From 7cce21c634a675068b66949b02c37f8f38c63261 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Mon, 22 Dec 2025 15:41:58 +0800 Subject: [PATCH 01/41] Enhance threadblock swizzle templates with default offset parameter and streamline parser.py for better readability --- src/tl_templates/cuda/threadblock_swizzle.h | 5 ++-- tilelang/language/overrides/parser.py | 29 ++++++++++----------- 2 files changed, 17 insertions(+), 17 deletions(-) diff --git a/src/tl_templates/cuda/threadblock_swizzle.h b/src/tl_templates/cuda/threadblock_swizzle.h index 1539b657dd..00a230c1a1 100644 --- a/src/tl_templates/cuda/threadblock_swizzle.h +++ b/src/tl_templates/cuda/threadblock_swizzle.h @@ -4,7 +4,7 @@ namespace tl { -template TL_DEVICE dim3 rasterization2DRow() { +template TL_DEVICE dim3 rasterization2DRow() { const unsigned int block_idx = blockIdx.x + blockIdx.y * gridDim.x; const unsigned int grid_size = gridDim.x * gridDim.y; const unsigned int panel_size = panel_width * gridDim.x; @@ -23,7 +23,8 @@ template TL_DEVICE dim3 rasterization2DRow() { return {col_idx, row_idx, blockIdx.z}; } -template TL_DEVICE dim3 rasterization2DColumn() { +template +TL_DEVICE dim3 rasterization2DColumn() { const unsigned int block_idx = blockIdx.x + blockIdx.y * gridDim.x; const unsigned int grid_size = gridDim.x * gridDim.y; const unsigned int panel_size = panel_width * gridDim.y; diff --git a/tilelang/language/overrides/parser.py b/tilelang/language/overrides/parser.py index 6c028efc18..28cb9d554c 100644 --- a/tilelang/language/overrides/parser.py +++ b/tilelang/language/overrides/parser.py @@ -164,10 +164,10 @@ def tilelang_visit_for(self, node: doc.For) -> None: # pylint: disable=unused-a "Expect the for loop to be one of the following: " "range, T.serial, T.grid, T.parallel, T.vectorized, T.unroll, T.thread_binding", ) - with self.var_table.with_frame(): - with iter_val as iters: - self.eval_assign(target=node.target, source=iters, bind_value=tvm_tir_parser.bind_for_value) - self.visit_body(node.body) + with self.var_table.with_frame(), iter_val as iters: + self.eval_assign( + target=node.target, source=iters, bind_value=tvm_tir_parser.bind_for_value) + self.visit_body(node.body) return # Stepped inclusive serial: require positive integer step @@ -192,16 +192,15 @@ def tilelang_visit_for(self, node: doc.For) -> None: # pylint: disable=unused-a # Use tvm.tir.floordiv via builder ops from tilelang.tir.ir if available # Avoid importing op wrappers; compute using arithmetic to keep it simple. # We construct: T.ceildiv((end - start), step) - extent = T.ceildiv(end - start, step_val) # type: ignore[operator] + extent = T.ceildiv(end - start, step_val) # type: ignore[operator] for_frame = T.serial(0, extent, annotations=annotations) - with self.var_table.with_frame(): - with for_frame as t: - # Bind loop target as Let var: i = start + t * step - stepped_index = start + t * step_val # type: ignore[operator] - self.eval_assign( - target=node.target, - source=stepped_index, - bind_value=tvm_tir_parser.bind_assign_value, - ) - self.visit_body(node.body) + with self.var_table.with_frame(), for_frame as t: + # Bind loop target as Let var: i = start + t * step + stepped_index = start + t * step_val # type: ignore[operator] + self.eval_assign( + target=node.target, + source=stepped_index, + bind_value=tvm_tir_parser.bind_assign_value, + ) + self.visit_body(node.body) From da0eea12a54ba76e5883b5b278f6abd65c201cc3 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Mon, 22 Dec 2025 16:57:17 +0800 Subject: [PATCH 02/41] [Cache] Rename sparse compress cache directory --- tilelang/utils/sparse.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tilelang/utils/sparse.py b/tilelang/utils/sparse.py index cd364b8bb5..9260aa8e15 100644 --- a/tilelang/utils/sparse.py +++ b/tilelang/utils/sparse.py @@ -6,10 +6,13 @@ from torch.utils.cpp_extension import load, _import_module_from_library from tilelang import env +# Include version information to ensure different versions use separate caches +from tilelang import __version__ + # Define paths compress_util = os.path.join(env.TILELANG_TEMPLATE_PATH, "tl_templates/cuda/compress_sm90.cu") # Cache directory for compiled extensions -_CACHE_DIR = os.path.join(env.TILELANG_CACHE_DIR, "sparse_compressor") +_CACHE_DIR = os.path.join(env.TILELANG_CACHE_DIR, "sparse_compressor", __version__) os.makedirs(_CACHE_DIR, exist_ok=True) From 75b67b04df43651d40e34ae47d9793d8e8a1b363 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Mon, 22 Dec 2025 21:08:32 +0800 Subject: [PATCH 03/41] Temporarily exclude sink tests from non-distributed example tests in CI to address timeout issues --- .github/workflows/ci.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index bf75d80869..995998b9b9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -122,7 +122,7 @@ jobs: fi # run remaining example tests (non-distributed) - mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' 2>/dev/null || true) + mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' | grep -vE 'sink|vs_sparse' 2>/dev/null || true) # temporarily disable problematic tests if [ "${#OTHER_TESTS[@]}" -gt 0 ]; then echo "Running non-distributed examples:" printf '%s\n' "${OTHER_TESTS[@]}" @@ -148,7 +148,7 @@ jobs: fi # run remaining tests - mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' 2>/dev/null || true) + mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' | grep -vE 'tilelibrary_gemm|jit_gemm_ctypes' 2>/dev/null || true) # temporarily disable problematic tests if [ "${#OTHER_TESTS[@]}" -gt 0 ]; then echo "Running non-distributed tests:" printf '%s\n' "${OTHER_TESTS[@]}" From f6df001b89587d4dbc1454351fb42b533c8d9952 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 19 Nov 2025 06:20:45 +0000 Subject: [PATCH 04/41] [DeepEP] Move deepep benchmark to example and allow compatible with new version DeepEP --- .../distributed/deepseek_deepep}/deepep.md | 3 +- .../intranode/get_dispatch_layout.py | 67 +++---- examples/distributed/deepseek_deepep/utils.py | 188 ++++++++++++++++++ 3 files changed, 213 insertions(+), 45 deletions(-) rename {benchmark/distributed/deepep => examples/distributed/deepseek_deepep}/deepep.md (86%) rename {benchmark/distributed/deepep => examples/distributed/deepseek_deepep}/intranode/get_dispatch_layout.py (85%) create mode 100644 examples/distributed/deepseek_deepep/utils.py diff --git a/benchmark/distributed/deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md similarity index 86% rename from benchmark/distributed/deepep/deepep.md rename to examples/distributed/deepseek_deepep/deepep.md index 61f4c8ae21..e2b9fb231a 100644 --- a/benchmark/distributed/deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -1,4 +1,4 @@ -# DeepEp in TileLang +# DeepEP To install and compare with DeepEP, please refer to https://github.com/deepseek-ai/DeepEP. @@ -6,6 +6,7 @@ To install and compare with DeepEP, please refer to https://github.com/deepseek- - [] Intranode Normal Mode - [x] get_dispatch_layout - [] dispatch + - [] notify_dispatch - [] combine - [] Internode Normal Mode - [] Low-latency Mode \ No newline at end of file diff --git a/benchmark/distributed/deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py similarity index 85% rename from benchmark/distributed/deepep/intranode/get_dispatch_layout.py rename to examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index f7685d48b2..7ebe4b2b03 100644 --- a/benchmark/distributed/deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -1,21 +1,19 @@ # For intranode only -from __future__ import annotations - import torch import tilelang import tilelang.language as T from tilelang.profiler import do_bench -from typing import tuple -import sys +from typing import Tuple from argparse import ArgumentParser - -tilelang.disable_cache() +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) +from utils import gen_inputs # noqa: F403 # TODO(wt): Add async functionality def get_dispatch_layout( topk_idx: torch.Tensor, num_experts: int, - num_ranks: int) -> tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor]: + num_ranks: int) -> Tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor]: """Calculate the layout required for later communication. Arguments: @@ -165,23 +163,6 @@ def main( return main -# Check: DeepEP/tests/test_intranode.py:test_main -def gen_topk_idx(num_tokens: int, num_topk: int, num_experts: int): - """Generate a random topk_idx tensor for testing. - Arguments: - num_tokens: the number of tokens. - num_topk: the number of top-k experts to select for each token. - num_experts: the number of experts. - Returns: - topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, - `-1` means no selections. - """ - assert num_topk <= num_experts, "num_topk must be less than or equal to num_experts" - scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 - topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] - return topk_idx - - def test_get_dispatch_layout( num_tokens: int, num_topk: int, @@ -190,27 +171,25 @@ def test_get_dispatch_layout( ): try: import deep_ep_cpp # noqa: F403 - except Exception as e: - print( - "Please install DeepEP to run this test.", - flush=True, - file=sys.stderr, - ) - raise e + except ModuleNotFoundError as e: + raise ModuleNotFoundError("Please install DeepEP to run this test.") # Validate correctness - topk_idx = gen_topk_idx(num_tokens, num_topk, num_experts) - buffer = deep_ep_cpp.Buffer(0, num_ranks, 0, 0, False, False) - - def deepep_impl(): - return buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False) - - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_impl() + topk_idx = gen_inputs(num_tokens, 1, num_topk, num_experts, num_ranks)[1] + buffer = deep_ep_cpp.Buffer( + 0, # rank + num_ranks, + 0, # num_nvl_bytes + 0, # num_rdma_bytes + False, # low_latency_mode + False, # explicit_destroy + False, # enable_shrink + False, # use fabric + ) - def tl_impl(): - return get_dispatch_layout(topk_idx, num_experts, num_ranks) + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False) - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = tl_impl() + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) assert torch.allclose(num_tokens_per_expert, ref_num_tokens_per_expert), \ f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" @@ -224,10 +203,10 @@ def tl_impl(): print("All checks passed.✅") # Benchmark - t1 = do_bench(deepep_impl) - t2 = do_bench(tl_impl) + t1 = do_bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False)) + t2 = do_bench(lambda: get_dispatch_layout(topk_idx, num_experts, num_ranks)) print(f"DeepEP: {t1:.3f} ms") - print(f"TileLang: {t2:.3f} ms") + print(f"TileScale: {t2:.3f} ms") print(f"Speedup: {t1 / t2:.2f}x") diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py new file mode 100644 index 0000000000..705788186c --- /dev/null +++ b/examples/distributed/deepseek_deepep/utils.py @@ -0,0 +1,188 @@ +from typing import Union, Tuple +import torch +import os +from dataclasses import dataclass + +# Pre-defined constants in DeepEP +NUM_MAX_NVL_PEERS = 8 # Maximum number of NVLink peers per GPU +NUM_MAX_RDMA_PEERS = 20 # Maximum number of RDMA peers per GPU +NUM_MAX_LOCAL_EXPERTS = 1024 # Maximum number of local experts per GPU +NUM_WORKSPACE_BYTES = 32 * 1024 * 1024 # 32 MiB +NUM_BUFFER_ALIGNMENT_BYTES = 128 + +num_sms: int = 20 + + +@dataclass +class Config: + num_max_nvl_chunked_send_tokens : int + num_max_nvl_chunked_recv_tokens : int + num_max_rdma_chunked_send_tokens : int + num_max_rdma_chunked_recv_tokens : int + + num_sms : int = 20 # the SMs used in high-throughput kernels + + def __post_init__(self): + assert self.num_sms % 2 == 0, "num_sms must be even" + + @staticmethod + def get_dispatch_config(num_ranks: int) -> 'Config': + """ + Get a recommended dispatch config. + + Argument: + num_ranks: the number of ranks. + + Returns: + config: the recommended config. + """ + + # TODO: automatically tune + config_map = { + 2: Config(num_sms, 24, 256, 6, 128), + 4: Config(num_sms, 6, 256, 6, 128), + 8: Config(num_sms, 6, 256, 6, 128), + 16: Config(num_sms, 36, 288, 20, 128), + 24: Config(num_sms, 32, 288, 8, 128), + 32: Config(num_sms, 32, 288, 8, 128), + 48: Config(num_sms, 32, 288, 8, 128), + 64: Config(num_sms, 32, 288, 8, 128), + 96: Config(num_sms, 20, 480, 12, 128), + 128: Config(num_sms, 20, 560, 12, 128), + 144: Config(num_sms, 32, 720, 12, 128), + 160: Config(num_sms, 28, 720, 12, 128), + } + assert num_ranks in config_map, f'Unsupported number of EP ranks: {num_ranks}' + return config_map[num_ranks] + + + @staticmethod + def get_combine_config(num_ranks: int) -> 'Config': + """ + Get a recommended combine config. + + Argument: + num_ranks: the number of ranks. + + Returns: + config: the recommended config. + """ + + # TODO: automatically tune + config_map = { + 2: Config(num_sms, 10, 256, 6, 128), + 4: Config(num_sms, 9, 256, 6, 128), + 8: Config(num_sms, 4, 256, 6, 128), + 16: Config(num_sms, 4, 288, 12, 128), + 24: Config(num_sms, 1, 288, 8, 128), + 32: Config(num_sms, 1, 288, 8, 128), + 48: Config(num_sms, 1, 288, 8, 128), + 64: Config(num_sms, 1, 288, 8, 128), + 96: Config(num_sms, 1, 480, 8, 128), + 128: Config(num_sms, 1, 560, 8, 128), + 144: Config(num_sms, 2, 720, 8, 128), + 160: Config(num_sms, 2, 720, 8, 128), + } + assert num_ranks in config_map, f'Unsupported number of EP ranks: {num_ranks}' + return config_map[num_ranks] + + +# Only necessary in inter-node cases +def set_rdma_env_args(num_qps_per_rank: int = 24, allow_nvlink_for_low_latency_mode: bool = True, allow_mnnvl: bool = False): + os.environ['NVSHMEM_DISABLE_P2P'] = '0' if allow_nvlink_for_low_latency_mode else '1' + os.environ['NVSHMEM_IB_ENABLE_IBGDA'] = '1' + os.environ['NVSHMEM_IBGDA_NUM_RC_PER_PE'] = f'{num_qps_per_rank}' + + # Make sure QP depth is always larger than the number of on-flight WRs, so that we can skip WQ slot check + nvshmem_qp_depth = int(os.environ.get('NVSHMEM_QP_DEPTH', '1024')) + os.environ['NVSHMEM_QP_DEPTH'] = str(nvshmem_qp_depth) + + # Reduce gpu memory usage + # 6 default teams + 1 extra team + os.environ['NVSHMEM_MAX_TEAMS'] = '7' + # Disable NVLink SHArP + os.environ['NVSHMEM_DISABLE_NVLS'] = '1' + # NOTES: NVSHMEM initialization requires at least 256 MiB + os.environ['NVSHMEM_CUMEM_GRANULARITY'] = f'{2 ** 29}' + + if not allow_mnnvl: + # Disable multi-node NVLink detection + os.environ['NVSHMEM_DISABLE_MNNVL'] = '1' + + +def unpack_bias(bias: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]): + bias_0, bias_1 = None, None + if isinstance(bias, torch.Tensor): + bias_0 = bias + elif isinstance(bias, tuple): + assert len(bias) == 2 + bias_0, bias_1 = bias + return bias_0, bias_1 + + +# Check: DeepEP/tests/test_intranode.py:test_main +def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, num_ranks: int): + """Generate random inputs for testing purpose. + Args: + num_tokens: the number of tokens. + hidden: the hidden dimension. + num_topk: the number of top-k experts to select for each token. + num_experts: the number of experts. + num_ranks: the number of total ranks. + + Returns: + x: `[num_tokens, hidden]` with `torch.bfloat16`, the input to MoE layer. + topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, + `-1` means no selections. + topk_weights: `[num_tokens, num_topk]` with `torch.float32`, the weights corresponding to + each selected expert for each token. + rank_idx: `[num_tokens, num_topk]` with `torch.int64`, the rank indices corresponding to + each selected expert, `-1` means no selections. + """ + assert num_topk <= num_experts, "num_topk must be less than or equal to num_experts" + assert num_experts % num_ranks == 0, "num_experts must be divisible by num_ranks" + + x = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') + scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 + topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] + topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda') + rank_idx = topk_idx // (num_experts // num_ranks) + rank_idx.masked_fill_(topk_idx == -1, -1) + inplace_unique(rank_idx, num_ranks) + + return x, topk_idx, topk_weights, rank_idx + + +def inplace_unique(x: torch.Tensor, num_slots: int): + """ + Keep at most `num_slots` different values in each row of `x`, + and fill `x` with -1 in other positions. + """ + assert x.dim() == 2 and num_slots <= x.size(-1) + mask = x < 0 + x_padded = x.masked_fill(mask, num_slots) + bin_count = torch.zeros((x.size(0), num_slots + 1), dtype=x.dtype, device=x.device) + bin_count.scatter_add_(1, x_padded, torch.ones_like(x_padded)) + bin_count = bin_count[:, :num_slots] + sorted_bin_count, sorted_bin_idx = torch.sort(bin_count, dim=-1, descending=True) + sorted_bin_idx.masked_fill_(sorted_bin_count == 0, -1) + sorted_bin_idx = torch.sort(sorted_bin_idx, descending=True, dim=-1).values + x[:, :].fill_(-1) + valid_len = min(num_slots, x.size(1)) + x[:, :valid_len] = sorted_bin_idx[:, :valid_len] + + +# Check: csrc/deep_ep.cpp:Buffer::Buffer +def create_moe_recv_counters(num_ranks: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: + num_rdma_ranks = max(1, num_ranks // NUM_MAX_NVL_PEERS) # noqa: F841 + num_nvl_ranks = min(num_ranks, NUM_MAX_NVL_PEERS) # noqa: F841 + + moe_recv_counter = torch.tensor( + -1, dtype=torch.int64, device='cuda', pin_memory=True) # MoE counter + moe_recv_expert_counter = torch.tensor( + [-1] * NUM_MAX_LOCAL_EXPERTS, dtype=torch.int32, device='cuda', + pin_memory=True) # MoE expert-level counter + moe_recv_rdma_counter = torch.tensor( + -1, dtype=torch.int, device='cuda', pin_memory=True) # MoE RDMA-level counter + + return moe_recv_counter, moe_recv_expert_counter, moe_recv_rdma_counter \ No newline at end of file From da032593031e35112911cad5974f4e42d27b98f7 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Thu, 20 Nov 2025 11:16:43 +0000 Subject: [PATCH 05/41] [Feat] Enhance `T.st` to support intra-node store to peer's symm memory --- .../primitives/example_remote_st.py | 81 +++++++++ src/op/builtin.cc | 2 - src/op/builtin.h | 8 - src/op/remote_copy.cc | 67 ++++++- src/op/remote_copy.h | 56 ++++++ src/target/codegen_cuda.cc | 6 - src/tl_templates/cuda/sync.h | 164 +++++++++++++++--- tilelang/language/builtin.py | 24 ++- 8 files changed, 361 insertions(+), 47 deletions(-) create mode 100644 examples/distributed/primitives/example_remote_st.py diff --git a/examples/distributed/primitives/example_remote_st.py b/examples/distributed/primitives/example_remote_st.py new file mode 100644 index 0000000000..b09ad8839e --- /dev/null +++ b/examples/distributed/primitives/example_remote_st.py @@ -0,0 +1,81 @@ +import os +import tilelang +import tilelang.language as T +import argparse +import torch +import torch.distributed as dist +import torch.multiprocessing +from tilelang.distributed import init_dist + +tilelang.disable_cache() +os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log + + +def kernel_(M, num_rank, block_M, threads): + + @T.prim_func + def main( + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), + ): + with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): + rank = T.alloc_local([1], "uint64") + num_rank = T.alloc_local([1], "uint64") + rank[0] = T.get_rank() + num_rank[0] = T.get_num_ranks() + tx = T.get_thread_binding() + T.st(dst[bx * block_M + tx], src[bx * block_M + tx], dst_pe=1-rank[0]) + + return main + + +def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): + M = args.M + BLOCK_M = threads = 128 + assert num_local_ranks == 2, "this example only supports 2 ranks copying to each other" + + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + allocator = tilelang.get_allocator( + size=2**25, + device="cuda", + is_distributed=True, + local_rank=local_rank, + num_local_ranks=num_local_ranks, + group=group) + kernel = tilelang.compile(kernel_(M, num_ranks, BLOCK_M, threads)) + kernel.initialize(allocator=allocator) + if local_rank == 0: + print(kernel.get_kernel_source()) + + src = tilelang.tensor((M), torch.float32, allocator=allocator).normal_() + dst = tilelang.tensor((M), torch.float32, allocator=allocator) + + torch.cuda.synchronize() + torch.distributed.barrier(group) + kernel(dst, src) + torch.cuda.synchronize() + torch.distributed.barrier(group) + + dst_torchs = [torch.empty_like(src) for _ in range(num_local_ranks)] + dist.all_gather(dst_torchs, src, group) + dst_torch = dst_torchs[local_rank ^ 1] + + if torch.allclose(dst_torch, dst, atol=1e-6, rtol=1e-6): + print(f"rank {local_rank} check passed.✅") + else: + print(f"rank {local_rank} check failed.❌") + print(f"dst_torch: {dst_torch}, dst: {dst}") + raise ValueError("Test failed") + + dist.destroy_process_group() + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') + parser.add_argument('--M', type=int, default=1024, help='M dimension') + args = parser.parse_args() + num_processes = args.num_processes + + torch.multiprocessing.spawn(main, args=(num_processes, args), nprocs=num_processes) diff --git a/src/op/builtin.cc b/src/op/builtin.cc index a3c8a024e2..a00643e2a5 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -315,7 +315,5 @@ TIR_DEFINE_TL_BUILTIN(atomicadd_elem_op) TIR_DEFINE_TL_BUILTIN(atom_add).set_num_inputs(4).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_DEFINE_TL_BUILTIN(st).set_num_inputs(4).set_attr( - "TCallEffectKind", Integer(CallEffectKind::kOpaque)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index 85a95f11a9..478921bf2d 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -545,14 +545,6 @@ TVM_DLL const Op &atomicadd_elem_op(); */ TVM_DLL const Op &atom_add(); -/*! - * \brief tilelang intrinsic for atomic store with semantic. - * - * This op is used to represent an atomic store operation with semantic in - * tilelang. - */ -TVM_DLL const Op &st(); - } // namespace tl } // namespace tvm diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 059d545b91..28de033a89 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -233,18 +233,79 @@ TileOperator GetOpNode::Clone() const { return GetOp(node); } -TIR_REGISTER_TL_OP(PutOp, put) +StOp::StOp(Array args, BufferMap vmap) { + ObjectPtr node = make_object(); + node->dst = args[0]; + ICHECK(node->dst.as()) << "dst must be a call node"; + ICHECK(node->dst.as()->op.same_as(builtin::address_of())) + << "dst must be address_of op"; + + node->value = args[1]; + node->sem = args[2].as().value()->value; + node->scope = args[3].as().value()->value; + node->dst_pe = args[4]; + data_ = std::move(node); + (void)vmap; +} + +bool StOpNode::is_distributed() const { + return !(dst_pe->IsInstance() && dst_pe.as()->value == -1); +} + +Stmt StOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { + (void)analyzer; + (void)T; + Array new_args; + std::stringstream ss; + + // Build function name: tl::st__ + ss << "tl::st_" << sem << "_" << scope; + + new_args.push_back(StringImm(ss.str())); + if (is_distributed()) { + PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); + PrimExpr local_base_ptr = + Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); + PrimExpr offset_to_base = + Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {dst}), + local_base_ptr); + new_args.push_back( + Call(DataType::Handle(), tl::get_remote_base_ptr(), {dst_pe}) + + offset_to_base); + } else { + new_args.push_back(dst); + } + new_args.push_back(value); + + auto st = Call(DataType::Handle(), builtin::call_extern(), new_args); + return Evaluate(st); +} + +LayoutMap StOpNode::InferLayout(const LayoutInferArgs &T, + InferLevel level) const { + (void)T; + (void)level; + return {}; +} + +TileOperator StOpNode::Clone() const { + auto node = make_object(*this); + return StOp(node); +} + +TIR_REGISTER_TL_OP(GetOp, get) .set_num_inputs(6) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_REGISTER_TL_OP(GetOp, get) - .set_num_inputs(6) +TIR_REGISTER_TL_OP(StOp, st) + .set_num_inputs(5) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); TVM_FFI_STATIC_INIT_BLOCK({ PutOpNode::RegisterReflection(); }); TVM_FFI_STATIC_INIT_BLOCK({ GetOpNode::RegisterReflection(); }); +TVM_FFI_STATIC_INIT_BLOCK({ StOpNode::RegisterReflection(); }); } // namespace tl } // namespace tvm diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index d390394fa1..54017779eb 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -200,6 +200,62 @@ class GetOp : public TileOperator { static const Op &Get(); }; +class StOpNode : public TileOperatorNode { +public: + PrimExpr dst; ///< Destination address + PrimExpr value; ///< Value to store + PrimExpr dst_pe; ///< Destination processing element (optional) + std::string scope; ///< Scope: {warp, block} + std::string sem; ///< Semantic: {relaxed, release} + + bool is_distributed() const; + + static constexpr const char *_type_key = "tl.StOp"; + TVM_DECLARE_FINAL_OBJECT_INFO(StOpNode, TileOperatorNode); + + Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; + LayoutMap InferLayout(const LayoutInferArgs &T, + InferLevel level) const override; + static const Op &Get(); + TileOperator Clone() const override; + + static void RegisterReflection() { + namespace refl = tvm::ffi::reflection; + refl::ObjectDef() + .def_ro("dst", &StOpNode::dst) + .def_ro("value", &StOpNode::value) + .def_ro("dst_pe", &StOpNode::dst_pe) + .def_ro("scope", &StOpNode::scope) + .def_ro("sem", &StOpNode::sem); + } + + bool SEqualReduce(const StOpNode *other, SEqualReducer equal) const { + return equal(dst, other->dst) && + equal(value, other->value) && + equal(dst_pe, other->dst_pe) && + scope == other->scope && + sem == other->sem; + } + + void SHashReduce(SHashReducer hash_reduce) const { + hash_reduce(dst); + hash_reduce(value); + hash_reduce(dst_pe); + hash_reduce(scope); + hash_reduce(sem); + } + + static constexpr bool _type_has_method_sequal_reduce = true; + static constexpr bool _type_has_method_shash_reduce = true; +}; + +class StOp : public TileOperator { +public: + TVM_DEFINE_OBJECT_REF_METHODS(StOp, TileOperator, StOpNode); + TVM_DLL StOp(Array args, BufferMap vmap); + static const Op &Get(); +}; + } // namespace tl } // namespace tvm diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index e93b6fc4ef..2ce7fcca64 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -1522,12 +1522,6 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { op->args[3].as()->value; os << func_name << "(" << this->PrintExpr(op->args[0]) << ", " << this->PrintExpr(op->args[1]) << ")"; - } else if (op->op.same_as(tl::st())) { - this->PrintIndent(); - std::string func_name = "tl::st_" + op->args[2].as()->value + - "_" + op->args[3].as()->value; - this->stream << func_name << "(" << this->PrintExpr(op->args[0]) << ", " - << this->PrintExpr(op->args[1]) << ");\n"; } else if (op->op.same_as(tl::get_clock())) { os << "get_clock()"; } else if (op->op.same_as(tl::loop_break())) { diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index b7b4b8cb9b..012b913447 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -186,32 +186,156 @@ template TL_DEVICE void wait_eq(void *barrier, T val = 1) { } } -TL_DEVICE void st_release_gpu(uint32_t *ptr, uint32_t value) { - asm volatile("st.release.gpu.global.b32 [%0], %1;" - : - : "l"(ptr), "r"(value) - : "memory"); +template +TL_DEVICE void st_release_gpu(P ptr, T value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); + static_assert(std::is_pointer_v

|| std::is_same_v); + T *ptr_ = reinterpret_cast(ptr); + + if constexpr (sizeof(T) == 2) { + asm volatile("st.release.gpu.global.b16 [%0], %1;" + : + : "l"(ptr_), "h"(value) + : "memory"); + } else if constexpr (sizeof(T) == 4) { + if constexpr (std::is_floating_point_v) { + asm volatile("st.release.gpu.global.b32 [%0], %1;" + : + : "l"(ptr_), "f"(value) + : "memory"); + } else { + asm volatile("st.release.gpu.global.b32 [%0], %1;" + : + : "l"(ptr_), "r"(value) + : "memory"); + } + } else { + if constexpr (std::is_floating_point_v) { + asm volatile("st.release.gpu.global.b64 [%0], %1;" + : + : "l"(ptr_), "d"(value) + : "memory"); + } else { + asm volatile("st.release.gpu.global.b64 [%0], %1;" + : + : "l"(ptr_), "l"(value) + : "memory"); + } + } } -TL_DEVICE void st_relaxed_gpu(uint32_t *ptr, uint32_t value) { - asm volatile("st.relaxed.gpu.global.b32 [%0], %1;" - : - : "l"(ptr), "r"(value) - : "memory"); +template +TL_DEVICE void st_relaxed_gpu(P ptr, T value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); + static_assert(std::is_pointer_v

|| std::is_same_v); + T *ptr_ = reinterpret_cast(ptr); + + if constexpr (sizeof(T) == 2) { + asm volatile("st.relaxed.gpu.global.b16 [%0], %1;" + : + : "l"(ptr_), "h"(value) + : "memory"); + } else if constexpr (sizeof(T) == 4) { + if constexpr (std::is_floating_point_v) { + asm volatile("st.relaxed.gpu.global.b32 [%0], %1;" + : + : "l"(ptr_), "f"(value) + : "memory"); + } else { + asm volatile("st.relaxed.gpu.global.b32 [%0], %1;" + : + : "l"(ptr_), "r"(value) + : "memory"); + } + } else { + if constexpr (std::is_floating_point_v) { + asm volatile("st.relaxed.gpu.global.b64 [%0], %1;" + : + : "l"(ptr_), "d"(value) + : "memory"); + } else { + asm volatile("st.relaxed.gpu.global.b64 [%0], %1;" + : + : "l"(ptr_), "l"(value) + : "memory"); + } + } } -TL_DEVICE void st_release_sys(uint32_t *ptr, uint32_t value) { - asm volatile("st.release.sys.global.b32 [%0], %1;" - : - : "l"(ptr), "r"(value) - : "memory"); +template +TL_DEVICE void st_release_sys(P ptr, T value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); + static_assert(std::is_pointer_v

|| std::is_same_v); + T *ptr_ = reinterpret_cast(ptr); + + if constexpr (sizeof(T) == 2) { + asm volatile("st.release.sys.global.b16 [%0], %1;" + : + : "l"(ptr_), "h"(value) + : "memory"); + } else if constexpr (sizeof(T) == 4) { + if constexpr (std::is_floating_point_v) { + asm volatile("st.release.sys.global.b32 [%0], %1;" + : + : "l"(ptr_), "f"(value) + : "memory"); + } else { + asm volatile("st.release.sys.global.b32 [%0], %1;" + : + : "l"(ptr_), "r"(value) + : "memory"); + } + } else { + if constexpr (std::is_floating_point_v) { + asm volatile("st.release.sys.global.b64 [%0], %1;" + : + : "l"(ptr_), "d"(value) + : "memory"); + } else { + asm volatile("st.release.sys.global.b64 [%0], %1;" + : + : "l"(ptr_), "l"(value) + : "memory"); + } + } } -TL_DEVICE void st_relaxed_sys(uint32_t *ptr, uint32_t value) { - asm volatile("st.relaxed.sys.global.b32 [%0], %1;" - : - : "l"(ptr), "r"(value) - : "memory"); +template +TL_DEVICE void st_relaxed_sys(P ptr, T value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); + static_assert(std::is_pointer_v

|| std::is_same_v); + T *ptr_ = reinterpret_cast(ptr); + + if constexpr (sizeof(T) == 2) { + asm volatile("st.relaxed.sys.global.b16 [%0], %1;" + : + : "l"(ptr_), "h"(value) + : "memory"); + } else if constexpr (sizeof(T) == 4) { + if constexpr (std::is_floating_point_v) { + asm volatile("st.relaxed.sys.global.b32 [%0], %1;" + : + : "l"(ptr_), "f"(value) + : "memory"); + } else { + asm volatile("st.relaxed.sys.global.b32 [%0], %1;" + : + : "l"(ptr_), "r"(value) + : "memory"); + } + } else { + if constexpr (std::is_floating_point_v) { + asm volatile("st.relaxed.sys.global.b64 [%0], %1;" + : + : "l"(ptr_), "d"(value) + : "memory"); + } else { + asm volatile("st.relaxed.sys.global.b64 [%0], %1;" + : + : "l"(ptr_), "l"(value) + : "memory"); + } + } } } // namespace tl diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index 1b088beff0..afc20c7e4d 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -737,18 +737,26 @@ def atom_add(barrier: PrimExpr, value: PrimExpr, scope: str = "gpu", sem: str = scope) -def st(barrier: PrimExpr, value: PrimExpr, scope: str = "gpu", sem: str = "relaxed"): - """Store a value to a given address with specified scope and semantic. +def st( + dst: PrimExpr, + value: PrimExpr, + scope: str = "gpu", + sem: str = "relaxed", + dst_pe: tir.PrimExpr | tir.IntImm | None = -1, +): + """Store a value to a given address with specified scope, semantic, and optional destination PE. Args: - address: The address to store the value to - value: The value to store - scope: The memory scope (default is "gpu") - semantic: The memory semantic (default is "relaxed") + dst: The destination to store the value to. + value: The value to store. + scope: The memory scope, either "gpu" (default) or "sys". + sem: The memory semantic, either "relaxed" (default) or "release". + dst_pe: The destination processing element (PE) identifier. + Use -1 (default) for local PE, or a non-negative integer to target a remote PE. Returns: - tir.Call: A handle to the store operation + tir.Call: A handle to the store operation. """ assert scope in ["gpu", "sys"], "Scope must be one of 'gpu', or 'sys'." assert sem in ["relaxed", "release"], "Semantic must be one of 'relaxed', or 'release'." - return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(barrier), value, sem, scope) + return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, dst_pe) From 158e98a2d57af477954ff01b7bbc70ddfc387892 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Thu, 20 Nov 2025 17:05:11 +0000 Subject: [PATCH 06/41] use strided loop to simplify get_dispatch a bit --- .../intranode/get_dispatch_layout.py | 28 ++++++++----------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 7ebe4b2b03..cb3fa6efbe 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -1,12 +1,14 @@ # For intranode only +# This op is non-distributed +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + import torch import tilelang import tilelang.language as T from tilelang.profiler import do_bench from typing import Tuple from argparse import ArgumentParser -import os, sys -sys.path.append(os.path.dirname(os.path.dirname(__file__))) from utils import gen_inputs # noqa: F403 @@ -91,16 +93,13 @@ def main( expert_begin_idx = T.alloc_local([1], "int32") expert_begin_idx[0] = bid * experts_per_sm expert_end_idx = T.alloc_local([1], "int32") - expert_end_idx[0] = expert_begin_idx[0] + experts_per_sm - if expert_end_idx[0] > num_experts: - expert_end_idx[0] = num_experts # tl does not support min/max + expert_end_idx[0] = T.min(expert_begin_idx[0] + experts_per_sm, num_experts) if expert_begin_idx[0] < expert_end_idx[0]: - for i in T.serial(0, T.ceildiv(num_tokens - tid, - threads)): # tl does not support strided loop + for i in T.serial(tid, num_tokens, threads): for j in T.serial(0, num_topk): expert_idx = T.alloc_local([1], "int32") - expert_idx[0] = T.cast(topk_idx[tid + i * threads, j], "int32") + expert_idx[0] = T.cast(topk_idx[i, j], "int32") if expert_begin_idx[0] <= expert_idx[0] and expert_idx[0] < expert_end_idx[ 0]: tokens_per_expert_per_thread[tid, @@ -119,9 +118,7 @@ def main( rank_begin_idx = T.alloc_local([1], "int32") rank_begin_idx[0] = (bid - sm_begin[0]) * ranks_per_sm rank_end_idx = T.alloc_local([1], "int32") - rank_end_idx[0] = rank_begin_idx[0] + ranks_per_sm - if rank_end_idx[0] > num_ranks: - rank_end_idx[0] = num_ranks # tl does not support min/max + rank_end_idx[0] = T.min(rank_begin_idx[0] + ranks_per_sm, num_ranks) if rank_begin_idx[0] >= 0 and rank_begin_idx[0] < rank_end_idx[0]: tokens_per_rank_per_thread = T.alloc_shared([threads, ranks_per_sm], "int32") @@ -132,15 +129,14 @@ def main( expert_end = T.alloc_local([1], "int32") expert_end[0] = rank_end_idx[0] * experts_per_rank - for i in T.serial(0, T.ceildiv(num_tokens - tid, - threads)): # tl does not support strided loop + for i in T.serial(tid, num_tokens, threads): is_in_rank = T.alloc_local([ranks_per_sm], "int32") T.clear(is_in_rank) for j in T.serial(0, num_topk): expert_idx = T.alloc_local([1], "int32") rank_idx = T.alloc_local([1], "int32") - expert_idx[0] = T.cast(topk_idx[tid + i * threads, j], "int32") + expert_idx[0] = T.cast(topk_idx[i, j], "int32") if expert_begin[0] <= expert_idx[0] and expert_idx[0] < expert_end[0]: rank_idx[0] = expert_idx[0] // experts_per_rank - rank_begin_idx[0] @@ -148,10 +144,10 @@ def main( for j in T.serial(rank_begin_idx[0], rank_end_idx[0]): if is_in_rank[j - rank_begin_idx[0]] > 0: - is_token_in_rank[tid + i * threads, j] = True + is_token_in_rank[i, j] = True tokens_per_rank_per_thread[tid, j - rank_begin_idx[0]] += 1 else: - is_token_in_rank[tid + i * threads, j] = False + is_token_in_rank[i, j] = False if rank_begin_idx[0] + tid < rank_end_idx[0]: sum = T.alloc_local([1], "int32") From 43e6965de48bc30a3b329cb71ed384c740b83a4b Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 21 Nov 2025 08:11:45 +0000 Subject: [PATCH 07/41] [Feat] Support warp reduce operators --- .../primitives/example_warp_reduce.py | 30 ++++++ src/op/builtin.cc | 24 +++++ src/op/builtin.h | 25 +++++ src/target/codegen_cuda.cc | 10 ++ src/tl_templates/cuda/reduce.h | 38 +++++++ tilelang/language/__init__.py | 5 + tilelang/language/reduce.py | 102 ++++++++++++++++++ 7 files changed, 234 insertions(+) create mode 100644 examples/distributed/primitives/example_warp_reduce.py diff --git a/examples/distributed/primitives/example_warp_reduce.py b/examples/distributed/primitives/example_warp_reduce.py new file mode 100644 index 0000000000..4ec10d276b --- /dev/null +++ b/examples/distributed/primitives/example_warp_reduce.py @@ -0,0 +1,30 @@ +import torch +import tilelang +import tilelang.language as T + + +@tilelang.jit +def get_kernel(): + @T.prim_func + def main( + x: T.Tensor((32), "float32") + + ): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding(0) + local_val = T.alloc_local([1], "float32") + local_val[0] = x[tx] + reduced_val = T.warp_reduce_sum(local_val[0]) + x[tx] = reduced_val + return main + + +if __name__ == '__main__': + a = torch.randn((32,), dtype=torch.float32, device='cuda') + kernel = get_kernel() + print(kernel.get_kernel_source()) + ref = torch.full_like(a, a.sum()) + kernel(a) + torch.testing.assert_close(a, ref) + print('Test passed for warp reduce sum ✅') + diff --git a/src/op/builtin.cc b/src/op/builtin.cc index a00643e2a5..89c1e85942 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -315,5 +315,29 @@ TIR_DEFINE_TL_BUILTIN(atomicadd_elem_op) TIR_DEFINE_TL_BUILTIN(atom_add).set_num_inputs(4).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); +TIR_DEFINE_TL_BUILTIN(warp_reduce_sum) + .set_num_inputs(1) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(warp_reduce_max) + .set_num_inputs(1) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(warp_reduce_min) + .set_num_inputs(1) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(warp_reduce_bitand) + .set_num_inputs(1) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(warp_reduce_bitor) + .set_num_inputs(1) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index 478921bf2d..3f2336bf6c 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -545,6 +545,31 @@ TVM_DLL const Op &atomicadd_elem_op(); */ TVM_DLL const Op &atom_add(); +/*! + * \brief tilelang intrinsic for warp reduction sum. + */ +TVM_DLL const Op &warp_reduce_sum(); + +/*! + * \brief tilelang intrinsic for warp reduction max. + */ +TVM_DLL const Op &warp_reduce_max(); + +/*! + * \brief tilelang intrinsic for warp reduction min. + */ +TVM_DLL const Op &warp_reduce_min(); + +/*! + * \brief tilelang intrinsic for warp reduction bitand. + */ +TVM_DLL const Op &warp_reduce_bitand(); + +/*! + * \brief tilelang intrinsic for warp reduction bitor. + */ +TVM_DLL const Op &warp_reduce_bitor(); + } // namespace tl } // namespace tvm diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 2ce7fcca64..705a15847f 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -1539,6 +1539,16 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { os << "tl::get_remote_base_ptr(" << pe_str << ")"; } else if (op->op.same_as(tl::get_uintptr_t())) { os << "tl::get_uintptr_t(" << this->PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_reduce_sum())) { + os << "tl::warp_reduce_sum(" << this->PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_reduce_max())) { + os << "tl::warp_reduce_max(" << this->PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_reduce_min())) { + os << "tl::warp_reduce_min(" << this->PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_reduce_bitand())) { + os << "tl::warp_reduce_bitand(" << this->PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_reduce_bitor())) { + os << "tl::warp_reduce_bitor(" << this->PrintExpr(op->args[0]) << ")"; } else if (op->op.same_as(builtin::tvm_fill_fragment())) { need_mma_h_ = true; ICHECK_EQ(op->args.size(), 6U); diff --git a/src/tl_templates/cuda/reduce.h b/src/tl_templates/cuda/reduce.h index 331da6dc87..a0f2318650 100644 --- a/src/tl_templates/cuda/reduce.h +++ b/src/tl_templates/cuda/reduce.h @@ -280,4 +280,42 @@ template struct CumSum2D { } }; +// TileScale extra + +template +TL_DEVICE T warp_reduce(T value, ReduceOp op) { + constexpr uint32_t mask = 0xffffffff; + value = op(value, __shfl_xor_sync(mask, value, 16)); + value = op(value, __shfl_xor_sync(mask, value, 8)); + value = op(value, __shfl_xor_sync(mask, value, 4)); + value = op(value, __shfl_xor_sync(mask, value, 2)); + value = op(value, __shfl_xor_sync(mask, value, 1)); + return value; +} + +template +TL_DEVICE T warp_reduce_sum(T value) { + return warp_reduce(value, SumOp()); +} + +template +TL_DEVICE T warp_reduce_max(T value) { + return warp_reduce(value, MaxOp()); +} + +template +TL_DEVICE T warp_reduce_min(T value) { + return warp_reduce(value, MinOp()); +} + +template +TL_DEVICE T warp_reduce_bitand(T value) { + return warp_reduce(value, BitAndOp()); +} + +template +TL_DEVICE T warp_reduce_bitor(T value) { + return warp_reduce(value, BitOrOp()); +} + } // namespace tl diff --git a/tilelang/language/__init__.py b/tilelang/language/__init__.py index 27f5432fcf..bb182ae64d 100644 --- a/tilelang/language/__init__.py +++ b/tilelang/language/__init__.py @@ -63,6 +63,11 @@ reduce_bitxor, # noqa: F401 cumsum, # noqa: F401 finalize_reducer, # noqa: F401 + warp_reduce_sum, # noqa: F401 + warp_reduce_max, # noqa: F401 + warp_reduce_min, # noqa: F401 + warp_reduce_bitand, # noqa: F401 + warp_reduce_bitor, # noqa: F401 ) from .print import print # noqa: F401 from .customize import ( diff --git a/tilelang/language/reduce.py b/tilelang/language/reduce.py index 55ac2bb0d8..603ada0733 100644 --- a/tilelang/language/reduce.py +++ b/tilelang/language/reduce.py @@ -270,3 +270,105 @@ def finalize_reducer(reducer: tir.Buffer): tir.op.Op.get("tl.finalize_reducer"), reducer.access_ptr("w"), ) + + +# TileScale extra + +def warp_reduce_sum(value: tir.PrimExpr): + """Perform warp reduction sum on a register value. + + This function reduces a value across all threads in a warp using shuffle operations. + Each thread provides a register `value`, and after the reduction, all threads + will have the sum of all values across the warp. + + Args: + x (tir.PrimExpr): The input register value to reduce + + Returns: + tir.PrimExpr: The reduced sum value (same on all threads in the warp) + """ + return tir.call_intrin( + value.dtype, + tir.op.Op.get("tl.warp_reduce_sum"), + value + ) + + +def warp_reduce_max(value: tir.PrimExpr): + """Perform warp reduction max on a register value. + + This function reduces a value across all threads in a warp using shuffle operations. + Each thread provides a register `value`, and after the reduction, all threads + will have the max of all values across the warp. + + Args: + value (tir.PrimExpr): The input register value to reduce + + Returns: + tir.PrimExpr: The reduced max value (same on all threads in the warp) + """ + return tir.call_intrin( + value.dtype, + tir.op.Op.get("tl.warp_reduce_max"), + value + ) + + +def warp_reduce_min(value: tir.PrimExpr): + """Perform warp reduction min on a register value. + + This function reduces a value across all threads in a warp using shuffle operations. + Each thread provides a register `value`, and after the reduction, all threads + will have the min of all values across the warp. + + Args: + value (tir.PrimExpr): The input register value to reduce + + Returns: + tir.PrimExpr: The reduced min value (same on all threads in the warp) + """ + return tir.call_intrin( + value.dtype, + tir.op.Op.get("tl.warp_reduce_min"), + value + ) + + +def warp_reduce_bitand(value: tir.PrimExpr): + """Perform warp reduction bitwise-and on a register value. + + This function reduces a value across all threads in a warp using shuffle operations. + Each thread provides a register `value`, and after the reduction, all threads + will have the bitwise-and of all values across the warp. + + Args: + value (tir.PrimExpr): The input register value to reduce + + Returns: + tir.PrimExpr: The reduced bitwise-and value (same on all threads in the warp) + """ + return tir.call_intrin( + value.dtype, + tir.op.Op.get("tl.warp_reduce_bitand"), + value + ) + + +def warp_reduce_bitor(value: tir.PrimExpr): + """Perform warp reduction bitwise-or on a register value. + + This function reduces a value across all threads in a warp using shuffle operations. + Each thread provides a register `value`, and after the reduction, all threads + will have the bitwise-or of all values across the warp. + + Args: + value (tir.PrimExpr): The input register value to reduce + + Returns: + tir.PrimExpr: The reduced bitwise-or value (same on all threads in the warp) + """ + return tir.call_intrin( + value.dtype, + tir.op.Op.get("tl.warp_reduce_bitor"), + value + ) \ No newline at end of file From b13fe3ff45e22fb160d9ec3f9918dd76bda24a30 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 21 Nov 2025 09:03:49 +0000 Subject: [PATCH 08/41] draft notify dispatch --- .../deepseek_deepep/intranode/dispatch.py | 87 +++++ .../intranode/get_dispatch_layout.py | 10 +- .../intranode/notify_dispatch.py | 314 ++++++++++++++++++ examples/distributed/deepseek_deepep/utils.py | 8 +- 4 files changed, 413 insertions(+), 6 deletions(-) create mode 100644 examples/distributed/deepseek_deepep/intranode/dispatch.py create mode 100644 examples/distributed/deepseek_deepep/intranode/notify_dispatch.py diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py new file mode 100644 index 0000000000..8942bd981f --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -0,0 +1,87 @@ +# For intranode only +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + +import torch +import torch.nn.functional as F +import tilelang +from tilelang.autotuner import * +import tilelang.language as T +import argparse +from typing import Optional, Tuple, List +from utils import Config + + +# todo: support cached-mode via handle +def intranode_dispatch( + # data + x: torch.Tensor, # todo: support fp8 quant + # handle + handle: Optional[Tuple] = None, + # meta + num_tokens_per_rank: Optional[torch.Tensor] = None, + is_token_in_rank: Optional[torch.Tensor] = None, + num_tokens_per_expert: Optional[torch.Tensor] = None, + topk_idx: Optional[torch.Tensor] = None, + topk_weights: Optional[torch.Tensor] = None, + expert_alignment: int = 1, + # todo: support expert alignment and num_worst_tokens + # tuning cfg + config: Optional[Config] = None, + # todo: support async functionality +) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int], Tuple]: + """ + Dispatch tokens to different intranode ranks. + Intranode kernels require all the ranks should be visible via NVLink. + + Arguments: + x: `torch.Tensor` or tuple of `torch.Tensor`, for the first type, the shape must be `[num_tokens, hidden]`, + and type must be `torch.bfloat16`; for the second type, the first element of the tuple must be shaped as + `[num_tokens, hidden]` with type `torch.float8_e4m3fn`, the second must be `[num_tokens, hidden // 128]` + (requiring divisible) with type `torch.float`. + num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. + is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. + num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. + topk_idx: `[num_tokens, num_topk]` with `deep_ep.topk_idx_t` (typically `torch.int64`), the expert indices + selected by each token, `-1` means no selections. + topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. + expert_alignment: align the number of tokens received by each local expert to this variable. + config: the performance tuning config. + + Returns: + recv_x: received tokens, the same type and tuple as the input `x`, but the number of tokens equals to the + received token count. + recv_topk_idx: received expert indices. + recv_topk_weights: received expert weights. + num_recv_tokens_per_expert_list: Python list shaped `[num_local_experts]`, the received token count by + each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list + will be empty. + handle: the returned communication handle. + """ + + assert handle is None # Currently only support non-cached mode + assert num_tokens_per_rank is not None or is_token_in_rank is not None and num_tokens_per_expert is not None, \ + "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" + + # acquire shapes + num_tokens, hidden = x.shape + num_experts = num_tokens_per_expert.shape[0] + num_ranks = num_tokens_per_rank.shape[0] + num_local_experts = num_experts // num_ranks + num_topk = topk_idx.shape[1] + + # Default config + config = Config.get_dispatch_config(num_ranks) if config is None else config + + num_memset_int = config.num_channels * num_ranks * 4 + + # Size prefix by ranks, shaped as `[num_ranks, num_ranks]` + # Size prefix by experts (not used later), shaped as `[num_ranks, num_local_experts]` + rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') + channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') + + notify_dispatch( + num_tokens_per_rank, + + ) + diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index cb3fa6efbe..1705b489b7 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -1,5 +1,7 @@ # For intranode only # This op is non-distributed +### python get_dispatch_layout.py + import os, sys sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path @@ -78,7 +80,7 @@ def get_dispatch_layout_kernel( experts_per_rank = num_experts // num_ranks @T.prim_func - def main( + def get_dispatch_layout_main( topk_idx: T.Tensor([num_tokens, num_topk], "int64"), # type: ignore num_tokens_per_rank: T.Tensor([num_ranks], "int32"), # type: ignore num_tokens_per_expert: T.Tensor([num_experts], "int32"), # type: ignore @@ -156,7 +158,7 @@ def main( sum[0] += tokens_per_rank_per_thread[i, tid] num_tokens_per_rank[rank_begin_idx[0] + tid] = sum[0] - return main + return get_dispatch_layout_main def test_get_dispatch_layout( @@ -187,7 +189,7 @@ def test_get_dispatch_layout( num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) - assert torch.allclose(num_tokens_per_expert, ref_num_tokens_per_expert), \ + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ @@ -196,7 +198,7 @@ def test_get_dispatch_layout( assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - print("All checks passed.✅") + print("All checks passed for TileScale get_dispatch_layout.✅") # Benchmark t1 = do_bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False)) diff --git a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py new file mode 100644 index 0000000000..690479e342 --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py @@ -0,0 +1,314 @@ +# For intranode only +# This op is distributed +### TILELANG_USE_DISTRIBUTED=1 python notify_dispatch.py + +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + +import tilelang +import tilelang.language as T +import torch +from argparse import ArgumentParser +from tilelang.distributed.utils import init_dist +from utils import gen_inputs # noqa: F403 + +from get_dispatch_layout import get_dispatch_layout + + +# TileScale notify-dispatch kernel for non-cached mode +# Check: DeepEP/csrc/kernels/intranode.cu::notify_dispatch +@tilelang.jit +def notify_dispatch_kernel( + rank: int, + num_ranks: int, + num_experts: int, + num_tokens: int, + num_channels: int, + expert_alignment: int, +): + + threads = 128 + num_local_experts = num_experts // num_ranks + num_warps = threads // 32 + + @T.prim_func + def notify_dispatch_main( + num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), + num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), + is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), + moe_recv_counter_mapped: T.Tensor((1,), 'int64'), + moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), + per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), + per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), + channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), + ): + with T.Kernel(num_ranks+1, threads=threads) as bx: + tx = T.get_thread_binding() + lane_id, warp_id = tx % 32, tx // 32 + + if bx == 0: + # Barrier first + T.barrier_all_blocks_sys(barrier_signal) + + # `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j + # `per_expert_buffer[rank][i, j]` means the number of tokens from rank i to local expert j + if tx < num_ranks: + T.st(per_rank_buffer[rank, tx], num_tokens_per_rank[tx], dst_pe=tx) + for i in T.serial(num_local_experts): + T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) + + T.barrier_all_blocks_sys(barrier_signal) + + # Sum per-rank cnts and pre-compute the prefix sum for data sending + if tx < num_ranks: + for i in T.serial(1, num_ranks): + per_rank_buffer[i, tx] += per_rank_buffer[i-1, tx] + if tx == rank: + moe_recv_counter_mapped[0] = per_rank_buffer[num_ranks-1, rank] + + # Sum per-expert cnts + if tx < num_local_experts: + sum = T.alloc_local([1], 'int32') + sum[0] = 0 + for i in T.serial(0, num_ranks): + sum[0] += per_expert_buffer[i, tx] + sum[0] = T.ceildiv(sum[0], expert_alignment) * expert_alignment # align up + moe_recv_expert_counter_mapped[tx] = sum[0] + T.sync_threads() + + # Copy rank size prefix matrix to another tensor + T.copy(per_rank_buffer, rank_prefix_matrix) + + #? We don't cleanup the buffer for later use, as it is one time used? + T.barrier_all_blocks_sys(barrier_signal) + else: + dst_rank = bx - 1 + for channel_id in T.serial(warp_id, num_channels, num_warps): + num_tokens_per_channel = T.ceildiv(num_tokens, num_channels) + token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) + token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) + cnt = T.alloc_local([1], 'int32') + cnt[0] = 0 + for i in T.serial(token_start_idx + lane_id, token_end_idx, 32): + cnt[0] += is_token_in_rank[i, dst_rank] + cnt[0] = T.warp_reduce_sum(cnt[0]) + if lane_id == 0: # todo: replace with elect_one_sync() for sm90 + channel_prefix_matrix[dst_rank, channel_id] = cnt[0] + T.sync_threads() + + if tx == 0: + for i in T.serial(1, num_channels): + channel_prefix_matrix[dst_rank, i] += channel_prefix_matrix[dst_rank, i-1] + + return notify_dispatch_main + + +# TileScale notify-dispatch op +def notify_dispatch( + # meta + rank: int, + num_ranks: int, + num_experts: int, + num_tokens: int, + num_channels: int, + expert_alignment: int, + # dispatch layout + num_tokens_per_rank: torch.Tensor, + num_tokens_per_expert: torch.Tensor, + is_token_in_rank: torch.Tensor, + # counter + moe_recv_counter_mapped: torch.Tensor, + moe_recv_expert_counter_mapped: torch.Tensor, + # symm buffers + per_rank_buffer: torch.Tensor, + per_expert_buffer: torch.Tensor, + barrier_signal: torch.Tensor, + # allocator + allocator, +): + """ + TileScale notify-dispatch op. + + Args: + rank (int): The current rank (process or device index). + num_ranks (int): Total number of participating ranks (nodes). + num_experts (int): Global number of experts in the MoE layer. + num_tokens (int): Number of tokens being dispatched. + num_channels (int): Number of communication channels. + expert_alignment (int): Alignment constraint for expert buffer. + + num_tokens_per_rank (torch.Tensor): [num_ranks] + - For each rank r, num_tokens_per_rank[r] is the number of tokens assigned for dispatch to rank r across the cluster. + num_tokens_per_expert (torch.Tensor): [num_experts] + - For each expert e, num_tokens_per_expert[e] is the number of tokens rank r will send to global expert e. + is_token_in_rank (torch.Tensor): [num_tokens, num_ranks] + - For each (token t, rank r), is_token_in_rank[t, r] indicates (bool) whether token t belongs to rank r after dispatch. + + moe_recv_counter_mapped (torch.Tensor): [1] + - The number of tokens received by the current rank from other ranks. + moe_recv_expert_counter_mapped (torch.Tensor): [num_local_experts] + - The number of tokens received by the current rank for its local experts. + + per_rank_buffer (torch.Tensor): num_ranks * [num_ranks, num_ranks], symm tensor, should be zeroed before use + - Symmetric buffer for per-rank communication; [src_rank, dst_rank] region. + per_expert_buffer (torch.Tensor): num_ranks * [num_ranks, num_local_experts], symm tensor, should be zeroed before use + - Buffer for per-expert communication; [rank, local_expert] region. + barrier_signal (torch.Tensor): num_ranks * [num_ranks], symm_tensor, should be zeroed before use + - Synchronization tensor used as a system-wide barrier. + + allocator: TileScale allocator for symm tensors + + Returns + rank_prefix_matrix (torch.Tensor): [num_ranks, num_ranks] + - For each (rank r, other_rank), rank_prefix_matrix[r, other_rank] records prefix sums/statistics for token dispatch between r and other_rank. + channel_prefix_matrix (torch.Tensor): [num_ranks, num_channels] + - For each (rank r, channel c), channel_prefix_matrix[r, c] records prefix sums/statistics for tokens on communication channel c for rank r. + """ + kernel = notify_dispatch_kernel( + rank, + num_ranks, + num_experts, + num_tokens, + num_channels, + expert_alignment, + ) + kernel.initialize(allocator=allocator) + + rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') + channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') + + kernel( + num_tokens_per_rank, + num_tokens_per_expert, + is_token_in_rank, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, + per_rank_buffer, + per_expert_buffer, + barrier_signal, + rank_prefix_matrix, + channel_prefix_matrix, + ) + + return rank_prefix_matrix, channel_prefix_matrix + + +# todo: impl cached_notify_dispatch + + +def test_notify_dispatch( + num_tokens: int, + hidden: int, + num_topk: int, + num_experts: int, + rank: int, + num_ranks: int, + expert_alignment: int, + group: torch.distributed.ProcessGroup, +): + try: + import deep_ep # noqa: F403 + except ModuleNotFoundError as e: + raise ModuleNotFoundError("Please install DeepEP to run this test.") + + num_local_experts = num_experts // num_ranks + + allocator = tilelang.get_allocator( + size=2**30, + device="cuda", + is_distributed=True, + local_rank=rank, + num_local_ranks=num_ranks, + group=group) + + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) + buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) + + if rank == 0: + print(f'get dispatch layout...') + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ + "is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" + + if rank == 0: + print(f'notify dispatch...') + handle = buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights)[-2] + ref_rank_prefix_matrix, ref_channel_prefix_matrix = handle[:2] + + # create buffers in need + moe_recv_counter_mapped = torch.empty([1], dtype=torch.int64, device='cuda') + moe_recv_counter_mapped[0] = -1 + moe_recv_expert_counter_mapped = torch.empty([num_local_experts], dtype=torch.int32, device='cuda') + moe_recv_expert_counter_mapped.fill_(-1) + + per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + barrier_signal = tilelang.tensor((num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + + rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( + rank, + num_ranks, + num_experts, + num_tokens, + 10, # 20 sms by default + expert_alignment, + num_tokens_per_rank, + num_tokens_per_expert, + is_token_in_rank, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, + per_rank_buffer, + per_expert_buffer, + barrier_signal, + allocator + ) + + assert torch.allclose(rank_prefix_matrix, ref_rank_prefix_matrix), \ + f"rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}" + assert torch.allclose(channel_prefix_matrix, ref_channel_prefix_matrix), \ + f"channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}" + print(f'[rank {rank}] All checks passed for TileScale notify_dispatch. ✅') + + # todo: benchmark + + +def main( + local_rank: int, num_local_ranks: int, args +): + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + + test_notify_dispatch( + args.num_tokens, + args.hidden, + args.num_topk, + args.num_experts, + rank, + num_ranks, + args.expert_alignment, + group, + ) + + +def parse_args(): + parser = ArgumentParser(description="Test notify_dispatch") + parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") + parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") + parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") + parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") + parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + + num_ranks = args.num_ranks + torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 705788186c..dc6deceae0 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -1,7 +1,7 @@ from typing import Union, Tuple import torch import os -from dataclasses import dataclass +from dataclasses import dataclass, field # Pre-defined constants in DeepEP NUM_MAX_NVL_PEERS = 8 # Maximum number of NVLink peers per GPU @@ -15,15 +15,19 @@ @dataclass class Config: + num_sms : int # the SMs used in high-throughput kernels num_max_nvl_chunked_send_tokens : int num_max_nvl_chunked_recv_tokens : int num_max_rdma_chunked_send_tokens : int num_max_rdma_chunked_recv_tokens : int - num_sms : int = 20 # the SMs used in high-throughput kernels + num_channels: int = field(init=False) + def __post_init__(self): assert self.num_sms % 2 == 0, "num_sms must be even" + self.num_channels = self.num_sms // 2 + # 1 sm for send, 1 sm for recv in each channel @staticmethod def get_dispatch_config(num_ranks: int) -> 'Config': From 2f65de98a10c0275d032cfa73f040dc3f702e804 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 21 Nov 2025 12:44:55 +0000 Subject: [PATCH 09/41] rename and refactor `T.barrier/sync_blocks` --- .../intranode/notify_dispatch.py | 8 ++-- .../distributed/sp_ag_attention_intra_node.py | 2 +- src/op/sync.cc | 40 ++++++++++--------- src/op/sync.h | 31 +++++++------- src/tl_templates/cuda/sync.h | 23 ++++++----- .../testing/sync/test_barrierall_sys.py | 2 +- tilelang/language/builtin.py | 15 +++++-- 7 files changed, 70 insertions(+), 51 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py index 690479e342..4e13c63626 100644 --- a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py @@ -50,7 +50,7 @@ def notify_dispatch_main( if bx == 0: # Barrier first - T.barrier_all_blocks_sys(barrier_signal) + T.sync_blocks(barrier_signal) # `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j # `per_expert_buffer[rank][i, j]` means the number of tokens from rank i to local expert j @@ -59,7 +59,7 @@ def notify_dispatch_main( for i in T.serial(num_local_experts): T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) - T.barrier_all_blocks_sys(barrier_signal) + T.barrier_blocks(barrier_signal) # Sum per-rank cnts and pre-compute the prefix sum for data sending if tx < num_ranks: @@ -81,8 +81,8 @@ def notify_dispatch_main( # Copy rank size prefix matrix to another tensor T.copy(per_rank_buffer, rank_prefix_matrix) - #? We don't cleanup the buffer for later use, as it is one time used? - T.barrier_all_blocks_sys(barrier_signal) + # We don't cleanup the buffer for later use, as it is one time used? + T.barrier_blocks(barrier_signal) else: dst_rank = bx - 1 for channel_id in T.serial(warp_id, num_channels, num_warps): diff --git a/examples/distributed/sp_ag_attention_intra_node.py b/examples/distributed/sp_ag_attention_intra_node.py index 42e5493e42..421f133931 100644 --- a/examples/distributed/sp_ag_attention_intra_node.py +++ b/examples/distributed/sp_ag_attention_intra_node.py @@ -15,7 +15,7 @@ def barrier_all_blocks_sys_kernel(num_local_rank,): @T.prim_func def main(barrier: T.Tensor((num_local_rank), "int32"),): with T.Kernel(1, threads=32): - T.barrier_all_blocks_sys(barrier) + T.barrier_blocks(barrier) return main diff --git a/src/op/sync.cc b/src/op/sync.cc index 0c83a7b8d4..b0b7fc6e2c 100644 --- a/src/op/sync.cc +++ b/src/op/sync.cc @@ -18,7 +18,7 @@ namespace tl { using namespace tir; PrimExpr -BarrierAllBlocksSysOpNode::get_offset(const BufferLoadNode *load) const { +BarrierBlocksOpNode::get_offset(const BufferLoadNode *load) const { PrimExpr offset = 0; PrimExpr stride = 1; auto buffer_shape = load->buffer->shape; @@ -63,11 +63,12 @@ TIR_DEFINE_TL_BUILTIN(sync_barrier_gpu) TIR_DEFINE_TL_BUILTIN(sync_grid).set_num_inputs(1).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -BarrierAllBlocksSysOp::BarrierAllBlocksSysOp(Array args, - BufferMap vmap) { - ObjectPtr node = - make_object(); +BarrierBlocksOp::BarrierBlocksOp(Array args, + BufferMap vmap) { + ObjectPtr node = + make_object(); node->local_bar_addr = args[0]; + node->need_fence = bool(args[1].as()->value); const auto *call = node->local_bar_addr.as(); ICHECK(call) << "local_bar_addr must be a call node"; ICHECK(call->op.same_as(builtin::address_of())) @@ -82,12 +83,15 @@ BarrierAllBlocksSysOp::BarrierAllBlocksSysOp(Array args, (void)vmap; } -Stmt BarrierAllBlocksSysOpNode::Lower(const LowerArgs &T, - arith::Analyzer *analyzer) const { +Stmt BarrierBlocksOpNode::Lower(const LowerArgs &T, + arith::Analyzer *analyzer) const { (void)analyzer; Array new_args; std::stringstream ss; - ss << "tl::barrier_all_blocks_sys"; + ss << "tl::barrier_blocks"; + if (!need_fence) { + ss << ""; + } new_args.push_back(StringImm(ss.str())); PrimExpr bar_addr = MakeLocalBarAddr(T); @@ -103,24 +107,24 @@ Stmt BarrierAllBlocksSysOpNode::Lower(const LowerArgs &T, new_args.push_back(rank); new_args.push_back(num_ranks); - auto barrier_all_blocks_sys = + auto barrier_blocks = Call(DataType::Handle(), builtin::call_extern(), new_args); - return Evaluate(barrier_all_blocks_sys); + return Evaluate(barrier_blocks); } -LayoutMap BarrierAllBlocksSysOpNode::InferLayout(const LayoutInferArgs &T, - InferLevel level) const { +LayoutMap BarrierBlocksOpNode::InferLayout(const LayoutInferArgs &T, + InferLevel level) const { (void)T; (void)level; return {}; } -TileOperator BarrierAllBlocksSysOpNode::Clone() const { - auto node = make_object(*this); - return BarrierAllBlocksSysOp(node); +TileOperator BarrierBlocksOpNode::Clone() const { + auto node = make_object(*this); + return BarrierBlocksOp(node); } -PrimExpr BarrierAllBlocksSysOpNode::MakeLocalBarAddr(const LowerArgs &T) const { +PrimExpr BarrierBlocksOpNode::MakeLocalBarAddr(const LowerArgs &T) const { const auto *call = local_bar_addr.as(); ICHECK(call && call->op.same_as(builtin::address_of())) << "local_bar_addr must remain an address_of call"; @@ -134,7 +138,7 @@ PrimExpr BarrierAllBlocksSysOpNode::MakeLocalBarAddr(const LowerArgs &T) const { {BufferLoad(buffer, local_indices)}); } -TIR_REGISTER_TL_OP(BarrierAllBlocksSysOp, barrier_all_blocks_sys) +TIR_REGISTER_TL_OP(BarrierBlocksOp, barrier_blocks) .set_num_inputs(1) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); @@ -148,7 +152,7 @@ TIR_DEFINE_TL_BUILTIN(fence_gpu).set_num_inputs(0).set_attr( TIR_DEFINE_TL_BUILTIN(fence_sys).set_num_inputs(0).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TVM_FFI_STATIC_INIT_BLOCK({ BarrierAllBlocksSysOpNode::RegisterReflection(); }); +TVM_FFI_STATIC_INIT_BLOCK({ BarrierBlocksOpNode::RegisterReflection(); }); } // namespace tl } // namespace tvm diff --git a/src/op/sync.h b/src/op/sync.h index e0833ea06b..c2b3f8d7f7 100644 --- a/src/op/sync.h +++ b/src/op/sync.h @@ -63,18 +63,19 @@ TVM_DLL const Op &sync_grid(); /*! * \brief Synchronize all blocks at a system-level barrier * - * void barrier_all_blocks_sys(barrier, rank, num_ranks) + * void barrier_blocks(barrier, rank, num_ranks) * */ -class BarrierAllBlocksSysOpNode : public TileOperatorNode { +class BarrierBlocksOpNode : public TileOperatorNode { public: PrimExpr local_bar_addr; ///< Address expression for the local barrier PrimExpr offset; ///< Byte offset within the barrier buffer Buffer local_bar; ///< Local barrier buffer reference Array local_indices; ///< Indices used to access the barrier buffer + bool need_fence; ///< Whether need sys-level fence - static constexpr const char *_type_key = "tl.BarrierAllBlocksSysOp"; - TVM_DECLARE_FINAL_OBJECT_INFO(BarrierAllBlocksSysOpNode, TileOperatorNode); + static constexpr const char *_type_key = "tl.BarrierBlocksOp"; + TVM_DECLARE_FINAL_OBJECT_INFO(BarrierBlocksOpNode, TileOperatorNode); Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; LayoutMap InferLayout(const LayoutInferArgs &T, @@ -84,14 +85,14 @@ class BarrierAllBlocksSysOpNode : public TileOperatorNode { static void RegisterReflection() { namespace refl = tvm::ffi::reflection; - refl::ObjectDef() - .def_ro("local_bar_addr", &BarrierAllBlocksSysOpNode::local_bar_addr) - .def_ro("offset", &BarrierAllBlocksSysOpNode::offset) - .def_ro("local_bar", &BarrierAllBlocksSysOpNode::local_bar) - .def_ro("local_indices", &BarrierAllBlocksSysOpNode::local_indices); + refl::ObjectDef() + .def_ro("local_bar_addr", &BarrierBlocksOpNode::local_bar_addr) + .def_ro("offset", &BarrierBlocksOpNode::offset) + .def_ro("local_bar", &BarrierBlocksOpNode::local_bar) + .def_ro("local_indices", &BarrierBlocksOpNode::local_indices); } - bool SEqualReduce(const BarrierAllBlocksSysOpNode *other, + bool SEqualReduce(const BarrierBlocksOpNode *other, SEqualReducer equal) const { return equal(local_bar_addr, other->local_bar_addr) && equal(offset, other->offset) && equal(local_bar, other->local_bar) && @@ -115,13 +116,13 @@ class BarrierAllBlocksSysOpNode : public TileOperatorNode { }; /*! - * \brief Wrapper for the BarrierAllBlocksSys operator + * \brief Wrapper for the BarrierBlocks operator */ -class BarrierAllBlocksSysOp : public TileOperator { +class BarrierBlocksOp : public TileOperator { public: - TVM_DEFINE_OBJECT_REF_METHODS(BarrierAllBlocksSysOp, TileOperator, - BarrierAllBlocksSysOpNode); - TVM_DLL BarrierAllBlocksSysOp(Array args, BufferMap vmap); + TVM_DEFINE_OBJECT_REF_METHODS(BarrierBlocksOp, TileOperator, + BarrierBlocksOpNode); + TVM_DLL BarrierBlocksOp(Array args, BufferMap vmap); static const Op &Get(); }; diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index 012b913447..2800080784 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -149,21 +149,25 @@ TL_DEVICE void sync_grid(uint32_t *barrier) { sync_grids_wait(token, barrier); } -// Synchronize all blocks at a system-level barrier -// TODO(wt): Add sync-only option and timeout handling +// Sync blocks at a system-level barrier with an optinal fence +// TODO(wt): Add timeout handling -TL_DEVICE void barrier_all_blocks_sys(int offset, int rank, int num_ranks) { +template +TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { // Macro to compute the barrier pointer for a given target rank -#define BARRIER_PTR(tgt_rank) \ +#define BARRIER_PTR(tgt_rank) \ (reinterpret_cast(get_remote_base_ptr(tgt_rank) + offset)) +#define FINISHED_SUM_TAG (1024) - memory_fence_sys(); - __syncthreads(); - + if constexpr (need_fence) { + memory_fence_sys(); + __syncthreads(); + } + int tid = threadIdx.x; if (tid < num_ranks) { - atomicAdd_system(BARRIER_PTR(rank) + tid, 1); - atomicAdd_system(BARRIER_PTR(tid) + rank, -1); + atomicAdd_system(BARRIER_PTR(rank) + tid, FINISHED_SUM_TAG); + atomicSub_system(BARRIER_PTR(tid) + rank, FINISHED_SUM_TAG); } while (true) { @@ -176,6 +180,7 @@ TL_DEVICE void barrier_all_blocks_sys(int offset, int rank, int num_ranks) { __syncthreads(); #undef BARRIER_PTR +#undef FINISHED_SUM_TAG } template TL_DEVICE void wait_eq(void *barrier, T val = 1) { diff --git a/tilelang/distributed/testing/sync/test_barrierall_sys.py b/tilelang/distributed/testing/sync/test_barrierall_sys.py index 96c65eca3e..a7a5c0dfd8 100644 --- a/tilelang/distributed/testing/sync/test_barrierall_sys.py +++ b/tilelang/distributed/testing/sync/test_barrierall_sys.py @@ -33,7 +33,7 @@ def main( val[0] = 1 T.atomic_add(A[tid], val[0]) - T.barrier_all_blocks_sys(barrier) + T.barrier_blocks(barrier) if tid < 32: T.put_warp( diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index afc20c7e4d..610be8a815 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -622,14 +622,23 @@ def sync_grid(barrier: PrimExpr): return tir.call_intrin("handle", tir.op.Op.get("tl.sync_grid"), address_of(barrier)) -def barrier_all_blocks_sys(barrier: PrimExpr): +def barrier_blocks(barrier: PrimExpr): + """Barrier all blocks at a system-level barrier. + Compare to sync_blocks, barrier_blocks have an extra system-level fence effect + + Args: + barrier: The barrier to synchronize at, should be [num_ranks] of int32 + """ + return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), 1) # whether need fence + + +def sync_blocks(barrier: PrimExpr): """Synchronize all blocks at a system-level barrier. Args: barrier: The barrier to synchronize at, should be [num_ranks] of int32 """ - return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_all_blocks_sys"), - address_of(barrier)) + return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), 0) # whether need fence def fence_cta(): From 5d686109ad54d2428044de141105e793362c069a Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 21 Nov 2025 12:51:08 +0000 Subject: [PATCH 10/41] fix prev typo --- tilelang/distributed/testing/sync/test_barrierall_sys.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tilelang/distributed/testing/sync/test_barrierall_sys.py b/tilelang/distributed/testing/sync/test_barrierall_sys.py index a7a5c0dfd8..307daee466 100644 --- a/tilelang/distributed/testing/sync/test_barrierall_sys.py +++ b/tilelang/distributed/testing/sync/test_barrierall_sys.py @@ -40,7 +40,7 @@ def main( src=T.address_of(A), dst=T.address_of(B[bid, 0]), size=threads, - src_pe=rank[0] ^ 1, + dst_pe=rank[0] ^ 1, unroll_factor=4) return main From c28e0c61023301fbe80d005d58363fa31e1edd13 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Sat, 22 Nov 2025 03:47:40 +0000 Subject: [PATCH 11/41] [Feat] Add `get_device_tensor` function and related test --- .../testing/test_get_device_tensor.py | 21 ++++++++++ tilelang/distributed/utils.py | 17 ++++++++- tilelang/utils/ts_ext/__init__.py | 10 ++++- tilelang/utils/ts_ext/exception.h | 37 ++++++++++++++++++ tilelang/utils/ts_ext/ipc_ops.cpp | 38 ++----------------- tilelang/utils/ts_ext/setup.py | 2 +- .../{tensor_from_ptr.cpp => tensor.cpp} | 9 +++++ tilelang/utils/ts_ext/ts_ext_bindings.cpp | 2 + tilelang/utils/ts_ext/ts_ext_ops.h | 2 + 9 files changed, 101 insertions(+), 37 deletions(-) create mode 100644 tilelang/distributed/testing/test_get_device_tensor.py create mode 100644 tilelang/utils/ts_ext/exception.h rename tilelang/utils/ts_ext/{tensor_from_ptr.cpp => tensor.cpp} (85%) diff --git a/tilelang/distributed/testing/test_get_device_tensor.py b/tilelang/distributed/testing/test_get_device_tensor.py new file mode 100644 index 0000000000..4e0d1d74a3 --- /dev/null +++ b/tilelang/distributed/testing/test_get_device_tensor.py @@ -0,0 +1,21 @@ +import torch +from tilelang.distributed.utils import get_device_tensor + + +if __name__ == "__main__": + shape = (1024, 1024) + dtype = torch.float32 + host_tensor = torch.randn(shape, dtype=dtype, pin_memory=True) + device_tensor = get_device_tensor(host_tensor) + + # test meta-data + assert device_tensor.device.type == "cuda" + assert device_tensor.shape == shape, f"{device_tensor.shape=}" + assert device_tensor.dtype == dtype, f"{device_tensor.dtype=}" + assert torch.equal(host_tensor, device_tensor.cpu()), f"{host_tensor=}, {device_tensor=}" + + # test modification + device_tensor.random_() + assert torch.equal(host_tensor, device_tensor.cpu()), f"{host_tensor=}, {device_tensor=}" + + print("All checks passed for get_device_tensor. ✅") \ No newline at end of file diff --git a/tilelang/distributed/utils.py b/tilelang/distributed/utils.py index ae7e1bfd79..bc153b92bc 100644 --- a/tilelang/distributed/utils.py +++ b/tilelang/distributed/utils.py @@ -19,7 +19,7 @@ from cuda import cuda, cudart import ctypes -from tilescale_ext import _create_tensor, _create_ipc_handle, _sync_ipc_handles +from tilescale_ext import _create_tensor, _create_ipc_handle, _sync_ipc_handles, _get_device_tensor import functools from functools import lru_cache from threading import Lock @@ -399,3 +399,18 @@ def has_fullmesh_nvlink(): stacklevel=2, ) return _has_fullmesh_nvlink + + +def get_device_tensor(tensor: torch.Tensor) -> torch.Tensor: + """Get the device tensor from the host tensor. + This is implemented via `cudaHostGetDevicePointer` + + Args: + tensor: The host tensor. + + Returns: + The device tensor with same meta-data. + """ + assert tensor.device.type == "cpu" + assert tensor.is_pinned() + return _get_device_tensor(tensor) diff --git a/tilelang/utils/ts_ext/__init__.py b/tilelang/utils/ts_ext/__init__.py index 087cdfafb2..a295a87fd9 100644 --- a/tilelang/utils/ts_ext/__init__.py +++ b/tilelang/utils/ts_ext/__init__.py @@ -6,5 +6,13 @@ _create_tensor = _C._create_tensor _create_ipc_handle = _C._create_ipc_handle _sync_ipc_handles = _C._sync_ipc_handles +_get_device_tensor = _C.get_device_tensor -__all__ = ["tensor_from_ptr", "_create_tensor", "_create_ipc_handle", "_sync_ipc_handles", "_C"] +__all__ = [ + "tensor_from_ptr", + "_create_tensor", + "_create_ipc_handle", + "_sync_ipc_handles", + "_get_device_tensor", + "_C", +] diff --git a/tilelang/utils/ts_ext/exception.h b/tilelang/utils/ts_ext/exception.h new file mode 100644 index 0000000000..8e5bf69ded --- /dev/null +++ b/tilelang/utils/ts_ext/exception.h @@ -0,0 +1,37 @@ +#pragma once + +#include +#include +#include + +class TSException : public std::exception { + std::string message; + +public: + TSException(const char *name, const char *file, int line, + const std::string &error) { + message = std::string("Failed: ") + name + " error " + file + ":" + + std::to_string(line) + " '" + error + "'"; + } + const char *what() const noexcept override { return message.c_str(); } +}; + +#ifndef TS_HOST_ASSERT +#define TS_HOST_ASSERT(cond) \ + do { \ + if (!(cond)) { \ + throw TSException("Assertion", __FILE__, __LINE__, #cond); \ + } \ + } while (0) +#endif + +#ifndef CUDA_CHECK +#define CUDA_CHECK(cmd) \ + do { \ + cudaError_t e = (cmd); \ + if (e != cudaSuccess) { \ + throw TSException("CUDA", __FILE__, __LINE__, cudaGetErrorString(e)); \ + } \ + } while (0) +#endif + diff --git a/tilelang/utils/ts_ext/ipc_ops.cpp b/tilelang/utils/ts_ext/ipc_ops.cpp index c3c1e166a1..755914c3ad 100644 --- a/tilelang/utils/ts_ext/ipc_ops.cpp +++ b/tilelang/utils/ts_ext/ipc_ops.cpp @@ -19,40 +19,10 @@ #include #include "ts_ext_ops.h" +#include "exception.h" namespace py = pybind11; -class EPException : public std::exception { - std::string message; - -public: - EPException(const char *name, const char *file, int line, - const std::string &error) { - message = std::string("Failed: ") + name + " error " + file + ":" + - std::to_string(line) + " '" + error + "'"; - } - const char *what() const noexcept override { return message.c_str(); } -}; - -#ifndef EP_HOST_ASSERT -#define EP_HOST_ASSERT(cond) \ - do { \ - if (!(cond)) { \ - throw EPException("Assertion", __FILE__, __LINE__, #cond); \ - } \ - } while (0) -#endif - -#ifndef CUDA_CHECK -#define CUDA_CHECK(cmd) \ - do { \ - cudaError_t e = (cmd); \ - if (e != cudaSuccess) { \ - throw EPException("CUDA", __FILE__, __LINE__, cudaGetErrorString(e)); \ - } \ - } while (0) -#endif - static size_t numel_of(const std::vector &shape) { return std::accumulate(shape.begin(), shape.end(), size_t{1}, [](size_t a, int64_t b) { @@ -105,15 +75,15 @@ void sync_ipc_handles( const int num = (int)device_ids.size(); const int rdma_rank = 0; - EP_HOST_ASSERT((size_t)num == all_gathered_handles.size()); + TS_HOST_ASSERT((size_t)num == all_gathered_handles.size()); std::vector ipc_handles(num); std::vector buffer_ptrs(num, nullptr); for (int i = 0, offset = rdma_rank * num; i < num; ++i) { - EP_HOST_ASSERT(all_gathered_handles[offset + i].has_value()); + TS_HOST_ASSERT(all_gathered_handles[offset + i].has_value()); std::string s = (std::string)all_gathered_handles[offset + i].value(); - EP_HOST_ASSERT(s.size() == CUDA_IPC_HANDLE_SIZE); + TS_HOST_ASSERT(s.size() == CUDA_IPC_HANDLE_SIZE); if (offset + i != rank) { std::memcpy(ipc_handles[i].reserved, s.data(), CUDA_IPC_HANDLE_SIZE); CUDA_CHECK(cudaIpcOpenMemHandle(&buffer_ptrs[i], ipc_handles[i], diff --git a/tilelang/utils/ts_ext/setup.py b/tilelang/utils/ts_ext/setup.py index a60b147dc2..1b7a2ee871 100644 --- a/tilelang/utils/ts_ext/setup.py +++ b/tilelang/utils/ts_ext/setup.py @@ -42,7 +42,7 @@ name="tilescale_ext._C", sources=[ "ts_ext_bindings.cpp", - "tensor_from_ptr.cpp", + "tensor.cpp", "ipc_ops.cpp", ], include_dirs=include_dirs, diff --git a/tilelang/utils/ts_ext/tensor_from_ptr.cpp b/tilelang/utils/ts_ext/tensor.cpp similarity index 85% rename from tilelang/utils/ts_ext/tensor_from_ptr.cpp rename to tilelang/utils/ts_ext/tensor.cpp index 3349267dc0..5d09b2a763 100644 --- a/tilelang/utils/ts_ext/tensor_from_ptr.cpp +++ b/tilelang/utils/ts_ext/tensor.cpp @@ -9,6 +9,7 @@ #include #include "ts_ext_ops.h" +#include "exception.h" static int64_t safe_mul_int64(int64_t a, int64_t b) { if (a == 0 || b == 0) @@ -84,3 +85,11 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, return at::from_blob(data_ptr, shape, deleter, options); } } + +torch::Tensor get_device_tensor(torch::Tensor tensor) { + void* device_ptr = nullptr; + CUDA_CHECK(cudaHostGetDevicePointer(&device_ptr, tensor.data_ptr(), 0)); + std::vector shape(tensor.sizes().begin(), tensor.sizes().end()); + std::string dtype_name(tensor.dtype().name()); + return tensor_from_ptr(reinterpret_cast(device_ptr), shape, dtype_name, tensor.device().index(), false); +} \ No newline at end of file diff --git a/tilelang/utils/ts_ext/ts_ext_bindings.cpp b/tilelang/utils/ts_ext/ts_ext_bindings.cpp index b1274f1aef..b3a39ab00c 100644 --- a/tilelang/utils/ts_ext/ts_ext_bindings.cpp +++ b/tilelang/utils/ts_ext/ts_ext_bindings.cpp @@ -22,6 +22,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { }, py::arg("shape"), py::arg("dtype")); + m.def("get_device_tensor", &get_device_tensor, py::arg("tensor")); + m.def( "_create_ipc_handle", [](uintptr_t ptr_value) { diff --git a/tilelang/utils/ts_ext/ts_ext_ops.h b/tilelang/utils/ts_ext/ts_ext_ops.h index 61b4758288..16fdf1a2aa 100644 --- a/tilelang/utils/ts_ext/ts_ext_ops.h +++ b/tilelang/utils/ts_ext/ts_ext_ops.h @@ -13,6 +13,8 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, torch::Tensor create_tensor(const std::vector &shape, c10::ScalarType dtype); +torch::Tensor get_device_tensor(torch::Tensor tensor); + pybind11::bytearray create_ipc_handle(void *ptr); void sync_ipc_handles( From b745a761c50451d7589f9d8041c96800652f972e Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Sun, 23 Nov 2025 06:16:04 +0000 Subject: [PATCH 12/41] support elect_one_sync() and add test --- .../primitives/example_warp_reduce.py | 30 ------- src/op/builtin.cc | 5 ++ src/op/builtin.h | 5 ++ src/target/codegen_cuda.cc | 2 + .../language/test_tilelang_language_elect.py | 29 +++++++ .../test_tilelang_language_warp_reduce.py | 83 +++++++++++++++++++ tilelang/language/builtin.py | 12 ++- 7 files changed, 133 insertions(+), 33 deletions(-) delete mode 100644 examples/distributed/primitives/example_warp_reduce.py create mode 100644 testing/python/language/test_tilelang_language_elect.py create mode 100644 testing/python/language/test_tilelang_language_warp_reduce.py diff --git a/examples/distributed/primitives/example_warp_reduce.py b/examples/distributed/primitives/example_warp_reduce.py deleted file mode 100644 index 4ec10d276b..0000000000 --- a/examples/distributed/primitives/example_warp_reduce.py +++ /dev/null @@ -1,30 +0,0 @@ -import torch -import tilelang -import tilelang.language as T - - -@tilelang.jit -def get_kernel(): - @T.prim_func - def main( - x: T.Tensor((32), "float32") - - ): - with T.Kernel(1, threads=32): - tx = T.get_thread_binding(0) - local_val = T.alloc_local([1], "float32") - local_val[0] = x[tx] - reduced_val = T.warp_reduce_sum(local_val[0]) - x[tx] = reduced_val - return main - - -if __name__ == '__main__': - a = torch.randn((32,), dtype=torch.float32, device='cuda') - kernel = get_kernel() - print(kernel.get_kernel_source()) - ref = torch.full_like(a, a.sum()) - kernel(a) - torch.testing.assert_close(a, ref) - print('Test passed for warp reduce sum ✅') - diff --git a/src/op/builtin.cc b/src/op/builtin.cc index 89c1e85942..02035590c8 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -339,5 +339,10 @@ TIR_DEFINE_TL_BUILTIN(warp_reduce_bitor) .set_num_inputs(1) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(elect_one_sync) + .set_num_inputs(0) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index 3f2336bf6c..e2bf445871 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -570,6 +570,11 @@ TVM_DLL const Op &warp_reduce_bitand(); */ TVM_DLL const Op &warp_reduce_bitor(); +/*! + * \brief tilelang intrinsic for electing exactly one lane within a logical thread group. + */ +TVM_DLL const Op &elect_one_sync(); + } // namespace tl } // namespace tvm diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 705a15847f..6f0309145a 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -2363,6 +2363,8 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { std::string func_name = math_func(op->dtype, "fdiv", rounding_mode); os << func_name << "(" << PrintExpr(op->args[0]) << ", " << PrintExpr(op->args[1]) << ")"; + } else if (op->op.same_as(tl::elect_one_sync())) { + os << "cute::elect_one_sync()"; } else { CodeGenC::VisitExpr_(op, os); } diff --git a/testing/python/language/test_tilelang_language_elect.py b/testing/python/language/test_tilelang_language_elect.py new file mode 100644 index 0000000000..c096f61663 --- /dev/null +++ b/testing/python/language/test_tilelang_language_elect.py @@ -0,0 +1,29 @@ +import torch + +import tilelang +import tilelang.testing +import tilelang.language as T + + +@tilelang.jit +def get_kernel(): + @T.prim_func + def main(x: T.Tensor((1), 'int32')): + with T.Kernel(1, threads=32): + if T.elect_one_sync(): + x[0] += 1 + + return main + + +@tilelang.testing.requires_cuda_compute_version(9, 0) +def test_elect_one_sync(): + a = torch.tensor([0], dtype=torch.int32, device='cuda') + kernel = get_kernel() + kernel(a) + assert 'cute::elect_one_sync' in kernel.get_kernel_source() + assert a[0] == 1 + + +if __name__ == "__main__": + tilelang.testing.main() \ No newline at end of file diff --git a/testing/python/language/test_tilelang_language_warp_reduce.py b/testing/python/language/test_tilelang_language_warp_reduce.py new file mode 100644 index 0000000000..83cac9c4da --- /dev/null +++ b/testing/python/language/test_tilelang_language_warp_reduce.py @@ -0,0 +1,83 @@ +import torch + +import tilelang +import tilelang.testing +import tilelang.language as T + + +@tilelang.jit +def get_kernel(reduce_op: str, dtype: str): + + assert reduce_op in ["sum", "max", "min", "bitand", "bitor"] + + @T.prim_func + def main(x: T.Tensor((32), dtype)): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding(0) + local_val = T.alloc_local([1], dtype) + local_val[0] = x[tx] + reduced_val = T.alloc_local([1], dtype) + if reduce_op == "sum": + reduced_val[0] = T.warp_reduce_sum(local_val[0]) + elif reduce_op == "max": + reduced_val[0] = T.warp_reduce_max(local_val[0]) + elif reduce_op == "min": + reduced_val[0] = T.warp_reduce_min(local_val[0]) + elif reduce_op == "bitand": + reduced_val[0] = T.warp_reduce_bitand(local_val[0]) + elif reduce_op == "bitor": + reduced_val[0] = T.warp_reduce_bitor(local_val[0]) + x[tx] = reduced_val[0] + + return main + + +def test_warp_reduce_sum(): + a = torch.randn((32,), dtype=torch.float32, device='cuda') + kernel = get_kernel('sum', 'float32') + ref = torch.full_like(a, a.sum()) + kernel(a) + torch.testing.assert_close(a, ref) + + +def test_warp_reduce_max(): + a = torch.randn((32,), dtype=torch.float32, device='cuda') + kernel = get_kernel("max", 'float32') + print(kernel.get_kernel_source()) + ref = torch.full_like(a, a.max()) + kernel(a) + torch.testing.assert_close(a, ref) + + +def test_warp_reduce_min(): + a = torch.randn((32,), dtype=torch.float32, device='cuda') + kernel = get_kernel("min", 'float32') + ref = torch.full_like(a, a.min()) + kernel(a) + torch.testing.assert_close(a, ref) + + +def test_warp_reduce_bitand(): + a = torch.randint(0, 100, size=(32,), dtype=torch.int32, device='cuda') + kernel = get_kernel("bitand", 'int32') + ref_val = a[0] + for i in range(1, a.shape[0]): + ref_val = ref_val & a[i] + ref = torch.full_like(a, ref_val) + kernel(a) + torch.testing.assert_close(a, ref) + + +def test_warp_reduce_bitor(): + a = torch.randint(0, 100, size=(32,), dtype=torch.int32, device='cuda') + kernel = get_kernel("bitor", 'int32') + ref_val = a[0] + for i in range(1, a.shape[0]): + ref_val = ref_val | a[i] + ref = torch.full_like(a, ref_val) + kernel(a) + torch.testing.assert_close(a, ref) + + +if __name__ == "__main__": + tilelang.testing.main() \ No newline at end of file diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index 610be8a815..acc857a9e0 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -6,7 +6,7 @@ from tilelang.language.kernel import get_thread_bindings, get_block_extents from tilelang.utils.target import check_hip_availability from tvm import tir -from typing import Any +from typing import Any, Literal, str import tilelang.language as T from tvm.tir import PrimExpr, Var, Call, Buffer, BufferLoad @@ -749,8 +749,8 @@ def atom_add(barrier: PrimExpr, value: PrimExpr, scope: str = "gpu", sem: str = def st( dst: PrimExpr, value: PrimExpr, - scope: str = "gpu", - sem: str = "relaxed", + scope: Literal["gpu", "sys"] = "gpu", + sem: Literal["relaxed", "release"] = "relaxed", dst_pe: tir.PrimExpr | tir.IntImm | None = -1, ): """Store a value to a given address with specified scope, semantic, and optional destination PE. @@ -769,3 +769,9 @@ def st( assert scope in ["gpu", "sys"], "Scope must be one of 'gpu', or 'sys'." assert sem in ["relaxed", "release"], "Semantic must be one of 'relaxed', or 'release'." return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, dst_pe) + + +def elect_one_sync(): + """Efficiently elect exactly one lane within a logical thread group. + """ + return tir.call_intrin("bool", tir.op.Op.get("tl.elect_one_sync")) \ No newline at end of file From 01b9996c3d596348d046a88e6b3513cfc3586aab Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Sun, 23 Nov 2025 06:19:48 +0000 Subject: [PATCH 13/41] draft dispatch --- .../distributed/deepseek_deepep/buffer.py | 91 +++++++++++++++ .../deepseek_deepep/intranode/__init__.py | 1 + .../deepseek_deepep/intranode/dispatch.py | 109 +++++++++++++++++- .../intranode/notify_dispatch.py | 40 +++++-- examples/distributed/deepseek_deepep/utils.py | 30 ++++- tilelang/language/builtin.py | 2 +- 6 files changed, 254 insertions(+), 19 deletions(-) create mode 100644 examples/distributed/deepseek_deepep/buffer.py create mode 100644 examples/distributed/deepseek_deepep/intranode/__init__.py diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py new file mode 100644 index 0000000000..62b845a33b --- /dev/null +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -0,0 +1,91 @@ +import os +import torch +import torch.distributed as dist +from typing import Callable, List, Tuple, Optional, Union + +import tilelang +import tilelang.language as T +from utils import Config +from intranode import get_dispatch_layout + + +class TSBuffer: + """ + TileScale communication buffers for DeepEP + + Attributes: + num_sms: the number of SMs used in high-throughput kernels + group: the communication process group + rank: the local rank + num_ranks: the total number of ranks + num_nvl_bytes: the buffer size for intranode NVLink communication. + """ + + num_sms: int = 20 + + def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int): + """ + Initialize the communication buffer. + + Args: + group: the communication group + num_nvl_bytes: the buffer size for intranode NVLink communication. + """ + self.group = group + self.rank = group.rank() + self.num_ranks = group.size() + self.num_nvl_bytes = num_nvl_bytes + assert self.num_ranks <= 8, "currently only support intranode" + + self._allocator= tilelang.get_allocator( + size=2**30, + device="cuda", + is_distributed=True, + local_rank=self.rank, + num_local_ranks=self.num_ranks, + group=group) + + @staticmethod + def set_num_sms(num_sms: int): + """Set the number of SMs used in high-throughput kernels + + Args: + num_sms: the number of SMs used in high-throughput kernels + """ + assert num_sms % 2 == 0, "num_sms must be even" + TSBuffer.num_sms = num_sms + + @property + def num_channels(self): + """Get the number of communication channels + + Returns: + the number of communication channels + """ + return self.num_sms // 2 + # 1 sm for send, 1 sm for recv in each channel + + @property + def default_dispatch_config(self): + return Config.get_dispatch_config(self.num_ranks) + + @property + def default_combine_config(self): + return Config.get_combine_config(self.num_ranks) + + def get_dispatch_layout(self, topk_idx: torch.Tensor, num_experts: int): + return get_dispatch_layout(topk_idx, num_experts, self.num_ranks) + + def dispatch( + self, + x: torch.Tensor, + num_tokens_per_rank: torch.Tensor, + is_token_in_rank: torch.Tensor, + num_tokens_per_expert: torch.Tensor, + topk_idx: torch.Tensor, + topk_weights: torch.Tensor, + expert_alignment: int = 1, + ): + per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() + per_expert_buffer = tilelang.tensor((self.num_ranks, num_tokens_per_expert.shape[0]), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() + barrier_signal = tilelang.tensor((self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/__init__.py b/examples/distributed/deepseek_deepep/intranode/__init__.py new file mode 100644 index 0000000000..2422961691 --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/__init__.py @@ -0,0 +1 @@ +from get_dispatch_layout import get_dispatch_layout \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 8942bd981f..3b41b8aaca 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -1,4 +1,7 @@ # For intranode only +# This op is distributed +### TILELANG_USE_DISTRIBUTED=1 python dispatch.py + import os, sys sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path @@ -9,8 +12,82 @@ import tilelang.language as T import argparse from typing import Optional, Tuple, List -from utils import Config +from utils import Config, create_moe_recv_counters # noqa: F403 + +from get_dispatch_layout import get_dispatch_layout +from notify_dispatch import notify_dispatch + + +@tilelang.jit +def dispatch_kernel( + rank, num_ranks, + num_tokens, + num_recv_tokens, + hidden, + num_topk, + num_experts, + num_sms, + dtype: str = 'bfloat16', +): + threads = 768 # 24 warps1 + TMABytesPerWarp = 8192 + smem_size = TMABytesPerWarp * threads // 32 + + num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) + num_channels = num_sms // 2 # 10 (2 SMs for each channel) + num_channels_total = num_channels * num_ranks # 80 + num_local_experts = num_experts // num_ranks + + num_send_warps = num_threads_per_rank // 32 # 24 + num_send_warps_per_rank = num_send_warps // num_ranks # 3 + + @T.prim_func + def dispatch_main( + # output + recv_x: T.Tensor((num_recv_tokens, hidden), 'bfloat16'), + recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), + recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int64'), + recv_topk_weights: T.Tensor((num_recv_tokens, num_topk), 'float'), + recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), + send_head: T.Tensor([num_tokens, num_ranks], "int32"), + # input + x: T.Tensor([num_tokens, hidden], "int32"), + topk_idx: T.Tensor([num_tokens, num_topk], "int64"), + topk_weights: T.Tensor([num_tokens, num_topk], "float32"), + is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), + channel_prefix_matrix: T.Tensor([num_ranks, num_channels], "int32"), + # For now we use NVSHMEM to allocate buffer + # instead of using CUDA IPC on the host side + # buffer_ptrs: T.Tensor([...], "int32"), + # channel metadatas (for local rank) + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + # channel buffers (for remote ranks) + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden_int4], "int4"), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "uint64"), + channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), + ): + with T.Kernel(num_sms, threads=threads) as bx: + tx = T.get_thread_binding() + lane_id = tx // 32 + responsible_rank = tx // num_threads_per_rank + responsible_channel = bx // 2 + tgt_rank = rank if bx % 2 == 0 else (rank + 1) % num_ranks + channel_rank_offset = responsible_channel * num_ranks + tgt_rank + + if bx % 2 == 0: # sender + send_warp_id_in_rank = (tx % num_send_warps_per_rank) // 32 + + # send offset by `-value-1` e.g. 0->-1, 1->-2 + if send_warp_id_in_rank == 0 and T.elect_one_sync(): + T.st + + # todo: support cached-mode via handle def intranode_dispatch( @@ -19,16 +96,18 @@ def intranode_dispatch( # handle handle: Optional[Tuple] = None, # meta + rank: int, num_tokens_per_rank: Optional[torch.Tensor] = None, is_token_in_rank: Optional[torch.Tensor] = None, num_tokens_per_expert: Optional[torch.Tensor] = None, topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, - # todo: support expert alignment and num_worst_tokens + # todo: support num_worst_tokens # tuning cfg config: Optional[Config] = None, # todo: support async functionality + allocator, ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int], Tuple]: """ Dispatch tokens to different intranode ranks. @@ -47,6 +126,7 @@ def intranode_dispatch( topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. expert_alignment: align the number of tokens received by each local expert to this variable. config: the performance tuning config. + allocator: TileScale allocator for symm tensors Returns: recv_x: received tokens, the same type and tuple as the input `x`, but the number of tokens equals to the @@ -73,15 +153,32 @@ def intranode_dispatch( # Default config config = Config.get_dispatch_config(num_ranks) if config is None else config - num_memset_int = config.num_channels * num_ranks * 4 - # Size prefix by ranks, shaped as `[num_ranks, num_ranks]` # Size prefix by experts (not used later), shaped as `[num_ranks, num_local_experts]` rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') - notify_dispatch( + moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks)[3:5] + + per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + barrier_signal = tilelang.tensor((num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + + rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( + rank, + num_ranks, + num_experts, + num_tokens, + config.num_channels, + expert_alignment, num_tokens_per_rank, - + num_tokens_per_expert, + is_token_in_rank, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, + per_rank_buffer, + per_expert_buffer, + barrier_signal, + allocator, ) diff --git a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py index 4e13c63626..f805de0365 100644 --- a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py @@ -10,7 +10,7 @@ import torch from argparse import ArgumentParser from tilelang.distributed.utils import init_dist -from utils import gen_inputs # noqa: F403 +from utils import gen_inputs, create_moe_recv_counters # noqa: F403 from get_dispatch_layout import get_dispatch_layout @@ -81,7 +81,7 @@ def notify_dispatch_main( # Copy rank size prefix matrix to another tensor T.copy(per_rank_buffer, rank_prefix_matrix) - # We don't cleanup the buffer for later use, as it is one time used? + #? We don't cleanup the buffer for later use, as it is one time used? T.barrier_blocks(barrier_signal) else: dst_rank = bx - 1 @@ -94,7 +94,7 @@ def notify_dispatch_main( for i in T.serial(token_start_idx + lane_id, token_end_idx, 32): cnt[0] += is_token_in_rank[i, dst_rank] cnt[0] = T.warp_reduce_sum(cnt[0]) - if lane_id == 0: # todo: replace with elect_one_sync() for sm90 + if T.elect_one_sync(): channel_prefix_matrix[dst_rank, channel_id] = cnt[0] T.sync_threads() @@ -195,6 +195,35 @@ def notify_dispatch( return rank_prefix_matrix, channel_prefix_matrix +@tilelang.jit +def cached_notify_dispatch( + rank: int, + num_ranks: int, + num_experts: int, + num_tokens: int, + num_channels: int, + expert_alignment: int, +): + + threads = 128 + + @T.prim_func + def cached_notify_dispatch_main( + rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), + per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + ): + with T.Kernel(1, threads=threads): + tx = T.get_thread_binding() + + T.sync_blocks(barrier_signal) + T.copy(rank_prefix_matrix, per_rank_buffer) + #? We don't cleanup the buffer for later use, as it is one time used? + T.barrier_blocks(barrier_signal) + + return cached_notify_dispatch_main + + # todo: impl cached_notify_dispatch @@ -243,10 +272,7 @@ def test_notify_dispatch( ref_rank_prefix_matrix, ref_channel_prefix_matrix = handle[:2] # create buffers in need - moe_recv_counter_mapped = torch.empty([1], dtype=torch.int64, device='cuda') - moe_recv_counter_mapped[0] = -1 - moe_recv_expert_counter_mapped = torch.empty([num_local_experts], dtype=torch.int32, device='cuda') - moe_recv_expert_counter_mapped.fill_(-1) + moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks)[3:5] per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index dc6deceae0..443ee26f32 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -2,6 +2,7 @@ import torch import os from dataclasses import dataclass, field +from tilelang.distributed.utils import get_device_tensor # Pre-defined constants in DeepEP NUM_MAX_NVL_PEERS = 8 # Maximum number of NVLink peers per GPU @@ -177,16 +178,35 @@ def inplace_unique(x: torch.Tensor, num_slots: int): # Check: csrc/deep_ep.cpp:Buffer::Buffer -def create_moe_recv_counters(num_ranks: int) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]: +def create_moe_recv_counters(num_ranks: int): + """Create MoE receive counters. + All allocated tensors are initialized with -1. + + Args: + num_ranks: the number of ranks. + + Returns: + moe_recv_counter: the MoE counter, allocated on pinned host memory. + moe_recv_expert_counter: the MoE expert-level counter, allocated on pinned host memory. + moe_recv_rdma_counter: the MoE RDMA-level counter, allocated on pinned host memory. + + moe_recv_counter_mapped: the MoE counter on device, mapped from the pinned host memory. + moe_recv_expert_counter_mapped: the MoE expert-level counter on device, mapped from the pinned host memory. + moe_recv_rdma_counter_mapped: the MoE RDMA-level counter on device, mapped from the pinned host memory. + """ num_rdma_ranks = max(1, num_ranks // NUM_MAX_NVL_PEERS) # noqa: F841 num_nvl_ranks = min(num_ranks, NUM_MAX_NVL_PEERS) # noqa: F841 moe_recv_counter = torch.tensor( - -1, dtype=torch.int64, device='cuda', pin_memory=True) # MoE counter + -1, dtype=torch.int64, pin_memory=True) # MoE counter moe_recv_expert_counter = torch.tensor( - [-1] * NUM_MAX_LOCAL_EXPERTS, dtype=torch.int32, device='cuda', + [-1] * NUM_MAX_LOCAL_EXPERTS, dtype=torch.int32, pin_memory=True) # MoE expert-level counter moe_recv_rdma_counter = torch.tensor( - -1, dtype=torch.int, device='cuda', pin_memory=True) # MoE RDMA-level counter + -1, dtype=torch.int32, pin_memory=True) # MoE RDMA-level counter - return moe_recv_counter, moe_recv_expert_counter, moe_recv_rdma_counter \ No newline at end of file + moe_recv_counter_mapped = get_device_tensor(moe_recv_counter) + moe_recv_expert_counter_mapped = get_device_tensor(moe_recv_expert_counter) + moe_recv_rdma_counter_mapped = get_device_tensor(moe_recv_rdma_counter) + return moe_recv_counter, moe_recv_expert_counter, moe_recv_rdma_counter, \ + moe_recv_counter_mapped, moe_recv_expert_counter_mapped, moe_recv_rdma_counter_mapped \ No newline at end of file diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index acc857a9e0..c1fc099a08 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -6,7 +6,7 @@ from tilelang.language.kernel import get_thread_bindings, get_block_extents from tilelang.utils.target import check_hip_availability from tvm import tir -from typing import Any, Literal, str +from typing import Any, Literal import tilelang.language as T from tvm.tir import PrimExpr, Var, Call, Buffer, BufferLoad From 9a4e5e56ef704944c5dc1bc41f2b24be2ac85f9b Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Mon, 24 Nov 2025 07:55:25 +0000 Subject: [PATCH 14/41] suupport ld, st, warp_sync, continue and add test --- .../distributed/primitives/test_ld_options.py | 52 +++++ .../distributed/primitives/test_st_options.py | 51 +++++ src/op/builtin.cc | 10 + src/op/builtin.h | 10 + src/op/remote_copy.cc | 89 +++++++- src/op/remote_copy.h | 77 ++++++- src/target/codegen_cuda.cc | 5 + src/tl_templates/cuda/ldst.h | 192 ++++++++++++++++++ src/tl_templates/cuda/sync.h | 152 -------------- tilelang/language/builtin.py | 67 +++++- 10 files changed, 533 insertions(+), 172 deletions(-) create mode 100644 examples/distributed/primitives/test_ld_options.py create mode 100644 examples/distributed/primitives/test_st_options.py create mode 100644 src/tl_templates/cuda/ldst.h diff --git a/examples/distributed/primitives/test_ld_options.py b/examples/distributed/primitives/test_ld_options.py new file mode 100644 index 0000000000..7c9f6aeb6a --- /dev/null +++ b/examples/distributed/primitives/test_ld_options.py @@ -0,0 +1,52 @@ +import torch +import tilelang +import tilelang.language as T +tilelang.disable_cache() + + +@tilelang.jit +def get_kernel(scope, sem, na, nc): + @T.prim_func + def main( + x: T.Tensor((32), "int32"), + y: T.Tensor((32), "int32") + ): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding() + reg = T.alloc_var('int32') + T.ld(x[tx], reg, scope=scope, sem=sem, na=na, nc=nc) + y[tx] = reg + return main + + +def test_ld_options(scope, sem, na, nc): + kernel = get_kernel(scope, sem, na, nc) + x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) + y = torch.zeros_like(x) + kernel(x, y) + assert torch.equal(x, y) + print(f'check passed for {scope=}.{sem=}.{na=}.{nc=} ✅') + + + +if __name__ == "__main__": + # from DeepEP all ld instructions + + # ld.acquire.sys.global.s32 / u64 + test_ld_options(scope="sys", sem="acquire", na=False, nc=False) + + # ld.acquire.gpu.global.s32 + test_ld_options(scope="gpu", sem="acquire", na=False, nc=False) + + # ld.acquire.cta.s32 + test_ld_options(scope="cta", sem="acquire", na=False, nc=False) + + # ld.relaxed.gpu.global.L1::no_allocate.b8/b16/b32/b64 + test_ld_options(scope="gpu", sem="relaxed", na=True, nc=False) + + # ld.volatile.global.s32/f32/s64/u64 + test_ld_options(scope="gpu", sem="volatile", na=False, nc=False) + + # ld.global.nc.L1::no_allocate.L2::256B (or ld.volatile.global when DISABLE_AGGRESSIVE_PTX_INSTRS) + test_ld_options(scope="gpu", sem="weak", na=True, nc=True) + diff --git a/examples/distributed/primitives/test_st_options.py b/examples/distributed/primitives/test_st_options.py new file mode 100644 index 0000000000..d42e9d4247 --- /dev/null +++ b/examples/distributed/primitives/test_st_options.py @@ -0,0 +1,51 @@ +import torch +import tilelang +import tilelang.language as T +tilelang.disable_cache() + + +@tilelang.jit +def get_kernel(scope, sem, na): + @T.prim_func + def main( + x: T.Tensor((32), "int32") + ): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding() + T.st(x[tx], tx, scope=scope, sem=sem, na=na) + return main + + +def test_st_options(scope, sem, na): + kernel = get_kernel(scope, sem, na) + x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) + kernel(x) + assert x.equal(torch.arange(32, device="cuda")) + print(f'check passed for {scope=}.{sem=}.{na=} ✅') + + + +if __name__ == "__main__": + # from DeepEP all st instructions + + # st.relaxed.sys.global.s32 + test_st_options("sys", "relaxed", False) + + # # st.release.sys.global.s32 + test_st_options("sys", "release", False) + + # st.release.cta.s32 + test_st_options("cta", "release", False) + + # st.relaxed.gpu.global.L1::no_allocate.b* + test_st_options("gpu", "relaxed", True) + + # st.release.gpu.global.L1::no_allocate.b* + test_st_options("gpu", "release", True) + + # test_st_options("gpu", "weak", False) + test_st_options("gpu", "weak", False) + test_st_options("gpu", "weak", True) + + + \ No newline at end of file diff --git a/src/op/builtin.cc b/src/op/builtin.cc index 02035590c8..f0a3f1b729 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -344,5 +344,15 @@ TIR_DEFINE_TL_BUILTIN(elect_one_sync) .set_num_inputs(0) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(sync_warp) + .set_num_inputs(0) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(loop_continue) + .set_num_inputs(0) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index e2bf445871..b44730d887 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -575,6 +575,16 @@ TVM_DLL const Op &warp_reduce_bitor(); */ TVM_DLL const Op &elect_one_sync(); +/*! + * \brief tilelang intrinsic for synchronizing all threads in a warp. + */ +TVM_DLL const Op &sync_warp(); + +/*! + * \brief tilelang intrinsic for continuing the innermost loop. + */ +TVM_DLL const Op &loop_continue(); + } // namespace tl } // namespace tvm diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 28de033a89..8bbd316930 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -241,9 +241,10 @@ StOp::StOp(Array args, BufferMap vmap) { << "dst must be address_of op"; node->value = args[1]; - node->sem = args[2].as().value()->value; - node->scope = args[3].as().value()->value; - node->dst_pe = args[4]; + node->sem = args[2].as().value()->value; + node->scope = args[3].as().value()->value; + node->na = args[4].as().value()->value; + node->dst_pe = args[5]; data_ = std::move(node); (void)vmap; } @@ -258,8 +259,12 @@ Stmt StOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { Array new_args; std::stringstream ss; - // Build function name: tl::st__ - ss << "tl::st_" << sem << "_" << scope; + // Map integers to enum literal strings + const char* sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", "Semantic::ACQUIRE", "Semantic::RELEASE", "Semantic::RELAXED"}; + const char* scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; + + // Build function name: tl::st + ss << "tl::st<" << sem_str[sem] << ", " << scope_str[scope] << ", " << (na ? "true" : "false") << ">"; new_args.push_back(StringImm(ss.str())); if (is_distributed()) { @@ -293,19 +298,91 @@ TileOperator StOpNode::Clone() const { return StOp(node); } +LdOp::LdOp(Array args, BufferMap vmap) { + ObjectPtr node = make_object(); + node->src = args[0]; + ICHECK(node->src.as()) << "src must be a call node"; + ICHECK(node->src.as()->op.same_as(builtin::address_of())) + << "src must be address_of op"; + + node->value = args[1]; + node->sem = args[2].as().value()->value; + node->scope = args[3].as().value()->value; + node->na = args[4].as().value()->value; + node->nc = args[5].as().value()->value; + node->src_pe = args[6]; + data_ = std::move(node); + (void)vmap; +} + +bool LdOpNode::is_distributed() const { + return !(src_pe->IsInstance() && src_pe.as()->value == -1); +} + +Stmt LdOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { + (void)analyzer; + (void)T; + Array new_args; + std::stringstream ss; + + // Map integers to enum literal strings + const char* sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", "Semantic::ACQUIRE", "Semantic::RELEASE", "Semantic::RELAXED"}; + const char* scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; + + // Build function name: tl::ld + ss << "tl::ld<" << sem_str[sem] << ", " << scope_str[scope] << ", " << (nc ? "true" : "false") << ", " << (na ? "true" : "false") << ">"; + + new_args.push_back(StringImm(ss.str())); + if (is_distributed()) { + PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); + PrimExpr local_base_ptr = + Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); + PrimExpr offset_to_base = + Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {src}), + local_base_ptr); + new_args.push_back( + Call(DataType::Handle(), tl::get_remote_base_ptr(), {src_pe}) + + offset_to_base); + } else { + new_args.push_back(src); + } + new_args.push_back(value); + + auto ld = Call(DataType::Handle(), builtin::call_extern(), new_args); + return Evaluate(ld); +} + +LayoutMap LdOpNode::InferLayout(const LayoutInferArgs &T, + InferLevel level) const { + (void)T; + (void)level; + return {}; +} + +TileOperator LdOpNode::Clone() const { + auto node = make_object(*this); + return LdOp(node); +} + TIR_REGISTER_TL_OP(GetOp, get) .set_num_inputs(6) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); TIR_REGISTER_TL_OP(StOp, st) - .set_num_inputs(5) + .set_num_inputs(6) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + +TIR_REGISTER_TL_OP(LdOp, ld) + .set_num_inputs(7) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); TVM_FFI_STATIC_INIT_BLOCK({ PutOpNode::RegisterReflection(); }); TVM_FFI_STATIC_INIT_BLOCK({ GetOpNode::RegisterReflection(); }); TVM_FFI_STATIC_INIT_BLOCK({ StOpNode::RegisterReflection(); }); +TVM_FFI_STATIC_INIT_BLOCK({ LdOpNode::RegisterReflection(); }); } // namespace tl } // namespace tvm diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index 54017779eb..b87f893ab7 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -205,8 +205,9 @@ class StOpNode : public TileOperatorNode { PrimExpr dst; ///< Destination address PrimExpr value; ///< Value to store PrimExpr dst_pe; ///< Destination processing element (optional) - std::string scope; ///< Scope: {warp, block} - std::string sem; ///< Semantic: {relaxed, release} + int scope; + int sem; + int na; bool is_distributed() const; @@ -226,7 +227,8 @@ class StOpNode : public TileOperatorNode { .def_ro("value", &StOpNode::value) .def_ro("dst_pe", &StOpNode::dst_pe) .def_ro("scope", &StOpNode::scope) - .def_ro("sem", &StOpNode::sem); + .def_ro("sem", &StOpNode::sem) + .def_ro("na", &StOpNode::na); } bool SEqualReduce(const StOpNode *other, SEqualReducer equal) const { @@ -234,7 +236,8 @@ class StOpNode : public TileOperatorNode { equal(value, other->value) && equal(dst_pe, other->dst_pe) && scope == other->scope && - sem == other->sem; + sem == other->sem && + na == other->na; } void SHashReduce(SHashReducer hash_reduce) const { @@ -243,6 +246,7 @@ class StOpNode : public TileOperatorNode { hash_reduce(dst_pe); hash_reduce(scope); hash_reduce(sem); + hash_reduce(na); } static constexpr bool _type_has_method_sequal_reduce = true; @@ -256,6 +260,71 @@ class StOp : public TileOperator { static const Op &Get(); }; +class LdOpNode : public TileOperatorNode { + public: + PrimExpr src; ///< Source address + PrimExpr value; ///< Value to store + PrimExpr src_pe; ///< Source PE (optional) + int scope; + int sem; + int na; + int nc; + + bool is_distributed() const; + + static constexpr const char *_type_key = "tl.LdOp"; + TVM_DECLARE_FINAL_OBJECT_INFO(LdOpNode, TileOperatorNode); + + Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; + LayoutMap InferLayout(const LayoutInferArgs &T, + InferLevel level) const override; + static const Op &Get(); + TileOperator Clone() const override; + + static void RegisterReflection() { + namespace refl = tvm::ffi::reflection; + refl::ObjectDef() + .def_ro("src", &LdOpNode::src) + .def_ro("value", &LdOpNode::value) + .def_ro("src_pe", &LdOpNode::src_pe) + .def_ro("scope", &LdOpNode::scope) + .def_ro("sem", &LdOpNode::sem) + .def_ro("na", &LdOpNode::na) + .def_ro("nc", &LdOpNode::nc); + } + + bool SEqualReduce(const LdOpNode *other, SEqualReducer equal) const { + return equal(src, other->src) && + equal(value, other->value) && + equal(src_pe, other->src_pe) && + scope == other->scope && + sem == other->sem && + na == other->na && + nc == other->nc; + } + + void SHashReduce(SHashReducer hash_reduce) const { + hash_reduce(src); + hash_reduce(value); + hash_reduce(src_pe); + hash_reduce(scope); + hash_reduce(sem); + hash_reduce(na); + hash_reduce(nc); + } + + static constexpr bool _type_has_method_sequal_reduce = true; + static constexpr bool _type_has_method_shash_reduce = true; + }; + +class LdOp : public TileOperator { +public: + TVM_DEFINE_OBJECT_REF_METHODS(LdOp, TileOperator, LdOpNode); + TVM_DLL LdOp(Array args, BufferMap vmap); + static const Op &Get(); +}; + + } // namespace tl } // namespace tvm diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 6f0309145a..2e1698237c 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -290,6 +290,7 @@ std::string CodeGenTileLangCUDA::Finish() { if (use_distributed_) { decl_stream << "#include \n"; decl_stream << "#include \n"; + decl_stream << "#include \n"; } decl_stream << "#ifdef ENABLE_BF16\n"; decl_stream << "#include \n"; @@ -2365,6 +2366,10 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { << PrintExpr(op->args[1]) << ")"; } else if (op->op.same_as(tl::elect_one_sync())) { os << "cute::elect_one_sync()"; + } else if (op->op.same_as(tl::sync_warp())) { + os << "__syncwarp()"; + } else if (op->op.same_as(tl::loop_continue())) { + os << "continue"; } else { CodeGenC::VisitExpr_(op, os); } diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h new file mode 100644 index 0000000000..1a4b4ee3e6 --- /dev/null +++ b/src/tl_templates/cuda/ldst.h @@ -0,0 +1,192 @@ +#pragma once + +#include "common.h" + +// Memory semantic and scope enums +enum class Semantic { WEAK, VOLATILE, ACQUIRE, RELEASE, RELAXED }; +enum class Scope { CTA, GPU, SYS }; + +#ifndef TL_ALWAYS_FALSE_V_DEFINED +#define TL_ALWAYS_FALSE_V_DEFINED +template inline constexpr bool always_false_v = false; +#endif + +// Fallback template for unsupported configurations +template +struct StImpl { + template + TL_DEVICE static void execute(T *ptr, T value) { + static_assert(always_false_v, + "tl::st: unsupported configuration. "); + } +}; + +template +struct LdImpl { + template + TL_DEVICE static void execute(const T *ptr, T &value) { + static_assert(always_false_v, + "tl::ld: unsupported configuration. "); + } +}; + +// Macro to define implementation with generic type T +#define TL_ST_IMPL(SEM, SCOPE, NA, SEM_LIT, SCOPE_LIT, NA_LIT) \ + template <> \ + struct StImpl { \ + template \ + TL_DEVICE static void execute(T *ptr, T value) { \ + if constexpr (sizeof(T) == 2) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ + :: "l"(ptr), "h"(value) : "memory"); \ + } else if constexpr (sizeof(T) == 4) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b32 [%0], %1;" \ + :: "l"(ptr), "f"(value) : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b32 [%0], %1;" \ + :: "l"(ptr), "r"(value) : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 8) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b64 [%0], %1;" \ + :: "l"(ptr), "d"(value) : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b64 [%0], %1;" \ + :: "l"(ptr), "l"(value) : "memory"); \ + } \ + } \ + } \ + }; + +// Macro to define implementation of tl::ld with generic type T +#define TL_LD_IMPL(SEM, SCOPE, NC, NA, SEM_LIT, SCOPE_LIT, NC_LIT, NA_LIT) \ + template <> \ + struct LdImpl { \ + template \ + TL_DEVICE static void execute(const T *ptr, T &value) { \ + if constexpr (sizeof(T) == 2) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ + : "=h"(value) : "l"(ptr) : "memory"); \ + } else if constexpr (sizeof(T) == 4) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ + : "=f"(value) : "l"(ptr) : "memory"); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ + : "=r"(value) : "l"(ptr) : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 8) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ + : "=d"(value) : "l"(ptr) : "memory"); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ + : "=l"(value) : "l"(ptr) : "memory"); \ + } \ + } \ + } \ + }; + +// Register all combinations of arguments for tl::st in need here +// WEAK (always .global) +TL_ST_IMPL(WEAK, CTA, false, ".weak", ".global", "") +TL_ST_IMPL(WEAK, GPU, false, ".weak", ".global", "") +TL_ST_IMPL(WEAK, GPU, true, ".weak", ".global", ".L1::no_allocate") +TL_ST_IMPL(WEAK, SYS, false, ".weak", ".global", "") +TL_ST_IMPL(WEAK, SYS, true, ".weak", ".global", ".L1::no_allocate") + +// VOLATILE (always .global, no na) +TL_ST_IMPL(VOLATILE, CTA, false, ".volatile", ".global", "") +TL_ST_IMPL(VOLATILE, GPU, false, ".volatile", ".global", "") +TL_ST_IMPL(VOLATILE, SYS, false, ".volatile", ".global", "") + +// RELAXED (scope-aware) +TL_ST_IMPL(RELAXED, CTA, false, ".relaxed", ".cta", "") +TL_ST_IMPL(RELAXED, GPU, false, ".relaxed", ".gpu.global", "") +TL_ST_IMPL(RELAXED, GPU, true, ".relaxed", ".gpu.global", ".L1::no_allocate") +TL_ST_IMPL(RELAXED, SYS, false, ".relaxed", ".sys.global", "") +TL_ST_IMPL(RELAXED, SYS, true, ".relaxed", ".sys.global", ".L1::no_allocate") + +// RELEASE (scope-aware) +TL_ST_IMPL(RELEASE, CTA, false, ".release", ".cta", "") +TL_ST_IMPL(RELEASE, GPU, false, ".release", ".gpu.global", "") +TL_ST_IMPL(RELEASE, GPU, true, ".release", ".gpu.global", ".L1::no_allocate") +TL_ST_IMPL(RELEASE, SYS, false, ".release", ".sys.global", "") +TL_ST_IMPL(RELEASE, SYS, true, ".release", ".sys.global", ".L1::no_allocate") + +// Register all combinations of arguments for tl::ld in need here +// nc (must with no scope and semantic) +TL_LD_IMPL(WEAK, CTA, true, false, "", ".global", ".nc", "") +TL_LD_IMPL(WEAK, GPU, true, false, "", ".global", ".nc", "") +TL_LD_IMPL(WEAK, SYS, true, false, "", ".global", ".nc", "") +TL_LD_IMPL(WEAK, GPU, true, true, "", ".global", ".nc", ".L1::no_allocate") +TL_LD_IMPL(WEAK, SYS, true, true, "", ".global", ".nc", ".L1::no_allocate") + +// WEAK +TL_LD_IMPL(WEAK, CTA, false, false, ".weak", ".cta", "", "") +TL_LD_IMPL(WEAK, GPU, false, false, ".weak", ".gpu.global", "", "") +TL_LD_IMPL(WEAK, SYS, false, false, ".weak", ".sys.global", "", "") +TL_LD_IMPL(WEAK, GPU, false, true, ".weak", ".gpu.global", "", ".L1::no_allocate") +TL_LD_IMPL(WEAK, SYS, false, true, ".weak", ".sys.global", "", ".L1::no_allocate") + +// VOLATILE (always .global, no na) +TL_LD_IMPL(VOLATILE, CTA, false, false, ".volatile", ".global", "", "") +TL_LD_IMPL(VOLATILE, GPU, false, false, ".volatile", ".global", "", "") +TL_LD_IMPL(VOLATILE, SYS, false, false, ".volatile", ".global", "", "") + +// RELAXED (scope-aware) +TL_LD_IMPL(RELAXED, CTA, false, false, ".relaxed", ".cta", "", "") +TL_LD_IMPL(RELAXED, GPU, false, false, ".relaxed", ".gpu.global", "", "") +TL_LD_IMPL(RELAXED, SYS, false, false, ".relaxed", ".sys.global", "", "") +TL_LD_IMPL(RELAXED, GPU, false, true, ".relaxed", ".gpu.global", "", ".L1::no_allocate") +TL_LD_IMPL(RELAXED, SYS, false, true, ".relaxed", ".sys.global", "", ".L1::no_allocate") + +// ACQUIRE (scope-aware) +TL_LD_IMPL(ACQUIRE, CTA, false, false, ".acquire", ".cta", "", "") +TL_LD_IMPL(ACQUIRE, GPU, false, false, ".acquire", ".gpu.global", "", "") +TL_LD_IMPL(ACQUIRE, SYS, false, false, ".acquire", ".sys.global", "", "") +TL_LD_IMPL(ACQUIRE, GPU, false, true, ".acquire", ".gpu.global", "", ".L1::no_allocate") +TL_LD_IMPL(ACQUIRE, SYS, false, true, ".acquire", ".sys.global", "", ".L1::no_allocate") + +#undef TL_ST_IMPL +#undef TL_LD_IMPL + +namespace tl { + +// Public interface +template +TL_DEVICE void st(P ptr, T value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8, + "tl::st: T must be 2, 4, or 8 bytes"); + static_assert(std::is_pointer_v

|| std::is_same_v, + "tl::st: P must be a pointer or uint64_t"); + static_assert(semantic == Semantic::WEAK + || semantic == Semantic::RELAXED + || semantic == Semantic::RELEASE + || semantic == Semantic::VOLATILE, + "tl::st: semantic must be WEAK, VOLATILE, RELAXED, or RELEASE"); + + T *ptr_ = reinterpret_cast(ptr); + StImpl::execute(ptr_, value); +} + +template +TL_DEVICE void ld(const P ptr, T &value) { + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8, + "tl::ld: T must be 2, 4, or 8 bytes"); + static_assert(std::is_pointer_v

|| std::is_same_v, + "tl::ld: P must be a pointer or uint64_t"); + static_assert(semantic == Semantic::WEAK + || semantic == Semantic::RELAXED + || semantic == Semantic::ACQUIRE + || semantic == Semantic::VOLATILE, + "tl::ld: semantic must be WEAK, RELAXED, ACQUIRE, or VOLATILE"); + + const T *ptr_ = reinterpret_cast(ptr); + LdImpl::execute(ptr_, value); +} + +// todo: support "ld.global.nc.L1::no_allocate.L2::256B" + +} // namespace tl diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index 2800080784..8580c1f496 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -191,156 +191,4 @@ template TL_DEVICE void wait_eq(void *barrier, T val = 1) { } } -template -TL_DEVICE void st_release_gpu(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); - static_assert(std::is_pointer_v

|| std::is_same_v); - T *ptr_ = reinterpret_cast(ptr); - - if constexpr (sizeof(T) == 2) { - asm volatile("st.release.gpu.global.b16 [%0], %1;" - : - : "l"(ptr_), "h"(value) - : "memory"); - } else if constexpr (sizeof(T) == 4) { - if constexpr (std::is_floating_point_v) { - asm volatile("st.release.gpu.global.b32 [%0], %1;" - : - : "l"(ptr_), "f"(value) - : "memory"); - } else { - asm volatile("st.release.gpu.global.b32 [%0], %1;" - : - : "l"(ptr_), "r"(value) - : "memory"); - } - } else { - if constexpr (std::is_floating_point_v) { - asm volatile("st.release.gpu.global.b64 [%0], %1;" - : - : "l"(ptr_), "d"(value) - : "memory"); - } else { - asm volatile("st.release.gpu.global.b64 [%0], %1;" - : - : "l"(ptr_), "l"(value) - : "memory"); - } - } -} - -template -TL_DEVICE void st_relaxed_gpu(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); - static_assert(std::is_pointer_v

|| std::is_same_v); - T *ptr_ = reinterpret_cast(ptr); - - if constexpr (sizeof(T) == 2) { - asm volatile("st.relaxed.gpu.global.b16 [%0], %1;" - : - : "l"(ptr_), "h"(value) - : "memory"); - } else if constexpr (sizeof(T) == 4) { - if constexpr (std::is_floating_point_v) { - asm volatile("st.relaxed.gpu.global.b32 [%0], %1;" - : - : "l"(ptr_), "f"(value) - : "memory"); - } else { - asm volatile("st.relaxed.gpu.global.b32 [%0], %1;" - : - : "l"(ptr_), "r"(value) - : "memory"); - } - } else { - if constexpr (std::is_floating_point_v) { - asm volatile("st.relaxed.gpu.global.b64 [%0], %1;" - : - : "l"(ptr_), "d"(value) - : "memory"); - } else { - asm volatile("st.relaxed.gpu.global.b64 [%0], %1;" - : - : "l"(ptr_), "l"(value) - : "memory"); - } - } -} - -template -TL_DEVICE void st_release_sys(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); - static_assert(std::is_pointer_v

|| std::is_same_v); - T *ptr_ = reinterpret_cast(ptr); - - if constexpr (sizeof(T) == 2) { - asm volatile("st.release.sys.global.b16 [%0], %1;" - : - : "l"(ptr_), "h"(value) - : "memory"); - } else if constexpr (sizeof(T) == 4) { - if constexpr (std::is_floating_point_v) { - asm volatile("st.release.sys.global.b32 [%0], %1;" - : - : "l"(ptr_), "f"(value) - : "memory"); - } else { - asm volatile("st.release.sys.global.b32 [%0], %1;" - : - : "l"(ptr_), "r"(value) - : "memory"); - } - } else { - if constexpr (std::is_floating_point_v) { - asm volatile("st.release.sys.global.b64 [%0], %1;" - : - : "l"(ptr_), "d"(value) - : "memory"); - } else { - asm volatile("st.release.sys.global.b64 [%0], %1;" - : - : "l"(ptr_), "l"(value) - : "memory"); - } - } -} - -template -TL_DEVICE void st_relaxed_sys(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8); - static_assert(std::is_pointer_v

|| std::is_same_v); - T *ptr_ = reinterpret_cast(ptr); - - if constexpr (sizeof(T) == 2) { - asm volatile("st.relaxed.sys.global.b16 [%0], %1;" - : - : "l"(ptr_), "h"(value) - : "memory"); - } else if constexpr (sizeof(T) == 4) { - if constexpr (std::is_floating_point_v) { - asm volatile("st.relaxed.sys.global.b32 [%0], %1;" - : - : "l"(ptr_), "f"(value) - : "memory"); - } else { - asm volatile("st.relaxed.sys.global.b32 [%0], %1;" - : - : "l"(ptr_), "r"(value) - : "memory"); - } - } else { - if constexpr (std::is_floating_point_v) { - asm volatile("st.relaxed.sys.global.b64 [%0], %1;" - : - : "l"(ptr_), "d"(value) - : "memory"); - } else { - asm volatile("st.relaxed.sys.global.b64 [%0], %1;" - : - : "l"(ptr_), "l"(value) - : "memory"); - } - } -} - } // namespace tl diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index c1fc099a08..b3f6b344d8 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -745,12 +745,44 @@ def atom_add(barrier: PrimExpr, value: PrimExpr, scope: str = "gpu", sem: str = return tir.call_intrin("uint32", tir.op.Op.get("tl.atom_add"), address_of(barrier), value, sem, scope) +def ld( + src: PrimExpr, + value: PrimExpr, + scope: Literal["cta", "gpu", "sys"] = "gpu", + sem: Literal["weak", "volatile", "acquire", "release", "relaxed"] = "weak", + na: bool = False, + nc: bool = False, + src_pe: tir.PrimExpr | tir.IntImm | None = -1, +): + """Load a value from a given address with specified scope, semantic, and optional destination PE. + + Args: + src: The source address to load from. + value: The value to load. + scope: The memory scope. + sem: The memory semantic. + na: Whether to use no-allocate L1 policy. + nc: Whether to use non-coherent cache. + src_pe: The source processing element (PE) identifier. + Use -1 (default) for local PE, or a non-negative integer to target a remote PE. + + Returns: + tir.Call: A handle to the load operation. + """ + assert scope in ["cta", "gpu", "sys"], "Scope must be one of 'cta', 'gpu', or 'sys'." + assert sem in ["weak", "volatile", "acquire", "relaxed"], "Semantic must be one of 'weak', 'volatile', 'acquire', 'release', or 'relaxed'." + scope = {"cta": 0, "gpu": 1, "sys": 2}[scope] + sem = {"weak": 0, "volatile": 1, "acquire": 2, "release": 3, "relaxed": 4}[sem] + na = 1 if na else 0 + nc = 1 if nc else 0 + return tir.call_intrin("handle", tir.op.Op.get("tl.ld"), address_of(src), value, sem, scope, na, nc, src_pe) def st( dst: PrimExpr, value: PrimExpr, - scope: Literal["gpu", "sys"] = "gpu", - sem: Literal["relaxed", "release"] = "relaxed", + scope: Literal["cta", "gpu", "sys"] = "gpu", + sem: Literal["weak", "volatile", "release", "relaxed"] = "weak", + na: bool = False, dst_pe: tir.PrimExpr | tir.IntImm | None = -1, ): """Store a value to a given address with specified scope, semantic, and optional destination PE. @@ -758,20 +790,35 @@ def st( Args: dst: The destination to store the value to. value: The value to store. - scope: The memory scope, either "gpu" (default) or "sys". - sem: The memory semantic, either "relaxed" (default) or "release". + scope: The memory scope. + sem: The memory semantic. + na: Whether to use no-allocate L1 policy. dst_pe: The destination processing element (PE) identifier. Use -1 (default) for local PE, or a non-negative integer to target a remote PE. Returns: tir.Call: A handle to the store operation. """ - assert scope in ["gpu", "sys"], "Scope must be one of 'gpu', or 'sys'." - assert sem in ["relaxed", "release"], "Semantic must be one of 'relaxed', or 'release'." - return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, dst_pe) + assert scope in ["cta", "gpu", "sys"], "Scope must be one of 'cta', 'gpu', or 'sys'." + assert sem in ["weak", "volatile", "release", "relaxed"], "Semantic must be one of 'weak', 'volatile', 'release', or 'relaxed'." + + # convert to int + scope = {"cta": 0, "gpu": 1, "sys": 2}[scope] + sem = {"weak": 0, "volatile": 1, "acquire": 2, "release": 3, "relaxed": 4}[sem] + na = 1 if na else 0 + return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, na, dst_pe) def elect_one_sync(): - """Efficiently elect exactly one lane within a logical thread group. - """ - return tir.call_intrin("bool", tir.op.Op.get("tl.elect_one_sync")) \ No newline at end of file + """Efficiently elect exactly one lane within a warp.""" + return tir.call_intrin("bool", tir.op.Op.get("tl.elect_one_sync")) + + +def sync_warp(): + """Synchronize all threads in a warp.""" + return tir.call_intrin("handle", tir.op.Op.get("tl.sync_warp")) + + +def loop_continue(): + """Continue the innermost loop.""" + return tir.call_intrin("handle", tir.op.Op.get("tl.loop_continue")) \ No newline at end of file From ea25c7f8e0e5103e7cf0e5a97f5118a57beca4b0 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Mon, 24 Nov 2025 10:27:45 +0000 Subject: [PATCH 15/41] support warp vote and add test --- src/op/builtin.cc | 10 ++++ src/op/builtin.h | 10 ++++ src/target/codegen_cuda.cc | 4 ++ .../language/test_tilelang_language_vote.py | 48 +++++++++++++++++++ tilelang/language/builtin.py | 28 ++++++++++- 5 files changed, 99 insertions(+), 1 deletion(-) create mode 100644 testing/python/language/test_tilelang_language_vote.py diff --git a/src/op/builtin.cc b/src/op/builtin.cc index f0a3f1b729..d325654cdf 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -354,5 +354,15 @@ TIR_DEFINE_TL_BUILTIN(loop_continue) .set_num_inputs(0) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); + +TIR_DEFINE_TL_BUILTIN(warp_any) + .set_num_inputs(2) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kPure)); + +TIR_DEFINE_TL_BUILTIN(warp_all) + .set_num_inputs(2) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kPure)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index b44730d887..354114ea33 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -585,6 +585,16 @@ TVM_DLL const Op &sync_warp(); */ TVM_DLL const Op &loop_continue(); +/*! + * \brief tilelang intrinsic for checking if any lane in the warp has a true value. + */ +TVM_DLL const Op &warp_any(); + +/*! + * \brief tilelang intrinsic for checking if all lanes in the warp have a true value. + */ +TVM_DLL const Op &warp_all(); + } // namespace tl } // namespace tvm diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 2e1698237c..847c66e146 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -2370,6 +2370,10 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { os << "__syncwarp()"; } else if (op->op.same_as(tl::loop_continue())) { os << "continue"; + } else if (op->op.same_as(tl::warp_any())) { + os << "__any_sync(" << PrintExpr(op->args[1]) << ", " << PrintExpr(op->args[0]) << ")"; + } else if (op->op.same_as(tl::warp_all())) { + os << "__all_sync(" << PrintExpr(op->args[1]) << ", " << PrintExpr(op->args[0]) << ")"; } else { CodeGenC::VisitExpr_(op, os); } diff --git a/testing/python/language/test_tilelang_language_vote.py b/testing/python/language/test_tilelang_language_vote.py new file mode 100644 index 0000000000..ba55e63f80 --- /dev/null +++ b/testing/python/language/test_tilelang_language_vote.py @@ -0,0 +1,48 @@ +import torch + +import tilelang +import tilelang.testing +import tilelang.language as T + + +@tilelang.jit +def get_kernel(): + @T.prim_func + def main(output: T.Tensor((6), 'int32')): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding(0) + value = T.alloc_var('int32') + result_any = T.alloc_var('int32') + result_all = T.alloc_var('int32') + value = 1 + result_any = T.warp_any(value) + result_all = T.warp_all(value) + if tx == 0: + output[0] = result_any + output[1] = result_all + value = 0 + result_any = T.warp_any(value) + result_all = T.warp_all(value) + if tx == 0: + output[2] = result_any + output[3] = result_all + value = tx % 2 + result_any = T.warp_any(value) + result_all = T.warp_all(value) + if tx == 0: + output[4] = result_any + output[5] = result_all + return main + + +def test_vote(): + output = torch.tensor(6 * [-1], dtype=torch.int32, device='cuda') + kernel = get_kernel() + kernel(output) + assert '__any_sync' and '__all_sync' in kernel.get_kernel_source() + ref = torch.tensor([1, 1, 0, 0, 1, 0], dtype=torch.int32, device='cuda') + assert output.equal(ref) + + +if __name__ == "__main__": + test_vote() \ No newline at end of file diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index b3f6b344d8..e53c30ee4f 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -821,4 +821,30 @@ def sync_warp(): def loop_continue(): """Continue the innermost loop.""" - return tir.call_intrin("handle", tir.op.Op.get("tl.loop_continue")) \ No newline at end of file + return tir.call_intrin("handle", tir.op.Op.get("tl.loop_continue")) + + +def warp_any(value, mask = -1): + """Check if any lane in the warp has a true value. + + Args: + value (int): The value to vote. + mask (uint32): The mask to use, default is 0xFFFFFFFF(-1), which means all lanes. + + Returns: + result (int): The result of the vote. + """ + return tir.call_intrin("int32", tir.op.Op.get("tl.warp_any"), value, mask) + + +def warp_all(value, mask = -1): + """Check if all lane in the warp have a true value. + + Args: + value (int): The value to vote. + mask (uint32): The mask to use, default is 0xFFFFFFFF(-1), which means all lanes. + + Returns: + result (int): The result of the vote. + """ + return tir.call_intrin("int32", tir.op.Op.get("tl.warp_all"), value, mask) \ No newline at end of file From 8333785aea31b379f6b985b28d48a5e798642995 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Tue, 25 Nov 2025 02:33:08 +0000 Subject: [PATCH 16/41] support device-side wait_ne --- src/op/sync.cc | 3 +++ src/op/sync.h | 8 +++++++- src/target/codegen_cuda.cc | 4 ++++ src/tl_templates/cuda/sync.h | 19 ++++++++++++++----- tilelang/language/builtin.py | 7 ++++++- 5 files changed, 34 insertions(+), 7 deletions(-) diff --git a/src/op/sync.cc b/src/op/sync.cc index b0b7fc6e2c..3c0401a240 100644 --- a/src/op/sync.cc +++ b/src/op/sync.cc @@ -55,6 +55,9 @@ TIR_DEFINE_TL_BUILTIN(wait_barrier_gpu) TIR_DEFINE_TL_BUILTIN(wait_eq).set_num_inputs(2).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); +TIR_DEFINE_TL_BUILTIN(wait_ne).set_num_inputs(2).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kOpaque)); + TIR_DEFINE_TL_BUILTIN(sync_barrier_gpu) .set_num_inputs(1) .set_attr("TCallEffectKind", diff --git a/src/op/sync.h b/src/op/sync.h index c2b3f8d7f7..25e1f6b119 100644 --- a/src/op/sync.h +++ b/src/op/sync.h @@ -40,11 +40,17 @@ TVM_DLL const Op &wait_barrier_gpu(); /*! * \brief Wait until *addr == expected* for GPU-level synchronization - * void wait_eq(barrier, expected) + * void wait_eq(addr, expected) */ TVM_DLL const Op &wait_eq(); +/*! + * \brief Wait until *addr != expected* for GPU-level synchronization + * void wait_ne(addr, expected) + */ +TVM_DLL const Op &wait_ne(); + /*! * \brief Synchronize at a barrier for GPU-level synchronization * diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 847c66e146..7efd6629d5 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -1517,6 +1517,10 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { this->PrintIndent(); this->stream << "tl::wait_eq(" << this->PrintExpr(op->args[0]) << ", " << this->PrintExpr(op->args[1]) << ");\n"; + } else if (op->op.same_as(tl::wait_ne())) { + this->PrintIndent(); + this->stream << "tl::wait_ne(" << this->PrintExpr(op->args[0]) << ", " + << this->PrintExpr(op->args[1]) << ");\n"; } else if (op->op.same_as(tl::atom_add())) { std::string func_name = "tl::ptx_atom_add_" + op->args[2].as()->value + "_" + diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index 8580c1f496..a2170da30c 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -1,6 +1,7 @@ #pragma once #include "common.h" +#include "ldst.h" #define IS_MASTER_THREAD() \ (threadIdx.x == 0 && threadIdx.y == 0 && threadIdx.z == 0) @@ -55,7 +56,7 @@ TL_DEVICE int atomic_load_acquire_sys_s32(const int *ptr) { return ret; } -TL_DEVICE int ld_volatile_global_s32(const int *ptr) { +TL_DEVICE int ld_volatile_global(const int *ptr) { int ret; asm volatile("ld.volatile.global.s32 %0, [%1];\n" : "=r"(ret) : "l"(ptr)); return ret; @@ -172,7 +173,7 @@ TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { while (true) { int value = - tid < num_ranks ? ld_volatile_global_s32(BARRIER_PTR(rank) + tid) : 0; + tid < num_ranks ? ld_volatile_global(BARRIER_PTR(rank) + tid) : 0; if (__all_sync(0xffffffff, value <= 0)) { break; } @@ -183,12 +184,20 @@ TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { #undef FINISHED_SUM_TAG } -template TL_DEVICE void wait_eq(void *barrier, T val = 1) { +template +TL_DEVICE void wait_eq(void *barrier, T val = 1) { T *flag_ptr = reinterpret_cast(barrier); // Spin-loop #pragma unroll 1 - while (ld_acquire(flag_ptr) != val) { - } + while (ld_acquire(flag_ptr) != val); +} + +template +TL_DEVICE void wait_ne(void *barrier, T val = 0) { + T *flag_ptr = reinterpret_cast(barrier); +// Spin-loop +#pragma unroll 1 + while (ld_volatile_global_acquire(flag_ptr) == val); } } // namespace tl diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index e53c30ee4f..4918b6656c 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -604,6 +604,11 @@ def wait_eq(barrier: PrimExpr, expected: PrimExpr): return tir.call_intrin("handle", tir.op.Op.get("tl.wait_eq"), address_of(barrier), expected) +def wait_ne(ptr: PrimExpr, expected: PrimExpr): + """Wait until *ptr != expected using ld_volatile_global()""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait_ne"), address_of(ptr), expected) + + def sync_barrier_gpu(barrier: PrimExpr): """Synchronize at a barrier for GPU-level synchronization. @@ -847,4 +852,4 @@ def warp_all(value, mask = -1): Returns: result (int): The result of the vote. """ - return tir.call_intrin("int32", tir.op.Op.get("tl.warp_all"), value, mask) \ No newline at end of file + return tir.call_intrin("int32", tir.op From 3cefc96b6fb12c291367aed13205904e0697a354 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Tue, 25 Nov 2025 03:38:48 +0000 Subject: [PATCH 17/41] refactor T.wait_* and refine dispatch test logic --- .../deepseek_deepep/intranode/dispatch.py | 421 ++++++++++++++++-- .../intranode/get_dispatch_layout.py | 6 +- .../distributed/deepseek_deepep/intranode/log | 1 + .../intranode/notify_dispatch.py | 5 +- examples/distributed/deepseek_deepep/utils.py | 19 +- src/op/remote_copy.cc | 5 + src/op/sync.cc | 64 ++- src/op/sync.h | 59 ++- src/target/codegen_cuda.cc | 16 +- src/tl_templates/cuda/common.h | 4 +- src/tl_templates/cuda/sync.h | 42 +- tilelang/language/builtin.py | 19 +- tilelang/language/distributed/common.py | 46 ++ tilelang/utils/ts_ext/tensor.cpp | 4 +- 14 files changed, 618 insertions(+), 93 deletions(-) create mode 100644 examples/distributed/deepseek_deepep/intranode/log diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 3b41b8aaca..f0a6a26feb 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -10,93 +10,292 @@ import tilelang from tilelang.autotuner import * import tilelang.language as T -import argparse -from typing import Optional, Tuple, List -from utils import Config, create_moe_recv_counters # noqa: F403 +from argparse import ArgumentParser +from typing import Any, Optional, Tuple, List +from tilelang.distributed.utils import init_dist +from utils import Config, create_moe_recv_counters, gen_inputs # noqa: F403 from get_dispatch_layout import get_dispatch_layout from notify_dispatch import notify_dispatch +tilelang.disable_cache() -@tilelang.jit + +@tilelang.jit( + pass_configs={"tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True}, debug_root_path='/root/workspace/wt/debug/dispatch') def dispatch_kernel( rank, num_ranks, num_tokens, - num_recv_tokens, + num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens + num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens hidden, num_topk, num_experts, num_sms, dtype: str = 'bfloat16', ): - threads = 768 # 24 warps1 + threads = 768 # 24 warps TMABytesPerWarp = 8192 smem_size = TMABytesPerWarp * threads // 32 num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) num_channels = num_sms // 2 # 10 (2 SMs for each channel) - num_channels_total = num_channels * num_ranks # 80 num_local_experts = num_experts // num_ranks - num_send_warps = num_threads_per_rank // 32 # 24 - num_send_warps_per_rank = num_send_warps // num_ranks # 3 + num_warps = threads // 32 # 24 + num_warps_per_rank = num_warps // num_ranks # 3 + num_recv_tokens = T.dynamic('num_recv_tokens') @T.prim_func def dispatch_main( # output - recv_x: T.Tensor((num_recv_tokens, hidden), 'bfloat16'), + recv_x: T.Tensor((num_recv_tokens, hidden), dtype), recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), - recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int64'), + recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int32'), recv_topk_weights: T.Tensor((num_recv_tokens, num_topk), 'float'), recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), send_head: T.Tensor([num_tokens, num_ranks], "int32"), # input - x: T.Tensor([num_tokens, hidden], "int32"), - topk_idx: T.Tensor([num_tokens, num_topk], "int64"), + x: T.Tensor([num_tokens, hidden], dtype), + topk_idx: T.Tensor([num_tokens, num_topk], "int32"), topk_weights: T.Tensor([num_tokens, num_topk], "float32"), is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), + rank_prefix_matrix: T.Tensor([num_ranks, num_ranks], "int32"), channel_prefix_matrix: T.Tensor([num_ranks, num_channels], "int32"), - # For now we use NVSHMEM to allocate buffer - # instead of using CUDA IPC on the host side - # buffer_ptrs: T.Tensor([...], "int32"), - # channel metadatas (for local rank) + ###### below are symm buffers, one on each rank ###### + # channel buffer metadatas, stored on the receiver side + # senders are responsible for tails, and receivers are responsible for heads channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), - # channel buffers (for remote ranks) - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden_int4], "int4"), + # channel data buffers, stored on the receiver side + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), - channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "uint64"), + channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int32"), channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), - channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), + # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: tx = T.get_thread_binding() - lane_id = tx // 32 + lane_id = tx % 32 responsible_rank = tx // num_threads_per_rank responsible_channel = bx // 2 - tgt_rank = rank if bx % 2 == 0 else (rank + 1) % num_ranks - channel_rank_offset = responsible_channel * num_ranks + tgt_rank if bx % 2 == 0: # sender - send_warp_id_in_rank = (tx % num_send_warps_per_rank) // 32 + send_warp_id_in_rank = (tx % num_threads_per_rank) // 32 # send offset by `-value-1` e.g. 0->-1, 1->-2 + # this is for distinguishing zero tokens if send_warp_id_in_rank == 0 and T.elect_one_sync(): - T.st + value = T.alloc_var('int32') + value = T.if_then_else( + responsible_channel > 0, + channel_prefix_matrix[responsible_rank, responsible_channel - 1], + 0) + T.st(channel_start_offset[responsible_channel, rank], -value-1, + scope='sys', sem='relaxed', dst_pe=responsible_rank) + value = channel_prefix_matrix[responsible_rank, responsible_channel] + T.st(channel_end_offset[responsible_channel, rank], -value-1, + scope='sys', sem='relaxed', dst_pe=responsible_rank) + T.sync_warp() + + # get task + num_tokens_per_channel = T.alloc_var('int32', init=T.ceildiv(num_tokens, num_channels)) + token_start_idx = T.alloc_var('int32') + token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) + token_end_idx = T.alloc_var('int32') + token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) + + # sender mainloop: iterate over all tokens and send by trunk + cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = 0 + token_idx = T.alloc_var('int32') + token_idx = token_start_idx + with T.While(token_idx < token_end_idx): + if T.elect_one_sync(): + T.wait_ge(channel_head_idx[responsible_channel, rank], + num_max_send_tokens+cached_channel_tail_idx-num_recv_buffer_tokens, + responsible_rank) + T.sync_warp() + # T.print(token_idx, 'start sender mainloop') + chunk_token_idx = T.alloc_var('int32') + chunk_token_idx = 0 + while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: + # for the same token, the warp assigned to save `send_head` may be different from the warp + # assigned to send the following data + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync(): + send_head[token_idx, responsible_rank] = T.if_then_else( + is_token_in_rank[token_idx, responsible_rank], + cached_channel_tail_idx, + -1 + ) + + # skip if not selected + if not is_token_in_rank[token_idx, responsible_rank]: + token_idx += 1 + T.loop_continue() + + # selected, get an empty slot + dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = cached_channel_tail_idx % num_recv_buffer_tokens + cached_channel_tail_idx += 1 + if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: + # copy data, all are remote copy + # 1. copy data + # todo: support ld_nc and st_na + T.put_warp(T.address_of(x[token_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), + hidden, + responsible_rank, 5) + # T.copy(x[token_idx, :], channel_x_buffers[responsible_channel, rank, dst_slot_idx, :], + # dst_pe=responsible_rank) #! we need this feature, but it's in another pr + + # 2. copy src idx + if T.elect_one_sync(): + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, + dst_pe=responsible_rank) + + # 3. copy `topk_idx` and `topk_weights` with transformed index + if lane_id < num_topk: + # topk_idx + recv_expert_begin = responsible_rank * num_local_experts + recv_expert_end = recv_expert_begin + num_local_experts + + idx_value = T.alloc_var('int32') + T.ld(topk_idx[token_idx, lane_id], idx_value, nc=True) + idx_value = T.if_then_else( + recv_expert_begin <= idx_value and idx_value < recv_expert_end, + idx_value - recv_expert_begin, + -1 + ) + T.st(channel_topk_idx_buffers[responsible_channel, rank, dst_slot_idx, lane_id], idx_value, + dst_pe=responsible_rank) + + # topk_weights + weight_value = T.alloc_var('float32') + T.ld(topk_weights[token_idx, lane_id], weight_value, nc=True) + weight_value = T.if_then_else(idx_value >= 0, weight_value, 0) + T.st(channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight_value, + dst_pe=responsible_rank) + + # 4. copy scale (support fp8 later) + + chunk_token_idx += 1 + token_idx += 1 + + # move tail index + # here all warps should share the same new tail + T.sync_threads(responsible_rank, num_threads_per_rank) + if send_warp_id_in_rank == 0 and T.elect_one_sync(): + T.st(channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, + scope='sys', sem='release', + dst_pe=responsible_rank) + + else: # receiver + recv_thread_id_in_rank = tx % num_threads_per_rank + recv_warp_id_in_rank = recv_thread_id_in_rank // 32 + + # calculate offset first + rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank-1, rank], 0) + + # receive channel offset + total_offset = T.alloc_var('int32') + num_tokens_to_recv = T.alloc_var('int32') + if T.elect_one_sync(): + T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) + T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem='volatile') + T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) + T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem='volatile') + total_offset = -total_offset - 1 + num_tokens_to_recv = -num_tokens_to_recv - 1 + if recv_warp_id_in_rank == 0: + recv_channel_offset[responsible_rank, responsible_channel] = total_offset + num_tokens_to_recv -= total_offset + total_offset = T.tvm_warp_shuffle(-1, total_offset, 0, 32, 32) + total_offset += rank_offset + num_tokens_to_recv = T.tvm_warp_shuffle(-1, num_tokens_to_recv, 0, 32, 32) + + # Shared tail indices for different warps + shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') + + cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = 0 + cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = 0 + with T.While(num_tokens_to_recv > 0): + with T.While(recv_thread_id_in_rank == 0): + T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem='acquire', scope='sys') + + # read to copy + if cached_channel_head_idx != cached_channel_tail_idx: + shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx + T.loop_break() + + # sync queue tail + T.sync_threads(responsible_rank, num_threads_per_rank) + cached_channel_tail_idx = shared_channel_tail_idx[responsible_rank] + + # copy data + # 1. recv x + num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + # T.copy(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, :], recv_x[total_offset+chunk_idx, :]) # todo: add ld_nc and st_na + #! T.copy will cause layout inference error + T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), + T.address_of(recv_x[total_offset+chunk_idx, 0]), + hidden, + rank, + 5) + + # 2. recv src_idx + for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, + cached_channel_tail_idx, + num_threads_per_rank): + local_src_idx = T.alloc_var('int32') + T.ld(channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, nc=True) + recv_src_idx[total_offset+chunk_idx-cached_channel_head_idx] = local_src_idx + + # 3. recv topk_idx and topk_weights + for idx in T.serial(recv_thread_id_in_rank, num_cur_recv_tokens*num_topk, num_threads_per_rank): + chunk_idx = idx // num_topk + token_topk_idx = idx % num_topk + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + recv_topk_idx[total_offset+chunk_idx, token_topk_idx] = channel_topk_idx_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx] + recv_topk_weights[total_offset+chunk_idx, token_topk_idx] = channel_topk_weights_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx] + + # 4. recv scale (support fp8 later) + + # Move queue + cached_channel_head_idx += num_cur_recv_tokens + total_offset += num_cur_recv_tokens + T.sync_threads(responsible_rank, num_threads_per_rank) + if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): + T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, + scope='sys', sem='relaxed') + + # Exit + num_tokens_to_recv -= num_cur_recv_tokens + if bx == 0 and tx == rank: + T.print(num_tokens_to_recv) + + # todo: support num_worst_tokens > 0 later + + return dispatch_main - # todo: support cached-mode via handle def intranode_dispatch( + rank: int, + allocator, # data x: torch.Tensor, # todo: support fp8 quant # handle handle: Optional[Tuple] = None, # meta - rank: int, num_tokens_per_rank: Optional[torch.Tensor] = None, is_token_in_rank: Optional[torch.Tensor] = None, num_tokens_per_expert: Optional[torch.Tensor] = None, @@ -107,8 +306,8 @@ def intranode_dispatch( # tuning cfg config: Optional[Config] = None, # todo: support async functionality - allocator, -) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, List[int], Tuple]: + +): """ Dispatch tokens to different intranode ranks. Intranode kernels require all the ranks should be visible via NVLink. @@ -121,7 +320,7 @@ def intranode_dispatch( num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. - topk_idx: `[num_tokens, num_topk]` with `deep_ep.topk_idx_t` (typically `torch.int64`), the expert indices + topk_idx: `[num_tokens, num_topk]` with `torch.int32`, the expert indices selected by each token, `-1` means no selections. topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. expert_alignment: align the number of tokens received by each local expert to this variable. @@ -136,11 +335,10 @@ def intranode_dispatch( num_recv_tokens_per_expert_list: Python list shaped `[num_local_experts]`, the received token count by each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list will be empty. - handle: the returned communication handle. """ assert handle is None # Currently only support non-cached mode - assert num_tokens_per_rank is not None or is_token_in_rank is not None and num_tokens_per_expert is not None, \ + assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, \ "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" # acquire shapes @@ -158,7 +356,7 @@ def intranode_dispatch( rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') - moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks)[3:5] + moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_experts // num_ranks)[3:5] per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() @@ -181,4 +379,159 @@ def intranode_dispatch( barrier_signal, allocator, ) + torch.cuda.synchronize() # todo: replace it with host-side wait_ne + + num_recv_tokens = moe_recv_counter_mapped.item() + assert num_recv_tokens >= 0 + num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() + + # create normal buffers + recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') + recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') + recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int32, device='cuda') + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') + recv_channel_offset = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') + send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') + + # create symm buffers + channel_start_offset = tilelang.tensor( + [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + channel_end_offset = tilelang.tensor( + [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + channel_head_idx = tilelang.tensor( + [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + channel_tail_idx = tilelang.tensor( + [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + channel_x_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, hidden], dtype=torch.bfloat16, device='cuda', allocator=allocator) + channel_src_idx_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, device='cuda', allocator=allocator) + channel_topk_idx_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.int32, device='cuda', allocator=allocator) + channel_topk_weights_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.float32, device='cuda', allocator=allocator) + + # get dispatch kernel + kernel = dispatch_kernel( + rank, + num_ranks, + num_tokens, + config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, + hidden, + num_topk, + num_experts, + config.num_sms, + dtype='bfloat16' + ) + kernel.initialize(allocator=allocator) + + # run dispatch + if rank == 0: + print('Start running dispatch kernel...') + kernel( + recv_x, + recv_src_idx, + recv_topk_idx, + recv_topk_weights, + recv_channel_offset, + send_head, + x, + topk_idx, + topk_weights, + is_token_in_rank, + rank_prefix_matrix, + channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_idx_buffers, + channel_topk_weights_buffers, + ) + + return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list + +def test_intranode_dispatch( + num_tokens: int, + hidden: int, + num_topk: int, + num_experts: int, + rank: int, + num_ranks: int, + expert_alignment: int, + group: torch.distributed.ProcessGroup, +): + try: + import deep_ep # noqa: F403 + except ModuleNotFoundError as e: + raise ModuleNotFoundError("Please install DeepEP to run this test.") + + allocator = tilelang.get_allocator( + size=2**30, + device="cuda", + is_distributed=True, + local_rank=rank, + num_local_ranks=num_ranks, + group=group) + + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) + buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) + + # Assume get_dispatch_layout is correct + if rank == 0: + print('get dispatch layout...') + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx.to(torch.int64), num_experts) # DeepEP requires int64 topk_idx + + if rank == 0: + print('intranode dispatch (notify_dispatch included...)') + + # golden + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, _, _ = \ + buffer.dispatch(x, None, num_tokens_per_rank, None, is_token_in_rank, num_tokens_per_expert, topk_idx.to(torch.int64), topk_weights, expert_alignment) # DeepEP requires int64 topk_idx`` + + # ours + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list = \ + intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) + + assert torch.equal(recv_x[0, :100], ref_recv_x[0, :100]), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + assert torch.equal(recv_topk_idx[0], ref_recv_topk_idx[0]), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal(recv_topk_weights[0], ref_recv_topk_weights[0]), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' + print(f'[rank {rank}] All checks passed for TileScale intranode_dispatch. ✅') + + # todo: benchmark + + +def main(local_rank: int, num_local_ranks: int, args): + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + + test_intranode_dispatch( + args.num_tokens, + args.hidden, + args.num_topk, + args.num_experts, + rank, + num_ranks, + args.expert_alignment, + group, + ) + +def parse_args(): + parser = ArgumentParser(description="Test notify_dispatch") + parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") + parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") + parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") + parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") + parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + num_ranks = args.num_ranks + torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 1705b489b7..b6cd2bfb85 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -21,7 +21,7 @@ def get_dispatch_layout( """Calculate the layout required for later communication. Arguments: - topk_idx: `[num_tokens, num_topk]`, dtype must be `torch.int64`, the expert indices selected by each token, + topk_idx: `[num_tokens, num_topk]`, dtype must be `torch.int32`, the expert indices selected by each token, `-1` means no selections. num_experts: the number of experts. num_ranks: the number of ranks. @@ -35,7 +35,7 @@ def get_dispatch_layout( """ # Check inputs - assert topk_idx.dtype == torch.int64, "topk_idx must be of dtype torch.int64" + assert topk_idx.dtype == torch.int32, "topk_idx must be of dtype torch.int32" assert topk_idx.ndim == 2, "topk_idx must be a 2D tensor" assert topk_idx.is_contiguous(), "topk_idx must be a contiguous tensor" assert num_experts > 0, "num_experts must be greater than 0" @@ -81,7 +81,7 @@ def get_dispatch_layout_kernel( @T.prim_func def get_dispatch_layout_main( - topk_idx: T.Tensor([num_tokens, num_topk], "int64"), # type: ignore + topk_idx: T.Tensor([num_tokens, num_topk], "int32"), # type: ignore num_tokens_per_rank: T.Tensor([num_ranks], "int32"), # type: ignore num_tokens_per_expert: T.Tensor([num_experts], "int32"), # type: ignore is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), # type: ignore diff --git a/examples/distributed/deepseek_deepep/intranode/log b/examples/distributed/deepseek_deepep/intranode/log new file mode 100644 index 0000000000..25cdab95b5 --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/log @@ -0,0 +1 @@ +2025-11-25 12:11:41 [TileLang:tilelang.env:WARNING]: Loading tilelang libs from dev root: /root/workspace/wt/tilescale/build diff --git a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py index f805de0365..d207a6e9e6 100644 --- a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py @@ -36,7 +36,7 @@ def notify_dispatch_main( num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), - moe_recv_counter_mapped: T.Tensor((1,), 'int64'), + moe_recv_counter_mapped: T.Tensor((1,), 'int32'), moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), @@ -272,7 +272,7 @@ def test_notify_dispatch( ref_rank_prefix_matrix, ref_channel_prefix_matrix = handle[:2] # create buffers in need - moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks)[3:5] + moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_local_experts)[3:5] per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() @@ -304,7 +304,6 @@ def test_notify_dispatch( # todo: benchmark - def main( local_rank: int, num_local_ranks: int, args ): diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 443ee26f32..1f7b8e9eee 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -137,11 +137,11 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu Returns: x: `[num_tokens, hidden]` with `torch.bfloat16`, the input to MoE layer. - topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, + topk_idx: `[num_tokens, num_topk]` with `torch.int32`, the expert indices selected by each token, `-1` means no selections. topk_weights: `[num_tokens, num_topk]` with `torch.float32`, the weights corresponding to each selected expert for each token. - rank_idx: `[num_tokens, num_topk]` with `torch.int64`, the rank indices corresponding to + rank_idx: `[num_tokens, num_topk]` with `torch.int32`, the rank indices corresponding to each selected expert, `-1` means no selections. """ assert num_topk <= num_experts, "num_topk must be less than or equal to num_experts" @@ -149,7 +149,7 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu x = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 - topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] + topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1].to(torch.int32) topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda') rank_idx = topk_idx // (num_experts // num_ranks) rank_idx.masked_fill_(topk_idx == -1, -1) @@ -178,12 +178,13 @@ def inplace_unique(x: torch.Tensor, num_slots: int): # Check: csrc/deep_ep.cpp:Buffer::Buffer -def create_moe_recv_counters(num_ranks: int): +def create_moe_recv_counters(num_ranks: int, num_local_experts: int): """Create MoE receive counters. All allocated tensors are initialized with -1. Args: num_ranks: the number of ranks. + num_local_experts: the number of local experts. Returns: moe_recv_counter: the MoE counter, allocated on pinned host memory. @@ -194,16 +195,14 @@ def create_moe_recv_counters(num_ranks: int): moe_recv_expert_counter_mapped: the MoE expert-level counter on device, mapped from the pinned host memory. moe_recv_rdma_counter_mapped: the MoE RDMA-level counter on device, mapped from the pinned host memory. """ - num_rdma_ranks = max(1, num_ranks // NUM_MAX_NVL_PEERS) # noqa: F841 - num_nvl_ranks = min(num_ranks, NUM_MAX_NVL_PEERS) # noqa: F841 moe_recv_counter = torch.tensor( - -1, dtype=torch.int64, pin_memory=True) # MoE counter + [-1], dtype=torch.int32, pin_memory=True, device='cpu') # MoE counter moe_recv_expert_counter = torch.tensor( - [-1] * NUM_MAX_LOCAL_EXPERTS, dtype=torch.int32, - pin_memory=True) # MoE expert-level counter + [-1] * num_local_experts, dtype=torch.int32, + pin_memory=True, device='cpu') # MoE expert-level counter moe_recv_rdma_counter = torch.tensor( - -1, dtype=torch.int32, pin_memory=True) # MoE RDMA-level counter + -1, dtype=torch.int32, pin_memory=True, device='cpu') # MoE RDMA-level counter moe_recv_counter_mapped = get_device_tensor(moe_recv_counter) moe_recv_expert_counter_mapped = get_device_tensor(moe_recv_expert_counter) diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 8bbd316930..78ff3295a3 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -364,6 +364,11 @@ TileOperator LdOpNode::Clone() const { return LdOp(node); } +TIR_REGISTER_TL_OP(PutOp, put) + .set_num_inputs(6) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + TIR_REGISTER_TL_OP(GetOp, get) .set_num_inputs(6) .set_attr("TCallEffectKind", diff --git a/src/op/sync.cc b/src/op/sync.cc index 3c0401a240..852013fc03 100644 --- a/src/op/sync.cc +++ b/src/op/sync.cc @@ -55,9 +55,6 @@ TIR_DEFINE_TL_BUILTIN(wait_barrier_gpu) TIR_DEFINE_TL_BUILTIN(wait_eq).set_num_inputs(2).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_DEFINE_TL_BUILTIN(wait_ne).set_num_inputs(2).set_attr( - "TCallEffectKind", Integer(CallEffectKind::kOpaque)); - TIR_DEFINE_TL_BUILTIN(sync_barrier_gpu) .set_num_inputs(1) .set_attr("TCallEffectKind", @@ -141,11 +138,71 @@ PrimExpr BarrierBlocksOpNode::MakeLocalBarAddr(const LowerArgs &T) const { {BufferLoad(buffer, local_indices)}); } +WaitOp::WaitOp(Array args, BufferMap vmap) { + ObjectPtr node = make_object(); + node->relation = args[0].as()->value; + node->addr = args[1]; + node->expected = args[2]; + node->peer = args[3]; + data_ = std::move(node); + (void)vmap; +} + +bool WaitOpNode::is_distributed() const { + return !(peer->IsInstance() && peer.as()->value == -1); +} + +Stmt WaitOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { + (void)analyzer; + (void)T; + Array new_args; + std::stringstream ss; + + // Map relation as int to literal_strings + const char* relation_str[] = {"eq", "ne", "ge", "le", "gt", "lt"}; + ss << "tl::wait_" << relation_str[relation]; + + new_args.push_back(StringImm(ss.str())); + if (is_distributed()) { + PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); + PrimExpr local_base_ptr = + Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); + PrimExpr offset_to_base = + Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {addr}), + local_base_ptr); + new_args.push_back( + Call(DataType::Handle(), tl::get_remote_base_ptr(), {peer}) + + offset_to_base); + } else { + new_args.push_back(addr); + } + new_args.push_back(expected); + + auto wait = Call(DataType::Handle(), builtin::call_extern(), new_args); + return Evaluate(wait); +} + +LayoutMap WaitOpNode::InferLayout(const LayoutInferArgs &T, InferLevel level) const { + (void)T; + (void)level; + return {}; +} + +TileOperator WaitOpNode::Clone() const { + auto node = make_object(*this); + return WaitOp(node); +} + TIR_REGISTER_TL_OP(BarrierBlocksOp, barrier_blocks) .set_num_inputs(1) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); +TIR_REGISTER_TL_OP(WaitOp, wait) + .set_num_inputs(4) + .set_attr("TCallEffectKind", + Integer(CallEffectKind::kOpaque)); + TIR_DEFINE_TL_BUILTIN(fence_cta).set_num_inputs(0).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); @@ -156,6 +213,7 @@ TIR_DEFINE_TL_BUILTIN(fence_sys).set_num_inputs(0).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); TVM_FFI_STATIC_INIT_BLOCK({ BarrierBlocksOpNode::RegisterReflection(); }); +TVM_FFI_STATIC_INIT_BLOCK({ WaitOpNode::RegisterReflection(); }); } // namespace tl } // namespace tvm diff --git a/src/op/sync.h b/src/op/sync.h index 25e1f6b119..718e4f116b 100644 --- a/src/op/sync.h +++ b/src/op/sync.h @@ -45,11 +45,64 @@ TVM_DLL const Op &wait_barrier_gpu(); TVM_DLL const Op &wait_eq(); + /*! - * \brief Wait until *addr != expected* for GPU-level synchronization - * void wait_ne(addr, expected) + * \brief TileOperatorNode for wait operation. + * + * WaitOpNode represents a wait primitive, + * which waits until a condition on a memory address is met. */ -TVM_DLL const Op &wait_ne(); +class WaitOpNode : public TileOperatorNode { + public: + PrimExpr addr; ///< The address to watch. + PrimExpr expected; ///< The expected value to compare against. + PrimExpr peer; ///< The peer to compare against. + int relation; ///< The relation to compare against. + + bool is_distributed() const; + + static constexpr const char* _type_key = "tl.WaitOp"; + TVM_DECLARE_FINAL_OBJECT_INFO(WaitOpNode, TileOperatorNode); + + Stmt Lower(const LowerArgs& T, arith::Analyzer* analyzer) const override; + LayoutMap InferLayout(const LayoutInferArgs& T, InferLevel level) const override; + static const Op& Get(); + TileOperator Clone() const override; + + static void RegisterReflection() { + namespace refl = tvm::ffi::reflection; + refl::ObjectDef() + .def_ro("addr", &WaitOpNode::addr) + .def_ro("expected", &WaitOpNode::expected) + .def_ro("peer", &WaitOpNode::peer) + .def_ro("relation", &WaitOpNode::relation); + } + + bool SEqualReduce(const WaitOpNode* other, SEqualReducer equal) const { + return equal(addr, other->addr) && equal(expected, other->expected) && + equal(peer, other->peer) && equal(relation, other->relation); + } + + void SHashReduce(SHashReducer hash_reduce) const { + hash_reduce(addr); + hash_reduce(expected); + hash_reduce(peer); + hash_reduce(relation); + } + + static constexpr bool _type_has_method_sequal_reduce = true; + static constexpr bool _type_has_method_shash_reduce = true; +}; + +/*! + * \brief Wrapper for the WaitOp operator. + */ +class WaitOp : public TileOperator { + public: + TVM_DEFINE_OBJECT_REF_METHODS(WaitOp, TileOperator, WaitOpNode); + TVM_DLL WaitOp(Array args, BufferMap vmap); + static const Op& Get(); +}; /*! * \brief Synchronize at a barrier for GPU-level synchronization diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 7efd6629d5..0ecc1969d8 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -823,13 +823,13 @@ void CodeGenTileLangCUDA::PrintStorageSync(const CallNode *op) { if (args.size() == 1) { this->stream << "__syncthreads();\n"; } else if (args.size() == 2) { - auto barrier_id = args[1].as()->value; - this->stream << "tl::__sync_thread_partial<" << barrier_id << ">();\n"; + std::string barrier_id = PrintExpr(args[1]); + this->stream << "tl::__sync_thread_partial(" << barrier_id << ");\n"; } else if (args.size() == 3) { - auto barrier_id = args[1].as()->value; - auto thread_count = args[2].as()->value; - this->stream << "tl::__sync_thread_partial<" << barrier_id << ", " - << thread_count << ">();\n"; + std::string barrier_id = PrintExpr(args[1]); + std::string thread_count = PrintExpr(args[2]); + this->stream << "tl::__sync_thread_partial(" << barrier_id << ", " + << thread_count << ");\n"; } else { LOG(FATAL) << "Invalid number of arguments for storage sync: " << args.size(); @@ -1517,10 +1517,6 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { this->PrintIndent(); this->stream << "tl::wait_eq(" << this->PrintExpr(op->args[0]) << ", " << this->PrintExpr(op->args[1]) << ");\n"; - } else if (op->op.same_as(tl::wait_ne())) { - this->PrintIndent(); - this->stream << "tl::wait_ne(" << this->PrintExpr(op->args[0]) << ", " - << this->PrintExpr(op->args[1]) << ");\n"; } else if (op->op.same_as(tl::atom_add())) { std::string func_name = "tl::ptx_atom_add_" + op->args[2].as()->value + "_" + diff --git a/src/tl_templates/cuda/common.h b/src/tl_templates/cuda/common.h index dfbc062cf1..057f6a25c9 100644 --- a/src/tl_templates/cuda/common.h +++ b/src/tl_templates/cuda/common.h @@ -295,11 +295,9 @@ template TL_DEVICE T pow_of_int(T x) { // Thread partial barrier synchronization // https://docs.nvidia.com/cuda/parallel-thread-execution/#memory-consistency-model -template -TL_DEVICE void __sync_thread_partial() { +TL_DEVICE void __sync_thread_partial(int barrier_id = 0, int thread_count = 0) { asm volatile("bar.sync %0, %1;" : : "r"(barrier_id), "r"(thread_count)); } - template TL_DEVICE void initialize_descriptor(GmmaDescriptor &descriptor, diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index a2170da30c..a6807f72f9 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -185,19 +185,51 @@ TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { } template -TL_DEVICE void wait_eq(void *barrier, T val = 1) { - T *flag_ptr = reinterpret_cast(barrier); +TL_DEVICE void wait_eq(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 while (ld_acquire(flag_ptr) != val); } template -TL_DEVICE void wait_ne(void *barrier, T val = 0) { - T *flag_ptr = reinterpret_cast(barrier); +TL_DEVICE void wait_ne(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global_acquire(flag_ptr) == val); + while (ld_volatile_global(flag_ptr) == val); +} + +template +TL_DEVICE void wait_ge(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); +// Spin-loop +#pragma unroll 1 + while (ld_volatile_global(flag_ptr) < val); +} + +template +TL_DEVICE void wait_le(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); +// Spin-loop +#pragma unroll 1 + while (ld_volatile_global(flag_ptr) > val); +} + +template +TL_DEVICE void wait_gt(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); +// Spin-loop +#pragma unroll 1 + while (ld_volatile_global(flag_ptr) <= val); +} + +template +TL_DEVICE void wait_lt(void *ptr, T val) { + T *flag_ptr = reinterpret_cast(ptr); +// Spin-loop +#pragma unroll 1 + while (ld_volatile_global(flag_ptr) >= val); } } // namespace tl diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index 4918b6656c..188811172c 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -511,7 +511,7 @@ def shfl_up(value: int | PrimExpr | tir.Call, offset: int | PrimExpr | tir.Call) return tir.call_extern(value.dtype, "__shfl_up_sync", 0xffffffff, value, offset) -def sync_threads(barrier_id: int = None, arrive_count: int = None): +def sync_threads(barrier_id: int | PrimExpr = None, arrive_count: int = None): """Synchronize all threads in a block. """ args = [] @@ -594,21 +594,6 @@ def wait_barrier_gpu(barrier: PrimExpr): return tir.call_intrin("handle", tir.op.Op.get("tl.wait_barrier_gpu"), address_of(barrier)) -def wait_eq(barrier: PrimExpr, expected: PrimExpr): - """Wait until *barrier == expected* for GPU-level synchronization. - - Args: - barrier: The barrier to wait at - expected: The expected value to wait for - """ - return tir.call_intrin("handle", tir.op.Op.get("tl.wait_eq"), address_of(barrier), expected) - - -def wait_ne(ptr: PrimExpr, expected: PrimExpr): - """Wait until *ptr != expected using ld_volatile_global()""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait_ne"), address_of(ptr), expected) - - def sync_barrier_gpu(barrier: PrimExpr): """Synchronize at a barrier for GPU-level synchronization. @@ -852,4 +837,4 @@ def warp_all(value, mask = -1): Returns: result (int): The result of the vote. """ - return tir.call_intrin("int32", tir.op + return tir.call_intrin("int32", tir.op.Op.get("tl.warp_all"), value, mask) diff --git a/tilelang/language/distributed/common.py b/tilelang/language/distributed/common.py index 5e7368bd19..153df91336 100644 --- a/tilelang/language/distributed/common.py +++ b/tilelang/language/distributed/common.py @@ -2,7 +2,9 @@ from __future__ import annotations from tvm import tir +from tvm.tir import address_of from tvm.tir import PrimExpr +from enum import Enum def get_rank(): @@ -105,3 +107,47 @@ def get_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, src_pe: PrimExpr | N return tir.call_intrin( "handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, 0, "block" ) # NOTE(wt): unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy + + +class BinaryRelation(Enum): + EQ = 0 + NE = 1 + GE = 2 + LE = 3 + GT = 4 + LT = 5 + + +def wait_eq(barrier: PrimExpr, expected: PrimExpr): + """Wait until *barrier == expected* for GPU-level synchronization. + # todo: have different semantic compared to 3 fns below currently + Args: + barrier: The barrier to wait at + expected: The expected value to wait for + """ + return tir.call_intrin("handle", tir.op.Op.get("tl.wait_eq"), address_of(barrier), expected) + + +def wait_ne(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): + """Wait until *ptr != expected""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.NE.value, address_of(ptr), expected, peer) + + +def wait_ge(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): + """Wait until *ptr >= expected""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GE.value, address_of(ptr), expected, peer) + + +def wait_le(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): + """Wait until *ptr <= expected""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LE.value, address_of(ptr), expected, peer) + + +def wait_gt(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): + """Wait until *ptr > expected""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GT.value, address_of(ptr), expected, peer) + + +def wait_lt(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): + """Wait until *ptr < expected""" + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LT.value, address_of(ptr), expected, peer) diff --git a/tilelang/utils/ts_ext/tensor.cpp b/tilelang/utils/ts_ext/tensor.cpp index 5d09b2a763..5ae90ebd88 100644 --- a/tilelang/utils/ts_ext/tensor.cpp +++ b/tilelang/utils/ts_ext/tensor.cpp @@ -35,7 +35,7 @@ static at::ScalarType dtype_from_string(const std::string &s) { return at::kUInt64; if (s == "int32" || s == "int") return at::kInt; - if (s == "int64" || s == "long") + if (s == "int64" || s == "long" || s == "long int") return at::kLong; if (s == "uint8" || s == "byte") return at::kByte; @@ -43,7 +43,7 @@ static at::ScalarType dtype_from_string(const std::string &s) { return at::kChar; if (s == "bool") return at::kBool; - throw std::runtime_error("Unsupported dtype string: " + s); + throw std::runtime_error("Unsupported dtype string: '"+ s + "'"); } torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, From 1cb41e7a2ccb097ca446e69ea7b84a5cd6d20778 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Tue, 25 Nov 2025 17:25:22 +0000 Subject: [PATCH 18/41] intra-node dispatch test passed --- .../deepseek_deepep/intranode/dispatch.py | 34 ++++++++----------- .../primitives/example_put_warp.py | 8 ++--- src/op/remote_copy.cc | 16 ++++++--- src/op/remote_copy.h | 12 +++---- src/target/codegen_cuda.cc | 24 +++++++++++++ src/tl_templates/cuda/copy.h | 20 +++++++++++ src/tl_templates/cuda/sync.h | 25 ++++++++------ tilelang/language/distributed/common.py | 33 ++++++++++-------- 8 files changed, 110 insertions(+), 62 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index f0a6a26feb..4c59fe812b 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -19,11 +19,12 @@ from notify_dispatch import notify_dispatch tilelang.disable_cache() +os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}, debug_root_path='/root/workspace/wt/debug/dispatch') + "tl.disable_warp_specialized": True}) def dispatch_kernel( rank, num_ranks, num_tokens, @@ -120,7 +121,7 @@ def dispatch_main( num_max_send_tokens+cached_channel_tail_idx-num_recv_buffer_tokens, responsible_rank) T.sync_warp() - # T.print(token_idx, 'start sender mainloop') + chunk_token_idx = T.alloc_var('int32') chunk_token_idx = 0 while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: @@ -144,15 +145,11 @@ def dispatch_main( cached_channel_tail_idx += 1 if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: # copy data, all are remote copy - # 1. copy data - # todo: support ld_nc and st_na + # 1. copy data (why useless???) T.put_warp(T.address_of(x[token_idx, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, - responsible_rank, 5) - # T.copy(x[token_idx, :], channel_x_buffers[responsible_channel, rank, dst_slot_idx, :], - # dst_pe=responsible_rank) #! we need this feature, but it's in another pr - + hidden, dst_pe=responsible_rank, unroll_factor=4) + # 2. copy src idx if T.elect_one_sync(): T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, @@ -248,8 +245,8 @@ def dispatch_main( T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), T.address_of(recv_x[total_offset+chunk_idx, 0]), hidden, - rank, - 5) + -1, + 4) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, @@ -279,8 +276,6 @@ def dispatch_main( # Exit num_tokens_to_recv -= num_cur_recv_tokens - if bx == 0 and tx == rank: - T.print(num_tokens_to_recv) # todo: support num_worst_tokens > 0 later @@ -382,7 +377,6 @@ def intranode_dispatch( torch.cuda.synchronize() # todo: replace it with host-side wait_ne num_recv_tokens = moe_recv_counter_mapped.item() - assert num_recv_tokens >= 0 num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() # create normal buffers @@ -399,9 +393,9 @@ def intranode_dispatch( channel_end_offset = tilelang.tensor( [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() channel_head_idx = tilelang.tensor( - [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() channel_tail_idx = tilelang.tensor( - [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() + shape=[config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() channel_x_buffers = tilelang.tensor( [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, hidden], dtype=torch.bfloat16, device='cuda', allocator=allocator) channel_src_idx_buffers = tilelang.tensor( @@ -470,7 +464,7 @@ def test_intranode_dispatch( raise ModuleNotFoundError("Please install DeepEP to run this test.") allocator = tilelang.get_allocator( - size=2**30, + size=2**33, device="cuda", is_distributed=True, local_rank=rank, @@ -496,9 +490,9 @@ def test_intranode_dispatch( recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list = \ intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) - assert torch.equal(recv_x[0, :100], ref_recv_x[0, :100]), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' - assert torch.equal(recv_topk_idx[0], ref_recv_topk_idx[0]), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal(recv_topk_weights[0], ref_recv_topk_weights[0]), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' print(f'[rank {rank}] All checks passed for TileScale intranode_dispatch. ✅') diff --git a/examples/distributed/primitives/example_put_warp.py b/examples/distributed/primitives/example_put_warp.py index 205eacec4f..a0351f6bf6 100644 --- a/examples/distributed/primitives/example_put_warp.py +++ b/examples/distributed/primitives/example_put_warp.py @@ -15,8 +15,8 @@ def kernel_(M, num_rank, block_M, threads): @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "bfloat16"), + src: T.Tensor((M), "bfloat16"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -55,8 +55,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if local_rank == 0: print(kernel.get_kernel_source()) - src = tilelang.tensor((M), torch.float32, allocator=allocator).normal_() - dst = tilelang.tensor((M), torch.float32, allocator=allocator) + src = tilelang.tensor(shape=(M), dtype=torch.bfloat16, allocator=allocator).normal_() + dst = tilelang.tensor(shape=(M), dtype=torch.bfloat16, allocator=allocator) torch.cuda.synchronize() torch.distributed.barrier(group) diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 78ff3295a3..7595e2fff2 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -78,11 +78,14 @@ PutOp::PutOp(Array args, BufferMap vmap) { node->dst_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; - node->is_symmetric = node->dst_pe.defined(); data_ = std::move(node); (void)vmap; } +bool PutOpNode::is_distributed() const { + return !(dst_pe->IsInstance() && dst_pe.as()->value == -1); +} + Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { (void)analyzer; Array new_args; @@ -96,7 +99,7 @@ Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { } new_args.push_back(StringImm(ss.str())); - if (is_symmetric) { + if (is_distributed()) { PrimExpr dst_addr_expr = MakeRemappedAddress(T, dst_buffer, dst_indices); PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); PrimExpr local_base_ptr = @@ -182,11 +185,14 @@ GetOp::GetOp(Array args, BufferMap vmap) { node->src_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; - node->is_symmetric = node->src_pe.defined(); data_ = std::move(node); (void)vmap; } +bool GetOpNode::is_distributed() const { + return !(src_pe->IsInstance() && src_pe.as()->value == -1); +} + Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { (void)analyzer; Array new_args; @@ -202,9 +208,9 @@ Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { new_args.push_back(StringImm(ss.str())); PrimExpr dst_addr_expr = MakeRemappedAddress(T, dst_buffer, dst_indices); new_args.push_back(dst_addr_expr); // Always dst first in tl_templates - if (is_symmetric) { + if (is_distributed()) { PrimExpr src_addr_expr = MakeRemappedAddress(T, src_buffer, src_indices); - PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); + PrimExpr local_rank = Call(DataType::In`t(64), tl::get_rank(), {}); PrimExpr local_base_ptr = Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); PrimExpr offset_to_base = diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index b87f893ab7..a6272db366 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -27,13 +27,14 @@ class PutOpNode : public TileOperatorNode { PrimExpr copy_size; ///< Number of bytes/elements to copy PrimExpr dst_pe; ///< Destination processing element (optional) int unroll_factor; ///< Unroll factor for warp copies - bool is_symmetric{false}; ///< Whether remote copy is symmetric Buffer src_buffer; ///< Source buffer reference Buffer dst_buffer; ///< Destination buffer reference Array src_indices; ///< Source indices used for address computation Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} + + bool is_distributed() const; static constexpr const char *_type_key = "tl.PutOp"; TVM_DECLARE_FINAL_OBJECT_INFO(PutOpNode, TileOperatorNode); @@ -52,7 +53,6 @@ class PutOpNode : public TileOperatorNode { .def_ro("copy_size", &PutOpNode::copy_size) .def_ro("dst_pe", &PutOpNode::dst_pe) .def_ro("unroll_factor", &PutOpNode::unroll_factor) - .def_ro("is_symmetric", &PutOpNode::is_symmetric) .def_ro("src_buffer", &PutOpNode::src_buffer) .def_ro("dst_buffer", &PutOpNode::dst_buffer) .def_ro("src_indices", &PutOpNode::src_indices) @@ -67,7 +67,6 @@ class PutOpNode : public TileOperatorNode { equal(dst_offset, other->dst_offset) && equal(copy_size, other->copy_size) && equal(dst_pe, other->dst_pe) && equal(unroll_factor, other->unroll_factor) && - equal(is_symmetric, other->is_symmetric) && equal(src_buffer, other->src_buffer) && equal(dst_buffer, other->dst_buffer) && equal(src_indices, other->src_indices) && @@ -82,7 +81,6 @@ class PutOpNode : public TileOperatorNode { hash_reduce(copy_size); hash_reduce(dst_pe); hash_reduce(unroll_factor); - hash_reduce(is_symmetric); hash_reduce(src_buffer); hash_reduce(dst_buffer); hash_reduce(src_indices); @@ -118,7 +116,6 @@ class GetOpNode : public TileOperatorNode { PrimExpr copy_size; ///< Number of bytes/elements to copy PrimExpr src_pe; ///< Source processing element (optional) int unroll_factor; ///< Unroll factor for warp copies - bool is_symmetric{false}; ///< Whether remote copy is symmetric Buffer src_buffer; ///< Source buffer reference Buffer dst_buffer; ///< Destination buffer reference Array src_indices; ///< Source indices used for address computation @@ -126,6 +123,8 @@ class GetOpNode : public TileOperatorNode { dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} + bool is_distributed() const; + static constexpr const char *_type_key = "tl.GetOp"; TVM_DECLARE_FINAL_OBJECT_INFO(GetOpNode, TileOperatorNode); @@ -143,7 +142,6 @@ class GetOpNode : public TileOperatorNode { .def_ro("copy_size", &GetOpNode::copy_size) .def_ro("src_pe", &GetOpNode::src_pe) .def_ro("unroll_factor", &GetOpNode::unroll_factor) - .def_ro("is_symmetric", &GetOpNode::is_symmetric) .def_ro("src_buffer", &GetOpNode::src_buffer) .def_ro("dst_buffer", &GetOpNode::dst_buffer) .def_ro("src_indices", &GetOpNode::src_indices) @@ -158,7 +156,6 @@ class GetOpNode : public TileOperatorNode { equal(dst_offset, other->dst_offset) && equal(copy_size, other->copy_size) && equal(src_pe, other->src_pe) && equal(unroll_factor, other->unroll_factor) && - equal(is_symmetric, other->is_symmetric) && equal(src_buffer, other->src_buffer) && equal(dst_buffer, other->dst_buffer) && equal(src_indices, other->src_indices) && @@ -173,7 +170,6 @@ class GetOpNode : public TileOperatorNode { hash_reduce(copy_size); hash_reduce(src_pe); hash_reduce(unroll_factor); - hash_reduce(is_symmetric); hash_reduce(src_buffer); hash_reduce(dst_buffer); hash_reduce(src_indices); diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 0ecc1969d8..5b45ac1d1f 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -2049,6 +2049,30 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { } } os << ")"; + } else if (op->op.same_as(tl::PutmemWarp())) { + this->use_distributed_ = true; + this->use_nvshmem_ = true; + os << "nvshmemx_putmem_warp("; + this->PrintExpr(op->args[0], os); + os << ", "; + this->PrintExpr(op->args[1], os); + os << ", "; + this->PrintExpr(op->args[2], os); + os << ", "; + this->PrintExpr(op->args[3], os); + os << ")"; + } else if (op->op.same_as(tl::PutmemNbiWarp())) { + this->use_distributed_ = true; + this->use_nvshmem_ = true; + os << "nvshmemx_putmem_nbi_warp("; + this->PrintExpr(op->args[0], os); + os << ", "; + this->PrintExpr(op->args[1], os); + os << ", "; + this->PrintExpr(op->args[2], os); + os << ", "; + this->PrintExpr(op->args[3], os); + os << ")"; } else if (op->op.same_as(tl::GetmemBlock())) { this->use_distributed_ = true; this->use_nvshmem_ = true; diff --git a/src/tl_templates/cuda/copy.h b/src/tl_templates/cuda/copy.h index 5264207faa..6dc07704d0 100644 --- a/src/tl_templates/cuda/copy.h +++ b/src/tl_templates/cuda/copy.h @@ -111,6 +111,18 @@ template <> TL_DEVICE uint8_t ld_nc_global(const uint8_t *ptr) { return static_cast(ret); } +template <> TL_DEVICE int16_t ld_nc_global(const int16_t *ptr) { + uint16_t ret; + asm volatile(LD_NC_FUNC ".s16 %0, [%1];" : "=h"(ret) : "l"(ptr)); + return static_cast(ret); +} + +template <> TL_DEVICE uint16_t ld_nc_global(const uint16_t *ptr) { + uint16_t ret; + asm volatile(LD_NC_FUNC ".u16 %0, [%1];" : "=h"(ret) : "l"(ptr)); + return ret; +} + template <> TL_DEVICE int ld_nc_global(const int *ptr) { int ret; asm volatile(LD_NC_FUNC ".s32 %0, [%1];" : "=r"(ret) : "l"(ptr)); @@ -153,6 +165,14 @@ TL_DEVICE void st_na_global(const dtype_t *ptr, const dtype_t &value) { &value)); } +template <> TL_DEVICE void st_na_global(const int16_t *ptr, const int16_t &value) { + asm volatile(ST_NA_FUNC ".s16 [%0], %1;" ::"l"(ptr), "h"(value)); +} + +template <> TL_DEVICE void st_na_global(const uint16_t *ptr, const uint16_t &value) { + asm volatile(ST_NA_FUNC ".u16 [%0], %1;" ::"l"(ptr), "h"(value)); +} + template <> TL_DEVICE void st_na_global(const int *ptr, const int &value) { asm volatile(ST_NA_FUNC ".s32 [%0], %1;" ::"l"(ptr), "r"(value)); } diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index a6807f72f9..e9514d90a8 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -192,40 +192,45 @@ TL_DEVICE void wait_eq(void *ptr, T val) { while (ld_acquire(flag_ptr) != val); } -template -TL_DEVICE void wait_ne(void *ptr, T val) { +template +TL_DEVICE void wait_ne(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 while (ld_volatile_global(flag_ptr) == val); } -template -TL_DEVICE void wait_ge(void *ptr, T val) { +template +TL_DEVICE void wait_ge(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 while (ld_volatile_global(flag_ptr) < val); } -template -TL_DEVICE void wait_le(void *ptr, T val) { +template +TL_DEVICE void wait_le(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 while (ld_volatile_global(flag_ptr) > val); } -template -TL_DEVICE void wait_gt(void *ptr, T val) { +template +TL_DEVICE void wait_gt(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 while (ld_volatile_global(flag_ptr) <= val); } -template -TL_DEVICE void wait_lt(void *ptr, T val) { +template +TL_DEVICE void wait_lt(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 diff --git a/tilelang/language/distributed/common.py b/tilelang/language/distributed/common.py index 153df91336..8cbaf5a174 100644 --- a/tilelang/language/distributed/common.py +++ b/tilelang/language/distributed/common.py @@ -3,7 +3,7 @@ from tvm import tir from tvm.tir import address_of -from tvm.tir import PrimExpr +from tvm.tir import PrimExpr, IntImm from enum import Enum @@ -22,7 +22,7 @@ def get_num_ranks(): def put_warp(src: PrimExpr, dst: PrimExpr, size: PrimExpr, - dst_pe: PrimExpr | None = None, + dst_pe: PrimExpr | IntImm | None = -1, unroll_factor: int = 4): """Put to a remote buffer with unrolled loop. @@ -35,8 +35,7 @@ def put_warp(src: PrimExpr, The size of the put in elements. dst_pe: PrimExpr | None The PE index of the destination. - If provided, the dst is a symmetric address, otherwise it is a UVA address. - If not provided, the dst is a UVA address and dst_pe is None. + -1 by default, which means local copy. unroll_factor: int The unroll factor """ @@ -47,7 +46,7 @@ def put_warp(src: PrimExpr, def get_warp(src: PrimExpr, dst: PrimExpr, size: PrimExpr, - src_pe: PrimExpr | None = None, + src_pe: PrimExpr | IntImm | None = -1, unroll_factor: int = 4): """Get from a remote buffer with unrolled loop. @@ -60,8 +59,7 @@ def get_warp(src: PrimExpr, The size of the get in elements. src_pe: PrimExpr | None The PE index of the source. - If provided, the src is a symmetric address, otherwise it is a UVA address. - If not provided, the src is a UVA address and src_pe is None. + -1 by default, which means local copy. unroll_factor: int The unroll factor """ @@ -69,7 +67,10 @@ def get_warp(src: PrimExpr, "warp") -def put_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | None = None): +def put_block(src: PrimExpr, + dst: PrimExpr, + size: PrimExpr, + dst_pe: PrimExpr | IntImm | None = -1): """Put to a remote buffer. Args: @@ -81,15 +82,17 @@ def put_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | N The size of the put in elements. dst_pe: PrimExpr | None The PE index of the destination. - If provided, the dst is a symmetric address, otherwise it is a UVA address. - If not provided, the dst is a UVA address and dst_pe is None. + -1 by default, which means local copy. """ return tir.call_intrin( "handle", tir.op.Op.get("tl.put"), src, dst, size, dst_pe, 0, "block" - ) # NOTE(wt): unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy + ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy -def get_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, src_pe: PrimExpr | None = None): +def get_block(src: PrimExpr, + dst: PrimExpr, + size: PrimExpr, + src_pe: PrimExpr | IntImm | None = -1): """Get from a remote buffer. Args: @@ -101,12 +104,12 @@ def get_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, src_pe: PrimExpr | N The size of the get in elements. src_pe: PrimExpr | None The PE index of the source. - If provided, the src is a symmetric address, otherwise it is a UVA address. - If not provided, the src is a UVA address and src_pe is None. + -1 by default, which means local copy. """ return tir.call_intrin( "handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, 0, "block" - ) # NOTE(wt): unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy + ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy + class BinaryRelation(Enum): From 1d7c4569f5fde29c256e826d449543233a2f8218 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Thu, 27 Nov 2025 05:54:44 +0000 Subject: [PATCH 19/41] draft combine --- .../deepseek_deepep/intranode/combine.py | 505 ++++++++++++++++++ .../deepseek_deepep/intranode/dispatch.py | 40 +- .../intranode/get_dispatch_layout.py | 10 +- src/op/remote_copy.cc | 2 +- src/target/codegen_cuda.cc | 30 -- src/tl_templates/cuda/ldst.h | 38 +- src/transform/storage_access.cc | 5 + tilelang/language/builtin.py | 2 +- 8 files changed, 586 insertions(+), 46 deletions(-) create mode 100644 examples/distributed/deepseek_deepep/intranode/combine.py diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py new file mode 100644 index 0000000000..ce596babae --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -0,0 +1,505 @@ +# For intranode only +# This op is distributed +### TILELANG_USE_DISTRIBUTED=1 python combine.py + +from asyncio import Handle +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + +import torch +import tilelang +import tilelang.language as T +from tilelang.profiler import do_bench +from tilelang.distributed.utils import init_dist +from utils import Config, create_moe_recv_counters, gen_inputs # noqa: F403 +from argparse import ArgumentParser + +from get_dispatch_layout import get_dispatch_layout +from notify_dispatch import notify_dispatch +from dispatch import intranode_dispatch + +# tilelang.disable_cache() +os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log + + +@tilelang.jit( + pass_configs={"tl.disable_tma_lower": True, + "tl.disable_warp_specialized": True}, + debug_root_path='/root/workspace/wt/debug/notify_combine') +def cached_notify_combine_kernel( + num_recv_tokens, + num_ranks, + num_sms, +): + num_channels = num_sms // 2 + threads = max(128, 32 * num_ranks) + + @T.prim_func + def cached_notify_combine_main( + send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), + ##### symm buffers ##### + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + ): + with T.Kernel(num_channels + 1, threads=threads) as bx: + tx = T.get_thread_binding() + + if bx == 0: + # block 0 is responsible for clearing channel_head/tail_idx buffers + # note that the buffer layout is slightly different from original DeepEP logic + T.sync_blocks(barrier_signal) + T.clear(channel_head_idx) + T.clear(channel_tail_idx) + T.barrier_blocks(barrier_signal) + else: + channel_id = bx - 1 + rank_id = tx // 32 + lane_id = tx % 32 + if rank_id >= num_ranks: + T.thread_return() + + tokens_per_channel = T.ceildiv(num_recv_tokens, num_channels) + token_start_idx = T.min(tokens_per_channel * channel_id, num_recv_tokens) + token_end_idx = T.min(token_start_idx + tokens_per_channel, num_recv_tokens) + + last_head = T.alloc_var('int32', init=2**25) # a heuristic large number + # todo: tilelang doesn't support reverse loop, we simulate this + for i in T.serial(0, token_end_idx-token_start_idx, 32): + token_idx_tail = token_end_idx - i - 1 + token_idx = token_idx_tail - lane_id + current_head = T.alloc_var('int32') + if token_idx >= token_start_idx: + T.ld(send_head[token_idx, rank_id], current_head, nc=True) + else: + current_head = -1 + expected_head = T.alloc_var('int32') + expected_head = 0 + for j in T.serial(T.min(32, token_idx_tail-token_start_idx + 1)): + head = T.tvm_warp_shuffle(-1, current_head, j, 32, 32) + if head < 0: + if lane_id == j: + expected_head = -last_head - 1 + else: + last_head = head + if current_head < 0 and token_idx >= token_start_idx: + send_head[token_idx, rank_id] = expected_head + + return cached_notify_combine_main + + +def cached_notify_combine( + num_ranks, + num_sms, + num_recv_tokens, #! means the original #tokens on each rank here + ##### symm buffers ##### + send_head: torch.Tensor, + channel_head_idx: torch.Tensor, + channel_tail_idx: torch.Tensor, + barrier_signal: torch.Tensor, + allocator +): + kernel = cached_notify_combine_kernel(num_recv_tokens, num_ranks, num_sms) + kernel.initialize(allocator=allocator) + + kernel( + send_head, + channel_head_idx, + channel_tail_idx, + barrier_signal, + ) + + +@tilelang.jit( + pass_configs={"tl.disable_tma_lower": True, # use TMA later + "tl.disable_warp_specialized": True}, debug_root_path='/root/workspace/wt/debug/combine') +def combine_kernel( + rank, num_ranks, + num_recv_tokens, + num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens + num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens + hidden, + num_topk, + num_sms, + dtype: str = 'bfloat16', +): + num_tokens = T.dynamic('num_tokens') + + num_channels = num_sms // 2 + threads = 768 # 24 warps + warps = threads // 32 + warps_per_rank = warps // num_ranks # 3 + threads_per_rank = threads // num_ranks # 96 + TMABytesPerWarp = 4096 + smem_size = TMABytesPerWarp * (threads // 32) + num_stages = 8 + + assert hidden % 8 == 0 # manual vectorize on recv-side + + @T.prim_func + def combine_main( + # inputs + x: T.Tensor([num_tokens, hidden], dtype), + topk_weights: T.Tensor([num_tokens, num_topk], "float32"), + src_idx: T.Tensor([num_tokens], "int32"), + # todo: support bias as inputs + # outputs + recv_x: T.Tensor([num_recv_tokens, hidden], dtype), + recv_topk_weights: T.Tensor([num_recv_tokens, num_topk], "float32"), + # metadata + rank_prefix_matrix: T.Tensor([num_ranks, num_ranks], "int32"), + channel_prefix_matrix: T.Tensor([num_ranks, num_channels], "int32"), + send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), + # symm buffers + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + ): + with T.Kernel(num_sms, threads=threads) as bx: + tx = T.get_thread_binding() + lane_id = tx % 32 + warp_id = tx // 32 + responsible_channel = bx // 2 + + if bx % 2 == 0: # sender + send_rank_id = (responsible_channel + warp_id) % num_ranks + send_warp_id_in_rank = warp_id // num_ranks + + # get tasks + rank_offset = T.if_then_else(send_rank_id > 0, rank_prefix_matrix[send_rank_id-1, rank], 0) + num_rank_tokens = rank_prefix_matrix[send_rank_id, rank] - rank_offset + channel_offset = channel_prefix_matrix[send_rank_id, responsible_channel] + num_channel_tokens= T.if_then_else( + responsible_channel == num_channels - 1, + num_rank_tokens, + channel_prefix_matrix[send_rank_id, responsible_channel + 1] - channel_offset, + ) + token_start_idx = rank_offset + channel_offset + token_end_idx = token_start_idx + num_channel_tokens + + # Iterate over all tokens and send by trunk + current_channel_tail_idx = T.alloc_var('int32') + current_channel_tail_idx = 0 + token_idx = T.alloc_var('int32') + token_idx = token_start_idx + with T.While(token_idx < token_end_idx): + # Check destination queue emptiness, or wait a buffer to be released (rare cases) + num_round_tokens = T.min(num_max_send_tokens, token_end_idx - token_idx) + if T.elect_one_sync(): + T.wait_ge(channel_head_idx[responsible_channel, rank], current_channel_tail_idx + num_round_tokens - num_recv_buffer_tokens, peer=send_rank_id) + T.sync_warp() + + # Send by trunk + for i in T.serial(send_warp_id_in_rank, num_round_tokens, warps_per_rank): + # Get an empty slot + dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = (current_channel_tail_idx + i) % num_recv_buffer_tokens + + # 1. copy data + T.put_warp(T.address_of(x[token_idx + i, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), + hidden, dst_pe=send_rank_id, unroll_factor=4) + + # 2. send src idx + idx = T.alloc_var('int32') + if T.elect_one_sync(): + T.ld(src_idx[token_idx + i], idx, nc=True) + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], idx, + dst_pe=send_rank_id) + + # 3. send topk_weights + if num_topk > 0 and lane_id < num_topk: + weight = T.alloc_var('float32') + T.ld(topk_weights[token_idx + i, lane_id], weight, nc=True) + T.st(channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight, + dst_pe=send_rank_id) + + token_idx += num_round_tokens + current_channel_tail_idx += num_round_tokens + + # move tail index + T.sync_threads(send_rank_id, threads_per_rank) + if send_warp_id_in_rank == 0 and T.elect_one_sync(): + T.st(channel_tail_idx[responsible_channel, rank], current_channel_tail_idx, + scope='sys', sem='release', + dst_pe=send_rank_id) + + else: # receiver + warp_channel_head_idx = T.alloc_shared([warps, num_ranks], 'int32') + shared_channel_tail_idx = T.alloc_shared([32], 'int32') #! workaround for illegal address + warp_retired = T.alloc_shared([warps], 'bool') + if tx < warps: + warp_retired[tx] = False + if lane_id < num_ranks: + warp_channel_head_idx[warp_id, lane_id] = 0 + if tx < 32: + shared_channel_tail_idx[tx] = 0 + T.sync_threads() + + if tx < 32: # one warp for moving the queue head + last_head = T.alloc_var('int32') + last_head = 0 + with T.While(lane_id < num_ranks): + # check retired + retired = T.alloc_var('bool') + retired = True + for i in T.serial(1, warps): + retired = retired and warp_retired[i] + if retired: + T.loop_break() + + # Update queue tail + new_tail = T.alloc_var('int32') + T.ld(channel_tail_idx[responsible_channel, lane_id], new_tail, sem="acquire", scope="sys") + # Use release semantics to ensure receiver warps see the update + T.st(shared_channel_tail_idx[lane_id], new_tail, sem="release", scope="cta") + + # Update minimum head + min_head = T.alloc_var('int32') + min_head = 2**31 - 1 # int32 max + for i in T.serial(1, warps): + if not warp_retired[i]: + min_head = T.min(min_head, warp_channel_head_idx[i, lane_id]) + if min_head != 2**31 - 1 and min_head > last_head: + last_head = min_head + T.st(channel_head_idx[responsible_channel, lane_id], min_head, sem="relaxed", scope="sys") + else: # other warps for reduction + # All lanes will use data buffer, but only rank lane will use `head/tail/src_idx` + # for *_buffers[i] channel_rank_offset = responsible_channel * kNumRanks + i; + + # The same tokens as the dispatch process + num_tokens_per_channel = T.ceildiv(num_recv_tokens, num_channels) + token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_recv_tokens) + token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_recv_tokens) + + # Iterate over all tokens and combine + for token_idx in T.serial(token_start_idx+warp_id-1, token_end_idx, warps-1): + # Read expected head + expected_head = T.alloc_var('int32') + expected_head = -1 + if lane_id < num_ranks: + T.ld(send_head[token_idx, lane_id], expected_head, nc=True) + + condvar = T.alloc_var('int32') + if bx == 1 and tx == 32: + T.print(condvar) + T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") + with T.While(T.warp_any(condvar <= expected_head and expected_head >= 0)): + T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") + T.print(condvar-expected_head) + T.loop_continue() + # can we simplify this ? + T.sync_warp() + + # Broadcast current heads + num_topk_ranks = T.alloc_var('int32') + num_topk_ranks = 0 + topk_ranks= T.alloc_local([num_ranks], 'int32') + slot_indices = T.alloc_local([num_ranks], 'int32') + for i in T.serial(num_ranks): + expected_head_i = T.tvm_warp_shuffle(-1, expected_head, i, 32, 32) + if expected_head_i >= 0: + slot_indices[num_topk_ranks] = expected_head_i % num_recv_buffer_tokens + topk_ranks[num_topk_ranks] = i + num_topk_ranks += 1 + if bx == 0 and tx == 32: + T.print(num_topk_ranks, 'broadcast finished') + + # Reduce data with pipeline + # todo: vectorize + recv_value = T.alloc_local([num_ranks, 8], dtype) + values = T.alloc_local([8], "float32") + + for i in T.serial(lane_id, hidden // 8, 32): + T.clear(values) + for j in T.serial(num_topk_ranks): + for k in T.vectorized(8): + T.ld(channel_x_buffers[responsible_channel, topk_ranks[j], slot_indices[j], i*8+k], recv_value[j, k], nc=True) + + # todo: support bias + + # Reduce a2a results + for j in T.serial(num_topk_ranks): + for k in T.vectorized(8): + values[k] += recv_value[j, k] + for j in T.vectorized(8): + recv_x[token_idx, i*8+j] = values[j] + + # Reduce topk_weights + if lane_id < num_topk: + weight_sum = T.alloc_var('float32') + weight_sum = 0 + for i in T.serial(num_topk_ranks): + weight = T.alloc_var('float32') + T.ld(channel_topk_weights_buffers[responsible_channel, topk_ranks[i], slot_indices[i], lane_id], weight, nc=True) + weight_sum += weight + recv_topk_weights[token_idx, lane_id] = weight_sum + + # Update head + if lane_id < num_ranks: + warp_channel_head_idx[warp_id, lane_id] = T.if_then_else( + expected_head < 0, + -expected_head - 1, + expected_head + 1) + + if bx == 1 and tx == 32: + T.print(warp_channel_head_idx[warp_id, lane_id]) + + # Retired + T.sync_warp() + if T.elect_one_sync(): + warp_retired[warp_id] = True + if bx == 1 and tx == 32: + T.print(warp_channel_head_idx, 'retired') + + return combine_main + + +def intranode_combine(rank: int, allocator, x, topk_weights, src_idx, + rank_prefix_matrix, channel_prefix_matrix, send_head, + channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers, + config=None): + + # acquire_shapes + num_tokens, hidden = x.shape + _, num_topk = topk_weights.shape + num_ranks, num_channels = channel_prefix_matrix.shape + num_recv_tokens = send_head.shape[0] + + # Default config + config = Config.get_combine_config(num_ranks) if config is None else config + + ### notify combine ### + kernel1 = cached_notify_combine_kernel(num_recv_tokens, num_ranks, config.num_sms) + kernel1.initialize(allocator=allocator) + kernel1( + send_head, + channel_head_idx, + channel_tail_idx, + barrier_signal, + ) + + ### combine ### + recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') + + kernel2 = combine_kernel( + rank, num_ranks, + num_recv_tokens, + config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, + hidden, + num_topk, + config.num_sms, + dtype='bfloat16' + ) + kernel2.initialize(allocator=allocator) + kernel2( + x, + topk_weights, + src_idx, + recv_x, + recv_topk_weights, + rank_prefix_matrix, + channel_prefix_matrix, + send_head, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_weights_buffers, + ) + + return recv_x, recv_topk_weights + + +def test_intranode_combine( + num_tokens: int, + hidden: int, + num_topk: int, + num_experts: int, + rank: int, + num_ranks: int, + expert_alignment: int, + group: torch.distributed.ProcessGroup, +): + try: + import deep_ep # noqa: F403 + except ModuleNotFoundError as e: + raise ModuleNotFoundError("Please install DeepEP to run this test.") + + allocator = tilelang.get_allocator( + size=2**30, + device="cuda", + is_distributed=True, + local_rank=rank, + num_local_ranks=num_ranks, + group=group) + + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) + buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) + + if rank == 0: + print('get dispatch layout...') + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx.to(torch.int64), num_experts) # DeepEP requires int64 topk_idx + + if rank == 0: + print('intranode dispatch...') + + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ + buffer.dispatch(x, None, num_tokens_per_rank, None, is_token_in_rank, num_tokens_per_expert, topk_idx.to(torch.int64), topk_weights, expert_alignment) # DeepEP requires int64 topk_idx`` + + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers = \ + intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) + + assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' + + if rank == 0: + print('Start combine...') + + ref_combine_x, ref_combine_topk_weights, _, ref_send_head = buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights, previous_event=event) + + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers = symm_buffers + combine_x, combine_topk_weights = intranode_combine(rank, allocator, recv_x, recv_topk_weights, recv_src_idx, rank_prefix_matrix, recv_channel_prefix_matrix, send_head, channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers) + + assert torch.equal(combine_x, ref_combine_x), f'combine_x mismatch, max err: {(combine_x - ref_combine_x).abs().max()}' + assert torch.equal(combine_topk_weights, ref_combine_topk_weights), f'combine_topk_weights mismatch, max err: {(combine_topk_weights - ref_combine_topk_weights).abs().max()}' + print(f'[rank {rank}] All checks passed for TileScale intranode_combine. ✅') + + +def main(local_rank: int, num_local_ranks: int, args): + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + + test_intranode_combine( + args.num_tokens, + args.hidden, + args.num_topk, + args.num_experts, + rank, + num_ranks, + args.expert_alignment, + group, + ) + +def parse_args(): + parser = ArgumentParser(description="Test notify_dispatch") + parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") + parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") + parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") + parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") + parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + + num_ranks = args.num_ranks + torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 4c59fe812b..390d4321af 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -10,6 +10,7 @@ import tilelang from tilelang.autotuner import * import tilelang.language as T +from tilelang.profiler import do_bench from argparse import ArgumentParser from typing import Any, Optional, Tuple, List from tilelang.distributed.utils import init_dist @@ -18,7 +19,7 @@ from get_dispatch_layout import get_dispatch_layout from notify_dispatch import notify_dispatch -tilelang.disable_cache() +# tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -246,7 +247,7 @@ def dispatch_main( T.address_of(recv_x[total_offset+chunk_idx, 0]), hidden, -1, - 4) + 5) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, @@ -330,6 +331,7 @@ def intranode_dispatch( num_recv_tokens_per_expert_list: Python list shaped `[num_local_experts]`, the received token count by each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list will be empty. + handle: the handle for combine, has `(rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)`. """ assert handle is None # Currently only support non-cached mode @@ -384,7 +386,7 @@ def intranode_dispatch( recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int32, device='cuda') recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') - recv_channel_offset = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') + recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') # create symm buffers @@ -428,7 +430,7 @@ def intranode_dispatch( recv_src_idx, recv_topk_idx, recv_topk_weights, - recv_channel_offset, + recv_channel_prefix_matrix, send_head, x, topk_idx, @@ -446,7 +448,13 @@ def intranode_dispatch( channel_topk_weights_buffers, ) - return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list + handle = (rank_prefix_matrix, channel_prefix_matrix, + recv_channel_prefix_matrix, recv_src_idx, + is_token_in_rank, send_head + ) + symm_buffers = (channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers) + return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers # todo: reconsider hierachy + def test_intranode_dispatch( num_tokens: int, @@ -483,20 +491,36 @@ def test_intranode_dispatch( print('intranode dispatch (notify_dispatch included...)') # golden - ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, _, _ = \ + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, _ = \ buffer.dispatch(x, None, num_tokens_per_rank, None, is_token_in_rank, num_tokens_per_expert, topk_idx.to(torch.int64), topk_weights, expert_alignment) # DeepEP requires int64 topk_idx`` # ours - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list = \ + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) + # check dispatch output assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' + + # check handle + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle + assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' + assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_src_idx, ref_recv_src_idx), f'recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' + assert torch.equal(send_head, ref_send_head), f'send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + print(f'[rank {rank}] All checks passed for TileScale intranode_dispatch. ✅') - # todo: benchmark + buffer.combine( + recv_x, + ref_handle, + recv_topk_weights, + ) def main(local_rank: int, num_local_ranks: int, args): diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index b6cd2bfb85..006fb11d22 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -201,8 +201,14 @@ def test_get_dispatch_layout( print("All checks passed for TileScale get_dispatch_layout.✅") # Benchmark - t1 = do_bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False)) - t2 = do_bench(lambda: get_dispatch_layout(topk_idx, num_experts, num_ranks)) + t1 = do_bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False), + _n_warmup=1, + _n_repeat=1, + ) + t2 = do_bench(lambda: get_dispatch_layout(topk_idx, num_experts, num_ranks), + _n_warmup=1, + _n_repeat=1, + ) print(f"DeepEP: {t1:.3f} ms") print(f"TileScale: {t2:.3f} ms") print(f"Speedup: {t1 / t2:.2f}x") diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 7595e2fff2..8cd3c6b22e 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -210,7 +210,7 @@ Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { new_args.push_back(dst_addr_expr); // Always dst first in tl_templates if (is_distributed()) { PrimExpr src_addr_expr = MakeRemappedAddress(T, src_buffer, src_indices); - PrimExpr local_rank = Call(DataType::In`t(64), tl::get_rank(), {}); + PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); PrimExpr local_base_ptr = Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); PrimExpr offset_to_base = diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 5b45ac1d1f..4b1949a292 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -291,12 +291,6 @@ std::string CodeGenTileLangCUDA::Finish() { decl_stream << "#include \n"; decl_stream << "#include \n"; decl_stream << "#include \n"; - } - decl_stream << "#ifdef ENABLE_BF16\n"; - decl_stream << "#include \n"; - decl_stream << "#endif\n"; - - if (use_distributed_) { decl_stream << "uint64_t __constant__ meta_data[1024];\n"; } decl_stream << "#ifdef ENABLE_BF16\n"; @@ -2049,30 +2043,6 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { } } os << ")"; - } else if (op->op.same_as(tl::PutmemWarp())) { - this->use_distributed_ = true; - this->use_nvshmem_ = true; - os << "nvshmemx_putmem_warp("; - this->PrintExpr(op->args[0], os); - os << ", "; - this->PrintExpr(op->args[1], os); - os << ", "; - this->PrintExpr(op->args[2], os); - os << ", "; - this->PrintExpr(op->args[3], os); - os << ")"; - } else if (op->op.same_as(tl::PutmemNbiWarp())) { - this->use_distributed_ = true; - this->use_nvshmem_ = true; - os << "nvshmemx_putmem_nbi_warp("; - this->PrintExpr(op->args[0], os); - os << ", "; - this->PrintExpr(op->args[1], os); - os << ", "; - this->PrintExpr(op->args[2], os); - os << ", "; - this->PrintExpr(op->args[3], os); - os << ")"; } else if (op->op.same_as(tl::GetmemBlock())) { this->use_distributed_ = true; this->use_nvshmem_ = true; diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h index 1a4b4ee3e6..73b7e1ba0e 100644 --- a/src/tl_templates/cuda/ldst.h +++ b/src/tl_templates/cuda/ldst.h @@ -11,6 +11,23 @@ enum class Scope { CTA, GPU, SYS }; template inline constexpr bool always_false_v = false; #endif +// Type trait to detect bfloat16 types +template +struct is_bfloat16 : std::false_type {}; + +#ifdef __CUDA_BF16_TYPES_EXIST__ +template <> +struct is_bfloat16<__nv_bfloat16> : std::true_type {}; +#endif + +// Detect cutlass bfloat16_t +namespace cutlass { struct bfloat16_t; } +template <> +struct is_bfloat16 : std::true_type {}; + +template +inline constexpr bool is_bfloat16_v = is_bfloat16::value; + // Fallback template for unsupported configurations template struct StImpl { @@ -37,8 +54,14 @@ struct LdImpl { template \ TL_DEVICE static void execute(T *ptr, T value) { \ if constexpr (sizeof(T) == 2) { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ - :: "l"(ptr), "h"(value) : "memory"); \ + if constexpr (is_bfloat16_v) { \ + uint16_t value_bits = *reinterpret_cast(&value); \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ + :: "l"(ptr), "h"(value_bits) : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ + :: "l"(ptr), "h"(value) : "memory"); \ + } \ } else if constexpr (sizeof(T) == 4) { \ if constexpr (std::is_floating_point_v) { \ asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b32 [%0], %1;" \ @@ -66,8 +89,15 @@ struct LdImpl { template \ TL_DEVICE static void execute(const T *ptr, T &value) { \ if constexpr (sizeof(T) == 2) { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ - : "=h"(value) : "l"(ptr) : "memory"); \ + if constexpr (is_bfloat16_v) { \ + uint16_t value_bits; \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ + : "=h"(value_bits) : "l"(ptr) : "memory"); \ + value = *reinterpret_cast(&value_bits); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ + : "=h"(value) : "l"(ptr) : "memory"); \ + } \ } else if constexpr (sizeof(T) == 4) { \ if constexpr (std::is_floating_point_v) { \ asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ diff --git a/src/transform/storage_access.cc b/src/transform/storage_access.cc index 0adaf712ba..38c53e607e 100644 --- a/src/transform/storage_access.cc +++ b/src/transform/storage_access.cc @@ -287,7 +287,12 @@ void TileLangStorageAccessVisitor::VisitStmt_(const WhileNode *op) { if (!is_thread_invariant) { ++condition_counter_; } + + allow_append_ = true; this->VisitExpr(op->condition); + curr_stmt_.access.clear(); + allow_append_ = false; + scope_.push_back(std::vector()); this->VisitStmt(op->body); StmtEntry s; diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index 188811172c..830e3da669 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -819,7 +819,7 @@ def warp_any(value, mask = -1): Args: value (int): The value to vote. - mask (uint32): The mask to use, default is 0xFFFFFFFF(-1), which means all lanes. + mask (uint32): The mask to use, default is 0xFFFFFFFF, which means all lanes. Returns: result (int): The result of the vote. From 6b6b9903dca29c08d314f997f6168a969e8bcca4 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 28 Nov 2025 06:55:48 +0000 Subject: [PATCH 20/41] support massage-only debug print --- src/tl_templates/cuda/debug.h | 7 +++++++ tilelang/language/print.py | 17 ++++++++++++++--- 2 files changed, 21 insertions(+), 3 deletions(-) diff --git a/src/tl_templates/cuda/debug.h b/src/tl_templates/cuda/debug.h index 1e65ea6977..d198840020 100644 --- a/src/tl_templates/cuda/debug.h +++ b/src/tl_templates/cuda/debug.h @@ -258,6 +258,13 @@ __device__ void debug_print_buffer_value(const char *msg, threadIdx.z, buf_name, index, (int32_t)var); } +// Specialization for msg-only debug print +__device__ void debug_print_msg(const char *msg) { + printf("msg='%s' BlockIdx=(%d, %d, %d), ThreadIdx=(%d, %d, %d)\n", + msg, blockIdx.x, blockIdx.y, blockIdx.z, threadIdx.x, threadIdx.y, + threadIdx.z); +} + __device__ uint64_t get_clock() { uint64_t gpu_clock; asm volatile("mov.u64 %0, %%clock64;\n" : "=l"(gpu_clock) : : "memory"); diff --git a/tilelang/language/print.py b/tilelang/language/print.py index 9661419bcd..6c473aa1f8 100644 --- a/tilelang/language/print.py +++ b/tilelang/language/print.py @@ -133,7 +133,15 @@ def print_local_buffer_with_condition(condition: tir.PrimExpr, buffer[coords]) -def print(obj: Any, msg: str = "", warp_group_id: int = 0, warp_id: int = 0) -> tir.PrimExpr: +@macro +def print_msg(msg: str) -> tir.PrimExpr: + """ + Prints a message for debugging purposes. + """ + tir.call_extern("handle", "debug_print_msg", msg) + + +def print(obj: Any = None, msg: str = "", warp_group_id: int = 0, warp_id: int = 0) -> tir.PrimExpr: """ A generic print function that handles both TIR buffers and primitive expressions. @@ -141,7 +149,7 @@ def print(obj: Any, msg: str = "", warp_group_id: int = 0, warp_id: int = 0) -> - If the input is a TIR primitive expression, it prints its value directly. Parameters: - obj (Any): The object to print. It can be either a tir.Buffer or tir.PrimExpr. + obj (Any): The object to print. It can be either a tir.Buffer, tir.PrimExpr or None. msg (str): An optional message to include in the print statement. warp_group_id (int): The warp group id to print. warp_id (int): The warp id to print. @@ -210,7 +218,10 @@ def print(obj: Any, msg: str = "", warp_group_id: int = 0, warp_id: int = 0) -> # Directly print primitive expressions. return print_var(obj, msg) + elif obj is None: + return print_msg(msg) + else: # Unsupported object type. raise ValueError( - f"Unexpected type: {type(obj)}. Supported types are tir.Buffer and tir.PrimExpr.") + f"Unexpected type: {type(obj)}. Supported types are tir.Buffer, tir.PrimExpr and None") From 449be5bc6df8989a78404a87c88023401dbdda0c Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 28 Nov 2025 07:02:44 +0000 Subject: [PATCH 21/41] intra-node combine test passed --- .../deepseek_deepep/intranode/combine.py | 27 +++++++------------ 1 file changed, 9 insertions(+), 18 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index ce596babae..9ee3395b7f 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -24,8 +24,7 @@ @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True}, - debug_root_path='/root/workspace/wt/debug/notify_combine') + "tl.disable_warp_specialized": True}) def cached_notify_combine_kernel( num_recv_tokens, num_ranks, @@ -112,7 +111,7 @@ def cached_notify_combine( @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # use TMA later - "tl.disable_warp_specialized": True}, debug_root_path='/root/workspace/wt/debug/combine') + "tl.disable_warp_specialized": True}) def combine_kernel( rank, num_ranks, num_recv_tokens, @@ -163,6 +162,7 @@ def combine_main( warp_id = tx // 32 responsible_channel = bx // 2 + if bx % 2 == 0: # sender send_rank_id = (responsible_channel + warp_id) % num_ranks send_warp_id_in_rank = warp_id // num_ranks @@ -174,8 +174,8 @@ def combine_main( num_channel_tokens= T.if_then_else( responsible_channel == num_channels - 1, num_rank_tokens, - channel_prefix_matrix[send_rank_id, responsible_channel + 1] - channel_offset, - ) + channel_prefix_matrix[send_rank_id, responsible_channel + 1] + ) - channel_offset token_start_idx = rank_offset + channel_offset token_end_idx = token_start_idx + num_channel_tokens @@ -227,9 +227,10 @@ def combine_main( dst_pe=send_rank_id) else: # receiver - warp_channel_head_idx = T.alloc_shared([warps, num_ranks], 'int32') - shared_channel_tail_idx = T.alloc_shared([32], 'int32') #! workaround for illegal address - warp_retired = T.alloc_shared([warps], 'bool') + #? Why we must need scope='shared', not 'shared.dynamic' here? + warp_channel_head_idx = T.alloc_shared([warps, num_ranks], 'int32', scope='shared') + shared_channel_tail_idx = T.alloc_shared([32], 'int32', scope='shared') #! workaround for illegal address + warp_retired = T.alloc_shared([warps], 'bool', scope='shared') if tx < warps: warp_retired[tx] = False if lane_id < num_ranks: @@ -283,12 +284,9 @@ def combine_main( T.ld(send_head[token_idx, lane_id], expected_head, nc=True) condvar = T.alloc_var('int32') - if bx == 1 and tx == 32: - T.print(condvar) T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") with T.While(T.warp_any(condvar <= expected_head and expected_head >= 0)): T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") - T.print(condvar-expected_head) T.loop_continue() # can we simplify this ? T.sync_warp() @@ -304,8 +302,6 @@ def combine_main( slot_indices[num_topk_ranks] = expected_head_i % num_recv_buffer_tokens topk_ranks[num_topk_ranks] = i num_topk_ranks += 1 - if bx == 0 and tx == 32: - T.print(num_topk_ranks, 'broadcast finished') # Reduce data with pipeline # todo: vectorize @@ -343,16 +339,11 @@ def combine_main( expected_head < 0, -expected_head - 1, expected_head + 1) - - if bx == 1 and tx == 32: - T.print(warp_channel_head_idx[warp_id, lane_id]) # Retired T.sync_warp() if T.elect_one_sync(): warp_retired[warp_id] = True - if bx == 1 and tx == 32: - T.print(warp_channel_head_idx, 'retired') return combine_main From bc4c6d65fdd3abc441f6d5f6ca0e5a4dfbeeecae Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 28 Nov 2025 13:22:13 +0000 Subject: [PATCH 22/41] unify dispatch, migrate topk_idx to u64, support cached dispatch --- .../deepseek_deepep/intranode/combine.py | 52 +- .../deepseek_deepep/intranode/dispatch.py | 623 +++++++++++++++--- .../intranode/get_dispatch_layout.py | 119 ++-- .../distributed/deepseek_deepep/intranode/log | 1 - .../intranode/notify_dispatch.py | 339 ---------- examples/distributed/deepseek_deepep/utils.py | 4 +- 6 files changed, 613 insertions(+), 525 deletions(-) delete mode 100644 examples/distributed/deepseek_deepep/intranode/log delete mode 100644 examples/distributed/deepseek_deepep/intranode/notify_dispatch.py diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 9ee3395b7f..244d667a46 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -2,7 +2,6 @@ # This op is distributed ### TILELANG_USE_DISTRIBUTED=1 python combine.py -from asyncio import Handle import os, sys sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path @@ -11,20 +10,17 @@ import tilelang.language as T from tilelang.profiler import do_bench from tilelang.distributed.utils import init_dist -from utils import Config, create_moe_recv_counters, gen_inputs # noqa: F403 +from utils import Config, gen_inputs # noqa: F403 from argparse import ArgumentParser from get_dispatch_layout import get_dispatch_layout -from notify_dispatch import notify_dispatch from dispatch import intranode_dispatch # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log -@tilelang.jit( - pass_configs={"tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True}) +@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def cached_notify_combine_kernel( num_recv_tokens, num_ranks, @@ -44,14 +40,13 @@ def cached_notify_combine_main( with T.Kernel(num_channels + 1, threads=threads) as bx: tx = T.get_thread_binding() - if bx == 0: - # block 0 is responsible for clearing channel_head/tail_idx buffers - # note that the buffer layout is slightly different from original DeepEP logic + if bx == 0: # clearing channel_head/tail_idx buffers + # note that the buffer layout here is slightly different from DeepEP T.sync_blocks(barrier_signal) T.clear(channel_head_idx) T.clear(channel_tail_idx) T.barrier_blocks(barrier_signal) - else: + else: # calculate send_head channel_id = bx - 1 rank_id = tx // 32 lane_id = tx % 32 @@ -255,7 +250,7 @@ def combine_main( new_tail = T.alloc_var('int32') T.ld(channel_tail_idx[responsible_channel, lane_id], new_tail, sem="acquire", scope="sys") # Use release semantics to ensure receiver warps see the update - T.st(shared_channel_tail_idx[lane_id], new_tail, sem="release", scope="cta") + T.st(shared_channel_tail_idx[lane_id], new_tail, sem="release", scope="cta") # todo: weaker sem pair # Update minimum head min_head = T.alloc_var('int32') @@ -268,7 +263,6 @@ def combine_main( T.st(channel_head_idx[responsible_channel, lane_id], min_head, sem="relaxed", scope="sys") else: # other warps for reduction # All lanes will use data buffer, but only rank lane will use `head/tail/src_idx` - # for *_buffers[i] channel_rank_offset = responsible_channel * kNumRanks + i; # The same tokens as the dispatch process num_tokens_per_channel = T.ceildiv(num_recv_tokens, num_channels) @@ -434,13 +428,20 @@ def test_intranode_combine( if rank == 0: print('get dispatch layout...') - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx.to(torch.int64), num_experts) # DeepEP requires int64 topk_idx + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ + "is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" if rank == 0: print('intranode dispatch...') ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ - buffer.dispatch(x, None, num_tokens_per_rank, None, is_token_in_rank, num_tokens_per_expert, topk_idx.to(torch.int64), topk_weights, expert_alignment) # DeepEP requires int64 topk_idx`` + buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers = \ intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) @@ -451,9 +452,9 @@ def test_intranode_combine( assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' if rank == 0: - print('Start combine...') + print('cached notify combine and intranode combine...') - ref_combine_x, ref_combine_topk_weights, _, ref_send_head = buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights, previous_event=event) + ref_combine_x, ref_combine_topk_weights, _ = buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights, previous_event=event) rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers = symm_buffers @@ -463,6 +464,19 @@ def test_intranode_combine( assert torch.equal(combine_topk_weights, ref_combine_topk_weights), f'combine_topk_weights mismatch, max err: {(combine_topk_weights - ref_combine_topk_weights).abs().max()}' print(f'[rank {rank}] All checks passed for TileScale intranode_combine. ✅') + # benchmark + t1 = do_bench(lambda: buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights), + _n_warmup=1, + _n_repeat=1, + ) + t2 = do_bench(lambda: intranode_combine(rank, allocator, recv_x, recv_topk_weights, recv_src_idx, rank_prefix_matrix, recv_channel_prefix_matrix, send_head, channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers), + _n_warmup=1, + _n_repeat=1, + ) + print(f"DeepEP: {t1:.3f} ms") + print(f"TileScale: {t2:.3f} ms") + print(f"Speedup: {t1 / t2:.2f}x") + def main(local_rank: int, num_local_ranks: int, args): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) @@ -478,8 +492,12 @@ def main(local_rank: int, num_local_ranks: int, args): group, ) + torch.distributed.destroy_process_group(group) + torch.distributed.destroy_process_group() + + def parse_args(): - parser = ArgumentParser(description="Test notify_dispatch") + parser = ArgumentParser(description="Test combine") parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 390d4321af..6af9584cda 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -3,26 +3,204 @@ ### TILELANG_USE_DISTRIBUTED=1 python dispatch.py import os, sys +from torch.types import Number sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch -import torch.nn.functional as F import tilelang -from tilelang.autotuner import * import tilelang.language as T from tilelang.profiler import do_bench from argparse import ArgumentParser -from typing import Any, Optional, Tuple, List +from typing import Optional, Tuple from tilelang.distributed.utils import init_dist from utils import Config, create_moe_recv_counters, gen_inputs # noqa: F403 from get_dispatch_layout import get_dispatch_layout -from notify_dispatch import notify_dispatch # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) +def notify_dispatch_kernel( + rank: int, + num_ranks: int, + num_experts: int, + num_tokens: int, + num_channels: int, + expert_alignment: int, +): + threads = 128 + num_local_experts = num_experts // num_ranks + num_warps = threads // 32 + + @T.prim_func + def notify_dispatch_main( + num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), + num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), + is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), + moe_recv_counter_mapped: T.Tensor((1,), 'int32'), + moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), + per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), + per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), + channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), + ): + with T.Kernel(num_ranks+1, threads=threads) as bx: + tx = T.get_thread_binding() + lane_id, warp_id = tx % 32, tx // 32 + + if bx == 0: + # Barrier first + T.sync_blocks(barrier_signal) + + # `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j + # `per_expert_buffer[rank][i, j]` means the number of tokens from rank i to local expert j + if tx < num_ranks: + T.st(per_rank_buffer[rank, tx], num_tokens_per_rank[tx], dst_pe=tx) + for i in T.serial(num_local_experts): + T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) + + T.barrier_blocks(barrier_signal) + + # Sum per-rank cnts and pre-compute the prefix sum for data sending + if tx < num_ranks: + for i in T.serial(1, num_ranks): + per_rank_buffer[i, tx] += per_rank_buffer[i-1, tx] + if tx == rank: + moe_recv_counter_mapped[0] = per_rank_buffer[num_ranks-1, rank] + + # Sum per-expert cnts + if tx < num_local_experts: + sum = T.alloc_local([1], 'int32') + sum[0] = 0 + for i in T.serial(0, num_ranks): + sum[0] += per_expert_buffer[i, tx] + sum[0] = T.ceildiv(sum[0], expert_alignment) * expert_alignment # align up + moe_recv_expert_counter_mapped[tx] = sum[0] + T.sync_threads() + + # Copy rank size prefix matrix to another tensor + # TODO: simply returns per_rank_buffer as rank_prefix_matrix + T.copy(per_rank_buffer, rank_prefix_matrix) + + # NOTE: We don't cleanup the buffer for later use + T.barrier_blocks(barrier_signal) + else: + dst_rank = bx - 1 + for channel_id in T.serial(warp_id, num_channels, num_warps): + num_tokens_per_channel = T.ceildiv(num_tokens, num_channels) + token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) + token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) + cnt = T.alloc_var('int32') + cnt = 0 + for i in T.serial(token_start_idx + lane_id, token_end_idx, 32): + cnt += is_token_in_rank[i, dst_rank] + cnt = T.warp_reduce_sum(cnt) + if T.elect_one_sync(): + channel_prefix_matrix[dst_rank, channel_id] = cnt + T.sync_threads() + + if tx == 0: + for i in T.serial(1, num_channels): + channel_prefix_matrix[dst_rank, i] += channel_prefix_matrix[dst_rank, i-1] + + return notify_dispatch_main + + +# TileScale notify-dispatch op +def notify_dispatch( + # meta + rank: int, + num_ranks: int, + num_experts: int, + num_tokens: int, + num_channels: int, + expert_alignment: int, + # dispatch layout + num_tokens_per_rank: torch.Tensor, + num_tokens_per_expert: torch.Tensor, + is_token_in_rank: torch.Tensor, + # counter + moe_recv_counter_mapped: torch.Tensor, + moe_recv_expert_counter_mapped: torch.Tensor, + # symm buffers + per_rank_buffer: torch.Tensor, + per_expert_buffer: torch.Tensor, + barrier_signal: torch.Tensor, + # allocator + allocator, +): + """ + TileScale notify-dispatch op. + + Args: + rank (int): The current rank (process or device index). + num_ranks (int): Total number of participating ranks (nodes). + num_experts (int): Global number of experts in the MoE layer. + num_tokens (int): Number of tokens being dispatched. + num_channels (int): Number of communication channels. + expert_alignment (int): Alignment constraint for expert buffer. + + num_tokens_per_rank (torch.Tensor): [num_ranks] + - For each rank r, num_tokens_per_rank[r] is the number of tokens assigned for dispatch to rank r across the cluster. + num_tokens_per_expert (torch.Tensor): [num_experts] + - For each expert e, num_tokens_per_expert[e] is the number of tokens rank r will send to global expert e. + is_token_in_rank (torch.Tensor): [num_tokens, num_ranks] + - For each (token t, rank r), is_token_in_rank[t, r] indicates (bool) whether token t belongs to rank r after dispatch. + + moe_recv_counter_mapped (torch.Tensor): [1] + - The number of tokens received by the current rank from other ranks. + moe_recv_expert_counter_mapped (torch.Tensor): [num_local_experts] + - The number of tokens received by the current rank for its local experts. + + per_rank_buffer (torch.Tensor): num_ranks * [num_ranks, num_ranks], symm tensor, should be zeroed before use + - Symmetric buffer for per-rank communication; [src_rank, dst_rank] region. + per_expert_buffer (torch.Tensor): num_ranks * [num_ranks, num_local_experts], symm tensor, should be zeroed before use + - Buffer for per-expert communication; [rank, local_expert] region. + barrier_signal (torch.Tensor): num_ranks * [num_ranks], symm_tensor, should be zeroed before use + - Synchronization tensor used as a system-wide barrier. + + allocator: TileScale allocator for symm tensors + + Returns + rank_prefix_matrix (torch.Tensor): [num_ranks, num_ranks] + - For each (rank r, other_rank), rank_prefix_matrix[r, other_rank] records prefix sums/statistics for token dispatch between r and other_rank. + channel_prefix_matrix (torch.Tensor): [num_ranks, num_channels] + - For each (rank r, channel c), channel_prefix_matrix[r, c] records prefix sums/statistics for tokens on communication channel c for rank r. + """ + kernel = notify_dispatch_kernel( + rank, + num_ranks, + num_experts, + num_tokens, + num_channels, + expert_alignment, + ) + kernel.initialize(allocator=allocator) + + rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') + channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') + + kernel( + num_tokens_per_rank, + num_tokens_per_expert, + is_token_in_rank, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, + per_rank_buffer, + per_expert_buffer, + barrier_signal, + rank_prefix_matrix, + channel_prefix_matrix, + ) + + return rank_prefix_matrix, channel_prefix_matrix + +# NOTE: We don't need cached_notify_dispatch, as per-rank-buffer is for one-time use + + @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # enable TMA later "tl.disable_warp_specialized": True}) @@ -55,13 +233,13 @@ def dispatch_main( # output recv_x: T.Tensor((num_recv_tokens, hidden), dtype), recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), - recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int32'), + recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int64'), recv_topk_weights: T.Tensor((num_recv_tokens, num_topk), 'float'), recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), send_head: T.Tensor([num_tokens, num_ranks], "int32"), # input x: T.Tensor([num_tokens, hidden], dtype), - topk_idx: T.Tensor([num_tokens, num_topk], "int32"), + topk_idx: T.Tensor([num_tokens, num_topk], "int64"), topk_weights: T.Tensor([num_tokens, num_topk], "float32"), is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), rank_prefix_matrix: T.Tensor([num_ranks, num_ranks], "int32"), @@ -76,7 +254,7 @@ def dispatch_main( # channel data buffers, stored on the receiver side channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), - channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int32"), + channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int64"), channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): @@ -162,10 +340,10 @@ def dispatch_main( recv_expert_begin = responsible_rank * num_local_experts recv_expert_end = recv_expert_begin + num_local_experts - idx_value = T.alloc_var('int32') + idx_value = T.alloc_var('int64') T.ld(topk_idx[token_idx, lane_id], idx_value, nc=True) idx_value = T.if_then_else( - recv_expert_begin <= idx_value and idx_value < recv_expert_end, + recv_expert_begin <= T.cast(idx_value, 'int32') < recv_expert_end, idx_value - recv_expert_begin, -1 ) @@ -278,11 +456,232 @@ def dispatch_main( # Exit num_tokens_to_recv -= num_cur_recv_tokens - # todo: support num_worst_tokens > 0 later - return dispatch_main +@tilelang.jit( + pass_configs={"tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True}) +def cached_dispatch_kernel( + rank, num_ranks, + num_tokens, + num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens + num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens + hidden, + num_topk, + num_experts, + num_sms, + dtype: str = 'bfloat16', +): + threads = 768 # 24 warps + TMABytesPerWarp = 8192 + smem_size = TMABytesPerWarp * threads // 32 + + num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) + num_channels = num_sms // 2 # 10 (2 SMs for each channel) + num_local_experts = num_experts // num_ranks + + num_warps = threads // 32 # 24 + num_warps_per_rank = num_warps // num_ranks # 3 + + num_recv_tokens = T.dynamic('num_recv_tokens') + + @T.prim_func + def cached_dispatch_main( + # output + recv_x: T.Tensor((num_recv_tokens, hidden), dtype), + recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), + recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), + send_head: T.Tensor([num_tokens, num_ranks], "int32"), + # input + x: T.Tensor([num_tokens, hidden], dtype), + is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), + rank_prefix_matrix: T.Tensor([num_ranks, num_ranks], "int32"), + channel_prefix_matrix: T.Tensor([num_ranks, num_channels], "int32"), + ###### below are symm buffers, one on each rank ###### + # channel buffer metadatas, stored on the receiver side + # senders are responsible for tails, and receivers are responsible for heads + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + # channel data buffers, stored on the receiver side + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), + ): + with T.Kernel(num_sms, threads=threads) as bx: + tx = T.get_thread_binding() + lane_id = tx % 32 + responsible_rank = tx // num_threads_per_rank + responsible_channel = bx // 2 + + if bx % 2 == 0: # sender + send_warp_id_in_rank = (tx % num_threads_per_rank) // 32 + + # send offset by `-value-1` e.g. 0->-1, 1->-2 + # this is for distinguishing zero tokens + if send_warp_id_in_rank == 0 and T.elect_one_sync(): + value = T.alloc_var('int32') + value = T.if_then_else( + responsible_channel > 0, + channel_prefix_matrix[responsible_rank, responsible_channel - 1], + 0) + T.st(channel_start_offset[responsible_channel, rank], -value-1, + scope='sys', sem='relaxed', dst_pe=responsible_rank) + value = channel_prefix_matrix[responsible_rank, responsible_channel] + T.st(channel_end_offset[responsible_channel, rank], -value-1, + scope='sys', sem='relaxed', dst_pe=responsible_rank) + T.sync_warp() + + # get task + num_tokens_per_channel = T.alloc_var('int32', init=T.ceildiv(num_tokens, num_channels)) + token_start_idx = T.alloc_var('int32') + token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) + token_end_idx = T.alloc_var('int32') + token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) + + # sender mainloop: iterate over all tokens and send by trunk + cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = 0 + token_idx = T.alloc_var('int32') + token_idx = token_start_idx + with T.While(token_idx < token_end_idx): + if T.elect_one_sync(): + T.wait_ge(channel_head_idx[responsible_channel, rank], + num_max_send_tokens+cached_channel_tail_idx-num_recv_buffer_tokens, + responsible_rank) + T.sync_warp() + + chunk_token_idx = T.alloc_var('int32') + chunk_token_idx = 0 + while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: + # for the same token, the warp assigned to save `send_head` may be different from the warp + # assigned to send the following data + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync(): + send_head[token_idx, responsible_rank] = T.if_then_else( + is_token_in_rank[token_idx, responsible_rank], + cached_channel_tail_idx, + -1 + ) + + # skip if not selected + if not is_token_in_rank[token_idx, responsible_rank]: + token_idx += 1 + T.loop_continue() + + # selected, get an empty slot + dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = cached_channel_tail_idx % num_recv_buffer_tokens + cached_channel_tail_idx += 1 + if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: + # copy data, all are remote copy + # 1. copy data (why useless???) + T.put_warp(T.address_of(x[token_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), + hidden, dst_pe=responsible_rank, unroll_factor=4) + + # 2. copy src idx + if T.elect_one_sync(): + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, + dst_pe=responsible_rank) + + # 4. copy scale (support fp8 later) + + chunk_token_idx += 1 + token_idx += 1 + + # move tail index + # here all warps should share the same new tail + T.sync_threads(responsible_rank, num_threads_per_rank) + if send_warp_id_in_rank == 0 and T.elect_one_sync(): + T.st(channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, + scope='sys', sem='release', + dst_pe=responsible_rank) + + else: # receiver + recv_thread_id_in_rank = tx % num_threads_per_rank + recv_warp_id_in_rank = recv_thread_id_in_rank // 32 + + # calculate offset first + rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank-1, rank], 0) + + # receive channel offset + total_offset = T.alloc_var('int32') + num_tokens_to_recv = T.alloc_var('int32') + if T.elect_one_sync(): + T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) + T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem='volatile') + T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) + T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem='volatile') + total_offset = -total_offset - 1 + num_tokens_to_recv = -num_tokens_to_recv - 1 + if recv_warp_id_in_rank == 0: + recv_channel_offset[responsible_rank, responsible_channel] = total_offset + num_tokens_to_recv -= total_offset + total_offset = T.tvm_warp_shuffle(-1, total_offset, 0, 32, 32) + total_offset += rank_offset + num_tokens_to_recv = T.tvm_warp_shuffle(-1, num_tokens_to_recv, 0, 32, 32) + + # Shared tail indices for different warps + shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') + + cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = 0 + cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = 0 + with T.While(num_tokens_to_recv > 0): + with T.While(recv_thread_id_in_rank == 0): + T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem='acquire', scope='sys') + + # read to copy + if cached_channel_head_idx != cached_channel_tail_idx: + shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx + T.loop_break() + + # sync queue tail + T.sync_threads(responsible_rank, num_threads_per_rank) + cached_channel_tail_idx = shared_channel_tail_idx[responsible_rank] + + # copy data + # 1. recv x + num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + # T.copy(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, :], recv_x[total_offset+chunk_idx, :]) # todo: add ld_nc and st_na + #! T.copy will cause layout inference error + T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), + T.address_of(recv_x[total_offset+chunk_idx, 0]), + hidden, + -1, + 5) + + # 2. recv src_idx + for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, + cached_channel_tail_idx, + num_threads_per_rank): + local_src_idx = T.alloc_var('int32') + T.ld(channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, nc=True) + recv_src_idx[total_offset+chunk_idx-cached_channel_head_idx] = local_src_idx + + # 4. recv scale (support fp8 later) + + # Move queue + cached_channel_head_idx += num_cur_recv_tokens + total_offset += num_cur_recv_tokens + T.sync_threads(responsible_rank, num_threads_per_rank) + if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): + T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, + scope='sys', sem='relaxed') + + # Exit + num_tokens_to_recv -= num_cur_recv_tokens + + # todo: support num_worst_tokens > 0 later + + return cached_dispatch_main + + # todo: support cached-mode via handle def intranode_dispatch( rank: int, @@ -316,7 +715,8 @@ def intranode_dispatch( num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. - topk_idx: `[num_tokens, num_topk]` with `torch.int32`, the expert indices + Returns None for cached-mode. + topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, `-1` means no selections. topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. expert_alignment: align the number of tokens received by each local expert to this variable. @@ -334,58 +734,66 @@ def intranode_dispatch( handle: the handle for combine, has `(rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)`. """ - assert handle is None # Currently only support non-cached mode - assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, \ + if handle is None: + assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, \ "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" + else: + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - # acquire shapes num_tokens, hidden = x.shape - num_experts = num_tokens_per_expert.shape[0] + num_experts = num_tokens_per_expert.shape[0] if handle is None else 0 num_ranks = num_tokens_per_rank.shape[0] num_local_experts = num_experts // num_ranks - num_topk = topk_idx.shape[1] + num_topk = topk_idx.shape[1] if handle is None else 0 # Default config config = Config.get_dispatch_config(num_ranks) if config is None else config - # Size prefix by ranks, shaped as `[num_ranks, num_ranks]` - # Size prefix by experts (not used later), shaped as `[num_ranks, num_local_experts]` - rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') - channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') - - moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_experts // num_ranks)[3:5] - - per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + # Alloc public barrier barrier_signal = tilelang.tensor((num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( - rank, - num_ranks, - num_experts, - num_tokens, - config.num_channels, - expert_alignment, - num_tokens_per_rank, - num_tokens_per_expert, - is_token_in_rank, - moe_recv_counter_mapped, - moe_recv_expert_counter_mapped, - per_rank_buffer, - per_expert_buffer, - barrier_signal, - allocator, - ) - torch.cuda.synchronize() # todo: replace it with host-side wait_ne - - num_recv_tokens = moe_recv_counter_mapped.item() - num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() + if handle is None: + # Size prefix by ranks, shaped as `[num_ranks, num_ranks]` + # Size prefix by experts (not used later), shaped as `[num_ranks, num_local_experts]` + rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') + channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') + + moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_experts // num_ranks)[3:5] + + per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + + rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( + rank, + num_ranks, + num_experts, + num_tokens, + config.num_channels, + expert_alignment, + num_tokens_per_rank, + num_tokens_per_expert, + is_token_in_rank, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, + per_rank_buffer, + per_expert_buffer, + barrier_signal, + allocator, + ) + torch.cuda.synchronize() # todo: replace it with host-side wait_ne + + num_recv_tokens = moe_recv_counter_mapped.item() + num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() + else: + num_recv_tokens = recv_src_idx.size(0) + num_recv_tokens_per_expert_list = None # create normal buffers recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') - recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int32, device='cuda') - recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') + if handle is None: + recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int64, device='cuda') + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') @@ -402,13 +810,19 @@ def intranode_dispatch( [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, hidden], dtype=torch.bfloat16, device='cuda', allocator=allocator) channel_src_idx_buffers = tilelang.tensor( [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, device='cuda', allocator=allocator) - channel_topk_idx_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.int32, device='cuda', allocator=allocator) - channel_topk_weights_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.float32, device='cuda', allocator=allocator) - # get dispatch kernel - kernel = dispatch_kernel( + if handle is None: + channel_topk_idx_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.int64, device='cuda', allocator=allocator) + channel_topk_weights_buffers = tilelang.tensor( + [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.float32, device='cuda', allocator=allocator) + else: + channel_topk_idx_buffers = None # todo: double-check this (may affect combine) + channel_topk_weights_buffers = None + + # get dispatch + _kernel = dispatch_kernel if handle is None else cached_dispatch_kernel + kernel = _kernel( rank, num_ranks, num_tokens, @@ -418,41 +832,27 @@ def intranode_dispatch( num_topk, num_experts, config.num_sms, - dtype='bfloat16' + 'bfloat16' ) kernel.initialize(allocator=allocator) # run dispatch if rank == 0: print('Start running dispatch kernel...') - kernel( - recv_x, - recv_src_idx, - recv_topk_idx, - recv_topk_weights, - recv_channel_prefix_matrix, - send_head, - x, - topk_idx, - topk_weights, - is_token_in_rank, - rank_prefix_matrix, - channel_prefix_matrix, - channel_start_offset, - channel_end_offset, - channel_head_idx, - channel_tail_idx, - channel_x_buffers, - channel_src_idx_buffers, - channel_topk_idx_buffers, - channel_topk_weights_buffers, - ) + if handle is None: + args = (recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + else: + args = (recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers) + kernel(*args) handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head ) symm_buffers = (channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers) + + if handle is not None: + recv_topk_idx = recv_topk_weights = None return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers # todo: reconsider hierachy @@ -464,6 +864,7 @@ def test_intranode_dispatch( rank: int, num_ranks: int, expert_alignment: int, + cached: bool, group: torch.distributed.ProcessGroup, ): try: @@ -472,7 +873,7 @@ def test_intranode_dispatch( raise ModuleNotFoundError("Please install DeepEP to run this test.") allocator = tilelang.get_allocator( - size=2**33, + size=2**30, device="cuda", is_distributed=True, local_rank=rank, @@ -482,45 +883,51 @@ def test_intranode_dispatch( x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) - # Assume get_dispatch_layout is correct if rank == 0: - print('get dispatch layout...') - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx.to(torch.int64), num_experts) # DeepEP requires int64 topk_idx + print(f'get dispatch layout ...') + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) + num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ + "is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" if rank == 0: - print('intranode dispatch (notify_dispatch included...)') + print('notify dispatch and intranode dispatch ...') # golden ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, _ = \ - buffer.dispatch(x, None, num_tokens_per_rank, None, is_token_in_rank, num_tokens_per_expert, topk_idx.to(torch.int64), topk_weights, expert_alignment) # DeepEP requires int64 topk_idx`` + buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) # ours - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ - intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) + if cached: + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ + intranode_dispatch(rank, allocator, x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment, None) + else: + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ + intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) # check dispatch output assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' - assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' - assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' + if not cached: + assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' # check handle - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle - assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' - assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_src_idx, ref_recv_src_idx), f'recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' - assert torch.equal(send_head, ref_send_head), f'send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + if not cached: + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle + assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' + assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_src_idx, ref_recv_src_idx), f'recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' + assert torch.equal(send_head, ref_send_head), f'send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' - print(f'[rank {rank}] All checks passed for TileScale intranode_dispatch. ✅') - - buffer.combine( - recv_x, - ref_handle, - recv_topk_weights, - ) + print(f'[rank {rank}] All checks passed for {'cached' if cached else 'non-cached'} TileScale intranode_dispatch. ✅') def main(local_rank: int, num_local_ranks: int, args): @@ -534,17 +941,23 @@ def main(local_rank: int, num_local_ranks: int, args): rank, num_ranks, args.expert_alignment, + args.cached, group, ) + torch.distributed.destroy_process_group(group) + torch.distributed.destroy_process_group() + + def parse_args(): - parser = ArgumentParser(description="Test notify_dispatch") + parser = ArgumentParser(description="Test dispatch") parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + parser.add_argument("-cached", action="store_true", default=False, help="Use cached mode") return parser.parse_args() diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 006fb11d22..05134c4856 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -21,7 +21,7 @@ def get_dispatch_layout( """Calculate the layout required for later communication. Arguments: - topk_idx: `[num_tokens, num_topk]`, dtype must be `torch.int32`, the expert indices selected by each token, + topk_idx: `[num_tokens, num_topk]`, dtype must be `torch.int64`, the expert indices selected by each token, `-1` means no selections. num_experts: the number of experts. num_ranks: the number of ranks. @@ -35,7 +35,7 @@ def get_dispatch_layout( """ # Check inputs - assert topk_idx.dtype == torch.int32, "topk_idx must be of dtype torch.int32" + assert topk_idx.dtype == torch.int64, "topk_idx must be of dtype torch.int64" assert topk_idx.ndim == 2, "topk_idx must be a 2D tensor" assert topk_idx.is_contiguous(), "topk_idx must be a contiguous tensor" assert num_experts > 0, "num_experts must be greater than 0" @@ -49,7 +49,7 @@ def get_dispatch_layout( is_token_in_rank = torch.empty((num_tokens, num_ranks), dtype=torch.bool, device='cuda') # Launch the kernel - kernel = get_dispatch_layout_kernel(num_tokens, num_topk, num_experts, num_ranks) + kernel = get_dispatch_layout_kernel(num_topk, num_experts, num_ranks) kernel( topk_idx, num_tokens_per_rank, @@ -63,100 +63,97 @@ def get_dispatch_layout( return num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank -@tilelang.jit +@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def get_dispatch_layout_kernel( - num_tokens: int, num_topk: int, num_experts: int, num_ranks: int, ) -> tilelang.JITKernel: - """Kernel to compute the dispatch layout.""" - - # Work partition from DeepEP/csrc/kernels/layout.cu:get_dispatch_layout threads = 256 experts_per_sm = 4 ranks_per_sm = 8 num_sms = T.ceildiv(num_experts, experts_per_sm) + T.ceildiv(num_ranks, ranks_per_sm) experts_per_rank = num_experts // num_ranks + num_tokens = T.dynamic('num_tokens') + @T.prim_func def get_dispatch_layout_main( - topk_idx: T.Tensor([num_tokens, num_topk], "int32"), # type: ignore + topk_idx: T.Tensor([num_tokens, num_topk], "int64"), # type: ignore num_tokens_per_rank: T.Tensor([num_ranks], "int32"), # type: ignore num_tokens_per_expert: T.Tensor([num_experts], "int32"), # type: ignore is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), # type: ignore ): - with T.Kernel(num_sms, threads=threads) as bid: - tid = T.get_thread_binding() + with T.Kernel(num_sms, threads=threads) as bx: + tx = T.get_thread_binding() # Calculate expert statistics tokens_per_expert_per_thread = T.alloc_shared([threads, experts_per_sm], "int32") T.clear(tokens_per_expert_per_thread) - expert_begin_idx = T.alloc_local([1], "int32") - expert_begin_idx[0] = bid * experts_per_sm - expert_end_idx = T.alloc_local([1], "int32") - expert_end_idx[0] = T.min(expert_begin_idx[0] + experts_per_sm, num_experts) - - if expert_begin_idx[0] < expert_end_idx[0]: - for i in T.serial(tid, num_tokens, threads): - for j in T.serial(0, num_topk): - expert_idx = T.alloc_local([1], "int32") - expert_idx[0] = T.cast(topk_idx[i, j], "int32") - if expert_begin_idx[0] <= expert_idx[0] and expert_idx[0] < expert_end_idx[ - 0]: - tokens_per_expert_per_thread[tid, - expert_idx[0] - expert_begin_idx[0]] += 1 - - if expert_begin_idx[0] + tid < expert_end_idx[0]: - sum = T.alloc_local([1], "int32") - sum[0] = 0 - for i in T.serial(0, threads): - sum[0] += tokens_per_expert_per_thread[i, tid] - num_tokens_per_expert[expert_begin_idx[0] + tid] = sum[0] + expert_begin_idx = T.alloc_var("int32") + expert_begin_idx = bx * experts_per_sm + expert_end_idx = T.alloc_var("int32") + expert_end_idx = T.min(expert_begin_idx + experts_per_sm, num_experts) + + if expert_begin_idx < expert_end_idx: + for i in T.serial(tx, num_tokens, threads): + for j in T.serial(num_topk): + expert_idx = T.alloc_var("int32") + expert_idx = topk_idx[i, j] + if expert_begin_idx <= expert_idx and expert_idx < expert_end_idx: + tokens_per_expert_per_thread[tx, + expert_idx - expert_begin_idx] += 1 + + if expert_begin_idx + tx < expert_end_idx: + sum = T.alloc_var("int32") + sum = 0 + for i in T.serial(threads): + sum += tokens_per_expert_per_thread[i, tx] + num_tokens_per_expert[expert_begin_idx + tx] = sum # Calculate rank statistics - sm_begin = T.alloc_local([1], "int32") - sm_begin[0] = T.ceildiv(num_experts, experts_per_sm) - rank_begin_idx = T.alloc_local([1], "int32") - rank_begin_idx[0] = (bid - sm_begin[0]) * ranks_per_sm - rank_end_idx = T.alloc_local([1], "int32") - rank_end_idx[0] = T.min(rank_begin_idx[0] + ranks_per_sm, num_ranks) - - if rank_begin_idx[0] >= 0 and rank_begin_idx[0] < rank_end_idx[0]: + sm_begin = T.alloc_var("int32") + sm_begin = T.ceildiv(num_experts, experts_per_sm) + rank_begin_idx = T.alloc_var("int32") + rank_begin_idx = (bx - sm_begin) * ranks_per_sm + rank_end_idx = T.alloc_var("int32") + rank_end_idx = T.min(rank_begin_idx + ranks_per_sm, num_ranks) + + if rank_begin_idx >= 0 and rank_begin_idx < rank_end_idx: tokens_per_rank_per_thread = T.alloc_shared([threads, ranks_per_sm], "int32") T.clear(tokens_per_rank_per_thread) - expert_begin = T.alloc_local([1], "int32") - expert_begin[0] = rank_begin_idx[0] * experts_per_rank - expert_end = T.alloc_local([1], "int32") - expert_end[0] = rank_end_idx[0] * experts_per_rank + expert_begin = T.alloc_var("int32") + expert_begin = rank_begin_idx * experts_per_rank + expert_end = T.alloc_var("int32") + expert_end = rank_end_idx * experts_per_rank - for i in T.serial(tid, num_tokens, threads): + for i in T.serial(tx, num_tokens, threads): is_in_rank = T.alloc_local([ranks_per_sm], "int32") T.clear(is_in_rank) - for j in T.serial(0, num_topk): - expert_idx = T.alloc_local([1], "int32") - rank_idx = T.alloc_local([1], "int32") - expert_idx[0] = T.cast(topk_idx[i, j], "int32") - if expert_begin[0] <= expert_idx[0] and expert_idx[0] < expert_end[0]: - rank_idx[0] = expert_idx[0] // experts_per_rank - rank_begin_idx[0] + for j in T.serial(num_topk): + expert_idx = T.alloc_var("int32") + rank_idx = T.alloc_var("int32") + expert_idx = topk_idx[i, j] + if expert_begin <= expert_idx and expert_idx < expert_end: + rank_idx = expert_idx // experts_per_rank - rank_begin_idx - is_in_rank[rank_idx[0]] += 1 + is_in_rank[rank_idx] += 1 - for j in T.serial(rank_begin_idx[0], rank_end_idx[0]): - if is_in_rank[j - rank_begin_idx[0]] > 0: + for j in T.serial(rank_begin_idx, rank_end_idx): + if is_in_rank[j - rank_begin_idx] > 0: is_token_in_rank[i, j] = True - tokens_per_rank_per_thread[tid, j - rank_begin_idx[0]] += 1 + tokens_per_rank_per_thread[tx, j - rank_begin_idx] += 1 else: is_token_in_rank[i, j] = False - if rank_begin_idx[0] + tid < rank_end_idx[0]: - sum = T.alloc_local([1], "int32") - sum[0] = 0 - for i in T.serial(0, threads): - sum[0] += tokens_per_rank_per_thread[i, tid] - num_tokens_per_rank[rank_begin_idx[0] + tid] = sum[0] + if rank_begin_idx + tx < rank_end_idx: + sum = T.alloc_var("int32") + sum = 0 + for i in T.serial(threads): + sum += tokens_per_rank_per_thread[i, tx] + num_tokens_per_rank[rank_begin_idx + tx] = sum return get_dispatch_layout_main diff --git a/examples/distributed/deepseek_deepep/intranode/log b/examples/distributed/deepseek_deepep/intranode/log deleted file mode 100644 index 25cdab95b5..0000000000 --- a/examples/distributed/deepseek_deepep/intranode/log +++ /dev/null @@ -1 +0,0 @@ -2025-11-25 12:11:41 [TileLang:tilelang.env:WARNING]: Loading tilelang libs from dev root: /root/workspace/wt/tilescale/build diff --git a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py b/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py deleted file mode 100644 index d207a6e9e6..0000000000 --- a/examples/distributed/deepseek_deepep/intranode/notify_dispatch.py +++ /dev/null @@ -1,339 +0,0 @@ -# For intranode only -# This op is distributed -### TILELANG_USE_DISTRIBUTED=1 python notify_dispatch.py - -import os, sys -sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path - -import tilelang -import tilelang.language as T -import torch -from argparse import ArgumentParser -from tilelang.distributed.utils import init_dist -from utils import gen_inputs, create_moe_recv_counters # noqa: F403 - -from get_dispatch_layout import get_dispatch_layout - - -# TileScale notify-dispatch kernel for non-cached mode -# Check: DeepEP/csrc/kernels/intranode.cu::notify_dispatch -@tilelang.jit -def notify_dispatch_kernel( - rank: int, - num_ranks: int, - num_experts: int, - num_tokens: int, - num_channels: int, - expert_alignment: int, -): - - threads = 128 - num_local_experts = num_experts // num_ranks - num_warps = threads // 32 - - @T.prim_func - def notify_dispatch_main( - num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), - num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), - is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), - moe_recv_counter_mapped: T.Tensor((1,), 'int32'), - moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), - per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), - per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), - barrier_signal: T.Tensor((num_ranks,), 'int32'), - rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), - channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), - ): - with T.Kernel(num_ranks+1, threads=threads) as bx: - tx = T.get_thread_binding() - lane_id, warp_id = tx % 32, tx // 32 - - if bx == 0: - # Barrier first - T.sync_blocks(barrier_signal) - - # `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j - # `per_expert_buffer[rank][i, j]` means the number of tokens from rank i to local expert j - if tx < num_ranks: - T.st(per_rank_buffer[rank, tx], num_tokens_per_rank[tx], dst_pe=tx) - for i in T.serial(num_local_experts): - T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) - - T.barrier_blocks(barrier_signal) - - # Sum per-rank cnts and pre-compute the prefix sum for data sending - if tx < num_ranks: - for i in T.serial(1, num_ranks): - per_rank_buffer[i, tx] += per_rank_buffer[i-1, tx] - if tx == rank: - moe_recv_counter_mapped[0] = per_rank_buffer[num_ranks-1, rank] - - # Sum per-expert cnts - if tx < num_local_experts: - sum = T.alloc_local([1], 'int32') - sum[0] = 0 - for i in T.serial(0, num_ranks): - sum[0] += per_expert_buffer[i, tx] - sum[0] = T.ceildiv(sum[0], expert_alignment) * expert_alignment # align up - moe_recv_expert_counter_mapped[tx] = sum[0] - T.sync_threads() - - # Copy rank size prefix matrix to another tensor - T.copy(per_rank_buffer, rank_prefix_matrix) - - #? We don't cleanup the buffer for later use, as it is one time used? - T.barrier_blocks(barrier_signal) - else: - dst_rank = bx - 1 - for channel_id in T.serial(warp_id, num_channels, num_warps): - num_tokens_per_channel = T.ceildiv(num_tokens, num_channels) - token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) - token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) - cnt = T.alloc_local([1], 'int32') - cnt[0] = 0 - for i in T.serial(token_start_idx + lane_id, token_end_idx, 32): - cnt[0] += is_token_in_rank[i, dst_rank] - cnt[0] = T.warp_reduce_sum(cnt[0]) - if T.elect_one_sync(): - channel_prefix_matrix[dst_rank, channel_id] = cnt[0] - T.sync_threads() - - if tx == 0: - for i in T.serial(1, num_channels): - channel_prefix_matrix[dst_rank, i] += channel_prefix_matrix[dst_rank, i-1] - - return notify_dispatch_main - - -# TileScale notify-dispatch op -def notify_dispatch( - # meta - rank: int, - num_ranks: int, - num_experts: int, - num_tokens: int, - num_channels: int, - expert_alignment: int, - # dispatch layout - num_tokens_per_rank: torch.Tensor, - num_tokens_per_expert: torch.Tensor, - is_token_in_rank: torch.Tensor, - # counter - moe_recv_counter_mapped: torch.Tensor, - moe_recv_expert_counter_mapped: torch.Tensor, - # symm buffers - per_rank_buffer: torch.Tensor, - per_expert_buffer: torch.Tensor, - barrier_signal: torch.Tensor, - # allocator - allocator, -): - """ - TileScale notify-dispatch op. - - Args: - rank (int): The current rank (process or device index). - num_ranks (int): Total number of participating ranks (nodes). - num_experts (int): Global number of experts in the MoE layer. - num_tokens (int): Number of tokens being dispatched. - num_channels (int): Number of communication channels. - expert_alignment (int): Alignment constraint for expert buffer. - - num_tokens_per_rank (torch.Tensor): [num_ranks] - - For each rank r, num_tokens_per_rank[r] is the number of tokens assigned for dispatch to rank r across the cluster. - num_tokens_per_expert (torch.Tensor): [num_experts] - - For each expert e, num_tokens_per_expert[e] is the number of tokens rank r will send to global expert e. - is_token_in_rank (torch.Tensor): [num_tokens, num_ranks] - - For each (token t, rank r), is_token_in_rank[t, r] indicates (bool) whether token t belongs to rank r after dispatch. - - moe_recv_counter_mapped (torch.Tensor): [1] - - The number of tokens received by the current rank from other ranks. - moe_recv_expert_counter_mapped (torch.Tensor): [num_local_experts] - - The number of tokens received by the current rank for its local experts. - - per_rank_buffer (torch.Tensor): num_ranks * [num_ranks, num_ranks], symm tensor, should be zeroed before use - - Symmetric buffer for per-rank communication; [src_rank, dst_rank] region. - per_expert_buffer (torch.Tensor): num_ranks * [num_ranks, num_local_experts], symm tensor, should be zeroed before use - - Buffer for per-expert communication; [rank, local_expert] region. - barrier_signal (torch.Tensor): num_ranks * [num_ranks], symm_tensor, should be zeroed before use - - Synchronization tensor used as a system-wide barrier. - - allocator: TileScale allocator for symm tensors - - Returns - rank_prefix_matrix (torch.Tensor): [num_ranks, num_ranks] - - For each (rank r, other_rank), rank_prefix_matrix[r, other_rank] records prefix sums/statistics for token dispatch between r and other_rank. - channel_prefix_matrix (torch.Tensor): [num_ranks, num_channels] - - For each (rank r, channel c), channel_prefix_matrix[r, c] records prefix sums/statistics for tokens on communication channel c for rank r. - """ - kernel = notify_dispatch_kernel( - rank, - num_ranks, - num_experts, - num_tokens, - num_channels, - expert_alignment, - ) - kernel.initialize(allocator=allocator) - - rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') - channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') - - kernel( - num_tokens_per_rank, - num_tokens_per_expert, - is_token_in_rank, - moe_recv_counter_mapped, - moe_recv_expert_counter_mapped, - per_rank_buffer, - per_expert_buffer, - barrier_signal, - rank_prefix_matrix, - channel_prefix_matrix, - ) - - return rank_prefix_matrix, channel_prefix_matrix - - -@tilelang.jit -def cached_notify_dispatch( - rank: int, - num_ranks: int, - num_experts: int, - num_tokens: int, - num_channels: int, - expert_alignment: int, -): - - threads = 128 - - @T.prim_func - def cached_notify_dispatch_main( - rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), - per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), - barrier_signal: T.Tensor((num_ranks,), 'int32'), - ): - with T.Kernel(1, threads=threads): - tx = T.get_thread_binding() - - T.sync_blocks(barrier_signal) - T.copy(rank_prefix_matrix, per_rank_buffer) - #? We don't cleanup the buffer for later use, as it is one time used? - T.barrier_blocks(barrier_signal) - - return cached_notify_dispatch_main - - -# todo: impl cached_notify_dispatch - - -def test_notify_dispatch( - num_tokens: int, - hidden: int, - num_topk: int, - num_experts: int, - rank: int, - num_ranks: int, - expert_alignment: int, - group: torch.distributed.ProcessGroup, -): - try: - import deep_ep # noqa: F403 - except ModuleNotFoundError as e: - raise ModuleNotFoundError("Please install DeepEP to run this test.") - - num_local_experts = num_experts // num_ranks - - allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=rank, - num_local_ranks=num_ranks, - group=group) - - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) - buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) - - if rank == 0: - print(f'get dispatch layout...') - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ - f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - "is_token_in_rank mismatch" - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ - f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - - if rank == 0: - print(f'notify dispatch...') - handle = buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights)[-2] - ref_rank_prefix_matrix, ref_channel_prefix_matrix = handle[:2] - - # create buffers in need - moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_local_experts)[3:5] - - per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - barrier_signal = tilelang.tensor((num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - - rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( - rank, - num_ranks, - num_experts, - num_tokens, - 10, # 20 sms by default - expert_alignment, - num_tokens_per_rank, - num_tokens_per_expert, - is_token_in_rank, - moe_recv_counter_mapped, - moe_recv_expert_counter_mapped, - per_rank_buffer, - per_expert_buffer, - barrier_signal, - allocator - ) - - assert torch.allclose(rank_prefix_matrix, ref_rank_prefix_matrix), \ - f"rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}" - assert torch.allclose(channel_prefix_matrix, ref_channel_prefix_matrix), \ - f"channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}" - print(f'[rank {rank}] All checks passed for TileScale notify_dispatch. ✅') - - # todo: benchmark - -def main( - local_rank: int, num_local_ranks: int, args -): - rank, num_ranks, group = init_dist(local_rank, num_local_ranks) - - test_notify_dispatch( - args.num_tokens, - args.hidden, - args.num_topk, - args.num_experts, - rank, - num_ranks, - args.expert_alignment, - group, - ) - - -def parse_args(): - parser = ArgumentParser(description="Test notify_dispatch") - parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") - parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") - parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") - parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") - parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_args() - - num_ranks = args.num_ranks - torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 1f7b8e9eee..0d14adf42c 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -137,7 +137,7 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu Returns: x: `[num_tokens, hidden]` with `torch.bfloat16`, the input to MoE layer. - topk_idx: `[num_tokens, num_topk]` with `torch.int32`, the expert indices selected by each token, + topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, `-1` means no selections. topk_weights: `[num_tokens, num_topk]` with `torch.float32`, the weights corresponding to each selected expert for each token. @@ -149,7 +149,7 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu x = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 - topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1].to(torch.int32) + topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda') rank_idx = topk_idx // (num_experts // num_ranks) rank_idx.masked_fill_(topk_idx == -1, -1) From ed2ca7bf9b52a7f28cf6e30a3afb2e6476d27fe6 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 3 Dec 2025 11:37:45 +0800 Subject: [PATCH 23/41] Refactor to pre-alloc buffers and expose interface, add benchmark --- .../distributed/deepseek_deepep/buffer.py | 195 +++++++++- .../deepseek_deepep/intranode/__init__.py | 4 +- .../deepseek_deepep/intranode/combine.py | 173 ++------- .../deepseek_deepep/intranode/dispatch.py | 353 +++++------------- .../intranode/get_dispatch_layout.py | 4 +- .../intranode/test_intranode.py | 188 ++++++++++ examples/distributed/deepseek_deepep/utils.py | 42 ++- tilelang/distributed/utils.py | 2 +- 8 files changed, 529 insertions(+), 432 deletions(-) create mode 100644 examples/distributed/deepseek_deepep/intranode/test_intranode.py diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index 62b845a33b..d0b808f201 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -1,3 +1,5 @@ +""" The interface for DeepEP. """ + import os import torch import torch.distributed as dist @@ -6,10 +8,11 @@ import tilelang import tilelang.language as T from utils import Config -from intranode import get_dispatch_layout +from tilelang.distributed.utils import get_device_tensor +from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine -class TSBuffer: +class EPBuffer: """ TileScale communication buffers for DeepEP @@ -22,29 +25,97 @@ class TSBuffer: """ num_sms: int = 20 + symm_heap_size: int = 2**30 # size of the symm heap for allocators - def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int): + def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, + num_topk: int, num_experts: int, hidden: int, + dispatch_cfg: Optional[Config] = None, combine_cfg: Optional[Config] = None): """ Initialize the communication buffer. Args: group: the communication group num_nvl_bytes: the buffer size for intranode NVLink communication. + num_topk: the number of topk experts to select. + num_experts: the number of experts. + hidden: the hidden dimension. + dispatch_cfg: the performance tuning config for dispatch. + combine_cfg: the performance tuning config for combine. """ self.group = group self.rank = group.rank() self.num_ranks = group.size() + self.num_nvl_bytes = num_nvl_bytes - assert self.num_ranks <= 8, "currently only support intranode" + assert self.num_ranks <= 8, "currently only support intranode" # todo: rm this + self.num_topk = num_topk + self.num_experts = num_experts + assert num_experts % self.num_ranks == 0, "num_experts must be divisible by num_ranks" + self.num_local_experts = num_experts // self.num_ranks + self.hidden = hidden + + self.dispatch_cfg = dispatch_cfg if dispatch_cfg is not None else self.default_dispatch_config + self.combine_cfg = combine_cfg if combine_cfg is not None else self.default_combine_config self._allocator= tilelang.get_allocator( - size=2**30, + size=EPBuffer.symm_heap_size, device="cuda", is_distributed=True, local_rank=self.rank, num_local_ranks=self.num_ranks, group=group) + self._pre_alloc_symm_buffers() + self._prepare_counters() + + def _pre_alloc_symm_buffers(self): + """Pre-allocate the symmetric buffers via the alloctor for later communication.""" + if self.num_ranks <= 8: + self._pre_alloc_symm_buffers_intranode() # todo: rm this + else: + self._pre_alloc_symm_buffers_internode() + + def _pre_alloc_symm_buffers_intranode(self): + # barrier signal is always zeroed after each usage, so we can pre-init here + barrier_signal = tilelang.tensor((self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() + + per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator) + per_expert_buffer = tilelang.tensor((self.num_ranks, self.num_local_experts), dtype=torch.int32, device='cuda', allocator=self._allocator) + + channel_start_offset = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) + channel_end_offset = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) + channel_head_idx = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) + channel_tail_idx = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) + # NOTE: for each #ranks, dispatch and combine cfg have the same num_max_nvl_chunked_recv_tokens, so we can use the same buffer here + channel_x_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden], dtype=torch.bfloat16, device='cuda', allocator=self._allocator) + channel_src_idx_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, device='cuda', allocator=self._allocator) + channel_topk_idx_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], dtype=torch.int64, device='cuda', allocator=self._allocator) + channel_topk_weights_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], dtype=torch.float32, device='cuda', allocator=self._allocator) + + self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, + channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + + def _pre_alloc_symm_buffers_internode(self): + raise NotImplementedError("internode is not supported yet") + + def _prepare_counters(self): + self._moe_recv_counter = torch.empty((1,), dtype=torch.int32, pin_memory=True, device='cpu') # MoE counter + self._moe_recv_counter_mapped = get_device_tensor(self._moe_recv_counter) + self._moe_recv_expert_counter = torch.empty((self.num_local_experts,), dtype=torch.int32, pin_memory=True, device='cpu') # MoE expert-level counter + self._moe_recv_expert_counter_mapped = get_device_tensor(self._moe_recv_expert_counter) + + if self.num_ranks > 8: # internode + self._moe_recv_rdma_counter = torch.tensor(-1, dtype=torch.int32, pin_memory=True, device='cpu') # MoE RDMA-level counter + self._moe_recv_rdma_counter_mapped = get_device_tensor(self._moe_recv_rdma_counter) + @staticmethod def set_num_sms(num_sms: int): """Set the number of SMs used in high-throughput kernels @@ -53,7 +124,7 @@ def set_num_sms(num_sms: int): num_sms: the number of SMs used in high-throughput kernels """ assert num_sms % 2 == 0, "num_sms must be even" - TSBuffer.num_sms = num_sms + EPBuffer.num_sms = num_sms @property def num_channels(self): @@ -73,19 +144,109 @@ def default_dispatch_config(self): def default_combine_config(self): return Config.get_combine_config(self.num_ranks) - def get_dispatch_layout(self, topk_idx: torch.Tensor, num_experts: int): - return get_dispatch_layout(topk_idx, num_experts, self.num_ranks) + def get_dispatch_layout(self, topk_idx: torch.Tensor): + """ + Calculate the layout required for later communication. + + Arguments: + topk_idx: `[num_tokens, num_topk]`, dtype must be `deep_ep.topk_idx_t` (typically `torch.int64`), the expert + indices selected by each token, `-1` means no selections. + + Returns: + num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. + num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. + is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. + """ + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, self.num_experts, self.num_ranks) + return num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank def dispatch( - self, + self, x: torch.Tensor, - num_tokens_per_rank: torch.Tensor, - is_token_in_rank: torch.Tensor, - num_tokens_per_expert: torch.Tensor, - topk_idx: torch.Tensor, - topk_weights: torch.Tensor, + handle: Optional[Tuple] = None, + num_tokens_per_rank: Optional[torch.Tensor] = None, + is_token_in_rank: Optional[torch.Tensor] = None, + num_tokens_per_expert: Optional[torch.Tensor] = None, + topk_idx: Optional[torch.Tensor] = None, + topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, ): - per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() - per_expert_buffer = tilelang.tensor((self.num_ranks, num_tokens_per_expert.shape[0]), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() - barrier_signal = tilelang.tensor((self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() \ No newline at end of file + """ + Dispatch tokens to different ranks, both intranode and internode settings are supported. + Intranode kernels require all the ranks should be visible via NVLink. + Internode kernels require the ranks in a node should be visible via NVLink, while the ranks with the same GPU + index should be visible via RDMA. + + Arguments: + x: `torch.Tensor` or tuple of `torch.Tensor`, for the first type, the shape must be `[num_tokens, hidden]`, + and type must be `torch.bfloat16`; for the second type, the first element of the tuple must be shaped as + `[num_tokens, hidden]` with type `torch.float8_e4m3fn`, the second must be `[num_tokens, hidden // 128]` + (requiring divisible) with type `torch.float`. + handle: an optional communication handle, if set, the CPU will reuse the layout information to save some time. + num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. + is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. + num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. + topk_idx: `[num_tokens, num_topk]` with `deep_ep.topk_idx_t` (typically `torch.int64`), the expert indices + selected by each token, `-1` means no selections. + topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. + expert_alignment: align the number of tokens received by each local expert to this variable. + + Returns: + recv_x: received tokens, the same type and tuple as the input `x`, but the number of tokens equals to the + received token count. + recv_topk_idx: received expert indices. + recv_topk_weights: received expert weights. + num_recv_tokens_per_expert_list: Python list shaped `[num_local_experts]`, the received token count by + each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list + will be empty. + handle: the returned communication handle. + """ + if handle is not None: + assert topk_idx is None and topk_weights is None + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + recv_x = intranode_dispatch( + self.rank, + self._allocator, + self._symm_buffers, + self._moe_recv_counter_mapped, + self._moe_recv_expert_counter_mapped, + x, + self.dispatch_cfg, + handle, + num_tokens_per_rank, + is_token_in_rank, + num_tokens_per_expert, + topk_idx, + topk_weights, + expert_alignment, + ) + return recv_x # cached-mode, only return recv_x + else: + assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( + self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle + + def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): + # todo: support bias + """ + Combine (reduce) tokens (addition **without** weights) from different ranks, both intranode and internode + settings are supported. + Intranode kernels require all the ranks should be visible via NVLink. + Internode kernels require the ranks in a node should be visible via NVLink, while the ranks with the same GPU + index should be visible via RDMA. + + Arguments: + x: `[num_tokens, hidden]` with `torch.bfloat16`, the tokens to send for reducing to its original ranks. + handle: a must-set communication handle, you can obtain this from the dispatch function. + topk_weights: `[num_tokens, num_topk]` with `torch.float`, the tokens' top-k weights for reducing to its original ranks. + + Returns: + recv_x: the reduced token from its dispatched ranks. + recv_topk_weights: the reduced top-k weights from its dispatch ranks. + """ + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + recv_x, recv_topk_weights = intranode_combine( + self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights) + return recv_x, recv_topk_weights + diff --git a/examples/distributed/deepseek_deepep/intranode/__init__.py b/examples/distributed/deepseek_deepep/intranode/__init__.py index 2422961691..acae046858 100644 --- a/examples/distributed/deepseek_deepep/intranode/__init__.py +++ b/examples/distributed/deepseek_deepep/intranode/__init__.py @@ -1 +1,3 @@ -from get_dispatch_layout import get_dispatch_layout \ No newline at end of file +from .get_dispatch_layout import get_dispatch_layout +from .dispatch import intranode_dispatch +from .combine import intranode_combine \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 244d667a46..8be288db7a 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -1,6 +1,5 @@ # For intranode only # This op is distributed -### TILELANG_USE_DISTRIBUTED=1 python combine.py import os, sys sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path @@ -14,7 +13,6 @@ from argparse import ArgumentParser from get_dispatch_layout import get_dispatch_layout -from dispatch import intranode_dispatch # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -22,13 +20,14 @@ @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def cached_notify_combine_kernel( - num_recv_tokens, num_ranks, num_sms, ): num_channels = num_sms // 2 threads = max(128, 32 * num_ranks) + num_recv_tokens = T.dynamic('num_recv_tokens') + @T.prim_func def cached_notify_combine_main( send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), @@ -41,7 +40,6 @@ def cached_notify_combine_main( tx = T.get_thread_binding() if bx == 0: # clearing channel_head/tail_idx buffers - # note that the buffer layout here is slightly different from DeepEP T.sync_blocks(barrier_signal) T.clear(channel_head_idx) T.clear(channel_tail_idx) @@ -85,7 +83,6 @@ def cached_notify_combine_main( def cached_notify_combine( num_ranks, num_sms, - num_recv_tokens, #! means the original #tokens on each rank here ##### symm buffers ##### send_head: torch.Tensor, channel_head_idx: torch.Tensor, @@ -93,15 +90,10 @@ def cached_notify_combine( barrier_signal: torch.Tensor, allocator ): - kernel = cached_notify_combine_kernel(num_recv_tokens, num_ranks, num_sms) + kernel = cached_notify_combine_kernel(num_ranks, num_sms) kernel.initialize(allocator=allocator) - kernel( - send_head, - channel_head_idx, - channel_tail_idx, - barrier_signal, - ) + kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal) @tilelang.jit( @@ -342,10 +334,18 @@ def combine_main( return combine_main -def intranode_combine(rank: int, allocator, x, topk_weights, src_idx, - rank_prefix_matrix, channel_prefix_matrix, send_head, - channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers, - config=None): +def intranode_combine( + rank: int, + allocator, + symm_buffers, + x, + config, + handle, + topk_weights, +): + assert handle is not None + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, _, send_head = handle + barrier_signal, _, _, _, _, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, _, channel_topk_weights_buffers = symm_buffers # acquire_shapes num_tokens, hidden = x.shape @@ -353,24 +353,14 @@ def intranode_combine(rank: int, allocator, x, topk_weights, src_idx, num_ranks, num_channels = channel_prefix_matrix.shape num_recv_tokens = send_head.shape[0] - # Default config - config = Config.get_combine_config(num_ranks) if config is None else config - - ### notify combine ### - kernel1 = cached_notify_combine_kernel(num_recv_tokens, num_ranks, config.num_sms) - kernel1.initialize(allocator=allocator) - kernel1( - send_head, - channel_head_idx, - channel_tail_idx, - barrier_signal, - ) + # notify combine + cached_notify_combine(num_ranks, config.num_sms, send_head, channel_head_idx, channel_tail_idx, barrier_signal, allocator) - ### combine ### + # combine recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') - kernel2 = combine_kernel( + kernel = combine_kernel( rank, num_ranks, num_recv_tokens, config.num_max_nvl_chunked_send_tokens, @@ -380,15 +370,15 @@ def intranode_combine(rank: int, allocator, x, topk_weights, src_idx, config.num_sms, dtype='bfloat16' ) - kernel2.initialize(allocator=allocator) - kernel2( + kernel.initialize(allocator=allocator) + kernel( x, topk_weights, - src_idx, + recv_src_idx, recv_x, recv_topk_weights, rank_prefix_matrix, - channel_prefix_matrix, + recv_channel_prefix_matrix, send_head, channel_head_idx, channel_tail_idx, @@ -396,119 +386,4 @@ def intranode_combine(rank: int, allocator, x, topk_weights, src_idx, channel_src_idx_buffers, channel_topk_weights_buffers, ) - return recv_x, recv_topk_weights - - -def test_intranode_combine( - num_tokens: int, - hidden: int, - num_topk: int, - num_experts: int, - rank: int, - num_ranks: int, - expert_alignment: int, - group: torch.distributed.ProcessGroup, -): - try: - import deep_ep # noqa: F403 - except ModuleNotFoundError as e: - raise ModuleNotFoundError("Please install DeepEP to run this test.") - - allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=rank, - num_local_ranks=num_ranks, - group=group) - - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) - buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) - - if rank == 0: - print('get dispatch layout...') - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ - f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - "is_token_in_rank mismatch" - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ - f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - - if rank == 0: - print('intranode dispatch...') - - ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ - buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) - - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers = \ - intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) - - assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' - assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' - assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' - - if rank == 0: - print('cached notify combine and intranode combine...') - - ref_combine_x, ref_combine_topk_weights, _ = buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights, previous_event=event) - - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers = symm_buffers - combine_x, combine_topk_weights = intranode_combine(rank, allocator, recv_x, recv_topk_weights, recv_src_idx, rank_prefix_matrix, recv_channel_prefix_matrix, send_head, channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers) - - assert torch.equal(combine_x, ref_combine_x), f'combine_x mismatch, max err: {(combine_x - ref_combine_x).abs().max()}' - assert torch.equal(combine_topk_weights, ref_combine_topk_weights), f'combine_topk_weights mismatch, max err: {(combine_topk_weights - ref_combine_topk_weights).abs().max()}' - print(f'[rank {rank}] All checks passed for TileScale intranode_combine. ✅') - - # benchmark - t1 = do_bench(lambda: buffer.combine(ref_recv_x, ref_handle, ref_recv_topk_weights), - _n_warmup=1, - _n_repeat=1, - ) - t2 = do_bench(lambda: intranode_combine(rank, allocator, recv_x, recv_topk_weights, recv_src_idx, rank_prefix_matrix, recv_channel_prefix_matrix, send_head, channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers), - _n_warmup=1, - _n_repeat=1, - ) - print(f"DeepEP: {t1:.3f} ms") - print(f"TileScale: {t2:.3f} ms") - print(f"Speedup: {t1 / t2:.2f}x") - - -def main(local_rank: int, num_local_ranks: int, args): - rank, num_ranks, group = init_dist(local_rank, num_local_ranks) - - test_intranode_combine( - args.num_tokens, - args.hidden, - args.num_topk, - args.num_experts, - rank, - num_ranks, - args.expert_alignment, - group, - ) - - torch.distributed.destroy_process_group(group) - torch.distributed.destroy_process_group() - - -def parse_args(): - parser = ArgumentParser(description="Test combine") - parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") - parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") - parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") - parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") - parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_args() - - num_ranks = args.num_ranks - torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 6af9584cda..827488a286 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -1,6 +1,5 @@ # For intranode only # This op is distributed -### TILELANG_USE_DISTRIBUTED=1 python dispatch.py import os, sys from torch.types import Number @@ -21,6 +20,9 @@ os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +# notify_dispatch is responible for: +# 1. Pre-compute rank/channel prefix for dispatch +# 2. Zero 4 symm buffers before a system-level barrier @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def notify_dispatch_kernel( rank: int, @@ -46,6 +48,11 @@ def notify_dispatch_main( barrier_signal: T.Tensor((num_ranks,), 'int32'), rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), ): with T.Kernel(num_ranks+1, threads=threads) as bx: tx = T.get_thread_binding() @@ -85,7 +92,12 @@ def notify_dispatch_main( # TODO: simply returns per_rank_buffer as rank_prefix_matrix T.copy(per_rank_buffer, rank_prefix_matrix) - # NOTE: We don't cleanup the buffer for later use + # Clear 4 symm buffers for later use + T.clear(channel_start_offset) + T.clear(channel_end_offset) + T.clear(channel_head_idx) + T.clear(channel_tail_idx) + T.barrier_blocks(barrier_signal) else: dst_rank = bx - 1 @@ -129,47 +141,13 @@ def notify_dispatch( per_rank_buffer: torch.Tensor, per_expert_buffer: torch.Tensor, barrier_signal: torch.Tensor, + channel_start_offset: torch.Tensor, + channel_end_offset: torch.Tensor, + channel_head_idx: torch.Tensor, + channel_tail_idx: torch.Tensor, # allocator allocator, ): - """ - TileScale notify-dispatch op. - - Args: - rank (int): The current rank (process or device index). - num_ranks (int): Total number of participating ranks (nodes). - num_experts (int): Global number of experts in the MoE layer. - num_tokens (int): Number of tokens being dispatched. - num_channels (int): Number of communication channels. - expert_alignment (int): Alignment constraint for expert buffer. - - num_tokens_per_rank (torch.Tensor): [num_ranks] - - For each rank r, num_tokens_per_rank[r] is the number of tokens assigned for dispatch to rank r across the cluster. - num_tokens_per_expert (torch.Tensor): [num_experts] - - For each expert e, num_tokens_per_expert[e] is the number of tokens rank r will send to global expert e. - is_token_in_rank (torch.Tensor): [num_tokens, num_ranks] - - For each (token t, rank r), is_token_in_rank[t, r] indicates (bool) whether token t belongs to rank r after dispatch. - - moe_recv_counter_mapped (torch.Tensor): [1] - - The number of tokens received by the current rank from other ranks. - moe_recv_expert_counter_mapped (torch.Tensor): [num_local_experts] - - The number of tokens received by the current rank for its local experts. - - per_rank_buffer (torch.Tensor): num_ranks * [num_ranks, num_ranks], symm tensor, should be zeroed before use - - Symmetric buffer for per-rank communication; [src_rank, dst_rank] region. - per_expert_buffer (torch.Tensor): num_ranks * [num_ranks, num_local_experts], symm tensor, should be zeroed before use - - Buffer for per-expert communication; [rank, local_expert] region. - barrier_signal (torch.Tensor): num_ranks * [num_ranks], symm_tensor, should be zeroed before use - - Synchronization tensor used as a system-wide barrier. - - allocator: TileScale allocator for symm tensors - - Returns - rank_prefix_matrix (torch.Tensor): [num_ranks, num_ranks] - - For each (rank r, other_rank), rank_prefix_matrix[r, other_rank] records prefix sums/statistics for token dispatch between r and other_rank. - channel_prefix_matrix (torch.Tensor): [num_ranks, num_channels] - - For each (rank r, channel c), channel_prefix_matrix[r, c] records prefix sums/statistics for tokens on communication channel c for rank r. - """ kernel = notify_dispatch_kernel( rank, num_ranks, @@ -183,6 +161,10 @@ def notify_dispatch( rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') + # clear buffers and counters + moe_recv_counter_mapped.fill_(-1) + moe_recv_expert_counter_mapped.fill_(-1) + kernel( num_tokens_per_rank, num_tokens_per_expert, @@ -194,11 +176,59 @@ def notify_dispatch( barrier_signal, rank_prefix_matrix, channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, ) return rank_prefix_matrix, channel_prefix_matrix -# NOTE: We don't need cached_notify_dispatch, as per-rank-buffer is for one-time use + +# cached_notify_dispatch only needs to clear symm buffers +@tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) +def cached_notify_dispatch_kernel( + num_ranks: int, + num_channels: int +): + @T.prim_func + def cached_notify_dispatch_main( + barrier_signal: T.Tensor((num_ranks,), 'int32'), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + ): + with T.Kernel(1, threads=128): + T.sync_blocks(barrier_signal) + + T.clear(channel_start_offset) + T.clear(channel_end_offset) + T.clear(channel_head_idx) + T.clear(channel_tail_idx) + + T.barrier_blocks(barrier_signal) + + return cached_notify_dispatch_main + + +def cached_notify_dispatch( + num_ranks: int, + num_channels: int, + # symm buffers to be cleared + channel_start_offset: torch.Tensor, + channel_end_offset: torch.Tensor, + channel_head_idx: torch.Tensor, + channel_tail_idx: torch.Tensor, + # barrier + barrier_signal: torch.Tensor, + # allocator + allocator +): + kernel = cached_notify_dispatch_kernel(num_ranks, num_channels) + kernel.initialize(allocator=allocator) # we still comm on barrier_signal + kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx) @tilelang.jit( @@ -682,15 +712,15 @@ def cached_dispatch_main( return cached_dispatch_main -# todo: support cached-mode via handle def intranode_dispatch( rank: int, allocator, - # data + symm_buffers, + moe_recv_counter_mapped, + moe_recv_expert_counter_mapped, x: torch.Tensor, # todo: support fp8 quant - # handle + config: Config, handle: Optional[Tuple] = None, - # meta num_tokens_per_rank: Optional[torch.Tensor] = None, is_token_in_rank: Optional[torch.Tensor] = None, num_tokens_per_expert: Optional[torch.Tensor] = None, @@ -698,42 +728,8 @@ def intranode_dispatch( topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, # todo: support num_worst_tokens - # tuning cfg - config: Optional[Config] = None, # todo: support async functionality - ): - """ - Dispatch tokens to different intranode ranks. - Intranode kernels require all the ranks should be visible via NVLink. - - Arguments: - x: `torch.Tensor` or tuple of `torch.Tensor`, for the first type, the shape must be `[num_tokens, hidden]`, - and type must be `torch.bfloat16`; for the second type, the first element of the tuple must be shaped as - `[num_tokens, hidden]` with type `torch.float8_e4m3fn`, the second must be `[num_tokens, hidden // 128]` - (requiring divisible) with type `torch.float`. - num_tokens_per_rank: `[num_ranks]` with `torch.int`, the number of tokens to be sent to each rank. - is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. - num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. - Returns None for cached-mode. - topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices - selected by each token, `-1` means no selections. - topk_weights: `[num_tokens, num_topk]` with `torch.float`, the expert weights of each token to dispatch. - expert_alignment: align the number of tokens received by each local expert to this variable. - config: the performance tuning config. - allocator: TileScale allocator for symm tensors - - Returns: - recv_x: received tokens, the same type and tuple as the input `x`, but the number of tokens equals to the - received token count. - recv_topk_idx: received expert indices. - recv_topk_weights: received expert weights. - num_recv_tokens_per_expert_list: Python list shaped `[num_local_experts]`, the received token count by - each local expert, aligned to the input `expert_alignment`. If `num_worst_tokens` is specified, the list - will be empty. - handle: the handle for combine, has `(rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)`. - """ - if handle is None: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, \ "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" @@ -749,20 +745,10 @@ def intranode_dispatch( # Default config config = Config.get_dispatch_config(num_ranks) if config is None else config - # Alloc public barrier - barrier_signal = tilelang.tensor((num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() + barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, \ + channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers = symm_buffers if handle is None: - # Size prefix by ranks, shaped as `[num_ranks, num_ranks]` - # Size prefix by experts (not used later), shaped as `[num_ranks, num_local_experts]` - rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') - channel_prefix_matrix = torch.empty([num_ranks, config.num_channels], dtype=torch.int32, device='cuda') - - moe_recv_counter_mapped, moe_recv_expert_counter_mapped = create_moe_recv_counters(num_ranks, num_experts // num_ranks)[3:5] - - per_rank_buffer = tilelang.tensor((num_ranks, num_ranks), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - per_expert_buffer = tilelang.tensor((num_ranks, num_local_experts), dtype=torch.int32, device='cuda', allocator=allocator).zero_() - rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( rank, num_ranks, @@ -778,17 +764,21 @@ def intranode_dispatch( per_rank_buffer, per_expert_buffer, barrier_signal, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, allocator, ) - torch.cuda.synchronize() # todo: replace it with host-side wait_ne + # todo: replace it with host-side wait_ne num_recv_tokens = moe_recv_counter_mapped.item() num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() else: + cached_notify_dispatch(num_ranks, config.num_channels, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, barrier_signal, allocator) num_recv_tokens = recv_src_idx.size(0) - num_recv_tokens_per_expert_list = None - # create normal buffers + # create output buffers recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') if handle is None: @@ -797,172 +787,15 @@ def intranode_dispatch( recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') - # create symm buffers - channel_start_offset = tilelang.tensor( - [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() - channel_end_offset = tilelang.tensor( - [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() - channel_head_idx = tilelang.tensor( - [config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() - channel_tail_idx = tilelang.tensor( - shape=[config.num_channels, num_ranks], dtype=torch.int32, device='cuda', allocator=allocator).zero_() - channel_x_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, hidden], dtype=torch.bfloat16, device='cuda', allocator=allocator) - channel_src_idx_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, device='cuda', allocator=allocator) - - if handle is None: - channel_topk_idx_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.int64, device='cuda', allocator=allocator) - channel_topk_weights_buffers = tilelang.tensor( - [config.num_channels, num_ranks, config.num_max_nvl_chunked_recv_tokens, num_topk], dtype=torch.float32, device='cuda', allocator=allocator) - else: - channel_topk_idx_buffers = None # todo: double-check this (may affect combine) - channel_topk_weights_buffers = None - - # get dispatch - _kernel = dispatch_kernel if handle is None else cached_dispatch_kernel - kernel = _kernel( - rank, - num_ranks, - num_tokens, - config.num_max_nvl_chunked_send_tokens, - config.num_max_nvl_chunked_recv_tokens, - hidden, - num_topk, - num_experts, - config.num_sms, - 'bfloat16' - ) - kernel.initialize(allocator=allocator) - # run dispatch - if rank == 0: - print('Start running dispatch kernel...') if handle is None: - args = (recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) - else: - args = (recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers) - kernel(*args) - - handle = (rank_prefix_matrix, channel_prefix_matrix, - recv_channel_prefix_matrix, recv_src_idx, - is_token_in_rank, send_head - ) - symm_buffers = (channel_head_idx, channel_tail_idx, barrier_signal, channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers) - - if handle is not None: - recv_topk_idx = recv_topk_weights = None - return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, symm_buffers # todo: reconsider hierachy - - -def test_intranode_dispatch( - num_tokens: int, - hidden: int, - num_topk: int, - num_experts: int, - rank: int, - num_ranks: int, - expert_alignment: int, - cached: bool, - group: torch.distributed.ProcessGroup, -): - try: - import deep_ep # noqa: F403 - except ModuleNotFoundError as e: - raise ModuleNotFoundError("Please install DeepEP to run this test.") - - allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=rank, - num_local_ranks=num_ranks, - group=group) - - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) - buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) - - if rank == 0: - print(f'get dispatch layout ...') - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts) - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ - f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - "is_token_in_rank mismatch" - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ - f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - - if rank == 0: - print('notify dispatch and intranode dispatch ...') - - # golden - ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, _ = \ - buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) - - # ours - if cached: - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ - intranode_dispatch(rank, allocator, x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment, None) + kernel = dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel.initialize(allocator=allocator) + kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) + return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle, _ = \ - intranode_dispatch(rank, allocator, x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, None) - - # check dispatch output - assert torch.equal(recv_x, ref_recv_x), f'recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' - if not cached: - assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' - assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, 'num_recv_tokens_per_expert_list mismatch' - - # check handle - if not cached: - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle - assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' - assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_src_idx, ref_recv_src_idx), f'recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' - assert torch.equal(send_head, ref_send_head), f'send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' - - print(f'[rank {rank}] All checks passed for {'cached' if cached else 'non-cached'} TileScale intranode_dispatch. ✅') - - -def main(local_rank: int, num_local_ranks: int, args): - rank, num_ranks, group = init_dist(local_rank, num_local_ranks) - - test_intranode_dispatch( - args.num_tokens, - args.hidden, - args.num_topk, - args.num_experts, - rank, - num_ranks, - args.expert_alignment, - args.cached, - group, - ) - - torch.distributed.destroy_process_group(group) - torch.distributed.destroy_process_group() - - -def parse_args(): - parser = ArgumentParser(description="Test dispatch") - parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") - parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") - parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") - parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") - parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - parser.add_argument("-cached", action="store_true", default=False, help="Use cached mode") - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_args() - - num_ranks = args.num_ranks - torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) \ No newline at end of file + kernel = cached_dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel.initialize(allocator=allocator) + kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers) + return recv_x diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 05134c4856..b10dd993a7 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -44,7 +44,6 @@ def get_dispatch_layout( # TODO(wt): Wait on previous events and allocate on comm stream when adding async functionality num_tokens, num_topk = topk_idx.shape num_tokens_per_rank = torch.empty(num_ranks, dtype=torch.int32, device='cuda') - num_tokens_per_rdma_rank = None # No RDMA ranks in intranode settings num_tokens_per_expert = torch.empty(num_experts, dtype=torch.int32, device='cuda') is_token_in_rank = torch.empty((num_tokens, num_ranks), dtype=torch.bool, device='cuda') @@ -53,14 +52,13 @@ def get_dispatch_layout( kernel( topk_idx, num_tokens_per_rank, - # num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank, ) # TODO(wt): Wait streams when adding async functionality - return num_tokens_per_rank, num_tokens_per_rdma_rank, num_tokens_per_expert, is_token_in_rank + return num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py new file mode 100644 index 0000000000..368799f0df --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -0,0 +1,188 @@ +### TILELANG_USE_DISTRIBUTED=1 python test_intranode.py (--cached, optionally) + +import os, sys +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + +import torch +import tilelang +import tilelang.language as T +from argparse import ArgumentParser +from typing import Optional, Tuple +from tilelang.distributed.utils import init_dist, perf_fn +from functools import partial + +from buffer import EPBuffer +from utils import gen_inputs, ep_bench + +# tilelang.disable_cache() +os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log + + +def test_intranode( + num_tokens: int, + hidden: int, + num_topk: int, + num_experts: int, + rank: int, + num_ranks: int, + expert_alignment: int, + cached_dispatch: bool, + group: torch.distributed.ProcessGroup, +): + try: + import deep_ep # noqa: F403 + except ModuleNotFoundError as e: + raise ModuleNotFoundError("Please install DeepEP to run this test.") + + # Create interface buffers + ts_buffer = EPBuffer(group, 2**30, num_topk, num_experts, hidden) + deepep_buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) + + # Generate inputs for testing + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) + + # 1. test get_dispatch_layout + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout(topk_idx, num_experts) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout(topk_idx) + + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + f"[rank {rank}] num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ + f"[rank {rank}] is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + f"[rank {rank}] num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" + + group.barrier() + if rank == 0: + print('Check passed for get_dispatch_layout. ✅') + + # 2. test dispatch + # ref + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ + deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + # ours + if cached_dispatch: + recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment) + else: + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + + # check dispatch output + assert torch.equal(recv_x, ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + if not cached_dispatch: + assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, f'[rank {rank}] num_recv_tokens_per_expert_list mismatch' + + # check handle + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle + assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' + assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' + assert torch.equal(recv_src_idx, ref_recv_src_idx), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' + assert torch.equal(send_head, ref_send_head), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + + group.barrier() + if rank == 0: + print(f'Check passed for {"cached" if cached_dispatch else "non-cached"} dispatch. ✅') + + # 3. test combine + ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights) + if cached_dispatch: # acquire handle first + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + combined_x, combined_topk_weights = ts_buffer.combine(recv_x, handle, recv_topk_weights) + assert torch.equal(combined_x, ref_combined_x), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' + assert torch.equal(combined_topk_weights, ref_combined_topk_weights), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' + + group.barrier() + if rank == 0: + print(f'Check passed for combine. ✅') + + if rank == 0: + print('All checks passed for TileScale intranode DeepEP. ✅') + + # benchmark + if rank == 0: + print(f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========') + if not cached_dispatch: + group.barrier() + deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), + warmup=10, rep=10) + print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + group.barrier() + if rank == 0: + print(f'avg_time: {deepep_dispatch_time:.4f}ms') + ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), + warmup=10, rep=10) + print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + group.barrier() + else: + group.barrier() + deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, None, None, expert_alignment), + warmup=10, rep=10) + print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + group.barrier() + ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment), + warmup=10, rep=10) + print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + group.barrier() + + if rank == 0: + print('========== Benchmarking combine ==========') + group.barrier() + deepep_combine_time = ep_bench(lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), + warmup=10, rep=10) + print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') + group.barrier() + ts_combine_time = ep_bench(lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), + warmup=10, rep=10) + print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') + group.barrier() + + if rank == 0: + print('========== Benchmarking report ==========') + dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 + combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes + if rank == 0: + print(f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s') + print(f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s') + print(f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)') + print(f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)') + + +def main(local_rank: int, num_local_ranks: int, args): + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + + test_intranode( + args.num_tokens, + args.hidden, + args.num_topk, + args.num_experts, + rank, + num_ranks, + args.expert_alignment, + args.cached, + group, + ) + + torch.distributed.destroy_process_group() + + +def parse_args(): + parser = ArgumentParser(description="Test dispatch") + parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") + parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") + parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") + parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") + parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + parser.add_argument("--cached", action="store_true", default=False, help="Whether to use cached dispatch") + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + + num_ranks = args.num_ranks + torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 0d14adf42c..7de630a833 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -208,4 +208,44 @@ def create_moe_recv_counters(num_ranks: int, num_local_experts: int): moe_recv_expert_counter_mapped = get_device_tensor(moe_recv_expert_counter) moe_recv_rdma_counter_mapped = get_device_tensor(moe_recv_rdma_counter) return moe_recv_counter, moe_recv_expert_counter, moe_recv_rdma_counter, \ - moe_recv_counter_mapped, moe_recv_expert_counter_mapped, moe_recv_rdma_counter_mapped \ No newline at end of file + moe_recv_counter_mapped, moe_recv_expert_counter_mapped, moe_recv_rdma_counter_mapped + + +def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): + """DeepEP style benchmark function. + Args: + fn: the function to benchmark. + warmup: the number of warmup iterations. + rep: the number of repetitions. + post_fn: the function to post-process the results. + + Returns: + time (ms): the average time of the function. + """ + import numpy as np + + # Flush L2 cache with 256 MB data + torch.cuda.synchronize() + cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda') + + # Warmup + for _ in range(warmup): + fn() + + # Flush L2 + cache.zero_() + + # Testing + start_events = [torch.cuda.Event(enable_timing=True) for _ in range(rep)] + end_events = [torch.cuda.Event(enable_timing=True) for _ in range(rep)] + for i in range(rep): + # Record + start_events[i].record() + fn() + end_events[i].record() + if post_fn is not None: + post_fn() + torch.cuda.synchronize() + + times = np.array([s.elapsed_time(e) for s, e in zip(start_events, end_events)])[1:] + return np.average(times).item() \ No newline at end of file diff --git a/tilelang/distributed/utils.py b/tilelang/distributed/utils.py index bc153b92bc..b346f238aa 100644 --- a/tilelang/distributed/utils.py +++ b/tilelang/distributed/utils.py @@ -228,7 +228,7 @@ def dist_print(*args, **kwargs): print(*args, **kwargs) -def perf_fn(fn, rep, warmup): +def perf_fn(fn, warmup, rep): start_event = torch.cuda.Event(enable_timing=True) stop_event = torch.cuda.Event(enable_timing=True) for n in range(rep + warmup): From 32804bcdcf930ae7e002b48a064afc49315202ec Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 3 Dec 2025 17:36:44 +0800 Subject: [PATCH 24/41] remove redundant test --- .../intranode/get_dispatch_layout.py | 73 ------------------- 1 file changed, 73 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index b10dd993a7..ff01ea1702 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -154,76 +154,3 @@ def get_dispatch_layout_main( num_tokens_per_rank[rank_begin_idx + tx] = sum return get_dispatch_layout_main - - -def test_get_dispatch_layout( - num_tokens: int, - num_topk: int, - num_experts: int, - num_ranks: int, -): - try: - import deep_ep_cpp # noqa: F403 - except ModuleNotFoundError as e: - raise ModuleNotFoundError("Please install DeepEP to run this test.") - - # Validate correctness - topk_idx = gen_inputs(num_tokens, 1, num_topk, num_experts, num_ranks)[1] - buffer = deep_ep_cpp.Buffer( - 0, # rank - num_ranks, - 0, # num_nvl_bytes - 0, # num_rdma_bytes - False, # low_latency_mode - False, # explicit_destroy - False, # enable_shrink - False, # use fabric - ) - - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False) - - num_tokens_per_rank, _, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, num_experts, num_ranks) - - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ - f"num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - "is_token_in_rank mismatch" - - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ - f"num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - - print("All checks passed for TileScale get_dispatch_layout.✅") - - # Benchmark - t1 = do_bench(lambda: buffer.get_dispatch_layout(topk_idx, num_experts, None, False, False), - _n_warmup=1, - _n_repeat=1, - ) - t2 = do_bench(lambda: get_dispatch_layout(topk_idx, num_experts, num_ranks), - _n_warmup=1, - _n_repeat=1, - ) - print(f"DeepEP: {t1:.3f} ms") - print(f"TileScale: {t2:.3f} ms") - print(f"Speedup: {t1 / t2:.2f}x") - - -def parse_args(): - parser = ArgumentParser(description="Test get_dispatch_layout") - parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") - parser.add_argument( - "--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") - parser.add_argument("--num_experts", type=int, default=256, help="Number of experts") - parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") - return parser.parse_args() - - -if __name__ == "__main__": - args = parse_args() - - test_get_dispatch_layout( - num_tokens=args.num_tokens, - num_topk=args.num_topk, - num_experts=args.num_experts, - num_ranks=args.num_ranks) From 2db7a385310f0af3e9ffd64dc50ec782b9a7831f Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 5 Dec 2025 21:03:22 +0800 Subject: [PATCH 25/41] update doc --- .../distributed/deepseek_deepep/deepep.md | 192 +++++++++++++++++- .../deepseek_deepep/intranode/dispatch.py | 7 +- 2 files changed, 186 insertions(+), 13 deletions(-) diff --git a/examples/distributed/deepseek_deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md index e2b9fb231a..490a0a526f 100644 --- a/examples/distributed/deepseek_deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -1,12 +1,188 @@ -# DeepEP +# DeepEP -To install and compare with DeepEP, please refer to https://github.com/deepseek-ai/DeepEP. +To install and compare with the original DeepEP implementation, please refer to https://github.com/deepseek-ai/DeepEP. ## TODO -- [] Intranode Normal Mode - - [x] get_dispatch_layout - - [] dispatch - - [] notify_dispatch - - [] combine +- [x] Intranode Normal Mode - [] Internode Normal Mode -- [] Low-latency Mode \ No newline at end of file +- [] Low-latency Mode + +# DeepEP Intra-node + +This example implements DeepEP’s intra‑node (NVLink) dispatch/combine using TileScale kernels. + +The intra‑node path lives under `intranode/` and provides a minimal public API that mirrors DeepEP’s behavior for NVLink‑connected ranks. + + +## Overview + +- Scope: intra‑node (NVLink) only; all ranks must be within one node and NVLink‑visible. +- Topology: experts are evenly partitioned across ranks (`num_experts % num_ranks == 0`). +- Datatypes: inputs are `torch.bfloat16`; routing `topk_idx` is `torch.int64`; `topk_weights` is `torch.float32`. +- Channels: each channel uses 2 SMs (send/recv). With default `num_sms=20`, there are `num_channels=10`. + + +## Public API (intranode) + +- `intranode.get_dispatch_layout(topk_idx, num_experts, num_ranks)` + - Computes the routing layout entirely on device. + - Returns `(num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank)`: + - `num_tokens_per_rank`: `[num_ranks]`, `torch.int32` — tokens destined for each rank. + - `num_tokens_per_expert`: `[num_experts]`, `torch.int32` — tokens per expert. + - `is_token_in_rank`: `[num_tokens, num_ranks]`, `torch.bool` — whether a token should be sent to a rank. + +- `intranode.intranode_dispatch(...)` + - Sends selected tokens to destination ranks over NVLink and prepares a reusable communication handle. + - Non‑cached mode (no handle input): + - Inputs: `rank`, `allocator`, `symm_buffers`, MoE counters, `x`, `config`, `num_tokens_per_rank`, `is_token_in_rank`, `num_tokens_per_expert`, `topk_idx`, `topk_weights`, `expert_alignment`. + - Returns: `(recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle)`. + - Cached mode (pass handle): + - Reuses precomputed matrices/buffers and returns only `recv_x`. + +- `intranode.intranode_combine(rank, allocator, symm_buffers, x, config, handle, topk_weights)` + - Reduces contributions back to origin ranks (sum, no weighting) and returns reduced weights for external use. + - Returns `(recv_x, recv_topk_weights)`. + +Convenience wrapper used by examples/tests: + +- `EPBuffer` in `buffer.py` + - Exposes the interface for the functions above via methods: `get_dispatch_layout`, `dispatch`, `combine`. + - Manages TileScale allocator, symmetric buffers, and recommended kernel configs. + + +## Core Data Structures and Handle + +- `rank_prefix_matrix` (num_ranks × num_ranks): cumulative per‑rank token counts; used to compute global offsets for receiver writes. +- `channel_prefix_matrix` (num_ranks × num_channels): per‑channel cumulative counts for each destination rank; senders split work across channels. +- `recv_channel_prefix_matrix` (num_ranks × num_channels): receiver‑side channel offsets populated during dispatch; consumed by combine. +- `send_head` (num_recv_tokens × num_ranks): per received token, expected per‑rank head index in the receiver’s ring buffer. Negative values encode “not yet present” via `-head-1` convention. +- `recv_src_idx` (num_recv_tokens): original source token index; forwarded during dispatch and used by combine senders to tag return traffic. +- `is_token_in_rank` (num_tokens × num_ranks): boolean mask whether a token contributes to a destination rank; reused in cached dispatch. +- `moe_recv_counter(_mapped)`: pinned host + device mapping, total tokens the current rank will receive. +- `moe_recv_expert_counter(_mapped)`: per‑local‑expert received counts (rounded up to `expert_alignment`). +- Symmetric ring buffers per channel/rank: + - Metadata: `channel_start_offset`, `channel_end_offset`, `channel_head_idx`, `channel_tail_idx`. + - Payload: `channel_x_buffers`, `channel_src_idx_buffers`, `channel_topk_idx_buffers`, `channel_topk_weights_buffers`. + +Dispatch returns the handle: +`(rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)` +which can be reused for cached re‑dispatch and is required by the combine stage. + + +## Kernel Responsibilities (high level) + +- Layout + - `get_dispatch_layout_kernel`: counts per‑rank/per‑expert and builds `is_token_in_rank` in one device pass. + +- Notify + Dispatch (A2A send) + - `notify_dispatch_kernel`: computes per‑rank and per‑channel prefixes, writes MoE counters, and zeros the 4 symmetric metadata buffers. + - `dispatch_kernel`: senders push `x`, `src_idx`, and remapped `topk_idx`/`topk_weights` to remote buffers; receivers drain via head/tail indices and assemble `recv_x`, `recv_topk_idx`, `recv_topk_weights`, plus `recv_channel_prefix_matrix`. Also fills `send_head` used by combine. + - Cached variants (`cached_notify_dispatch_kernel`, `cached_dispatch_kernel`) reuse matrices/handle and only clear or advance necessary state. + +- Notify + Combine (reduce back) + - `cached_notify_combine_kernel`: recalculates `send_head` expectations and zeros `channel_head_idx`/`channel_tail_idx` for the combine round. + - `combine_kernel`: senders return expert outputs; receivers reduce by sum per token. `recv_topk_weights` is the sum of returned weights per token. Requires `hidden % 8 == 0` for vectorized access on the receiver side. + + +## Configuration and Tuning + +- `utils.Config` provides recommended values for `num_max_nvl_chunked_send_tokens` and `num_max_nvl_chunked_recv_tokens` per `num_ranks`. These control per‑round trunk sizes and receiver buffer depth per channel. +- `EPBuffer.num_sms` controls total SMs assigned to high‑throughput kernels. Channels = `num_sms // 2` (one send SM + one recv SM per channel). +- `expert_alignment` pads per‑local‑expert MoE receive counters up to the specified multiple, which can be used to size per‑expert workspace. + + +## Execution Flow (non‑cached) + +1) Prepare group and buffers +- Initialize the distributed process group. +- Construct `EPBuffer(group, num_nvl_bytes, num_topk, num_experts, hidden)`; it creates a TileScale distributed allocator, pre‑allocates symmetric buffers and counters, and selects recommended configs based on `num_ranks`. + +2) Routing layout +- Call `EPBuffer.get_dispatch_layout(topk_idx)` to obtain `(num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank)`. + - Inputs must satisfy: `topk_idx.dtype == torch.int64`, 2D contiguous; `num_experts > 0` and divisible by `num_ranks`. + +3) Dispatch +- Call `EPBuffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment)`. +- Internals: + - `notify_dispatch` computes prefix matrices, zeros channel metadata, and populates MoE counters (including per‑expert counts aligned to `expert_alignment`). + - `dispatch_kernel` executes A2A via channels. For each token and destination rank: + - `topk_idx` is remapped into local‑expert indices for the destination rank; non‑local selections become `-1` with weight `0`. + - Sender writes `x`, `src_idx` (token id), and the remapped `topk_idx`/`topk_weights` into receiver buffers; receiver drains and assembles `recv_x`, `recv_topk_idx`, `recv_topk_weights`. + - `send_head` is produced to orchestrate the subsequent combine. +- Returns `(recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle)`. + +4) Expert compute +- Run local experts on `recv_x` to produce expert outputs (shape `[num_recv_tokens, hidden]`). + +5) Combine (reduce back to origin) +- Call `EPBuffer.combine(expert_out, handle, recv_topk_weights)`. +- Internals: + - `cached_notify_combine` recomputes `send_head` expectations per token/rank and zeros receiver heads/tails. + - `combine_kernel` sends expert outputs back and receiver reduces by sum. It also returns `recv_topk_weights` as the sum of returned weights per token, enabling external weighted aggregation if desired. +- Returns `(reduced_x, reduced_topk_weights)`. + +6) Cached re‑dispatch (optional) +- For repeated communication with the same layout, pass `handle` back into `EPBuffer.dispatch(x, handle, ...)` to skip layout/notify work and return only `recv_x`. + + +## Usage + +Quick start (intra‑node test): + +``` +TILELANG_USE_DISTRIBUTED=1 python intranode/test_intranode.py \ + --num_ranks 8 --num_tokens 4096 --hidden 7168 --num_topk 8 --num_experts 32 [--cached] +``` + +Minimal pattern via EPBuffer: + +```python +from buffer import EPBuffer +from tilelang.distributed.utils import init_dist +from utils import gen_inputs + +rank, world_size, group = init_dist(local_rank, num_local_ranks) +buf = EPBuffer(group, num_nvl_bytes=1<<30, num_topk=8, num_experts=32, hidden=7168) + +# Prepare inputs +x, topk_idx, topk_weights, _ = gen_inputs(num_tokens, 7168, 8, 32, world_size) + +# 1) Layout +num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = buf.get_dispatch_layout(topk_idx) + +# 2) Dispatch (non-cached) +recv_x, recv_topk_idx, recv_topk_weights, per_expert_counts, handle = buf.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment=1) + +# 3) Expert compute on recv_x -> expert_out + +# 4) Combine back +reduced_x, reduced_weights = buf.combine(expert_out, handle, recv_topk_weights) +``` + + +## Notes and Limits + +- Intra‑node only: ranks must be NVLink‑visible; current code asserts `num_ranks <= 8` and `num_experts % num_ranks == 0`. +- Combine requires `hidden % 8 == 0` for vectorized receiver loads/stores. +- `dispatch` currently targets BF16 paths. FP8 is not wired end‑to‑end. +- Combine reduces data by sum (no weighting). Reduced weights are returned to enable external weighting logic. +- Ensure `topk_idx` is contiguous, 2D, and `torch.int64`. +- Set `TILELANG_USE_DISTRIBUTED=1` to enable TileScale’s distributed runtime. + + +## Files + +- `intranode/__init__.py` — re‑exports `get_dispatch_layout`, `intranode_dispatch`, `intranode_combine`. +- `intranode/get_dispatch_layout.py` — layout computation function and kernel. +- `intranode/dispatch.py` — notify and main dispatch kernels; host orchestration and cached variants. +- `intranode/combine.py` — notify for combine and main combine kernel; host orchestration. +- `buffer.py` — EPBuffer wrapper: allocator and symmetric buffers, public methods. +- `utils.py` — recommended configs and MoE counter helpers. + + +## Implementation Notes + +- Negative offset encoding: senders write channel start/end offsets as `-value-1` so that a zero token count is distinguishable from an uninitialized `0`. +- Queue semantics: senders update `channel_tail_idx` with release semantics; receivers poll heads/tails with acquire/volatile loads to ensure visibility across PEs. +- `send_head` orchestration: combine waits until each contributing rank’s head meets the expected position for a token, ensuring all contributions are present before reduction. diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 827488a286..c9577aa25a 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -354,7 +354,7 @@ def dispatch_main( cached_channel_tail_idx += 1 if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: # copy data, all are remote copy - # 1. copy data (why useless???) + # 1. copy data T.put_warp(T.address_of(x[token_idx, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), hidden, dst_pe=responsible_rank, unroll_factor=4) @@ -606,7 +606,7 @@ def cached_dispatch_main( cached_channel_tail_idx += 1 if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: # copy data, all are remote copy - # 1. copy data (why useless???) + # 1. copy data T.put_warp(T.address_of(x[token_idx, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), hidden, dst_pe=responsible_rank, unroll_factor=4) @@ -742,9 +742,6 @@ def intranode_dispatch( num_local_experts = num_experts // num_ranks num_topk = topk_idx.shape[1] if handle is None else 0 - # Default config - config = Config.get_dispatch_config(num_ranks) if config is None else config - barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, \ channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers = symm_buffers From f66691d4f1ee5032f05f2e72a431557ea5bc1ffc Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 10 Dec 2025 20:39:51 +0800 Subject: [PATCH 26/41] use int4 vectorization for dispatch --- .../distributed/deepseek_deepep/buffer.py | 27 ++++--- .../deepseek_deepep/intranode/__init__.py | 2 +- .../deepseek_deepep/intranode/combine.py | 20 ++--- .../deepseek_deepep/intranode/dispatch.py | 63 +++++++-------- .../intranode/test_intranode.py | 22 +++--- examples/distributed/deepseek_deepep/utils.py | 78 ++++++++++--------- src/op/remote_copy.cc | 8 +- src/op/remote_copy.h | 2 + src/tl_templates/cuda/copy.h | 77 +++++++++--------- ...tensor.py => test_create_mapped_tensor.py} | 7 +- tilelang/distributed/utils.py | 17 +--- tilelang/language/distributed/common.py | 20 +++-- tilelang/utils/ts_ext/__init__.py | 4 +- tilelang/utils/ts_ext/tensor.cpp | 41 ++++++++-- tilelang/utils/ts_ext/ts_ext_bindings.cpp | 4 +- tilelang/utils/ts_ext/ts_ext_ops.h | 3 +- 16 files changed, 216 insertions(+), 179 deletions(-) rename tilelang/distributed/testing/{test_get_device_tensor.py => test_create_mapped_tensor.py} (70%) diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index d0b808f201..9c24a09805 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -1,6 +1,5 @@ """ The interface for DeepEP. """ -import os import torch import torch.distributed as dist from typing import Callable, List, Tuple, Optional, Union @@ -8,8 +7,8 @@ import tilelang import tilelang.language as T from utils import Config -from tilelang.distributed.utils import get_device_tensor -from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine +from tilelang.distributed.utils import create_mapped_tensor +from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine, dispatch_kernel class EPBuffer: @@ -68,6 +67,9 @@ def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, self._pre_alloc_symm_buffers() self._prepare_counters() + torch.cuda.synchronize() + self.group.barrier() + def _pre_alloc_symm_buffers(self): """Pre-allocate the symmetric buffers via the alloctor for later communication.""" if self.num_ranks <= 8: @@ -103,18 +105,19 @@ def _pre_alloc_symm_buffers_intranode(self): self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + # exp: prepare kernels AOT + self._dispatch_kernel = dispatch_kernel(self.rank, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_send_tokens, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden, self.num_topk, self.num_experts, self.num_sms, 'bfloat16') + self._dispatch_kernel.initialize(allocator=self._allocator) + def _pre_alloc_symm_buffers_internode(self): raise NotImplementedError("internode is not supported yet") def _prepare_counters(self): - self._moe_recv_counter = torch.empty((1,), dtype=torch.int32, pin_memory=True, device='cpu') # MoE counter - self._moe_recv_counter_mapped = get_device_tensor(self._moe_recv_counter) - self._moe_recv_expert_counter = torch.empty((self.num_local_experts,), dtype=torch.int32, pin_memory=True, device='cpu') # MoE expert-level counter - self._moe_recv_expert_counter_mapped = get_device_tensor(self._moe_recv_expert_counter) + self._moe_recv_counter, self._moe_recv_counter_mapped = create_mapped_tensor([1], torch.int32) + self._moe_recv_expert_counter, self._moe_recv_expert_counter_mapped = create_mapped_tensor([self.num_local_experts], torch.int32) if self.num_ranks > 8: # internode - self._moe_recv_rdma_counter = torch.tensor(-1, dtype=torch.int32, pin_memory=True, device='cpu') # MoE RDMA-level counter - self._moe_recv_rdma_counter_mapped = get_device_tensor(self._moe_recv_rdma_counter) + self._moe_recv_rdma_counter, self._moe_recv_rdma_counter_mapped = create_mapped_tensor([1], torch.int32) @staticmethod def set_num_sms(num_sms: int): @@ -169,7 +172,7 @@ def dispatch( num_tokens_per_expert: Optional[torch.Tensor] = None, topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, - expert_alignment: int = 1, + expert_alignment: int = 1 ): """ Dispatch tokens to different ranks, both intranode and internode settings are supported. @@ -208,6 +211,8 @@ def dispatch( self.rank, self._allocator, self._symm_buffers, + self._moe_recv_counter, + self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, @@ -224,7 +229,7 @@ def dispatch( else: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( - self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self._dispatch_kernel) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): diff --git a/examples/distributed/deepseek_deepep/intranode/__init__.py b/examples/distributed/deepseek_deepep/intranode/__init__.py index acae046858..dbbd611dc3 100644 --- a/examples/distributed/deepseek_deepep/intranode/__init__.py +++ b/examples/distributed/deepseek_deepep/intranode/__init__.py @@ -1,3 +1,3 @@ from .get_dispatch_layout import get_dispatch_layout -from .dispatch import intranode_dispatch +from .dispatch import intranode_dispatch, dispatch_kernel from .combine import intranode_combine \ No newline at end of file diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 8be288db7a..7cbcbf790f 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -7,12 +7,6 @@ import torch import tilelang import tilelang.language as T -from tilelang.profiler import do_bench -from tilelang.distributed.utils import init_dist -from utils import Config, gen_inputs # noqa: F403 -from argparse import ArgumentParser - -from get_dispatch_layout import get_dispatch_layout # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -100,8 +94,8 @@ def cached_notify_combine( pass_configs={"tl.disable_tma_lower": True, # use TMA later "tl.disable_warp_specialized": True}) def combine_kernel( - rank, num_ranks, - num_recv_tokens, + rank, + num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens hidden, @@ -110,6 +104,7 @@ def combine_kernel( dtype: str = 'bfloat16', ): num_tokens = T.dynamic('num_tokens') + num_recv_tokens = T.dynamic('num_recv_tokens') num_channels = num_sms // 2 threads = 768 # 24 warps @@ -187,7 +182,7 @@ def combine_main( # 1. copy data T.put_warp(T.address_of(x[token_idx + i, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=send_rank_id, unroll_factor=4) + hidden, dst_pe=send_rank_id, unroll_factor=4, enable_aggresive_vectorize=True) # 2. send src idx idx = T.alloc_var('int32') @@ -257,7 +252,8 @@ def combine_main( # All lanes will use data buffer, but only rank lane will use `head/tail/src_idx` # The same tokens as the dispatch process - num_tokens_per_channel = T.ceildiv(num_recv_tokens, num_channels) + num_tokens_per_channel = T.truncdiv(num_recv_tokens+num_channels-1, num_channels) + # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_recv_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_recv_tokens) @@ -361,8 +357,8 @@ def intranode_combine( recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') kernel = combine_kernel( - rank, num_ranks, - num_recv_tokens, + rank, + num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index c9577aa25a..539a7e62f9 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -2,7 +2,6 @@ # This op is distributed import os, sys -from torch.types import Number sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch @@ -12,9 +11,7 @@ from argparse import ArgumentParser from typing import Optional, Tuple from tilelang.distributed.utils import init_dist -from utils import Config, create_moe_recv_counters, gen_inputs # noqa: F403 - -from get_dispatch_layout import get_dispatch_layout +from utils import Config, ep_ext # noqa: F403 # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -28,7 +25,6 @@ def notify_dispatch_kernel( rank: int, num_ranks: int, num_experts: int, - num_tokens: int, num_channels: int, expert_alignment: int, ): @@ -36,6 +32,8 @@ def notify_dispatch_kernel( num_local_experts = num_experts // num_ranks num_warps = threads // 32 + num_tokens = T.dynamic('num_tokens') + @T.prim_func def notify_dispatch_main( num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), @@ -102,7 +100,8 @@ def notify_dispatch_main( else: dst_rank = bx - 1 for channel_id in T.serial(warp_id, num_channels, num_warps): - num_tokens_per_channel = T.ceildiv(num_tokens, num_channels) + num_tokens_per_channel = T.truncdiv(num_tokens+num_channels-1, num_channels) + # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) cnt = T.alloc_var('int32') @@ -135,6 +134,8 @@ def notify_dispatch( num_tokens_per_expert: torch.Tensor, is_token_in_rank: torch.Tensor, # counter + moe_recv_counter: torch.Tensor, + moe_recv_expert_counter: torch.Tensor, moe_recv_counter_mapped: torch.Tensor, moe_recv_expert_counter_mapped: torch.Tensor, # symm buffers @@ -152,7 +153,6 @@ def notify_dispatch( rank, num_ranks, num_experts, - num_tokens, num_channels, expert_alignment, ) @@ -162,8 +162,8 @@ def notify_dispatch( channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') # clear buffers and counters - moe_recv_counter_mapped.fill_(-1) - moe_recv_expert_counter_mapped.fill_(-1) + moe_recv_counter.fill_(-1) + moe_recv_expert_counter.fill_(-1) kernel( num_tokens_per_rank, @@ -182,15 +182,13 @@ def notify_dispatch( channel_tail_idx, ) - return rank_prefix_matrix, channel_prefix_matrix + num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready(moe_recv_counter, moe_recv_expert_counter) + return num_recv_tokens, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix # cached_notify_dispatch only needs to clear symm buffers @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) -def cached_notify_dispatch_kernel( - num_ranks: int, - num_channels: int -): +def cached_notify_dispatch_kernel(num_ranks: int, num_channels: int): @T.prim_func def cached_notify_dispatch_main( barrier_signal: T.Tensor((num_ranks,), 'int32'), @@ -233,10 +231,10 @@ def cached_notify_dispatch( @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}) + "tl.disable_warp_specialized": True}, debug_root_path='/home/wt/debug/dispatch_static') def dispatch_kernel( - rank, num_ranks, - num_tokens, + rank, + num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens hidden, @@ -256,6 +254,7 @@ def dispatch_kernel( num_warps = threads // 32 # 24 num_warps_per_rank = num_warps // num_ranks # 3 + num_tokens = T.dynamic('num_tokens') num_recv_tokens = T.dynamic('num_recv_tokens') @T.prim_func @@ -313,7 +312,8 @@ def dispatch_main( T.sync_warp() # get task - num_tokens_per_channel = T.alloc_var('int32', init=T.ceildiv(num_tokens, num_channels)) + num_tokens_per_channel = T.truncdiv(num_tokens+num_channels-1, num_channels) + # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.alloc_var('int32') token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) token_end_idx = T.alloc_var('int32') @@ -357,7 +357,7 @@ def dispatch_main( # 1. copy data T.put_warp(T.address_of(x[token_idx, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=responsible_rank, unroll_factor=4) + hidden, dst_pe=responsible_rank, unroll_factor=4, enable_aggresive_vectorize=True) # 2. copy src idx if T.elect_one_sync(): @@ -455,7 +455,8 @@ def dispatch_main( T.address_of(recv_x[total_offset+chunk_idx, 0]), hidden, -1, - 5) + 5, + enable_aggresive_vectorize=True) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, @@ -609,7 +610,7 @@ def cached_dispatch_main( # 1. copy data T.put_warp(T.address_of(x[token_idx, 0]), T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=responsible_rank, unroll_factor=4) + hidden, dst_pe=responsible_rank, unroll_factor=4, enable_aggresive_vectorize=True) # 2. copy src idx if T.elect_one_sync(): @@ -678,13 +679,13 @@ def cached_dispatch_main( num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens - # T.copy(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, :], recv_x[total_offset+chunk_idx, :]) # todo: add ld_nc and st_na #! T.copy will cause layout inference error T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), T.address_of(recv_x[total_offset+chunk_idx, 0]), hidden, -1, - 5) + 5, + enable_aggresive_vectorize=True) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, @@ -716,6 +717,8 @@ def intranode_dispatch( rank: int, allocator, symm_buffers, + moe_recv_counter, + moe_recv_expert_counter, moe_recv_counter_mapped, moe_recv_expert_counter_mapped, x: torch.Tensor, # todo: support fp8 quant @@ -727,6 +730,7 @@ def intranode_dispatch( topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, + kernel = None # todo: support num_worst_tokens # todo: support async functionality ): @@ -746,7 +750,7 @@ def intranode_dispatch( channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers = symm_buffers if handle is None: - rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( + num_recv_tokens, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( rank, num_ranks, num_experts, @@ -756,6 +760,8 @@ def intranode_dispatch( num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank, + moe_recv_counter, + moe_recv_expert_counter, moe_recv_counter_mapped, moe_recv_expert_counter_mapped, per_rank_buffer, @@ -767,15 +773,10 @@ def intranode_dispatch( channel_tail_idx, allocator, ) - # todo: replace it with host-side wait_ne - - num_recv_tokens = moe_recv_counter_mapped.item() - num_recv_tokens_per_expert_list = moe_recv_expert_counter_mapped.tolist() else: cached_notify_dispatch(num_ranks, config.num_channels, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, barrier_signal, allocator) num_recv_tokens = recv_src_idx.size(0) - # create output buffers recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') if handle is None: @@ -786,8 +787,8 @@ def intranode_dispatch( # run dispatch if handle is None: - kernel = dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') - kernel.initialize(allocator=allocator) + # kernel = dispatch_kernel(rank, num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + # kernel.initialize(allocator=allocator) kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index 368799f0df..4898b26454 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -5,11 +5,8 @@ import torch import tilelang -import tilelang.language as T from argparse import ArgumentParser -from typing import Optional, Tuple from tilelang.distributed.utils import init_dist, perf_fn -from functools import partial from buffer import EPBuffer from utils import gen_inputs, ep_bench @@ -108,23 +105,21 @@ def test_intranode( if not cached_dispatch: group.barrier() deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=10, rep=10) + warmup=5, rep=5) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() - if rank == 0: - print(f'avg_time: {deepep_dispatch_time:.4f}ms') ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=10, rep=10) + warmup=5, rep=5) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() else: group.barrier() deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, None, None, expert_alignment), - warmup=10, rep=10) + warmup=5, rep=5) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment), - warmup=10, rep=10) + warmup=5, rep=5) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() @@ -132,11 +127,12 @@ def test_intranode( print('========== Benchmarking combine ==========') group.barrier() deepep_combine_time = ep_bench(lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), - warmup=10, rep=10) + warmup=50, rep=50) print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') + group.barrier() ts_combine_time = ep_bench(lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), - warmup=10, rep=10) + warmup=50, rep=50) print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') group.barrier() @@ -145,8 +141,8 @@ def test_intranode( dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes if rank == 0: - print(f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s') - print(f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s') + print(f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)') + print(f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)') print(f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)') print(f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)') diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 7de630a833..5518e66ac3 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -1,8 +1,8 @@ from typing import Union, Tuple import torch +from torch.utils.cpp_extension import load_inline import os from dataclasses import dataclass, field -from tilelang.distributed.utils import get_device_tensor # Pre-defined constants in DeepEP NUM_MAX_NVL_PEERS = 8 # Maximum number of NVLink peers per GPU @@ -176,40 +176,6 @@ def inplace_unique(x: torch.Tensor, num_slots: int): valid_len = min(num_slots, x.size(1)) x[:, :valid_len] = sorted_bin_idx[:, :valid_len] - -# Check: csrc/deep_ep.cpp:Buffer::Buffer -def create_moe_recv_counters(num_ranks: int, num_local_experts: int): - """Create MoE receive counters. - All allocated tensors are initialized with -1. - - Args: - num_ranks: the number of ranks. - num_local_experts: the number of local experts. - - Returns: - moe_recv_counter: the MoE counter, allocated on pinned host memory. - moe_recv_expert_counter: the MoE expert-level counter, allocated on pinned host memory. - moe_recv_rdma_counter: the MoE RDMA-level counter, allocated on pinned host memory. - - moe_recv_counter_mapped: the MoE counter on device, mapped from the pinned host memory. - moe_recv_expert_counter_mapped: the MoE expert-level counter on device, mapped from the pinned host memory. - moe_recv_rdma_counter_mapped: the MoE RDMA-level counter on device, mapped from the pinned host memory. - """ - - moe_recv_counter = torch.tensor( - [-1], dtype=torch.int32, pin_memory=True, device='cpu') # MoE counter - moe_recv_expert_counter = torch.tensor( - [-1] * num_local_experts, dtype=torch.int32, - pin_memory=True, device='cpu') # MoE expert-level counter - moe_recv_rdma_counter = torch.tensor( - -1, dtype=torch.int32, pin_memory=True, device='cpu') # MoE RDMA-level counter - - moe_recv_counter_mapped = get_device_tensor(moe_recv_counter) - moe_recv_expert_counter_mapped = get_device_tensor(moe_recv_expert_counter) - moe_recv_rdma_counter_mapped = get_device_tensor(moe_recv_rdma_counter) - return moe_recv_counter, moe_recv_expert_counter, moe_recv_rdma_counter, \ - moe_recv_counter_mapped, moe_recv_expert_counter_mapped, moe_recv_rdma_counter_mapped - def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): """DeepEP style benchmark function. @@ -248,4 +214,44 @@ def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): torch.cuda.synchronize() times = np.array([s.elapsed_time(e) for s, e in zip(start_events, end_events)])[1:] - return np.average(times).item() \ No newline at end of file + return np.average(times).item() + + +_src = r""" +#include +#include + +std::tuple> wait_for_counters_ready( + torch::Tensor& moe_recv_counter, torch::Tensor& moe_recv_expert_counter) { + volatile int *counter_ptr = moe_recv_counter.data_ptr(); // volatile is necessary + volatile int *expert_ptr = moe_recv_expert_counter.data_ptr(); + const int num_local_experts = moe_recv_expert_counter.size(0); + + // Wait for counters to be ready + while (true) { + bool ready = counter_ptr[0] >= 0; + for (int i = 0; i < num_local_experts and ready; ++i) + ready &= expert_ptr[i] >= 0; + + if (ready) break; + } + + // After ready, get counter values to return + int counter_value = counter_ptr[0]; + + std::vector expert_counter_values = std::vector( + expert_ptr, + expert_ptr + num_local_experts); + + return std::make_tuple(counter_value, expert_counter_values); +} +""" + +ep_ext = load_inline( + name="ep_ext", + cpp_sources=_src, + functions=["wait_for_counters_ready"], + extra_cflags=["-O3", "-march=native"], + verbose=False +) + diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 8cd3c6b22e..3ccf78a2cc 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -78,6 +78,7 @@ PutOp::PutOp(Array args, BufferMap vmap) { node->dst_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; + node->enable_aggresive_vectorize = bool(args[6].as().value()->value); data_ = std::move(node); (void)vmap; } @@ -91,7 +92,8 @@ Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { Array new_args; std::stringstream ss; if (scope == "warp") { - ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ">"; + ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " + << (enable_aggresive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { @@ -185,6 +187,7 @@ GetOp::GetOp(Array args, BufferMap vmap) { node->src_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; + node->enable_aggresive_vectorize = bool(args[6].as().value()->value); data_ = std::move(node); (void)vmap; } @@ -198,7 +201,8 @@ Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { Array new_args; std::stringstream ss; if (scope == "warp") { - ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ">"; + ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " + << (enable_aggresive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index a6272db366..d089e9ce66 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -33,6 +33,7 @@ class PutOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} + bool enable_aggresive_vectorize; ///< Whether to enable aggressive vectorization, only effctive for warp-scope bool is_distributed() const; @@ -122,6 +123,7 @@ class GetOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} + bool enable_aggresive_vectorize; ///< Whether to enable aggressive vectorization, only effctive for warp-scope bool is_distributed() const; diff --git a/src/tl_templates/cuda/copy.h b/src/tl_templates/cuda/copy.h index 6dc07704d0..b33af49c9a 100644 --- a/src/tl_templates/cuda/copy.h +++ b/src/tl_templates/cuda/copy.h @@ -94,7 +94,7 @@ template <> struct VecInt<16> { using vec_t = int4; }; -#define LD_NC_FUNC "ld.volatile.global" +#define LD_NC_FUNC "ld.nc.global" #define ST_NA_FUNC "st.global" template TL_DEVICE dtype_t ld_nc_global(const dtype_t *ptr) { @@ -195,7 +195,7 @@ template <> TL_DEVICE void st_na_global(const int4 *ptr, const int4 &value) { #define ST_FUNC(ptr, value) st_na_global(ptr, value) template -TL_DEVICE void cp_warp(dtype_t const *const dst_addr, +TL_DEVICE void cp_warp_impl(dtype_t const *const dst_addr, dtype_t const *const src_addr) { int lane_id; asm("mov.s32 %0, %laneid;" : "=r"(lane_id)); @@ -216,51 +216,50 @@ TL_DEVICE void cp_warp(dtype_t const *const dst_addr, ST_FUNC(__dst + __i, LD_FUNC(__src + __i)); } -template -TL_DEVICE void cp_warp(uint64_t dst_addr_uint64, +/** + * @param enable_aggresive_vectorize If set to true, the copy will be performed with aggressive vectorization + * (e.g., using int4 for aligned and sized transfers), which requires that both source and destination addresses + * are 16-byte aligned and N*sizeof(dtype_t) is a multiple of 16 for optimal memory access and throughput. + * If false, performs a standard element-wise copy. + */ + // todo: support more auto-vectorize later +template +TL_DEVICE void cp_warp(dtype_t const *const dst_addr, dtype_t const *const src_addr) { - dtype_t *dst_addr = reinterpret_cast(dst_addr_uint64); + if constexpr (enable_aggresive_vectorize) { + int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; + const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; + constexpr int N_int4 = sizeof(dtype_t) * N / 16; + cp_warp_impl(dst_addr_int4, src_addr_int4); + } else { + cp_warp_impl(dst_addr, src_addr); + } +} - int lane_id; - asm("mov.s32 %0, %laneid;" : "=r"(lane_id)); - constexpr int kLoopStride = 32 * (UNROLL_FACTOR); - typename std::remove_reference::type - unrolled_values[(UNROLL_FACTOR)]; - auto __src = (src_addr); - auto __dst = (dst_addr); - for (int __i = (lane_id); __i < ((N) / kLoopStride) * kLoopStride; - __i += kLoopStride) { - _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) - unrolled_values[__j] = LD_FUNC(__src + __i + __j * 32); - _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) - ST_FUNC(__dst + __i + __j * 32, unrolled_values[__j]); +template +TL_DEVICE void cp_warp(uint64_t dst_addr_uint64,dtype_t const *const src_addr) { + dtype_t *dst_addr = reinterpret_cast(dst_addr_uint64); + if constexpr (enable_aggresive_vectorize) { + int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; + const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; + constexpr int N_int4 = sizeof(dtype_t) * N / 16; + cp_warp_impl(dst_addr_int4, src_addr_int4); + } else { + cp_warp_impl(dst_addr, src_addr); } - for (int __i = ((N) / kLoopStride) * kLoopStride + (lane_id); __i < (N); - __i += 32) - ST_FUNC(__dst + __i, LD_FUNC(__src + __i)); } -template +template TL_DEVICE void cp_warp(dtype_t *const dst_addr, uint64_t src_addr_uint64) { const dtype_t *src_addr = reinterpret_cast(src_addr_uint64); - - int lane_id; - asm("mov.s32 %0, %laneid;" : "=r"(lane_id)); - constexpr int kLoopStride = 32 * (UNROLL_FACTOR); - typename std::remove_reference::type - unrolled_values[(UNROLL_FACTOR)]; - auto __src = (src_addr); - auto __dst = (dst_addr); - for (int __i = (lane_id); __i < ((N) / kLoopStride) * kLoopStride; - __i += kLoopStride) { - _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) - unrolled_values[__j] = LD_FUNC(__src + __i + __j * 32); - _Pragma("unroll") for (int __j = 0; __j < (UNROLL_FACTOR); ++__j) - ST_FUNC(__dst + __i + __j * 32, unrolled_values[__j]); + if constexpr (enable_aggresive_vectorize) { + int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; + const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; + constexpr int N_int4 = sizeof(dtype_t) * N / 16; + cp_warp_impl(dst_addr_int4, src_addr_int4); + } else { + cp_warp_impl(dst_addr, src_addr); } - for (int __i = ((N) / kLoopStride) * kLoopStride + (lane_id); __i < (N); - __i += 32) - ST_FUNC(__dst + __i, LD_FUNC(__src + __i)); } /** diff --git a/tilelang/distributed/testing/test_get_device_tensor.py b/tilelang/distributed/testing/test_create_mapped_tensor.py similarity index 70% rename from tilelang/distributed/testing/test_get_device_tensor.py rename to tilelang/distributed/testing/test_create_mapped_tensor.py index 4e0d1d74a3..3bd4fb8c26 100644 --- a/tilelang/distributed/testing/test_get_device_tensor.py +++ b/tilelang/distributed/testing/test_create_mapped_tensor.py @@ -1,12 +1,11 @@ import torch -from tilelang.distributed.utils import get_device_tensor +from tilelang.distributed.utils import create_mapped_tensor if __name__ == "__main__": shape = (1024, 1024) dtype = torch.float32 - host_tensor = torch.randn(shape, dtype=dtype, pin_memory=True) - device_tensor = get_device_tensor(host_tensor) + host_tensor, device_tensor = create_mapped_tensor(shape, dtype) # test meta-data assert device_tensor.device.type == "cuda" @@ -18,4 +17,4 @@ device_tensor.random_() assert torch.equal(host_tensor, device_tensor.cpu()), f"{host_tensor=}, {device_tensor=}" - print("All checks passed for get_device_tensor. ✅") \ No newline at end of file + print("All checks passed for create_mapped_tensor. ✅") \ No newline at end of file diff --git a/tilelang/distributed/utils.py b/tilelang/distributed/utils.py index b346f238aa..42166435e9 100644 --- a/tilelang/distributed/utils.py +++ b/tilelang/distributed/utils.py @@ -19,7 +19,7 @@ from cuda import cuda, cudart import ctypes -from tilescale_ext import _create_tensor, _create_ipc_handle, _sync_ipc_handles, _get_device_tensor +from tilescale_ext import _create_tensor, _create_ipc_handle, _sync_ipc_handles, create_host_device_tensor import functools from functools import lru_cache from threading import Lock @@ -401,16 +401,5 @@ def has_fullmesh_nvlink(): return _has_fullmesh_nvlink -def get_device_tensor(tensor: torch.Tensor) -> torch.Tensor: - """Get the device tensor from the host tensor. - This is implemented via `cudaHostGetDevicePointer` - - Args: - tensor: The host tensor. - - Returns: - The device tensor with same meta-data. - """ - assert tensor.device.type == "cpu" - assert tensor.is_pinned() - return _get_device_tensor(tensor) +def create_mapped_tensor(shape: list[int], dtype: torch.dtype) -> torch.Tensor: + return create_host_device_tensor(shape, dtype) diff --git a/tilelang/language/distributed/common.py b/tilelang/language/distributed/common.py index 8cbaf5a174..6f7c2f01db 100644 --- a/tilelang/language/distributed/common.py +++ b/tilelang/language/distributed/common.py @@ -23,7 +23,8 @@ def put_warp(src: PrimExpr, dst: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | IntImm | None = -1, - unroll_factor: int = 4): + unroll_factor: int = 4, + enable_aggresive_vectorize: bool = False): """Put to a remote buffer with unrolled loop. Args: @@ -38,16 +39,20 @@ def put_warp(src: PrimExpr, -1 by default, which means local copy. unroll_factor: int The unroll factor + enable_aggresive_vectorize: bool + Whether to enable aggressive vectorization. + If True, the compiler with try to vectorize the copy via int4. """ return tir.call_intrin("handle", tir.op.Op.get("tl.put"), src, dst, size, dst_pe, unroll_factor, - "warp") + "warp", enable_aggresive_vectorize) def get_warp(src: PrimExpr, dst: PrimExpr, size: PrimExpr, src_pe: PrimExpr | IntImm | None = -1, - unroll_factor: int = 4): + unroll_factor: int = 4, + enable_aggresive_vectorize: bool = False): """Get from a remote buffer with unrolled loop. Args: @@ -62,9 +67,12 @@ def get_warp(src: PrimExpr, -1 by default, which means local copy. unroll_factor: int The unroll factor + enable_aggresive_vectorize: bool + Whether to enable aggressive vectorization. + If True, the compiler with try to vectorize the copy via int4. """ return tir.call_intrin("handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, unroll_factor, - "warp") + "warp", enable_aggresive_vectorize) def put_block(src: PrimExpr, @@ -85,7 +93,7 @@ def put_block(src: PrimExpr, -1 by default, which means local copy. """ return tir.call_intrin( - "handle", tir.op.Op.get("tl.put"), src, dst, size, dst_pe, 0, "block" + "handle", tir.op.Op.get("tl.put"), src, dst, size, dst_pe, 0, "block", True ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy @@ -107,7 +115,7 @@ def get_block(src: PrimExpr, -1 by default, which means local copy. """ return tir.call_intrin( - "handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, 0, "block" + "handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, 0, "block", True ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy diff --git a/tilelang/utils/ts_ext/__init__.py b/tilelang/utils/ts_ext/__init__.py index a295a87fd9..3f6484b92d 100644 --- a/tilelang/utils/ts_ext/__init__.py +++ b/tilelang/utils/ts_ext/__init__.py @@ -6,13 +6,13 @@ _create_tensor = _C._create_tensor _create_ipc_handle = _C._create_ipc_handle _sync_ipc_handles = _C._sync_ipc_handles -_get_device_tensor = _C.get_device_tensor +create_host_device_tensor = _C.create_host_device_tensor __all__ = [ "tensor_from_ptr", "_create_tensor", "_create_ipc_handle", "_sync_ipc_handles", - "_get_device_tensor", + "create_host_device_tensor", "_C", ] diff --git a/tilelang/utils/ts_ext/tensor.cpp b/tilelang/utils/ts_ext/tensor.cpp index 5ae90ebd88..e4eaf039d0 100644 --- a/tilelang/utils/ts_ext/tensor.cpp +++ b/tilelang/utils/ts_ext/tensor.cpp @@ -86,10 +86,39 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, } } -torch::Tensor get_device_tensor(torch::Tensor tensor) { - void* device_ptr = nullptr; - CUDA_CHECK(cudaHostGetDevicePointer(&device_ptr, tensor.data_ptr(), 0)); - std::vector shape(tensor.sizes().begin(), tensor.sizes().end()); - std::string dtype_name(tensor.dtype().name()); - return tensor_from_ptr(reinterpret_cast(device_ptr), shape, dtype_name, tensor.device().index(), false); +std::pair +create_host_device_tensor(const std::vector &shape, c10::ScalarType dtype) { + size_t elem_size = at::elementSize(dtype); + int64_t numel = 1; + for (int64_t s : shape) numel *= s; + + size_t bytes = numel * elem_size; + + void* host_ptr = nullptr; + CUDA_CHECK(cudaHostAlloc( + &host_ptr, + bytes, + cudaHostAllocMapped + )); + + void* device_ptr = nullptr; + CUDA_CHECK(cudaHostGetDevicePointer( + &device_ptr, + host_ptr, + 0 + )); + + auto host_tensor = torch::from_blob( + host_ptr, + shape, + torch::TensorOptions().dtype(dtype).device(torch::kCPU) + ); + + auto device_tensor = torch::from_blob( + device_ptr, + shape, + torch::TensorOptions().dtype(dtype).device(torch::kCUDA) + ); + + return std::make_pair(host_tensor, device_tensor); } \ No newline at end of file diff --git a/tilelang/utils/ts_ext/ts_ext_bindings.cpp b/tilelang/utils/ts_ext/ts_ext_bindings.cpp index b3a39ab00c..81c3b1932c 100644 --- a/tilelang/utils/ts_ext/ts_ext_bindings.cpp +++ b/tilelang/utils/ts_ext/ts_ext_bindings.cpp @@ -22,7 +22,9 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { }, py::arg("shape"), py::arg("dtype")); - m.def("get_device_tensor", &get_device_tensor, py::arg("tensor")); + m.def("create_host_device_tensor", + &create_host_device_tensor, + "Create host/device shared pinned-mapped tensor (shape + dtype)"); m.def( "_create_ipc_handle", diff --git a/tilelang/utils/ts_ext/ts_ext_ops.h b/tilelang/utils/ts_ext/ts_ext_ops.h index 16fdf1a2aa..22a4d1c52b 100644 --- a/tilelang/utils/ts_ext/ts_ext_ops.h +++ b/tilelang/utils/ts_ext/ts_ext_ops.h @@ -13,7 +13,8 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, torch::Tensor create_tensor(const std::vector &shape, c10::ScalarType dtype); -torch::Tensor get_device_tensor(torch::Tensor tensor); +std::pair create_host_device_tensor(const std::vector &shape, + c10::ScalarType dtype); pybind11::bytearray create_ipc_handle(void *ptr); From 1e8ad160ac881799816ef9cc2d73a765a3455517 Mon Sep 17 00:00:00 2001 From: Yu Cheng Date: Tue, 16 Dec 2025 02:34:20 +0800 Subject: [PATCH 27/41] use comm_stream for comm kernels --- .../distributed/deepseek_deepep/buffer.py | 9 ++++--- .../deepseek_deepep/intranode/combine.py | 17 +++++++----- .../deepseek_deepep/intranode/dispatch.py | 27 +++++++++++-------- tilelang/jit/adapter/wrapper.py | 6 ++--- tilelang/jit/kernel.py | 3 ++- 5 files changed, 38 insertions(+), 24 deletions(-) diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index 9c24a09805..77754be038 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -55,6 +55,8 @@ def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, self.dispatch_cfg = dispatch_cfg if dispatch_cfg is not None else self.default_dispatch_config self.combine_cfg = combine_cfg if combine_cfg is not None else self.default_combine_config + + self.comm_stream = torch.cuda.Stream() self._allocator= tilelang.get_allocator( size=EPBuffer.symm_heap_size, @@ -107,7 +109,7 @@ def _pre_alloc_symm_buffers_intranode(self): # exp: prepare kernels AOT self._dispatch_kernel = dispatch_kernel(self.rank, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_send_tokens, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden, self.num_topk, self.num_experts, self.num_sms, 'bfloat16') - self._dispatch_kernel.initialize(allocator=self._allocator) + self._dispatch_kernel.initialize(allocator=self._allocator, stream=self.comm_stream.cuda_stream) def _pre_alloc_symm_buffers_internode(self): raise NotImplementedError("internode is not supported yet") @@ -224,12 +226,13 @@ def dispatch( topk_idx, topk_weights, expert_alignment, + self.comm_stream, ) return recv_x # cached-mode, only return recv_x else: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( - self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self._dispatch_kernel) + self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self._dispatch_kernel, self.comm_stream) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): @@ -252,6 +255,6 @@ def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): """ rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle recv_x, recv_topk_weights = intranode_combine( - self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights) + self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights, self.comm_stream,) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 7cbcbf790f..0ce137c79d 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -8,7 +8,7 @@ import tilelang import tilelang.language as T -# tilelang.disable_cache() +tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -82,12 +82,13 @@ def cached_notify_combine( channel_head_idx: torch.Tensor, channel_tail_idx: torch.Tensor, barrier_signal: torch.Tensor, - allocator + allocator, + comm_stream=None ): kernel = cached_notify_combine_kernel(num_ranks, num_sms) - kernel.initialize(allocator=allocator) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal) + kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal, stream=comm_stream.cuda_stream) @tilelang.jit( @@ -338,6 +339,7 @@ def intranode_combine( config, handle, topk_weights, + comm_stream=None ): assert handle is not None rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, _, send_head = handle @@ -350,7 +352,7 @@ def intranode_combine( num_recv_tokens = send_head.shape[0] # notify combine - cached_notify_combine(num_ranks, config.num_sms, send_head, channel_head_idx, channel_tail_idx, barrier_signal, allocator) + cached_notify_combine(num_ranks, config.num_sms, send_head, channel_head_idx, channel_tail_idx, barrier_signal, allocator, comm_stream=comm_stream) # combine recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') @@ -366,7 +368,7 @@ def intranode_combine( config.num_sms, dtype='bfloat16' ) - kernel.initialize(allocator=allocator) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) kernel( x, topk_weights, @@ -381,5 +383,8 @@ def intranode_combine( channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers, + stream=comm_stream.cuda_stream ) + compute_stream = torch.cuda.current_stream() + compute_stream.wait_stream(comm_stream) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 539a7e62f9..f2578dcc71 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -13,7 +13,7 @@ from tilelang.distributed.utils import init_dist from utils import Config, ep_ext # noqa: F403 -# tilelang.disable_cache() +tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -148,6 +148,7 @@ def notify_dispatch( channel_tail_idx: torch.Tensor, # allocator allocator, + comm_stream=None, ): kernel = notify_dispatch_kernel( rank, @@ -156,7 +157,7 @@ def notify_dispatch( num_channels, expert_alignment, ) - kernel.initialize(allocator=allocator) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') @@ -180,6 +181,7 @@ def notify_dispatch( channel_end_offset, channel_head_idx, channel_tail_idx, + stream=comm_stream.cuda_stream ) num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready(moe_recv_counter, moe_recv_expert_counter) @@ -222,16 +224,17 @@ def cached_notify_dispatch( # barrier barrier_signal: torch.Tensor, # allocator - allocator + allocator, + comm_stream=None, ): kernel = cached_notify_dispatch_kernel(num_ranks, num_channels) - kernel.initialize(allocator=allocator) # we still comm on barrier_signal - kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) # we still comm on barrier_signal + kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, stream=comm_stream.cuda_stream) @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}, debug_root_path='/home/wt/debug/dispatch_static') + "tl.disable_warp_specialized": True}, debug_root_path='/home/yu/debug/dispatch_static') def dispatch_kernel( rank, num_ranks, @@ -730,7 +733,8 @@ def intranode_dispatch( topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, - kernel = None + kernel = None, + comm_stream = None, # todo: support num_worst_tokens # todo: support async functionality ): @@ -772,9 +776,10 @@ def intranode_dispatch( channel_head_idx, channel_tail_idx, allocator, + comm_stream=comm_stream, ) else: - cached_notify_dispatch(num_ranks, config.num_channels, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, barrier_signal, allocator) + cached_notify_dispatch(num_ranks, config.num_channels, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, barrier_signal, allocator, comm_stream=comm_stream) num_recv_tokens = recv_src_idx.size(0) recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') @@ -789,11 +794,11 @@ def intranode_dispatch( if handle is None: # kernel = dispatch_kernel(rank, num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') # kernel.initialize(allocator=allocator) - kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream) handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: kernel = cached_dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') - kernel.initialize(allocator=allocator) - kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) + kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream) return recv_x diff --git a/tilelang/jit/adapter/wrapper.py b/tilelang/jit/adapter/wrapper.py index c88cbc6cca..4cd001cce9 100644 --- a/tilelang/jit/adapter/wrapper.py +++ b/tilelang/jit/adapter/wrapper.py @@ -44,7 +44,7 @@ """ PREDEF_INIT_TABLE_FUNC = """ -extern "C" int init_table(const void* host_table, size_t n) {{ +extern "C" int init_table(const void* host_table, size_t n, cudaStream_t stream) {{ if (error_buf) error_buf[0] = '\\0'; if (host_table == nullptr) {{ @@ -56,9 +56,9 @@ }} size_t bytes = n * sizeof(uint64_t); - cudaError_t err = cudaMemcpyToSymbol(meta_data, host_table, bytes, 0, cudaMemcpyHostToDevice); + cudaError_t err = cudaMemcpyToSymbolAsync(meta_data, host_table, bytes, 0, cudaMemcpyHostToDevice, stream); if (err != cudaSuccess) {{ - if (error_buf) std::snprintf(error_buf, 256, "cudaMemcpyToSymbol failed: %s", cudaGetErrorString(err)); + if (error_buf) std::snprintf(error_buf, 256, "cudaMemcpyToSymbolAsync failed: %s", cudaGetErrorString(err)); return static_cast(err); }} return 0; diff --git a/tilelang/jit/kernel.py b/tilelang/jit/kernel.py index 981162284c..0dba70f058 100644 --- a/tilelang/jit/kernel.py +++ b/tilelang/jit/kernel.py @@ -407,10 +407,11 @@ def get_host_source(self) -> str: def initialize( self, allocator: BaseAllocator, + stream: int = None, ): assert allocator.initialized(), "Allocator is not initialized" result = self.adapter.lib.init_table( - ctypes.c_void_p(allocator.table.data_ptr()), allocator.table_size) + ctypes.c_void_p(allocator.table.data_ptr()), allocator.table_size, ctypes.c_void_p(stream) if stream is not None else ctypes.c_void_p(0)) if result != 0: error_msg = self.adapter.lib.get_last_error().decode('utf-8') raise RuntimeError(f"Initialization failed: {error_msg}") From 05a93004efc5d902a76a126b49c1826e75db9944 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 17 Dec 2025 16:58:18 +0800 Subject: [PATCH 28/41] optimze dispatch perf via skipping tensor validation --- .../deepseek_deepep/intranode/dispatch.py | 17 +++++++++-------- .../intranode/get_dispatch_layout.py | 3 --- .../deepseek_deepep/intranode/test_intranode.py | 2 +- 3 files changed, 10 insertions(+), 12 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index f2578dcc71..57d95541c0 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -7,10 +7,7 @@ import torch import tilelang import tilelang.language as T -from tilelang.profiler import do_bench -from argparse import ArgumentParser from typing import Optional, Tuple -from tilelang.distributed.utils import init_dist from utils import Config, ep_ext # noqa: F403 tilelang.disable_cache() @@ -181,7 +178,8 @@ def notify_dispatch( channel_end_offset, channel_head_idx, channel_tail_idx, - stream=comm_stream.cuda_stream + stream=comm_stream.cuda_stream, + skip_tensor_validation=True # reduce runtime overhead ) num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready(moe_recv_counter, moe_recv_expert_counter) @@ -229,12 +227,13 @@ def cached_notify_dispatch( ): kernel = cached_notify_dispatch_kernel(num_ranks, num_channels) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) # we still comm on barrier_signal - kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, stream=comm_stream.cuda_stream) + kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}, debug_root_path='/home/yu/debug/dispatch_static') + "tl.disable_warp_specialized": True}) def dispatch_kernel( rank, num_ranks, @@ -794,11 +793,13 @@ def intranode_dispatch( if handle is None: # kernel = dispatch_kernel(rank, num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') # kernel.initialize(allocator=allocator) - kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream) + kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: kernel = cached_dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream) + kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead return recv_x diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index ff01ea1702..339033ebee 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -8,10 +8,7 @@ import torch import tilelang import tilelang.language as T -from tilelang.profiler import do_bench from typing import Tuple -from argparse import ArgumentParser -from utils import gen_inputs # noqa: F403 # TODO(wt): Add async functionality diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index 4898b26454..ac80388ec9 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -6,7 +6,7 @@ import torch import tilelang from argparse import ArgumentParser -from tilelang.distributed.utils import init_dist, perf_fn +from tilelang.distributed.utils import init_dist from buffer import EPBuffer from utils import gen_inputs, ep_bench From 072324bda50d5d22d8b6d5822e9d23c2f55bb902 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 17 Dec 2025 16:58:43 +0800 Subject: [PATCH 29/41] add dispatch benchmark result --- .../distributed/deepseek_deepep/deepep.md | 19 ++++++++++++++++++- 1 file changed, 18 insertions(+), 1 deletion(-) diff --git a/examples/distributed/deepseek_deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md index 490a0a526f..4a53f4e93f 100644 --- a/examples/distributed/deepseek_deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -7,7 +7,24 @@ To install and compare with the original DeepEP implementation, please refer to - [] Internode Normal Mode - [] Low-latency Mode -# DeepEP Intra-node +# Benchmark Results + +The table below shows a latency and bandwidth comparison for DeepEP and TileScale on the same NVLink hardware (as reported by the example): + +*Measured on: NVL8, H100, 10 channels, 8 ranks, 32 experts, 7168 hidden, 4096 tokens.* + +## Normal Mode Dispatch + +| Method | Dispatch Time (ms) | Bandwidth (GB/s) | +|-------------|--------------------|------------------| +| DeepEP | 1.0045 | 328.97 | +| TileScale | 1.0720 | 308.25 | + +## Normal Mode Combine + +> Coming soon... + +# Intra-node Introduction This example implements DeepEP’s intra‑node (NVLink) dispatch/combine using TileScale kernels. From a36afea872ed351d43577b9dfb12ad202247ed8d Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Wed, 17 Dec 2025 21:02:51 +0800 Subject: [PATCH 30/41] make rank as an argument of the kernel --- .../distributed/deepseek_deepep/buffer.py | 14 +- .../deepseek_deepep/intranode/combine.py | 233 +++++++++++++++++- .../deepseek_deepep/intranode/dispatch.py | 26 +- .../intranode/test_intranode.py | 8 +- 4 files changed, 252 insertions(+), 29 deletions(-) diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index 77754be038..d141bb1321 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -2,13 +2,12 @@ import torch import torch.distributed as dist -from typing import Callable, List, Tuple, Optional, Union +from typing import Tuple, Optional import tilelang -import tilelang.language as T from utils import Config from tilelang.distributed.utils import create_mapped_tensor -from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine, dispatch_kernel +from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine class EPBuffer: @@ -107,10 +106,6 @@ def _pre_alloc_symm_buffers_intranode(self): self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) - # exp: prepare kernels AOT - self._dispatch_kernel = dispatch_kernel(self.rank, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_send_tokens, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden, self.num_topk, self.num_experts, self.num_sms, 'bfloat16') - self._dispatch_kernel.initialize(allocator=self._allocator, stream=self.comm_stream.cuda_stream) - def _pre_alloc_symm_buffers_internode(self): raise NotImplementedError("internode is not supported yet") @@ -232,7 +227,7 @@ def dispatch( else: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( - self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self._dispatch_kernel, self.comm_stream) + self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self.comm_stream) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): @@ -253,8 +248,7 @@ def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): recv_x: the reduced token from its dispatched ranks. recv_topk_weights: the reduced top-k weights from its dispatch ranks. """ - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle recv_x, recv_topk_weights = intranode_combine( - self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights, self.comm_stream,) + self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights, self.comm_stream) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 0ce137c79d..3f4dfd55e3 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -95,7 +95,6 @@ def cached_notify_combine( pass_configs={"tl.disable_tma_lower": True, # use TMA later "tl.disable_warp_specialized": True}) def combine_kernel( - rank, num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens @@ -120,6 +119,7 @@ def combine_kernel( @T.prim_func def combine_main( + rank: T.int32, # inputs x: T.Tensor([num_tokens, hidden], dtype), topk_weights: T.Tensor([num_tokens, num_topk], "float32"), @@ -331,6 +331,235 @@ def combine_main( return combine_main +# @tilelang.engine.register_cuda_postproc +def _(code, _): + if not 'void combine_main_kernel' in code: + return code + return r''' +#include +#include +#include +#include +#include +#include +#include +#include +#include +uint64_t __constant__ meta_data[1024]; +#ifdef ENABLE_BF16 +#include +#endif + +extern "C" __global__ void combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens); +extern "C" __global__ void __launch_bounds__(768, 1) combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens) { + int current_channel_tail_idx = 0; + int token_idx = 0; + int dst_slot_idx = 0; + __shared__ signed char warp_retired[24]; + __shared__ int warp_channel_head_idx[192]; + __shared__ int shared_channel_tail_idx[32]; + int last_head = 0; + signed char retired = (signed char)0; + int new_tail = 0; + int min_head = 0; + int idx = 0; + int condvar = 0; + int slot_indices[8]; + int topk_ranks[8]; + float values[8]; + bfloat16_t recv_value[64]; + float weight_sum = 0x0p+0f/*0.000000e+00*/; + float weight = 0x0p+0f/*0.000000e+00*/; + if ((((int)blockIdx.x) % 2) == 0) { + int condval; + if ((0 < (((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7))) { + condval = rank_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 8) - 6)]; + } else { + condval = 0; + } + int rank_offset = condval; + int num_rank_tokens = (rank_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 8) + 2)] - rank_offset); + int channel_offset = channel_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 10) + (((int)blockIdx.x) >> 1))]; + int condval_1; + if (((((int)blockIdx.x) >> 1) == 9)) { + condval_1 = num_rank_tokens; + } else { + condval_1 = channel_prefix_matrix[((((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 10) + (((int)blockIdx.x) >> 1)) + 1)]; + } + int num_channel_tokens = (condval_1 - channel_offset); + current_channel_tail_idx = 0; + token_idx = (rank_offset + channel_offset); + while (1) { + if (!((token_idx < ((rank_offset + channel_offset) + num_channel_tokens)))) { break; } + int num_round_tokens = min(4, (((rank_offset + channel_offset) + num_channel_tokens) - token_idx)); + if (cute::elect_one_sync()) { + tl::wait_ge((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_head_idx[(((((int)blockIdx.x) >> 1) * 8) + 2)]))) - tl::get_remote_base_ptr(tl::get_rank()))), ((current_channel_tail_idx + num_round_tokens) - 256)); + } + __syncwarp(); + for (int v = 0; v < ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) / 3) + ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) % 3) >> 31)); ++v) { + dst_slot_idx = ((((v * 3) + (((int)threadIdx.x) >> 8)) + current_channel_tail_idx) & 255); + if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { + if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { + tl::cp_warp<7168, 4, true>((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_x_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)14680064) + (((int64_t)dst_slot_idx) * (int64_t)7168)) + (int64_t)3670016)]))) - tl::get_remote_base_ptr(tl::get_rank()))), (&(x[(((((int64_t)v) * (int64_t)21504) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)7168)) + (((int64_t)token_idx) * (int64_t)7168))]))); + } + } + if (cute::elect_one_sync()) { + if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { + if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { + tl::ld((&(src_idx[(((((int64_t)v) * (int64_t)3) + (((int64_t)((int)threadIdx.x)) >> (int64_t)8)) + ((int64_t)token_idx))])), idx); + } + } + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_src_idx_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)2048) + ((int64_t)dst_slot_idx)) + (int64_t)512)]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + } + if ((((int)threadIdx.x) & 31) < 8) { + if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { + if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { + tl::ld((&(topk_weights[((((((int64_t)v) * (int64_t)24) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)8)) + (((int64_t)token_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31))])), idx); + } + } + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_topk_weights_buffers[(((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)16384) + (((int64_t)dst_slot_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) + (int64_t)4096)]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + } + } + token_idx = (token_idx + num_round_tokens); + current_channel_tail_idx = (current_channel_tail_idx + num_round_tokens); + tl::__sync_thread_partial((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7), 96); + if (((((int)threadIdx.x) >> 8) == 0) && cute::elect_one_sync()) { + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_tail_idx[(((((int)blockIdx.x) >> 1) * 8) + 2)]))) - tl::get_remote_base_ptr(tl::get_rank()))), current_channel_tail_idx); + } + } + } else { + if (((int)threadIdx.x) < 24) { + warp_retired[((int)threadIdx.x)] = (signed char)0; + } + if ((((int)threadIdx.x) & 31) < 8) { + warp_channel_head_idx[(((((int)threadIdx.x) >> 5) * 8) + (((int)threadIdx.x) & 31))] = 0; + } + if (((int)threadIdx.x) < 32) { + shared_channel_tail_idx[((int)threadIdx.x)] = 0; + } + __syncthreads(); + if (((int)threadIdx.x) < 32) { + last_head = 0; + while (1) { + if (!((((int)threadIdx.x) < 8))) { break; } + retired = (signed char)1; + for (int i = 1; i < 24; ++i) { + retired = ((signed char)(((bool)retired) && ((bool)warp_retired[i]))); + } + if ((bool)retired) { + break; + } + tl::ld((&(channel_tail_idx[(((((int)blockIdx.x) >> 1) * 8) + ((int)threadIdx.x))])), new_tail); + tl::st((&(shared_channel_tail_idx[((int)threadIdx.x)])), new_tail); + min_head = 2147483647; + for (int i_1 = 1; i_1 < 24; ++i_1) { + if (!((bool)warp_retired[i_1])) { + min_head = min(min_head, warp_channel_head_idx[((i_1 * 8) + ((int)threadIdx.x))]); + } + } + if ((min_head < 2147483647) && (last_head < min_head)) { + last_head = min_head; + tl::st((&(channel_head_idx[(((((int)blockIdx.x) >> 1) * 8) + ((int)threadIdx.x))])), min_head); + } + } + } else { + for (int v_1 = 0; v_1 < ((((min(((num_recv_tokens + 9) / 10), max((num_recv_tokens - (((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1))), 0)) + 214748401) - (((int)threadIdx.x) >> 5)) / 23) - 9336886); ++v_1) { + idx = -1; + if ((((int)threadIdx.x) & 31) < 8) { + tl::ld((&(send_head[(((((((int64_t)v_1) * (int64_t)184) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)8)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) - (int64_t)8)])), idx); + } + tl::ld((&(shared_channel_tail_idx[(((int)threadIdx.x) & 31)])), condvar); + while (1) { + if (!(__any_sync(-1, ((condvar <= idx) && (0 <= idx))))) { break; } + tl::ld((&(shared_channel_tail_idx[(((int)threadIdx.x) & 31)])), condvar); + continue; + } + __syncwarp(); + condvar = 0; + for (int i_2 = 0; i_2 < 8; ++i_2) { + int expected_head_i = __shfl_sync(-1, idx, i_2, 32); + if (0 <= expected_head_i) { + slot_indices[condvar] = (expected_head_i & 255); + topk_ranks[condvar] = i_2; + condvar = (condvar + 1); + } + } + for (int v_2 = 0; v_2 < ((927 - (((int)threadIdx.x) & 31)) >> 5); ++v_2) { + for (int i_3 = 0; i_3 < 2; ++i_3) { + *(float4*)(values + (i_3 * 4)) = make_float4(0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/); + } + for (int j = 0; j < condvar; ++j) { + if (0 <= slot_indices[j]) { + if (slot_indices[j] < 256) { + if (0 <= topk_ranks[j]) { + if (topk_ranks[j] < 8) { + auto src = (&(channel_x_buffers[(((((((((int)blockIdx.x) >> 1) * 14680064) + (topk_ranks[j] * 1835008)) + (slot_indices[j] * 7168)) + (v_2 * 256)) + ((((int)threadIdx.x) & 31) * 8)) + 0)])); + auto dst = &(recv_value[((((int64_t)j) * (int64_t)8))]); + *reinterpret_cast(dst) = *reinterpret_cast(src); + } + } + } + } + } + for (int j_1 = 0; j_1 < condvar; ++j_1) { + for (int k_1 = 0; k_1 < 2; ++k_1) { + float4 __1; + float4 v_ = *(float4*)(values + (k_1 * 4)); + float4 __2; + uint2 v__1 = *(uint2*)(recv_value + ((((int64_t)j_1) * (int64_t)8) + (((int64_t)k_1) * (int64_t)4))); + ((float2*)(&__2))[0] = __bfloat1622float2(*reinterpret_cast<__nv_bfloat162*>(&(v__1))); + ((float2*)(&__2))[1] = __bfloat1622float2(*(reinterpret_cast<__nv_bfloat162*>(&(v__1))+1)); + __1.x = (v_.x+__2.x); + __1.y = (v_.y+__2.y); + __1.z = (v_.z+__2.z); + __1.w = (v_.w+__2.w); + *(float4*)(values + (k_1 * 4)) = __1; + } + } + for (int j_2 = 0; j_2 < 2; ++j_2) { + if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { + uint2 __3; + float4 v__2 = *(float4*)(values + (j_2 * 4)); + (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(&(v__2))); + (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(&(v__2))+1)); + *(uint2*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)j_2) * (int64_t)4)) - (int64_t)7168)) = __3; + } + } + } + if ((((int)threadIdx.x) & 31) < 8) { + weight_sum = 0x0p+0f/*0.000000e+00*/; + for (int i_4 = 0; i_4 < condvar; ++i_4) { + if (0 <= slot_indices[i_4]) { + if (slot_indices[i_4] < 256) { + if (0 <= topk_ranks[i_4]) { + if (topk_ranks[i_4] < 8) { + tl::ld((&(channel_topk_weights_buffers[(((((((int)blockIdx.x) >> 1) * 16384) + (topk_ranks[i_4] * 2048)) + (slot_indices[i_4] * 8)) + (((int)threadIdx.x) & 31))])), weight); + } + } + } + } + weight_sum = (weight_sum + weight); + } + recv_topk_weights[(((((((int64_t)v_1) * (int64_t)184) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)8)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) - (int64_t)8)] = weight_sum; + int condval_2; + if ((idx < 0)) { + condval_2 = ((0 - idx) - 1); + } else { + condval_2 = (idx + 1); + } + warp_channel_head_idx[(((((int)threadIdx.x) >> 5) * 8) + (((int)threadIdx.x) & 31))] = condval_2; + } + } + __syncwarp(); + if (cute::elect_one_sync()) { + warp_retired[(((int)threadIdx.x) >> 5)] = (signed char)1; + } + } + } +} +''' + + def intranode_combine( rank: int, allocator, @@ -359,7 +588,6 @@ def intranode_combine( recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') kernel = combine_kernel( - rank, num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, @@ -370,6 +598,7 @@ def intranode_combine( ) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) kernel( + rank, x, topk_weights, recv_src_idx, diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 57d95541c0..0f58127231 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -10,7 +10,7 @@ from typing import Optional, Tuple from utils import Config, ep_ext # noqa: F403 -tilelang.disable_cache() +# tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log @@ -19,7 +19,6 @@ # 2. Zero 4 symm buffers before a system-level barrier @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def notify_dispatch_kernel( - rank: int, num_ranks: int, num_experts: int, num_channels: int, @@ -33,6 +32,7 @@ def notify_dispatch_kernel( @T.prim_func def notify_dispatch_main( + rank: T.int32, num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), @@ -148,7 +148,6 @@ def notify_dispatch( comm_stream=None, ): kernel = notify_dispatch_kernel( - rank, num_ranks, num_experts, num_channels, @@ -164,6 +163,7 @@ def notify_dispatch( moe_recv_expert_counter.fill_(-1) kernel( + rank, num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank, @@ -235,7 +235,6 @@ def cached_notify_dispatch( pass_configs={"tl.disable_tma_lower": True, # enable TMA later "tl.disable_warp_specialized": True}) def dispatch_kernel( - rank, num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens @@ -261,6 +260,7 @@ def dispatch_kernel( @T.prim_func def dispatch_main( + rank: T.int32, # output recv_x: T.Tensor((num_recv_tokens, hidden), dtype), recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), @@ -496,7 +496,7 @@ def dispatch_main( pass_configs={"tl.disable_tma_lower": True, # enable TMA later "tl.disable_warp_specialized": True}) def cached_dispatch_kernel( - rank, num_ranks, + num_ranks, num_tokens, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens @@ -521,6 +521,7 @@ def cached_dispatch_kernel( @T.prim_func def cached_dispatch_main( + rank: T.int32, # output recv_x: T.Tensor((num_recv_tokens, hidden), dtype), recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), @@ -732,7 +733,6 @@ def intranode_dispatch( topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, - kernel = None, comm_stream = None, # todo: support num_worst_tokens # todo: support async functionality @@ -791,15 +791,15 @@ def intranode_dispatch( # run dispatch if handle is None: - # kernel = dispatch_kernel(rank, num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') - # kernel.initialize(allocator=allocator) - kernel(recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel = dispatch_kernel(num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel.initialize(allocator=allocator) + kernel(rank, recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: - kernel = cached_dispatch_kernel(rank, num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel = cached_dispatch_kernel(num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel(rank, recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead return recv_x diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index ac80388ec9..9053f167a3 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -105,21 +105,21 @@ def test_intranode( if not cached_dispatch: group.barrier() deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=5, rep=5) + warmup=50, rep=50) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=5, rep=5) + warmup=50, rep=50) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() else: group.barrier() deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, None, None, expert_alignment), - warmup=5, rep=5) + warmup=50, rep=50) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment), - warmup=5, rep=5) + warmup=50, rep=50) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() From 08281a689a8c79db72641a6c8fca584422020f2d Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Thu, 18 Dec 2025 15:09:17 +0800 Subject: [PATCH 31/41] use cuda postproc for vectorization in combine --- .../deepseek_deepep/intranode/combine.py | 86 ++++++++++++------- 1 file changed, 53 insertions(+), 33 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 3f4dfd55e3..22fe11ff2d 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -88,12 +88,15 @@ def cached_notify_combine( kernel = cached_notify_combine_kernel(num_ranks, num_sms) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal, stream=comm_stream.cuda_stream) + kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal, stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # use TMA later - "tl.disable_warp_specialized": True}) + "tl.disable_warp_specialized": True}, + # debug_root_path='/home/wt/debug/combine' +) def combine_kernel( num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens @@ -331,7 +334,7 @@ def combine_main( return combine_main -# @tilelang.engine.register_cuda_postproc +@tilelang.engine.register_cuda_postproc def _(code, _): if not 'void combine_main_kernel' in code: return code @@ -350,8 +353,8 @@ def _(code, _): #include #endif -extern "C" __global__ void combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens); -extern "C" __global__ void __launch_bounds__(768, 1) combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens) { +extern "C" __global__ void combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens, int rank); +extern "C" __global__ void __launch_bounds__(768, 1) combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens, int rank) { int current_channel_tail_idx = 0; int token_idx = 0; int dst_slot_idx = 0; @@ -373,12 +376,12 @@ def _(code, _): if ((((int)blockIdx.x) % 2) == 0) { int condval; if ((0 < (((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7))) { - condval = rank_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 8) - 6)]; + condval = rank_prefix_matrix[((((((((int64_t)((int)threadIdx.x)) >> (int64_t)5) + (((int64_t)((int)blockIdx.x)) >> (int64_t)1)) & (int64_t)7) * (int64_t)8) + ((int64_t)rank)) - (int64_t)8)]; } else { condval = 0; } int rank_offset = condval; - int num_rank_tokens = (rank_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 8) + 2)] - rank_offset); + int num_rank_tokens = (rank_prefix_matrix[(((((((int64_t)((int)threadIdx.x)) >> (int64_t)5) + (((int64_t)((int)blockIdx.x)) >> (int64_t)1)) & (int64_t)7) * (int64_t)8) + ((int64_t)rank))] - rank_offset); int channel_offset = channel_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 10) + (((int)blockIdx.x) >> 1))]; int condval_1; if (((((int)blockIdx.x) >> 1) == 9)) { @@ -393,14 +396,18 @@ def _(code, _): if (!((token_idx < ((rank_offset + channel_offset) + num_channel_tokens)))) { break; } int num_round_tokens = min(4, (((rank_offset + channel_offset) + num_channel_tokens) - token_idx)); if (cute::elect_one_sync()) { - tl::wait_ge((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_head_idx[(((((int)blockIdx.x) >> 1) * 8) + 2)]))) - tl::get_remote_base_ptr(tl::get_rank()))), ((current_channel_tail_idx + num_round_tokens) - 256)); + tl::wait_ge((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_head_idx[(((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)8) + ((int64_t)rank))]))) - tl::get_remote_base_ptr(tl::get_rank()))), ((current_channel_tail_idx + num_round_tokens) - 256)); } __syncwarp(); for (int v = 0; v < ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) / 3) + ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) % 3) >> 31)); ++v) { dst_slot_idx = ((((v * 3) + (((int)threadIdx.x) >> 8)) + current_channel_tail_idx) & 255); if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { - tl::cp_warp<7168, 4, true>((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_x_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)14680064) + (((int64_t)dst_slot_idx) * (int64_t)7168)) + (int64_t)3670016)]))) - tl::get_remote_base_ptr(tl::get_rank()))), (&(x[(((((int64_t)v) * (int64_t)21504) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)7168)) + (((int64_t)token_idx) * (int64_t)7168))]))); + if (0 <= rank) { + if (rank < 8) { + tl::cp_warp<7168, 4, true>((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_x_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)14680064) + (((int64_t)rank) * (int64_t)1835008)) + (((int64_t)dst_slot_idx) * (int64_t)7168))]))) - tl::get_remote_base_ptr(tl::get_rank()))), (&(x[(((((int64_t)v) * (int64_t)21504) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)7168)) + (((int64_t)token_idx) * (int64_t)7168))]))); + } + } } } if (cute::elect_one_sync()) { @@ -409,7 +416,11 @@ def _(code, _): tl::ld((&(src_idx[(((((int64_t)v) * (int64_t)3) + (((int64_t)((int)threadIdx.x)) >> (int64_t)8)) + ((int64_t)token_idx))])), idx); } } - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_src_idx_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)2048) + ((int64_t)dst_slot_idx)) + (int64_t)512)]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + if (0 <= rank) { + if (rank < 8) { + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_src_idx_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)2048) + (((int64_t)rank) * (int64_t)256)) + ((int64_t)dst_slot_idx))]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + } + } } if ((((int)threadIdx.x) & 31) < 8) { if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { @@ -417,14 +428,18 @@ def _(code, _): tl::ld((&(topk_weights[((((((int64_t)v) * (int64_t)24) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)8)) + (((int64_t)token_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31))])), idx); } } - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_topk_weights_buffers[(((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)16384) + (((int64_t)dst_slot_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) + (int64_t)4096)]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + if (0 <= rank) { + if (rank < 8) { + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_topk_weights_buffers[(((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)16384) + (((int64_t)rank) * (int64_t)2048)) + (((int64_t)dst_slot_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31))]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); + } + } } } token_idx = (token_idx + num_round_tokens); current_channel_tail_idx = (current_channel_tail_idx + num_round_tokens); tl::__sync_thread_partial((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7), 96); if (((((int)threadIdx.x) >> 8) == 0) && cute::elect_one_sync()) { - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_tail_idx[(((((int)blockIdx.x) >> 1) * 8) + 2)]))) - tl::get_remote_base_ptr(tl::get_rank()))), current_channel_tail_idx); + tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_tail_idx[(((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)8) + ((int64_t)rank))]))) - tl::get_remote_base_ptr(tl::get_rank()))), current_channel_tail_idx); } } } else { @@ -488,19 +503,13 @@ def _(code, _): for (int i_3 = 0; i_3 < 2; ++i_3) { *(float4*)(values + (i_3 * 4)) = make_float4(0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/); } + /// change 1 (major) for (int j = 0; j < condvar; ++j) { - if (0 <= slot_indices[j]) { - if (slot_indices[j] < 256) { - if (0 <= topk_ranks[j]) { - if (topk_ranks[j] < 8) { - auto src = (&(channel_x_buffers[(((((((((int)blockIdx.x) >> 1) * 14680064) + (topk_ranks[j] * 1835008)) + (slot_indices[j] * 7168)) + (v_2 * 256)) + ((((int)threadIdx.x) & 31) * 8)) + 0)])); - auto dst = &(recv_value[((((int64_t)j) * (int64_t)8))]); - *reinterpret_cast(dst) = *reinterpret_cast(src); - } - } - } - } + auto src = (&(channel_x_buffers[(((((((((int)blockIdx.x) >> 1) * 14680064) + (topk_ranks[j] * 1835008)) + (slot_indices[j] * 7168)) + (v_2 * 256)) + ((((int)threadIdx.x) & 31) * 8)))])); + auto dst = &(recv_value[((((int64_t)j) * (int64_t)8))]); + *reinterpret_cast(dst) = __ldg(reinterpret_cast(src)); } + /// for (int j_1 = 0; j_1 < condvar; ++j_1) { for (int k_1 = 0; k_1 < 2; ++k_1) { float4 __1; @@ -516,15 +525,25 @@ def _(code, _): *(float4*)(values + (k_1 * 4)) = __1; } } - for (int j_2 = 0; j_2 < 2; ++j_2) { - if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { - uint2 __3; - float4 v__2 = *(float4*)(values + (j_2 * 4)); - (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(&(v__2))); - (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(&(v__2))+1)); - *(uint2*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)j_2) * (int64_t)4)) - (int64_t)7168)) = __3; - } + /// change 2 (minor) + // for (int j_2 = 0; j_2 < 2; ++j_2) { + // if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { + // uint2 __3; + // float4 v__2 = *(float4*)(values + (j_2 * 4)); + // (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(&(v__2))); + // (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(&(v__2))+1)); + // *(uint2*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)j_2) * (int64_t)4)) - (int64_t)7168)) = __3; + // } + // } + if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { + int4 __3; + (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(values)); + (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(values)+1)); + (reinterpret_cast<__nv_bfloat162*>(&__3))[2] = __float22bfloat162_rn(*((float2*)(values)+2)); + (reinterpret_cast<__nv_bfloat162*>(&__3))[3] = __float22bfloat162_rn(*((float2*)(values)+3)); + *(int4*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)0) * (int64_t)4)) - (int64_t)7168)) = __3; } + /// } if ((((int)threadIdx.x) & 31) < 8) { weight_sum = 0x0p+0f/*0.000000e+00*/; @@ -612,8 +631,9 @@ def intranode_combine( channel_x_buffers, channel_src_idx_buffers, channel_topk_weights_buffers, - stream=comm_stream.cuda_stream - ) + stream=comm_stream.cuda_stream, + skip_tensor_validation=True + ) # reduce runtime overhead compute_stream = torch.cuda.current_stream() compute_stream.wait_stream(comm_stream) return recv_x, recv_topk_weights From 2c1bd1fbfd2ffe71b5779d97bd1d147863590eb9 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Thu, 18 Dec 2025 21:43:37 +0800 Subject: [PATCH 32/41] support int4 ld/st ptx in cuda template --- src/tl_templates/cuda/ldst.h | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h index 73b7e1ba0e..34f5ae24f0 100644 --- a/src/tl_templates/cuda/ldst.h +++ b/src/tl_templates/cuda/ldst.h @@ -78,6 +78,10 @@ struct LdImpl { asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b64 [%0], %1;" \ :: "l"(ptr), "l"(value) : "memory"); \ } \ + } else if constexpr (sizeof(T) == 16) { \ + static_assert(std::is_same_v, "tl::st: T must be int4"); \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ + :: "l"(ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); \ } \ } \ }; @@ -114,6 +118,11 @@ struct LdImpl { asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ : "=l"(value) : "l"(ptr) : "memory"); \ } \ + } else if constexpr (sizeof(T) == 16) { \ + static_assert(std::is_same_v, "tl::ld: T must be int4"); \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ + : "=r"(value.x), "=r"(value.y), "=r"(value.z), "=r"(value.w) \ + : "l"(ptr) : "memory"); \ } \ } \ }; From d23a65e18b73aca6dda885c60b8d7cb39aa00c2c Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 17:13:58 +0800 Subject: [PATCH 33/41] [Feat] Support auto vectorization for ld/st to optimize combine to surpass deepep --- .../deepseek_deepep/intranode/combine.py | 259 +----------------- .../deepseek_deepep/intranode/dispatch.py | 5 - src/tl_templates/cuda/ldst.h | 10 +- .../common/loop_vectorization_utils.h | 153 +++++++++++ src/transform/loop_vectorize.cc | 37 ++- src/transform/vectorize_loop.cc | 141 ++++++++++ 6 files changed, 339 insertions(+), 266 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 22fe11ff2d..d796ee5f0f 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -13,10 +13,7 @@ @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) -def cached_notify_combine_kernel( - num_ranks, - num_sms, -): +def cached_notify_combine_kernel(num_ranks, num_sms): num_channels = num_sms // 2 threads = max(128, 32 * num_ranks) @@ -94,8 +91,7 @@ def cached_notify_combine( @tilelang.jit( pass_configs={"tl.disable_tma_lower": True, # use TMA later - "tl.disable_warp_specialized": True}, - # debug_root_path='/home/wt/debug/combine' + "tl.disable_warp_specialized": True} ) def combine_kernel( num_ranks, @@ -307,7 +303,7 @@ def combine_main( for k in T.vectorized(8): values[k] += recv_value[j, k] for j in T.vectorized(8): - recv_x[token_idx, i*8+j] = values[j] + recv_x[token_idx, i*8+j] = values[j] # todo: further vectorize this # Reduce topk_weights if lane_id < num_topk: @@ -334,251 +330,6 @@ def combine_main( return combine_main -@tilelang.engine.register_cuda_postproc -def _(code, _): - if not 'void combine_main_kernel' in code: - return code - return r''' -#include -#include -#include -#include -#include -#include -#include -#include -#include -uint64_t __constant__ meta_data[1024]; -#ifdef ENABLE_BF16 -#include -#endif - -extern "C" __global__ void combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens, int rank); -extern "C" __global__ void __launch_bounds__(768, 1) combine_main_kernel(int* __restrict__ channel_head_idx, int* __restrict__ channel_prefix_matrix, int* __restrict__ channel_src_idx_buffers, int* __restrict__ channel_tail_idx, float* __restrict__ channel_topk_weights_buffers, bfloat16_t* __restrict__ channel_x_buffers, int* __restrict__ rank_prefix_matrix, float* __restrict__ recv_topk_weights, bfloat16_t* __restrict__ recv_x, int* __restrict__ send_head, int* __restrict__ src_idx, float* __restrict__ topk_weights, bfloat16_t* __restrict__ x, int num_recv_tokens, int num_tokens, int rank) { - int current_channel_tail_idx = 0; - int token_idx = 0; - int dst_slot_idx = 0; - __shared__ signed char warp_retired[24]; - __shared__ int warp_channel_head_idx[192]; - __shared__ int shared_channel_tail_idx[32]; - int last_head = 0; - signed char retired = (signed char)0; - int new_tail = 0; - int min_head = 0; - int idx = 0; - int condvar = 0; - int slot_indices[8]; - int topk_ranks[8]; - float values[8]; - bfloat16_t recv_value[64]; - float weight_sum = 0x0p+0f/*0.000000e+00*/; - float weight = 0x0p+0f/*0.000000e+00*/; - if ((((int)blockIdx.x) % 2) == 0) { - int condval; - if ((0 < (((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7))) { - condval = rank_prefix_matrix[((((((((int64_t)((int)threadIdx.x)) >> (int64_t)5) + (((int64_t)((int)blockIdx.x)) >> (int64_t)1)) & (int64_t)7) * (int64_t)8) + ((int64_t)rank)) - (int64_t)8)]; - } else { - condval = 0; - } - int rank_offset = condval; - int num_rank_tokens = (rank_prefix_matrix[(((((((int64_t)((int)threadIdx.x)) >> (int64_t)5) + (((int64_t)((int)blockIdx.x)) >> (int64_t)1)) & (int64_t)7) * (int64_t)8) + ((int64_t)rank))] - rank_offset); - int channel_offset = channel_prefix_matrix[(((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 10) + (((int)blockIdx.x) >> 1))]; - int condval_1; - if (((((int)blockIdx.x) >> 1) == 9)) { - condval_1 = num_rank_tokens; - } else { - condval_1 = channel_prefix_matrix[((((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7) * 10) + (((int)blockIdx.x) >> 1)) + 1)]; - } - int num_channel_tokens = (condval_1 - channel_offset); - current_channel_tail_idx = 0; - token_idx = (rank_offset + channel_offset); - while (1) { - if (!((token_idx < ((rank_offset + channel_offset) + num_channel_tokens)))) { break; } - int num_round_tokens = min(4, (((rank_offset + channel_offset) + num_channel_tokens) - token_idx)); - if (cute::elect_one_sync()) { - tl::wait_ge((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_head_idx[(((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)8) + ((int64_t)rank))]))) - tl::get_remote_base_ptr(tl::get_rank()))), ((current_channel_tail_idx + num_round_tokens) - 256)); - } - __syncwarp(); - for (int v = 0; v < ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) / 3) + ((((num_round_tokens + 2) - (((int)threadIdx.x) >> 8)) % 3) >> 31)); ++v) { - dst_slot_idx = ((((v * 3) + (((int)threadIdx.x) >> 8)) + current_channel_tail_idx) & 255); - if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { - if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { - if (0 <= rank) { - if (rank < 8) { - tl::cp_warp<7168, 4, true>((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_x_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)14680064) + (((int64_t)rank) * (int64_t)1835008)) + (((int64_t)dst_slot_idx) * (int64_t)7168))]))) - tl::get_remote_base_ptr(tl::get_rank()))), (&(x[(((((int64_t)v) * (int64_t)21504) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)7168)) + (((int64_t)token_idx) * (int64_t)7168))]))); - } - } - } - } - if (cute::elect_one_sync()) { - if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { - if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { - tl::ld((&(src_idx[(((((int64_t)v) * (int64_t)3) + (((int64_t)((int)threadIdx.x)) >> (int64_t)8)) + ((int64_t)token_idx))])), idx); - } - } - if (0 <= rank) { - if (rank < 8) { - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_src_idx_buffers[((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)2048) + (((int64_t)rank) * (int64_t)256)) + ((int64_t)dst_slot_idx))]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); - } - } - } - if ((((int)threadIdx.x) & 31) < 8) { - if (0 <= (((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx)) { - if ((((v * 3) + (((int)threadIdx.x) >> 8)) + token_idx) < num_tokens) { - tl::ld((&(topk_weights[((((((int64_t)v) * (int64_t)24) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)8) * (int64_t)8)) + (((int64_t)token_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31))])), idx); - } - } - if (0 <= rank) { - if (rank < 8) { - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_topk_weights_buffers[(((((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)16384) + (((int64_t)rank) * (int64_t)2048)) + (((int64_t)dst_slot_idx) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31))]))) - tl::get_remote_base_ptr(tl::get_rank()))), idx); - } - } - } - } - token_idx = (token_idx + num_round_tokens); - current_channel_tail_idx = (current_channel_tail_idx + num_round_tokens); - tl::__sync_thread_partial((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7), 96); - if (((((int)threadIdx.x) >> 8) == 0) && cute::elect_one_sync()) { - tl::st((tl::get_remote_base_ptr((((((int)threadIdx.x) >> 5) + (((int)blockIdx.x) >> 1)) & 7)) + (tl::get_uintptr_t((&(channel_tail_idx[(((((int64_t)((int)blockIdx.x)) >> (int64_t)1) * (int64_t)8) + ((int64_t)rank))]))) - tl::get_remote_base_ptr(tl::get_rank()))), current_channel_tail_idx); - } - } - } else { - if (((int)threadIdx.x) < 24) { - warp_retired[((int)threadIdx.x)] = (signed char)0; - } - if ((((int)threadIdx.x) & 31) < 8) { - warp_channel_head_idx[(((((int)threadIdx.x) >> 5) * 8) + (((int)threadIdx.x) & 31))] = 0; - } - if (((int)threadIdx.x) < 32) { - shared_channel_tail_idx[((int)threadIdx.x)] = 0; - } - __syncthreads(); - if (((int)threadIdx.x) < 32) { - last_head = 0; - while (1) { - if (!((((int)threadIdx.x) < 8))) { break; } - retired = (signed char)1; - for (int i = 1; i < 24; ++i) { - retired = ((signed char)(((bool)retired) && ((bool)warp_retired[i]))); - } - if ((bool)retired) { - break; - } - tl::ld((&(channel_tail_idx[(((((int)blockIdx.x) >> 1) * 8) + ((int)threadIdx.x))])), new_tail); - tl::st((&(shared_channel_tail_idx[((int)threadIdx.x)])), new_tail); - min_head = 2147483647; - for (int i_1 = 1; i_1 < 24; ++i_1) { - if (!((bool)warp_retired[i_1])) { - min_head = min(min_head, warp_channel_head_idx[((i_1 * 8) + ((int)threadIdx.x))]); - } - } - if ((min_head < 2147483647) && (last_head < min_head)) { - last_head = min_head; - tl::st((&(channel_head_idx[(((((int)blockIdx.x) >> 1) * 8) + ((int)threadIdx.x))])), min_head); - } - } - } else { - for (int v_1 = 0; v_1 < ((((min(((num_recv_tokens + 9) / 10), max((num_recv_tokens - (((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1))), 0)) + 214748401) - (((int)threadIdx.x) >> 5)) / 23) - 9336886); ++v_1) { - idx = -1; - if ((((int)threadIdx.x) & 31) < 8) { - tl::ld((&(send_head[(((((((int64_t)v_1) * (int64_t)184) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)8)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) - (int64_t)8)])), idx); - } - tl::ld((&(shared_channel_tail_idx[(((int)threadIdx.x) & 31)])), condvar); - while (1) { - if (!(__any_sync(-1, ((condvar <= idx) && (0 <= idx))))) { break; } - tl::ld((&(shared_channel_tail_idx[(((int)threadIdx.x) & 31)])), condvar); - continue; - } - __syncwarp(); - condvar = 0; - for (int i_2 = 0; i_2 < 8; ++i_2) { - int expected_head_i = __shfl_sync(-1, idx, i_2, 32); - if (0 <= expected_head_i) { - slot_indices[condvar] = (expected_head_i & 255); - topk_ranks[condvar] = i_2; - condvar = (condvar + 1); - } - } - for (int v_2 = 0; v_2 < ((927 - (((int)threadIdx.x) & 31)) >> 5); ++v_2) { - for (int i_3 = 0; i_3 < 2; ++i_3) { - *(float4*)(values + (i_3 * 4)) = make_float4(0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/, 0x0p+0f/*0.000000e+00*/); - } - /// change 1 (major) - for (int j = 0; j < condvar; ++j) { - auto src = (&(channel_x_buffers[(((((((((int)blockIdx.x) >> 1) * 14680064) + (topk_ranks[j] * 1835008)) + (slot_indices[j] * 7168)) + (v_2 * 256)) + ((((int)threadIdx.x) & 31) * 8)))])); - auto dst = &(recv_value[((((int64_t)j) * (int64_t)8))]); - *reinterpret_cast(dst) = __ldg(reinterpret_cast(src)); - } - /// - for (int j_1 = 0; j_1 < condvar; ++j_1) { - for (int k_1 = 0; k_1 < 2; ++k_1) { - float4 __1; - float4 v_ = *(float4*)(values + (k_1 * 4)); - float4 __2; - uint2 v__1 = *(uint2*)(recv_value + ((((int64_t)j_1) * (int64_t)8) + (((int64_t)k_1) * (int64_t)4))); - ((float2*)(&__2))[0] = __bfloat1622float2(*reinterpret_cast<__nv_bfloat162*>(&(v__1))); - ((float2*)(&__2))[1] = __bfloat1622float2(*(reinterpret_cast<__nv_bfloat162*>(&(v__1))+1)); - __1.x = (v_.x+__2.x); - __1.y = (v_.y+__2.y); - __1.z = (v_.z+__2.z); - __1.w = (v_.w+__2.w); - *(float4*)(values + (k_1 * 4)) = __1; - } - } - /// change 2 (minor) - // for (int j_2 = 0; j_2 < 2; ++j_2) { - // if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { - // uint2 __3; - // float4 v__2 = *(float4*)(values + (j_2 * 4)); - // (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(&(v__2))); - // (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(&(v__2))+1)); - // *(uint2*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)j_2) * (int64_t)4)) - (int64_t)7168)) = __3; - // } - // } - if ((((v_1 * 23) + min((((num_recv_tokens + 9) / 10) * (((int)blockIdx.x) >> 1)), num_recv_tokens)) + (((int)threadIdx.x) >> 5)) <= num_recv_tokens) { - int4 __3; - (reinterpret_cast<__nv_bfloat162*>(&__3))[0] = __float22bfloat162_rn(*(float2*)(values)); - (reinterpret_cast<__nv_bfloat162*>(&__3))[1] = __float22bfloat162_rn(*((float2*)(values)+1)); - (reinterpret_cast<__nv_bfloat162*>(&__3))[2] = __float22bfloat162_rn(*((float2*)(values)+2)); - (reinterpret_cast<__nv_bfloat162*>(&__3))[3] = __float22bfloat162_rn(*((float2*)(values)+3)); - *(int4*)(recv_x + (((((((((int64_t)v_1) * (int64_t)164864) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)7168)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)7168)) + (((int64_t)v_2) * (int64_t)256)) + ((((int64_t)((int)threadIdx.x)) & (int64_t)31) * (int64_t)8)) + (((int64_t)0) * (int64_t)4)) - (int64_t)7168)) = __3; - } - /// - } - if ((((int)threadIdx.x) & 31) < 8) { - weight_sum = 0x0p+0f/*0.000000e+00*/; - for (int i_4 = 0; i_4 < condvar; ++i_4) { - if (0 <= slot_indices[i_4]) { - if (slot_indices[i_4] < 256) { - if (0 <= topk_ranks[i_4]) { - if (topk_ranks[i_4] < 8) { - tl::ld((&(channel_topk_weights_buffers[(((((((int)blockIdx.x) >> 1) * 16384) + (topk_ranks[i_4] * 2048)) + (slot_indices[i_4] * 8)) + (((int)threadIdx.x) & 31))])), weight); - } - } - } - } - weight_sum = (weight_sum + weight); - } - recv_topk_weights[(((((((int64_t)v_1) * (int64_t)184) + ((((int64_t)((int)threadIdx.x)) >> (int64_t)5) * (int64_t)8)) + (min((((((int64_t)num_recv_tokens) + (int64_t)9) / (int64_t)10) * (((int64_t)((int)blockIdx.x)) >> (int64_t)1)), ((int64_t)num_recv_tokens)) * (int64_t)8)) + (((int64_t)((int)threadIdx.x)) & (int64_t)31)) - (int64_t)8)] = weight_sum; - int condval_2; - if ((idx < 0)) { - condval_2 = ((0 - idx) - 1); - } else { - condval_2 = (idx + 1); - } - warp_channel_head_idx[(((((int)threadIdx.x) >> 5) * 8) + (((int)threadIdx.x) & 31))] = condval_2; - } - } - __syncwarp(); - if (cute::elect_one_sync()) { - warp_retired[(((int)threadIdx.x) >> 5)] = (signed char)1; - } - } - } -} -''' - - def intranode_combine( rank: int, allocator, @@ -594,9 +345,9 @@ def intranode_combine( barrier_signal, _, _, _, _, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, _, channel_topk_weights_buffers = symm_buffers # acquire_shapes - num_tokens, hidden = x.shape + _, hidden = x.shape _, num_topk = topk_weights.shape - num_ranks, num_channels = channel_prefix_matrix.shape + num_ranks, _ = channel_prefix_matrix.shape num_recv_tokens = send_head.shape[0] # notify combine diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 0f58127231..1a192e8f84 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -123,7 +123,6 @@ def notify_dispatch( rank: int, num_ranks: int, num_experts: int, - num_tokens: int, num_channels: int, expert_alignment: int, # dispatch layout @@ -512,7 +511,6 @@ def cached_dispatch_kernel( num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) num_channels = num_sms // 2 # 10 (2 SMs for each channel) - num_local_experts = num_experts // num_ranks num_warps = threads // 32 # 24 num_warps_per_rank = num_warps // num_ranks # 3 @@ -546,7 +544,6 @@ def cached_dispatch_main( ): with T.Kernel(num_sms, threads=threads) as bx: tx = T.get_thread_binding() - lane_id = tx % 32 responsible_rank = tx // num_threads_per_rank responsible_channel = bx // 2 @@ -746,7 +743,6 @@ def intranode_dispatch( num_tokens, hidden = x.shape num_experts = num_tokens_per_expert.shape[0] if handle is None else 0 num_ranks = num_tokens_per_rank.shape[0] - num_local_experts = num_experts // num_ranks num_topk = topk_idx.shape[1] if handle is None else 0 barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, \ @@ -757,7 +753,6 @@ def intranode_dispatch( rank, num_ranks, num_experts, - num_tokens, config.num_channels, expert_alignment, num_tokens_per_rank, diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h index 34f5ae24f0..4dbe319d3f 100644 --- a/src/tl_templates/cuda/ldst.h +++ b/src/tl_templates/cuda/ldst.h @@ -79,7 +79,6 @@ struct LdImpl { :: "l"(ptr), "l"(value) : "memory"); \ } \ } else if constexpr (sizeof(T) == 16) { \ - static_assert(std::is_same_v, "tl::st: T must be int4"); \ asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ :: "l"(ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); \ } \ @@ -119,7 +118,6 @@ struct LdImpl { : "=l"(value) : "l"(ptr) : "memory"); \ } \ } else if constexpr (sizeof(T) == 16) { \ - static_assert(std::is_same_v, "tl::ld: T must be int4"); \ asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ : "=r"(value.x), "=r"(value.y), "=r"(value.z), "=r"(value.w) \ : "l"(ptr) : "memory"); \ @@ -196,8 +194,8 @@ namespace tl { // Public interface template TL_DEVICE void st(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8, - "tl::st: T must be 2, 4, or 8 bytes"); + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || sizeof(T) == 16, + "tl::st: T must be 2, 4, 8, or 16 bytes"); static_assert(std::is_pointer_v

|| std::is_same_v, "tl::st: P must be a pointer or uint64_t"); static_assert(semantic == Semantic::WEAK @@ -212,8 +210,8 @@ TL_DEVICE void st(P ptr, T value) { template TL_DEVICE void ld(const P ptr, T &value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8, - "tl::ld: T must be 2, 4, or 8 bytes"); + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || sizeof(T) == 16, + "tl::ld: T must be 2, 4, 8, or 16 bytes"); static_assert(std::is_pointer_v

|| std::is_same_v, "tl::ld: P must be a pointer or uint64_t"); static_assert(semantic == Semantic::WEAK diff --git a/src/transform/common/loop_vectorization_utils.h b/src/transform/common/loop_vectorization_utils.h index 3f033c9666..84f8ed7e28 100644 --- a/src/transform/common/loop_vectorization_utils.h +++ b/src/transform/common/loop_vectorization_utils.h @@ -29,6 +29,7 @@ #include #include +#include #include #include "../../op/parallel.h" @@ -377,6 +378,146 @@ class Vectorizer : public StmtMutator, return std::move(var); } } + // tl::ld or tl::st expr vectorization + // Transform: for k in vectorized(N): tl::ld(&buf[base+k], val[k]) + // Into: tl::ld(&buf[base], reinterpret(val[base])) with vectorized load + // + // This function handles the vectorization of tl::ld and tl::st calls. + // The key insight is that for 8 consecutive bf16 loads (128 bits total), + // we can use a single int4 load which is more efficient by reinterpreting + // the value as int4. + PrimExpr MutateTlLdStExpr_(const CallNode *op, bool is_load) { + // Structure: call_extern("tl::ld<...>", address_of(BufferLoad), value, ...) + // or: call_extern("tl::st<...>", address_of(BufferLoad), value, ...) + ICHECK(op->args.size() >= 3) << "tl::ld/st expects at least 3 arguments"; + + PrimExpr func_name = op->args[0]; + PrimExpr addr_arg = op->args[1]; + PrimExpr value_arg = op->args[2]; + + // Visit the address argument to vectorize indices + PrimExpr new_addr = this->VisitExpr(addr_arg); + PrimExpr new_value = this->VisitExpr(value_arg); + + // Helper to extract base from Ramp and get lanes + auto extract_ramp_info = [](const Array& indices) + -> std::pair, int> { + Array base_indices; + int ramp_lanes = 1; + for (const auto& idx : indices) { + auto ramp = idx.as(); + if (ramp && is_one(ramp->stride)) { + auto lanes_imm = ramp->lanes.as(); + if (lanes_imm) { + ramp_lanes = lanes_imm->value; + } + base_indices.push_back(ramp->base); + } else { + base_indices.push_back(idx); + } + } + return {base_indices, ramp_lanes}; + }; + + // Check source address for Ramp pattern + int src_ramp_lanes = 1; + auto addr_call = new_addr.as(); + if (addr_call && addr_call->op.same_as(builtin::address_of())) { + auto buffer_load = addr_call->args[0].as(); + if (buffer_load) { + auto [base_indices, lanes] = extract_ramp_info(buffer_load->indices); + if (lanes > 1) { + src_ramp_lanes = lanes; + // Create new address with base indices only + BufferLoad new_buffer_load(buffer_load->buffer, base_indices); + new_addr = Call(DataType::Handle(), builtin::address_of(), {new_buffer_load}); + } + } + } + + // Check destination value for Ramp pattern (for local buffer stores) + int dst_ramp_lanes = 1; + auto value_load = new_value.as(); + if (value_load) { + auto [base_indices, lanes] = extract_ramp_info(value_load->indices); + if (lanes > 1) { + dst_ramp_lanes = lanes; + // Create new value with base indices only + new_value = BufferLoad(value_load->buffer, base_indices); + } + } + + // Determine vectorization lanes + int vector_lanes = std::max(src_ramp_lanes, dst_ramp_lanes); + if (vector_lanes > 1) { + // Determine the vector type based on total bytes + // 8 x 16-bit = 128 bits = int4, 4 x 32-bit = 128 bits = int4 + // 4 x 16-bit = 64 bits = int2, 2 x 32-bit = 64 bits = int2 + DataType vec_dtype; + int elem_bits = 16; // Default assumption for bf16/f16 + + // Try to get element dtype from source buffer + auto addr_call_check = new_addr.as(); + if (addr_call_check && addr_call_check->op.same_as(builtin::address_of())) { + auto buffer_load = addr_call_check->args[0].as(); + if (buffer_load) { + elem_bits = buffer_load->buffer->dtype.bits(); + } + } + + int total_bits = vector_lanes * elem_bits; + if (total_bits == 128) { + vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) + } else if (total_bits == 64) { + vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) + } else if (total_bits == 32) { + vec_dtype = DataType::Int(32); + } else { + // Can't vectorize to a standard type, fall back to scalarize + need_scalarize_ = true; + return GetRef(op); + } + + // Reinterpret the value to vector type (e.g., int4 for 8xbf16) + // This generates: reinterpret_cast(dst[base]) + PrimExpr vec_value = Call(vec_dtype, builtin::reinterpret(), {new_value}); + + // Build new args with base addresses and reinterpreted value + Array new_args; + new_args.push_back(func_name); + new_args.push_back(new_addr); + new_args.push_back(vec_value); + // Copy remaining args (sem, scope, etc.) + for (size_t i = 3; i < op->args.size(); ++i) { + new_args.push_back(this->VisitExpr(op->args[i])); + } + + // Return the vectorized call with same function but vectorized value type + return Call(op->dtype, op->op, new_args); + } + + // If we couldn't vectorize but args became vectors, need to scalarize + if (new_addr.dtype().is_scalable_or_fixed_length_vector() || + new_value.dtype().is_scalable_or_fixed_length_vector()) { + need_scalarize_ = true; + return GetRef(op); + } + + // No vectorization needed, return with updated args if changed + if (new_addr.same_as(addr_arg) && new_value.same_as(value_arg)) { + return GetRef(op); + } + + Array new_args; + new_args.push_back(func_name); + new_args.push_back(new_addr); + new_args.push_back(new_value); + for (size_t i = 3; i < op->args.size(); ++i) { + new_args.push_back(this->VisitExpr(op->args[i])); + } + return Call(op->dtype, op->op, new_args); + } + // IfThenElse expr PrimExpr MutateIfThenElseExpr_(const CallNode *op) { PrimExpr cond = this->VisitExpr(op->args[0]); @@ -425,6 +566,18 @@ class Vectorizer : public StmtMutator, PrimExpr VisitExpr_(const CallNode *op) final { if (op->op.same_as(builtin::if_then_else())) { return MutateIfThenElseExpr_(op); + } else if (op->op.same_as(builtin::call_extern())) { + // Check if this is a tl::ld or tl::st call which can be vectorized + if (op->args.size() >= 3) { + auto func_name_node = op->args[0].as(); + if (func_name_node) { + std::string func_name = func_name_node->value; + // Check for tl::ld<...> or tl::st<...> patterns + if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + return MutateTlLdStExpr_(op, func_name.rfind("tl::ld<", 0) == 0); + } + } + } } else if (op->op.same_as(builtin::texture2d_load())) { int lane = 0; Array fcd = MutateArray({op->args.back()}, &lane); diff --git a/src/transform/loop_vectorize.cc b/src/transform/loop_vectorize.cc index cda4ad2e16..567c42e732 100644 --- a/src/transform/loop_vectorize.cc +++ b/src/transform/loop_vectorize.cc @@ -138,7 +138,42 @@ class VectorizePlanner : public arith::IRVisitorWithAnalyzer { if (node->op == builtin::if_then_else()) { CheckConditionVectorized(node->args[0]); } else if (node->op == builtin::call_extern()) { - // do not vectorize extern calls + // Check if this is a tl::ld or tl::st call which can be vectorized + if (node->args.size() >= 3) { + auto func_name_node = node->args[0].as(); + if (func_name_node) { + std::string func_name = func_name_node->value; + // Check for tl::ld<...> or tl::st<...> patterns + if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + bool can_vectorize = true; + + // Check source address (args[1]) for vectorizable pattern + auto addr_call = node->args[1].as(); + if (addr_call && addr_call->op.same_as(builtin::address_of())) { + auto buffer_load = addr_call->args[0].as(); + if (buffer_load) { + has_nonlocal_memory_access_ = true; + UpdateVectorSize(buffer_load->indices, buffer_load->buffer); + } else { + can_vectorize = false; + } + } else { + can_vectorize = false; + } + + // Check destination value (args[2]) for vectorizable pattern + auto value_load = node->args[2].as(); + if (value_load) { + UpdateVectorSize(value_load->indices, value_load->buffer); + } + + if (can_vectorize) { + return arith::IRVisitorWithAnalyzer::VisitExpr_(node); + } + } + } + } + // do not vectorize other extern calls vector_size_ = 1; } return arith::IRVisitorWithAnalyzer::VisitExpr_(node); diff --git a/src/transform/vectorize_loop.cc b/src/transform/vectorize_loop.cc index 8891b0084d..20c37a11f1 100644 --- a/src/transform/vectorize_loop.cc +++ b/src/transform/vectorize_loop.cc @@ -456,6 +456,17 @@ class TLVectorizer : public StmtMutator, PrimExpr VisitExpr_(const CallNode *op) final { if (op->op.same_as(builtin::if_then_else())) { return MutateIfThenElseExpr_(op); + } else if (op->op.same_as(builtin::call_extern())) { + // Check if this is a tl::ld or tl::st call which can be vectorized + if (op->args.size() >= 3) { + auto func_name_node = op->args[0].as(); + if (func_name_node) { + std::string func_name = func_name_node->value; + if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + return MutateTlLdStExpr_(op, func_name.rfind("tl::ld<", 0) == 0); + } + } + } } else if (op->op.same_as(builtin::texture2d_load())) { int lane = 0; Array fcd = MutateArray({op->args.back()}, &lane); @@ -687,6 +698,136 @@ class TLVectorizer : public StmtMutator, return StmtMutator::VisitStmt_(op); } + // Vectorize tl::ld or tl::st call + PrimExpr MutateTlLdStExpr_(const CallNode *op, bool is_load) { + // Structure: call_extern("tl::ld<...>", address_of(BufferLoad), value, ...) + ICHECK(op->args.size() >= 3) << "tl::ld/st expects at least 3 arguments"; + + PrimExpr func_name = op->args[0]; + PrimExpr addr_arg = op->args[1]; + PrimExpr value_arg = op->args[2]; + + // Helper to visit indices and extract Ramp info + // Returns: (visited_indices, base_indices, ramp_lanes) + auto visit_and_extract_ramp = [this](const Array& indices) + -> std::tuple, Array, int> { + Array visited_indices; + Array base_indices; + int ramp_lanes = 1; + for (const auto& idx : indices) { + PrimExpr visited = this->VisitExpr(idx); + visited_indices.push_back(visited); + auto ramp = visited.as(); + if (ramp && is_one(ramp->stride)) { + auto lanes_imm = ramp->lanes.as(); + if (lanes_imm) { + ramp_lanes = lanes_imm->value; + } + base_indices.push_back(ramp->base); + } else { + base_indices.push_back(visited); + } + } + return {visited_indices, base_indices, ramp_lanes}; + }; + + // Process source address - directly handle address_of(BufferLoad) + int src_ramp_lanes = 1; + PrimExpr new_addr = addr_arg; + auto addr_call = addr_arg.as(); + if (addr_call && addr_call->op.same_as(builtin::address_of())) { + auto buffer_load = addr_call->args[0].as(); + if (buffer_load) { + auto [visited_indices, base_indices, lanes] = visit_and_extract_ramp(buffer_load->indices); + src_ramp_lanes = lanes; + // Create new address with base indices only (for vectorized load) + BufferLoad new_buffer_load(buffer_load->buffer, base_indices); + new_addr = Call(DataType::Handle(), builtin::address_of(), {new_buffer_load}); + } + } + + // Process destination value - directly handle BufferLoad + int dst_ramp_lanes = 1; + PrimExpr new_value = value_arg; + auto value_load = value_arg.as(); + if (value_load) { + auto [visited_indices, base_indices, lanes] = visit_and_extract_ramp(value_load->indices); + dst_ramp_lanes = lanes; + // Create new value with base indices only + new_value = BufferLoad(value_load->buffer, base_indices); + } + + // Determine vectorization lanes + int vector_lanes = std::max(src_ramp_lanes, dst_ramp_lanes); + if (vector_lanes > 1) { + // Determine the vector type based on total bytes + // 8 x 16-bit = 128 bits = int4, 4 x 32-bit = 128 bits = int4 + // 4 x 16-bit = 64 bits = int2, 2 x 32-bit = 64 bits = int2 + DataType vec_dtype; + int elem_bits = 16; // Default assumption for bf16/f16 + + // Try to get element dtype from source buffer + auto addr_call_check = new_addr.as(); + if (addr_call_check && addr_call_check->op.same_as(builtin::address_of())) { + auto buffer_load = addr_call_check->args[0].as(); + if (buffer_load) { + elem_bits = buffer_load->buffer->dtype.bits(); + } + } + + int total_bits = vector_lanes * elem_bits; + if (total_bits == 128) { + vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) + } else if (total_bits == 64) { + vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) + } else if (total_bits == 32) { + vec_dtype = DataType::Int(32); + } else { + // Can't vectorize to a standard type, fall back to scalarize + need_scalarize_ = true; + return GetRef(op); + } + + // Reinterpret the value to vector type (e.g., int4 for 8xbf16) + PrimExpr vec_value = Call(vec_dtype, builtin::reinterpret(), {new_value}); + PrimExpr vec_value_slice = vec_value.as()->args[0]; + + // Build new args with base addresses and reinterpreted value + Array new_args; + new_args.push_back(func_name); + new_args.push_back(new_addr); + new_args.push_back(vec_value_slice); + // Copy remaining args (sem, scope, etc.) + for (size_t i = 3; i < op->args.size(); ++i) { + new_args.push_back(this->VisitExpr(op->args[i])); + } + + // Return the vectorized call + return Call(op->dtype, op->op, new_args); + } + + // If we couldn't vectorize but args became vectors, need to scalarize + if (new_addr.dtype().is_scalable_or_fixed_length_vector() || + new_value.dtype().is_scalable_or_fixed_length_vector()) { + need_scalarize_ = true; + return GetRef(op); + } + + // No vectorization needed, return with updated args if changed + if (new_addr.same_as(addr_arg) && new_value.same_as(value_arg)) { + return GetRef(op); + } + + Array new_args; + new_args.push_back(func_name); + new_args.push_back(new_addr); + new_args.push_back(new_value); + for (size_t i = 3; i < op->args.size(); ++i) { + new_args.push_back(this->VisitExpr(op->args[i])); + } + return Call(op->dtype, op->op, new_args); + } + // scalarize the statement Stmt Scalarize(Stmt stmt) { Var idx(var_->name_hint + ".s", var_->dtype); From 7eddf31a44fc078254f811bcf3dccf6402ffb7b9 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 17:34:09 +0800 Subject: [PATCH 34/41] lint --- .../distributed/deepseek_deepep/buffer.py | 158 ++++-- .../deepseek_deepep/intranode/__init__.py | 6 +- .../deepseek_deepep/intranode/combine.py | 227 +++++--- .../deepseek_deepep/intranode/dispatch.py | 534 ++++++++++++------ .../intranode/get_dispatch_layout.py | 7 +- .../intranode/test_intranode.py | 155 +++-- examples/distributed/deepseek_deepep/utils.py | 40 +- .../primitives/example_remote_st.py | 2 +- .../distributed/primitives/test_ld_options.py | 26 +- .../distributed/primitives/test_st_options.py | 21 +- src/op/builtin.cc | 18 +- src/op/builtin.h | 9 +- src/op/remote_copy.cc | 76 +-- src/op/remote_copy.h | 134 +++-- src/op/sync.cc | 26 +- src/op/sync.h | 28 +- src/target/codegen_cuda.cc | 6 +- src/tl_templates/cuda/copy.h | 31 +- src/tl_templates/cuda/debug.h | 4 +- src/tl_templates/cuda/ldst.h | 256 +++++---- src/tl_templates/cuda/reduce.h | 17 +- src/tl_templates/cuda/sync.h | 55 +- .../common/loop_vectorization_utils.h | 51 +- src/transform/loop_vectorize.cc | 9 +- src/transform/storage_access.cc | 2 +- src/transform/vectorize_loop.cc | 25 +- .../language/test_tilelang_language_elect.py | 3 +- .../language/test_tilelang_language_vote.py | 4 +- .../test_tilelang_language_warp_reduce.py | 2 +- .../testing/test_create_mapped_tensor.py | 5 +- tilelang/jit/kernel.py | 3 +- tilelang/language/builtin.py | 53 +- tilelang/language/distributed/common.py | 30 +- tilelang/language/reduce.py | 63 +-- tilelang/utils/ts_ext/__init__.py | 8 +- tilelang/utils/ts_ext/exception.h | 1 - tilelang/utils/ts_ext/ipc_ops.cpp | 2 +- tilelang/utils/ts_ext/tensor.cpp | 51 +- tilelang/utils/ts_ext/ts_ext_bindings.cpp | 5 +- tilelang/utils/ts_ext/ts_ext_ops.h | 5 +- 40 files changed, 1251 insertions(+), 907 deletions(-) diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index d141bb1321..4b6e643408 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -13,7 +13,7 @@ class EPBuffer: """ TileScale communication buffers for DeepEP - + Attributes: num_sms: the number of SMs used in high-throughput kernels group: the communication process group @@ -25,9 +25,14 @@ class EPBuffer: num_sms: int = 20 symm_heap_size: int = 2**30 # size of the symm heap for allocators - def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, - num_topk: int, num_experts: int, hidden: int, - dispatch_cfg: Optional[Config] = None, combine_cfg: Optional[Config] = None): + def __init__(self, + group: dist.ProcessGroup, + num_nvl_bytes: int, + num_topk: int, + num_experts: int, + hidden: int, + dispatch_cfg: Optional[Config] = None, + combine_cfg: Optional[Config] = None): """ Initialize the communication buffer. @@ -43,7 +48,7 @@ def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, self.group = group self.rank = group.rank() self.num_ranks = group.size() - + self.num_nvl_bytes = num_nvl_bytes assert self.num_ranks <= 8, "currently only support intranode" # todo: rm this self.num_topk = num_topk @@ -51,13 +56,13 @@ def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, assert num_experts % self.num_ranks == 0, "num_experts must be divisible by num_ranks" self.num_local_experts = num_experts // self.num_ranks self.hidden = hidden - + self.dispatch_cfg = dispatch_cfg if dispatch_cfg is not None else self.default_dispatch_config self.combine_cfg = combine_cfg if combine_cfg is not None else self.default_combine_config - + self.comm_stream = torch.cuda.Stream() - self._allocator= tilelang.get_allocator( + self._allocator = tilelang.get_allocator( size=EPBuffer.symm_heap_size, device="cuda", is_distributed=True, @@ -72,7 +77,7 @@ def __init__(self, group: dist.ProcessGroup, num_nvl_bytes: int, self.group.barrier() def _pre_alloc_symm_buffers(self): - """Pre-allocate the symmetric buffers via the alloctor for later communication.""" + """Pre-allocate the symmetric buffers via the allocator for later communication.""" if self.num_ranks <= 8: self._pre_alloc_symm_buffers_intranode() # todo: rm this else: @@ -80,46 +85,86 @@ def _pre_alloc_symm_buffers(self): def _pre_alloc_symm_buffers_intranode(self): # barrier signal is always zeroed after each usage, so we can pre-init here - barrier_signal = tilelang.tensor((self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator).zero_() - - per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), dtype=torch.int32, device='cuda', allocator=self._allocator) - per_expert_buffer = tilelang.tensor((self.num_ranks, self.num_local_experts), dtype=torch.int32, device='cuda', allocator=self._allocator) - - channel_start_offset = tilelang.tensor( - [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) - channel_end_offset = tilelang.tensor( - [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) - channel_head_idx = tilelang.tensor( - [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) - channel_tail_idx = tilelang.tensor( - [self.num_channels, self.num_ranks], dtype=torch.int32, device='cuda', allocator=self._allocator) + barrier_signal = tilelang.tensor((self.num_ranks), + dtype=torch.int32, + device='cuda', + allocator=self._allocator).zero_() + + per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + per_expert_buffer = tilelang.tensor((self.num_ranks, self.num_local_experts), + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + + channel_start_offset = tilelang.tensor([self.num_channels, self.num_ranks], + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + channel_end_offset = tilelang.tensor([self.num_channels, self.num_ranks], + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + channel_head_idx = tilelang.tensor([self.num_channels, self.num_ranks], + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + channel_tail_idx = tilelang.tensor([self.num_channels, self.num_ranks], + dtype=torch.int32, + device='cuda', + allocator=self._allocator) # NOTE: for each #ranks, dispatch and combine cfg have the same num_max_nvl_chunked_recv_tokens, so we can use the same buffer here - channel_x_buffers = tilelang.tensor( - [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden], dtype=torch.bfloat16, device='cuda', allocator=self._allocator) + channel_x_buffers = tilelang.tensor([ + self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, + self.hidden + ], + dtype=torch.bfloat16, + device='cuda', + allocator=self._allocator) channel_src_idx_buffers = tilelang.tensor( - [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, device='cuda', allocator=self._allocator) - channel_topk_idx_buffers = tilelang.tensor( - [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], dtype=torch.int64, device='cuda', allocator=self._allocator) - channel_topk_weights_buffers = tilelang.tensor( - [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], dtype=torch.float32, device='cuda', allocator=self._allocator) - - self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, - channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers) + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens], + dtype=torch.int32, + device='cuda', + allocator=self._allocator) + channel_topk_idx_buffers = tilelang.tensor([ + self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, + self.num_topk + ], + dtype=torch.int64, + device='cuda', + allocator=self._allocator) + channel_topk_weights_buffers = tilelang.tensor([ + self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, + self.num_topk + ], + dtype=torch.float32, + device='cuda', + allocator=self._allocator) + + self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, + channel_start_offset, channel_end_offset, channel_head_idx, + channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, + channel_topk_idx_buffers, channel_topk_weights_buffers) def _pre_alloc_symm_buffers_internode(self): raise NotImplementedError("internode is not supported yet") def _prepare_counters(self): - self._moe_recv_counter, self._moe_recv_counter_mapped = create_mapped_tensor([1], torch.int32) - self._moe_recv_expert_counter, self._moe_recv_expert_counter_mapped = create_mapped_tensor([self.num_local_experts], torch.int32) - + self._moe_recv_counter, self._moe_recv_counter_mapped = create_mapped_tensor([1], + torch.int32) + self._moe_recv_expert_counter, self._moe_recv_expert_counter_mapped = create_mapped_tensor( + [self.num_local_experts], torch.int32) + if self.num_ranks > 8: # internode - self._moe_recv_rdma_counter, self._moe_recv_rdma_counter_mapped = create_mapped_tensor([1], torch.int32) + self._moe_recv_rdma_counter, self._moe_recv_rdma_counter_mapped = create_mapped_tensor( + [1], torch.int32) @staticmethod def set_num_sms(num_sms: int): """Set the number of SMs used in high-throughput kernels - + Args: num_sms: the number of SMs used in high-throughput kernels """ @@ -129,7 +174,7 @@ def set_num_sms(num_sms: int): @property def num_channels(self): """Get the number of communication channels - + Returns: the number of communication channels """ @@ -157,20 +202,19 @@ def get_dispatch_layout(self, topk_idx: torch.Tensor): num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. """ - num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, self.num_experts, self.num_ranks) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout( + topk_idx, self.num_experts, self.num_ranks) return num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank - def dispatch( - self, - x: torch.Tensor, - handle: Optional[Tuple] = None, - num_tokens_per_rank: Optional[torch.Tensor] = None, - is_token_in_rank: Optional[torch.Tensor] = None, - num_tokens_per_expert: Optional[torch.Tensor] = None, - topk_idx: Optional[torch.Tensor] = None, - topk_weights: Optional[torch.Tensor] = None, - expert_alignment: int = 1 - ): + def dispatch(self, + x: torch.Tensor, + handle: Optional[Tuple] = None, + num_tokens_per_rank: Optional[torch.Tensor] = None, + is_token_in_rank: Optional[torch.Tensor] = None, + num_tokens_per_expert: Optional[torch.Tensor] = None, + topk_idx: Optional[torch.Tensor] = None, + topk_weights: Optional[torch.Tensor] = None, + expert_alignment: int = 1): """ Dispatch tokens to different ranks, both intranode and internode settings are supported. Intranode kernels require all the ranks should be visible via NVLink. @@ -227,9 +271,13 @@ def dispatch( else: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( - self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, self._moe_recv_expert_counter, self._moe_recv_counter_mapped, self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment, self.comm_stream) + self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, + self._moe_recv_expert_counter, self._moe_recv_counter_mapped, + self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, + num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, + topk_weights, expert_alignment, self.comm_stream) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle - + def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): # todo: support bias """ @@ -248,7 +296,7 @@ def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): recv_x: the reduced token from its dispatched ranks. recv_topk_weights: the reduced top-k weights from its dispatch ranks. """ - recv_x, recv_topk_weights = intranode_combine( - self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights, self.comm_stream) + recv_x, recv_topk_weights = intranode_combine(self.rank, self._allocator, + self._symm_buffers, x, self.combine_cfg, + handle, topk_weights, self.comm_stream) return recv_x, recv_topk_weights - diff --git a/examples/distributed/deepseek_deepep/intranode/__init__.py b/examples/distributed/deepseek_deepep/intranode/__init__.py index dbbd611dc3..f637779377 100644 --- a/examples/distributed/deepseek_deepep/intranode/__init__.py +++ b/examples/distributed/deepseek_deepep/intranode/__init__.py @@ -1,3 +1,3 @@ -from .get_dispatch_layout import get_dispatch_layout -from .dispatch import intranode_dispatch, dispatch_kernel -from .combine import intranode_combine \ No newline at end of file +from .get_dispatch_layout import get_dispatch_layout # noqa: F401 +from .dispatch import intranode_dispatch # noqa: F401 +from .combine import intranode_combine # noqa: F401 diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index d796ee5f0f..a00a7fd1df 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -1,7 +1,9 @@ # For intranode only # This op is distributed -import os, sys +import os +import sys + sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch @@ -21,11 +23,11 @@ def cached_notify_combine_kernel(num_ranks, num_sms): @T.prim_func def cached_notify_combine_main( - send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), - ##### symm buffers ##### - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), - barrier_signal: T.Tensor((num_ranks,), 'int32'), + send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), + ##### symm buffers ##### + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + barrier_signal: T.Tensor((num_ranks,), 'int32'), ): with T.Kernel(num_channels + 1, threads=threads) as bx: tx = T.get_thread_binding() @@ -45,10 +47,10 @@ def cached_notify_combine_main( tokens_per_channel = T.ceildiv(num_recv_tokens, num_channels) token_start_idx = T.min(tokens_per_channel * channel_id, num_recv_tokens) token_end_idx = T.min(token_start_idx + tokens_per_channel, num_recv_tokens) - + last_head = T.alloc_var('int32', init=2**25) # a heuristic large number # todo: tilelang doesn't support reverse loop, we simulate this - for i in T.serial(0, token_end_idx-token_start_idx, 32): + for i in T.serial(0, token_end_idx - token_start_idx, 32): token_idx_tail = token_end_idx - i - 1 token_idx = token_idx_tail - lane_id current_head = T.alloc_var('int32') @@ -58,7 +60,7 @@ def cached_notify_combine_main( current_head = -1 expected_head = T.alloc_var('int32') expected_head = 0 - for j in T.serial(T.min(32, token_idx_tail-token_start_idx + 1)): + for j in T.serial(T.min(32, token_idx_tail - token_start_idx + 1)): head = T.tvm_warp_shuffle(-1, current_head, j, 32, 32) if head < 0: if lane_id == j: @@ -67,38 +69,42 @@ def cached_notify_combine_main( last_head = head if current_head < 0 and token_idx >= token_start_idx: send_head[token_idx, rank_id] = expected_head - + return cached_notify_combine_main def cached_notify_combine( - num_ranks, - num_sms, - ##### symm buffers ##### - send_head: torch.Tensor, - channel_head_idx: torch.Tensor, - channel_tail_idx: torch.Tensor, - barrier_signal: torch.Tensor, - allocator, - comm_stream=None -): + num_ranks, + num_sms, + ##### symm buffers ##### + send_head: torch.Tensor, + channel_head_idx: torch.Tensor, + channel_tail_idx: torch.Tensor, + barrier_signal: torch.Tensor, + allocator, + comm_stream=None): kernel = cached_notify_combine_kernel(num_ranks, num_sms) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal, stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel( + send_head, + channel_head_idx, + channel_tail_idx, + barrier_signal, + stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead -@tilelang.jit( - pass_configs={"tl.disable_tma_lower": True, # use TMA later - "tl.disable_warp_specialized": True} -) +@tilelang.jit(pass_configs={ + "tl.disable_tma_lower": True, # use TMA later + "tl.disable_warp_specialized": True +}) def combine_kernel( num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens - hidden, - num_topk, + hidden, + num_topk, num_sms, dtype: str = 'bfloat16', ): @@ -111,8 +117,8 @@ def combine_kernel( warps_per_rank = warps // num_ranks # 3 threads_per_rank = threads // num_ranks # 96 TMABytesPerWarp = 4096 - smem_size = TMABytesPerWarp * (threads // 32) - num_stages = 8 + smem_size = TMABytesPerWarp * (threads // 32) # noqa: F841 + num_stages = 8 # noqa: F841 assert hidden % 8 == 0 # manual vectorize on recv-side @@ -134,9 +140,12 @@ def combine_main( # symm buffers channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), - channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], + dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], + "int32"), + channel_topk_weights_buffers: T.Tensor( + [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: tx = T.get_thread_binding() @@ -144,20 +153,18 @@ def combine_main( warp_id = tx // 32 responsible_channel = bx // 2 - if bx % 2 == 0: # sender send_rank_id = (responsible_channel + warp_id) % num_ranks send_warp_id_in_rank = warp_id // num_ranks # get tasks - rank_offset = T.if_then_else(send_rank_id > 0, rank_prefix_matrix[send_rank_id-1, rank], 0) + rank_offset = T.if_then_else(send_rank_id > 0, rank_prefix_matrix[send_rank_id - 1, + rank], 0) num_rank_tokens = rank_prefix_matrix[send_rank_id, rank] - rank_offset channel_offset = channel_prefix_matrix[send_rank_id, responsible_channel] - num_channel_tokens= T.if_then_else( - responsible_channel == num_channels - 1, - num_rank_tokens, - channel_prefix_matrix[send_rank_id, responsible_channel + 1] - ) - channel_offset + num_channel_tokens = T.if_then_else( + responsible_channel == num_channels - 1, num_rank_tokens, + channel_prefix_matrix[send_rank_id, responsible_channel + 1]) - channel_offset token_start_idx = rank_offset + channel_offset token_end_idx = token_start_idx + num_channel_tokens @@ -170,7 +177,10 @@ def combine_main( # Check destination queue emptiness, or wait a buffer to be released (rare cases) num_round_tokens = T.min(num_max_send_tokens, token_end_idx - token_idx) if T.elect_one_sync(): - T.wait_ge(channel_head_idx[responsible_channel, rank], current_channel_tail_idx + num_round_tokens - num_recv_buffer_tokens, peer=send_rank_id) + T.wait_ge( + channel_head_idx[responsible_channel, rank], + current_channel_tail_idx + num_round_tokens - num_recv_buffer_tokens, + peer=send_rank_id) T.sync_warp() # Send by trunk @@ -180,22 +190,32 @@ def combine_main( dst_slot_idx = (current_channel_tail_idx + i) % num_recv_buffer_tokens # 1. copy data - T.put_warp(T.address_of(x[token_idx + i, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=send_rank_id, unroll_factor=4, enable_aggresive_vectorize=True) - + T.put_warp( + T.address_of(x[token_idx + i, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, + 0]), + hidden, + dst_pe=send_rank_id, + unroll_factor=4, + enable_aggresive_vectorize=True) + # 2. send src idx idx = T.alloc_var('int32') if T.elect_one_sync(): T.ld(src_idx[token_idx + i], idx, nc=True) - T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], idx, + T.st( + channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], + idx, dst_pe=send_rank_id) # 3. send topk_weights if num_topk > 0 and lane_id < num_topk: weight = T.alloc_var('float32') T.ld(topk_weights[token_idx + i, lane_id], weight, nc=True) - T.st(channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight, + T.st( + channel_topk_weights_buffers[responsible_channel, rank, + dst_slot_idx, lane_id], + weight, dst_pe=send_rank_id) token_idx += num_round_tokens @@ -204,14 +224,18 @@ def combine_main( # move tail index T.sync_threads(send_rank_id, threads_per_rank) if send_warp_id_in_rank == 0 and T.elect_one_sync(): - T.st(channel_tail_idx[responsible_channel, rank], current_channel_tail_idx, - scope='sys', sem='release', + T.st( + channel_tail_idx[responsible_channel, rank], + current_channel_tail_idx, + scope='sys', + sem='release', dst_pe=send_rank_id) - + else: # receiver #? Why we must need scope='shared', not 'shared.dynamic' here? warp_channel_head_idx = T.alloc_shared([warps, num_ranks], 'int32', scope='shared') - shared_channel_tail_idx = T.alloc_shared([32], 'int32', scope='shared') #! workaround for illegal address + shared_channel_tail_idx = T.alloc_shared( + [32], 'int32', scope='shared') #! workaround for illegal address warp_retired = T.alloc_shared([warps], 'bool', scope='shared') if tx < warps: warp_retired[tx] = False @@ -232,12 +256,18 @@ def combine_main( retired = retired and warp_retired[i] if retired: T.loop_break() - + # Update queue tail new_tail = T.alloc_var('int32') - T.ld(channel_tail_idx[responsible_channel, lane_id], new_tail, sem="acquire", scope="sys") + T.ld( + channel_tail_idx[responsible_channel, lane_id], + new_tail, + sem="acquire", + scope="sys") # Use release semantics to ensure receiver warps see the update - T.st(shared_channel_tail_idx[lane_id], new_tail, sem="release", scope="cta") # todo: weaker sem pair + T.st( + shared_channel_tail_idx[lane_id], new_tail, sem="release", + scope="cta") # todo: weaker sem pair # Update minimum head min_head = T.alloc_var('int32') @@ -247,18 +277,25 @@ def combine_main( min_head = T.min(min_head, warp_channel_head_idx[i, lane_id]) if min_head != 2**31 - 1 and min_head > last_head: last_head = min_head - T.st(channel_head_idx[responsible_channel, lane_id], min_head, sem="relaxed", scope="sys") + T.st( + channel_head_idx[responsible_channel, lane_id], + min_head, + sem="relaxed", + scope="sys") else: # other warps for reduction # All lanes will use data buffer, but only rank lane will use `head/tail/src_idx` # The same tokens as the dispatch process - num_tokens_per_channel = T.truncdiv(num_recv_tokens+num_channels-1, num_channels) + num_tokens_per_channel = T.truncdiv(num_recv_tokens + num_channels - 1, + num_channels) # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var - token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_recv_tokens) + token_start_idx = T.min(num_tokens_per_channel * responsible_channel, + num_recv_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_recv_tokens) # Iterate over all tokens and combine - for token_idx in T.serial(token_start_idx+warp_id-1, token_end_idx, warps-1): + for token_idx in T.serial(token_start_idx + warp_id - 1, token_end_idx, + warps - 1): # Read expected head expected_head = T.alloc_var('int32') expected_head = -1 @@ -268,7 +305,11 @@ def combine_main( condvar = T.alloc_var('int32') T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") with T.While(T.warp_any(condvar <= expected_head and expected_head >= 0)): - T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") + T.ld( + shared_channel_tail_idx[lane_id], + condvar, + sem="acquire", + scope="cta") T.loop_continue() # can we simplify this ? T.sync_warp() @@ -276,12 +317,13 @@ def combine_main( # Broadcast current heads num_topk_ranks = T.alloc_var('int32') num_topk_ranks = 0 - topk_ranks= T.alloc_local([num_ranks], 'int32') + topk_ranks = T.alloc_local([num_ranks], 'int32') slot_indices = T.alloc_local([num_ranks], 'int32') for i in T.serial(num_ranks): expected_head_i = T.tvm_warp_shuffle(-1, expected_head, i, 32, 32) if expected_head_i >= 0: - slot_indices[num_topk_ranks] = expected_head_i % num_recv_buffer_tokens + slot_indices[ + num_topk_ranks] = expected_head_i % num_recv_buffer_tokens topk_ranks[num_topk_ranks] = i num_topk_ranks += 1 @@ -289,13 +331,17 @@ def combine_main( # todo: vectorize recv_value = T.alloc_local([num_ranks, 8], dtype) values = T.alloc_local([8], "float32") - + for i in T.serial(lane_id, hidden // 8, 32): T.clear(values) for j in T.serial(num_topk_ranks): for k in T.vectorized(8): - T.ld(channel_x_buffers[responsible_channel, topk_ranks[j], slot_indices[j], i*8+k], recv_value[j, k], nc=True) - + T.ld( + channel_x_buffers[responsible_channel, topk_ranks[j], + slot_indices[j], i * 8 + k], + recv_value[j, k], + nc=True) + # todo: support bias # Reduce a2a results @@ -303,7 +349,8 @@ def combine_main( for k in T.vectorized(8): values[k] += recv_value[j, k] for j in T.vectorized(8): - recv_x[token_idx, i*8+j] = values[j] # todo: further vectorize this + recv_x[token_idx, + i * 8 + j] = values[j] # todo: further vectorize this # Reduce topk_weights if lane_id < num_topk: @@ -311,16 +358,18 @@ def combine_main( weight_sum = 0 for i in T.serial(num_topk_ranks): weight = T.alloc_var('float32') - T.ld(channel_topk_weights_buffers[responsible_channel, topk_ranks[i], slot_indices[i], lane_id], weight, nc=True) + T.ld( + channel_topk_weights_buffers[responsible_channel, topk_ranks[i], + slot_indices[i], lane_id], + weight, + nc=True) weight_sum += weight recv_topk_weights[token_idx, lane_id] = weight_sum # Update head if lane_id < num_ranks: warp_channel_head_idx[warp_id, lane_id] = T.if_then_else( - expected_head < 0, - -expected_head - 1, - expected_head + 1) + expected_head < 0, -expected_head - 1, expected_head + 1) # Retired T.sync_warp() @@ -330,18 +379,16 @@ def combine_main( return combine_main -def intranode_combine( - rank: int, - allocator, - symm_buffers, - x, - config, - handle, - topk_weights, - comm_stream=None -): +def intranode_combine(rank: int, + allocator, + symm_buffers, + x, + config, + handle, + topk_weights, + comm_stream=None): assert handle is not None - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, _, send_head = handle + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, _, send_head = handle barrier_signal, _, _, _, _, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, _, channel_topk_weights_buffers = symm_buffers # acquire_shapes @@ -349,9 +396,17 @@ def intranode_combine( _, num_topk = topk_weights.shape num_ranks, _ = channel_prefix_matrix.shape num_recv_tokens = send_head.shape[0] - + # notify combine - cached_notify_combine(num_ranks, config.num_sms, send_head, channel_head_idx, channel_tail_idx, barrier_signal, allocator, comm_stream=comm_stream) + cached_notify_combine( + num_ranks, + config.num_sms, + send_head, + channel_head_idx, + channel_tail_idx, + barrier_signal, + allocator, + comm_stream=comm_stream) # combine recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') @@ -364,11 +419,10 @@ def intranode_combine( hidden, num_topk, config.num_sms, - dtype='bfloat16' - ) + dtype='bfloat16') kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) kernel( - rank, + rank, x, topk_weights, recv_src_idx, @@ -383,8 +437,7 @@ def intranode_combine( channel_src_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream, - skip_tensor_validation=True - ) # reduce runtime overhead + skip_tensor_validation=True) # reduce runtime overhead compute_stream = torch.cuda.current_stream() compute_stream.wait_stream(comm_stream) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 1a192e8f84..96e7af70e0 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -1,7 +1,9 @@ # For intranode only # This op is distributed -import os, sys +import os +import sys + sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch @@ -14,7 +16,7 @@ os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log -# notify_dispatch is responible for: +# notify_dispatch is responsible for: # 1. Pre-compute rank/channel prefix for dispatch # 2. Zero 4 symm buffers before a system-level barrier @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) @@ -32,29 +34,29 @@ def notify_dispatch_kernel( @T.prim_func def notify_dispatch_main( - rank: T.int32, - num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), - num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), - is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), - moe_recv_counter_mapped: T.Tensor((1,), 'int32'), - moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), - per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), - per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), - barrier_signal: T.Tensor((num_ranks,), 'int32'), - rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), - channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), - # 4 symm buffers to be zeroed - channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + rank: T.int32, + num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), + num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), + is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), + moe_recv_counter_mapped: T.Tensor((1,), 'int32'), + moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), + per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), + per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), + channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), ): - with T.Kernel(num_ranks+1, threads=threads) as bx: + with T.Kernel(num_ranks + 1, threads=threads) as bx: tx = T.get_thread_binding() lane_id, warp_id = tx % 32, tx // 32 if bx == 0: - # Barrier first + # Barrier first T.sync_blocks(barrier_signal) # `per_rank_buffer[rank][i, j]` means the number of tokens from rank i to rank j @@ -62,17 +64,20 @@ def notify_dispatch_main( if tx < num_ranks: T.st(per_rank_buffer[rank, tx], num_tokens_per_rank[tx], dst_pe=tx) for i in T.serial(num_local_experts): - T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) - + T.st( + per_expert_buffer[rank, i], + num_tokens_per_expert[tx * num_local_experts + i], + dst_pe=tx) + T.barrier_blocks(barrier_signal) # Sum per-rank cnts and pre-compute the prefix sum for data sending if tx < num_ranks: for i in T.serial(1, num_ranks): - per_rank_buffer[i, tx] += per_rank_buffer[i-1, tx] + per_rank_buffer[i, tx] += per_rank_buffer[i - 1, tx] if tx == rank: - moe_recv_counter_mapped[0] = per_rank_buffer[num_ranks-1, rank] - + moe_recv_counter_mapped[0] = per_rank_buffer[num_ranks - 1, rank] + # Sum per-expert cnts if tx < num_local_experts: sum = T.alloc_local([1], 'int32') @@ -83,11 +88,11 @@ def notify_dispatch_main( moe_recv_expert_counter_mapped[tx] = sum[0] T.sync_threads() - # Copy rank size prefix matrix to another tensor - # TODO: simply returns per_rank_buffer as rank_prefix_matrix + # Copy rank size prefix matrix to another tensor + # TODO: simply returns per_rank_buffer as rank_prefix_matrix T.copy(per_rank_buffer, rank_prefix_matrix) - # Clear 4 symm buffers for later use + # Clear 4 symm buffers for later use T.clear(channel_start_offset) T.clear(channel_end_offset) T.clear(channel_head_idx) @@ -97,7 +102,7 @@ def notify_dispatch_main( else: dst_rank = bx - 1 for channel_id in T.serial(warp_id, num_channels, num_warps): - num_tokens_per_channel = T.truncdiv(num_tokens+num_channels-1, num_channels) + num_tokens_per_channel = T.truncdiv(num_tokens + num_channels - 1, num_channels) # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) @@ -112,7 +117,7 @@ def notify_dispatch_main( if tx == 0: for i in T.serial(1, num_channels): - channel_prefix_matrix[dst_rank, i] += channel_prefix_matrix[dst_rank, i-1] + channel_prefix_matrix[dst_rank, i] += channel_prefix_matrix[dst_rank, i - 1] return notify_dispatch_main @@ -120,7 +125,7 @@ def notify_dispatch_main( # TileScale notify-dispatch op def notify_dispatch( # meta - rank: int, + rank: int, num_ranks: int, num_experts: int, num_channels: int, @@ -181,21 +186,23 @@ def notify_dispatch( skip_tensor_validation=True # reduce runtime overhead ) - num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready(moe_recv_counter, moe_recv_expert_counter) + num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready( + moe_recv_counter, moe_recv_expert_counter) return num_recv_tokens, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix # cached_notify_dispatch only needs to clear symm buffers @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def cached_notify_dispatch_kernel(num_ranks: int, num_channels: int): + @T.prim_func def cached_notify_dispatch_main( - barrier_signal: T.Tensor((num_ranks,), 'int32'), - # 4 symm buffers to be zeroed - channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + barrier_signal: T.Tensor((num_ranks,), 'int32'), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), ): with T.Kernel(1, threads=128): T.sync_blocks(barrier_signal) @@ -225,28 +232,36 @@ def cached_notify_dispatch( comm_stream=None, ): kernel = cached_notify_dispatch_kernel(num_ranks, num_channels) - kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) # we still comm on barrier_signal - kernel(barrier_signal, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel.initialize( + allocator=allocator, stream=comm_stream.cuda_stream) # we still comm on barrier_signal + kernel( + barrier_signal, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + stream=comm_stream.cuda_stream, + skip_tensor_validation=True) # reduce runtime overhead -@tilelang.jit( - pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}) +@tilelang.jit(pass_configs={ + "tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True +}) def dispatch_kernel( num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens - hidden, - num_topk, + hidden, + num_topk, num_experts, num_sms, dtype: str = 'bfloat16', ): threads = 768 # 24 warps TMABytesPerWarp = 8192 - smem_size = TMABytesPerWarp * threads // 32 - + smem_size = TMABytesPerWarp * threads // 32 # noqa: F841 + num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) num_channels = num_sms // 2 # 10 (2 SMs for each channel) num_local_experts = num_experts // num_ranks @@ -282,10 +297,14 @@ def dispatch_main( channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # channel data buffers, stored on the receiver side - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), - channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int64"), - channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], + dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], + "int32"), + channel_topk_idx_buffers: T.Tensor( + [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int64"), + channel_topk_weights_buffers: T.Tensor( + [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: @@ -302,24 +321,31 @@ def dispatch_main( if send_warp_id_in_rank == 0 and T.elect_one_sync(): value = T.alloc_var('int32') value = T.if_then_else( - responsible_channel > 0, - channel_prefix_matrix[responsible_rank, responsible_channel - 1], - 0) - T.st(channel_start_offset[responsible_channel, rank], -value-1, - scope='sys', sem='relaxed', dst_pe=responsible_rank) + responsible_channel > 0, channel_prefix_matrix[responsible_rank, + responsible_channel - 1], 0) + T.st( + channel_start_offset[responsible_channel, rank], + -value - 1, + scope='sys', + sem='relaxed', + dst_pe=responsible_rank) value = channel_prefix_matrix[responsible_rank, responsible_channel] - T.st(channel_end_offset[responsible_channel, rank], -value-1, - scope='sys', sem='relaxed', dst_pe=responsible_rank) + T.st( + channel_end_offset[responsible_channel, rank], + -value - 1, + scope='sys', + sem='relaxed', + dst_pe=responsible_rank) T.sync_warp() # get task - num_tokens_per_channel = T.truncdiv(num_tokens+num_channels-1, num_channels) + num_tokens_per_channel = T.truncdiv(num_tokens + num_channels - 1, num_channels) # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.alloc_var('int32') token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) token_end_idx = T.alloc_var('int32') token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) - + # sender mainloop: iterate over all tokens and send by trunk cached_channel_tail_idx = T.alloc_var('int32') cached_channel_tail_idx = 0 @@ -327,23 +353,23 @@ def dispatch_main( token_idx = token_start_idx with T.While(token_idx < token_end_idx): if T.elect_one_sync(): - T.wait_ge(channel_head_idx[responsible_channel, rank], - num_max_send_tokens+cached_channel_tail_idx-num_recv_buffer_tokens, + T.wait_ge( + channel_head_idx[responsible_channel, rank], + num_max_send_tokens + cached_channel_tail_idx - num_recv_buffer_tokens, responsible_rank) T.sync_warp() chunk_token_idx = T.alloc_var('int32') chunk_token_idx = 0 while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: - # for the same token, the warp assigned to save `send_head` may be different from the warp + # for the same token, the warp assigned to save `send_head` may be different from the warp # assigned to send the following data - if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync(): + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync( + ): send_head[token_idx, responsible_rank] = T.if_then_else( is_token_in_rank[token_idx, responsible_rank], - cached_channel_tail_idx, - -1 - ) - + cached_channel_tail_idx, -1) + # skip if not selected if not is_token_in_rank[token_idx, responsible_rank]: token_idx += 1 @@ -356,13 +382,21 @@ def dispatch_main( if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: # copy data, all are remote copy # 1. copy data - T.put_warp(T.address_of(x[token_idx, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=responsible_rank, unroll_factor=4, enable_aggresive_vectorize=True) - + T.put_warp( + T.address_of(x[token_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, + dst_slot_idx, 0]), + hidden, + dst_pe=responsible_rank, + unroll_factor=4, + enable_aggresive_vectorize=True) + # 2. copy src idx if T.elect_one_sync(): - T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, + T.st( + channel_src_idx_buffers[responsible_channel, rank, + dst_slot_idx], + token_idx, dst_pe=responsible_rank) # 3. copy `topk_idx` and `topk_weights` with transformed index @@ -370,52 +404,66 @@ def dispatch_main( # topk_idx recv_expert_begin = responsible_rank * num_local_experts recv_expert_end = recv_expert_begin + num_local_experts - + idx_value = T.alloc_var('int64') T.ld(topk_idx[token_idx, lane_id], idx_value, nc=True) idx_value = T.if_then_else( - recv_expert_begin <= T.cast(idx_value, 'int32') < recv_expert_end, - idx_value - recv_expert_begin, - -1 - ) - T.st(channel_topk_idx_buffers[responsible_channel, rank, dst_slot_idx, lane_id], idx_value, + recv_expert_begin <= T.cast(idx_value, 'int32') < + recv_expert_end, idx_value - recv_expert_begin, -1) + T.st( + channel_topk_idx_buffers[responsible_channel, rank, + dst_slot_idx, lane_id], + idx_value, dst_pe=responsible_rank) # topk_weights weight_value = T.alloc_var('float32') T.ld(topk_weights[token_idx, lane_id], weight_value, nc=True) weight_value = T.if_then_else(idx_value >= 0, weight_value, 0) - T.st(channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight_value, + T.st( + channel_topk_weights_buffers[responsible_channel, rank, + dst_slot_idx, lane_id], + weight_value, dst_pe=responsible_rank) # 4. copy scale (support fp8 later) chunk_token_idx += 1 token_idx += 1 - + # move tail index # here all warps should share the same new tail T.sync_threads(responsible_rank, num_threads_per_rank) if send_warp_id_in_rank == 0 and T.elect_one_sync(): - T.st(channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, - scope='sys', sem='release', + T.st( + channel_tail_idx[responsible_channel, rank], + cached_channel_tail_idx, + scope='sys', + sem='release', dst_pe=responsible_rank) - + else: # receiver recv_thread_id_in_rank = tx % num_threads_per_rank recv_warp_id_in_rank = recv_thread_id_in_rank // 32 # calculate offset first - rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank-1, rank], 0) + rank_offset = T.if_then_else(responsible_rank > 0, + rank_prefix_matrix[responsible_rank - 1, rank], 0) # receive channel offset total_offset = T.alloc_var('int32') num_tokens_to_recv = T.alloc_var('int32') if T.elect_one_sync(): T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) - T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem='volatile') + T.ld( + channel_start_offset[responsible_channel, responsible_rank], + total_offset, + sem='volatile') T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) - T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem='volatile') + T.ld( + channel_end_offset[responsible_channel, responsible_rank], + num_tokens_to_recv, + sem='volatile') total_offset = -total_offset - 1 num_tokens_to_recv = -num_tokens_to_recv - 1 if recv_warp_id_in_rank == 0: @@ -428,17 +476,21 @@ def dispatch_main( # Shared tail indices for different warps shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') - cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = T.alloc_var('int32') cached_channel_head_idx = 0 cached_channel_tail_idx = T.alloc_var('int32') cached_channel_tail_idx = 0 with T.While(num_tokens_to_recv > 0): with T.While(recv_thread_id_in_rank == 0): - T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem='acquire', scope='sys') - + T.ld( + channel_tail_idx[responsible_channel, responsible_rank], + cached_channel_tail_idx, + sem='acquire', + scope='sys') + # read to copy if cached_channel_head_idx != cached_channel_tail_idx: - shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx + shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx T.loop_break() # sync queue tail @@ -448,32 +500,48 @@ def dispatch_main( # copy data # 1. recv x num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx - for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): - token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, + num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + + chunk_idx) % num_recv_buffer_tokens # T.copy(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, :], recv_x[total_offset+chunk_idx, :]) # todo: add ld_nc and st_na #! T.copy will cause layout inference error - T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), - T.address_of(recv_x[total_offset+chunk_idx, 0]), + T.put_warp( + T.address_of(channel_x_buffers[responsible_channel, responsible_rank, + token_idx_in_buffer, 0]), + T.address_of(recv_x[total_offset + chunk_idx, 0]), hidden, - -1, + -1, 5, enable_aggresive_vectorize=True) - + # 2. recv src_idx - for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, - cached_channel_tail_idx, - num_threads_per_rank): + for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, + cached_channel_tail_idx, num_threads_per_rank): local_src_idx = T.alloc_var('int32') - T.ld(channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, nc=True) - recv_src_idx[total_offset+chunk_idx-cached_channel_head_idx] = local_src_idx - + T.ld( + channel_src_idx_buffers[responsible_channel, responsible_rank, + chunk_idx % num_recv_buffer_tokens], + local_src_idx, + nc=True) + recv_src_idx[total_offset + chunk_idx - + cached_channel_head_idx] = local_src_idx + # 3. recv topk_idx and topk_weights - for idx in T.serial(recv_thread_id_in_rank, num_cur_recv_tokens*num_topk, num_threads_per_rank): + for idx in T.serial(recv_thread_id_in_rank, num_cur_recv_tokens * num_topk, + num_threads_per_rank): chunk_idx = idx // num_topk token_topk_idx = idx % num_topk - token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens - recv_topk_idx[total_offset+chunk_idx, token_topk_idx] = channel_topk_idx_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx] - recv_topk_weights[total_offset+chunk_idx, token_topk_idx] = channel_topk_weights_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx] + token_idx_in_buffer = (cached_channel_head_idx + + chunk_idx) % num_recv_buffer_tokens + recv_topk_idx[total_offset + chunk_idx, + token_topk_idx] = channel_topk_idx_buffers[ + responsible_channel, responsible_rank, + token_idx_in_buffer, token_topk_idx] + recv_topk_weights[total_offset + chunk_idx, + token_topk_idx] = channel_topk_weights_buffers[ + responsible_channel, responsible_rank, + token_idx_in_buffer, token_topk_idx] # 4. recv scale (support fp8 later) @@ -482,33 +550,37 @@ def dispatch_main( total_offset += num_cur_recv_tokens T.sync_threads(responsible_rank, num_threads_per_rank) if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): - T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, - scope='sys', sem='relaxed') - + T.st( + channel_head_idx[responsible_channel, responsible_rank], + cached_channel_head_idx, + scope='sys', + sem='relaxed') + # Exit num_tokens_to_recv -= num_cur_recv_tokens - + return dispatch_main -@tilelang.jit( - pass_configs={"tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True}) +@tilelang.jit(pass_configs={ + "tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True +}) def cached_dispatch_kernel( - num_ranks, - num_tokens, + num_ranks, + num_tokens, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens - hidden, - num_topk, + hidden, + num_topk, num_experts, num_sms, dtype: str = 'bfloat16', ): threads = 768 # 24 warps TMABytesPerWarp = 8192 - smem_size = TMABytesPerWarp * threads // 32 - + smem_size = TMABytesPerWarp * threads // 32 # noqa: F841 + num_threads_per_rank = threads // num_ranks # 96 (3 warps for each rank) num_channels = num_sms // 2 # 10 (2 SMs for each channel) @@ -538,8 +610,10 @@ def cached_dispatch_main( channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # channel data buffers, stored on the receiver side - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], + dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], + "int32"), # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: @@ -555,23 +629,31 @@ def cached_dispatch_main( if send_warp_id_in_rank == 0 and T.elect_one_sync(): value = T.alloc_var('int32') value = T.if_then_else( - responsible_channel > 0, - channel_prefix_matrix[responsible_rank, responsible_channel - 1], - 0) - T.st(channel_start_offset[responsible_channel, rank], -value-1, - scope='sys', sem='relaxed', dst_pe=responsible_rank) + responsible_channel > 0, channel_prefix_matrix[responsible_rank, + responsible_channel - 1], 0) + T.st( + channel_start_offset[responsible_channel, rank], + -value - 1, + scope='sys', + sem='relaxed', + dst_pe=responsible_rank) value = channel_prefix_matrix[responsible_rank, responsible_channel] - T.st(channel_end_offset[responsible_channel, rank], -value-1, - scope='sys', sem='relaxed', dst_pe=responsible_rank) + T.st( + channel_end_offset[responsible_channel, rank], + -value - 1, + scope='sys', + sem='relaxed', + dst_pe=responsible_rank) T.sync_warp() # get task - num_tokens_per_channel = T.alloc_var('int32', init=T.ceildiv(num_tokens, num_channels)) + num_tokens_per_channel = T.alloc_var( + 'int32', init=T.ceildiv(num_tokens, num_channels)) token_start_idx = T.alloc_var('int32') token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) token_end_idx = T.alloc_var('int32') token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) - + # sender mainloop: iterate over all tokens and send by trunk cached_channel_tail_idx = T.alloc_var('int32') cached_channel_tail_idx = 0 @@ -579,23 +661,23 @@ def cached_dispatch_main( token_idx = token_start_idx with T.While(token_idx < token_end_idx): if T.elect_one_sync(): - T.wait_ge(channel_head_idx[responsible_channel, rank], - num_max_send_tokens+cached_channel_tail_idx-num_recv_buffer_tokens, + T.wait_ge( + channel_head_idx[responsible_channel, rank], + num_max_send_tokens + cached_channel_tail_idx - num_recv_buffer_tokens, responsible_rank) T.sync_warp() chunk_token_idx = T.alloc_var('int32') chunk_token_idx = 0 while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: - # for the same token, the warp assigned to save `send_head` may be different from the warp + # for the same token, the warp assigned to save `send_head` may be different from the warp # assigned to send the following data - if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync(): + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync( + ): send_head[token_idx, responsible_rank] = T.if_then_else( is_token_in_rank[token_idx, responsible_rank], - cached_channel_tail_idx, - -1 - ) - + cached_channel_tail_idx, -1) + # skip if not selected if not is_token_in_rank[token_idx, responsible_rank]: token_idx += 1 @@ -608,43 +690,61 @@ def cached_dispatch_main( if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: # copy data, all are remote copy # 1. copy data - T.put_warp(T.address_of(x[token_idx, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), - hidden, dst_pe=responsible_rank, unroll_factor=4, enable_aggresive_vectorize=True) - + T.put_warp( + T.address_of(x[token_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, + dst_slot_idx, 0]), + hidden, + dst_pe=responsible_rank, + unroll_factor=4, + enable_aggresive_vectorize=True) + # 2. copy src idx if T.elect_one_sync(): - T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, + T.st( + channel_src_idx_buffers[responsible_channel, rank, + dst_slot_idx], + token_idx, dst_pe=responsible_rank) # 4. copy scale (support fp8 later) chunk_token_idx += 1 token_idx += 1 - + # move tail index # here all warps should share the same new tail T.sync_threads(responsible_rank, num_threads_per_rank) if send_warp_id_in_rank == 0 and T.elect_one_sync(): - T.st(channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, - scope='sys', sem='release', + T.st( + channel_tail_idx[responsible_channel, rank], + cached_channel_tail_idx, + scope='sys', + sem='release', dst_pe=responsible_rank) - + else: # receiver recv_thread_id_in_rank = tx % num_threads_per_rank recv_warp_id_in_rank = recv_thread_id_in_rank // 32 # calculate offset first - rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank-1, rank], 0) + rank_offset = T.if_then_else(responsible_rank > 0, + rank_prefix_matrix[responsible_rank - 1, rank], 0) # receive channel offset total_offset = T.alloc_var('int32') num_tokens_to_recv = T.alloc_var('int32') if T.elect_one_sync(): T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) - T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem='volatile') + T.ld( + channel_start_offset[responsible_channel, responsible_rank], + total_offset, + sem='volatile') T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) - T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem='volatile') + T.ld( + channel_end_offset[responsible_channel, responsible_rank], + num_tokens_to_recv, + sem='volatile') total_offset = -total_offset - 1 num_tokens_to_recv = -num_tokens_to_recv - 1 if recv_warp_id_in_rank == 0: @@ -657,17 +757,21 @@ def cached_dispatch_main( # Shared tail indices for different warps shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') - cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = T.alloc_var('int32') cached_channel_head_idx = 0 cached_channel_tail_idx = T.alloc_var('int32') cached_channel_tail_idx = 0 with T.While(num_tokens_to_recv > 0): with T.While(recv_thread_id_in_rank == 0): - T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem='acquire', scope='sys') - + T.ld( + channel_tail_idx[responsible_channel, responsible_rank], + cached_channel_tail_idx, + sem='acquire', + scope='sys') + # read to copy if cached_channel_head_idx != cached_channel_tail_idx: - shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx + shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx T.loop_break() # sync queue tail @@ -677,23 +781,31 @@ def cached_dispatch_main( # copy data # 1. recv x num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx - for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): - token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, + num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + + chunk_idx) % num_recv_buffer_tokens #! T.copy will cause layout inference error - T.put_warp(T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), - T.address_of(recv_x[total_offset+chunk_idx, 0]), + T.put_warp( + T.address_of(channel_x_buffers[responsible_channel, responsible_rank, + token_idx_in_buffer, 0]), + T.address_of(recv_x[total_offset + chunk_idx, 0]), hidden, - -1, + -1, 5, enable_aggresive_vectorize=True) - + # 2. recv src_idx - for chunk_idx in T.serial(cached_channel_head_idx+recv_thread_id_in_rank, - cached_channel_tail_idx, - num_threads_per_rank): + for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, + cached_channel_tail_idx, num_threads_per_rank): local_src_idx = T.alloc_var('int32') - T.ld(channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, nc=True) - recv_src_idx[total_offset+chunk_idx-cached_channel_head_idx] = local_src_idx + T.ld( + channel_src_idx_buffers[responsible_channel, responsible_rank, + chunk_idx % num_recv_buffer_tokens], + local_src_idx, + nc=True) + recv_src_idx[total_offset + chunk_idx - + cached_channel_head_idx] = local_src_idx # 4. recv scale (support fp8 later) @@ -702,14 +814,17 @@ def cached_dispatch_main( total_offset += num_cur_recv_tokens T.sync_threads(responsible_rank, num_threads_per_rank) if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): - T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, - scope='sys', sem='relaxed') - + T.st( + channel_head_idx[responsible_channel, responsible_rank], + cached_channel_head_idx, + scope='sys', + sem='relaxed') + # Exit num_tokens_to_recv -= num_cur_recv_tokens - + # todo: support num_worst_tokens > 0 later - + return cached_dispatch_main @@ -730,7 +845,7 @@ def intranode_dispatch( topk_idx: Optional[torch.Tensor] = None, topk_weights: Optional[torch.Tensor] = None, expert_alignment: int = 1, - comm_stream = None, + comm_stream=None, # todo: support num_worst_tokens # todo: support async functionality ): @@ -773,28 +888,85 @@ def intranode_dispatch( comm_stream=comm_stream, ) else: - cached_notify_dispatch(num_ranks, config.num_channels, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, barrier_signal, allocator, comm_stream=comm_stream) + cached_notify_dispatch( + num_ranks, + config.num_channels, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + barrier_signal, + allocator, + comm_stream=comm_stream) num_recv_tokens = recv_src_idx.size(0) recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') if handle is None: recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int64, device='cuda') - recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') - recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device='cuda') + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), + dtype=torch.float32, + device='cuda') + recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), + dtype=torch.int32, + device='cuda') send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') # run dispatch if handle is None: - kernel = dispatch_kernel(num_ranks, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel = dispatch_kernel(num_ranks, config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, + num_experts, config.num_sms, 'bfloat16') kernel.initialize(allocator=allocator) - kernel(rank, recv_x, recv_src_idx, recv_topk_idx, recv_topk_weights, recv_channel_prefix_matrix, send_head, x, topk_idx, topk_weights, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers, stream=comm_stream.cuda_stream, + kernel( + rank, + recv_x, + recv_src_idx, + recv_topk_idx, + recv_topk_weights, + recv_channel_prefix_matrix, + send_head, + x, + topk_idx, + topk_weights, + is_token_in_rank, + rank_prefix_matrix, + channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_idx_buffers, + channel_topk_weights_buffers, + stream=comm_stream.cuda_stream, skip_tensor_validation=True) # reduce runtime overhead - handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) + handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, + recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: - kernel = cached_dispatch_kernel(num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, num_experts, config.num_sms, 'bfloat16') + kernel = cached_dispatch_kernel(num_ranks, num_tokens, + config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, + num_experts, config.num_sms, 'bfloat16') kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel(rank, recv_x, recv_src_idx, recv_channel_prefix_matrix, send_head, x, is_token_in_rank, rank_prefix_matrix, channel_prefix_matrix, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, stream=comm_stream.cuda_stream, + kernel( + rank, + recv_x, + recv_src_idx, + recv_channel_prefix_matrix, + send_head, + x, + is_token_in_rank, + rank_prefix_matrix, + channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + stream=comm_stream.cuda_stream, skip_tensor_validation=True) # reduce runtime overhead return recv_x diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 339033ebee..97b67d1a44 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -2,7 +2,9 @@ # This op is non-distributed ### python get_dispatch_layout.py -import os, sys +import os +import sys + sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch @@ -96,8 +98,7 @@ def get_dispatch_layout_main( expert_idx = T.alloc_var("int32") expert_idx = topk_idx[i, j] if expert_begin_idx <= expert_idx and expert_idx < expert_end_idx: - tokens_per_expert_per_thread[tx, - expert_idx - expert_begin_idx] += 1 + tokens_per_expert_per_thread[tx, expert_idx - expert_begin_idx] += 1 if expert_begin_idx + tx < expert_end_idx: sum = T.alloc_var("int32") diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index 9053f167a3..aed291c9db 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -1,10 +1,11 @@ ### TILELANG_USE_DISTRIBUTED=1 python test_intranode.py (--cached, optionally) -import os, sys +import os +import sys + sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path import torch -import tilelang from argparse import ArgumentParser from tilelang.distributed.utils import init_dist @@ -26,29 +27,32 @@ def test_intranode( cached_dispatch: bool, group: torch.distributed.ProcessGroup, ): - try: + try: import deep_ep # noqa: F403 - except ModuleNotFoundError as e: - raise ModuleNotFoundError("Please install DeepEP to run this test.") + except ModuleNotFoundError: + raise ModuleNotFoundError("Please install DeepEP to run this test.") from None # Create interface buffers ts_buffer = EPBuffer(group, 2**30, num_topk, num_experts, hidden) deepep_buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) # Generate inputs for testing - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, + num_ranks) # 1. test get_dispatch_layout - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout(topk_idx, num_experts) - num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout(topk_idx) - + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout( + topk_idx, num_experts) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout( + topk_idx) + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ f"[rank {rank}] num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ f"[rank {rank}] is_token_in_rank mismatch" assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ f"[rank {rank}] num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - + group.barrier() if rank == 0: print('Check passed for get_dispatch_layout. ✅') @@ -59,80 +63,125 @@ def test_intranode( deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) # ours if cached_dispatch: - recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment) + recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, + num_tokens_per_expert, None, None, expert_alignment) else: - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, + topk_weights, expert_alignment) # check dispatch output - assert torch.equal(recv_x, ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + assert torch.equal( + recv_x, + ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' if not cached_dispatch: - assert torch.equal(recv_topk_idx, ref_recv_topk_idx), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal(recv_topk_weights, ref_recv_topk_weights), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert torch.equal( + recv_topk_idx, ref_recv_topk_idx + ), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal( + recv_topk_weights, ref_recv_topk_weights + ), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, f'[rank {rank}] num_recv_tokens_per_expert_list mismatch' - + # check handle rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle - assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' - assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' - assert torch.equal(recv_src_idx, ref_recv_src_idx), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' - assert torch.equal(send_head, ref_send_head), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' - + assert torch.equal( + rank_prefix_matrix, ref_rank_prefix_matrix + ), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' + assert torch.equal( + channel_prefix_matrix, ref_channel_prefix_matrix + ), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' + assert torch.equal( + recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix + ), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' + assert torch.equal( + recv_src_idx, ref_recv_src_idx + ), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' + assert torch.equal( + is_token_in_rank, ref_is_token_in_rank + ), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' + assert torch.equal( + send_head, ref_send_head + ), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + group.barrier() if rank == 0: print(f'Check passed for {"cached" if cached_dispatch else "non-cached"} dispatch. ✅') # 3. test combine - ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights) + ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine( + recv_x, ref_handle, ref_recv_topk_weights) if cached_dispatch: # acquire handle first - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, + topk_weights, expert_alignment) combined_x, combined_topk_weights = ts_buffer.combine(recv_x, handle, recv_topk_weights) - assert torch.equal(combined_x, ref_combined_x), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' - assert torch.equal(combined_topk_weights, ref_combined_topk_weights), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' + assert torch.equal( + combined_x, ref_combined_x + ), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' + assert torch.equal( + combined_topk_weights, ref_combined_topk_weights + ), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' group.barrier() if rank == 0: - print(f'Check passed for combine. ✅') + print('Check passed for combine. ✅') if rank == 0: print('All checks passed for TileScale intranode DeepEP. ✅') # benchmark if rank == 0: - print(f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========') + print( + f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========' + ) if not cached_dispatch: group.barrier() - deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=50, rep=50) + deepep_dispatch_time = ep_bench( + lambda: deepep_buffer. + dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, + ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), + warmup=50, + rep=50) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() - ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=50, rep=50) + ts_dispatch_time = ep_bench( + lambda: ts_buffer. + dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, + topk_idx, topk_weights, expert_alignment), + warmup=50, + rep=50) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() else: group.barrier() - deepep_dispatch_time = ep_bench(lambda: deepep_buffer.dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, None, None, expert_alignment), - warmup=50, rep=50) + deepep_dispatch_time = ep_bench( + lambda: deepep_buffer. + dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, + ref_num_tokens_per_expert, None, None, expert_alignment), + warmup=50, + rep=50) print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') group.barrier() - ts_dispatch_time = ep_bench(lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment), - warmup=50, rep=50) + ts_dispatch_time = ep_bench( + lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, + num_tokens_per_expert, None, None, expert_alignment), + warmup=50, + rep=50) print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') group.barrier() if rank == 0: print('========== Benchmarking combine ==========') group.barrier() - deepep_combine_time = ep_bench(lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), - warmup=50, rep=50) + deepep_combine_time = ep_bench( + lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), warmup=50, rep=50) print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') - + group.barrier() - ts_combine_time = ep_bench(lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), - warmup=50, rep=50) + ts_combine_time = ep_bench( + lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), warmup=50, rep=50) print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') group.barrier() @@ -141,10 +190,18 @@ def test_intranode( dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes if rank == 0: - print(f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)') - print(f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)') - print(f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)') - print(f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)') + print( + f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)' + ) def main(local_rank: int, num_local_ranks: int, args): @@ -170,10 +227,12 @@ def parse_args(): parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument( + "--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - parser.add_argument("--cached", action="store_true", default=False, help="Whether to use cached dispatch") + parser.add_argument( + "--cached", action="store_true", default=False, help="Whether to use cached dispatch") return parser.parse_args() diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/utils.py index 5518e66ac3..1294acb316 100644 --- a/examples/distributed/deepseek_deepep/utils.py +++ b/examples/distributed/deepseek_deepep/utils.py @@ -16,20 +16,19 @@ @dataclass class Config: - num_sms : int # the SMs used in high-throughput kernels - num_max_nvl_chunked_send_tokens : int - num_max_nvl_chunked_recv_tokens : int - num_max_rdma_chunked_send_tokens : int - num_max_rdma_chunked_recv_tokens : int + num_sms: int # the SMs used in high-throughput kernels + num_max_nvl_chunked_send_tokens: int + num_max_nvl_chunked_recv_tokens: int + num_max_rdma_chunked_send_tokens: int + num_max_rdma_chunked_recv_tokens: int num_channels: int = field(init=False) - def __post_init__(self): assert self.num_sms % 2 == 0, "num_sms must be even" self.num_channels = self.num_sms // 2 # 1 sm for send, 1 sm for recv in each channel - + @staticmethod def get_dispatch_config(num_ranks: int) -> 'Config': """ @@ -59,8 +58,7 @@ def get_dispatch_config(num_ranks: int) -> 'Config': } assert num_ranks in config_map, f'Unsupported number of EP ranks: {num_ranks}' return config_map[num_ranks] - - + @staticmethod def get_combine_config(num_ranks: int) -> 'Config': """ @@ -93,7 +91,9 @@ def get_combine_config(num_ranks: int) -> 'Config': # Only necessary in inter-node cases -def set_rdma_env_args(num_qps_per_rank: int = 24, allow_nvlink_for_low_latency_mode: bool = True, allow_mnnvl: bool = False): +def set_rdma_env_args(num_qps_per_rank: int = 24, + allow_nvlink_for_low_latency_mode: bool = True, + allow_mnnvl: bool = False): os.environ['NVSHMEM_DISABLE_P2P'] = '0' if allow_nvlink_for_low_latency_mode else '1' os.environ['NVSHMEM_IB_ENABLE_IBGDA'] = '1' os.environ['NVSHMEM_IBGDA_NUM_RC_PER_PE'] = f'{num_qps_per_rank}' @@ -134,14 +134,14 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu num_topk: the number of top-k experts to select for each token. num_experts: the number of experts. num_ranks: the number of total ranks. - + Returns: x: `[num_tokens, hidden]` with `torch.bfloat16`, the input to MoE layer. topk_idx: `[num_tokens, num_topk]` with `torch.int64`, the expert indices selected by each token, `-1` means no selections. - topk_weights: `[num_tokens, num_topk]` with `torch.float32`, the weights corresponding to + topk_weights: `[num_tokens, num_topk]` with `torch.float32`, the weights corresponding to each selected expert for each token. - rank_idx: `[num_tokens, num_topk]` with `torch.int32`, the rank indices corresponding to + rank_idx: `[num_tokens, num_topk]` with `torch.int32`, the rank indices corresponding to each selected expert, `-1` means no selections. """ assert num_topk <= num_experts, "num_topk must be less than or equal to num_experts" @@ -160,7 +160,7 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu def inplace_unique(x: torch.Tensor, num_slots: int): """ - Keep at most `num_slots` different values in each row of `x`, + Keep at most `num_slots` different values in each row of `x`, and fill `x` with -1 in other positions. """ assert x.dim() == 2 and num_slots <= x.size(-1) @@ -176,7 +176,7 @@ def inplace_unique(x: torch.Tensor, num_slots: int): valid_len = min(num_slots, x.size(1)) x[:, :valid_len] = sorted_bin_idx[:, :valid_len] - + def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): """DeepEP style benchmark function. Args: @@ -238,11 +238,11 @@ def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): // After ready, get counter values to return int counter_value = counter_ptr[0]; - + std::vector expert_counter_values = std::vector( - expert_ptr, + expert_ptr, expert_ptr + num_local_experts); - + return std::make_tuple(counter_value, expert_counter_values); } """ @@ -252,6 +252,4 @@ def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): cpp_sources=_src, functions=["wait_for_counters_ready"], extra_cflags=["-O3", "-march=native"], - verbose=False -) - + verbose=False) diff --git a/examples/distributed/primitives/example_remote_st.py b/examples/distributed/primitives/example_remote_st.py index b09ad8839e..251e5e08b3 100644 --- a/examples/distributed/primitives/example_remote_st.py +++ b/examples/distributed/primitives/example_remote_st.py @@ -24,7 +24,7 @@ def main( rank[0] = T.get_rank() num_rank[0] = T.get_num_ranks() tx = T.get_thread_binding() - T.st(dst[bx * block_M + tx], src[bx * block_M + tx], dst_pe=1-rank[0]) + T.st(dst[bx * block_M + tx], src[bx * block_M + tx], dst_pe=1 - rank[0]) return main diff --git a/examples/distributed/primitives/test_ld_options.py b/examples/distributed/primitives/test_ld_options.py index 7c9f6aeb6a..1b60f18a27 100644 --- a/examples/distributed/primitives/test_ld_options.py +++ b/examples/distributed/primitives/test_ld_options.py @@ -1,21 +1,21 @@ import torch import tilelang import tilelang.language as T + tilelang.disable_cache() @tilelang.jit def get_kernel(scope, sem, na, nc): + @T.prim_func - def main( - x: T.Tensor((32), "int32"), - y: T.Tensor((32), "int32") - ): - with T.Kernel(1, threads=32): + def main(x: T.Tensor((32), "int32"), y: T.Tensor((32), "int32")): + with T.Kernel(1, threads=32): tx = T.get_thread_binding() reg = T.alloc_var('int32') T.ld(x[tx], reg, scope=scope, sem=sem, na=na, nc=nc) y[tx] = reg + return main @@ -26,27 +26,25 @@ def test_ld_options(scope, sem, na, nc): kernel(x, y) assert torch.equal(x, y) print(f'check passed for {scope=}.{sem=}.{na=}.{nc=} ✅') - if __name__ == "__main__": - # from DeepEP all ld instructions - + # from DeepEP all ld instructions + # ld.acquire.sys.global.s32 / u64 test_ld_options(scope="sys", sem="acquire", na=False, nc=False) - + # ld.acquire.gpu.global.s32 test_ld_options(scope="gpu", sem="acquire", na=False, nc=False) - + # ld.acquire.cta.s32 test_ld_options(scope="cta", sem="acquire", na=False, nc=False) - + # ld.relaxed.gpu.global.L1::no_allocate.b8/b16/b32/b64 test_ld_options(scope="gpu", sem="relaxed", na=True, nc=False) - + # ld.volatile.global.s32/f32/s64/u64 test_ld_options(scope="gpu", sem="volatile", na=False, nc=False) - + # ld.global.nc.L1::no_allocate.L2::256B (or ld.volatile.global when DISABLE_AGGRESSIVE_PTX_INSTRS) test_ld_options(scope="gpu", sem="weak", na=True, nc=True) - diff --git a/examples/distributed/primitives/test_st_options.py b/examples/distributed/primitives/test_st_options.py index d42e9d4247..ac5c900adf 100644 --- a/examples/distributed/primitives/test_st_options.py +++ b/examples/distributed/primitives/test_st_options.py @@ -1,18 +1,19 @@ import torch import tilelang import tilelang.language as T + tilelang.disable_cache() @tilelang.jit def get_kernel(scope, sem, na): + @T.prim_func - def main( - x: T.Tensor((32), "int32") - ): - with T.Kernel(1, threads=32): + def main(x: T.Tensor((32), "int32")): + with T.Kernel(1, threads=32): tx = T.get_thread_binding() T.st(x[tx], tx, scope=scope, sem=sem, na=na) + return main @@ -22,7 +23,6 @@ def test_st_options(scope, sem, na): kernel(x) assert x.equal(torch.arange(32, device="cuda")) print(f'check passed for {scope=}.{sem=}.{na=} ✅') - if __name__ == "__main__": @@ -30,22 +30,19 @@ def test_st_options(scope, sem, na): # st.relaxed.sys.global.s32 test_st_options("sys", "relaxed", False) - + # # st.release.sys.global.s32 test_st_options("sys", "release", False) - + # st.release.cta.s32 test_st_options("cta", "release", False) - + # st.relaxed.gpu.global.L1::no_allocate.b* test_st_options("gpu", "relaxed", True) - + # st.release.gpu.global.L1::no_allocate.b* test_st_options("gpu", "release", True) # test_st_options("gpu", "weak", False) test_st_options("gpu", "weak", False) test_st_options("gpu", "weak", True) - - - \ No newline at end of file diff --git a/src/op/builtin.cc b/src/op/builtin.cc index d325654cdf..18baaae3c6 100644 --- a/src/op/builtin.cc +++ b/src/op/builtin.cc @@ -345,24 +345,18 @@ TIR_DEFINE_TL_BUILTIN(elect_one_sync) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_DEFINE_TL_BUILTIN(sync_warp) - .set_num_inputs(0) - .set_attr("TCallEffectKind", - Integer(CallEffectKind::kOpaque)); +TIR_DEFINE_TL_BUILTIN(sync_warp).set_num_inputs(0).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kOpaque)); TIR_DEFINE_TL_BUILTIN(loop_continue) .set_num_inputs(0) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_DEFINE_TL_BUILTIN(warp_any) - .set_num_inputs(2) - .set_attr("TCallEffectKind", - Integer(CallEffectKind::kPure)); +TIR_DEFINE_TL_BUILTIN(warp_any).set_num_inputs(2).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kPure)); -TIR_DEFINE_TL_BUILTIN(warp_all) - .set_num_inputs(2) - .set_attr("TCallEffectKind", - Integer(CallEffectKind::kPure)); +TIR_DEFINE_TL_BUILTIN(warp_all).set_num_inputs(2).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kPure)); } // namespace tl } // namespace tvm diff --git a/src/op/builtin.h b/src/op/builtin.h index 354114ea33..b4b9bf934b 100644 --- a/src/op/builtin.h +++ b/src/op/builtin.h @@ -571,7 +571,8 @@ TVM_DLL const Op &warp_reduce_bitand(); TVM_DLL const Op &warp_reduce_bitor(); /*! - * \brief tilelang intrinsic for electing exactly one lane within a logical thread group. + * \brief tilelang intrinsic for electing exactly one lane within a logical + * thread group. */ TVM_DLL const Op &elect_one_sync(); @@ -586,12 +587,14 @@ TVM_DLL const Op &sync_warp(); TVM_DLL const Op &loop_continue(); /*! - * \brief tilelang intrinsic for checking if any lane in the warp has a true value. + * \brief tilelang intrinsic for checking if any lane in the warp has a true + * value. */ TVM_DLL const Op &warp_any(); /*! - * \brief tilelang intrinsic for checking if all lanes in the warp have a true value. + * \brief tilelang intrinsic for checking if all lanes in the warp have a true + * value. */ TVM_DLL const Op &warp_all(); diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index 3ccf78a2cc..f7430c30ad 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -84,7 +84,8 @@ PutOp::PutOp(Array args, BufferMap vmap) { } bool PutOpNode::is_distributed() const { - return !(dst_pe->IsInstance() && dst_pe.as()->value == -1); + return !(dst_pe->IsInstance() && + dst_pe.as()->value == -1); } Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { @@ -92,8 +93,8 @@ Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { Array new_args; std::stringstream ss; if (scope == "warp") { - ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " - << (enable_aggresive_vectorize ? "true" : "false") << ">"; + ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " + << (enable_aggresive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { @@ -193,7 +194,8 @@ GetOp::GetOp(Array args, BufferMap vmap) { } bool GetOpNode::is_distributed() const { - return !(src_pe->IsInstance() && src_pe.as()->value == -1); + return !(src_pe->IsInstance() && + src_pe.as()->value == -1); } Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { @@ -201,8 +203,8 @@ Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { Array new_args; std::stringstream ss; if (scope == "warp") { - ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " - << (enable_aggresive_vectorize ? "true" : "false") << ">"; + ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " + << (enable_aggresive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { @@ -260,7 +262,8 @@ StOp::StOp(Array args, BufferMap vmap) { } bool StOpNode::is_distributed() const { - return !(dst_pe->IsInstance() && dst_pe.as()->value == -1); + return !(dst_pe->IsInstance() && + dst_pe.as()->value == -1); } Stmt StOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { @@ -268,22 +271,24 @@ Stmt StOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { (void)T; Array new_args; std::stringstream ss; - + // Map integers to enum literal strings - const char* sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", "Semantic::ACQUIRE", "Semantic::RELEASE", "Semantic::RELAXED"}; - const char* scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; - + const char *sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", + "Semantic::ACQUIRE", "Semantic::RELEASE", + "Semantic::RELAXED"}; + const char *scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; + // Build function name: tl::st - ss << "tl::st<" << sem_str[sem] << ", " << scope_str[scope] << ", " << (na ? "true" : "false") << ">"; - + ss << "tl::st<" << sem_str[sem] << ", " << scope_str[scope] << ", " + << (na ? "true" : "false") << ">"; + new_args.push_back(StringImm(ss.str())); if (is_distributed()) { PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); PrimExpr local_base_ptr = Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); - PrimExpr offset_to_base = - Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {dst}), - local_base_ptr); + PrimExpr offset_to_base = Sub( + Call(DataType::Handle(), tl::get_uintptr_t(), {dst}), local_base_ptr); new_args.push_back( Call(DataType::Handle(), tl::get_remote_base_ptr(), {dst_pe}) + offset_to_base); @@ -291,7 +296,7 @@ Stmt StOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { new_args.push_back(dst); } new_args.push_back(value); - + auto st = Call(DataType::Handle(), builtin::call_extern(), new_args); return Evaluate(st); } @@ -326,7 +331,8 @@ LdOp::LdOp(Array args, BufferMap vmap) { } bool LdOpNode::is_distributed() const { - return !(src_pe->IsInstance() && src_pe.as()->value == -1); + return !(src_pe->IsInstance() && + src_pe.as()->value == -1); } Stmt LdOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { @@ -334,22 +340,24 @@ Stmt LdOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { (void)T; Array new_args; std::stringstream ss; - + // Map integers to enum literal strings - const char* sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", "Semantic::ACQUIRE", "Semantic::RELEASE", "Semantic::RELAXED"}; - const char* scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; - + const char *sem_str[] = {"Semantic::WEAK", "Semantic::VOLATILE", + "Semantic::ACQUIRE", "Semantic::RELEASE", + "Semantic::RELAXED"}; + const char *scope_str[] = {"Scope::CTA", "Scope::GPU", "Scope::SYS"}; + // Build function name: tl::ld - ss << "tl::ld<" << sem_str[sem] << ", " << scope_str[scope] << ", " << (nc ? "true" : "false") << ", " << (na ? "true" : "false") << ">"; - + ss << "tl::ld<" << sem_str[sem] << ", " << scope_str[scope] << ", " + << (nc ? "true" : "false") << ", " << (na ? "true" : "false") << ">"; + new_args.push_back(StringImm(ss.str())); if (is_distributed()) { PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); PrimExpr local_base_ptr = Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); - PrimExpr offset_to_base = - Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {src}), - local_base_ptr); + PrimExpr offset_to_base = Sub( + Call(DataType::Handle(), tl::get_uintptr_t(), {src}), local_base_ptr); new_args.push_back( Call(DataType::Handle(), tl::get_remote_base_ptr(), {src_pe}) + offset_to_base); @@ -357,7 +365,7 @@ Stmt LdOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { new_args.push_back(src); } new_args.push_back(value); - + auto ld = Call(DataType::Handle(), builtin::call_extern(), new_args); return Evaluate(ld); } @@ -384,15 +392,11 @@ TIR_REGISTER_TL_OP(GetOp, get) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_REGISTER_TL_OP(StOp, st) - .set_num_inputs(6) - .set_attr("TCallEffectKind", - Integer(CallEffectKind::kOpaque)); +TIR_REGISTER_TL_OP(StOp, st).set_num_inputs(6).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -TIR_REGISTER_TL_OP(LdOp, ld) - .set_num_inputs(7) - .set_attr("TCallEffectKind", - Integer(CallEffectKind::kOpaque)); +TIR_REGISTER_TL_OP(LdOp, ld).set_num_inputs(7).set_attr( + "TCallEffectKind", Integer(CallEffectKind::kOpaque)); TVM_FFI_STATIC_INIT_BLOCK({ PutOpNode::RegisterReflection(); }); TVM_FFI_STATIC_INIT_BLOCK({ GetOpNode::RegisterReflection(); }); diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index d089e9ce66..c5397d73d4 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -33,8 +33,10 @@ class PutOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} - bool enable_aggresive_vectorize; ///< Whether to enable aggressive vectorization, only effctive for warp-scope - + bool enable_aggresive_vectorize; ///< Whether to enable aggressive + ///< vectorization, only effctive for + ///< warp-scope + bool is_distributed() const; static constexpr const char *_type_key = "tl.PutOp"; @@ -123,7 +125,9 @@ class GetOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} - bool enable_aggresive_vectorize; ///< Whether to enable aggressive vectorization, only effctive for warp-scope + bool enable_aggresive_vectorize; ///< Whether to enable aggressive + ///< vectorization, only effctive for + ///< warp-scope bool is_distributed() const; @@ -200,9 +204,9 @@ class GetOp : public TileOperator { class StOpNode : public TileOperatorNode { public: - PrimExpr dst; ///< Destination address - PrimExpr value; ///< Value to store - PrimExpr dst_pe; ///< Destination processing element (optional) + PrimExpr dst; ///< Destination address + PrimExpr value; ///< Value to store + PrimExpr dst_pe; ///< Destination processing element (optional) int scope; int sem; int na; @@ -230,12 +234,9 @@ class StOpNode : public TileOperatorNode { } bool SEqualReduce(const StOpNode *other, SEqualReducer equal) const { - return equal(dst, other->dst) && - equal(value, other->value) && - equal(dst_pe, other->dst_pe) && - scope == other->scope && - sem == other->sem && - na == other->na; + return equal(dst, other->dst) && equal(value, other->value) && + equal(dst_pe, other->dst_pe) && scope == other->scope && + sem == other->sem && na == other->na; } void SHashReduce(SHashReducer hash_reduce) const { @@ -259,69 +260,64 @@ class StOp : public TileOperator { }; class LdOpNode : public TileOperatorNode { - public: - PrimExpr src; ///< Source address - PrimExpr value; ///< Value to store - PrimExpr src_pe; ///< Source PE (optional) - int scope; - int sem; - int na; - int nc; - - bool is_distributed() const; - - static constexpr const char *_type_key = "tl.LdOp"; - TVM_DECLARE_FINAL_OBJECT_INFO(LdOpNode, TileOperatorNode); - - Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; - LayoutMap InferLayout(const LayoutInferArgs &T, - InferLevel level) const override; - static const Op &Get(); - TileOperator Clone() const override; - - static void RegisterReflection() { - namespace refl = tvm::ffi::reflection; - refl::ObjectDef() - .def_ro("src", &LdOpNode::src) - .def_ro("value", &LdOpNode::value) - .def_ro("src_pe", &LdOpNode::src_pe) - .def_ro("scope", &LdOpNode::scope) - .def_ro("sem", &LdOpNode::sem) - .def_ro("na", &LdOpNode::na) - .def_ro("nc", &LdOpNode::nc); - } - - bool SEqualReduce(const LdOpNode *other, SEqualReducer equal) const { - return equal(src, other->src) && - equal(value, other->value) && - equal(src_pe, other->src_pe) && - scope == other->scope && - sem == other->sem && - na == other->na && - nc == other->nc; - } - - void SHashReduce(SHashReducer hash_reduce) const { - hash_reduce(src); - hash_reduce(value); - hash_reduce(src_pe); - hash_reduce(scope); - hash_reduce(sem); - hash_reduce(na); - hash_reduce(nc); - } - - static constexpr bool _type_has_method_sequal_reduce = true; - static constexpr bool _type_has_method_shash_reduce = true; - }; - +public: + PrimExpr src; ///< Source address + PrimExpr value; ///< Value to store + PrimExpr src_pe; ///< Source PE (optional) + int scope; + int sem; + int na; + int nc; + + bool is_distributed() const; + + static constexpr const char *_type_key = "tl.LdOp"; + TVM_DECLARE_FINAL_OBJECT_INFO(LdOpNode, TileOperatorNode); + + Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; + LayoutMap InferLayout(const LayoutInferArgs &T, + InferLevel level) const override; + static const Op &Get(); + TileOperator Clone() const override; + + static void RegisterReflection() { + namespace refl = tvm::ffi::reflection; + refl::ObjectDef() + .def_ro("src", &LdOpNode::src) + .def_ro("value", &LdOpNode::value) + .def_ro("src_pe", &LdOpNode::src_pe) + .def_ro("scope", &LdOpNode::scope) + .def_ro("sem", &LdOpNode::sem) + .def_ro("na", &LdOpNode::na) + .def_ro("nc", &LdOpNode::nc); + } + + bool SEqualReduce(const LdOpNode *other, SEqualReducer equal) const { + return equal(src, other->src) && equal(value, other->value) && + equal(src_pe, other->src_pe) && scope == other->scope && + sem == other->sem && na == other->na && nc == other->nc; + } + + void SHashReduce(SHashReducer hash_reduce) const { + hash_reduce(src); + hash_reduce(value); + hash_reduce(src_pe); + hash_reduce(scope); + hash_reduce(sem); + hash_reduce(na); + hash_reduce(nc); + } + + static constexpr bool _type_has_method_sequal_reduce = true; + static constexpr bool _type_has_method_shash_reduce = true; +}; + class LdOp : public TileOperator { public: TVM_DEFINE_OBJECT_REF_METHODS(LdOp, TileOperator, LdOpNode); TVM_DLL LdOp(Array args, BufferMap vmap); static const Op &Get(); }; - } // namespace tl } // namespace tvm diff --git a/src/op/sync.cc b/src/op/sync.cc index 852013fc03..892fc22203 100644 --- a/src/op/sync.cc +++ b/src/op/sync.cc @@ -17,8 +17,7 @@ namespace tl { using namespace tir; -PrimExpr -BarrierBlocksOpNode::get_offset(const BufferLoadNode *load) const { +PrimExpr BarrierBlocksOpNode::get_offset(const BufferLoadNode *load) const { PrimExpr offset = 0; PrimExpr stride = 1; auto buffer_shape = load->buffer->shape; @@ -63,10 +62,8 @@ TIR_DEFINE_TL_BUILTIN(sync_barrier_gpu) TIR_DEFINE_TL_BUILTIN(sync_grid).set_num_inputs(1).set_attr( "TCallEffectKind", Integer(CallEffectKind::kOpaque)); -BarrierBlocksOp::BarrierBlocksOp(Array args, - BufferMap vmap) { - ObjectPtr node = - make_object(); +BarrierBlocksOp::BarrierBlocksOp(Array args, BufferMap vmap) { + ObjectPtr node = make_object(); node->local_bar_addr = args[0]; node->need_fence = bool(args[1].as()->value); const auto *call = node->local_bar_addr.as(); @@ -149,7 +146,8 @@ WaitOp::WaitOp(Array args, BufferMap vmap) { } bool WaitOpNode::is_distributed() const { - return !(peer->IsInstance() && peer.as()->value == -1); + return !(peer->IsInstance() && + peer.as()->value == -1); } Stmt WaitOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { @@ -159,17 +157,16 @@ Stmt WaitOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { std::stringstream ss; // Map relation as int to literal_strings - const char* relation_str[] = {"eq", "ne", "ge", "le", "gt", "lt"}; + const char *relation_str[] = {"eq", "ne", "ge", "le", "gt", "lt"}; ss << "tl::wait_" << relation_str[relation]; - + new_args.push_back(StringImm(ss.str())); if (is_distributed()) { PrimExpr local_rank = Call(DataType::Int(64), tl::get_rank(), {}); PrimExpr local_base_ptr = Call(DataType::Handle(), tl::get_remote_base_ptr(), {local_rank}); - PrimExpr offset_to_base = - Sub(Call(DataType::Handle(), tl::get_uintptr_t(), {addr}), - local_base_ptr); + PrimExpr offset_to_base = Sub( + Call(DataType::Handle(), tl::get_uintptr_t(), {addr}), local_base_ptr); new_args.push_back( Call(DataType::Handle(), tl::get_remote_base_ptr(), {peer}) + offset_to_base); @@ -177,12 +174,13 @@ Stmt WaitOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { new_args.push_back(addr); } new_args.push_back(expected); - + auto wait = Call(DataType::Handle(), builtin::call_extern(), new_args); return Evaluate(wait); } -LayoutMap WaitOpNode::InferLayout(const LayoutInferArgs &T, InferLevel level) const { +LayoutMap WaitOpNode::InferLayout(const LayoutInferArgs &T, + InferLevel level) const { (void)T; (void)level; return {}; diff --git a/src/op/sync.h b/src/op/sync.h index 718e4f116b..16487877e3 100644 --- a/src/op/sync.h +++ b/src/op/sync.h @@ -45,7 +45,6 @@ TVM_DLL const Op &wait_barrier_gpu(); TVM_DLL const Op &wait_eq(); - /*! * \brief TileOperatorNode for wait operation. * @@ -53,20 +52,21 @@ TVM_DLL const Op &wait_eq(); * which waits until a condition on a memory address is met. */ class WaitOpNode : public TileOperatorNode { - public: - PrimExpr addr; ///< The address to watch. - PrimExpr expected; ///< The expected value to compare against. - PrimExpr peer; ///< The peer to compare against. - int relation; ///< The relation to compare against. +public: + PrimExpr addr; ///< The address to watch. + PrimExpr expected; ///< The expected value to compare against. + PrimExpr peer; ///< The peer to compare against. + int relation; ///< The relation to compare against. bool is_distributed() const; - static constexpr const char* _type_key = "tl.WaitOp"; + static constexpr const char *_type_key = "tl.WaitOp"; TVM_DECLARE_FINAL_OBJECT_INFO(WaitOpNode, TileOperatorNode); - Stmt Lower(const LowerArgs& T, arith::Analyzer* analyzer) const override; - LayoutMap InferLayout(const LayoutInferArgs& T, InferLevel level) const override; - static const Op& Get(); + Stmt Lower(const LowerArgs &T, arith::Analyzer *analyzer) const override; + LayoutMap InferLayout(const LayoutInferArgs &T, + InferLevel level) const override; + static const Op &Get(); TileOperator Clone() const override; static void RegisterReflection() { @@ -78,7 +78,7 @@ class WaitOpNode : public TileOperatorNode { .def_ro("relation", &WaitOpNode::relation); } - bool SEqualReduce(const WaitOpNode* other, SEqualReducer equal) const { + bool SEqualReduce(const WaitOpNode *other, SEqualReducer equal) const { return equal(addr, other->addr) && equal(expected, other->expected) && equal(peer, other->peer) && equal(relation, other->relation); } @@ -98,10 +98,10 @@ class WaitOpNode : public TileOperatorNode { * \brief Wrapper for the WaitOp operator. */ class WaitOp : public TileOperator { - public: +public: TVM_DEFINE_OBJECT_REF_METHODS(WaitOp, TileOperator, WaitOpNode); TVM_DLL WaitOp(Array args, BufferMap vmap); - static const Op& Get(); + static const Op &Get(); }; /*! @@ -131,7 +131,7 @@ class BarrierBlocksOpNode : public TileOperatorNode { PrimExpr offset; ///< Byte offset within the barrier buffer Buffer local_bar; ///< Local barrier buffer reference Array local_indices; ///< Indices used to access the barrier buffer - bool need_fence; ///< Whether need sys-level fence + bool need_fence; ///< Whether need sys-level fence static constexpr const char *_type_key = "tl.BarrierBlocksOp"; TVM_DECLARE_FINAL_OBJECT_INFO(BarrierBlocksOpNode, TileOperatorNode); diff --git a/src/target/codegen_cuda.cc b/src/target/codegen_cuda.cc index 4b1949a292..b39e9b042e 100644 --- a/src/target/codegen_cuda.cc +++ b/src/target/codegen_cuda.cc @@ -2365,9 +2365,11 @@ void CodeGenTileLangCUDA::VisitExpr_(const CallNode *op, std::ostream &os) { } else if (op->op.same_as(tl::loop_continue())) { os << "continue"; } else if (op->op.same_as(tl::warp_any())) { - os << "__any_sync(" << PrintExpr(op->args[1]) << ", " << PrintExpr(op->args[0]) << ")"; + os << "__any_sync(" << PrintExpr(op->args[1]) << ", " + << PrintExpr(op->args[0]) << ")"; } else if (op->op.same_as(tl::warp_all())) { - os << "__all_sync(" << PrintExpr(op->args[1]) << ", " << PrintExpr(op->args[0]) << ")"; + os << "__all_sync(" << PrintExpr(op->args[1]) << ", " + << PrintExpr(op->args[0]) << ")"; } else { CodeGenC::VisitExpr_(op, os); } diff --git a/src/tl_templates/cuda/copy.h b/src/tl_templates/cuda/copy.h index b33af49c9a..eaf4091558 100644 --- a/src/tl_templates/cuda/copy.h +++ b/src/tl_templates/cuda/copy.h @@ -165,11 +165,13 @@ TL_DEVICE void st_na_global(const dtype_t *ptr, const dtype_t &value) { &value)); } -template <> TL_DEVICE void st_na_global(const int16_t *ptr, const int16_t &value) { +template <> +TL_DEVICE void st_na_global(const int16_t *ptr, const int16_t &value) { asm volatile(ST_NA_FUNC ".s16 [%0], %1;" ::"l"(ptr), "h"(value)); } -template <> TL_DEVICE void st_na_global(const uint16_t *ptr, const uint16_t &value) { +template <> +TL_DEVICE void st_na_global(const uint16_t *ptr, const uint16_t &value) { asm volatile(ST_NA_FUNC ".u16 [%0], %1;" ::"l"(ptr), "h"(value)); } @@ -196,7 +198,7 @@ template <> TL_DEVICE void st_na_global(const int4 *ptr, const int4 &value) { template TL_DEVICE void cp_warp_impl(dtype_t const *const dst_addr, - dtype_t const *const src_addr) { + dtype_t const *const src_addr) { int lane_id; asm("mov.s32 %0, %laneid;" : "=r"(lane_id)); constexpr int kLoopStride = 32 * (UNROLL_FACTOR); @@ -217,13 +219,15 @@ TL_DEVICE void cp_warp_impl(dtype_t const *const dst_addr, } /** - * @param enable_aggresive_vectorize If set to true, the copy will be performed with aggressive vectorization - * (e.g., using int4 for aligned and sized transfers), which requires that both source and destination addresses - * are 16-byte aligned and N*sizeof(dtype_t) is a multiple of 16 for optimal memory access and throughput. - * If false, performs a standard element-wise copy. + * @param enable_aggresive_vectorize If set to true, the copy will be performed + * with aggressive vectorization (e.g., using int4 for aligned and sized + * transfers), which requires that both source and destination addresses are + * 16-byte aligned and N*sizeof(dtype_t) is a multiple of 16 for optimal memory + * access and throughput. If false, performs a standard element-wise copy. */ - // todo: support more auto-vectorize later -template +// todo: support more auto-vectorize later +template TL_DEVICE void cp_warp(dtype_t const *const dst_addr, dtype_t const *const src_addr) { if constexpr (enable_aggresive_vectorize) { @@ -236,8 +240,10 @@ TL_DEVICE void cp_warp(dtype_t const *const dst_addr, } } -template -TL_DEVICE void cp_warp(uint64_t dst_addr_uint64,dtype_t const *const src_addr) { +template +TL_DEVICE void cp_warp(uint64_t dst_addr_uint64, + dtype_t const *const src_addr) { dtype_t *dst_addr = reinterpret_cast(dst_addr_uint64); if constexpr (enable_aggresive_vectorize) { int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; @@ -249,7 +255,8 @@ TL_DEVICE void cp_warp(uint64_t dst_addr_uint64,dtype_t const *const src_addr) { } } -template +template TL_DEVICE void cp_warp(dtype_t *const dst_addr, uint64_t src_addr_uint64) { const dtype_t *src_addr = reinterpret_cast(src_addr_uint64); if constexpr (enable_aggresive_vectorize) { diff --git a/src/tl_templates/cuda/debug.h b/src/tl_templates/cuda/debug.h index d198840020..3a71f60ba4 100644 --- a/src/tl_templates/cuda/debug.h +++ b/src/tl_templates/cuda/debug.h @@ -260,8 +260,8 @@ __device__ void debug_print_buffer_value(const char *msg, // Specialization for msg-only debug print __device__ void debug_print_msg(const char *msg) { - printf("msg='%s' BlockIdx=(%d, %d, %d), ThreadIdx=(%d, %d, %d)\n", - msg, blockIdx.x, blockIdx.y, blockIdx.z, threadIdx.x, threadIdx.y, + printf("msg='%s' BlockIdx=(%d, %d, %d), ThreadIdx=(%d, %d, %d)\n", msg, + blockIdx.x, blockIdx.y, blockIdx.z, threadIdx.x, threadIdx.y, threadIdx.z); } diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h index 4dbe319d3f..a34595910b 100644 --- a/src/tl_templates/cuda/ldst.h +++ b/src/tl_templates/cuda/ldst.h @@ -12,126 +12,145 @@ template inline constexpr bool always_false_v = false; #endif // Type trait to detect bfloat16 types -template -struct is_bfloat16 : std::false_type {}; +template struct is_bfloat16 : std::false_type {}; #ifdef __CUDA_BF16_TYPES_EXIST__ -template <> -struct is_bfloat16<__nv_bfloat16> : std::true_type {}; +template <> struct is_bfloat16<__nv_bfloat16> : std::true_type {}; #endif // Detect cutlass bfloat16_t -namespace cutlass { struct bfloat16_t; } -template <> -struct is_bfloat16 : std::true_type {}; +namespace cutlass { +struct bfloat16_t; +} +template <> struct is_bfloat16 : std::true_type {}; template inline constexpr bool is_bfloat16_v = is_bfloat16::value; // Fallback template for unsupported configurations -template -struct StImpl { - template - TL_DEVICE static void execute(T *ptr, T value) { - static_assert(always_false_v, - "tl::st: unsupported configuration. "); +template struct StImpl { + template TL_DEVICE static void execute(T *ptr, T value) { + static_assert(always_false_v, "tl::st: unsupported configuration. "); } }; -template -struct LdImpl { - template - TL_DEVICE static void execute(const T *ptr, T &value) { - static_assert(always_false_v, - "tl::ld: unsupported configuration. "); +template struct LdImpl { + template TL_DEVICE static void execute(const T *ptr, T &value) { + static_assert(always_false_v, "tl::ld: unsupported configuration. "); } }; // Macro to define implementation with generic type T -#define TL_ST_IMPL(SEM, SCOPE, NA, SEM_LIT, SCOPE_LIT, NA_LIT) \ - template <> \ - struct StImpl { \ - template \ - TL_DEVICE static void execute(T *ptr, T value) { \ - if constexpr (sizeof(T) == 2) { \ - if constexpr (is_bfloat16_v) { \ - uint16_t value_bits = *reinterpret_cast(&value); \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ - :: "l"(ptr), "h"(value_bits) : "memory"); \ - } else { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b16 [%0], %1;" \ - :: "l"(ptr), "h"(value) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 4) { \ - if constexpr (std::is_floating_point_v) { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b32 [%0], %1;" \ - :: "l"(ptr), "f"(value) : "memory"); \ - } else { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b32 [%0], %1;" \ - :: "l"(ptr), "r"(value) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 8) { \ - if constexpr (std::is_floating_point_v) { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b64 [%0], %1;" \ - :: "l"(ptr), "d"(value) : "memory"); \ - } else { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".b64 [%0], %1;" \ - :: "l"(ptr), "l"(value) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 16) { \ - asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ - :: "l"(ptr), "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) : "memory"); \ - } \ - } \ +#define TL_ST_IMPL(SEM, SCOPE, NA, SEM_LIT, SCOPE_LIT, NA_LIT) \ + template <> struct StImpl { \ + template TL_DEVICE static void execute(T *ptr, T value) { \ + if constexpr (sizeof(T) == 2) { \ + if constexpr (is_bfloat16_v) { \ + uint16_t value_bits = *reinterpret_cast(&value); \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b16 [%0], %1;" ::"l"(ptr), \ + "h"(value_bits) \ + : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b16 [%0], %1;" ::"l"(ptr), \ + "h"(value) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 4) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b32 [%0], %1;" ::"l"(ptr), \ + "f"(value) \ + : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b32 [%0], %1;" ::"l"(ptr), \ + "r"(value) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 8) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b64 [%0], %1;" ::"l"(ptr), \ + "d"(value) \ + : "memory"); \ + } else { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".b64 [%0], %1;" ::"l"(ptr), \ + "l"(value) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 16) { \ + asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ + ".v4.s32 {%0, %1, %2, %3}, [%4];" ::"l"(ptr), \ + "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) \ + : "memory"); \ + } \ + } \ }; // Macro to define implementation of tl::ld with generic type T -#define TL_LD_IMPL(SEM, SCOPE, NC, NA, SEM_LIT, SCOPE_LIT, NC_LIT, NA_LIT) \ - template <> \ - struct LdImpl { \ - template \ - TL_DEVICE static void execute(const T *ptr, T &value) { \ - if constexpr (sizeof(T) == 2) { \ - if constexpr (is_bfloat16_v) { \ - uint16_t value_bits; \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ - : "=h"(value_bits) : "l"(ptr) : "memory"); \ - value = *reinterpret_cast(&value_bits); \ - } else { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ - : "=h"(value) : "l"(ptr) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 4) { \ - if constexpr (std::is_floating_point_v) { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ - : "=f"(value) : "l"(ptr) : "memory"); \ - } else { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ - : "=r"(value) : "l"(ptr) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 8) { \ - if constexpr (std::is_floating_point_v) { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ - : "=d"(value) : "l"(ptr) : "memory"); \ - } else { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ - : "=l"(value) : "l"(ptr) : "memory"); \ - } \ - } else if constexpr (sizeof(T) == 16) { \ - asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".v4.s32 {%0, %1, %2, %3}, [%4];" \ - : "=r"(value.x), "=r"(value.y), "=r"(value.z), "=r"(value.w) \ - : "l"(ptr) : "memory"); \ - } \ - } \ +#define TL_LD_IMPL(SEM, SCOPE, NC, NA, SEM_LIT, SCOPE_LIT, NC_LIT, NA_LIT) \ + template <> struct LdImpl { \ + template \ + TL_DEVICE static void execute(const T *ptr, T &value) { \ + if constexpr (sizeof(T) == 2) { \ + if constexpr (is_bfloat16_v) { \ + uint16_t value_bits; \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ + : "=h"(value_bits) \ + : "l"(ptr) \ + : "memory"); \ + value = *reinterpret_cast(&value_bits); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b16 %0, [%1];" \ + : "=h"(value) \ + : "l"(ptr) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 4) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ + : "=f"(value) \ + : "l"(ptr) \ + : "memory"); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b32 %0, [%1];" \ + : "=r"(value) \ + : "l"(ptr) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 8) { \ + if constexpr (std::is_floating_point_v) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ + : "=d"(value) \ + : "l"(ptr) \ + : "memory"); \ + } else { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT ".b64 %0, [%1];" \ + : "=l"(value) \ + : "l"(ptr) \ + : "memory"); \ + } \ + } else if constexpr (sizeof(T) == 16) { \ + asm volatile("ld" SEM_LIT SCOPE_LIT NC_LIT NA_LIT \ + ".v4.s32 {%0, %1, %2, %3}, [%4];" \ + : "=r"(value.x), "=r"(value.y), "=r"(value.z), \ + "=r"(value.w) \ + : "l"(ptr) \ + : "memory"); \ + } \ + } \ }; // Register all combinations of arguments for tl::st in need here // WEAK (always .global) TL_ST_IMPL(WEAK, CTA, false, ".weak", ".global", "") TL_ST_IMPL(WEAK, GPU, false, ".weak", ".global", "") -TL_ST_IMPL(WEAK, GPU, true, ".weak", ".global", ".L1::no_allocate") +TL_ST_IMPL(WEAK, GPU, true, ".weak", ".global", ".L1::no_allocate") TL_ST_IMPL(WEAK, SYS, false, ".weak", ".global", "") -TL_ST_IMPL(WEAK, SYS, true, ".weak", ".global", ".L1::no_allocate") +TL_ST_IMPL(WEAK, SYS, true, ".weak", ".global", ".L1::no_allocate") // VOLATILE (always .global, no na) TL_ST_IMPL(VOLATILE, CTA, false, ".volatile", ".global", "") @@ -141,16 +160,16 @@ TL_ST_IMPL(VOLATILE, SYS, false, ".volatile", ".global", "") // RELAXED (scope-aware) TL_ST_IMPL(RELAXED, CTA, false, ".relaxed", ".cta", "") TL_ST_IMPL(RELAXED, GPU, false, ".relaxed", ".gpu.global", "") -TL_ST_IMPL(RELAXED, GPU, true, ".relaxed", ".gpu.global", ".L1::no_allocate") +TL_ST_IMPL(RELAXED, GPU, true, ".relaxed", ".gpu.global", ".L1::no_allocate") TL_ST_IMPL(RELAXED, SYS, false, ".relaxed", ".sys.global", "") -TL_ST_IMPL(RELAXED, SYS, true, ".relaxed", ".sys.global", ".L1::no_allocate") +TL_ST_IMPL(RELAXED, SYS, true, ".relaxed", ".sys.global", ".L1::no_allocate") // RELEASE (scope-aware) TL_ST_IMPL(RELEASE, CTA, false, ".release", ".cta", "") TL_ST_IMPL(RELEASE, GPU, false, ".release", ".gpu.global", "") -TL_ST_IMPL(RELEASE, GPU, true, ".release", ".gpu.global", ".L1::no_allocate") +TL_ST_IMPL(RELEASE, GPU, true, ".release", ".gpu.global", ".L1::no_allocate") TL_ST_IMPL(RELEASE, SYS, false, ".release", ".sys.global", "") -TL_ST_IMPL(RELEASE, SYS, true, ".release", ".sys.global", ".L1::no_allocate") +TL_ST_IMPL(RELEASE, SYS, true, ".release", ".sys.global", ".L1::no_allocate") // Register all combinations of arguments for tl::ld in need here // nc (must with no scope and semantic) @@ -164,8 +183,10 @@ TL_LD_IMPL(WEAK, SYS, true, true, "", ".global", ".nc", ".L1::no_allocate") TL_LD_IMPL(WEAK, CTA, false, false, ".weak", ".cta", "", "") TL_LD_IMPL(WEAK, GPU, false, false, ".weak", ".gpu.global", "", "") TL_LD_IMPL(WEAK, SYS, false, false, ".weak", ".sys.global", "", "") -TL_LD_IMPL(WEAK, GPU, false, true, ".weak", ".gpu.global", "", ".L1::no_allocate") -TL_LD_IMPL(WEAK, SYS, false, true, ".weak", ".sys.global", "", ".L1::no_allocate") +TL_LD_IMPL(WEAK, GPU, false, true, ".weak", ".gpu.global", "", + ".L1::no_allocate") +TL_LD_IMPL(WEAK, SYS, false, true, ".weak", ".sys.global", "", + ".L1::no_allocate") // VOLATILE (always .global, no na) TL_LD_IMPL(VOLATILE, CTA, false, false, ".volatile", ".global", "", "") @@ -176,15 +197,19 @@ TL_LD_IMPL(VOLATILE, SYS, false, false, ".volatile", ".global", "", "") TL_LD_IMPL(RELAXED, CTA, false, false, ".relaxed", ".cta", "", "") TL_LD_IMPL(RELAXED, GPU, false, false, ".relaxed", ".gpu.global", "", "") TL_LD_IMPL(RELAXED, SYS, false, false, ".relaxed", ".sys.global", "", "") -TL_LD_IMPL(RELAXED, GPU, false, true, ".relaxed", ".gpu.global", "", ".L1::no_allocate") -TL_LD_IMPL(RELAXED, SYS, false, true, ".relaxed", ".sys.global", "", ".L1::no_allocate") +TL_LD_IMPL(RELAXED, GPU, false, true, ".relaxed", ".gpu.global", "", + ".L1::no_allocate") +TL_LD_IMPL(RELAXED, SYS, false, true, ".relaxed", ".sys.global", "", + ".L1::no_allocate") // ACQUIRE (scope-aware) TL_LD_IMPL(ACQUIRE, CTA, false, false, ".acquire", ".cta", "", "") TL_LD_IMPL(ACQUIRE, GPU, false, false, ".acquire", ".gpu.global", "", "") TL_LD_IMPL(ACQUIRE, SYS, false, false, ".acquire", ".sys.global", "", "") -TL_LD_IMPL(ACQUIRE, GPU, false, true, ".acquire", ".gpu.global", "", ".L1::no_allocate") -TL_LD_IMPL(ACQUIRE, SYS, false, true, ".acquire", ".sys.global", "", ".L1::no_allocate") +TL_LD_IMPL(ACQUIRE, GPU, false, true, ".acquire", ".gpu.global", "", + ".L1::no_allocate") +TL_LD_IMPL(ACQUIRE, SYS, false, true, ".acquire", ".sys.global", "", + ".L1::no_allocate") #undef TL_ST_IMPL #undef TL_LD_IMPL @@ -194,30 +219,31 @@ namespace tl { // Public interface template TL_DEVICE void st(P ptr, T value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || sizeof(T) == 16, + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || + sizeof(T) == 16, "tl::st: T must be 2, 4, 8, or 16 bytes"); static_assert(std::is_pointer_v

|| std::is_same_v, "tl::st: P must be a pointer or uint64_t"); - static_assert(semantic == Semantic::WEAK - || semantic == Semantic::RELAXED - || semantic == Semantic::RELEASE - || semantic == Semantic::VOLATILE, + static_assert(semantic == Semantic::WEAK || semantic == Semantic::RELAXED || + semantic == Semantic::RELEASE || + semantic == Semantic::VOLATILE, "tl::st: semantic must be WEAK, VOLATILE, RELAXED, or RELEASE"); - + T *ptr_ = reinterpret_cast(ptr); StImpl::execute(ptr_, value); } -template +template TL_DEVICE void ld(const P ptr, T &value) { - static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || sizeof(T) == 16, + static_assert(sizeof(T) == 2 || sizeof(T) == 4 || sizeof(T) == 8 || + sizeof(T) == 16, "tl::ld: T must be 2, 4, 8, or 16 bytes"); static_assert(std::is_pointer_v

|| std::is_same_v, "tl::ld: P must be a pointer or uint64_t"); - static_assert(semantic == Semantic::WEAK - || semantic == Semantic::RELAXED - || semantic == Semantic::ACQUIRE - || semantic == Semantic::VOLATILE, + static_assert(semantic == Semantic::WEAK || semantic == Semantic::RELAXED || + semantic == Semantic::ACQUIRE || + semantic == Semantic::VOLATILE, "tl::ld: semantic must be WEAK, RELAXED, ACQUIRE, or VOLATILE"); const T *ptr_ = reinterpret_cast(ptr); diff --git a/src/tl_templates/cuda/reduce.h b/src/tl_templates/cuda/reduce.h index a0f2318650..5981fa0714 100644 --- a/src/tl_templates/cuda/reduce.h +++ b/src/tl_templates/cuda/reduce.h @@ -280,7 +280,7 @@ template struct CumSum2D { } }; -// TileScale extra +// TileScale extra template TL_DEVICE T warp_reduce(T value, ReduceOp op) { @@ -293,28 +293,23 @@ TL_DEVICE T warp_reduce(T value, ReduceOp op) { return value; } -template -TL_DEVICE T warp_reduce_sum(T value) { +template TL_DEVICE T warp_reduce_sum(T value) { return warp_reduce(value, SumOp()); } -template -TL_DEVICE T warp_reduce_max(T value) { +template TL_DEVICE T warp_reduce_max(T value) { return warp_reduce(value, MaxOp()); } -template -TL_DEVICE T warp_reduce_min(T value) { +template TL_DEVICE T warp_reduce_min(T value) { return warp_reduce(value, MinOp()); } -template -TL_DEVICE T warp_reduce_bitand(T value) { +template TL_DEVICE T warp_reduce_bitand(T value) { return warp_reduce(value, BitAndOp()); } -template -TL_DEVICE T warp_reduce_bitor(T value) { +template TL_DEVICE T warp_reduce_bitor(T value) { return warp_reduce(value, BitOrOp()); } diff --git a/src/tl_templates/cuda/sync.h b/src/tl_templates/cuda/sync.h index e9514d90a8..cad94ee7ef 100644 --- a/src/tl_templates/cuda/sync.h +++ b/src/tl_templates/cuda/sync.h @@ -156,7 +156,7 @@ TL_DEVICE void sync_grid(uint32_t *barrier) { template TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { // Macro to compute the barrier pointer for a given target rank -#define BARRIER_PTR(tgt_rank) \ +#define BARRIER_PTR(tgt_rank) \ (reinterpret_cast(get_remote_base_ptr(tgt_rank) + offset)) #define FINISHED_SUM_TAG (1024) @@ -164,7 +164,7 @@ TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { memory_fence_sys(); __syncthreads(); } - + int tid = threadIdx.x; if (tid < num_ranks) { atomicAdd_system(BARRIER_PTR(rank) + tid, FINISHED_SUM_TAG); @@ -184,57 +184,62 @@ TL_DEVICE void barrier_blocks(int offset, int rank, int num_ranks) { #undef FINISHED_SUM_TAG } -template -TL_DEVICE void wait_eq(void *ptr, T val) { +template TL_DEVICE void wait_eq(void *ptr, T val) { T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_acquire(flag_ptr) != val); + while (ld_acquire(flag_ptr) != val) + ; } -template -TL_DEVICE void wait_ne(P ptr, T val) { - static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); +template TL_DEVICE void wait_ne(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, + "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global(flag_ptr) == val); + while (ld_volatile_global(flag_ptr) == val) + ; } -template -TL_DEVICE void wait_ge(P ptr, T val) { - static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); +template TL_DEVICE void wait_ge(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, + "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global(flag_ptr) < val); + while (ld_volatile_global(flag_ptr) < val) + ; } -template -TL_DEVICE void wait_le(P ptr, T val) { - static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); +template TL_DEVICE void wait_le(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, + "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global(flag_ptr) > val); + while (ld_volatile_global(flag_ptr) > val) + ; } -template -TL_DEVICE void wait_gt(P ptr, T val) { - static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); +template TL_DEVICE void wait_gt(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, + "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global(flag_ptr) <= val); + while (ld_volatile_global(flag_ptr) <= val) + ; } -template -TL_DEVICE void wait_lt(P ptr, T val) { - static_assert(std::is_same_v || std::is_pointer_v

, "P must be a pointer or uint64_t"); +template TL_DEVICE void wait_lt(P ptr, T val) { + static_assert(std::is_same_v || std::is_pointer_v

, + "P must be a pointer or uint64_t"); T *flag_ptr = reinterpret_cast(ptr); // Spin-loop #pragma unroll 1 - while (ld_volatile_global(flag_ptr) >= val); + while (ld_volatile_global(flag_ptr) >= val) + ; } } // namespace tl diff --git a/src/transform/common/loop_vectorization_utils.h b/src/transform/common/loop_vectorization_utils.h index 84f8ed7e28..890597464f 100644 --- a/src/transform/common/loop_vectorization_utils.h +++ b/src/transform/common/loop_vectorization_utils.h @@ -381,7 +381,7 @@ class Vectorizer : public StmtMutator, // tl::ld or tl::st expr vectorization // Transform: for k in vectorized(N): tl::ld(&buf[base+k], val[k]) // Into: tl::ld(&buf[base], reinterpret(val[base])) with vectorized load - // + // // This function handles the vectorization of tl::ld and tl::st calls. // The key insight is that for 8 consecutive bf16 loads (128 bits total), // we can use a single int4 load which is more efficient by reinterpreting @@ -390,21 +390,21 @@ class Vectorizer : public StmtMutator, // Structure: call_extern("tl::ld<...>", address_of(BufferLoad), value, ...) // or: call_extern("tl::st<...>", address_of(BufferLoad), value, ...) ICHECK(op->args.size() >= 3) << "tl::ld/st expects at least 3 arguments"; - + PrimExpr func_name = op->args[0]; PrimExpr addr_arg = op->args[1]; PrimExpr value_arg = op->args[2]; - + // Visit the address argument to vectorize indices PrimExpr new_addr = this->VisitExpr(addr_arg); PrimExpr new_value = this->VisitExpr(value_arg); - + // Helper to extract base from Ramp and get lanes - auto extract_ramp_info = [](const Array& indices) - -> std::pair, int> { + auto extract_ramp_info = + [](const Array &indices) -> std::pair, int> { Array base_indices; int ramp_lanes = 1; - for (const auto& idx : indices) { + for (const auto &idx : indices) { auto ramp = idx.as(); if (ramp && is_one(ramp->stride)) { auto lanes_imm = ramp->lanes.as(); @@ -418,7 +418,7 @@ class Vectorizer : public StmtMutator, } return {base_indices, ramp_lanes}; }; - + // Check source address for Ramp pattern int src_ramp_lanes = 1; auto addr_call = new_addr.as(); @@ -430,11 +430,12 @@ class Vectorizer : public StmtMutator, src_ramp_lanes = lanes; // Create new address with base indices only BufferLoad new_buffer_load(buffer_load->buffer, base_indices); - new_addr = Call(DataType::Handle(), builtin::address_of(), {new_buffer_load}); + new_addr = Call(DataType::Handle(), builtin::address_of(), + {new_buffer_load}); } } } - + // Check destination value for Ramp pattern (for local buffer stores) int dst_ramp_lanes = 1; auto value_load = new_value.as(); @@ -446,7 +447,7 @@ class Vectorizer : public StmtMutator, new_value = BufferLoad(value_load->buffer, base_indices); } } - + // Determine vectorization lanes int vector_lanes = std::max(src_ramp_lanes, dst_ramp_lanes); if (vector_lanes > 1) { @@ -454,22 +455,23 @@ class Vectorizer : public StmtMutator, // 8 x 16-bit = 128 bits = int4, 4 x 32-bit = 128 bits = int4 // 4 x 16-bit = 64 bits = int2, 2 x 32-bit = 64 bits = int2 DataType vec_dtype; - int elem_bits = 16; // Default assumption for bf16/f16 - + int elem_bits = 16; // Default assumption for bf16/f16 + // Try to get element dtype from source buffer auto addr_call_check = new_addr.as(); - if (addr_call_check && addr_call_check->op.same_as(builtin::address_of())) { + if (addr_call_check && + addr_call_check->op.same_as(builtin::address_of())) { auto buffer_load = addr_call_check->args[0].as(); if (buffer_load) { elem_bits = buffer_load->buffer->dtype.bits(); } } - + int total_bits = vector_lanes * elem_bits; if (total_bits == 128) { - vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) + vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) } else if (total_bits == 64) { - vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) + vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) } else if (total_bits == 32) { vec_dtype = DataType::Int(32); } else { @@ -477,11 +479,11 @@ class Vectorizer : public StmtMutator, need_scalarize_ = true; return GetRef(op); } - + // Reinterpret the value to vector type (e.g., int4 for 8xbf16) // This generates: reinterpret_cast(dst[base]) PrimExpr vec_value = Call(vec_dtype, builtin::reinterpret(), {new_value}); - + // Build new args with base addresses and reinterpreted value Array new_args; new_args.push_back(func_name); @@ -491,23 +493,23 @@ class Vectorizer : public StmtMutator, for (size_t i = 3; i < op->args.size(); ++i) { new_args.push_back(this->VisitExpr(op->args[i])); } - + // Return the vectorized call with same function but vectorized value type return Call(op->dtype, op->op, new_args); } - + // If we couldn't vectorize but args became vectors, need to scalarize if (new_addr.dtype().is_scalable_or_fixed_length_vector() || new_value.dtype().is_scalable_or_fixed_length_vector()) { need_scalarize_ = true; return GetRef(op); } - + // No vectorization needed, return with updated args if changed if (new_addr.same_as(addr_arg) && new_value.same_as(value_arg)) { return GetRef(op); } - + Array new_args; new_args.push_back(func_name); new_args.push_back(new_addr); @@ -573,7 +575,8 @@ class Vectorizer : public StmtMutator, if (func_name_node) { std::string func_name = func_name_node->value; // Check for tl::ld<...> or tl::st<...> patterns - if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + if (func_name.rfind("tl::ld<", 0) == 0 || + func_name.rfind("tl::st<", 0) == 0) { return MutateTlLdStExpr_(op, func_name.rfind("tl::ld<", 0) == 0); } } diff --git a/src/transform/loop_vectorize.cc b/src/transform/loop_vectorize.cc index 567c42e732..9eddb213f2 100644 --- a/src/transform/loop_vectorize.cc +++ b/src/transform/loop_vectorize.cc @@ -144,9 +144,10 @@ class VectorizePlanner : public arith::IRVisitorWithAnalyzer { if (func_name_node) { std::string func_name = func_name_node->value; // Check for tl::ld<...> or tl::st<...> patterns - if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + if (func_name.rfind("tl::ld<", 0) == 0 || + func_name.rfind("tl::st<", 0) == 0) { bool can_vectorize = true; - + // Check source address (args[1]) for vectorizable pattern auto addr_call = node->args[1].as(); if (addr_call && addr_call->op.same_as(builtin::address_of())) { @@ -160,13 +161,13 @@ class VectorizePlanner : public arith::IRVisitorWithAnalyzer { } else { can_vectorize = false; } - + // Check destination value (args[2]) for vectorizable pattern auto value_load = node->args[2].as(); if (value_load) { UpdateVectorSize(value_load->indices, value_load->buffer); } - + if (can_vectorize) { return arith::IRVisitorWithAnalyzer::VisitExpr_(node); } diff --git a/src/transform/storage_access.cc b/src/transform/storage_access.cc index 38c53e607e..e8ba0ed78d 100644 --- a/src/transform/storage_access.cc +++ b/src/transform/storage_access.cc @@ -292,7 +292,7 @@ void TileLangStorageAccessVisitor::VisitStmt_(const WhileNode *op) { this->VisitExpr(op->condition); curr_stmt_.access.clear(); allow_append_ = false; - + scope_.push_back(std::vector()); this->VisitStmt(op->body); StmtEntry s; diff --git a/src/transform/vectorize_loop.cc b/src/transform/vectorize_loop.cc index 20c37a11f1..8adb2c31d9 100644 --- a/src/transform/vectorize_loop.cc +++ b/src/transform/vectorize_loop.cc @@ -462,7 +462,8 @@ class TLVectorizer : public StmtMutator, auto func_name_node = op->args[0].as(); if (func_name_node) { std::string func_name = func_name_node->value; - if (func_name.rfind("tl::ld<", 0) == 0 || func_name.rfind("tl::st<", 0) == 0) { + if (func_name.rfind("tl::ld<", 0) == 0 || + func_name.rfind("tl::st<", 0) == 0) { return MutateTlLdStExpr_(op, func_name.rfind("tl::ld<", 0) == 0); } } @@ -709,12 +710,12 @@ class TLVectorizer : public StmtMutator, // Helper to visit indices and extract Ramp info // Returns: (visited_indices, base_indices, ramp_lanes) - auto visit_and_extract_ramp = [this](const Array& indices) + auto visit_and_extract_ramp = [this](const Array &indices) -> std::tuple, Array, int> { Array visited_indices; Array base_indices; int ramp_lanes = 1; - for (const auto& idx : indices) { + for (const auto &idx : indices) { PrimExpr visited = this->VisitExpr(idx); visited_indices.push_back(visited); auto ramp = visited.as(); @@ -738,11 +739,13 @@ class TLVectorizer : public StmtMutator, if (addr_call && addr_call->op.same_as(builtin::address_of())) { auto buffer_load = addr_call->args[0].as(); if (buffer_load) { - auto [visited_indices, base_indices, lanes] = visit_and_extract_ramp(buffer_load->indices); + auto [visited_indices, base_indices, lanes] = + visit_and_extract_ramp(buffer_load->indices); src_ramp_lanes = lanes; // Create new address with base indices only (for vectorized load) BufferLoad new_buffer_load(buffer_load->buffer, base_indices); - new_addr = Call(DataType::Handle(), builtin::address_of(), {new_buffer_load}); + new_addr = + Call(DataType::Handle(), builtin::address_of(), {new_buffer_load}); } } @@ -751,7 +754,8 @@ class TLVectorizer : public StmtMutator, PrimExpr new_value = value_arg; auto value_load = value_arg.as(); if (value_load) { - auto [visited_indices, base_indices, lanes] = visit_and_extract_ramp(value_load->indices); + auto [visited_indices, base_indices, lanes] = + visit_and_extract_ramp(value_load->indices); dst_ramp_lanes = lanes; // Create new value with base indices only new_value = BufferLoad(value_load->buffer, base_indices); @@ -764,11 +768,12 @@ class TLVectorizer : public StmtMutator, // 8 x 16-bit = 128 bits = int4, 4 x 32-bit = 128 bits = int4 // 4 x 16-bit = 64 bits = int2, 2 x 32-bit = 64 bits = int2 DataType vec_dtype; - int elem_bits = 16; // Default assumption for bf16/f16 + int elem_bits = 16; // Default assumption for bf16/f16 // Try to get element dtype from source buffer auto addr_call_check = new_addr.as(); - if (addr_call_check && addr_call_check->op.same_as(builtin::address_of())) { + if (addr_call_check && + addr_call_check->op.same_as(builtin::address_of())) { auto buffer_load = addr_call_check->args[0].as(); if (buffer_load) { elem_bits = buffer_load->buffer->dtype.bits(); @@ -777,9 +782,9 @@ class TLVectorizer : public StmtMutator, int total_bits = vector_lanes * elem_bits; if (total_bits == 128) { - vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) + vec_dtype = DataType::Int(32, 4); // int4 equivalent (128 bits) } else if (total_bits == 64) { - vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) + vec_dtype = DataType::Int(32, 2); // int2 equivalent (64 bits) } else if (total_bits == 32) { vec_dtype = DataType::Int(32); } else { diff --git a/testing/python/language/test_tilelang_language_elect.py b/testing/python/language/test_tilelang_language_elect.py index c096f61663..2b5d97d9bf 100644 --- a/testing/python/language/test_tilelang_language_elect.py +++ b/testing/python/language/test_tilelang_language_elect.py @@ -7,6 +7,7 @@ @tilelang.jit def get_kernel(): + @T.prim_func def main(x: T.Tensor((1), 'int32')): with T.Kernel(1, threads=32): @@ -26,4 +27,4 @@ def test_elect_one_sync(): if __name__ == "__main__": - tilelang.testing.main() \ No newline at end of file + tilelang.testing.main() diff --git a/testing/python/language/test_tilelang_language_vote.py b/testing/python/language/test_tilelang_language_vote.py index ba55e63f80..b4beaf30d4 100644 --- a/testing/python/language/test_tilelang_language_vote.py +++ b/testing/python/language/test_tilelang_language_vote.py @@ -7,6 +7,7 @@ @tilelang.jit def get_kernel(): + @T.prim_func def main(output: T.Tensor((6), 'int32')): with T.Kernel(1, threads=32): @@ -32,6 +33,7 @@ def main(output: T.Tensor((6), 'int32')): if tx == 0: output[4] = result_any output[5] = result_all + return main @@ -45,4 +47,4 @@ def test_vote(): if __name__ == "__main__": - test_vote() \ No newline at end of file + test_vote() diff --git a/testing/python/language/test_tilelang_language_warp_reduce.py b/testing/python/language/test_tilelang_language_warp_reduce.py index 83cac9c4da..681b234708 100644 --- a/testing/python/language/test_tilelang_language_warp_reduce.py +++ b/testing/python/language/test_tilelang_language_warp_reduce.py @@ -80,4 +80,4 @@ def test_warp_reduce_bitor(): if __name__ == "__main__": - tilelang.testing.main() \ No newline at end of file + tilelang.testing.main() diff --git a/tilelang/distributed/testing/test_create_mapped_tensor.py b/tilelang/distributed/testing/test_create_mapped_tensor.py index 3bd4fb8c26..1706e5ce34 100644 --- a/tilelang/distributed/testing/test_create_mapped_tensor.py +++ b/tilelang/distributed/testing/test_create_mapped_tensor.py @@ -1,12 +1,11 @@ import torch from tilelang.distributed.utils import create_mapped_tensor - if __name__ == "__main__": shape = (1024, 1024) dtype = torch.float32 host_tensor, device_tensor = create_mapped_tensor(shape, dtype) - + # test meta-data assert device_tensor.device.type == "cuda" assert device_tensor.shape == shape, f"{device_tensor.shape=}" @@ -17,4 +16,4 @@ device_tensor.random_() assert torch.equal(host_tensor, device_tensor.cpu()), f"{host_tensor=}, {device_tensor=}" - print("All checks passed for create_mapped_tensor. ✅") \ No newline at end of file + print("All checks passed for create_mapped_tensor. ✅") diff --git a/tilelang/jit/kernel.py b/tilelang/jit/kernel.py index 0dba70f058..4e6e320896 100644 --- a/tilelang/jit/kernel.py +++ b/tilelang/jit/kernel.py @@ -411,7 +411,8 @@ def initialize( ): assert allocator.initialized(), "Allocator is not initialized" result = self.adapter.lib.init_table( - ctypes.c_void_p(allocator.table.data_ptr()), allocator.table_size, ctypes.c_void_p(stream) if stream is not None else ctypes.c_void_p(0)) + ctypes.c_void_p(allocator.table.data_ptr()), allocator.table_size, + ctypes.c_void_p(stream) if stream is not None else ctypes.c_void_p(0)) if result != 0: error_msg = self.adapter.lib.get_last_error().decode('utf-8') raise RuntimeError(f"Initialization failed: {error_msg}") diff --git a/tilelang/language/builtin.py b/tilelang/language/builtin.py index 830e3da669..8db6383089 100644 --- a/tilelang/language/builtin.py +++ b/tilelang/language/builtin.py @@ -619,7 +619,8 @@ def barrier_blocks(barrier: PrimExpr): Args: barrier: The barrier to synchronize at, should be [num_ranks] of int32 """ - return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), 1) # whether need fence + return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), + 1) # whether need fence def sync_blocks(barrier: PrimExpr): @@ -628,7 +629,8 @@ def sync_blocks(barrier: PrimExpr): Args: barrier: The barrier to synchronize at, should be [num_ranks] of int32 """ - return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), 0) # whether need fence + return tir.call_intrin("handle", tir.op.Op.get("tl.barrier_blocks"), address_of(barrier), + 0) # whether need fence def fence_cta(): @@ -735,17 +737,18 @@ def atom_add(barrier: PrimExpr, value: PrimExpr, scope: str = "gpu", sem: str = return tir.call_intrin("uint32", tir.op.Op.get("tl.atom_add"), address_of(barrier), value, sem, scope) + def ld( - src: PrimExpr, - value: PrimExpr, - scope: Literal["cta", "gpu", "sys"] = "gpu", + src: PrimExpr, + value: PrimExpr, + scope: Literal["cta", "gpu", "sys"] = "gpu", sem: Literal["weak", "volatile", "acquire", "release", "relaxed"] = "weak", na: bool = False, nc: bool = False, - src_pe: tir.PrimExpr | tir.IntImm | None = -1, + src_pe: tir.PrimExpr | tir.IntImm | None = -1, ): """Load a value from a given address with specified scope, semantic, and optional destination PE. - + Args: src: The source address to load from. value: The value to load. @@ -753,27 +756,31 @@ def ld( sem: The memory semantic. na: Whether to use no-allocate L1 policy. nc: Whether to use non-coherent cache. - src_pe: The source processing element (PE) identifier. + src_pe: The source processing element (PE) identifier. Use -1 (default) for local PE, or a non-negative integer to target a remote PE. Returns: tir.Call: A handle to the load operation. """ assert scope in ["cta", "gpu", "sys"], "Scope must be one of 'cta', 'gpu', or 'sys'." - assert sem in ["weak", "volatile", "acquire", "relaxed"], "Semantic must be one of 'weak', 'volatile', 'acquire', 'release', or 'relaxed'." + assert sem in [ + "weak", "volatile", "acquire", "relaxed" + ], "Semantic must be one of 'weak', 'volatile', 'acquire', 'release', or 'relaxed'." scope = {"cta": 0, "gpu": 1, "sys": 2}[scope] sem = {"weak": 0, "volatile": 1, "acquire": 2, "release": 3, "relaxed": 4}[sem] na = 1 if na else 0 nc = 1 if nc else 0 - return tir.call_intrin("handle", tir.op.Op.get("tl.ld"), address_of(src), value, sem, scope, na, nc, src_pe) + return tir.call_intrin("handle", tir.op.Op.get("tl.ld"), address_of(src), value, sem, scope, na, + nc, src_pe) + def st( - dst: PrimExpr, - value: PrimExpr, - scope: Literal["cta", "gpu", "sys"] = "gpu", + dst: PrimExpr, + value: PrimExpr, + scope: Literal["cta", "gpu", "sys"] = "gpu", sem: Literal["weak", "volatile", "release", "relaxed"] = "weak", na: bool = False, - dst_pe: tir.PrimExpr | tir.IntImm | None = -1, + dst_pe: tir.PrimExpr | tir.IntImm | None = -1, ): """Store a value to a given address with specified scope, semantic, and optional destination PE. @@ -783,20 +790,22 @@ def st( scope: The memory scope. sem: The memory semantic. na: Whether to use no-allocate L1 policy. - dst_pe: The destination processing element (PE) identifier. + dst_pe: The destination processing element (PE) identifier. Use -1 (default) for local PE, or a non-negative integer to target a remote PE. Returns: tir.Call: A handle to the store operation. """ assert scope in ["cta", "gpu", "sys"], "Scope must be one of 'cta', 'gpu', or 'sys'." - assert sem in ["weak", "volatile", "release", "relaxed"], "Semantic must be one of 'weak', 'volatile', 'release', or 'relaxed'." - + assert sem in ["weak", "volatile", "release", "relaxed" + ], "Semantic must be one of 'weak', 'volatile', 'release', or 'relaxed'." + # convert to int scope = {"cta": 0, "gpu": 1, "sys": 2}[scope] sem = {"weak": 0, "volatile": 1, "acquire": 2, "release": 3, "relaxed": 4}[sem] na = 1 if na else 0 - return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, na, dst_pe) + return tir.call_intrin("handle", tir.op.Op.get("tl.st"), address_of(dst), value, sem, scope, na, + dst_pe) def elect_one_sync(): @@ -814,9 +823,9 @@ def loop_continue(): return tir.call_intrin("handle", tir.op.Op.get("tl.loop_continue")) -def warp_any(value, mask = -1): +def warp_any(value, mask=-1): """Check if any lane in the warp has a true value. - + Args: value (int): The value to vote. mask (uint32): The mask to use, default is 0xFFFFFFFF, which means all lanes. @@ -827,9 +836,9 @@ def warp_any(value, mask = -1): return tir.call_intrin("int32", tir.op.Op.get("tl.warp_any"), value, mask) -def warp_all(value, mask = -1): +def warp_all(value, mask=-1): """Check if all lane in the warp have a true value. - + Args: value (int): The value to vote. mask (uint32): The mask to use, default is 0xFFFFFFFF(-1), which means all lanes. diff --git a/tilelang/language/distributed/common.py b/tilelang/language/distributed/common.py index 6f7c2f01db..31264c0ae2 100644 --- a/tilelang/language/distributed/common.py +++ b/tilelang/language/distributed/common.py @@ -75,10 +75,7 @@ def get_warp(src: PrimExpr, "warp", enable_aggresive_vectorize) -def put_block(src: PrimExpr, - dst: PrimExpr, - size: PrimExpr, - dst_pe: PrimExpr | IntImm | None = -1): +def put_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | IntImm | None = -1): """Put to a remote buffer. Args: @@ -97,10 +94,7 @@ def put_block(src: PrimExpr, ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy -def get_block(src: PrimExpr, - dst: PrimExpr, - size: PrimExpr, - src_pe: PrimExpr | IntImm | None = -1): +def get_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, src_pe: PrimExpr | IntImm | None = -1): """Get from a remote buffer. Args: @@ -119,7 +113,6 @@ def get_block(src: PrimExpr, ) # NOTE: unroll_factor is not needed because currently we implement block-level comm based on NVSHMEM-style copy - class BinaryRelation(Enum): EQ = 0 NE = 1 @@ -127,8 +120,8 @@ class BinaryRelation(Enum): LE = 3 GT = 4 LT = 5 - - + + def wait_eq(barrier: PrimExpr, expected: PrimExpr): """Wait until *barrier == expected* for GPU-level synchronization. # todo: have different semantic compared to 3 fns below currently @@ -141,24 +134,29 @@ def wait_eq(barrier: PrimExpr, expected: PrimExpr): def wait_ne(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): """Wait until *ptr != expected""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.NE.value, address_of(ptr), expected, peer) + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.NE.value, + address_of(ptr), expected, peer) def wait_ge(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): """Wait until *ptr >= expected""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GE.value, address_of(ptr), expected, peer) + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GE.value, + address_of(ptr), expected, peer) def wait_le(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): """Wait until *ptr <= expected""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LE.value, address_of(ptr), expected, peer) + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LE.value, + address_of(ptr), expected, peer) def wait_gt(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): """Wait until *ptr > expected""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GT.value, address_of(ptr), expected, peer) + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.GT.value, + address_of(ptr), expected, peer) def wait_lt(ptr: PrimExpr, expected: PrimExpr, peer: PrimExpr | None = -1): """Wait until *ptr < expected""" - return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LT.value, address_of(ptr), expected, peer) + return tir.call_intrin("handle", tir.op.Op.get("tl.wait"), BinaryRelation.LT.value, + address_of(ptr), expected, peer) diff --git a/tilelang/language/reduce.py b/tilelang/language/reduce.py index 603ada0733..23167bdbfc 100644 --- a/tilelang/language/reduce.py +++ b/tilelang/language/reduce.py @@ -274,101 +274,82 @@ def finalize_reducer(reducer: tir.Buffer): # TileScale extra + def warp_reduce_sum(value: tir.PrimExpr): """Perform warp reduction sum on a register value. - + This function reduces a value across all threads in a warp using shuffle operations. - Each thread provides a register `value`, and after the reduction, all threads + Each thread provides a register `value`, and after the reduction, all threads will have the sum of all values across the warp. - + Args: x (tir.PrimExpr): The input register value to reduce Returns: tir.PrimExpr: The reduced sum value (same on all threads in the warp) """ - return tir.call_intrin( - value.dtype, - tir.op.Op.get("tl.warp_reduce_sum"), - value - ) + return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_sum"), value) def warp_reduce_max(value: tir.PrimExpr): """Perform warp reduction max on a register value. - + This function reduces a value across all threads in a warp using shuffle operations. - Each thread provides a register `value`, and after the reduction, all threads + Each thread provides a register `value`, and after the reduction, all threads will have the max of all values across the warp. - + Args: value (tir.PrimExpr): The input register value to reduce Returns: tir.PrimExpr: The reduced max value (same on all threads in the warp) """ - return tir.call_intrin( - value.dtype, - tir.op.Op.get("tl.warp_reduce_max"), - value - ) - + return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_max"), value) + def warp_reduce_min(value: tir.PrimExpr): """Perform warp reduction min on a register value. - + This function reduces a value across all threads in a warp using shuffle operations. - Each thread provides a register `value`, and after the reduction, all threads + Each thread provides a register `value`, and after the reduction, all threads will have the min of all values across the warp. - + Args: value (tir.PrimExpr): The input register value to reduce Returns: tir.PrimExpr: The reduced min value (same on all threads in the warp) """ - return tir.call_intrin( - value.dtype, - tir.op.Op.get("tl.warp_reduce_min"), - value - ) + return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_min"), value) def warp_reduce_bitand(value: tir.PrimExpr): """Perform warp reduction bitwise-and on a register value. - + This function reduces a value across all threads in a warp using shuffle operations. - Each thread provides a register `value`, and after the reduction, all threads + Each thread provides a register `value`, and after the reduction, all threads will have the bitwise-and of all values across the warp. - + Args: value (tir.PrimExpr): The input register value to reduce Returns: tir.PrimExpr: The reduced bitwise-and value (same on all threads in the warp) """ - return tir.call_intrin( - value.dtype, - tir.op.Op.get("tl.warp_reduce_bitand"), - value - ) + return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_bitand"), value) def warp_reduce_bitor(value: tir.PrimExpr): """Perform warp reduction bitwise-or on a register value. - + This function reduces a value across all threads in a warp using shuffle operations. - Each thread provides a register `value`, and after the reduction, all threads + Each thread provides a register `value`, and after the reduction, all threads will have the bitwise-or of all values across the warp. - + Args: value (tir.PrimExpr): The input register value to reduce Returns: tir.PrimExpr: The reduced bitwise-or value (same on all threads in the warp) """ - return tir.call_intrin( - value.dtype, - tir.op.Op.get("tl.warp_reduce_bitor"), - value - ) \ No newline at end of file + return tir.call_intrin(value.dtype, tir.op.Op.get("tl.warp_reduce_bitor"), value) diff --git a/tilelang/utils/ts_ext/__init__.py b/tilelang/utils/ts_ext/__init__.py index 3f6484b92d..e8f1bb87c5 100644 --- a/tilelang/utils/ts_ext/__init__.py +++ b/tilelang/utils/ts_ext/__init__.py @@ -9,10 +9,10 @@ create_host_device_tensor = _C.create_host_device_tensor __all__ = [ - "tensor_from_ptr", - "_create_tensor", - "_create_ipc_handle", - "_sync_ipc_handles", + "tensor_from_ptr", + "_create_tensor", + "_create_ipc_handle", + "_sync_ipc_handles", "create_host_device_tensor", "_C", ] diff --git a/tilelang/utils/ts_ext/exception.h b/tilelang/utils/ts_ext/exception.h index 8e5bf69ded..cd3b7f1961 100644 --- a/tilelang/utils/ts_ext/exception.h +++ b/tilelang/utils/ts_ext/exception.h @@ -34,4 +34,3 @@ class TSException : public std::exception { } \ } while (0) #endif - diff --git a/tilelang/utils/ts_ext/ipc_ops.cpp b/tilelang/utils/ts_ext/ipc_ops.cpp index 755914c3ad..eaa820a545 100644 --- a/tilelang/utils/ts_ext/ipc_ops.cpp +++ b/tilelang/utils/ts_ext/ipc_ops.cpp @@ -18,8 +18,8 @@ #include #include -#include "ts_ext_ops.h" #include "exception.h" +#include "ts_ext_ops.h" namespace py = pybind11; diff --git a/tilelang/utils/ts_ext/tensor.cpp b/tilelang/utils/ts_ext/tensor.cpp index e4eaf039d0..26efca64c9 100644 --- a/tilelang/utils/ts_ext/tensor.cpp +++ b/tilelang/utils/ts_ext/tensor.cpp @@ -8,8 +8,8 @@ #include #include -#include "ts_ext_ops.h" #include "exception.h" +#include "ts_ext_ops.h" static int64_t safe_mul_int64(int64_t a, int64_t b) { if (a == 0 || b == 0) @@ -43,7 +43,7 @@ static at::ScalarType dtype_from_string(const std::string &s) { return at::kChar; if (s == "bool") return at::kBool; - throw std::runtime_error("Unsupported dtype string: '"+ s + "'"); + throw std::runtime_error("Unsupported dtype string: '" + s + "'"); } torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, @@ -87,38 +87,27 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, } std::pair -create_host_device_tensor(const std::vector &shape, c10::ScalarType dtype) { - size_t elem_size = at::elementSize(dtype); - int64_t numel = 1; - for (int64_t s : shape) numel *= s; +create_host_device_tensor(const std::vector &shape, + c10::ScalarType dtype) { + size_t elem_size = at::elementSize(dtype); + int64_t numel = 1; + for (int64_t s : shape) + numel *= s; + + size_t bytes = numel * elem_size; - size_t bytes = numel * elem_size; + void *host_ptr = nullptr; + CUDA_CHECK(cudaHostAlloc(&host_ptr, bytes, cudaHostAllocMapped)); - void* host_ptr = nullptr; - CUDA_CHECK(cudaHostAlloc( - &host_ptr, - bytes, - cudaHostAllocMapped - )); + void *device_ptr = nullptr; + CUDA_CHECK(cudaHostGetDevicePointer(&device_ptr, host_ptr, 0)); - void* device_ptr = nullptr; - CUDA_CHECK(cudaHostGetDevicePointer( - &device_ptr, - host_ptr, - 0 - )); + auto host_tensor = torch::from_blob( + host_ptr, shape, torch::TensorOptions().dtype(dtype).device(torch::kCPU)); - auto host_tensor = torch::from_blob( - host_ptr, - shape, - torch::TensorOptions().dtype(dtype).device(torch::kCPU) - ); - - auto device_tensor = torch::from_blob( - device_ptr, - shape, - torch::TensorOptions().dtype(dtype).device(torch::kCUDA) - ); + auto device_tensor = torch::from_blob( + device_ptr, shape, + torch::TensorOptions().dtype(dtype).device(torch::kCUDA)); - return std::make_pair(host_tensor, device_tensor); + return std::make_pair(host_tensor, device_tensor); } \ No newline at end of file diff --git a/tilelang/utils/ts_ext/ts_ext_bindings.cpp b/tilelang/utils/ts_ext/ts_ext_bindings.cpp index 81c3b1932c..685ba11098 100644 --- a/tilelang/utils/ts_ext/ts_ext_bindings.cpp +++ b/tilelang/utils/ts_ext/ts_ext_bindings.cpp @@ -22,9 +22,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { }, py::arg("shape"), py::arg("dtype")); - m.def("create_host_device_tensor", - &create_host_device_tensor, - "Create host/device shared pinned-mapped tensor (shape + dtype)"); + m.def("create_host_device_tensor", &create_host_device_tensor, + "Create host/device shared pinned-mapped tensor (shape + dtype)"); m.def( "_create_ipc_handle", diff --git a/tilelang/utils/ts_ext/ts_ext_ops.h b/tilelang/utils/ts_ext/ts_ext_ops.h index 22a4d1c52b..224ace2968 100644 --- a/tilelang/utils/ts_ext/ts_ext_ops.h +++ b/tilelang/utils/ts_ext/ts_ext_ops.h @@ -13,8 +13,9 @@ torch::Tensor tensor_from_ptr(uint64_t ptr_val, std::vector shape, torch::Tensor create_tensor(const std::vector &shape, c10::ScalarType dtype); -std::pair create_host_device_tensor(const std::vector &shape, - c10::ScalarType dtype); +std::pair +create_host_device_tensor(const std::vector &shape, + c10::ScalarType dtype); pybind11::bytearray create_ipc_handle(void *ptr); From c37575a859b861bed47cd4733a0b8933f16b87b7 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 17:40:42 +0800 Subject: [PATCH 35/41] upd doc --- examples/distributed/deepseek_deepep/deepep.md | 15 ++++++--------- 1 file changed, 6 insertions(+), 9 deletions(-) diff --git a/examples/distributed/deepseek_deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md index 4a53f4e93f..97f3f00aad 100644 --- a/examples/distributed/deepseek_deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -11,18 +11,15 @@ To install and compare with the original DeepEP implementation, please refer to The table below shows a latency and bandwidth comparison for DeepEP and TileScale on the same NVLink hardware (as reported by the example): -*Measured on: NVL8, H100, 10 channels, 8 ranks, 32 experts, 7168 hidden, 4096 tokens.* +*Measured on: 8xH100 on NVL, 10 channels, 8 ranks, 32 experts, 7168 hidden, 4096 tokens.* -## Normal Mode Dispatch +## Normal Mode -| Method | Dispatch Time (ms) | Bandwidth (GB/s) | -|-------------|--------------------|------------------| -| DeepEP | 1.0045 | 328.97 | -| TileScale | 1.0720 | 308.25 | +| Method | Dispatch Time (ms) | Dispatch Bandwidth (GB/s) | Combine Time (ms) | Combine Bandwidth (GB/s) | +|-------------|--------------------|---------------------------|-------------------|--------------------------| +| DeepEP | 1.0045 | 328.97 | 1.1552 | 287.14 | +| TileScale | 1.0720 | 308.25 | 1.0809 | 306.86 | -## Normal Mode Combine - -> Coming soon... # Intra-node Introduction From 71ece5e1ca293f1da3f99865f9e17c4e06ac488b Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 18:21:29 +0800 Subject: [PATCH 36/41] make ci happy --- .../distributed/deepseek_deepep/buffer.py | 2 +- .../distributed/deepseek_deepep/deepep.md | 4 +- .../{utils.py => deepep_utils.py} | 0 .../deepseek_deepep/intranode/dispatch.py | 2 +- .../intranode/example_intranode.py | 247 ++++++++++++++++++ .../intranode/test_intranode.py | 245 +---------------- .../distributed/primitives/test_ld_options.py | 50 ---- .../distributed/primitives/test_st_options.py | 48 ---- .../test_tilelang_language_ldst_options.py | 92 +++++++ 9 files changed, 351 insertions(+), 339 deletions(-) rename examples/distributed/deepseek_deepep/{utils.py => deepep_utils.py} (100%) create mode 100644 examples/distributed/deepseek_deepep/intranode/example_intranode.py delete mode 100644 examples/distributed/primitives/test_ld_options.py delete mode 100644 examples/distributed/primitives/test_st_options.py create mode 100644 testing/python/language/test_tilelang_language_ldst_options.py diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index 4b6e643408..976b8b7739 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -5,7 +5,7 @@ from typing import Tuple, Optional import tilelang -from utils import Config +from deepep_utils import Config from tilelang.distributed.utils import create_mapped_tensor from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine diff --git a/examples/distributed/deepseek_deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md index 97f3f00aad..d3cea90dc4 100644 --- a/examples/distributed/deepseek_deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -24,7 +24,7 @@ The table below shows a latency and bandwidth comparison for DeepEP and TileScal # Intra-node Introduction This example implements DeepEP’s intra‑node (NVLink) dispatch/combine using TileScale kernels. - +z The intra‑node path lives under `intranode/` and provides a minimal public API that mirrors DeepEP’s behavior for NVLink‑connected ranks. @@ -144,7 +144,7 @@ which can be reused for cached re‑dispatch and is required by the combine stag Quick start (intra‑node test): ``` -TILELANG_USE_DISTRIBUTED=1 python intranode/test_intranode.py \ +CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 TILELANG_USE_DISTRIBUTED=1 python intranode/example_intranode.py \ --num_ranks 8 --num_tokens 4096 --hidden 7168 --num_topk 8 --num_experts 32 [--cached] ``` diff --git a/examples/distributed/deepseek_deepep/utils.py b/examples/distributed/deepseek_deepep/deepep_utils.py similarity index 100% rename from examples/distributed/deepseek_deepep/utils.py rename to examples/distributed/deepseek_deepep/deepep_utils.py diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 96e7af70e0..55096a5030 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -10,7 +10,7 @@ import tilelang import tilelang.language as T from typing import Optional, Tuple -from utils import Config, ep_ext # noqa: F403 +from deepep_utils import Config, ep_ext # noqa: F403 # tilelang.disable_cache() os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log diff --git a/examples/distributed/deepseek_deepep/intranode/example_intranode.py b/examples/distributed/deepseek_deepep/intranode/example_intranode.py new file mode 100644 index 0000000000..8f555dfeea --- /dev/null +++ b/examples/distributed/deepseek_deepep/intranode/example_intranode.py @@ -0,0 +1,247 @@ +### TILELANG_USE_DISTRIBUTED=1 python test_intranode.py (--cached, optionally) + +import os +import sys + +sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path + +import torch +from argparse import ArgumentParser +from tilelang.distributed.utils import init_dist + +from buffer import EPBuffer +from deepep_utils import gen_inputs, ep_bench + +# tilelang.disable_cache() +os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log + + +def test_intranode( + num_tokens: int, + hidden: int, + num_topk: int, + num_experts: int, + rank: int, + num_ranks: int, + expert_alignment: int, + cached_dispatch: bool, + group: torch.distributed.ProcessGroup, +): + try: + import deep_ep # noqa: F403 + except ModuleNotFoundError: + raise ModuleNotFoundError("Please install DeepEP to run this test.") from None + + # Create interface buffers + ts_buffer = EPBuffer(group, 2**30, num_topk, num_experts, hidden) + deepep_buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) + + # Generate inputs for testing + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, + num_ranks) + + # 1. test get_dispatch_layout + ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout( + topk_idx, num_experts) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout( + topk_idx) + + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + f"[rank {rank}] num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ + f"[rank {rank}] is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + f"[rank {rank}] num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" + + group.barrier() + if rank == 0: + print('Check passed for get_dispatch_layout. ✅') + + # 2. test dispatch + # ref + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ + deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + # ours + if cached_dispatch: + recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, + num_tokens_per_expert, None, None, expert_alignment) + else: + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, + topk_weights, expert_alignment) + + # check dispatch output + assert torch.equal( + recv_x, + ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + if not cached_dispatch: + assert torch.equal( + recv_topk_idx, ref_recv_topk_idx + ), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' + assert torch.equal( + recv_topk_weights, ref_recv_topk_weights + ), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, f'[rank {rank}] num_recv_tokens_per_expert_list mismatch' + + # check handle + rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle + ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle + assert torch.equal( + rank_prefix_matrix, ref_rank_prefix_matrix + ), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' + assert torch.equal( + channel_prefix_matrix, ref_channel_prefix_matrix + ), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' + assert torch.equal( + recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix + ), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' + assert torch.equal( + recv_src_idx, ref_recv_src_idx + ), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' + assert torch.equal( + is_token_in_rank, ref_is_token_in_rank + ), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' + assert torch.equal( + send_head, ref_send_head + ), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + + group.barrier() + if rank == 0: + print(f'Check passed for {"cached" if cached_dispatch else "non-cached"} dispatch. ✅') + + # 3. test combine + ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine( + recv_x, ref_handle, ref_recv_topk_weights) + if cached_dispatch: # acquire handle first + recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, + topk_weights, expert_alignment) + combined_x, combined_topk_weights = ts_buffer.combine(recv_x, handle, recv_topk_weights) + assert torch.equal( + combined_x, ref_combined_x + ), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' + assert torch.equal( + combined_topk_weights, ref_combined_topk_weights + ), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' + + group.barrier() + if rank == 0: + print('Check passed for combine. ✅') + + if rank == 0: + print('All checks passed for TileScale intranode DeepEP. ✅') + + # benchmark + if rank == 0: + print( + f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========' + ) + if not cached_dispatch: + group.barrier() + deepep_dispatch_time = ep_bench( + lambda: deepep_buffer. + dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, + ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), + warmup=50, + rep=50) + print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + group.barrier() + ts_dispatch_time = ep_bench( + lambda: ts_buffer. + dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, + topk_idx, topk_weights, expert_alignment), + warmup=50, + rep=50) + print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + group.barrier() + else: + group.barrier() + deepep_dispatch_time = ep_bench( + lambda: deepep_buffer. + dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, + ref_num_tokens_per_expert, None, None, expert_alignment), + warmup=50, + rep=50) + print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + group.barrier() + ts_dispatch_time = ep_bench( + lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, + num_tokens_per_expert, None, None, expert_alignment), + warmup=50, + rep=50) + print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + group.barrier() + + if rank == 0: + print('========== Benchmarking combine ==========') + group.barrier() + deepep_combine_time = ep_bench( + lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), warmup=50, rep=50) + print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') + + group.barrier() + ts_combine_time = ep_bench( + lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), warmup=50, rep=50) + print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') + group.barrier() + + if rank == 0: + print('========== Benchmarking report ==========') + dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 + combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes + if rank == 0: + print( + f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)' + ) + print( + f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)' + ) + + +def run(local_rank: int, num_local_ranks: int, args): + rank, num_ranks, group = init_dist(local_rank, num_local_ranks) + + test_intranode( + args.num_tokens, + args.hidden, + args.num_topk, + args.num_experts, + rank, + num_ranks, + args.expert_alignment, + args.cached, + group, + ) + + torch.distributed.destroy_process_group() + + +def parse_args(): + parser = ArgumentParser(description="Test dispatch") + parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") + parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") + parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") + parser.add_argument( + "--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") + parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") + parser.add_argument( + "--cached", action="store_true", default=False, help="Whether to use cached dispatch") + return parser.parse_args() + + +def main(): + args = parse_args() + + num_ranks = args.num_ranks + torch.multiprocessing.spawn(run, args=(num_ranks, args), nprocs=num_ranks) + + +if __name__ == "__main__": + main() diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index aed291c9db..3b7d52807f 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -1,243 +1,14 @@ -### TILELANG_USE_DISTRIBUTED=1 python test_intranode.py (--cached, optionally) +import tilelang +import tilelang.testing -import os -import sys +import example_intranode -sys.path.append(os.path.dirname(os.path.dirname(__file__))) # add parent folder to path -import torch -from argparse import ArgumentParser -from tilelang.distributed.utils import init_dist - -from buffer import EPBuffer -from utils import gen_inputs, ep_bench - -# tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log - - -def test_intranode( - num_tokens: int, - hidden: int, - num_topk: int, - num_experts: int, - rank: int, - num_ranks: int, - expert_alignment: int, - cached_dispatch: bool, - group: torch.distributed.ProcessGroup, -): - try: - import deep_ep # noqa: F403 - except ModuleNotFoundError: - raise ModuleNotFoundError("Please install DeepEP to run this test.") from None - - # Create interface buffers - ts_buffer = EPBuffer(group, 2**30, num_topk, num_experts, hidden) - deepep_buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) - - # Generate inputs for testing - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, - num_ranks) - - # 1. test get_dispatch_layout - ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout( - topk_idx, num_experts) - num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout( - topk_idx) - - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ - f"[rank {rank}] num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - f"[rank {rank}] is_token_in_rank mismatch" - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ - f"[rank {rank}] num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" - - group.barrier() - if rank == 0: - print('Check passed for get_dispatch_layout. ✅') - - # 2. test dispatch - # ref - ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ - deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) - # ours - if cached_dispatch: - recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, - num_tokens_per_expert, None, None, expert_alignment) - else: - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( - x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, - topk_weights, expert_alignment) - - # check dispatch output - assert torch.equal( - recv_x, - ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' - if not cached_dispatch: - assert torch.equal( - recv_topk_idx, ref_recv_topk_idx - ), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal( - recv_topk_weights, ref_recv_topk_weights - ), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' - assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, f'[rank {rank}] num_recv_tokens_per_expert_list mismatch' - - # check handle - rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle - assert torch.equal( - rank_prefix_matrix, ref_rank_prefix_matrix - ), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' - assert torch.equal( - channel_prefix_matrix, ref_channel_prefix_matrix - ), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' - assert torch.equal( - recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix - ), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' - assert torch.equal( - recv_src_idx, ref_recv_src_idx - ), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' - assert torch.equal( - is_token_in_rank, ref_is_token_in_rank - ), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' - assert torch.equal( - send_head, ref_send_head - ), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' - - group.barrier() - if rank == 0: - print(f'Check passed for {"cached" if cached_dispatch else "non-cached"} dispatch. ✅') - - # 3. test combine - ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine( - recv_x, ref_handle, ref_recv_topk_weights) - if cached_dispatch: # acquire handle first - recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( - x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, - topk_weights, expert_alignment) - combined_x, combined_topk_weights = ts_buffer.combine(recv_x, handle, recv_topk_weights) - assert torch.equal( - combined_x, ref_combined_x - ), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' - assert torch.equal( - combined_topk_weights, ref_combined_topk_weights - ), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' - - group.barrier() - if rank == 0: - print('Check passed for combine. ✅') - - if rank == 0: - print('All checks passed for TileScale intranode DeepEP. ✅') - - # benchmark - if rank == 0: - print( - f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========' - ) - if not cached_dispatch: - group.barrier() - deepep_dispatch_time = ep_bench( - lambda: deepep_buffer. - dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, - ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), - warmup=50, - rep=50) - print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') - group.barrier() - ts_dispatch_time = ep_bench( - lambda: ts_buffer. - dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, - topk_idx, topk_weights, expert_alignment), - warmup=50, - rep=50) - print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') - group.barrier() - else: - group.barrier() - deepep_dispatch_time = ep_bench( - lambda: deepep_buffer. - dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, - ref_num_tokens_per_expert, None, None, expert_alignment), - warmup=50, - rep=50) - print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') - group.barrier() - ts_dispatch_time = ep_bench( - lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, - num_tokens_per_expert, None, None, expert_alignment), - warmup=50, - rep=50) - print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') - group.barrier() - - if rank == 0: - print('========== Benchmarking combine ==========') - group.barrier() - deepep_combine_time = ep_bench( - lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), warmup=50, rep=50) - print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') - - group.barrier() - ts_combine_time = ep_bench( - lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), warmup=50, rep=50) - print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') - group.barrier() - - if rank == 0: - print('========== Benchmarking report ==========') - dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 - combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes - if rank == 0: - print( - f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)' - ) - print( - f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)' - ) - print( - f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)' - ) - print( - f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)' - ) - - -def main(local_rank: int, num_local_ranks: int, args): - rank, num_ranks, group = init_dist(local_rank, num_local_ranks) - - test_intranode( - args.num_tokens, - args.hidden, - args.num_topk, - args.num_experts, - rank, - num_ranks, - args.expert_alignment, - args.cached, - group, - ) - - torch.distributed.destroy_process_group() - - -def parse_args(): - parser = ArgumentParser(description="Test dispatch") - parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") - parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") - parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument( - "--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") - parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") - parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - parser.add_argument( - "--cached", action="store_true", default=False, help="Whether to use cached dispatch") - return parser.parse_args() +@tilelang.testing.requires_cuda +def test_intranode(monkeypatch): + monkeypatch.setattr("sys.argv", ["example_intranode.py"]) # optionally add testing params here + example_intranode.main() if __name__ == "__main__": - args = parse_args() - - num_ranks = args.num_ranks - torch.multiprocessing.spawn(main, args=(num_ranks, args), nprocs=num_ranks) + tilelang.testing.main() diff --git a/examples/distributed/primitives/test_ld_options.py b/examples/distributed/primitives/test_ld_options.py deleted file mode 100644 index 1b60f18a27..0000000000 --- a/examples/distributed/primitives/test_ld_options.py +++ /dev/null @@ -1,50 +0,0 @@ -import torch -import tilelang -import tilelang.language as T - -tilelang.disable_cache() - - -@tilelang.jit -def get_kernel(scope, sem, na, nc): - - @T.prim_func - def main(x: T.Tensor((32), "int32"), y: T.Tensor((32), "int32")): - with T.Kernel(1, threads=32): - tx = T.get_thread_binding() - reg = T.alloc_var('int32') - T.ld(x[tx], reg, scope=scope, sem=sem, na=na, nc=nc) - y[tx] = reg - - return main - - -def test_ld_options(scope, sem, na, nc): - kernel = get_kernel(scope, sem, na, nc) - x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) - y = torch.zeros_like(x) - kernel(x, y) - assert torch.equal(x, y) - print(f'check passed for {scope=}.{sem=}.{na=}.{nc=} ✅') - - -if __name__ == "__main__": - # from DeepEP all ld instructions - - # ld.acquire.sys.global.s32 / u64 - test_ld_options(scope="sys", sem="acquire", na=False, nc=False) - - # ld.acquire.gpu.global.s32 - test_ld_options(scope="gpu", sem="acquire", na=False, nc=False) - - # ld.acquire.cta.s32 - test_ld_options(scope="cta", sem="acquire", na=False, nc=False) - - # ld.relaxed.gpu.global.L1::no_allocate.b8/b16/b32/b64 - test_ld_options(scope="gpu", sem="relaxed", na=True, nc=False) - - # ld.volatile.global.s32/f32/s64/u64 - test_ld_options(scope="gpu", sem="volatile", na=False, nc=False) - - # ld.global.nc.L1::no_allocate.L2::256B (or ld.volatile.global when DISABLE_AGGRESSIVE_PTX_INSTRS) - test_ld_options(scope="gpu", sem="weak", na=True, nc=True) diff --git a/examples/distributed/primitives/test_st_options.py b/examples/distributed/primitives/test_st_options.py deleted file mode 100644 index ac5c900adf..0000000000 --- a/examples/distributed/primitives/test_st_options.py +++ /dev/null @@ -1,48 +0,0 @@ -import torch -import tilelang -import tilelang.language as T - -tilelang.disable_cache() - - -@tilelang.jit -def get_kernel(scope, sem, na): - - @T.prim_func - def main(x: T.Tensor((32), "int32")): - with T.Kernel(1, threads=32): - tx = T.get_thread_binding() - T.st(x[tx], tx, scope=scope, sem=sem, na=na) - - return main - - -def test_st_options(scope, sem, na): - kernel = get_kernel(scope, sem, na) - x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) - kernel(x) - assert x.equal(torch.arange(32, device="cuda")) - print(f'check passed for {scope=}.{sem=}.{na=} ✅') - - -if __name__ == "__main__": - # from DeepEP all st instructions - - # st.relaxed.sys.global.s32 - test_st_options("sys", "relaxed", False) - - # # st.release.sys.global.s32 - test_st_options("sys", "release", False) - - # st.release.cta.s32 - test_st_options("cta", "release", False) - - # st.relaxed.gpu.global.L1::no_allocate.b* - test_st_options("gpu", "relaxed", True) - - # st.release.gpu.global.L1::no_allocate.b* - test_st_options("gpu", "release", True) - - # test_st_options("gpu", "weak", False) - test_st_options("gpu", "weak", False) - test_st_options("gpu", "weak", True) diff --git a/testing/python/language/test_tilelang_language_ldst_options.py b/testing/python/language/test_tilelang_language_ldst_options.py new file mode 100644 index 0000000000..10704b93fc --- /dev/null +++ b/testing/python/language/test_tilelang_language_ldst_options.py @@ -0,0 +1,92 @@ +import tilelang +import tilelang.language as T +import torch +import tilelang.testing + + +@tilelang.jit +def get_ld_kernel(scope, sem, na, nc): + + @T.prim_func + def main(x: T.Tensor((32), "int32"), y: T.Tensor((32), "int32")): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding() + reg = T.alloc_var('int32') + T.ld(x[tx], reg, scope=scope, sem=sem, na=na, nc=nc) + y[tx] = reg + + return main + + +@tilelang.jit +def get_st_kernel(scope, sem, na): + + @T.prim_func + def main(x: T.Tensor((32), "int32")): + with T.Kernel(1, threads=32): + tx = T.get_thread_binding() + T.st(x[tx], tx, scope=scope, sem=sem, na=na) + + return main + + +def _test_ld_options(scope, sem, na, nc): + kernel = get_ld_kernel(scope, sem, na, nc) + x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) + y = torch.zeros_like(x) + kernel(x, y) + assert torch.equal(x, y) + + +@tilelang.testing.requires_cuda +def test_ld_options(): + # ld.acquire.sys.global.s32 / u64 + _test_ld_options(scope="sys", sem="acquire", na=False, nc=False) + + # ld.acquire.gpu.global.s32 + _test_ld_options(scope="gpu", sem="acquire", na=False, nc=False) + + # ld.acquire.cta.s32 + _test_ld_options(scope="cta", sem="acquire", na=False, nc=False) + + # ld.relaxed.gpu.global.L1::no_allocate.b8/b16/b32/b64 + _test_ld_options(scope="gpu", sem="relaxed", na=True, nc=False) + + # ld.volatile.global.s32/f32/s64/u64 + _test_ld_options(scope="gpu", sem="volatile", na=False, nc=False) + + # ld.global.nc.L1::no_allocate.L2::256B (or ld.volatile.global when DISABLE_AGGRESSIVE_PTX_INSTRS) + _test_ld_options(scope="gpu", sem="weak", na=True, nc=True) + + +def _test_st_options(scope, sem, na): + kernel = get_st_kernel(scope, sem, na) + x = torch.randint(0, 100, (32,), device="cuda", dtype=torch.int32) + kernel(x) + assert x.equal(torch.arange(32, device="cuda")) + + +@tilelang.testing.requires_cuda +def test_st_options(): + # st.relaxed.sys.global.s32 + _test_st_options("sys", "relaxed", False) + + # # st.release.sys.global.s32 + _test_st_options("sys", "release", False) + + # st.release.cta.s32 + _test_st_options("cta", "release", False) + + # st.relaxed.gpu.global.L1::no_allocate.b* + _test_st_options("gpu", "relaxed", True) + + # st.release.gpu.global.L1::no_allocate.b* + _test_st_options("gpu", "release", True) + + # test_st_options("gpu", "weak", False) + _test_st_options("gpu", "weak", False) + _test_st_options("gpu", "weak", True) + + +if __name__ == "__main__": + tilelang.testing.main() \ No newline at end of file From 47dc3668a0fab192310e94ad2a7dd479ca225a50 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 18:36:01 +0800 Subject: [PATCH 37/41] fix review issues --- .../deepseek_deepep/intranode/combine.py | 2 +- .../deepseek_deepep/intranode/dispatch.py | 14 ++++++-------- src/op/remote_copy.cc | 12 ++++++------ src/op/remote_copy.h | 12 ++++++------ src/tl_templates/cuda/copy.h | 14 +++++++------- src/tl_templates/cuda/ldst.h | 2 +- src/transform/vectorize_loop.cc | 6 ++++-- .../test_tilelang_language_ldst_options.py | 7 +++---- tilelang/distributed/utils.py | 2 +- tilelang/language/distributed/common.py | 12 ++++++------ 10 files changed, 41 insertions(+), 42 deletions(-) diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index a00a7fd1df..17c5f175c7 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -197,7 +197,7 @@ def combine_main( hidden, dst_pe=send_rank_id, unroll_factor=4, - enable_aggresive_vectorize=True) + enable_aggressive_vectorize=True) # 2. send src idx idx = T.alloc_var('int32') diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 55096a5030..0811a4eb17 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -389,7 +389,7 @@ def dispatch_main( hidden, dst_pe=responsible_rank, unroll_factor=4, - enable_aggresive_vectorize=True) + enable_aggressive_vectorize=True) # 2. copy src idx if T.elect_one_sync(): @@ -513,7 +513,7 @@ def dispatch_main( hidden, -1, 5, - enable_aggresive_vectorize=True) + enable_aggressive_vectorize=True) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, @@ -572,8 +572,6 @@ def cached_dispatch_kernel( num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens hidden, - num_topk, - num_experts, num_sms, dtype: str = 'bfloat16', ): @@ -697,7 +695,7 @@ def cached_dispatch_main( hidden, dst_pe=responsible_rank, unroll_factor=4, - enable_aggresive_vectorize=True) + enable_aggressive_vectorize=True) # 2. copy src idx if T.elect_one_sync(): @@ -793,7 +791,7 @@ def cached_dispatch_main( hidden, -1, 5, - enable_aggresive_vectorize=True) + enable_aggressive_vectorize=True) # 2. recv src_idx for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, @@ -948,8 +946,8 @@ def intranode_dispatch( else: kernel = cached_dispatch_kernel(num_ranks, num_tokens, config.num_max_nvl_chunked_send_tokens, - config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, - num_experts, config.num_sms, 'bfloat16') + config.num_max_nvl_chunked_recv_tokens, hidden, + config.num_sms, 'bfloat16') kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) kernel( rank, diff --git a/src/op/remote_copy.cc b/src/op/remote_copy.cc index f7430c30ad..fba501e487 100644 --- a/src/op/remote_copy.cc +++ b/src/op/remote_copy.cc @@ -78,7 +78,7 @@ PutOp::PutOp(Array args, BufferMap vmap) { node->dst_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; - node->enable_aggresive_vectorize = bool(args[6].as().value()->value); + node->enable_aggressive_vectorize = bool(args[6].as().value()->value); data_ = std::move(node); (void)vmap; } @@ -94,7 +94,7 @@ Stmt PutOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { std::stringstream ss; if (scope == "warp") { ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " - << (enable_aggresive_vectorize ? "true" : "false") << ">"; + << (enable_aggressive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { @@ -188,7 +188,7 @@ GetOp::GetOp(Array args, BufferMap vmap) { node->src_pe = args[3]; node->unroll_factor = args[4].as().value()->value; node->scope = args[5].as().value()->value; - node->enable_aggresive_vectorize = bool(args[6].as().value()->value); + node->enable_aggressive_vectorize = bool(args[6].as().value()->value); data_ = std::move(node); (void)vmap; } @@ -204,7 +204,7 @@ Stmt GetOpNode::Lower(const LowerArgs &T, arith::Analyzer *analyzer) const { std::stringstream ss; if (scope == "warp") { ss << "tl::cp_warp<" << copy_size << ", " << unroll_factor << ", " - << (enable_aggresive_vectorize ? "true" : "false") << ">"; + << (enable_aggressive_vectorize ? "true" : "false") << ">"; } else if (scope == "block") { ss << "tl::cp_block<" << copy_size << ">"; } else { @@ -383,12 +383,12 @@ TileOperator LdOpNode::Clone() const { } TIR_REGISTER_TL_OP(PutOp, put) - .set_num_inputs(6) + .set_num_inputs(7) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); TIR_REGISTER_TL_OP(GetOp, get) - .set_num_inputs(6) + .set_num_inputs(7) .set_attr("TCallEffectKind", Integer(CallEffectKind::kOpaque)); diff --git a/src/op/remote_copy.h b/src/op/remote_copy.h index c5397d73d4..3c118f33af 100644 --- a/src/op/remote_copy.h +++ b/src/op/remote_copy.h @@ -33,9 +33,9 @@ class PutOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} - bool enable_aggresive_vectorize; ///< Whether to enable aggressive - ///< vectorization, only effctive for - ///< warp-scope + bool enable_aggressive_vectorize; ///< Whether to enable aggressive + ///< vectorization, only effctive for + ///< warp-scope bool is_distributed() const; @@ -125,9 +125,9 @@ class GetOpNode : public TileOperatorNode { Array dst_indices; ///< Destination indices used for address computation std::string scope; ///< Scope: {warp, block} - bool enable_aggresive_vectorize; ///< Whether to enable aggressive - ///< vectorization, only effctive for - ///< warp-scope + bool enable_aggressive_vectorize; ///< Whether to enable aggressive + ///< vectorization, only effctive for + ///< warp-scope bool is_distributed() const; diff --git a/src/tl_templates/cuda/copy.h b/src/tl_templates/cuda/copy.h index eaf4091558..df68287cb6 100644 --- a/src/tl_templates/cuda/copy.h +++ b/src/tl_templates/cuda/copy.h @@ -219,18 +219,18 @@ TL_DEVICE void cp_warp_impl(dtype_t const *const dst_addr, } /** - * @param enable_aggresive_vectorize If set to true, the copy will be performed + * @param enable_aggressive_vectorize If set to true, the copy will be performed * with aggressive vectorization (e.g., using int4 for aligned and sized * transfers), which requires that both source and destination addresses are * 16-byte aligned and N*sizeof(dtype_t) is a multiple of 16 for optimal memory * access and throughput. If false, performs a standard element-wise copy. */ // todo: support more auto-vectorize later -template TL_DEVICE void cp_warp(dtype_t const *const dst_addr, dtype_t const *const src_addr) { - if constexpr (enable_aggresive_vectorize) { + if constexpr (enable_aggressive_vectorize) { int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; constexpr int N_int4 = sizeof(dtype_t) * N / 16; @@ -240,12 +240,12 @@ TL_DEVICE void cp_warp(dtype_t const *const dst_addr, } } -template TL_DEVICE void cp_warp(uint64_t dst_addr_uint64, dtype_t const *const src_addr) { dtype_t *dst_addr = reinterpret_cast(dst_addr_uint64); - if constexpr (enable_aggresive_vectorize) { + if constexpr (enable_aggressive_vectorize) { int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; constexpr int N_int4 = sizeof(dtype_t) * N / 16; @@ -255,11 +255,11 @@ TL_DEVICE void cp_warp(uint64_t dst_addr_uint64, } } -template TL_DEVICE void cp_warp(dtype_t *const dst_addr, uint64_t src_addr_uint64) { const dtype_t *src_addr = reinterpret_cast(src_addr_uint64); - if constexpr (enable_aggresive_vectorize) { + if constexpr (enable_aggressive_vectorize) { int4 *__restrict__ dst_addr_int4 = (int4 *)dst_addr; const int4 *__restrict__ src_addr_int4 = (const int4 *)src_addr; constexpr int N_int4 = sizeof(dtype_t) * N / 16; diff --git a/src/tl_templates/cuda/ldst.h b/src/tl_templates/cuda/ldst.h index a34595910b..c875832ebc 100644 --- a/src/tl_templates/cuda/ldst.h +++ b/src/tl_templates/cuda/ldst.h @@ -83,7 +83,7 @@ template struct LdImpl { } \ } else if constexpr (sizeof(T) == 16) { \ asm volatile("st" SEM_LIT SCOPE_LIT NA_LIT \ - ".v4.s32 {%0, %1, %2, %3}, [%4];" ::"l"(ptr), \ + ".v4.s32 [%0], {%1, %2, %3, %4};" ::"l"(ptr), \ "r"(value.x), "r"(value.y), "r"(value.z), "r"(value.w) \ : "memory"); \ } \ diff --git a/src/transform/vectorize_loop.cc b/src/transform/vectorize_loop.cc index 8adb2c31d9..56a6ec3b67 100644 --- a/src/transform/vectorize_loop.cc +++ b/src/transform/vectorize_loop.cc @@ -795,13 +795,15 @@ class TLVectorizer : public StmtMutator, // Reinterpret the value to vector type (e.g., int4 for 8xbf16) PrimExpr vec_value = Call(vec_dtype, builtin::reinterpret(), {new_value}); - PrimExpr vec_value_slice = vec_value.as()->args[0]; + + // A trick to get the lvalue of the vectorized value + PrimExpr vec_value_lvalue = vec_value.as()->args[0]; // Build new args with base addresses and reinterpreted value Array new_args; new_args.push_back(func_name); new_args.push_back(new_addr); - new_args.push_back(vec_value_slice); + new_args.push_back(vec_value_lvalue); // Copy remaining args (sem, scope, etc.) for (size_t i = 3; i < op->args.size(); ++i) { new_args.push_back(this->VisitExpr(op->args[i])); diff --git a/testing/python/language/test_tilelang_language_ldst_options.py b/testing/python/language/test_tilelang_language_ldst_options.py index 10704b93fc..3bcf44e0c4 100644 --- a/testing/python/language/test_tilelang_language_ldst_options.py +++ b/testing/python/language/test_tilelang_language_ldst_options.py @@ -67,11 +67,11 @@ def _test_st_options(scope, sem, na): @tilelang.testing.requires_cuda -def test_st_options(): +def test_st_options(): # st.relaxed.sys.global.s32 _test_st_options("sys", "relaxed", False) - # # st.release.sys.global.s32 + # st.release.sys.global.s32 _test_st_options("sys", "release", False) # st.release.cta.s32 @@ -83,10 +83,9 @@ def test_st_options(): # st.release.gpu.global.L1::no_allocate.b* _test_st_options("gpu", "release", True) - # test_st_options("gpu", "weak", False) _test_st_options("gpu", "weak", False) _test_st_options("gpu", "weak", True) if __name__ == "__main__": - tilelang.testing.main() \ No newline at end of file + tilelang.testing.main() diff --git a/tilelang/distributed/utils.py b/tilelang/distributed/utils.py index 42166435e9..d994d5094f 100644 --- a/tilelang/distributed/utils.py +++ b/tilelang/distributed/utils.py @@ -401,5 +401,5 @@ def has_fullmesh_nvlink(): return _has_fullmesh_nvlink -def create_mapped_tensor(shape: list[int], dtype: torch.dtype) -> torch.Tensor: +def create_mapped_tensor(shape: list[int], dtype: torch.dtype) -> tuple[torch.Tensor, torch.Tensor]: return create_host_device_tensor(shape, dtype) diff --git a/tilelang/language/distributed/common.py b/tilelang/language/distributed/common.py index 31264c0ae2..adb559e924 100644 --- a/tilelang/language/distributed/common.py +++ b/tilelang/language/distributed/common.py @@ -24,7 +24,7 @@ def put_warp(src: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | IntImm | None = -1, unroll_factor: int = 4, - enable_aggresive_vectorize: bool = False): + enable_aggressive_vectorize: bool = False): """Put to a remote buffer with unrolled loop. Args: @@ -39,12 +39,12 @@ def put_warp(src: PrimExpr, -1 by default, which means local copy. unroll_factor: int The unroll factor - enable_aggresive_vectorize: bool + enable_aggressive_vectorize: bool Whether to enable aggressive vectorization. If True, the compiler with try to vectorize the copy via int4. """ return tir.call_intrin("handle", tir.op.Op.get("tl.put"), src, dst, size, dst_pe, unroll_factor, - "warp", enable_aggresive_vectorize) + "warp", enable_aggressive_vectorize) def get_warp(src: PrimExpr, @@ -52,7 +52,7 @@ def get_warp(src: PrimExpr, size: PrimExpr, src_pe: PrimExpr | IntImm | None = -1, unroll_factor: int = 4, - enable_aggresive_vectorize: bool = False): + enable_aggressive_vectorize: bool = False): """Get from a remote buffer with unrolled loop. Args: @@ -67,12 +67,12 @@ def get_warp(src: PrimExpr, -1 by default, which means local copy. unroll_factor: int The unroll factor - enable_aggresive_vectorize: bool + enable_aggressive_vectorize: bool Whether to enable aggressive vectorization. If True, the compiler with try to vectorize the copy via int4. """ return tir.call_intrin("handle", tir.op.Op.get("tl.get"), src, dst, size, src_pe, unroll_factor, - "warp", enable_aggresive_vectorize) + "warp", enable_aggressive_vectorize) def put_block(src: PrimExpr, dst: PrimExpr, size: PrimExpr, dst_pe: PrimExpr | IntImm | None = -1): From 35433fe468b024b2fa1a3def9aa986e309ca9daf Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Fri, 19 Dec 2025 20:09:18 +0800 Subject: [PATCH 38/41] fix import error --- examples/distributed/deepseek_deepep/buffer.py | 4 +++- examples/distributed/deepseek_deepep/intranode/__init__.py | 3 --- .../distributed/deepseek_deepep/intranode/test_intranode.py | 1 - 3 files changed, 3 insertions(+), 5 deletions(-) delete mode 100644 examples/distributed/deepseek_deepep/intranode/__init__.py diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index 976b8b7739..f281f19e30 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -7,7 +7,9 @@ import tilelang from deepep_utils import Config from tilelang.distributed.utils import create_mapped_tensor -from intranode import get_dispatch_layout, intranode_dispatch, intranode_combine +from intranode.get_dispatch_layout import get_dispatch_layout +from intranode.dispatch import intranode_dispatch +from intranode.combine import intranode_combine class EPBuffer: diff --git a/examples/distributed/deepseek_deepep/intranode/__init__.py b/examples/distributed/deepseek_deepep/intranode/__init__.py deleted file mode 100644 index f637779377..0000000000 --- a/examples/distributed/deepseek_deepep/intranode/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -from .get_dispatch_layout import get_dispatch_layout # noqa: F401 -from .dispatch import intranode_dispatch # noqa: F401 -from .combine import intranode_combine # noqa: F401 diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index 3b7d52807f..3177219969 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -1,4 +1,3 @@ -import tilelang import tilelang.testing import example_intranode From 298cb04b560f7da4ba222f53601faa85aed8f683 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Sat, 20 Dec 2025 17:23:25 +0800 Subject: [PATCH 39/41] Add DeepEP submodule and installation script for CI --- .github/workflows/ci.yml | 1 + .gitmodules | 3 +++ pyproject.toml | 1 + tilelang/distributed/install_deepep.sh | 28 ++++++++++++++++++++++++++ 4 files changed, 33 insertions(+) create mode 100644 tilelang/distributed/install_deepep.sh diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 995998b9b9..f1824c4bc9 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -41,6 +41,7 @@ jobs: [[ -f requirements-test.txt ]] && \ PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user pip install flash_attn==2.5.8 --no-user --no-build-isolation + bash tilelang/distributed/install_deepep.sh # Install DeepEP for testing purpose touch "$MARKER" fi diff --git a/.gitmodules b/.gitmodules index 67ce3488ae..0410c30cfb 100644 --- a/.gitmodules +++ b/.gitmodules @@ -7,3 +7,6 @@ [submodule "3rdparty/composable_kernel"] path = 3rdparty/composable_kernel url = https://github.com/ROCm/composable_kernel +[submodule "3rdparty/DeepEP"] + path = 3rdparty/DeepEP + url = https://github.com/deepseek-ai/DeepEP diff --git a/pyproject.toml b/pyproject.toml index 9537cbd59f..17a65115db 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -31,6 +31,7 @@ dependencies = [ "torch>=2.7; platform_system == 'Darwin'", "tqdm>=4.62.3", "typing-extensions>=4.10.0", + "nvidia-nvshmem-cu12", "tilescale_ext @ file:./tilelang/utils/ts_ext", ] diff --git a/tilelang/distributed/install_deepep.sh b/tilelang/distributed/install_deepep.sh new file mode 100644 index 0000000000..7e21b143ef --- /dev/null +++ b/tilelang/distributed/install_deepep.sh @@ -0,0 +1,28 @@ +# This script is for automatic installation of DeepEP for CI workflow + +# Ensure DeepEP is cloned into 3rdparty folder +if [ ! -d "3rdparty/DeepEP" ]; then + echo "DeepEP is not cloned into 3rdparty folder" + exit 1 +fi + +# Ensure NVSHMEM installed +if pip list | grep nvshmem > /dev/null 2>&1; then + echo "nvshmem is already installed." +else + pip install nvidia-nvshmem-cu12 +fi + +# Fix a bug of NVSHMEM path +export NVSHMEM_DIR=$(python -c "import site; print(site.getsitepackages()[0])")/nvidia/nvshmem +echo "NVSHMEM_DIR is set to $NVSHMEM_DIR" +ln -sf $NVSHMEM_DIR/lib/libnvshmem_host.so.3 $NVSHMEM_DIR/lib/libnvshmem_host.so + +# Install DeepEP +cd 3rdparty/DeepEP +python setup.py install +cd - + +# Validate +python -c "import deep_ep; print(deep_ep.__version__)" +echo "DeepEP is installed successfully. ✅" From 5bbd6dd4ed38825aa5d3d018dc4e5e9d50849e51 Mon Sep 17 00:00:00 2001 From: Rachmanino <18805904201@163.com> Date: Sat, 20 Dec 2025 17:48:47 +0800 Subject: [PATCH 40/41] fix ci bug --- .github/workflows/ci.yml | 29 +++++++++++--------------- 3rdparty/DeepEP | 1 + tilelang/distributed/install_deepep.sh | 6 +++++- 3 files changed, 18 insertions(+), 18 deletions(-) create mode 160000 3rdparty/DeepEP diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index f1824c4bc9..a04edc1eb1 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -7,7 +7,7 @@ env: jobs: format-check: - runs-on: [self-hosted, nvidia] + runs-on: [self-hosted, nvidia, hopper] permissions: contents: write @@ -41,7 +41,6 @@ jobs: [[ -f requirements-test.txt ]] && \ PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user pip install flash_attn==2.5.8 --no-user --no-build-isolation - bash tilelang/distributed/install_deepep.sh # Install DeepEP for testing purpose touch "$MARKER" fi @@ -85,26 +84,22 @@ jobs: set -e REQS_HASH=$(cat requirements-test.txt 2>/dev/null || true) MARKER="${{ runner.tool_cache }}/.venv_marker_${{ env.PYTHON_VERSION }}_${REQS_HASH:0:8}" - - if [[ -f "$MARKER" ]] && [[ -f "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" ]]; then - echo "venv exists and hash matches – reuse it" - else - echo "venv stale or missing – recreating" - rm -rf "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" "$MARKER" - python -m venv "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - python -m pip install --upgrade pip --no-user - [[ -f requirements-test.txt ]] && \ - PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user - # flash attention usually requires no isolation build - pip install flash_attn==2.5.8 --no-user --no-build-isolation - touch "$MARKER" - fi + # NOTE(wt): We disable the venv reuse for now to allow installing DeepEP + # echo "venv stale or missing – recreating" + rm -rf "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" + python -m venv "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" + source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" + python -m pip install --upgrade pip --no-user + [[ -f requirements-test.txt ]] && \ + PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user + # flash attention usually requires no isolation build + pip install flash_attn==2.5.8 --no-user --no-build-isolation - name: Install project (wheel form) run: | source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" pip install . --no-user -v + bash tilelang/distributed/install_deepep.sh # Install DeepEP for testing purpose - name: Run examples run: | diff --git a/3rdparty/DeepEP b/3rdparty/DeepEP new file mode 160000 index 0000000000..b57e5e212a --- /dev/null +++ b/3rdparty/DeepEP @@ -0,0 +1 @@ +Subproject commit b57e5e212ab75350f53c72064333e4fe1076b1da diff --git a/tilelang/distributed/install_deepep.sh b/tilelang/distributed/install_deepep.sh index 7e21b143ef..2d369a2394 100644 --- a/tilelang/distributed/install_deepep.sh +++ b/tilelang/distributed/install_deepep.sh @@ -24,5 +24,9 @@ python setup.py install cd - # Validate -python -c "import deep_ep; print(deep_ep.__version__)" +python -c "import deep_ep" +if [ $? -ne 0 ]; then + echo "Failed to import deep_ep" + exit 1 +fi echo "DeepEP is installed successfully. ✅" From 5f37623905549f5de9ea51ab8ffcd20d8825ad04 Mon Sep 17 00:00:00 2001 From: XIAO YOUWEI Date: Fri, 6 Feb 2026 15:46:13 +0800 Subject: [PATCH 41/41] [Sync] Merge mainstream TileLang TVM-FFI features into TileScale (#47) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [Example] Add GQA decoding kernel with varlen page table (#1265) * [Example] Add page table for gqa decode * [Example] Page table for varlen decoding * [Lint] * [Refactor] Remove redundant code * [Lint] * [Lint] * [Lint] * [Refactor] add support for numpy dtype conversion (#1255) * add typing stub for tir.ir * remove idents * minor update * [Refactor] add numpy conversion for dtype * fix lint error * remove unused np.float_ in dtype conversion * fix type in np.int_ * fix typo * minor fix * remove debug files * [EXAMPLE] In the flash attention example keep the max of all blocks seen in scores_max numerical stability (#1148) * Keep the max of all blocks seen in scores_max for stability * ruff formatting * [Docs] Improve Installation Guide (#1270) * [Docs] Improve installation guide * address comments * [Enhancement] Keep max score attention across blocks in FlashAttention for better numerical stablity (#1269) * Implement max score retention across blocks in FlashAttention for improved stability * fix manual pipeline parameters * Update examples/flash_attention/example_gqa_fwd_varlen.py Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * fix typo * more * fix a previous typo --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * [Bugfix] Fix multiple cg defination when using T.sync_grid (#1272) * [Minor] Remove from __future__ import annotations for python 3.8 (#1273) * [BugFix] Adding extra parameters into autotune hashkey (#1274) * [BugFix] Adding extra parameters into autotune hashkey * lint * None check * check serializable * Fix various issues under `int64_t` static and dynamic shape. (#1218) * Fix various issues under int64_t static and dynamic shape. * Resolve reviewed issues. * Add unit test. * fix --------- Co-authored-by: LeiWang1999 * Bug fix for Gated Delta Net benchmark script (#1267) * fix argument order for fla chunk_gated_delta_rule_fwd_h * explicit import assert_similar from utils * rename utils module to avoid name clash * set store_final_state and save_new_value to True * fix --------- Co-authored-by: LeiWang1999 * [Bugfix] Minor fix for some cases (#1278) * [Language] Add shape check in `T.view/reshape` (#1277) * [Language] Add shape check in T.view/reshape * address comments * [FFI] Use tvm ffi as the default execution backend (#1259) * [Refactor] Update FFI type handling and simplify argument management * Refactored FFI type definitions in runtime and code generation files to use `TVMFFIAny` instead of `TVMValue`, enhancing type clarity. * Updated function registration in `runtime.cc` to utilize canonical names for better consistency. * Simplified argument handling in the `simplify` transformation, ensuring unused buffer parameters are removed only when simplification is enabled. * Adjusted autotuner and profiler parameters to standardize the execution backend to `tvm_ffi`, improving clarity in backend selection. * Removed obsolete `adapt_torch2tvm` function from tensor utilities to streamline the codebase and reduce complexity. * [Update] Sync TVM submodule and enhance kernel source handling * Updated the TVM submodule to commit cdc2aced, ensuring compatibility with recent changes. * Added functionality to print kernel source in `example_blocksparse_gemm.py` for better debugging. * Commented out the main execution call in test files to prevent unintended execution during testing. * Introduced `tilelang.disable_cache()` in various test files to streamline testing and avoid cache-related issues. * Refactored kernel source retrieval methods to improve clarity and consistency across different execution backends. * [Refactor] Clean up imports and improve code formatting * Removed unused import of `tilelang.testing` in `test_example_blocksparse_gemm.py` to streamline the code. * Reformatted several lines in `arg_binder.cc`, `make_packed_api.cc`, `tvm_ffi.py`, and `adapter.py` for improved readability and consistency. * Updated comments and spacing in `tvm_ffi.py` to enhance clarity without altering functionality. * Update execution backend options and improve resolution logic - Changed default execution backend from "cython" to "auto" in multiple locations to allow automatic selection based on the target. - Expanded the list of supported execution backends to include "torch" and "nvrtc" across various classes and functions. - Enhanced backend resolution logic in `KernelCache` and `AutoTuner` to ensure appropriate backend selection based on the target. - Updated documentation to reflect changes in execution backend options and their defaults. * lint fix * fix * Enhance argument handling in CUDA and HIP runtime modules - Updated `ExtractFuncInfo` in `rt_mod_cuda.cc` and `rt_mod_hip.cc` to map boolean argument types to int32, ensuring compatibility with device runtime. - Refactored `BindDLTensor` in `arg_binder.cc` to improve null handling and validation checks for DLTensor parameters, utilizing expression-level guards to prevent dereferencing null pointers. - Enhanced error checking for buffer shape, strides, and data fields, ensuring robust handling of optional inputs and maintaining consistency across various checks. * lint fix * lint fix * lint fix * lint fix * minor fix * fix * recover check * Refactor argument binding and validation in `arg_binder.cc` - Improved null handling and validation checks in `BindDLTensor`, ensuring safe dereferencing of pointers. - Enhanced consistency checks for buffer shape, strides, and data fields, utilizing expression-level guards. - Updated `MakePackedAPI` to maintain code clarity and consistency in argument handling. - Minor adjustments in test files to streamline kernel execution and improve readability. * lint fix * stride fix * minor fix * fix * lint fix * lint fix * Add CUDA stream access policy window helpers and integrate with L2 persistent cache management - Introduced functions to set and reset the CUDA stream access policy window, allowing for better control over L2 cache usage. - Updated runtime files to include new FFI packed functions for managing stream attributes. - Modified lower_hopper_intrin to incorporate prologue and epilogue statements for L2 cache setup and teardown. - Enhanced tests to verify the inclusion of new FFI calls in the generated kernel source. * check with symbolic * support null ptr * Update CMakeLists and lower.py for code generation and subproject status - Added `codegen_c_host.cc` to the list of source files in CMakeLists.txt for improved code generation support. - Updated the function call in `lower.py` to use `target.build.tilelang_c` for C target host code generation, enhancing compatibility. - Marked the TVM subproject as dirty to indicate local modifications. * lint fix * Update comments for clarity in quickstart.py * [Bugfix] Supply missing `T.print` for bool type (#1279) * fix for bool dtype * lint fix * fix * ci fix * [Fix] Fix memory leak bug (#1281) * add typing stub for tir.ir * remove idents * minor update * [Refactor] add numpy conversion for dtype * fix lint error * remove unused np.float_ in dtype conversion * fix type in np.int_ * fix typo * minor fix * remove debug files * fix memory leak bug * fix lint error * add comments * fix lint error * remove duplicated, because tilelang doesn't dependent deprecated * [Enhancement] Enhance CUDA compilation by integrating pass context configuration (#1283) - Updated the `tilelang_callback_cuda_compile` function to accept a `pass_config` parameter, allowing for more flexible compilation options. - Introduced handling for fast math and PTXAS options based on the provided pass configuration. - Modified the CUDA build process in `rt_mod_cuda.cc` to utilize the current pass context, improving the integration of compilation settings. - Refactored NVCC command construction to use a dedicated function for better clarity and maintainability. * Fix the bug in issue #1266 (#1284) Co-authored-by: cheeryBloosm * [Language][UX] Nested loop checker in pre-lowering stage (#1288) * [Language][UX] Nested loop checker in pre-lowering stage * rename * comment * address comments * [Compatibility] Support CUDA 11.3 (#1290) * [Feat] Add support for using `T.Tensor(n * 2 + 1)` in function annotation (#1285) * [Feature] Add support for A: T.Tensor(n + 1) and A: T.Tensor(2*n) * issue fix * fix * fix * decreate nproc for debugging --------- Co-authored-by: Lei Wang * [Feat] add support for passing reference in T.Var annotation (#1291) * [Enhancement] Shared Memory Size Can be Dynamic (#1294) * bugfix * lint fix * test * lint fix * increate procs * recover * [Fix] Remove unused let_bindings_ in CodeGenC to fix #1300 (#1305) * [Feat] add missing support of uint32x2 * [Feat] Add `T.Ref` annotation and tests * fix lint error * minor update for error message on twice decl * Remove unused let_bindings_ in CodeGenC to fix #1300 * [Bugfix] Fallback to the old AtomicAdd implementation for legacy architectures (#1306) * [Fix] Fix frame scope error in T.macro (#1308) * [Fix] Fix #1307 by adding macro inside function * fix lint error * add comments and fix lint error * Remove debug print from enter_frame method Removed debug print statement from enter_frame method. --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [WIP] support more dtypes for tcgen05 (#1229) support ld with pack for fp32 dtype add dump add tempalte expand remove unused dtype and change to rebased apis * Improve memory access safety and `T.assume` handling (#1292) * Improve memory access safety and T.assume handling * Improve memory access safety and T.assume handling * bugfix * lint fix * bugfix * bugfix * refactor legalize safe memory access pass --------- Co-authored-by: Lei Wang * [Bugfix] Fix autotune cache (#1315) * [Refactor] Backup Analyzer to get the appropriate arith informations (#1311) * [Refactor] Update Vectorization Functions to Accept Analyzer Parameter - Modified `VectorizeLoop` and related functions to accept an `arith::Analyzer` parameter, enhancing their capability to perform analysis during vectorization. - Updated multiple instances in `copy.cc`, `fill.cc`, `parallel.cc`, and layout inference files to utilize the new analyzer parameter for improved performance and correctness. - Ensured consistency across vectorization logic by integrating the analyzer into existing workflows, facilitating better optimization opportunities. * [Fix] Corrected PostOrderVisit call in loop_vectorize.cc - Updated the PostOrderVisit function to analyze the body of the loop node instead of the node itself, ensuring proper handling of nested loops during vectorization analysis. * fix * lint fix * fix * Revert "[WIP] support more dtypes for tcgen05 (#1229)" (#1323) This reverts commit 0d101c110f74ebf2ef8c11a5ece9dfb314b48baa. Co-authored-by: Zhiwen Mo * [CI]: Bump actions/checkout from 5 to 6 (#1319) Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [CI]: Bump pypa/cibuildwheel from 3.2 to 3.3 (#1318) Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [Installation] Fix building using customized TVM path (#1326) * [Release] Allow developer with write permission to trigger wheel release (#1322) * [Feat] Support warp reduce (#1316) * [Feat] Support warp reduce * lint * add test * lint * [Enhancement] Support more dtype in `T.print` (#1329) * [Enhancement] Support more dtype in `T.print` * upd * upd * [BugFix] Use BufferRegion in tl.cumsum to infer buffer shape (#1321) * [BugFix] Use BufferRegion in tl.cumsum to infer buffer shape * remove debug lines * remove rubbish * Fix decorator syntax for atomic_different_memory_orders_program --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Fix] fix wrong uint narrowing bug in tvm in #1310 (#1320) * [Refactor] Disable strided buffer load inside tvm (#1301) (#1332) * [Refactor] Moving `NormalizeToBufferRegion` and `MakeAccessPtrFromRegion` to utils (#1333) * Refactor GEMM and Reduce operations by moving NormalizeToBufferRegion and MakeAccessPtrFromRegion to utils.{h,cc} for better code organization and reuse. * lint fix * [Fix] Fix bug copying from or to local buffer (#1304) (#1324) * [Fix] fix copy from or to local buffer (#1304) * fix lint error * minor fix testing script * [Language][UX] Semantic check for parallel fragment access (#1338) * Add unit tests for T.assume (#1341) * Add test for T.assume * Add unit test for T.assume * Add unit test for T.assume * Add unit tests for T.assume * Remove debug print for kernel source Remove print statement for kernel source in tests. * Update test_tilelang_language_assume.py --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Feat] Extend LegalizeNegativeIndex to support buffer store stmts (#1339) This commit enhances the LegalizeNegativeIndex transformation pass to handle both buffer load and store operations with negative indices and adds some test cases. * [Refactor] Phaseout vmap for Tile Operators (#1334) * Refactor GEMM and Reduce operations by moving NormalizeToBufferRegion and MakeAccessPtrFromRegion to utils.{h,cc} for better code organization and reuse. * lint fix * Refactor region handling by removing the RegionOp and updating NormalizeToBufferRegion to only accept BufferLoad and BufferRegion. This change improves code organization and simplifies the handling of memory regions across various operations. * fix * Refactor memory region handling by introducing `tl.region` calls across various operations, including GEMM and fill functions. This change enhances the consistency of region management and improves code organization by utilizing utility functions for buffer region conversions. * fix * fix * test fix * lint fix * Refactor GEMM operations to improve memory region handling by replacing `mbarPtr_` with `mbarRegion_` and updating related logic in both C++ and Python implementations. This change enhances the clarity and consistency of buffer region management. * fix * lint fix * fix * fix * test fix * lint fix * lint fix * minor fix * fix --------- Co-authored-by: Zhiwen Mo * [Enhancement] add more dtype and fix mma.ws for fp16 for tcgen05 (#1327) * feat: add fp8 variants; add placeholder for fp6/fp4 in meta support ld with pack for fp32 dtype add dump add tempalte expand remove unused dtype and change to rebased apis * fix: when atom-m!=128, enable_ws * fix: typo in tcgen05 meta; dispatch in gemm sm100 * [Refactor] Enhance CopyNode's IterVar Creation and Range Handling (#1346) * [Refactor] Enhance CopyNode's IterVar Creation and Range Handling This commit refines the `MakeIterVars` method in `CopyNode` to select base ranges based on memory scope levels, ensuring that the chosen ranges are not smaller than the original source ranges. Additionally, it updates the Python `copy` function to clarify range handling, including broadcasting logic and extent alignment. These changes improve the robustness and clarity of the copy operation's implementation. * test fix * [Fix] Fix missing `not` rewrite in frontend (#1348) * [Enhancement] Add support for k_pack in gemm_mfma (#1344) * add support for k_pack * support benchmark on ROCm * fix format * Add sparse fine-tuning kernel for deepseek sparse attention to example (#1296) * [EXAMPLE] add example for dsa sparse finetuning * [Refactor] * [Refactor] Improve assertion handling in CodeGenCHost and ArgBinder (#1352) * [Refactor] Improve assertion handling in CodeGenCHost and ArgBinder This commit refines the assertion message generation in CodeGenCHost by optimizing the handling of equality checks and reducing buffer size for error messages. Additionally, it enhances the ArgBinder by introducing a nullable guard mechanism for assertions, allowing for more precise error handling when binding arguments. The changes improve the clarity and efficiency of assertion handling across the codebase. * [Enhancement] Update matmul kernel and optimize argument binding This commit enhances the matmul kernel by introducing additional tensor parameters and refining the pipeline stages for improved performance. It also updates the argument binding mechanism to include a flag indicating whether buffers are used, enhancing the efficiency of buffer management. Furthermore, the optimization phase in the engine is improved by adding a simplification step, ensuring better performance and clarity in the generated code. * lint fix * [Enhancement] Add tensor checks documentation and improve argument binding assertions This commit introduces a new documentation page for host-side tensor checks, detailing the automatic validations performed by TileLang on kernel arguments. It enhances the ArgBinder by adding assertions for non-null pointers when arguments are used, improving error handling. Additionally, the optimization phase in the engine is updated to include a simplification step, ensuring better performance and clarity in the generated code. * [Enhancement] Update .gitignore and refine matmul kernel for improved performance This commit adds host checks logs to the .gitignore file to prevent unnecessary log files from being tracked. Additionally, it refines the matmul kernel by adjusting pipeline stages, updating tensor parameters, and enhancing argument handling for better performance. The changes also include improved error messages in the argument binding process, ensuring clearer diagnostics for users. * lint fix * lint fix * [Refactor] Simplify tensor_null_test function and remove ptr_null_test This commit refactors the tensor_null_test function by adding a with_bias parameter and removing the ptr_null_test function, which was previously unused. The run_test function is updated to reflect these changes, streamlining the testing process for tensor operations. * lint fix * fix * [Refactor] Simplify index sign state handling in LegalizeNegativeIndex (#1354) This commit refines the logic for determining the sign state of indices in the LegalizeNegativeIndex transformation. It prioritizes vector patterns, specifically Ramp and Broadcast nodes, to avoid compile-time lane queries. The handling of scalar indices is also streamlined, ensuring clearer diagnostics when non-negativity cannot be proven. These changes enhance the robustness and clarity of index handling in the transformation pass. * [Enhancement] Improve error handling and assertion messages across runtime and argument binding (#1356) This commit enhances the error handling mechanisms in the runtime by introducing CPU-safe runtime helpers and refining assertion messages in the CodeGenCHost and ArgBinder. It includes structured packed error messages for various conditions, improving clarity in diagnostics. Additionally, the CMake configuration is updated to always include necessary runtime helpers, ensuring consistent error reporting. The changes aim to provide clearer feedback during runtime errors and improve the overall robustness of the argument binding process. * [Bugfix] Disable floordiv optimization due to integer overflow risk (#1355) * disable overflow-prone floordiv optimization in lower_intrin.cc * disable overflow-prone floordiv optimization in lower_intrin.cc * [Bugfix] Fix the jit_kernel issue (#1357) * [Bugfix] Fix the jit_kernel issue * Update README.md --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Refactor] Update Fragment Indexing in ParallelOpNode's InferLayout Method (#1359) This commit refines the Fragment creation process in the InferLayout method of ParallelOpNode. It removes the unnecessary forward_index array and utilizes default fragment indexing for consistency with other operations. Additionally, it binds the thread range to enhance comparability across different operations. * [Analysis] Enhance NestedLoopChecker with tile op cases (#1358) * [Analysis] Enhance NestedLoopChecker with tile op cases * fix tileop issue * [Language] support `T.gemm_sp_v2` on sm80 and sm89 (#1056) * [misc] add a cpp side wrapper for gemm_sp_py * [misc] typing * [IR] bind GemmSPWarpPolicy * [chore] add wrapper code * [IR] fix GemmSPWarpPolicy * [codegen] apply ptxas instructions * [intrinsic] add typical (unused) mma layout * [template] add uint16 debug func * [intrinsic] add b matrix layout * [gemm_sp] enable fp16/bf16 on sm8x * [layout] refactor fp16/bf16 layout * [gemm_sp] enable int8 * [chore] update test case dtype * [gemm_sp] enable fp32 * [layout] refactor layouts * [intrinsic] enable ldmatrix for mat A * [layout] enable ldsm for matrix b * [layout] add ldmatrix for fp32 and fp8 * [chore] refine * [chore] refactor * [chore] add fp8 efactor * [chore] refactor * [chore] add remove negative zero util * [example] add a custom compress kernel * [chore] minor update * [test] refactor gemm_sp test * [refactor] make metadata layout func * [example] add option for using cutlass layout * [doc] add a gemm_sp doc * [doc] minor polish * [chore] remove unused * [bugfix] fix non replicate b case * [test] refactor * [chore] add a check * [bugfix] fix util bug * [wip] init a new test case for v2 * [chore] minor refactor * [chore] minor update * [bugfix] enable 16bit rs * [language] enable rs * [language] enable gemm_sp_sr * [language] enable gemm_sp_rr * [test] enable more tests * [tvm] update ffi binding * [chore] remove print * [chore] fix benchmark script * [lint] precommit lint * [chore] apply feedback * [test] use arch 8.0 * [chore] rollback ::ordered_metadata for backward compatibility * [bugfix] fix captialized * [example] keep gemm_sp on hopper * [test] fix no fp8 normal kernel * [test] reduce matmul size to satisfy accum error * [test] use cal_diff for assertion * [bugfix] expand float8 type * [lib] add make_int4 for short type * [language] add transpose E * [bugfix] fix wrong var * [format] format * [chore] refactor binding * [chore] fix wrong passing var * [Bugfix] Update TIR registration for GemmSPPy to use tile operation (#1361) * [Enhancement] Implement dynamic unroll factor in CUDA code generation (#1360) * [Enhancement] Implement dynamic unroll factor in CUDA code generation This commit introduces support for specifying a dynamic unroll factor in the CUDA code generation. The `unroll_factor` map is added to store unroll factors for loop variables, allowing for more flexible and optimized loop unrolling. Additionally, the `unroll` function is integrated into the loop language, enabling users to define unroll factors directly in their code. This enhancement improves performance by allowing tailored unrolling strategies based on specific loop characteristics. * lint fix * [Bugfix] Correct initialization of non-zero counters in custom compress kernel and update TIR registration for gemm_sp_py to use the correct tile operation * [CI] [pre-commit.ci] autoupdate (#1362) updates: - [github.com/pre-commit/mirrors-clang-format: v21.1.2 → v21.1.6](https://github.com/pre-commit/mirrors-clang-format/compare/v21.1.2...v21.1.6) - [github.com/astral-sh/ruff-pre-commit: v0.14.3 → v0.14.7](https://github.com/astral-sh/ruff-pre-commit/compare/v0.14.3...v0.14.7) Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> * [Bugfix] Remove debug print in PyStmtFunctionVisitor (#1363) * [Debug] Always include line info in NVCC command for improved profiling and mapping (#1364) * [Refactor] Update condition for benchmarking in example_gemv.py and simplify cached library path handling in sparse.py (#1365) * [Enhancement] Add DISABLE_CACHE environment variables (#1368) * [Refactor]: Remove useless include in atomicadd_vectorize.h (#1371) * [Refactor] Generalize fp8 process (#1372) * [Refactor] Update condition for benchmarking in example_gemv.py and simplify cached library path handling in sparse.py * [Enhancement] Extend support for float8 data types in GEMM operations - Updated GEMM operations to recognize additional float8 data types: `float8_e4m3fn` and `float8_e5m2fnuz`. - Refactored condition checks in `checkWgmma` methods to simplify float8 type handling. - Adjusted test cases to ensure compatibility with the new float8 types in tile language examples. * lint fix * [Layout] Enhance Free Layout Inference (#1375) * [Refactor] Update condition for benchmarking in example_gemv.py and simplify cached library path handling in sparse.py * [Enhancement] Extend support for float8 data types in GEMM operations - Updated GEMM operations to recognize additional float8 data types: `float8_e4m3fn` and `float8_e5m2fnuz`. - Refactored condition checks in `checkWgmma` methods to simplify float8 type handling. - Adjusted test cases to ensure compatibility with the new float8 types in tile language examples. * lint fix * [Enhancement] Add injective layout detection and exception handling - Introduced `DetectInjective` method in `FragmentNode` to check for injective layouts. - Added `LoopLayoutInjectiveException` to handle errors related to non-injective layouts. - Updated `InferLayout` methods in `ParallelOpNode` to utilize injective checks and log relevant information. - Refactored layout inference queue management to use `std::deque` for improved performance and added prioritization logic for buffer layouts. * remove debug print * remove debug print * remove debug print * minor layout fix * fix for T.view * [Enhancement] Improve injective layout detection in FragmentNode - Updated the `DetectInjective` method to handle symbolic dimensions more effectively by introducing a mechanism to collect symbolic shapes and adjust the detection level accordingly. - Added logging for cases where the layout detection falls back to NoCheck due to symbolic dimensions. - Minor update to the test file to include the tilelang testing module. * [Refactor] Simplify layout inference for bulk copy operations - Removed unnecessary conditions for bulk load/store operations in the layout inference logic. - Streamlined the handling of layout application for bulk copy instances to enhance clarity and maintainability. * remove debug print * [Enhancement] Introduce layout-related exceptions and improve error handling - Added `LayoutConflictException` and `LoopLayoutInjectiveException` classes for better exception management in layout operations. - Updated `InferLayout` method in `ParallelOpNode` to throw `LoopLayoutInjectiveException` with detailed error information when injective layout checks fail. - Removed redundant exception class definitions from `parallel.h` to streamline code organization. * [Enhancement] Introduce buffer var lca analysis for pass plan buffer allocations (#1376) * Update submodule TVM to latest commit and add PlanAndUpdateBufferAllocationLocation function to transform module - Updated the TVM submodule to commit 3a32b763. - Added a new function `PlanAndUpdateBufferAllocationLocation` in the transform module to facilitate buffer allocation planning within PrimFuncs. * Refactor buffer allocation code for improved readability and consistency - Updated formatting and spacing in `plan_update_buffer_allocation_location.cc` for better code clarity. - Standardized the use of pointer and reference syntax across various class methods. - Enhanced comments for better understanding of buffer allocation logic. - Removed unnecessary lines and improved overall code structure. * Refactor buffer allocation checks for improved clarity - Replaced size checks with empty checks for `ffi::Array` in `plan_update_buffer_allocation_location.cc` to enhance code readability. - Updated conditions in multiple methods to use `empty()` instead of comparing size to zero, streamlining the logic. * [Tool] Provide layout visualization tool (#1353) * Provide layout visualization tool Adds a layout visualization tool to TileLang, which helps users understand and debug the layout transformations applied during compilation. This tool visualizes the memory layout of tensors at different stages of the compilation process, allowing developers to identify potential inefficiencies and optimize their code for better performance. The visualization can be enabled via a pass config option. * format * add layout visual example * Adds vis extra with matplotlib dependency * rafactor pass config name * fix lint * Enables configurable layout visualization formats Allows users to specify the output formats (png, pdf, svg) for layout visualization through a pass config option. This change provides more flexibility in how layout visualizations are generated, allowing users to choose the formats that best suit their needs. It also fixes a bug where layout visualization was not correctly disabled when the config option was set to "false". * Adds visual layout inference tool docs * fix lint * fix lint * Rafactor configurable layout visualization formats * fix lint * fix typo * add some comments * fix lints * add some warnings for user * Moves layout visualization * Refactors layout visualization pass configuration Updates the layout visualization pass configuration to use boolean flag for enabling and a string for specifying formats. * Enables multiple layout visualization formats * Updates layout visualization docs * Moves layout visualization to analysis * [Release] Relax constraint of tvm-ffi to compatible version (#1373) Co-authored-by: LeiWang1999 * [Language] Tilelang LazyJIT Experimental Version (#1337) * initial step * modify builder * scratch version of new frontend * write some tests * add many tests * add typing stub for tir.ir * remove idents * minor update * minor update * First version of jitv2 (renamed to LazyJIT) * fix pre-commit error * minor fix * fix lint error * fix lint error * Fix conditional check for PrimFunc instance --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Builder] Enhance variable name binding and scope management (#1378) - Improved handling of TVM Var/Buffer names to prevent out-of-scope errors when reusing Python names across different for-frames. - Added assertions to ensure variables are defined within the correct control flow frame, enhancing error checking and code reliability. * [Bugfix] make cuda driver api compat with cuda12/13, along with tests (#1379) * [Fix] typo in cuda attr (#1380) * [Bugfix] make cuda driver api compat with cuda12/13, along with tests * fix typo in cudaDevAttr * [Language V2] Minor fix for complex annotations (#1381) * [Release] Bump Version into 0.1.7 (#1377) * Update VERSION to 0.1.7 * Update Python version in distribution scripts to support CPython 3.9 and log output * [Typing] Enhance compatibility for advanced typing features in Python (#1382) - Updated `allocate.py` and `annot.py` to improve compatibility with Python 3.9 and later by conditionally importing advanced typing features such as `TypeVarTuple`, `Unpack`, and `ParamSpec`. - Added fallback imports from `typing_extensions` for environments using earlier Python versions. - Improved handling of generic alias detection to ensure consistent behavior across different Python versions. * [Bugfix][Build] Update CMake configuration to remove project root injection for sys.path (#1385) * [Build] Update CMake configuration for tilelang_cython_wrapper installation - Adjusted output directories for the tilelang_cython_wrapper to ensure that development builds place the extension in build/lib. - Updated installation paths to place the extension in tilelang/lib within the wheel, improving organization and avoiding potential conflicts with other modules. - Modified the internal library path exposure in env.py to prevent shadowing of common module names, enhancing compatibility and usability in user projects. * [Build] Standardize output directories for tilelang libraries - Set output directories for both tilelang and tilelang_module libraries to "${CMAKE_BINARY_DIR}/lib" for consistency in development builds. - This change enhances organization and ensures that all build artifacts are located in a unified directory structure. * [BugFix] Fix split kernel layout bug of GQA decode (#1386) * [BugFix] Fix split kernel layout bug of GQA decode * [BugFix] Avoid local with Parallel; use robust fragment instead * [Enhancement] Add debug output methods for Layout and Fragment classes (#1392) * [Doc] Update logging docs (#1395) * [Enhancement] Refactor inflight computing to support dynamic pipeline extents (#1399) * [Build] Update CMake configuration for tilelang_cython_wrapper installation - Adjusted output directories for the tilelang_cython_wrapper to ensure that development builds place the extension in build/lib. - Updated installation paths to place the extension in tilelang/lib within the wheel, improving organization and avoiding potential conflicts with other modules. - Modified the internal library path exposure in env.py to prevent shadowing of common module names, enhancing compatibility and usability in user projects. * [Build] Standardize output directories for tilelang libraries - Set output directories for both tilelang and tilelang_module libraries to "${CMAKE_BINARY_DIR}/lib" for consistency in development builds. - This change enhances organization and ensures that all build artifacts are located in a unified directory structure. * [Refactor] Update TVM subproject and enhance pipeline loop handling - Updated the TVM subproject to commit 90581fe9e5287bbcf1844ad14255a1e1e8cdf7f0. - Added new fields to `PipelineAnnotation` and `RewrittenBlockInfo` structures to track original statement indices and improve async state management. - Refactored `EmitImpl` and `PopulateWaitCounts` methods to enhance clarity and functionality, including better handling of commit groups and wait counts. - Simplified access index calculations and strengthened analyzer constraints for loop bounds. * [Cleanup] Remove license block and unused includes from inject_pipeline.cc - Eliminated the Apache license block from the top of the file to streamline the code. - Removed unused include directives for memory and stringstream to enhance code clarity and reduce unnecessary dependencies. * [Refactor] Enhance transformation pipeline and test execution - Added an additional Simplify transformation in the InjectSoftwarePipeline to improve optimization. - Updated the test file to call `test_trival_pipeline()` directly, commenting out the previous main execution for better test isolation. * [AMD] Fix 3 bugs when build docker on amd mi3x gpu (#1401) * [Typo] Fix tilelang link in README.md (#1402) * [Dependency] Update apache-tvm-ffi version to >=0.1.2 (#1400) * [Dependency] Update apache-tvm-ffi version to >=0.1.2 in project files * [Dependency] Update subproject commit for TVM to latest version afc07935 * [Enhancement] Add support for optional step parameter in loop constructs - Updated loop creation functions to accept an optional step parameter, enhancing flexibility in loop definitions. - Modified ForFrame implementations to utilize the new step parameter across various loop types including serial, parallel, and pipelined loops. - Adjusted related vectorization transformations to accommodate the step parameter, ensuring consistent behavior in loop vectorization processes. * lint fix * [AMD] Enable FA2 fwd on AMD MI300X (#1406) * enable FA2 on AMD MI300X * make lint happy * [TypoFix] fix typo for SM120 (#1408) * [Doc] Minor documentation update (#1410) * [Dependency] Add torch-c-dlpack-ext to project requirements (#1403) * [Dependency] Add torch-c-dlpack-ext to project requirements * Added torch-c-dlpack-ext to both pyproject.toml and requirements.txt to provide prebuilt torch extensions, which may prevent JIT compilation on first import of TVM FFI. * [Build] Update manylinux images in project configuration * Changed the manylinux image for x86_64 from "manylinux2014" to "manylinux_2_28" in both pyproject.toml and the Dockerfile to align with updated standards for compatibility and performance. * [Build] Update CUDA repository configuration in pyproject.toml * Changed the package manager command from `yum-config-manager` to `dnf config-manager` for adding the CUDA repository, ensuring compatibility with newer systems. * fix * [Build] Update CUDA repository to RHEL 8 * Changed the CUDA repository configuration in both pyproject.toml and the manylinux Dockerfile from RHEL 7 to RHEL 8, ensuring compatibility with newer systems. * test: run out of space * use cu130 to reduce size * upd * upd comment * upd --------- Co-authored-by: Your Name * [Dependency] Update TVM subproject to latest commit 2b1ead1a (#1412) * [Enhancement] Introduce `T.__ldg` (#1414) * [Enhancement] Add __ldg intrinsic for CUDA read-only cache loads * Introduced the __ldg intrinsic to enable explicit read-only cached loads from global memory in CUDA. * Updated the corresponding documentation and added support in both CUDA and HIP code generation. * Enhanced the Python interface for __ldg to accept BufferLoad and Buffer types, improving usability. * [Enhancement] Update formatting and linting rules in pyproject.toml; minor test adjustment * Added new formatting rules in pyproject.toml to enforce consistent code style, including hanging indents and argument splitting. * Updated test_tilelang_language_intrinsics_codegen.py to improve readability by adding a blank line before the main execution block. * Refactored error messages in builtin.py for better clarity and consistency, ensuring proper formatting in function definitions and raising ValueErrors. * lint fix * [Enhancement] Improve vectorization invariant check (#1398) * Improve loop vectorize * Improve loop vectorize * Improve loop vectorize * Improve loop vectorize * Improve loop vectorize * Add some vectorize tests and comments * [Lint] Phaseout Yapf format and embrace ruff format (#1417) * [Atomic] Use ptr for atomicAdd dst instead of reference (#1425) * [Enhancement] Update AtomicAdd function signature to accept pointer to destination * Modified AtomicAdd in CUDA to take a pointer instead of a reference for the destination argument. * Updated related code in atomicadd_vectorize.cc to ensure compatibility with the new signature. * Adjusted Python interface in atomic.py to pass the destination by pointer, aligning with device function requirements. * [Enhancement] Refactor AtomicAddRet function signature to accept pointer * Updated AtomicAddRet in both CUDA and HIP to take a pointer instead of a reference for the address argument, improving consistency with the AtomicAdd function. * Adjusted the implementation to ensure proper reinterpretation of the address type for atomic operations. * lint fix * [Enhancement] Refactor AtomicAddNode::MakeSIMTLoop to use destination pointer * Updated the MakeSIMTLoop function to build a pointer to the destination element using tvm_access_ptr instead of loading the destination value directly. * Simplified the handling of source and destination predicates, improving clarity and maintainability of the code. * Ensured compatibility with the new pointer-based approach for atomic operations. * lint fix * test fix * lint fix * [CUDA] Add read-only parameter annotation for CUDA codegen (#1416) * [Enhancement] Add read-only parameter annotation for CUDA codegen * Introduced the `AnnotateReadOnlyParams` transformation to annotate read-only handle parameters in PrimFuncs, enabling the generation of `const` qualifiers in CUDA codegen. * Updated `PrintFunctionSignature` and `AddFunction` methods to utilize the new attribute `tl.readonly_param_indices`, enhancing performance by allowing read-only cache loads. * Modified the optimization pipeline to include the new annotation step, improving the overall efficiency of the code generation process. * lint fix * [Dependency] Update apache-tvm-ffi version to >=0.1.3 * Updated the version of apache-tvm-ffi in pyproject.toml, requirements.txt, and requirements-dev.txt to ensure compatibility with the latest features and fixes. * Made adjustments in CUDA and HIP template files to use `const` qualifiers for global pointer parameters, enhancing code safety and clarity. * lint fix * [Enhancement] Refactor ReadWriteMarker for improved parameter handling * Updated the ReadWriteMarker class to accept a set of parameter or data variables, enhancing its ability to track written variables. * Introduced a new method, ResolveDataVarFromPtrArg, to resolve underlying buffer data from pointer-like arguments, improving accuracy in identifying written variables. * Modified the MarkReadOnlyParams function to gather handle parameters and their corresponding buffer data variables, streamlining the process of determining read-only parameters. * Enhanced the logic for identifying written variables to account for aliased data variables, ensuring comprehensive tracking of modifications. * lint fix * Update tma_load function to use const qualifier for global memory pointer * Changed the parameter type of gmem_ptr in the tma_load function from void* to void const* to enhance type safety and clarity in memory operations. * This modification ensures that the function correctly handles read-only global memory pointers, aligning with best practices in CUDA programming. * Remove commented-out code and reorder transformations in OptimizeForTarget function for clarity * Refactor buffer marking logic in annotate_read_only_params.cc to improve accuracy in identifying written variables. Update OptimizeForTarget function to reorder transformations for better clarity. * [Refactor] Phase out the primitives folder since its design has been merged into tileop (#1429) * Phase out primitives * revert changes * Refactor GemmWarpPolicy method signature for clarity Updated the `from_warp_partition` method in the `GemmWarpPolicy` class to return the type `GemmWarpPolicy` instead of a string, enhancing type safety and clarity in the codebase. Removed an unnecessary blank line for improved readability. * fix * [CI]: Bump actions/upload-artifact from 5 to 6 (#1431) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 5 to 6. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v5...v6) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [CI]: Bump actions/download-artifact from 6 to 7 (#1432) Bumps [actions/download-artifact](https://github.com/actions/download-artifact) from 6 to 7. - [Release notes](https://github.com/actions/download-artifact/releases) - [Commits](https://github.com/actions/download-artifact/compare/v6...v7) --- updated-dependencies: - dependency-name: actions/download-artifact dependency-version: '7' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [Bugfix] Convey `compile_flags` to ffi compilation path with pass_configs (#1434) * [Enhancement] Add device compile flags support in pass configuration * Introduced `kDeviceCompileFlags` option in the pass configuration to allow additional device compiler flags for CUDA compilation. * Updated the `tilelang_callback_cuda_compile` function to merge extra flags from the pass configuration, enhancing flexibility in compiler options. * Modified the `JITKernel` class to handle device compile flags appropriately, ensuring they are included during compilation. * Documented the new pass configuration key for clarity on usage and expected input formats. * lint fix * [Refactor] Simplify compile_flags handling in JIT functions * Removed redundant string check for compile_flags in the compile, jit, and lazy_jit functions, ensuring compile_flags is consistently treated as a list. * Updated the JITKernel class to handle compile_flags as a list when a string is provided, enhancing code clarity and maintainability. * lint fix * fix * [Enhancement] Improve buffer usage tracking in MakePackedAPI (#1435) * Added detailed logging for data and shape variable parameters during buffer usage detection in the MakePackedAPI function. * Refactored the UsedBufferDetector to differentiate between used parameters by data and shape variables, enhancing clarity in buffer management. * Updated logic to ensure minimal carrier buffers are selected for shape symbols, improving the efficiency of parameter handling. * [Enhancement] Improve InjectAssumes logic and make assumes work after SplitHostDevice (#1405) * [Refactor] Refactor InjectAssumes logic and make assumes work after SplitHostDevice * address comments * fix * fix submodule * fix * fix 3rdparty * [Enhancement] Include PrimFunc name in memory cache logs for better debugging (#1437) * Added the `get_prim_func_name` utility to extract human-readable function names from TVM PrimFuncs. * Updated memory cache logging in `AutoTuner` and `KernelCache` classes to include the kernel name, improving clarity during cache hits. * Enhanced debug logging to provide more informative messages when checking disk cache for kernels. * [CI] Update lint dependencies and fix lint on trunk (#1433) * [CI] Update pre-commit hooks * [Lint] Pass correct `exclude-header-filter` to `clang-tidy` * [Lint] Download latest `run-clang-tidy` script * [CI] Show compile commands * [CI] Add output grouping to GHA * [Lint] Re-order pre-commit hooks * [Enhancement] Refactor vectorization checks in loop_vectorize (#1440) * Introduced a new function, IsExprInvariantInVectorBoundary, to encapsulate the logic for checking if an expression is invariant within vector boundaries, improving code clarity and reusability. * Updated the existing vectorization logic to utilize this new function, streamlining the process of determining vectorization feasibility based on boundary conditions. * Enhanced comments for better understanding of the vectorization criteria and mathematical rationale behind the checks. * Enhance vectorized conversion support (#1438) * [Feature] Support region as input of T.cumsum (#1426) * [Feature] Support region as input of T.cumsum - Extend T.cumsum to accept BufferRegion and BufferLoad inputs in addition to Buffer - This enables operations on buffer slices/regions like: T.cumsum(InputG_fragment[i * chunk_size:(i + 1) * chunk_size], dim=0) - Update cumsum_fragment to handle region inputs properly - Add comprehensive tests for 1D and 2D region inputs including normal and reverse modes Fixes #879 * Fix formatting and add docstring for cumsum_fragment - Add comprehensive docstring for cumsum_fragment function - Format code according to ruff style guidelines * Fix CodeRabbit review issues - Fix negative dimension bounds check (dim < -len(shape) instead of dim <= -len(shape)) - Add src/dst shape compatibility validation for out-of-place cumsum - Update copy() type annotation to accept BufferRegion as dst parameter - Fix test in-place mutation issues by using out-of-place cumsum operations - Add non-divisible size test cases for tail region coverage * Fix out-of-bounds access in region tests - Add bounds clamping using T.min() for chunk_end calculations - Prevents accessing beyond tensor bounds for non-divisible sizes - Matches reference implementation behavior - Fixes both 1D and 2D region test cases * Fix region test: use simple slice expressions instead of T.min() - Remove T.min() which cannot be used directly in slice indices - Use chunk_start + chunk_size form instead - Rely on system's automatic bounds checking for non-divisible sizes - Update comments to reflect this approach * Fix cumsum region: use region extents in lowering and update tests for shared memory * Simplify fragment scope check using is_fragment() --------- Co-authored-by: LeiWang1999 * [Fix] Fix analyzer bind conflicting (#1446) * [Refactor] Reduce direct dependency on PyTorch due to its limited type support (#1444) * [Enhancement] Update KernelParam to use tvm.DataType directly and add torch_dtype conversion method - Changed dtype in KernelParam from torch.dtype to tvm.DataType to support a wider range of data types and prevent information loss during conversions. - Added a new method, torch_dtype, to convert tvm.DataType back to torch.dtype for tensor creation. - Updated various adapters to utilize the new torch_dtype method for parameter type conversion during initialization. * [Enhancement] Refactor CUDA type handling and add support for FP4 and FP8 types - Renamed functions for clarity: GetFP8Type, GetFP6Type, and GetFP4Type are now GetTileLangFP8Type, GetTileLangFP6Type, and GetTileLangFP4Type respectively. - Enhanced FP4 type handling to support additional lane sizes (2, 4, 8, 16, 32, 64). - Updated CUDA code generation to include new FP8 and FP4 types, ensuring proper type handling in PrintType and related functions. - Introduced new structures for FP8 types in cuda_fp8.h to facilitate better memory management and type packing. - Added methods in KernelParam and tensor utilities to recognize and handle float4 types, improving compatibility with PyTorch. - Enhanced logging for debugging purposes in various CUDA functions to track type handling and memory operations more effectively. * lint fix * Remove unnecessary logging statements from CUDA code generation and delete obsolete matrix multiplication test file. * [Enhancement] Add support for FP4 and FP8 types in CUDA code generation - Enhanced PrintVecElemLoad and PrintVecElemStore functions to handle new FP4 types. - Updated arg_binder to allow float4 to match int8 at runtime, improving compatibility with PyTorch. - Modified loop_vectorize to account for buffer dtype lanes in vectorization calculations. - Refactored tensor type mapping to support new float4 and float8 types, ensuring correct type handling in tensor operations. - Added tests for FP4 and FP8 copy operations to validate functionality and integration with existing workflows. --------- Co-authored-by: Zhiwen Mo * [Refactor] Use `pytest.mark.parameterize` to speedup parallel testing (#1447) * Refactor GEMM tests to use parameterized pytest fixtures - Converted multiple test cases for GEMM operations in `test_tilelang_tilelibrary_gemm_sp.py` to use `pytest.mark.parametrize` for better maintainability and readability. - Similar refactoring applied to `test_tilelang_tilelibrary_gemm_sp_v2.py`, consolidating test cases for `run_gemm_ss`, `run_gemm_rs`, `run_gemm_sr`, and `run_gemm_rr` into parameterized tests. - This change reduces code duplication and enhances the clarity of test configurations. * Update testing/python/amd/test_tilelang_gemm_mfma_preshuffle.py Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * [Docs] Improve installation instructions for developers (#1450) * [Feat] Integrate Z3 in TVM Arith Analyzer (#1367) * [Bugfix] Improve autotune from elementwise_add function in examples (#1445) * Remove JIT decorator from elementwise_add function in examples * fix kernel compilation without autotune * Refactor main function to accept parameters and update tests for autotune option * Refactor autotune test function for morden style * [Language] Introduce `T.annotate_restrict_buffers` (#1428) * [Enhancement] Introduce non-restrict parameter support in code generation - Added a new PrimFunc-level attribute `tl.non_restrict_params` to specify handle Vars that should not be marked with the restrict qualifier during code generation. - Updated `CodeGenTileLangCPP`, `CodeGenTileLangCUDA`, and `CodeGenTileLangHIP` to handle non-restrict parameters, ensuring proper treatment of overlapping buffer aliases. - Implemented a new annotation function `annotate_restrict_buffers` to facilitate the marking of buffer parameters as non-restrict. - Enhanced the `SplitHostDevice` transformation to propagate non-restrict parameters from host to device functions. - Added a new transform function `HoistNonRestrictParams` to manage non-restrict parameters effectively. * [Enhancement] Improve HoistNonRestrictParams transformation - Updated the HoistNonRestrictParams function to recursively collect all `tl.non_restrict_params` annotations from nested blocks, enhancing flexibility in annotation placement. - Introduced a new NonRestrictCollector class to manage the collection and deduplication of non-restrict parameters. - Modified the SplitHostDevice transformation to remove the non-restrict attribute from the host-side PrimFunc after propagation to device kernels. - Adjusted the LowerAndLegalize function to directly apply the HoistNonRestrictParams transformation without exception handling, streamlining the process. * [Refactor] Simplify non-restrict parameter handling in code generation - Removed unnecessary normalization logic and associated data structures from `CodeGenTileLangCPP`, `CodeGenTileLangCUDA`, and `CodeGenTileLangHIP`. - Streamlined the handling of non-restrict parameters by directly inserting them into the `non_restrict` set, improving code clarity and maintainability. - Updated conditional checks to eliminate redundant checks against normalized names, enhancing performance and readability. * [Dependency] Update TVM subproject to latest commit 68aa8461 - Updated the TVM subproject to the latest commit, ensuring compatibility with recent changes and improvements. - Refactored non-restrict parameter handling in `CodeGenTileLangCPP`, `CodeGenTileLangCUDA`, and `CodeGenTileLangHIP` to enhance code clarity and maintainability. - Adjusted the `SplitHostDevice` transformation to streamline the propagation of non-restrict parameters. * fix * [Analyzer] Require loop extent > 0 when entering loop (#1451) * Updat ROCm CI to Nightly-ROCm-7.1 (#1449) * [Enhancement] Update examples and tests for improved type handling functionality (#1448) * [Enhancement] Update examples and tests for improved type handling and functionality - Enhanced various example scripts to support new data types and improve compatibility with PyTorch. - Updated tests across multiple modules to ensure correct functionality with the latest changes in type handling. - Refactored code in examples to streamline operations and improve clarity, particularly in tensor operations and memory management. - Added comprehensive tests for new features and fixed existing issues related to type conversions and buffer handling. * [Refactor] Update accumulation data type to float32 across examples - Changed accumulation data type from "float" to T.float32 in multiple example scripts to ensure consistency and improve numerical stability. - This update affects various modules including flash attention, GEMM analysis, convolution, and deepseek MLA examples, enhancing type handling across the board. * [Refactor] Standardize data type usage across benchmark scripts - Updated data type definitions in benchmark scripts to use T.float16 and T.float32 consistently, enhancing clarity and type handling. - Adjusted dtype assignments in matmul functions and configuration setups to align with the new standard. - Improved overall code consistency and maintainability by ensuring uniform data type usage across various modules. * [Refactor] Standardize data type usage in templates and scripts - Updated data type definitions in various templates and scripts to use string representations (e.g., "float16", "int32") instead of T.float16 and T.int32 for improved consistency and clarity. - Enhanced overall code maintainability by ensuring uniform data type usage across multiple modules, including convolution, elementwise operations, and matrix multiplication templates. - This change aims to streamline type handling and improve compatibility with existing workflows. * [Refactor] Standardize data type usage in examples and benchmarks - Updated data type definitions in various example and benchmark scripts to use T.float16 and T.int32 consistently, enhancing clarity and maintainability. - Adjusted dtype assignments in kernel functions and configuration setups to align with the new standard. - Improved overall code consistency by ensuring uniform data type usage across multiple modules, including attention mechanisms, matrix multiplication, and GEMM examples. * [Refactor] Import dtypes from language.v2 module - Added import statement for dtypes from the language.v2 module to enhance type handling and maintain consistency across the codebase. - This change aims to streamline data type management and improve overall code clarity. * fix * [Refactor] Standardize data type usage across scripts - Updated data type definitions in various scripts to use string representations (e.g., "float16", "int8") instead of T.float16 and T.int8 for improved consistency and clarity. - Adjusted dtype assignments in functions and configuration setups to align with the new standard, enhancing overall code maintainability. - This change affects multiple modules, including benchmark and attention mechanisms, ensuring uniform data type usage throughout the codebase. * [Refactor] Update data type handling for consistency and clarity - Changed string representations of data types in the Hint class to use T.float32 and T.int32 for improved consistency. - Added new data types "int4" and "int16" to the dtypes module, enhancing type support across the codebase. - Updated function signatures and assertions in the lop3 and mxfp modules to utilize the new data types, ensuring uniformity in type handling. - This refactor aims to streamline data type management and improve overall code clarity and maintainability. * [Enhancement] Improve data type handling and error messaging - Introduced a mapping for canonical data types to their display strings, enhancing clarity in type representation. - Updated the dtype creation logic to utilize the new mapping, ensuring more intuitive handling of string inputs. - Refined error messages in the lop3 module to provide clearer feedback on invalid source formats, improving debugging and user experience. * [Fix] Correct boolean flag in GEMM SP test case - Updated the boolean flag in the test_gemm_sp_sm90 function to ensure proper functionality in the test case. - This change enhances the accuracy of the test and aligns it with expected behavior for the GEMM SP implementation. * [Refactor] Standardize data type usage across scripts - Updated data type definitions in various scripts to use T.float16 and T.bfloat16 consistently, enhancing clarity and maintainability. - Adjusted dtype assignments in function signatures and argument parsing to align with the new standard, ensuring uniform data type usage throughout the codebase. - This change affects multiple modules, including benchmarks and examples, improving overall code consistency and readability. * [Refactor] Standardize data type usage in various modules - Updated data type assignments in multiple scripts to utilize T.float32, T.int8, and T.int32 consistently, enhancing clarity and maintainability. - Adjusted function signatures and parameter types across benchmarks, examples, and tests to align with the new standard, ensuring uniform data type usage throughout the codebase. - This change improves overall code consistency and readability, impacting modules related to matrix multiplication, GEMM, and tensor operations. * [Refactor] Update argument parsing for data types in benchmarks - Changed argument parsing for data types in benchmark_matmul_intrinsic.py and benchmark_matmul_sp.py to use string representations ("float16", "int8", "float") instead of T.float16 and T.float. - This update enhances consistency in data type handling across benchmark scripts, improving clarity and maintainability. * [Refactor] Update data type handling in benchmark and example scripts - Changed data type arguments in benchmark and example scripts to use string representations ("float16") instead of T.float16 for improved consistency. - Updated function signatures and argument parsing to align with the new standard, enhancing clarity and maintainability across the codebase. - This change affects multiple modules related to attention mechanisms and tensor operations, ensuring uniform data type usage throughout the examples. * [Refactor] Fix data type conversion in multiple scripts - Corrected the usage of the data type conversion method from dtype..as_torch() to dtype.as_torch() across various benchmark and example scripts. - This change enhances consistency in data type handling and improves code readability, impacting modules related to attention mechanisms and tensor operations. * [Refactor] Update float8 data type usage across multiple scripts - Changed instances of T.float8_e4m3 to T.float8_e4m3fn in various benchmark, example, and test scripts to ensure consistency in data type handling. - This update enhances clarity and maintainability across the codebase, particularly in modules related to matrix multiplication and tensor operations. * [Refactor] Enhance float8 data type handling in CUDA code generation - Updated the handling of float8 data types in the CUDA code generation to include additional float8 variants, improving type conversion logic. - Adjusted conditions to ensure proper type checks for float8 conversions, enhancing clarity and maintainability in the codebase. - Modified layout inference to streamline float8 type checks, ensuring consistency across the implementation. - This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy. * [Refactor] Streamline float8 data type handling in CUDA and related modules - Enhanced float8 data type handling in CUDA code generation by refining type conversion logic and ensuring consistent type checks. - Updated layout inference for float8 types to improve clarity and maintainability across the implementation. - This change impacts modules related to matrix operations and CUDA code generation, improving overall type handling and conversion accuracy. * [Refactor] Remove unnecessary cache disabling in float8 example script - Eliminated the call to tilelang.disable_cache() in example_group_per_split_token_cast_to_fp8.py to streamline the code. - This change enhances clarity and maintainability of the example script without affecting its functionality. * [Refactor] Update data type usage in debug print tests - Changed the argument for dtype in the test_debug_print_buffer function from a string representation to the corresponding T.bool type. - This update enhances consistency in data type handling within the test suite, improving clarity and maintainability. * lint fix * Update function parameter types from `str` to `T.dtype` for improved type safety in attention sink and related examples * Refactor `gemv_alloc_reducer` function signature for improved readability by formatting parameters across multiple lines. * [Issue Template] Enable blank issues in GitHub issue template(#1453) * [CI] Moved the clang-tidy step to after pip install (#1456) * [Bug] Fix tvm build script when patchelf is not found #1459) * [Analyzer] Fix floordiv & floormod bug in z3 prover (#1458) * fix floordiv & floormod in z3 prover * fix lint error * [Cache] Rename sparse compress cache directory (#1460) * Enhance cache directory structure by including version information in sparse.py to ensure separate caches for different versions. * Fix formatting in sparse.py by adding a newline for improved readability and consistency. * [Language]Adds a random number generation capability through curand_kernel (#1461) * add curand.{curand_init, curand} * run format.sh * add default value for curand_init & add test for curand * Update testing/python/language/test_rand.py Remove unused thread binding Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * remove unused library * enable tilelang cache for testing * run format.sh * Revert "run format.sh" This reverts commit 5afaff782f31cdf653e2c45b469da8dead228b8a. * Revert "enable tilelang cache for testing" This reverts commit c277a43e77938bd88d47a108dd1bd65734d4a1ae. * Revert "remove unused library" This reverts commit 568ad20611f039380113937fd131151a2bffd801. * run format.sh * ensure FreshName for __philox_state * ensure FreshName for __philox_state * change the return type of T.rng_init --------- Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> * remove unused duplicated type check (#1462) Signed-off-by: Jinjie Liu * feat(cutedsl): add CuTeDSL backend (#1421) * feat: CuTeDSL backend * fix: clang-tidy * fix: clang-format * fix: ci * fix: revert example gemm fp8 * fix: remove duplicate code * fix: switch-case * fix: fp16 silence * fix: TVM IR print * fix: useless tir * fix: clang-format * fix: remove tilelang/contrib/cutedsl/.gitignore * fix: use hexfloat * fix: gsym guard * fix: unknown storage sync type * fix: string literal * fix: add args guard * fix: name hint dedup * fix: better find_kernel_by_pattern * fix: set libpath for from_database path * fix: guard buffer.strides * fix: from guard * fix: eviction guard * fix: use thread local tma descs * fix: ruff * fix: drop tma_init_cpp * fix: exc_info * fix: negative unmatch early return * fix: rename postproc func and add test * fix: handle fast math according to pass config * fix: dyn_sym parse * fix: wrap_forward * fix: use tvm_ffi.libinfo instead of cli * fix: keep signature * fix: C++ string safety * fix: mark tma_store_add as unsupported * fix: tvm version * resolve ldsm and cpasync issues. * fix: minor fixes * fix: parse signature using ast * fix: guard global_addr * fix: create tempfile only when necessary * fix: use logger.execption for exceptions * fix: guard lib_path and host_func * fix: remove tma_cpp_init and add timeout for cpp compile * add timeout for mbarrier_wait. * fix: _load_kernel_from_disk signature * resolve codegen issues. * fix: logger.exception * add comment for div_by=1 * merge * fix: reserve cutlass,cute,tl * fix: guard tma_store * fix: allow int64 offset in make_tensor_at_offset * fix: guard barrier * fix: add comments for div_by=16 * fix: div_by=1 issue * delete div_by when offset is 0 * use tl.make_tensor when offset is 0 * fix: explicitly check cutedsl target * fix: use param.torch_dtype() --------- Co-authored-by: yuxic Co-authored-by: Yong Co-authored-by: LeiWang1999 * [Refactor] Rename test for curand & add triton baseline in `test_tilelang_language_rand.py` (#1464) * rename test for curand & add triton baseline * add a comment for calling T.rng_rand() four times * refactor tilelang&triton kernel * Add boundary checks for M not divisible by 128 * [ArgBinder] Enhance shape variable handling and assertions (#1467) * feat(arg_binder): enhance shape variable handling and assertions - Implemented special handling for comparing if_then_else expressions to simplify conditions involving NULL checks. - Added methods to set shared shape variables and finalize deferred bindings, generating cascading if_then_else expressions and runtime assertions for non-NULL buffers. - Updated the binding logic to defer shape variable bindings for shared variables, ensuring proper handling across multiple nullable buffers. * refactor(arg_binder): clean up shape variable handling and remove unused code - Removed deprecated methods for setting shared shape variables and finalizing deferred bindings, streamlining the argument binding process. - Simplified the logic for handling shape values in the `BindDLTensor` function, ensuring immediate binding for normal shape variables. - Enhanced clarity by eliminating unnecessary comments and code related to cascading if_then_else expressions for shared variables. * refactor(arg_binder): enhance DLTensor binding with improved shape handling - Replaced the single `BindDLTensor` method with `BindDLTensors` to support multiple buffers, improving flexibility in handling DLTensor bindings. - Introduced a two-pass approach for shape variable handling, allowing for better management of symbolic dimensions and null checks. - Updated the logic to assert non-null conditions at runtime and utilize cascaded if_then_else expressions for shape retrieval, enhancing robustness. - Removed deprecated code and streamlined the binding process for clarity and maintainability. * fix(test_nullable_buffer_params): improve formatting and consistency in test output - Updated string formatting for better readability in the `test_nullable_shared_shape` function. - Ensured consistent use of double quotes for string literals. - Added a missing newline at the end of the file for proper formatting. * refactor(arg_binder): simplify allocation size calculation in BindDLTensors - Streamlined the calculation of allocation size by replacing a lambda function with a direct loop, enhancing readability and maintainability. - Improved clarity in the null check message for data pointers, ensuring better understanding of the binding process. * Remove debug prints from phase.py Removed debug print statements after MakePackedAPI transformation. * [Language] Make TL scripts friendly to Python syntax highlights (#1466) * Language] Make TL scripts friendly to Python syntax highlights * add comments * fix submodule * [Refactor] Remove triton dependence in testing & move triton baseline into examples (#1470) * remove triton dependence in testing & move triton baseline into example * use ceildiv and handles arbitrary M correctly for triton * [Language] Enhance T.dtype.as_torch conversion for compatibility (#1473) * [Language] Enhance dtype conversion for PyTorch compatibility - Added support for new float8 and float4 data types in the __dtype_as_torch__ method. - Implemented backend-specific handling for float8_e4m3 based on HIP or CUDA. - Included assertions to ensure compatibility with the required PyTorch versions for each dtype. - Improved error handling for unsupported dtypes. * Fix test script execution and improve error messages for dtype assertions - Commented out the main execution call in the test script and replaced it with a direct call to the test function `test_divmod()`. - Enhanced error messages in the dtype conversion assertions to improve clarity and readability, ensuring proper guidance for required PyTorch versions. * [News] update with latest news (#1475) * Update README.md with latest news, including CuTeDSL backend support, Z3 theorem prover integration, and migration to apache-tvm-ffi for improved compatibility. * Update README.md to enhance CuTeDSL backend announcement with a link to related issue and clarify migration benefits to apache-tvm-ffi, reducing CPU overhead. * [Enhancement] Use static Z3 context (#1482) * use static Z3 context * Update submodule reference for TVM to indicate a dirty state * [Enhancement] Enhance let binding handling in layout inference and warp specialized pass (#1484) * [Feature] Add FullyReplicated Fragment Layout and Enhance Layout Inference * Introduced a new static method `FullyReplicated` in the `Fragment` class to create fully replicated fragment layouts, ensuring all threads hold identical copies of the buffer. * Updated `CopyNode` to collect fragment layouts and mark them as fully replicated during layout inference. * Enhanced `ParallelOpNode` to expand let bindings for fragment buffer accesses, improving layout inference accuracy. * Added documentation for new methods and updated existing methods to support the new layout features. * lint fix * Remove debug logging statements from layout inference process to streamline output and improve performance. * [Refactor] Phaseout PassConfig `kDisableDynamicTailSplit` and `kDynamicAlignment` as they are legacy (#1486) * [Cleanup] Remove dynamic shape example and related tests * Deleted the dynamic shape example script `example_dynamic.py` and its corresponding test file `test_example_dynamic.py` to streamline the codebase. * Removed unused dynamic tail split and dynamic alignment configurations from `builtin.h` and `pass_config.py`. * Cleaned up the dynamic shape testing files to eliminate redundancy and improve maintainability. * build fix * [Enhancement] Optimize the time cost of critical path for IntervalSetEvaluator (#1491) * [Cleanup] Remove dynamic shape example and related tests * Deleted the dynamic shape example script `example_dynamic.py` and its corresponding test file `test_example_dynamic.py` to streamline the codebase. * Removed unused dynamic tail split and dynamic alignment configurations from `builtin.h` and `pass_config.py`. * Cleaned up the dynamic shape testing files to eliminate redundancy and improve maintainability. * build fix * Update submodule reference for TVM to latest commit 315036dc * phaseout z3 * [CI] Add preformance regression test script (#1489) * [Feature]: Add benchmark scripts for examples * apply cupti * fix * format * initial commit * fix * upd * upd * lint * fix * fake * Simplify PR regression test workflow Removed redundant 'Clean pip environment' steps from the workflow. * Update test_perf_regression.py * Enhance regression test bot workflow file handling Updated the GitHub Actions workflow to improve file handling for the regression test report. * Update regression test workflow for artifact naming * Update pr-regression-test-bot.yml * fix * lint * Update performance regression test trigger conditions --------- Co-authored-by: yyttt6 <1652272478@qq.com> * Pin nvidia-cutlass-dsl to 4.3.3 (#1497) * [Language] Remove ConstIf Frame for better meta programming (#1496) * [CI] Fix concurrency bug in regression test workflow (#1500) Updated concurrency group to use issue/PR number. * [Refactor] Phaseout legacy `alloc_local` statement in examples and introduce processing for floating fragment buffers (#1495) * [Refactor] Replace local allocations with variable allocations in various examples and operations * Updated multiple files to replace local buffer allocations with variable allocations for improved performance and clarity. * Changed `alloc_local` to `alloc_var` in examples related to attention mechanisms, deep learning models, and GEMM operations. * Enhanced code readability and maintainability by streamlining buffer management across different components. * Ensured consistent handling of buffer scopes and types throughout the codebase. * typo fix * test fix * [Refactor] Simplify index handling in sparse MLA forward pipelined example * Updated index handling in `sparse_mla_fwd_pipelined.py` to eliminate unnecessary local array usage, improving code clarity and performance. * Replaced instances of `indices_local[0]` with direct usage of `indices_local` for better readability and consistency in buffer access. * Commented out the main execution call in the GDN test script to focus on the specific test function, enhancing test clarity. * lint fix * [Enhancement] Optimize MHA varlen fwd and support autotune (#1499) * [Enhancement] Optimize MHA varlen fwd and support autotune * use fa2 instead of fa3 as baseline in ci * [Enhancement] Refactor CUDA vectorized cast generation and remove unsupported FP8 type (#1474) * Refactor CUDA vectorized cast generation and remove unsupported FP8 type * test fix * lint fix * Refactor CUDA vectorized cast function naming for clarity * Add support for float4_e2m1fn type conversions in CUDA vectorized casts - Implemented conversions between float4_e2m1fn and float32, half2, and float2 in utils.cc and cuda_fp4.h. - Updated test_tilelang_language_vectorized_cast.py to validate new conversions and ensure correctness. - Enhanced dtype conversion in dtypes.py to handle float4_e2m1fn appropriately, logging a warning for unsupported types in PyTorch. * Enhance vectorized cast tests for new data types - Added tests for vectorized casting of float8 and float4 data types, ensuring compatibility with CUDA compute versions. - Refactored existing test functions to improve clarity and organization, separating tests for different data types. - Updated parameterization to include additional test cases for new conversions. --------- Co-authored-by: LeiWang1999 Co-authored-by: Zhiwen Mo * [Dependency] Update apache-tvm-ffi to >=0.1.6 for memory safety when gc is not enabled (#1502) * Update cutedsl docs and version check(#1503) * [Misc] configure pymarkdown (#1505) * [Language] Fix gemm syntax highlight (#1476) * [Language] Fix gemm syntax highlight * fix proxy args * add docstring * [Fix] Fix TL_ENABLE_PTXAS_VERBOSE_OUTPUT has no effect in tvm-ffi (#1511) * [Refactor] Phaseout execution_backend `ctypes` (#1510) * Refactor execution backend options by removing 'ctypes' from the list of supported backends across multiple files. Update related documentation and tests to reflect this change, ensuring consistency in the autotuning and JIT compilation processes. * minor fix * [Testing] Add Memory Leak Test (#1516) * fix TL_ENABLE_PTXAS_VERBOSE_OUTPUT is not print in tvm-ffi * add memory leak test in tvm-ffi * fix lint error * fix typos * [Refactor] Support auto swizzling for tma store and phaseout related layout annotations (#1509) * Remove unnecessary swizzled layout annotations from various attention sink examples and kernel analysis script for improved clarity and performance. * Remove unnecessary swizzled layout annotations from various examples to enhance code clarity and performance. * lint fix * Uncomment main test execution for vectorized cast * Add swizzled layout annotation for B_shared in dequantize GEMM example This change introduces a layout annotation for the B_shared tensor in the example_dequant_gemm_bf16_mxfp4_hopper.py file, enhancing the memory layout optimization for better performance during matrix multiplication. * Remove unnecessary cache disabling in GQA sink example for improved clarity and performance. * lint fix * Refactor layout functions to support row-major linear layout for any dimension - Renamed `makeGemmLayoutLinear` to `makeLinearLayout` and updated its implementation to handle arbitrary dimensions. - Updated related function calls in `gemm_layouts.cc` and `layout.cc` to use the new layout function. - Enhanced layout inference in `CumSumOpNode` to enforce linear layout for shared buffers in strict mode. * Refactor `make_linear_layout` to accept a single argument and support arbitrary dimensions - Updated the function signature to take a `Buffer`, `BufferLoad`, or `BufferRegion` directly. - Simplified the implementation by removing argument checks and directly obtaining the shape from the buffer info. - Enhanced the documentation to clarify the function's purpose and usage. * skip callback test * Remove example_dequant_gemm_bf16_mxfp4_hopper_tma.py file, eliminating unused code related to dequantization GEMM example. * typo fix * [CuTeDSL][Fix] thread safety + context safety (#1513) * fix: cutedsl thread safety + context safety * fix: use get_device * fix: single process multiple gpu * fix: multi gpu * fix: pre-commit * fix: add cleanup * fix: device check * [BugFix] Phaseout unused tests for gqa decode kernels and add the kernels to CI (#1515) * [Cleanup] Remove unnecessary macros in tilelang examples (#1514) * Remove unnecessary macros in tilelang examples * fix typo --------- Co-authored-by: LeiWang1999 Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * Fix ramp_lanes calculation in CUDA codegen (#1518) * [Misc] add env for default target/backend/verbose (#1512) * [Misc] add env for default target/backend/verbose * fix: target_host signature * fix: move all env logic to kernel_cache * fix: example * fix: type hint * Update example_gqa_decode_varlen_logits.py --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> * [Dtype] Improve host codegen handling for subtype (#1517) * fp4 related update, require_cu13 * Enhance CUDA type conversion handling and optimize dtype management - Updated CUDA vectorized cast functions to ensure proper handling of float16, float32, bfloat16, and float8 conversions, adding checks for bit sizes. - Refactored dtype conversion logic in `cuda_fp4.h` to utilize `cudaRoundZero` for improved accuracy in floating-point conversions. - Introduced a new method in `KernelParam` to convert TVM DataType to TileLang dtype. - Adjusted argument binding logic in `arg_binder.cc` to allow for better subtype matching based on total bit counts. - Enhanced dtype handling in `dtypes.py` to accommodate new float4_e2m1fn types and ensure compatibility with PyTorch. This update aims to improve type safety and conversion accuracy across the codebase. * lint fix * lint fix * typo fix --------- Co-authored-by: Zhiwen Mo * [Bugfix] Fallback to a Linear Layout instead of raising errors (#1521) * Enhance GEMM layout functions to include a fallback for float64 when mat_stride % 8 != 0. Refactor swizzling layout conditions to check mat_stride before mat_continuous, improving layout selection logic for better performance. * lint fix * Use `TargetIsCuda` for all cuda target (#1522) * Fix fp4 pointer arithmetic in CUDA codegen (#1524) * Fix fp4 pointer arithmetic in CUDA codegen * Fix fp4 pointer arithmetic in CUDA codegen * [Enhancement] Improve GitHub Actions permissions check and refine performance regression testing (#1519) * [Release] Bump version into 0.1.7.post1 (#1506) * [Pipeline] Refactor buffer allocation in Inject Pipeline Pass (#1525) * [Feature] Introduce BufferUsageCollector for software pipelining * Added BufferUsageCollector class to identify and collect buffers used in pipeline loop bodies, enabling proper multi-versioning for software pipelining. * Updated PipelineRewriter to handle local and outer block buffer allocations more effectively, ensuring that only necessary buffers are included in the pipeline. * Enhanced buffer remapping logic to prevent conflicts when buffers from outer blocks are used in multiple pipeline loops. This update improves the efficiency and correctness of buffer management during software pipelining. * Refactor buffer allocation declarations in inject_pipeline.cc * Adjusted formatting of buffer allocation declarations for improved readability. * Ensured consistent style in the codebase by aligning variable declarations. This change enhances code clarity without altering functionality. * test fix * [Dev] Fix when build local version with isolated build (#1487) * fix when build local version with isolated build * fix * trivial update * upd * [Bugfix] Skip stride check for subtype (#1531) * fp4 related update, require_cu13 * Enhance CUDA type conversion handling and optimize dtype management - Updated CUDA vectorized cast functions to ensure proper handling of float16, float32, bfloat16, and float8 conversions, adding checks for bit sizes. - Refactored dtype conversion logic in `cuda_fp4.h` to utilize `cudaRoundZero` for improved accuracy in floating-point conversions. - Introduced a new method in `KernelParam` to convert TVM DataType to TileLang dtype. - Adjusted argument binding logic in `arg_binder.cc` to allow for better subtype matching based on total bit counts. - Enhanced dtype handling in `dtypes.py` to accommodate new float4_e2m1fn types and ensure compatibility with PyTorch. This update aims to improve type safety and conversion accuracy across the codebase. * lint fix * Enhance ArgBinder stride handling for subbyte types - Updated `BindDLTensors` method in `arg_binder.cc` to skip stride checks for subbyte types (bits < 8), as they utilize packed storage where stride semantics do not apply. - Added comments for clarity on the changes made to the stride binding logic. This change aims to improve the handling of data types in the argument binding process. * lint fix * refactor * fix --------- Co-authored-by: Zhiwen Mo * [Lint] Enable whitespace and permission bit hooks (#1439) * [Lint] Enable whitespace hooks * [Lint] Enable permission bit hooks --------- Co-authored-by: LeiWang1999 * [Enhancement][Tool] Tree-style pretty ASTPrinter (#1468) * fix rebase * improve printer * update docs * [Fix] Add support for non-var complement arithmetic computation (#1374) (#1533) * [Fix] Add complement for non-var IterExpr * fix lint error * lint fix --------- Co-authored-by: LeiWang1999 * [BugFix] Complete vectorized loading for common dtypes (#1536) * [Compat] Add CUDA version check for __nv_fp8_e8m0 type (#1537) __nv_fp8_e8m0 is only available in CUDA 12.6+. Add conditional compilation to provide a placeholder struct for older CUDA versions. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-authored-by: Claude Opus 4.5 * [Bug] Fix bugs of varlen attention forward examples caused by `S_q != S_kv` (#1530) * fix(examples): correct causal loop range in GQA varlen example Signed-off-by: hukongyi * WIP: implement right alignment logic (tests currently failing) Signed-off-by: hukongyi * use fa2 as gqa varlen fwd ref and add test to ci * fix sq>skv * use triple expr * lint --------- Signed-off-by: hukongyi Co-authored-by: Rachmanino <18805904201@163.com> * [Bug] Fix hanging from reduction on sm120 (#1540) * [example] use T.dynamic instead of tvm.te.var (#1538) * [Enhancement] Refactor KernelCache to use inheritance-based design (#1483) * refactor kernel_cache Signed-off-by: Jinjie Liu * set key to auto when execution_backend is None Signed-off-by: Jinjie Liu * fix type error on _load_kernel_source override Signed-off-by: Jinjie Liu * put kernel cache subclasses in adapter Signed-off-by: Jinjie Liu * remove auto in execution_backend Signed-off-by: Jinjie Liu * remove _save_kernel_source_code_to_disk for NVRTCKernelCache Signed-off-by: Jinjie Liu * refactor all cutedsl if statements Signed-off-by: Jinjie Liu * remove unused kernel_cubin_path Signed-off-by: Jinjie Liu * add func arguments Signed-off-by: Jinjie Liu * remove ctypes Signed-off-by: Jinjie Liu * update ci.yaml to test kernel cache Signed-off-by: Jinjie Liu * fix bugs after #1512 merged Signed-off-by: Jinjie Liu * test: fix kernel cache test * fix: ruff * fix: ruff * fix: coderabbit * fix: coderabbit * fix: ruff * fix: MMA * fix: coderabbit * fix: codex * fix: coderabbit * fix: coderabbit --------- Signed-off-by: Jinjie Liu Co-authored-by: Zihua Wu <13583761+lucifer1004@users.noreply.github.com> * [Bugfix] Avoid considering `local.var` buffer as `local` (#1541) * [Bugfix] Fix of `T.Fill` for local.var (#1543) * Remove logging from ExpandLetBindings in ParallelOpNode and simplify IsLocalBuffer function to check only for "local" scope. * Add IsLocalVarBuffer check in FillNode::Lower for improved buffer handling - Introduced IsLocalVarBuffer function to identify local variable buffers. - Updated FillNode::Lower to handle both local and local variable buffers in the vectorized thread loop logic. * Refactor variable allocation in compress_kernel function for improved local variable handling - Changed allocation of non_zero_cnt and non_zero_elt_log_idx from shared to local variables. - Updated logic to correctly reference the first element of non_zero_cnt for counting non-zero elements. - Adjusted conditions to use the updated local variable references. * [Z3] Change z3 timeout to rlimit for determistic prove behavior (#1542) * [Z3] Change timeout to rlimit for determistic behavior * update tvm --------- Co-authored-by: LeiWang1999 * [Feat] Adapt gemm v2 for cutedsl backend (#1544) * feat: adapt gemm v2 for cutedsl backend * fix: ruff * [Enhancement] Support larger `H` in deepseek sparse mla backward via split-H (#1548) * [Bugfix] Fix regression test to use installed package instead of source directory (#1550) * [Bugfix] Fix regression test to use installed package instead of source directory * fix * fix * lint * upd * fix * [Refactor] Introduce layout annotations for `ParallelOPNode` and `CopyNode` (#1539) * [Refactor] Enhance parallel loop handling and layout inference * Introduced new annotations for parallel loop layouts and predicates in layout.h to improve layout management. * Refactored loop lowering logic in copy.cc to utilize a new LowerParallelLoop function, consolidating partitioning and vectorization steps. * Updated layout inference to store layout and predicate annotations on For nodes, ensuring proper handling during subsequent transformations. * Added checks in lower_tile_op.cc to enforce layout annotations for parallel loops, enhancing error handling and clarity. This update aims to streamline parallel loop processing and improve the overall efficiency of layout inference in the codebase. * [Refactor] Introduce annotations for atomic operations and copy operations * Added support for annotations in AtomicAddNode and CopyNode to enhance flexibility in memory operations. * Updated constructors to parse annotations from input arguments, allowing for coalesced width, TMA usage, and memory order to be specified via an annotations map. * Refactored related logic in atomic_add.cc and copy.cc to utilize the new annotations, improving clarity and maintainability. * Enhanced Python bindings to support annotations in atomic and copy operations, ensuring consistency across the API. This update aims to streamline the handling of memory operation parameters and improve the overall usability of the API. * [Refactor] Enhance operator constructors to support annotations * Updated constructors for various operators (AtomicAdd, Copy, Fill, Gemm, etc.) to accept an annotations map, improving flexibility in handling additional parameters. * Refactored related logic in operator implementations to utilize the new annotations, ensuring consistency across the API. * Enhanced Python bindings to support annotations in operator calls, streamlining the interface for users. This update aims to improve the usability and extensibility of operator functionalities in the codebase. * lint fix * [Script] Provide regression test script to help benchmark regression in local env (#1551) * Add .perf_regression/ to .gitignore for performance regression tests * Enhance performance regression script to handle existing build directories by backing them up before installation and restoring them afterward. * Update performance regression script to clean additional build artifacts, including .perf_regression and build.bak.* * [Typing] Update Kernel signature and add type hints for buffer operations (#1545) * improve some typings * revoke min, max change to ir * add min, max to ir.pyi * fix format * [CI]: Bump actions/upload-artifact from 4 to 6 (#1555) Bumps [actions/upload-artifact](https://github.com/actions/upload-artifact) from 4 to 6. - [Release notes](https://github.com/actions/upload-artifact/releases) - [Commits](https://github.com/actions/upload-artifact/compare/v4...v6) --- updated-dependencies: - dependency-name: actions/upload-artifact dependency-version: '6' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * Use cuda capability from torch to be more generic (#1557) * Use cuda capability from torch to be more generic * fix * [CI]: Bump actions/github-script from 7 to 8 (#1556) Bumps [actions/github-script](https://github.com/actions/github-script) from 7 to 8. - [Release notes](https://github.com/actions/github-script/releases) - [Commits](https://github.com/actions/github-script/compare/v7...v8) --- updated-dependencies: - dependency-name: actions/github-script dependency-version: '8' dependency-type: direct:production update-type: version-update:semver-major ... Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> * [Host] Provide post process to customize host code and enhance nullable check (#1562) * [Enhancement] Add C host post-processing callback and improve nullable buffer handling - Introduced a new callback for C host code generation that allows for post-processing of generated code before it is wrapped into a CSourceModule. - Enhanced the ArgBinder to log shape variable sources and ensure safe binding of symbolic shape variables, preventing potential segmentation faults when dealing with nullable buffers. - Added a regression test to verify that a single buffer with a symbolic shape variable must be non-null, ensuring robustness against null inputs. * [Refactor] Clean up formatting and improve readability in C host code generation and ArgBinder - Adjusted indentation in the C host code generation to enhance clarity. - Reformatted comments and code structure in ArgBinder for better readability, ensuring consistent style throughout the file. - Minor whitespace adjustments to maintain code consistency. * [Release] Build tilelang against CUDA 13.1 in CI (#1532) * [Release] Build tilelang against CUDA 13.1 in CI * Remove toolchain version to reduce cache size * [LazyJIT] Move Type Annotations to Function Body (#1480) * [LazyJIT] Refactor to move outside annotation into function * [SyntaxSugar] Allow write expression bind before match buffer * [SyntaxSugar] Allow decoupled argument annotation * [SyntaxSugar] Add `T.empty` and return * Fix typos * fix lint error * [Testing] Update lazy_jit tests * fix typo * add assertion and error message * fix typo * fix double pop of __tune_params * fix double pop error * [bugfix] fix missing logic for clear_accum (#1563) * [Misc] Remove unused `tl_pipeline_sync`. (#1566) * Remove unused sync annotation setting Removed setting of 'tl_pipeline_sync' annotation if sync is empty. * Update nested_loop_checker.py * [Refactor] Improve scalarization handling in vectorization logic (#1565) * Enhanced the Vectorize function to retain the original body for scalarization if needed. * Updated VisitStmt and VisitStmt_ methods to return the original statement when scalarization is required, ensuring proper handling of statements during vectorization. * Added checks after visiting expressions and statements to maintain the integrity of the original structure when scalarization is triggered. This refactor aims to streamline the vectorization process and improve the handling of scalarization scenarios. * [Refactor] Simplify do_bench calls by using default warmup and rep parameters (#1568) * Refactor benchmarking calls to remove warmup parameter in do_bench function Updated multiple examples to streamline benchmarking by removing the warmup parameter from the do_bench function calls. This change simplifies the function signature while maintaining the backend specification for performance measurement. * Update regression performance parameters across multiple examples to support larger input sizes Modified the `run_regression_perf` function in various examples to set default parameters for matrix dimensions (M, N, K) and heads to 4096 or 32, enhancing the benchmarking capabilities for larger datasets. This change aims to improve performance testing and ensure consistency across examples. * [CI] Refactor PR regression test job conditions (#1569) Updated the conditions for the performance regression test job. * [Parallel][Infer] Free-mode chooses minimal replication between buffer-based and PlanLoopPartition (#1559) * [Enhancement] Improve layout inference in ParallelOpNode * Enhanced the layout inference mechanism in ParallelOpNode to utilize two strategies: compute_loop_layout_from_buffer and PlanLoopPartition, selecting the one that minimizes replication while ensuring compatibility. * Updated the logic to choose the best candidate layout based on replication size and containment checks. * Refactored the HasKnownLayoutAnchor function to clarify its purpose in prioritizing buffer layouts. * Added a new test case to validate the layout inference behavior, ensuring the correct fragments are generated in the output. This update aims to optimize layout inference for parallel operations, improving performance and resource utilization. * lint fix * bug fix and refactor * lint fix * [Refactor] Enhance deterministic ordering in shared memory allocation merge. (#1570) * [Refactor] Enhance deterministic ordering in shared memory allocation handling * Updated comparison logic in merge_shared_memory_allocations.cc to use name hints for deterministic ordering of variables instead of pointer comparisons. * Introduced a sorted vector of keys for shmem_allocs_ to ensure consistent iteration order when processing allocations. This refactor aims to improve the predictability of shared memory allocation handling in the transformation process. * lint fix * [Enhancement] Improve equality checks in layout nodes and fragment validation (#1573) * [Enhancement] Improve equality checks in layout nodes and fragment validation * Enhanced the IsEqual method in LayoutNode and FragmentNode to include detailed comparisons of forward mappings, ensuring accurate equality checks. * Introduced a new parameter in ProveFragmentContains to validate physical indices when checking fragment containment, improving correctness in layout validation. * Removed obsolete test file related to layout inference. This update aims to strengthen the integrity of layout comparisons and fragment validations in the system. * lint fix * [Feature] add kUseCooperativeLaunch tag for tvm_ffi (#1572) * add kUseCooperativeLaunch tag * add test for sync_grid in cooperative launch * add test for sync_grid in cooperative launch * [Refactor] Remove unnecessary logging configuration in Analyzer.py (#1574) * Removed the logging configuration line from Analyzer.py to streamline the logging setup. This change simplifies the code and relies on the default logging configuration, improving maintainability. * [Release] Bump version to 0.1.7.post2 (#1575) * [BugFix] Change default rounding mode for fp4 conversions (#1580) * [CI] Add CUDA-aware pytest scheduler + auto workers (#1584) * maint: add CUDA-aware pytest-xdist scheduler plugin and update run_local_ci_test.sh to auto-calc workers and support device selection * lint fix * maint: quote ROOT_DIR and exit on cd failure (SC2164) * maint: guard directory changes (cd) with || exit for reliability * [Enhancement] Improve performance regression output with timing and streaming (#1585) * [Bugfix] Add kernel_global_source property to TVMFFIKernelAdapter (#1589) * Add PrimExpr substitution support for AttrStmt nodes (#1583) * [BugFix] fix tcgen5mma example (#1577) * [Doc] Rename docs/merge_tilescale to docs/sync_with_tilelang and add comparison study - Rename documentation directory to better reflect purpose - Add comprehensive study comparing TileScale contributions to TileLang - Include generated patches and detailed analysis documents: - Distributed primitives (PutOp, GetOp, WaitOp, etc.) - Language extensions (T.ld, T.st, T.warp_any, etc.) - C++ TileOperators and CUDA templates - JIT infrastructure changes - Distributed examples catalog - Update internal document references Co-Authored-By: Claude Opus 4.5 * fix a typo * Remove symbols created by Claude's hallucination * fix include logic in cuda codegen * fix ldst.h * fix more files * migrate from `TIR_REGISTER_TL_OP` to `TIR_REGISTER_TL_TILE_OP` * let all distributed examples pass * fix deepep regression via applying vectorization * fix lint and remove Claude's merge doc * fix sdist * disable arm and macos * fix `dist.yml` * disable ci for arm and metal * fix ts_ext * use sdist for ci * use tilelang's new ci * use cmake rather than pyproject dependency for tilescale extension * install torch before ts_ext * fix torch lib link bug * add missing codegen * disable ci test for deepep * fix gitignore bug * disable ib for nccl * switch to new ci runner * lint * set num_procs to 2 * fix typo * using tsinghua src for pip * refactor CI workflow to remove SDist download step, simplifying artifact management * [BugFix] Add device_ids attribute to BaseAllocator for improved device management * [Doc] Update Installation Guide for TileScale: Simplify installation methods, update prerequisites, and enhance Docker instructions. * [Feature] Support tvm-ffi for TileScale * update DeepEP installation script and * draft for supporting tvm-ffi in deepep * [Refactor] Update memory management to use constant memory for meta_data, streamline kernel initialization, and enhance dispatch mechanisms with improved CUDA stream handling. * lint fix * [Bugfix]ci: add missing - for uv run --script stdin input * [Bugfix]dist: exclude nvshmem and nccl libs from auditwheel repair Added --exclude 'libnvshmem*' and --exclude 'libnccl*' to prevent auditwheel from trying to bundle NVIDIA runtime libraries that should be installed separately via nvidia-nvshmem-cu12 package. * [Bugfix]dist: disable abi3audit --strict to allow nvshmem builds The abi3audit strict mode fails for wheels that include nvidia-nvshmem libraries which may not be fully ABI3 compliant. Changed to report-only mode with || true to allow builds to continue while still logging any compatibility warnings. * dist: skip CUDA wheel tests on GitHub-hosted runners GitHub-hosted runners don't have CUDA installed. Skip the wheel import test for CUDA builds on these runners. * fix missing loop_break import * [CI] Enhance CI workflow and testing framework for distributed tests - Updated CI configuration to streamline the execution of distributed tests, ensuring they are run with the appropriate environment variable. - Refactored test execution commands to simplify the process and improve readability. - Added `@tilelang.testing.requires_distributed` decorator to relevant test functions to enforce distributed environment requirements. - Removed obsolete test discovery logic and replaced it with direct pytest markers for better performance and clarity. - Deleted unused test file related to tilelang primitives. * [CI] Refactor distributed test marker to a decorator - Introduced a new `requires_distributed` decorator to mark tests that require the TILELANG_USE_DISTRIBUTED environment variable. - The decorator combines both `pytest.mark.distributed` and `pytest.mark.skipif` for improved test management and clarity. - Updated the implementation to enhance readability and maintainability of test requirements. --------- Signed-off-by: dependabot[bot] Signed-off-by: Jinjie Liu Signed-off-by: hukongyi Co-authored-by: Zhengju Tang <97930865+tzj-fxz@users.noreply.github.com> Co-authored-by: Kuris <227995639+kurisu6912@users.noreply.github.com> Co-authored-by: Varuna Jayasiri Co-authored-by: Chaofan Lin Co-authored-by: Tong WU <109033598+Rachmanino@users.noreply.github.com> Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com> Co-authored-by: Yu Cheng <54519279+chengyupku@users.noreply.github.com> Co-authored-by: Yichen Yan Co-authored-by: Elevator14B Co-authored-by: LeiWang1999 Co-authored-by: Jay Zhuang <80731350+learning-chip@users.noreply.github.com> Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: liu yuhao Co-authored-by: cheeryBloosm Co-authored-by: Yunqian Fan Co-authored-by: LJC00118 <77378439+LJC00118@users.noreply.github.com> Co-authored-by: Zhiwen Mo Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> Co-authored-by: Wenhao Xie Co-authored-by: ConvolutedDog Co-authored-by: Gongen-Ali Co-authored-by: Yuxuan Hu Co-authored-by: Leon Lu Co-authored-by: botbw Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Yuqi Dong <134183314+yyttt6@users.noreply.github.com> Co-authored-by: Cunxiao Ni <85601223+Cunxiao2002@users.noreply.github.com> Co-authored-by: danielhua23 Co-authored-by: senlyu163 <70838408+senlyu163@users.noreply.github.com> Co-authored-by: Xuehai Pan Co-authored-by: Dayuxiaoshui <158081477+Dayuxiaoshui@users.noreply.github.com> Co-authored-by: silentCoder-dev Co-authored-by: Jinjie Liu <68475640+sgjzfzzf@users.noreply.github.com> Co-authored-by: Gabriel Wu <13583761+lucifer1004@users.noreply.github.com> Co-authored-by: yuxic Co-authored-by: Yong Co-authored-by: yyttt6 <1652272478@qq.com> Co-authored-by: Yichen Yan Co-authored-by: Claude Opus 4.5 Co-authored-by: hukongyi Co-authored-by: Rachmanino <18805904201@163.com> Co-authored-by: Clouds Co-authored-by: Connector Switch Co-authored-by: Hao Kang <89672451+haok1402@users.noreply.github.com> Co-authored-by: Yu Cheng Co-authored-by: rqfeng930 --- .clang-tidy | 6 +- .github/ISSUE_TEMPLATE/config.yml | 2 +- .github/ISSUE_TEMPLATE/release-plan.yml | 63 + .github/workflows/amd_ci.yml | 8 +- .github/workflows/ci.yml | 472 ++++-- .github/workflows/dist.yml | 125 +- .github/workflows/pr-regression-test-bot.yml | 273 +++ .github/workflows/publish-docs.yml | 2 +- .gitignore | 23 + .pre-commit-config.yaml | 39 +- .pymarkdown | 37 + 3rdparty/tvm | 2 +- CMakeLists.txt | 288 +++- CODE_OF_CONDUCT.md | 20 +- CONTRIBUTING.md | 4 +- LICENSE | 2 +- MANIFEST.in | 10 - README.md | 51 +- THIRDPARTYNOTICES.txt | 4 +- VERSION | 2 +- .../benchmark_library_dense_fmha.py | 13 +- .../benchmark_tilelang_block_sparse_fmha.py | 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docs/get_started/targets.md | 1 + docs/index.md | 24 +- docs/programming_guides/autotuning.md | 308 ++++ docs/programming_guides/control_flow.md | 145 ++ docs/programming_guides/instructions.md | 180 ++ docs/programming_guides/language_basics.md | 234 +++ docs/programming_guides/overview.md | 27 + docs/programming_guides/type_system.md | 41 + docs/spelling_wordlist.txt | 1 + docs/tutorials/auto_tuning.md | 4 +- docs/tutorials/debug_tools_for_tilelang.md | 33 +- docs/tutorials/logging.md | 116 ++ examples/amd/example_amd_flash_attn_bwd.py | 228 ++- examples/amd/example_amd_flash_attn_fwd.py | 128 +- examples/analyze/README.md | 20 +- examples/analyze/example_conv_analyze.py | 44 +- examples/analyze/example_gemm_analyze.py | 10 +- examples/attention_sink/README.md | 3 +- .../attention_sink/benchmark_gqa_sink_fwd.py | 57 +- .../attention_sink/benchmark_mha_sink_fwd.py | 71 +- .../example_gqa_sink_bwd_bhsd.py | 377 +++-- ...ample_gqa_sink_fwd_bhsd_wgmma_pipelined.py | 297 ++-- .../example_mha_sink_bwd_bhsd.py | 349 ++-- .../example_mha_sink_fwd_bhsd.py | 316 ++-- ...ample_mha_sink_fwd_bhsd_wgmma_pipelined.py | 322 ++-- .../regression_attention_sink.py | 64 + examples/bitnet-1.58b/.gitignore | 2 +- examples/bitnet-1.58b/README.md | 4 +- examples/bitnet-1.58b/benchmark.sh | 2 + examples/bitnet-1.58b/benchmark_generate.py | 35 +- .../benchmark_inference_latency.py | 9 +- examples/bitnet-1.58b/configuration_bitnet.py | 16 +- examples/bitnet-1.58b/eval_correctness.py | 22 +- examples/bitnet-1.58b/eval_gpu_memory.py | 13 +- examples/bitnet-1.58b/eval_ppl.py | 28 +- examples/bitnet-1.58b/eval_utils.py | 20 +- .../tilelang_bitnet_158_int8xint2_decode.py | 62 +- .../tilelang_bitnet_158_int8xint2_prefill.py | 119 +- .../kernel_benchmark/tl_int8xint8.py | 48 +- examples/bitnet-1.58b/load_from_quantized.py | 8 +- examples/bitnet-1.58b/maint/README.md | 3 +- .../bitnet-1.58b/maint/create_bitblas_ckpt.py | 21 +- .../generate_bitnet_model_bitblas_format.sh | 2 + 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+- .../jit/adapter/cython/cython_wrapper.pyx | 30 +- tilelang/jit/adapter/cython/kernel_cache.py | 4 + tilelang/jit/adapter/dlpack.py | 2 +- tilelang/jit/adapter/kernel_cache.py | 21 + tilelang/jit/adapter/libgen.py | 147 +- tilelang/jit/adapter/nvrtc/__init__.py | 31 +- tilelang/jit/adapter/nvrtc/adapter.py | 82 +- tilelang/jit/adapter/nvrtc/kernel_cache.py | 18 + tilelang/jit/adapter/nvrtc/libgen.py | 233 +++ tilelang/jit/adapter/nvrtc/wrapper.py | 581 +++++++ tilelang/jit/adapter/torch/__init__.py | 2 +- tilelang/jit/adapter/torch/kernel_cache.py | 4 + tilelang/jit/adapter/torch/metal.py | 14 +- tilelang/jit/adapter/tvm_ffi.py | 357 ++++ tilelang/jit/adapter/utils.py | 312 +++- tilelang/jit/adapter/wrapper.py | 737 +++------ tilelang/jit/execution_backend.py | 108 ++ tilelang/jit/kernel.py | 437 ++++- tilelang/language/__init__.py | 65 +- tilelang/language/allocate.py | 96 +- tilelang/language/annotations.py | 38 +- tilelang/language/ast/__init__.py | 1 + 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examples/gemm_fp8/regression_example_gemm_fp8.py create mode 100644 examples/gemm_sp/example_custom_compress.py create mode 100644 examples/gemm_sp/test_example_gemm_sp.py create mode 100644 examples/gemm_splitk/regression_example_gemm_splitk.py rename examples/gemm_streamk/{test_example_tilelang_gemm_splitk.py => test_example_tilelang_gemm_streamk.py} (100%) create mode 100644 examples/gemv/regression_example_gemv.py create mode 100644 examples/lazy_jit/lazyjit.en.ipynb create mode 100644 examples/lazy_jit/lazyjit.zh.ipynb create mode 100644 examples/linear_attention/regression_linear_attn.py create mode 100644 examples/minference/regression_vs_sparse_attn.py create mode 100644 examples/plot_layout/fragment_mfma_load_a.py create mode 100644 examples/rand/rand_uint.py create mode 100644 examples/seer_attention/regression_block_sparse_attn_tilelang.py create mode 100644 examples/sparse_tensorcore/regression_example_sparse_tensorcore.py create mode 100644 examples/topk/regression_topk_tilelang.py create mode 100644 examples/visual_layout_inference/visual_layout_inference.py create mode 100644 examples/warp_specialize/regression_example_warp_specialize.py create mode 100644 maint/gemm_v2/correctness_evaluation.py create mode 100644 maint/gemm_v2/correctness_evaluation_sm70.py create mode 100644 maint/gemm_v2/correctness_evaluation_tcgen05.py create mode 100644 maint/gemm_v2/latency.py create mode 100644 maint/gemm_v2/latency_gemm.py create mode 100644 maint/gemm_v2/latency_mha_fwd_bhsd.py create mode 100644 maint/host_checks/01_num_args_mismatch.py create mode 100644 maint/host_checks/02_pointer_type_error.py create mode 100644 maint/host_checks/03_ndim_mismatch.py create mode 100644 maint/host_checks/04_dtype_mismatch.py create mode 100644 maint/host_checks/05_shape_mismatch.py create mode 100644 maint/host_checks/06_strides_mismatch.py create mode 100644 maint/host_checks/07_device_type_mismatch.py create mode 100644 maint/host_checks/08_device_id_mismatch.py create mode 100644 maint/host_checks/09_null_data_pointer.py create mode 100644 maint/host_checks/10_scalar_type_mismatch.py create mode 100644 maint/host_checks/README.md create mode 100644 maint/host_checks/common.py create mode 100644 maint/host_checks/run_all.py mode change 100644 => 100755 maint/precision/compare_ops.py delete mode 100644 maint/scripts/ci_performance.py delete mode 100755 maint/scripts/docker_build_all.sh delete mode 100644 maint/scripts/performance.py create mode 100644 maint/scripts/regression_all.py create mode 100755 maint/scripts/run_perf_regression.sh create mode 100644 maint/scripts/test_perf_regression.py create mode 100644 src/op/gemm_sp_py.cc create mode 100644 src/op/gemm_sp_py.h create mode 100644 src/op/tcgen5_meta.h create mode 100644 src/op/utils.cc create mode 100644 src/op/utils.h create mode 100644 src/runtime/error_helpers.cc create mode 100644 src/runtime/error_helpers.h create mode 100644 src/runtime/tilescale_cuda_module.cc create mode 100644 src/runtime/tilescale_cuda_module.h create mode 100644 src/support/ffi_aliases.h create mode 100644 src/target/codegen_c_host.cc create mode 100644 src/target/codegen_c_host.h create mode 100644 src/target/codegen_cutedsl.cc create mode 100644 src/target/codegen_cutedsl.h create mode 100644 src/target/codegen_py.cc create mode 100644 src/target/codegen_py.h create mode 100644 src/target/codegen_utils.cc create mode 100644 src/target/codegen_utils.h delete mode 100644 src/target/codegen_webgpu.cc delete mode 100644 src/target/codegen_webgpu.h create mode 100644 src/target/rt_mod_cutedsl.cc create mode 100644 src/tl_templates/cuda/cuda_fp4.h create mode 100644 src/tl_templates/cuda/instruction/mma.h create mode 100644 src/tl_templates/cuda/instruction/mma_sm70.h create mode 100644 src/tl_templates/cuda/instruction/tcgen05mma.h create mode 100644 src/transform/annotate_read_only_params.cc create mode 100644 src/transform/arg_binder.cc create mode 100644 src/transform/arg_binder.h create mode 100644 src/transform/common/assume.cc create mode 100644 src/transform/common/assume.h create mode 100644 src/transform/hoist_nonrestrict_params.cc create mode 100644 src/transform/legalize_negative_index.cc delete mode 100644 src/transform/loop_vectorize_dynamic.cc mode change 100755 => 100644 src/transform/lower_tile_op.cc create mode 100644 src/transform/merge_if_stmt.h create mode 100644 src/transform/parallel_loop_layout_validator.h create mode 100644 src/transform/plan_update_buffer_allocation_location.cc create mode 100644 testing/python/analysis/test_tilelang_fragment_loop_checker.py create mode 100644 testing/python/analysis/test_tilelang_nested_loop_checker.py create mode 100644 testing/python/arith/test_arith_hard.py create mode 100644 testing/python/arith/test_arith_intset.py create mode 100644 testing/python/arith/test_arith_iter_affine_map.py create mode 100644 testing/python/arith/test_arith_simplify.py create mode 100644 testing/python/cache/test_tilelang_kernel_cache.py create mode 100644 testing/python/carver/test_tilelang_carver_cuda_driver_properties.py create mode 100644 testing/python/components/test_cuda_restrict_codegen.py create mode 100644 testing/python/debug/test_device_assert.py create mode 100644 testing/python/issue/test_tilelang_issue_1001.py create mode 100644 testing/python/issue/test_tilelang_issue_1008.py create mode 100644 testing/python/issue/test_tilelang_issue_1115.py create mode 100644 testing/python/issue/test_tilelang_issue_1198.py create mode 100644 testing/python/issue/test_tilelang_issue_1210.py create mode 100644 testing/python/issue/test_tilelang_issue_1237.py create mode 100644 testing/python/issue/test_tilelang_issue_1374.py rename testing/python/jit/{test_tilelang_jit_gemm_ctypes.py => test_tilelang_jit_cutedsl.py} (59%) create mode 100644 testing/python/jit/test_tilelang_jit_nullptr.py create mode 100644 testing/python/jit/test_tilelang_jit_nvrtc.py create mode 100644 testing/python/jit/test_tilelang_jit_parcompile.py create mode 100644 testing/python/jit/test_tilelang_jit_tvm_ffi.py create mode 100644 testing/python/language/test_tilelang_intimm.py create mode 100644 testing/python/language/test_tilelang_language_annot.py create mode 100644 testing/python/language/test_tilelang_language_assume.py create mode 100644 testing/python/language/test_tilelang_language_chain_equal.py delete mode 100644 testing/python/language/test_tilelang_language_elect.py create mode 100644 testing/python/language/test_tilelang_language_frontend_v2.py create mode 100644 testing/python/language/test_tilelang_language_infinity.py create mode 100644 testing/python/language/test_tilelang_language_int64.py create mode 100644 testing/python/language/test_tilelang_language_intrinsics_codegen.py create mode 100644 testing/python/language/test_tilelang_language_lazy_jit.py create mode 100644 testing/python/language/test_tilelang_language_let.py create mode 100644 testing/python/language/test_tilelang_language_let_layout.py create mode 100644 testing/python/language/test_tilelang_language_negative_index.py create mode 100644 testing/python/language/test_tilelang_language_rand.py create mode 100644 testing/python/language/test_tilelang_language_tma_1d.py create mode 100644 testing/python/language/test_tilelang_language_unroll.py create mode 100644 testing/python/language/test_tilelang_language_var_init.py create mode 100644 testing/python/language/test_tilelang_memory_leak.py create mode 100644 testing/python/layout/test_tilelang_layout_equal.py create mode 100644 testing/python/layout/test_tilelang_layout_fused_replicate.py create mode 100644 testing/python/layout/test_tilelang_layout_inference.py delete mode 100644 testing/python/primitives/test_tilelang_primitives_mma.py create mode 100644 testing/python/runtime/test_tilelang_runtime_dynamic_shared_memory.py create mode 100644 testing/python/tilelibrary/test_tilelang_tilelibrary_gemm_sp_v2.py create mode 100644 testing/python/transform/test_nullable_buffer_params.py create mode 100644 testing/python/transform/test_readonly_param_const_codegen.py create mode 100644 testing/python/transform/test_tilelang_transform_legalize_negative_index.py create mode 100644 tilelang/analysis/__init__.py create mode 100644 tilelang/analysis/ast_printer.py create mode 100644 tilelang/analysis/fragment_loop_checker.py create mode 100644 tilelang/analysis/layout_visual.py create mode 100644 tilelang/analysis/nested_loop_checker.py create mode 100644 tilelang/contrib/cutedsl/__init__.py create mode 100644 tilelang/contrib/cutedsl/cpasync.py create mode 100644 tilelang/contrib/cutedsl/gemm_V1.py create mode 100644 tilelang/contrib/cutedsl/ldsm.py create mode 100644 tilelang/contrib/cutedsl/math.py create mode 100644 tilelang/contrib/cutedsl/mbar.py create mode 100644 tilelang/contrib/cutedsl/reduce.py create mode 100644 tilelang/contrib/cutedsl/threadblock_swizzle.py mode change 100644 => 100755 tilelang/distributed/build_nvshmem.sh mode change 100644 => 100755 tilelang/distributed/pynvshmem/testing/cpp/run_nvshmem_example.sh mode change 100644 => 100755 tilelang/distributed/pynvshmem/testing/test_rs.sh create mode 100644 tilelang/intrinsics/mma_sm70_layout.py create mode 100644 tilelang/intrinsics/mma_sm70_macro_generator.py create mode 100644 tilelang/intrinsics/mma_sp_layout.py create mode 100644 tilelang/intrinsics/mma_sp_macro_generator.py create mode 100644 tilelang/intrinsics/tcgen05_macro_generator.py delete mode 100644 tilelang/jit/adapter/ctypes/__init__.py create mode 100644 tilelang/jit/adapter/cutedsl/__init__.py create mode 100644 tilelang/jit/adapter/cutedsl/adapter.py create mode 100644 tilelang/jit/adapter/cutedsl/checks.py create mode 100644 tilelang/jit/adapter/cutedsl/kernel_cache.py create mode 100644 tilelang/jit/adapter/cutedsl/libgen.py create mode 100644 tilelang/jit/adapter/cutedsl/wrapper.py create mode 100644 tilelang/jit/adapter/cython/kernel_cache.py create mode 100644 tilelang/jit/adapter/kernel_cache.py create mode 100644 tilelang/jit/adapter/nvrtc/kernel_cache.py create mode 100644 tilelang/jit/adapter/nvrtc/libgen.py create mode 100644 tilelang/jit/adapter/nvrtc/wrapper.py create mode 100644 tilelang/jit/adapter/torch/kernel_cache.py create mode 100644 tilelang/jit/adapter/tvm_ffi.py create mode 100644 tilelang/jit/execution_backend.py create mode 100644 tilelang/language/copy_op.py create mode 100644 tilelang/language/fill_op.py create mode 100644 tilelang/language/gemm_op.py create mode 100644 tilelang/language/loop.py rename tilelang/language/{print.py => print_op.py} (81%) create mode 100644 tilelang/language/random.py create mode 100644 tilelang/language/reduce_op.py create mode 100644 tilelang/language/tir/ir.pyi create mode 100644 tilelang/language/v2/__init__.py create mode 100644 tilelang/language/v2/ast.py create mode 100644 tilelang/language/v2/builder.py create mode 100644 tilelang/language/v2/dtypes.py create mode 100644 tilelang/language/v2/utils.py delete mode 100644 tilelang/primitives/__init__.py delete mode 100644 tilelang/primitives/gemm/gemm_mma.py create mode 100644 tilelang/testing/perf_regression.py rename tilelang/{primitives/gemm => tileop}/base.py (56%) create mode 100644 tilelang/tileop/gemm/gemm_cutedsl.py create mode 100644 tilelang/tileop/gemm/gemm_mfma.py create mode 100644 tilelang/tileop/gemm/gemm_mma_sm70.py create mode 100644 tilelang/tileop/gemm/gemm_tcgen05.py create mode 100644 tilelang/tileop/gemm_sp/__init__.py create mode 100644 tilelang/tileop/gemm_sp/gemm_sp_base.py create mode 100644 tilelang/tileop/gemm_sp/gemm_sp_mma.py create mode 100644 tilescale_ext/__init__.py diff --git a/.clang-tidy b/.clang-tidy index 2ddbefbf91..f9b77bce8a 100644 --- a/.clang-tidy +++ b/.clang-tidy @@ -1,10 +1,12 @@ --- InheritParentConfig: true -ExtraArgs: ['-v'] +ExtraArgs: [] FormatStyle: file UseColor: true WarningsAsErrors: '*' -ExcludeHeaderFilterRegex: '^(3rdparty|tvm)/.*$' +# FIXME: Use `ExcludeHeaderFilterRegex` instead when all maintainers upgraded their `clang-tidy` +HeaderFilterRegex: '^(?!.*(?:/|^)(3rdparty|tvm)/).*' +# ExcludeHeaderFilterRegex: '^(3rdparty|tvm)/.*$' # NOTE: there must be no spaces before the '-', so put the comma last. Checks: >- diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 3ba13e0cec..0086358db1 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -1 +1 @@ -blank_issues_enabled: false +blank_issues_enabled: true diff --git a/.github/ISSUE_TEMPLATE/release-plan.yml b/.github/ISSUE_TEMPLATE/release-plan.yml new file mode 100644 index 0000000000..a3528275c8 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/release-plan.yml @@ -0,0 +1,63 @@ +name: "Release Plan" +description: "Plan the next release" +title: "[Release Plan] vX.Y.Z" +labels: + - release-plan + - tracking +assignees: [] +body: + - type: input + id: version + attributes: + label: "Version" + placeholder: "v0.2.0" + validations: + required: true + + - type: input + id: milestone + attributes: + label: "Milestone" + description: "Link or name of the milestone for this release" + placeholder: "https://github.com/tile-ai/tilelang/milestone/XX" + + - type: textarea + id: scope + attributes: + label: "Scope" + description: "Goals and non-goals (brief)" + placeholder: | + - Goals: ... + - Non-goals: ... + + - type: textarea + id: tasks + attributes: + label: "Tasks" + description: "Task list; link issues/PRs" + value: | + - [ ] Features + - [ ] Fixes + - [ ] Docs + - [ ] API/Breaking changes + - [ ] Benchmarks + - [ ] Release notes + + - type: checkboxes + id: readiness + attributes: + label: "Readiness" + options: + - label: "All planned issues closed or deferred" + - label: "Docs updated" + - label: "CI green; artifacts verified" + - label: "Release notes drafted" + + - type: textarea + id: notes + attributes: + label: "Notes" + description: "Risks or communications (optional)" + placeholder: | + - Risk: ... + - Communication: ... diff --git a/.github/workflows/amd_ci.yml b/.github/workflows/amd_ci.yml index 2ef300b66e..144c0f09f1 100644 --- a/.github/workflows/amd_ci.yml +++ b/.github/workflows/amd_ci.yml @@ -11,7 +11,7 @@ jobs: runs-on: [self-hosted, amd, gpu] permissions: - contents: write + contents: write steps: - name: Checkout repository @@ -56,7 +56,7 @@ jobs: echo "------------------------------------" exit 1 fi - + - name: Commit and Push Changes uses: stefanzweifel/git-auto-commit-action@v5 with: @@ -86,7 +86,7 @@ jobs: set -e REQS_HASH=$(sha256sum requirements-rocm.txt | cut -d ' ' -f 1) MARKER="${{ runner.tool_cache }}/.venv_marker_${{ env.PYTHON_VERSION }}_${REQS_HASH:0:8}" - + echo "Installing requirements" if [[ -f "$MARKER" ]] && [[ -f "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" ]]; then echo "venv exists and hash matches – reuse it" @@ -117,4 +117,4 @@ jobs: source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" cd testing/python/amd unset PYTHONPATH - python -m pytest -v test_tilelang_test_amd.py \ No newline at end of file + python -m pytest -v test_tilelang_test_amd.py diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index a04edc1eb1..8d5f3ffb48 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -1,154 +1,342 @@ name: CI -on: [pull_request] +on: + pull_request: + types: + - labeled + - unlabeled + - opened + - synchronize + - reopened + # Allow to trigger the workflow manually + workflow_dispatch: + +permissions: + contents: read + +concurrency: + group: "${{ github.workflow }}-${{ github.ref }}" + cancel-in-progress: ${{ github.event_name == 'pull_request' }} env: - PYTHON_VERSION: '3.12' - VENV_DIR: tilelang_ci + CLANG_TIDY_CMAKE_OPTIONS: "-DCMAKE_EXPORT_COMPILE_COMMANDS=ON" # to be updated + PYTHONDEVMODE: "1" + PYTHONUNBUFFERED: "1" + PYTHONPATH: "" # explicit cleanup + PIP_USER: "" # explicit cleanup + COLUMNS: "100" + FORCE_COLOR: "1" + CLICOLOR_FORCE: "1" + UV_INDEX_STRATEGY: "unsafe-best-match" + UV_HTTP_TIMEOUT: "600" + XDG_CACHE_HOME: "${{ github.workspace }}/.cache" # to be updated + PIP_CACHE_DIR: "${{ github.workspace }}/.cache/pip" # to be updated + UV_CACHE_DIR: "${{ github.workspace }}/.cache/uv" # to be updated + PRE_COMMIT_HOME: "${{ github.workspace }}/.cache/pip/.pre-commit" # to be updated jobs: - format-check: - runs-on: [self-hosted, nvidia, hopper] + lint: + name: Quick Lint + runs-on: ubuntu-latest + timeout-minutes: 30 + steps: + - name: Checkout repository + uses: actions/checkout@v6 + with: + fetch-depth: 0 + submodules: recursive - permissions: - contents: write + - name: Setup Python 3.8 + id: setup-pylowest + uses: actions/setup-python@v6 + with: + python-version: "3.8" # use lowest supported version for linting + update-environment: false + + - name: Check AST with Python 3.8 + run: | + "${{ steps.setup-pylowest.outputs.python-path }}" -m compileall -q -f tilelang + + - name: Setup Python 3.9 + uses: actions/setup-python@v6 + with: + python-version: "3.9" + update-environment: true + cache: pip + cache-dependency-path: | + pyproject.toml + requirements*.txt + .pre-commit-config.yaml + + - name: Pre-commit Lint + run: | + if ! pipx run pre-commit run --all-files --color=always --show-diff-on-failure; then + echo "::error::Pre-commit checks failed. Please run 'pre-commit install' and 'pre-commit run --all-files' locally to see the issues." + exit 1 + fi + + tests: + name: Test for Python ${{ matrix.python-version }} with ${{ matrix.runner.toolkit }} (on ${{ matrix.runner.name }}) + if: | + github.repository_owner == 'tile-ai' && + (github.event_name != 'pull_request' || !github.event.pull_request.draft) + needs: [lint] + runs-on: ${{ matrix.runner.tags }} + strategy: + matrix: + runner: + - tags: [self-hosted, tilescale] + name: self-hosted-nvidia + # Format: [Nightly-]CUDA-.[.]. E.g., "CUDA-12.8" or "Nightly-CUDA-13.0". + # Use "Nightly-" prefix to use torch nightly builds. + toolkit: CUDA-12.8 + python-version: + - "3.12" + fail-fast: false + timeout-minutes: 120 steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: ${{ env.PYTHON_VERSION }} - - - name: Ensure venv (local & persistent) - run: | - set -e - REQS_HASH=$(sha256sum requirements-test.txt 2>/dev/null | awk '{print $1}' || echo "no_requirements") - MARKER="${{ runner.tool_cache }}/.venv_marker_${{ env.PYTHON_VERSION }}_${REQS_HASH:0:8}" - - if [[ -f "$MARKER" ]] && [[ -f "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" ]]; then - echo "venv exists and hash matches – reuse it" - else - echo "venv stale or missing – recreating" - rm -rf "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" "$MARKER" - python -m venv "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" - # shellcheck source=/dev/null - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - python -m pip install --upgrade pip --no-user - [[ -f requirements-test.txt ]] && \ - PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user - pip install flash_attn==2.5.8 --no-user --no-build-isolation - touch "$MARKER" - fi - - - name: Run format check - run: | - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - if ! output=$(./format.sh 2>&1); then - echo "------------------------------------" - echo "message:" - echo "$output" - printf '%s\n' "$output" | grep "Please review and stage the changes." - echo "------------------------------------" - exit 1 - fi - - - name: Commit and Push Changes - uses: stefanzweifel/git-auto-commit-action@v5 - with: - commit_message: "lint" - - build-test-nvidia: - runs-on: [self-hosted, nvidia, hopper] - needs: format-check - permissions: - contents: read - steps: - - name: Checkout repository - uses: actions/checkout@v4 - with: - fetch-depth: 0 - repository: ${{ github.event.pull_request.head.repo.full_name }} - ref: ${{ github.event.pull_request.head.ref }} - - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: ${{ env.PYTHON_VERSION }} - - - name: Ensure venv (local & persistent) - run: | - set -e - REQS_HASH=$(cat requirements-test.txt 2>/dev/null || true) - MARKER="${{ runner.tool_cache }}/.venv_marker_${{ env.PYTHON_VERSION }}_${REQS_HASH:0:8}" - # NOTE(wt): We disable the venv reuse for now to allow installing DeepEP - # echo "venv stale or missing – recreating" - rm -rf "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" - python -m venv "${{ runner.tool_cache }}/${{ env.VENV_DIR }}" - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - python -m pip install --upgrade pip --no-user - [[ -f requirements-test.txt ]] && \ - PIP_NO_BUILD_ISOLATION=1 pip install -r requirements-test.txt --no-user - # flash attention usually requires no isolation build - pip install flash_attn==2.5.8 --no-user --no-build-isolation - - - name: Install project (wheel form) - run: | - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - pip install . --no-user -v - bash tilelang/distributed/install_deepep.sh # Install DeepEP for testing purpose - - - name: Run examples - run: | - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - cd examples - unset PYTHONPATH - - # find and run distributed tests with TILELANG_USE_DISTRIBUTED=1 - mapfile -t DIST_TESTS < <(find . -type f -path '*/distributed/*' -name 'test*.py' 2>/dev/null || true) - if [ "${#DIST_TESTS[@]}" -gt 0 ]; then + - name: Checkout repository + uses: actions/checkout@v6 + with: + fetch-depth: 0 + submodules: recursive + + - name: Set environment (self-hosted runners) + if: startsWith(matrix.runner.name, 'self-hosted') + run: | + # Hide sensitive data in logs for self-hosted runners + if [[ -n "${{ secrets.SECRET_PATH_PREFIXES }}" ]]; then + echo "::add-mask::${{ secrets.SECRET_PATH_PREFIXES }}" + # Colon separated list of secrets to mask + for secret in $(echo "${{ secrets.SECRET_PATH_PREFIXES }}" | tr ':' '\n'); do + echo "::add-mask::${secret}" + done + fi + + # Use runner tool_cache as cache root for self-hosted runners to avoid internet connection + # issues and to share cache between jobs. + export XDG_CACHE_HOME="${{ runner.tool_cache }}/.ci-cache-${{ github.workflow }}" + echo "XDG_CACHE_HOME=${XDG_CACHE_HOME}" | tee -a "${GITHUB_ENV}" + echo "PIP_CACHE_DIR=${XDG_CACHE_HOME}/pip" | tee -a "${GITHUB_ENV}" + echo "UV_CACHE_DIR=${XDG_CACHE_HOME}/uv" | tee -a "${GITHUB_ENV}" + echo "PRE_COMMIT_HOME=${XDG_CACHE_HOME}/pip/.pre-commit" | tee -a "${GITHUB_ENV}" + + # Do not use ccache on self-hosted runners, as it will download/upload caches which is slow. + # Self-hosted runners usually have more CPU power to compile without ccache. + - name: Setup ccache (GitHub-hosted runners) + id: setup-ccache + if: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + uses: hendrikmuhs/ccache-action@v1 + with: + create-symlink: true + evict-old-files: "7d" + append-timestamp: false + key: ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }}-${{ hashFiles('**/*.cc') }} + restore-keys: | + ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }}-${{ hashFiles('**/*.cc') }} + ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }} + ${{ runner.os }}-${{ runner.arch }} + + - name: Set environment (CUDA) + if: contains(matrix.runner.toolkit, 'CUDA') + run: | + TOOLKIT="${{ matrix.runner.toolkit }}" + CUDA_VERSION="${TOOLKIT##*-}" + CUDA_VERSION_MAJMIN="$(echo ${CUDA_VERSION} | cut -d '.' -f-2)" + CUDA_VERSION_MAJMIN_NODOT="${CUDA_VERSION_MAJMIN//./}" + if [[ "${TOOLKIT}" == "Nightly-"* ]]; then + # Use torch nightly builds + export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/nightly/cu${CUDA_VERSION_MAJMIN_NODOT}" + else + export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cu${CUDA_VERSION_MAJMIN_NODOT}" + fi + export UV_INDEX="${PIP_EXTRA_INDEX_URL}" + export CLANG_TIDY_CMAKE_OPTIONS="${CLANG_TIDY_CMAKE_OPTIONS} -DUSE_CUDA=ON" + + echo "USE_CUDA=ON" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION=${CUDA_VERSION}" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION_MAJMIN=${CUDA_VERSION_MAJMIN}" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION_MAJMIN_NODOT=${CUDA_VERSION_MAJMIN_NODOT}" | tee -a "${GITHUB_ENV}" + echo "PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}" | tee -a "${GITHUB_ENV}" + echo "UV_INDEX=${UV_INDEX}" | tee -a "${GITHUB_ENV}" + echo "CLANG_TIDY_CMAKE_OPTIONS=${CLANG_TIDY_CMAKE_OPTIONS}" | tee -a "${GITHUB_ENV}" + + if [[ ! -x "$(command -v nvcc)" ]]; then + export PATH="/usr/local/cuda/bin:${PATH}" + export LD_LIBRARY_PATH="/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" + echo "PATH=${PATH}" | tee -a "${GITHUB_ENV}" + echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" | tee -a "${GITHUB_ENV}" + fi + if [[ -x "$(command -v nvcc)" ]]; then + echo "\$ $(command -v nvcc) --version" && nvcc --version + else + echo "::warning::nvcc not found in PATH!" + fi + + - name: Setup Python and uv with caching + id: setup-uv + uses: astral-sh/setup-uv@v7 + with: + python-version: ${{ matrix.python-version }} + activate-environment: true + # Do not use cache for self-hosted runners, as it will download/upload caches which is slow. + enable-cache: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + prune-cache: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + # Use runner tool_cache for self-hosted runners + cache-local-path: ${{ env.UV_CACHE_DIR }} + ignore-nothing-to-cache: true + # Extra cache key to upload/download caches on GitHub-hosted runners + cache-suffix: uv-${{ runner.os }}-${{ runner.arch }}-${{ matrix.python-version }}-${{ matrix.runner.name }}-${{ matrix.runner.toolkit }} + cache-dependency-glob: | + pyproject.toml + requirements*.txt + .pre-commit-config.yaml + + - name: Setup venv + id: setup-venv + run: | + set -o pipefail + + uv pip install --upgrade pip setuptools wheel + if [[ "${UV_INDEX}" == *"/nightly/"* ]]; then + uv pip install --prerelease=allow -v torch + fi + uv pip install -v -r requirements-test.txt -i https://pypi.tuna.tsinghua.edu.cn/simple + echo "import torch; print(f'torch: {torch.__version__}')" | uv run --no-project --script - + if [[ "${{ matrix.runner.toolkit }}" == *"CUDA"* ]]; then + uv pip install --no-build-isolation-package=flash-attn -v -r requirements-test-cuda.txt -i https://pypi.tuna.tsinghua.edu.cn/simple + echo "import flash_attn; print(f'flash_attn: {flash_attn.__version__}')" | uv run --no-project --script - + # elif [[ "${{ matrix.runner.toolkit }}" == *"ROCm"* ]]; then + # uv pip install -v -r requirements-test-rocm.txt + # elif [[ "${{ matrix.runner.toolkit }}" == *"Metal"* ]]; then + # uv pip install -v -r requirements-test-metal.txt + else + echo "::error::Unknown toolkit: ${{ matrix.runner.toolkit }}" + exit 1 + fi + echo "::group::torch.utils.collect_env" + uv run --no-project -m -- torch.utils.collect_env + echo "::endgroup::" + + - name: Clear uv cache for self-hosted runners (if setup failed) + if: >- + ${{ + failure() && + startsWith(matrix.runner.name, 'self-hosted') && + (steps.setup-uv.conclusion == 'failure' || steps.setup-venv.conclusion == 'failure') + }} + run: | + echo "Clearing uv cache at ${UV_CACHE_DIR} due to failure." + uv cache clean + + - name: Enable core dump generation (Linux / GitHub-hosted runners) + if: ${{ runner.os == 'Linux' && !startsWith(matrix.runner.name, 'self-hosted') }} + run: | + sudo sysctl -w kernel.core_pattern="core.${{ matrix.python-version }}.${{ matrix.runner.toolkit }}.%P" + sudo sysctl -w kernel.core_uses_pid=0 + sudo sysctl -w fs.suid_dumpable=1 + sysctl kernel.core_pattern kernel.core_uses_pid fs.suid_dumpable + + - name: Enable core dump generation (macOS / GitHub-hosted runners) + if: ${{ runner.os == 'macOS' && !startsWith(matrix.runner.name, 'self-hosted') }} + run: | + sudo sysctl -w kern.corefile="core.${{ matrix.python-version }}.${{ matrix.runner.toolkit }}.%P" + sudo sysctl -w kern.coredump=1 + sudo sysctl -w kern.sugid_coredump=1 + sysctl kern.corefile kern.coredump kern.sugid_coredump + + - name: Install project (wheel form) + run: | + uv pip install -v . + bash tilelang/distributed/install_deepep.sh # Install DeepEP for testing purpose + export NCCL_IB_DISABLE=1 # Our CI machine's IB is incomplete, disable it to avoid unnecessary error msgs + + # - name: Run clang-tidy + # id: clang-tidy + # if: runner.os == 'Linux' + # run: | + # echo "\$ $(command -v clang-tidy) --version" && clang-tidy --version + + # # Download run-clang-tidy script + # RCT_URL=https://raw.githubusercontent.com/llvm/llvm-project/refs/heads/release/21.x/clang-tools-extra/clang-tidy/tool/run-clang-tidy.py + # echo "Downloading run-clang-tidy script from ${RCT_URL}" + # echo "import urllib.request; url = '${RCT_URL}'.rstrip('/'); urllib.request.urlretrieve(url, url.split('/')[-1])" | uv run --no-project --script - + # RUN_CLANG_TIDY=(uv run --no-project --script -- run-clang-tidy.py) + + # if [[ -x "$(command -v clang-apply-replacements)" ]]; then + # echo "Using clang-apply-replacements from $(command -v clang-apply-replacements)" + # RUN_CLANG_TIDY+=(-fix -clang-apply-replacements-binary="$(command -v clang-apply-replacements)") + # else + # echo "::warning::clang-apply-replacements not found in PATH, automatic fixing disabled." + # fi + + # # Run cmake to create the build directory with compile_commands.json + # cmake -S . -B cmake-build --fresh ${CLANG_TIDY_CMAKE_OPTIONS} # no quotes here + # echo "::group::compile_commands.json" + # ls -alh cmake-build/compile_commands.json + # uv run --no-project -m -- json.tool --no-ensure-ascii cmake-build/compile_commands.json + # echo "::endgroup::" + + # CXX_FILES=$(find src -type f -iname "*.[ch]pp" -o -iname "*.cc" -o -iname "*.c" -o -iname "*.h") + # rc=0 + # echo "::group::run-clang-tidy" + # "${RUN_CLANG_TIDY[@]}" -clang-tidy-binary="$(command -v clang-tidy)" \ + # -exclude-header-filter='^(3rdparty|tvm)/.*$' \ + # -p="cmake-build" ${CXX_FILES} || rc="$?" + # echo "::endgroup::" + # rm -rf cmake-build run-clang-tidy.py + # if (( rc != 0 )); then + # echo "::error::clang-tidy found issues (exit code: ${rc}). Please run 'clang-tidy --fix' locally to fix them." + # git diff --color=always || true + # exit "${rc}" + # fi + + - name: Run examples with Python ${{ matrix.python-version }} (${{ matrix.runner.toolkit }}) + if: contains(matrix.runner.toolkit, 'CUDA') + run: | + cd examples + unset PYTHONPATH + PYTEST=( + uv run --no-project -m -- + pytest --verbose --color=yes --durations=0 --showlocals --cache-clear -r fE + ) + + # Run distributed tests (marked with @requires_distributed) with TILELANG_USE_DISTRIBUTED=1 + # DeepEP tests requires fullmesh nvl or internode environment, we disable for now echo "Running distributed examples with TILELANG_USE_DISTRIBUTED=1:" - printf '%s\n' "${DIST_TESTS[@]}" - TILELANG_USE_DISTRIBUTED=1 python -m pytest -n 1 "${DIST_TESTS[@]}" -v -r fE - else - echo "No distributed examples found." - fi - - # run remaining example tests (non-distributed) - mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' | grep -vE 'sink|vs_sparse' 2>/dev/null || true) # temporarily disable problematic tests - if [ "${#OTHER_TESTS[@]}" -gt 0 ]; then + TILELANG_USE_DISTRIBUTED=1 "${PYTEST[@]}" --maxfail=3 --numprocesses=1 -m distributed --ignore-glob='*deepep*' . || true + + # Run remaining example tests (non-distributed) + # Temporarily disable problematic tests: sink, vs_sparse echo "Running non-distributed examples:" - printf '%s\n' "${OTHER_TESTS[@]}" - python -m pytest -n 4 "${OTHER_TESTS[@]}" -v -r fE - else - echo "No non-distributed example tests found." - fi - - - name: Run tests - run: | - source "${{ runner.tool_cache }}/${{ env.VENV_DIR }}/bin/activate" - cd testing/python - unset PYTHONPATH - - # run distributed tests first with env var - mapfile -t DIST_TESTS < <(find . -type f -path '*/distributed/*' -name 'test*.py' 2>/dev/null || true) - if [ "${#DIST_TESTS[@]}" -gt 0 ]; then + "${PYTEST[@]}" --maxfail=3 --numprocesses=2 -m "not distributed" -k "not sink and not vs_sparse" . || true + + # NVIDIA CUDA tests + - name: Run CUDA tests with Python ${{ matrix.python-version }} (${{ matrix.runner.toolkit }}) + id: cuda-tests + if: contains(matrix.runner.toolkit, 'CUDA') + run: | + cd testing/python + unset PYTHONPATH + PYTEST=( + uv run --no-project -m -- + pytest --verbose --color=yes --durations=0 --showlocals --cache-clear -r fE + ) + + # Run distributed tests (marked with @requires_distributed) with TILELANG_USE_DISTRIBUTED=1 echo "Running distributed tests with TILELANG_USE_DISTRIBUTED=1:" - printf '%s\n' "${DIST_TESTS[@]}" - TILELANG_USE_DISTRIBUTED=1 python -m pytest -n 1 "${DIST_TESTS[@]}" -v -r fE - else - echo "No distributed tests found under testing/python." - fi - - # run remaining tests - mapfile -t OTHER_TESTS < <(find . -type f -name 'test*.py' ! -path '*/distributed/*' | grep -vE 'tilelibrary_gemm|jit_gemm_ctypes' 2>/dev/null || true) # temporarily disable problematic tests - if [ "${#OTHER_TESTS[@]}" -gt 0 ]; then + TILELANG_USE_DISTRIBUTED=1 "${PYTEST[@]}" --maxfail=3 --numprocesses=1 -m distributed . || true + + # Run remaining tests (non-distributed) + # Temporarily disable problematic tests: tilelibrary_gemm, jit_gemm_ctypes echo "Running non-distributed tests:" - printf '%s\n' "${OTHER_TESTS[@]}" - python -m pytest -n 4 "${OTHER_TESTS[@]}" -v -r fE - else - echo "No non-distributed tests found under testing/python." - fi + "${PYTEST[@]}" --maxfail=3 --numprocesses=2 -m "not distributed" -k "not tilelibrary_gemm and not jit_gemm_ctypes" . || true + + - name: List generated files + if: ${{ !cancelled() }} + run: | + find . -type f -name '*.py[co]' -delete + find . -depth -type d -name "__pycache__" -exec rm -r "{}" + + if git status --ignored --porcelain | grep -qvE '/$'; then + ls -alh $(git status --ignored --porcelain | grep -vE '/$' | grep -oE '\S+$') + fi diff --git a/.github/workflows/dist.yml b/.github/workflows/dist.yml index 904fbb13b1..74132ffb3f 100644 --- a/.github/workflows/dist.yml +++ b/.github/workflows/dist.yml @@ -1,5 +1,6 @@ name: Dist on: + workflow_dispatch: schedule: # gemini said this is 6:00 china time - cron: "0 22 * * *" @@ -28,6 +29,18 @@ concurrency: group: "${{ github.workflow }}-${{ github.ref }}" cancel-in-progress: true +env: + PYTHONDEVMODE: "1" + PYTHONUNBUFFERED: "1" + COLUMNS: "100" + FORCE_COLOR: "1" + CLICOLOR_FORCE: "1" + UV_INDEX_STRATEGY: "unsafe-best-match" + UV_HTTP_TIMEOUT: "600" + XDG_CACHE_HOME: "${{ github.workspace }}/.cache" # to be updated + PIP_CACHE_DIR: "${{ github.workspace }}/.cache/pip" # to be updated + UV_CACHE_DIR: "${{ github.workspace }}/.cache/uv" # to be updated + jobs: build-wheels: name: Build wheels for Python ${{ matrix.python-version }} on ${{ matrix.target.runner }} with ${{ matrix.target.toolkit }} @@ -37,39 +50,41 @@ jobs: strategy: matrix: target: - - { runner: ubuntu-latest, toolkit: "CUDA-12.1" } - - { runner: ubuntu-24.04-arm, toolkit: "CUDA-12.8" } - - { runner: macos-latest, toolkit: "Metal" } + # NOTE(wt): Temporarily disable ARM and MacOS, as NVSHMEM only supports x86 (?) + - { runner: ubuntu-latest, toolkit: "CUDA-12.8" } + # - { runner: ubuntu-24.04-arm, toolkit: "CUDA-12.8" } + - { runner: ubuntu-latest, toolkit: "Nightly-CUDA-13.0" } + # - { runner: ubuntu-24.04-arm, toolkit: "Nightly-CUDA-13.0" } + # - { runner: macos-latest, toolkit: "Metal" } python-version: - - "3.8" - # TVM is built with Python 3.8 Limited API, it should work with all Python >= 3.8. - # - "3.9" - # - "3.10" - # - "3.11" - # - "3.12" - # - "3.13" - # - "3.14" + # Wheels are built with Python 3.8 Limited API, they should work with all Python >= 3.8. + # Only build wheels against Python 3.8 Limited API to save CI resources. + - "3.9" fail-fast: false timeout-minutes: 120 runs-on: ${{ matrix.target.runner }} env: - NO_VERSION_LABEL: ${{ github.event_name == 'release' && 'OFF' || 'ON' }} + IS_RELEASE: ${{ github.event_name != 'pull_request' || contains(github.event.pull_request.title, '[Release]') }} + NO_VERSION_LABEL: "OFF" steps: - name: Checkout repository - uses: actions/checkout@v5 + uses: actions/checkout@v6 with: fetch-depth: 1 submodules: recursive - # NB: CIBW builds wheels in containers on Linux - - name: Setup ccache (macOS only) - if: runner.os == 'macOS' + - name: Setup ccache uses: hendrikmuhs/ccache-action@v1 with: + max-size: "200MB" create-symlink: true - key: ccache-${{ runner.os }}-${{ runner.arch }}-${{ matrix.python-version }}-${{ matrix.target.toolkit }} evict-old-files: "7d" + append-timestamp: false + key: wheel-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('**/*.cc') }} + restore-keys: | + wheel-${{ runner.os }}-${{ runner.arch }}-${{ hashFiles('**/*.cc') }} + wheel-${{ runner.os }}-${{ runner.arch }} - name: Set CIBW_BUILD run: | @@ -80,21 +95,81 @@ jobs: if [[ "${{ matrix.target.toolkit }}" == *"CUDA"* ]]; then CUDA_VERSION="${{ matrix.target.toolkit }}" - CUDA_VERSION="${CUDA_VERSION#CUDA-}" + CUDA_VERSION="${CUDA_VERSION##*-}" + CUDA_VERSION_MAJMIN="$(echo ${CUDA_VERSION} | cut -d '.' -f-2)" + CUDA_VERSION_MAJMIN_NODOT="${CUDA_VERSION_MAJMIN//./}" echo "CUDA_VERSION=${CUDA_VERSION}" | tee -a "${GITHUB_ENV}" + if [[ "${{ matrix.target.toolkit }}" == "Nightly-"* ]]; then + # Use torch nightly builds + export UV_INDEX="https://download.pytorch.org/whl/nightly/cu${CUDA_VERSION_MAJMIN_NODOT}" + else + export UV_INDEX="https://download.pytorch.org/whl/cu${CUDA_VERSION_MAJMIN_NODOT}" + echo "UV_TORCH_BACKEND=cu${CUDA_VERSION_MAJMIN_NODOT}" | tee -a "${GITHUB_ENV}" + fi + echo "UV_INDEX=${UV_INDEX}" | tee -a "${GITHUB_ENV}" + fi + + if [[ "${{ env.IS_RELEASE }}" == "true" ]]; then + if [[ "${{ matrix.target.toolkit }}" == "Nightly-"* ]]; then + # Avoid using same file name for different toolkit. + echo "NO_GIT_VERSION=ON" | tee -a "${GITHUB_ENV}" + else + echo "NO_VERSION_LABEL=ON" | tee -a "${GITHUB_ENV}" + fi + fi + + if [[ "${{ runner.os }}" == "Linux" ]]; then + HOST_CCACHE_DIR="$(ccache --get-config cache_dir)" + # Install torch for tilescale_ext._C build, then setup ccache + echo "CIBW_BEFORE_BUILD_LINUX=pip install torch --no-cache-dir && dnf install -y ccache && ccache -o cache_dir=/host${HOST_CCACHE_DIR}" | tee -a "${GITHUB_ENV}" fi - name: Build wheels - uses: pypa/cibuildwheel@v3.2 + uses: pypa/cibuildwheel@v3.3 with: package-dir: . output-dir: wheelhouse config-file: "{package}/pyproject.toml" + - name: Setup Python and uv with caching + id: setup-uv + uses: astral-sh/setup-uv@v7 + with: + python-version: "3.12" + activate-environment: true + + - name: Test built wheels + # Skip CUDA wheel tests on GitHub-hosted runners (no CUDA available) + # Tests should be run on self-hosted runners with CUDA or during release validation + if: ${{ !contains(matrix.target.toolkit, 'CUDA') || contains(matrix.target.runner, 'self-hosted') }} + run: | + for WHEEL in wheelhouse/*.whl; do + echo "Testing wheel: ${WHEEL}" + ( + set -e + uv venv --python=3.12 test-venv + source test-venv/bin/activate + + uv pip install --upgrade pip setuptools wheel + if [[ "${UV_INDEX}" == *"/nightly/"* ]]; then + uv pip install --prerelease=allow -v torch + fi + + uv pip install -v "${WHEEL}" + ( + set -e + cd / + uv run --no-project -- python -c "import tilelang; print(tilelang.__version__)" + ) + deactivate + rm -rf test-venv + ) + done + - name: Upload wheels # Not PR to save artifact storage, as wheels are only needed for releases. - if: github.event_name != 'pull_request' - uses: actions/upload-artifact@v4 + if: github.event_name != 'pull_request' || contains(github.event.pull_request.title, '[Release]') + uses: actions/upload-artifact@v6 with: name: wheels-${{ matrix.python-version }}-${{ runner.os }}-${{ runner.arch }}-${{ matrix.target.toolkit }} path: wheelhouse/*.whl @@ -102,14 +177,14 @@ jobs: list-artifacts: name: List artifacts - # Not PR to save artifact storage, as wheels are only needed for releases. - if: github.event_name != 'pull_request' + # Not PR to save artifact storage, as artifacts are only needed for releases. + if: github.event_name != 'pull_request' || contains(github.event.pull_request.title, '[Release]') runs-on: ubuntu-latest needs: [build-wheels] timeout-minutes: 15 steps: - name: Download built wheels - uses: actions/download-artifact@v5 + uses: actions/download-artifact@v7 with: pattern: wheels-* path: dist @@ -119,7 +194,7 @@ jobs: run: ls -lh dist/* - name: Upload artifacts - uses: actions/upload-artifact@v4 + uses: actions/upload-artifact@v6 with: name: artifacts path: dist/* diff --git a/.github/workflows/pr-regression-test-bot.yml b/.github/workflows/pr-regression-test-bot.yml new file mode 100644 index 0000000000..568ce85550 --- /dev/null +++ b/.github/workflows/pr-regression-test-bot.yml @@ -0,0 +1,273 @@ +name: Performance Regression Bot + +on: + issue_comment: + types: + - created + +permissions: + contents: read + issues: write + pull-requests: write + +concurrency: + # Use the issue/PR number to differentiate between different PRs + group: "${{ github.workflow }}-${{ github.event.issue.number }}" + cancel-in-progress: true + +env: + PYTHONDEVMODE: "1" + PYTHONUNBUFFERED: "1" + PYTHONPATH: "" # explicit cleanup + PIP_USER: "" # explicit cleanup + COLUMNS: "100" + FORCE_COLOR: "1" + CLICOLOR_FORCE: "1" + UV_INDEX_STRATEGY: "unsafe-best-match" + UV_HTTP_TIMEOUT: "600" + XDG_CACHE_HOME: "${{ github.workspace }}/.cache" # to be updated + PIP_CACHE_DIR: "${{ github.workspace }}/.cache/pip" # to be updated + UV_CACHE_DIR: "${{ github.workspace }}/.cache/uv" # to be updated + PRE_COMMIT_HOME: "${{ github.workspace }}/.cache/pip/.pre-commit" # to be updated + +jobs: + permissions-check: + name: Check bot permissions + if: | + github.repository_owner == 'tile-ai' && + github.event.issue.pull_request && + (contains(github.event.comment.body, '@regression-perf')) + runs-on: ubuntu-latest + steps: + - name: Get commenter permission + id: perm + uses: actions/github-script@v8 + with: + script: | + const username = context.payload.comment.user.login + const { owner, repo } = context.repo + const { data } = await github.rest.repos.getCollaboratorPermissionLevel({ owner, repo, username }) + core.setOutput('permission', data.permission) // admin|maintain|write|triage|read|none + + - name: Reject if not allowed + if: ${{ steps.perm.outputs.permission != 'admin' && steps.perm.outputs.permission != 'maintain' && steps.perm.outputs.permission != 'write' }} + run: | + echo "Not authorized: permission=${{ steps.perm.outputs.permission }}" + exit 1 + + pr-regression: + name: Performance regression test between PR and main + needs: [permissions-check] + runs-on: ${{ matrix.runner.tags }} + strategy: + matrix: + runner: + - tags: [self-hosted, nvidia] + name: self-hosted-nvidia + toolkit: CUDA-12.8 + python-version: + - "3.12" + fail-fast: false + timeout-minutes: 120 + steps: + - name: Checkout repository + uses: actions/checkout@v6 + with: + ref: refs/pull/${{ github.event.issue.number }}/merge + fetch-depth: 0 + submodules: recursive + + - name: Set environment (self-hosted runners) + if: startsWith(matrix.runner.name, 'self-hosted') + run: | + # Hide sensitive data in logs for self-hosted runners + if [[ -n "${{ secrets.SECRET_PATH_PREFIXES }}" ]]; then + echo "::add-mask::${{ secrets.SECRET_PATH_PREFIXES }}" + # Colon separated list of secrets to mask + for secret in $(echo "${{ secrets.SECRET_PATH_PREFIXES }}" | tr ':' '\n'); do + echo "::add-mask::${secret}" + done + fi + + # Use runner tool_cache as cache root for self-hosted runners to avoid internet connection + # issues and to share cache between jobs. + export XDG_CACHE_HOME="${{ runner.tool_cache }}/.ci-cache-${{ github.workflow }}" + echo "XDG_CACHE_HOME=${XDG_CACHE_HOME}" | tee -a "${GITHUB_ENV}" + echo "PIP_CACHE_DIR=${XDG_CACHE_HOME}/pip" | tee -a "${GITHUB_ENV}" + echo "UV_CACHE_DIR=${XDG_CACHE_HOME}/uv" | tee -a "${GITHUB_ENV}" + echo "PRE_COMMIT_HOME=${XDG_CACHE_HOME}/pip/.pre-commit" | tee -a "${GITHUB_ENV}" + + # Do not use ccache on self-hosted runners, as it will download/upload caches which is slow. + # Self-hosted runners usually have more CPU power to compile without ccache. + - name: Setup ccache (GitHub-hosted runners) + id: setup-ccache + if: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + uses: hendrikmuhs/ccache-action@v1 + with: + create-symlink: true + evict-old-files: "7d" + append-timestamp: false + key: ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }}-${{ hashFiles('**/*.cc') }} + restore-keys: | + ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }}-${{ hashFiles('**/*.cc') }} + ${{ runner.os }}-${{ runner.arch }}-${{ matrix.runner.toolkit }} + ${{ runner.os }}-${{ runner.arch }} + + - name: Set environment (CUDA) + if: contains(matrix.runner.toolkit, 'CUDA') + run: | + TOOLKIT="${{ matrix.runner.toolkit }}" + CUDA_VERSION="${TOOLKIT##*-}" + CUDA_VERSION_MAJMIN="$(echo ${CUDA_VERSION} | cut -d '.' -f-2)" + CUDA_VERSION_MAJMIN_NODOT="${CUDA_VERSION_MAJMIN//./}" + if [[ "${TOOLKIT}" == "Nightly-"* ]]; then + # Use torch nightly builds + export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/nightly/cu${CUDA_VERSION_MAJMIN_NODOT}" + else + export PIP_EXTRA_INDEX_URL="https://download.pytorch.org/whl/cu${CUDA_VERSION_MAJMIN_NODOT}" + fi + export UV_INDEX="${PIP_EXTRA_INDEX_URL}" + export CLANG_TIDY_CMAKE_OPTIONS="${CLANG_TIDY_CMAKE_OPTIONS} -DUSE_CUDA=ON" + + echo "USE_CUDA=ON" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION=${CUDA_VERSION}" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION_MAJMIN=${CUDA_VERSION_MAJMIN}" | tee -a "${GITHUB_ENV}" + echo "CUDA_VERSION_MAJMIN_NODOT=${CUDA_VERSION_MAJMIN_NODOT}" | tee -a "${GITHUB_ENV}" + echo "PIP_EXTRA_INDEX_URL=${PIP_EXTRA_INDEX_URL}" | tee -a "${GITHUB_ENV}" + echo "UV_INDEX=${UV_INDEX}" | tee -a "${GITHUB_ENV}" + echo "CLANG_TIDY_CMAKE_OPTIONS=${CLANG_TIDY_CMAKE_OPTIONS}" | tee -a "${GITHUB_ENV}" + + if [[ ! -x "$(command -v nvcc)" ]]; then + export PATH="/usr/local/cuda/bin:${PATH}" + export LD_LIBRARY_PATH="/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" + echo "PATH=${PATH}" | tee -a "${GITHUB_ENV}" + echo "LD_LIBRARY_PATH=${LD_LIBRARY_PATH}" | tee -a "${GITHUB_ENV}" + fi + if [[ -x "$(command -v nvcc)" ]]; then + echo "\$ $(command -v nvcc) --version" && nvcc --version + else + echo "::warning::nvcc not found in PATH!" + fi + + - name: Setup Python and uv with caching + id: setup-uv + uses: astral-sh/setup-uv@v7 + with: + python-version: ${{ matrix.python-version }} + activate-environment: true + # Do not use cache for self-hosted runners, as it will download/upload caches which is slow. + enable-cache: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + prune-cache: ${{ !startsWith(matrix.runner.name, 'self-hosted') }} + # Use runner tool_cache for self-hosted runners + cache-local-path: ${{ env.UV_CACHE_DIR }} + ignore-nothing-to-cache: true + # Extra cache key to upload/download caches on GitHub-hosted runners + cache-suffix: uv-${{ runner.os }}-${{ runner.arch }}-${{ matrix.python-version }}-${{ matrix.runner.name }}-${{ matrix.runner.toolkit }} + cache-dependency-glob: | + pyproject.toml + requirements*.txt + + - name: Setup environments + id: setup-venv + run: | + set -e + + uv venv --python "${{ matrix.python-version }}" new + + source new/bin/activate + uv pip install -v -r requirements-test.txt + uv pip install -v . + + - name: Install Main version (Baseline) + run: | + set -e + git clean -dxf -e new/ -e .cache/ + git checkout main + git submodule update --init --recursive + uv venv --python "${{ matrix.python-version }}" old + source old/bin/activate + + uv pip install -v -r requirements-test.txt + uv pip install -v . + rm -rf tilelang build + + uv venv --python "${{ matrix.python-version }}" test_regression + source test_regression/bin/activate + uv pip install -v -r requirements-test.txt + + - name: Clear uv cache for self-hosted runners (if setup failed) + if: >- + ${{ + failure() && + startsWith(matrix.runner.name, 'self-hosted') && + (steps.setup-uv.conclusion == 'failure' || steps.setup-venv.conclusion == 'failure') + }} + run: | + echo "Clearing uv cache at ${UV_CACHE_DIR} due to failure." + uv cache clean + + - name: Enable core dump generation (Linux / GitHub-hosted runners) + if: ${{ runner.os == 'Linux' && !startsWith(matrix.runner.name, 'self-hosted') }} + run: | + sudo sysctl -w kernel.core_pattern="core.${{ matrix.python-version }}.${{ matrix.runner.toolkit }}.%P" + sudo sysctl -w kernel.core_uses_pid=0 + sudo sysctl -w fs.suid_dumpable=1 + sysctl kernel.core_pattern kernel.core_uses_pid fs.suid_dumpable + + - name: Enable core dump generation (macOS / GitHub-hosted runners) + if: ${{ runner.os == 'macOS' && !startsWith(matrix.runner.name, 'self-hosted') }} + run: | + sudo sysctl -w kern.corefile="core.${{ matrix.python-version }}.${{ matrix.runner.toolkit }}.%P" + sudo sysctl -w kern.coredump=1 + sudo sysctl -w kern.sugid_coredump=1 + sysctl kern.corefile kern.coredump kern.sugid_coredump + + - name: Run performance regression test + run: | + source test_regression/bin/activate + OLD_PYTHON=./old/bin/python NEW_PYTHON=./new/bin/python \ + PERF_REGRESSION_MD=regression_result.md PERF_REGRESSION_PNG=regression_result.png \ + python ./maint/scripts/test_perf_regression.py + + - name: Read markdown table + id: read_md + run: | + echo "content<> $GITHUB_OUTPUT + cat regression_result.md >> $GITHUB_OUTPUT + echo "EOF" >> $GITHUB_OUTPUT + + - name: Upload result image as artifact + uses: actions/upload-artifact@v6 + with: + name: perf-regression-${{ github.run_id }} + path: regression_result.png + + - name: Post test results as PR comment + uses: actions/github-script@v8 + with: + github-token: ${{ secrets.GITHUB_TOKEN }} + script: | + const fs = require('fs'); + // Read the file directly instead of passing via env/outputs to avoid escaping issues + const md = fs.readFileSync('regression_result.md', 'utf8'); + + const runUrl = `${{ github.server_url }}/${{ github.repository }}/actions/runs/${{ github.run_id }}`; + + const body = + 'Performance Regression Test Report\n' + + '============================\n\n' + + `Triggered by: @${context.payload.comment.user.login}\n` + + `Workflow run: ${runUrl}\n\n` + + 'Results\n' + + '-------\n\n' + + md + '\n\n' + + 'Artifacts\n' + + '---------\n\n' + + '- regression_result.png (speedup plot) is attached as a workflow artifact. Download it from the workflow run page above.\n'; + + await github.rest.issues.createComment({ + owner: context.repo.owner, + repo: context.repo.repo, + issue_number: context.issue.number, + body + }); diff --git a/.github/workflows/publish-docs.yml b/.github/workflows/publish-docs.yml index 953303102c..2197015b66 100644 --- a/.github/workflows/publish-docs.yml +++ b/.github/workflows/publish-docs.yml @@ -25,7 +25,7 @@ jobs: runs-on: [self-hosted, nvidia] steps: - name: Checkout repository - uses: actions/checkout@v5 + uses: actions/checkout@v6 with: fetch-depth: 0 submodules: recursive diff --git a/.gitignore b/.gitignore index 75aa07f82f..e85c2c0943 100644 --- a/.gitignore +++ b/.gitignore @@ -20,6 +20,8 @@ debug/ build/ *dist/ +dist*/ +!distributed*/ wheelhouse/ __pycache__ nnfusion.tar.gz @@ -110,3 +112,24 @@ nvshmem_src/ # CMake cmake-build/ cmake-build-*/ + +# Git version for sdist +.git_commit.txt + +# pre-commit cache +.pre-commit-cache/* + +# host checks logs +maint/host_checks/logs/* + +# ncu +*.ncu-rep + +# csv +*.csv + +# clang-tidy +/run-clang-tidy.py + +# perf regression test +.perf_regression/ diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 99a05f4c63..f52f91b536 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,15 +13,13 @@ repos: hooks: - id: check-symlinks - id: destroyed-symlinks - # FIXME: enable these hooks - # - id: trailing-whitespace - # - id: end-of-file-fixer + - id: trailing-whitespace + - id: end-of-file-fixer - id: check-added-large-files - id: check-merge-conflict fail_fast: true - # FIXME: enable these hooks - # - id: check-executables-have-shebangs - # - id: check-shebang-scripts-are-executable + - id: check-executables-have-shebangs + - id: check-shebang-scripts-are-executable - id: detect-private-key - id: check-yaml - id: check-toml @@ -32,39 +30,30 @@ repos: args: [--ignore-case] files: ^docs/spelling_wordlist\.txt$ - repo: https://github.com/pre-commit/mirrors-clang-format - rev: v21.1.2 # sync with requirements-lint.txt + rev: v21.1.7 # sync with requirements-lint.txt hooks: - id: clang-format - exclude: | - (?ix)( - ^.+\.(cu|cuh)$| - ^.+\.json$ - ) + types_or: [c++, c] - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.14.1 # sync with requirements-lint.txt + rev: v0.14.9 # sync with requirements-lint.txt hooks: - id: ruff-check args: [--fix, --exit-non-zero-on-fix] - - repo: https://github.com/google/yapf - rev: v0.43.0 # sync with requirements-lint.txt - hooks: - - id: yapf - name: yapf-multiproc-bugfix - # yapf is not multiprocess safe, so we run a dummy yapf first. - args: [--in-place, docs/conf.py] - always_run: true - pass_filenames: false - - id: yapf - args: [--recursive, --in-place] + - id: ruff-format + args: [--exit-non-zero-on-format] - repo: https://github.com/codespell-project/codespell rev: v2.4.1 # sync with requirements-lint.txt hooks: - id: codespell additional_dependencies: [".[toml]"] - args: ["-L", "HDA"] exclude: | (?x)( ^.+\.(cpp|hpp|cxx|cc|c|h|cu|cuh)$| ^.+\.svg$| ^.*\brequirements\b.*\.txt$ ) + - repo: https://github.com/jackdewinter/pymarkdown + rev: v0.9.33 + hooks: + - id: pymarkdown + args: ["--config", ".pymarkdown", "fix"] diff --git a/.pymarkdown b/.pymarkdown new file mode 100644 index 0000000000..5394265ed8 --- /dev/null +++ b/.pymarkdown @@ -0,0 +1,37 @@ +{ + "plugins": { + "md003": { + "style": "atx" + }, + "md004": { + "style": "dash" + }, + "md013": { + "enabled": false + }, + "md026": { + "enabled": false + }, + "md029": { + "enabled": false + }, + "md031": { + "enabled": false + }, + "md032": { + "enabled": false + }, + "md033": { + "enabled": false + }, + "md034": { + "enabled": false + }, + "md040": { + "enabled": false + }, + "md041": { + "enabled": false + } + } +} diff --git a/3rdparty/tvm b/3rdparty/tvm index 5bf17a3460..23bce012ff 160000 --- a/3rdparty/tvm +++ b/3rdparty/tvm @@ -1 +1 @@ -Subproject commit 5bf17a34602931e7d7e01cbccf358a21fe972779 +Subproject commit 23bce012ffd255a24289eea6ceab74a40b94a096 diff --git a/CMakeLists.txt b/CMakeLists.txt index afeccacebf..4fb370d509 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -8,6 +8,11 @@ set(CMAKE_CXX_STANDARD 17) set(CMAKE_POSITION_INDEPENDENT_CODE ON) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) +if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND "$ENV{CIBUILDWHEEL}") + # Warning came from tvm submodule + string(APPEND CMAKE_CXX_FLAGS " -Wno-dangling-reference") +endif() + set(CMAKE_MODULE_PATH ${CMAKE_MODULE_PATH} ${CMAKE_CURRENT_SOURCE_DIR}/cmake) if(EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.gitmodules" AND EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/.git") @@ -36,15 +41,74 @@ endif() find_program(CCACHE_PROGRAM ccache) if(CCACHE_PROGRAM) + message(STATUS "Using ccache: ${CCACHE_PROGRAM} with base_dir=${CMAKE_SOURCE_DIR}") + if(APPLE) + # Passing configs like `ccache base_dir=/xxx cc ...` is supported + # (likely) since ccache 4.x, which has been provided by homebrew. + # Our Linux builder image (manylinux2014 & manylinux_2_28) still + # provides ccache 3.x and do not support this form. + # `cibuildwheel` uses fixed folder on Linux (`/project`) as working directory, + # so cache would work without setting `base_dir`. + set(CCACHE_PROGRAM "${CCACHE_PROGRAM};base_dir=${CMAKE_SOURCE_DIR}") + endif() set(CMAKE_C_COMPILER_LAUNCHER "${CCACHE_PROGRAM}" CACHE STRING "C compiler launcher") set(CMAKE_CXX_COMPILER_LAUNCHER "${CCACHE_PROGRAM}" CACHE STRING "CXX compiler launcher") set(CMAKE_CUDA_COMPILER_LAUNCHER "${CCACHE_PROGRAM}" CACHE STRING "CUDA compiler launcher") +else() + find_program(SCCACHE_PROGRAM sccache) + if(SCCACHE_PROGRAM) + message(STATUS "Using sccache: ${SCCACHE_PROGRAM}") + set(CMAKE_C_COMPILER_LAUNCHER "${SCCACHE_PROGRAM}" CACHE STRING "C compiler launcher") + set(CMAKE_CXX_COMPILER_LAUNCHER "${SCCACHE_PROGRAM}" CACHE STRING "CXX compiler launcher") + set(CMAKE_CUDA_COMPILER_LAUNCHER "${SCCACHE_PROGRAM}" CACHE STRING "CUDA compiler launcher") + endif() endif() # Configs -set(USE_CUDA OFF) -set(USE_ROCM OFF) -set(USE_METAL OFF) +set(TILELANG_BACKENDS CUDA ROCM METAL) + +set(TILELANG_BACKEND_DOC_CUDA "Enable CUDA backend (ON/OFF/or CUDA SDK path)") +set(TILELANG_BACKEND_DOC_ROCM "Enable ROCm backend (ON/OFF/or ROCm SDK path)") +set(TILELANG_BACKEND_DOC_METAL "Enable Metal backend") + +# TVM's config.cmake redefines USE_* options later, so we cache the user's choice +# (including explicit -DUSE_XXX arguments) before we include TVM and restore it +# afterwards. + +macro(tilelang_define_backend_option BACKEND) + set(_backend_var "USE_${BACKEND}") + set(_doc "${TILELANG_BACKEND_DOC_${BACKEND}}") + set(_user_override_var "TILELANG_USER_OVERRIDE_${_backend_var}") + + set(_user_override OFF) + if(DEFINED ${_user_override_var}) + set(_user_override "${${_user_override_var}}") + endif() + + if(DEFINED CACHE{${_backend_var}}) + get_property(_cache_type CACHE ${_backend_var} PROPERTY TYPE) + if(_cache_type STREQUAL "UNINITIALIZED") + set(_user_override ON) + endif() + endif() + + set(_default OFF) + if(DEFINED ${_backend_var}) + set(_default "${${_backend_var}}") + endif() + + option(${_backend_var} "${_doc}" "${_default}") + # Remember if the user explicitly set this option so that later logic + # won't auto-toggle backends they configured on the command line. + set(${_user_override_var} ${_user_override} CACHE INTERNAL + "User explicitly set ${_backend_var} during configuration" FORCE) + set(TILELANG_OPTION_${_backend_var} "${${_backend_var}}") +endmacro() + +foreach(BACKEND IN LISTS TILELANG_BACKENDS) + tilelang_define_backend_option(${BACKEND}) +endforeach() + set(PREBUILD_CYTHON ON) # Configs end @@ -55,6 +119,14 @@ if(EXISTS ${TVM_SOURCE}/cmake/config.cmake) else() message(FATAL_ERROR "Nor tvm provided or submodule checkout-ed.") endif() +# Re-apply TileLang's preferred backend settings after TVM's config may have +# overridden the USE_* cache entries. +foreach(BACKEND IN LISTS TILELANG_BACKENDS) + set(_backend_var "USE_${BACKEND}") + set(_doc "${TILELANG_BACKEND_DOC_${BACKEND}}") + set(${_backend_var} ${TILELANG_OPTION_${_backend_var}} CACHE STRING "${_doc}" FORCE) + set(${_backend_var} ${TILELANG_OPTION_${_backend_var}}) +endforeach() # Include directories for TileLang set(TILE_LANG_INCLUDES ${TVM_INCLUDES}) @@ -64,33 +136,50 @@ file(GLOB TILE_LANG_SRCS src/*.cc src/layout/*.cc src/transform/*.cc + src/transform/common/*.cc src/op/*.cc src/target/utils.cc + src/target/codegen_c_host.cc src/target/codegen_cpp.cc src/target/rt_mod_cpp.cc - # webgpu doesn't have system dependency - src/target/codegen_webgpu.cc # intrin_rule doesn't have system dependency src/target/intrin_rule*.cc ) -# Backend-specific checks and configs -if($ENV{USE_METAL}) - set(USE_METAL ON) -elseif(APPLE) - message(STATUS "Enable Metal support by default.") - set(USE_METAL ON) -elseif($ENV{USE_ROCM}) - set(USE_ROCM ON) -else() - if($ENV{USE_CUDA}) - set(USE_CUDA ON) - elseif(DEFINED ENV{USE_CUDA} AND NOT $ENV{USE_CUDA}) - # Build CPU-only when we explicitly disable CUDA - set(USE_CUDA OFF) +# Always include CPU-safe runtime helpers +list(APPEND TILE_LANG_SRCS + src/runtime/error_helpers.cc +) + +# Track if the user explicitly selected a backend via cache options. +set(TILELANG_BACKEND_USER_SELECTED OFF) +foreach(BACKEND IN LISTS TILELANG_BACKENDS) + set(_backend_var "USE_${BACKEND}") + set(_override_var "TILELANG_USER_OVERRIDE_${_backend_var}") + if(${_backend_var} OR ${_override_var}) + set(TILELANG_BACKEND_USER_SELECTED ON) + endif() +endforeach() + +# Only auto-select a backend when the user didn't specify one explicitly. +if(NOT TILELANG_BACKEND_USER_SELECTED) + if($ENV{USE_METAL}) + set(USE_METAL ON) + elseif(APPLE) + message(STATUS "Enable Metal support by default.") + set(USE_METAL ON) + elseif($ENV{USE_ROCM}) + set(USE_ROCM ON) else() - message(STATUS "Enable CUDA support by default.") - set(USE_CUDA ON) + if($ENV{USE_CUDA}) + set(USE_CUDA ON) + elseif(DEFINED ENV{USE_CUDA} AND NOT $ENV{USE_CUDA}) + # Build CPU-only when we explicitly disable CUDA + set(USE_CUDA OFF) + else() + message(STATUS "Enable CUDA support by default.") + set(USE_CUDA ON) + endif() endif() endif() @@ -104,7 +193,7 @@ if(USE_METAL) elseif(USE_ROCM) set(CMAKE_HIP_STANDARD 17) include(${TVM_SOURCE}/cmake/utils/FindROCM.cmake) - find_rocm($ENV{USE_ROCM}) + find_rocm(${USE_ROCM}) add_compile_definitions(__HIP_PLATFORM_AMD__ __HIP_PLATFORM_HCC__=1) file(GLOB TILE_LANG_HIP_SRCS @@ -123,16 +212,29 @@ elseif(USE_CUDA) cmake_path(GET CUDAToolkit_BIN_DIR PARENT_PATH USE_CUDA) file(GLOB TILE_LANG_CUDA_SRCS - src/runtime/*.cc + src/runtime/runtime.cc + src/runtime/tilescale_cuda_module.cc src/target/ptx.cc src/target/codegen_cuda.cc + src/target/codegen_py.cc + src/target/codegen_utils.cc + src/target/codegen_cutedsl.cc src/target/rt_mod_cuda.cc + src/target/rt_mod_cutedsl.cc ) list(APPEND TILE_LANG_SRCS ${TILE_LANG_CUDA_SRCS}) list(APPEND TILE_LANG_INCLUDES ${CUDAToolkit_INCLUDE_DIRS}) endif() +set(USE_Z3 ON CACHE STRING "Use Z3 SMT solver for TileLang optimizations") +set(USE_PYPI_Z3 ON CACHE BOOL "Use Z3 provided by PyPI z3-solver package") + +if(USE_Z3 AND USE_PYPI_Z3) + list(APPEND CMAKE_MODULE_PATH "${CMAKE_CURRENT_LIST_DIR}/cmake/pypi-z3") + find_package(Z3 REQUIRED) +endif() + # Include tvm after configs have been populated add_subdirectory(${TVM_SOURCE} tvm EXCLUDE_FROM_ALL) @@ -140,7 +242,11 @@ add_subdirectory(${TVM_SOURCE} tvm EXCLUDE_FROM_ALL) add_compile_definitions(DMLC_USE_LOGGING_LIBRARY=) add_library(tilelang_objs OBJECT ${TILE_LANG_SRCS}) + +# Set debug mode compile definitions +# We open the deubg option of TVM, i.e. TVM_LOG_DEBUG if(CMAKE_BUILD_TYPE STREQUAL "Debug") + message(STATUS "Building TileLang with DEBUG mode") target_compile_definitions(tilelang_objs PRIVATE "TVM_LOG_DEBUG") endif() @@ -148,12 +254,20 @@ target_include_directories(tilelang_objs PRIVATE ${TILE_LANG_INCLUDES}) add_library(tilelang SHARED $) add_library(tilelang_module SHARED $) -target_link_libraries(tilelang PUBLIC tvm_runtime) +target_link_libraries(tilelang PUBLIC tvm_runtime tvm) target_link_libraries(tilelang_module PUBLIC tvm) -if(APPLE) - # FIXME: libtilelang should only link against tvm runtime - target_link_libraries(tilelang PUBLIC tvm) -endif() + +# Place dev build outputs under build/lib for consistency +set_target_properties(tilelang PROPERTIES + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" +) +set_target_properties(tilelang_module PROPERTIES + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" +) # Build cython extension find_package(Python REQUIRED COMPONENTS Interpreter Development.Module ${SKBUILD_SABI_COMPONENT}) @@ -173,26 +287,112 @@ if(NOT "${SKBUILD_SABI_VERSION}" STREQUAL "") endif() python_add_library(tilelang_cython_wrapper MODULE "${CMAKE_BINARY_DIR}/tilelang_cython_wrapper.cpp" ${USE_SABI} WITH_SOABI) -# Install extension into the tilelang package directory + +# Ensure dev builds drop the extension into build/lib alongside other shared libs +set_target_properties(tilelang_cython_wrapper PROPERTIES + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + RUNTIME_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + ARCHIVE_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" +) + +# Install the extension into tilelang/lib inside the wheel install(TARGETS tilelang_cython_wrapper - LIBRARY DESTINATION tilelang - RUNTIME DESTINATION tilelang - ARCHIVE DESTINATION tilelang) + LIBRARY DESTINATION tilelang/lib + RUNTIME DESTINATION tilelang/lib + ARCHIVE DESTINATION tilelang/lib) + +# Copy libz3.so to build folder to workaround isolated build env issue +if(USE_Z3 AND USE_PYPI_Z3) + get_target_property(Z3_LIBRARY_PATH z3::libz3 IMPORTED_LOCATION) + install(FILES "${Z3_LIBRARY_PATH}" DESTINATION "${CMAKE_BINARY_DIR}/tvm") + if(APPLE) + set_target_properties(tvm PROPERTIES BUILD_RPATH "@loader_path") + else() + set_target_properties(tvm PROPERTIES BUILD_RPATH "\$ORIGIN") + endif() +endif() -# let libtilelang to search tvm/tvm_runtime in same dir if(APPLE) - set_target_properties(tilelang PROPERTIES INSTALL_RPATH "@loader_path") - set_target_properties(tilelang_module PROPERTIES INSTALL_RPATH "@loader_path") -else() - set_target_properties(tilelang PROPERTIES INSTALL_RPATH "\$ORIGIN") - set_target_properties(tilelang_module PROPERTIES INSTALL_RPATH "\$ORIGIN") + set(TILELANG_INSTALL_RPATH "@loader_path;@loader_path/../../tvm_ffi/lib") + if(USE_Z3 AND USE_PYPI_Z3) + # some z3 is placed in lib/ and some in bin/, we add both in rpath + list(APPEND TILELANG_INSTALL_RPATH "@loader_path/../../z3/lib" "@loader_path/../../z3/bin") + endif() +elseif(UNIX) + set(TILELANG_INSTALL_RPATH "\$ORIGIN:\$ORIGIN/../../tvm_ffi/lib") + if(USE_Z3 AND USE_PYPI_Z3) + # cmake uses ; by default, we explicitly use : for linux + string(APPEND TILELANG_INSTALL_RPATH ":\$ORIGIN/../../z3/lib") + endif() endif() -install(TARGETS tvm tvm_runtime tilelang_module tilelang LIBRARY DESTINATION tilelang/lib) +set_target_properties( + tilelang tilelang_module tvm tvm_runtime + PROPERTIES INSTALL_RPATH "${TILELANG_INSTALL_RPATH}") -# Copy tvm cython ext for wheels -# TODO: not necessary for editable builds -if(TVM_BUILD_FROM_SOURCE) - add_dependencies(tilelang tvm_cython) - install(FILES "${CMAKE_CURRENT_SOURCE_DIR}/3rdparty/tvm/python/tvm/ffi/core.abi3.so" DESTINATION tilelang/3rdparty/tvm/python/tvm/ffi/) +install( + TARGETS tvm tvm_runtime tilelang_module tilelang + LIBRARY DESTINATION tilelang/lib +) + +# Build tilescale_ext PyTorch C++ extension +if(USE_CUDA) + # Find Torch + execute_process( + COMMAND "${Python_EXECUTABLE}" -c "import torch; print(torch.utils.cmake_prefix_path)" + OUTPUT_VARIABLE TORCH_CMAKE_PREFIX_PATH + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE TORCH_CMAKE_RESULT + ) + if(TORCH_CMAKE_RESULT EQUAL 0 AND EXISTS "${TORCH_CMAKE_PREFIX_PATH}") + list(APPEND CMAKE_PREFIX_PATH "${TORCH_CMAKE_PREFIX_PATH}") + endif() + + find_package(Torch QUIET) + if(Torch_FOUND) + message(STATUS "Building tilescale_ext with Torch ${Torch_VERSION}") + + set(TILESCALE_EXT_SOURCES + ${CMAKE_CURRENT_SOURCE_DIR}/tilelang/utils/ts_ext/ts_ext_bindings.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/tilelang/utils/ts_ext/tensor.cpp + ${CMAKE_CURRENT_SOURCE_DIR}/tilelang/utils/ts_ext/ipc_ops.cpp + ) + + # Find libtorch_python.so + execute_process( + COMMAND "${Python_EXECUTABLE}" -c "import torch; import os; print(os.path.join(os.path.dirname(torch.__file__), 'lib', 'libtorch_python.so'))" + OUTPUT_VARIABLE TORCH_PYTHON_LIBRARY + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE TORCH_PYTHON_RESULT + ) + + python_add_library(tilescale_ext_C MODULE ${TILESCALE_EXT_SOURCES} WITH_SOABI) + target_compile_definitions(tilescale_ext_C PRIVATE TORCH_EXTENSION_NAME=_C) + target_include_directories(tilescale_ext_C PRIVATE + ${TORCH_INCLUDE_DIRS} + ${CUDAToolkit_INCLUDE_DIRS} + ) + + if(TORCH_PYTHON_RESULT EQUAL 0 AND EXISTS "${TORCH_PYTHON_LIBRARY}") + message(STATUS "Found libtorch_python: ${TORCH_PYTHON_LIBRARY}") + target_link_libraries(tilescale_ext_C PRIVATE ${TORCH_LIBRARIES} ${TORCH_PYTHON_LIBRARY} CUDA::cudart) + else() + message(WARNING "libtorch_python.so not found, extension may have undefined symbols") + target_link_libraries(tilescale_ext_C PRIVATE ${TORCH_LIBRARIES} CUDA::cudart) + endif() + + target_compile_options(tilescale_ext_C PRIVATE -fPIC) + set_target_properties(tilescale_ext_C PROPERTIES + OUTPUT_NAME "_C" + CXX_STANDARD 17 + LIBRARY_OUTPUT_DIRECTORY "${CMAKE_BINARY_DIR}/lib" + ) + + # Install as tilescale_ext/_C.so so it can be imported as tilescale_ext._C + install(TARGETS tilescale_ext_C + LIBRARY DESTINATION tilescale_ext + RUNTIME DESTINATION tilescale_ext) + else() + message(WARNING "Torch not found, tilescale_ext will not be built") + endif() endif() diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md index 9e380d8317..5eba9044ab 100644 --- a/CODE_OF_CONDUCT.md +++ b/CODE_OF_CONDUCT.md @@ -17,23 +17,23 @@ diverse, inclusive, and healthy community. Examples of behavior that contributes to a positive environment for our community include: -* Demonstrating empathy and kindness toward other people -* Being respectful of differing opinions, viewpoints, and experiences -* Giving and gracefully accepting constructive feedback -* Accepting responsibility and apologizing to those affected by our mistakes, +- Demonstrating empathy and kindness toward other people +- Being respectful of differing opinions, viewpoints, and experiences +- Giving and gracefully accepting constructive feedback +- Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience -* Focusing on what is best not just for us as individuals, but for the overall +- Focusing on what is best not just for us as individuals, but for the overall community Examples of unacceptable behavior include: -* The use of sexualized language or imagery, and sexual attention or advances of +- The use of sexualized language or imagery, and sexual attention or advances of any kind -* Trolling, insulting or derogatory comments, and personal or political attacks -* Public or private harassment -* Publishing others' private information, such as a physical or email address, +- Trolling, insulting or derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or email address, without their explicit permission -* Other conduct which could reasonably be considered inappropriate in a +- Other conduct which could reasonably be considered inappropriate in a professional setting ## Enforcement Responsibilities diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e4b45e24bb..45284e9800 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -2,7 +2,7 @@ That would be awesome if you want to contribute something to TileLang! -### Table of Contents +## Table of Contents - [Report Bugs](#report-bugs) - [Ask Questions](#ask-questions) @@ -81,6 +81,8 @@ in the main directory. This installation is removable by: python3 -m pip uninstall tilelang ``` +We also recommend installing TileLang in a more manual way for better control over the build process, by compiling the C++ extensions first and set the `PYTHONPATH`. See [Working from Source via `PYTHONPATH`](https://tilelang.com/get_started/Installation.html#working-from-source-via-pythonpath) for detailed instructions. + ## Lint Check To check the linting, run: diff --git a/LICENSE b/LICENSE index 2122252e91..09dd51c8c8 100644 --- a/LICENSE +++ b/LICENSE @@ -1,7 +1,7 @@ MIT License Copyright (c) Tile-AI. - **During the period from December 1, 2024, to Mar 14, 2025, this project is + **During the period from December 1, 2024, to Mar 14, 2025, this project is subject to additional collaboration terms with Microsoft Corporation.** Permission is hereby granted, free of charge, to any person obtaining a copy diff --git a/MANIFEST.in b/MANIFEST.in deleted file mode 100644 index 88b2068251..0000000000 --- a/MANIFEST.in +++ /dev/null @@ -1,10 +0,0 @@ -include VERSION -include CMakeLists.txt -include requirements.txt -include requirements-test.txt -include requirements-dev.txt -include tilelang/jit/adapter/cython/cython_wrapper.pyx -recursive-include src * -recursive-include 3rdparty * -recursive-exclude 3rdparty/clang* * -recursive-exclude 3rdparty/llvm* * diff --git a/README.md b/README.md index 3962010dfc..886a148688 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@ # TileScale: Tile-based AI Compute at All Scales -TileScale is a distributed extension of TileLang. It expands TileLang's tile-level programming to multi-GPU, multi-node, and even distributed chip architecture scopes, with some new feature designs like tile-level communication and hierarchical programming introduced. +TileScale is a distributed extension of TileLang. It expands TileLang's tile-level programming to multi-GPU, multi-node, and even distributed chip architecture scopes, with some new feature designs like tile-level communication and hierarchical programming introduced. -TileScale is a distributed-native domain-specific language (DSL) and compiler stack designed for deep learning on next-generation distributed architectures. +TileScale is a distributed-native domain-specific language (DSL) and compiler stack designed for deep learning on next-generation distributed architectures. As AI model entering the "scaling-law" era, modern AI infrastructure is also scaling the computation across both intra-chip and inter-chip scopes. On one side, current large AI models are already executing on multiple GPUs or even multiple nodes connected by the high-performance links like NVLink or InfiniBand. On the other side, a bunch of next-gen AI accelerators are embracing new chip architectures—such as 3D IC, near/in-memory computing, wafer-scale accelerators, etc., which are all in distributed form inner the chip for better scalability. Together, these trends are shaping modern AI compute systems into a hybrid, multi-level of "distributed architecture". TileScale is the first programming and compiler stack to unify these intra-chip and inter-chip compute resources into a unified, hierarchical, distributed architecture, which virtualizes the whole distributed system as a unified "mega-device" to users. To facilitate programming, TileScale provides a set of consistent tile-level primitives across all hardware layers for compute, memory, and communication. Thus, users can just write tile-level computing logic or flow at certain layers of interest, then TileScale automatically compiles and optimizes the scheduling of computation, communication, memory access, and their overlap. The goal of TileScale is to define an open, streamlined programming model for future distributed architectures and systems, addressing the emerging needs of modern AI computation, such as fine-grained computation and communication overlap, flexible parallel mechanisms, dataflow computation, NUMA programming, etc. -#### The full technical white-paper is coming soon. +## The full technical white-paper is coming soon. ## Hierarchical Distributed Architecture (HDA) Unlike traditional GPU SIMT programming, which assumes thread-level computation on a single device, TileScale is designed to manage compute, memory, and communication across all hierarchical scales, from threads and PEs to dies, chips, and nodes. It introduces a unified virtual device architecture, called Hierarchical Distributed Architecture (HDA), to abstract these distributed systems. @@ -32,16 +32,15 @@ At each layer, the associated memory may be shared among all units or distribute Following the hierarchical hardware architecture, TileScale exposes a hierarchical programming interface. The fundamental unit of computation in TileScale is at the *tile* granularity. TileScale provides consistent tile-level compute, memory, and communication operators corresponding to each hardware scales.

TileScale Programming Interface
- -* *Compute*: A compute primitive takes input tensor tiles at certain memory layer and produces output tensor tiles. The same compute primitive can be used at different scale level, which will be translated to different implementations. A primitive at a high-level scale can be implemented by the lower-level-scale primitives. For example, a block-scale operator can be implemented by a group of warp-scale or thread-scale primitives. - -* *Memory*: The memory primitives are used to copy data tiles at certain memory layer, as well as to copy data tile between different memory layers. - -* *Communicate*: The communication primitives are used to transfer data tiles between compute units over the network, as well as to manage the synchronization. TileScale provides both basic peer-to-peer communication primitives as well as the collective communication primitives like AllReduce, All2All, etc., at a specific scale level. + +- *Compute*: A compute primitive takes input tensor tiles at certain memory layer and produces output tensor tiles. The same compute primitive can be used at different scale level, which will be translated to different implementations. A primitive at a high-level scale can be implemented by the lower-level-scale primitives. For example, a block-scale operator can be implemented by a group of warp-scale or thread-scale primitives. + +- *Memory*: The memory primitives are used to copy data tiles at certain memory layer, as well as to copy data tile between different memory layers. + +- *Communicate*: The communication primitives are used to transfer data tiles between compute units over the network, as well as to manage the synchronization. TileScale provides both basic peer-to-peer communication primitives as well as the collective communication primitives like AllReduce, All2All, etc., at a specific scale level. A primitive for a certain scale level may have multiple implementations. For example, a copy primitive could be implemented using TMA or LSU, while a remote copy across GPUs might be implemented using copy engines, TMA, or LSU. TileScale provides default implementations for each primitive, along with a compilation process to tune the best implementation. Users can also specify particular implementations through arguments in the tile primitives. -With this hierarchical interface, user can easily customize the computation at certain scale level. For example, we can leverage the DSMEM feature to implement a general cluster-scale GEMM primitive. - +With this hierarchical interface, user can easily customize the computation at certain scale level. For example, we can leverage the DSMEM feature to implement a general cluster-scale GEMM primitive. ## System Overview and Design
TileScale system overview @@ -60,7 +59,7 @@ The layout and partition dimensions are either automatically inferred through a
### Parallel task scheduling -TileScale introduces a *T.Scale* primitive to control which hardware scale the current computations are conducted on. +TileScale introduces a *T.Scale* primitive to control which hardware scale the current computations are conducted on. It follows the SPMD (Single Program Multiple Data) programming model that scale the specified computation to all parallel units at this level. For example, the following *T.gemm* represents a warp GEMM, which executes on all warps in parallel. ```python @@ -81,18 +80,18 @@ with T.Kernel( T.gemm(A, B, C) ``` #### Task(warp) specialization -Additionally, the T.Scale primitive can also return the rank and the total number of ranks of the current scale level. This allows you to easily leverage the rank index for task specialization, such as warp specialization or any other scale-level specialization. +Additionally, the T.Scale primitive can also return the rank and the total number of ranks of the current scale level. This allows you to easily leverage the rank index for task specialization, such as warp specialization or any other scale-level specialization. ```python # warp specialize example with T.Scale("warpgroup") as wg_id, wg_num: if wg_id == 0: - # do something + # do something else: # do other thing ``` #### MPI-style programming -Combined with the communication primitives, you can also implement MPI-like programs if a communication channel exists across those ranks. For those compute units without hardware links, TileScale can also implement software channels by passing data through lower-level memory. +Combined with the communication primitives, you can also implement MPI-like programs if a communication channel exists across those ranks. For those compute units without hardware links, TileScale can also implement software channels by passing data through lower-level memory. ```python # communication example: send data to neighbor GPU with T.Scale("device") as dev_id, dev_num: @@ -100,7 +99,7 @@ with T.Scale("device") as dev_id, dev_num: T.barrier() ``` -## Example: +## Example: ```python # Example of GEMM # 4-GPU Tensor Parallelism, using L2 to communicate @@ -119,12 +118,12 @@ def gemm( A_global = T.view(A, layout=T.FullCol) B_global = T.view(B, layout=T.FullRow) C_global = T.view(C, layout=T.Replica) - + with T.Scale("block"): A_local = T.alloc((block_M, block_K), dtype, level="l0") B_local = T.alloc((block_K, block_N), dtype, level="l0") C_local = T.alloc((block_M, block_N), accum_dtype, level="l0") - T.clear(C_local) + T.clear(C_local) for k in T.Pipelined(T.ceildiv(A_global.shape[1], block_K), num_stages=3): with T.Scale("warpgroup") as wg_id, wg_num: @@ -134,7 +133,7 @@ def gemm( T.copy(A_local_wg, A_global[by * block_M, k * block_K]) T.copy(B_local_wg, B_global[k * block_K, bx * block_N]) T.gemm(A_local_wg, B_local_wg, C_local_wg) - + # Allreduce C_local_wg through software-defined channel on L1 T.allreduce(C_local_wg) T.copy(C_global[by * block_M, bx * block_N], C_local) @@ -142,7 +141,7 @@ def gemm( with T.Scale("device") as dev_id, dev_num: # Allreduce C on L2 T.allreduce(C_global) - + ``` ```python # Example of FlashMLA @@ -156,8 +155,8 @@ def flash_mla( Output: T.Tensor([batch, heads, dim], dtype), ): with T.Kernel( - device=(4), - block=(batch, heads // min(block_H, kv_group_num), + device=(4), + block=(batch, heads // min(block_H, kv_group_num), threads=256) ): with T.Scale("device"): @@ -182,8 +181,8 @@ def flash_mla( scores_scale = T.alloc([block_H], accum_dtype, level="l0") scores_sum = T.alloc([block_H], accum_dtype, level="l0") logsum = T.alloc([block_H], accum_dtype, level="l0") - - cur_kv_head = by // (kv_group_num // block_H) + + cur_kv_head = by // (kv_group_num // block_H) T.copy(Q_shared, Q_global[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :]) T.copy(Q_pe_shared, Q_pe_global[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :]) @@ -199,7 +198,7 @@ def flash_mla( T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - + T.copy(scores_max_prev, scores_max) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) @@ -217,7 +216,7 @@ def flash_mla( T.copy(acc_s_cast_local[:, block_N // 2:block_N], acc_s_local, dst=(wg_id + 1) % wg_num) # Or, you can use high level cooperative primitive # T.allgather(acc_s_local), and Cast ... - + for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_H, dim): diff --git a/THIRDPARTYNOTICES.txt b/THIRDPARTYNOTICES.txt index b7c4818411..3558662a8d 100644 --- a/THIRDPARTYNOTICES.txt +++ b/THIRDPARTYNOTICES.txt @@ -1,5 +1,5 @@ -BitBLAS uses third-party material as listed below. The attached notices are -provided for informational purposes only. +BitBLAS uses third-party material as listed below. The attached notices are +provided for informational purposes only. Notice for apache/tvm ------------------------------- diff --git a/VERSION b/VERSION index 70f6c676ef..e52aba075b 100644 --- a/VERSION +++ b/VERSION @@ -1 +1 @@ -0.1.6.post1 +0.1.7.post1 diff --git a/benchmark/blocksparse_attention/benchmark_library_dense_fmha.py b/benchmark/blocksparse_attention/benchmark_library_dense_fmha.py index 6401276ac0..3dd82aa5e5 100644 --- a/benchmark/blocksparse_attention/benchmark_library_dense_fmha.py +++ b/benchmark/blocksparse_attention/benchmark_library_dense_fmha.py @@ -7,10 +7,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -28,15 +25,15 @@ def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=F def benchmark_topk_sparse_attention(): from benchmark_configs import configs + torch.manual_seed(0) # Config for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs: - # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) import flash_attn diff --git a/benchmark/blocksparse_attention/benchmark_tilelang_block_sparse_fmha.py b/benchmark/blocksparse_attention/benchmark_tilelang_block_sparse_fmha.py index aefe4d4205..0018e9c930 100644 --- a/benchmark/blocksparse_attention/benchmark_tilelang_block_sparse_fmha.py +++ b/benchmark/blocksparse_attention/benchmark_tilelang_block_sparse_fmha.py @@ -15,10 +15,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -39,16 +36,15 @@ def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal) block_N = 64 num_stages = 2 threads = 128 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] block_mask_shape = [batch, heads, downsample_len, downsample_len] - dtype = "float16" - accum_dtype = "float" - block_mask_dtype = "bool" + dtype = T.float16 + accum_dtype = T.float32 + block_mask_dtype = T.bool def kernel_func(block_M, block_N, num_stages, threads): - @T.macro def MMA0( K: T.Tensor(shape, dtype), @@ -60,11 +56,10 @@ def MMA0( by: T.int32, bz: T.int32, ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @@ -79,22 +74,24 @@ def MMA1( by: T.int32, bz: T.int32, ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) @T.macro def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), + acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), + acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), + scores_max: T.FragmentBuffer([block_M], accum_dtype), + scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), + scores_scale: T.FragmentBuffer([block_M], accum_dtype), + scores_sum: T.FragmentBuffer([block_M], accum_dtype), + logsum: T.FragmentBuffer([block_M], accum_dtype), ): T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # To do causal softmax, we need to set the scores_max to 0 if it is -inf # This process is called Check_inf in FlashAttention3 code, and it only need to be done # in the first ceil_div(kBlockM, kBlockN) steps. @@ -114,22 +111,21 @@ def Softmax( @T.macro def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), + acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), + scores_scale: T.FragmentBuffer([block_M], accum_dtype), ): for i, j in T.Parallel(block_M, dim): acc_o[i, j] *= scores_scale[i] @T.prim_func def main( - Q: T.Tensor(shape, dtype), - K: T.Tensor(shape, dtype), - V: T.Tensor(shape, dtype), - BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), - Output: T.Tensor(shape, dtype), + Q: T.Tensor(shape, dtype), + K: T.Tensor(shape, dtype), + V: T.Tensor(shape, dtype), + BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), + Output: T.Tensor(shape, dtype), ): - with T.Kernel( - T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -142,31 +138,29 @@ def main( scores_scale = T.alloc_fragment([block_M], accum_dtype) scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - block_mask = T.alloc_local([downsample_len], block_mask_dtype) + block_mask = T.alloc_fragment([downsample_len], block_mask_dtype) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - for vj in T.serial(downsample_len): - block_mask[vj] = BlockSparseMask[bz, by, bx, vj] + T.copy(BlockSparseMask[bz, by, bx, :], block_mask) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): - if block_mask[k]: + if block_mask[k] != 0: MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, - scores_sum, logsum) + Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, logsum) Rescale(acc_o, scores_scale) MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main @@ -175,26 +169,23 @@ def main( def benchmark_topk_sparse_attention(): from benchmark_configs import configs + torch.manual_seed(0) # Config for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs: - # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) - program = blocksparse_flashattn( - BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) + program = blocksparse_flashattn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) kernel = tilelang.compile(program, out_idx=4) def benchmark_fn(): diff --git a/benchmark/blocksparse_attention/benchmark_torch_block_sparse_fmha.py b/benchmark/blocksparse_attention/benchmark_torch_block_sparse_fmha.py index e4828ce5f6..85d754ae3a 100644 --- a/benchmark/blocksparse_attention/benchmark_torch_block_sparse_fmha.py +++ b/benchmark/blocksparse_attention/benchmark_torch_block_sparse_fmha.py @@ -10,10 +10,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -31,39 +28,37 @@ def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=F def benchmark_topk_sparse_attention(): from benchmark_configs import configs + torch.manual_seed(0) # Config for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs: - # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) def benchmark_fn(): # Compute reference # Expand block mask to full attention matrix - full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')) + full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")) full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) # Apply causal # PyTorch reference implementation - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale - attn = attn.masked_fill(~full_mask, float('-inf')) + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale + attn = attn.masked_fill(~full_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) return ref_output ref_latency = do_bench( diff --git a/benchmark/blocksparse_attention/benchmark_triton_block_sparse_fmha.py b/benchmark/blocksparse_attention/benchmark_triton_block_sparse_fmha.py index 86ac894bc7..7ebca93a6a 100644 --- a/benchmark/blocksparse_attention/benchmark_triton_block_sparse_fmha.py +++ b/benchmark/blocksparse_attention/benchmark_triton_block_sparse_fmha.py @@ -15,10 +15,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -56,7 +53,6 @@ def _fwd_kernel_inner( BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, ): - mask_val = tl.load(block_mask_ptr + k_block_col_idx * stride_bmask_n) if mask_val == True: @@ -72,8 +68,7 @@ def _fwd_kernel_inner( # the following is needed only when LAST_K_BLOCK or BLOCK_M < BLOCK_N if LAST_K_BLOCK: - qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, - float('-inf')) + qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, float("-inf")) m_ij = tl.maximum(m_i, tl.max(qk, 1)) qk -= m_ij[:, None] @@ -153,7 +148,7 @@ def _fwd_kernel( v_ptrs = V + off_v mask_ptrs = block_mask_ptr + start_m * stride_bmm - m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") l_i = tl.zeros([BLOCK_M], dtype=tl.float32) acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) @@ -191,24 +186,12 @@ def _fwd_kernel( acc = acc * l_recip acc = acc.to(Out.dtype.element_ty) - off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[ - None, :] * stride_od + off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :] * stride_od out_ptrs = Out + off_o tl.store(out_ptrs, acc, mask=offs_m[:, None] < N_CTX) -def _forward(ctx, - q, - k, - v, - block_sparse_mask, - sm_scale, - BLOCK_M=64, - BLOCK_N=64, - num_warps=None, - num_stages=1, - out=None): - +def _forward(ctx, q, k, v, block_sparse_mask, sm_scale, BLOCK_M=64, BLOCK_N=64, num_warps=None, num_stages=1, out=None): assert q.shape[-1] == k.shape[-1] == v.shape[-1] assert k.shape[2] == v.shape[2] o = out if out is not None else torch.empty_like(q).contiguous() @@ -253,7 +236,6 @@ def _forward(ctx, class _sparse_attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, block_sparse_dense, sm_scale): # shape constraints @@ -271,24 +253,22 @@ def backward(ctx, do): def benchmark_topk_sparse_attention(): from benchmark_configs import configs + torch.manual_seed(0) # Config for BATCH, N_HEADS, SEQ_LEN, D_HEAD, TOPK, BLOCK in configs: - # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) diff --git a/benchmark/distributed/README.md b/benchmark/distributed/README.md index ac1cea257e..21db285310 100644 --- a/benchmark/distributed/README.md +++ b/benchmark/distributed/README.md @@ -1 +1 @@ -To compare with [TileLink](https://arxiv.org/abs/2503.20313), please install [Triton-distributed](https://github.com/ByteDance-Seed/Triton-distributed). \ No newline at end of file +To compare with [TileLink](https://arxiv.org/abs/2503.20313), please install [Triton-distributed](https://github.com/ByteDance-Seed/Triton-distributed). diff --git a/benchmark/distributed/benchmark_ag_gemm.py b/benchmark/distributed/benchmark_ag_gemm.py index a4b0bd7859..8ac3c244e3 100644 --- a/benchmark/distributed/benchmark_ag_gemm.py +++ b/benchmark/distributed/benchmark_ag_gemm.py @@ -1,4 +1,4 @@ -'''Bugfix first: +"""Bugfix first: Triton-distributed/python/triton_dist/kernels/nvidia/allgather_gemm.py:566 ```python M = M_per_rank * ctx.num_ranks @@ -7,9 +7,9 @@ ```python M = M_per_rank * num_ranks ``` -''' +""" -#TODO: further tune the performance +# TODO: further tune the performance import argparse import torch @@ -27,36 +27,27 @@ @tilelang.jit( out_idx=-1, - pass_configs={"tl.disable_rdc": True} - #FIXME: https://github.com/tile-ai/tilelang/issues/659 + pass_configs={"tl.disable_rdc": True}, + # FIXME: https://github.com/tile-ai/tilelang/issues/659 ) -def matmut_transpose(rank, - num_ranks, - M, - N_per_rank, - K, - block_M, - block_N, - block_K, - dtype="float16", - threads=256, - persistent=False) -> tilelang.JITKernel: +def matmut_transpose( + rank, num_ranks, M, N_per_rank, K, block_M, block_N, block_K, dtype="float16", threads=256, persistent=False +) -> tilelang.JITKernel: accum_dtype = "float32" signal_dtype = "uint64" # NVSHMEM requires uint64 for signal assert M % block_M == 0 and N_per_rank % block_N == 0 and K % block_K == 0 - M_blocks, N_blocks, K_stages = T.ceildiv(M, block_M), T.ceildiv(N_per_rank, - block_N), T.ceildiv(K, block_K) + M_blocks, N_blocks, K_stages = T.ceildiv(M, block_M), T.ceildiv(N_per_rank, block_N), T.ceildiv(K, block_K) M_blocks_per_rank = M_blocks // num_ranks sm_num = driver.get_num_sms() # Get # of SMs for persistent kernel @T.prim_func def nonpersistent_kernel( - A: T.Tensor((M, K), dtype), # type: ignore - B: T.Tensor((N_per_rank, K), dtype), # type: ignore - signal: T.Tensor((num_ranks), signal_dtype), # type: ignore - C: T.Tensor((M, N_per_rank), dtype), # type: ignore + A: T.Tensor((M, K), dtype), # type: ignore + B: T.Tensor((N_per_rank, K), dtype), # type: ignore + signal: T.Tensor((num_ranks), signal_dtype), # type: ignore + C: T.Tensor((M, N_per_rank), dtype), # type: ignore ): with T.Kernel(N_blocks, M_blocks, threads=threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -81,10 +72,10 @@ def nonpersistent_kernel( @T.prim_func def persistent_kernel( - A: T.Tensor((M, K), dtype), # type: ignore - B: T.Tensor((N_per_rank, K), dtype), # type: ignore - signal: T.Tensor((num_ranks), signal_dtype), # type: ignore - C: T.Tensor((M, N_per_rank), dtype), # type: ignore + A: T.Tensor((M, K), dtype), # type: ignore + B: T.Tensor((N_per_rank, K), dtype), # type: ignore + signal: T.Tensor((num_ranks), signal_dtype), # type: ignore + C: T.Tensor((M, N_per_rank), dtype), # type: ignore ): with T.Kernel(sm_num, threads=threads) as (block_id): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -145,9 +136,10 @@ def overlapped_ag_gemm( block_K=64, dtype=dtype, threads=threads, - persistent=persistent) + persistent=persistent, + ) if RANK == 0 and args.print_source: - print('We currently use cp-engine for producer, print consumer kernel code only...') + print("We currently use cp-engine for producer, print consumer kernel code only...") print(consumer.get_kernel_source()) ag_buffer = pynvshmem.nvshmem_create_tensor_list_intra_node( @@ -164,14 +156,13 @@ def overlapped_ag_gemm( gemm_stream.wait_stream(current_stream) with torch.cuda.stream(ag_stream): - ag_buffer[rank][rank * M_per_rank:(rank + 1) * M_per_rank, :].copy_(A) + ag_buffer[rank][rank * M_per_rank : (rank + 1) * M_per_rank, :].copy_(A) pynvshmem.write64_on_stream(signal_buffer[rank], 1, ag_stream) - pynvshmem.nvshmemx_barrier_all_on_stream( - ag_stream.cuda_stream) # Ensure visible to all ranks + pynvshmem.nvshmemx_barrier_all_on_stream(ag_stream.cuda_stream) # Ensure visible to all ranks rank_orders = [(rank + i) % num_ranks for i in range(1, num_ranks)] for src_rank in rank_orders: - dst = ag_buffer[rank][src_rank * M_per_rank:(src_rank + 1) * M_per_rank, :] - src = ag_buffer[src_rank][src_rank * M_per_rank:(src_rank + 1) * M_per_rank, :] + dst = ag_buffer[rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] + src = ag_buffer[src_rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] dst.copy_(src) pynvshmem.write64_on_stream(signal_buffer[src_rank], 1, ag_stream) @@ -188,19 +179,17 @@ def parse_args(): parser.add_argument("--M", type=int, default=8192) parser.add_argument("--N", type=int, default=49152) parser.add_argument("--K", type=int, default=12288) - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) parser.add_argument("--threads", type=int, default=256, help="number of threads in a block") - parser.add_argument( - "--persistent", action='store_true', default=False, help="use persistent GEMM consumers") + parser.add_argument("--persistent", action="store_true", default=False, help="use persistent GEMM consumers") parser.add_argument("--print_source", action="store_true", help="print kernel source code") parser.add_argument("--warmup", type=int, default=5, help="number of warmup iterations") parser.add_argument("--repeat", type=int, default=10, help="number of repeat iterations") return parser.parse_args() -if __name__ == '__main__': - assert torch.cuda.get_device_capability()[0] >= 9, '❗This benchmark requires sm_90 or higher' +if __name__ == "__main__": + assert torch.cuda.get_device_capability()[0] >= 9, "❗This benchmark requires sm_90 or higher" WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) assert WORLD_SIZE <= 8, "This benchmark is designed for intra-node AG-GEMM" @@ -231,12 +220,10 @@ def torch_ag_gemm(): # Benchmark Triton-dist (overlapped) ag_intranode_stream = torch.cuda.Stream(priority=-1) - ctx = create_ag_gemm_context( - A, B, RANK, PE_num, max_M=M, for_correctness=False, ag_intranode_stream=ag_intranode_stream) + ctx = create_ag_gemm_context(A, B, RANK, PE_num, max_M=M, for_correctness=False, ag_intranode_stream=ag_intranode_stream) def triton_ag_gemm(persistent, autotune): - return ag_gemm( - A, B, ctx=ctx, rank=RANK, num_ranks=PE_num, persistent=persistent, autotune=autotune) + return ag_gemm(A, B, ctx=ctx, rank=RANK, num_ranks=PE_num, persistent=persistent, autotune=autotune) dist.barrier(TP_GROUP) triton_ag_gemm = partial(triton_ag_gemm, persistent=False, autotune=False) @@ -257,8 +244,7 @@ def tilelang_ag_gemm(): print(f"rank {RANK} tilelang AG-GEMM avg time: {tl_t} ms") # Check correctness - assert torch.allclose( - tl_out, torch_out, atol=1e-2, rtol=1e-2), f'max error: {(tl_out - torch_out).abs().max()}' + assert torch.allclose(tl_out, torch_out, atol=1e-2, rtol=1e-2), f"max error: {(tl_out - torch_out).abs().max()}" print(f"rank {RANK} check passed.✅") dist.destroy_process_group() diff --git a/benchmark/distributed/benchmark_all_gather.py b/benchmark/distributed/benchmark_all_gather.py index 24d3445b22..676ad4853d 100644 --- a/benchmark/distributed/benchmark_all_gather.py +++ b/benchmark/distributed/benchmark_all_gather.py @@ -30,9 +30,8 @@ def cp_engine_producer_all_gather_full_mesh_pull( if src_rank == rank: continue # peer: src_rank, offset src_rank[src_rank] -> rank[src_rank] - dst = remote_tensor_buffers[rank][src_rank * M_per_rank:(src_rank + 1) * M_per_rank, :] - src = remote_tensor_buffers[src_rank][src_rank * M_per_rank:(src_rank + 1) * - M_per_rank, :] + dst = remote_tensor_buffers[rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] + src = remote_tensor_buffers[src_rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] dst.copy_(src) pynvshmem.write64_on_stream( barrier_buffers[rank][src_rank], @@ -47,8 +46,8 @@ def allgather(PE_num, M, N, dtype="float16", threads=128): @T.prim_func def a2a_pull( - A: T.Tensor((M_per_rank, N), dtype), # type: ignore - B: T.Tensor((M, N), dtype), # type: ignore + A: T.Tensor((M_per_rank, N), dtype), # type: ignore + B: T.Tensor((M, N), dtype), # type: ignore ): with T.Kernel(M_per_rank // block_M, PE_num - 1, threads=threads) as (bx, by): mype = T.get_pe() @@ -57,7 +56,10 @@ def a2a_pull( T.getmem_nbi_block( T.address_of(B[peer * M_per_rank + bx * block_M, 0]), - T.address_of(A[bx * block_M, 0]), block_M * N * dtype_map[dtype].itemsize, peer) + T.address_of(A[bx * block_M, 0]), + block_M * N * dtype_map[dtype].itemsize, + peer, + ) # We don't need a barrier for the pull mode return a2a_pull @@ -65,12 +67,9 @@ def a2a_pull( def parse_args(): parser = argparse.ArgumentParser() - parser.add_argument( - "--M", type=int, - default=8192) # Follow Triton-setting, we benchmark on (M, N) = (8192, 12288) + parser.add_argument("--M", type=int, default=8192) # Follow Triton-setting, we benchmark on (M, N) = (8192, 12288) parser.add_argument("--N", type=int, default=12288) - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) parser.add_argument("--threads", type=int, default=128, help="number of threads in a block") parser.add_argument("--print_source", action="store_true", help="print kernel source code") parser.add_argument("--warmup", type=int, default=5, help="number of warmup iterations") @@ -78,7 +77,7 @@ def parse_args(): return parser.parse_args() -if __name__ == '__main__': +if __name__ == "__main__": WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) assert WORLD_SIZE <= 8, "This benchmark is designed for intra-node communication" @@ -111,13 +110,9 @@ def torch_ag(): # Benchmark Triton-dist def triton_ag(): - ag_buffer_ptrs = pynvshmem.nvshmem_create_tensor_list_intra_node( - [M, N], torch_dtype) # buffer for dist-triton allgather - signal = pynvshmem.nvshmem_create_tensor_list_intra_node( - ([PE_num]), torch.uint64) # each rank corresponds to one barrier - ag_buffer_ptrs[RANK][ - RANK * M_per_rank:(RANK + 1) * M_per_rank, - ].copy_(local_data) + ag_buffer_ptrs = pynvshmem.nvshmem_create_tensor_list_intra_node([M, N], torch_dtype) # buffer for dist-triton allgather + signal = pynvshmem.nvshmem_create_tensor_list_intra_node(([PE_num]), torch.uint64) # each rank corresponds to one barrier + ag_buffer_ptrs[RANK][RANK * M_per_rank : (RANK + 1) * M_per_rank,].copy_(local_data) signal[RANK].zero_() pynvshmem.nvshmemx_barrier_all_on_stream(torch.cuda.current_stream().cuda_stream) cp_engine_producer_all_gather_full_mesh_pull( @@ -134,7 +129,7 @@ def tilelang_ag(): ag_buffer = pynvshmem.nvshmem_create_tensor([M_per_rank, N], torch_dtype) ag_buffer.copy_(local_data) out = pynvshmem.nvshmem_create_tensor([M, N], torch_dtype) - out[RANK * M_per_rank:(RANK + 1) * M_per_rank, :].copy_(local_data) + out[RANK * M_per_rank : (RANK + 1) * M_per_rank, :].copy_(local_data) kernel(ag_buffer, out) return out @@ -145,8 +140,7 @@ def tilelang_ag(): # Tested on 4A100 with full-mesh NVLink, comparable with Triton-dist and ~20x faster than Torch # Check correctness - assert torch.allclose( - tl_out, torch_out, atol=0, rtol=0), f'max error: {(tl_out - torch_out).abs().max()}' + assert torch.allclose(tl_out, torch_out, atol=0, rtol=0), f"max error: {(tl_out - torch_out).abs().max()}" print(f"rank {RANK} check passed.✅") dist.destroy_process_group() diff --git a/benchmark/distributed/benchmark_all_to_all.py b/benchmark/distributed/benchmark_all_to_all.py index 6aae8b2036..d2d0ded3a2 100644 --- a/benchmark/distributed/benchmark_all_to_all.py +++ b/benchmark/distributed/benchmark_all_to_all.py @@ -13,19 +13,18 @@ def all_to_all(max_m, hidden, num_tot_experts, WORLD_SIZE, threads=128, dtype="float16"): - scale_dtype = "float" EXPERTS_PER_RANK = num_tot_experts // WORLD_SIZE @T.prim_func def main( - send_buf: T.Tensor((max_m, hidden), dtype), # type: ignore - recv_buf: T.Tensor((WORLD_SIZE * max_m * 2, hidden), dtype), # type: ignore - scale_send_buf: T.Tensor((max_m), scale_dtype), # type: ignore - scale_recv_buf: T.Tensor((WORLD_SIZE * max_m * 2), scale_dtype), # type: ignore - split_send_buf: T.Tensor((num_tot_experts), "int32"), # type: ignore - split_recv_buf: T.Tensor((num_tot_experts * 2), "int32"), # type: ignore - signal_buf: T.Tensor((WORLD_SIZE * 2), "uint64"), # type: ignore + send_buf: T.Tensor((max_m, hidden), dtype), # type: ignore + recv_buf: T.Tensor((WORLD_SIZE * max_m * 2, hidden), dtype), # type: ignore + scale_send_buf: T.Tensor((max_m), scale_dtype), # type: ignore + scale_recv_buf: T.Tensor((WORLD_SIZE * max_m * 2), scale_dtype), # type: ignore + split_send_buf: T.Tensor((num_tot_experts), "int32"), # type: ignore + split_recv_buf: T.Tensor((num_tot_experts * 2), "int32"), # type: ignore + signal_buf: T.Tensor((WORLD_SIZE * 2), "uint64"), # type: ignore ): with T.Kernel(WORLD_SIZE, threads=threads) as (bx): peer = bx @@ -63,17 +62,14 @@ def main( class TilelangAllToAll: - def __init__(self, ctx: AllToAllContext): self.ctx = ctx - self.func = all_to_all( - ctx.max_m, ctx.hidden, ctx.num_tot_experts, ctx.WORLD_SIZE, threads=128) + self.func = all_to_all(ctx.max_m, ctx.hidden, ctx.num_tot_experts, ctx.WORLD_SIZE, threads=128) self.kernel = tilelang.compile(self.func, pass_configs={"tl.disable_tma_lower": True}) if self.ctx.rank == 0: print(self.kernel.get_kernel_source()) - def __call__(self, send_tensor: torch.Tensor, send_split_cumsum: torch.Tensor, - send_scale: torch.Tensor | None): + def __call__(self, send_tensor: torch.Tensor, send_split_cumsum: torch.Tensor, send_scale: torch.Tensor | None): """ low-latency all-to-all communication """ @@ -161,7 +157,6 @@ def calc_gather_index( row_end: int, BLOCK_SIZE: int = 1024, ): - @triton.jit def _kernel( scatter_index: torch.Tensor, @@ -202,8 +197,7 @@ def _kernel( def calc_scatter_index_stable(choosed_experts: torch.Tensor): - return (choosed_experts.flatten().argsort(stable=True).argsort().int().view( - choosed_experts.shape)) + return choosed_experts.flatten().argsort(stable=True).argsort().int().view(choosed_experts.shape) def main(): @@ -227,7 +221,6 @@ def main(): ) def perf_triton(input: torch.Tensor, scale_tensor: torch.Tensor, exp_indices: torch.Tensor): - # prepare the indexes splits_gpu_cur_rank = torch.bincount(exp_indices.view(-1), minlength=args.G).to(torch.int32) split_cumsum = splits_to_cumsum(splits_gpu_cur_rank) @@ -237,20 +230,17 @@ def perf_triton(input: torch.Tensor, scale_tensor: torch.Tensor, exp_indices: to # calculate the gather idx accordingly gather_idx_cur_rank, _ = calc_gather_index(scatter_idx_cur_rank, 0, token_num * args.topk) # use torch native scatter forward(will not be included in the e2e time measurement) - scattered_input = torch.empty( - input.size(0) * args.topk, input.size(1), dtype=input.dtype, device=input.device) + scattered_input = torch.empty(input.size(0) * args.topk, input.size(1), dtype=input.dtype, device=input.device) scattered_scale_tensor = torch.empty( (scale_tensor.size(0) * args.topk), dtype=scale_tensor.dtype, device=scale_tensor.device, ) scattered_input.copy_(torch.index_select(input, dim=0, index=gather_idx_cur_rank)) - scattered_scale_tensor.copy_( - torch.index_select(scale_tensor, dim=0, index=gather_idx_cur_rank)) + scattered_scale_tensor.copy_(torch.index_select(scale_tensor, dim=0, index=gather_idx_cur_rank)) def fwd(): - return fast_all_to_all(all_to_all_ctx, scattered_input, split_cumsum, - scattered_scale_tensor if args.with_scale else None) + return fast_all_to_all(all_to_all_ctx, scattered_input, split_cumsum, scattered_scale_tensor if args.with_scale else None) torch.cuda._sleep(1000000000) # warmup @@ -269,21 +259,22 @@ def fwd(): # 1. dispatch dispatch_splits, dispatch_token, dispatch_scale = fast_all_to_all( - all_to_all_ctx, scattered_input, split_cumsum, - scattered_scale_tensor if args.with_scale else None) + all_to_all_ctx, scattered_input, split_cumsum, scattered_scale_tensor if args.with_scale else None + ) dispatch_token, dispatch_scale = all_to_all_post_process( - all_to_all_ctx, dispatch_splits, dispatch_token, - dispatch_scale if args.with_scale else None) + all_to_all_ctx, dispatch_splits, dispatch_token, dispatch_scale if args.with_scale else None + ) # 2. compute: moe_compute(dispatch_token, dispatch_scale, moe_weight, ...) # ... # 3. combine combine_splits, combine_token, combine_scale = fast_all_to_all( - all_to_all_ctx, dispatch_token, splits_to_cumsum(dispatch_splits), dispatch_scale) + all_to_all_ctx, dispatch_token, splits_to_cumsum(dispatch_splits), dispatch_scale + ) combine_token, combine_scale = all_to_all_post_process( - all_to_all_ctx, combine_splits, combine_token, - combine_scale if args.with_scale else None) + all_to_all_ctx, combine_splits, combine_token, combine_scale if args.with_scale else None + ) # 3.1. reduce: [num_tokens_local_rank * topk] => [num_tokens_local_rank] combine_reduced_out = torch.zeros_like(input) @@ -293,8 +284,7 @@ def fwd(): torch.testing.assert_close(combine_reduced_out, input * args.topk, rtol=1e-2, atol=1e-2) tilelang_all_to_all = TilelangAllToAll(all_to_all_ctx) - tilelang_all_to_all(scattered_input, split_cumsum, - scattered_scale_tensor if args.with_scale else None) + tilelang_all_to_all(scattered_input, split_cumsum, scattered_scale_tensor if args.with_scale else None) # torch.testing.assert_close(tilelang_out[1], dispatch_token, rtol=1e-2, atol=1e-2) # torch.testing.assert_close(tilelang_scale, dispatch_scale, rtol=1e-2, atol=1e-2) @@ -307,8 +297,7 @@ def fwd(): exp_indices = generate_random_exp_indices(token_num, args.G, args.topk) assert exp_indices.size(0) == token_num and exp_indices.size(1) == args.topk exp_indices = exp_indices.to("cuda") - input = ( - torch.rand(token_num, args.N, dtype=torch.float32).to(dtype_map[args.dtype]).to("cuda")) + input = torch.rand(token_num, args.N, dtype=torch.float32).to(dtype_map[args.dtype]).to("cuda") scale_tensor = torch.rand(token_num, dtype=torch.float32).to("cuda") torch.cuda.synchronize() diff --git a/benchmark/distributed/benchmark_gemm_rs.py b/benchmark/distributed/benchmark_gemm_rs.py index 5be4431c3c..a4570d2f42 100644 --- a/benchmark/distributed/benchmark_gemm_rs.py +++ b/benchmark/distributed/benchmark_gemm_rs.py @@ -1,6 +1,6 @@ # Currently we only implement in Tilelang -#TODO: add Triton-dist v3.4 impl -#TODO: further tune the performance +# TODO: add Triton-dist v3.4 impl +# TODO: further tune the performance import argparse import torch @@ -8,40 +8,33 @@ import pynvshmem import tilelang import tilelang.language as T + # from tilelang.carver.arch import driver from tilelang.distributed import init_distributed, dtype_map, perf_fn tilelang.disable_cache() -@tilelang.jit(pass_configs={"tl.disable_rdc": True} - #FIXME: https://github.com/tile-ai/tilelang/issues/659 - ) -def fused_gemm_scatter(rank, - num_ranks, - M, - N, - K_per_rank, - block_M, - block_N, - block_K, - dtype="float16", - threads=128, - persistent=False) -> tilelang.JITKernel: +@tilelang.jit( + pass_configs={"tl.disable_rdc": True} + # FIXME: https://github.com/tile-ai/tilelang/issues/659 +) +def fused_gemm_scatter( + rank, num_ranks, M, N, K_per_rank, block_M, block_N, block_K, dtype="float16", threads=128, persistent=False +) -> tilelang.JITKernel: accum_dtype = "float32" assert M % block_M == 0 and N % block_N == 0 and K_per_rank % block_K == 0 - M_blocks, N_blocks, K_stages = T.ceildiv(M, block_M), T.ceildiv(N, block_N), T.ceildiv( - K_per_rank, block_K) + M_blocks, N_blocks, K_stages = T.ceildiv(M, block_M), T.ceildiv(N, block_N), T.ceildiv(K_per_rank, block_K) M_blocks_per_rank = M_blocks // num_ranks # sm_num = driver.get_num_sms() # Get # of SMs for persistent kernel @T.prim_func def nonpersistent_kernel( - A: T.Tensor((M, K_per_rank), dtype), # type: ignore - B: T.Tensor((N, K_per_rank), dtype), # type: ignore - C: T.Tensor((M_blocks, N_blocks, block_M, block_N), dtype), # type: ignore + A: T.Tensor((M, K_per_rank), dtype), # type: ignore + B: T.Tensor((N, K_per_rank), dtype), # type: ignore + C: T.Tensor((M_blocks, N_blocks, block_M, block_N), dtype), # type: ignore ): with T.Kernel(N_blocks, M_blocks, threads=threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -63,8 +56,8 @@ def nonpersistent_kernel( T.copy(C_shared, C[by, bx, :, :]) peer = by // M_blocks_per_rank T.putmem_nbi_block( - T.address_of(C[by, bx, 0, 0]), T.address_of(C[by, bx, 0, 0]), - block_M * block_N * dtype_map[dtype].itemsize, peer) + T.address_of(C[by, bx, 0, 0]), T.address_of(C[by, bx, 0, 0]), block_M * block_N * dtype_map[dtype].itemsize, peer + ) assert not persistent return nonpersistent_kernel @@ -110,10 +103,10 @@ def overlapped_gemm_rs( block_K=block_K, dtype=dtype, threads=threads, - persistent=persistent) + persistent=persistent, + ) - gemm_output = pynvshmem.nvshmem_create_tensor_list_intra_node( - [M_blocks, N_blocks, block_M, block_N], dtype=input.dtype) + gemm_output = pynvshmem.nvshmem_create_tensor_list_intra_node([M_blocks, N_blocks, block_M, block_N], dtype=input.dtype) output = torch.empty((M_per_rank, N), dtype=input.dtype, device="cuda") fused_gemm_scatter_kernel(input, weight, gemm_output[rank]) dist.barrier(TP_GROUP) @@ -126,19 +119,17 @@ def parse_args(): parser.add_argument("--M", type=int, default=16384) parser.add_argument("--N", type=int, default=12288) parser.add_argument("--K", type=int, default=49152) - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) parser.add_argument("--threads", type=int, default=128, help="number of threads in a block") - parser.add_argument( - "--persistent", action='store_true', default=False, help="use persistent GEMM producers") + parser.add_argument("--persistent", action="store_true", default=False, help="use persistent GEMM producers") parser.add_argument("--print_source", action="store_true", help="print kernel source code") parser.add_argument("--warmup", type=int, default=5, help="number of warmup iterations") parser.add_argument("--repeat", type=int, default=10, help="number of repeat iterations") return parser.parse_args() -if __name__ == '__main__': - assert torch.cuda.get_device_capability()[0] >= 9, '❗This benchmark requires sm_90 or higher' +if __name__ == "__main__": + assert torch.cuda.get_device_capability()[0] >= 9, "❗This benchmark requires sm_90 or higher" WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) assert WORLD_SIZE <= 8, "This benchmark is designed for intra-node GEMM-RS" @@ -176,16 +167,14 @@ def torch_gemm_rs(): print("Use non-persistent GEMM producers...") def tilelang_gemm_rs(): - return overlapped_gemm_rs( - input, weight, rank=RANK, num_ranks=PE_num, persistent=args.persistent) + return overlapped_gemm_rs(input, weight, rank=RANK, num_ranks=PE_num, persistent=args.persistent) dist.barrier(TP_GROUP) tl_out, tl_t = perf_fn(tilelang_gemm_rs, warmup, repeat) print(f"rank {RANK} tilelang GEMM avg time: {tl_t} ms") # Check correctness - assert torch.allclose( - tl_out, torch_out, atol=1e-2, rtol=1e-2), f'max error: {(tl_out - torch_out).abs().max()}' + assert torch.allclose(tl_out, torch_out, atol=1e-2, rtol=1e-2), f"max error: {(tl_out - torch_out).abs().max()}" print(f"rank {RANK} check passed.✅") dist.destroy_process_group() diff --git a/benchmark/distributed/benchmark_reduce_scatter.py b/benchmark/distributed/benchmark_reduce_scatter.py index c6431f79a4..277125bb6a 100644 --- a/benchmark/distributed/benchmark_reduce_scatter.py +++ b/benchmark/distributed/benchmark_reduce_scatter.py @@ -11,13 +11,13 @@ tilelang.disable_cache() -#TODO: Bench on 4/8 H100 -#TODO: split N? -'''init_nvshmem_by_torch_process_group(_TP_GROUP) +# TODO: Bench on 4/8 H100 +# TODO: split N? +"""init_nvshmem_by_torch_process_group(_TP_GROUP) Note: Minor numerical differences exist between Triton/TileLang and Torch (~1e-2) due to the order reductions are handled in different implementations. (No error when #PE = 2) -''' +""" def reducescatter(PE_num, M, N, dtype="float16", threads=128): @@ -27,8 +27,8 @@ def reducescatter(PE_num, M, N, dtype="float16", threads=128): @T.prim_func def pull_reduce( - A: T.Tensor((M, N), dtype), # type: ignore - B: T.Tensor((M_per_rank, N), dtype), # type: ignore + A: T.Tensor((M, N), dtype), # type: ignore + B: T.Tensor((M_per_rank, N), dtype), # type: ignore ): with T.Kernel(M_per_rank // block_M, threads=threads) as (bx): mype = T.get_pe() @@ -42,15 +42,17 @@ def pull_reduce( T.getmem_nbi_block( T.address_of(A_shared[peer, 0, 0]), T.address_of(A[mype * M_per_rank + bx * block_M, 0]), - block_M * N * dtype_map[dtype].itemsize, peer) + block_M * N * dtype_map[dtype].itemsize, + peer, + ) base = mype * M_per_rank + bx * block_M - T.copy(A[base:base + block_M, :], A_shared[mype, :, :]) + T.copy(A[base : base + block_M, :], A_shared[mype, :, :]) T.fence() # Ensure reduce happens after all IO T.copy(A_shared, A_local) T.reduce_sum(A_local, A_local_sum, dim=0) - T.copy(A_local_sum, B[bx * block_M:bx * block_M + block_M, :]) + T.copy(A_local_sum, B[bx * block_M : bx * block_M + block_M, :]) return pull_reduce @@ -59,8 +61,7 @@ def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--M", type=int, default=8192) parser.add_argument("--N", type=int, default=16384) - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) parser.add_argument("--threads", type=int, default=128, help="number of threads in a block") parser.add_argument("--print_source", action="store_true", help="print kernel source code") parser.add_argument("--warmup", type=int, default=5, help="number of warmup iterations") @@ -68,8 +69,8 @@ def parse_args(): return parser.parse_args() -if __name__ == '__main__': - assert torch.cuda.get_device_capability()[0] >= 9, '❗This benchmark requires sm_90 or higher' +if __name__ == "__main__": + assert torch.cuda.get_device_capability()[0] >= 9, "❗This benchmark requires sm_90 or higher" WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) assert WORLD_SIZE <= 8, "This benchmark is designed for intra-node RS" @@ -83,7 +84,7 @@ def parse_args(): nelems = M * PE_num func = reducescatter(PE_num, M, N, dtype=dtype, threads=threads) - kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True}, target='cuda') + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True}, target="cuda") # Get CUDA Source if RANK == 0 and args.print_source: @@ -142,8 +143,7 @@ def tilelang_rs(): print(f"rank {RANK} tilelang reduce_scatter avg time: {tl_t} ms") # Check correctness - assert torch.allclose( - tl_out, torch_out, atol=1e-2, rtol=1e-2), f'max error: {(tt_out - torch_out).abs().max()}' + assert torch.allclose(tl_out, torch_out, atol=1e-2, rtol=1e-2), f"max error: {(tt_out - torch_out).abs().max()}" print(f"rank {RANK} check passed.✅") dist.destroy_process_group() diff --git a/benchmark/distributed/ipc_impls/README.md b/benchmark/distributed/ipc_impls/README.md index d89d009568..59ad34e502 100644 --- a/benchmark/distributed/ipc_impls/README.md +++ b/benchmark/distributed/ipc_impls/README.md @@ -31,4 +31,3 @@ python benchmark/distributed/ipc_impls/benchmark_unrolledcp_p2p.py | 4,194,304 | 10.6560 | 2.2474 | 11.9145 | 2.2845 | > **Note:** All data presented above are unidirectional bandwidth. - diff --git a/benchmark/distributed/ipc_impls/benchmark_nvshmem_p2p.py b/benchmark/distributed/ipc_impls/benchmark_nvshmem_p2p.py index 5ab6265ae8..b4836d1c36 100644 --- a/benchmark/distributed/ipc_impls/benchmark_nvshmem_p2p.py +++ b/benchmark/distributed/ipc_impls/benchmark_nvshmem_p2p.py @@ -12,15 +12,14 @@ from tilelang.distributed import init_distributed, perf_fn import pynvshmem -os.environ['NCCL_DEBUG'] = 'WARN' +os.environ["NCCL_DEBUG"] = "WARN" def nvshmem_kernel_push(size, threads): - @T.prim_func def nvshmem_push( - dst: T.Tensor((size), "float32"), # type: ignore - src: T.Tensor((size), "float32"), # type: ignore + dst: T.Tensor((size), "float32"), # type: ignore + src: T.Tensor((size), "float32"), # type: ignore ): with T.Kernel(1, threads=threads): T.putmem_block( @@ -35,11 +34,10 @@ def nvshmem_push( def nvshmem_kernel_pull(size, threads): - @T.prim_func def nvshmem_pull( - dst: T.Tensor((size), "float32"), # type: ignore - src: T.Tensor((size), "float32"), # type: ignore + dst: T.Tensor((size), "float32"), # type: ignore + src: T.Tensor((size), "float32"), # type: ignore ): with T.Kernel(1, threads=threads): T.getmem_block( @@ -53,8 +51,7 @@ def nvshmem_pull( return nvshmem_pull -def benchmark_nvshmem_bw(rank: int, num_ranks: int, group: dist.ProcessGroup, size: int, - args: argparse.Namespace): +def benchmark_nvshmem_bw(rank: int, num_ranks: int, group: dist.ProcessGroup, size: int, args: argparse.Namespace): assert num_ranks == 2, "this benchmark only supports 2 ranks" assert args.threads % 32 == 0, "threads must be divisible by 32" @@ -90,10 +87,8 @@ def pull_fn(): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - "--warmup", type=int, default=10, help="number of warmup iterations (default: 10)") - parser.add_argument( - "--repeat", type=int, default=50, help="number of repeat iterations (default: 50)") + parser.add_argument("--warmup", type=int, default=10, help="number of warmup iterations (default: 10)") + parser.add_argument("--repeat", type=int, default=50, help="number of repeat iterations (default: 50)") parser.add_argument("--threads", type=int, default=128, help="Threads per block (default: 128)") args = parser.parse_args() @@ -102,8 +97,6 @@ def pull_fn(): size = 2**log_size push_bw, pull_bw = benchmark_nvshmem_bw(rank, num_ranks, group, size, args) if rank == 0: - print( - f"size={size*4} bytes, nvshmem push bw: {push_bw:.4f} GB/s, nvshmem pull bw: {pull_bw:.4f} GB/s" - ) + print(f"size={size * 4} bytes, nvshmem push bw: {push_bw:.4f} GB/s, nvshmem pull bw: {pull_bw:.4f} GB/s") dist.destroy_process_group() diff --git a/benchmark/distributed/ipc_impls/benchmark_unrolledcp_p2p.py b/benchmark/distributed/ipc_impls/benchmark_unrolledcp_p2p.py index c7d3f2556f..c320688ac8 100644 --- a/benchmark/distributed/ipc_impls/benchmark_unrolledcp_p2p.py +++ b/benchmark/distributed/ipc_impls/benchmark_unrolledcp_p2p.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist, perf_fn tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' +os.environ["NCCL_DEBUG"] = "WARN" def ipc_kernel_push(size, threads, unroll_factor): - @T.prim_func def ipc_push( - dst: T.Tensor((size), "float32"), # type: ignore - src: T.Tensor((size), "float32"), # type: ignore + dst: T.Tensor((size), "float32"), # type: ignore + src: T.Tensor((size), "float32"), # type: ignore ): with T.Kernel(1, threads=threads): rank = T.alloc_local([1], "uint64") @@ -29,18 +28,18 @@ def ipc_push( dst=T.address_of(dst[warp_start]), size=warp_copy_size, dst_pe=rank[0] ^ 1, - unroll_factor=unroll_factor) + unroll_factor=unroll_factor, + ) T.fence_sys() return ipc_push def ipc_kernel_pull(size, threads, unroll_factor): - @T.prim_func def ipc_pull( - dst: T.Tensor((size), "float32"), # type: ignore - src: T.Tensor((size), "float32"), # type: ignore + dst: T.Tensor((size), "float32"), # type: ignore + src: T.Tensor((size), "float32"), # type: ignore ): with T.Kernel(1, threads=threads): rank = T.alloc_local([1], "uint64") @@ -53,14 +52,14 @@ def ipc_pull( dst=T.address_of(dst[warp_start]), size=warp_copy_size, src_pe=rank[0] ^ 1, - unroll_factor=unroll_factor) + unroll_factor=unroll_factor, + ) T.fence_sys() return ipc_pull -def benchmark_ipc_bw(rank: int, num_ranks: int, group: dist.ProcessGroup, size: int, - args: argparse.Namespace, allocator): +def benchmark_ipc_bw(rank: int, num_ranks: int, group: dist.ProcessGroup, size: int, args: argparse.Namespace, allocator): assert num_ranks == 2, "this benchmark only supports 2 ranks" assert args.threads % 32 == 0, "threads must be divisible by 32" @@ -100,30 +99,22 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=rank, - num_local_ranks=num_ranks, - group=group) + size=2**30, device="cuda", is_distributed=True, local_rank=rank, num_local_ranks=num_ranks, group=group + ) for log_size in range(9, 21): size = 2**log_size push_bw, pull_bw = benchmark_ipc_bw(rank, num_ranks, group, size, args, allocator) if rank == 0: - print( - f"size={size*4} bytes, ipc push bw: {push_bw:.4f} GB/s, ipc pull bw: {pull_bw:.4f} GB/s" - ) + print(f"size={size * 4} bytes, ipc push bw: {push_bw:.4f} GB/s, ipc pull bw: {pull_bw:.4f} GB/s") dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - "--warmup", type=int, default=10, help="number of warmup iterations (default: 10)") - parser.add_argument( - "--repeat", type=int, default=50, help="number of repeat iterations (default: 50)") + parser.add_argument("--warmup", type=int, default=10, help="number of warmup iterations (default: 10)") + parser.add_argument("--repeat", type=int, default=50, help="number of repeat iterations (default: 50)") parser.add_argument("--threads", type=int, default=128, help="Threads per block (default: 128)") parser.add_argument("--unroll-factor", type=int, default=4, help="Unroll factor (default: 4)") args = parser.parse_args() diff --git a/benchmark/distributed/utils.py b/benchmark/distributed/utils.py index fba164121e..87cf9cc245 100644 --- a/benchmark/distributed/utils.py +++ b/benchmark/distributed/utils.py @@ -13,7 +13,6 @@ class AllToAllContext: - def __init__( self, max_m: int, diff --git a/benchmark/mamba2/README.md b/benchmark/mamba2/README.md index 8c6d933d5d..f0b4b7e80b 100644 --- a/benchmark/mamba2/README.md +++ b/benchmark/mamba2/README.md @@ -45,9 +45,14 @@ PY | 16384 | 2.531 | 135.711 | | 32768 | 5.076 | 135.379 | +## Compare with Baselines + +- Triton: v3.5.0, mamba-ssm: v2.2.6.post3 +- Helion: v0.2.1 +
Mamba2_chunk_scan Performance Comparison on H100
Performance comparison across compilers on NVIDIA H100
-
\ No newline at end of file + diff --git a/benchmark/mamba2/benchmark_mamba_chunk_scan.py b/benchmark/mamba2/benchmark_mamba_chunk_scan.py index 78dfb135e1..55f802b4f6 100644 --- a/benchmark/mamba2/benchmark_mamba_chunk_scan.py +++ b/benchmark/mamba2/benchmark_mamba_chunk_scan.py @@ -5,6 +5,20 @@ import tilelang.language as T from einops import rearrange, repeat import itertools +import math +from tilelang.profiler import do_bench + +try: + from mamba_ssm.ops.triton.ssd_chunk_scan import _chunk_scan_fwd +except ImportError as err: + raise ImportError("Please install mamba-ssm to use the triton chunk scan operator.") from err + +try: + import helion + from helion._testing import run_example + import helion.language as hl +except ImportError as err: + raise ImportError("Please install helion to use the helion chunk scan operator.") from err def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D): @@ -37,14 +51,15 @@ def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D): dt_segment_sum = dA_cumsum[:, :, :, :, None] - dA_cumsum[:, :, :, None, :] decay = torch.exp(dt_segment_sum) scores_decay = cb * rearrange(decay, "b h c l s -> b c h l s") - causal_mask = torch.tril( - torch.ones(chunk_size, chunk_size, device=x.device, dtype=bool), diagonal=0) + causal_mask = torch.tril(torch.ones(chunk_size, chunk_size, device=x.device, dtype=bool), diagonal=0) scores_decay = scores_decay.masked_fill(~causal_mask, 0) - out = torch.einsum('bchls,bhcs,bcshp->bclhp', scores_decay.to(x.dtype), dt.to(x.dtype), - rearrange(x, "b (c s) h p -> b c s h p", c=nchunks)) + out = torch.einsum( + "bchls,bhcs,bcshp->bclhp", scores_decay.to(x.dtype), dt.to(x.dtype), rearrange(x, "b (c s) h p -> b c s h p", c=nchunks) + ) state_decay_out = torch.exp(rearrange(dA_cumsum, "b h c l -> b c l h 1")) - out_prev = torch.einsum('bclhn,bchpn->bclhp', rearrange( - C, "b (c l) h n -> b c l h n", c=nchunks), prev_states.to(C.dtype)) * state_decay_out + out_prev = ( + torch.einsum("bclhn,bchpn->bclhp", rearrange(C, "b (c l) h n -> b c l h n", c=nchunks), prev_states.to(C.dtype)) * state_decay_out + ) out = out + out_prev out = rearrange(out, "b c l h p -> b (c l) h p") if D is not None: @@ -54,13 +69,114 @@ def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D): return out +def chunk_scan_triton(cb, x, dt, dA_cumsum, C, states, D): + out, _ = _chunk_scan_fwd(cb, x, dt, dA_cumsum, C, states, D) + return out + + +def chunk_scan_helion(cb, x, dt, dA_cumsum, C, states, D): + @helion.kernel() + def helion_mamba2_chunk_scan_kernel( + cb: torch.Tensor, + x: torch.Tensor, + dt: torch.Tensor, + dA_cumsum: torch.Tensor, + C: torch.Tensor, + prev_states: torch.Tensor, + D: torch.Tensor, + ) -> torch.Tensor: + """ + Argument: + cb: (batch, nchunks, ngroups, chunk_size, chunk_size) + x: (batch, seqlen, nheads, headdim) + dt: (batch, nheads, nchunks, chunk_size) + dA_cumsum: (batch, nheads, nchunks, chunk_size) + C: (batch, seqlen, ngroups, dstate) + prev_states: (batch, nchunks, nheads, headdim, dstate) + D: (nheads,) + Return: + out: (batch, seqlen, nheads, headdim) + """ + + batch, nchunks, ngroups, chunk_size, _ = cb.shape + _, seqlen, nheads, headdim = x.shape + _, _, _, dstate = C.shape + assert nchunks == (seqlen + chunk_size - 1) // chunk_size + + block_m = hl.register_block_size(chunk_size) + block_n = hl.register_block_size(headdim) + block_k = hl.register_block_size(64, 64) + dstate = hl.specialize(dstate) + + assert cb.shape == (batch, nchunks, ngroups, chunk_size, chunk_size) + assert x.shape == (batch, seqlen, nheads, headdim) + assert dt.shape == (batch, nheads, nchunks, chunk_size) + assert dA_cumsum.shape == (batch, nheads, nchunks, chunk_size) + assert C.shape == (batch, seqlen, ngroups, dstate) + assert prev_states.shape == (batch, nchunks, nheads, headdim, dstate) + assert D.shape == (nheads,) + + dtype = cb.dtype + accum_dtype = torch.float32 + assert x.dtype == dt.dtype == dA_cumsum.dtype == C.dtype == prev_states.dtype == D.dtype == dtype + + out = torch.empty_like(x) + + p = 1.44269504 + + for tile_h, tile_m, tile_n, tile_b, tile_c in hl.tile( + [nheads, chunk_size, headdim, batch, nchunks], + block_size=[1, block_m, block_n, 1, 1], + ): + acc_o = hl.zeros([tile_m, tile_n], dtype=accum_dtype) + dA_cumsum_local_m = dA_cumsum[tile_b.begin, tile_h.begin, tile_c.begin, tile_m].to(torch.float32) + scale_m_local = torch.exp2(dA_cumsum_local_m * p) + + C_local = C[ + tile_b.begin, + tile_m.index + tile_c.begin * chunk_size, + tile_h.begin // (nheads // ngroups), + :, + ] + prev_states_local = prev_states[tile_b.begin, tile_c.begin, tile_h.begin, tile_n, :] + acc_o = hl.dot(C_local, prev_states_local.T, acc=acc_o) + acc_o *= scale_m_local[:, None] + + for tile_k in hl.tile((tile_m.id + 1) * block_m, block_size=block_k): + cb_local = cb[ + tile_b.begin, + tile_c.begin, + tile_h.begin // (nheads // ngroups), + tile_m, + tile_k, + ] + dA_cumsum_local_k = dA_cumsum[tile_b.begin, tile_h.begin, tile_c.begin, tile_k].to(torch.float32) + cb_local *= torch.exp2(dA_cumsum_local_m[:, None] * p - dA_cumsum_local_k[None, :] * p) + dt_local = dt[tile_b.begin, tile_h.begin, tile_c.begin, tile_k].to(torch.float32) + cb_local = (cb_local * dt_local[None, :]).to(dtype) + pred = (tile_m.index + 0)[:, None] >= (tile_k.index + 0)[None, :] + cb_local = torch.where(pred, cb_local, torch.zeros_like(cb_local)) + x_local = x[ + tile_b.begin, + tile_c.begin * chunk_size + tile_k.index, + tile_h.begin, + tile_n, + ] + acc_o = hl.dot(cb_local, x_local, acc=acc_o) + + D_local = D[tile_h.begin].to(torch.float32) + x_residual = x[tile_b.begin, tile_c.begin * chunk_size + tile_m.index, tile_h.begin, tile_n].to(torch.float32) + acc_o += x_residual * D_local + out[tile_b.begin, tile_c.begin * chunk_size + tile_m.index, tile_h.begin, tile_n] = acc_o.to(dtype=dtype) + + return out + + args = (cb, x, dt, dA_cumsum, C, states, D) + run_example(helion_mamba2_chunk_scan_kernel, ref_program, args) + + def get_configs(): - iter_params = dict( - block_M=[64, 128, 256], - block_N=[32, 64], - block_K=[64, 128, 256], - block_Dstate=[128], - num_stages=[1, 2, 3, 4, 5]) + iter_params = dict(block_M=[64, 128, 256], block_N=[32, 64], block_K=[64, 128, 256], block_Dstate=[128], num_stages=[1, 2, 3, 4, 5]) return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] @@ -71,56 +187,58 @@ def get_configs(): tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, ) -def chunk_scan_fwd(batch, - seqlen, - chunk_size, - ngroups, - nheads, - headdim, - dstate, - block_M=64, - block_N=64, - block_K=64, - block_Dstate=128, - num_stages=2, - threads=128): - dtype = "float16" - accum_dtype = "float" +def chunk_scan_fwd( + batch, + seqlen, + chunk_size, + ngroups, + nheads, + headdim, + dstate, + block_M=64, + block_N=64, + block_K=64, + block_Dstate=128, + num_stages=2, + threads=128, +): + dtype = T.float16 + accum_dtype = T.float32 nchunks = T.ceildiv(seqlen, chunk_size) p = 1.44269504 @T.prim_func def main( - cb: T.Tensor((batch, nchunks, ngroups, chunk_size, chunk_size), dtype), # type: ignore - x: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore - dt: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore - dA_cumsum: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore - C: T.Tensor((batch, seqlen, ngroups, dstate), dtype), # type: ignore - prev_states: T.Tensor((batch, nchunks, nheads, headdim, dstate), dtype), # type: ignore - D: T.Tensor((nheads), dtype), # type: ignore - Output: T.Tensor((batch, seqlen, nheads, headdim), dtype) # type: ignore + cb: T.Tensor((batch, nchunks, ngroups, chunk_size, chunk_size), dtype), # type: ignore + x: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore + dt: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore + dA_cumsum: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore + C: T.Tensor((batch, seqlen, ngroups, dstate), dtype), # type: ignore + prev_states: T.Tensor((batch, nchunks, nheads, headdim, dstate), dtype), # type: ignore + D: T.Tensor((nheads), dtype), # type: ignore + Output: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore ): - with T.Kernel( - nheads, - T.ceildiv(chunk_size, block_M) * T.ceildiv(headdim, block_N), - batch * nchunks, - threads=threads) as (bz, bx, by): + with T.Kernel(nheads, T.ceildiv(chunk_size, block_M) * T.ceildiv(headdim, block_N), batch * nchunks, threads=threads) as ( + bz, + bx, + by, + ): acc_o = T.alloc_fragment((block_M, block_N), accum_dtype) acc_o_shared = T.alloc_shared((block_M, block_N), dtype) - cb_shared = T.alloc_shared((block_M, block_K), dtype, scope="shared.dyn") + cb_shared = T.alloc_shared((block_M, block_K), dtype) cb_local = T.alloc_fragment((block_M, block_K), dtype) - dA_cs_k_shared = T.alloc_shared((block_K), dtype, scope="shared") + dA_cs_k_shared = T.alloc_shared((block_K), dtype) dA_cs_k_local = T.alloc_fragment((block_K), accum_dtype) dA_cs_m_local = T.alloc_fragment((block_M), accum_dtype) - dt_shared = T.alloc_shared((block_K), dtype, scope="shared") + dt_shared = T.alloc_shared((block_K), dtype) dt_local = T.alloc_fragment((block_K), accum_dtype) - x_shared = T.alloc_shared((block_K, block_N), dtype, scope="shared.dyn") - dA_cs_m_shared = T.alloc_shared((block_M), dtype, scope="shared") + x_shared = T.alloc_shared((block_K, block_N), dtype) + dA_cs_m_shared = T.alloc_shared((block_M), dtype) scale_m_local = T.alloc_fragment((block_M), accum_dtype) C_shared = T.alloc_shared((block_M, block_Dstate), dtype) prev_state_shared = T.alloc_shared((block_N, block_Dstate), dtype) D_local = T.alloc_fragment((1), accum_dtype) - x_residual_shared = T.alloc_shared((block_M, block_N), dtype, scope="shared.dyn") + x_residual_shared = T.alloc_shared((block_M, block_N), dtype) x_residual_local = T.alloc_fragment((block_M, block_N), accum_dtype) batch_idx = by % batch @@ -130,27 +248,31 @@ def main( m_idx = bx // T.ceildiv(headdim, block_N) n_idx = bx % T.ceildiv(headdim, block_N) - T.annotate_layout({ - acc_o_shared: tilelang.layout.make_swizzled_layout(acc_o_shared), - cb_shared: tilelang.layout.make_swizzled_layout(cb_shared), - x_residual_shared: tilelang.layout.make_swizzled_layout(x_residual_shared) - }) + T.annotate_layout( + { + cb_shared: tilelang.layout.make_swizzled_layout(cb_shared), + x_residual_shared: tilelang.layout.make_swizzled_layout(x_residual_shared), + } + ) T.no_set_max_nreg() - T.copy(dA_cumsum[batch_idx, bz, chunk_idx, m_idx * block_M:(m_idx + 1) * block_M], - dA_cs_m_shared) + T.copy(dA_cumsum[batch_idx, bz, chunk_idx, m_idx * block_M : (m_idx + 1) * block_M], dA_cs_m_shared) T.copy(dA_cs_m_shared, dA_cs_m_local) T.clear(acc_o) for i in T.Parallel(block_M): scale_m_local[i] = T.exp2(dA_cs_m_local[i] * p) T.copy( - C[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz // (nheads // ngroups), 0:block_Dstate], C_shared) - T.copy( - prev_states[batch_idx, chunk_idx, bz, n_idx * block_N:(n_idx + 1) * block_N, - 0:block_Dstate], prev_state_shared) + C[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz // (nheads // ngroups), + 0:block_Dstate, + ], + C_shared, + ) + T.copy(prev_states[batch_idx, chunk_idx, bz, n_idx * block_N : (n_idx + 1) * block_N, 0:block_Dstate], prev_state_shared) T.gemm(C_shared, prev_state_shared, acc_o, transpose_B=True) for i, j in T.Parallel(block_M, block_N): acc_o[i, j] *= scale_m_local[i] @@ -159,34 +281,47 @@ def main( for k in T.Pipelined(loop_range, num_stages=num_stages): T.copy( - cb[batch_idx, chunk_idx, bz // (nheads // ngroups), - m_idx * block_M:(m_idx + 1) * block_M, k * block_K:(k + 1) * block_K], - cb_shared) + cb[ + batch_idx, + chunk_idx, + bz // (nheads // ngroups), + m_idx * block_M : (m_idx + 1) * block_M, + k * block_K : (k + 1) * block_K, + ], + cb_shared, + ) T.copy(cb_shared, cb_local) - T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], - dA_cs_k_shared) + T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dA_cs_k_shared) T.copy(dA_cs_k_shared, dA_cs_k_local) for i, j in T.Parallel(block_M, block_K): - cb_local[i, - j] = cb_local[i, - j] * T.exp2(dA_cs_m_local[i] * p - dA_cs_k_local[j] * p) - T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared) + cb_local[i, j] = cb_local[i, j] * T.exp2(dA_cs_m_local[i] * p - dA_cs_k_local[j] * p) + T.copy(dt[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dt_shared) T.copy(dt_shared, dt_local) for i, j in T.Parallel(block_M, block_K): cb_local[i, j] *= dt_local[j] for i, j in T.Parallel(block_M, block_K): - cb_local[i, j] = T.if_then_else(m_idx * block_M + i >= k * block_K + j, - cb_local[i, j], 0) + cb_local[i, j] = T.if_then_else(m_idx * block_M + i >= k * block_K + j, cb_local[i, j], 0) T.copy( - x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size + - (k + 1) * block_K, bz, n_idx * block_N:(n_idx + 1) * block_N], x_shared) + x[ + batch_idx, + chunk_idx * chunk_size + k * block_K : chunk_idx * chunk_size + (k + 1) * block_K, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + x_shared, + ) T.gemm(cb_local, x_shared, acc_o) D_local[0] = D[bz] T.copy( - x[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N], - x_residual_shared) + x[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + x_residual_shared, + ) T.copy(x_residual_shared, x_residual_local) for i, j in T.Parallel(block_M, block_N): acc_o[i, j] += x_residual_local[i, j] * D_local[0] @@ -194,26 +329,41 @@ def main( T.copy(acc_o, acc_o_shared) T.copy( acc_o_shared, - Output[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N]) + Output[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + ) return main if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=80, help='heads') - parser.add_argument('--groups', type=int, default=1, help='groups') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--chunk_size', type=int, default=256, help='chunk size') - parser.add_argument('--dim', type=int, default=64, help='dim') - parser.add_argument('--dstate', type=int, default=128, help='dstate') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=80, help="heads") + parser.add_argument("--groups", type=int, default=1, help="groups") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--chunk_size", type=int, default=256, help="chunk size") + parser.add_argument("--dim", type=int, default=64, help="dim") + parser.add_argument("--dstate", type=int, default=128, help="dstate") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - batch, heads, groups, seq_len, chunk_size, dim, dstate = args.batch, args.heads, args.groups, args.seq_len, args.chunk_size, args.dim, args.dstate + batch, heads, groups, seq_len, chunk_size, dim, dstate = ( + args.batch, + args.heads, + args.groups, + args.seq_len, + args.chunk_size, + args.dim, + args.dstate, + ) + nchunks = math.ceil(seq_len / chunk_size) total_flops = 2 * batch * seq_len * chunk_size * heads * dim * 0.5 + 2 * batch * seq_len * heads * dim * dstate + print("Benchmarking TileLang...") kernel = chunk_scan_fwd(batch, seq_len, chunk_size, groups, heads, dim, dstate) best_latency = kernel.latency best_config = kernel.config @@ -221,3 +371,18 @@ def main( print(f"Best latency: {best_latency}") print(f"Best TFlops: {total_flops / best_latency * 1e-9}") print(f"Best config: {best_config}") + + cb = torch.randn(batch, nchunks, groups, chunk_size, chunk_size).half().cuda() + x = torch.randn(batch, seq_len, heads, dim).half().cuda() + dt = torch.randn(batch, heads, nchunks, chunk_size).half().cuda() + dA_cumsum = torch.randn(batch, heads, nchunks, chunk_size).half().cuda() + C = torch.randn(batch, seq_len, groups, dstate).half().cuda() + states = torch.randn(batch, nchunks, heads, dim, dstate).half().cuda() + D = torch.randn(heads).half().cuda() + + print("Benchmarking Triton...") + triton_latency = do_bench(lambda: chunk_scan_triton(cb, x, dt, dA_cumsum, C, states, D), _n_warmup=10, _n_repeat=10) + print(f"Triton TFlops: {total_flops / triton_latency * 1e-9}") + + print("Benchmarking Helion...") + chunk_scan_helion(cb, x, dt, dA_cumsum, C, states, D) diff --git a/benchmark/matmul/benchmark_matmul.py b/benchmark/matmul/benchmark_matmul.py index c64f4fabf8..643c1fd5e9 100644 --- a/benchmark/matmul/benchmark_matmul.py +++ b/benchmark/matmul/benchmark_matmul.py @@ -6,6 +6,7 @@ import tilelang.language as T from tilelang.autotuner import autotune from tilelang import jit + # Configure logger logger = logging.getLogger(__name__) logger.setLevel(logging.DEBUG) @@ -61,9 +62,9 @@ def get_configs(args, kwargs): M=M, N=N, K=K, - in_dtype="float16", - out_dtype="float16", - accum_dtype="float", + in_dtype=T.float16, + out_dtype=T.float16, + accum_dtype=T.float32, ).with_arch(arch) func = carve_template.equivalent_function() @@ -101,9 +102,7 @@ def get_configs(args, kwargs): policy=[T.GemmWarpPolicy.Square], enable_rasteration=[True, False], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] return configs @@ -112,7 +111,9 @@ def get_configs(args, kwargs): warmup=3, rep=20, ) -@jit(out_idx=[2],) +@jit( + out_idx=[2], +) def matmul( M, N, @@ -154,14 +155,14 @@ def matmul( # Use half-precision for input data to reduce memory bandwidth, # accumulate in float for better numerical accuracy - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): """ The compiled TVM function for block-level matrix multiplication. @@ -176,7 +177,6 @@ def main( # Bind x-dimension to block index in N, # y-dimension to block index in M. with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): - # Allocate shared memory for A sub-block of shape (block_M, block_K) A_shared = T.alloc_shared((block_M, block_K), dtype) # Allocate shared memory for B sub-block of shape (block_N, block_K) diff --git a/benchmark/matmul/benchmark_matmul_intrinsic.py b/benchmark/matmul/benchmark_matmul_intrinsic.py index 94e36b385b..4ef860c210 100644 --- a/benchmark/matmul/benchmark_matmul_intrinsic.py +++ b/benchmark/matmul/benchmark_matmul_intrinsic.py @@ -6,7 +6,8 @@ import tilelang.language as T from tilelang.intrinsics import get_swizzle_layout from tilelang.intrinsics.mma_macro_generator import ( - TensorCoreIntrinEmitter,) + TensorCoreIntrinEmitter, +) from tilelang.transform import simplify_prim_func from tilelang.autotuner import autotune import itertools @@ -48,22 +49,22 @@ def tl_matmul( enable_rasteration=False, ): assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - if out_dtype == "int32": + if out_dtype == T.int32: micro_size_k = 32 # This is a debug config - # chunk = 32 if in_dtype == "float16" else 64 + # chunk = 32 if in_dtype == T.float16 else 64 shared_scope = "shared.dyn" block_M = block_row_warps * warp_row_tiles @@ -103,12 +104,11 @@ def tl_matmul( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) @@ -116,10 +116,12 @@ def main( B_local = T.alloc_local((warp_cols * local_size_b), in_dtype) C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype) - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - B_shared: make_swizzle_layout(B_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + B_shared: make_swizzle_layout(B_shared), + } + ) # Improve L2 Cache T.use_swizzle(panel_size=10, enable=enable_rasteration) @@ -127,7 +129,6 @@ def main( T.clear(C_local) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, block_K): A_shared[i, k] = A[by * block_M + i, ko * block_K + k] @@ -137,7 +138,6 @@ def main( B_shared[j, k] = B[bx * block_N + j, ko * block_K + k] for ki in T.serial(0, (block_K // micro_size_k)): - # Load A into fragment mma_emitter.ldmatrix_a(A_local, A_shared, ki) @@ -194,9 +194,9 @@ def get_configs(args, kwargs): M=M, N=N, K=K, - in_dtype="float16", - out_dtype="float16", - accum_dtype="float16", + in_dtype=T.float16, + out_dtype=T.float16, + accum_dtype=T.float16, ).with_arch(arch) func = carve_template.equivalent_function() @@ -223,7 +223,6 @@ def get_configs(args, kwargs): for config in configs: print(config) else: - iter_params = dict( block_row_warps=[1, 2, 4], block_col_warps=[1, 2, 4], @@ -233,9 +232,7 @@ def get_configs(args, kwargs): stage=[0, 2], enable_rasteration=[True, False], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] return configs @@ -247,14 +244,16 @@ def get_configs(args, kwargs): ref_prog=ref_program, skip_check=True, ) -@tl.jit(out_idx=[2],) +@tl.jit( + out_idx=[2], +) def matmul( M, N, K, - in_dtype="float16", - out_dtype="float16", - accum_dtype="float16", + in_dtype=T.float16, + out_dtype=T.float16, + accum_dtype=T.float16, with_roller=False, block_row_warps=None, block_col_warps=None, @@ -291,19 +290,14 @@ def kernel(): parser.add_argument("--m", type=int, default=16384, help="Matrix dimension M") parser.add_argument("--n", type=int, default=16384, help="Matrix dimension N") parser.add_argument("--k", type=int, default=16384, help="Matrix dimension K") - parser.add_argument( - "--with_roller", - type=bool, - default=False, - help="Whether to use roller to deduce search spaces") - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "int8"], help="Input data type") + parser.add_argument("--with_roller", type=bool, default=False, help="Whether to use roller to deduce search spaces") + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "int8"], help="Input data type") args = parser.parse_args() M, N, K = args.m, args.n, args.k - in_dtype = args.dtype - out_dtype = "float32" if in_dtype == "int8" else "float16" - accum_dtype = "float32" if in_dtype == "int8" else "float16" + in_dtype = T.dtype(args.dtype) + out_dtype = T.float32 if in_dtype == T.int8 else T.float16 + accum_dtype = T.float32 if in_dtype == T.int8 else T.float16 with_roller = args.with_roller with_roller = True # Compute total floating-point operations diff --git a/benchmark/matmul/benchmark_matmul_sp.py b/benchmark/matmul/benchmark_matmul_sp.py index 4e4ed61283..7ecffc26a2 100644 --- a/benchmark/matmul/benchmark_matmul_sp.py +++ b/benchmark/matmul/benchmark_matmul_sp.py @@ -9,7 +9,7 @@ from tilelang.autotuner import autotune from tilelang import jit from tilelang.contrib import nvcc -from tilelang.layout import make_metadata_layout +from tilelang.layout import make_cutlass_metadata_layout # Configure logger logger = logging.getLogger(__name__) @@ -70,7 +70,8 @@ def get_configs(M, N, K): thread_num, policy, enable_rasterization, - )) + ) + ) configs = [ { @@ -81,12 +82,13 @@ def get_configs(M, N, K): "thread_num": c[4], "policy": c[5], "enable_rasterization": c[6], # keep param name for backward-compat - } for c in _configs + } + for c in _configs ] return configs -def matmul_sp(M, N, K, accum_dtype): +def matmul_sp(M, N, K, in_dtype, accum_dtype): """ Create an autotuned matrix multiplication kernel for matrices of shape: - A: (M, K) @@ -126,7 +128,9 @@ def matmul_sp(M, N, K, accum_dtype): warmup=3, rep=20, ) - @jit(out_idx=[2],) + @jit( + out_idx=[2], + ) def kernel( block_M=None, block_N=None, @@ -161,15 +165,14 @@ def kernel( """ # Use half-precision for input data to reduce memory bandwidth, # accumulate in float for better numerical accuracy - dtype = "float16" e_factor, e_dtype = ARCH_INFO[arch] @T.prim_func def main( - A_sparse: T.Tensor((M, K // 2), dtype), - E: T.Tensor((M, K // e_factor), e_dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), accum_dtype), + A_sparse: T.Tensor((M, K // 2), in_dtype), + E: T.Tensor((M, K // e_factor), e_dtype), + B: T.Tensor((K, N), in_dtype), + C: T.Tensor((M, N), accum_dtype), ): """ The compiled TVM function for block-level matrix multiplication. @@ -183,13 +186,11 @@ def main( """ # Bind x-dimension to block index in N, # y-dimension to block index in M. - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): - + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): # Allocate shared memory for A sub-block of shape (block_M, block_K) - A_shared = T.alloc_shared((block_M, block_K // 2), dtype) + A_shared = T.alloc_shared((block_M, block_K // 2), in_dtype) # Allocate shared memory for B sub-block of shape (block_N, block_K) - B_shared = T.alloc_shared((block_K, block_N), dtype) + B_shared = T.alloc_shared((block_K, block_N), in_dtype) # Allocate shared memory for E sub-block of shape (block_M, block_K // E_factor) E_shared = T.alloc_shared((block_M, block_K // e_factor), e_dtype) # Allocate a local fragment for intermediate accumulation @@ -202,14 +203,12 @@ def main( T.disable_warp_group_reg_alloc() T.use_swizzle(panel_size=10, enable=enable_rasterization) - T.annotate_layout({ - E: - make_metadata_layout( - E, mma_dtype="float16", backend="cutlass", block_k=block_K), - E_shared: - make_metadata_layout( - E_shared, mma_dtype="float16", backend="cutlass", block_k=block_K), - }) + T.annotate_layout( + { + E: make_cutlass_metadata_layout(E, mma_dtype=in_dtype, block_k=block_K), + E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=in_dtype, block_k=block_K), + } + ) # Loop over sub-blocks in K dimension, pipelined by num_stages for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): # Load a sub-block of A from global memory into A_shared @@ -220,7 +219,7 @@ def main( T.copy(B[k * block_K, bx * block_N], B_shared) # Perform a partial matrix multiplication: # C_local += A_shared @ B_shared - T.gemm_sp( + T.gemm_sp_v2( A_shared, E_shared, B_shared, @@ -244,18 +243,13 @@ def main( parser.add_argument("--n", type=int, default=16384, help="Matrix dimension N") parser.add_argument("--k", type=int, default=16384, help="Matrix dimension K") parser.add_argument("--disable_cache", action="store_true") - parser.add_argument( - "--accum_dtype", - type=str, - default="float", - choices=["float", "float16"], - help="Accumulation datatype") + parser.add_argument("--accum_dtype", type=str, default="float", choices=["float", "float16"], help="Accumulation datatype") parser.add_argument( "--bench_torch_sparse", type=str, - choices=['cutlass', 'cusparselt'], + choices=["cutlass", "cusparselt"], default=None, - help="Whether to benchmark against torch sparse implementation, note that at current time only sm80 is supported" + help="Whether to benchmark against torch sparse implementation, note that at current time only sm80 is supported", ) args = parser.parse_args() @@ -268,7 +262,7 @@ def main( total_flops = 2 * M * N * K # matmul(...) returns (best_latency, best_config, ref_latency) - best_result = matmul_sp(M, N, K, args.accum_dtype) + best_result = matmul_sp(M, N, K, T.float16, args.accum_dtype) best_latency = best_result.latency best_config = best_result.config A = torch.randn(M, K, dtype=torch.float16, device="cuda") @@ -277,7 +271,8 @@ def main( if args.bench_torch_sparse is not None: from torch.sparse import to_sparse_semi_structured, SparseSemiStructuredTensor - if args.bench_torch_sparse == 'cutlass': + + if args.bench_torch_sparse == "cutlass": SparseSemiStructuredTensor._FORCE_CUTLASS = True A_sp = to_sparse_semi_structured(A, transposed=False) torch_sparse_latency = do_bench(lambda: A_sp @ B) @@ -288,8 +283,6 @@ def main( print(f"Best config: {best_config}") if args.bench_torch_sparse is not None: - print( - f"Torch sparse ({args.bench_torch_sparse}) TFlops: {total_flops / torch_sparse_latency * 1e-9:.3f}" - ) + print(f"Torch sparse ({args.bench_torch_sparse}) TFlops: {total_flops / torch_sparse_latency * 1e-9:.3f}") print(f"Reference Dense TFlops: {total_flops / ref_latency * 1e-9:.3f}") diff --git a/benchmark/matmul_fp8/benchmark_matmul.py b/benchmark/matmul_fp8/benchmark_matmul.py index 36b9103555..64714b6493 100644 --- a/benchmark/matmul_fp8/benchmark_matmul.py +++ b/benchmark/matmul_fp8/benchmark_matmul.py @@ -1,7 +1,7 @@ import argparse import itertools +import torch import logging -import tilelang import tilelang.language as T from tilelang.autotuner import autotune from tilelang import jit @@ -62,9 +62,9 @@ def get_configs(args, kwargs): M=M, N=N, K=K, - in_dtype="float16", - out_dtype="float16", - accum_dtype="float", + in_dtype=T.float16, + out_dtype=T.float16, + accum_dtype=T.float32, ).with_arch(arch) func = carve_template.equivalent_function() @@ -99,12 +99,11 @@ def get_configs(args, kwargs): block_K=[64, 128], num_stages=[0, 1, 2, 3], thread_num=[128, 256], + k_pack=[1, 2], policy=[T.GemmWarpPolicy.Square], enable_rasteration=[True, False], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] return configs @@ -114,7 +113,9 @@ def get_configs(args, kwargs): warmup=3, rep=20, ) -@jit(out_idx=[2],) +@jit( + out_idx=[2], +) def matmul( M, N, @@ -125,6 +126,7 @@ def matmul( block_K=None, num_stages=None, thread_num=None, + k_pack=None, policy=None, enable_rasteration=None, ): @@ -156,14 +158,14 @@ def matmul( # Use half-precision for input data to reduce memory bandwidth, # accumulate in float for better numerical accuracy - dtype = "float8_e4m3" - accum_dtype = "float" + dtype = T.float8_e4m3fnuz if torch.version.hip is not None else T.float8_e4m3fn + accum_dtype = T.float32 @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): """ The compiled TVM function for block-level matrix multiplication. @@ -178,7 +180,6 @@ def main( # Bind x-dimension to block index in N, # y-dimension to block index in M. with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): - # Allocate shared memory for A sub-block of shape (block_M, block_K) A_shared = T.alloc_shared((block_M, block_K), dtype) # Allocate shared memory for B sub-block of shape (block_N, block_K) @@ -190,8 +191,6 @@ def main( # Enable (or disable) swizzling optimization T.use_swizzle(panel_size=10, enable=enable_rasteration) - # to utilize swizzle tma layout - T.annotate_layout({C_shared: tilelang.layout.make_swizzled_layout(C_shared)}) # Clear out the accumulation buffer T.clear(C_local) @@ -210,6 +209,7 @@ def main( C_local, transpose_B=True, policy=policy, + k_pack=k_pack, ) # Write back the results from C_local to the global memory C T.copy(C_local, C_shared) diff --git a/cmake/load_tvm.cmake b/cmake/load_tvm.cmake index 21fe6dfb55..cb21be95f6 100644 --- a/cmake/load_tvm.cmake +++ b/cmake/load_tvm.cmake @@ -3,16 +3,28 @@ set(TVM_BUILD_FROM_SOURCE TRUE) set(TVM_SOURCE ${CMAKE_SOURCE_DIR}/3rdparty/tvm) -if(DEFINED $ENV{TVM_ROOT}) +if(DEFINED ENV{TVM_ROOT}) if(EXISTS $ENV{TVM_ROOT}/cmake/config.cmake) set(TVM_SOURCE $ENV{TVM_ROOT}) + message(STATUS "Using TVM_ROOT from environment variable: ${TVM_SOURCE}") endif() endif() +message(STATUS "Using TVM source: ${TVM_SOURCE}") + set(TVM_INCLUDES ${TVM_SOURCE}/include - ${TVM_SOURCE}/ffi/include ${TVM_SOURCE}/src ${TVM_SOURCE}/3rdparty/dlpack/include ${TVM_SOURCE}/3rdparty/dmlc-core/include ) + +if(EXISTS ${TVM_SOURCE}/ffi/include) + list(APPEND TVM_INCLUDES ${TVM_SOURCE}/ffi/include) +elseif(EXISTS ${TVM_SOURCE}/3rdparty/tvm-ffi/include) + list(APPEND TVM_INCLUDES ${TVM_SOURCE}/3rdparty/tvm-ffi/include) +endif() + +if(EXISTS ${TVM_SOURCE}/3rdparty/tvm-ffi/3rdparty/dlpack/include) + list(APPEND TVM_INCLUDES ${TVM_SOURCE}/3rdparty/tvm-ffi/3rdparty/dlpack/include) +endif() diff --git a/cmake/pypi-z3/FindZ3.cmake b/cmake/pypi-z3/FindZ3.cmake new file mode 100644 index 0000000000..d7920f8f9c --- /dev/null +++ b/cmake/pypi-z3/FindZ3.cmake @@ -0,0 +1,30 @@ +if(Z3_FOUND) + return() +endif() +find_package(Python3 COMPONENTS Interpreter REQUIRED) +execute_process( + COMMAND "${Python3_EXECUTABLE}" -c "import z3; print(z3.__path__[0])" + OUTPUT_VARIABLE Z3_PATH + OUTPUT_STRIP_TRAILING_WHITESPACE + RESULT_VARIABLE Z3_PYTHON_RESULT +) +if(NOT Z3_PYTHON_RESULT EQUAL 0 OR Z3_PATH STREQUAL "") + message(FATAL_ERROR "Failed to locate z3 Python package. Ensure z3-solver>=4.13.0 is installed.") +endif() +message("-- Find Z3 in path: ${Z3_PATH}") +find_path(Z3_INCLUDE_DIR NO_DEFAULT_PATH NAMES z3++.h PATHS ${Z3_PATH}/include) +find_library(Z3_LIBRARY NO_DEFAULT_PATH NAMES z3 libz3 PATHS ${Z3_PATH}/bin ${Z3_PATH}/lib ${Z3_PATH}/lib64) +message("-- Found Z3 include dir: ${Z3_INCLUDE_DIR}") +message("-- Found Z3 library: ${Z3_LIBRARY}") +add_library(z3::libz3 SHARED IMPORTED GLOBAL) +set_target_properties(z3::libz3 + PROPERTIES + IMPORTED_LOCATION ${Z3_LIBRARY} + INTERFACE_INCLUDE_DIRECTORIES ${Z3_INCLUDE_DIR} +) +if(NOT Z3_INCLUDE_DIR OR NOT Z3_LIBRARY) + message(FATAL_ERROR "Could not find Z3 library or include directory") +endif() +set(Z3_CXX_INCLUDE_DIRS ${Z3_INCLUDE_DIR}) +set(Z3_C_INCLUDE_DIRS ${Z3_INCLUDE_DIR}) +set(Z3_FOUND TRUE) diff --git a/docker/Dockerfile.cu118 b/docker/Dockerfile.cu118 index 9256fc09bb..969b0e43c3 100644 --- a/docker/Dockerfile.cu118 +++ b/docker/Dockerfile.cu118 @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/pytorch:22.12-py3 +FROM nvcr.io/nvidia/pytorch:22.12-py3 WORKDIR /root @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu120 b/docker/Dockerfile.cu120 index c89ce82ef7..341fe40c0c 100644 --- a/docker/Dockerfile.cu120 +++ b/docker/Dockerfile.cu120 @@ -1,4 +1,4 @@ -FROM nvcr.io/nvidia/pytorch:23.01-py3 +FROM nvcr.io/nvidia/pytorch:23.01-py3 WORKDIR /root @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu121 b/docker/Dockerfile.cu121 index 5b092773db..f91029d751 100644 --- a/docker/Dockerfile.cu121 +++ b/docker/Dockerfile.cu121 @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu123 b/docker/Dockerfile.cu123 index 2715536a8a..b3d1217fdd 100644 --- a/docker/Dockerfile.cu123 +++ b/docker/Dockerfile.cu123 @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu124 b/docker/Dockerfile.cu124 index fb9654f484..335f52565d 100644 --- a/docker/Dockerfile.cu124 +++ b/docker/Dockerfile.cu124 @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu125 b/docker/Dockerfile.cu125 index c409667cbf..148e44b41d 100644 --- a/docker/Dockerfile.cu125 +++ b/docker/Dockerfile.cu125 @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu126 b/docker/Dockerfile.cu126 index 93593b5dfe..c031c2bc98 100644 --- a/docker/Dockerfile.cu126 +++ b/docker/Dockerfile.cu126 @@ -23,6 +23,6 @@ RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && c RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.cu128 b/docker/Dockerfile.cu128 index 1617bc79c4..2b895ecd8a 100644 --- a/docker/Dockerfile.cu128 +++ b/docker/Dockerfile.cu128 @@ -20,9 +20,12 @@ ENV LIBGL_ALWAYS_INDIRECT=1 RUN conda install pip cmake && conda install -c conda-forge libstdcxx-ng=12 && conda clean --all -RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev +RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev \ + build-essential cmake libedit-dev libxml2-dev cython3 + +RUN pip install cython RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main TileLang \ - && cd TileLang && ./install_cuda.sh + && cd TileLang && USE_CUDA=1 pip install -e . -v CMD bash diff --git a/docker/Dockerfile.rocm b/docker/Dockerfile.rocm index 1fb23a9f34..5f61f0e2e8 100644 --- a/docker/Dockerfile.rocm +++ b/docker/Dockerfile.rocm @@ -9,23 +9,43 @@ ENV DEBIAN_FRONTEND=noninteractive RUN apt-get update && apt-get install -y --no-install-recommends \ build-essential git wget \ libgtest-dev libprotobuf-dev protobuf-compiler libgflags-dev libsqlite3-dev llvm-dev \ + rocm-dev rocm-libs hip-dev hipblas-dev rocblas-dev \ && apt-get clean autoclean && rm -rf /var/lib/apt/lists/{cache,log} /tmp/* /var/tmp/* ENV PATH="/opt/conda/bin:${PATH}" ENV LIBGL_ALWAYS_INDIRECT=1 +ENV USE_ROCM=1 +ENV USE_CUDA=0 +ENV ROCM_HOME=/opt/rocm +ENV HIP_PLATFORM=amd +ENV PYTORCH_ROCM_ARCH="gfx90a;gfx942" RUN conda run -n py_3.10 conda install pip cmake -y && \ conda run -n py_3.10 conda install -c conda-forge libstdcxx-ng=12 -y && \ conda clean --all -RUN apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev +RUN apt-get update && apt-get install -y python3 python3-dev python3-setuptools gcc libtinfo-dev zlib1g-dev build-essential cmake libedit-dev libxml2-dev && \ + apt-get clean autoclean && rm -rf /var/lib/apt/lists/{cache,log} /tmp/* /var/tmp/* -RUN git clone https://github.com/tile-ai/tilelang.git --recursive -b main tilelang && \ - conda run -n py_3.10 bash -c "cd tilelang && ./install_rocm.sh" +# Copy local tilelang directory instead of cloning from git +# Build from tilelang root: docker build -f docker/Dockerfile.rocm -t mi300:latest . +COPY . /root/tilelang -RUN conda init bash +RUN mv /opt/conda/envs/py_3.10/compiler_compat /opt/conda/envs/py_3.10/compiler_compat.bak || true && \ + conda run -n py_3.10 bash -c "export USE_ROCM=1 USE_CUDA=0 && pip install 'numpy<2.0' --force-reinstall" && \ + conda run -n py_3.10 bash -c "cd /root/tilelang && \ + # Backup and modify pyproject.toml to remove torch from dependencies \ + cp pyproject.toml pyproject.toml.bak && \ + sed -i '/^[[:space:]]*\"torch/d' pyproject.toml && \ + # Install tilelang with all dependencies except torch \ + USE_ROCM=1 USE_CUDA=0 pip install -e . -v && \ + # Restore original pyproject.toml \ + mv pyproject.toml.bak pyproject.toml" + +RUN conda init bash && \ + echo "conda activate py_3.10" >> /root/.bashrc SHELL ["/bin/bash", "-l", "-c"] -CMD ["bash", "-c", "source ~/.bashrc && conda activate py_3.10 && exec bash"] \ No newline at end of file +ENTRYPOINT ["/bin/bash", "--login", "-i"] diff --git a/docs/.gitignore b/docs/.gitignore index 4d8eb40499..79ba97163e 100644 --- a/docs/.gitignore +++ b/docs/.gitignore @@ -1,2 +1,2 @@ _build/ -autoapi/ \ No newline at end of file +autoapi/ diff --git a/docs/CNAME b/docs/CNAME index ca903c694a..6862cd2e98 100644 --- a/docs/CNAME +++ b/docs/CNAME @@ -1 +1 @@ -tilelang.com \ No newline at end of file +tilelang.com diff --git a/docs/README.md b/docs/README.md index 349c0eccc5..896d778d20 100644 --- a/docs/README.md +++ b/docs/README.md @@ -27,4 +27,4 @@ cd _build/html python3 -m http.server ``` -Then you can view the documentation in your browser at `http://localhost:8000` (the port can be customized by appending ` -p PORT_NUMBER` in the python command above). +Then you can view the documentation in your browser at `http://localhost:8000` (the port can be customized by appending `-p PORT_NUMBER` in the python command above). diff --git a/docs/_static/custom.css b/docs/_static/custom.css new file mode 100644 index 0000000000..a1fee9c3d6 --- /dev/null +++ b/docs/_static/custom.css @@ -0,0 +1,10 @@ +/* Reduce the displayed size of the sidebar logo in Furo */ +.sidebar-logo { + max-height: 125px; + width: auto; +} + +/* Optional: keep container from growing too tall due to spacing */ +.sidebar-logo-container { + line-height: 0; +} diff --git a/docs/_static/img/logo-row.svg b/docs/_static/img/logo-row.svg index 633243f3a9..e73244b743 100644 --- a/docs/_static/img/logo-row.svg +++ b/docs/_static/img/logo-row.svg @@ -1 +1 @@ - \ No newline at end of file + diff --git a/docs/_static/img/logo-v2.png b/docs/_static/img/logo-v2.png new file 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zDgM7>cX1w2U7vi9)mqpWqh}k)awfzu4c%E5?5KKDIci0HyFk06Q{?Fhz}%#Le#_Fy zIFtu>`ANvR=GAUOH_z4BsC=6G{-EQoK z&0D|fP%=3gu!B1HS5JD+D1sJD>q)ypVirmWGUylw&3tuRq9OniRP#5*UjTFEBpW}i zD~Yy>C5)GCxxSj;cWmF5o@BxabHIxKL9455bHXgUNsJ;e25hc7{$1aXm!nTZ0%KWB zala9?R)2e1UOJ(0IcW0P`?k=3Y3US#Bl(eS-x}Mjf P$f*R6Mb2Fm+Qf2f)}5P zqq5|_$}oA*(>-ClQij=~#|4D=0e!nLztD(28;4kiZvPt@64R*-EJWT$BOeVPFh=JoXC}j|55QE%F zu?u7P_ads}U$r(n&e3hw=1PTVJ1cQLU_zKjn^&3X`fLngKYUoT?6kUhSJKqhrm^>| z#hAKxN7&{F50Los(ocpz$rU|XmxzdnIQlujM9Yt|F)@Kd`a#E#nZC+-_v1&0OdL!I zGYbamLxx8P@C+Z|F!OIQV-k2?k`kHuSEG^w1J8q_>zRq+N1AxLZ<9fBUj5ST?`^xYpwTW?R(pX=|Edd6Ezm7-E%#QUT zg4mVixV!vpf3XiR_P)zEz4!Kd;E}?=jds5vi61*fE2`zH0O0 zZs^_5<^oTj8dv5D_Tuwd2`JXYKKP{ng(KmP{Jnnell@1mc{xnJ^I9B+q-pKahE*pW zTg|+i=;*njq)HtK;ePO^~#_Z6|2Ie?%HLyH)c;$X?Y?_`L0_yMJLz(A: num_args should be N; expected: , got: N` + +- Pointer-typed argument expected + - Trigger: scalar passed where a tensor is expected + - Error: `: Expect arg[i] to be pointer` + +- Rank (ndim) mismatch + - Trigger: runtime rank differs from compile-time rank + - Error: `..ndim is expected to equal R, but got mismatched ndim` + +- Dtype mismatch + - Trigger: dtype not equal to the compiled dtype and not within the tolerance set + - Error: `..dtype is expected to be , but got incompatible dtype` + +- Shape constraint violation + - Trigger: a dimension doesn’t match a constant/symbol binding + - Error: `Argument ..shape[i] has an unsatisfied constraint: ... == ` + +- Strides check failed (e.g., non-contiguous layout) + - Trigger: transposed/sliced tensors that violate expected strides + - Error: `Argument ..strides[j] has an unsatisfied constraint: ... == ` + +- Device type mismatch + - Trigger: calling a CUDA kernel with CPU tensors, etc. + - Error: `..device_type mismatch [expected: ()] ...` + +- Device id mismatch + - Trigger: mixing tensors from different GPUs + - Error: `Argument ..device_id has an unsatisfied constraint: ... == ...` + +- NULL data pointer + - Trigger: tensor required to be non-null has a NULL data pointer + - Error: `. is expected to have non-NULL data pointer, but got NULL` + +- Scalar type mismatch + - Trigger: passing float to `T.int32`, or non-boolean to `T.bool` + - Error: `: Expect arg[i] to be int/boolean` + +--- + +## Troubleshooting Tips +- Print the host source: `print(fn.get_host_source())` to see the exact assertion and expected vs. actual fields. +- Fix strides: call `.contiguous()` for non-contiguous tensors, or avoid generating transposed/sliced layouts that break assumptions. +- Align devices: ensure all participating tensors share the same `device_type` and `device_id`. +- Align dtype: use `.to()` or construct tensors with the correct dtype; pay attention to `float8` and `bool` tolerance. +- Dynamic shapes: ensure cross-tensor linear relations can be uniquely determined at the check point (only one unknown at a time). + +--- + +## FAQ +- Can I disable the checks? + - Not recommended and usually not supported. Checks are done on the host to preserve ABI stability and fail early close to the device call. +- Is the overhead noticeable? + - The checks are lightweight (branches and field reads). Compared to Python-side checks, it’s faster; the dominating cost remains the Python→C boundary. Overall it’s cheaper than equivalent checks in Python. + +--- + +## Reference Example (Matmul + ReLU) + +```python +@T.prim_func +def matmul_relu_kernel( + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), +): + # Initialize Kernel Context + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + B_shared = T.alloc_shared((block_K, block_N), dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=0): + T.copy(A[by * block_M, ko * block_K], A_shared) + T.copy(B[ko * block_K, bx * block_N], B_shared) + T.gemm(A_shared, B_shared, C_local) + T.copy(C_local, C[by * block_M, bx * block_N]) + +# For debugging, print the host source +print(matmul_relu_kernel.get_host_source()) +``` + +The host will insert all checks described above for this example. + +--- + +## Quick Error Reference (Short List) +- Argument count + - Trigger: missing/extra args; Error: `num_args should be N; expected: , got: N`. +- Pointer kind + - Trigger: scalar passed to tensor arg; Error: `Expect arg[i] to be pointer`. +- Rank (ndim) + - Trigger: runtime rank != compile-time; Error: `ndim ... expected to equal R`. +- Dtype + - Trigger: mismatch and not tolerated; Error: `dtype ... expected to be `. +- Shape + - Trigger: constant/symbol binding violated; Error: `shape[i] ... == `. +- Strides + - Trigger: layout mismatch; Error: `strides[j] ... == `. +- Device type + - Trigger: wrong backend device; Error: `device_type mismatch [expected: ...]`. +- Device id + - Trigger: tensors on different GPUs; Error: `device_id ... == ...`. +- Data pointer + - Trigger: required non-NULL but NULL; Error: `non-NULL data pointer`. +- Scalar types + - Trigger: wrong scalar type; Error: `Expect arg[i] to be int/boolean`. + +--- + +## Host Error Troubleshooting (Minimal Repros) + +Below are minimal repro snippets for common host-side errors, assuming a CUDA-targeted kernel like `matmul_relu_kernel` with: + +```python +# Convention: +# A: float16 [M, K] +# B: float16 [K, N] +# C: float16 [M, N] +# Target: CUDA (device_type=2) +fn = matmul_relu_kernel # your compiled function +M = N = K = 1024 +``` + +Adjust dtype/device if your kernel differs. + +### 0. Tip: print the host source +```python +print(fn.get_host_source()) +``` + +### 1. num_args mismatch +```python +import torch + +A = torch.empty((M, K), device='cuda', dtype=torch.float16) +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +# Missing C +fn(A, B) +``` +Expected: `: num_args should be 3; expected: , got: 3`. + +Fix: pass all arguments per the signature. + +### 2. Expect pointer (tensor) but got scalar +```python +import torch + +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +C = torch.empty((M, N), device='cuda', dtype=torch.float16) +fn(1, B, C) +``` +Expected: `: Expect arg[0] to be pointer`. + +Fix: pass a DLPack-compatible tensor (e.g., torch.Tensor). + +### 3. ndim mismatch +```python +import torch + +A = torch.empty((M, K, 1), device='cuda', dtype=torch.float16) # rank=3 +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +C = torch.empty((M, N), device='cuda', dtype=torch.float16) +fn(A, B, C) +``` +Expected: `.A_handle.ndim is expected to equal 2, but got mismatched ndim`. + +Fix: ensure runtime rank equals compiled rank. + +### 4. dtype mismatch +```python +import torch + +A = torch.empty((M, K), device='cuda', dtype=torch.float32) # should be float16 +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +C = torch.empty((M, N), device='cuda', dtype=torch.float16) +fn(A, B, C) +``` +Expected: `.A_handle.dtype is expected to be float16, but got incompatible dtype`. + +Fix: `A = A.to(torch.float16)` or create with the correct dtype. + +### 5. Shape constant/symbol mismatch +```python +import torch + +A = torch.empty((M, K + 1), device='cuda', dtype=torch.float16) # K mismatched +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +C = torch.empty((M, N), device='cuda', dtype=torch.float16) +fn(A, B, C) +``` +Expected: `Argument .A_handle.shape[i] has an unsatisfied constraint: ... == `. + +Fix: satisfy linear constraints and constants across tensors. + +### 6. Strides check failure (non-contiguous) +```python +import torch + +A = torch.empty((M, K), device='cuda', dtype=torch.float16) +A_nc = A.t() # transpose -> non-contiguous +B = torch.empty((K, N), device='cuda', dtype=torch.float16) +C = torch.empty((M, N), device='cuda', dtype=torch.float16) +fn(A_nc, B, C) +``` +Expected: `Argument .A_handle.strides[1] has an unsatisfied constraint: ... == 1`. + +Fix: pass `A_nc.contiguous()` or align the layout expectation in the kernel. + +### 7. device_type mismatch +```python +import torch + +A = torch.empty((M, K), device='cpu', dtype=torch.float16) +B = torch.empty((K, N), device='cpu', dtype=torch.float16) +C = torch.empty((M, N), device='cpu', dtype=torch.float16) +fn(A, B, C) # CUDA-targeted kernel +``` +Expected: `.A_handle.device_type mismatch [expected: 2 (cuda)] ...`. + +Fix: move tensors to the CUDA device. + +### 8. device_id mismatch (multi-GPU) +```python +import torch + +A = torch.empty((M, K), device='cuda:0', dtype=torch.float16) +B = torch.empty((K, N), device='cuda:1', dtype=torch.float16) +C = torch.empty((M, N), device='cuda:0', dtype=torch.float16) +fn(A, B, C) +``` +Expected: `Argument .B_handle.device_id has an unsatisfied constraint: ... == ...`. + +Fix: place all tensors on the same GPU (e.g., `cuda:0`). + +### 9. NULL data pointer (advanced) +This usually comes from hand-constructed DLTensor/NDArray, or external frameworks passing unallocated/freed storage. Regular `torch.Tensor` allocations rarely hit this. + +Expected: `. is expected to have non-NULL data pointer, but got NULL`. + +Fix: ensure valid underlying storage; in PyTorch scenarios, avoid constructing tensors from invalid external handles. + +### 10. Scalar type mismatch (int / bool) +```python +import tilelang.language as T + +@T.prim_func +def scalar_check(x: T.int32, flag: T.bool()): + T.evaluate(0) + +scalar_check(1.0, True) # x is float -> Expect arg[0] to be int +scalar_check(1, 2.5) # flag is float -> Expect arg[1] to be boolean +``` + +Fix: pass correct scalar types, e.g., `scalar_check(1, True)`. + +--- + +## Closing Notes +- Cross-check “shape / strides / device / dtype” against the kernel signature to localize issues efficiently. +- For complex symbolic relations, print the host source to confirm binding/solving order, then adjust runtime shapes/layouts accordingly. diff --git a/docs/conf.py b/docs/conf.py index 1b12890385..877b5582e1 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -1,5 +1,5 @@ # General information about the project. -project = "Tile Language
" +project = "TileLang
" author = "Tile Lang Contributors" copyright = f"2025-2025, {author}" @@ -20,33 +20,27 @@ "autoapi.extension", ] -autoapi_type = 'python' -autoapi_dirs = ['../tilelang'] +autoapi_type = "python" +autoapi_dirs = ["../tilelang"] autoapi_options = [ - 'members', - 'undoc-members', - 'show-inheritance', - 'show-module-summary', - 'special-members', + "members", + "undoc-members", + "show-inheritance", + "show-module-summary", + "special-members", ] autoapi_keep_files = False # Useful for debugging the generated rst files autoapi_generate_api_docs = True -autodoc_typehints = 'description' +autodoc_typehints = "description" autoapi_ignore = ["*language/ast*", "*version*", "*libinfo*", "*parser*"] -source_suffix = { - '.rst': 'restructuredtext', - '.md': 'markdown', -} +source_suffix = {".rst": "restructuredtext", ".md": "markdown"} -myst_enable_extensions = [ - "colon_fence", - "deflist", -] +myst_enable_extensions = ["colon_fence", "deflist"] redirects = {"get_started/try_out": "../index.html#getting-started"} @@ -62,13 +56,11 @@ html_theme = "furo" templates_path = [] html_static_path = ["_static"] -footer_copyright = "© 2025-2025 Tile Language" +html_css_files = ["custom.css"] +footer_copyright = "© 2025-2026 TileLang" footer_note = " " -html_theme_options = { - "light_logo": "img/logo-row.svg", - "dark_logo": "img/logo-row.svg", -} +html_theme_options = {"light_logo": "img/logo-v2.png", "dark_logo": "img/logo-v2.png"} header_links = [ ("Home", "https://github.com/tile-ai/tilelang"), diff --git a/docs/deeplearning_operators/deepseek_mla.md b/docs/deeplearning_operators/deepseek_mla.md index 08175778f0..ed02b58b15 100644 --- a/docs/deeplearning_operators/deepseek_mla.md +++ b/docs/deeplearning_operators/deepseek_mla.md @@ -1,8 +1,7 @@ # 🚀 Write High Performance FlashMLA with TileLang on Hopper -

- Author: Yu Cheng + Author: Yu Cheng Author: Lei Wang
@@ -32,14 +31,14 @@ Figure 1: Performance under batch size=64 Figure 2: Performance under batch size=128 ``` -As shown in the results, TileLang achieves performance comparable to FlashMLA in most cases, significantly outperforming both FlashInfer and Triton. +As shown in the results, TileLang achieves performance comparable to FlashMLA in most cases, significantly outperforming both FlashInfer and Triton. Notably, **TileLang accomplishes this with just around 80 lines of Python code**, demonstrating its exceptional ease of use and efficiency. Let's dive in and see how TileLang achieves this. ## Implementation First, let's review the core computation logic of traditional FlashAttention: -```python +```python # acc_s: [block_M, block_N] # scores_max: [block_M] # scores_scale: [block_M] @@ -62,7 +61,7 @@ Compared to traditional attention operators like MHA (Multi-Headed Attention) or This raises the question of how to partition the matrix multiplication operation. On the Hopper architecture, most computation kernels use [`wgmma.mma_async`](https://docs.nvidia.com/cuda/parallel-thread-execution/#asynchronous-warpgroup-level-matrix-instructions) instructions for optimal performance. The `wgmma.mma_async` instruction organizes 4 warps (128 threads) into a warpgroup for collective MMA operations. However, `wgmma.mma_async` instructions require a minimum M dimension of 64. This means each warpgroup's minimum M dimension can only be reduced to 64, but a tile size of 64*512 is too large for a single warpgroup, leading to register spilling. -Therefore, our only option is to partition `acc_o` along the `dim` dimension, with two warpgroups computing the left and right part of `acc_o` respectively. However, this introduces another challenge: both warpgroups require the complete `acc_s` result as input. +Therefore, our only option is to partition `acc_o` along the `dim` dimension, with two warpgroups computing the left and right part of `acc_o` respectively. However, this introduces another challenge: both warpgroups require the complete `acc_s` result as input. Our solution is to have each warpgroup compute half of `acc_s` during `Q @ K` computation, then obtain the other half computed by the other warpgroup through shared memory. @@ -106,7 +105,6 @@ T.use_swizzle(panel_size: int, order: str = "row") Here, `panel_size` specifies the width of the swizzled threadblock group, and `order` determines the swizzling pattern, which can be either "row" or "col". - ### Shared Memory Swizzling In CUDA programming, shared memory is divided into multiple memory banks, with each bank capable of servicing one thread request per clock cycle in parallel. Bank conflicts occur when multiple threads simultaneously access different addresses mapped to the same bank, forcing these accesses to be serialized and degrading performance. @@ -123,17 +121,14 @@ T.annotate_layout({ Here, `T.annotate_layout` allows users to specify any desired layout for a buffer. For convenience, TileLang provides the `make_swizzled_layout` primitive to automatically generate a swizzled layout. - ### Warp-Specialization The Hopper architecture commonly employs warp specialization for performance optimization. A typical approach is to designate one warpgroup as a producer that handles data movement using TMA (Tensor Memory Accelerator), while the remaining warpgroups serve as consumers performing computations. However, this programming pattern is complex, requiring developers to manually manage the execution logic for producers and consumers, including synchronization through the `mbarrier` objects. In TileLang, users are completely shielded from these implementation details. The frontend script is automatically transformed into a warp-specialized form, where TileLang handles all producer-consumer synchronization automatically, enabling efficient computation. - ### Pipeline - Pipeline is a technique used to improve memory access efficiency by overlapping memory access and computation. In TileLang, pipeline can be implemented through the `T.pipelined` annotation: ```python @@ -142,14 +137,12 @@ T.pipelined(range: int, stage: int) Here, `range` specifies the range of the pipeline, and `stage` specifies the stage of the pipeline. Multi-stage pipelining enables overlapping of computation and memory access, which can significantly improve performance for memory-intensive operators. However, setting a higher number of stages consumes more shared memory resources, so the optimal configuration needs to be determined based on specific use cases. - ### Split-KV We have also implemented Split-KV optimization similar to [FlashDecoding](https://pytorch.org/blog/flash-decoding/). Specifically, when the batch size is small, parallel SM resources cannot be fully utilized due to low parallelism. In such cases, we can split the kv_ctx dimension across multiple SMs for parallel computation and then merge the results. In our implementation, we have developed both split and combine kernels, allowing users to control the split size through a `num_split` parameter. - ## 🚀 On AMD MI300X Accelerators Following our previous demonstration of [high-performance FlashMLA implementation on NVIDIA Hopper architectures using TileLang](https://github.com/tile-ai/tilelang/blob/main/examples/deepseek_mla/README.md), this work presents an optimized implementation for AMD MI300X accelerators. We examine architectural differences and corresponding optimization strategies between these platforms. @@ -167,7 +160,7 @@ Key implementation differences between Hopper and MI300X architectures include: # Original shared memory allocation Q_shared = T.alloc_shared([block_H, dim], dtype) Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype) - + # Optimized register allocation Q_local = T.alloc_fragment([block_H, dim], dtype) Q_pe_local = T.alloc_fragment([block_H, pe_dim], dtype) diff --git a/docs/deeplearning_operators/elementwise.md b/docs/deeplearning_operators/elementwise.md index 5e1243c268..6aa8e4085a 100644 --- a/docs/deeplearning_operators/elementwise.md +++ b/docs/deeplearning_operators/elementwise.md @@ -8,7 +8,7 @@ :class: myclass1 myclass2 :name: a-tip-reference - This document is still **experimental** and may be incomplete. + This document is still **experimental** and may be incomplete. Suggestions and improvements are highly encouraged—please submit a PR! ::: @@ -24,7 +24,7 @@ Please note that this tutorial does not delve deeply into the design principles ## Elementwise add in TileLang ```python -def elementwise_add(N, threads=256, dtype="bfloat16"): +def elementwise_add(N, threads=256, dtype=T.bfloat16): @T.prim_func def main(A: T.Tensor((N), dtype), B: T.Tensor((N), dtype), C: T.Tensor((N), dtype)): @@ -43,7 +43,7 @@ Those familiar with CUDA programming might wonder where `threadIdx` fits into th The program can be compiled using the following code: ```python -program = elementwise_add(1024, threads=256, dtype="bfloat16") +program = elementwise_add(1024, threads=256, dtype=T.bfloat16) kernel = tilelang.compile(program, out_idx=-1, target="cuda", execution_backend="cython") ``` Launching the kernel is straightforward, just call it directly like a function: @@ -89,7 +89,7 @@ def elementwise_add( In the compilation process above, a fixed shape was used. However, in practical usage, we often want the kernel to support dynamic shapes. So, how can we compile a kernel in TileLang to handle dynamic shapes? In TileLang, we can replace the target size with a dynamic symbolic value, making the dimension dynamic. The following example illustrates this: ```python -program = elementwise_add(T.dynamic("N"), threads=256, dtype="bfloat16") +program = elementwise_add(T.dynamic("N"), threads=256, dtype=T.bfloat16) kernel = tilelang.compile(program, out_idx=-1, target="cuda", execution_backend="cython") ``` @@ -102,7 +102,7 @@ TileLang automatically incorporates boundary-checking conditions; however, this When compiling the example below, let's set `N` to 2047: ```python -def elementwise_add(N, num_per_thread=8, threads=256, dtype="bfloat16"): +def elementwise_add(N, num_per_thread=8, threads=256, dtype=T.bfloat16): @T.prim_func def main(A: T.Tensor((N), dtype), B: T.Tensor((N), dtype), C: T.Tensor((N), dtype)): @@ -176,7 +176,7 @@ While TileLang incorporates various optimizations for the aforementioned case, i In such scenarios, explicitly specifying the number of elements computed per thread can help "guide" TileLang's code generation process, leading to implementations that are more closely aligned with the intended design. ```python -def elementwise_add(N, num_per_thread=8, threads=256, dtype="bfloat16"): +def elementwise_add(N, num_per_thread=8, threads=256, dtype=T.bfloat16): @T.prim_func def main(A: T.Tensor((N), dtype), B: T.Tensor((N), dtype), C: T.Tensor((N), dtype)): @@ -212,7 +212,7 @@ Aha, this CUDA code aligns closely with conventional programming practices, maki But what happens if we provide additional hints to TileLang? For instance, by explicitly specifying register copies using the `T.copy(...)` operation. The example below demonstrates a vector addition implementation. Unlike the previous examples, this code explicitly loads data into registers before performing computations. ```python -def elementwise_add(N, NUM_ELE_PER_THREAD=8, threads=256, dtype="bfloat16"): +def elementwise_add(N, NUM_ELE_PER_THREAD=8, threads=256, dtype=T.bfloat16): @T.prim_func def main(A: T.Tensor((N), dtype), B: T.Tensor((N), dtype), C: T.Tensor((N), dtype)): @@ -280,8 +280,8 @@ To evaluate complexity, one could implement the same elementwise addition operat ```c++ template -__global__ void elementwise_add(nv_bfloat16* C, - const nv_bfloat16* A, +__global__ void elementwise_add(nv_bfloat16* C, + const nv_bfloat16* A, const nv_bfloat16* B, int N) { using namespace cute; diff --git a/docs/deeplearning_operators/gemv.md b/docs/deeplearning_operators/gemv.md index c75a961b80..38287f2205 100644 --- a/docs/deeplearning_operators/gemv.md +++ b/docs/deeplearning_operators/gemv.md @@ -6,7 +6,7 @@ :::{warning} - This document is still **experimental** and may be incomplete. + This document is still **experimental** and may be incomplete. Suggestions and improvements are highly encouraged—please submit a PR! ::: @@ -206,7 +206,6 @@ def splitk_gemv( return main ``` - ## Vectorized Reads GEMV is less computation intensive than GEMM as the computation intensity and memory throughput will be the optimization bottleneck. One effective strategy is to use vectorized load/store operations (e.g., `float2`, `float4`). In `TileLang`, you can specify vectorized operations via `T.vectorized`: @@ -254,7 +253,6 @@ def splitk_gemv_vectorized( With vectorized read, now the kernel finishes in **~0.0084 ms**, which is getting close to cuBLAS performance. - ## `tvm_thread_allreduce` Instead of `atomicAdd` [`tvm_thread_allreduce`](https://tvm.apache.org/docs/reference/api/python/tir/tir.html#tvm.tir.tvm_thread_allreduce) has implemented optimization when making an all-reduce across a number of threads, which should outperfrom out plain smem + `atomidAdd`: @@ -459,6 +457,5 @@ This corresponds closely to our `TileLang` program, with necessary synchronizati | splitk_gemv_vectorized | 0.00809 ms | | splitk_gemv_vectorized_tvm | 0.00675 ms | - Triton Time: 0.0077344514429569244 -In this tutorial, we implemented a simple GEMV kernel and learn that `TileLang` exposes low level control to user such as thread-level programming and CUDA primitives. \ No newline at end of file +In this tutorial, we implemented a simple GEMV kernel and learn that `TileLang` exposes low level control to user such as thread-level programming and CUDA primitives. diff --git a/docs/deeplearning_operators/matmul.md b/docs/deeplearning_operators/matmul.md index fea036ebe4..12189eb8fa 100644 --- a/docs/deeplearning_operators/matmul.md +++ b/docs/deeplearning_operators/matmul.md @@ -14,11 +14,11 @@ TileLang is a domain-specific language (DSL) designed for writing high-performance GPU kernels. It provides three main levels of abstraction: -* **Level 1:** A user writes pure compute logic without knowledge of or concern for hardware details (e.g., GPU caches, tiling, etc.). The compiler or runtime performs automatic scheduling and optimization. This level is conceptually similar to the idea behind TVM. +- **Level 1:** A user writes pure compute logic without knowledge of or concern for hardware details (e.g., GPU caches, tiling, etc.). The compiler or runtime performs automatic scheduling and optimization. This level is conceptually similar to the idea behind TVM. -* **Level 2:** A user is aware of GPU architecture concepts—such as shared memory, tiling, and thread blocks—but does not necessarily want to drop down to the lowest level of explicit thread control. This mode is somewhat comparable to Triton's programming model, where you can write tile-level operations and let the compiler do layout inference, pipelining, etc. +- **Level 2:** A user is aware of GPU architecture concepts—such as shared memory, tiling, and thread blocks—but does not necessarily want to drop down to the lowest level of explicit thread control. This mode is somewhat comparable to Triton's programming model, where you can write tile-level operations and let the compiler do layout inference, pipelining, etc. -* **Level 3:** A user takes full control of thread-level primitives and can write code that is almost as explicit as a hand-written CUDA/HIP kernel. This is useful for performance experts who need to manage every detail, such as PTX inline assembly, explicit thread behavior, etc. +- **Level 3:** A user takes full control of thread-level primitives and can write code that is almost as explicit as a hand-written CUDA/HIP kernel. This is useful for performance experts who need to manage every detail, such as PTX inline assembly, explicit thread behavior, etc. ```{figure} ../_static/img/overview.png :width: 50% @@ -52,12 +52,12 @@ While Level 1 in TileLang can be very comfortable for general users—since it r Below is a simplified code snippet for a 1024 x 1024 x 1024 matrix multiplication. It uses: -* **`T.Kernel(...)`** to initialize the thread block configuration (grid dimensions, block size, etc.). -* **`T.alloc_shared(...)`** to allocate GPU shared memory. -* **`T.alloc_fragment(...)`** to allocate a register fragment for accumulation. -* **`T.Pipelined(...)`** to express software pipelining across the K dimension. -* **`T.Parallel(...)`** to parallelize data copy loops. -* **`T.gemm(...)`** to perform tile-level GEMM operations (which map to the appropriate backends, such as MMA instructions on NVIDIA GPUs). +- **`T.Kernel(...)`** to initialize the thread block configuration (grid dimensions, block size, etc.). +- **`T.alloc_shared(...)`** to allocate GPU shared memory. +- **`T.alloc_fragment(...)`** to allocate a register fragment for accumulation. +- **`T.Pipelined(...)`** to express software pipelining across the K dimension. +- **`T.Parallel(...)`** to parallelize data copy loops. +- **`T.gemm(...)`** to perform tile-level GEMM operations (which map to the appropriate backends, such as MMA instructions on NVIDIA GPUs). ```python import tilelang @@ -147,14 +147,12 @@ with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, - This sets up the block grid dimensions based on N/block_N and M/block_M. - `threads=128` specifies that each thread block uses 128 threads. The compiler will infer how loops map to these threads. - ```{figure} ../_static/img/Parallel.png :alt: Parallel :align: center ``` - 2. **Shared & Fragment Memory**: ```python @@ -182,7 +180,6 @@ for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3): ``` - 4. **Parallel Copy**: ```python @@ -252,8 +249,8 @@ For more advanced usage—including partial lowering, explicitly controlling thr ## Further Resources -* [TileLang GitHub](https://github.com/tile-ai/tilelang) -* [BitBLAS](https://github.com/tile-ai/bitblas) -* [Triton](https://github.com/openai/triton) -* [Cutlass](https://github.com/NVIDIA/cutlass) -* [PyCUDA](https://documen.tician.de/pycuda/) +- [TileLang GitHub](https://github.com/tile-ai/tilelang) +- [BitBLAS](https://github.com/tile-ai/bitblas) +- [Triton](https://github.com/openai/triton) +- [Cutlass](https://github.com/NVIDIA/cutlass) +- [PyCUDA](https://documen.tician.de/pycuda/) diff --git a/docs/deeplearning_operators/matmul_sparse.md b/docs/deeplearning_operators/matmul_sparse.md new file mode 100644 index 0000000000..8caa6182f0 --- /dev/null +++ b/docs/deeplearning_operators/matmul_sparse.md @@ -0,0 +1,261 @@ +# Sparse Matrix-Matrix Multiplication with Tile Library + +
+ Author: botbw +
+ +:::{warning} + This document is still **experimental** and may be incomplete. + + This feature is still **experimental** and need further optimization. + + Suggestions and improvements are highly encouraged—please submit a PR! +::: + +:::{tip} +It's suggested to go through `docs/deeplearning_operators/matmul.md` first. + +Example code can be found at `examples/gemm_sp`. +::: + +## Structured sparsity in the NVIDIA Ampere architecture + +Since the Ampere architecture (sm80 and above), sparsity support has been integrated into Tensor Cores. This allows a 2:4 (or 1:2 for 32-bit data types) semi-structured matrix to be compressed into its non-zero values along with associated metadata, which can then be fed into the Tensor Core. This enables up to **2x throughput** compared to the equivalent dense computation. + +:::{warning} + This tutorial primarily focuses on CUDA, as this feature is not yet supported on ROCm. However, AMD provides a similar capability in the matrix cores of GPUs such as the MI300X. +::: + +```{figure} ../_static/img/sparse_mma_storage_example.png +:align: center + +Figure: Sparse MMA storage example (from PTX doc) +``` + +## Compress a dense tensor + +To utilize sparse Tensor Cores, a dense tensor must first be **compressed** into its non-zero values along with the corresponding metadata. + +Both `PyTorch` and `vLLM` use `CUTLASS` as their computation backend (see references [here](https://github.com/pytorch/pytorch/blob/a8d6afb511a69687bbb2b7e88a3cf67917e1697e/aten/src/ATen/native/sparse/cuda/SparseSemiStructuredOps.cu#L47) and [here](https://github.com/vllm-project/vllm/blob/a5dd03c1ebc5e4f56f3c9d3dc0436e9c582c978f/csrc/sparse/cutlass/sparse_scaled_mm_c3x.cuh#L116)), leveraging `CUTLASS`’s built-in compressor (or reimplementing it in `PyTorch`). + +A set of **CUTLASS-compatible** compressors is provided in `tilelang.utils.sparse`, where a dense tensor—along with other required arguments (e.g., block_K for sm90, transpose options)—can be passed in to perform the compression. + +```python +from tilelang.utils.sparse import compress +A_sparse, E = compress(A, transposed=trans_A, block_k=block_K) +``` + +Here, `A_sparse` contains all the non-zero elements of `A`, while `E` stores the corresponding metadata (indexing information) required to reconstruct the original sparse pattern. + +> NOTE: When using CUTLASS compressor, there is no naive position correspondence between the positions in `A_sparse`/`A` and `E`. (i.e. the 4-element group at [n, k] doesn't match the 4-bit metadata at [n, k] if you consider metadata as int4 tensor) +The metadata is reordered internally to optimize memory access patterns (e.g., for ldsm instructions and vectorized loads). +For more information, see **A note on `gemm_sp` and `gemm_sp_v2`**. + +## `T.gemm_sp` with CUTLASS's compressor + +:::{warning} + +It is strongly recommended to use T.gemm_sp_v2 due to its greater flexibility and faster compilation time. + +::: + +A 2:4 sparse GEMM kernel is similar to its dense counterpart, except that it also requires handling the associated metadata. + +Check comments in below kernel code for required modification. + +```python +def matmul_sp_sm80( + M, + N, + K, + block_M, + block_N, + block_K, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, + trans_A, + trans_B, +): + is_8_bit = "8" in in_dtype + metadata_dtype = 'int32' if is_8_bit else 'int16' + E_factor = SparseTensorCoreIntrinEmitter.E_FACTOR_MAP[in_dtype][metadata_dtype] # Calculate shape for given datatypes + A_sparse_shape = (M, K // 2) if not trans_A else (K // 2, M) + B_shape = (K, N) if not trans_B else (N, K) + A_shared_shape = (block_M, block_K // 2) if not trans_A else (block_K // 2, block_M) + B_shared_shape = (block_K, block_N) if not trans_B else (block_N, block_K) + + import tilelang.language as T + + @T.prim_func + def main( + A_sparse: T.Tensor(A_sparse_shape, in_dtype), + E: T.Tensor((M, K // E_factor), metadata_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype) + B_shared = T.alloc_shared(B_shared_shape, in_dtype) + E_shared = T.alloc_shared((block_M, block_K // E_factor), metadata_dtype) # Allocate smem for metadata + C_frag = T.alloc_fragment((block_M, block_N), accum_dtype) + T.annotate_layout({ # Annotate reordered cutlass metadata layout + E: + make_cutlass_metadata_layout(E, mma_dtype=in_dtype, arch="8.0"), + E_shared: + make_cutlass_metadata_layout( + E_shared, mma_dtype=in_dtype, arch="8.0"), + }) + T.clear(C_frag) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + T.copy(E[by * block_M, k * block_K // E_factor], E_shared) + if trans_A: + T.copy(A_sparse[k * block_K // 2, by * block_M], A_shared) + else: + T.copy(A_sparse[by * block_M, k * block_K // 2], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.gemm_sp(A_shared, E_shared, B_shared, C_frag, trans_A, trans_B) # Call gemm_sp with non-zero values and metadata + T.copy(C_frag, C[by * block_M, bx * block_N]) + + return main +``` + +Under the hood, `gemm_sp` invokes templates adapted from `CUTLASS`, and a compatible metadata layout must be specified using `T.annotate_layout`. + +## `T.gemm_sp_v2` with a custom compressor + +To migrate to `gemm_sp_v2`, simply replace occurrences of `gemm_sp`. + +Unlike `gemm_sp`, `gemm_sp_v2` can operate without `T.annotate_layout`, and it also supports user-defined layouts and compressors. + +The metadata is stored in a `(u)int8`/`(u)int16`/`(u)int32` tensor, where **each 4-bit chunk represents two 2-bit indices** of non-zero elements within four consecutive elements. Here, we start with an `int16` example, which is the **default dtype** for `bf16` and `fp16` on Ampere GPUs. + +Suppose we have the following row vector: +```python +t = tensor([[0, 7, 0, 3], [1, 5, 0, 0], [0, 0, 2, 4], [9, 0, 9, 0]], dtype=torch.float16).flatten() +``` + +The non-zero elements and their corresponding indices are: + +```python +t_sp = tensor([[7, 3], [1, 5], [2, 4], [9, 9]], dtype=torch.float16).flatten() +indices = tensor([[1, 3], [0, 1], [2, 3], [0, 2]], dtype=torch.float16).flatten() +``` + +The corresponding uint16 metadata is: +```python +# metadata_bits = tensor([0b1101, 0b0100, 0b1110, 0b1000]) +# Note: storage uses little-endian order: tensor(0b1000111001001101, dtype=torch.int16) +# Note: the above code is not runnable in python as the interpreter won't take the binary +# as 2's complement +metadata_int16 = tensor(-29107) +``` + +You can decode an int16 metadata tensor using the following utility: +```python +def decode_metadata(meta: torch.Tensor) -> torch.Tensor: + assert meta.dtype is torch.int16 + groups_per_meta = 16 // 4 + out = [] + for g in range(groups_per_meta): + group_bits = (meta >> (g * 4)) & 0xF + idx0 = group_bits & 0x3 + idx1 = (group_bits >> 2) & 0x3 + out.append(torch.stack([idx0, idx1], dim=-1)) + return torch.concat(out, dim=-1).view(meta.shape[0], -1) +``` + +The compressor can be implement at either `PyTorch`/`NumPy` level or kernel level. + +For example, `PyTorch` provides an Ampere compressor [here](https://github.com/pytorch/pytorch/blob/267d0197bfca0232488d51dd1ff735d619adc2cf/torch/sparse/_semi_structured_conversions.py#L47-L179). Note that in this implementation, a [permutation](https://github.com/pytorch/pytorch/blob/267d0197bfca0232488d51dd1ff735d619adc2cf/torch/sparse/_semi_structured_conversions.py#L173-L175) is applied to match CUTLASS’s metadata layout. If you do not annotate a metadata layout when using `gemm_sp_v2`, your compressor should replicate the same behavior as the PyTorch example—but without using the `_calculate_meta_reordering_scatter_offsets` function. + +If you want to use a custom metadata layout in your kernel, one approach is to define the layout in `TileLang` and then apply the same layout to both your compressor kernel and the matmul_sp kernel. + +```python + +@tilelang.jit(out_idx=[1, 2], pass_configs={ + tilelang.PassConfigKey.TIR_DISABLE_VECTORIZE: True, +}) +def compress_kernel(M, K, block_M, block_K, dtype, use_cutlass_layout): + e_factor, e_dtype = ARCH_INFO["8.0"] + e_K = K // e_factor + elem, group = 2, 4 + + assert M % block_M == 0, "M must be divisible by block_M" + assert K % block_K == 0, "K must be divisible by block_K" + assert K % e_factor == 0, "K must be divisible by e_factor" + assert block_K % e_factor == 0, "block_K must be divisible by e_factor" + + @T.prim_func + def kernel( + A: T.Tensor((M, K), dtype), + A_sp: T.Tensor((M, K // 2), dtype), + E: T.Tensor((M, e_K), e_dtype), + ): + with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(K, block_K), threads=block_M) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + A_sp_shared = T.alloc_shared((block_M, block_K // 2), dtype) + E_shared = T.alloc_shared((block_M, block_K // e_factor), e_dtype) + if use_cutlass_layout: # NOTE: Make sure compressor metadata layout + T.annotate_layout({ # is same with your computation kernel + E: + make_cutlass_metadata_layout( + E, mma_dtype="float16", arch="8.0", block_k=block_K), + E_shared: + make_cutlass_metadata_layout( + E_shared, + mma_dtype="float16", + arch="8.0", + block_k=block_K), + }) + T.clear(A_sp_shared) + T.clear(E_shared) + non_zero_cnt = T.alloc_local((1, ), dtype="uint8") + non_zero_elt_log_idx = T.alloc_local((elem, ), dtype="uint8") + T.copy(A[bx * block_M, by * block_K], A_shared) + for tm in T.Parallel(block_M): + for g_i in range(0, block_K // group): + a_k = g_i * group + T.clear(non_zero_cnt) + T.clear(non_zero_elt_log_idx) + for i in range(group): + val = A_shared[tm, a_k + i] + if val != 0.0: + non_zero_elt_log_idx[non_zero_cnt[0]] = i + A_sp_shared[tm, a_k // 2 + non_zero_cnt[0]] = val + non_zero_cnt[0] += 1 + if non_zero_cnt[0] == 1 and non_zero_elt_log_idx[0] == 3: + non_zero_elt_log_idx[0] = 0 + non_zero_elt_log_idx[1] = 3 + A_sp_shared[tm, a_k // 2 + 1] = A_sp_shared[tm, a_k // 2] + A_sp_shared[tm, a_k // 2] = 0.0 + elif non_zero_cnt[0] == 1: + A_sp_shared[tm, a_k // 2 + 1] = 0 + non_zero_elt_log_idx[1] = 3 + for i in T.serial(elem): + val = non_zero_elt_log_idx[i] + E_shared[tm, a_k // e_factor] |= T.shift_left(val, 4 * (g_i % (e_factor // group)) + 2 * i) + T.copy(A_sp_shared, A_sp[bx * block_M, by * block_K // 2]) + T.copy(E_shared, E[bx * block_M, by * block_K // e_factor]) + + return kernel +``` + +## A note on `gemm_sp` and `gemm_sp_v2` + +Initially, `T.gemm_sp` followed the same design as `T.gemm`, lowering to a `CUTLASS` template. This inherently requires metadata to be reordered offline following a predetermined layout. + +However, fixing a specific layout introduces several potential issues: + +1. Painful debugging experience: Debugging a failed kernel becomes difficult due to the reordered indexing, including permutations and swizzling. + +2. Limited flexibility: For example, concatenating two compressed tensors, such as `A_sparse_0` and `A_sparse_1`, into a new `A_sparse` makes sense. However, concatenating their metadata `E_0` and `E_1` may not be valid unless the layout allows it mathematically. + +3. Alignment requirements: `CUTLASS` enforces strict alignment checks, and many hyperparameter configurations can lead to compilation errors. (For reference, sm8x was implemented in `CUTLASS 2`.) + +`T.gemm_sp_v2` was designed to address these limitations, following the approach of `T.gemm_v2`. It lowers directly to PTX, removing the need for a fixed metadata layout. diff --git a/docs/get_started/Installation.md b/docs/get_started/Installation.md index f441d1a83e..ea980b59b6 100644 --- a/docs/get_started/Installation.md +++ b/docs/get_started/Installation.md @@ -15,7 +15,7 @@ We currently provide three methods to install **TileScale**: ```bash docker pull nvcr.io/nvidia/pytorch:25.03-py3 -docker run --name tilescale --ipc=host --network=host --privileged --cap-add=SYS_ADMIN --shm-size=10g --gpus=all -it nvcr.io/nvidia/pytorch:25.03-py3 /bin/bash +docker run --name tilescale --ipc=host --network=host --privileged --cap-add=SYS_ADMIN --shm-size=10g --gpus=all -it nvcr.io/nvidia/pytorch:25.03-py3 /bin/bash echo -n > /etc/pip/constraint.txt bash Miniconda3-latest-Linux-x86_64.sh # install conda conda install -c conda-forge libstdcxx-ng @@ -44,7 +44,7 @@ Verify that **TileScale** is working correctly: python -c "import tilelang; print(tilelang.__version__)" ``` -You can now run TileScale examples and develop your applications. +You can now run TileScale examples and develop your applications. **Example Usage:** @@ -55,12 +55,11 @@ cd /home/tilelang TILELANG_USE_DISTRIBUTED=1 python examples/distributed/example_allgather_gemm_overlapped.py ``` - ## To use NVSHMEM APIs Before running the examples using NVSHMEM APIs (e.g., [example_allgather.py](../../examples/distributed/example_allgather.py)), you need to build NVSHMEM library for device-side code generation. -```bash +```bash pip install mpich # building NVSHMEM needs MPI export NVSHMEM_SRC="your_custom_nvshmem_dir" # default to 3rdparty/nvshmem_src cd tilelang/distributed diff --git a/docs/get_started/overview.md b/docs/get_started/overview.md index 18fa9f1936..a7c154f31c 100644 --- a/docs/get_started/overview.md +++ b/docs/get_started/overview.md @@ -15,49 +15,49 @@ Figure 1: High-level overview of the TileLang compilation flow. ## Programming Interfaces 1. **Beginner Level (Hardware-Unaware)** - - Intended for users who need to write code that is independent of specific hardware details. - - The goal is to let developers focus on the basic logic without worrying about memory hierarchies or hardware-specific optimizations. + - Intended for users who need to write code that is independent of specific hardware details. + - The goal is to let developers focus on the basic logic without worrying about memory hierarchies or hardware-specific optimizations. - *Note:* This interface is not yet fully implemented. 2. **Developer Level (Hardware-Aware with Tile Library)** - - Designed for developers who have a basic understanding of GPU memory hierarchies and performance considerations. - - Provides a **Tile Library**, containing predefined operations and patterns optimized for various hardware architectures. + - Designed for developers who have a basic understanding of GPU memory hierarchies and performance considerations. + - Provides a **Tile Library**, containing predefined operations and patterns optimized for various hardware architectures. - Users at this level can leverage these ready-made primitives without diving into low-level threading details. 3. **Expert Level (Hardware-Aware with Thread Primitives)** - - For highly experienced users who have an in-depth understanding of low-level hardware characteristics (e.g., threading models, memory coalescing). - - Offers direct access to **thread primitives** and other low-level constructs, allowing for fine-grained control of performance-critical kernels. + - For highly experienced users who have an in-depth understanding of low-level hardware characteristics (e.g., threading models, memory coalescing). + - Offers direct access to **thread primitives** and other low-level constructs, allowing for fine-grained control of performance-critical kernels. - This level grants maximum flexibility for specialized optimizations tailored to specific GPU or multi-core architectures. ## Compilation Flow -1. **Tile Program** +1. **Tile Program** A high-level specification of the computation. Depending on the user’s expertise, they may write a purely hardware-unaware tile program or incorporate constructs from the Tile Library or thread primitives. -2. **Tile Program with Tile Library** +2. **Tile Program with Tile Library** When developers choose from the Tile Library, the original Tile Program is expanded with specialized library calls. These calls encapsulate efficient implementation patterns for different operations. -3. **Tile Program with Thread Primitives** +3. **Tile Program with Thread Primitives** Expert-level developers can explicitly use low-level threading constructs to hand-optimize data layout, synchronization, and memory usage. -4. **IRModule** +4. **IRModule** After the program is composed with libraries or thread primitives, it is lowered to an intermediate representation (IR) that captures the necessary hardware details. -5. **Source Code Generation (C/CUDA/HIP/LLVM/…)** +5. **Source Code Generation (C/CUDA/HIP/LLVM/…)** From the IR, the system generates target-specific source code. This source code is tuned for the desired backends or GPU architectures (e.g., NVIDIA, AMD). -6. **Hardware-Specific Executable/Runtime** +6. **Hardware-Specific Executable/Runtime** Finally, the generated source is compiled into hardware-specific executables, ready to run on the corresponding devices. The pipeline supports multiple GPU backends and can be extended to additional architectures. ## Tile-based Programming Model -[Figure 2](#fig-overview-gemm) provides a concise matrix multiplication (GEMM) example in ``TileLang``, -illustrating how developers can employ high-level constructs such as tiles, memory placement, pipelining, +[Figure 2](#fig-overview-gemm) provides a concise matrix multiplication (GEMM) example in ``TileLang``, +illustrating how developers can employ high-level constructs such as tiles, memory placement, pipelining, and operator calls to manage data movement and computation with fine-grained control. -In particular, this snippet ([Figure 2](#fig-overview-gemm) (a)) demonstrates how multi-level tiling -leverages different memory hierarchies (global, shared, and registers) to optimize bandwidth utilization +In particular, this snippet ([Figure 2](#fig-overview-gemm) (a)) demonstrates how multi-level tiling +leverages different memory hierarchies (global, shared, and registers) to optimize bandwidth utilization and reduce latency. -Overall, [Figure 2](#fig-overview-gemm) (b) showcases how the Python-like syntax of ``TileLang`` +Overall, [Figure 2](#fig-overview-gemm) (b) showcases how the Python-like syntax of ``TileLang`` allows developers to reason about performance-critical optimizations within a user-friendly programming model. ```{figure} ../_static/img/MatmulExample.png diff --git a/docs/get_started/run_example.md b/docs/get_started/run_example.md index aced5d5a83..e25f42fb8f 100644 --- a/docs/get_started/run_example.md +++ b/docs/get_started/run_example.md @@ -5,11 +5,11 @@ Before running, enable TileLang’s distributed mode: ```bash -export TILELANG_USE_DISTRIBUTED=1 +export TILELANG_USE_DISTRIBUTED=1 ``` Then start an example directly with Python: ```bash - python examples/distributed/primitives/example_put_warp.py + python examples/distributed/primitives/example_put_warp.py ``` ## Examples using NVSHMEM APIs @@ -18,4 +18,4 @@ Use the provided launcher `tilelang/distributed/launch.sh` to start programs tha ```bash GPUS=2 ./tilelang/distributed/launch.sh examples/distributed/example_allgather.py ``` -You can change GPUS to the number of local GPUs you want to use. The launcher will set the required environment variables and invoke `torch.distributed.run`. \ No newline at end of file +You can change GPUS to the number of local GPUs you want to use. The launcher will set the required environment variables and invoke `torch.distributed.run`. diff --git a/docs/get_started/targets.md b/docs/get_started/targets.md index c2b3f2fb5a..3a464bd660 100644 --- a/docs/get_started/targets.md +++ b/docs/get_started/targets.md @@ -14,6 +14,7 @@ the generated code. The most frequent choices are listed below: | --------- | ----------- | | `auto` | Detects CUDA → HIP → Metal in that order. Useful when running the same script across machines. | | `cuda` | NVIDIA GPUs. Supports options such as `-arch=sm_80`, `-max_num_threads=1024`, etc. | +| `cutedsl` | NVIDIA CUTLASS/CuTe DSL backend. Requires `nvidia-cutlass-dsl`. `cuda` options can also be applied to this target. | | `hip` | AMD GPUs via ROCm. Options like `-mcpu=gfx90a` can be appended. | | `metal` | Apple Silicon GPUs (arm64 Macs). | | `llvm` | CPU execution; accepts the standard TVM LLVM switches. | diff --git a/docs/index.md b/docs/index.md index 5d9a158f80..ca5a125ebd 100644 --- a/docs/index.md +++ b/docs/index.md @@ -2,10 +2,10 @@ [GitHub](https://github.com/tile-ai/tilelang) -Tile Language (tile-lang) is a concise domain-specific language designed to streamline -the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). -By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, -tile-lang allows developers to focus on productivity without sacrificing the +Tile Language (tile-lang) is a concise domain-specific language designed to streamline +the development of high-performance GPU/CPU kernels (e.g., GEMM, Dequant GEMM, FlashAttention, LinearAttention). +By employing a Pythonic syntax with an underlying compiler infrastructure on top of TVM, +tile-lang allows developers to focus on productivity without sacrificing the low-level optimizations necessary for state-of-the-art performance. :::{toctree} @@ -17,13 +17,25 @@ get_started/overview get_started/targets ::: - :::{toctree} :maxdepth: 1 :caption: TUTORIALS tutorials/debug_tools_for_tilelang tutorials/auto_tuning +tutorials/logging +::: + +:::{toctree} +:maxdepth: 1 +:caption: PROGRAMMING GUIDES + +programming_guides/overview +programming_guides/language_basics +programming_guides/instructions +programming_guides/control_flow +programming_guides/autotuning +programming_guides/type_system ::: :::{toctree} @@ -33,6 +45,7 @@ tutorials/auto_tuning deeplearning_operators/elementwise deeplearning_operators/gemv deeplearning_operators/matmul +deeplearning_operators/matmul_sparse deeplearning_operators/deepseek_mla ::: @@ -42,6 +55,7 @@ deeplearning_operators/deepseek_mla compiler_internals/letstmt_inline compiler_internals/inject_fence_proxy +compiler_internals/tensor_checks ::: :::{toctree} diff --git a/docs/programming_guides/autotuning.md b/docs/programming_guides/autotuning.md new file mode 100644 index 0000000000..9cc5a2d94c --- /dev/null +++ b/docs/programming_guides/autotuning.md @@ -0,0 +1,308 @@ +# Autotuning + +TileLang includes a built‑in autotuner that searches configuration spaces +for the best performing kernel, compiles candidates in parallel, validates +correctness, benchmarks them, and caches the best result for reuse. + +This guide covers two workflows: +- Decorator‑based: `@tilelang.autotune(configs=...)` stacked on `@tilelang.jit` +- Programmatic: `AutoTuner.from_kernel(...).set_*().run()` + +It also explains input tensor supply, validation, caching, and environment +variables that affect parallelism and cache behavior. + +## 1) Decorator‑based Autotune + +Use `@tilelang.autotune` above `@tilelang.jit` and expose tunable parameters as +function arguments with defaults. The autotuner overrides these parameters with +values from your config space. + +```python +import tilelang +import tilelang.language as T + +def matmul_configs(M, N, K): + # Example space — tailor to your target + tiles = [64, 128] + stages = [2, 3] + threads = [128, 256] + return [ + dict(block_M=BM, block_N=BN, block_K=BK, num_stages=S, threads=TH) + for BM in tiles + for BN in tiles + for BK in [32, 64] + for S in stages + for TH in threads + ] + +@tilelang.autotune(configs=matmul_configs, warmup=25, rep=100, timeout=60) +@tilelang.jit(out_idx=[-1]) +def matmul(M: int, N: int, K: int, + block_M: int = 128, block_N: int = 128, block_K: int = 32, + threads: int = 128, num_stages: int = 3, + dtype: str = 'float16', accum_dtype: str = 'float32'): + + @T.prim_func + def kernel(A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype)): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_s = T.alloc_shared((block_M, block_K), dtype) + B_s = T.alloc_shared((block_K, block_N), dtype) + C_f = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_f) + + for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + T.copy(A[by * block_M, ko * block_K], A_s) + T.copy(B[ko * block_K, bx * block_N], B_s) + T.gemm(A_s, B_s, C_f) + + T.copy(C_f, C[by * block_M, bx * block_N]) + + return kernel + +# Usage +# Provide inputs via context (recommended for reproducibility across configs) +import torch +M = N = K = 1024 +A = torch.randn(M, K, device='cuda', dtype=torch.float16) +B = torch.randn(K, N, device='cuda', dtype=torch.float16) +C = torch.empty(M, N, device='cuda', dtype=torch.float16) + +from tilelang.autotuner import set_autotune_inputs +with set_autotune_inputs(A, B, C): + tuned_kernel = matmul(M, N, K) # compiles, tunes, returns best kernel + tuned_kernel(A, B, C) # run best kernel +``` + +Notes +- `configs` can be a list of dicts or a callable `(args...) -> list[dict]`. Each + dict’s keys must match the tunable function arguments (e.g., `block_M`). +- The decorator returns a callable that runs autotune once per argument tuple + and caches the resulting best kernel in‑process. +- For explicit input control during tuning, wrap the call with + `set_autotune_inputs(...)`. Otherwise, `supply_type` (below) is used. + +## 2) Programmatic Autotune + +Use the `AutoTuner` class to manage configs and arguments more explicitly. + +```python +from tilelang.autotuner import AutoTuner + +kernel_factory = matmul # the function above (already @tilelang.jit) +tuner = AutoTuner.from_kernel(kernel_factory(M, N, K), configs=matmul_configs(M, N, K)) + +tuner.set_profile_args( + warmup=25, rep=100, timeout=60, + supply_type=tilelang.TensorSupplyType.Auto, # or provide supply_prog/ref_prog + ref_prog=lambda A, B, C: torch.allclose(C, (A @ B).to(C.dtype), rtol=1e-2, atol=1e-2), +) + +tuner.set_compile_args( + target='auto', # or 'cuda'/'hip'/'metal' + execution_backend='auto', # resolves per-target + out_idx=[-1], # which outputs to return if multiple + pass_configs={ # optional TVM passes/flags + # tilelang.PassConfigKey.EXAMPLE_KEY: value, + }, +) + +artifact = tuner.run() # compiles + runs + validates all configs +best_kernel = artifact.kernel # JITKernel +best_latency = artifact.latency +best_config = artifact.config + +# Reuse best kernel +best_kernel(A, B, C) +``` + +### Example Gallery (in repo) +- examples/gdn/example_chunk_delta_h.py:101 — uses `@autotune` to sweep configs +- examples/deepseek_nsa/benchmark/benchmark_nsa_fwd.py:451 — uses `@tilelang.autotune` +- examples/quickstart.py:84 — profiles a tuned kernel with `get_profiler` +- examples/hadamard_transform/example_hadamard.py:152 — profiler with custom warmup +- examples/dynamic_shape/example_dynamic.py:94 — profiler for dynamic shapes +- examples/gemm/example_gemm_persistent.py:135 — compare persistent vs non‑persistent + +Click any path to open the code and compare patterns. + +## Input Tensor Supply + +The tuner needs inputs to compile and benchmark kernels. Provide them in one of +three ways (priority order): + +1) Context manager (fixed inputs across configs) +```python +with set_autotune_inputs(A, B, C): + tuned = matmul(M, N, K) +``` + +2) Custom supplier program +```python +def supply_prog(signature): + # signature holds KernelParam objects describing shapes/dtypes + # Return a list of torch tensors matching the kernel’s arguments + return [A, B, C] + +tuner.set_profile_args(supply_prog=supply_prog) +``` + +3) Built‑in generators via `supply_type` +- `TensorSupplyType.Auto` (default): heuristic per dtype (uniform ints / fp ranges) +- `Integer`, `Uniform`, `Normal`, `Randn`, `Zero`, `One` + +Important +- Built‑in generators require static shapes; if your PrimFunc uses symbolic + dimensions (T.dyn), supply concrete inputs via (1) or (2). +- Float8 dtypes require PyTorch 2.1+ for `torch.float8_*` support. + +## Correctness Checking and Tolerances + +Use one of the following validation methods: +- `ref_prog`: Provide a reference program that receives the same inputs and + checks results. You can return a boolean or raise on mismatch. +- `manual_check_prog`: A callable that inspects outputs and raises on mismatch. +- `skip_check=True`: Skip correctness checks (faster, use with caution). + +Control numeric drift via: +- `rtol` and `atol` (defaults 1e‑2) +- `max_mismatched_ratio` (default 1%) + +## Configuration Spaces and Best Practices + +What to tune +- Tile sizes: `block_M`, `block_N`, `block_K` +- Software pipelining: `num_stages` +- Threads per block: `threads` (or (x, y) tuple) +- Optional: dtype variants, epilogues, small scheduling knobs + +Tips +- Start from a working baseline. Tune a small, meaningful space first. +- Respect hardware limits (shared memory bytes, registers per thread/block, + max threads per block). Eliminate impossible configs up‑front. +- Keep block sizes multiples of vector widths and warp sizes when relevant. +- Use `set_autotune_inputs` to ensure each config is measured on identical data. +- Record your best configs and bake them as defaults when stable. + +## Parallel Compilation/Benchmarking and Timeouts + +The tuner compiles configurations in parallel using a thread pool and benchmarks +them with a per‑config timeout. On CUDA, each worker sets the current device to +avoid context issues. + +Notes +- `timeout` uses POSIX signals; on non‑Unix systems, it may not take effect. +- Logs are written to `autotuner.log` in the working directory. + +## Caching + +The autotuner caches best artifacts both in‑memory (per process) and on disk under +`$TILELANG_CACHE_DIR/autotuner`. The cache key includes: +- TileLang version, function source, closure free‑vars +- Config list, compile args, profile args + +Disk cache contents (per key) +- Best config and latency: `best_config.json`, `latency.json` +- Kernel sources and library: `device_kernel.cu`, `host_kernel.cu`, `kernel_lib.so` (or `kernel.cubin`/`executable.so` depending on backend) +- Function and params: `function.pkl`, `params.pkl` + +Control via env vars (tilelang.env) +- `TILELANG_CACHE_DIR` (default `~/.tilelang/cache`) +- `TILELANG_TMP_DIR` (default `$TILELANG_CACHE_DIR/tmp`) +- Disable all kernel caches: `TILELANG_DISABLE_CACHE=1` +- Disable autotune disk cache only: `TILELANG_AUTO_TUNING_DISABLE_CACHE=1` + +CPU worker control +- `TILELANG_AUTO_TUNING_CPU_UTILITIES` (fraction, default 0.9) +- `TILELANG_AUTO_TUNING_CPU_COUNTS` (int, `-1` auto) +- `TILELANG_AUTO_TUNING_MAX_CPU_COUNT` (int, `-1` unlimited) + +Backend notes +- NVRTC backend persists `.cubin` and a Python launcher. +- Torch/DLPack backend may not save artifacts to disk; in this case, only + in‑memory caching applies and a warning is logged. + +## Alternative: Manual Sweeps with par_compile + +If you prefer manual control, use `JITImpl.par_compile` to compile a batch of +configs and drive your own benchmarking: + +```python +@tilelang.jit +def factory(M, N, K, block_M=128, block_N=128, block_K=32): + @T.prim_func + def k(A: T.Tensor((M, K), 'float16'), + B: T.Tensor((K, N), 'float16'), + C: T.Tensor((M, N), 'float16')): + ... + return k + +impl = factory # JITImpl +cfgs = [ + dict(block_M=64, block_N=128, block_K=32), + dict(block_M=128, block_N=128, block_K=64), +] +kernels = impl.par_compile(cfgs, num_workers=4) +# Now benchmark kernels[i](A, B, C) yourself +``` + +## Recording and Reusing Best Configs + +The programmatic path returns an `AutotuneResult` that can be saved and later +reloaded. This is useful for CI, multi‑host workflows, or shipping tuned configs. + +```python +artifact = tuner.run() # AutotuneResult + +# Save to disk +from pathlib import Path +save_dir = Path('out/best/matmul_1024') +artifact.save_to_disk(save_dir, verbose=True) + +# Reload later +from tilelang.autotuner.param import AutotuneResult, CompileArgs +restored = AutotuneResult.load_from_disk(save_dir, CompileArgs()) +best = restored.kernel +best(A, B, C) +``` + +Notes +- DLPack/Torch execution backend may not persist compiled binaries; in that + case, re‑compilation is needed on load or use a different backend. +- The directory contains human‑readable JSONs (best config/latency) and sources. + +## Advanced: Config Space Callables + +Derive config spaces from problem sizes to keep searches targeted and legal: + +```python +def matmul_configs(M, N, K): + large = min(M, N, K) >= 1024 + tiles = [128] if large else [64, 128] + for BM in tiles: + for BN in tiles: + for BK in [32, 64]: + for S in [2, 3]: + for TH in [128, 256]: + yield dict(block_M=BM, block_N=BN, block_K=BK, + num_stages=S, threads=TH) +``` + +## Device and Backend Selection + +Tune compile‑time options explicitly: +- `target='auto'|'cuda'|'hip'|'metal'` (normalized to a TVM Target) +- `execution_backend='auto'|'tvm_ffi'|'cython'|'nvrtc'|'torch'` +- `pass_configs={...}` to toggle TileLang/TVM passes for experiments + +On CUDA with multiple GPUs, the tuner sets the current device per worker thread +to avoid context mixups. + +## Troubleshooting +- “No configurations to tune”: Ensure `configs` is a non‑empty list or callable. +- Timeouts: Increase `timeout`; ensure inputs fit device memory; verify that + your reference check isn’t the bottleneck. +- Dynamic shapes: Provide concrete inputs via `set_autotune_inputs` or a custom + `supply_prog`. +- Disk cache disabled: Check `TILELANG_AUTO_TUNING_DISABLE_CACHE` and backend. diff --git a/docs/programming_guides/control_flow.md b/docs/programming_guides/control_flow.md new file mode 100644 index 0000000000..158c51166e --- /dev/null +++ b/docs/programming_guides/control_flow.md @@ -0,0 +1,145 @@ +# Control Flow + +This guide covers the control‑flow primitives in TileLang and how they lower to +efficient GPU code. You will use these to structure loops, handle boundaries, +and express pipelined compute. + +## Overview +- Conditionals: `if` / `elif` / `else`, ternary (`x if c else y`) +- Loops: `T.serial`, `T.unroll`, `T.Parallel`, `T.Pipelined` +- While loops: `while` with a TIR condition +- Flow control: Python `break` / `continue` +- Safety: automatic OOB guards via the LegalizeSafeMemoryAccess pass + +The examples assume `import tilelang.language as T`. + +## Conditionals + +Standard Python `if`/`elif`/`else` is supported inside `@T.prim_func` kernels. +Conditions should be TIR expressions (e.g., `i < N`). Python plain booleans are +treated as compile‑time constants and will be folded. + +```python +for i in T.serial(N): + if i < N: # TIR condition + C[i] = A[i] + B[i] + else: + pass + +# Ternary +x = (A[i] if i < N else 0) +``` + +Short‑circuit boolean ops are supported. For multi‑dimensional bounds, use +`T.any_of` / `T.all_of` for clarity: + +```python +if T.all_of(i < M, j < N): + C[i, j] = A[i, j] + B[i, j] +``` + +Boundary handling note +- The LegalizeSafeMemoryAccess pass automatically inserts guards when an access + may be out‑of‑bounds, and elides them when proven safe. You can often omit + explicit `if` checks for simple edge handling, but keep them when you need + custom logic or clarity. + +## Loops + +### Serial + +`T.serial` creates a plain for‑loop. Common forms: + +```python +for i in T.serial(N): + ... # 0..N-1 + +for i in T.serial(0, N, 2): + ... # 0, 2, 4, ... +``` + +### Unroll + +`T.unroll` requests loop unrolling for small trip counts. + +```python +for k in T.unroll(K_TILE): + acc += a[k] * b[k] +``` + +Advanced: TileLang forwards unroll hints to TIR; factor/explicit knobs are +available for expert tuning. + +### Parallel (elementwise) + +`T.Parallel(ext0, ext1, ...)` builds nested loops that map well to elementwise +operations. The body receives all indices in one `for` header: + +```python +for i, j in T.Parallel(M, N): + C[i, j] = A[i, j] + B[i, j] +``` + +Optional: `coalesced_width=` can hint memory coalescing for the innermost loop. + +### Pipelined (software pipelining) + +`T.Pipelined(iters, num_stages=...)` overlaps producer/consumer stages (e.g., +Global→Shared copies with compute). This is the backbone of GEMM/attention +pipelines. + +```python +for ko in T.Pipelined(T.ceildiv(K, BK), num_stages=3): + T.copy(A[by * BM, ko * BK], A_s) # stage: copy A tile + T.copy(B[ko * BK, bx * BN], B_s) # stage: copy B tile + T.gemm(A_s, B_s, C_f) # stage: compute +``` + +### Persistent (advanced) + +`T.Persistent(domain, wave_size, index, group_size=...)` exposes persistent +thread‑block style looping. It is an advanced construct that TileLang lowers in +later passes and is typically used by specialized templates. + +## While Loops + +`while` is supported when the condition is a TIR expression. Avoid infinite +loops; TileLang will error if it detects a constant‑true condition. + +```python +i = 0 +while i < N: + ... + if done: + break + i += 1 +``` + +## Break and Continue + +Use Python `break`/`continue` to exit or skip within `T.serial`/`T.unroll`/ +`T.Parallel`/`while` loops. Keep the body clean after a `break`/`continue` for +readability; the compiler will ignore the dead path. + +## Putting It Together: Residual Tile Handling + +Below is a typical edge pattern for a 2D kernel. With LegalizeSafeMemoryAccess, +the explicit guard can be omitted when you don’t need a custom edge path. + +```python +for i, j in T.Parallel(M, N): + gi = by * BM + i + gj = bx * BN + j + if T.all_of(gi < M, gj < N): # optional in many cases + C[gi, gj] = A[gi, gj] + B[gi, gj] +``` + +## Debugging Conditions + +Use `T.print` to inspect values under predicates. For buffers, TileLang prints +from a single thread to avoid duplicate outputs. + +```python +if i == 0: + T.print(C, msg='C tile:') +``` diff --git a/docs/programming_guides/instructions.md b/docs/programming_guides/instructions.md new file mode 100644 index 0000000000..69025c3473 --- /dev/null +++ b/docs/programming_guides/instructions.md @@ -0,0 +1,180 @@ +# Instructions + +This page summarizes the core TileLang “instructions” available at the DSL +level, how they map to hardware concepts, and how to use them correctly. + +## Quick Categories +- Data movement: `T.copy`, `T.c2d_im2col`, staging Global ↔ Shared ↔ Fragment +- Compute primitives: `T.gemm`/`T.gemm_sp`, elementwise math (`T.exp`, `T.max`), + reductions (`T.reduce_sum`, `T.cumsum`, warp reducers) +- Control helpers: `T.clear`/`T.fill`, `T.reshape`/`T.view` +- Diagnostics: `T.print`, `T.device_assert` +- Advanced: atomics, memory barriers, warp‑group ops + +## Data Movement + +Use `T.copy(src, dst, coalesced_width=None, disable_tma=False, eviction_policy=None)` +to move tiles between memory scopes. It accepts `tir.Buffer`, `BufferLoad`, or +`BufferRegion`; extents are inferred or broadcast when possible. + +```python +# Global → Shared tiles (extents inferred from dst) +T.copy(A[by * BM, ko * BK], A_s) +T.copy(B[ko * BK, bx * BN], B_s) + +# Fragment/Register → Global (store result) +T.copy(C_f, C[by * BM, bx * BN]) +``` + +Semantics +- Extents are deduced from arguments; missing sides broadcast to the other’s rank. +- Access patterns are legalized and coalesced during lowering. Explicit + vectorization is not required in HL mode. +- Safety: the LegalizeSafeMemoryAccess pass inserts boundary guards when an + access may be out‑of‑bounds and drops them when proven safe. + +Other helpers +- `T.c2d_im2col(img, col, ...)`: convenience for conv‑style transforms. + +## Compute Primitives + +GEMM and sparse GEMM +- `T.gemm(A_shared, B_shared, C_fragment)`: computes a tile GEMM using shared + inputs and a fragment accumulator; lowered to target‑specific tensor cores. +- `T.gemm_sp(...)`: 2:4 sparse tensor core variant (see examples and README). + +Reductions and scans +- `T.reduce_sum`, `T.reduce_max`, `T.reduce_min`, `T.cumsum`, plus warp + reducers (`T.warp_reduce_sum`, etc.). +- Allocate and initialize accumulators via `T.alloc_fragment` + `T.clear` or + `T.fill`. + +Elementwise math +- Most math ops mirror TVM TIR: `T.exp`, `T.log`, `T.max`, `T.min`, `T.rsqrt`, + `T.sigmoid`, etc. Compose freely inside loops. + +Reshape/view (no copy) +- `T.reshape(buf, new_shape)` and `T.view(buf, shape=None, dtype=None)` create + new views that share storage, with shape/dtype checks enforced. + +## Synchronization (HL usage) + +In HL pipelines, you usually don’t need to write explicit barriers. Passes such +as PipelinePlanning/InjectSoftwarePipeline/InjectTmaBarrier orchestrate +producer/consumer ordering and thread synchronization behind the scenes. + +If you need debugging or explicit checks: +- `T.device_assert(cond, msg='')` emits device‑side asserts on CUDA targets. +- `T.print(obj, msg='...')` prints scalars or buffers safely from one thread. + +## Putting It Together: GEMM Tile + +```python +@T.prim_func +def gemm( + A: T.Tensor((M, K), 'float16'), + B: T.Tensor((K, N), 'float16'), + C: T.Tensor((M, N), 'float16'), +): + with T.Kernel(T.ceildiv(N, BN), T.ceildiv(M, BM), threads=128) as (bx, by): + A_s = T.alloc_shared((BM, BK), 'float16') + B_s = T.alloc_shared((BK, BN), 'float16') + C_f = T.alloc_fragment((BM, BN), 'float32') + T.clear(C_f) + + for ko in T.Pipelined(T.ceildiv(K, BK), num_stages=3): + T.copy(A[by * BM, ko * BK], A_s) # Global → Shared + T.copy(B[ko * BK, bx * BN], B_s) + T.gemm(A_s, B_s, C_f) # compute into fragment + + T.copy(C_f, C[by * BM, bx * BN]) # store back +``` + +## Instruction Reference (Concise) + +Below is a concise list of TileLang instructions grouped by category. For full +signatures, behaviors, constraints, and examples, refer to API Reference +(`autoapi/tilelang/index`). + +Data movement +- `T.copy(src, dst, ...)`: Move tiles between Global/Shared/Fragment. +- `T.c2d_im2col(img, col, ...)`: 2D im2col transform for conv. + +Memory allocation and descriptors +- `T.alloc_shared(shape, dtype, scope='shared.dyn')`: Allocate shared buffer. +- `T.alloc_fragment(shape, dtype, scope='local.fragment')`: Allocate fragment. +- `T.alloc_var(dtype, [init], scope='local.var')`: Scalar var buffer (1 elem). +- `T.alloc_barrier(arrive_count)`: Shared barrier buffer. +- `T.alloc_tmem(shape, dtype)`: Tensor memory (TMEM) buffer (Hopper+). +- `T.alloc_reducer(shape, dtype, op='sum', replication=None)`: Reducer buf. +- `T.alloc_descriptor(kind, dtype)`: Generic descriptor allocator. + - `T.alloc_wgmma_desc(dtype='uint64')` + - `T.alloc_tcgen05_smem_desc(dtype='uint64')` + - `T.alloc_tcgen05_instr_desc(dtype='uint32')` +- `T.empty(shape, dtype='float32')`: Declare function output tensors. + +Compute primitives +- `T.gemm(A_s, B_s, C_f)`: Tile GEMM into fragment accumulator. +- `T.gemm_sp(...)`: Sparse (2:4) tensor core GEMM. +- Reductions: `T.reduce_sum/max/min/abssum/absmax`, bitwise `and/or/xor`. +- Scans: `T.cumsum`, finalize: `T.finalize_reducer`. +- Warp reducers: `T.warp_reduce_sum/max/min/bitand/bitor`. +- Elementwise math: TIR ops (`T.exp`, `T.log`, `T.max`, `T.min`, `T.rsqrt`, ...). +- Fast math: `T.__log/__log2/__log10/__exp/__exp2/__exp10/__sin/__cos/__tan`. +- IEEE math: `T.ieee_add/sub/mul/fmaf` (configurable rounding). +- Helpers: `T.clear(buf)`, `T.fill(buf, value)`. +- Views: `T.reshape(buf, shape)`, `T.view(buf, shape=None, dtype=None)`. + +Diagnostics +- `T.print(obj, msg='')`: Print scalar/buffer from one thread. +- `T.device_assert(cond, msg='')`: Device-side assert (CUDA). + +Logical helpers +- `T.any_of(a, b, ...)`, `T.all_of(a, b, ...)`: Multi-term predicates. + +Annotation helpers +- `T.use_swizzle(panel_size=..., enable=True)`: Rasterization hint. +- `T.annotate_layout({...})`: Attach explicit layouts to buffers. +- `T.annotate_safe_value(var, ...)`: Safety/const hints. +- `T.annotate_l2_hit_ratio(buf, ratio)`: Cache behavior hint. + +Atomics +- `T.atomic_add(dst, value, memory_order=None, return_prev=False, use_tma=False)`. +- `T.atomic_addx2(dst, value, return_prev=False)`; `T.atomic_addx4(...)`. +- `T.atomic_max(dst, value, memory_order=None, return_prev=False)`. +- `T.atomic_min(dst, value, memory_order=None, return_prev=False)`. +- `T.atomic_load(dst)`, `T.atomic_store(dst, value)`. + +Custom intrinsics +- `T.dp4a(A, B, C)`: 4‑element dot‑product accumulate. +- `T.clamp(x, lo, hi)`: Clamp to [lo, hi]. +- `T.loop_break()`: Break from current loop via intrinsic. + +Barriers, TMA, warp‑group +- Barriers: `T.create_list_of_mbarrier(...)`, `T.get_mbarrier(i)`. +- Parity ops: `T.mbarrier_wait_parity(barrier, parity)`, `T.mbarrier_arrive(barrier)`. +- Expect tx: `T.mbarrier_expect_tx(...)`; sugar: `T.barrier_wait(id, parity=None)`. +- TMA: `T.create_tma_descriptor(...)`, `T.tma_load(...)`, + `T.tma_store_arrive(...)`, `T.tma_store_wait(...)`. +- Proxy/fences: `T.fence_proxy_async(...)`, `T.warpgroup_fence_operand(...)`. +- Warp‑group: `T.warpgroup_arrive()`, `T.warpgroup_commit_batch()`, + `T.warpgroup_wait(num_mma)`, `T.wait_wgmma(id)`. + +Lane/warp index +- `T.get_lane_idx(warp_size=None)`: Lane id in warp. +- `T.get_warp_idx_sync(warp_size=None)`: Canonical warp id (sync). +- `T.get_warp_idx(warp_size=None)`: Canonical warp id (no sync). +- `T.get_warp_group_idx(warp_size=None, warps_per_group=None)`: Group id. + +Register control +- `T.set_max_nreg(reg_count, is_inc)`, `T.inc_max_nreg(n)`, `T.dec_max_nreg(n)`. +- `T.annotate_producer_reg_dealloc(n=24)`, `T.annotate_consumer_reg_alloc(n=240)`. +- `T.no_set_max_nreg()`, `T.disable_warp_group_reg_alloc()`. + +## Notes on Dtypes + +Dtypes accept three equivalent forms: +- String: `'float32'` +- TileLang dtype: `T.float32` +- Framework dtype: `torch.float32` +All are normalized internally. See Type System for details. diff --git a/docs/programming_guides/language_basics.md b/docs/programming_guides/language_basics.md new file mode 100644 index 0000000000..1152680c97 --- /dev/null +++ b/docs/programming_guides/language_basics.md @@ -0,0 +1,234 @@ +# Language Basics + +This page introduces the core TileLang (tile‑lang) DSL that you’ll use to write +high‑performance kernels. It focuses on how to define a kernel, express +iteration, move data across memory scopes, and run it with JIT. + +The examples use the conventional aliases: + +```python +import tilelang +import tilelang.language as T +from tilelang import jit +``` + +## 1. Defining a Kernel with `@T.prim_func` + +TileLang kernels are TIR (TVM IR) functions produced by the `@T.prim_func` +decorator. Arguments are annotated with shapes and dtypes via `T.Tensor` or +`T.Buffer`. + +Note on dtypes +- You can pass dtypes as a string (e.g., 'float32'), a TileLang dtype (e.g., `T.float32`), + or a framework dtype (e.g., `torch.float32`). TileLang normalizes all of these. + See Type System for details. + +```python +@T.prim_func +def add_kernel( + A: T.Tensor((N,), dtype), # dtype could be 'float32' | T.float32 | torch.float32 + B: T.Tensor((N,), dtype), + C: T.Tensor((N,), dtype), +): + ... # kernel body +``` + +- Shapes may be concrete integers or symbolic. For symbolic, you can pass + Python ints through the outer `@jit` wrapper (shown below), or annotate with + `T.dyn` when you want a named symbolic dimension. + +```python +# Named symbolic dimension (optional) +K = T.dyn['K'] +@T.prim_func +def uses_dyn(A: T.Tensor((K,), 'float32')): + ... +``` + +### Dynamic symbolic dimensions: two ways + +TileLang supports two complementary ways to introduce symbolic (dynamic) dims: + +- Type-level annotations via `T.dyn[...]` (recommended for function signatures) + - Use in `T.Tensor((T.dyn['K'], ...), dtype)` or bind once then reuse (as above). + - Inside the kernel body, prefer reading from the buffer’s shape, e.g. `M = A.shape[0]`. + +- Term-level variables via `T.dynamic(name, dtype)` + - Creates a TIR `tir.Var` you can use directly in expressions/loops. + - Handy when you need to reference the dimension symbol in the body. + +```python +# 1) Annotation-only symbol; read the bound size via shape +K = T.dyn['K'] # dtype defaults to int32 +@T.prim_func +def foo(A: T.Tensor((K,), 'float32')): + N = A.shape[0] + for i in T.serial(N): + ... + +# 2) Explicit Var symbol usable in the body +K = T.dynamic('K', 'int32') # or T.dynamic('K') defaults to int32 +@T.prim_func +def bar(A: T.Tensor((K,), 'float32')): + for i in T.serial(K): + ... +``` + +Notes +- `T.symbolic(name, dtype)` is a deprecated alias of `T.dynamic`; prefer `T.dynamic`. +- Under `@jit`, concrete sizes come from the actual tensor arguments at the first call. +- Symbols in annotations do not need to be separate kernel arguments; TileLang binds them from argument shapes. + +## 2. Launching Work with `T.Kernel` + +`with T.Kernel(...)` declares a launch context and creates block/thread +bindings. For GPU backends, specify a grid and threads per block. + +```python +with T.Kernel(grid_x, grid_y, threads=128) as (bx, by): + ... # bx/by are blockIdx.x/y +``` + +You rarely need raw thread indices; most kernels use structured loops +(`T.serial`, `T.unroll`, `T.Parallel`, `T.Pipelined`) inside a `T.Kernel`. + +## 3. Loops and Control Flow + +Core loop constructs map to familiar hardware patterns: + +- `T.serial(start, stop[, step])`: plain for‑loop +- `T.unroll(start, stop[, step])`: unrolled loop +- `T.Parallel(ext0, ext1, ...)`: nested parallel loops (elementwise‑friendly) +- `T.Pipelined(iters, num_stages=N)`: software pipelining for producer/consumer + +```python +for i in T.serial(N): + ... + +for i, j in T.Parallel(M, N): + C[i, j] = A[i, j] + B[i, j] + +for k in T.Pipelined(T.ceildiv(K, BK), num_stages=3): + # overlap copy/compute across stages + ... +``` + +Conditionals use standard Python `if`/`else`. Guard edges with predicates when +tile sizes do not divide problem sizes evenly. + +## 4. Memory Scopes and Allocation + +TileLang exposes key software‑managed scopes: + +- Global: device memory (default for `T.Tensor` arguments) +- Shared: on‑chip, block‑visible (`T.alloc_shared(shape, dtype)`) +- Fragment and scalars: per‑thread fragments and scalar vars but in Shared View + (`T.alloc_fragment`, `T.alloc_var`) + +```python +A_shared = T.alloc_shared((BM, BK), 'float16') +B_shared = T.alloc_shared((BK, BN), 'float16') +C_local = T.alloc_fragment((BM, BN), 'float32') +T.clear(C_local) # zero accumulators +``` + +## 5. Moving Data: `T.copy` + +Use `T.copy(src, dst)` to move tiles between scopes. It accepts buffers, +buffer regions, or buffer loads; extents are inferred or can be broadcast. + +```python +# Global -> Shared (tile copy), extents inferred from dst +T.copy(A[by * BM, ko * BK], A_shared) +T.copy(B[ko * BK, bx * BN], B_shared) + +# Fragment -> Global (store back) +T.copy(C_local, C[by * BM, bx * BN]) +``` + +`T.copy` performs coalescing and scope‑specific lowering during compilation. + +## 6. A Minimal End‑to‑End Example (Vector Add) + +```python +import tilelang +import tilelang.language as T +from tilelang import jit + +@jit # infers target from tensors at first call +def add(N: int, block: int = 256, dtype: str = 'float32'): + + @T.prim_func + def add_kernel( + A: T.Tensor((N,), dtype), + B: T.Tensor((N,), dtype), + C: T.Tensor((N,), dtype), + ): + with T.Kernel(T.ceildiv(N, block), threads=block) as bx: + for i in T.Parallel(block): + gi = bx * block + i + # Optional — LegalizeSafeMemoryAccess inserts a guard when an access may be OOB + C[gi] = A[gi] + B[gi] + + return add_kernel + +# Host side (PyTorch shown; NumPy/DLPack also supported) +import torch +N = 1 << 20 +A = torch.randn(N, device='cuda', dtype=torch.float32) +B = torch.randn(N, device='cuda', dtype=torch.float32) +C = torch.empty(N, device='cuda', dtype=torch.float32) + +kernel = add(N) +kernel(A, B, C) # runs on GPU +torch.testing.assert_close(C, A + B) +``` + +Notes +- The `@jit` wrapper returns a callable kernel after the first compilation. +- You can pass compile‑time tunables (tile sizes, dtypes) through the outer + Python function and bake them into the generated TIR. + +## 7. Tiled GEMM Skeleton + +Below is a minimal pattern for a tiled GEMM using shared memory staging and a +fragment accumulator. It mirrors the quickstart style found in the repository. + +```python +@T.prim_func +def gemm( + A: T.Tensor((M, K), 'float16'), + B: T.Tensor((K, N), 'float16'), + C: T.Tensor((M, N), 'float16'), +): + with T.Kernel(T.ceildiv(N, BN), T.ceildiv(M, BM), threads=128) as (bx, by): + A_s = T.alloc_shared((BM, BK), 'float16') + B_s = T.alloc_shared((BK, BN), 'float16') + C_f = T.alloc_fragment((BM, BN), 'float32') + T.clear(C_f) + + for ko in T.Pipelined(T.ceildiv(K, BK), num_stages=3): + T.copy(A[by * BM, ko * BK], A_s) + T.copy(B[ko * BK, bx * BN], B_s) + T.gemm(A_s, B_s, C_f) # lowered to tensor‑core/ISA specific kernels + + T.copy(C_f, C[by * BM, bx * BN]) +``` + +## 8. Debugging and Printing + +Use `T.print` inside a kernel for quick introspection. TileLang emits printing +from a single thread for shared/fragment scopes to avoid floods. + +```python +T.print(C_f, msg='accumulator:') +T.print(A_s, msg='A tile:') +T.print(C[0], msg='C[0] = ') +``` + +## 9. Where to Go Next + +- Control flow details: see Programming Guides → Control Flow +- Memory topics: see Programming Guides → (removed cache/layout); basics are covered inline +- Autotuning tile sizes and mappings: Programming Guides → Autotuning +- Operator examples (GEMM, GEMV, attention): see Deep Learning Operators diff --git a/docs/programming_guides/overview.md b/docs/programming_guides/overview.md new file mode 100644 index 0000000000..64b6d20390 --- /dev/null +++ b/docs/programming_guides/overview.md @@ -0,0 +1,27 @@ +# Programming Guides Overview + +This section provides a practical guide to writing high‑performance kernels with Tile Language (tile‑lang). +It mirrors the structure of a similar guide in another project and adapts it to tile‑lang concepts and APIs. + +- Audience: Developers implementing custom GPU/CPU kernels with tile‑lang +- Prereqs: Basic Python, NumPy/Tensor concepts, and familiarity with GPU programming notions +- Scope: Language basics, control flow, instructions, autotuning, and type system + +## What You’ll Learn +- How to structure kernels with TileLang’s core DSL constructs +- How to move data across global/shared/fragment and pipeline compute +- How to apply autotuning to tile sizes and schedules +- How to specify and work with dtypes in kernels + +## Suggested Reading Order +1. Language Basics +2. Control Flow +3. Instructions +4. Autotuning +5. Type System + +## Related Docs +- Tutorials: see existing guides in `tutorials/` +- Operators: examples in `deeplearning_operators/` + +> NOTE: This is a draft scaffold. Fill in code snippets and benchmarks as APIs evolve. diff --git a/docs/programming_guides/type_system.md b/docs/programming_guides/type_system.md new file mode 100644 index 0000000000..60061df3f4 --- /dev/null +++ b/docs/programming_guides/type_system.md @@ -0,0 +1,41 @@ +# Type System + +This page lists the data types supported by TileLang and how to specify them in +kernels. For full details and the authoritative list, see the API Reference +(`autoapi/tilelang/index`) and `tilelang.language.v2.dtypes`. + +How to specify dtypes +- Use any of the following forms; TileLang normalizes them internally: + - String: `'float32'`, `'int8'`, `'bfloat16'`, ... + - TileLang dtype object: `T.float32`, `T.int8`, `T.bfloat16`, ... + - Framework dtype: `torch.float32`, `torch.int8`, `torch.bfloat16`, ... + +Common scalar types +- Boolean: `bool` +- Signed integers: `int8`, `int16`, `int32`, `int64` +- Unsigned integers: `uint8`, `uint16`, `uint32`, `uint64` +- Floating‑point: `float16` (half), `bfloat16`, `float32`, `float64` + +Float8 and low‑precision families +- Float8: `float8_e3m4`, `float8_e4m3`, `float8_e4m3b11fnuz`, `float8_e4m3fn`, + `float8_e4m3fnuz`, `float8_e5m2`, `float8_e5m2fnuz`, `float8_e8m0fnu` +- Float6: `float6_e2m3fn`, `float6_e3m2fn` +- Float4: `float4_e2m1fn` + +Vectorized element types (SIMD packs) +- For many base types, vector‑packed variants are available by lane count: + `x2`, `x4`, `x8`, `x16`, `x32`, `x64`. +- Examples: + - Integers: `int8x2`, `int8x4`, ..., `int32x2`, `int32x4`, ... + - Unsigned: `uint8x2`, `uint8x4`, ... + - Floats: `float16x2`, `float16x4`, `float32x2`, `float32x4`, ... + - Float8/6/4 families also provide `x2/x4/x8/x16/x32/x64` where applicable, + e.g., `float8_e4m3x2`, `float8_e4m3x4`, `float6_e2m3fnx8`, `float4_e2m1fnx16`. + +Notes +- Availability of certain low‑precision formats (float8/6/4) depends on target + architecture and backend support. +- Choose accumulation dtypes explicitly for mixed‑precision compute (e.g., + GEMM with `float16` inputs and `float32` accumulators). +- The complete, up‑to‑date list is exposed in + `tilelang.language.v2.dtypes` and rendered in the API Reference. diff --git a/docs/spelling_wordlist.txt b/docs/spelling_wordlist.txt index e859d0e7b1..6fd4334594 100644 --- a/docs/spelling_wordlist.txt +++ b/docs/spelling_wordlist.txt @@ -1,4 +1,5 @@ cancelled +HDA hsa ist LOD diff --git a/docs/tutorials/auto_tuning.md b/docs/tutorials/auto_tuning.md index 3f3cad8322..33368a2f0c 100644 --- a/docs/tutorials/auto_tuning.md +++ b/docs/tutorials/auto_tuning.md @@ -14,7 +14,7 @@ Auto-tuning a Tile Language program involves three main steps: ## Matrix Multiplication Example -The following example demonstrates auto-tuning matrix multiplication. Code has been simplified for readability - see `examples/gemm/example_gemm.py` for complete implementation. +The following example demonstrates auto-tuning matrix multiplication. Code has been simplified for readability - see `examples/gemm/example_gemm.py` for complete implementation. ### Step 1: Implement with Reserved Parameters Users can implement matrix multiplication in Tile Language while reserving parameters for optimization: @@ -145,4 +145,4 @@ for hint in roller_hints: config["thread_num"] = block_rows * block_cols * 32 config["enable_rasteration"] = hint.rasterization_plan is not NoRasterization -``` \ No newline at end of file +``` diff --git a/docs/tutorials/debug_tools_for_tilelang.md b/docs/tutorials/debug_tools_for_tilelang.md index e18b132795..d98d4cb5e1 100644 --- a/docs/tutorials/debug_tools_for_tilelang.md +++ b/docs/tutorials/debug_tools_for_tilelang.md @@ -12,7 +12,6 @@ A Tile Language program (hereafter referred to as a *program*) is transformed in 2. The program undergoes multiple *Passes* for transformation and optimization (the *lower* stage, see `tilelang/engine/lower.py`), finally producing an intermediate representation (e.g., LLVM or C for CPU, CUDA for NVIDIA GPUs, etc.). 3. The generated code is compiled by the respective compiler (e.g., nvcc) into a hardware-executable file. - ```{figure} ../_static/img/overview.png :width: 300 :alt: Overview of the compilation process @@ -22,9 +21,9 @@ A Tile Language program (hereafter referred to as a *program*) is transformed in During this process, users may encounter roughly three categories of issues: -* **Generation issues**: The Tile Language program fails to generate a valid hardware-executable file (i.e., errors during the lowering process). -* **Correctness issues**: The resulting executable runs, but produces incorrect results. -* **Performance issues**: The executable runs with performance significantly below the expected theoretical hardware limits. +- **Generation issues**: The Tile Language program fails to generate a valid hardware-executable file (i.e., errors during the lowering process). +- **Correctness issues**: The resulting executable runs, but produces incorrect results. +- **Performance issues**: The executable runs with performance significantly below the expected theoretical hardware limits. This tutorial focuses on the first two issues—how to debug generation and correctness problems. Performance tuning often requires using vendor-provided profiling tools (e.g., **Nsight Compute**, **rocProf**, etc.) for further hardware-level analysis, which we will address in future materials. @@ -52,7 +51,6 @@ func = matmul(1024, 1024, 1024, 128, 128, 32) TileLang essentially performs *progressive lowering*. For example, a `T.copy` may first be expanded into `T.Parallel` (see the pass `LowerTileOP`), which is then expanded again, eventually resulting in lower-level statements that can be translated to CUDA C code. - ```{figure} ../_static/img/ir_transform_diagram.png :width: 400 :alt: IR transformation diagram @@ -171,6 +169,31 @@ The output messages will include something like: msg='hello world' BlockIdx=(0, 0, 0), ThreadIdx=(0, 0, 0): 0 ``` +### Visual Layout Inference For TileLang + The **Visual Layout Inference** tool automatically generates visual diagrams that illustrate the mapping between logical indices, thread IDs, and register file locations. + +When TileLang performs layout inference, it determines how fragment buffers are distributed across threads. The visual layout tool captures this information and generates: +1. **Textual output**: A human-readable description of the layout mapping +2. **Visual diagrams**: Color-coded plots showing the thread-to-data mapping + +The visual layout inference tool is controlled through the `TL_LAYOUT_VISUALIZATION_ENABLE` and `TL_LAYOUT_VISUALIZATION_FORMATS` pass configuration. By default, `TL_LAYOUT_VISUALIZATION_ENABLE` is **disabled** to avoid performance overhead during compilation. + +When enabled, `TL_LAYOUT_VISUALIZATION_FORMATS` accepts string values to control output formats: +- "txt": Text output only (same as default) +- "all": Generates all formats (TXT, PDF, PNG, SVG) +- "png": Generate PNG format only +- "pdf": Generate PDF format only +- "svg": Generate SVG format only +- "txt,svg": Generate multiple formats (comma-separated) in addition to text output + +The output messages of "txt" will include something like: +``` +C_local inferenced layout: + Shape: [32, 32] -> [8] + Thread: _j // 16 * 64 + _i // 16 * 32 + _i % 8 * 4 + _j % 8 // 2 + Index: [_j % 16 // 8 * 4 + _i % 16 // 8 * 2 + _j % 2] +``` + ## Conclusion By carefully examining intermediate representations (IR) before final code generation—and by leveraging runtime printing through `T.print`—one can quickly diagnose where index calculations, copy logic, or other kernel operations deviate from the intended behavior. This two-pronged approach (inspecting IR transformations and using runtime prints) is often sufficient for resolving generation and correctness issues in TileLang programs. diff --git a/docs/tutorials/logging.md b/docs/tutorials/logging.md new file mode 100644 index 0000000000..1a015432db --- /dev/null +++ b/docs/tutorials/logging.md @@ -0,0 +1,116 @@ +Logging in Tilelang/TVM +=================================================== +
+Author: SiriusNEO +
+ +## TVM Logging Overview + +Tilelang currently utilizes the logging system from TVM. The implementation can be found in: + +- [include/tvm/runtime/logging.h](https://github.com/apache/tvm/blob/main/include/tvm/runtime/logging.h): Macro definitions +- [src/runtime/logging.cc](https://github.com/apache/tvm/blob/main/src/runtime/logging.cc): Logging logic implementation + +The design style is inspired by [Google's glog](https://google.github.io/glog/stable/). + +## Logging Categories + +There are three primary macro types: + +```c++ +LOG(INFO) << "aaa"; +DLOG(INFO) << "aaa"; +VLOG(1) << "aaa"; +``` + +- **LOG**: Standard logging preserved in code for displaying necessary information at different levels during runtime. Most Tilelang C++ error reporting is implemented via `LOG(FATAL) << "error msg"`. +- **DLOG**: Debug logging for developer debugging output. DLOG is controlled at build time by the TVM_LOG_DEBUG environment variable and is **eliminated in Release builds through dead code elimination**. + - The key difference between LOG(DEBUG) and DLOG is this build-time elimination. We recommend using DLOG over LOG(DEBUG), as the latter has overlapping functionality and gets compiled into the release runtime. +- **VLOG**: [Verbose logging](https://google.github.io/glog/stable/logging/#verbose-logging), primarily for debugging. Its main feature is customizable verbosity levels. For example, VLOG(n) where n can be 1, 2, 3, 4, 5, or 6, enabling complex tracing requirements. In contrast, LOG and DLOG typically use predefined verbose levels like INFO and DEBUG. + - In practical Tilelang development, VLOG is used less frequently. + - TVM's VLOG is implemented using DLOG, thus inheriting DLOG's characteristics. + +Additional useful macros include various **CHECK** variants: + +```c++ +CHECK(cond) << "error msg"; +DCHECK(cond) << "error msg"; +ICHECK(cond) << "error msg"; +``` + +The implementation routes errors to LogFatal: + +```c++ +#define CHECK(x) \ + if (!(x)) \ + ::tvm::runtime::detail::LogFatal(__FILE__, __LINE__).stream() \ + << "Check failed: (" #x << ") is false: " +``` +- **DCHECK**: Debug mode CHECK, only compiled in debug builds +- **ICHECK**: Internal Check that should exist in Release builds. When ICHECK fails, the entire system should report an error. + +## Logging Verbose Levels + +TVM defines 5 levels for LOG and DLOG (adding DEBUG compared to glog): + +```c++ +#define TVM_LOG_LEVEL_DEBUG 0 +#define TVM_LOG_LEVEL_INFO 1 +#define TVM_LOG_LEVEL_WARNING 2 +#define TVM_LOG_LEVEL_ERROR 3 +#define TVM_LOG_LEVEL_FATAL 4 +``` + +## Using Logging in TileLang Development + +### Guidelines + +For temporary debugging output in your code, there are no restrictions (you can even use std::cout). Just remember to remove it before submitting a PR. + +For meaningful logging that should remain in the Tilelang codebase: + +- Critical correctness checks: Use ICHECK with sufficient error messages to facilitate debugging when issues arise. +- Complex Pass debugging: For passes requiring intermediate output that may need future review (e.g., LayoutInference), use DLOG. +- General INFO/WARNING messages: Use standard LOG. + +### Enabling Log Output in Tilelang + +To specify current log level at runtime, we need to set the environment variable `TVM_LOG_LEVEL`. An example usage is: + +```c++ +TVM_LOG_DEBUG=1 python3 code.py +``` + +which enables all DEBUG/INFO (level <= 1) logs for all files. + +#### Detailed Rules for TVM_LOG_DEBUG Specification + +The parsing logic is in `logging.cc`. Reference: [HyperAI Zhihu Article](https://zhuanlan.zhihu.com/p/1933106843468665163). + +Launch Python with `TVM_LOG_DEBUG=`, where `` is a comma-separated list of level assignments in the form `=`. Important notes: + +- The special filename DEFAULT sets the LOG level for all files. +- `` can be set to -1 to disable LOG for that file. +- `` is the C++ source filename (e.g., .cc, not .h) relative to the `src/` directory in the TVM repository. The `src/` prefix is optional when specifying file paths. + +### Enabling Debug Mode + +To enable DLOG/DCHECK, developers need to first build Tilelang in Debug mode: + +```bash +cmake .. -DCMAKE_BUILD_TYPE=Debug -DUSE_CUDA=ON +``` + +Tilelang's CMake logic automatically adds the `TVM_LOG_DEBUG` macro, compiling all DLOG statements: + +```cmake +target_compile_definitions(tilelang_objs PRIVATE "TVM_LOG_DEBUG") +``` + +Then you also need to specify the runtime environment variables. For example, to use `DLOG(INFO) << "xxx"` for debugging, run your code with INFO level (1): `TVM_LOG_DEBUG=1`. + +:::{note} + **Important**: There are two TVM_LOG_DEBUG variables. (1) Compile-time macro: Determines whether debug content (like DLOG) is compiled into the .so file. Referenced in C++ source via #ifdef TVM_LOG_DEBUG. This is automatically enabled when using Debug build mode in CMake. (2) Runtime environment variable: Controls logging level at runtime. TVM provides a specification for this variable, allowing control over per-file logging levels. + + These two should ideally have different names, but TVM uses the same name for both, which can cause confusion. +::: diff --git a/examples/amd/example_amd_flash_attn_bwd.py b/examples/amd/example_amd_flash_attn_bwd.py index d47866e1e2..788aec367c 100644 --- a/examples/amd/example_amd_flash_attn_bwd.py +++ b/examples/amd/example_amd_flash_attn_bwd.py @@ -2,7 +2,7 @@ import torch.nn.functional as F import tilelang import tilelang.language as T -from tilelang.primitives.gemm.base import GemmWarpPolicy +from tilelang.tileop.base import GemmWarpPolicy import itertools import argparse from functools import partial @@ -11,22 +11,20 @@ def ref_program(Q, K, V, is_causal, groups=1): - assert Q.size( - 2) == K.size(2) * groups, f"Q heads {Q.size(2)} K heads {K.size(2)} groups {groups}" - assert Q.size( - 2) == V.size(2) * groups, f"Q heads {Q.size(2)} V heads {V.size(2)} groups {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q heads {Q.size(2)} K heads {K.size(2)} groups {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q heads {Q.size(2)} V heads {V.size(2)} groups {groups}" dim = Q.size(-1) K_ref = K.repeat_interleave(groups, dim=2) V_ref = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K_ref) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K_ref) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V_ref) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V_ref) lse = torch.logsumexp(scores, dim=-1).float() return output, lse @@ -45,23 +43,23 @@ def get_fwd_configs(): valid_configs = [] - for m, n, s, t, stages, r, k, p, qkw, vw in itertools.product(block_M, block_N, num_split_q, - threads, num_stages, - enable_rasterization, k_pack, - panel_size, qk_coalesced_width, - v_coalesced_width): - valid_configs.append({ - "block_M": m, - "block_N": n, - "num_split_q": s, - "threads": t, - "num_stages": stages, - "enable_rasterization": r, - "k_pack": k, - "panel_size": p, - "qk_coalesced_width": qkw, - "v_coalesced_width": vw, - }) + for m, n, s, t, stages, r, k, p, qkw, vw in itertools.product( + block_M, block_N, num_split_q, threads, num_stages, enable_rasterization, k_pack, panel_size, qk_coalesced_width, v_coalesced_width + ): + valid_configs.append( + { + "block_M": m, + "block_N": n, + "num_split_q": s, + "threads": t, + "num_stages": stages, + "enable_rasterization": r, + "k_pack": k, + "panel_size": p, + "qk_coalesced_width": qkw, + "v_coalesced_width": vw, + } + ) return valid_configs @@ -85,23 +83,23 @@ def fast_flashattn( qk_coalesced_width: int, v_coalesced_width: int, ): - scale = (1.0 / dim)**0.5 + scale = (1.0 / dim) ** 0.5 head_kv = heads // groups q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 vec_size = qk_coalesced_width v_vec_size = v_coalesced_width @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), - LSE: T.Tensor([batch, heads, seq_len], accum_dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), + LSE: T.Tensor([batch, heads, seq_len], accum_dtype), ): with T.Kernel(num_split_q, batch * heads, threads=threads) as (b_split, byz_combined): T.use_swizzle(panel_size, enable=enable_rasterization) @@ -111,7 +109,7 @@ def main( num_q_blocks = T.ceildiv(seq_len, block_M) - bx_loop_var = T.alloc_var("int32") + bx_loop_var = T.alloc_var(T.int32) bx_loop_var = b_split with T.While(bx_loop_var < num_q_blocks): @@ -135,33 +133,21 @@ def main( m_prev = T.alloc_fragment([block_M], accum_dtype) scale_factor = T.alloc_fragment([block_M], accum_dtype) - T.copy( - Q[bz, q_block_offset:q_block_offset + block_M, by, :], - Q_shared, - coalesced_width=vec_size) + T.copy(Q[bz, q_block_offset : q_block_offset + block_M, by, :], Q_shared, coalesced_width=vec_size) - loop_end_k = ( - T.ceildiv(q_block_offset + - block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_end_k = T.ceildiv(q_block_offset + block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) row_sum = T.alloc_fragment([block_M], accum_dtype) for k in T.Pipelined(loop_end_k, num_stages=num_stages): kv_idx = k * block_N - T.copy( - K[bz, kv_idx:kv_idx + block_N, by // groups, :], - K_shared, - coalesced_width=vec_size) - T.copy( - V[bz, kv_idx:kv_idx + block_N, by // groups, :], - V_shared, - coalesced_width=v_vec_size) + T.copy(K[bz, kv_idx : kv_idx + block_N, by // groups, :], K_shared, coalesced_width=vec_size) + T.copy(V[bz, kv_idx : kv_idx + block_N, by // groups, :], V_shared, coalesced_width=v_vec_size) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(q_block_offset + i >= kv_idx + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(q_block_offset + i >= kv_idx + j, 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) T.gemm( @@ -178,6 +164,8 @@ def main( T.copy(m_i, m_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for i in T.Parallel(block_M): + m_i[i] = T.max(m_i[i], m_prev[i]) for i in T.Parallel(block_M): if m_prev[i] == -T.infinity(accum_dtype): @@ -214,8 +202,7 @@ def main( for i in T.Parallel(block_M): if q_block_offset + i < seq_len: - lse_val = T.if_then_else(l_i[i] > 0, - T.log(l_i[i]) + m_i[i], -T.infinity(accum_dtype)) + lse_val = T.if_then_else(l_i[i] > 0, T.log(l_i[i]) + m_i[i], -T.infinity(accum_dtype)) LSE[bz, by, q_block_offset + i] = lse_val bx_loop_var = current_bx + num_split_q @@ -232,30 +219,30 @@ def get_bwd_configs(): panel_size = [7, 8, 9, 10] configs = [] - for m, n, stages, t, r, p in itertools.product(block_M, block_N, num_stages, threads, - enable_rasterization, panel_size): - configs.append({ - "block_M": m, - "block_N": n, - "num_stages": stages, - "threads": t, - "enable_rasterization": r, - "panel_size": p, - }) + for m, n, stages, t, r, p in itertools.product(block_M, block_N, num_stages, threads, enable_rasterization, panel_size): + configs.append( + { + "block_M": m, + "block_N": n, + "num_stages": stages, + "threads": t, + "enable_rasterization": r, + "panel_size": p, + } + ) return configs @tilelang.jit(out_idx=[2]) def flashattn_bwd_preprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim] blk = 32 @T.prim_func - def flash_bwd_prep(O: T.Tensor(shape, dtype), dO: T.Tensor(shape, dtype), - Delta: T.Tensor([batch, heads, seq_len], accum_dtype)): + def flash_bwd_prep(O: T.Tensor(shape, dtype), dO: T.Tensor(shape, dtype), Delta: T.Tensor([batch, heads, seq_len], accum_dtype)): with T.Kernel(batch, heads, T.ceildiv(seq_len, blk)) as (bz, bx, by): o = T.alloc_fragment([blk, blk], dtype) do = T.alloc_fragment([blk, blk], dtype) @@ -263,36 +250,51 @@ def flash_bwd_prep(O: T.Tensor(shape, dtype), dO: T.Tensor(shape, dtype), delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep @tilelang.autotune(configs=get_bwd_configs(), cache_input_tensors=True) @tilelang.jit -def flashattn_bwd(batch, heads, seq_len, dim, is_causal, groups, block_M: int, block_N: int, - num_stages: int, threads: int, enable_rasterization: bool, panel_size: int): - sm_scale = (1.0 / dim)**0.5 +def flashattn_bwd( + batch, + heads, + seq_len, + dim, + is_causal, + groups, + block_M: int, + block_N: int, + num_stages: int, + threads: int, + enable_rasterization: bool, + panel_size: int, +): + sm_scale = (1.0 / dim) ** 0.5 head_kv = heads // groups q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func - def flash_bwd_kernel(Q: T.Tensor(q_shape, - dtype), K: T.Tensor(kv_shape, - dtype), V: T.Tensor(kv_shape, dtype), - dO: T.Tensor(q_shape, dtype), lse: T.Tensor([batch, heads, seq_len], - accum_dtype), - Delta: T.Tensor([batch, heads, seq_len], - accum_dtype), dQ: T.Tensor(q_shape, accum_dtype), - dK: T.Tensor(kv_shape, accum_dtype), dV: T.Tensor(kv_shape, accum_dtype)): + def flash_bwd_kernel( + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + dO: T.Tensor(q_shape, dtype), + lse: T.Tensor([batch, heads, seq_len], accum_dtype), + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), + dQ: T.Tensor(q_shape, accum_dtype), + dK: T.Tensor(kv_shape, accum_dtype), + dV: T.Tensor(kv_shape, accum_dtype), + ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): T.use_swizzle(panel_size, enable=enable_rasterization) @@ -313,8 +315,8 @@ def flash_bwd_kernel(Q: T.Tensor(q_shape, dk = T.alloc_fragment([block_M, dim], accum_dtype) dq = T.alloc_fragment([block_N, dim], accum_dtype) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) @@ -322,22 +324,21 @@ def flash_bwd_kernel(Q: T.Tensor(q_shape, loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q_shared) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q_shared) T.clear(qkT) T.gemm(K_shared, q_shared, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): P_acc[i, j] = T.exp(qkT[i, j] * sm_scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - P_acc[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, - P_acc[i, j], 0.0) + P_acc[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, P_acc[i, j], 0.0) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do_shared) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do_shared) T.clear(dP) T.gemm(V_shared, do_shared, dP, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @@ -345,7 +346,7 @@ def flash_bwd_kernel(Q: T.Tensor(q_shape, T.copy(P_acc, p_cast) T.gemm(p_cast, do_shared, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta_shared) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta_shared) for i, j in T.Parallel(block_M, block_N): p_cast[i, j] = P_acc[i, j] * (dP[i, j] - delta_shared[j]) * sm_scale @@ -367,8 +368,8 @@ def flash_bwd_kernel(Q: T.Tensor(q_shape, @tilelang.jit(out_idx=[1]) def flashattn_bwd_postprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim] blk = 64 @@ -376,8 +377,8 @@ def flashattn_bwd_postprocess(batch, heads, seq_len, dim): def flash_bwd_post(dQ_in: T.Tensor(shape, accum_dtype), dQ_out: T.Tensor(shape, dtype)): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.copy( - dQ_in[bz, bx * blk:(bx + 1) * blk, by, :], - dQ_out[bz, bx * blk:(bx + 1) * blk, by, :], + dQ_in[bz, bx * blk : (bx + 1) * blk, by, :], + dQ_out[bz, bx * blk : (bx + 1) * blk, by, :], ) return flash_bwd_post @@ -444,22 +445,14 @@ def benchmark_function(func, *args, warmup=10, repeat=100): return np.median(times) -def main(batch: int = 1, - heads: int = 8, - seq_len: int = 4096, - dim: int = 128, - is_causal: bool = False, - groups: int = 1): - +def main(batch: int = 1, heads: int = 8, seq_len: int = 4096, dim: int = 128, is_causal: bool = False, groups: int = 1): device = "cuda" dtype = torch.float16 torch.manual_seed(42) torch.cuda.manual_seed(42) - print( - f"Test configuration: batch={batch}, heads={heads}, seq_len={seq_len}, dim={dim}, is_causal={is_causal}, groups={groups}" - ) + print(f"Test configuration: batch={batch}, heads={heads}, seq_len={seq_len}, dim={dim}, is_causal={is_causal}, groups={groups}") flops_per_gemm = 2.0 * batch * heads * seq_len * seq_len * dim total_flops = 5 * flops_per_gemm @@ -515,22 +508,19 @@ def main(batch: int = 1, o_ref.backward(dO) print("Verifying backward pass correctness...") - dq_close, dq_max_diff, dq_mean_diff = debug_tensor_comparison( - dQ_tl, q_ref.grad, "dQ", rtol=0.05, atol=0.05) + dq_close, dq_max_diff, dq_mean_diff = debug_tensor_comparison(dQ_tl, q_ref.grad, "dQ", rtol=0.05, atol=0.05) if dq_close: print("dQ is correct.") else: print("dQ mismatch detected.") - dk_close, dk_max_diff, dk_mean_diff = debug_tensor_comparison( - dK_tl.to(torch.float16), k_ref.grad, "dK", rtol=0.05, atol=0.05) + dk_close, dk_max_diff, dk_mean_diff = debug_tensor_comparison(dK_tl.to(torch.float16), k_ref.grad, "dK", rtol=0.05, atol=0.05) if dk_close: print("dK is correct.") else: print("dK mismatch detected.") - dv_close, dv_max_diff, dv_mean_diff = debug_tensor_comparison( - dV_tl.to(torch.float16), v_ref.grad, "dV", rtol=0.05, atol=0.05) + dv_close, dv_max_diff, dv_mean_diff = debug_tensor_comparison(dV_tl.to(torch.float16), v_ref.grad, "dV", rtol=0.05, atol=0.05) if dv_close: print("dV is correct.") else: @@ -551,9 +541,7 @@ def run_reference_fwd_bwd(): torch.cuda.synchronize() ref_latency = benchmark_function(run_reference_fwd_bwd, warmup=10, repeat=100) - print( - f"Reference PyTorch Forward+Backward: {ref_latency:.2f} ms | {total_flops / ref_latency * 1e-9:.2f} TFlops" - ) + print(f"Reference PyTorch Forward+Backward: {ref_latency:.2f} ms | {total_flops / ref_latency * 1e-9:.2f} TFlops") def run_complete_fwd_bwd(): o_tl_bench, lse_tl_bench = fwd_kernel(q, k, v) @@ -591,12 +579,12 @@ def run_complete_fwd_bwd(): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=8, help='heads') - parser.add_argument('--seq_len', type=int, default=1024, help='sequence length') - parser.add_argument('--dim', type=int, default=64, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--groups', type=int, default=1, help='groups') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=8, help="heads") + parser.add_argument("--seq_len", type=int, default=1024, help="sequence length") + parser.add_argument("--dim", type=int, default=64, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--groups", type=int, default=1, help="groups") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups) diff --git a/examples/amd/example_amd_flash_attn_fwd.py b/examples/amd/example_amd_flash_attn_fwd.py index 6ec5db1e50..ca9c361ff1 100644 --- a/examples/amd/example_amd_flash_attn_fwd.py +++ b/examples/amd/example_amd_flash_attn_fwd.py @@ -2,29 +2,42 @@ import torch.nn.functional as F import tilelang import tilelang.language as T -from tilelang.primitives.gemm.base import GemmWarpPolicy +from tilelang.tileop.base import GemmWarpPolicy import itertools import argparse from functools import partial +# Custom supply function to ensure tensors are created on GPU +def supply_tensors_gpu(params): + """Supply function that creates tensors on GPU for ROCm/HIP.""" + tensors = [] + for param in params: + if hasattr(param, "shape") and hasattr(param, "dtype"): + # Force creation on GPU device + shape = [int(s) for s in param.shape] + tensor = torch.randn(shape, dtype=param.dtype, device="cuda") + tensors.append(tensor) + else: + tensors.append(param) + return tensors + + def ref_program(Q, K, V, is_causal, groups=1): - assert Q.size( - 2) == K.size(2) * groups, f"Q heads {Q.size(2)} K heads {K.size(2)} groups {groups}" - assert Q.size( - 2) == V.size(2) * groups, f"Q heads {Q.size(2)} V heads {V.size(2)} groups {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q heads {Q.size(2)} K heads {K.size(2)} groups {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q heads {Q.size(2)} V heads {V.size(2)} groups {groups}" dim = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -43,27 +56,27 @@ def get_configs(): valid_configs = [] - for m, n, s, t, stages, r, k, p, qkw, vw in itertools.product(block_M, block_N, num_split_q, - threads, num_stages, - enable_rasterization, k_pack, - panel_size, qk_coalesced_width, - v_coalesced_width): - valid_configs.append({ - "block_M": m, - "block_N": n, - "num_split_q": s, - "threads": t, - "num_stages": stages, - "enable_rasterization": r, - "k_pack": k, - "panel_size": p, - "qk_coalesced_width": qkw, - "v_coalesced_width": vw, - }) + for m, n, s, t, stages, r, k, p, qkw, vw in itertools.product( + block_M, block_N, num_split_q, threads, num_stages, enable_rasterization, k_pack, panel_size, qk_coalesced_width, v_coalesced_width + ): + valid_configs.append( + { + "block_M": m, + "block_N": n, + "num_split_q": s, + "threads": t, + "num_stages": stages, + "enable_rasterization": r, + "k_pack": k, + "panel_size": p, + "qk_coalesced_width": qkw, + "v_coalesced_width": vw, + } + ) return valid_configs -@tilelang.autotune(configs=get_configs(), cache_input_tensors=True) +@tilelang.autotune(configs=get_configs(), cache_input_tensors=True, supply_prog=supply_tensors_gpu) @tilelang.jit(out_idx=[3]) def fast_flashattn( batch, @@ -83,22 +96,22 @@ def fast_flashattn( qk_coalesced_width: int, v_coalesced_width: int, ): - scale = (1.0 / dim)**0.5 + scale = (1.0 / dim) ** 0.5 head_kv = heads // groups q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 vec_size = qk_coalesced_width v_vec_size = v_coalesced_width @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(num_split_q, batch * heads, threads=threads) as (b_split, byz_combined): T.use_swizzle(panel_size, enable=enable_rasterization) @@ -108,7 +121,7 @@ def main( num_q_blocks = T.ceildiv(seq_len, block_M) - bx = T.alloc_var("int32") + bx = T.alloc_var(T.int32) bx = b_split with T.While(bx < num_q_blocks): @@ -132,32 +145,21 @@ def main( m_prev = T.alloc_fragment([block_M], accum_dtype) scale_factor = T.alloc_fragment([block_M], accum_dtype) - T.copy( - Q[bz, q_block_offset:q_block_offset + block_M, by, :], - Q_shared, - coalesced_width=vec_size) + T.copy(Q[bz, q_block_offset : q_block_offset + block_M, by, :], Q_shared, coalesced_width=vec_size) - loop_end_k = T.ceildiv(q_block_offset + block_M, - block_N) if is_causal else T.ceildiv(seq_len, block_N) + loop_end_k = T.ceildiv(q_block_offset + block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) row_sum = T.alloc_fragment([block_M], accum_dtype) for k in T.Pipelined(loop_end_k, num_stages=num_stages): kv_idx = k * block_N - T.copy( - K[bz, kv_idx:kv_idx + block_N, by // groups, :], - K_shared, - coalesced_width=vec_size) - T.copy( - V[bz, kv_idx:kv_idx + block_N, by // groups, :], - V_shared, - coalesced_width=v_vec_size) + T.copy(K[bz, kv_idx : kv_idx + block_N, by // groups, :], K_shared, coalesced_width=vec_size) + T.copy(V[bz, kv_idx : kv_idx + block_N, by // groups, :], V_shared, coalesced_width=v_vec_size) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(q_block_offset + i >= kv_idx + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(q_block_offset + i >= kv_idx + j, 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) T.gemm( @@ -171,6 +173,8 @@ def main( T.copy(m_i, m_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for i in T.Parallel(block_M): + m_i[i] = T.max(m_i[i], m_prev[i]) for i in T.Parallel(block_M): sf = T.exp(m_prev[i] * scale - m_i[i] * scale) @@ -205,13 +209,7 @@ def main( return main -def main(batch: int = 1, - heads: int = 8, - seq_len: int = 4096, - dim: int = 128, - is_causal: bool = False, - groups: int = 1): - +def main(batch: int = 1, heads: int = 8, seq_len: int = 4096, dim: int = 128, is_causal: bool = False, groups: int = 1): flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim total_flops = 2 * flops_per_matmul if is_causal: @@ -233,18 +231,16 @@ def main(batch: int = 1, print(f"Reference (PyTorch): {latency:.2f} ms | {total_flops / latency * 1e-9:.2f} TFlops") latency = profiler.do_bench(warmup=100) - print( - f"Fast Flash Attention V2 (Tile-lang): {latency:.2f} ms | {total_flops / latency * 1e-9:.2f} TFlops" - ) + print(f"Fast Flash Attention V2 (Tile-lang): {latency:.2f} ms | {total_flops / latency * 1e-9:.2f} TFlops") if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=8, help='heads') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--groups', type=int, default=1, help='groups') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=8, help="heads") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--groups", type=int, default=1, help="groups") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups) diff --git a/examples/analyze/README.md b/examples/analyze/README.md index 8171d88268..1c2788b0b8 100644 --- a/examples/analyze/README.md +++ b/examples/analyze/README.md @@ -21,9 +21,9 @@ M = N = K = 1024 def kernel(block_M=128, block_N=128, block_K=32, num_stages=3, thread_num=128): @T.prim_func - def main(A: T.Tensor((M, K), "float16"), - B: T.Tensor((N, K), "float16"), - C: T.Tensor((M, N), "float")): + def main(A: T.Tensor((M, K), T.float16), + B: T.Tensor((N, K), T.float16), + C: T.Tensor((M, N), T.float)): # ... (kernel definition) return main @@ -40,9 +40,9 @@ from tilelang.carver.arch import CUDA def kernel(N=64, C=256, H=512, W=512, F=512, K=3, block_M=64, block_N=128): @T.prim_func - def main(data: T.Tensor((N, H, W, C), "float16"), - kernel: T.Tensor((K, K, C, F), "float16"), - out: T.Tensor((N, (H-K+1), (W-K+1), F), "float")): + def main(data: T.Tensor((N, H, W, C), T.float16), + kernel: T.Tensor((K, K, C, F), T.float16), + out: T.Tensor((N, (H-K+1), (W-K+1), F), T.float)): # ... (convolution kernel definition) return main @@ -64,10 +64,10 @@ class AnalysisResult: ``` ### `Analyzer` Class Methods #### `analysis(fn, device)` -* ​Parameters: - * fn: TVM IRModule or PrimFunc - * device: Device configuration object -* Returns: AnalysisResult +- ​Parameters: + - fn: TVM IRModule or PrimFunc + - device: Device configuration object +- Returns: AnalysisResult #### Supported Architectures ```python # Extendable to custom hardware via: "compute_capability": (cores_per_SM, clock_GHz, flops_per_cycle, max_SM_count) diff --git a/examples/analyze/example_conv_analyze.py b/examples/analyze/example_conv_analyze.py index 540fcf4b74..06e5a86e9d 100644 --- a/examples/analyze/example_conv_analyze.py +++ b/examples/analyze/example_conv_analyze.py @@ -2,7 +2,6 @@ from tilelang.tools import Analyzer from tilelang.carver.arch import CUDA from tilelang.carver.arch import CDNA -from tilelang.layout import make_swizzled_layout import torch N = 64 @@ -25,38 +24,21 @@ def check_hopper(): return False -def kernel(N, - C, - H, - W, - F, - K, - S, - D, - P, - block_M, - block_N, - block_K, - num_stages, - threads, - dtype="float16", - accum_dtype="float"): +def kernel(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, threads, dtype=T.float16, accum_dtype=T.float32): KH, KW = K, K OH = (H + 2 * P - D * (K - 1) - 1) // S + 1 OW = (W + 2 * P - D * (K - 1) - 1) // S + 1 - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 is_hopper = check_hopper() @T.prim_func def conv( - data: T.Tensor((N, H, W, C), dtype), - kernel: T.Tensor((KH, KW, C, F), dtype), - out: T.Tensor((N, OH, OW, F), dtype), + data: T.Tensor((N, H, W, C), dtype), + kernel: T.Tensor((KH, KW, C, F), dtype), + out: T.Tensor((N, OH, OW, F), dtype), ): - with T.Kernel( - T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), - threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=threads) as (bx, by): data_shared = T.alloc_shared((block_M, block_K), dtype) kernel_shared = T.alloc_shared((block_K, block_N), dtype) out_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -65,12 +47,6 @@ def conv( kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data) out_flat = T.Tensor((N * OH * OW, F), dtype, out.data) - T.annotate_layout({ - out_shared: make_swizzled_layout(out_shared), - data_shared: make_swizzled_layout(data_shared), - kernel_shared: make_swizzled_layout(kernel_shared), - }) - T.clear(out_local) for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages): if is_hopper: @@ -81,10 +57,8 @@ def conv( m = by * block_M + i access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P access_w = m % OW * S + k // C % KW * D - P - in_bound = ((access_h >= 0) and (access_w >= 0) and (access_h < H) and - (access_w < W)) - data_shared[i, j] = T.if_then_else( - in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) + in_bound = (access_h >= 0) and (access_w >= 0) and (access_h < H) and (access_w < W) + data_shared[i, j] = T.if_then_else(in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared) T.gemm(data_shared, kernel_shared, out_local) diff --git a/examples/analyze/example_gemm_analyze.py b/examples/analyze/example_gemm_analyze.py index bfd934f6aa..0367af126e 100644 --- a/examples/analyze/example_gemm_analyze.py +++ b/examples/analyze/example_gemm_analyze.py @@ -15,14 +15,14 @@ def kernel( thread_num=None, enable_rasteration=None, ): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def matmul( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) diff --git a/examples/attention_sink/README.md b/examples/attention_sink/README.md index ed4b7004e6..2cba8f0cc3 100644 --- a/examples/attention_sink/README.md +++ b/examples/attention_sink/README.md @@ -2,7 +2,6 @@ We compare with an optimized version of the official Triton implementation [here](https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py). - ## Algorithm ### Forward The only change from vanilla FlashAttention is that `sinks` should be taken into consideration in the softmax, which requires an extra rescaling at the epilogue stage. @@ -43,4 +42,4 @@ where $P_{b, h, q}$ is the proportion of $sink_h$ in the softmax in the $b$-th b | 16384 | 64 | 309.46 | **400.62** | 1.29x | | 16384 | 128 | 418.99 | **549.11** | 1.31x | -> The backward performance will be further optimized in the future. \ No newline at end of file +> The backward performance will be further optimized in the future. diff --git a/examples/attention_sink/benchmark_gqa_sink_fwd.py b/examples/attention_sink/benchmark_gqa_sink_fwd.py index 00256286bd..211ef1d18c 100644 --- a/examples/attention_sink/benchmark_gqa_sink_fwd.py +++ b/examples/attention_sink/benchmark_gqa_sink_fwd.py @@ -1,10 +1,12 @@ import torch import argparse from tilelang.profiler import do_bench +from tilelang import language as T import triton import triton.language as tl from triton.tools.tensor_descriptor import TensorDescriptor from example_gqa_sink_fwd_bhsd_wgmma_pipelined import flashattn, ref_program, gen_inputs +from typing import Optional @triton.jit @@ -50,8 +52,7 @@ def triton_kernel( q = Q.load([off_z, off_h, start_m * BLOCK_M, 0]).reshape([BLOCK_M, HEAD_DIM]) if BANDWIDTH: - lo, hi = tl.maximum(0, start_q + start_m * BLOCK_M - - BANDWIDTH), start_q + (start_m + 1) * BLOCK_M + lo, hi = tl.maximum(0, start_q + start_m * BLOCK_M - BANDWIDTH), start_q + (start_m + 1) * BLOCK_M else: lo, hi = 0, start_q + (start_m + 1) * BLOCK_M @@ -94,7 +95,7 @@ def triton_kernel( Out.store([off_z, off_h, start_m * BLOCK_M, 0], acc) -def triton_program(Q, K, V, Sinks, window_size: int | None = None) -> torch.Tensor: +def triton_program(Q, K, V, Sinks, window_size: Optional[int] = None) -> torch.Tensor: bs, n_heads, seq_q, head_dim = Q.shape _, n_heads_kv, seq_kv, _ = K.shape BLOCK_M = 64 @@ -119,7 +120,8 @@ def triton_program(Q, K, V, Sinks, window_size: int | None = None) -> torch.Tens BANDWIDTH=window_size, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, - start_q=seq_kv - seq_q) + start_q=seq_kv - seq_q, + ) return o @@ -130,18 +132,18 @@ def main( seq_kv: int = 256, dim: int = 128, groups: int = 8, - window_size: int | None = None, + window_size: Optional[int] = None, dtype: str = "float16", tune: bool = False, ): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= seq_q - flops_per_matmul = 2.0 * batch * heads * min( - window_size, seq_kv // 2) * seq_q * dim # just a rough estimation + flops_per_matmul = 2.0 * batch * heads * min(window_size, seq_kv // 2) * seq_q * dim # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5 total_flops = 2 * flops_per_matmul @@ -169,15 +171,14 @@ def main( block_N=block_N, num_stages=num_stages, threads=threads, - dtype=dtype) + dtype=dtype, + ) Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, groups, dtype=torch_dtype) if torch.allclose( - triton_program(Q, K, V, sinks, window_size), - ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), - rtol=1e-2, - atol=1e-2): + triton_program(Q, K, V, sinks, window_size), ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), rtol=1e-2, atol=1e-2 + ): print("Checks for triton passed.✅") else: print("Checks for triton failed.❌") @@ -197,20 +198,14 @@ def main( if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='heads') - parser.add_argument('--seq_q', type=int, default=2048, help='sequence length of query') - parser.add_argument('--seq_kv', type=int, default=2048, help='sequence length of key/value') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--groups', type=int, default=8, help='groups') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="heads") + parser.add_argument("--seq_q", type=int, default=2048, help="sequence length of query") + parser.add_argument("--seq_kv", type=int, default=2048, help="sequence length of key/value") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--groups", type=int, default=8, help="groups") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default="float16", help="dtype, can be float16 or bfloat16") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.groups, args.window_size, - args.dtype, args.tune) + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.groups, args.window_size, args.dtype, args.tune) diff --git a/examples/attention_sink/benchmark_mha_sink_fwd.py b/examples/attention_sink/benchmark_mha_sink_fwd.py index 734870fe40..50747e6b09 100644 --- a/examples/attention_sink/benchmark_mha_sink_fwd.py +++ b/examples/attention_sink/benchmark_mha_sink_fwd.py @@ -1,10 +1,12 @@ import torch import argparse from tilelang.profiler import do_bench +from tilelang import language as T import triton import triton.language as tl from triton.tools.tensor_descriptor import TensorDescriptor from example_mha_sink_fwd_bhsd_wgmma_pipelined import flashattn, ref_program, gen_inputs +from typing import Optional @triton.jit @@ -49,8 +51,7 @@ def triton_kernel( q = Q.load([off_z, off_h, start_m * BLOCK_M, 0]).reshape([BLOCK_M, HEAD_DIM]) if BANDWIDTH: - lo, hi = tl.maximum(0, start_q + start_m * BLOCK_M - - BANDWIDTH), start_q + (start_m + 1) * BLOCK_M + lo, hi = tl.maximum(0, start_q + start_m * BLOCK_M - BANDWIDTH), start_q + (start_m + 1) * BLOCK_M else: lo, hi = 0, start_q + (start_m + 1) * BLOCK_M @@ -93,7 +94,7 @@ def triton_kernel( Out.store([off_z, off_h, start_m * BLOCK_M, 0], acc) -def triton_program(Q, K, V, Sinks, window_size: int | None = None) -> torch.Tensor: +def triton_program(Q, K, V, Sinks, window_size: Optional[int] = None) -> torch.Tensor: bs, n_heads, seq_q, head_dim = Q.shape seq_kv = K.shape[2] BLOCK_M = 64 @@ -116,26 +117,29 @@ def triton_program(Q, K, V, Sinks, window_size: int | None = None) -> torch.Tens BANDWIDTH=window_size, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, - start_q=seq_kv - seq_q) + start_q=seq_kv - seq_q, + ) return o -def main(batch: int = 1, - heads: int = 32, - seq_q: int = 256, - seq_kv: int = 256, - dim: int = 128, - window_size: int | None = None, - dtype: str = "float16", - tune: bool = False): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] +def main( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + window_size: Optional[int] = None, + dtype: str = "float16", + tune: bool = False, +): + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= seq_q - flops_per_matmul = 2.0 * batch * heads * min( - window_size, seq_kv // 2) * seq_q * dim # just a rough estimation + flops_per_matmul = 2.0 * batch * heads * min(window_size, seq_kv // 2) * seq_q * dim # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5 total_flops = 2 * flops_per_matmul @@ -162,15 +166,14 @@ def main(batch: int = 1, block_N=block_N, num_stages=num_stages, threads=threads, - dtype=dtype) + dtype=dtype, + ) Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype) torch.testing.assert_close( - kernel(Q, K, V, sinks), - ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), - rtol=1e-2, - atol=1e-2) + kernel(Q, K, V, sinks), ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), rtol=1e-2, atol=1e-2 + ) print("All checks passed.✅") latency = do_bench(lambda: triton_program(Q, K, V, sinks, window_size), warmup=500) @@ -183,19 +186,13 @@ def main(batch: int = 1, if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_q', type=int, default=4096, help='sequence length of query') - parser.add_argument('--seq_kv', type=int, default=4096, help='sequence length of key/value') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") - parser.add_argument('--tune', action='store_true', help='tune') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_q", type=int, default=4096, help="sequence length of query") + parser.add_argument("--seq_kv", type=int, default=4096, help="sequence length of key/value") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default="float16", help="dtype, can be float16 or bfloat16") + parser.add_argument("--tune", action="store_true", help="tune") args = parser.parse_args() - main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, - args.tune) + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, args.tune) diff --git a/examples/attention_sink/example_gqa_sink_bwd_bhsd.py b/examples/attention_sink/example_gqa_sink_bwd_bhsd.py index f8f970ea42..cfdcd21b58 100644 --- a/examples/attention_sink/example_gqa_sink_bwd_bhsd.py +++ b/examples/attention_sink/example_gqa_sink_bwd_bhsd.py @@ -13,50 +13,50 @@ def get_bwd_configs(): sm_version = sm_major * 10 + sm_minor if sm_version == 80: return 64, 32, 1, 128 - elif sm_version == 90: - return 128, 32, 2, 256 else: - raise ValueError(f"Unsupported SM version: {sm_version}") + return 128, 32, 2, 256 @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd( - batch, - heads, - seq_len, - dim, - groups=1, - window_size=None, # None for full attention - sm_scale=None, - block_M=64, - block_N=64, - num_stages=1, - threads=128, - dtype: str = "float16"): - + batch, + heads, + seq_len, + dim, + groups=1, + window_size=None, # None for full attention + sm_scale=None, + block_M=64, + block_N=64, + num_stages=1, + threads=128, + dtype: T.dtype = T.float16, +): if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, heads, seq_len, dim] kv_shape = [batch, head_kv, seq_len, dim] - accum_dtype = "float" + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(kv_shape, dtype), # type: ignore - V: T.Tensor(kv_shape, dtype), # type: ignore - Output: T.Tensor(q_shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Sinks: T.Tensor([heads], dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(kv_shape, dtype), # type: ignore + V: T.Tensor(kv_shape, dtype), # type: ignore + Output: T.Tensor(q_shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Sinks: T.Tensor([heads], dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -72,8 +72,7 @@ def flash_fwd( logsum = T.alloc_fragment([block_M], accum_dtype) sinks = T.alloc_fragment([heads], dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) @@ -81,34 +80,30 @@ def flash_fwd( sinks[i] = Sinks[by] end = T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) - start = T.alloc_local([1], 'int32') - if window_size is not None: - start[0] = T.max(0, (bx * block_M - window_size) // block_N) - else: - start[0] = 0 - - for k in T.Pipelined(start[0], end, num_stages=num_stages): - T.copy(K[bz, by // groups, k * block_N:(k + 1) * block_N, :], K_shared) + start = T.max(0, (bx * block_M - window_size) // block_N) if window_size is not None else 0 + + for k in T.Pipelined(start, end, num_stages=num_stages): + T.copy(K[bz, by // groups, k * block_N : (k + 1) * block_N, :], K_shared) for i, j in T.Parallel(block_M, block_N): q_idx = bx * block_M + i k_idx = k * block_N + j if window_size is not None: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, - 0, -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, -T.infinity(acc_s.dtype)) else: acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, by // groups, k * block_N:(k + 1) * block_N, :], V_shared) + T.copy(V[bz, by // groups, k * block_N : (k + 1) * block_N, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # To do causal softmax, we need to set the scores_max to 0 if it is -inf # This process is called Check_inf in FlashAttention3 code, and it only need to be done # NOTE(wt): check_inf is necessary for sliding window attention. for i in T.Parallel(block_M): if window_size is not None: - scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, - scores_max[i]) + scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): @@ -125,32 +120,33 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i in T.Parallel(block_M): - logsum[i] += T.exp2(sinks[i] * 1.44269504 - - scores_max[i] * scale) # The only change for attention sink + logsum[i] += T.exp2(sinks[i] * 1.44269504 - scores_max[i] * scale) # The only change for attention sink for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(acc_o, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_bwd_preprocess(batch, heads, seq_len, dim, dtype: str = "float16"): - accum_dtype = "float" + }, +) +def flashattn_bwd_preprocess(batch, heads, seq_len, dim, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -159,65 +155,61 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], do) + T.copy(O[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep def make_dq_layout(dQ): # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment - return T.Layout(dQ.shape, - lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) + return T.Layout(dQ.shape, lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) @tilelang.jit( - out_idx=[1], pass_configs={ + out_idx=[1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_bwd_postprocess(batch, heads, seq_len, dim, dtype: str = "float16"): - accum_dtype = "float" + }, +) +def flashattn_bwd_postprocess(batch, heads, seq_len, dim, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 64 @T.prim_func def flash_bwd_post( - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(shape, dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) T.copy( - dQ[bz, by, bx * blk:(bx + 1) * blk, :], - dQ_out[bz, by, bx * blk:(bx + 1) * blk, :], + dQ[bz, by, bx * blk : (bx + 1) * blk, :], + dQ_out[bz, by, bx * blk : (bx + 1) * blk, :], ) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd(batch, - heads, - seq_len, - dim, - groups, - window_size=None, - sm_scale=None, - dtype="float16"): # None for full attention +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd(batch, heads, seq_len, dim, groups, window_size=None, sm_scale=None, dtype=T.float16): # None for full attention if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, heads, seq_len, dim] kv_shape = [batch, head_kv, seq_len, dim] - accum_dtype = "float" + accum_dtype = T.float32 block_M, block_N, num_stages, threads = get_bwd_configs() @@ -226,15 +218,15 @@ def flashattn_bwd(batch, @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(kv_shape, dtype), # type: ignore - V: T.Tensor(kv_shape, dtype), # type: ignore - dO: T.Tensor(q_shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(kv_shape, accum_dtype), # type: ignore - dV: T.Tensor(kv_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(kv_shape, dtype), # type: ignore + V: T.Tensor(kv_shape, dtype), # type: ignore + dO: T.Tensor(q_shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(kv_shape, accum_dtype), # type: ignore + dV: T.Tensor(kv_shape, accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim], dtype) @@ -254,47 +246,44 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim], accum_dtype) dk_shared = T.alloc_shared([block_M, dim], accum_dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) - T.copy(K[bz, bx // groups, by * block_M:(by + 1) * block_M, :], K_shared) - T.copy(V[bz, bx // groups, by * block_M:(by + 1) * block_M, :], V_shared) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) + T.copy(K[bz, bx // groups, by * block_M : (by + 1) * block_M, :], K_shared) + T.copy(V[bz, bx // groups, by * block_M : (by + 1) * block_M, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) - loop_ed = T.alloc_local([1], 'int32') - if window_size is not None: - loop_ed[0] = T.min( - T.ceildiv((by + 1) * block_M + window_size, block_N), - T.ceildiv(seq_len, block_N)) - else: - loop_ed[0] = T.ceildiv(seq_len, block_N) - for k in T.Pipelined(loop_st, loop_ed[0], num_stages=num_stages): - T.copy(Q[bz, bx, k * block_N:(k + 1) * block_N, :], q) + loop_ed = ( + T.min(T.ceildiv((by + 1) * block_M + window_size, block_N), T.ceildiv(seq_len, block_N)) + if window_size is not None + else T.ceildiv(seq_len, block_N) + ) + + for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): + T.copy(Q[bz, bx, k * block_N : (k + 1) * block_N, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) for i, j in T.Parallel(block_M, block_N): if window_size is not None: qkT[i, j] = T.if_then_else( - by * block_M + i <= k * block_N + j and - by * block_M + i > k * block_N + j - window_size, qkT[i, j], 0) + by * block_M + i <= k * block_N + j and by * block_M + i > k * block_N + j - window_size, qkT[i, j], 0 + ) else: - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, bx, k * block_N:(k + 1) * block_N, :], dst=do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + T.copy(dO[bz, bx, k * block_N : (k + 1) * block_N, :], dst=do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -303,50 +292,46 @@ def flash_bwd( T.copy(dsT_cast, dsT_shared) T.clear(dq) T.gemm(dsT_shared, K_shared, dq, transpose_A=True) - T.atomic_add(dQ[bz, bx, k * block_N:(k + 1) * block_N, :], dq) + T.atomic_add(dQ[bz, bx, k * block_N : (k + 1) * block_N, :], dq) T.copy(dv, dv_shared) - T.atomic_add(dV[bz, bx // groups, by * block_M:(by + 1) * block_M, :], dv_shared) + T.atomic_add(dV[bz, bx // groups, by * block_M : (by + 1) * block_M, :], dv_shared) T.copy(dk, dk_shared) - T.atomic_add(dK[bz, bx // groups, by * block_M:(by + 1) * block_M, :], dk_shared) + T.atomic_add(dK[bz, bx // groups, by * block_M : (by + 1) * block_M, :], dk_shared) return flash_bwd @tilelang.jit(out_idx=-1) -def flashattn_bwd_dsink(batch, heads, seq_len, block=256, dtype: str = "float16"): - accum_dtype = "float" +def flashattn_bwd_dsink(batch, heads, seq_len, block=256, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len] @T.prim_func def flash_bwd_dsink( - Sinks: T.Tensor([heads], dtype), # type: ignore - Delta: T.Tensor(shape, accum_dtype), # type: ignore - lse: T.Tensor(shape, accum_dtype), # type: ignore - dsinks: T.Tensor(shape, dtype), # type: ignore + Sinks: T.Tensor([heads], dtype), # type: ignore + Delta: T.Tensor(shape, accum_dtype), # type: ignore + lse: T.Tensor(shape, accum_dtype), # type: ignore + dsinks: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block), batch, threads=256) as (bx, by, bz): - sink = T.alloc_local([1], dtype) lse_fragment = T.alloc_fragment([block], accum_dtype) delta_fragment = T.alloc_fragment([block], accum_dtype) dsink_fragment = T.alloc_fragment([block], dtype) - sink[0] = Sinks[bx] - T.copy(lse[bz, bx, by * block:(by + 1) * block], lse_fragment) - T.copy(Delta[bz, bx, by * block:(by + 1) * block], delta_fragment) + sink = Sinks[bx] + T.copy(lse[bz, bx, by * block : (by + 1) * block], lse_fragment) + T.copy(Delta[bz, bx, by * block : (by + 1) * block], delta_fragment) for i in T.Parallel(block): - dsink_fragment[i] = -T.exp2(Sinks[bx] * 1.44269504 - - lse_fragment[i]) * delta_fragment[i] - T.copy(dsink_fragment, dsinks[bz, bx, by * block:(by + 1) * block]) + dsink_fragment[i] = -T.exp2(sink * 1.44269504 - lse_fragment[i]) * delta_fragment[i] + T.copy(dsink_fragment, dsinks[bz, bx, by * block : (by + 1) * block]) return flash_bwd_dsink class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, sinks, window_size, groups): - def maybe_contiguous(x): if x.stride(-1) != 1: return x.contiguous() @@ -354,7 +339,7 @@ def maybe_contiguous(x): q, k, v, sinks = [maybe_contiguous(x) for x in (q, k, v, sinks)] BATCH, H, N_CTX, D_HEAD = q.shape - dtype = "float16" if q.dtype == torch.float16 else "bfloat16" + dtype = T.float16 if q.dtype == torch.float16 else T.bfloat16 kernel = flashattn_fwd(BATCH, H, N_CTX, D_HEAD, groups, window_size, dtype=dtype) o, lse = kernel(q, k, v, sinks) ctx.save_for_backward(q, k, v, sinks, o, lse) @@ -367,7 +352,7 @@ def backward(ctx, do): q, k, v, sinks, o, lse = ctx.saved_tensors BATCH, H, N_CTX, D_HEAD = q.shape groups = ctx.groups - dtype = "float16" if q.dtype == torch.float16 else "bfloat16" + dtype = T.float16 if q.dtype == torch.float16 else T.bfloat16 kernel_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) kernel_post = flashattn_bwd_postprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) @@ -392,13 +377,14 @@ def backward(ctx, do): # Adapted and optimized from # https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py -def ref_program(query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - sinks: torch.Tensor, - sliding_window: Optional[int] = None, - dtype: torch.dtype = torch.float16) -> torch.Tensor: - +def ref_program( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + sinks: torch.Tensor, + sliding_window: Optional[int] = None, + dtype: torch.dtype = torch.float16, +) -> torch.Tensor: key = key.transpose(1, 2).contiguous() value = value.transpose(1, 2).contiguous() batch_size, num_keys, num_key_value_heads, head_dim = key.shape @@ -434,32 +420,32 @@ def ref_program(query: torch.Tensor, output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float()) - output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, - head_dim).to(dtype) + output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, head_dim).to(dtype) return output.transpose(1, 2).contiguous() -def main(BATCH: int = 1, - H: int = 8, - N_CTX: int = 512, - D_HEAD: int = 64, - groups: int = 2, - window_size: int | None = None, - dtype: str = "float16"): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] +def main( + BATCH: int = 1, + H: int = 8, + N_CTX: int = 512, + D_HEAD: int = 64, + groups: int = 2, + window_size: Optional[int] = None, + dtype: str = "float16", +): + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= N_CTX - flops_per_matmul = 2.0 * BATCH * H * min( - window_size, N_CTX // 2) * N_CTX * D_HEAD # just a rough estimation + flops_per_matmul = 2.0 * BATCH * H * min(window_size, N_CTX // 2) * N_CTX * D_HEAD # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD * 0.5 total_flops = 5 * flops_per_matmul - Q = (torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_()) - K = torch.randn( - BATCH, H // groups, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_() + Q = torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_() + K = torch.randn(BATCH, H // groups, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_() V = torch.randn_like(K).requires_grad_() sinks = torch.randn(H, dtype=torch_dtype, device="cuda").requires_grad_() dO = torch.randn_like(Q) @@ -480,19 +466,14 @@ def main(BATCH: int = 1, # Checks rtol, atol = { - "float16": (1e-2, 1e-2), - "bfloat16": (2e-2, 2e-2), + T.float16: (1e-2, 1e-2), + T.bfloat16: (2e-2, 2e-2), }[dtype] - assert torch.allclose(O, O_ref, rtol=rtol, atol=atol), f'O max err: {(O-O_ref).abs().max()}' - assert torch.allclose( - dV, dV_ref, rtol=rtol, atol=atol), f'dV max err: {(dV-dV_ref).abs().max()}' - assert torch.allclose( - dK, dK_ref, rtol=rtol, atol=atol), f'dK max err: {(dK-dK_ref).abs().max()}' - assert torch.allclose( - dQ, dQ_ref, rtol=rtol, atol=atol), f'dq max err: {(dQ-dQ_ref).abs().max()}' - assert torch.allclose( - dsinks, dsinks_ref, rtol=rtol, - atol=atol), f'dsinks max err: {(dsinks-dsinks_ref).abs().max()}' + assert torch.allclose(O, O_ref, rtol=rtol, atol=atol), f"O max err: {(O - O_ref).abs().max()}" + assert torch.allclose(dV, dV_ref, rtol=rtol, atol=atol), f"dV max err: {(dV - dV_ref).abs().max()}" + assert torch.allclose(dK, dK_ref, rtol=rtol, atol=atol), f"dK max err: {(dK - dK_ref).abs().max()}" + assert torch.allclose(dQ, dQ_ref, rtol=rtol, atol=atol), f"dq max err: {(dQ - dQ_ref).abs().max()}" + assert torch.allclose(dsinks, dsinks_ref, rtol=rtol, atol=atol), f"dsinks max err: {(dsinks - dsinks_ref).abs().max()}" print("All checks passed for tilelang kernels.✅") @@ -511,19 +492,57 @@ def tl_bwd(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf( + BATCH: int = 1, + H: int = 8, + N_CTX: int = 512, + D_HEAD: int = 64, + groups: int = 2, + window_size: Optional[int] = None, + dtype: str = "float16", +): + torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + with torch.no_grad(): + Q = torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda") + K = torch.randn(BATCH, H // groups, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda") + V = torch.randn_like(K) + sinks = torch.randn(H, dtype=torch_dtype, device="cuda") + dO = torch.randn_like(Q) + fwd = flashattn_fwd(BATCH, H, N_CTX, D_HEAD, groups, window_size, dtype=dtype) + O, lse = fwd(Q, K, V, sinks) + + def maybe_contiguous(x): + return x if x.stride(-1) == 1 else x.contiguous() + + do, q, k, v, sinks_c, o = [maybe_contiguous(x) for x in (dO, Q, K, V, sinks, O)] + k_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) + Delta = k_prep(o, do) + k_bwd = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, groups, window_size, dtype=dtype) + k_dsink = flashattn_bwd_dsink(BATCH, H, N_CTX, dtype=dtype) + q_shape = (BATCH, H, N_CTX, D_HEAD) + head_kv = H // groups + kv_shape = (BATCH, head_kv, N_CTX, D_HEAD) + dq = torch.zeros(q_shape, dtype=torch.float32, device="cuda") + dk = torch.zeros(kv_shape, dtype=torch.float32, device="cuda") + dv = torch.zeros(kv_shape, dtype=torch.float32, device="cuda") + k_bwd(q, k, v, do, lse, Delta, dq, dk, dv) + _ = k_dsink(sinks_c, Delta, lse).sum(0).sum(1) + + def run_kernel_only(): + k_bwd(q, k, v, do, lse, Delta, dq, dk, dv) + + latency_ms = do_bench(run_kernel_only, backend="cupti") + return latency_ms + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='Batch size') - parser.add_argument('--h', type=int, default=64, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=4096, help='Context size') - parser.add_argument('--d_head', type=int, default=128, help='Head dimension') - parser.add_argument('--groups', type=int, default=8, help='Groups') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") + parser.add_argument("--batch", type=int, default=1, help="Batch size") + parser.add_argument("--h", type=int, default=64, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=4096, help="Context size") + parser.add_argument("--d_head", type=int, default=128, help="Head dimension") + parser.add_argument("--groups", type=int, default=8, help="Groups") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default="float16", help="dtype, can be float16 or bfloat16") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head, args.groups, args.window_size, args.dtype) diff --git a/examples/attention_sink/example_gqa_sink_fwd_bhsd_wgmma_pipelined.py b/examples/attention_sink/example_gqa_sink_fwd_bhsd_wgmma_pipelined.py index 49a3ecbd82..fa73df0af7 100644 --- a/examples/attention_sink/example_gqa_sink_fwd_bhsd_wgmma_pipelined.py +++ b/examples/attention_sink/example_gqa_sink_fwd_bhsd_wgmma_pipelined.py @@ -6,7 +6,6 @@ from tilelang.autotuner import autotune from tilelang.profiler import do_bench import tilelang.language as T -from tilelang.layout import make_swizzled_layout import itertools import argparse from typing import Optional @@ -23,9 +22,11 @@ def get_configs(): rep=100, ) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn( batch, heads, @@ -39,106 +40,30 @@ def flashattn( block_N=128, num_stages=2, threads=256, - dtype: str = "float16", + dtype: T.dtype = T.float16, ): - if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, head_kv, seq_kv, dim] - accum_dtype = "float" + accum_dtype = T.float32 past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by // groups, k * block_N:(k + 1) * block_N, :], K_shared) - for i, j in T.Parallel(block_M, block_N): - q_idx = bx * block_M + i + past_len - k_idx = k * block_N + j - if window_size is not None: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, - -T.infinity(acc_s.dtype)) - else: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_M, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by // groups, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # NOTE(wt): check_inf is necessary for sliding window attention. - for i in T.Parallel(block_M): - if window_size is not None: - scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, - scores_max[i]) - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), - Sinks: T.Tensor([heads], dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), + Sinks: T.Tensor([heads], dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -155,61 +80,83 @@ def main( logsum = T.alloc_fragment([block_M], accum_dtype) sinks = T.alloc_fragment([block_M], dtype) - T.annotate_layout({ - Q_shared: make_swizzled_layout(Q_shared), - K_shared: make_swizzled_layout(K_shared), - V_shared: make_swizzled_layout(V_shared), - O_shared: make_swizzled_layout(O_shared), - }) - - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) for i in T.Parallel(block_M): sinks[i] = Sinks[by] - end = T.min( - T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + end = T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) - start = T.alloc_local([1], 'int32') - if window_size is not None: - start[0] = T.max(0, (bx * block_M + past_len - window_size) // block_N) - else: - start[0] = 0 + start = T.max(0, (bx * block_M + past_len - window_size) // block_N) if window_size is not None else 0 for k in T.Pipelined( - start[0], - end, - num_stages=num_stages, - order=[-1, 0, 3, 1, -1, 2], - stage=[-1, 0, 0, 1, -1, 1], - group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + start, + end, + num_stages=num_stages, + order=[-1, 0, 3, 1, -1, 2], + stage=[-1, 0, 0, 1, -1, 1], + group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10, 11], [12], [13], [14]], + ): + T.copy(K[bz, by // groups, k * block_N : (k + 1) * block_N, :], K_shared) + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + if window_size is not None: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, -T.infinity(acc_s.dtype)) + else: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # NOTE(wt): check_inf is necessary for sliding window attention. + for i in T.Parallel(block_M): + if window_size is not None: + scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by // groups, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i in T.Parallel(block_M): - logsum[i] += T.exp2(sinks[i] * 1.44269504 - - scores_max[i] * scale) # The only change for attention sink + logsum[i] += T.exp2(sinks[i] * 1.44269504 - scores_max[i] * scale) # The only change for attention sink for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main # Following functions are adapted and optimized from # https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py -def ref_program(query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - sinks: torch.Tensor, - sliding_window: Optional[int] = None, - dtype: torch.dtype = torch.float16) -> torch.Tensor: - +def ref_program( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + sinks: torch.Tensor, + sliding_window: Optional[int] = None, + dtype: torch.dtype = torch.float16, +) -> torch.Tensor: key = key.transpose(1, 2).contiguous() value = value.transpose(1, 2).contiguous() batch_size, num_keys, num_key_value_heads, head_dim = key.shape @@ -245,23 +192,15 @@ def ref_program(query: torch.Tensor, output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float()) - output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, - head_dim).to(dtype) + output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, head_dim).to(dtype) return output.transpose(1, 2).contiguous() -def gen_inputs( - B, - H, - Sq, - Skv, - D, - groups, - dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - query = torch.randn([B, H, Sq, D], dtype=dtype, device='cuda') - key = torch.randn([B, H // groups, Skv, D], dtype=dtype, device='cuda') - value = torch.randn([B, H // groups, Skv, D], dtype=dtype, device='cuda') - sinks = torch.randn([H], dtype=dtype, device='cuda') +def gen_inputs(B, H, Sq, Skv, D, groups, dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + query = torch.randn([B, H, Sq, D], dtype=dtype, device="cuda") + key = torch.randn([B, H // groups, Skv, D], dtype=dtype, device="cuda") + value = torch.randn([B, H // groups, Skv, D], dtype=dtype, device="cuda") + sinks = torch.randn([H], dtype=dtype, device="cuda") return query, key, value, sinks @@ -272,18 +211,18 @@ def main( seq_kv: int = 256, dim: int = 128, groups: int = 8, - window_size: int | None = None, - dtype: str = "float16", + window_size: Optional[int] = None, + dtype: T.dtype = T.float16, tune: bool = False, ): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= seq_q - flops_per_matmul = 2.0 * batch * heads * min( - window_size, seq_kv // 2) * seq_q * dim # just a rough estimation + flops_per_matmul = 2.0 * batch * heads * min(window_size, seq_kv // 2) * seq_q * dim # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5 total_flops = 2 * flops_per_matmul @@ -311,15 +250,14 @@ def main( block_N=block_N, num_stages=num_stages, threads=threads, - dtype=dtype) + dtype=dtype, + ) Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, groups, dtype=torch_dtype) torch.testing.assert_close( - kernel(Q, K, V, sinks), - ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), - rtol=1e-2, - atol=1e-2) + kernel(Q, K, V, sinks), ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), rtol=1e-2, atol=1e-2 + ) print("All checks passed.✅") # Benchmark tilelang @@ -328,22 +266,51 @@ def main( print("Tilelang: {:.2f} TFlops".format(total_flops / latency_tilelang * 1e-9)) +def run_regression_perf( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + groups: int = 8, + window_size: Optional[int] = None, + dtype: str = "float16", + tune: bool = False, +): + torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + block_M = 128 + block_N = 128 + num_stages = 2 + threads = 256 + kernel = flashattn( + batch, + heads, + seq_q, + seq_kv, + dim, + groups, + window_size, + block_M=block_M, + block_N=block_N, + num_stages=num_stages, + threads=threads, + dtype=dtype, + ) + Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, groups, dtype=torch_dtype) + latency = do_bench(lambda: kernel(Q, K, V, sinks), backend="cupti") + return latency + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='heads') - parser.add_argument('--seq_q', type=int, default=2048, help='sequence length of query') - parser.add_argument('--seq_kv', type=int, default=2048, help='sequence length of key/value') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--groups', type=int, default=8, help='groups') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="heads") + parser.add_argument("--seq_q", type=int, default=2048, help="sequence length of query") + parser.add_argument("--seq_kv", type=int, default=2048, help="sequence length of key/value") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--groups", type=int, default=8, help="groups") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default="float16", help="dtype, can be float16 or bfloat16") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.groups, args.window_size, - args.dtype, args.tune) + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.groups, args.window_size, args.dtype, args.tune) diff --git a/examples/attention_sink/example_mha_sink_bwd_bhsd.py b/examples/attention_sink/example_mha_sink_bwd_bhsd.py index ee1c35ece2..66905f55d1 100644 --- a/examples/attention_sink/example_mha_sink_bwd_bhsd.py +++ b/examples/attention_sink/example_mha_sink_bwd_bhsd.py @@ -20,40 +20,42 @@ def get_bwd_configs(): @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd( - batch, - heads, - seq_len, - dim, - window_size=None, # None for full attention, - sm_scale=None, - block_M=64, - block_N=64, - num_stages=1, - threads=128, - dtype: str = "float16"): - + batch, + heads, + seq_len, + dim, + window_size=None, # None for full attention, + sm_scale=None, + block_M=64, + block_N=64, + num_stages=1, + threads=128, + dtype: T.dtype = T.float16, +): if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] - accum_dtype = "float" + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - Output: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Sinks: T.Tensor([heads], dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + Output: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Sinks: T.Tensor([heads], dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -69,8 +71,7 @@ def flash_fwd( logsum = T.alloc_fragment([block_M], accum_dtype) sinks = T.alloc_fragment([heads], dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) @@ -78,34 +79,30 @@ def flash_fwd( sinks[i] = Sinks[by] end = T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) - start = T.alloc_local([1], 'int32') - if window_size is not None: - start[0] = T.max(0, (bx * block_M - window_size) // block_N) - else: - start[0] = 0 - - for k in T.Pipelined(start[0], end, num_stages=num_stages): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) + start = T.max(0, (bx * block_M - window_size) // block_N) if window_size is not None else 0 + + for k in T.Pipelined(start, end, num_stages=num_stages): + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) for i, j in T.Parallel(block_M, block_N): q_idx = bx * block_M + i k_idx = k * block_N + j if window_size is not None: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, - 0, -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, -T.infinity(acc_s.dtype)) else: acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # To do causal softmax, we need to set the scores_max to 0 if it is -inf # This process is called Check_inf in FlashAttention3 code, and it only need to be done # NOTE(wt): check_inf is necessary for sliding window attention. for i in T.Parallel(block_M): if window_size is not None: - scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, - scores_max[i]) + scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): @@ -122,32 +119,33 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i in T.Parallel(block_M): - logsum[i] += T.exp2(sinks[i] * 1.44269504 - - scores_max[i] * scale) # The only change for attention sink + logsum[i] += T.exp2(sinks[i] * 1.44269504 - scores_max[i] * scale) # The only change for attention sink for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(acc_o, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_bwd_preprocess(batch, heads, seq_len, dim, dtype: str = "float16"): - accum_dtype = "float" + }, +) +def flashattn_bwd_preprocess(batch, heads, seq_len, dim, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -156,49 +154,52 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], do) + T.copy(O[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep def make_dq_layout(dQ): # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment - return T.Layout(dQ.shape, - lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) + return T.Layout(dQ.shape, lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) @tilelang.jit( - out_idx=[1], pass_configs={ + out_idx=[1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_bwd_postprocess(batch, heads, seq_len, dim, dtype: str = "float16"): - accum_dtype = "float" + }, +) +def flashattn_bwd_postprocess(batch, heads, seq_len, dim, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 64 @T.prim_func def flash_bwd_post( - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(shape, dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) T.copy( - dQ[bz, by, bx * blk:(bx + 1) * blk, :], - dQ_out[bz, by, bx * blk:(bx + 1) * blk, :], + dQ[bz, by, bx * blk : (bx + 1) * blk, :], + dQ_out[bz, by, bx * blk : (bx + 1) * blk, :], ) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def flashattn_bwd( batch, heads, @@ -206,32 +207,31 @@ def flashattn_bwd( dim, window_size=None, # None for full attention sm_scale=None, - dtype: str = "float16", + dtype: T.dtype = T.float16, ): - block_M, block_N, num_stages, threads = get_bwd_configs() if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] - accum_dtype = "float" + accum_dtype = T.float32 if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" @T.prim_func def flash_bwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dK: T.Tensor(shape, dtype), # type: ignore - dV: T.Tensor(shape, dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dK: T.Tensor(shape, dtype), # type: ignore + dV: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim], dtype) @@ -255,47 +255,43 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim], dtype) dk_shared = T.alloc_shared([block_M, dim], dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) - T.copy(K[bz, bx, by * block_M:(by + 1) * block_M, :], K_shared) - T.copy(V[bz, bx, by * block_M:(by + 1) * block_M, :], V_shared) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) + T.copy(K[bz, bx, by * block_M : (by + 1) * block_M, :], K_shared) + T.copy(V[bz, bx, by * block_M : (by + 1) * block_M, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) - loop_ed = T.alloc_local([1], 'int32') - if window_size is not None: - loop_ed[0] = T.min( - T.ceildiv((by + 1) * block_M + window_size, block_N), - T.ceildiv(seq_len, block_N)) - else: - loop_ed[0] = T.ceildiv(seq_len, block_N) - for k in T.Pipelined(loop_st, loop_ed[0], num_stages=num_stages): - T.copy(Q[bz, bx, k * block_N:(k + 1) * block_N, :], q) + loop_ed = ( + T.min(T.ceildiv((by + 1) * block_M + window_size, block_N), T.ceildiv(seq_len, block_N)) + if window_size is not None + else T.ceildiv(seq_len, block_N) + ) + for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): + T.copy(Q[bz, bx, k * block_N : (k + 1) * block_N, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) for i, j in T.Parallel(block_M, block_N): if window_size is not None: qkT[i, j] = T.if_then_else( - by * block_M + i <= k * block_N + j and - by * block_M + i > k * block_N + j - window_size, qkT[i, j], 0) + by * block_M + i <= k * block_N + j and by * block_M + i > k * block_N + j - window_size, qkT[i, j], 0 + ) else: - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, bx, k * block_N:(k + 1) * block_N, :], dst=do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + T.copy(dO[bz, bx, k * block_N : (k + 1) * block_N, :], dst=do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, B=do, C=dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -304,51 +300,48 @@ def flash_bwd( T.copy(dsT_cast, dsT_shared) T.clear(dq) T.gemm(dsT_shared, K_shared, dq, transpose_A=True) - T.atomic_add(dQ[bz, bx, k * block_N:(k + 1) * block_N, :], dq) + T.atomic_add(dQ[bz, bx, k * block_N : (k + 1) * block_N, :], dq) T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, dV[bz, bx, by * block_M:(by + 1) * block_M, :]) - T.copy(dk_shared, dK[bz, bx, by * block_M:(by + 1) * block_M, :]) + T.copy(dv_shared, dV[bz, bx, by * block_M : (by + 1) * block_M, :]) + T.copy(dk_shared, dK[bz, bx, by * block_M : (by + 1) * block_M, :]) return flash_bwd @tilelang.jit(out_idx=-1) -def flashattn_bwd_dsink(batch, heads, seq_len, block=128, dtype: str = "float16"): - accum_dtype = "float" +def flashattn_bwd_dsink(batch, heads, seq_len, block=128, dtype: T.dtype = T.float16): + accum_dtype = T.float32 shape = [batch, heads, seq_len] @T.prim_func def flash_bwd_dsink( - Sinks: T.Tensor([heads], dtype), # type: ignore - Delta: T.Tensor(shape, accum_dtype), # type: ignore - lse: T.Tensor(shape, accum_dtype), # type: ignore - dsinks: T.Tensor(shape, accum_dtype), # type: ignore + Sinks: T.Tensor([heads], dtype), # type: ignore + Delta: T.Tensor(shape, accum_dtype), # type: ignore + lse: T.Tensor(shape, accum_dtype), # type: ignore + dsinks: T.Tensor(shape, accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block), batch, threads=128) as (bx, by, bz): - sink = T.alloc_local([1], dtype) lse_fragment = T.alloc_fragment([block], accum_dtype) delta_fragment = T.alloc_fragment([block], accum_dtype) dsink_fragment = T.alloc_fragment([block], accum_dtype) - sink[0] = Sinks[bx] - T.copy(lse[bz, bx, by * block:(by + 1) * block], lse_fragment) - T.copy(Delta[bz, bx, by * block:(by + 1) * block], delta_fragment) + sink = Sinks[bx] + T.copy(lse[bz, bx, by * block : (by + 1) * block], lse_fragment) + T.copy(Delta[bz, bx, by * block : (by + 1) * block], delta_fragment) for i in T.Parallel(block): - dsink_fragment[i] = -T.exp2(Sinks[bx] * 1.44269504 - - lse_fragment[i]) * delta_fragment[i] - T.copy(dsink_fragment, dsinks[bz, bx, by * block:(by + 1) * block]) + dsink_fragment[i] = -T.exp2(sink * 1.44269504 - lse_fragment[i]) * delta_fragment[i] + T.copy(dsink_fragment, dsinks[bz, bx, by * block : (by + 1) * block]) return flash_bwd_dsink class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, sinks, window_size): BATCH, H, N_CTX, D_HEAD = q.shape - dtype = "float16" if q.dtype == torch.float16 else "bfloat16" + dtype = T.float16 if q.dtype == torch.float16 else T.bfloat16 kernel = flashattn_fwd(BATCH, H, N_CTX, D_HEAD, window_size, dtype=dtype) o, lse = kernel(q, k, v, sinks) ctx.save_for_backward(q, k, v, sinks, o, lse) @@ -366,7 +359,7 @@ def maybe_contiguous(x): return x do, q, k, v, sinks, o = [maybe_contiguous(x) for x in (do, q, k, v, sinks, o)] - dtype = "float16" if q.dtype == torch.float16 else "bfloat16" + dtype = T.float16 if q.dtype == torch.float16 else T.bfloat16 kernel_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) kernel_post = flashattn_bwd_postprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) delta = kernel_prep(o, do) @@ -388,15 +381,15 @@ def maybe_contiguous(x): # Adapted and optimized from # https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py -def ref_program(query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - sinks: torch.Tensor, - sliding_window: Optional[int] = None, - dtype: torch.dtype = torch.float16) -> torch.Tensor: - - query = query.transpose(1, 2).contiguous().unsqueeze( - 3) # align with the original function's interface +def ref_program( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + sinks: torch.Tensor, + sliding_window: Optional[int] = None, + dtype: torch.dtype = torch.float16, +) -> torch.Tensor: + query = query.transpose(1, 2).contiguous().unsqueeze(3) # align with the original function's interface key = key.transpose(1, 2).contiguous() value = value.transpose(1, 2).contiguous() @@ -431,29 +424,23 @@ def ref_program(query: torch.Tensor, output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float()) - output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, - head_dim).to(dtype) + output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, head_dim).to(dtype) return output.transpose(1, 2).contiguous() -def main(BATCH: int = 1, - H: int = 1, - N_CTX: int = 512, - D_HEAD: int = 128, - window_size: int | None = None, - dtype: str = "float16"): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] +def main(BATCH: int = 1, H: int = 1, N_CTX: int = 512, D_HEAD: int = 128, window_size: Optional[int] = None, dtype: T.dtype = T.float16): + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= N_CTX - flops_per_matmul = 2.0 * BATCH * H * min( - window_size, N_CTX // 2) * N_CTX * D_HEAD # just a rough estimation + flops_per_matmul = 2.0 * BATCH * H * min(window_size, N_CTX // 2) * N_CTX * D_HEAD # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD * 0.5 total_flops = 5 * flops_per_matmul - Q = (torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_()) + Q = torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda").requires_grad_() K = torch.randn_like(Q).requires_grad_() V = torch.randn_like(Q).requires_grad_() sinks = torch.randn(H, dtype=torch_dtype, device=Q.device).requires_grad_() @@ -475,19 +462,14 @@ def main(BATCH: int = 1, # Checks rtol, atol = { - "float16": (1e-2, 1e-2), - "bfloat16": (2e-2, 2e-2), + T.float16: (1e-2, 1e-2), + T.bfloat16: (2e-2, 2e-2), }[dtype] - assert torch.allclose(O, O_ref, rtol=rtol, atol=atol), f'O max err: {(O-O_ref).abs().max()}' - assert torch.allclose( - dV, dV_ref, rtol=rtol, atol=atol), f'dV max err: {(dV-dV_ref).abs().max()}' - assert torch.allclose( - dK, dK_ref, rtol=rtol, atol=atol), f'dK max err: {(dK-dK_ref).abs().max()}' - assert torch.allclose( - dQ, dQ_ref, rtol=rtol, atol=atol), f'dq max err: {(dQ-dQ_ref).abs().max()}' - assert torch.allclose( - dsinks, dsinks_ref, rtol=rtol, - atol=atol), f'dsinks max err: {(dsinks-dsinks_ref).abs().max()}' + assert torch.allclose(O, O_ref, rtol=rtol, atol=atol), f"O max err: {(O - O_ref).abs().max()}" + assert torch.allclose(dV, dV_ref, rtol=rtol, atol=atol), f"dV max err: {(dV - dV_ref).abs().max()}" + assert torch.allclose(dK, dK_ref, rtol=rtol, atol=atol), f"dK max err: {(dK - dK_ref).abs().max()}" + assert torch.allclose(dQ, dQ_ref, rtol=rtol, atol=atol), f"dq max err: {(dQ - dQ_ref).abs().max()}" + assert torch.allclose(dsinks, dsinks_ref, rtol=rtol, atol=atol), f"dsinks max err: {(dsinks - dsinks_ref).abs().max()}" print("All checks passed for tilelang kernels.✅") @@ -506,18 +488,53 @@ def tl_bwd(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf( + BATCH: int = 1, + H: int = 32, + N_CTX: int = 512, + D_HEAD: int = 128, + window_size: Optional[int] = None, + dtype: str = "float16", +): + torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + with torch.no_grad(): + Q = torch.randn(BATCH, H, N_CTX, D_HEAD, dtype=torch_dtype, device="cuda") + K = torch.randn_like(Q) + V = torch.randn_like(Q) + sinks = torch.randn(H, dtype=torch_dtype, device=Q.device) + dO = torch.randn_like(Q) + fwd = flashattn_fwd(BATCH, H, N_CTX, D_HEAD, window_size=window_size, dtype=dtype) + O, lse = fwd(Q, K, V, sinks) + + def maybe_contiguous(x): + return x if x.stride(-1) == 1 else x.contiguous() + + do, q, k, v, sinks_c, o = [maybe_contiguous(x) for x in (dO, Q, K, V, sinks, O)] + k_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD, dtype=dtype) + Delta = k_prep(o, do) + k_bwd = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, window_size, dtype=dtype) + k_dsink = flashattn_bwd_dsink(BATCH, H, N_CTX, dtype=dtype) + shape = (BATCH, H, N_CTX, D_HEAD) + dq = torch.zeros(shape, dtype=torch.float32, device=Q.device) + dk = torch.empty(shape, dtype=torch_dtype, device=Q.device) + dv = torch.empty(shape, dtype=torch_dtype, device=Q.device) + k_bwd(q, k, v, do, lse, Delta, dq, dk, dv) + _ = k_dsink(sinks_c, Delta, lse).sum(0).sum(1) + + def run_kernel_only(): + k_bwd(q, k, v, do, lse, Delta, dq, dk, dv) + + latency_ms = do_bench(run_kernel_only, backend="cupti") + return latency_ms + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='Batch size') - parser.add_argument('--h', type=int, default=64, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=4096, help='Context size') - parser.add_argument('--d_head', type=int, default=128, help='Head dimension') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") + parser.add_argument("--batch", type=int, default=1, help="Batch size") + parser.add_argument("--h", type=int, default=64, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=4096, help="Context size") + parser.add_argument("--d_head", type=int, default=128, help="Head dimension") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default="float16", help="dtype, can be float16 or bfloat16") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head, args.window_size, args.dtype) diff --git a/examples/attention_sink/example_mha_sink_fwd_bhsd.py b/examples/attention_sink/example_mha_sink_fwd_bhsd.py index 7e59e277e4..f24aa38b72 100644 --- a/examples/attention_sink/example_mha_sink_fwd_bhsd.py +++ b/examples/attention_sink/example_mha_sink_fwd_bhsd.py @@ -5,7 +5,6 @@ from tilelang.autotuner import autotune from tilelang.profiler import do_bench import tilelang.language as T -from tilelang.layout import make_swizzled_layout import itertools import argparse from typing import Optional @@ -18,117 +17,45 @@ def get_configs(): @autotune(configs=get_configs(), warmup=500, rep=100) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn( - batch, - heads, - seq_q, - seq_kv, - dim, - window_size=None, # None for full attention - sm_scale=None, - block_M=64, - block_N=64, - num_stages=1, - threads=128, - dtype: str = "float16"): + batch, + heads, + seq_q, + seq_kv, + dim, + window_size=None, # None for full attention + sm_scale=None, + block_M=64, + block_N=64, + num_stages=1, + threads=128, + dtype: T.dtype = T.float16, +): if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] - accum_dtype = "float" + accum_dtype = T.float32 past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) - for i, j in T.Parallel(block_M, block_N): - q_idx = bx * block_M + i + past_len - k_idx = k * block_N + j - if window_size is not None: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, - -T.infinity(acc_s.dtype)) - else: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_M, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # NOTE(wt): check_inf is necessary for sliding window attention. - for i in T.Parallel(block_M): - if window_size is not None: - scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, - scores_max[i]) - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), - Sinks: T.Tensor([heads], dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), + Sinks: T.Tensor([heads], dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -145,56 +72,76 @@ def main( logsum = T.alloc_fragment([block_M], accum_dtype) sinks = T.alloc_fragment([block_M], dtype) - T.annotate_layout({ - Q_shared: make_swizzled_layout(Q_shared), - K_shared: make_swizzled_layout(K_shared), - V_shared: make_swizzled_layout(V_shared), - O_shared: make_swizzled_layout(O_shared), - }) - - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) for i in T.Parallel(block_M): sinks[i] = Sinks[by] - end = T.min( - T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) - - start = T.alloc_local([1], 'int32') - if window_size is not None: - start[0] = T.max(0, (bx * block_M + past_len - window_size) // block_N) - else: - start[0] = 0 - - for k in T.Pipelined(start[0], end, num_stages=num_stages): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + end = T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + + start = T.max(0, (bx * block_M + past_len - window_size) // block_N) if window_size is not None else 0 + + for k in T.Pipelined(start, end, num_stages=num_stages): + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + if window_size is not None: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, -T.infinity(acc_s.dtype)) + else: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # NOTE(wt): check_inf is necessary for sliding window attention. + for i in T.Parallel(block_M): + if window_size is not None: + scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i in T.Parallel(block_M): - logsum[i] += T.exp2(sinks[i] * 1.44269504 - - scores_max[i] * scale) # The only change for attention sink + logsum[i] += T.exp2(sinks[i] * 1.44269504 - scores_max[i] * scale) # The only change for attention sink for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main # Modified from https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py -def ref_program(query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - sinks: torch.Tensor, - sliding_window: Optional[int] = None, - dtype: torch.dtype = torch.float16) -> torch.Tensor: - - query = query.transpose(1, 2).contiguous().unsqueeze( - 3) # align with the original function's interface +def ref_program( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + sinks: torch.Tensor, + sliding_window: Optional[int] = None, + dtype: torch.dtype = torch.float16, +) -> torch.Tensor: + query = query.transpose(1, 2).contiguous().unsqueeze(3) # align with the original function's interface key = key.transpose(1, 2).contiguous() value = value.transpose(1, 2).contiguous() @@ -229,41 +176,36 @@ def ref_program(query: torch.Tensor, output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float()) - output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, - head_dim).to(dtype) + output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, head_dim).to(dtype) return output.transpose(1, 2).contiguous() -def gen_inputs( - B, - H, - Sq, - Skv, - D, - dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - query = torch.randn([B, H, Sq, D], dtype=dtype, device='cuda') - key = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda') - value = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda') - sinks = torch.randn([H], dtype=dtype, device='cuda') +def gen_inputs(B, H, Sq, Skv, D, dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + query = torch.randn([B, H, Sq, D], dtype=dtype, device="cuda") + key = torch.randn([B, H, Skv, D], dtype=dtype, device="cuda") + value = torch.randn([B, H, Skv, D], dtype=dtype, device="cuda") + sinks = torch.randn([H], dtype=dtype, device="cuda") return query, key, value, sinks -def main(batch: int = 1, - heads: int = 1, - seq_q: int = 256, - seq_kv: int = 256, - dim: int = 128, - window_size: int | None = None, - dtype: str = "float16", - tune: bool = False): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] +def main( + batch: int = 1, + heads: int = 1, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + window_size: Optional[int] = None, + dtype: T.dtype = T.float16, + tune: bool = False, +): + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= seq_q - flops_per_matmul = 2.0 * batch * heads * min( - window_size, seq_kv // 2) * seq_q * dim # just a rough estimation + flops_per_matmul = 2.0 * batch * heads * min(window_size, seq_kv // 2) * seq_q * dim # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5 total_flops = 2 * flops_per_matmul @@ -290,19 +232,17 @@ def main(batch: int = 1, block_N=block_N, num_stages=num_stages, threads=threads, - dtype=dtype) + dtype=dtype, + ) Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype) torch.testing.assert_close( - kernel(Q, K, V, sinks), - ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), - rtol=1e-2, - atol=1e-2) + kernel(Q, K, V, sinks), ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), rtol=1e-2, atol=1e-2 + ) print("All checks passed.✅") - latency = do_bench( - lambda: ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), warmup=500) + latency = do_bench(lambda: ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), warmup=500) print("Ref: {:.2f} ms".format(latency)) print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9)) latency = do_bench(lambda: kernel(Q, K, V, sinks), warmup=500) @@ -310,21 +250,37 @@ def main(batch: int = 1, print("Tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + window_size: Optional[int] = None, + dtype: str = "float16", +): + torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + block_M = 128 + block_N = 128 + num_stages = 2 + threads = 256 + kernel = flashattn( + batch, heads, seq_q, seq_kv, dim, window_size, block_M=block_M, block_N=block_N, num_stages=num_stages, threads=threads, dtype=dtype + ) + Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype) + latency = do_bench(lambda: kernel(Q, K, V, sinks), backend="cupti") + return latency + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_q', type=int, default=4096, help='sequence length of query') - parser.add_argument('--seq_kv', type=int, default=4096, help='sequence length of key/value') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") - parser.add_argument('--tune', action='store_true', help='tune') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_q", type=int, default=4096, help="sequence length of query") + parser.add_argument("--seq_kv", type=int, default=4096, help="sequence length of key/value") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default=T.float16, help="dtype, can be float16 or bfloat16") + parser.add_argument("--tune", action="store_true", help="tune") args = parser.parse_args() - main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, - args.tune) + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, args.tune) diff --git a/examples/attention_sink/example_mha_sink_fwd_bhsd_wgmma_pipelined.py b/examples/attention_sink/example_mha_sink_fwd_bhsd_wgmma_pipelined.py index eee2f3ac5a..b47c8175f1 100644 --- a/examples/attention_sink/example_mha_sink_fwd_bhsd_wgmma_pipelined.py +++ b/examples/attention_sink/example_mha_sink_fwd_bhsd_wgmma_pipelined.py @@ -6,7 +6,6 @@ from tilelang.autotuner import autotune from tilelang.profiler import do_bench import tilelang.language as T -from tilelang.layout import make_swizzled_layout import itertools import argparse from typing import Optional @@ -19,119 +18,46 @@ def get_configs(): @autotune(configs=get_configs(), warmup=500, rep=100) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn( - batch, - heads, - seq_q, - seq_kv, - dim, - window_size=None, # None for full attention - sm_scale=None, - block_M=128, - block_N=128, - num_stages=2, - threads=256, - dtype: str = "float16"): - + batch, + heads, + seq_q, + seq_kv, + dim, + window_size=None, # None for full attention + sm_scale=None, + block_M=128, + block_N=128, + num_stages=2, + threads=256, + dtype: T.dtype = T.float16, +): if window_size is not None: assert window_size % block_N == 0, "window_size must be divisible by block_N" if sm_scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 scale = sm_scale * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] - accum_dtype = "float" + accum_dtype = T.float32 past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) - for i, j in T.Parallel(block_M, block_N): - q_idx = bx * block_M + i + past_len - k_idx = k * block_N + j - if window_size is not None: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, - -T.infinity(acc_s.dtype)) - else: - acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_M, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # NOTE(wt): check_inf is necessary for sliding window attention. - for i in T.Parallel(block_M): - if window_size is not None: - scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, - scores_max[i]) - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), - Sinks: T.Tensor([heads], dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), + Sinks: T.Tensor([heads], dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -148,63 +74,84 @@ def main( logsum = T.alloc_fragment([block_M], accum_dtype) sinks = T.alloc_fragment([block_M], dtype) - T.annotate_layout({ - Q_shared: make_swizzled_layout(Q_shared), - K_shared: make_swizzled_layout(K_shared), - V_shared: make_swizzled_layout(V_shared), - O_shared: make_swizzled_layout(O_shared), - }) - - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) for i in T.Parallel(block_M): sinks[i] = Sinks[by] - end = T.min( - T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + end = T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) - start = T.alloc_local([1], 'int32') - if window_size is not None: - start[0] = T.max(0, (bx * block_M + past_len - window_size) // block_N) - else: - start[0] = 0 + start = T.max(0, (bx * block_M + past_len - window_size) // block_N) if window_size is not None else 0 for k in T.Pipelined( - start[0], - end, - num_stages=num_stages, - order=[-1, 0, 3, 1, -1, 2], - stage=[-1, 0, 0, 1, -1, 1], - group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + start, + end, + num_stages=num_stages, + order=[-1, 0, 3, 1, -1, 2], + stage=[-1, 0, 0, 1, -1, 1], + group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10, 11], [12], [13], [14]], + ): + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + if window_size is not None: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0, -T.infinity(acc_s.dtype)) + else: + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # NOTE(wt): check_inf is necessary for sliding window attention. + for i in T.Parallel(block_M): + if window_size is not None: + scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i in T.Parallel(block_M): - logsum[i] += T.exp2(sinks[i] * 1.44269504 - - scores_max[i] * scale) # The only change for attention sink + logsum[i] += T.exp2(sinks[i] * 1.44269504 - scores_max[i] * scale) # The only change for attention sink for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main # Following functions are adapted and optimized from # https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py -def ref_program(query: torch.Tensor, - key: torch.Tensor, - value: torch.Tensor, - sinks: torch.Tensor, - sliding_window: Optional[int] = None, - dtype: torch.dtype = torch.float16) -> torch.Tensor: - - query = query.transpose(1, 2).contiguous().unsqueeze( - 3) # align with the original function'sinterface +def ref_program( + query: torch.Tensor, + key: torch.Tensor, + value: torch.Tensor, + sinks: torch.Tensor, + sliding_window: Optional[int] = None, + dtype: torch.dtype = torch.float16, +) -> torch.Tensor: + query = query.transpose(1, 2).contiguous().unsqueeze(3) # align with the original function'sinterface key = key.transpose(1, 2).contiguous() value = value.transpose(1, 2).contiguous() @@ -239,41 +186,36 @@ def ref_program(query: torch.Tensor, output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float()) - output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, - head_dim).to(dtype) + output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups, head_dim).to(dtype) return output.transpose(1, 2).contiguous() -def gen_inputs( - B, - H, - Sq, - Skv, - D, - dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: - query = torch.randn([B, H, Sq, D], dtype=dtype, device='cuda') - key = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda') - value = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda') - sinks = torch.randn([H], dtype=dtype, device='cuda') +def gen_inputs(B, H, Sq, Skv, D, dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]: + query = torch.randn([B, H, Sq, D], dtype=dtype, device="cuda") + key = torch.randn([B, H, Skv, D], dtype=dtype, device="cuda") + value = torch.randn([B, H, Skv, D], dtype=dtype, device="cuda") + sinks = torch.randn([H], dtype=dtype, device="cuda") return query, key, value, sinks -def main(batch: int = 1, - heads: int = 32, - seq_q: int = 256, - seq_kv: int = 256, - dim: int = 128, - window_size: int | None = None, - dtype: str = "float16", - tune: bool = False): - torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] +def main( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + window_size: Optional[int] = None, + dtype: T.dtype = T.float16, + tune: bool = False, +): + dtype = T.dtype(dtype) + torch_dtype = dtype.as_torch() if window_size is not None: - print('Using sliding window attention.') + print("Using sliding window attention.") assert window_size <= seq_q - flops_per_matmul = 2.0 * batch * heads * min( - window_size, seq_kv // 2) * seq_q * dim # just a rough estimation + flops_per_matmul = 2.0 * batch * heads * min(window_size, seq_kv // 2) * seq_q * dim # just a rough estimation else: - print('Using full attention.') + print("Using full attention.") flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5 total_flops = 2 * flops_per_matmul @@ -300,15 +242,14 @@ def main(batch: int = 1, block_N=block_N, num_stages=num_stages, threads=threads, - dtype=dtype) + dtype=dtype, + ) Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype) torch.testing.assert_close( - kernel(Q, K, V, sinks), - ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), - rtol=1e-2, - atol=1e-2) + kernel(Q, K, V, sinks), ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), rtol=1e-2, atol=1e-2 + ) print("All checks passed.✅") latency = do_bench(lambda: kernel(Q, K, V, sinks), warmup=500) @@ -316,21 +257,38 @@ def main(batch: int = 1, print("Tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + window_size: Optional[int] = None, + dtype: str = "float16", + tune: bool = False, +): + torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype] + block_M = 128 + block_N = 128 + num_stages = 2 + threads = 256 + kernel = flashattn( + batch, heads, seq_q, seq_kv, dim, window_size, block_M=block_M, block_N=block_N, num_stages=num_stages, threads=threads, dtype=dtype + ) + Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype) + latency = do_bench(lambda: kernel(Q, K, V, sinks), backend="cupti") + return latency + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_q', type=int, default=4096, help='sequence length of query') - parser.add_argument('--seq_kv', type=int, default=4096, help='sequence length of key/value') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument( - '--window_size', - type=int, - default=None, - help='window size (default: None, which means full attention)') - parser.add_argument( - '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16") - parser.add_argument('--tune', action='store_true', help='tune') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_q", type=int, default=4096, help="sequence length of query") + parser.add_argument("--seq_kv", type=int, default=4096, help="sequence length of key/value") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--window_size", type=int, default=None, help="window size (default: None, which means full attention)") + parser.add_argument("--dtype", type=str, default=T.float16, help="dtype, can be float16 or bfloat16") + parser.add_argument("--tune", action="store_true", help="tune") args = parser.parse_args() - main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, - args.tune) + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype, args.tune) diff --git a/examples/attention_sink/regression_attention_sink.py b/examples/attention_sink/regression_attention_sink.py new file mode 100644 index 0000000000..e2453173cf --- /dev/null +++ b/examples/attention_sink/regression_attention_sink.py @@ -0,0 +1,64 @@ +import tilelang.testing +import example_mha_sink_fwd_bhsd +import example_mha_sink_fwd_bhsd_wgmma_pipelined +import example_mha_sink_bwd_bhsd +import example_gqa_sink_bwd_bhsd +import example_gqa_sink_fwd_bhsd_wgmma_pipelined + + +def regression_example_mha_sink_fwd_bhsd(): + tilelang.testing.process_func(example_mha_sink_fwd_bhsd.run_regression_perf) + + +def regression_example_mha_sink_fwd_bhsd_sliding_window(): + tilelang.testing.process_func( + example_mha_sink_fwd_bhsd.run_regression_perf, "regression_example_mha_sink_fwd_bhsd_sliding_window", window_size=128 + ) + + +def regression_example_mha_sink_fwd_bhsd_wgmma_pipelined(): + tilelang.testing.process_func(example_mha_sink_fwd_bhsd_wgmma_pipelined.run_regression_perf) + + +def regression_example_mha_sink_fwd_bhsd_wgmma_pipelined_sliding_window(): + tilelang.testing.process_func( + example_mha_sink_fwd_bhsd_wgmma_pipelined.run_regression_perf, + "regression_example_mha_sink_fwd_bhsd_wgmma_pipelined_sliding_window", + window_size=128, + ) + + +def regression_example_gqa_sink_fwd_bhsd_wgmma_pipelined(): + tilelang.testing.process_func(example_gqa_sink_fwd_bhsd_wgmma_pipelined.run_regression_perf) + + +def regression_example_gqa_sink_fwd_bhsd_wgmma_pipelined_sliding_window(): + tilelang.testing.process_func( + example_gqa_sink_fwd_bhsd_wgmma_pipelined.run_regression_perf, + "regression_example_gqa_sink_fwd_bhsd_wgmma_pipelined_sliding_window", + window_size=128, + ) + + +def regression_example_mha_sink_bwd_bhsd(): + tilelang.testing.process_func(example_mha_sink_bwd_bhsd.run_regression_perf) + + +def regression_example_mha_sink_bwd_bhsd_sliding_window(): + tilelang.testing.process_func( + example_mha_sink_bwd_bhsd.run_regression_perf, "regression_example_mha_sink_bwd_bhsd_sliding_window", window_size=128 + ) + + +def regression_example_gqa_sink_bwd_bhsd(): + tilelang.testing.process_func(example_gqa_sink_bwd_bhsd.run_regression_perf) + + +def regression_example_gqa_sink_bwd_bhsd_sliding_window(): + tilelang.testing.process_func( + example_gqa_sink_bwd_bhsd.run_regression_perf, "regression_example_gqa_sink_bwd_bhsd_sliding_window", window_size=128 + ) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/bitnet-1.58b/.gitignore b/examples/bitnet-1.58b/.gitignore index 6ea8874968..2bcdfd92ba 100644 --- a/examples/bitnet-1.58b/.gitignore +++ b/examples/bitnet-1.58b/.gitignore @@ -1 +1 @@ -models/ \ No newline at end of file +models/ diff --git a/examples/bitnet-1.58b/README.md b/examples/bitnet-1.58b/README.md index 2b587eab4c..b9898741b8 100644 --- a/examples/bitnet-1.58b/README.md +++ b/examples/bitnet-1.58b/README.md @@ -2,7 +2,6 @@ license: mit --- - This is a Tilelang Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with INT8xINT2 Kernel. We also evaluated the model's correctness and performance through `eval_correctness.py` and `benchmark_inference_latency.py`. ## Make Checkpoints for vLLM @@ -43,7 +42,6 @@ python3 inference_with_bitblas_format.py | bitnet-3b-1.58bits | vllm-tilelang | 379.25 | 117.43 | 752.55 | | bitnet-3b-1.58bits | vllm-tilelang-cuda-graph | 2543.58 | 1621.08 | 2731.79 | - ## BitBLAS Results ### Performance @@ -94,4 +92,4 @@ The differences between the reported numbers and the reproduced results are poss journal={arXiv preprint arXiv:2402.17764}, year={2024} } -``` \ No newline at end of file +``` diff --git a/examples/bitnet-1.58b/benchmark.sh b/examples/bitnet-1.58b/benchmark.sh index 6a2550d455..839443dc68 100755 --- a/examples/bitnet-1.58b/benchmark.sh +++ b/examples/bitnet-1.58b/benchmark.sh @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + python benchmark_generate.py --bs 16 --in_seq_len 32 --out_seq_len 128 | tee b16_i32_o128.log python benchmark_generate.py --bs 1 --in_seq_len 512 --out_seq_len 64 | tee b1_i512_o64.log diff --git a/examples/bitnet-1.58b/benchmark_generate.py b/examples/bitnet-1.58b/benchmark_generate.py index d6f21ed502..d678b91a4e 100644 --- a/examples/bitnet-1.58b/benchmark_generate.py +++ b/examples/bitnet-1.58b/benchmark_generate.py @@ -12,8 +12,7 @@ def generate_text_batch(model, tokenizer, prompts, max_length=100): # Encode the input prompts as a batch - input_ids = tokenizer( - prompts, return_tensors="pt", padding=True, truncation=True).input_ids.to(model.device) + input_ids = tokenizer(prompts, return_tensors="pt", padding=True, truncation=True).input_ids.to(model.device) # Generate cos and sin values (commented out as not used in generation) seq_length = input_ids.size(1) @@ -37,9 +36,7 @@ def generate_text_batch(model, tokenizer, prompts, max_length=100): end_time = time.time() # Decode the output ids to text - generated_texts = [ - tokenizer.decode(output_id, skip_special_tokens=True) for output_id in output_ids - ] + generated_texts = [tokenizer.decode(output_id, skip_special_tokens=True) for output_id in output_ids] generation_time = end_time - start_time num_tokens = sum(len(output_id) for output_id in output_ids) @@ -52,8 +49,8 @@ def generate_text_batch(model, tokenizer, prompts, max_length=100): def profile(model, input_data): - import numpy as np + model = model.cuda() model.eval() @@ -74,25 +71,29 @@ def get_runtime(num_repeats=1): return np.mean(times) -model_path = '1bitLLM/bitnet_b1_58-3B' +model_path = "1bitLLM/bitnet_b1_58-3B" def main(): parser = argparse.ArgumentParser() - parser.add_argument('--bs', default=16, type=int) - parser.add_argument('--in_seq_len', default=32, type=int) - parser.add_argument('--out_seq_len', default=128, type=int) - parser.add_argument('--bitblas', action='store_true') + parser.add_argument("--bs", default=16, type=int) + parser.add_argument("--in_seq_len", default=32, type=int) + parser.add_argument("--out_seq_len", default=128, type=int) + parser.add_argument("--bitblas", action="store_true") args = parser.parse_args() bs = args.bs in_seq_len = args.in_seq_len out_seq_len = args.out_seq_len is_bitblas = args.bitblas - model = BitnetForCausalLM.from_pretrained( - model_path, - use_flash_attention_2=True, - torch_dtype=torch.float16, - ).cuda().half() + model = ( + BitnetForCausalLM.from_pretrained( + model_path, + use_flash_attention_2=True, + torch_dtype=torch.float16, + ) + .cuda() + .half() + ) if is_bitblas: with torch.no_grad(): model.quantize() @@ -109,5 +110,5 @@ def main(): print(generate_text_batch(model, tokenizer, prompts, max_length=max_length)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/bitnet-1.58b/benchmark_inference_latency.py b/examples/bitnet-1.58b/benchmark_inference_latency.py index 9ce7a3898c..788fc5565d 100644 --- a/examples/bitnet-1.58b/benchmark_inference_latency.py +++ b/examples/bitnet-1.58b/benchmark_inference_latency.py @@ -6,13 +6,14 @@ torch.set_grad_enabled(False) parser = argparse.ArgumentParser() -parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str) +parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str) def profile(model, input_data): import time import numpy as np + model = model.cuda() model.eval() @@ -35,8 +36,8 @@ def get_runtime(num_repeats=1): def main(): model = BitnetForCausalLM.from_pretrained( - '1bitLLM/bitnet_b1_58-3B', - device_map='auto', + "1bitLLM/bitnet_b1_58-3B", + device_map="auto", low_cpu_mem_usage=True, use_flash_attention_2=True, torch_dtype=torch.float16, @@ -52,5 +53,5 @@ def main(): print(f"Batch size: {batch_size}, Seq len: {seq_len}, Latency: {latency}") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/bitnet-1.58b/configuration_bitnet.py b/examples/bitnet-1.58b/configuration_bitnet.py index 5f4937b87b..63c499db36 100644 --- a/examples/bitnet-1.58b/configuration_bitnet.py +++ b/examples/bitnet-1.58b/configuration_bitnet.py @@ -17,7 +17,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. -""" LLaMA model configuration""" +"""LLaMA model configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging @@ -180,16 +180,10 @@ def _rope_scaling_validation(self): return if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: - raise ValueError( - "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " - f"got {self.rope_scaling}") + raise ValueError(f"`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, got {self.rope_scaling}") rope_scaling_type = self.rope_scaling.get("type", None) rope_scaling_factor = self.rope_scaling.get("factor", None) if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: - raise ValueError( - f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" - ) - if rope_scaling_factor is None or not isinstance(rope_scaling_factor, - float) or rope_scaling_factor <= 1.0: - raise ValueError( - f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") + raise ValueError(f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}") + if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: + raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") diff --git a/examples/bitnet-1.58b/eval_correctness.py b/examples/bitnet-1.58b/eval_correctness.py index ac1e340729..11d47004b8 100644 --- a/examples/bitnet-1.58b/eval_correctness.py +++ b/examples/bitnet-1.58b/eval_correctness.py @@ -47,8 +47,8 @@ def generate_text(model, tokenizer, prompt, max_length=100): def profile(model, input_data): - import numpy as np + model = model.cuda() model.eval() @@ -69,18 +69,22 @@ def get_runtime(num_repeats=1): return np.mean(times) -model_path = '1bitLLM/bitnet_b1_58-3B' +model_path = "1bitLLM/bitnet_b1_58-3B" def main(): - model = BitnetForCausalLM.from_pretrained( - model_path, - use_flash_attention_2=False, - torch_dtype=torch.float16, - ).cuda().half() + model = ( + BitnetForCausalLM.from_pretrained( + model_path, + use_flash_attention_2=False, + torch_dtype=torch.float16, + ) + .cuda() + .half() + ) tokenizer = BitnetTokenizer.from_pretrained(model_path, use_fast=False) - input_id = tokenizer("Hello")['input_ids'] + input_id = tokenizer("Hello")["input_ids"] input_id = torch.tensor(input_id).unsqueeze(0).cuda() print("original model generated text:") @@ -91,5 +95,5 @@ def main(): print(generate_text(model, tokenizer, "Hello", max_length=100)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/bitnet-1.58b/eval_gpu_memory.py b/examples/bitnet-1.58b/eval_gpu_memory.py index 597cbbfcda..00c914cb31 100644 --- a/examples/bitnet-1.58b/eval_gpu_memory.py +++ b/examples/bitnet-1.58b/eval_gpu_memory.py @@ -6,13 +6,14 @@ torch.set_grad_enabled(False) parser = argparse.ArgumentParser() -parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str) +parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str) def profile(model, input_data): import time import numpy as np + model = model.cuda() model.eval() @@ -35,17 +36,17 @@ def get_runtime(num_repeats=1): def main(): model = BitnetForCausalLM.from_pretrained( - '1bitLLM/bitnet_b1_58-3B', - device_map='auto', + "1bitLLM/bitnet_b1_58-3B", + device_map="auto", low_cpu_mem_usage=True, use_flash_attention_2=True, torch_dtype=torch.float16, ).half() - print(f"gpu memory: {torch.cuda.memory_allocated() / 1024 ** 3} GB") + print(f"gpu memory: {torch.cuda.memory_allocated() / 1024**3} GB") with torch.no_grad(): model._post_process_weights() - print(f"gpu memory BitBLAS: {torch.cuda.memory_allocated() / 1024 ** 3} GB") + print(f"gpu memory BitBLAS: {torch.cuda.memory_allocated() / 1024**3} GB") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/bitnet-1.58b/eval_ppl.py b/examples/bitnet-1.58b/eval_ppl.py index 61c8488e46..97db2d0f52 100644 --- a/examples/bitnet-1.58b/eval_ppl.py +++ b/examples/bitnet-1.58b/eval_ppl.py @@ -15,9 +15,9 @@ torch.set_grad_enabled(False) parser = argparse.ArgumentParser() -parser.add_argument('--seed', default=0, type=int) -parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str) -parser.add_argument('--seqlen', default=2048, type=int) +parser.add_argument("--seed", default=0, type=int) +parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str) +parser.add_argument("--seqlen", default=2048, type=int) def calulate_loss(model, input, loss_fct): @@ -29,12 +29,16 @@ def calulate_loss(model, input, loss_fct): def main(args): - datasets = ['c4', 'wikitext2'] - model = BitnetForCausalLM.from_pretrained( - args.hf_path, - use_flash_attention_2=True, - torch_dtype=torch.float16, - ).cuda().half() + datasets = ["c4", "wikitext2"] + model = ( + BitnetForCausalLM.from_pretrained( + args.hf_path, + use_flash_attention_2=True, + torch_dtype=torch.float16, + ) + .cuda() + .half() + ) with torch.no_grad(): model._post_process_weights() tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False) @@ -48,9 +52,9 @@ def main(args): for ii in progress: input = torch.Tensor(testdata[ii]).long().cuda().view(1, -1) loss = calulate_loss(model, input, loss_fct) - count += (input.size(-1) - 1) + count += input.size(-1) - 1 acc_loss += loss.item() - progress.set_description(f"avg_loss = {acc_loss/ count / math.log(2)}") + progress.set_description(f"avg_loss = {acc_loss / count / math.log(2)}") avg_loss = acc_loss / count / math.log(2) ppl.append(2**avg_loss) @@ -60,7 +64,7 @@ def main(args): print("Avg PPL:", sum(ppl) / len(ppl)) -if __name__ == '__main__': +if __name__ == "__main__": torch.set_grad_enabled(False) args = parser.parse_args() random.seed(args.seed) diff --git a/examples/bitnet-1.58b/eval_utils.py b/examples/bitnet-1.58b/eval_utils.py index 46241eedf0..72480c392a 100644 --- a/examples/bitnet-1.58b/eval_utils.py +++ b/examples/bitnet-1.58b/eval_utils.py @@ -15,21 +15,17 @@ def set_seed(seed): def get_test_dataset(dataset_name, tokenizer, seqlen=2048): if dataset_name == "wikitext2": - testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test') - testdata = "".join(testdata['text']).split('\n') + testdata = load_dataset("wikitext", "wikitext-2-raw-v1", split="test") + testdata = "".join(testdata["text"]).split("\n") elif dataset_name == "c4": - testdata = load_dataset( - 'allenai/c4', - data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'}, - split='validation')['text'] + testdata = load_dataset("allenai/c4", data_files={"validation": "en/c4-validation.00000-of-00008.json.gz"}, split="validation")[ + "text" + ] else: raise NotImplementedError testdata = [item for item in testdata if item != ""] - tokenized_text = [ - tokenizer(item, add_special_tokens=False)['input_ids'] + [tokenizer.eos_token_id] - for item in testdata - ] + tokenized_text = [tokenizer(item, add_special_tokens=False)["input_ids"] + [tokenizer.eos_token_id] for item in testdata] data, doc = [], [tokenizer.bos_token_id] for sen in tokenized_text: @@ -45,7 +41,6 @@ def get_test_dataset(dataset_name, tokenizer, seqlen=2048): class LMEvalAdaptor(BaseLM): - def __init__(self, model_name, model, tokenizer, batch_size=1, max_length=-1): super().__init__() @@ -137,5 +132,4 @@ def _model_call(self, inps): return out def _model_generate(self, context, max_length, eos_token_id): - return self.model.generate( - context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False) + return self.model.generate(context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False) diff --git a/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py b/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py index e5af16cc48..7b8b7b95cd 100644 --- a/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py +++ b/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py @@ -76,13 +76,13 @@ def bitnet_158_int8xint2_decode( reduce_thread=32, ): assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" storage_nbit = 8 num_bits = 2 @@ -94,7 +94,7 @@ def bitnet_158_int8xint2_decode( MAX_TRANSACTION_SIZE_IN_BITS = 128 micro_size_k = MAX_TRANSACTION_SIZE_IN_BITS // DataType(in_dtype).bits micro_size_k_compressed = micro_size_k // num_elems_per_byte - storage_dtype = "int8" + storage_dtype = T.int8 block_K = reduce_thread * micro_size_k use_dp4a = True @@ -102,17 +102,17 @@ def bitnet_158_int8xint2_decode( @T.prim_func def kernel( - A: T.Buffer(A_shape, in_dtype), - B: T.Buffer(B_shape, storage_dtype), - C: T.Buffer(C_shape, out_dtype), + A: T.Buffer(A_shape, in_dtype), + B: T.Buffer(B_shape, storage_dtype), + C: T.Buffer(C_shape, out_dtype), ): with T.Kernel( - T.ceildiv(N, n_partition), - M, - threads=(reduce_thread, n_partition), + T.ceildiv(N, n_partition), + M, + threads=(reduce_thread, n_partition), ) as ( - bx, - by, + bx, + by, ): A_local = T.alloc_local((micro_size_k,), in_dtype) B_quant_local = T.alloc_local([micro_size_k_compressed], storage_dtype) @@ -133,8 +133,7 @@ def kernel( for v in T.vectorized(micro_size_k_compressed): B_quant_local[v] = B[ bx * n_partition + ni, - ko * (reduce_thread * micro_size_k_compressed) + - kr * micro_size_k_compressed + v, + ko * (reduce_thread * micro_size_k_compressed) + kr * micro_size_k_compressed + v, ] T.call_extern( @@ -156,9 +155,9 @@ def kernel( accum_res[0] += A_local[ki] * B_dequantize_local[ki] with T.attr( - T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), - "reduce_scope", - T.reinterpret(T.uint64(0), dtype="handle"), + T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), + "reduce_scope", + T.reinterpret(T.uint64(0), dtype="handle"), ): T.evaluate( T.tvm_thread_allreduce( @@ -168,7 +167,8 @@ def kernel( reduced_accum_res[0], kr, dtype="handle", - )) + ) + ) if kr == 0: C[by, bx * n_partition + ni] = reduced_accum_res[0] @@ -194,12 +194,12 @@ def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8): # interleave weight numpy implementation -def interleave_weight(qweight, nbits=4, target_dtype="float16"): - assert target_dtype in ["float16", "int8"] +def interleave_weight(qweight, nbits=4, target_dtype=T.float16): + assert target_dtype in [T.float16, T.int8] # reinterpret the data type of qweight to int32 qweight = qweight.view(np.int32) new_qweight = np.zeros_like(qweight) - bits_stride = 8 if target_dtype == "int8" else 16 + bits_stride = 8 if target_dtype == T.int8 else 16 mask = (1 << nbits) - 1 # for 4bit the val is 0x0000000f num_groups = 32 // bits_stride elems_per_group = bits_stride // nbits @@ -209,7 +209,7 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift - if nbits == 1 and target_dtype == "int8": + if nbits == 1 and target_dtype == T.int8: # special handling for 1b interleave n16_weight = new_qweight & np.int32(0xF0F00F0F) n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16 @@ -217,12 +217,12 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4 n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12 return n16_weight.view(np.int8) - elif nbits == 2 and target_dtype == "float16": + elif nbits == 2 and target_dtype == T.float16: n8_weight = new_qweight & np.int32(0xFF0000FF) n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16 n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8 return n8_weight.view(np.int8) - elif nbits == 1 and target_dtype == "float16": + elif nbits == 1 and target_dtype == T.float16: n8_weight = new_qweight & 0xF000000F n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8 n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16 @@ -234,13 +234,7 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): return new_qweight.view(np.int8) -def assert_bitnet_158_int8xint2_decode_correctness(M, - N, - K, - in_dtype, - out_dtype, - accum_dtype, - fast_decoding=True): +def assert_bitnet_158_int8xint2_decode_correctness(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding=True): program = bitnet_158_int8xint2_decode(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding) print(program) kernel = tilelang.compile(program) @@ -265,4 +259,4 @@ def assert_bitnet_158_int8xint2_decode_correctness(M, if __name__ == "__main__": - assert_bitnet_158_int8xint2_decode_correctness(1, 256, 256, "int8", "int32", "int32") + assert_bitnet_158_int8xint2_decode_correctness(1, 256, 256, T.int8, T.int32, T.int32) diff --git a/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py b/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py index d8b1f6228e..f4a60098a5 100644 --- a/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py +++ b/examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py @@ -8,11 +8,13 @@ from tilelang import tvm as tvm from tvm import DataType from tilelang.intrinsics.mma_layout import ( - make_mma_swizzle_layout as make_swizzle_layout,) + make_mma_swizzle_layout as make_swizzle_layout, +) import numpy as np from tilelang.intrinsics.mma_macro_generator import ( - INT4TensorCoreIntrinEmitter,) + INT4TensorCoreIntrinEmitter, +) from tilelang.transform import simplify_prim_func torch.manual_seed(42) @@ -86,9 +88,9 @@ def bitnet_158_int8xint2_prefill( Create a TVM GPU prim_func implementing a block-tiled matrix multiply that multiplies dense A by compressed/interleaved low‑precision B (2-bit packed into int8 storage), decoding B to int8 on-chip and accumulating into C. The returned prim_func expects: - - A: shape (M, K) with dtype `in_dtype` ("float16" or "int8"). + - A: shape (M, K) with dtype `in_dtype` (T.float16 or T.int8). - B: compressed storage with shape (N, K/4) and int8 storage layout (packing 4 2-bit elements per byte). - - C: output buffer shape (M, N) with dtype `out_dtype` ("float16", "float32", or "int32"). + - C: output buffer shape (M, N) with dtype `out_dtype` (T.float16, T.float32, or T.int32). Details: - Builds a tiled, pipelined kernel using shared memory and warp-level MMA intrinsics (INT4TensorCoreIntrinEmitter). B is loaded from compressed storage, decoded to int8 in threads (via decode_i2u_to_i8s / decode_i2s_to_i8s), and dequantized into a shared buffer used by the MMA emitter. @@ -96,15 +98,15 @@ def bitnet_158_int8xint2_prefill( - block_row_warps, block_col_warps: number of warps per block in row/col. - warp_row_tiles, warp_col_tiles: tiles per warp. - chunk: K-sized chunk per block (block_K). - - micro sizes are fixed (16x16x16, except micro_k=32 when accum_dtype == "int32"). + - micro sizes are fixed (16x16x16, except micro_k=32 when accum_dtype == T.int32). - Uses 2-stage pipelining by default to overlap loads and compute and applies a swizzle layout to improve L2 behavior. - Assertions: raises AssertionError if in_dtype or out_dtype are not among supported values. Parameters: M, N, K (int): Global matrix dimensions. - in_dtype (str): Input and decoded B element dtype; "float16" or "int8". - out_dtype (str): Output C dtype; one of "float16", "float32", "int32". - accum_dtype (str): Accumulator dtype used by MMA (e.g., "int32"). + in_dtype (str): Input and decoded B element dtype; T.float16 or T.int8. + out_dtype (str): Output C dtype; one of T.float16, T.float32, T.int32. + accum_dtype (str): Accumulator dtype used by MMA (e.g., T.int32). fast_decoding (bool): If True, enable the fast decoding path (affects which device decode is used). block_row_warps (int): Warps in block row dimension. block_col_warps (int): Warps in block column dimension. @@ -116,18 +118,18 @@ def bitnet_158_int8xint2_prefill( T.prim_func: A TVM prim_func implementing the described GPU kernel suitable for compilation and execution. """ assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - if accum_dtype == "int32": + if accum_dtype == T.int32: micro_size_k = 32 num_elems_per_byte = 4 @@ -136,7 +138,7 @@ def bitnet_158_int8xint2_prefill( local_size_compressed = local_size // num_elems_per_byte shared_scope = "shared.dyn" - storage_dtype = "int8" + storage_dtype = T.int8 # Pipeline Stage stage = 2 @@ -181,38 +183,36 @@ def bitnet_158_int8xint2_prefill( @T.prim_func def main( - A: T.Buffer(A_shape, in_dtype), - B: T.Buffer(B_shape, storage_dtype), - C: T.Buffer((M, N), out_dtype), + A: T.Buffer(A_shape, in_dtype), + B: T.Buffer(B_shape, storage_dtype), + C: T.Buffer((M, N), out_dtype), ): """ - GPU kernel entry that performs a blocked, pipelined matrix multiplication A @ B.T writing into C. + GPU kernel entry that performs a blocked, pipelined matrix multiplication A @ B.T writing into C. - This kernel: - - Loads tiles of A and a compressed/interleaved representation of B from global memory into shared memory. - - Decodes B's packed low-precision format (storage_dtype, e.g., 2-bit packed) into element values of `in_dtype` in shared memory via an external decode routine. - - Uses Warp/MMA tiled fragments and an INT4/INT2-capable MMA emitter to compute accumulation across K in a pipelined fashion with configurable stages. - - Writes accumulated tile results from shared memory back to global C with the expected block/micro-tile indexing. + This kernel: + - Loads tiles of A and a compressed/interleaved representation of B from global memory into shared memory. + - Decodes B's packed low-precision format (storage_dtype, e.g., 2-bit packed) into element values of `in_dtype` in shared memory via an external decode routine. + - Uses Warp/MMA tiled fragments and an INT4/INT2-capable MMA emitter to compute accumulation across K in a pipelined fashion with configurable stages. + - Writes accumulated tile results from shared memory back to global C with the expected block/micro-tile indexing. - Parameters: - A: Input matrix buffer of shape A_shape and element type `in_dtype`. Represents the MxK activations. - B: Compressed/interleaved weight buffer of shape B_shape and storage type `storage_dtype`. Must contain B in the packed low-precision layout expected by the decode routine used by this kernel. - C: Output buffer of shape (M, N) and type `out_dtype`; receives the resulting matrix (accumulated values are produced in `accum_dtype` and stored into C). + Parameters: + A: Input matrix buffer of shape A_shape and element type `in_dtype`. Represents the MxK activations. + B: Compressed/interleaved weight buffer of shape B_shape and storage type `storage_dtype`. Must contain B in the packed low-precision layout expected by the decode routine used by this kernel. + C: Output buffer of shape (M, N) and type `out_dtype`; receives the resulting matrix (accumulated values are produced in `accum_dtype` and stored into C). - Side effects: - Writes results into C. Calls external device decode functions to expand B from its packed representation into shared memory before computation. + Side effects: + Writes results into C. Calls external device decode functions to expand B from its packed representation into shared memory before computation. """ with T.Kernel( - T.ceildiv(N, block_N), - T.ceildiv(M, block_M), - threads=threads, - prelude=decode_i2s_to_i8s, + T.ceildiv(N, block_N), + T.ceildiv(M, block_M), + threads=threads, + prelude=decode_i2s_to_i8s, ) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, storage_dtype, scope=shared_scope) - B_dequantize_shared = T.alloc_shared( - B_dequantize_shared_shape, in_dtype, scope=shared_scope) + B_dequantize_shared = T.alloc_shared(B_dequantize_shared_shape, in_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) A_frag = T.alloc_local((warp_rows * fragement_size_a), in_dtype) B_frag = T.alloc_local((warp_cols * fragement_size_b), in_dtype) @@ -221,12 +221,14 @@ def main( B_local = T.alloc_local([local_size_compressed], storage_dtype) B_dequantize_local = T.alloc_local([local_size], in_dtype) - thread_bindings = T.thread_binding(0, threads, "threadIdx.x") + thread_bindings = T.get_thread_binding(0) - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - B_dequantize_shared: make_swizzle_layout(B_dequantize_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + B_dequantize_shared: make_swizzle_layout(B_dequantize_shared), + } + ) # Improve L2 Cache T.use_swizzle(panel_size=10) @@ -234,7 +236,6 @@ def main( T.clear(C_frag) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, block_K): A_shared[i, k] = A[by * block_M + i, ko * block_K + k] @@ -243,12 +244,9 @@ def main( for j, k in T.Parallel(block_N, block_K // num_elems_per_byte): B_shared[j, k] = B[bx * block_N + j, ko * (block_K // num_elems_per_byte) + k] - for i in T.serial(block_N * block_K // num_elems_per_byte // - (threads * local_size_compressed)): + for i in T.serial(block_N * block_K // num_elems_per_byte // (threads * local_size_compressed)): for v in T.vectorized(0, local_size_compressed): - index = ( - i * threads * local_size_compressed + - thread_bindings * local_size_compressed + v) + index = i * threads * local_size_compressed + thread_bindings * local_size_compressed + v vi, vj = T.index_to_coordinates(index, B_shared_shape) B_local[v] = B_shared[vi, vj] @@ -260,12 +258,11 @@ def main( ) for v in T.vectorized(0, local_size): - index = (i * threads * local_size + thread_bindings * local_size + v) + index = i * threads * local_size + thread_bindings * local_size + v vi, vj = T.index_to_coordinates(index, B_dequantize_shared_shape) B_dequantize_shared[vi, vj] = B_dequantize_local[v] for ki in T.serial(0, (block_K // micro_size_k)): - # Load A into fragment mma_emitter.ldmatrix_a( A_frag, @@ -320,12 +317,12 @@ def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8): # interleave weight numpy implementation -def interleave_weight(qweight, nbits=4, target_dtype="float16"): - assert target_dtype in ["float16", "int8"] +def interleave_weight(qweight, nbits=4, target_dtype=T.float16): + assert target_dtype in [T.float16, T.int8] # reinterpret the data type of qweight to int32 qweight = qweight.view(np.int32) new_qweight = np.zeros_like(qweight) - bits_stride = 8 if target_dtype == "int8" else 16 + bits_stride = 8 if target_dtype == T.int8 else 16 mask = (1 << nbits) - 1 # for 4bit the val is 0x0000000f num_groups = 32 // bits_stride elems_per_group = bits_stride // nbits @@ -335,7 +332,7 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift - if nbits == 1 and target_dtype == "int8": + if nbits == 1 and target_dtype == T.int8: # special handling for 1b interleave n16_weight = new_qweight & np.int32(0xF0F00F0F) n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16 @@ -343,12 +340,12 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4 n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12 return n16_weight.view(np.int8) - elif nbits == 2 and target_dtype == "float16": + elif nbits == 2 and target_dtype == T.float16: n8_weight = new_qweight & np.int32(0xFF0000FF) n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16 n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8 return n8_weight.view(np.int8) - elif nbits == 1 and target_dtype == "float16": + elif nbits == 1 and target_dtype == T.float16: n8_weight = new_qweight & 0xF000000F n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8 n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16 @@ -360,13 +357,7 @@ def interleave_weight(qweight, nbits=4, target_dtype="float16"): return new_qweight.view(np.int8) -def assert_bitnet_158_int8xint2_prefill_correctness(M, - N, - K, - in_dtype, - out_dtype, - accum_dtype, - fast_decoding=True): +def assert_bitnet_158_int8xint2_prefill_correctness(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding=True): program = bitnet_158_int8xint2_prefill(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding) print(program) kernel = tilelang.compile(program) @@ -391,4 +382,4 @@ def assert_bitnet_158_int8xint2_prefill_correctness(M, if __name__ == "__main__": - assert_bitnet_158_int8xint2_prefill_correctness(256, 256, 256, "int8", "int32", "int32") + assert_bitnet_158_int8xint2_prefill_correctness(256, 256, 256, T.int8, T.int32, T.int32) diff --git a/examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py b/examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py index 9864635988..e3d35df4b2 100644 --- a/examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py +++ b/examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py @@ -6,7 +6,8 @@ import tvm.tl.language as T from bitblas.tl.utils import get_swizzle_layout from bitblas.tl.mma_macro_generator import ( - TensorCoreIntrinEmitter,) + TensorCoreIntrinEmitter, +) from bitblas.base import simplify_prim_func torch.manual_seed(0) @@ -37,18 +38,18 @@ def tl_matmul( accum_dtype, ): assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - if out_dtype == "int32": + if out_dtype == T.int32: micro_size_k = 32 # This is a debug config @@ -56,7 +57,7 @@ def tl_matmul( block_col_warps = 2 warp_row_tiles = 64 warp_col_tiles = 64 - chunk = 32 if in_dtype == "float16" else 64 + chunk = 32 if in_dtype == T.float16 else 64 shared_scope = "shared.dyn" # Pipeline Stage @@ -101,12 +102,11 @@ def tl_matmul( @T.prim_func def main( - A: T.Buffer(A_shape, in_dtype), - B: T.Buffer(B_shape, in_dtype), - C: T.Buffer((M, N), out_dtype), + A: T.Buffer(A_shape, in_dtype), + B: T.Buffer(B_shape, in_dtype), + C: T.Buffer((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) @@ -116,10 +116,12 @@ def main( thread_bindings = T.thread_binding(0, threads, "threadIdx.x") - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - B_shared: make_swizzle_layout(B_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + B_shared: make_swizzle_layout(B_shared), + } + ) # Improve L2 Cache T.use_swizzle(panel_size=10) @@ -127,7 +129,6 @@ def main( T.clear(C_local) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, block_K): A_shared[i, k] = A[by * block_M + i, ko * block_K + k] @@ -137,7 +138,6 @@ def main( B_shared[j, k] = B[bx * block_N + j, ko * block_K + k] for ki in T.serial(0, (block_K // micro_size_k)): - # Load A into fragment mma_emitter.ldmatrix_a( A_local, @@ -183,7 +183,7 @@ def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype): # src_code is the generated cuda source assert src_code is not None print(src_code) - if in_dtype == "int8": + if in_dtype == T.int8: A = torch.randint(-7, 7, (M, K), device="cuda", dtype=torch.int8) B = torch.randint(-7, 7, (N, K), device="cuda", dtype=torch.int8) else: @@ -209,12 +209,12 @@ def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype): def test_assert_tl_matmul(): - assert_tl_matmul_correctness(128, 128, 128, "float16", "float16", "float16") - assert_tl_matmul_correctness(128, 256, 256, "float16", "float32", "float32") + assert_tl_matmul_correctness(128, 128, 128, T.float16, T.float16, T.float16) + assert_tl_matmul_correctness(128, 256, 256, T.float16, T.float32, T.float32) if __name__ == "__main__": # bitblas.testing.main() - # assert_tl_matmul_correctness(128, 128, 128, "float16", "float16", "float16") - # assert_tl_matmul_correctness(128, 128, 128, "int8", "int32", "int32") - assert_tl_matmul_correctness(16384, 16384, 16384, "int8", "int32", "int32") + # assert_tl_matmul_correctness(128, 128, 128, T.float16, T.float16, T.float16) + # assert_tl_matmul_correctness(128, 128, 128, T.int8, T.int32, T.int32) + assert_tl_matmul_correctness(16384, 16384, 16384, T.int8, T.int32, T.int32) diff --git a/examples/bitnet-1.58b/load_from_quantized.py b/examples/bitnet-1.58b/load_from_quantized.py index 26a32f9747..8c775aa4c8 100644 --- a/examples/bitnet-1.58b/load_from_quantized.py +++ b/examples/bitnet-1.58b/load_from_quantized.py @@ -49,7 +49,13 @@ def generate_text(model, tokenizer, prompt, max_length=100): def main(): # load quantized model - qmodel = BitnetForCausalLM.from_quantized(saved_model_path,).cuda().half() + qmodel = ( + BitnetForCausalLM.from_quantized( + saved_model_path, + ) + .cuda() + .half() + ) tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False) # print("original model generated text:") # print(generate_text(model, tokenizer, "Hi, ", max_length=100)) diff --git a/examples/bitnet-1.58b/maint/README.md b/examples/bitnet-1.58b/maint/README.md index 63cc3e275f..6bccdf93a2 100644 --- a/examples/bitnet-1.58b/maint/README.md +++ b/examples/bitnet-1.58b/maint/README.md @@ -2,7 +2,6 @@ license: mit --- - This is a BitBLAS Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with BitBLAS INT8xINT2 Kernel. We also evaluated the model's correctness and performance through `eval_correctness.py` and `benchmark_inference_latency.py`. ## Latest News @@ -88,4 +87,4 @@ The differences between the reported numbers and the reproduced results are poss journal={arXiv preprint arXiv:2402.17764}, year={2024} } -``` \ No newline at end of file +``` diff --git a/examples/bitnet-1.58b/maint/create_bitblas_ckpt.py b/examples/bitnet-1.58b/maint/create_bitblas_ckpt.py index 1e29a553ab..2604ef3877 100644 --- a/examples/bitnet-1.58b/maint/create_bitblas_ckpt.py +++ b/examples/bitnet-1.58b/maint/create_bitblas_ckpt.py @@ -25,9 +25,9 @@ args = parser.parse_args() model_name_or_path = args.model_name_or_path -saved_model_path = os.path.join( - dirpath, "models", - f"{model_name_or_path}_bitblas") if args.saved_model_path is None else args.saved_model_path +saved_model_path = ( + os.path.join(dirpath, "models", f"{model_name_or_path}_bitblas") if args.saved_model_path is None else args.saved_model_path +) def generate_text(model, tokenizer, prompt, max_length=100): @@ -67,7 +67,10 @@ def main(): model_name_or_path, use_flash_attention_2=False, torch_dtype=torch.float16, - ).cuda().half()) + ) + .cuda() + .half() + ) tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False) # print("original model generated text:") @@ -112,10 +115,16 @@ def main(): file_path = cached_file(model_name_or_path, file) os.system(f"cp {file_path} {saved_model_path}") # load quantized model - qmodel = BitnetForCausalLM.from_quantized(saved_model_path,).cuda().half() + qmodel = ( + BitnetForCausalLM.from_quantized( + saved_model_path, + ) + .cuda() + .half() + ) print("quantized model generated text:") print(generate_text(qmodel, tokenizer, "Hi, ", max_length=100)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/bitnet-1.58b/maint/generate_bitnet_model_bitblas_format.sh b/examples/bitnet-1.58b/maint/generate_bitnet_model_bitblas_format.sh index 741c3a124a..b0430588a0 100755 --- a/examples/bitnet-1.58b/maint/generate_bitnet_model_bitblas_format.sh +++ b/examples/bitnet-1.58b/maint/generate_bitnet_model_bitblas_format.sh @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + # retrieve the native model input and saved model directory MODEL_DIR=$1 SAVED_MODEL_DIR=$2 diff --git a/examples/bitnet-1.58b/maint/generate_bitnet_model_native_format.sh b/examples/bitnet-1.58b/maint/generate_bitnet_model_native_format.sh index a2df0eb8cb..66356d3d84 100755 --- a/examples/bitnet-1.58b/maint/generate_bitnet_model_native_format.sh +++ b/examples/bitnet-1.58b/maint/generate_bitnet_model_native_format.sh @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + # require git lfs if ! command -v git-lfs &> /dev/null; then echo "Please install git-lfs first by running 'sudo apt install git-lfs'" diff --git a/examples/bitnet-1.58b/maint/quantize_config.json b/examples/bitnet-1.58b/maint/quantize_config.json index e2b24123a1..80fbf02f03 100644 --- a/examples/bitnet-1.58b/maint/quantize_config.json +++ b/examples/bitnet-1.58b/maint/quantize_config.json @@ -7,4 +7,4 @@ "model_name_or_path": "1bitLLM/bitnet_b1_58-3B", "quant_method": "bitnet", "checkpoint_format": "bitnet" -} \ No newline at end of file +} diff --git a/examples/bitnet-1.58b/maint/upload_models.sh b/examples/bitnet-1.58b/maint/upload_models.sh index b764b0da67..7c6d76e322 100755 --- a/examples/bitnet-1.58b/maint/upload_models.sh +++ b/examples/bitnet-1.58b/maint/upload_models.sh @@ -1,3 +1,5 @@ +#!/usr/bin/env bash + MODEL_DIR=$1 REMOTE_DIR=$2 diff --git a/examples/bitnet-1.58b/modeling_bitnet.py b/examples/bitnet-1.58b/modeling_bitnet.py index 6e3c42b6f9..1830995ee6 100644 --- a/examples/bitnet-1.58b/modeling_bitnet.py +++ b/examples/bitnet-1.58b/modeling_bitnet.py @@ -64,8 +64,7 @@ def find_layers(module, layers=None, name=""): return {name: module} res = {} for name1, child in module.named_children(): - res.update( - find_layers(child, layers=layers, name=name + "." + name1 if name != "" else name1)) + res.update(find_layers(child, layers=layers, name=name + "." + name1 if name != "" else name1)) return res @@ -87,7 +86,6 @@ def _get_unpad_data(attention_mask): class BitnetRMSNorm(nn.Module): - def __init__(self, hidden_size, eps=1e-6): """ BitnetRMSNorm is equivalent to T5LayerNorm @@ -108,34 +106,23 @@ def forward(self, hidden_states): class BitnetRotaryEmbedding(nn.Module): - - def __init__(self, - dim, - max_position_embeddings=2048, - base=10000, - device=None, - scaling_factor=1.0): + def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0): super().__init__() self.scaling_factor = scaling_factor self.dim = dim self.max_position_embeddings = max_position_embeddings self.base = base - inv_freq = 1.0 / ( - self.base - **(torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) + inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2, dtype=torch.int64).float().to(device) / self.dim)) self.register_buffer("inv_freq", inv_freq) # For BC we register cos and sin cached self.max_seq_len_cached = max_position_embeddings - t = torch.arange( - self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) + t = torch.arange(self.max_seq_len_cached, device=device, dtype=torch.int64).type_as(self.inv_freq) t = t / self.scaling_factor freqs = torch.outer(t, self.inv_freq) # Different from paper, but it uses a different permutation in order to obtain the same calculation emb = torch.cat((freqs, freqs), dim=-1) - self.register_buffer( - "_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False) - self.register_buffer( - "_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False) + self.register_buffer("_cos_cached", emb.cos().to(torch.get_default_dtype()), persistent=False) + self.register_buffer("_sin_cached", emb.sin().to(torch.get_default_dtype()), persistent=False) @property def sin_cached(self): @@ -156,14 +143,12 @@ def cos_cached(self): @torch.no_grad() def forward(self, x, position_ids): # x: [bs, num_attention_heads, seq_len, head_size] - inv_freq_expanded = self.inv_freq[None, :, - None].float().expand(position_ids.shape[0], -1, 1) + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) position_ids_expanded = position_ids[:, None, :].float() # Force float32 since bfloat16 loses precision on long contexts # See https://github.com/huggingface/transformers/pull/29285 device_type = x.device.type - device_type = device_type if isinstance(device_type, - str) and device_type != "mps" else "cpu" + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" with torch.autocast(device_type=device_type, enabled=False): freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2) emb = torch.cat((freqs, freqs), dim=-1) @@ -174,8 +159,8 @@ def forward(self, x, position_ids): def rotate_half(x): """Rotates half the hidden dims of the input.""" - x1 = x[..., :x.shape[-1] // 2] - x2 = x[..., x.shape[-1] // 2:] + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] return torch.cat((-x2, x1), dim=-1) @@ -207,7 +192,6 @@ def apply_rotary_pos_emb(q, k, cos, sin, position_ids=None, unsqueeze_dim=1): class BitnetMLP(nn.Module): - def __init__(self, config): super().__init__() self.config = config @@ -245,7 +229,6 @@ def forward(self, x): class BitnetMLPFuseGateUp(nn.Module): - def __init__(self, config): super().__init__() self.config = config @@ -272,8 +255,7 @@ def __init__(self, config): def from_bit_mlp(cls, bit_mlp: BitnetMLP): module = cls(bit_mlp.config) # assign the weights - module.gate_up_proj.weight = nn.Parameter( - torch.cat([bit_mlp.gate_proj.weight, bit_mlp.up_proj.weight], dim=0)) + module.gate_up_proj.weight = nn.Parameter(torch.cat([bit_mlp.gate_proj.weight, bit_mlp.up_proj.weight], dim=0)) module.down_proj = bit_mlp.down_proj module.ffn_layernorm = bit_mlp.ffn_layernorm return module @@ -295,8 +277,7 @@ def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: batch, num_key_value_heads, slen, head_dim = hidden_states.shape if n_rep == 1: return hidden_states - hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, - head_dim) + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) @@ -311,7 +292,8 @@ def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None): logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " - "when creating this class.") + "when creating this class." + ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size @@ -325,8 +307,8 @@ def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None): if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( - f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" - f" and `num_heads`: {self.num_heads}).") + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`: {self.num_heads})." + ) self.q_proj = BitLinear( self.hidden_size, @@ -387,10 +369,8 @@ def forward( value_states = self.v_proj(hidden_states) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) - key_states = key_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) - value_states = value_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) past_key_value = getattr(self, "past_key_value", past_key_value) cos, sin = self.rotary_emb(value_states, position_ids) @@ -399,30 +379,24 @@ def forward( if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} - key_states, value_states = past_key_value.update(key_states, value_states, - self.layer_idx, cache_kwargs) + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) - attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt( - self.head_dim) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: # no matter the length, we just slice it - causal_mask = attention_mask[:, :, :, :key_states.shape[-2]] + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 - attn_weights = nn.functional.softmax( - attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) - attn_weights = nn.functional.dropout( - attn_weights, p=self.attention_dropout, training=self.training) + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" - f" {attn_output.size()}") + raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}") attn_output = attn_output.transpose(1, 2).contiguous() @@ -448,7 +422,8 @@ def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None): logger.warning_once( f"Instantiating {self.__class__.__name__} without passing a `layer_idx` is not recommended and will " "lead to errors during the forward call if caching is used. Please make sure to provide a `layer_idx` " - "when creating this class.") + "when creating this class." + ) self.attention_dropout = config.attention_dropout self.hidden_size = config.hidden_size @@ -462,8 +437,8 @@ def __init__(self, config: BitnetConfig, layer_idx: Optional[int] = None): if (self.head_dim * self.num_heads) != self.hidden_size: raise ValueError( - f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}" - f" and `num_heads`: {self.num_heads}).") + f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size} and `num_heads`: {self.num_heads})." + ) self.qkv_proj = BitLinear( self.hidden_size, @@ -497,17 +472,12 @@ def from_bit_attention(cls, bit_attention: BitnetAttention): module = cls(bit_attention.config, bit_attention.layer_idx) # assign the weights module.qkv_proj.weight = nn.Parameter( - torch.cat([ - bit_attention.q_proj.weight, bit_attention.k_proj.weight, - bit_attention.v_proj.weight - ], - dim=0)) + torch.cat([bit_attention.q_proj.weight, bit_attention.k_proj.weight, bit_attention.v_proj.weight], dim=0) + ) if bit_attention.q_proj.bias is not None and bit_attention.k_proj.bias is not None and bit_attention.v_proj.bias is not None: module.qkv_proj.bias = nn.Parameter( - torch.cat([ - bit_attention.q_proj.bias, bit_attention.k_proj.bias, bit_attention.v_proj.bias - ], - dim=0)) + torch.cat([bit_attention.q_proj.bias, bit_attention.k_proj.bias, bit_attention.v_proj.bias], dim=0) + ) module.o_proj = bit_attention.o_proj module.inner_attn_ln = bit_attention.inner_attn_ln if bit_attention.config.rope_scaling is None: @@ -528,16 +498,13 @@ def forward( bsz, q_len, _ = hidden_states.size() qkv_states = self.qkv_proj(hidden_states) query_states, key_states, value_states = torch.split( - qkv_states, [ - self.num_heads * self.head_dim, self.num_key_value_heads * self.head_dim, - self.num_key_value_heads * self.head_dim - ], - dim=-1) + qkv_states, + [self.num_heads * self.head_dim, self.num_key_value_heads * self.head_dim, self.num_key_value_heads * self.head_dim], + dim=-1, + ) query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) - key_states = key_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) - value_states = value_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) past_key_value = getattr(self, "past_key_value", past_key_value) cos, sin = self.rotary_emb(value_states, position_ids) @@ -546,30 +513,24 @@ def forward( if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} - key_states, value_states = past_key_value.update(key_states, value_states, - self.layer_idx, cache_kwargs) + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) key_states = repeat_kv(key_states, self.num_key_value_groups) value_states = repeat_kv(value_states, self.num_key_value_groups) - attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt( - self.head_dim) + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) if attention_mask is not None: # no matter the length, we just slice it - causal_mask = attention_mask[:, :, :, :key_states.shape[-2]] + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] attn_weights = attn_weights + causal_mask # upcast attention to fp32 - attn_weights = nn.functional.softmax( - attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) - attn_weights = nn.functional.dropout( - attn_weights, p=self.attention_dropout, training=self.training) + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout, training=self.training) attn_output = torch.matmul(attn_weights, value_states) if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): - raise ValueError( - f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" - f" {attn_output.size()}") + raise ValueError(f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is {attn_output.size()}") attn_output = attn_output.transpose(1, 2).contiguous() @@ -622,10 +583,8 @@ def forward( # batch_size x seq_length x head_dim x hidden_dim # therefore we just need to keep the original shape query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) - key_states = key_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) - value_states = value_states.view(bsz, q_len, self.num_key_value_heads, - self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) cos, sin = self.rotary_emb(value_states, position_ids) query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) @@ -635,8 +594,7 @@ def forward( if past_key_value is not None: # sin and cos are specific to RoPE models; cache_position needed for the static cache cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} - key_states, value_states = past_key_value.update(key_states, value_states, - self.layer_idx, cache_kwargs) + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache # to be able to avoid many of these transpose/reshape/view. @@ -665,14 +623,14 @@ def forward( logger.warning_once( f"The input hidden states seems to be silently casted in float32, this might be related to" f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" - f" {target_dtype}.") + f" {target_dtype}." + ) query_states = query_states.to(target_dtype) key_states = key_states.to(target_dtype) value_states = value_states.to(target_dtype) - attn_output = self._flash_attention_forward( - query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate) + attn_output = self._flash_attention_forward(query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate) attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous() attn_output = self.inner_attn_ln(attn_output) @@ -683,14 +641,9 @@ def forward( return attn_output, attn_weights, past_key_value - def _flash_attention_forward(self, - query_states, - key_states, - value_states, - attention_mask, - query_length, - dropout=0.0, - softmax_scale=None): + def _flash_attention_forward( + self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None + ): """ Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token first unpad the input, then computes the attention scores and pad the final attention scores. @@ -720,7 +673,8 @@ def _flash_attention_forward(self, if attention_mask is not None: batch_size = query_states.shape[0] query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input( - query_states, key_states, value_states, attention_mask, query_length) + query_states, key_states, value_states, attention_mask, query_length + ) cu_seqlens_q, cu_seqlens_k = cu_seq_lens max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens @@ -740,13 +694,7 @@ def _flash_attention_forward(self, attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length) else: - attn_output = flash_attn_func( - query_states, - key_states, - value_states, - dropout, - softmax_scale=softmax_scale, - causal=causal) + attn_output = flash_attn_func(query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal) return attn_output @@ -754,28 +702,24 @@ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask) batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape - key_layer = index_first_axis( - key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k) - value_layer = index_first_axis( - value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k) + key_layer = index_first_axis(key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k) + value_layer = index_first_axis(value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k) if query_length == kv_seq_len: - query_layer = index_first_axis( - query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k) + query_layer = index_first_axis(query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k) cu_seqlens_q = cu_seqlens_k max_seqlen_in_batch_q = max_seqlen_in_batch_k indices_q = indices_k elif query_length == 1: max_seqlen_in_batch_q = 1 cu_seqlens_q = torch.arange( - batch_size + 1, dtype=torch.int32, - device=query_layer.device) # There is a memcpy here, that is very bad. + batch_size + 1, dtype=torch.int32, device=query_layer.device + ) # There is a memcpy here, that is very bad. indices_q = cu_seqlens_q[:-1] query_layer = query_layer.squeeze(1) else: # The -q_len: slice assumes left padding. attention_mask = attention_mask[:, -query_length:] - query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input( - query_layer, attention_mask) + query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask) return ( query_layer, @@ -794,13 +738,11 @@ def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query class BitnetDecoderLayer(nn.Module): - def __init__(self, config: BitnetConfig, layer_idx: int): super().__init__() self.hidden_size = config.hidden_size - self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation]( - config=config, layer_idx=layer_idx) + self.self_attn = LLAMA_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx) self.mlp = BitnetMLP(config) self.input_layernorm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) @@ -834,7 +776,8 @@ def forward( if "padding_mask" in kwargs: warnings.warn( "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`", - stacklevel=2) + stacklevel=2, + ) residual = hidden_states @@ -925,8 +868,7 @@ def _setup_cache(self, cache_cls, max_batch_size, max_cache_len: Optional[int] = dtype = self.config._pre_quantization_dtype else: dtype = layer.self_attn.o_proj.weight.dtype - layer.self_attn.past_key_value = cache_cls( - self.config, max_batch_size, max_cache_len, device=device, dtype=dtype) + layer.self_attn.past_key_value = cache_cls(self.config, max_batch_size, max_cache_len, device=device, dtype=dtype) def _reset_cache(self): for layer in self.model.layers: @@ -1025,9 +967,7 @@ def __init__(self, config: BitnetConfig): self.vocab_size = config.vocab_size self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx) - self.layers = nn.ModuleList([ - BitnetDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers) - ]) + self.layers = nn.ModuleList([BitnetDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]) self.norm = BitnetRMSNorm(config.hidden_size, eps=config.rms_norm_eps) self.gradient_checkpointing = False @@ -1055,21 +995,15 @@ def forward( cache_position: Optional[torch.LongTensor] = None, ) -> Union[Tuple, BaseModelOutputWithPast]: output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states use_cache = use_cache if use_cache is not None else self.config.use_cache return_dict = return_dict if return_dict is not None else self.config.use_return_dict if (input_ids is None) ^ (inputs_embeds is not None): - raise ValueError( - "You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one" - ) + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time, and must specify either one") if self.gradient_checkpointing and self.training and use_cache: - logger.warning_once( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`." - ) + logger.warning_once("`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.") use_cache = False if inputs_embeds is None: @@ -1083,10 +1017,7 @@ def forward( if cache_position is None: if isinstance(past_key_values, StaticCache): raise ValueError("cache_position is a required argument when using StaticCache.") - cache_position = torch.arange( - past_seen_tokens, - past_seen_tokens + inputs_embeds.shape[1], - device=inputs_embeds.device) + cache_position = torch.arange(past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device) if position_ids is None: position_ids = cache_position.unsqueeze(0) @@ -1143,12 +1074,9 @@ def forward( next_cache = None if use_cache: - next_cache = ( - next_decoder_cache.to_legacy_cache() - if isinstance(next_decoder_cache, Cache) else next_decoder_cache) + next_cache = next_decoder_cache.to_legacy_cache() if isinstance(next_decoder_cache, Cache) else next_decoder_cache if not return_dict: - return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] - if v is not None) + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) return BaseModelOutputWithPast( last_hidden_state=hidden_states, past_key_values=next_cache, @@ -1172,14 +1100,9 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position): if hasattr(self.layers[0].self_attn, "past_key_value"): # static cache target_length = self.config.max_position_embeddings else: # dynamic cache - target_length = ( - attention_mask.shape[-1] - if isinstance(attention_mask, torch.Tensor) else cache_position[-1] + 1) - - causal_mask = torch.full((sequence_length, target_length), - fill_value=min_dtype, - dtype=dtype, - device=device) + target_length = attention_mask.shape[-1] if isinstance(attention_mask, torch.Tensor) else cache_position[-1] + 1 + + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) if sequence_length != 1: causal_mask = torch.triu(causal_mask, diagonal=1) causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) @@ -1188,10 +1111,8 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position): causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit if attention_mask.dim() == 2: mask_length = attention_mask.shape[-1] - padding_mask = causal_mask[..., :mask_length].eq( - 0.0) * attention_mask[:, None, None, :].eq(0.0) - causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill( - padding_mask, min_dtype) + padding_mask = causal_mask[..., :mask_length].eq(0.0) * attention_mask[:, None, None, :].eq(0.0) + causal_mask[..., :mask_length] = causal_mask[..., :mask_length].masked_fill(padding_mask, min_dtype) elif attention_mask.dim() == 4: # backwards compatibility: we allow passing a 4D attention mask shorter than the input length with # cache. In that case, the 4D attention mask attends to the newest tokens only. @@ -1201,8 +1122,7 @@ def _update_causal_mask(self, attention_mask, input_tensor, cache_position): offset = 0 mask_shape = attention_mask.shape mask_slice = (attention_mask.eq(0.0)).to(dtype=dtype) * min_dtype - causal_mask[:mask_shape[0], :mask_shape[1], - offset:mask_shape[2] + offset, :mask_shape[3]] = mask_slice + causal_mask[: mask_shape[0], : mask_shape[1], offset : mask_shape[2] + offset, : mask_shape[3]] = mask_slice return causal_mask @@ -1279,9 +1199,7 @@ def forward( "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you." ```""" output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions - output_hidden_states = ( - output_hidden_states - if output_hidden_states is not None else self.config.output_hidden_states) + output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states return_dict = return_dict if return_dict is not None else self.config.use_return_dict # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn) @@ -1327,13 +1245,9 @@ def forward( attentions=outputs.attentions, ) - def prepare_inputs_for_generation(self, - input_ids, - past_key_values=None, - attention_mask=None, - inputs_embeds=None, - cache_position=None, - **kwargs): + def prepare_inputs_for_generation( + self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, cache_position=None, **kwargs + ): # With static cache, the `past_key_values` is None # TODO joao: standardize interface for the different Cache classes and remove of this if has_static_cache = False @@ -1344,13 +1258,13 @@ def prepare_inputs_for_generation(self, past_length = 0 if past_key_values is not None: if isinstance(past_key_values, Cache): - past_length = cache_position[ - 0] if cache_position is not None else past_key_values.get_seq_length() + past_length = cache_position[0] if cache_position is not None else past_key_values.get_seq_length() max_cache_length = ( torch.tensor(past_key_values.get_max_length(), device=input_ids.device) - if past_key_values.get_max_length() is not None else None) - cache_length = past_length if max_cache_length is None else torch.min( - max_cache_length, past_length) + if past_key_values.get_max_length() is not None + else None + ) + cache_length = past_length if max_cache_length is None else torch.min(max_cache_length, past_length) # TODO joao: remove this `else` after `generate` prioritizes `Cache` objects else: cache_length = past_length = past_key_values[0][0].shape[2] @@ -1361,7 +1275,7 @@ def prepare_inputs_for_generation(self, # some of the inputs are exclusively passed as part of the cache (e.g. when passing input_embeds as # input) if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]: - input_ids = input_ids[:, -(attention_mask.shape[1] - past_length):] + input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :] # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard # input_ids based on the past_length. elif past_length < input_ids.shape[1]: @@ -1369,8 +1283,7 @@ def prepare_inputs_for_generation(self, # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens. # If we are about to go beyond the maximum cache length, we need to crop the input attention mask. - if (max_cache_length is not None and attention_mask is not None and - cache_length + input_ids.shape[1] > max_cache_length): + if max_cache_length is not None and attention_mask is not None and cache_length + input_ids.shape[1] > max_cache_length: attention_mask = attention_mask[:, -max_cache_length:] position_ids = kwargs.get("position_ids") @@ -1379,7 +1292,7 @@ def prepare_inputs_for_generation(self, position_ids = attention_mask.long().cumsum(-1) - 1 position_ids.masked_fill_(attention_mask == 0, 1) if past_key_values: - position_ids = position_ids[:, -input_ids.shape[1]:] + position_ids = position_ids[:, -input_ids.shape[1] :] # if `inputs_embeds` are passed, we only want to use them in the 1st generation step if inputs_embeds is not None and past_key_values is None: @@ -1392,39 +1305,38 @@ def prepare_inputs_for_generation(self, input_length = position_ids.shape[-1] if position_ids is not None else input_ids.shape[-1] if cache_position is None: - cache_position = torch.arange( - past_length, past_length + input_length, device=input_ids.device) + cache_position = torch.arange(past_length, past_length + input_length, device=input_ids.device) else: cache_position = cache_position[-input_length:] if has_static_cache: past_key_values = None - model_inputs.update({ - "position_ids": position_ids, - "cache_position": cache_position, - "past_key_values": past_key_values, - "use_cache": kwargs.get("use_cache"), - "attention_mask": attention_mask, - }) + model_inputs.update( + { + "position_ids": position_ids, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": kwargs.get("use_cache"), + "attention_mask": attention_mask, + } + ) return model_inputs @staticmethod def _reorder_cache(past_key_values, beam_idx): reordered_past = () for layer_past in past_key_values: - reordered_past += (tuple( - past_state.index_select(0, beam_idx.to(past_state.device)) - for past_state in layer_past),) + reordered_past += (tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),) return reordered_past @staticmethod def recursive_set(model, name, attr): - ''' - set layers.25.mlp.up_proj to attr - ''' + """ + set layers.25.mlp.up_proj to attr + """ - names = name.split('.') + names = name.split(".") obj = model for n in names[:-1]: obj = getattr(obj, n) @@ -1521,6 +1433,7 @@ def from_quantized( fuse_gateup = quant_config.get("fuse_gateup", True) import accelerate + if checkpoint_format == "bitblas": model = cls(config) for name, module in model.named_modules(): @@ -1567,7 +1480,6 @@ def from_quantized( LLAMA_START_DOCSTRING, ) class BitnetForSequenceClassification(BitnetPreTrainedModel): - def __init__(self, config): super().__init__(config) self.num_labels = config.num_labels @@ -1631,8 +1543,7 @@ def forward( else: if input_ids is not None: # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility - sequence_lengths = torch.eq(input_ids, - self.config.pad_token_id).int().argmax(-1) - 1 + sequence_lengths = torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1 sequence_lengths = sequence_lengths % input_ids.shape[-1] sequence_lengths = sequence_lengths.to(logits.device) else: @@ -1646,8 +1557,7 @@ def forward( if self.config.problem_type is None: if self.num_labels == 1: self.config.problem_type = "regression" - elif self.num_labels > 1 and (labels.dtype == torch.long or - labels.dtype == torch.int): + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): self.config.problem_type = "single_label_classification" else: self.config.problem_type = "multi_label_classification" diff --git a/examples/bitnet-1.58b/nvidia_measure_memory.sh b/examples/bitnet-1.58b/nvidia_measure_memory.sh index e8998f3092..82cf4855f5 100755 --- a/examples/bitnet-1.58b/nvidia_measure_memory.sh +++ b/examples/bitnet-1.58b/nvidia_measure_memory.sh @@ -1 +1,3 @@ +#!/usr/bin/env bash + nvidia-smi --query-gpu=memory.used --format=csv -lms 500 diff --git a/examples/bitnet-1.58b/tokenization_bitnet.py b/examples/bitnet-1.58b/tokenization_bitnet.py index 6fea3252a9..2adfd6dee1 100644 --- a/examples/bitnet-1.58b/tokenization_bitnet.py +++ b/examples/bitnet-1.58b/tokenization_bitnet.py @@ -18,6 +18,7 @@ # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes for LLaMA.""" + import os from shutil import copyfile from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple @@ -37,12 +38,10 @@ PRETRAINED_VOCAB_FILES_MAP = { "vocab_file": { - "hf-internal-testing/llama-tokenizer": - "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model", + "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer.model", }, "tokenizer_file": { - "hf-internal-testing/llama-tokenizer": - "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json", + "hf-internal-testing/llama-tokenizer": "https://huggingface.co/hf-internal-testing/llama-tokenizer/resolve/main/tokenizer_config.json", }, } PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { @@ -159,14 +158,10 @@ def __init__( **kwargs, ): self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs - bos_token = AddedToken( - bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token - eos_token = AddedToken( - eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token - unk_token = AddedToken( - unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token - pad_token = AddedToken( - pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token + bos_token = AddedToken(bos_token, normalized=False, special=True) if isinstance(bos_token, str) else bos_token + eos_token = AddedToken(eos_token, normalized=False, special=True) if isinstance(eos_token, str) else eos_token + unk_token = AddedToken(unk_token, normalized=False, special=True) if isinstance(unk_token, str) else unk_token + pad_token = AddedToken(pad_token, normalized=False, special=True) if isinstance(pad_token, str) else pad_token if legacy is None: logger.warning_once( @@ -174,7 +169,8 @@ def __init__( " expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you." " If you want to use the new behavior, set `legacy=False`. This should only be set if you understand what it" " means, and thoroughly read the reason why this was added as explained in" - " https://github.com/huggingface/transformers/pull/24565") + " https://github.com/huggingface/transformers/pull/24565" + ) legacy = True self.legacy = legacy @@ -214,8 +210,7 @@ def get_spm_processor(self, from_slow=False): with open(self.vocab_file, "rb") as f: sp_model = f.read() - model_pb2 = import_protobuf( - f"The new behavior of {self.__class__.__name__} (with `self.legacy = False`)") + model_pb2 = import_protobuf(f"The new behavior of {self.__class__.__name__} (with `self.legacy = False`)") model = model_pb2.ModelProto.FromString(sp_model) normalizer_spec = model_pb2.NormalizerSpec() normalizer_spec.add_dummy_prefix = False @@ -261,8 +256,7 @@ def tokenize(self, text: "TextInput", **kwargs) -> List[str]: tokens = super().tokenize(text, **kwargs) - if len(tokens - ) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: + if len(tokens) > 1 and tokens[0] == SPIECE_UNDERLINE and tokens[1] in self.all_special_tokens: tokens = tokens[1:] return tokens @@ -284,7 +278,7 @@ def _tokenize(self, text, **kwargs): # 1. Encode string + prefix ex: " Hey" tokens = self.sp_model.encode(self.unk_token + text, out_type=str) # 2. Remove self.unk_token from ['<','unk','>', '▁Hey'] - return tokens[self.unk_token_length:] if len(tokens) >= self.unk_token_length else tokens + return tokens[self.unk_token_length :] if len(tokens) >= self.unk_token_length else tokens def _convert_token_to_id(self, token): """Converts a token (str) in an id using the vocab.""" @@ -332,12 +326,9 @@ def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) if not os.path.isdir(save_directory): logger.error(f"Vocabulary path ({save_directory}) should be a directory") return - out_vocab_file = os.path.join(save_directory, - (filename_prefix + "-" if filename_prefix else "") + - VOCAB_FILES_NAMES["vocab_file"]) + out_vocab_file = os.path.join(save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]) - if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile( - self.vocab_file): + if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file): copyfile(self.vocab_file, out_vocab_file) elif not os.path.isfile(self.vocab_file): with open(out_vocab_file, "wb") as fi: @@ -357,10 +348,9 @@ def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None): return output - def get_special_tokens_mask(self, - token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None, - already_has_special_tokens: bool = False) -> List[int]: + def get_special_tokens_mask( + self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False + ) -> List[int]: """ Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding special tokens using the tokenizer `prepare_for_model` method. @@ -377,20 +367,16 @@ def get_special_tokens_mask(self, `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token. """ if already_has_special_tokens: - return super().get_special_tokens_mask( - token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True) + return super().get_special_tokens_mask(token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True) bos_token_id = [1] if self.add_bos_token else [] eos_token_id = [1] if self.add_eos_token else [] if token_ids_1 is None: return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id - return (bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + - ([0] * len(token_ids_1)) + eos_token_id) + return bos_token_id + ([0] * len(token_ids_0)) + eos_token_id + bos_token_id + ([0] * len(token_ids_1)) + eos_token_id - def create_token_type_ids_from_sequences(self, - token_ids_0: List[int], - token_ids_1: Optional[List[int]] = None) -> List[int]: + def create_token_type_ids_from_sequences(self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None) -> List[int]: """ Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT sequence pair mask has the following format: @@ -473,9 +459,9 @@ def default_chat_template(self): "{% elif message['role'] == 'assistant' %}" "{{ ' ' + content.strip() + ' ' + eos_token }}" "{% endif %}" - "{% endfor %}") - template = template.replace("USE_DEFAULT_PROMPT", - "true" if self.use_default_system_prompt else "false") + "{% endfor %}" + ) + template = template.replace("USE_DEFAULT_PROMPT", "true" if self.use_default_system_prompt else "false") default_message = DEFAULT_SYSTEM_PROMPT.replace("\n", "\\n").replace("'", "\\'") template = template.replace("DEFAULT_SYSTEM_MESSAGE", default_message) diff --git a/examples/bitnet-1.58b/utils_quant.py b/examples/bitnet-1.58b/utils_quant.py index 5f5db5dbc0..5a50edb392 100644 --- a/examples/bitnet-1.58b/utils_quant.py +++ b/examples/bitnet-1.58b/utils_quant.py @@ -24,15 +24,14 @@ def weight_quant(weight, num_bits=1): def activation_quant(x, num_bits=8): dtype = x.dtype x = x.float() - Qn = -(2**(num_bits - 1)) - Qp = 2**(num_bits - 1) - 1 + Qn = -(2 ** (num_bits - 1)) + Qp = 2 ** (num_bits - 1) - 1 s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5) result = (x * s).round().clamp(Qn, Qp) / s return result.type(dtype) class BitLinearBitBLAS(nn.Module): - def __init__( self, in_features: int, @@ -68,7 +67,7 @@ def __init__( self.bitblas_matmul = self._get_or_create_bitblas_operator(matmul_config, ENABLE_TUNING) self.format = "bitnet" - self.Qp = 2**(self.input_bits - 1) - 1 + self.Qp = 2 ** (self.input_bits - 1) - 1 def _get_or_create_bitblas_operator(self, config, enable_tuning): if global_operator_cache.size() == 0: @@ -99,8 +98,7 @@ def replace_weight_param_with_qweight(self): @classmethod def from_bit_linear(cls, bitlinear, weight_group=1): - bitblas_linear = cls( - bitlinear.in_features, bitlinear.out_features, weight_bits=1, input_bits=8) + bitblas_linear = cls(bitlinear.in_features, bitlinear.out_features, weight_bits=1, input_bits=8) sw, qweight = bitblas_linear.create_bitblas_weights(bitlinear.weight, weight_group) bitblas_linear.register_buffer("qweight", qweight) bitblas_linear.register_buffer("sw", sw) @@ -158,8 +156,8 @@ def weight_quant(weight): @torch.compile def activation_quant(self, x, num_bits=8): x = x.float() - Qn = -(2**(num_bits - 1)) - Qp = 2**(num_bits - 1) - 1 + Qn = -(2 ** (num_bits - 1)) + Qp = 2 ** (num_bits - 1) - 1 s = Qp / x.abs().max(dim=-1, keepdim=True).values.clamp(min=1e-5) result = (x * s).round().clamp(Qn, Qp) return result.type(torch.int8), s @@ -173,9 +171,8 @@ def post_quant_process(self, input, si, sw): # for the correctness evaluation. def native_forward(self, input): - quant_input = (input + (activation_quant(input, self.input_bits) - input).detach()) - quant_weight = ( - self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach()) + quant_input = input + (activation_quant(input, self.input_bits) - input).detach() + quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach() out = nn.functional.linear(quant_input, quant_weight) if self.bias is not None: @@ -214,7 +211,6 @@ def forward(self, input): # Naive BitLinear from HuggingFace class BitLinear(nn.Linear): - def __init__(self, *kargs, weight_bits=1, input_bits=8, **kwargs): super(BitLinear, self).__init__(*kargs, **kwargs) """ @@ -224,10 +220,8 @@ def __init__(self, *kargs, weight_bits=1, input_bits=8, **kwargs): self.input_bits = input_bits def forward(self, input): - quant_input = input + (activation_quant(input, self.input_bits) - input).detach() - quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - - self.weight).detach() + quant_weight = self.weight + (weight_quant(self.weight, self.weight_bits) - self.weight).detach() out = nn.functional.linear(quant_input, quant_weight) if self.bias is not None: diff --git a/examples/bitnet-1.58b/vllm_workspace/conftest.py b/examples/bitnet-1.58b/vllm_workspace/conftest.py index 951f389914..e9e2997ef6 100644 --- a/examples/bitnet-1.58b/vllm_workspace/conftest.py +++ b/examples/bitnet-1.58b/vllm_workspace/conftest.py @@ -20,7 +20,7 @@ from vllm import LLM, SamplingParams from vllm.assets.image import ImageAsset from vllm.config import TokenizerPoolConfig -from vllm.distributed import (destroy_distributed_environment, destroy_model_parallel) +from vllm.distributed import destroy_distributed_environment, destroy_model_parallel from vllm.inputs import TextPrompt from vllm.logger import init_logger from vllm.sequence import SampleLogprobs @@ -56,12 +56,13 @@ class _ImageAssetsBase(UserList[ImageAsset]): class _ImageAssets(_ImageAssetsBase): - def __init__(self) -> None: - super().__init__([ - ImageAsset("stop_sign"), - ImageAsset("cherry_blossom"), - ]) + super().__init__( + [ + ImageAsset("stop_sign"), + ImageAsset("cherry_blossom"), + ] + ) def prompts(self, prompts: _ImageAssetPrompts) -> List[str]: """ @@ -136,7 +137,6 @@ def image_assets() -> _ImageAssets: class HfRunner: - def wrap_device(self, input: _T) -> _T: if not is_cpu(): return input.to("cuda") @@ -166,7 +166,8 @@ def __init__( SentenceTransformer( model_name, device="cpu", - ).to(dtype=torch_dtype)) + ).to(dtype=torch_dtype) + ) else: if is_vision_model: auto_cls = AutoModelForVision2Seq @@ -184,7 +185,8 @@ def __init__( torch_dtype=torch_dtype, trust_remote_code=True, **model_kwargs, - )) + ) + ) self.tokenizer = AutoTokenizer.from_pretrained( model_name, @@ -204,8 +206,7 @@ def __init__( ) except Exception: logger.warning( - "Unable to auto-load processor from HuggingFace for " - "model %s. Using tokenizer instead.", + "Unable to auto-load processor from HuggingFace for model %s. Using tokenizer instead.", model_name, ) self.processor = self.tokenizer @@ -362,7 +363,7 @@ def generate_greedy_logprobs_limit( last_hidden_states, self.model.get_output_embeddings().weight.t(), ) - if (getattr(self.model.get_output_embeddings(), "bias", None) is not None): + if getattr(self.model.get_output_embeddings(), "bias", None) is not None: logits += self.model.get_output_embeddings().bias.unsqueeze(0) logprobs = F.log_softmax(logits, dim=-1, dtype=torch.float32) seq_logprobs.append(logprobs) @@ -389,8 +390,7 @@ def generate_greedy_logprobs_limit( all_output_strs.append(self.tokenizer.decode(output_ids)) outputs = zip(all_output_ids, all_output_strs, all_logprobs) - return [(output_ids, output_str, output_logprobs) - for output_ids, output_str, output_logprobs in outputs] + return [(output_ids, output_str, output_logprobs) for output_ids, output_str, output_logprobs in outputs] def encode(self, prompts: List[str]) -> List[List[torch.Tensor]]: return self.model.encode(prompts) @@ -409,7 +409,6 @@ def hf_runner(): class VllmRunner: - def __init__( self, model_name: str, @@ -514,12 +513,10 @@ def generate_greedy_logprobs( num_logprobs: int, images: Optional[List[Image.Image]] = None, ) -> List[Tuple[List[int], str, Optional[SampleLogprobs]]]: - greedy_logprobs_params = SamplingParams( - temperature=0.0, max_tokens=max_tokens, logprobs=num_logprobs) + greedy_logprobs_params = SamplingParams(temperature=0.0, max_tokens=max_tokens, logprobs=num_logprobs) outputs = self.generate_w_logprobs(prompts, greedy_logprobs_params, images=images) - return [(output_ids, output_str, output_logprobs) - for output_ids, output_str, output_logprobs in outputs] + return [(output_ids, output_str, output_logprobs) for output_ids, output_str, output_logprobs in outputs] def generate_beam_search( self, diff --git a/examples/bitnet-1.58b/vllm_workspace/inference_with_compress_format.py b/examples/bitnet-1.58b/vllm_workspace/inference_with_compress_format.py index 55a24543e3..ea18239cbc 100644 --- a/examples/bitnet-1.58b/vllm_workspace/inference_with_compress_format.py +++ b/examples/bitnet-1.58b/vllm_workspace/inference_with_compress_format.py @@ -32,15 +32,14 @@ ckpt_path = args.ckpt_path with VllmRunner( - ckpt_path, - dtype="half", - quantization="bitblas", - # set enforce_eager = False to enable cuda graph - # set enforce_eager = True to disable cuda graph - enforce_eager=False, + ckpt_path, + dtype="half", + quantization="bitblas", + # set enforce_eager = False to enable cuda graph + # set enforce_eager = True to disable cuda graph + enforce_eager=False, ) as bitnet_model: - bitbnet_outputs = bitnet_model.generate_greedy(["Hi, tell me about microsoft?"], - max_tokens=1024) + bitbnet_outputs = bitnet_model.generate_greedy(["Hi, tell me about microsoft?"], max_tokens=1024) print("bitnet inference:") print(bitbnet_outputs[0][0]) print(bitbnet_outputs[0][1]) diff --git a/examples/bitnet-1.58b/vllm_workspace/inference_with_native_format.py b/examples/bitnet-1.58b/vllm_workspace/inference_with_native_format.py index 4f5f87f6ff..f631fb3067 100644 --- a/examples/bitnet-1.58b/vllm_workspace/inference_with_native_format.py +++ b/examples/bitnet-1.58b/vllm_workspace/inference_with_native_format.py @@ -33,13 +33,13 @@ ckpt_path = args.ckpt_path with VllmRunner( - ckpt_path, - dtype="half", - quantization="bitnet_bitblas", - gpu_memory_utilization=0.5, - # set enforce_eager = False to enable cuda graph - # set enforce_eager = True to disable cuda graph - enforce_eager=False, + ckpt_path, + dtype="half", + quantization="bitnet_bitblas", + gpu_memory_utilization=0.5, + # set enforce_eager = False to enable cuda graph + # set enforce_eager = True to disable cuda graph + enforce_eager=False, ) as bitnet_model: bitbnet_outputs = bitnet_model.generate_greedy(["Hi, tell me about microsoft?"], max_tokens=128) print("bitnet inference output:") diff --git a/examples/bitnet-1.58b/vllm_workspace/utils.py b/examples/bitnet-1.58b/vllm_workspace/utils.py index daa9d8f52b..e96b19e28c 100644 --- a/examples/bitnet-1.58b/vllm_workspace/utils.py +++ b/examples/bitnet-1.58b/vllm_workspace/utils.py @@ -3,8 +3,7 @@ TokensText = Tuple[List[int], str] -def check_outputs_equal(outputs_0_lst: List[TokensText], outputs_1_lst: List[TokensText], - name_0: str, name_1: str): +def check_outputs_equal(outputs_0_lst: List[TokensText], outputs_1_lst: List[TokensText], name_0: str, name_1: str): """ Compare the two sequences generated by different models, which should be equal. @@ -15,19 +14,14 @@ def check_outputs_equal(outputs_0_lst: List[TokensText], outputs_1_lst: List[Tok output_ids_0, output_str_0 = outputs_0 output_ids_1, output_str_1 = outputs_1 - assert output_str_0 == output_str_1, (f"Test{prompt_idx}:" - f"\n{name_0}:\t{output_str_0!r}" - f"\n{name_1}:\t{output_str_1!r}") - assert output_ids_0 == output_ids_1, (f"Test{prompt_idx}:" - f"\n{name_0}:\t{output_str_0!r}" - f"\n{name_1}:\t{output_str_1!r}") + assert output_str_0 == output_str_1, f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}" + assert output_ids_0 == output_ids_1, f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}" TokensTextLogprobs = Tuple[List[int], str, List[Dict[int, float]]] -def check_logprobs_close(outputs_0_lst: List[TokensTextLogprobs], - outputs_1_lst: List[TokensTextLogprobs], name_0: str, name_1: str): +def check_logprobs_close(outputs_0_lst: List[TokensTextLogprobs], outputs_1_lst: List[TokensTextLogprobs], name_0: str, name_1: str): """ Compare the logprobs of two sequences generated by different models, which should be similar but not necessarily equal. @@ -41,16 +35,11 @@ def check_logprobs_close(outputs_0_lst: List[TokensTextLogprobs], # Loop through generated tokens. for idx, (output_id_0, output_id_1) in enumerate(zip(output_ids_0, output_ids_1)): - # If generated tokens don't match, then if output_id_0 != output_id_1: # Each predicted token must be in top N logprobs of the other - assert output_id_0 in logprobs_1[idx], (f"Test{prompt_idx}:" - f"\n{name_0}:\t{output_str_0!r}" - f"\n{name_1}:\t{output_str_1!r}") - assert output_id_1 in logprobs_0[idx], (f"Test{prompt_idx}:" - f"\n{name_0}:\t{output_str_0!r}" - f"\n{name_1}:\t{output_str_1!r}") + assert output_id_0 in logprobs_1[idx], f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}" + assert output_id_1 in logprobs_0[idx], f"Test{prompt_idx}:\n{name_0}:\t{output_str_0!r}\n{name_1}:\t{output_str_1!r}" # Break out since sequences will now diverge. break diff --git a/examples/blocksparse_attention/README.md b/examples/blocksparse_attention/README.md index 89f75b81de..34bf3c6375 100644 --- a/examples/blocksparse_attention/README.md +++ b/examples/blocksparse_attention/README.md @@ -1,6 +1,5 @@ # Block-Sparse Flash-Attention -Tilelang implementation of block-sparse flash-attention kernels. - -The kernels have been used in [Rectified Sparse Attention](https://arxiv.org/abs/2506.04108) and [SeerAttention-R](https://arxiv.org/abs/2506.08889). +Tilelang implementation of block-sparse flash-attention kernels. +The kernels have been used in [Rectified Sparse Attention](https://arxiv.org/abs/2506.04108) and [SeerAttention-R](https://arxiv.org/abs/2506.08889). diff --git a/examples/blocksparse_attention/block_sparse_attn_triton.py b/examples/blocksparse_attention/block_sparse_attn_triton.py index 014f0c5fcb..b94e602f60 100644 --- a/examples/blocksparse_attention/block_sparse_attn_triton.py +++ b/examples/blocksparse_attention/block_sparse_attn_triton.py @@ -1,7 +1,6 @@ # ruff: noqa: E712 import math import torch - import triton import triton.language as tl import torch.nn.functional as F @@ -15,10 +14,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -56,7 +52,6 @@ def _fwd_kernel_inner( BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, ): - mask_val = tl.load(block_mask_ptr + k_block_col_idx * stride_bmask_n) # print @@ -73,8 +68,7 @@ def _fwd_kernel_inner( # the following is needed only when LAST_K_BLOCK or BLOCK_M < BLOCK_N if LAST_K_BLOCK: - qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, - float('-inf')) + qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, float("-inf")) m_ij = tl.maximum(m_i, tl.max(qk, 1)) qk -= m_ij[:, None] @@ -154,7 +148,7 @@ def _fwd_kernel( v_ptrs = V + off_v mask_ptrs = block_mask_ptr + start_m * stride_bmm - m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") l_i = tl.zeros([BLOCK_M], dtype=tl.float32) acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) @@ -192,24 +186,12 @@ def _fwd_kernel( acc = acc * l_recip acc = acc.to(Out.dtype.element_ty) - off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[ - None, :] * stride_od + off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :] * stride_od out_ptrs = Out + off_o tl.store(out_ptrs, acc, mask=offs_m[:, None] < N_CTX) -def _forward(ctx, - q, - k, - v, - block_sparse_mask, - sm_scale, - BLOCK_M=64, - BLOCK_N=64, - num_warps=None, - num_stages=1, - out=None): - +def _forward(ctx, q, k, v, block_sparse_mask, sm_scale, BLOCK_M=64, BLOCK_N=64, num_warps=None, num_stages=1, out=None): assert q.shape[-1] == k.shape[-1] == v.shape[-1] assert k.shape[2] == v.shape[2] o = out if out is not None else torch.empty_like(q).contiguous() @@ -254,7 +236,6 @@ def _forward(ctx, class _sparse_attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, block_sparse_dense, sm_scale): # shape constraints @@ -278,9 +259,9 @@ def test_topk_sparse_attention(): torch.manual_seed(0) # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) @@ -288,9 +269,7 @@ def test_topk_sparse_attention(): downsample_len = math.ceil(SEQ_LEN / downsample_factor) print("downsample_len", downsample_len) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 print("x_ds.shape", x_ds.shape) block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) @@ -302,22 +281,21 @@ def test_topk_sparse_attention(): # Compute reference # Expand block mask to full attention matrix - full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')) + full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")) full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) # Apply causal # PyTorch reference implementation - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale - attn = attn.masked_fill(~full_mask, float('-inf')) + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale + attn = attn.masked_fill(~full_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) # print("ref_output", ref_output) # print("triton_output", triton_output) # Verify accuracy - assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), \ - "Triton output doesn't match reference" + assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference" print("Pass topk sparse attention test with qlen == klen") @@ -329,9 +307,9 @@ def test_topk_sparse_attention_qlt_kl(): torch.manual_seed(0) # Create inputs. - q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) + q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) # softmax scale sm_scale = 1.0 / (D_HEAD**0.5) @@ -339,8 +317,7 @@ def test_topk_sparse_attention_qlt_kl(): print("downsample_factor", downsample_factor) downsample_len = math.ceil(K_LEN / downsample_factor) # number of blocks along one dimension print("downsample_len", downsample_len) - x_ds = torch.randn( - BATCH, N_HEADS, downsample_len, downsample_len, device='cuda', dtype=torch.bfloat16) + x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="cuda", dtype=torch.bfloat16) # Force the first column to be high so that the first block is always selected. x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) @@ -351,26 +328,25 @@ def test_topk_sparse_attention_qlt_kl(): past_len = K_LEN - Q_LEN - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale - full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')).bool() + full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")).bool() full_mask_full = full_mask_full[..., :K_LEN, :K_LEN] effective_mask = full_mask_full[..., past_len:K_LEN, :] # shape: (B, H, Q_LEN, K_LEN) i_global = torch.arange(past_len, K_LEN, device=k.device).unsqueeze(1) # shape: (Q_LEN, 1) j_global = torch.arange(K_LEN, device=k.device).unsqueeze(0) # shape: (1, K_LEN) - causal_mask = (j_global <= i_global) # shape: (Q_LEN, K_LEN) + causal_mask = j_global <= i_global # shape: (Q_LEN, K_LEN) final_mask = effective_mask & causal_mask # shape: (B, H, Q_LEN, K_LEN) - attn = attn.masked_fill(~final_mask, float('-inf')) + attn = attn.masked_fill(~final_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) # Verify accuracy. - assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), \ - "Triton output doesn't match reference when qlen < klen" + assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference when qlen < klen" print("Pass topk sparse attention test with qlen < klen") diff --git a/examples/blocksparse_attention/example_tilelang_block_sparse_attn.py b/examples/blocksparse_attention/example_tilelang_block_sparse_attn.py index 7e90db7e5f..9a394710f1 100644 --- a/examples/blocksparse_attention/example_tilelang_block_sparse_attn.py +++ b/examples/blocksparse_attention/example_tilelang_block_sparse_attn.py @@ -1,8 +1,8 @@ import math import torch - import tilelang import tilelang.language as T +from tilelang.profiler import do_bench import torch.nn.functional as F @@ -10,10 +10,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -30,105 +27,34 @@ def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=F @tilelang.jit( - out_idx=[4], pass_configs={ + out_idx=[4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def blocksparse_flashattn(batch, heads, seq_len, dim, downsample_len, is_causal): block_M = 64 block_N = 64 num_stages = 1 threads = 128 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] block_mask_shape = [batch, heads, downsample_len, downsample_len] - dtype = "float16" - accum_dtype = "float" - block_mask_dtype = "bool" + dtype = T.float16 + accum_dtype = T.float32 + block_mask_dtype = T.bool def kernel_func(block_M, block_N, num_stages, threads): - - @T.macro - def MMA0( - K: T.Tensor(shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(shape, dtype), - V_shared: T.SharedBuffer([block_M, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def blocksparse_flashattn( - Q: T.Tensor(shape, dtype), - K: T.Tensor(shape, dtype), - V: T.Tensor(shape, dtype), - BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), - Output: T.Tensor(shape, dtype), + Q: T.Tensor(shape, dtype), + K: T.Tensor(shape, dtype), + V: T.Tensor(shape, dtype), + BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), + Output: T.Tensor(shape, dtype), ): - with T.Kernel( - T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -141,31 +67,59 @@ def blocksparse_flashattn( scores_scale = T.alloc_fragment([block_M], accum_dtype) scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - block_mask = T.alloc_local([downsample_len], block_mask_dtype) + block_mask = T.alloc_fragment([downsample_len], block_mask_dtype) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - for vj in T.serial(downsample_len): - block_mask[vj] = BlockSparseMask[bz, by, bx, vj] + T.copy(BlockSparseMask[bz, by, bx, :], block_mask) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): if block_mask[k] != 0: - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, - scores_sum, logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + T.clear(acc_s) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # in the first ceil_div(kBlockM, kBlockN) steps. + # for i in T.Parallel(block_M): + # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return blocksparse_flashattn @@ -180,18 +134,16 @@ def test_topk_sparse_attention(): torch.manual_seed(0) # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) @@ -202,15 +154,15 @@ def test_topk_sparse_attention(): # Compute reference # Expand block mask to full attention matrix - full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')) + full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")) full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) # Apply causal # PyTorch reference implementation - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale - attn = attn.masked_fill(~full_mask, float('-inf')) + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale + attn = attn.masked_fill(~full_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) print("ref_output", ref_output) print("tilelang_output", tilelang_output) @@ -224,5 +176,26 @@ def main(): test_topk_sparse_attention() +def run_regression_perf(): + BATCH, N_HEADS, SEQ_LEN, D_HEAD = 1, 32, 256, 64 + TOPK = 2 + BLOCK = 64 + torch.manual_seed(0) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + downsample_factor = BLOCK + downsample_len = math.ceil(SEQ_LEN / downsample_factor) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) + x_ds[:, :, :, 0] = 100 + block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) + kernel = blocksparse_flashattn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) + + def run_kernel_only(): + kernel(q, k, v, block_mask) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py index e299821620..6e73214522 100644 --- a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py +++ b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_paged.py @@ -8,22 +8,26 @@ import argparse import time import math +from tilelang.profiler import do_bench from heuristic import num_splits_heuristic def flashattn(batch, heads, heads_kv, dim, dim_v): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // heads_kv @tilelang.jit( - out_idx=[-1], pass_configs={ + out_idx=[-1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) - def kernel_func(block_N, block_H, page_block_size, num_split, num_stages, threads, num_pages, - max_num_blocks_per_seq, max_selected_blocks): + }, + ) + def kernel_func( + block_N, block_H, page_block_size, num_split, num_stages, threads, num_pages, max_num_blocks_per_seq, max_selected_blocks + ): shape_q = [batch, heads, dim] shape_k = [num_pages, page_block_size, heads_kv, dim] shape_v = [num_pages, page_block_size, heads_kv, dim_v] @@ -35,19 +39,20 @@ def kernel_func(block_N, block_H, page_block_size, num_split, num_stages, thread assert block_N <= page_block_size and page_block_size % block_N == 0 block_ratio = page_block_size // block_N - @T.macro - def flash_attn_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_indices: T.Tensor(shape_indices, "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - block_table: T.Tensor(shape_block_table, "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), + @T.prim_func + def main( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + block_indices: T.Tensor(shape_indices, T.int32), + cache_seqlens: T.Tensor([batch], T.int32), + block_table: T.Tensor(shape_block_table, T.int32), + glse: T.Tensor([batch, heads, num_split], accum_dtype), + Output_partial: T.Tensor(part_shape, accum_dtype), + Output: T.Tensor(shape_o, dtype), ): - with T.Kernel( - batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): + # flash_attn_split + with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim_v], dtype) @@ -67,7 +72,7 @@ def flash_attn_split( sid = bz cur_kv_head = hid // (kv_group_num // valid_block_H) - T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared) + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) @@ -75,7 +80,7 @@ def flash_attn_split( num_blocks = max_selected_blocks blocks_per_split = T.floordiv(num_blocks, num_split) remaining_blocks = T.floormod(num_blocks, num_split) - loop_range = (blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)) + loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0) start = blocks_per_split * sid + T.min(sid, remaining_blocks) has_valid_block = False for k in T.Pipelined(loop_range, num_stages=num_stages): @@ -85,30 +90,20 @@ def flash_attn_split( block_table_idx = T.floordiv(logical_block_idx, block_ratio) block_tile_idx = T.floormod(logical_block_idx, block_ratio) physical_block_idx = block_table[bid, block_table_idx] - T.copy( - K[physical_block_idx, - block_tile_idx * block_N:(block_tile_idx + 1) * block_N, - cur_kv_head, :], K_shared) + T.copy(K[physical_block_idx, block_tile_idx * block_N : (block_tile_idx + 1) * block_N, cur_kv_head, :], K_shared) T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) if k == 0: # assume block_indices is sorted in reverse order, otherwise, remove this if condition for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.if_then_else( - logical_block_idx * block_N + j >= cache_seqlens[bid], - -T.infinity(accum_dtype), acc_s[i, j]) + logical_block_idx * block_N + j >= cache_seqlens[bid], -T.infinity(accum_dtype), acc_s[i, j] + ) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) for i in T.Parallel(block_H): - scores_max[i] = T.if_then_else(scores_max[i] > scores_max_prev[i], - scores_max[i], scores_max_prev[i]) - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - - scores_max[i] * scale) + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) @@ -117,10 +112,7 @@ def flash_attn_split( T.copy(acc_s, acc_s_cast) for i, j in T.Parallel(block_H, dim_v): acc_o[i, j] *= scores_scale[i] - T.copy( - V[physical_block_idx, - block_tile_idx * block_N:(block_tile_idx + 1) * block_N, - cur_kv_head, :], V_shared) + T.copy(V[physical_block_idx, block_tile_idx * block_N : (block_tile_idx + 1) * block_N, cur_kv_head, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) if has_valid_block: for i, j in T.Parallel(block_H, dim_v): @@ -137,74 +129,47 @@ def flash_attn_split( if i < valid_block_H: Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j] - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): + # combine with T.Kernel(heads, batch, threads=128) as (by, bz): po_local = T.alloc_fragment([dim_v], accum_dtype) o_accum_local = T.alloc_fragment([dim_v], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - max_split = T.alloc_local([1], "int32") - - T.annotate_layout({ - lse_logsum_local: - T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + max_split = T.alloc_var(T.int32) T.clear(lse_logsum_local) T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) + lse_max_local = -T.infinity(accum_dtype) for k in T.serial(num_split): - lse_local_split[0] = glse[bz, by, k] - if (lse_local_split[0] != 0): - max_split[0] = k - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k]) + lse_local_split = glse[bz, by, k] + if lse_local_split != 0: + max_split = k + lse_max_local = T.max(lse_max_local, glse[bz, by, k]) for k in T.Pipelined(num_split, num_stages=1): - if k <= max_split[0]: - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] + if k <= max_split: + lse_local_split = glse[bz, by, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local for k in T.serial(num_split): - if k <= max_split[0]: + if k <= max_split: for i in T.Parallel(dim_v): po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) + lse_local_split = glse[bz, by, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) for i in T.Parallel(dim_v): - o_accum_local[i] += po_local[i] * scale_local[0] + o_accum_local[i] += po_local[i] * scale_local for i in T.Parallel(dim_v): Output[bz, by, i] = o_accum_local[i] - @T.prim_func - def main( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_indices: T.Tensor(shape_indices, "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - block_table: T.Tensor(shape_block_table, "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): - flash_attn_split(Q, K, V, block_indices, cache_seqlens, block_table, glse, - Output_partial) - combine(glse, Output_partial, Output) - return main return kernel_func class SparseFlashAttn(torch.nn.Module): - def __init__(self, batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, num_pages): super(SparseFlashAttn, self).__init__() self.batch = batch @@ -250,18 +215,11 @@ def forward(self, query, key, value, block_indices, cache_seqlens, block_table): num_sm = self.num_sm num_split = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) - - glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda') - output_partial = torch.empty((batch, heads, num_split, dim_v), - dtype=torch.float32, - device='cuda') + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") output = self.kernel( query, @@ -276,14 +234,13 @@ def forward(self, query, key, value, block_indices, cache_seqlens, block_table): return output -def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_seqlens, - block_table, page_block_size, block_size): +def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_seqlens, block_table, page_block_size, block_size): """ Paged version of sparse attention reference implementation. - + Args: query: [batch, heads, dim] - key_cache: [num_pages, page_block_size, heads_kv, dim] + key_cache: [num_pages, page_block_size, heads_kv, dim] value_cache: [num_pages, page_block_size, heads_kv, dim] block_indices: [batch, heads_kv, max_selected_blocks] - logical block indices cache_seqlens: [batch] - actual sequence lengths @@ -299,12 +256,8 @@ def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_ # Reconstruct the full key and value tensors from paged cache max_cache_seqlen = max(cache_seqlens).item() - key_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim), - dtype=key_cache.dtype, - device=key_cache.device) - value_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim_v), - dtype=value_cache.dtype, - device=value_cache.device) + key_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim), dtype=key_cache.dtype, device=key_cache.device) + value_full = torch.zeros((batch, heads_kv, max_cache_seqlen, dim_v), dtype=value_cache.dtype, device=value_cache.device) # Reconstruct full tensors from paged cache using block_table for b in range(batch): @@ -320,20 +273,14 @@ def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_ actual_block_size = end_token - start_token # Copy from paged cache to full tensors - key_full[b, :, start_token:end_token, :] = key_cache[ - physical_block_idx, :actual_block_size, :, :].transpose(0, 1) - value_full[b, :, start_token:end_token, :] = value_cache[ - physical_block_idx, :actual_block_size, :, :].transpose(0, 1) + key_full[b, :, start_token:end_token, :] = key_cache[physical_block_idx, :actual_block_size, :, :].transpose(0, 1) + value_full[b, :, start_token:end_token, :] = value_cache[physical_block_idx, :actual_block_size, :, :].transpose(0, 1) # Reshape query for grouped attention - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] # Compute attention scores - scores = einsum( - query, key_full, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = einsum(query, key_full, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, heads_kv, seqlen_kv] # Create sparse mask based on block_indices sparse_mask = torch.zeros_like(scores) @@ -349,24 +296,23 @@ def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_ sparse_mask[b, :, h, start_pos:end_pos] = 1 # Apply sparse mask - scores = scores.masked_fill(sparse_mask == 0, float('-inf')) + scores = scores.masked_fill(sparse_mask == 0, float("-inf")) # Apply causal mask based on actual sequence lengths range_len = torch.arange(scores.shape[-1], device=scores.device).unsqueeze(0) cache_seqlens_expanded = cache_seqlens.unsqueeze(1) pad_mask = range_len >= cache_seqlens_expanded pad_mask = pad_mask[:, None, None, :] - scores = scores.masked_fill(pad_mask, float('-inf')) + scores = scores.masked_fill(pad_mask, float("-inf")) # Compute attention weights attention = F.softmax(scores / scale, dim=-1) # Apply attention to values - out = einsum(attention, value_full, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, heads_kv, dim] + out = einsum(attention, value_full, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, heads_kv, dim] # Reshape output back to original format - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out @@ -374,17 +320,23 @@ def ref_program_torch_paged(query, key_cache, value_cache, block_indices, cache_ def ref_program_fa(query, kcache, vcache, cache_seqlens, block_table): # latency reference # from flash_attn_interface import flash_attn_with_kvcache # fa3 - from flash_attn import flash_attn_with_kvcache #fa2 + from flash_attn import flash_attn_with_kvcache # fa2 + query = query.unsqueeze(1) - output = flash_attn_with_kvcache( - query, kcache, vcache, cache_seqlens=cache_seqlens, block_table=block_table) + output = flash_attn_with_kvcache(query, kcache, vcache, cache_seqlens=cache_seqlens, block_table=block_table) output = output.squeeze(1) return output def main(args): - - batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v + batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = ( + args.batch, + args.heads, + args.heads_kv, + args.max_cache_seqlen, + args.dim, + args.dim_v, + ) sparse_ratio = args.sparse_ratio block_N = args.block_N page_block_size = args.page_block_size @@ -396,35 +348,30 @@ def main(args): dtype = torch.float16 # Generate random inputs - Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda') - cache_seqlens = torch.randint( - max_cache_seqlen // 2, max_cache_seqlen + 1, (batch,), dtype=torch.int32, device='cuda') + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(max_cache_seqlen // 2, max_cache_seqlen + 1, (batch,), dtype=torch.int32, device="cuda") print("cache_seqlens: ", cache_seqlens) - K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda') - V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda') + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") # Create paged KV cache - K_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim), dtype=dtype, device='cuda') - V_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim_v), - dtype=dtype, - device='cuda') + K_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim), dtype=dtype, device="cuda") + V_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim_v), dtype=dtype, device="cuda") # Create block table and block indices for dense case (all blocks selected) max_num_blocks_per_seq = int(math.ceil(max_cache_seqlen / page_block_size)) print("max_num_blocks_per_seq: ", max_num_blocks_per_seq) - block_table = torch.zeros((batch, max_num_blocks_per_seq), dtype=torch.int32, device='cuda') - block_indices = torch.zeros((batch, heads_kv, max_selected_blocks), - dtype=torch.int32, - device='cuda') + block_table = torch.zeros((batch, max_num_blocks_per_seq), dtype=torch.int32, device="cuda") + block_indices = torch.zeros((batch, heads_kv, max_selected_blocks), dtype=torch.int32, device="cuda") # Fill block table and block indices and cache # Create a pool of available physical blocks - total_blocks_needed = sum( - int(math.ceil(cache_seqlens[seq_idx].item() / page_block_size)) for seq_idx in range(batch)) + total_blocks_needed = sum(int(math.ceil(cache_seqlens[seq_idx].item() / page_block_size)) for seq_idx in range(batch)) available_blocks = list(range(total_blocks_needed)) import random + random.seed(42) # For reproducibility random.shuffle(available_blocks) @@ -459,10 +406,8 @@ def main(args): actual_block_size = end_token - start_token # Copy K and V data to the paged cache - K_cache[physical_block_idx, :actual_block_size, :, :] = K[seq_idx, - start_token:end_token, :, :] - V_cache[physical_block_idx, :actual_block_size, :, :] = V[seq_idx, - start_token:end_token, :, :] + K_cache[physical_block_idx, :actual_block_size, :, :] = K[seq_idx, start_token:end_token, :, :] + V_cache[physical_block_idx, :actual_block_size, :, :] = V[seq_idx, start_token:end_token, :, :] # Fill block_indices for sparse attention # For dense case (verification), we select all blocks in reverse order @@ -497,10 +442,9 @@ def main(args): remaining_blocks = [b for b in all_blocks if b not in selected_blocks] if remaining_blocks: import random + random.seed(42) # For reproducibility - additional_blocks = random.sample( - remaining_blocks, - min(num_selected - recent_blocks, len(remaining_blocks))) + additional_blocks = random.sample(remaining_blocks, min(num_selected - recent_blocks, len(remaining_blocks))) selected_blocks.extend(additional_blocks) # Sort selected blocks in reverse order (most recent first) @@ -513,25 +457,20 @@ def main(args): block_indices[seq_idx, head_idx, i] = -1 # Initialize sparse attention module - sparse_attn = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, - num_blocks) - output_sparse = sparse_attn.forward(Q, K_cache, V_cache, block_indices, cache_seqlens, - block_table) + sparse_attn = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, num_blocks) + output_sparse = sparse_attn.forward(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table) import flash_attn # noqa: F401 - output_ref_torch = ref_program_torch_paged(Q, K_cache, V_cache, block_indices, cache_seqlens, - block_table, page_block_size, block_N) + output_ref_torch = ref_program_torch_paged(Q, K_cache, V_cache, block_indices, cache_seqlens, block_table, page_block_size, block_N) output_ref_fa = ref_program_fa(Q, K_cache, V_cache, cache_seqlens, block_table) # Check correctness if sparse_ratio == 0.0: max_diff = torch.max(torch.abs(output_sparse - output_ref_fa)).item() mean_diff = torch.mean(torch.abs(output_sparse - output_ref_fa)).item() - assert torch.allclose( - output_ref_fa, output_ref_torch, atol=1e-2), "Reference outputs do not match!" + assert torch.allclose(output_ref_fa, output_ref_torch, atol=1e-2), "Reference outputs do not match!" else: - max_diff = torch.max(torch.abs(output_sparse - output_ref_torch)).item() mean_diff = torch.mean(torch.abs(output_sparse - output_ref_torch)).item() @@ -573,18 +512,140 @@ def main(args): print(f"Speedup: {kernel_time_fa / kernel_time:.2f}x") +def run_regression_perf(args): + batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = ( + args.batch, + args.heads, + args.heads_kv, + args.max_cache_seqlen, + args.dim, + args.dim_v, + ) + sparse_ratio = args.sparse_ratio + block_N = args.block_N + page_block_size = args.page_block_size + num_blocks = args.num_pages + max_selected_blocks = int(math.ceil(max_cache_seqlen / block_N)) + dtype = torch.float16 + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(max_cache_seqlen // 2, max_cache_seqlen + 1, (batch,), dtype=torch.int32, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + K_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim), dtype=dtype, device="cuda") + V_cache = torch.zeros((num_blocks, page_block_size, heads_kv, dim_v), dtype=dtype, device="cuda") + max_num_blocks_per_seq = int(math.ceil(max_cache_seqlen / page_block_size)) + block_table = torch.zeros((batch, max_num_blocks_per_seq), dtype=torch.int32, device="cuda") + block_indices = torch.zeros((batch, heads_kv, max_selected_blocks), dtype=torch.int32, device="cuda") + total_blocks_needed = sum(int(math.ceil(cache_seqlens[seq_idx].item() / page_block_size)) for seq_idx in range(batch)) + available_blocks = list(range(total_blocks_needed)) + import random + + random.seed(42) + random.shuffle(available_blocks) + block_assignment = {} + block_idx_counter = 0 + for seq_idx in range(batch): + seq_len = cache_seqlens[seq_idx].item() + num_blocks_needed = int(math.ceil(seq_len / page_block_size)) + for block_idx in range(num_blocks_needed): + physical_block_idx = available_blocks[block_idx_counter] + block_table[seq_idx, block_idx] = physical_block_idx + block_assignment[(seq_idx, block_idx)] = physical_block_idx + block_idx_counter += 1 + for seq_idx in range(batch): + seq_len = cache_seqlens[seq_idx].item() + num_blocks_needed = int(math.ceil(seq_len / page_block_size)) + for block_idx in range(num_blocks_needed): + physical_block_idx = block_assignment[(seq_idx, block_idx)] + start_token = block_idx * page_block_size + end_token = min(start_token + page_block_size, seq_len) + actual_block_size = end_token - start_token + K_cache[physical_block_idx, :actual_block_size, :, :] = K[seq_idx, start_token:end_token, :, :] + V_cache[physical_block_idx, :actual_block_size, :, :] = V[seq_idx, start_token:end_token, :, :] + for seq_idx in range(batch): + seq_len = cache_seqlens[seq_idx].item() + num_tile = int(math.ceil(seq_len / block_N)) + if sparse_ratio == 0.0: + selected_blocks = min(num_tile, max_selected_blocks) + for head_idx in range(heads_kv): + for i in range(selected_blocks): + block_indices[seq_idx, head_idx, i] = num_tile - 1 - i + for i in range(selected_blocks, max_selected_blocks): + block_indices[seq_idx, head_idx, i] = -1 + else: + num_selected = int(num_tile * (1.0 - sparse_ratio)) + num_selected = max(1, min(num_selected, max_selected_blocks)) + all_blocks = list(range(num_tile)) + for head_idx in range(heads_kv): + selected_blocks = [] + recent_blocks = 1 + selected_blocks.append(num_tile - 1) + if num_selected > recent_blocks: + remaining_blocks = [b for b in all_blocks if b not in selected_blocks] + if remaining_blocks: + import random + + random.seed(42) + additional_blocks = random.sample(remaining_blocks, min(num_selected - recent_blocks, len(remaining_blocks))) + selected_blocks.extend(additional_blocks) + + selected_blocks.sort(reverse=True) + + for i in range(len(selected_blocks)): + block_indices[seq_idx, head_idx, i] = selected_blocks[i] + for i in range(len(selected_blocks), max_selected_blocks): + block_indices[seq_idx, head_idx, i] = -1 + + sparse_attn = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, page_block_size, block_N, num_blocks) + kernel = sparse_attn.kernel + batch = sparse_attn.batch + heads = sparse_attn.heads + heads_kv = sparse_attn.heads_kv + dim_v = sparse_attn.dim_v + dim = sparse_attn.dim + block_size = sparse_attn.block_N + max_selected_blocks = block_indices.shape[-1] + + num_m_blocks = 1 * (heads // heads_kv + sparse_attn.block_H - 1) // sparse_attn.block_H + num_n_blocks = max_selected_blocks + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 + total_mblocks = batch * heads_kv * num_m_blocks + + num_sm = sparse_attn.num_sm + + num_split = num_splits_heuristic( + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") + + def run_kernel_only(): + kernel( + Q, + K_cache, + V_cache, + block_indices, + cache_seqlens, + block_table, + glse, + output_partial, + ) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv') - parser.add_argument( - '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--dim_v', type=int, default=128, help='dim_v') - parser.add_argument('--sparse_ratio', type=float, default=0.0, help='sparse ratio') - parser.add_argument('--block_N', type=int, default=64, help='block_N') - parser.add_argument('--page_block_size', type=int, default=256, help='block size of pages') - parser.add_argument('--num_pages', type=int, default=1024, help='total number of pages') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv") + parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--dim_v", type=int, default=128, help="dim_v") + parser.add_argument("--sparse_ratio", type=float, default=0.0, help="sparse ratio") + parser.add_argument("--block_N", type=int, default=64, help="block_N") + parser.add_argument("--page_block_size", type=int, default=256, help="block size of pages") + parser.add_argument("--num_pages", type=int, default=1024, help="total number of pages") args = parser.parse_args() main(args) diff --git a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py index ae30042674..d6cf7d9176 100644 --- a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py +++ b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_indice.py @@ -7,20 +7,22 @@ import time import math from heuristic import num_splits_heuristic +from tilelang.profiler import do_bench def flashattn(batch, heads, heads_kv, dim, dim_v): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // heads_kv @tilelang.jit( - out_idx=[-1], pass_configs={ + out_idx=[-1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) - def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, - max_selected_blocks): + }, + ) + def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, max_selected_blocks): shape_q = [batch, heads, dim] shape_k = [batch, max_cache_seqlen, heads_kv, dim] shape_v = [batch, max_cache_seqlen, heads_kv, dim_v] @@ -29,19 +31,21 @@ def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seql part_shape = [batch, heads, num_split, dim_v] valid_block_H = min(block_H, kv_group_num) - @T.macro - def flash_attn_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_indices: T.Tensor(shape_indices, "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - # actual_num_blocks: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), + @T.prim_func + def main( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + block_indices: T.Tensor(shape_indices, T.int32), + cache_seqlens: T.Tensor([batch], T.int32), + # actual_num_blocks: T.Tensor([batch], T.int32), + glse: T.Tensor([batch, heads, num_split], accum_dtype), + Output_partial: T.Tensor(part_shape, accum_dtype), + Output: T.Tensor(shape_o, dtype), ): - with T.Kernel( - batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): + # flash_attn_split(Q, K, V, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial) + # flash_attn_split + with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim_v], dtype) @@ -62,7 +66,7 @@ def flash_attn_split( sid = bz cur_kv_head = hid // (kv_group_num // valid_block_H) - T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared) + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) @@ -70,7 +74,7 @@ def flash_attn_split( num_blocks = max_selected_blocks blocks_per_split = T.floordiv(num_blocks, num_split) remaining_blocks = T.floormod(num_blocks, num_split) - loop_range = (blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)) + loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0) start = blocks_per_split * sid + T.min(sid, remaining_blocks) has_valid_block = False @@ -78,27 +82,18 @@ def flash_attn_split( i_s = block_indices[bid, cur_kv_head, start + k] if i_s >= 0: has_valid_block = True - T.copy(K[bid, i_s * block_N:(i_s + 1) * block_N, cur_kv_head, :], K_shared) + T.copy(K[bid, i_s * block_N : (i_s + 1) * block_N, cur_kv_head, :], K_shared) T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) if k == 0: # assume block_indices is sorted in reverse order, otherwise, remove this if condition for i, j in T.Parallel(block_H, block_N): - acc_s[i, - j] = T.if_then_else(i_s * block_N + j >= cache_seqlens[bid], - -T.infinity(accum_dtype), acc_s[i, j]) + acc_s[i, j] = T.if_then_else(i_s * block_N + j >= cache_seqlens[bid], -T.infinity(accum_dtype), acc_s[i, j]) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) for i in T.Parallel(block_H): - scores_max[i] = T.if_then_else(scores_max[i] > scores_max_prev[i], - scores_max[i], scores_max_prev[i]) - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - - scores_max[i] * scale) + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) @@ -107,7 +102,7 @@ def flash_attn_split( T.copy(acc_s, acc_s_cast) for i, j in T.Parallel(block_H, dim_v): acc_o[i, j] *= scores_scale[i] - T.copy(V[bid, i_s * block_N:(i_s + 1) * block_N, cur_kv_head, :], V_shared) + T.copy(V[bid, i_s * block_N : (i_s + 1) * block_N, cur_kv_head, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) if has_valid_block: for i, j in T.Parallel(block_H, dim_v): @@ -124,74 +119,47 @@ def flash_attn_split( if i < valid_block_H: Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j] - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): + # combine with T.Kernel(heads, batch, threads=128) as (by, bz): po_local = T.alloc_fragment([dim_v], accum_dtype) o_accum_local = T.alloc_fragment([dim_v], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - max_split = T.alloc_local([1], "int32") - - T.annotate_layout({ - lse_logsum_local: - T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + max_split = T.alloc_var(T.int32) T.clear(lse_logsum_local) T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) + lse_max_local = -T.infinity(accum_dtype) for k in T.serial(num_split): - lse_local_split[0] = glse[bz, by, k] - if (lse_local_split[0] != 0): - max_split[0] = k - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k]) + lse_local_split = glse[bz, by, k] + if lse_local_split != 0: + max_split = k + lse_max_local = T.max(lse_max_local, glse[bz, by, k]) for k in T.Pipelined(num_split, num_stages=1): - if k <= max_split[0]: - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] + if k <= max_split: + lse_local_split = glse[bz, by, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local for k in T.serial(num_split): - if k <= max_split[0]: + if k <= max_split: for i in T.Parallel(dim_v): po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) + lse_local_split = glse[bz, by, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) for i in T.Parallel(dim_v): - o_accum_local[i] += po_local[i] * scale_local[0] + o_accum_local[i] += po_local[i] * scale_local for i in T.Parallel(dim_v): Output[bz, by, i] = o_accum_local[i] - @T.prim_func - def main( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_indices: T.Tensor(shape_indices, "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - # actual_num_blocks: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): - # flash_attn_split(Q, K, V, block_indices, cache_seqlens, actual_num_blocks, glse, Output_partial) - flash_attn_split(Q, K, V, block_indices, cache_seqlens, glse, Output_partial) - combine(glse, Output_partial, Output) - return main return kernel_func class SparseFlashAttn(torch.nn.Module): - def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size): super(SparseFlashAttn, self).__init__() self.batch = batch @@ -210,7 +178,8 @@ def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size): num_stages=2, threads=128, max_cache_seqlen=T.dynamic("max_cache_seqlen"), - max_selected_blocks=T.dynamic("max_selected_blocks")) + max_selected_blocks=T.dynamic("max_selected_blocks"), + ) props = torch.cuda.get_device_properties(torch.device("cuda:0")) self.num_sm = props.multi_processor_count @@ -233,25 +202,17 @@ def forward(self, query, key, value, block_indices, cache_seqlens): num_sm = self.num_sm num_split = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) - - glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda') - output_partial = torch.empty((batch, heads, num_split, dim_v), - dtype=torch.float32, - device='cuda') + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") output = self.kernel(query, key, value, block_indices, cache_seqlens, glse, output_partial) return output -def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seqlens, - max_cache_seqlen, block_size): +def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, block_size): """ Args: query: [batch, heads, dim] @@ -273,31 +234,24 @@ def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seql block_H = 64 actual_num_blocks = torch.sum(block_indices != -1, dim=-1).to(torch.int32) - actual_num_blocks = actual_num_blocks[:, - 0] #[batch], number of valid blocks, assume all groups in the same batch have the same number of blocks + actual_num_blocks = actual_num_blocks[ + :, 0 + ] # [batch], number of valid blocks, assume all groups in the same batch have the same number of blocks # get num_split num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H - num_n_blocks = max_selected_blocks #(kv_seqlen + block_size - 1 ) // block_size + num_n_blocks = max_selected_blocks # (kv_seqlen + block_size - 1 ) // block_size # num_n_blocks = torch.sum(actual_num_blocks, dim=-1).item() * heads_kv # total number of blocks - size_one_kv_head = max_selected_blocks * block_size * ( - dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2 + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 # kv_seqlen * (dim + dim_v) * 2 total_mblocks = batch * heads_kv * num_m_blocks num_sm = 132 num_split = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) - - glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda') - Output_partial = torch.empty((batch, heads, num_split, dim_v), - dtype=torch.float32, - device='cuda') + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") kernel = flashattn(batch, heads, heads_kv, dim, dim_v)( block_N=block_size, block_H=block_H, @@ -305,29 +259,24 @@ def sparse_gqa_decode_varlen_indice(query, key, value, block_indices, cache_seql num_stages=2, threads=128, max_cache_seqlen=T.dynamic("max_cache_seqlen"), - max_selected_blocks=T.dynamic("max_selected_blocks")) + max_selected_blocks=T.dynamic("max_selected_blocks"), + ) output = kernel(query, key, value, block_indices, cache_seqlens, glse, Output_partial) return output -def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): - +def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size): batch, heads, dim = query.shape heads_kv = key.shape[2] num_head_groups = query.shape[1] // key.shape[2] scale = dim**0.5 - key = rearrange(key, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] - value = rearrange(value, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] + key = rearrange(key, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] + value = rearrange(value, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, heads_kv, seqlen_kv] sparse_mask = torch.zeros_like(scores) # Assign mask values based on block_indices @@ -336,28 +285,26 @@ def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache valid_indices = block_indices[b, h] # Extract indices for this batch and head for idx in valid_indices: if idx >= 0: - sparse_mask[b, :, h, idx * block_size:(idx + 1) * block_size] = 1 - scores = scores.masked_fill(sparse_mask == 0, float('-inf')) + sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1 + scores = scores.masked_fill(sparse_mask == 0, float("-inf")) - range_len = torch.arange(scores.shape[-1], device='cuda').unsqueeze(0) + range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0) cache_seqlens_expanded = cache_seqlens.unsqueeze(1) pad_mask = range_len >= cache_seqlens_expanded pad_mask = pad_mask[:, None, None, :] - scores = scores.masked_fill(pad_mask, float('-inf')) - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = scores.masked_fill(pad_mask, float("-inf")) + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] - out = einsum(attention, value, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, heads_kv, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, value, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, heads_kv, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out -def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): +def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size): # latency reference # from flash_attn_interface import flash_attn_with_kvcache # fa3 - from flash_attn import flash_attn_with_kvcache #fa2 + from flash_attn import flash_attn_with_kvcache # fa2 + query = query.unsqueeze(1) output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens) output = output.squeeze(1) @@ -369,23 +316,13 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3): print(name + " all_close={}".format(all_close)) if not all_close: diff = (expect - actual).abs() - print("all_close={}, max={}, min={}, mean={}".format(all_close, - diff.max().item(), - diff.min().item(), - diff.mean().item())) + print("all_close={}, max={}, min={}, mean={}".format(all_close, diff.max().item(), diff.min().item(), diff.mean().item())) max_indices = torch.nonzero(diff == diff.max().item()) first_index = tuple(max_indices[0].tolist()) print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}") -def main(batch=8, - heads=32, - heads_kv=8, - max_cache_seqlen=8192, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32): +def main(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v sparse_ratio = sparse_ratio block_size = block_size @@ -393,10 +330,10 @@ def main(batch=8, print("max_selected_blocks: ", max_selected_blocks) dtype = torch.float16 - Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda') - K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda') - V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda') - cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device='cuda') + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda') # # Ensure at least one element equals cache_seqlen # random_index = torch.randint(0, batch, (1,), device='cuda').item() # Select a random index @@ -407,10 +344,7 @@ def main(batch=8, max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() print("max_valid_num_blocks: ", max_valid_num_blocks) # Initialize block_indices with -1 (for padding blocks) - block_indices = torch.full((batch, heads_kv, max_selected_blocks), - -1, - dtype=torch.int32, - device='cuda') + block_indices = torch.full((batch, heads_kv, max_selected_blocks), -1, dtype=torch.int32, device="cuda") # max_num_blocks = int((max_cache_seqlen + block_size - 1)/ block_size) # block_indices = torch.full((batch, heads_kv, max_num_blocks), -1, dtype=torch.int32, device='cuda') @@ -419,10 +353,9 @@ def main(batch=8, max_valid_block = max_valid_num_blocks[b].item() # Max valid blocks for this batch if max_valid_block > 0: # Ensure there's at least one valid block for h in range(heads_kv): - valid_indices = torch.randperm( - max_valid_block, device='cuda', dtype=torch.int32)[:max_selected_blocks] + valid_indices = torch.randperm(max_valid_block, device="cuda", dtype=torch.int32)[:max_selected_blocks] # valid_indices = torch.randperm(max_valid_block, device='cuda', dtype=torch.int32)[:max_num_blocks] - block_indices[b, h, :len(valid_indices)] = valid_indices + block_indices[b, h, : len(valid_indices)] = valid_indices # Sort indices within each batch-group for consistency block_indices, _ = block_indices.sort(dim=-1, descending=True) @@ -435,8 +368,7 @@ def main(batch=8, print("max_num_blocks: ", max_num_blocks) # parity reference - ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, - block_size) + ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size) sparse_kernel = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size) out = sparse_kernel(Q, K, V, block_indices, cache_seqlens) @@ -446,13 +378,11 @@ def main(batch=8, ## latency reference for _ in range(10): - ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, - max_num_blocks, block_size) + ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size) torch.cuda.synchronize() start = time.time() for _ in range(100): - ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, - max_num_blocks, block_size) + ref = ref_program_fa(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size) torch.cuda.synchronize() print("dense time: ", (time.time() - start) / 100 * 1000) @@ -468,17 +398,67 @@ def main(batch=8, print("sparse time: ", (time.time() - start) / 100 * 1000) +def run_regression_perf(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): + torch.manual_seed(42) + torch.cuda.manual_seed_all(42) + batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v + sparse_ratio = sparse_ratio + block_size = block_size + max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size)) + dtype = torch.float16 + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") + max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() + block_indices = torch.full((batch, heads_kv, max_selected_blocks), -1, dtype=torch.int32, device="cuda") + + for b in range(batch): + max_valid_block = max_valid_num_blocks[b].item() + if max_valid_block > 0: + for h in range(heads_kv): + valid_indices = torch.randperm(max_valid_block, device="cuda", dtype=torch.int32)[:max_selected_blocks] + block_indices[b, h, : len(valid_indices)] = valid_indices + + block_indices, _ = block_indices.sort(dim=-1, descending=True) + sparse_kernel = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size) + batch = sparse_kernel.batch + heads = sparse_kernel.heads + heads_kv = sparse_kernel.heads_kv + dim_v = sparse_kernel.dim_v + dim = sparse_kernel.dim + block_size = sparse_kernel.block_size + max_selected_blocks = block_indices.shape[-1] + + num_m_blocks = 1 * (heads // heads_kv + sparse_kernel.block_H - 1) // sparse_kernel.block_H + num_n_blocks = max_selected_blocks + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 + total_mblocks = batch * heads_kv * num_m_blocks + num_sm = sparse_kernel.num_sm + + num_split = num_splits_heuristic( + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") + kernel = sparse_kernel.kernel + + def run_kernel_only(): + kernel(Q, K, V, block_indices, cache_seqlens, glse, output_partial) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv') - parser.add_argument( - '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--dim_v', type=int, default=128, help='dim_v') - parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio') - parser.add_argument('--block_size', type=int, default=32, help='block_size') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv") + parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--dim_v", type=int, default=128, help="dim_v") + parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio") + parser.add_argument("--block_size", type=int, default=32, help="block_size") args = parser.parse_args() - main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, - args.sparse_ratio, args.block_size) + main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size) diff --git a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py index ad62817dd5..e48428fb89 100644 --- a/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py +++ b/examples/blocksparse_attention/example_tilelang_sparse_gqa_decode_varlen_mask.py @@ -5,22 +5,24 @@ import tilelang.language as T from einops import rearrange, einsum import argparse - import time import math from heuristic import num_splits_heuristic +from tilelang.profiler import do_bench def flashattn(batch, heads, heads_kv, dim, dim_v): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // heads_kv @tilelang.jit( - out_idx=[-1], pass_configs={ + out_idx=[-1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, + ) def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seqlen, num_blocks): shape_q = [batch, heads, dim] shape_k = [batch, max_cache_seqlen, heads_kv, dim] @@ -30,22 +32,21 @@ def kernel_func(block_N, block_H, num_split, num_stages, threads, max_cache_seql part_shape = [batch, heads, num_split, dim_v] valid_block_H = min(block_H, kv_group_num) - @T.macro - def flash_attn_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_mask: T.Tensor(shape_mask, "bool"), - cache_seqlens: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), + @T.prim_func + def main( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + block_mask: T.Tensor(shape_mask, T.bool), + cache_seqlens: T.Tensor([batch], T.int32), + glse: T.Tensor([batch, heads, num_split], accum_dtype), + Output_partial: T.Tensor(part_shape, accum_dtype), + Output: T.Tensor(shape_o, dtype), ): - with T.Kernel( - batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): + with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim_v], dtype) - # O_shared = T.alloc_shared([valid_block_H, dim_v], dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o = T.alloc_fragment([block_H, dim_v], accum_dtype) @@ -62,38 +63,31 @@ def flash_attn_split( sid = bz cur_kv_head = hid // (kv_group_num // valid_block_H) - T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared) + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) blocks_per_split = T.floordiv(num_blocks, num_split) remaining_blocks = T.floormod(num_blocks, num_split) - loop_range = (blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0)) + loop_range = blocks_per_split + T.if_then_else(sid < remaining_blocks, 1, 0) start = blocks_per_split * sid + T.min(sid, remaining_blocks) has_valid_block = False for k in T.Pipelined(loop_range, num_stages=num_stages): if block_mask[bid, hid, start + k]: has_valid_block = True - T.copy( - K[bid, (start + k) * block_N:(start + k + 1) * block_N, cur_kv_head, :], - K_shared) + T.copy(K[bid, (start + k) * block_N : (start + k + 1) * block_N, cur_kv_head, :], K_shared) T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, block_N): - acc_s[i, j] = T.if_then_else((start + k) * block_N + j - >= cache_seqlens[bx], - -T.infinity(accum_dtype), acc_s[i, j]) + acc_s[i, j] = T.if_then_else( + (start + k) * block_N + j >= cache_seqlens[bx], -T.infinity(accum_dtype), acc_s[i, j] + ) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) for i in T.Parallel(block_H): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - - scores_max[i] * scale) + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) @@ -102,9 +96,7 @@ def flash_attn_split( T.copy(acc_s, acc_s_cast) for i, j in T.Parallel(block_H, dim_v): acc_o[i, j] *= scores_scale[i] - T.copy( - V[bid, (start + k) * block_N:(start + k + 1) * block_N, cur_kv_head, :], - V_shared) + T.copy(V[bid, (start + k) * block_N : (start + k + 1) * block_N, cur_kv_head, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) if has_valid_block: for i, j in T.Parallel(block_H, dim_v): @@ -120,65 +112,39 @@ def flash_attn_split( if i < valid_block_H: Output_partial[bid, hid * valid_block_H + i, sid, j] = acc_o[i, j] - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): with T.Kernel(heads, batch, threads=128) as (by, bz): po_local = T.alloc_fragment([dim_v], accum_dtype) o_accum_local = T.alloc_fragment([dim_v], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: - T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) T.clear(lse_logsum_local) T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) + lse_max_local = -T.infinity(accum_dtype) for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k]) + lse_max_local = T.max(lse_max_local, glse[bz, by, k]) for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] + lse_local_split = glse[bz, by, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local for k in T.serial(num_split): for i in T.Parallel(dim_v): po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) + lse_local_split = glse[bz, by, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) for i in T.Parallel(dim_v): - o_accum_local[i] += po_local[i] * scale_local[0] + o_accum_local[i] += po_local[i] * scale_local for i in T.Parallel(dim_v): Output[bz, by, i] = o_accum_local[i] - @T.prim_func - def main( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - block_mask: T.Tensor(shape_mask, "bool"), - cache_seqlens: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, heads, num_split], accum_dtype), - Output_partial: T.Tensor(part_shape, accum_dtype), - Output: T.Tensor(shape_o, dtype), - ): - flash_attn_split(Q, K, V, block_mask, cache_seqlens, glse, Output_partial) - combine(glse, Output_partial, Output) - return main return kernel_func class SparseFlashAttn(torch.nn.Module): - def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size): super(SparseFlashAttn, self).__init__() self.batch = batch @@ -197,7 +163,8 @@ def __init__(self, batch, heads, heads_kv, dim, dim_v, block_size): num_stages=2, threads=128, max_cache_seqlen=T.dynamic("max_cache_seqlen"), - num_blocks=T.dynamic("num_blocks")) + num_blocks=T.dynamic("num_blocks"), + ) props = torch.cuda.get_device_properties(torch.device("cuda:0")) self.num_sm = props.multi_processor_count @@ -216,24 +183,16 @@ def forward(self, query, key, value, block_mask, cache_seqlens): num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H num_n_blocks = max_selected_blocks - size_one_kv_head = max_selected_blocks * block_size * ( - dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2 + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 # kv_seqlen * (dim + dim_v) * 2 total_mblocks = batch * heads_kv * num_m_blocks # num_sm = 132 num_sm = self.num_sm num_split = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) # print("num_split: ", num_split) - glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda') - Output_partial = torch.empty((batch, heads, num_split, dim_v), - dtype=torch.float32, - device='cuda') + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") output = self.kernel(query, key, value, block_mask, cache_seqlens, glse, Output_partial) return output @@ -258,26 +217,21 @@ def sparse_gqa_decode_varlen_mask(query, key, value, block_mask, cache_seqlens, block_H = 64 actual_num_blocks = torch.sum(block_mask, dim=-1).to(torch.int32) - actual_num_blocks = actual_num_blocks[:, - 0] #[batch], number of valid blocks, assume all groups in the same batch have the same number of blocks + actual_num_blocks = actual_num_blocks[ + :, 0 + ] # [batch], number of valid blocks, assume all groups in the same batch have the same number of blocks max_selected_blocks = actual_num_blocks.max().item() # get num_split num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H - num_n_blocks = max_selected_blocks #(kv_seqlen + block_size - 1 ) // block_size + num_n_blocks = max_selected_blocks # (kv_seqlen + block_size - 1 ) // block_size # num_n_blocks = torch.sum(actual_num_blocks, dim=-1).item() * heads_kv # total number of blocks - size_one_kv_head = max_selected_blocks * block_size * ( - dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2 + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 # kv_seqlen * (dim + dim_v) * 2 total_mblocks = batch * heads_kv * num_m_blocks num_sm = 132 num_split = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) kernel = flashattn(batch, heads, heads_kv, dim, dim_v)( block_N=block_size, @@ -286,11 +240,10 @@ def sparse_gqa_decode_varlen_mask(query, key, value, block_mask, cache_seqlens, num_stages=2, threads=128, max_cache_seqlen=T.dynamic("max_cache_seqlen"), - num_blocks=T.dynamic("num_blocks")) - glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device='cuda') - Output_partial = torch.empty((batch, heads, num_split, dim_v), - dtype=torch.float32, - device='cuda') + num_blocks=T.dynamic("num_blocks"), + ) + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") # print(kernel.get_kernel_source()) output = kernel(query, key, value, block_mask, cache_seqlens, glse, Output_partial) @@ -298,24 +251,18 @@ def sparse_gqa_decode_varlen_mask(query, key, value, block_mask, cache_seqlens, return output -def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): - +def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size): batch, heads, dim = query.shape heads_kv = key.shape[2] num_head_groups = query.shape[1] // key.shape[2] scale = dim**0.5 - key = rearrange(key, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] - value = rearrange(value, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] + key = rearrange(key, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] + value = rearrange(value, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, heads_kv, seqlen_kv] sparse_mask = torch.zeros_like(scores) # Assign mask values @@ -323,29 +270,27 @@ def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_se for h in range(heads_kv): for idx in range(num_blocks): if block_mask[b, h, idx]: - sparse_mask[b, :, h, idx * block_size:(idx + 1) * block_size] = 1 + sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1 - scores = scores.masked_fill(sparse_mask == 0, float('-inf')) + scores = scores.masked_fill(sparse_mask == 0, float("-inf")) - range_len = torch.arange(scores.shape[-1], device='cuda').unsqueeze(0) + range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0) cache_seqlens_expanded = cache_seqlens.unsqueeze(1) pad_mask = range_len >= cache_seqlens_expanded pad_mask = pad_mask[:, None, None, :] - scores = scores.masked_fill(pad_mask, float('-inf')) - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = scores.masked_fill(pad_mask, float("-inf")) + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] - out = einsum(attention, value, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, heads_kv, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, value, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, heads_kv, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out -def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): +def ref_program_fa(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size): # latency reference # from flash_attn_interface import flash_attn_with_kvcache # fa3 - from flash_attn import flash_attn_with_kvcache #fa2 + from flash_attn import flash_attn_with_kvcache # fa2 + query = query.unsqueeze(1) output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens) output = output.squeeze(1) @@ -359,23 +304,13 @@ def debug(name, expect, actual, atol=1e-3, rtol=1e-3): # print(expect[3, 28]) # print(actual[3, 28]) diff = (expect - actual).abs() - print("all_close={}, max={}, min={}, mean={}".format(all_close, - diff.max().item(), - diff.min().item(), - diff.mean().item())) + print("all_close={}, max={}, min={}, mean={}".format(all_close, diff.max().item(), diff.min().item(), diff.mean().item())) max_indices = torch.nonzero(diff == diff.max().item()) first_index = tuple(max_indices[0].tolist()) print(f"Index: {first_index}, expect: {expect[first_index]}, actual: {actual[first_index]}") -def main(batch=8, - heads=32, - heads_kv=8, - max_cache_seqlen=8192, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32): +def main(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v sparse_ratio = sparse_ratio block_size = block_size @@ -383,14 +318,13 @@ def main(batch=8, print("max_selected_blocks: ", max_selected_blocks) dtype = torch.float16 - Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda') - K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda') - V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda') - cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device='cuda') + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") # Ensure at least one element equals cache_seqlen - random_index = torch.randint(0, batch, (1,), device='cuda').item() # Select a random index - cache_seqlens[ - random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence + random_index = torch.randint(0, batch, (1,), device="cuda").item() # Select a random index + cache_seqlens[random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda') print("cache_seqlens: ", cache_seqlens) @@ -402,7 +336,7 @@ def main(batch=8, max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() print("max_valid_num_blocks: ", max_valid_num_blocks) # Initialize block_mask with false (for padding blocks) - block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device='cuda') + block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device="cuda") # Assign valid indices while ensuring no duplicates within each batch-group for b in range(batch): @@ -410,13 +344,12 @@ def main(batch=8, valid_num_block = valid_num_blocks[b].item() # Valid blocks for this batch if valid_num_block > 0: # Ensure there's at least one valid block for h in range(heads_kv): - perm = torch.randperm(max_valid_block, device='cuda')[:valid_num_block] + perm = torch.randperm(max_valid_block, device="cuda")[:valid_num_block] block_mask[b, h, perm] = True # print("block_mask: ", block_mask) # parity reference - ref = ref_program_torch(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size) + ref = ref_program_torch(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size) # out = sparse_gqa_decode_varlen_mask(Q, K, V, block_mask, cache_seqlens, block_size) model = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size) out = model(Q, K, V, block_mask, cache_seqlens) @@ -426,13 +359,11 @@ def main(batch=8, ## latency reference for _ in range(10): - ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size) + ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size) torch.cuda.synchronize() start = time.time() for _ in range(100): - ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size) + ref = ref_program_fa(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size) torch.cuda.synchronize() print("dense time: ", (time.time() - start) / 100 * 1000) @@ -449,17 +380,72 @@ def main(batch=8, print("sparse time: ", (time.time() - start) / 100 * 1000) +def run_regression_perf(batch=8, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): + batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v + sparse_ratio = sparse_ratio + block_size = block_size + max_selected_blocks = int(math.ceil(max_cache_seqlen * (1 - sparse_ratio) / block_size)) + dtype = torch.float16 + + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") + random_index = torch.randint(0, batch, (1,), device="cuda").item() + cache_seqlens[random_index] = max_cache_seqlen + + num_blocks = (max_cache_seqlen + block_size - 1) // block_size + + valid_num_blocks = torch.ceil(cache_seqlens * (1 - sparse_ratio) / block_size).int() + max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() + block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device="cuda") + + for b in range(batch): + max_valid_block = max_valid_num_blocks[b].item() + valid_num_block = valid_num_blocks[b].item() + if valid_num_block > 0: + for h in range(heads_kv): + perm = torch.randperm(max_valid_block, device="cuda")[:valid_num_block] + block_mask[b, h, perm] = True + + model = SparseFlashAttn(batch, heads, heads_kv, dim, dim_v, block_size) + batch = model.batch + heads = model.heads + heads_kv = model.heads_kv + dim_v = model.dim_v + dim = model.dim + block_size = model.block_size + block_H = model.block_H + max_cache_seqlen = K.shape[1] + max_selected_blocks = (max_cache_seqlen + block_size - 1) // block_size + num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H + num_n_blocks = max_selected_blocks + + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 + total_mblocks = batch * heads_kv * num_m_blocks + num_sm = model.num_sm + num_split = num_splits_heuristic( + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) + glse = torch.empty((batch, heads, num_split), dtype=torch.float32, device="cuda") + Output_partial = torch.empty((batch, heads, num_split, dim_v), dtype=torch.float32, device="cuda") + kernel = model.kernel + + def run_kernel_only(): + kernel(Q, K, V, block_mask, cache_seqlens, glse, Output_partial) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv') - parser.add_argument( - '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--dim_v', type=int, default=128, help='dim_v') - parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio') - parser.add_argument('--block_size', type=int, default=32, help='block_size') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv") + parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--dim_v", type=int, default=128, help="dim_v") + parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio") + parser.add_argument("--block_size", type=int, default=32, help="block_size") args = parser.parse_args() - main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, - args.sparse_ratio, args.block_size) + main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size) diff --git a/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_indice.py b/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_indice.py index 85b72b775e..01695742b5 100644 --- a/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_indice.py +++ b/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_indice.py @@ -5,19 +5,15 @@ import argparse from einops import rearrange, einsum import torch.nn.functional as F - import math import time from heuristic import num_splits_heuristic +from tilelang.profiler import do_bench @triton.autotune( - configs=[ - triton.Config({}, num_warps=num_warps, num_stages=num_stages) - for num_warps in [1, 2, 4]\ - for num_stages in [1, 2, 3, 4, 7] - ], - key=['BLOCK_H', 'BLOCK_N', 'BLOCK_D'], + configs=[triton.Config({}, num_warps=num_warps, num_stages=num_stages) for num_warps in [1, 2, 4] for num_stages in [1, 2, 3, 4, 7]], + key=["BLOCK_H", "BLOCK_N", "BLOCK_D"], ) @triton.jit def _split_kernel( @@ -79,16 +75,11 @@ def _split_kernel( loop_range = blocks_per_split q_ptr += batch_idx * stride_q_b + head_idx_q * stride_q_h - k_cache_ptr += batch_idx * stride_k_b + head_idx_kv * stride_k_h + offs_n[ - None, :] * stride_k_s + offs_d[:, None] * stride_k_d - v_cache_ptr += batch_idx * stride_v_b + head_idx_kv * stride_v_h + offs_n[:, - None] * stride_v_s + offs_d[ - None, :] * stride_v_d + k_cache_ptr += batch_idx * stride_k_b + head_idx_kv * stride_k_h + offs_n[None, :] * stride_k_s + offs_d[:, None] * stride_k_d + v_cache_ptr += batch_idx * stride_v_b + head_idx_kv * stride_v_h + offs_n[:, None] * stride_v_s + offs_d[None, :] * stride_v_d mask_ptr += batch_idx * stride_mask_b + head_idx_kv * stride_mask_h - q = tl.load( - q_ptr + offs_h[:, None] * stride_q_h + offs_d[None, :] * stride_q_d, - mask=offs_h[:, None] < gqa_group_size) + q = tl.load(q_ptr + offs_h[:, None] * stride_q_h + offs_d[None, :] * stride_q_d, mask=offs_h[:, None] < gqa_group_size) start = blocks_per_split * split_idx + tl.minimum(split_idx, remaining_blocks) for i in range(loop_range): block_idx = tl.load(mask_ptr + (start + i) * stride_mask_s) @@ -119,23 +110,18 @@ def _split_kernel( acc = acc * l_recip acc = acc.to(o_partial_ptr.dtype.element_ty) - lse_partial_ptr += batch_idx * stride_lse_b + ( - head_idx_q + offs_h) * stride_lse_h + split_idx * stride_lse_split + lse_partial_ptr += batch_idx * stride_lse_b + (head_idx_q + offs_h) * stride_lse_h + split_idx * stride_lse_split tl.store(lse_partial_ptr, m_i, mask=offs_h < gqa_group_size) - o_partial_ptr += batch_idx * stride_o_b + ( - head_idx_q + - offs_h[:, None]) * stride_o_h + split_idx * stride_o_split + offs_d[None, :] * stride_o_d + o_partial_ptr += ( + batch_idx * stride_o_b + (head_idx_q + offs_h[:, None]) * stride_o_h + split_idx * stride_o_split + offs_d[None, :] * stride_o_d + ) tl.store(o_partial_ptr, acc, mask=offs_h[:, None] < gqa_group_size) @triton.autotune( - configs=[ - triton.Config({}, num_warps=num_warps, num_stages=num_stages) - for num_warps in [1, 2, 4]\ - for num_stages in [1, 2, 3, 4, 7] - ], - key=['BLOCK_D'], + configs=[triton.Config({}, num_warps=num_warps, num_stages=num_stages) for num_warps in [1, 2, 4] for num_stages in [1, 2, 3, 4, 7]], + key=["BLOCK_D"], ) @triton.jit def _merge_kernel( @@ -163,18 +149,15 @@ def _merge_kernel( offs_d = tl.arange(0, BLOCK_D) lse_offsets = lse_partial_ptr + batch_idx * lse_partial_stride_b + head_idx * lse_partial_stride_h - lse = tl.load( - lse_offsets + offs_splits * lse_partial_stride_split, - mask=offs_splits < num_splits, - other=float("-inf")) + lse = tl.load(lse_offsets + offs_splits * lse_partial_stride_split, mask=offs_splits < num_splits, other=float("-inf")) lse_max = tl.max(lse) o_offsets = o_partial_ptr + batch_idx * o_partial_stride_b + head_idx * o_partial_stride_h o_partial = tl.load( - o_offsets + offs_splits[:, None] * o_partial_stride_split + - offs_d[None, :] * o_partial_stride_d, - mask=offs_splits[:, None] < num_splits) + o_offsets + offs_splits[:, None] * o_partial_stride_split + offs_d[None, :] * o_partial_stride_d, + mask=offs_splits[:, None] < num_splits, + ) sumexp_normalized_splitk = tl.exp(lse - lse_max) sumexp_normalized = tl.sum(sumexp_normalized_splitk, axis=0) numerator_normalized = tl.sum(o_partial * sumexp_normalized_splitk[:, None], axis=0) @@ -209,19 +192,13 @@ def block_sparse_flash_decode_gqa_indice_triton( num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H num_n_blocks = max_selected_blocks - size_one_kv_head = max_selected_blocks * block_size * ( - dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2 + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 # kv_seqlen * (dim + dim_v) * 2 total_mblocks = batch * heads_kv * num_m_blocks num_sm = 64 # num_sm = self.num_sm num_splits = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) # print("num_splits:", num_splits, "num_blocks:", num_n_blocks) @@ -295,24 +272,18 @@ def block_sparse_flash_decode_gqa_indice_triton( return output -def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): - +def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache_seqlen, num_blocks, block_size): batch, heads, dim = query.shape heads_kv = key.shape[2] dim_v = value.shape[-1] num_head_groups = query.shape[1] // key.shape[2] scale = dim**0.5 - key = rearrange(key, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] - value = rearrange(value, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] + key = rearrange(key, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] + value = rearrange(value, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, heads_kv, seqlen_kv] sparse_mask = torch.zeros_like(scores) # Assign mask values based on block_indices @@ -321,42 +292,33 @@ def ref_program_torch(query, key, value, block_indices, cache_seqlens, max_cache valid_indices = block_indices[b, h] # Extract indices for this batch and head for idx in valid_indices: if idx >= 0: - sparse_mask[b, :, h, idx * block_size:(idx + 1) * block_size] = 1 - scores = scores.masked_fill(sparse_mask == 0, float('-inf')) + sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1 + scores = scores.masked_fill(sparse_mask == 0, float("-inf")) - range_len = torch.arange(scores.shape[-1], device='cuda').unsqueeze(0) + range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0) cache_seqlens_expanded = cache_seqlens.unsqueeze(1) pad_mask = range_len >= cache_seqlens_expanded pad_mask = pad_mask[:, None, None, :] - scores = scores.masked_fill(pad_mask, float('-inf')) - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = scores.masked_fill(pad_mask, float("-inf")) + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] - out = einsum(attention, value, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, heads_kv, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, value, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, heads_kv, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out def ref_program_fa(query, key, value, cache_seqlens): # latency reference # from flash_attn_interface import flash_attn_with_kvcache # fa3 - from flash_attn import flash_attn_with_kvcache #fa2 + from flash_attn import flash_attn_with_kvcache # fa2 + query = query.unsqueeze(1) output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens) output = output.squeeze(1) return output -def main(batch=64, - heads=32, - heads_kv=8, - max_cache_seqlen=8192, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32): - +def main(batch=64, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v sparse_ratio = sparse_ratio block_size = block_size @@ -369,34 +331,29 @@ def main(batch=64, dtype = torch.float16 block_H = 64 - Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda') - K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda') - V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda') - cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device='cuda') + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") # cache_seqlens = torch.full((batch,), max_cache_seqlen, dtype=torch.int32, device='cuda') # Ensure at least one element equals cache_seqlen - random_index = torch.randint(0, batch, (1,), device='cuda').item() # Select a random index - cache_seqlens[ - random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence + random_index = torch.randint(0, batch, (1,), device="cuda").item() # Select a random index + cache_seqlens[random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence print("cache_seqlens: ", cache_seqlens) max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() print("max_valid_num_blocks: ", max_valid_num_blocks) # Initialize block_indices with -1 (for padding blocks) - block_indices = torch.full((batch, heads_kv, max_selected_blocks), - -1, - dtype=torch.int32, - device='cuda') + block_indices = torch.full((batch, heads_kv, max_selected_blocks), -1, dtype=torch.int32, device="cuda") # Assign valid indices while ensuring no duplicates within each batch-group for b in range(batch): max_valid_block = max_valid_num_blocks[b].item() # Max valid blocks for this batch if max_valid_block > 0: # Ensure there's at least one valid block for h in range(heads_kv): - valid_indices = torch.randperm( - max_valid_block, device='cuda', dtype=torch.int32)[:max_selected_blocks] - block_indices[b, h, :len(valid_indices)] = valid_indices + valid_indices = torch.randperm(max_valid_block, device="cuda", dtype=torch.int32)[:max_selected_blocks] + block_indices[b, h, : len(valid_indices)] = valid_indices # Sort indices within each batch-group for consistency block_indices, _ = block_indices.sort(dim=-1, descending=True) @@ -408,8 +365,7 @@ def main(batch=64, max_num_blocks = torch.max(max_valid_num_blocks).item() print("max_num_blocks: ", max_num_blocks) - ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, - block_size) + ref = ref_program_torch(Q, K, V, block_indices, cache_seqlens, max_cache_seqlen, max_num_blocks, block_size) triton_out = block_sparse_flash_decode_gqa_indice_triton( Q, @@ -423,8 +379,7 @@ def main(batch=64, ) print("max difference: ", torch.max(torch.abs(ref - triton_out))) - assert torch.allclose( - ref, triton_out, atol=1e-2), "Output mismatch between Triton and reference implementation" + assert torch.allclose(ref, triton_out, atol=1e-2), "Output mismatch between Triton and reference implementation" print("Passed the ref test!") # Measure performance @@ -466,15 +421,13 @@ def main(batch=64, if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=64, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv') - parser.add_argument( - '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--dim_v', type=int, default=128, help='dim_v') - parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio') - parser.add_argument('--block_size', type=int, default=32, help='block_size') + parser.add_argument("--batch", type=int, default=64, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv") + parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--dim_v", type=int, default=128, help="dim_v") + parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio") + parser.add_argument("--block_size", type=int, default=32, help="block_size") args = parser.parse_args() - main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, - args.sparse_ratio, args.block_size) + main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size) diff --git a/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_mask.py b/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_mask.py index 3485725265..232bcacafc 100644 --- a/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_mask.py +++ b/examples/blocksparse_attention/example_triton_sparse_gqa_decode_varlen_mask.py @@ -4,19 +4,14 @@ import argparse from einops import rearrange, einsum import torch.nn.functional as F - import math import time from heuristic import num_splits_heuristic @triton.autotune( - configs=[ - triton.Config({}, num_warps=num_warps, num_stages=num_stages) - for num_warps in [1, 2, 4]\ - for num_stages in [1, 2, 3, 4, 7] - ], - key=['BLOCK_H', 'BLOCK_N', 'BLOCK_D'], + configs=[triton.Config({}, num_warps=num_warps, num_stages=num_stages) for num_warps in [1, 2, 4] for num_stages in [1, 2, 3, 4, 7]], + key=["BLOCK_H", "BLOCK_N", "BLOCK_D"], ) @triton.jit def _split_kernel( @@ -77,16 +72,11 @@ def _split_kernel( loop_range = blocks_per_split q_ptr += batch_idx * stride_q_b + head_idx_q * stride_q_h - k_cache_ptr += batch_idx * stride_k_b + head_idx_kv * stride_k_h + offs_n[ - None, :] * stride_k_s + offs_d[:, None] * stride_k_d - v_cache_ptr += batch_idx * stride_v_b + head_idx_kv * stride_v_h + offs_n[:, - None] * stride_v_s + offs_d[ - None, :] * stride_v_d + k_cache_ptr += batch_idx * stride_k_b + head_idx_kv * stride_k_h + offs_n[None, :] * stride_k_s + offs_d[:, None] * stride_k_d + v_cache_ptr += batch_idx * stride_v_b + head_idx_kv * stride_v_h + offs_n[:, None] * stride_v_s + offs_d[None, :] * stride_v_d mask_ptr += batch_idx * stride_mask_b + head_idx_kv * stride_mask_h - q = tl.load( - q_ptr + offs_h[:, None] * stride_q_h + offs_d[None, :] * stride_q_d, - mask=offs_h[:, None] < gqa_group_size) + q = tl.load(q_ptr + offs_h[:, None] * stride_q_h + offs_d[None, :] * stride_q_d, mask=offs_h[:, None] < gqa_group_size) start = blocks_per_split * split_idx + tl.minimum(split_idx, remaining_blocks) for block_idx in range(loop_range): start_n = (start + block_idx) * BLOCK_N @@ -117,23 +107,18 @@ def _split_kernel( acc = acc * l_recip acc = acc.to(o_partial_ptr.dtype.element_ty) - lse_partial_ptr += batch_idx * stride_lse_b + ( - head_idx_q + offs_h) * stride_lse_h + split_idx * stride_lse_split + lse_partial_ptr += batch_idx * stride_lse_b + (head_idx_q + offs_h) * stride_lse_h + split_idx * stride_lse_split tl.store(lse_partial_ptr, m_i, mask=offs_h < gqa_group_size) - o_partial_ptr += batch_idx * stride_o_b + ( - head_idx_q + - offs_h[:, None]) * stride_o_h + split_idx * stride_o_split + offs_d[None, :] * stride_o_d + o_partial_ptr += ( + batch_idx * stride_o_b + (head_idx_q + offs_h[:, None]) * stride_o_h + split_idx * stride_o_split + offs_d[None, :] * stride_o_d + ) tl.store(o_partial_ptr, acc, mask=offs_h[:, None] < gqa_group_size) @triton.autotune( - configs=[ - triton.Config({}, num_warps=num_warps, num_stages=num_stages) - for num_warps in [1, 2, 4]\ - for num_stages in [1, 2, 3, 4, 7] - ], - key=['BLOCK_D'], + configs=[triton.Config({}, num_warps=num_warps, num_stages=num_stages) for num_warps in [1, 2, 4] for num_stages in [1, 2, 3, 4, 7]], + key=["BLOCK_D"], ) @triton.jit def _merge_kernel( @@ -161,18 +146,15 @@ def _merge_kernel( offs_d = tl.arange(0, BLOCK_D) lse_offsets = lse_partial_ptr + batch_idx * lse_partial_stride_b + head_idx * lse_partial_stride_h - lse = tl.load( - lse_offsets + offs_splits * lse_partial_stride_split, - mask=offs_splits < num_splits, - other=float("-inf")) + lse = tl.load(lse_offsets + offs_splits * lse_partial_stride_split, mask=offs_splits < num_splits, other=float("-inf")) lse_max = tl.max(lse) o_offsets = o_partial_ptr + batch_idx * o_partial_stride_b + head_idx * o_partial_stride_h o_partial = tl.load( - o_offsets + offs_splits[:, None] * o_partial_stride_split + - offs_d[None, :] * o_partial_stride_d, - mask=offs_splits[:, None] < num_splits) + o_offsets + offs_splits[:, None] * o_partial_stride_split + offs_d[None, :] * o_partial_stride_d, + mask=offs_splits[:, None] < num_splits, + ) sumexp_normalized_splitk = tl.exp(lse - lse_max) sumexp_normalized = tl.sum(sumexp_normalized_splitk, axis=0) numerator_normalized = tl.sum(o_partial * sumexp_normalized_splitk[:, None], axis=0) @@ -207,19 +189,13 @@ def block_sparse_flash_decode_gqa_mask_triton( num_m_blocks = 1 * (heads // heads_kv + block_H - 1) // block_H num_n_blocks = max_selected_blocks - size_one_kv_head = max_selected_blocks * block_size * ( - dim + dim_v) * 2 #kv_seqlen * (dim + dim_v) * 2 + size_one_kv_head = max_selected_blocks * block_size * (dim + dim_v) * 2 # kv_seqlen * (dim + dim_v) * 2 total_mblocks = batch * heads_kv * num_m_blocks num_sm = 64 # num_sm = self.num_sm num_splits = num_splits_heuristic( - total_mblocks, - num_sm, - num_n_blocks, - num_m_blocks, - size_one_kv_head, - is_causal_or_local=True, - max_splits=128) + total_mblocks, num_sm, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local=True, max_splits=128 + ) # print("num_splits:", num_splits, "num_blocks:", num_n_blocks) @@ -292,24 +268,18 @@ def block_sparse_flash_decode_gqa_mask_triton( return output -def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size): - +def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size): batch, heads, dim = query.shape heads_kv = key.shape[2] num_head_groups = query.shape[1] // key.shape[2] scale = dim**0.5 - key = rearrange(key, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] - value = rearrange(value, 'b n h d -> b h n d') # [batch_size, heads_kv, seqlen_kv, dim] + key = rearrange(key, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] + value = rearrange(value, "b n h d -> b h n d") # [batch_size, heads_kv, seqlen_kv, dim] - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, heads_kv, dim] - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, heads_kv, seqlen_kv] sparse_mask = torch.zeros_like(scores) # Assign mask values @@ -317,43 +287,34 @@ def ref_program_torch(query, key, value, block_mask, cache_seqlens, max_cache_se for h in range(heads_kv): for idx in range(num_blocks): if block_mask[b, h, idx]: - sparse_mask[b, :, h, idx * block_size:(idx + 1) * block_size] = 1 + sparse_mask[b, :, h, idx * block_size : (idx + 1) * block_size] = 1 - scores = scores.masked_fill(sparse_mask == 0, float('-inf')) + scores = scores.masked_fill(sparse_mask == 0, float("-inf")) - range_len = torch.arange(scores.shape[-1], device='cuda').unsqueeze(0) + range_len = torch.arange(scores.shape[-1], device="cuda").unsqueeze(0) cache_seqlens_expanded = cache_seqlens.unsqueeze(1) pad_mask = range_len >= cache_seqlens_expanded pad_mask = pad_mask[:, None, None, :] - scores = scores.masked_fill(pad_mask, float('-inf')) - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] + scores = scores.masked_fill(pad_mask, float("-inf")) + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, heads_kv, seqlen_kv] - out = einsum(attention, value, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, heads_kv, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, value, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, heads_kv, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out def ref_program_fa(query, key, value, cache_seqlens): # latency reference # from flash_attn_interface import flash_attn_with_kvcache # fa3 - from flash_attn import flash_attn_with_kvcache #fa2 + from flash_attn import flash_attn_with_kvcache # fa2 + query = query.unsqueeze(1) output = flash_attn_with_kvcache(query, key, value, cache_seqlens=cache_seqlens) output = output.squeeze(1) return output -def main(batch=64, - heads=32, - heads_kv=8, - max_cache_seqlen=8192, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32): - +def main(batch=64, heads=32, heads_kv=8, max_cache_seqlen=8192, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32): batch, heads, heads_kv, max_cache_seqlen, dim, dim_v = batch, heads, heads_kv, max_cache_seqlen, dim, dim_v block_size = block_size sparse_ratio = sparse_ratio @@ -363,14 +324,13 @@ def main(batch=64, dtype = torch.float16 - Q = torch.randn((batch, heads, dim), dtype=dtype, device='cuda') - K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device='cuda') - V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device='cuda') - cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device='cuda') + Q = torch.randn((batch, heads, dim), dtype=dtype, device="cuda") + K = torch.randn((batch, max_cache_seqlen, heads_kv, dim), dtype=dtype, device="cuda") + V = torch.randn((batch, max_cache_seqlen, heads_kv, dim_v), dtype=dtype, device="cuda") + cache_seqlens = torch.randint(1, max_cache_seqlen, (batch,), dtype=torch.int32, device="cuda") # Ensure at least one element equals cache_seqlen - random_index = torch.randint(0, batch, (1,), device='cuda').item() # Select a random index - cache_seqlens[ - random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence + random_index = torch.randint(0, batch, (1,), device="cuda").item() # Select a random index + cache_seqlens[random_index] = max_cache_seqlen # Assign cache_seqlen to ensure at least one occurrence num_blocks = (max_cache_seqlen + block_size - 1) // block_size @@ -379,7 +339,7 @@ def main(batch=64, max_valid_num_blocks = torch.ceil(cache_seqlens / block_size).int() print("max_valid_num_blocks: ", max_valid_num_blocks) # Initialize block_mask with false (for padding blocks) - block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device='cuda') + block_mask = torch.zeros((batch, heads_kv, num_blocks), dtype=torch.bool, device="cuda") # Assign valid indices while ensuring no duplicates within each batch-group for b in range(batch): @@ -387,11 +347,10 @@ def main(batch=64, valid_num_block = valid_num_blocks[b].item() # Valid blocks for this batch if valid_num_block > 0: # Ensure there's at least one valid block for h in range(heads_kv): - perm = torch.randperm(max_valid_block, device='cuda')[:valid_num_block] + perm = torch.randperm(max_valid_block, device="cuda")[:valid_num_block] block_mask[b, h, perm] = True - ref = ref_program_torch(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, - block_size) + ref = ref_program_torch(Q, K, V, block_mask, cache_seqlens, max_cache_seqlen, num_blocks, block_size) triton_out = block_sparse_flash_decode_gqa_mask_triton( Q, @@ -404,8 +363,7 @@ def main(batch=64, ) # print("max difference: ", torch.max(torch.abs(ref - triton_out))) - assert torch.allclose( - ref, triton_out, atol=1e-2), "Output mismatch between Triton and reference implementation" + assert torch.allclose(ref, triton_out, atol=1e-2), "Output mismatch between Triton and reference implementation" print("Passed the ref test!") # Measure performance @@ -448,15 +406,13 @@ def main(batch=64, if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=64, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--heads_kv', type=int, default=8, help='heads_kv') - parser.add_argument( - '--max_cache_seqlen', type=int, default=8192, help='kvcache sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--dim_v', type=int, default=128, help='dim_v') - parser.add_argument('--sparse_ratio', type=float, default=0.8, help='sparse ratio') - parser.add_argument('--block_size', type=int, default=32, help='block_size') + parser.add_argument("--batch", type=int, default=64, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--heads_kv", type=int, default=8, help="heads_kv") + parser.add_argument("--max_cache_seqlen", type=int, default=8192, help="kvcache sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--dim_v", type=int, default=128, help="dim_v") + parser.add_argument("--sparse_ratio", type=float, default=0.8, help="sparse ratio") + parser.add_argument("--block_size", type=int, default=32, help="block_size") args = parser.parse_args() - main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, - args.sparse_ratio, args.block_size) + main(args.batch, args.heads, args.heads_kv, args.max_cache_seqlen, args.dim, args.dim_v, args.sparse_ratio, args.block_size) diff --git a/examples/blocksparse_attention/heuristic.py b/examples/blocksparse_attention/heuristic.py index b60a81dc35..0e6fc52819 100644 --- a/examples/blocksparse_attention/heuristic.py +++ b/examples/blocksparse_attention/heuristic.py @@ -1,8 +1,7 @@ import math -def num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, num_m_blocks, size_one_kv_head, - is_causal_or_local, max_splits): +def num_splits_heuristic(total_mblocks, num_SMs, num_n_blocks, num_m_blocks, size_one_kv_head, is_causal_or_local, max_splits): """ Determines the optimal number of splits for maximizing GPU occupancy while balancing memory efficiency. diff --git a/examples/blocksparse_attention/regression_example_blocksparse_attention.py b/examples/blocksparse_attention/regression_example_blocksparse_attention.py new file mode 100644 index 0000000000..26fa60df50 --- /dev/null +++ b/examples/blocksparse_attention/regression_example_blocksparse_attention.py @@ -0,0 +1,20 @@ +import tilelang.testing +import example_tilelang_block_sparse_attn +import example_tilelang_sparse_gqa_decode_varlen_indice +import example_tilelang_sparse_gqa_decode_varlen_mask + + +def regression_example_tilelang_block_sparse_attn(): + tilelang.testing.process_func(example_tilelang_block_sparse_attn.run_regression_perf) + + +def regression_example_tilelang_sparse_gqa_decode_varlen_indice(): + tilelang.testing.process_func(example_tilelang_sparse_gqa_decode_varlen_indice.run_regression_perf, batch=1, max_cache_seqlen=2048) + + +def regression_example_tilelang_sparse_gqa_decode_varlen_mask(): + tilelang.testing.process_func(example_tilelang_sparse_gqa_decode_varlen_mask.run_regression_perf, batch=1, max_cache_seqlen=2048) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/blocksparse_attention/test_example_blocksparse_attention.py b/examples/blocksparse_attention/test_example_blocksparse_attention.py index 88527f7b3d..dd33f46c4e 100644 --- a/examples/blocksparse_attention/test_example_blocksparse_attention.py +++ b/examples/blocksparse_attention/test_example_blocksparse_attention.py @@ -25,26 +25,14 @@ def test_example_tilelang_sparse_gqa_decode_varlen_mask(): def test_example_triton_sparse_gqa_decode_varlen_indice(): example_triton_sparse_gqa_decode_varlen_indice.main( - batch=16, - heads=16, - heads_kv=8, - max_cache_seqlen=4096, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32) + batch=8, heads=8, heads_kv=4, max_cache_seqlen=2048, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32 + ) def test_example_triton_sparse_gqa_decode_varlen_mask(): example_triton_sparse_gqa_decode_varlen_mask.main( - batch=16, - heads=16, - heads_kv=8, - max_cache_seqlen=4096, - dim=128, - dim_v=128, - sparse_ratio=0.8, - block_size=32) + batch=16, heads=16, heads_kv=8, max_cache_seqlen=1024, dim=128, dim_v=128, sparse_ratio=0.8, block_size=32 + ) if __name__ == "__main__": diff --git a/examples/blocksparse_gemm/example_blocksparse_gemm.py b/examples/blocksparse_gemm/example_blocksparse_gemm.py index 7b9cff7c12..178cc59842 100644 --- a/examples/blocksparse_gemm/example_blocksparse_gemm.py +++ b/examples/blocksparse_gemm/example_blocksparse_gemm.py @@ -6,6 +6,7 @@ from tilelang.utils.tensor import get_tensor_supply, TensorSupplyType import torch from typing import List +from tilelang.profiler import do_bench DEFAULT_BLOCK_M = 128 DEFAULT_BLOCK_N = 128 @@ -19,8 +20,7 @@ parser.add_argument("--n", type=int, default=1024, help="Matrix dimension N") parser.add_argument("--k", type=int, default=1024, help="Matrix dimension K") parser.add_argument("--sparsity", type=float, default=0.5, help="Sparsity ratio (0-1)") -parser.add_argument( - "--use_autotune", action="store_true", default=False, help="Whether to use autotune") +parser.add_argument("--use_autotune", action="store_true", default=False, help="Whether to use autotune") args, _ = parser.parse_known_args() M, N, K = args.m, args.n, args.k @@ -41,17 +41,19 @@ def get_configs(): thread_num = [128, 256] enable_rasterization = [True, False] - _configs = list( - itertools.product(block_M, block_N, block_K, num_stages, thread_num, enable_rasterization)) + _configs = list(itertools.product(block_M, block_N, block_K, num_stages, thread_num, enable_rasterization)) - return [{ - "block_M": c[0], - "block_N": c[1], - "block_K": c[2], - "num_stages": c[3], - "thread_num": c[4], - "enable_rasteration": c[5], - } for c in _configs] + return [ + { + "block_M": c[0], + "block_N": c[1], + "block_K": c[2], + "num_stages": c[3], + "thread_num": c[4], + "enable_rasteration": c[5], + } + for c in _configs + ] def ref_program(A, B, BlockMask, block_M, block_N, block_K): @@ -61,12 +63,10 @@ def ref_program(A, B, BlockMask, block_M, block_N, block_K): accu = torch.zeros((block_M, block_N), dtype=torch.float32, device=A.device) for k in range(K // block_K): if BlockMask[i, j, k]: - accu += ( - A[i * block_M:(i + 1) * block_M, k * block_K:(k + 1) * block_K].to( - torch.float32) @ B[k * block_K:(k + 1) * block_K, - j * block_N:(j + 1) * block_N].to(torch.float32)) - ref_c[i * block_M:(i + 1) * block_M, - j * block_N:(j + 1) * block_N] = accu.to(torch.float16) + accu += A[i * block_M : (i + 1) * block_M, k * block_K : (k + 1) * block_K].to(torch.float32) @ B[ + k * block_K : (k + 1) * block_K, j * block_N : (j + 1) * block_N + ].to(torch.float32) + ref_c[i * block_M : (i + 1) * block_M, j * block_N : (j + 1) * block_N] = accu.to(torch.float16) return ref_c @@ -89,28 +89,21 @@ def supply_program(params: List[KernelParam]): return input_tensors -@tilelang.autotune(configs=get_configs(),) +@tilelang.autotune( + configs=get_configs(), +) @tilelang.jit(out_idx=[-1]) -def blocksparse_matmul(M, - N, - K, - block_M, - block_N, - block_K, - num_stages, - thread_num, - enable_rasteration, - dtype="float16", - accum_dtype="float"): - +def blocksparse_matmul( + M, N, K, block_M, block_N, block_K, num_stages, thread_num, enable_rasteration, dtype=T.float16, accum_dtype=T.float32 +): block_mask_shape = (M // block_M, N // block_N, K // block_K) @T.prim_func def block_sparse_matmul( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - BlockMask: T.Tensor(block_mask_shape, "bool"), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + BlockMask: T.Tensor(block_mask_shape, "bool"), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -134,7 +127,6 @@ def block_sparse_matmul( def main(): - # Initialize input matrices A and B on the GPU with half precision a = torch.randn(M, K).cuda().half() b = torch.randn(K, N).cuda().half() @@ -147,8 +139,7 @@ def main(): best_config = kernel.config best_latency = kernel.latency - block_M, block_N, block_K = best_config["block_M"], best_config["block_N"], best_config[ - "block_K"] + block_M, block_N, block_K = best_config["block_M"], best_config["block_N"], best_config["block_K"] print(f"Best Config: {best_config}") print(f"Sparsity Ratio: {sparsity}") @@ -163,10 +154,10 @@ def main(): block_K=DEFAULT_BLOCK_K, num_stages=DEFAULT_NUM_STAGES, thread_num=DEFAULT_THREAD_NUM, - enable_rasteration=DEFAULT_ENABLE_RASTERIZATION) + enable_rasteration=DEFAULT_ENABLE_RASTERIZATION, + ) block_M, block_N, block_K = DEFAULT_BLOCK_M, DEFAULT_BLOCK_N, DEFAULT_BLOCK_K print(f"Using default kernel with block size ({block_M}, {block_N}, {block_K})") - # Create block mask with desired sparsity mask_shape = (M // block_M, N // block_N, K // block_K) block_mask = torch.rand(mask_shape).cuda() > sparsity @@ -185,5 +176,32 @@ def main(): print(e) +def run_regression_perf(): + torch.manual_seed(42) + torch.cuda.manual_seed_all(42) + a = torch.randn(M, K).cuda().half() + b = torch.randn(K, N).cuda().half() + + kernel = blocksparse_matmul( + M, + N, + K, + block_M=DEFAULT_BLOCK_M, + block_N=DEFAULT_BLOCK_N, + block_K=DEFAULT_BLOCK_K, + num_stages=DEFAULT_NUM_STAGES, + thread_num=DEFAULT_THREAD_NUM, + enable_rasteration=DEFAULT_ENABLE_RASTERIZATION, + ) + block_M, block_N, block_K = DEFAULT_BLOCK_M, DEFAULT_BLOCK_N, DEFAULT_BLOCK_K + mask_shape = (M // block_M, N // block_N, K // block_K) + block_mask = torch.rand(mask_shape).cuda() > sparsity + + def run_kernel_only(): + kernel(a, b, block_mask) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/blocksparse_gemm/regression_example_blocksparse_gemm.py b/examples/blocksparse_gemm/regression_example_blocksparse_gemm.py new file mode 100644 index 0000000000..81900a00cc --- /dev/null +++ b/examples/blocksparse_gemm/regression_example_blocksparse_gemm.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_blocksparse_gemm + + +def regression_example_blocksparse_gemm(): + tilelang.testing.process_func(example_blocksparse_gemm.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/cast/example_group_per_split_token_cast_to_fp8.py b/examples/cast/example_group_per_split_token_cast_to_fp8.py index 4c2f574c06..db6beab1e5 100644 --- a/examples/cast/example_group_per_split_token_cast_to_fp8.py +++ b/examples/cast/example_group_per_split_token_cast_to_fp8.py @@ -5,8 +5,8 @@ from tilelang.utils.tensor import torch_assert_close # support bfloat16, float, float16 -dtype = "bfloat16" -accum_dtype = "float" +dtype = T.bfloat16 +accum_dtype = T.float32 @tilelang.jit(out_idx=[2, 3]) @@ -16,11 +16,13 @@ def group_per_split_token_cast_to_fp8(M, M_max, N, BG, blk_m): fp8_max = 448.0 @T.prim_func - def group_per_split_token_cast(X: T.Tensor((M, N), dtype), batch_sizes: T.Tensor( - (BG,), "int32"), X_fp8: T.Tensor((BG, M_max, N), "float8_e4m3"), X_amax: T.Tensor( - (BG, M_max, T.ceildiv(N, group_size)), accum_dtype)): - with T.Kernel( - T.ceildiv(M_max, blk_m), T.ceildiv(N, group_size), BG, threads=128) as (bx, by, bz): + def group_per_split_token_cast( + X: T.Tensor((M, N), dtype), + batch_sizes: T.Tensor((BG,), T.int32), + X_fp8: T.Tensor((BG, M_max, N), T.float8_e4m3fn), + X_amax: T.Tensor((BG, M_max, T.ceildiv(N, group_size)), accum_dtype), + ): + with T.Kernel(T.ceildiv(M_max, blk_m), T.ceildiv(N, group_size), BG, threads=128) as (bx, by, bz): row = bx row_g_id = by bg = bz @@ -28,39 +30,29 @@ def group_per_split_token_cast(X: T.Tensor((M, N), dtype), batch_sizes: T.Tensor y_amax_local = T.alloc_fragment((blk_m,), accum_dtype) y_s_local = T.alloc_fragment((blk_m,), accum_dtype) y_q_local = T.alloc_fragment((blk_m, group_size), accum_dtype) - y_q_local_fp8 = T.alloc_fragment((blk_m, group_size), "float8_e4m3") - row_offset = T.alloc_fragment((1,), "int32") + y_q_local_fp8 = T.alloc_fragment((blk_m, group_size), T.float8_e4m3fn) + row_offset = T.alloc_var(dtype=T.int32) - T.annotate_layout({ - y_local: - T.Fragment( - y_local.shape, - forward_thread_fn=lambda i, j: (i // (blk_m // 4)) * 32 + j % 32), - }) - - row_offset[0] = 0 + row_offset = 0 for i in T.serial(bg): - row_offset[0] += batch_sizes[i] + row_offset += batch_sizes[i] T.copy( - X[row_offset[0] + row * blk_m:row_offset[0] + (row + 1) * blk_m, - row_g_id * group_size:(row_g_id + 1) * group_size], y_local) + X[row_offset + row * blk_m : row_offset + (row + 1) * blk_m, row_g_id * group_size : (row_g_id + 1) * group_size], + y_local, + ) T.reduce_absmax(y_local, y_amax_local, dim=1) for i in T.Parallel(blk_m): y_amax_local[i] = T.max(y_amax_local[i], 1e-4) - y_s_local[i] = T.if_then_else(row * blk_m + i < batch_sizes[bg], - y_amax_local[i] / fp8_max, 0) + y_s_local[i] = T.if_then_else(row * blk_m + i < batch_sizes[bg], y_amax_local[i] / fp8_max, 0) for i, j in T.Parallel(blk_m, group_size): y_q_local[i, j] = T.clamp(y_local[i, j] / y_s_local[i], fp8_min, fp8_max) T.copy(y_q_local, y_q_local_fp8) for i, j in T.Parallel(blk_m, group_size): - y_q_local_fp8[i, j] = T.if_then_else(row * blk_m + i < batch_sizes[bg], - y_q_local[i, j], 0) + y_q_local_fp8[i, j] = T.if_then_else(row * blk_m + i < batch_sizes[bg], y_q_local[i, j], 0) for i in T.Parallel(blk_m): X_amax[bg, row * blk_m + i, row_g_id] = y_s_local[i] - T.copy( - y_q_local_fp8, X_fp8[bg, row * blk_m:(row + 1) * blk_m, - row_g_id * group_size:(row_g_id + 1) * group_size]) + T.copy(y_q_local_fp8, X_fp8[bg, row * blk_m : (row + 1) * blk_m, row_g_id * group_size : (row_g_id + 1) * group_size]) return group_per_split_token_cast @@ -127,8 +119,7 @@ def get_col_major_tma_aligned_tensor(x: torch.Tensor) -> torch.Tensor: return x.squeeze(0) if remove_dim else x # Normal layout requires transposing - aligned_x = torch.transpose( - torch.empty((b, n, aligned_m), device=x.device, dtype=x.dtype), 1, 2) + aligned_x = torch.transpose(torch.empty((b, n, aligned_m), device=x.device, dtype=x.dtype), 1, 2) aligned_x[:, :m, :] = x aligned_x = aligned_x[:, :m, :] return aligned_x.squeeze(0) if remove_dim else aligned_x @@ -146,31 +137,35 @@ def ref_per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tens x_fp8 = x_fp8.view(m, -1)[:, :n].contiguous() return x_fp8, (x_amax / 448.0).view(m, -1) -def ref_program(x: torch.Tensor, batch_sizes: torch.Tensor) -> \ - Tuple[torch.Tensor, torch.Tensor]: + +def ref_program(x: torch.Tensor, batch_sizes: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: # assert x.shape[0] == batch_sizes.sum() M_max = ceil_div(batch_sizes.max(), 128) * 128 split_x = torch.split(x, batch_sizes.tolist(), dim=0) padded_x = [torch.nn.functional.pad(t, (0, 0, 0, M_max - t.shape[0])) for t in split_x] num_groups, m, n = batch_sizes.shape[0], M_max, x.shape[1] - x_fp8 = (torch.empty((num_groups, m, n), device='cuda', dtype=torch.float8_e4m3fn), - torch.empty((num_groups, m, n // 128), device='cuda', dtype=torch.float)) + x_fp8 = ( + torch.empty((num_groups, m, n), device="cuda", dtype=torch.float8_e4m3fn), + torch.empty((num_groups, m, n // 128), device="cuda", dtype=torch.float), + ) for i in range(num_groups): x_fp8[0][i], x_fp8[1][i] = ref_per_token_cast_to_fp8(padded_x[i]) x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1])) return x_fp8 -def main(M=8192, N=8192, BG=2, blk_m=8): - if dtype == "float": +def main(M=8192, N=8192, BG=2, blk_m=8, batch_sizes=None): + if batch_sizes is None: + batch_sizes = [2048, 6144] + if dtype == T.float: x = torch.randn(M, N, device="cuda", dtype=torch.float32) - elif dtype == "float16": + elif dtype == T.float16: x = torch.randn(M, N, device="cuda", dtype=torch.float16) - elif dtype == "bfloat16": + elif dtype == T.bfloat16: x = torch.randn(M, N, device="cuda", dtype=torch.bfloat16) else: raise ValueError(f"Unsupported dtype: {dtype}") - batch_sizes = torch.tensor([2048, 6144], device="cuda", dtype=torch.int32) + batch_sizes = torch.tensor(batch_sizes, device="cuda", dtype=torch.int32) M_max = int(ceil_div(batch_sizes.max(), 128) * 128) print("batch_sizes:", batch_sizes) @@ -204,5 +199,35 @@ def run_torch(): print("Torch: {:.2f} ms".format(latency)) +def run_regression_perf(M=8192, N=8192, BG=2, blk_m=8, batch_sizes=None): + if batch_sizes is None: + batch_sizes = [2048, 6144] + if dtype == "float": + x = torch.randn(M, N, device="cuda", dtype=torch.float32) + elif dtype == "float16": + x = torch.randn(M, N, device="cuda", dtype=torch.float16) + elif dtype == "bfloat16": + x = torch.randn(M, N, device="cuda", dtype=torch.bfloat16) + else: + raise ValueError(f"Unsupported dtype: {dtype}") + batch_sizes = torch.tensor(batch_sizes, device="cuda", dtype=torch.int32) + M_max = int(ceil_div(batch_sizes.max(), 128) * 128) + + kernel = group_per_split_token_cast_to_fp8(M, M_max, N, BG, blk_m) + + x_fp8, x_amax = kernel(x, batch_sizes) + x_fp8_ref, x_amax_ref = ref_program(x, batch_sizes) + + torch_assert_close(x_fp8.to(torch.float32), x_fp8_ref.to(torch.float32), rtol=0.01, atol=0.01) + torch_assert_close(x_amax, x_amax_ref, rtol=0.01, atol=0.01) + + from tilelang.profiler import do_bench + + def run_tilelang(): + kernel(x, batch_sizes) + + return do_bench(run_tilelang, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/cast/example_per_token_cast_to_fp8.py b/examples/cast/example_per_token_cast_to_fp8.py index 484a092f09..4b3730b4b9 100644 --- a/examples/cast/example_per_token_cast_to_fp8.py +++ b/examples/cast/example_per_token_cast_to_fp8.py @@ -7,14 +7,15 @@ @tilelang.jit(out_idx=[1, 2]) def per_token_cast_to_fp8(M, N, blk_m): - dtype = "float" + dtype = T.float group_size = 128 fp8_min = -448.0 fp8_max = 448.0 @T.prim_func - def per_token_cast(X: T.Tensor((M, N), dtype), X_fp8: T.Tensor((M, N), "float8_e4m3"), - X_amax: T.Tensor((M, T.ceildiv(N, group_size)), dtype)): + def per_token_cast( + X: T.Tensor((M, N), dtype), X_fp8: T.Tensor((M, N), T.float8_e4m3fn), X_amax: T.Tensor((M, T.ceildiv(N, group_size)), dtype) + ): with T.Kernel(T.ceildiv(M, blk_m), T.ceildiv(N, group_size), threads=128) as (bx, by): row = bx row_g_id = by @@ -22,18 +23,9 @@ def per_token_cast(X: T.Tensor((M, N), dtype), X_fp8: T.Tensor((M, N), "float8_e y_amax_local = T.alloc_fragment((blk_m,), dtype) y_s_local = T.alloc_fragment((blk_m,), dtype) y_q_local = T.alloc_fragment((blk_m, group_size), dtype) - y_q_local_fp8 = T.alloc_fragment((blk_m, group_size), "float8_e4m3") - - T.annotate_layout({ - y_local: - T.Fragment( - y_local.shape, - forward_thread_fn=lambda i, j: (i // (blk_m // 4)) * 32 + j % 32), - }) - - T.copy( - X[row * blk_m:(row + 1) * blk_m, row_g_id * group_size:(row_g_id + 1) * group_size], - y_local) + y_q_local_fp8 = T.alloc_fragment((blk_m, group_size), T.float8_e4m3fn) + + T.copy(X[row * blk_m : (row + 1) * blk_m, row_g_id * group_size : (row_g_id + 1) * group_size], y_local) T.reduce_absmax(y_local, y_amax_local, dim=1) for i in T.Parallel(blk_m): y_amax_local[i] = T.max(y_amax_local[i], 1e-4) @@ -43,9 +35,7 @@ def per_token_cast(X: T.Tensor((M, N), dtype), X_fp8: T.Tensor((M, N), "float8_e T.copy(y_q_local, y_q_local_fp8) for i in T.Parallel(blk_m): X_amax[row * blk_m + i, row_g_id] = y_s_local[i] - T.copy( - y_q_local_fp8, X_fp8[row * blk_m:(row + 1) * blk_m, - row_g_id * group_size:(row_g_id + 1) * group_size]) + T.copy(y_q_local_fp8, X_fp8[row * blk_m : (row + 1) * blk_m, row_g_id * group_size : (row_g_id + 1) * group_size]) return per_token_cast @@ -102,16 +92,32 @@ def main(M=8192, N=8192, blk_m=8): print("Tile-lang: {:.2f} ms".format(latency)) from tilelang.profiler import do_bench - from example_triton_cast_to_fp8 import per_token_group_quant_fp8 - def run_triton(): - x_fp8_triton_, x_amax_triton_ = per_token_group_quant_fp8( - x, 128, 1e-4, dtype=torch.float8_e4m3fn, column_major_scales=False) - return x_fp8_triton_, x_amax_triton_ + # Triton fp8e4nv is only supported on Hopper (SM90) and later + major, _ = torch.cuda.get_device_capability() + if major >= 9: + from example_triton_cast_to_fp8 import per_token_group_quant_fp8 + + def run_triton(): + x_fp8_triton_, x_amax_triton_ = per_token_group_quant_fp8(x, 128, 1e-4, dtype=torch.float8_e4m3fn, column_major_scales=False) + return x_fp8_triton_, x_amax_triton_ + + x_fp8_triton, x_amax_triton = run_triton() + latency = do_bench(run_triton) + print("Triton: {:.2f} ms".format(latency)) + else: + print("Triton fp8e4nv benchmark skipped (requires SM90+)") + + +def run_regression_perf(M=8192, N=8192, blk_m=8): + kernel = per_token_cast_to_fp8(M, N, blk_m) + x = torch.randn(M, N, device="cuda", dtype=torch.float32) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(x) - x_fp8_triton, x_amax_triton = run_triton() - latency = do_bench(run_triton) - print("Triton: {:.2f} ms".format(latency)) + return do_bench(run_kernel_only, backend="cupti") if __name__ == "__main__": diff --git a/examples/cast/example_triton_cast_to_fp8.py b/examples/cast/example_triton_cast_to_fp8.py index cc56defe77..1859433f10 100644 --- a/examples/cast/example_triton_cast_to_fp8.py +++ b/examples/cast/example_triton_cast_to_fp8.py @@ -128,9 +128,7 @@ def per_token_group_quant_fp8( Tuple[torch.Tensor, torch.Tensor]: The quantized tensor and the scaling factor for quantization. """ - assert (x.shape[-1] % - group_size == 0), (f"the last dimension of `x` {x.shape[-1]} must be divisible " - f"by `group_size` {group_size}") + assert x.shape[-1] % group_size == 0, f"the last dimension of `x` {x.shape[-1]} must be divisible by `group_size` {group_size}" assert x.stride(-1) == 1, "`x` groups must be contiguous" finfo = torch.finfo(dtype) diff --git a/examples/cast/regression_example_cast.py b/examples/cast/regression_example_cast.py new file mode 100644 index 0000000000..4bdfb99e77 --- /dev/null +++ b/examples/cast/regression_example_cast.py @@ -0,0 +1,17 @@ +import tilelang.testing +import example_group_per_split_token_cast_to_fp8 +import example_per_token_cast_to_fp8 + + +def regression_example_group_per_split_token_cast_to_fp8(): + tilelang.testing.process_func( + example_group_per_split_token_cast_to_fp8.run_regression_perf, M=1024, N=1024, BG=2, blk_m=4, batch_sizes=[128, 896] + ) + + +def regression_example_per_token_cast_to_fp8(): + tilelang.testing.process_func(example_per_token_cast_to_fp8.run_regression_perf, M=2048, N=512, blk_m=8) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/cast/test_example_cast.py b/examples/cast/test_example_cast.py index 2f978c1d45..e8b10a7979 100644 --- a/examples/cast/test_example_cast.py +++ b/examples/cast/test_example_cast.py @@ -4,11 +4,11 @@ def test_example_group_per_split_token_cast_to_fp8(): - example_group_per_split_token_cast_to_fp8.main(M=8192, N=2048, BG=2, blk_m=8) + example_group_per_split_token_cast_to_fp8.main(M=1024, N=1024, BG=2, blk_m=4, batch_sizes=[128, 896]) def test_example_per_token_cast_to_fp8(): - example_per_token_cast_to_fp8.main(M=8192, N=2048, blk_m=8) + example_per_token_cast_to_fp8.main(M=2048, N=512, blk_m=8) if __name__ == "__main__": diff --git a/examples/compile_flags/usecase.py b/examples/compile_flags/usecase.py index 8451b04fcf..80e2b784b2 100644 --- a/examples/compile_flags/usecase.py +++ b/examples/compile_flags/usecase.py @@ -4,12 +4,11 @@ # @tilelang.jit(compile_flags=["-O3", "--use_fast_math", "--expt-relaxed-constexpr"]) def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): @@ -36,8 +35,7 @@ def main( func = matmul(M, N, K, block_M, block_N, block_K) -jit_kernel = tilelang.compile( - func, out_idx=[2], target="cuda", compile_flags="-O3 --use_fast_math --expt-relaxed-constexpr") +jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda", compile_flags="-O3 --use_fast_math --expt-relaxed-constexpr") # or jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda", compile_flags=["-O3", "--use_fast_math", "--expt-relaxed-constexpr"]) # or jit_kernel = tilelang.compile(func, out_idx=[2], target="cuda", compile_flags=["-O3 --use_fast_math --expt-relaxed-constexpr"]) diff --git a/examples/conftest.py b/examples/conftest.py index 9f49d40a9b..4010e0d83a 100644 --- a/examples/conftest.py +++ b/examples/conftest.py @@ -33,12 +33,9 @@ def pytest_terminal_summary(terminalreporter, exitstatus, config): "warnings", "error", } - if (sum( - len(terminalreporter.stats.get(k, [])) - for k in known_types.difference({"skipped", "deselected"})) == 0): + if sum(len(terminalreporter.stats.get(k, [])) for k in known_types.difference({"skipped", "deselected"})) == 0: terminalreporter.write_sep( "!", - (f"Error: No tests were collected. " - f"{dict(sorted((k, len(v)) for k, v in terminalreporter.stats.items()))}"), + (f"Error: No tests were collected. {dict(sorted((k, len(v)) for k, v in terminalreporter.stats.items()))}"), ) pytest.exit("No tests were collected.", returncode=5) diff --git a/examples/convolution/example_convolution.py b/examples/convolution/example_convolution.py index b2696ba8f5..1599d3464f 100644 --- a/examples/convolution/example_convolution.py +++ b/examples/convolution/example_convolution.py @@ -14,7 +14,6 @@ def check_hopper(): def ref_program(stride, padding, dilation): - def main(A, B): A = A.permute(0, 3, 1, 2) # N, H, W, C -> N, C, H, W B = B.permute(3, 2, 0, 1) # H, W, C, F -> F, C, H, W @@ -26,38 +25,21 @@ def main(A, B): @tilelang.jit(out_idx=[2]) -def convolution(N, - C, - H, - W, - F, - K, - S, - D, - P, - block_M, - block_N, - block_K, - num_stages, - threads, - dtype="float16", - accum_dtype="float"): +def convolution(N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, threads, dtype=T.float16, accum_dtype=T.float32): KH, KW = K, K OH = (H + 2 * P - D * (K - 1) - 1) // S + 1 OW = (W + 2 * P - D * (K - 1) - 1) // S + 1 - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 is_hopper = check_hopper() @T.prim_func def main( - data: T.Tensor((N, H, W, C), dtype), - kernel: T.Tensor((KH, KW, C, F), dtype), - out: T.Tensor((N, OH, OW, F), dtype), + data: T.Tensor((N, H, W, C), dtype), + kernel: T.Tensor((KH, KW, C, F), dtype), + out: T.Tensor((N, OH, OW, F), dtype), ): - with T.Kernel( - T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), - threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=threads) as (bx, by): data_shared = T.alloc_shared((block_M, block_K), dtype) kernel_shared = T.alloc_shared((block_K, block_N), dtype) out_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -66,12 +48,6 @@ def main( kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data) out_flat = T.Tensor((N * OH * OW, F), dtype, out.data) - T.annotate_layout({ - out_shared: tilelang.layout.make_swizzled_layout(out_shared), - data_shared: tilelang.layout.make_swizzled_layout(data_shared), - kernel_shared: tilelang.layout.make_swizzled_layout(kernel_shared), - }) - T.clear(out_local) for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages): if is_hopper: @@ -82,10 +58,8 @@ def main( m = by * block_M + i access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P access_w = m % OW * S + k // C % KW * D - P - in_bound = ((access_h >= 0) and (access_w >= 0) and (access_h < H) and - (access_w < W)) - data_shared[i, j] = T.if_then_else( - in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) + in_bound = (access_h >= 0) and (access_w >= 0) and (access_h < H) and (access_w < W) + data_shared[i, j] = T.if_then_else(in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared) T.gemm(data_shared, kernel_shared, out_local) @@ -97,15 +71,15 @@ def main( def main(argv=None): parser = argparse.ArgumentParser() - parser.add_argument('--n', type=int, default=128, help='n') - parser.add_argument('--c', type=int, default=128, help='c') - parser.add_argument('--h', type=int, default=64, help='h') - parser.add_argument('--w', type=int, default=64, help='w') - parser.add_argument('--f', type=int, default=128, help='f') - parser.add_argument('--k', type=int, default=3, help='k') - parser.add_argument('--s', type=int, default=1, help='s') - parser.add_argument('--d', type=int, default=1, help='d') - parser.add_argument('--p', type=int, default=1, help='p') + parser.add_argument("--n", type=int, default=128, help="n") + parser.add_argument("--c", type=int, default=128, help="c") + parser.add_argument("--h", type=int, default=64, help="h") + parser.add_argument("--w", type=int, default=64, help="w") + parser.add_argument("--f", type=int, default=128, help="f") + parser.add_argument("--k", type=int, default=3, help="k") + parser.add_argument("--s", type=int, default=1, help="s") + parser.add_argument("--d", type=int, default=1, help="d") + parser.add_argument("--p", type=int, default=1, help="p") args = parser.parse_args(argv) N, C, H, W, F, K, S, D, P = args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p @@ -125,5 +99,30 @@ def main(argv=None): print("All checks passed.✅") +def run_regression_perf(argv=None): + parser = argparse.ArgumentParser() + parser.add_argument("--n", type=int, default=128, help="n") + parser.add_argument("--c", type=int, default=128, help="c") + parser.add_argument("--h", type=int, default=64, help="h") + parser.add_argument("--w", type=int, default=64, help="w") + parser.add_argument("--f", type=int, default=128, help="f") + parser.add_argument("--k", type=int, default=3, help="k") + parser.add_argument("--s", type=int, default=1, help="s") + parser.add_argument("--d", type=int, default=1, help="d") + parser.add_argument("--p", type=int, default=1, help="p") + + args = parser.parse_args(argv) + N, C, H, W, F, K, S, D, P = args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p + + block_m = 64 + block_n = 128 + block_k = 32 + num_stages = 3 + threads = 256 + kernel = convolution(N, C, H, W, F, K, S, D, P, block_m, block_n, block_k, num_stages, threads) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/convolution/example_convolution_autotune.py b/examples/convolution/example_convolution_autotune.py index 393677489b..c0c666402a 100644 --- a/examples/convolution/example_convolution_autotune.py +++ b/examples/convolution/example_convolution_autotune.py @@ -14,7 +14,6 @@ def check_hopper(): def ref_program(stride, padding, dilation): - def main(A, B): A = A.permute(0, 3, 1, 2) # N, H, W, C -> N, C, H, W B = B.permute(3, 2, 0, 1) # H, W, C, F -> F, C, H, W @@ -40,7 +39,8 @@ def get_configs(): num_stages, thread_num, enable_rasterization, - )) + ) + ) configs = [ { @@ -50,7 +50,8 @@ def get_configs(): "num_stages": c[3], "thread_num": c[4], "enable_rasteration": c[5], # keep param name for backward-compat - } for c in _configs + } + for c in _configs ] return configs @@ -64,69 +65,32 @@ def get_heuristic_config() -> dict: sm_version = sm_major * 10 + sm_minor print(f"CUDA device capability: {sm_version}") if sm_version in {80}: - return { - "block_M": 128, - "block_N": 256, - "block_K": 32, - "num_stages": 2, - "thread_num": 128, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 2, "thread_num": 128, "enable_rasteration": True} elif sm_version in {90}: - return { - "block_M": 128, - "block_N": 256, - "block_K": 64, - "num_stages": 3, - "thread_num": 256, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 64, "num_stages": 3, "thread_num": 256, "enable_rasteration": True} else: - return { - "block_M": 128, - "block_N": 256, - "block_K": 32, - "num_stages": 0, - "thread_num": 128, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 0, "thread_num": 128, "enable_rasteration": True} @tilelang.autotune(configs=get_configs()) @tilelang.jit(out_idx=[2]) -def convolution(N, - C, - H, - W, - F, - K, - S, - D, - P, - block_M, - block_N, - block_K, - num_stages, - thread_num, - enable_rasteration, - dtype="float16", - accum_dtype="float"): +def convolution( + N, C, H, W, F, K, S, D, P, block_M, block_N, block_K, num_stages, thread_num, enable_rasteration, dtype=T.float16, accum_dtype=T.float32 +): KH, KW = K, K OH = (H + 2 * P - D * (K - 1) - 1) // S + 1 OW = (W + 2 * P - D * (K - 1) - 1) // S + 1 - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 is_hopper = check_hopper() @T.prim_func def main( - data: T.Tensor((N, H, W, C), dtype), - kernel: T.Tensor((KH, KW, C, F), dtype), - out: T.Tensor((N, OH, OW, F), dtype), + data: T.Tensor((N, H, W, C), dtype), + kernel: T.Tensor((KH, KW, C, F), dtype), + out: T.Tensor((N, OH, OW, F), dtype), ): - with T.Kernel( - T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), - threads=thread_num) as (bx, by): + with T.Kernel(T.ceildiv(F, block_N), T.ceildiv(N * OH * OW, block_M), threads=thread_num) as (bx, by): data_shared = T.alloc_shared((block_M, block_K), dtype) kernel_shared = T.alloc_shared((block_K, block_N), dtype) out_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -135,11 +99,6 @@ def main( kernel_flat = T.Tensor((KH * KW * C, F), dtype, kernel.data) out_flat = T.Tensor((N * OH * OW, F), dtype, out.data) - if is_hopper: - T.annotate_layout({ - out_shared: tilelang.layout.make_swizzled_layout(out_shared), - }) - T.clear(out_local) for k_iter in T.Pipelined(T.ceildiv(KH * KW * C, block_K), num_stages=num_stages): if is_hopper: @@ -150,10 +109,8 @@ def main( m = by * block_M + i access_h = m % (OH * OW) // OW * S + k // (KW * C) * D - P access_w = m % OW * S + k // C % KW * D - P - in_bound = ((access_h >= 0) and (access_w >= 0) and (access_h < H) and - (access_w < W)) - data_shared[i, j] = T.if_then_else( - in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) + in_bound = (access_h >= 0) and (access_w >= 0) and (access_h < H) and (access_w < W) + data_shared[i, j] = T.if_then_else(in_bound, data[m // (OH * OW), access_h, access_w, k % C], 0) T.copy(kernel_flat[k_iter * block_K, bx * block_N], kernel_shared) T.gemm(data_shared, kernel_shared, out_local) @@ -166,17 +123,19 @@ def main( return main -def main(n: int = 128, - c: int = 128, - h: int = 64, - w: int = 64, - f: int = 128, - k: int = 3, - s: int = 1, - d: int = 1, - p: int = 1, - use_autotune: bool = False, - with_roller: bool = True): +def main( + n: int = 128, + c: int = 128, + h: int = 64, + w: int = 64, + f: int = 128, + k: int = 3, + s: int = 1, + d: int = 1, + p: int = 1, + use_autotune: bool = False, + with_roller: bool = True, +): N, C, H, W, F, K, S, D, P = n, c, h, w, f, k, s, d, p ref_prog = ref_program(S, P, D) @@ -194,27 +153,38 @@ def main(n: int = 128, print(f"Ref latency: {ref_latency}") +def run_regression_perf( + n: int = 128, + c: int = 128, + h: int = 64, + w: int = 64, + f: int = 128, + k: int = 3, + s: int = 1, + d: int = 1, + p: int = 1, + use_autotune: bool = False, + with_roller: bool = True, +): + N, C, H, W, F, K, S, D, P = n, c, h, w, f, k, s, d, p + config = get_heuristic_config() + kernel = convolution(N, C, H, W, F, K, S, D, P, **config) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") - parser.add_argument('--n', type=int, default=128, help='n') - parser.add_argument('--c', type=int, default=128, help='c') - parser.add_argument('--h', type=int, default=64, help='h') - parser.add_argument('--w', type=int, default=64, help='w') - parser.add_argument('--f', type=int, default=128, help='f') - parser.add_argument('--k', type=int, default=3, help='k') - parser.add_argument('--s', type=int, default=1, help='s') - parser.add_argument('--d', type=int, default=1, help='d') - parser.add_argument('--p', type=int, default=1, help='p') - parser.add_argument( - "--use_autotune", - action="store_true", - default=False, - help="Whether to use autotune for matmul configs") - parser.add_argument( - "--with_roller", - action="store_true", - default=True, - help="Whether to enable BitBLAS roller for search space") + parser.add_argument("--n", type=int, default=128, help="n") + parser.add_argument("--c", type=int, default=128, help="c") + parser.add_argument("--h", type=int, default=64, help="h") + parser.add_argument("--w", type=int, default=64, help="w") + parser.add_argument("--f", type=int, default=128, help="f") + parser.add_argument("--k", type=int, default=3, help="k") + parser.add_argument("--s", type=int, default=1, help="s") + parser.add_argument("--d", type=int, default=1, help="d") + parser.add_argument("--p", type=int, default=1, help="p") + parser.add_argument("--use_autotune", action="store_true", default=False, help="Whether to use autotune for matmul configs") + parser.add_argument("--with_roller", action="store_true", default=True, help="Whether to enable BitBLAS roller for search space") args = parser.parse_args() - main(args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p, args.use_autotune, - args.with_roller) + main(args.n, args.c, args.h, args.w, args.f, args.k, args.s, args.d, args.p, args.use_autotune, args.with_roller) diff --git a/examples/convolution/regression_example_convolution.py b/examples/convolution/regression_example_convolution.py new file mode 100644 index 0000000000..18d4bcb682 --- /dev/null +++ b/examples/convolution/regression_example_convolution.py @@ -0,0 +1,15 @@ +import tilelang.testing +import example_convolution +import example_convolution_autotune + + +def regression_example_convolution(): + tilelang.testing.process_func(example_convolution.run_regression_perf) + + +def regression_example_convolution_autotune(): + tilelang.testing.process_func(example_convolution_autotune.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/deepseek_deepgemm/example_deepgemm_fp8_2xAcc.py b/examples/deepseek_deepgemm/example_deepgemm_fp8_2xAcc.py index 715f09a9b1..18467a8118 100644 --- a/examples/deepseek_deepgemm/example_deepgemm_fp8_2xAcc.py +++ b/examples/deepseek_deepgemm/example_deepgemm_fp8_2xAcc.py @@ -20,11 +20,11 @@ def tl_gemm( accum_dtype, ): assert in_dtype in [ - "float8_e4m3", + T.float8_e4m3fn, ], "Currently only float8_e4m3 is supported" assert out_dtype in [ - "bfloat16", - "float32", + T.bfloat16, + T.float32, ], "Currently only float16 and float32 are supported" group_size = 128 @@ -41,18 +41,17 @@ def tl_gemm( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), - scales_a: T.Tensor(Scales_A_shape, "float32"), - scales_b: T.Tensor(Scales_B_shape, "float32"), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + scales_a: T.Tensor(Scales_A_shape, T.float32), + scales_b: T.Tensor(Scales_B_shape, T.float32), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, in_dtype) C_shared = T.alloc_shared(C_shared_shape, out_dtype) - Scale_C_shared = T.alloc_shared((block_M), "float32") + Scale_C_shared = T.alloc_shared((block_M), T.float32) C_local = T.alloc_fragment(C_shared_shape, accum_dtype) C_local_accum = T.alloc_fragment(C_shared_shape, accum_dtype) @@ -93,21 +92,18 @@ def per_token_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: m, n = x.shape x_view = x.view(m, -1, 128) x_amax = x_view.abs().float().amax(dim=2).view(m, -1).clamp(1e-4) - return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view( - m, n), (x_amax / 448.0).view(m, -1) + return (x_view * (448.0 / x_amax.unsqueeze(2))).to(torch.float8_e4m3fn).view(m, n), (x_amax / 448.0).view(m, -1) def per_block_cast_to_fp8(x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: assert x.dim() == 2 m, n = x.shape - x_padded = torch.zeros( - ceildiv(m, 128) * 128, ceildiv(n, 128) * 128, dtype=x.dtype, device=x.device) + x_padded = torch.zeros(ceildiv(m, 128) * 128, ceildiv(n, 128) * 128, dtype=x.dtype, device=x.device) x_padded[:m, :n] = x x_view = x_padded.view(-1, 128, x_padded.size(1) // 128, 128) x_amax = x_view.abs().float().amax(dim=(1, 3), keepdim=True).clamp(1e-4) x_scaled = (x_view * (448.0 / x_amax)).to(torch.float8_e4m3fn) - return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view( - x_view.size(0), x_view.size(2)) + return x_scaled.view_as(x_padded)[:m, :n].contiguous(), (x_amax / 448.0).view(x_view.size(0), x_view.size(2)) def ref_deepgemm_fp8(A_fp8, B_fp8, A_scale, B_scale, out_dtype): @@ -127,13 +123,14 @@ def ref_deepgemm_fp8(A_fp8, B_fp8, A_scale, B_scale, out_dtype): c_acc.zero_() for k in range(ceildiv(K, 128)): c = torch._scaled_mm( - A_fp8[i * 128:(i + 1) * 128, k * 128:(k + 1) * 128], - B_fp8[j * 128:(j + 1) * 128, k * 128:(k + 1) * 128].T, + A_fp8[i * 128 : (i + 1) * 128, k * 128 : (k + 1) * 128], + B_fp8[j * 128 : (j + 1) * 128, k * 128 : (k + 1) * 128].T, scale_a=A_scales[i, k].view(128, 1).contiguous(), scale_b=B_scales[j, k].view(1, 128).contiguous(), - out_dtype=torch.bfloat16) + out_dtype=torch.bfloat16, + ) c_acc += c.to(torch.float32) - C[i * 128:(i + 1) * 128, j * 128:(j + 1) * 128] = c_acc.to(out_dtype) + C[i * 128 : (i + 1) * 128, j * 128 : (j + 1) * 128] = c_acc.to(out_dtype) return C @@ -179,11 +176,11 @@ def assert_tl_gemm_correctness(M, N, K, block_N, in_dtype, out_dtype, accum_dtyp def main(): - assert_tl_gemm_correctness(1024, 1024, 8192, 128, "float8_e4m3", "bfloat16", "float32") + assert_tl_gemm_correctness(1024, 1024, 8192, 128, T.float8_e4m3fn, T.bfloat16, T.float32) if __name__ == "__main__": - for dtype in ["float8_e4m3"]: - for out_dtype in ["bfloat16", "float32"]: + for dtype in [T.float8_e4m3fn]: + for out_dtype in [T.bfloat16, T.float32]: for block_N in [16, 32, 64, 128]: - assert_tl_gemm_correctness(1024, 1024, 8192, block_N, dtype, out_dtype, "float32") + assert_tl_gemm_correctness(1024, 1024, 8192, block_N, dtype, out_dtype, T.float32) diff --git a/examples/deepseek_mla/README.md b/examples/deepseek_mla/README.md index e64b1c37d0..bd3539d269 100644 --- a/examples/deepseek_mla/README.md +++ b/examples/deepseek_mla/README.md @@ -24,14 +24,14 @@ We benchmarked the performance of FlashMLA, TileLang, Torch, Triton, and FlashIn
Figure 2：Performance under batch size=128
-As shown in the results, TileLang achieves performance comparable to FlashMLA in most cases, significantly outperforming both FlashInfer and Triton. +As shown in the results, TileLang achieves performance comparable to FlashMLA in most cases, significantly outperforming both FlashInfer and Triton. Notably, **TileLang accomplishes this with just around 80 lines of Python code**, demonstrating its exceptional ease of use and efficiency. Let's dive in and see how TileLang achieves this. ## Implementation First, let's review the core computation logic of traditional FlashAttention: -```python +```python # acc_s: [block_M, block_N] # scores_max: [block_M] # scores_scale: [block_M] @@ -54,7 +54,7 @@ Compared to traditional attention operators like MHA (Multi-Headed Attention) or This raises the question of how to partition the matrix multiplication operation. On the Hopper architecture, most computation kernels use [`wgmma.mma_async`](https://docs.nvidia.com/cuda/parallel-thread-execution/#asynchronous-warpgroup-level-matrix-instructions) instructions for optimal performance. The `wgmma.mma_async` instruction organizes 4 warps (128 threads) into a warpgroup for collective MMA operations. However, `wgmma.mma_async` instructions require a minimum M dimension of 64. This means each warpgroup's minimum M dimension can only be reduced to 64, but a tile size of 64*512 is too large for a single warpgroup, leading to register spilling. -Therefore, our only option is to partition `acc_o` along the `dim` dimension, with two warpgroups computing the left and right part of `acc_o` respectively. However, this introduces another challenge: both warpgroups require the complete `acc_s` result as input. +Therefore, our only option is to partition `acc_o` along the `dim` dimension, with two warpgroups computing the left and right part of `acc_o` respectively. However, this introduces another challenge: both warpgroups require the complete `acc_s` result as input. Our solution is to have each warpgroup compute half of `acc_s` during `Q @ K` computation, then obtain the other half computed by the other warpgroup through shared memory. @@ -96,7 +96,6 @@ T.use_swizzle(panel_size: int, order: str = "row") Here, `panel_size` specifies the width of the swizzled threadblock group, and `order` determines the swizzling pattern, which can be either "row" or "col". - ### Shared Memory Swizzling In CUDA programming, shared memory is divided into multiple memory banks, with each bank capable of servicing one thread request per clock cycle in parallel. Bank conflicts occur when multiple threads simultaneously access different addresses mapped to the same bank, forcing these accesses to be serialized and degrading performance. @@ -113,17 +112,14 @@ T.annotate_layout({ Here, `T.annotate_layout` allows users to specify any desired layout for a buffer. For convenience, TileLang provides the `make_swizzled_layout` primitive to automatically generate a swizzled layout. - ### Warp-Specialization The Hopper architecture commonly employs warp specialization for performance optimization. A typical approach is to designate one warpgroup as a producer that handles data movement using TMA (Tensor Memory Accelerator), while the remaining warpgroups serve as consumers performing computations. However, this programming pattern is complex, requiring developers to manually manage the execution logic for producers and consumers, including synchronization through the `mbarrier` objects. In TileLang, users are completely shielded from these implementation details. The frontend script is automatically transformed into a warp-specialized form, where TileLang handles all producer-consumer synchronization automatically, enabling efficient computation. - ### Pipeline - Pipeline is a technique used to improve memory access efficiency by overlapping memory access and computation. In TileLang, pipeline can be implemented through the `T.pipelined` annotation: ```python @@ -132,9 +128,8 @@ T.pipelined(range: int, stage: int) Here, `range` specifies the range of the pipeline, and `stage` specifies the stage of the pipeline. Multi-stage pipelining enables overlapping of computation and memory access, which can significantly improve performance for memory-intensive operators. However, setting a higher number of stages consumes more shared memory resources, so the optimal configuration needs to be determined based on specific use cases. - ### Split-KV We have also implemented Split-KV optimization similar to [FlashDecoding](https://pytorch.org/blog/flash-decoding/). Specifically, when the batch size is small, parallel SM resources cannot be fully utilized due to low parallelism. In such cases, we can split the kv_ctx dimension across multiple SMs for parallel computation and then merge the results. -In our implementation, we have developed both split and combine kernels, allowing users to control the split size through a `num_split` parameter. \ No newline at end of file +In our implementation, we have developed both split and combine kernels, allowing users to control the split size through a `num_split` parameter. diff --git a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_tilelang.py b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_tilelang.py index db460437fd..dccf333ad3 100644 --- a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_tilelang.py +++ b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_tilelang.py @@ -8,6 +8,7 @@ def get_configs(): import itertools + BLOCK_N = [16, 32, 64, 128] BLOCK_H = [16, 32, 64, 128] num_split = [1, 2, 4, 8, 16, 32] @@ -15,45 +16,44 @@ def get_configs(): _configs = list(itertools.product(BLOCK_N, BLOCK_H, num_split, threads)) - return [{ - "block_N": c[0], - "block_H": c[1], - "num_split": c[2], - "threads": c[3], - } for c in _configs] + return [ + { + "block_N": c[0], + "block_H": c[1], + "num_split": c[2], + "threads": c[3], + } + for c in _configs + ] @tilelang.autotune(configs=get_configs()) @tilelang.jit( - out_idx=[6], pass_configs={ + out_idx=[6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashmla_decode(batch, - heads, - kv_head_num, - seqlen_kv, - dim, - pe_dim, - block_N, - block_H, - num_split, - threads=128): - scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + }, +) +def flashmla_decode(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, threads=128): + scale = (1.0 / (dim + pe_dim)) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" - @T.macro - def flash_attn( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + @T.prim_func + def main_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel(batch, heads // min(block_H, kv_group_num), threads=threads) as (bx, by): + # flash_attn_split + with T.Kernel(batch, heads // min(block_H, kv_group_num), num_split, threads=threads) as (bx, by, bz): Q_local = T.alloc_fragment([block_H, dim], dtype) Q_pe_local = T.alloc_fragment([block_H, pe_dim], dtype) KV_shared = T.alloc_shared([block_N, dim], dtype) @@ -69,34 +69,31 @@ def flash_attn( cur_kv_head = by // (kv_group_num // block_H) T.use_swizzle(10) - - T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_local) - T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_local) + T.copy(Q[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_local) + T.copy(Q_pe[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_pe_local) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv(seqlen_kv, block_N) + loop_range = T.ceildiv((seqlen_kv // num_split), block_N) for k in T.Pipelined(loop_range, num_stages=0): - T.copy(KV[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], KV_shared) - T.copy(K_pe[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared) + kv_start = (seqlen_kv // num_split) * bz + k * block_N + kv_end = (seqlen_kv // num_split) * bz + (k + 1) * block_N + T.copy(KV[bx, kv_start:kv_end, cur_kv_head, :], KV_shared) + T.copy(K_pe[bx, kv_start:kv_end, cur_kv_head, :], K_pe_shared) T.clear(acc_s) T.gemm(Q_local, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.gemm( - Q_pe_local, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_pe_local, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) - # T.copy(acc_s, S_shared) T.copy(acc_s, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] @@ -105,20 +102,50 @@ def flash_attn( T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :]) + for i in T.Parallel(block_H): + logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + T.copy(logsum, glse[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, bz]) + T.copy(acc_o, Output_partial[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, bz, :]) + + # combine + with T.Kernel(heads, batch, threads=128) as (by, bz): + po_local = T.alloc_fragment([dim], dtype) + o_accum_local = T.alloc_fragment([dim], accum_dtype) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + + T.clear(lse_logsum_local) + T.clear(o_accum_local) + lse_max_local = -T.infinity(accum_dtype) + for k in T.serial(num_split): + lse_max_local = T.max(lse_max_local, glse[bz, by, k]) + for k in T.Pipelined(num_split, num_stages=1): + lse_local_split = glse[bz, by, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local + for k in T.serial(num_split): + for i in T.Parallel(dim): + po_local[i] = Output_partial[bz, by, k, i] + lse_local_split = glse[bz, by, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) + for i in T.Parallel(dim): + o_accum_local[i] += po_local[i] * scale_local[0] + for i in T.Parallel(dim): + Output[bz, by, i] = o_accum_local[i] - @T.macro - def flash_attn_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + @T.prim_func + def main_no_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel( - batch, heads // min(block_H, kv_group_num), num_split, - threads=threads) as (bx, by, bz): + with T.Kernel(batch, heads // min(block_H, kv_group_num), threads=threads) as (bx, by): Q_local = T.alloc_fragment([block_H, dim], dtype) Q_pe_local = T.alloc_fragment([block_H, pe_dim], dtype) KV_shared = T.alloc_shared([block_N, dim], dtype) @@ -134,34 +161,31 @@ def flash_attn_split( cur_kv_head = by // (kv_group_num // block_H) T.use_swizzle(10) - T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_local) - T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_local) + + T.copy(Q[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_local) + T.copy(Q_pe[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_pe_local) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv((seqlen_kv // num_split), block_N) + loop_range = T.ceildiv(seqlen_kv, block_N) for k in T.Pipelined(loop_range, num_stages=0): - kv_start = (seqlen_kv // num_split) * bz + k * block_N - kv_end = (seqlen_kv // num_split) * bz + (k + 1) * block_N - T.copy(KV[bx, kv_start:kv_end, cur_kv_head, :], KV_shared) - T.copy(K_pe[bx, kv_start:kv_end, cur_kv_head, :], K_pe_shared) + T.copy(KV[bx, k * block_N : (k + 1) * block_N, cur_kv_head, :], KV_shared) + T.copy(K_pe[bx, k * block_N : (k + 1) * block_N, cur_kv_head, :], K_pe_shared) T.clear(acc_s) T.gemm(Q_local, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.gemm( - Q_pe_local, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_pe_local, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) + # T.copy(acc_s, S_shared) T.copy(acc_s, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] @@ -170,72 +194,7 @@ def flash_attn_split( T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] - for i in T.Parallel(block_H): - logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, glse[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz]) - T.copy(acc_o, Output_partial[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz, :]) - - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - with T.Kernel(heads, batch, threads=128) as (by, bz): - po_local = T.alloc_fragment([dim], dtype) - o_accum_local = T.alloc_fragment([dim], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) - - T.clear(lse_logsum_local) - T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) - for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k]) - for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] - for k in T.serial(num_split): - for i in T.Parallel(dim): - po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) - for i in T.Parallel(dim): - o_accum_local[i] += po_local[i] * scale_local[0] - for i in T.Parallel(dim): - Output[bz, by, i] = o_accum_local[i] - - @T.prim_func - def main_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn_split(Q, Q_pe, KV, K_pe, glse, Output_partial) - combine(glse, Output_partial, Output) - - @T.prim_func - def main_no_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn(Q, Q_pe, KV, K_pe, Output) + T.copy(acc_o, Output[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :]) if num_split > 1: return main_split @@ -258,43 +217,36 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=128, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') - parser.add_argument('--autotune', action='store_true', help='auto tune') + parser.add_argument("--batch", type=int, default=128, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") + parser.add_argument("--autotune", action="store_true", help="auto tune") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim enable_autotune = args.autotune @@ -310,17 +262,7 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): if enable_autotune: kernel = flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim) else: - kernel = flashmla_decode( - batch, - heads, - kv_heads, - kv_ctx, - dim, - pe_dim, - BLOCK_N, - BLOCK_H, - num_split, - threads=threads) + kernel = flashmla_decode(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, threads=threads) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) input_tensors = profiler._get_inputs() tilelang_output = kernel(*input_tensors) diff --git a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_torch.py b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_torch.py index 0006d94687..18c0a5f86d 100644 --- a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_torch.py +++ b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_torch.py @@ -32,8 +32,7 @@ def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): @torch.inference_mode() -def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, - h_kv, d, dv, causal, dtype): +def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] def ref_mla(): @@ -94,8 +93,7 @@ def _mla_attn_kernel( offs_d_ckv = tl.arange(0, HEAD_DIM_CKV) cur_head = cur_head_id * BLOCK_H + tl.arange(0, BLOCK_H) - offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[ - None, :] + offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[None, :] q_nope = tl.load(Q_nope + offs_q_nope) offs_d_kpe = tl.arange(0, HEAD_DIM_KPE) @@ -141,9 +139,7 @@ def _mla_attn_kernel( e_sum = e_sum * re_scale + tl.sum(p, 1) e_max = n_e_max - offs_o = cur_batch * stride_o_b + cur_head[:, - None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[ - None, :] + offs_o = cur_batch * stride_o_b + cur_head[:, None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[None, :] tl.store(O + offs_o, acc / e_sum[:, None]) offs_o_1 = cur_batch * stride_o_b + cur_head * stride_o_h + split_kv_id * stride_o_s + HEAD_DIM_CKV tl.store(O + offs_o_1, e_max + tl.log(e_sum)) @@ -309,24 +305,30 @@ def mla_decode_triton( @torch.inference_mode() -def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, - cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - +def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() def flash_mla_triton(): num_kv_splits = 32 o = torch.empty([b * s_q, h_q, dv]) attn_logits = torch.empty([b * s_q, h_q, num_kv_splits, dv + 1]) mla_decode_triton( - q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d - dv), blocked_k_nope.view(-1, dv), - blocked_k_pe.view(-1, d - dv), o, block_table, cache_seqlens, attn_logits, - num_kv_splits, 1 / math.sqrt(d), block_size) + q_nope.view(-1, h_q, dv), + q_pe.view(-1, h_q, d - dv), + blocked_k_nope.view(-1, dv), + blocked_k_pe.view(-1, d - dv), + o, + block_table, + cache_seqlens, + attn_logits, + num_kv_splits, + 1 / math.sqrt(d), + block_size, + ) return o.view([b, s_q, h_q, dv]) out_flash = flash_mla_triton() @@ -362,14 +364,15 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_a, lse_a, perf_a = baseline_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_a, lse_a, perf_a = baseline_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) torch.testing.assert_close(out_b.float(), out_a.float(), atol=1e-2, rtol=1e-2), "out" if target not in ["flash_mla_triton"]: @@ -377,21 +380,14 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal torch.testing.assert_close(lse_b.float(), lse_a.float(), atol=1e-2, rtol=1e-2), "lse" FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10 ** 9 / perf_a:.0f} TFLOPS, {bytes / 10 ** 6 / perf_a:.0f} GB/s" - ) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10**9 / perf_a:.0f} TFLOPS, {bytes / 10**6 / perf_a:.0f} GB/s") + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_a, bytes / 10**6 / perf_b def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - print( - f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}" - ) + print(f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}") torch.set_default_dtype(dtype) device = torch.device("cuda:0") torch.set_default_device(device) @@ -408,19 +404,16 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_b @@ -429,26 +422,22 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): "flash_mla_triton", ] -shape_configs = [{ - "b": - batch, - "s_q": - 1, - "cache_seqlens": - torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), - "h_q": - head, - "h_kv": - 1, - "d": - 512 + 64, - "dv": - 512, - "causal": - True, - "dtype": - torch.float16 -} for batch in [128] for seqlen in [1024, 2048, 4096, 8192, 16384] for head in [128]] +shape_configs = [ + { + "b": batch, + "s_q": 1, + "cache_seqlens": torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), + "h_q": head, + "h_kv": 1, + "d": 512 + 64, + "dv": 512, + "causal": True, + "dtype": torch.float16, + } + for batch in [128] + for seqlen in [1024, 2048, 4096, 8192, 16384] + for head in [128] +] def get_args(): @@ -470,26 +459,54 @@ def get_args(): for shape in shape_configs: if args.all: for target in available_targets: - perf = compare_a(target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) elif args.compare: - perfa, prefb = compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], - shape["cache_seqlens"], shape["h_q"], shape["h_kv"], - shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + perfa, prefb = compare_ab( + args.baseline, + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.baseline},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perfa:.0f}\n' + f"{args.baseline},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perfa:.0f}\n" ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{prefb:.0f}\n" ) elif args.one: - perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) diff --git a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_triton.py b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_triton.py index 644f97da15..861e841c4e 100644 --- a/examples/deepseek_mla/amd/benchmark_mla_decode_amd_triton.py +++ b/examples/deepseek_mla/amd/benchmark_mla_decode_amd_triton.py @@ -29,8 +29,7 @@ def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): @torch.inference_mode() -def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, - h_kv, d, dv, causal, dtype): +def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] def ref_mla(): @@ -91,8 +90,7 @@ def _mla_attn_kernel( offs_d_ckv = tl.arange(0, HEAD_DIM_CKV) cur_head = cur_head_id * BLOCK_H + tl.arange(0, BLOCK_H) - offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[ - None, :] + offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[None, :] q_nope = tl.load(Q_nope + offs_q_nope) offs_d_kpe = tl.arange(0, HEAD_DIM_KPE) @@ -138,9 +136,7 @@ def _mla_attn_kernel( e_sum = e_sum * re_scale + tl.sum(p, 1) e_max = n_e_max - offs_o = cur_batch * stride_o_b + cur_head[:, - None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[ - None, :] + offs_o = cur_batch * stride_o_b + cur_head[:, None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[None, :] tl.store(O + offs_o, acc / e_sum[:, None]) offs_o_1 = cur_batch * stride_o_b + cur_head * stride_o_h + split_kv_id * stride_o_s + HEAD_DIM_CKV tl.store(O + offs_o_1, e_max + tl.log(e_sum)) @@ -306,24 +302,30 @@ def mla_decode_triton( @torch.inference_mode() -def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, - cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - +def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() def flash_mla_triton(): num_kv_splits = 32 o = torch.empty([b * s_q, h_q, dv]) attn_logits = torch.empty([b * s_q, h_q, num_kv_splits, dv + 1]) mla_decode_triton( - q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d - dv), blocked_k_nope.view(-1, dv), - blocked_k_pe.view(-1, d - dv), o, block_table, cache_seqlens, attn_logits, - num_kv_splits, 1 / math.sqrt(d), block_size) + q_nope.view(-1, h_q, dv), + q_pe.view(-1, h_q, d - dv), + blocked_k_nope.view(-1, dv), + blocked_k_pe.view(-1, d - dv), + o, + block_table, + cache_seqlens, + attn_logits, + num_kv_splits, + 1 / math.sqrt(d), + block_size, + ) return o.view([b, s_q, h_q, dv]) out_flash = flash_mla_triton() @@ -359,14 +361,15 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_a, lse_a, perf_a = baseline_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_a, lse_a, perf_a = baseline_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) torch.testing.assert_close(out_b.float(), out_a.float(), atol=1e-2, rtol=1e-2), "out" if target not in ["flash_mla_triton"]: @@ -374,21 +377,14 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal torch.testing.assert_close(lse_b.float(), lse_a.float(), atol=1e-2, rtol=1e-2), "lse" FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10 ** 9 / perf_a:.0f} TFLOPS, {bytes / 10 ** 6 / perf_a:.0f} GB/s" - ) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10**9 / perf_a:.0f} TFLOPS, {bytes / 10**6 / perf_a:.0f} GB/s") + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_a, bytes / 10**6 / perf_b def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - print( - f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}" - ) + print(f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}") torch.set_default_dtype(dtype) device = torch.device("cuda:0") torch.set_default_device(device) @@ -405,19 +401,16 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_b @@ -426,26 +419,22 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): "flash_mla_triton", ] -shape_configs = [{ - "b": - batch, - "s_q": - 1, - "cache_seqlens": - torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), - "h_q": - head, - "h_kv": - 1, - "d": - 512 + 64, - "dv": - 512, - "causal": - True, - "dtype": - torch.float16 -} for batch in [64, 128] for seqlen in [1024, 2048, 4096, 8192, 16384] for head in [128]] +shape_configs = [ + { + "b": batch, + "s_q": 1, + "cache_seqlens": torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), + "h_q": head, + "h_kv": 1, + "d": 512 + 64, + "dv": 512, + "causal": True, + "dtype": torch.float16, + } + for batch in [64, 128] + for seqlen in [1024, 2048, 4096, 8192, 16384] + for head in [128] +] def get_args(): @@ -467,26 +456,54 @@ def get_args(): for shape in shape_configs: if args.all: for target in available_targets: - perf = compare_a(target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) elif args.compare: - perfa, prefb = compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], - shape["cache_seqlens"], shape["h_q"], shape["h_kv"], - shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + perfa, prefb = compare_ab( + args.baseline, + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.baseline},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perfa:.0f}\n' + f"{args.baseline},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perfa:.0f}\n" ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{prefb:.0f}\n" ) elif args.one: - perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) diff --git a/examples/deepseek_mla/benchmark_mla.py b/examples/deepseek_mla/benchmark_mla.py index a542ff611d..544b5e1285 100644 --- a/examples/deepseek_mla/benchmark_mla.py +++ b/examples/deepseek_mla/benchmark_mla.py @@ -33,8 +33,7 @@ def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): @torch.inference_mode() -def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, - h_kv, d, dv, causal, dtype): +def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] def ref_mla(): @@ -61,8 +60,7 @@ def ref_mla(): @torch.inference_mode() -def run_flash_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, - h_kv, d, dv, causal, dtype): +def run_flash_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): from flash_mla import flash_mla_with_kvcache, get_mla_metadata blocked_v = blocked_k[..., :dv] @@ -87,14 +85,13 @@ def flash_mla(): @torch.inference_mode() -def run_flashinfer(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, - h_q, h_kv, d, dv, causal, dtype): +def run_flashinfer(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): # pip install flashinfer-python import flashinfer + assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() kv_indptr = [0] kv_indices = [] @@ -111,8 +108,7 @@ def run_flashinfer(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q kv_indptr = torch.tensor(kv_indptr, dtype=torch.int32) kv_indices = torch.tensor(kv_indices, dtype=torch.int32) - mla_wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper( - torch.empty(128 * 1024 * 1024, dtype=torch.int8), backend="fa3") + mla_wrapper = flashinfer.mla.BatchMLAPagedAttentionWrapper(torch.empty(128 * 1024 * 1024, dtype=torch.int8), backend="fa3") mla_wrapper.plan( q_indptr, kv_indptr, @@ -129,12 +125,7 @@ def run_flashinfer(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q ) def flashinfer(): - output, lse = mla_wrapper.run( - q_nope.view(-1, h_q, dv), - q_pe.view(-1, h_q, d - dv), - blocked_k_nope, - blocked_k_pe, - return_lse=True) + output, lse = mla_wrapper.run(q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d - dv), blocked_k_nope, blocked_k_pe, return_lse=True) return output.view(b, -1, h_q, dv), lse.view(b, h_q, 1) out_flash, lse_flash = flashinfer() @@ -177,8 +168,7 @@ def _mla_attn_kernel( offs_d_ckv = tl.arange(0, HEAD_DIM_CKV) cur_head = cur_head_id * BLOCK_H + tl.arange(0, BLOCK_H) - offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[ - None, :] + offs_q_nope = cur_batch * stride_q_nope_bs + cur_head[:, None] * stride_q_nope_h + offs_d_ckv[None, :] q_nope = tl.load(Q_nope + offs_q_nope) offs_d_kpe = tl.arange(0, HEAD_DIM_KPE) @@ -224,9 +214,7 @@ def _mla_attn_kernel( e_sum = e_sum * re_scale + tl.sum(p, 1) e_max = n_e_max - offs_o = cur_batch * stride_o_b + cur_head[:, - None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[ - None, :] + offs_o = cur_batch * stride_o_b + cur_head[:, None] * stride_o_h + split_kv_id * stride_o_s + offs_d_ckv[None, :] tl.store(O + offs_o, acc / e_sum[:, None]) offs_o_1 = cur_batch * stride_o_b + cur_head * stride_o_h + split_kv_id * stride_o_s + HEAD_DIM_CKV tl.store(O + offs_o_1, e_max + tl.log(e_sum)) @@ -393,24 +381,30 @@ def mla_decode_triton( @torch.inference_mode() -def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, - cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - +def run_flash_mla_triton(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): blocked_v = blocked_k[..., :dv] assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() def flash_mla_triton(): num_kv_splits = 32 o = torch.empty([b * s_q, h_q, dv]) attn_logits = torch.empty([b * s_q, h_q, num_kv_splits, dv + 1]) mla_decode_triton( - q_nope.view(-1, h_q, dv), q_pe.view(-1, h_q, d - dv), blocked_k_nope.view(-1, dv), - blocked_k_pe.view(-1, d - dv), o, block_table, cache_seqlens, attn_logits, - num_kv_splits, 1 / math.sqrt(d), block_size) + q_nope.view(-1, h_q, dv), + q_pe.view(-1, h_q, d - dv), + blocked_k_nope.view(-1, dv), + blocked_k_pe.view(-1, d - dv), + o, + block_table, + cache_seqlens, + attn_logits, + num_kv_splits, + 1 / math.sqrt(d), + block_size, + ) return o.view([b, s_q, h_q, dv]) out_flash = flash_mla_triton() @@ -419,13 +413,10 @@ def flash_mla_triton(): @torch.inference_mode() -def run_flash_mla_tilelang(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, - cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - +def run_flash_mla_tilelang(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() dpe = d - dv num_kv_splits = 1 @@ -434,8 +425,7 @@ def run_flash_mla_tilelang(q, block_table, blocked_k, max_seqlen_pad, block_size out_partial = torch.empty(b, h_q, num_kv_splits, dv, dtype=dtype, device=q.device) glse = torch.empty(b, h_q, num_kv_splits, dtype=dtype, device=q.device) - kernel = mla_decode_tilelang(b, h_q, h_kv, max_seqlen_pad, dv, dpe, BLOCK_N, BLOCK_H, - num_kv_splits, block_size) + kernel = mla_decode_tilelang(b, h_q, h_kv, max_seqlen_pad, dv, dpe, BLOCK_N, BLOCK_H, num_kv_splits, block_size) def flash_mla_tilelang(): out = kernel( @@ -486,38 +476,31 @@ def compare_ab(baseline, target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_a, lse_a, perf_a = baseline_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_a, lse_a, perf_a = baseline_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) torch.testing.assert_close(out_b.float(), out_a.float(), atol=1e-2, rtol=1e-2), "out" - if target not in ["flashinfer", "flash_mla_triton", "tilelang" - ] and baseline not in ["flashinfer", "flash_mla_triton", "tilelang"]: + if target not in ["flashinfer", "flash_mla_triton", "tilelang"] and baseline not in ["flashinfer", "flash_mla_triton", "tilelang"]: # flashinfer has a different lse return value # flash_mla_triton and flash_mla_tilelang doesn't return lse torch.testing.assert_close(lse_b.float(), lse_a.float(), atol=1e-2, rtol=1e-2), "lse" FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10 ** 9 / perf_a:.0f} TFLOPS, {bytes / 10 ** 6 / perf_a:.0f} GB/s" - ) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {baseline}: {perf_a:.3f} ms, {FLOPS / 10**9 / perf_a:.0f} TFLOPS, {bytes / 10**6 / perf_a:.0f} GB/s") + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_a, bytes / 10**6 / perf_b def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): - print( - f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}" - ) + print(f"{target}: {b=}, {s_q=}, mean_seqlens={cache_seqlens.float().mean()}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {dtype=}") torch.set_default_dtype(dtype) device = torch.device("cuda:0") torch.set_default_device(device) @@ -534,19 +517,16 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): q = torch.randn(b, s_q, h_q, d) block_size = 64 - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d) - out_b, lse_b, perf_b = target_func(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_b, lse_b, perf_b = target_func( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) FLOPS = s_q * total_seqlens * h_q * (d + dv) * 2 - bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * ( - torch.finfo(dtype).bits // 8) - print( - f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10 ** 9 / perf_b:.0f} TFLOPS, {bytes / 10 ** 6 / perf_b:.0f} GB/s" - ) + bytes = (total_seqlens * h_kv * d + b * s_q * h_q * d + b * s_q * h_q * dv) * (torch.finfo(dtype).bits // 8) + print(f"perf {target}: {perf_b:.3f} ms, {FLOPS / 10**9 / perf_b:.0f} TFLOPS, {bytes / 10**6 / perf_b:.0f} GB/s") return bytes / 10**6 / perf_b @@ -558,26 +538,22 @@ def compare_a(target, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): "flash_mla_triton", ] -shape_configs = [{ - "b": - batch, - "s_q": - 1, - "cache_seqlens": - torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), - "h_q": - head, - "h_kv": - 1, - "d": - 512 + 64, - "dv": - 512, - "causal": - True, - "dtype": - torch.float16 -} for batch in [128] for seqlen in [1024, 2048, 4096, 8192, 16384, 32768] for head in [128]] +shape_configs = [ + { + "b": batch, + "s_q": 1, + "cache_seqlens": torch.tensor([seqlen + 2 * i for i in range(batch)], dtype=torch.int32, device="cuda"), + "h_q": head, + "h_kv": 1, + "d": 512 + 64, + "dv": 512, + "causal": True, + "dtype": torch.float16, + } + for batch in [128] + for seqlen in [1024, 2048, 4096, 8192, 16384, 32768] + for head in [128] +] def get_args(): @@ -599,26 +575,54 @@ def get_args(): for shape in shape_configs: if args.all: for target in available_targets: - perf = compare_a(target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) elif args.compare: - perfa, prefb = compare_ab(args.baseline, args.target, shape["b"], shape["s_q"], - shape["cache_seqlens"], shape["h_q"], shape["h_kv"], - shape["d"], shape["dv"], shape["causal"], shape["dtype"]) + perfa, prefb = compare_ab( + args.baseline, + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.baseline},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perfa:.0f}\n' + f"{args.baseline},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perfa:.0f}\n" ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{prefb:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{prefb:.0f}\n" ) elif args.one: - perf = compare_a(args.target, shape["b"], shape["s_q"], shape["cache_seqlens"], - shape["h_q"], shape["h_kv"], shape["d"], shape["dv"], - shape["causal"], shape["dtype"]) + perf = compare_a( + args.target, + shape["b"], + shape["s_q"], + shape["cache_seqlens"], + shape["h_q"], + shape["h_kv"], + shape["d"], + shape["dv"], + shape["causal"], + shape["dtype"], + ) fout.write( - f'{args.target},{shape["b"]},{shape["cache_seqlens"].float().mean().cpu().item():.0f},{shape["h_q"]},{perf:.0f}\n' + f"{args.target},{shape['b']},{shape['cache_seqlens'].float().mean().cpu().item():.0f},{shape['h_q']},{perf:.0f}\n" ) diff --git a/examples/deepseek_mla/example_mla_decode.py b/examples/deepseek_mla/example_mla_decode.py index e1dd0b4d63..7de4faf089 100644 --- a/examples/deepseek_mla/example_mla_decode.py +++ b/examples/deepseek_mla/example_mla_decode.py @@ -10,27 +10,31 @@ @tilelang.jit( - out_idx=[6], pass_configs={ + out_idx=[6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, - softmax_scale): + }, +) +def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, softmax_scale): scale = float(softmax_scale * 1.44269504) # log2(e) - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" - @T.macro - def flash_attn( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + @T.prim_func + def main_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=256) as (hid, bid): + # flash_attn_split + with T.Kernel(batch, heads // min(block_H, kv_group_num), num_split, threads=256) as (bid, hid, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype) @@ -38,6 +42,7 @@ def flash_attn( K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype) O_shared = T.alloc_shared([block_H, dim], dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) + acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o = T.alloc_fragment([block_H, dim], accum_dtype) scores_max = T.alloc_fragment([block_H], accum_dtype) scores_max_prev = T.alloc_fragment([block_H], accum_dtype) @@ -46,64 +51,87 @@ def flash_attn( logsum = T.alloc_fragment([block_H], accum_dtype) cur_kv_head = hid // (kv_group_num // block_H) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - }) + T.use_swizzle(10) - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv(seqlen_kv, block_N) + loop_range = T.ceildiv((seqlen_kv // num_split), block_N) for k in T.Pipelined(loop_range, num_stages=2): - T.copy(KV[bid, k * block_N:(k + 1) * block_N, cur_kv_head, :], KV_shared) - T.copy(K_pe[bid, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared) - T.gemm( - Q_shared, - KV_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol, - clear_accum=True) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + kv_start = (seqlen_kv // num_split) * bz + k * block_N + kv_end = (seqlen_kv // num_split) * bz + (k + 1) * block_N + T.copy(KV[bid, kv_start:kv_end, cur_kv_head, :], KV_shared) + T.copy(K_pe[bid, kv_start:kv_end, cur_kv_head, :], K_pe_shared) + T.clear(acc_s) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) T.copy(acc_s, S_shared) + T.copy(S_shared, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_H, dim): acc_o[i, j] *= scores_scale[i] - T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) + T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] + for i in T.Parallel(block_H): + logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + T.copy(logsum, glse[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, bz]) T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :]) + T.copy(O_shared, Output_partial[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, bz, :]) + + # combine + with T.Kernel(heads, batch, threads=128) as (hid, bz): + po_local = T.alloc_fragment([dim], dtype) + o_accum_local = T.alloc_fragment([dim], accum_dtype) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + + T.clear(lse_logsum_local) + T.clear(o_accum_local) + lse_max_local = -T.infinity(accum_dtype) + for k in T.serial(num_split): + lse_max_local = T.max(lse_max_local, glse[bz, hid, k]) + for k in T.Pipelined(num_split, num_stages=1): + lse_local_split = glse[bz, hid, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local + for k in T.serial(num_split): + for i in T.Parallel(dim): + po_local[i] = Output_partial[bz, hid, k, i] + lse_local_split = glse[bz, hid, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) + for i in T.Parallel(dim): + o_accum_local[i] += po_local[i] * scale_local + for i in T.Parallel(dim): + Output[bz, hid, i] = o_accum_local[i] - @T.macro - def flash_attn_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + @T.prim_func + def main_no_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel( - batch, heads // min(block_H, kv_group_num), num_split, - threads=256) as (bid, hid, bz): + with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=256) as (hid, bid): Q_shared = T.alloc_shared([block_H, dim], dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) Q_pe_shared = T.alloc_shared([block_H, pe_dim], dtype) @@ -111,7 +139,6 @@ def flash_attn_split( K_pe_shared = T.alloc_shared([block_N, pe_dim], dtype) O_shared = T.alloc_shared([block_H, dim], dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) - acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o = T.alloc_fragment([block_H, dim], accum_dtype) scores_max = T.alloc_fragment([block_H], accum_dtype) scores_max_prev = T.alloc_fragment([block_H], accum_dtype) @@ -120,118 +147,39 @@ def flash_attn_split( logsum = T.alloc_fragment([block_H], accum_dtype) cur_kv_head = hid // (kv_group_num // block_H) - T.use_swizzle(10) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - S_shared: tilelang.layout.make_swizzled_layout(S_shared), - }) - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv((seqlen_kv // num_split), block_N) + loop_range = T.ceildiv(seqlen_kv, block_N) for k in T.Pipelined(loop_range, num_stages=2): - kv_start = (seqlen_kv // num_split) * bz + k * block_N - kv_end = (seqlen_kv // num_split) * bz + (k + 1) * block_N - T.copy(KV[bid, kv_start:kv_end, cur_kv_head, :], KV_shared) - T.copy(K_pe[bid, kv_start:kv_end, cur_kv_head, :], K_pe_shared) - T.clear(acc_s) - T.gemm( - Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + T.copy(KV[bid, k * block_N : (k + 1) * block_N, cur_kv_head, :], KV_shared) + T.copy(K_pe[bid, k * block_N : (k + 1) * block_N, cur_kv_head, :], K_pe_shared) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol, clear_accum=True) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) T.copy(acc_s, S_shared) - T.copy(S_shared, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_H, dim): acc_o[i, j] *= scores_scale[i] - T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) + T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] - for i in T.Parallel(block_H): - logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, glse[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz]) T.copy(acc_o, O_shared) - T.copy(O_shared, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - bz, :]) - - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - with T.Kernel(heads, batch, threads=128) as (hid, bz): - po_local = T.alloc_fragment([dim], dtype) - o_accum_local = T.alloc_fragment([dim], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) - - T.clear(lse_logsum_local) - T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) - for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, hid, k]) - for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, hid, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] - for k in T.serial(num_split): - for i in T.Parallel(dim): - po_local[i] = Output_partial[bz, hid, k, i] - lse_local_split[0] = glse[bz, hid, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) - for i in T.Parallel(dim): - o_accum_local[i] += po_local[i] * scale_local[0] - for i in T.Parallel(dim): - Output[bz, hid, i] = o_accum_local[i] - - @T.prim_func - def main_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn_split(Q, Q_pe, KV, K_pe, glse, Output_partial) - combine(glse, Output_partial, Output) - - @T.prim_func - def main_no_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn(Q, Q_pe, KV, K_pe, Output) + T.copy(O_shared, Output[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :]) if num_split > 1: return main_split @@ -254,31 +202,24 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out @@ -296,10 +237,9 @@ def main( BLOCK_N = 64 BLOCK_H = min(64, heads // kv_heads) num_split = 1 - softmax_scale = (dim + pe_dim)**-0.5 + softmax_scale = (dim + pe_dim) ** -0.5 - kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, - softmax_scale) + kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, softmax_scale) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) profiler.assert_allclose(ref_program, rtol=1e-4, atol=1e-4) latency = profiler.do_bench(warmup=500) @@ -307,14 +247,33 @@ def main( print(f"TFlops: {total_flops / latency * 1e-9} TFlops") +def run_regression_perf( + batch=1, + heads=128, + kv_heads=1, + kv_ctx=8192, + dim=512, + pe_dim=64, +): + BLOCK_N = 64 + BLOCK_H = min(64, heads // kv_heads) + num_split = 1 + softmax_scale = (dim + pe_dim) ** -0.5 + + kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, softmax_scale) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) + profiler.assert_allclose(ref_program, rtol=1e-4, atol=1e-4) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=132, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') + parser.add_argument("--batch", type=int, default=132, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim main(batch, heads, kv_heads, kv_ctx, dim, pe_dim) diff --git a/examples/deepseek_mla/example_mla_decode_paged.py b/examples/deepseek_mla/example_mla_decode_paged.py index fe50d4d4fd..2e1911028c 100644 --- a/examples/deepseek_mla/example_mla_decode_paged.py +++ b/examples/deepseek_mla/example_mla_decode_paged.py @@ -8,41 +8,36 @@ @tilelang.jit( - out_idx=[8], pass_configs={ + out_idx=[8], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def mla_decode_tilelang(batch, - h_q, - h_kv, - max_seqlen_pad, - dv, - dpe, - block_N, - block_H, - num_split, - block_size, - softmax_scale=None): + }, +) +def mla_decode_tilelang(batch, h_q, h_kv, max_seqlen_pad, dv, dpe, block_N, block_H, num_split, block_size, softmax_scale=None): if softmax_scale is None: - softmax_scale = (dv + dpe)**-0.5 + softmax_scale = (dv + dpe) ** -0.5 scale = float(softmax_scale * 1.44269504) # log2(e) - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = h_q // h_kv VALID_BLOCK_H = min(block_H, kv_group_num) assert h_kv == 1, "h_kv must be 1" assert block_size >= block_N and block_size % block_N == 0, "block_size must be larger than block_N and a multiple of block_N" - @T.macro - def flash_mla_kernel( - Q: T.Tensor([batch, h_q, dv], dtype), - Q_pe: T.Tensor([batch, h_q, dpe], dtype), - KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), - K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), - BLOCK_TABLE: T.Tensor([batch, max_seqlen_pad // block_size], "int32"), - CACHE_SEQLENS: T.Tensor([batch], "int32"), - Output: T.Tensor([batch, h_q, dv], dtype), + @T.prim_func + def main_split( + Q: T.Tensor([batch, h_q, dv], dtype), + Q_pe: T.Tensor([batch, h_q, dpe], dtype), + KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), + K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), + block_table: T.Tensor([batch, max_seqlen_pad // block_size], T.int32), + cache_seqlens: T.Tensor([batch], T.int32), + glse: T.Tensor([batch, h_q, num_split], dtype), + Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), + Output: T.Tensor([batch, h_q, dv], dtype), ): - with T.Kernel(batch, h_q // min(block_H, kv_group_num), threads=256) as (bx, by): + # split kv + with T.Kernel(batch, h_q // min(block_H, kv_group_num), num_split, threads=256) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dv], dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) Q_pe_shared = T.alloc_shared([block_H, dpe], dtype) @@ -50,6 +45,7 @@ def flash_mla_kernel( K_pe_shared = T.alloc_shared([block_N, dpe], dtype) O_shared = T.alloc_shared([block_H, dv], dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) + acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o = T.alloc_fragment([block_H, dv], accum_dtype) scores_max = T.alloc_fragment([block_H], accum_dtype) scores_max_prev = T.alloc_fragment([block_H], accum_dtype) @@ -59,69 +55,94 @@ def flash_mla_kernel( cur_kv_head = by // (kv_group_num // block_H) T.use_swizzle(10) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - S_shared: tilelang.layout.make_swizzled_layout(S_shared), - }) - T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv(CACHE_SEQLENS[bx], block_N) - for kr in T.Pipelined(loop_range, num_stages=2): - k = loop_range - 1 - kr - kv_start = BLOCK_TABLE[bx, (k * block_N) // - block_size] * block_size + (k * block_N) % block_size - T.copy(KV[kv_start:kv_start + block_N, cur_kv_head, :], KV_shared) - T.copy(K_pe[kv_start:kv_start + block_N, cur_kv_head, :], K_pe_shared) + total_blocks = T.ceildiv(cache_seqlens[bx], block_N) + blocks_per_split = T.floordiv(total_blocks, num_split) + remaining_blocks = T.floormod(total_blocks, num_split) + loop_range = blocks_per_split + T.if_then_else(bz < remaining_blocks, 1, 0) + start = (blocks_per_split * bz + T.min(bz, remaining_blocks)) * block_N + + for k in T.Pipelined(loop_range, num_stages=2): + kv_start = block_table[bx, (start + k * block_N) // block_size] * block_size + (k * block_N) % block_size + T.copy(KV[kv_start : kv_start + block_N, cur_kv_head, :], KV_shared) + T.copy(K_pe[kv_start : kv_start + block_N, cur_kv_head, :], K_pe_shared) T.clear(acc_s) - T.gemm( - Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) - if kr == 0: - for i, j in T.Parallel(block_H, block_N): - acc_s[i, j] = T.if_then_else(k * block_N + j >= CACHE_SEQLENS[bx], - -T.infinity(accum_dtype), acc_s[i, j]) + for i, j in T.Parallel(block_H, block_N): + acc_s[i, j] = T.if_then_else(start + k * block_N + j >= cache_seqlens[bx], -T.infinity(accum_dtype), acc_s[i, j]) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) T.copy(acc_s, S_shared) + T.copy(S_shared, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_H, dv): acc_o[i, j] *= scores_scale[i] - T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) + T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) for i, j in T.Parallel(block_H, dv): acc_o[i, j] /= logsum[i] + for i in T.Parallel(block_H): + logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + T.copy(logsum, glse[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, bz]) T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :]) - - @T.macro - def flash_mla_split_kv_kernel( - Q: T.Tensor([batch, h_q, dv], dtype), - Q_pe: T.Tensor([batch, h_q, dpe], dtype), - KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), - K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), - BLOCK_TABLE: T.Tensor([batch, max_seqlen_pad // block_size], "int32"), - CACHE_SEQLENS: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, h_q, num_split], dtype), - Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), + T.copy(O_shared, Output_partial[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, bz, :]) + + # combine + with T.Kernel(h_q, batch, threads=128) as (by, bz): + po_local = T.alloc_fragment([dv], dtype) + o_accum_local = T.alloc_fragment([dv], accum_dtype) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + + T.clear(lse_logsum_local) + T.clear(o_accum_local) + lse_max_local = -T.infinity(accum_dtype) + for k in T.serial(num_split): + lse_max_local = T.max(lse_max_local, glse[bz, by, k]) + for k in T.Pipelined(num_split, num_stages=1): + lse_local_split = glse[bz, by, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local + for k in T.serial(num_split): + for i in T.Parallel(dv): + po_local[i] = Output_partial[bz, by, k, i] + lse_local_split = glse[bz, by, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) + for i in T.Parallel(dv): + o_accum_local[i] += po_local[i] * scale_local + for i in T.Parallel(dv): + Output[bz, by, i] = o_accum_local[i] + + @T.prim_func + def main_no_split( + Q: T.Tensor([batch, h_q, dv], dtype), + Q_pe: T.Tensor([batch, h_q, dpe], dtype), + KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), + K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), + block_table: T.Tensor([batch, max_seqlen_pad // block_size], T.int32), + cache_seqlens: T.Tensor([batch], T.int32), + glse: T.Tensor([batch, h_q, num_split], dtype), + Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), + Output: T.Tensor([batch, h_q, dv], dtype), ): - with T.Kernel( - batch, h_q // min(block_H, kv_group_num), num_split, threads=256) as (bx, by, bz): + with T.Kernel(batch, h_q // min(block_H, kv_group_num), threads=256) as (bx, by): Q_shared = T.alloc_shared([block_H, dv], dtype) S_shared = T.alloc_shared([block_H, block_N], dtype) Q_pe_shared = T.alloc_shared([block_H, dpe], dtype) @@ -129,7 +150,6 @@ def flash_mla_split_kv_kernel( K_pe_shared = T.alloc_shared([block_N, dpe], dtype) O_shared = T.alloc_shared([block_H, dv], dtype) acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) - acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) acc_o = T.alloc_fragment([block_H, dv], accum_dtype) scores_max = T.alloc_fragment([block_H], accum_dtype) scores_max_prev = T.alloc_fragment([block_H], accum_dtype) @@ -139,129 +159,45 @@ def flash_mla_split_kv_kernel( cur_kv_head = by // (kv_group_num // block_H) T.use_swizzle(10) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - S_shared: tilelang.layout.make_swizzled_layout(S_shared), - }) - T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - total_blocks = T.ceildiv(CACHE_SEQLENS[bx], block_N) - blocks_per_split = T.floordiv(total_blocks, num_split) - remaining_blocks = T.floormod(total_blocks, num_split) - loop_range = (blocks_per_split + T.if_then_else(bz < remaining_blocks, 1, 0)) - start = (blocks_per_split * bz + T.min(bz, remaining_blocks)) * block_N - - for k in T.Pipelined(loop_range, num_stages=2): - kv_start = BLOCK_TABLE[bx, (start + k * block_N) // - block_size] * block_size + (k * block_N) % block_size - T.copy(KV[kv_start:kv_start + block_N, cur_kv_head, :], KV_shared) - T.copy(K_pe[kv_start:kv_start + block_N, cur_kv_head, :], K_pe_shared) + loop_range = T.ceildiv(cache_seqlens[bx], block_N) + for kr in T.Pipelined(loop_range, num_stages=2): + k = loop_range - 1 - kr + kv_start = block_table[bx, (k * block_N) // block_size] * block_size + (k * block_N) % block_size + T.copy(KV[kv_start : kv_start + block_N, cur_kv_head, :], KV_shared) + T.copy(K_pe[kv_start : kv_start + block_N, cur_kv_head, :], K_pe_shared) T.clear(acc_s) - T.gemm( - Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) - for i, j in T.Parallel(block_H, block_N): - acc_s[i, j] = T.if_then_else(start + k * block_N + j >= CACHE_SEQLENS[bx], - -T.infinity(accum_dtype), acc_s[i, j]) + if kr == 0: + for i, j in T.Parallel(block_H, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= cache_seqlens[bx], -T.infinity(accum_dtype), acc_s[i, j]) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) T.copy(acc_s, S_shared) - T.copy(S_shared, acc_s_cast) for i in T.Parallel(block_H): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_H, dv): acc_o[i, j] *= scores_scale[i] - T.gemm(acc_s_cast, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) + T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullCol) for i, j in T.Parallel(block_H, dv): acc_o[i, j] /= logsum[i] - for i in T.Parallel(block_H): - logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, glse[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz]) T.copy(acc_o, O_shared) - T.copy(O_shared, Output_partial[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, bz, :]) - - @T.macro - def combine( - glse: T.Tensor([batch, h_q, num_split], dtype), - Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), - Output: T.Tensor([batch, h_q, dv], dtype), - ): - with T.Kernel(h_q, batch, threads=128) as (by, bz): - po_local = T.alloc_fragment([dv], dtype) - o_accum_local = T.alloc_fragment([dv], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) - - T.clear(lse_logsum_local) - T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) - for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, by, k]) - for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] - for k in T.serial(num_split): - for i in T.Parallel(dv): - po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) - for i in T.Parallel(dv): - o_accum_local[i] += po_local[i] * scale_local[0] - for i in T.Parallel(dv): - Output[bz, by, i] = o_accum_local[i] - - @T.prim_func - def main_split( - Q: T.Tensor([batch, h_q, dv], dtype), - Q_pe: T.Tensor([batch, h_q, dpe], dtype), - KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), - K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), - block_table: T.Tensor([batch, max_seqlen_pad // block_size], "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, h_q, num_split], dtype), - Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), - Output: T.Tensor([batch, h_q, dv], dtype), - ): - flash_mla_split_kv_kernel(Q, Q_pe, KV, K_pe, block_table, cache_seqlens, glse, - Output_partial) - combine(glse, Output_partial, Output) - - @T.prim_func - def main_no_split( - Q: T.Tensor([batch, h_q, dv], dtype), - Q_pe: T.Tensor([batch, h_q, dpe], dtype), - KV: T.Tensor([batch * max_seqlen_pad, h_kv, dv], dtype), - K_pe: T.Tensor([batch * max_seqlen_pad, h_kv, dpe], dtype), - block_table: T.Tensor([batch, max_seqlen_pad // block_size], "int32"), - cache_seqlens: T.Tensor([batch], "int32"), - glse: T.Tensor([batch, h_q, num_split], dtype), - Output_partial: T.Tensor([batch, h_q, num_split, dv], dtype), - Output: T.Tensor([batch, h_q, dv], dtype), - ): - flash_mla_kernel(Q, Q_pe, KV, K_pe, block_table, cache_seqlens, Output) + T.copy(O_shared, Output[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :]) if num_split > 1: return main_split @@ -280,8 +216,7 @@ def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): s_q = query.shape[-2] s_k = key.shape[-2] attn_bias = torch.zeros(s_q, s_k, dtype=query.dtype, device=query.device) - temp_mask = torch.ones( - s_q, s_k, dtype=torch.bool, device=query.device).tril(diagonal=s_k - s_q) + temp_mask = torch.ones(s_q, s_k, dtype=torch.bool, device=query.device).tril(diagonal=s_k - s_q) attn_bias.masked_fill_(temp_mask.logical_not(), float("-inf")) attn_bias.to(query.dtype) attn_weight += attn_bias @@ -291,8 +226,7 @@ def scaled_dot_product_attention(query, key, value, h_q, h_kv, is_causal=False): @torch.inference_mode() -def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, - h_kv, d, dv, causal, dtype): +def run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): # q: [b, s_q, h_q, d] # block_table: [b, max_seqlen_pad // block_size] # blocked_k: [b * max_seqlen_pad // block_size, block_size, h_kv, d] @@ -321,13 +255,10 @@ def ref_mla(): return out_torch -def run_tilelang_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, - h_q, h_kv, d, dv, causal, dtype): - +def run_tilelang_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype): assert d > dv, "mla with rope dim should be larger than no rope dim" q_nope, q_pe = q[..., :dv].contiguous(), q[..., dv:].contiguous() - blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., - dv:].contiguous() + blocked_k_nope, blocked_k_pe = blocked_k[..., :dv].contiguous(), blocked_k[..., dv:].contiguous() dpe = d - dv num_kv_splits = 1 @@ -337,8 +268,7 @@ def run_tilelang_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s out_partial = torch.empty(b, h_q, num_kv_splits, dv, dtype=dtype, device=q.device) glse = torch.empty(b, h_q, num_kv_splits, dtype=dtype, device=q.device) - kernel = mla_decode_tilelang(b, h_q, h_kv, max_seqlen_pad, dv, dpe, BLOCK_N, BLOCK_H, - num_kv_splits, block_size, softmax_scale) + kernel = mla_decode_tilelang(b, h_q, h_kv, max_seqlen_pad, dv, dpe, BLOCK_N, BLOCK_H, num_kv_splits, block_size, softmax_scale) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) def flash_mla_tilelang(): @@ -356,8 +286,7 @@ def flash_mla_tilelang(): out_flash = flash_mla_tilelang() t = do_bench(flash_mla_tilelang) - out_ref = run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, - cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_ref = run_torch_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) torch.testing.assert_close(out_flash, out_ref, rtol=0.01, atol=0.01) print("All close") return out_flash, t @@ -365,12 +294,12 @@ def flash_mla_tilelang(): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=128, help='batch size') - parser.add_argument('--h_q', type=int, default=128, help='q heads number') - parser.add_argument('--h_kv', type=int, default=1, help='kv heads number') - parser.add_argument('--cache_seqlen', type=int, default=8192, help='kv cache context length') - parser.add_argument('--d', type=int, default=576, help='query/key head dim, d = dv + dpe') - parser.add_argument('--dv', type=int, default=512, help='value head dim') + parser.add_argument("--batch", type=int, default=128, help="batch size") + parser.add_argument("--h_q", type=int, default=128, help="q heads number") + parser.add_argument("--h_kv", type=int, default=1, help="kv heads number") + parser.add_argument("--cache_seqlen", type=int, default=8192, help="kv cache context length") + parser.add_argument("--d", type=int, default=576, help="query/key head dim, d = dv + dpe") + parser.add_argument("--dv", type=int, default=512, help="value head dim") args = parser.parse_args() b, h_q, h_kv, cache_seqlen, d, dv = args.batch, args.h_q, args.h_kv, args.cache_seqlen, args.d, args.dv @@ -379,9 +308,7 @@ def flash_mla_tilelang(): s_q = 1 # for decode, s_q = 1 block_size = 64 - cache_seqlens = torch.tensor([cache_seqlen + 2 * i for i in range(b)], - dtype=torch.int32, - device=device) + cache_seqlens = torch.tensor([cache_seqlen + 2 * i for i in range(b)], dtype=torch.int32, device=device) dpe = d - dv causal = True @@ -393,12 +320,11 @@ def flash_mla_tilelang(): total_flops = s_q * total_seqlens * h_q * d * 2 q = torch.randn(b, s_q, h_q, d, dtype=dtype, device=device) - block_table = torch.arange( - b * max_seqlen_pad // block_size, dtype=torch.int32, - device=device).view(b, max_seqlen_pad // block_size) + block_table = torch.arange(b * max_seqlen_pad // block_size, dtype=torch.int32, device=device).view(b, max_seqlen_pad // block_size) blocked_k = torch.randn(block_table.numel(), block_size, h_kv, d, dtype=dtype, device=device) - out_flash, latency = run_tilelang_mla(q, block_table, blocked_k, max_seqlen_pad, block_size, b, - s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype) + out_flash, latency = run_tilelang_mla( + q, block_table, blocked_k, max_seqlen_pad, block_size, b, s_q, cache_seqlens, h_q, h_kv, d, dv, causal, dtype + ) print("Tile-lang: {:.2f} ms".format(latency)) print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) diff --git a/examples/deepseek_mla/example_mla_decode_persistent.py b/examples/deepseek_mla/example_mla_decode_persistent.py index 3f57ea0518..74d974fbb6 100644 --- a/examples/deepseek_mla/example_mla_decode_persistent.py +++ b/examples/deepseek_mla/example_mla_decode_persistent.py @@ -9,13 +9,15 @@ @tilelang.jit( - out_idx=[6], pass_configs={ + out_idx=[6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split): - scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + scale = (1.0 / (dim + pe_dim)) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" @@ -23,13 +25,13 @@ def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_ @T.prim_func def main_split_persistent( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): with T.Kernel(sm_num, threads=256) as (block_id): Q_shared = T.alloc_shared([block_H, dim], dtype) @@ -48,16 +50,11 @@ def main_split_persistent( logsum = T.alloc_fragment([block_H], accum_dtype) po_local = T.alloc_fragment([dim], dtype) o_accum_local = T.alloc_fragment([dim], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - # O_shared: tilelang.layout.make_swizzled_layout(O_shared), - S_shared: tilelang.layout.make_swizzled_layout(S_shared), - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + T.use_swizzle(10) total_tiles = batch * (heads // min(block_H, kv_group_num)) * num_split @@ -70,8 +67,8 @@ def main_split_persistent( cur_kv_head = hid // (kv_group_num // block_H) if bid < batch and hid * VALID_BLOCK_H < heads and sid < num_split: - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) @@ -83,24 +80,15 @@ def main_split_persistent( T.copy(KV[bid, kv_start:kv_end, cur_kv_head, :], KV_shared) T.copy(K_pe[bid, kv_start:kv_end, cur_kv_head, :], K_pe_shared) T.clear(acc_s) - T.gemm( - Q_shared, - KV_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) for i in T.Parallel(block_H): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - - scores_max[i] * scale) + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_H): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) @@ -115,11 +103,9 @@ def main_split_persistent( acc_o[i, j] /= logsum[i] for i in T.Parallel(block_H): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, glse[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, sid]) + T.copy(logsum, glse[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, sid]) # T.copy(acc_o, O_shared) - T.copy( - acc_o, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - sid, :]) + T.copy(acc_o, Output_partial[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, sid, :]) T.sync_grid() waves = T.ceildiv(heads * batch, sm_num) @@ -130,20 +116,20 @@ def main_split_persistent( if bid < batch and hid < heads: T.clear(lse_logsum_local) T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) + lse_max_local = -T.infinity(accum_dtype) for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bid, hid, k]) + lse_max_local = T.max(lse_max_local, glse[bid, hid, k]) for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bid, hid, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] + lse_local_split = glse[bid, hid, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local for k in T.serial(num_split): for i in T.Parallel(dim): po_local[i] = Output_partial[bid, hid, k, i] - lse_local_split[0] = glse[bid, hid, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) + lse_local_split = glse[bid, hid, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) for i in T.Parallel(dim): - o_accum_local[i] += po_local[i] * scale_local[0] + o_accum_local[i] += po_local[i] * scale_local for i in T.Parallel(dim): Output[bid, hid, i] = o_accum_local[i] @@ -165,42 +151,35 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out def main(): parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=128, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') + parser.add_argument("--batch", type=int, default=128, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim) diff --git a/examples/deepseek_mla/example_mla_decode_ws.py b/examples/deepseek_mla/example_mla_decode_ws.py index 6554d57de4..32eb0d4754 100644 --- a/examples/deepseek_mla/example_mla_decode_ws.py +++ b/examples/deepseek_mla/example_mla_decode_ws.py @@ -13,30 +13,38 @@ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, compile_flags=[ - "-O3", "-Wno-deprecated-declarations", "-U__CUDA_NO_HALF_OPERATORS__", - "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_HALF2_OPERATORS__", - "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda", - "--ptxas-options=-v,--register-usage-level=10", "-DNDEBUG" + "-O3", + "-Wno-deprecated-declarations", + "-U__CUDA_NO_HALF_OPERATORS__", + "-U__CUDA_NO_HALF_CONVERSIONS__", + "-U__CUDA_NO_HALF2_OPERATORS__", + "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", + "--expt-relaxed-constexpr", + "--expt-extended-lambda", + "--ptxas-options=-v,--register-usage-level=10", + "-DNDEBUG", ], ) -def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, - softmax_scale): +def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split, softmax_scale): sm_scale = float(softmax_scale * 1.44269504) # log2(e) - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" - @T.macro - def flash_attn( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + @T.prim_func + def main_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=384) as (hid, bid): + # flash_attn_split + with T.Kernel(batch, heads // min(block_H, kv_group_num), num_split, threads=384) as (bid, hid, bz): Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype) Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype) Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype) @@ -75,16 +83,16 @@ def flash_attn( tx = T.get_thread_binding() - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l) - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, 0 : dim // 2], Q_shared_l) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, dim // 2 : dim], Q_shared_r) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) T.barrier_arrive(bar_q) if tx < 128: T.set_max_nreg(240, 1) T.fill(sumexp, 0) - T.fill(m_i, -2**30) # avoid -inf - inf to cause nan + T.fill(m_i, -(2**30)) # avoid -inf - inf to cause nan T.fill(acc_o_l, 0) T.barrier_wait(bar_q, 0) @@ -105,6 +113,8 @@ def flash_attn( T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(block_H): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): @@ -137,6 +147,8 @@ def flash_attn( T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(block_H): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): @@ -162,8 +174,8 @@ def flash_attn( for h_i in T.Parallel(block_H): sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale T.copy(acc_o_l, O_shared_l) - T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - 0:dim // 2]) + T.copy(O_shared_l, Output_partial[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, bz, 0 : dim // 2]) + T.copy(sumexp, glse[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, bz]) elif tx >= 128 and tx < 256: T.set_max_nreg(168, 1) @@ -193,8 +205,7 @@ def flash_attn( acc_o_r[h_i, d_i] /= sum_exp_shared[h_i] T.copy(acc_o_r, O_shared_r) - T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - dim // 2:dim]) + T.copy(O_shared_r, Output_partial[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, bz, dim // 2 : dim]) elif tx >= 256: # producer @@ -203,59 +214,82 @@ def flash_attn( # Buffer 0 T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - kv_indices = (i_i * 2) * block_N + r * 16 + (tx - 256) // 8 + kv_indices = (seqlen_kv // num_split) * bz + (i_i * 2) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_0_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_0_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_0_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_0_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = K_pe[bid, kv_indices, cur_kv_head, - (tx - 256) % 8 * 8 + v] + K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[ + bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_0_ready[0]) # Buffer 1 T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - kv_indices = (i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 + kv_indices = (seqlen_kv // num_split) * bz + (i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_1_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_1_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_1_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_1_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = K_pe[bid, kv_indices, cur_kv_head, - (tx - 256) % 8 * 8 + v] + K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[ + bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_1_ready[0]) - @T.macro - def flash_attn_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + # combine + with T.Kernel(heads, batch, threads=128) as (hid, bz): + po_local = T.alloc_fragment([dim], dtype) + o_accum_local = T.alloc_fragment([dim], accum_dtype) + lse_local_split = T.alloc_var(accum_dtype) + lse_logsum_local = T.alloc_var(accum_dtype) + lse_max_local = T.alloc_var(accum_dtype) + scale_local = T.alloc_var(accum_dtype) + + T.clear(lse_logsum_local) + T.clear(o_accum_local) + lse_max_local = -T.infinity(accum_dtype) + for k in T.serial(num_split): + lse_max_local = T.max(lse_max_local, glse[bz, hid, k]) + for k in T.Pipelined(num_split, num_stages=1): + lse_local_split = glse[bz, hid, k] + lse_logsum_local += T.exp2(lse_local_split - lse_max_local) + lse_logsum_local = T.log2(lse_logsum_local) + lse_max_local + for k in T.serial(num_split): + for i in T.Parallel(dim): + po_local[i] = Output_partial[bz, hid, k, i] + lse_local_split = glse[bz, hid, k] + scale_local = T.exp2(lse_local_split - lse_logsum_local) + for i in T.Parallel(dim): + o_accum_local[i] += po_local[i] * scale_local + for i in T.Parallel(dim): + Output[bz, hid, i] = o_accum_local[i] + + @T.prim_func + def main_no_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): - with T.Kernel( - batch, heads // min(block_H, kv_group_num), num_split, - threads=384) as (bid, hid, bz): + with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=384) as (hid, bid): Q_shared_l = T.alloc_shared([block_H, dim // 2], dtype) Q_shared_r = T.alloc_shared([block_H, dim // 2], dtype) Q_tail_shared = T.alloc_shared([block_H, pe_dim], dtype) @@ -294,16 +328,16 @@ def flash_attn_split( tx = T.get_thread_binding() - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, 0:dim // 2], Q_shared_l) - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, dim // 2:dim], Q_shared_r) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, 0 : dim // 2], Q_shared_l) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, dim // 2 : dim], Q_shared_r) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_tail_shared) T.barrier_arrive(bar_q) if tx < 128: T.set_max_nreg(240, 1) T.fill(sumexp, 0) - T.fill(m_i, -2**30) # avoid -inf - inf to cause nan + T.fill(m_i, -(2**30)) # avoid -inf - inf to cause nan T.fill(acc_o_l, 0) T.barrier_wait(bar_q, 0) @@ -323,7 +357,9 @@ def flash_attn_split( T.barrier_wait(bar_sScale_and_sS_free, ((i_i * 2) & 1) ^ 1) T.copy(m_i, m_i_prev) - T.reduce_max(acc_s, m_i, dim=1, clear=False) + T.reduce_max(acc_s, out=m_i, dim=1, clear=False) + for h_i in T.Parallel(block_H): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): @@ -356,6 +392,8 @@ def flash_attn_split( T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(block_H): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(block_H): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(block_H, block_N): @@ -381,10 +419,7 @@ def flash_attn_split( for h_i in T.Parallel(block_H): sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale T.copy(acc_o_l, O_shared_l) - T.copy( - O_shared_l, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - bz, 0:dim // 2]) - T.copy(sumexp, glse[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, bz]) + T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, 0 : dim // 2]) elif tx >= 128 and tx < 256: T.set_max_nreg(168, 1) @@ -414,9 +449,7 @@ def flash_attn_split( acc_o_r[h_i, d_i] /= sum_exp_shared[h_i] T.copy(acc_o_r, O_shared_r) - T.copy( - O_shared_r, Output_partial[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - bz, dim // 2:dim]) + T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, dim // 2 : dim]) elif tx >= 256: # producer @@ -425,111 +458,43 @@ def flash_attn_split( # Buffer 0 T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - kv_indices = (seqlen_kv // num_split) * bz + ( - i_i * 2) * block_N + r * 16 + (tx - 256) // 8 + kv_indices = (i_i * 2) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_0_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_0_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_0_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_0_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = K_pe[bid, kv_indices, cur_kv_head, - (tx - 256) % 8 * 8 + v] + K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[ + bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_0_ready[0]) # Buffer 1 T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - kv_indices = (seqlen_kv // num_split) * bz + ( - i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 + kv_indices = (i_i * 2 + 1) * block_N + r * 16 + (tx - 256) // 8 with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_1_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_1_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[bid, kv_indices, cur_kv_head, dim // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_1_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_1_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + bid, kv_indices, cur_kv_head, dim // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = K_pe[bid, kv_indices, cur_kv_head, - (tx - 256) % 8 * 8 + v] + K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = K_pe[ + bid, kv_indices, cur_kv_head, (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_1_ready[0]) - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - with T.Kernel(heads, batch, threads=128) as (hid, bz): - po_local = T.alloc_fragment([dim], dtype) - o_accum_local = T.alloc_fragment([dim], accum_dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_local([1], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - }) - - T.clear(lse_logsum_local) - T.clear(o_accum_local) - lse_max_local[0] = -T.infinity(accum_dtype) - for k in T.serial(num_split): - lse_max_local[0] = T.max(lse_max_local[0], glse[bz, hid, k]) - for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, hid, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] - for k in T.serial(num_split): - for i in T.Parallel(dim): - po_local[i] = Output_partial[bz, hid, k, i] - lse_local_split[0] = glse[bz, hid, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) - for i in T.Parallel(dim): - o_accum_local[i] += po_local[i] * scale_local[0] - for i in T.Parallel(dim): - Output[bz, hid, i] = o_accum_local[i] - - @T.prim_func - def main_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn_split(Q, Q_pe, KV, K_pe, glse, Output_partial) - combine(glse, Output_partial, Output) - - @T.prim_func - def main_no_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn(Q, Q_pe, KV, K_pe, Output) - if num_split > 1: return main_split else: @@ -551,31 +516,24 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out @@ -593,10 +551,9 @@ def main( BLOCK_N = 64 BLOCK_H = min(64, heads // kv_heads) num_split = 1 - softmax_scale = (dim + pe_dim)**-0.5 + softmax_scale = (dim + pe_dim) ** -0.5 - kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, - softmax_scale) + kernel = flashattn(batch, heads, kv_heads, kv_ctx, dim, pe_dim, BLOCK_N, BLOCK_H, num_split, softmax_scale) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) profiler.assert_allclose(ref_program, rtol=1e-4, atol=1e-4) latency = profiler.do_bench(warmup=500) @@ -606,12 +563,12 @@ def main( if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=132, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') + parser.add_argument("--batch", type=int, default=132, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim main(batch, heads, kv_heads, kv_ctx, dim, pe_dim) diff --git a/examples/deepseek_mla/experimental/example_mla_decode_kv_fp8.py b/examples/deepseek_mla/experimental/example_mla_decode_kv_fp8.py index 1b1447e88f..e70c35349e 100644 --- a/examples/deepseek_mla/experimental/example_mla_decode_kv_fp8.py +++ b/examples/deepseek_mla/experimental/example_mla_decode_kv_fp8.py @@ -8,25 +8,27 @@ @tilelang.jit( - out_idx=[-1], pass_configs={ + out_idx=[-1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H): - scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) - dtype = "float16" - q_dtype = "float8_e4m3" - accum_dtype = "float" + scale = (1.0 / (dim + pe_dim)) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + q_dtype = T.float8_e4m3fn + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" @T.prim_func def main_no_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], q_dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], q_dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): with T.Kernel(batch, heads // min(block_H, kv_group_num), threads=256) as (bx, by): Q_shared = T.alloc_shared([block_H, dim], dtype) @@ -46,34 +48,27 @@ def main_no_split( cur_kv_head = by // (kv_group_num // block_H) T.use_swizzle(10) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - }) - T.copy(Q[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_shared) - T.copy(Q_pe[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_shared) + T.copy(Q_pe[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) T.disable_warp_group_reg_alloc() loop_range = T.ceildiv(seqlen_kv, block_N) for k in T.Pipelined(loop_range, num_stages=2): - T.copy(KV[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], qKV_shared) - T.copy(K_pe[bx, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_pe_shared) + T.copy(KV[bx, k * block_N : (k + 1) * block_N, cur_kv_head, :], qKV_shared) + T.copy(K_pe[bx, k * block_N : (k + 1) * block_N, cur_kv_head, :], K_pe_shared) T.copy(qKV_shared, KV_shared) T.clear(acc_s) - T.gemm( - Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - T.gemm( - Q_pe_shared, - K_pe_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_shared, KV_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_pe_shared, K_pe_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): @@ -88,7 +83,7 @@ def main_no_split( for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bx, by * VALID_BLOCK_H:(by + 1) * VALID_BLOCK_H, :]) + T.copy(O_shared, Output[bx, by * VALID_BLOCK_H : (by + 1) * VALID_BLOCK_H, :]) return main_no_split @@ -106,42 +101,35 @@ def ref_program(q, q_pe, kv, k_pe): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=128, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') + parser.add_argument("--batch", type=int, default=128, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim qk_flops = 2 * batch * heads * kv_ctx * (dim + pe_dim) diff --git a/examples/deepseek_mla/regression_example_mla_decode.py b/examples/deepseek_mla/regression_example_mla_decode.py new file mode 100644 index 0000000000..64e1c436a0 --- /dev/null +++ b/examples/deepseek_mla/regression_example_mla_decode.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_mla_decode + + +def regression_example_mla_decode(): + tilelang.testing.process_func(example_mla_decode.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/deepseek_mla/test_example_mla_decode.py b/examples/deepseek_mla/test_example_mla_decode.py index 66a750f7df..a269ea57ae 100644 --- a/examples/deepseek_mla/test_example_mla_decode.py +++ b/examples/deepseek_mla/test_example_mla_decode.py @@ -1,5 +1,4 @@ import tilelang.testing - import example_mla_decode diff --git a/examples/deepseek_mla/torch_refs.py b/examples/deepseek_mla/torch_refs.py index 4b4c888cd2..aae6c7cd2b 100644 --- a/examples/deepseek_mla/torch_refs.py +++ b/examples/deepseek_mla/torch_refs.py @@ -11,7 +11,7 @@ def flash_split_ref(Q, Q_pe, KV, K_pe): block_N = 64 seqlen_kv = KV.size(1) - scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) + scale = (1.0 / (dim + pe_dim)) ** 0.5 * 1.44269504 # log2(e) acc_s = torch.empty((batch, nheads, block_N), device="cuda", dtype=torch.float) acc_s_cast = torch.empty((batch, nheads, block_N), device="cuda", dtype=torch.float16) acc_o = torch.empty((batch, nheads, dim), device="cuda", dtype=torch.float) @@ -31,18 +31,20 @@ def flash_split_ref(Q, Q_pe, KV, K_pe): for ks in range(num_split): acc_o.fill_(0) logsum.fill_(0) - scores_max.fill_(float('-inf')) - scores_max_prev.fill_(float('-inf')) + scores_max.fill_(float("-inf")) + scores_max_prev.fill_(float("-inf")) for i in range(int((seqlen_kv // num_split) / block_N)): acc_s.fill_(0) - acc_s = torch.einsum('bhd,bkhd->bhk', Q_, - KV_[:, (seqlen_kv // num_split) * ks + - i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) # [batch, nheads, block_N] + acc_s = torch.einsum( + "bhd,bkhd->bhk", + Q_, + KV_[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) # [batch, nheads, block_N] acc_s += torch.einsum( - 'bhd,bkhd->bhk', Q_pe_, - K_pe_[:, (seqlen_kv // num_split) * ks + i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) + "bhd,bkhd->bhk", + Q_pe_, + K_pe_[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) scores_max_prev = scores_max scores_max = acc_s.max(dim=-1, keepdim=False).values # [batch, nheads] scores_scale = torch.exp2(scores_max_prev - scores_max) # [batch, nheads] @@ -50,9 +52,10 @@ def flash_split_ref(Q, Q_pe, KV, K_pe): acc_s = torch.exp2(acc_s - scores_max[:, :, None]) acc_s_cast = acc_s.to(torch.float16) # [batch, nheads, block_N] acc_o += torch.einsum( - 'bhk,bkhd->bhd', acc_s_cast, - KV_[:, (seqlen_kv // num_split) * ks + i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) + "bhk,bkhd->bhd", + acc_s_cast, + KV_[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) scores_sum = acc_s.sum(dim=-1, keepdim=False) logsum = logsum * scores_scale + scores_sum acc_o /= logsum[:, :, None] diff --git a/examples/deepseek_nsa/benchmark/benchmark_nsa_fwd.py b/examples/deepseek_nsa/benchmark/benchmark_nsa_fwd.py index daee39865c..ca98d01be9 100644 --- a/examples/deepseek_nsa/benchmark/benchmark_nsa_fwd.py +++ b/examples/deepseek_nsa/benchmark/benchmark_nsa_fwd.py @@ -14,21 +14,44 @@ from fla.utils import autocast_custom_fwd, contiguous -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor), -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) @triton.jit -def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices, - block_counts, offsets, token_indices, T, H: tl.constexpr, - HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr, - S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr, - BV: tl.constexpr, USE_OFFSETS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_fwd_kernel( + q, + k, + v, + o_slc, + o_swa, + lse_slc, + lse_swa, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H @@ -40,20 +63,18 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc NS = S - p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # the Q block is kept in the shared memory throughout the whole kernel # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), - (G, BV), (1, 0)) + p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_slc = tl.zeros([G, BV], dtype=tl.float32) - b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_slc = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_slc = tl.zeros([G], dtype=tl.float32) for i in range(NS): i_s = tl.load(block_indices + i).to(tl.int32) * BS @@ -66,7 +87,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1)) # [G, BS] b_s_slc = tl.dot(b_q, b_k_slc) - b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf')) + b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float("-inf")) # [G] b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc @@ -87,7 +108,6 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -100,8 +120,7 @@ def forward(ctx, q, k, v, block_indices, block_size, scale, offsets): # [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]] token_indices = prepare_token_indices(offsets) if offsets is not None else None - o, lse = parallel_nsa_fwd( - q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) + o, lse = parallel_nsa_fwd(q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) ctx.save_for_backward(q, k, v, o, lse) ctx.block_indices = block_indices ctx.block_size = block_size @@ -172,7 +191,6 @@ def parallel_nsa_fwd( @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -195,7 +213,8 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, window_size=window_size, scale=scale, offsets=offsets, - token_indices=token_indices) + token_indices=token_indices, + ) ctx.save_for_backward(q, k, v, o_slc, lse_slc, o_swa, lse_swa) ctx.block_indices = block_indices ctx.block_counts = block_counts @@ -207,18 +226,20 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, return o_slc.to(q.dtype), o_swa.to(q.dtype) if o_swa is not None else o_swa -def parallel_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def parallel_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -258,44 +279,44 @@ def parallel_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, - window_size, scale, cu_seqlens) + o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: o = torch.addcmul(o_slc * g_slc.unsqueeze(-1), o_swa, g_swa.unsqueeze(-1)) else: o = o_slc * g_slc.unsqueeze(-1) if head_first: - o = rearrange(o, 'b t h d -> b h t d') + o = rearrange(o, "b t h d -> b h t d") return o -def naive_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def naive_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -335,26 +356,24 @@ def naive_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - raise RuntimeError( - "Sequences with variable lengths are not supported for head-first mode") + raise RuntimeError("Sequences with variable lengths are not supported for head-first mode") if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") dtype = q.dtype G = q.shape[2] // k.shape[2] BS = block_size S = block_indices.shape[-1] - k, v, block_indices = (repeat(x, 'b t h d -> b t (h g) d', g=G) for x in (k, v, block_indices)) + k, v, block_indices = (repeat(x, "b t h d -> b t (h g) d", g=G) for x in (k, v, block_indices)) if isinstance(block_counts, torch.Tensor): - block_counts = repeat(block_counts, 'b t h -> b t (h g)', g=G) + block_counts = repeat(block_counts, "b t h -> b t (h g)", g=G) c = torch.arange(S).repeat_interleave(BS).unsqueeze(1).expand(-1, q.shape[2]).to(q.device) q, k, v = map(lambda x: x.float(), (q, k, v)) @@ -364,14 +383,11 @@ def naive_nsa(q: torch.Tensor, if cu_seqlens is None: varlen = False B, T = q.shape[:2] - cu_seqlens = torch.cat( - [block_indices.new_tensor(range(0, B * T, T)), - block_indices.new_tensor([B * T])]) + cu_seqlens = torch.cat([block_indices.new_tensor(range(0, B * T, T)), block_indices.new_tensor([B * T])]) for i in range(len(cu_seqlens) - 1): if not varlen: - q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = q[i], k[i], v[i], g_slc[i], g_swa[ - i], block_indices[i] + q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = q[i], k[i], v[i], g_slc[i], g_swa[i], block_indices[i] if isinstance(block_counts, torch.Tensor): s_b = block_counts[i] else: @@ -379,10 +395,10 @@ def naive_nsa(q: torch.Tensor, else: T = cu_seqlens[i + 1] - cu_seqlens[i] q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = map( - lambda x: x[0][cu_seqlens[i]:cu_seqlens[i + 1]], - (q, k, v, g_slc, g_swa, block_indices)) + lambda x: x[0][cu_seqlens[i] : cu_seqlens[i + 1]], (q, k, v, g_slc, g_swa, block_indices) + ) if isinstance(block_counts, torch.Tensor): - s_b = block_counts[0][cu_seqlens[i]:cu_seqlens[i + 1]] + s_b = block_counts[0][cu_seqlens[i] : cu_seqlens[i + 1]] else: s_b = block_counts @@ -404,71 +420,58 @@ def naive_nsa(q: torch.Tensor, else: s_i = s_b # [S*BS, HQ, -1] - k_i_slc, v_i_slc = map( - lambda x: x.gather( - 0, - i_i.clamp(0, T - 1).unsqueeze(-1).expand(*i_i.shape, x.shape[-1])), (k_b, v_b)) + k_i_slc, v_i_slc = map(lambda x: x.gather(0, i_i.clamp(0, T - 1).unsqueeze(-1).expand(*i_i.shape, x.shape[-1])), (k_b, v_b)) # [S*BS, HQ] - attn_slc = torch.einsum('h d, n h d -> n h', q_i, k_i_slc).masked_fill( - torch.logical_or(i_i < 0, i_i > i_q) | - (c >= s_i if block_counts is not None else False), float('-inf')).softmax(0) + attn_slc = ( + torch.einsum("h d, n h d -> n h", q_i, k_i_slc) + .masked_fill(torch.logical_or(i_i < 0, i_i > i_q) | (c >= s_i if block_counts is not None else False), float("-inf")) + .softmax(0) + ) if not varlen: - o_slc[i, i_q] = torch.einsum('n h, n h v -> h v', attn_slc, - v_i_slc) * g_slc_i.unsqueeze(-1) + o_slc[i, i_q] = torch.einsum("n h, n h v -> h v", attn_slc, v_i_slc) * g_slc_i.unsqueeze(-1) else: - o_slc[0][cu_seqlens[i] + i_q] = torch.einsum('n h, n h v -> h v', attn_slc, - v_i_slc) * g_slc_i.unsqueeze(-1) + o_slc[0][cu_seqlens[i] + i_q] = torch.einsum("n h, n h v -> h v", attn_slc, v_i_slc) * g_slc_i.unsqueeze(-1) if window_size > 0: - k_i_swa, v_i_swa = map(lambda x: x[max(0, i_q - window_size + 1):i_q + 1], - (k_b, v_b)) - attn_swa = torch.einsum('h d, n h d -> n h', q_i, k_i_swa).softmax(0) + k_i_swa, v_i_swa = map(lambda x: x[max(0, i_q - window_size + 1) : i_q + 1], (k_b, v_b)) + attn_swa = torch.einsum("h d, n h d -> n h", q_i, k_i_swa).softmax(0) if not varlen: - o_swa[i, i_q] = torch.einsum('n h, n h v -> h v', attn_swa, - v_i_swa) * g_swa_i.unsqueeze(-1) + o_swa[i, i_q] = torch.einsum("n h, n h v -> h v", attn_swa, v_i_swa) * g_swa_i.unsqueeze(-1) else: - o_swa[0][cu_seqlens[i] + i_q] = torch.einsum('n h, n h v -> h v', attn_swa, - v_i_swa) * g_swa_i.unsqueeze(-1) + o_swa[0][cu_seqlens[i] + i_q] = torch.einsum("n h, n h v -> h v", attn_swa, v_i_swa) * g_swa_i.unsqueeze(-1) if head_first: - o_slc = rearrange(o_slc, 'b t h d -> b h t d') - o_swa = rearrange(o_swa, 'b t h d -> b h t d') + o_slc = rearrange(o_slc, "b t h d -> b h t d") + o_swa = rearrange(o_swa, "b t h d -> b h t d") return o_slc.to(dtype) + o_swa.to(dtype) if o_swa is not None else o_slc.to(dtype) def get_configs(): import itertools + iter_params = dict( block_T=[128, 256, 512], num_stages=[0, 1, 2, 4, 5], threads=[32, 64, 128, 256, 512], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] -@tilelang.autotune(configs=get_configs(),) +@tilelang.autotune( + configs=get_configs(), +) @tilelang.jit( pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) -def tilelang_sparse_attention(batch, - heads, - seq_len, - dim, - is_causal, - scale=None, - block_size=64, - groups=1, - selected_blocks=16, - block_T=128, - num_stages=2, - threads=32): + } +) +def tilelang_sparse_attention( + batch, heads, seq_len, dim, is_causal, scale=None, block_size=64, groups=1, selected_blocks=16, block_T=128, num_stages=2, threads=32 +): if scale is None: - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) else: scale = scale * 1.44269504 # log2(e) @@ -476,9 +479,9 @@ def tilelang_sparse_attention(batch, q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] block_indices_shape = [batch, seq_len, head_kv, selected_blocks] - block_indices_dtype = "int32" - dtype = "float16" - accum_dtype = "float" + block_indices_dtype = T.int32 + dtype = T.float16 + accum_dtype = T.float32 block_S = block_size block_T = min(block_T, tilelang.math.next_power_of_2(dim)) @@ -493,11 +496,11 @@ def tilelang_sparse_attention(batch, @T.prim_func def tilelang_sparse_attention( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(seq_len, NV, batch * head_kv, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([G, BK], dtype) @@ -514,13 +517,11 @@ def tilelang_sparse_attention( scores_sum = T.alloc_fragment([G], accum_dtype) logsum = T.alloc_fragment([G], accum_dtype) - T.annotate_layout({O_shared: tilelang.layout.make_swizzled_layout(O_shared)}) - i_t, i_v, i_bh = bx, by, bz i_b, i_h = i_bh // head_kv, i_bh % head_kv NS = S - T.copy(Q[i_b, i_t, i_h * G:(i_h + 1) * G, :], Q_shared) + T.copy(Q[i_b, i_t, i_h * G : (i_h + 1) * G, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) @@ -530,21 +531,15 @@ def tilelang_sparse_attention( i_s = BlockIndices[i_b, i_t, i_h, i] * BS if i_s <= i_t and i_s >= 0: # [BS, BK] - T.copy(K[i_b, i_s:i_s + BS, i_h, :], K_shared) + T.copy(K[i_b, i_s : i_s + BS, i_h, :], K_shared) if is_causal: for i, j in T.Parallel(G, BS): - acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) # Softmax T.copy(scores_max, scores_max_prev) @@ -564,45 +559,33 @@ def tilelang_sparse_attention( acc_o[i, j] *= scores_scale[i] # V * softmax(Q * K) - T.copy(V[i_b, i_s:i_s + BS, i_h, i_v * BV:(i_v + 1) * BV], V_shared) + T.copy(V[i_b, i_s : i_s + BS, i_h, i_v * BV : (i_v + 1) * BV], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(G, BV): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[i_b, i_t, i_h * G:(i_h + 1) * G, i_v * BV:(i_v + 1) * BV]) + T.copy(O_shared, Output[i_b, i_t, i_h * G : (i_h + 1) * G, i_v * BV : (i_v + 1) * BV]) return tilelang_sparse_attention def generate_block_indices(batch, seq_len, heads, selected_blocks, block_size): """Generate random block indices for the benchmark.""" - block_indices = torch.full((batch, seq_len, heads, selected_blocks), - seq_len, - dtype=torch.long, - device='cuda') + block_indices = torch.full((batch, seq_len, heads, selected_blocks), seq_len, dtype=torch.long, device="cuda") for b in range(batch): for t in range(seq_len): for h in range(heads): i_i = torch.randperm(max(1, (t // block_size)))[:selected_blocks] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i return block_indices.sort(-1)[0] -def benchmark_nsa(batch_size, - seq_len, - heads, - head_query, - dim, - selected_blocks, - block_size, - dtype, - scale, - warmup=10, - iterations=100, - validate=False): +def benchmark_nsa( + batch_size, seq_len, heads, head_query, dim, selected_blocks, block_size, dtype, scale, warmup=10, iterations=100, validate=False +): """Benchmark the TileLang Sparse Attention implementation.""" # Set random seed for reproducibility @@ -628,14 +611,13 @@ def benchmark_nsa(batch_size, print(f"Profiler latency: {profiler_latency} ms") # Create input tensors - Q = torch.randn((batch_size, seq_len, head_query, dim), dtype=dtype, device='cuda') - K = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device='cuda') - V = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device='cuda') - out = torch.empty((batch_size, seq_len, head_query, dim), dtype=dtype, device='cuda') + Q = torch.randn((batch_size, seq_len, head_query, dim), dtype=dtype, device="cuda") + K = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device="cuda") + V = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device="cuda") + out = torch.empty((batch_size, seq_len, head_query, dim), dtype=dtype, device="cuda") # Generate block indices - block_indices = generate_block_indices(batch_size, seq_len, heads, selected_blocks, - block_size).to(torch.int32) + block_indices = generate_block_indices(batch_size, seq_len, heads, selected_blocks, block_size).to(torch.int32) # Warmup for _ in range(warmup): @@ -666,10 +648,9 @@ def benchmark_nsa(batch_size, # Validate result against reference if requested if validate: - g_slc = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device='cuda') - g_swa = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device='cuda') - block_counts = torch.randint( - 1, selected_blocks + 1, (batch_size, seq_len, heads), device='cuda') + g_slc = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device="cuda") + g_swa = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device="cuda") + block_counts = torch.randint(1, selected_blocks + 1, (batch_size, seq_len, heads), device="cuda") ref = naive_nsa( q=Q, @@ -700,22 +681,13 @@ def benchmark_nsa(batch_size, "head_query": head_query, "dim": dim, "selected_blocks": selected_blocks, - "block_size": block_size + "block_size": block_size, } -def benchmark_triton_nsa(batch_size, - seq_len, - heads, - head_query, - dim, - selected_blocks, - block_size, - dtype, - scale, - warmup=10, - iterations=100, - validate=False): +def benchmark_triton_nsa( + batch_size, seq_len, heads, head_query, dim, selected_blocks, block_size, dtype, scale, warmup=10, iterations=100, validate=False +): """Benchmark the Triton-based TileLang Sparse Attention implementation.""" # Set random seed for reproducibility @@ -723,18 +695,17 @@ def benchmark_triton_nsa(batch_size, torch.random.manual_seed(0) # Create input tensors - Q = torch.randn((batch_size, seq_len, head_query, dim), dtype=dtype, device='cuda') - K = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device='cuda') - V = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device='cuda') - g_slc = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device='cuda') - g_swa = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device='cuda') + Q = torch.randn((batch_size, seq_len, head_query, dim), dtype=dtype, device="cuda") + K = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device="cuda") + V = torch.randn((batch_size, seq_len, heads, dim), dtype=dtype, device="cuda") + g_slc = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device="cuda") + g_swa = torch.ones((batch_size, seq_len, head_query), dtype=dtype, device="cuda") # Generate block indices block_indices = generate_block_indices(batch_size, seq_len, heads, selected_blocks, block_size) - block_counts = torch.randint( - 1, selected_blocks + 1, (batch_size, seq_len, heads), device='cuda') - o_slc = torch.empty((batch_size, seq_len, head_query, dim), dtype=dtype, device='cuda') - lse_slc = torch.empty((batch_size, seq_len, head_query), dtype=torch.float, device='cuda') + block_counts = torch.randint(1, selected_blocks + 1, (batch_size, seq_len, heads), device="cuda") + o_slc = torch.empty((batch_size, seq_len, head_query, dim), dtype=dtype, device="cuda") + lse_slc = torch.empty((batch_size, seq_len, head_query), dtype=torch.float, device="cuda") # Warmup for _ in range(warmup): @@ -750,7 +721,8 @@ def benchmark_triton_nsa(batch_size, block_counts=block_counts, block_size=block_size, window_size=0, - scale=scale) + scale=scale, + ) # Synchronize before timing torch.cuda.synchronize() @@ -770,7 +742,8 @@ def benchmark_triton_nsa(batch_size, block_counts=block_counts, block_size=block_size, window_size=0, - scale=scale) + scale=scale, + ) torch.cuda.synchronize() end_time = time.time() @@ -815,54 +788,28 @@ def benchmark_triton_nsa(batch_size, "head_query": head_query, "dim": dim, "selected_blocks": selected_blocks, - "block_size": block_size + "block_size": block_size, } -def run_benchmark_suite(impl='all'): +def run_benchmark_suite(impl="all"): """Run a suite of benchmarks with different configurations.""" # Define configurations to benchmark configs = [ # Small model config - Note: head_query must be a multiple of heads*16 for Triton - { - "batch_size": 2, - "seq_len": 1024, - "heads": 8, - "head_query": 8 * 16, - "dim": 64, - "selected_blocks": 8, - "block_size": 32 - }, - + {"batch_size": 2, "seq_len": 1024, "heads": 8, "head_query": 8 * 16, "dim": 64, "selected_blocks": 8, "block_size": 32}, # Medium model config - { - "batch_size": 2, - "seq_len": 2048, - "heads": 16, - "head_query": 16 * 16, - "dim": 64, - "selected_blocks": 16, - "block_size": 64 - }, - + {"batch_size": 2, "seq_len": 2048, "heads": 16, "head_query": 16 * 16, "dim": 64, "selected_blocks": 16, "block_size": 64}, # Large model config - { - "batch_size": 1, - "seq_len": 4096, - "heads": 32, - "head_query": 32 * 16, - "dim": 128, - "selected_blocks": 32, - "block_size": 128 - }, + {"batch_size": 1, "seq_len": 4096, "heads": 32, "head_query": 32 * 16, "dim": 128, "selected_blocks": 32, "block_size": 128}, ] results = [] for config in configs: print(f"Running benchmark with config: {config}") - if impl in ['all', 'tilelang']: + if impl in ["all", "tilelang"]: print("Benchmarking TileLang implementation:") result = benchmark_nsa( batch_size=config["batch_size"], @@ -874,12 +821,13 @@ def run_benchmark_suite(impl='all'): block_size=config["block_size"], dtype=torch.float16, scale=0.1, - validate=False) + validate=False, + ) results.append({"impl": "tilelang", **result}) print(f"Average time: {result['avg_time_ms']:.2f} ms") print(f"Performance: {result['tflops']:.2f} TFLOPs") - if impl in ['all', 'triton']: + if impl in ["all", "triton"]: print("Benchmarking Triton implementation:") result = benchmark_triton_nsa( batch_size=config["batch_size"], @@ -891,19 +839,24 @@ def run_benchmark_suite(impl='all'): block_size=config["block_size"], dtype=torch.float16, scale=0.1, - validate=False) + validate=False, + ) results.append({"impl": "triton", **result}) print(f"Average time: {result['avg_time_ms']:.2f} ms") print(f"Performance: {result['tflops']:.2f} TFLOPs") - if impl in ['all']: + if impl in ["all"]: # Print comparison if both implementations were run tilelang_result = next( - r for r in results if r["impl"] == "tilelang" and - r["batch_size"] == config["batch_size"] and r["seq_len"] == config["seq_len"]) + r + for r in results + if r["impl"] == "tilelang" and r["batch_size"] == config["batch_size"] and r["seq_len"] == config["seq_len"] + ) triton_result = next( - r for r in results if r["impl"] == "triton" and - r["batch_size"] == config["batch_size"] and r["seq_len"] == config["seq_len"]) + r + for r in results + if r["impl"] == "triton" and r["batch_size"] == config["batch_size"] and r["seq_len"] == config["seq_len"] + ) speedup = tilelang_result["avg_time_ms"] / triton_result["avg_time_ms"] print(f"Speedup (Triton vs TileLang): {speedup:.2f}x") @@ -921,8 +874,7 @@ def run_benchmark_suite(impl='all'): parser.add_argument("--dim", type=int, default=128, help="Head dimension") parser.add_argument("--selected_blocks", type=int, default=16, help="Number of selected blocks") parser.add_argument("--block_size", type=int, default=32, help="Block size") - parser.add_argument( - "--dtype", type=str, default="float16", help="Data type (float16 or float32)") + parser.add_argument("--dtype", type=str, default=T.float16, help="Data type (float16 or float32)") parser.add_argument("--scale", type=float, default=0.1, help="Attention scale factor") parser.add_argument("--iterations", type=int, default=100, help="Number of iterations") parser.add_argument("--warmup", type=int, default=10, help="Warmup iterations") @@ -933,7 +885,8 @@ def run_benchmark_suite(impl='all'): type=str, default="all", choices=["tilelang", "triton", "all"], - help="Implementation to benchmark (tilelang, triton, or all)") + help="Implementation to benchmark (tilelang, triton, or all)", + ) args = parser.parse_args() @@ -941,13 +894,12 @@ def run_benchmark_suite(impl='all'): if args.impl in ["triton", "all"] and args.head_query % (args.heads * 16) != 0: # Adjust head_query to nearest valid value args.head_query = ((args.head_query // (args.heads * 16)) + 1) * (args.heads * 16) - print( - f"Adjusted head_query to {args.head_query} to be compatible with Triton implementation") + print(f"Adjusted head_query to {args.head_query} to be compatible with Triton implementation") if args.suite: run_benchmark_suite(impl=args.impl) else: - dtype = torch.float16 if args.dtype == "float16" else torch.float32 + dtype = torch.float16 if args.dtype == T.float16 else torch.float32 if args.impl in ["tilelang", "all"]: print("Benchmarking TileLang implementation:") @@ -963,12 +915,14 @@ def run_benchmark_suite(impl='all'): scale=args.scale, warmup=args.warmup, iterations=args.iterations, - validate=args.validate) + validate=args.validate, + ) print("\nBenchmark Results (TileLang):") print( - f"Configuration: batch={args.batch}, seq_len={args.seq_len}, heads={args.heads}, " + - f"head_query={args.head_query}, dim={args.dim}, blocks={args.selected_blocks}, " + - f"block_size={args.block_size}") + f"Configuration: batch={args.batch}, seq_len={args.seq_len}, heads={args.heads}, " + + f"head_query={args.head_query}, dim={args.dim}, blocks={args.selected_blocks}, " + + f"block_size={args.block_size}" + ) print(f"Average time: {result['avg_time_ms']:.2f} ms") print(f"Performance: {result['tflops']:.2f} TFLOPs") @@ -986,11 +940,13 @@ def run_benchmark_suite(impl='all'): scale=args.scale, warmup=args.warmup, iterations=args.iterations, - validate=args.validate) + validate=args.validate, + ) print("\nBenchmark Results (Triton):") print( - f"Configuration: batch={args.batch}, seq_len={args.seq_len}, heads={args.heads}, " + - f"head_query={args.head_query}, dim={args.dim}, blocks={args.selected_blocks}, " + - f"block_size={args.block_size}") + f"Configuration: batch={args.batch}, seq_len={args.seq_len}, heads={args.heads}, " + + f"head_query={args.head_query}, dim={args.dim}, blocks={args.selected_blocks}, " + + f"block_size={args.block_size}" + ) print(f"Average time: {result['avg_time_ms']:.2f} ms") print(f"Performance: {result['tflops']:.2f} TFLOPs") diff --git a/examples/deepseek_nsa/example_tilelang_nsa_bwd.py b/examples/deepseek_nsa/example_tilelang_nsa_bwd.py index 8387d22714..3da285a9ba 100644 --- a/examples/deepseek_nsa/example_tilelang_nsa_bwd.py +++ b/examples/deepseek_nsa/example_tilelang_nsa_bwd.py @@ -7,6 +7,7 @@ import triton import fla + if parse(fla.__version__) < parse("0.2.1"): from fla.ops.common.utils import prepare_token_indices else: @@ -22,7 +23,8 @@ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + } +) def tilelang_kernel_fwd( batch, heads, @@ -34,11 +36,10 @@ def tilelang_kernel_fwd( groups=1, selected_blocks=16, ): - from tilelang import language as T if scale is None: - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) else: scale = scale * 1.44269504 # log2(e) @@ -48,9 +49,9 @@ def tilelang_kernel_fwd( o_slc_shape = [batch, seq_len, heads, dim] lse_slc_shape = [batch, seq_len, heads] block_indices_shape = [batch, seq_len, head_kv, selected_blocks] - block_indices_dtype = "int32" - dtype = "float16" - accum_dtype = "float" + block_indices_dtype = T.int32 + dtype = T.float16 + accum_dtype = T.float32 block_S = block_size block_T = min(128, tilelang.math.next_power_of_2(dim)) @@ -67,12 +68,12 @@ def tilelang_kernel_fwd( @T.prim_func def native_sparse_attention( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), - O_slc: T.Tensor(o_slc_shape, dtype), - LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), + O_slc: T.Tensor(o_slc_shape, dtype), + LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), ): with T.Kernel(seq_len, NV, batch * head_kv, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([G, BK], dtype) @@ -93,7 +94,7 @@ def native_sparse_attention( i_b, i_h = i_bh // head_kv, i_bh % head_kv NS = S - T.copy(Q[i_b, i_t, i_h * G:(i_h + 1) * G, :], Q_shared) + T.copy(Q[i_b, i_t, i_h * G : (i_h + 1) * G, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) @@ -103,12 +104,11 @@ def native_sparse_attention( i_s = BlockIndices[i_b, i_t, i_h, i] * BS if i_s <= i_t and i_s >= 0: # [BS, BK] - T.copy(K[i_b, i_s:i_s + BS, i_h, :], K_shared) + T.copy(K[i_b, i_s : i_s + BS, i_h, :], K_shared) if is_causal: - for i, j in T.Parallel(G, BS): - acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, - -T.infinity(acc_s.dtype)) + for k, j in T.Parallel(G, BS): + acc_s[k, j] = T.if_then_else(i_t >= (i_s + j), 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) @@ -124,21 +124,21 @@ def native_sparse_attention( T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=True) - for i in T.Parallel(G): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(G, BS): - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + for k in T.Parallel(G): + scores_scale[k] = T.exp2(scores_max_prev[k] * scale - scores_max[k] * scale) + for k, j in T.Parallel(G, BS): + acc_s[k, j] = T.exp2(acc_s[k, j] * scale - scores_max[k] * scale) T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(G): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + for k in T.Parallel(G): + logsum[k] = logsum[k] * scores_scale[k] + scores_sum[k] T.copy(acc_s, acc_s_cast) # Rescale - for i, j in T.Parallel(G, BV): - acc_o[i, j] *= scores_scale[i] + for k, j in T.Parallel(G, BV): + acc_o[k, j] *= scores_scale[k] # V * softmax(Q * K) - T.copy(V[i_b, i_s:i_s + BS, i_h, i_v * BV:(i_v + 1) * BV], V_shared) + T.copy(V[i_b, i_s : i_s + BS, i_h, i_v * BV : (i_v + 1) * BV], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(G, BV): @@ -146,18 +146,20 @@ def native_sparse_attention( T.copy(acc_o, O_shared) T.copy( O_shared, - O_slc[i_b, i_t, i_h * G:(i_h + 1) * G, i_v * BV:(i_v + 1) * BV], + O_slc[i_b, i_t, i_h * G : (i_h + 1) * G, i_v * BV : (i_v + 1) * BV], ) for i in T.Parallel(G): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, LSE_slc[i_b, i_t, i_h * G:(i_h + 1) * G]) + T.copy(logsum, LSE_slc[i_b, i_t, i_h * G : (i_h + 1) * G]) return native_sparse_attention -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def tilelang_kernel_bwd_dkv( batch, heads, @@ -168,11 +170,11 @@ def tilelang_kernel_bwd_dkv( block_size=64, groups=1, selected_blocks=16, - dtype="float16", - accum_dtype="float", + dtype=T.float16, + accum_dtype=T.float32, ): if scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 else: sm_scale = scale @@ -207,15 +209,15 @@ def tilelang_kernel_bwd_dkv( @T.prim_func def flash_bwd_dkv( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(k_shape, dtype), - V: T.Tensor(v_shape, dtype), - LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), - Delta_slc: T.Tensor(delta_slc_shape, accum_dtype), - DO_slc: T.Tensor(do_slc_shape, dtype), - DK: T.Tensor(dk_shape, dtype), - DV: T.Tensor(dv_shape, dtype), - BlockMask: T.Tensor(block_mask_shape, "int32"), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(k_shape, dtype), + V: T.Tensor(v_shape, dtype), + LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), + Delta_slc: T.Tensor(delta_slc_shape, accum_dtype), + DO_slc: T.Tensor(do_slc_shape, dtype), + DK: T.Tensor(dk_shape, dtype), + DV: T.Tensor(dv_shape, dtype), + BlockMask: T.Tensor(block_mask_shape, T.int32), ): with T.Kernel(NV, NS, B * H, threads=num_threads) as (i_v, i_s, i_bh): K_shared = T.alloc_shared([BS, BK], dtype) @@ -238,31 +240,25 @@ def flash_bwd_dkv( i_b, i_h = i_bh // H, i_bh % H - T.copy(K[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BK], K_shared) - T.copy(V[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BV], V_shared) + T.copy(K[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BK], K_shared) + T.copy(V[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BV], V_shared) # [BS, BK] T.clear(dk) # [BS, BV] T.clear(dv) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) - loop_st = i_s * BS loop_ed = seq_len for i in T.Pipelined( - start=loop_st, - stop=loop_ed, - num_stages=0, + start=loop_st, + stop=loop_ed, + num_stages=0, ): b_m_slc = BlockMask[i_b, i, i_h, i_s] if b_m_slc != 0: # [G, BK] - T.copy(Q[i_b, i, i_h * G:(i_h + 1) * G, :BK], Q_shared) + T.copy(Q[i_b, i, i_h * G : (i_h + 1) * G, :BK], Q_shared) T.clear(qkT) # [BS, BK] @ [G, BK] -> [BS, G] T.gemm( @@ -273,7 +269,7 @@ def flash_bwd_dkv( policy=T.GemmWarpPolicy.FullRow, ) # [G] - T.copy(LSE_slc[i_b, i, i_h * G:(i_h + 1) * G], lse_shared) + T.copy(LSE_slc[i_b, i, i_h * G : (i_h + 1) * G], lse_shared) for _i, _j in T.Parallel(BS, G): qkT[_i, _j] = T.exp2(qkT[_i, _j] * scale - lse_shared[_j]) @@ -282,7 +278,7 @@ def flash_bwd_dkv( qkT[_i, _j] = T.if_then_else(i >= (i_s * BS + _i), qkT[_i, _j], 0) # [G, BV] - T.copy(DO_slc[i_b, i, i_h * G:(i_h + 1) * G, :BV], do) + T.copy(DO_slc[i_b, i, i_h * G : (i_h + 1) * G, :BV], do) T.clear(dsT) # [BS, BV] @ [G, BV] -> [BS, G] T.gemm( @@ -296,7 +292,7 @@ def flash_bwd_dkv( # [BS, G] @ [G, BV] -> [BS, BV] T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) # [G] - T.copy(Delta_slc[i_b, i, i_h * G:(i_h + 1) * G], delta) + T.copy(Delta_slc[i_b, i, i_h * G : (i_h + 1) * G], delta) for i, j in T.Parallel(BS, G): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -305,8 +301,8 @@ def flash_bwd_dkv( T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, DV[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BV]) - T.copy(dk_shared, DK[i_v, i_b, i_s * BS:(i_s + 1) * BS, i_h, :BK]) + T.copy(dv_shared, DV[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BV]) + T.copy(dk_shared, DK[i_v, i_b, i_s * BS : (i_s + 1) * BS, i_h, :BK]) return flash_bwd_dkv @@ -321,9 +317,11 @@ def make_dq_layout(dQ): ) -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def tilelang_kernel_bwd_dqkv( batch, heads, @@ -334,11 +332,11 @@ def tilelang_kernel_bwd_dqkv( block_size=64, groups=1, selected_blocks=16, - dtype="float16", - accum_dtype="float", + dtype=T.float16, + accum_dtype=T.float32, ): if scale is None: - sm_scale = (1.0 / dim)**0.5 + sm_scale = (1.0 / dim) ** 0.5 else: sm_scale = scale @@ -373,16 +371,16 @@ def tilelang_kernel_bwd_dqkv( @T.prim_func def flash_bwd_dqkv( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(k_shape, dtype), - V: T.Tensor(v_shape, dtype), - LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), - Delta_slc: T.Tensor(delta_slc_shape, accum_dtype), - DO_slc: T.Tensor(do_slc_shape, dtype), - DQ: T.Tensor(dq_shape, dtype), - DK: T.Tensor(dk_shape, dtype), - DV: T.Tensor(dv_shape, dtype), - BlockMask: T.Tensor(block_mask_shape, "int32"), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(k_shape, dtype), + V: T.Tensor(v_shape, dtype), + LSE_slc: T.Tensor(lse_slc_shape, accum_dtype), + Delta_slc: T.Tensor(delta_slc_shape, accum_dtype), + DO_slc: T.Tensor(do_slc_shape, dtype), + DQ: T.Tensor(dq_shape, dtype), + DK: T.Tensor(dk_shape, dtype), + DV: T.Tensor(dv_shape, dtype), + BlockMask: T.Tensor(block_mask_shape, T.int32), ): with T.Kernel(NV, NS, B * H, threads=num_threads) as (i_v, i_s, i_bh): K_shared = T.alloc_shared([BS, BK], dtype) @@ -406,31 +404,25 @@ def flash_bwd_dqkv( i_b, i_h = i_bh // H, i_bh % H - T.copy(K[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BK], K_shared) - T.copy(V[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BV], V_shared) + T.copy(K[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BK], K_shared) + T.copy(V[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BV], V_shared) # [BS, BK] T.clear(dk) # [BS, BV] T.clear(dv) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) - loop_st = i_s * BS loop_ed = seq_len for i in T.Pipelined( - start=loop_st, - stop=loop_ed, - num_stages=0, + start=loop_st, + stop=loop_ed, + num_stages=0, ): b_m_slc = BlockMask[i_b, i, i_h, i_s] if b_m_slc != 0: # [G, BK] - T.copy(Q[i_b, i, i_h * G:(i_h + 1) * G, :BK], Q_shared) + T.copy(Q[i_b, i, i_h * G : (i_h + 1) * G, :BK], Q_shared) T.clear(qkT) # [BS, BK] @ [G, BK] -> [BS, G] T.gemm( @@ -441,7 +433,7 @@ def flash_bwd_dqkv( policy=T.GemmWarpPolicy.FullRow, ) # [G] - T.copy(LSE_slc[i_b, i, i_h * G:(i_h + 1) * G], lse_shared) + T.copy(LSE_slc[i_b, i, i_h * G : (i_h + 1) * G], lse_shared) for _i, _j in T.Parallel(BS, G): qkT[_i, _j] = T.exp2(qkT[_i, _j] * scale - lse_shared[_j]) @@ -450,7 +442,7 @@ def flash_bwd_dqkv( qkT[_i, _j] = T.if_then_else(i >= (i_s * BS + _i), qkT[_i, _j], 0) # [G, BV] - T.copy(DO_slc[i_b, i, i_h * G:(i_h + 1) * G, :BV], do) + T.copy(DO_slc[i_b, i, i_h * G : (i_h + 1) * G, :BV], do) T.clear(dsT) # [BS, BV] @ [G, BV] -> [BS, G] T.gemm( @@ -464,9 +456,9 @@ def flash_bwd_dqkv( # [BS, G] @ [G, BV] -> [BS, BV] T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) # [G] - T.copy(Delta_slc[i_b, i, i_h * G:(i_h + 1) * G], delta) - for i, j in T.Parallel(BS, G): - dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale + T.copy(Delta_slc[i_b, i, i_h * G : (i_h + 1) * G], delta) + for _i, _j in T.Parallel(BS, G): + dsT_cast[_i, _j] = qkT[_i, _j] * (dsT[_i, _j] - delta[_j]) * sm_scale # [BS, G] @ [G, BK] -> [BS, BK] T.gemm(dsT_cast, Q_shared, dk, policy=T.GemmWarpPolicy.FullRow) @@ -480,23 +472,25 @@ def flash_bwd_dqkv( T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, DV[i_b, i_s * BS:(i_s + 1) * BS, i_h, :BV]) - T.copy(dk_shared, DK[i_v, i_b, i_s * BS:(i_s + 1) * BS, i_h, :BK]) + T.copy(dv_shared, DV[i_b, i_s * BS : (i_s + 1) * BS, i_h, :BV]) + T.copy(dk_shared, DK[i_v, i_b, i_s * BS : (i_s + 1) * BS, i_h, :BK]) return flash_bwd_dqkv @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def tilelang_kernel_preprocess( batch, heads, seq_len, dim, - dtype="float16", - accum_dtype="float", + dtype=T.float16, + accum_dtype=T.float32, blk=32, ): from tilelang import language as T @@ -505,9 +499,9 @@ def tilelang_kernel_preprocess( @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, seq_len, heads], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, seq_len, heads], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -516,27 +510,29 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, by * blk:(by + 1) * blk, bx]) + T.copy(delta, Delta[bz, by * blk : (by + 1) * blk, bx]) return flash_bwd_prep @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def tilelang_kernel_block_mask( batch, heads, seq_len, selected_blocks, block_size, - dtype="int32", + dtype=T.int32, ): from tilelang import language as T @@ -551,9 +547,9 @@ def tilelang_kernel_block_mask( @T.prim_func def flash_bwd_block_mask( - BlockIndices: T.Tensor(block_indices_shape, dtype), # type: ignore - BlockCounts: T.Tensor(block_counts_shape, dtype), # type: ignore - BlockMask: T.Tensor(block_mask_shape, dtype), # type: ignore + BlockIndices: T.Tensor(block_indices_shape, dtype), # type: ignore + BlockCounts: T.Tensor(block_counts_shape, dtype), # type: ignore + BlockMask: T.Tensor(block_mask_shape, dtype), # type: ignore ): with T.Kernel(seq_len, batch, heads * S) as (bx, by, bz): i_t, i_b, i_hs = bx, by, bz @@ -603,9 +599,7 @@ def parallel_nsa_bwd( dk = torch.empty(NV, *k.shape, dtype=k.dtype, device=q.device) dv = torch.empty(v.shape, dtype=v.dtype, device=q.device) - block_mask = tilelang_kernel_block_mask(B, H, T, S, - BS)(block_indices.to(torch.int32), - block_counts.to(torch.int32)).to(torch.bool) + block_mask = tilelang_kernel_block_mask(B, H, T, S, BS)(block_indices.to(torch.int32), block_counts.to(torch.int32)).to(torch.bool) fused_qkv_bwd_kernel = tilelang_kernel_bwd_dqkv( batch=B, @@ -618,8 +612,7 @@ def parallel_nsa_bwd( selected_blocks=S, scale=scale, ) - fused_qkv_bwd_kernel(q, k, v, lse_slc, delta_slc, do_slc, dq, dk, dv, - block_mask.to(torch.int32)) + fused_qkv_bwd_kernel(q, k, v, lse_slc, delta_slc, do_slc, dq, dk, dv, block_mask.to(torch.int32)) dq = dq.sum(0) dk = dk.sum(0) @@ -628,7 +621,6 @@ def parallel_nsa_bwd( @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -773,23 +765,21 @@ def parallel_nsa( Outputs of shape `[B, SEQLEN, HQ, V]` if `head_first=False` else `[B, HQ, SEQLEN, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), - (q, k, v, block_indices)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): block_counts = rearrange(block_counts, "b h t -> b t h") - assert (q.shape[2] % (k.shape[2] * 16) == 0), "Group size must be a multiple of 16 in NSA" + assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, - window_size, scale, cu_seqlens) + o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: o = torch.addcmul(o_slc * g_slc.unsqueeze(-1), o_swa, g_swa.unsqueeze(-1)) else: @@ -814,7 +804,7 @@ def parallel_nsa( for t in range(T): for h in range(H): i_i = torch.randperm(max(1, (t // block_size)))[:S] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] block_counts = torch.randint(1, S + 1, (B, T, H), device="cuda") diff --git a/examples/deepseek_nsa/example_tilelang_nsa_decode.py b/examples/deepseek_nsa/example_tilelang_nsa_decode.py index 58f4355094..381d92493e 100644 --- a/examples/deepseek_nsa/example_tilelang_nsa_decode.py +++ b/examples/deepseek_nsa/example_tilelang_nsa_decode.py @@ -16,7 +16,8 @@ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def native_sparse_attention( batch, heads, @@ -25,18 +26,18 @@ def native_sparse_attention( scale=None, block_size=64, # Tile size for attention computation groups=1, # Grouped query attention (GQA) groups - selected_blocks=16 # Number of blocks to select per attention head + selected_blocks=16, # Number of blocks to select per attention head ): if scale is None: - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups # Modified shapes for inference (q has seq_len=1)a q_shape = [batch, 1, heads, dim] # Changed seq_len to 1 kv_shape = [batch, seq_len, head_kv, dim] block_indices_shape = [batch, 1, head_kv, selected_blocks] # Changed seq_len to 1 - block_indices_dtype = "int32" - dtype = "float16" - accum_dtype = "float" + block_indices_dtype = T.int32 + dtype = T.float16 + accum_dtype = T.float32 block_S = block_size block_T = min(128, tilelang.math.next_power_of_2(dim)) @@ -53,12 +54,11 @@ def native_sparse_attention( @T.prim_func def native_sparse_attention( - Q: T.Tensor(q_shape, dtype), # [batch, 1, heads, dim] - K: T.Tensor(kv_shape, dtype), # [batch, seq_len, head_kv, dim] - V: T.Tensor(kv_shape, dtype), # Same shape as K - BlockIndices: T.Tensor(block_indices_shape, - block_indices_dtype), # Selected block indices - Output: T.Tensor(q_shape, dtype), # Output attention tensor + Q: T.Tensor(q_shape, dtype), # [batch, 1, heads, dim] + K: T.Tensor(kv_shape, dtype), # [batch, seq_len, head_kv, dim] + V: T.Tensor(kv_shape, dtype), # Same shape as K + BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), # Selected block indices + Output: T.Tensor(q_shape, dtype), # Output attention tensor ): with T.Kernel(1, NV, batch * head_kv, threads=threads) as (bx, by, bz): # Shared memory allocations for tile storage @@ -82,7 +82,7 @@ def native_sparse_attention( NS = S # Copy Q for the single position - T.copy(Q[i_b, 0, i_h * G:(i_h + 1) * G, :], Q_shared) # Changed i_t to 0 + T.copy(Q[i_b, 0, i_h * G : (i_h + 1) * G, :], Q_shared) # Changed i_t to 0 T.fill(acc_o, 0) T.fill(logsum, 0) @@ -93,16 +93,11 @@ def native_sparse_attention( i_s = BlockIndices[i_b, 0, i_h, i] * BS # Get block offset if i_s >= 0: # Skip invalid/padding blocks # Load current key block to shared memory - T.copy(K[i_b, i_s:i_s + BS, i_h, :], K_shared) + T.copy(K[i_b, i_s : i_s + BS, i_h, :], K_shared) # Compute QK^T attention scores T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) # Online softmax with numerical stability # 1. Compute max for scaling @@ -122,15 +117,14 @@ def native_sparse_attention( T.copy(acc_s, acc_s_cast) # Accumulate attention-weighted values - T.copy(V[i_b, i_s:i_s + BS, i_h, i_v * BV:(i_v + 1) * BV], V_shared) + T.copy(V[i_b, i_s : i_s + BS, i_h, i_v * BV : (i_v + 1) * BV], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) # Final normalization and output for i, j in T.Parallel(G, BV): acc_o[i, j] /= logsum[i] # Normalize by logsum T.copy(acc_o, O_shared) - T.copy(O_shared, Output[i_b, 0, i_h * G:(i_h + 1) * G, - i_v * BV:(i_v + 1) * BV]) # Changed i_t to 0 + T.copy(O_shared, Output[i_b, 0, i_h * G : (i_h + 1) * G, i_v * BV : (i_v + 1) * BV]) # Changed i_t to 0 return native_sparse_attention @@ -149,21 +143,21 @@ def main(): selected_blocks=S, ) - Q = torch.randn((B, SEQ_LEN_Q, HQ, D), dtype=dtype, device='cuda').requires_grad_(True) - K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device='cuda').requires_grad_(True) - V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device='cuda').requires_grad_(True) + Q = torch.randn((B, SEQ_LEN_Q, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) - mask = torch.randint(0, 2, (B, SEQ_LEN, groups), device='cuda') - DO = torch.randn((B, SEQ_LEN_Q, HQ, D), dtype=dtype, device='cuda') + mask = torch.randint(0, 2, (B, SEQ_LEN, groups), device="cuda") + DO = torch.randn((B, SEQ_LEN_Q, HQ, D), dtype=dtype, device="cuda") - block_indices = torch.full((B, SEQ_LEN_Q, H, S), SEQ_LEN, dtype=torch.long, device='cuda') + block_indices = torch.full((B, SEQ_LEN_Q, H, S), SEQ_LEN, dtype=torch.long, device="cuda") for b in range(B): for t in range(SEQ_LEN_Q): for h in range(H): i_i = torch.randperm(max(1, (t // block_size)))[:S] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (B, SEQ_LEN_Q, H), device='cuda') + block_counts = torch.randint(1, S + 1, (B, SEQ_LEN_Q, H), device="cuda") out = kernel(Q, K, V, block_indices.to(torch.int32)) @@ -178,5 +172,38 @@ def main(): torch.testing.assert_close(ref, out, atol=1e-2, rtol=1e-2) +def run_regression_perf(): + B, SEQ_LEN, H, HQ, D, S, block_size, dtype = 2, 64, 1, 16, 16, 1, 32, torch.float16 + groups = HQ // H + SEQ_LEN_Q = 1 + kernel = native_sparse_attention( + batch=B, + heads=HQ, + seq_len=SEQ_LEN, + dim=D, + block_size=block_size, + groups=HQ // H, + selected_blocks=S, + ) + + Q = torch.randn((B, SEQ_LEN_Q, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + block_indices = torch.full((B, SEQ_LEN_Q, H, S), SEQ_LEN, dtype=torch.long, device="cuda") + for b in range(B): + for t in range(SEQ_LEN_Q): + for h in range(H): + i_i = torch.randperm(max(1, (t // block_size)))[:S] + block_indices[b, t, h, : len(i_i)] = i_i + block_indices = block_indices.sort(-1)[0] + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, block_indices.to(torch.int32)) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/deepseek_nsa/example_tilelang_nsa_fwd.py b/examples/deepseek_nsa/example_tilelang_nsa_fwd.py index f8a7ebfb0c..7b36d6e26f 100644 --- a/examples/deepseek_nsa/example_tilelang_nsa_fwd.py +++ b/examples/deepseek_nsa/example_tilelang_nsa_fwd.py @@ -14,18 +14,11 @@ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) -def native_sparse_attention(batch, - heads, - seq_len, - dim, - is_causal, - scale=None, - block_size=64, - groups=1, - selected_blocks=16): + }, +) +def native_sparse_attention(batch, heads, seq_len, dim, is_causal, scale=None, block_size=64, groups=1, selected_blocks=16): if scale is None: - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) else: scale = scale * 1.44269504 # log2(e) @@ -33,9 +26,9 @@ def native_sparse_attention(batch, q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] block_indices_shape = [batch, seq_len, head_kv, selected_blocks] - block_indices_dtype = "int32" - dtype = "float16" - accum_dtype = "float" + block_indices_dtype = T.int32 + dtype = T.float16 + accum_dtype = T.float32 block_S = block_size block_T = min(128, tilelang.math.next_power_of_2(dim)) @@ -52,11 +45,11 @@ def native_sparse_attention(batch, @T.prim_func def native_sparse_attention( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(seq_len, NV, batch * head_kv, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([G, BK], dtype) @@ -77,7 +70,7 @@ def native_sparse_attention( i_b, i_h = i_bh // head_kv, i_bh % head_kv NS = S - T.copy(Q[i_b, i_t, i_h * G:(i_h + 1) * G, :], Q_shared) + T.copy(Q[i_b, i_t, i_h * G : (i_h + 1) * G, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) @@ -87,21 +80,15 @@ def native_sparse_attention( i_s = BlockIndices[i_b, i_t, i_h, i] * BS if i_s <= i_t and i_s >= 0: # [BS, BK] - T.copy(K[i_b, i_s:i_s + BS, i_h, :], K_shared) + T.copy(K[i_b, i_s : i_s + BS, i_h, :], K_shared) if is_causal: for i, j in T.Parallel(G, BS): - acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) # Softmax T.copy(scores_max, scores_max_prev) @@ -121,13 +108,13 @@ def native_sparse_attention( acc_o[i, j] *= scores_scale[i] # V * softmax(Q * K) - T.copy(V[i_b, i_s:i_s + BS, i_h, i_v * BV:(i_v + 1) * BV], V_shared) + T.copy(V[i_b, i_s : i_s + BS, i_h, i_v * BV : (i_v + 1) * BV], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(G, BV): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[i_b, i_t, i_h * G:(i_h + 1) * G, i_v * BV:(i_v + 1) * BV]) + T.copy(O_shared, Output[i_b, i_t, i_h * G : (i_h + 1) * G, i_v * BV : (i_v + 1) * BV]) return native_sparse_attention @@ -148,21 +135,22 @@ def main(): ) print(kernel.get_kernel_source()) torch.random.manual_seed(0) - Q = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device='cuda').requires_grad_(True) - K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device='cuda').requires_grad_(True) - V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device='cuda').requires_grad_(True) - g_slc = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device='cuda').requires_grad_(True) - g_swa = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device='cuda').requires_grad_(True) - DO = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device='cuda') - - block_indices = torch.full((B, SEQ_LEN, H, S), SEQ_LEN, dtype=torch.long, device='cuda') + Q = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + g_slc = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + DO = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device="cuda") + + block_indices = torch.full((B, SEQ_LEN, H, S), SEQ_LEN, dtype=torch.long, device="cuda") + block_counts = torch.zeros((B, SEQ_LEN, H), dtype=torch.long, device="cuda") for b in range(B): for t in range(SEQ_LEN): for h in range(H): i_i = torch.randperm(max(1, (t // block_size)))[:S] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i + block_counts[b, t, h] = (block_indices[b, t, h] != SEQ_LEN).sum().item() block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (B, SEQ_LEN, H), device='cuda') out = kernel(Q, K, V, block_indices.to(torch.int32)) @@ -183,5 +171,43 @@ def main(): torch.testing.assert_close(ref, out, atol=1e-2, rtol=1e-2) +def run_regression_perf(): + B, SEQ_LEN, H, HQ, D, S, block_size, dtype, scale = 2, 64, 1, 16, 32, 1, 32, torch.float16, 0.1 + kernel = native_sparse_attention( + batch=B, + heads=HQ, + seq_len=SEQ_LEN, + dim=D, + is_causal=True, + block_size=block_size, + groups=HQ // H, + selected_blocks=S, + scale=scale, + ) + torch.random.manual_seed(0) + Q = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + K = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + V = torch.randn((B, SEQ_LEN, H, D), dtype=dtype, device="cuda").requires_grad_(True) + g_slc = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.ones((B, SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + DO = torch.randn((B, SEQ_LEN, HQ, D), dtype=dtype, device="cuda") + block_indices = torch.full((B, SEQ_LEN, H, S), SEQ_LEN, dtype=torch.long, device="cuda") + block_counts = torch.zeros((B, SEQ_LEN, H), dtype=torch.long, device="cuda") + for b in range(B): + for t in range(SEQ_LEN): + for h in range(H): + i_i = torch.randperm(max(1, (t // block_size)))[:S] + block_indices[b, t, h, : len(i_i)] = i_i + block_counts[b, t, h] = (block_indices[b, t, h] != SEQ_LEN).sum().item() + block_indices = block_indices.sort(-1)[0] + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, block_indices.to(torch.int32)) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/deepseek_nsa/example_tilelang_nsa_fwd_varlen.py b/examples/deepseek_nsa/example_tilelang_nsa_fwd_varlen.py index d365e7a5f9..b52ebe42e2 100644 --- a/examples/deepseek_nsa/example_tilelang_nsa_fwd_varlen.py +++ b/examples/deepseek_nsa/example_tilelang_nsa_fwd_varlen.py @@ -8,6 +8,7 @@ import tilelang.testing import fla + if parse(fla.__version__) < parse("0.2.1"): from fla.ops.common.utils import prepare_token_indices else: @@ -21,18 +22,11 @@ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) -def native_sparse_attention_varlen(batch, - heads, - c_seq_len, - dim, - is_causal, - scale=None, - block_size=64, - groups=1, - selected_blocks=16): + } +) +def native_sparse_attention_varlen(batch, heads, c_seq_len, dim, is_causal, scale=None, block_size=64, groups=1, selected_blocks=16): if scale is None: - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [c_seq_len, heads, dim] kv_shape = [c_seq_len, head_kv, dim] @@ -44,12 +38,12 @@ def native_sparse_attention_varlen(batch, block_counts_shape = [c_seq_len, head_kv] offsets_shape = [batch + 1] token_indices_shape = [c_seq_len, 2] - block_indices_dtype = "int32" - block_counts_dtype = "int32" - offsets_dtype = "int32" - token_indices_dtype = "int32" - dtype = "float16" - accum_dtype = "float" + block_indices_dtype = T.int32 + block_counts_dtype = T.int32 + offsets_dtype = T.int32 + token_indices_dtype = T.int32 + dtype = T.float16 + accum_dtype = T.float32 block_S = block_size block_T = min(128, tilelang.math.next_power_of_2(dim)) @@ -66,14 +60,14 @@ def native_sparse_attention_varlen(batch, @T.prim_func def native_sparse_attention_varlen( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - O_slc: T.Tensor(o_slc_shape, dtype), - BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), - BlockCounts: T.Tensor(block_counts_shape, block_counts_dtype), - Offsets: T.Tensor(offsets_shape, offsets_dtype), - TokenIndices: T.Tensor(token_indices_shape, token_indices_dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + O_slc: T.Tensor(o_slc_shape, dtype), + BlockIndices: T.Tensor(block_indices_shape, block_indices_dtype), + BlockCounts: T.Tensor(block_counts_shape, block_counts_dtype), + Offsets: T.Tensor(offsets_shape, offsets_dtype), + TokenIndices: T.Tensor(token_indices_shape, token_indices_dtype), ): with T.Kernel(c_seq_len, NV, batch * head_kv, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([G, BK], dtype) @@ -100,7 +94,7 @@ def native_sparse_attention_varlen( current_seq_len = eos - bos NS = BlockCounts[i_t, i_h] - T.copy(Q[bos + i_t, i_h * G:(i_h + 1) * G, :BK], Q_shared) + T.copy(Q[bos + i_t, i_h * G : (i_h + 1) * G, :BK], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) @@ -112,21 +106,15 @@ def native_sparse_attention_varlen( # [BS, BK] # Lei: may have some padding issues # we should learn from mha varlen templates to handle this - T.copy(K[bos + i_s:bos + i_s + BS, i_h, :BK], K_shared) + T.copy(K[bos + i_s : bos + i_s + BS, i_h, :BK], K_shared) if is_causal: for i, j in T.Parallel(G, BS): - acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(i_t >= (i_s + j), 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) # Softmax T.copy(scores_max, scores_max_prev) @@ -146,13 +134,13 @@ def native_sparse_attention_varlen( acc_o[i, j] *= scores_scale[i] # V * softmax(Q * K) - T.copy(V[bos + i_s:bos + i_s + BS, i_h, i_v * BV:(i_v + 1) * BV], V_shared) + T.copy(V[bos + i_s : bos + i_s + BS, i_h, i_v * BV : (i_v + 1) * BV], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(G, BV): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, O_slc[bos + i_t, i_h * G:(i_h + 1) * G, i_v * BV:(i_v + 1) * BV]) + T.copy(O_shared, O_slc[bos + i_t, i_h * G : (i_h + 1) * G, i_v * BV : (i_v + 1) * BV]) return native_sparse_attention_varlen @@ -190,17 +178,20 @@ def parallel_nsa_fwd( o_slc = torch.empty(B, C_SEQ_LEN, HQ, V, dtype=v.dtype, device=q.device) kernel( - q.view(C_SEQ_LEN, HQ, D), k.view(C_SEQ_LEN, H, D), v.view(C_SEQ_LEN, H, D), + q.view(C_SEQ_LEN, HQ, D), + k.view(C_SEQ_LEN, H, D), + v.view(C_SEQ_LEN, H, D), o_slc.view(C_SEQ_LEN, HQ, V), block_indices.to(torch.int32).view(C_SEQ_LEN, H, S), - block_counts.to(torch.int32).view(C_SEQ_LEN, H), offsets.to(torch.int32), - token_indices.to(torch.int32)) + block_counts.to(torch.int32).view(C_SEQ_LEN, H), + offsets.to(torch.int32), + token_indices.to(torch.int32), + ) return o_slc @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, scale, offsets): ctx.dtype = q.dtype @@ -221,22 +212,25 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, window_size=window_size, scale=scale, offsets=offsets, - token_indices=token_indices) + token_indices=token_indices, + ) return o_slc.to(q.dtype) -def parallel_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def parallel_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -276,29 +270,27 @@ def parallel_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, - scale, cu_seqlens) + o_slc = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: assert False, "Window size is not supported yet" else: o = o_slc * g_slc.unsqueeze(-1) if head_first: - o = rearrange(o, 'b t h d -> b h t d') + o = rearrange(o, "b t h d -> b h t d") return o @@ -306,41 +298,57 @@ def parallel_nsa(q: torch.Tensor, N, C_SEQ_LEN, H, HQ, D, S, block_size, dtype = 2, 64, 1, 16, 64, 1, 32, torch.float16 torch.manual_seed(42) # randomly split the sequence into N segments - offsets = torch.cat([ - torch.tensor([0], dtype=torch.long), - torch.arange(16, C_SEQ_LEN)[torch.randperm(C_SEQ_LEN - 1)[:N - 1]], - torch.tensor([C_SEQ_LEN], dtype=torch.long) - ], 0).cuda().sort()[0] + offsets = ( + torch.cat( + [ + torch.tensor([0], dtype=torch.long), + torch.arange(16, C_SEQ_LEN)[torch.randperm(C_SEQ_LEN - 1)[: N - 1]], + torch.tensor([C_SEQ_LEN], dtype=torch.long), + ], + 0, + ) + .cuda() + .sort()[0] + ) # seq-first required for inputs with variable lengths - perm_q = torch.randperm(C_SEQ_LEN, device='cuda') - perm_k = torch.randperm(C_SEQ_LEN, device='cuda') - perm_v = torch.randperm(C_SEQ_LEN, device='cuda') - q = torch.linspace( - 0, 1, steps=C_SEQ_LEN, dtype=dtype, - device='cuda')[perm_q].view(1, C_SEQ_LEN, 1, 1).expand(1, C_SEQ_LEN, HQ, - D).clone().requires_grad_(True) - k = torch.linspace( - 0, 1, steps=C_SEQ_LEN, dtype=dtype, - device='cuda')[perm_k].view(1, C_SEQ_LEN, 1, 1).expand(1, C_SEQ_LEN, H, - D).clone().requires_grad_(True) - v = torch.linspace( - 0, 1, steps=C_SEQ_LEN, dtype=dtype, - device='cuda')[perm_v].view(1, C_SEQ_LEN, 1, 1).expand(1, C_SEQ_LEN, H, - D).clone().requires_grad_(True) - g_slc = torch.rand((1, C_SEQ_LEN, HQ), dtype=dtype, device='cuda').requires_grad_(True) - g_swa = torch.rand((1, C_SEQ_LEN, HQ), dtype=dtype, device='cuda').requires_grad_(True) - do = torch.randn((1, C_SEQ_LEN, HQ, D), dtype=dtype, device='cuda') + perm_q = torch.randperm(C_SEQ_LEN, device="cuda") + perm_k = torch.randperm(C_SEQ_LEN, device="cuda") + perm_v = torch.randperm(C_SEQ_LEN, device="cuda") + q = ( + torch.linspace(0, 1, steps=C_SEQ_LEN, dtype=dtype, device="cuda")[perm_q] + .view(1, C_SEQ_LEN, 1, 1) + .expand(1, C_SEQ_LEN, HQ, D) + .clone() + .requires_grad_(True) + ) + k = ( + torch.linspace(0, 1, steps=C_SEQ_LEN, dtype=dtype, device="cuda")[perm_k] + .view(1, C_SEQ_LEN, 1, 1) + .expand(1, C_SEQ_LEN, H, D) + .clone() + .requires_grad_(True) + ) + v = ( + torch.linspace(0, 1, steps=C_SEQ_LEN, dtype=dtype, device="cuda")[perm_v] + .view(1, C_SEQ_LEN, 1, 1) + .expand(1, C_SEQ_LEN, H, D) + .clone() + .requires_grad_(True) + ) + g_slc = torch.rand((1, C_SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.rand((1, C_SEQ_LEN, HQ), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((1, C_SEQ_LEN, HQ, D), dtype=dtype, device="cuda") token_indices = prepare_token_indices(offsets).tolist() - block_indices = torch.full((1, C_SEQ_LEN, H, S), C_SEQ_LEN, dtype=torch.long, device='cuda') + block_indices = torch.full((1, C_SEQ_LEN, H, S), C_SEQ_LEN, dtype=torch.long, device="cuda") for i in range(C_SEQ_LEN): _, t = token_indices[i] for h in range(H): i_i = torch.randperm(max(1, tilelang.cdiv(t, block_size)))[:S] - block_indices[0, i, h, :len(i_i)] = i_i + block_indices[0, i, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (1, C_SEQ_LEN, H), device='cuda') + block_counts = torch.randint(1, S + 1, (1, C_SEQ_LEN, H), device="cuda") ref = naive_nsa( q=q, @@ -351,7 +359,8 @@ def parallel_nsa(q: torch.Tensor, block_indices=block_indices, block_counts=block_counts, block_size=block_size, - cu_seqlens=offsets) + cu_seqlens=offsets, + ) tri = parallel_nsa( q=q, @@ -362,7 +371,8 @@ def parallel_nsa(q: torch.Tensor, block_indices=block_indices, block_counts=block_counts, block_size=block_size, - cu_seqlens=offsets) + cu_seqlens=offsets, + ) print("tri", tri) print("ref", ref) diff --git a/examples/deepseek_nsa/example_triton_nsa_bwd.py b/examples/deepseek_nsa/example_triton_nsa_bwd.py index e912794a45..af05bfa701 100644 --- a/examples/deepseek_nsa/example_triton_nsa_bwd.py +++ b/examples/deepseek_nsa/example_triton_nsa_bwd.py @@ -8,6 +8,7 @@ import triton.language as tl import fla + if parse(fla.__version__) < parse("0.2.1"): from fla.ops.common.utils import prepare_token_indices else: @@ -17,21 +18,44 @@ from einops import rearrange -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor), -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) @triton.jit -def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices, - block_counts, offsets, token_indices, T, H: tl.constexpr, - HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr, - S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr, - BV: tl.constexpr, USE_OFFSETS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_fwd_kernel( + q, + k, + v, + o_slc, + o_swa, + lse_slc, + lse_swa, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H @@ -46,20 +70,18 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc # else: NS = S - p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # the Q block is kept in the shared memory throughout the whole kernel # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), - (G, BV), (1, 0)) + p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_slc = tl.zeros([G, BV], dtype=tl.float32) - b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_slc = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_slc = tl.zeros([G], dtype=tl.float32) for i in range(NS): i_s = tl.load(block_indices + i).to(tl.int32) * BS @@ -72,7 +94,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1)) # [G, BS] b_s_slc = tl.dot(b_q, b_k_slc) - b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf')) + b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float("-inf")) # [G] b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc @@ -92,7 +114,6 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -105,8 +126,7 @@ def forward(ctx, q, k, v, block_indices, block_size, scale, offsets): # [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]] token_indices = prepare_token_indices(offsets) if offsets is not None else None - o, lse = parallel_nsa_fwd( - q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) + o, lse = parallel_nsa_fwd(q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) ctx.save_for_backward(q, k, v, o, lse) ctx.block_indices = block_indices ctx.block_size = block_size @@ -134,7 +154,8 @@ def backward(ctx, do_slc, do_swa): window_size=ctx.window_size, scale=ctx.scale, offsets=ctx.offsets, - token_indices=ctx.token_indices) + token_indices=ctx.token_indices, + ) return dq.to(q), dk.to(k), dv.to(v), None, None, None, None, None, None, None, None @@ -199,37 +220,56 @@ def parallel_nsa_fwd( return o_slc, lse_slc, o_swa, lse_swa -@triton.heuristics({'USE_OFFSETS': lambda args: args['offsets'] is not None}) +@triton.heuristics({"USE_OFFSETS": lambda args: args["offsets"] is not None}) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) -@triton.jit(do_not_specialize=['T']) -def parallel_nsa_bwd_kernel_dkv(q, k, v, lse_slc, lse_swa, delta_slc, delta_swa, do_slc, do_swa, dk, - dv, block_mask, offsets, chunk_indices, scale, T, B: tl.constexpr, - H: tl.constexpr, HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, - V: tl.constexpr, M: tl.constexpr, BS: tl.constexpr, - WS: tl.constexpr, BK: tl.constexpr, BV: tl.constexpr, - USE_OFFSETS: tl.constexpr): +@triton.jit(do_not_specialize=["T"]) +def parallel_nsa_bwd_kernel_dkv( + q, + k, + v, + lse_slc, + lse_swa, + delta_slc, + delta_swa, + do_slc, + do_swa, + dk, + dv, + block_mask, + offsets, + chunk_indices, + scale, + T, + B: tl.constexpr, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + M: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, +): i_v, i_s, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H if USE_OFFSETS: - i_n, i_s = tl.load(chunk_indices + i_s * 2).to(tl.int32), tl.load(chunk_indices + i_s * 2 + - 1).to(tl.int32) + i_n, i_s = tl.load(chunk_indices + i_s * 2).to(tl.int32), tl.load(chunk_indices + i_s * 2 + 1).to(tl.int32) bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) T = eos - bos else: bos, eos = i_b * T, i_b * T + T - p_k = tl.make_block_ptr(k + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_s * BS, 0), (BS, BK), - (1, 0)) - p_v = tl.make_block_ptr(v + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_s * BS, i_v * BV), - (BS, BV), (1, 0)) - p_dk = tl.make_block_ptr(dk + (i_v * B * T * H + bos * H + i_h) * K, (T, K), (H * K, 1), - (i_s * BS, 0), (BS, BK), (1, 0)) - p_dv = tl.make_block_ptr(dv + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_s * BS, i_v * BV), - (BS, BV), (1, 0)) + p_k = tl.make_block_ptr(k + (bos * H + i_h) * K, (T, K), (H * K, 1), (i_s * BS, 0), (BS, BK), (1, 0)) + p_v = tl.make_block_ptr(v + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_s * BS, i_v * BV), (BS, BV), (1, 0)) + p_dk = tl.make_block_ptr(dk + (i_v * B * T * H + bos * H + i_h) * K, (T, K), (H * K, 1), (i_s * BS, 0), (BS, BK), (1, 0)) + p_dv = tl.make_block_ptr(dv + (bos * H + i_h) * V, (T, V), (H * V, 1), (i_s * BS, i_v * BV), (BS, BV), (1, 0)) # [BS, BK] b_k = tl.load(p_k, boundary_check=(0, 1)) @@ -241,14 +281,12 @@ def parallel_nsa_bwd_kernel_dkv(q, k, v, lse_slc, lse_swa, delta_slc, delta_swa, for i in range(i_s * BS, T): b_m_slc = tl.load(block_mask + (bos + i) * H * M + i_h * M + i_s) if b_m_slc: - p_q = tl.make_block_ptr(q + (bos + i) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_do_slc = tl.make_block_ptr(do_slc + (bos + i) * HQ * V, (HQ, V), (V, 1), - (i_h * G, i_v * BV), (G, BV), (1, 0)) + p_do_slc = tl.make_block_ptr(do_slc + (bos + i) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i) * HQ + i_h * G + tl.arange(0, G) p_delta_slc = delta_slc + (bos + i) * HQ + i_h * G + tl.arange(0, G) # [G, BV] @@ -272,14 +310,12 @@ def parallel_nsa_bwd_kernel_dkv(q, k, v, lse_slc, lse_swa, delta_slc, delta_swa, if WS > 0: o_s = i_s * BS + tl.arange(0, BS) if max(i_s * BS, i - WS + 1) < min((i_s + 1) * BS, i + 1): - p_q = tl.make_block_ptr(q + (bos + i) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), - (G, BK), (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_do_swa = tl.make_block_ptr(do_swa + (bos + i) * HQ * V, (HQ, V), (V, 1), - (i_h * G, i_v * BV), (G, BV), (1, 0)) + p_do_swa = tl.make_block_ptr(do_swa + (bos + i) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_swa = lse_swa + (bos + i) * HQ + i_h * G + tl.arange(0, G) p_delta_swa = delta_swa + (bos + i) * HQ + i_h * G + tl.arange(0, G) # [G, BV] @@ -304,12 +340,19 @@ def parallel_nsa_bwd_kernel_dkv(q, k, v, lse_slc, lse_swa, delta_slc, delta_swa, tl.store(p_dv, b_dv.to(p_dv.dtype.element_ty), boundary_check=(0, 1)) -@triton.heuristics( - {'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor)}) +@triton.heuristics({"USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor)}) @triton.jit -def parallel_nsa_kernel_mask(block_indices, block_counts, block_mask, T: tl.constexpr, - H: tl.constexpr, S: tl.constexpr, BS: tl.constexpr, NS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_kernel_mask( + block_indices, + block_counts, + block_mask, + T: tl.constexpr, + H: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + NS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_b, i_hs = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_h, i_s = i_hs // S, i_hs % S @@ -320,31 +363,56 @@ def parallel_nsa_kernel_mask(block_indices, block_counts, block_mask, T: tl.cons b_m = b_i * BS <= i_t if b_i < NS and b_i >= 0: - tl.store(block_mask + i_b * T * H * NS + i_t * H * NS + i_h * NS + b_i, - b_m.to(block_mask.dtype.element_ty)) + tl.store(block_mask + i_b * T * H * NS + i_t * H * NS + i_h * NS + b_i, b_m.to(block_mask.dtype.element_ty)) -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor) -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) -@triton.jit(do_not_specialize=['T']) -def parallel_nsa_bwd_kernel_dq(q, k, v, lse_slc, delta_slc, do_slc, lse_swa, delta_swa, do_swa, dq, - scale, block_indices, block_counts, offsets, token_indices, T, - B: tl.constexpr, H: tl.constexpr, HQ: tl.constexpr, G: tl.constexpr, - K: tl.constexpr, V: tl.constexpr, S: tl.constexpr, BS: tl.constexpr, - WS: tl.constexpr, BK: tl.constexpr, BV: tl.constexpr, - USE_OFFSETS: tl.constexpr, USE_BLOCK_COUNTS: tl.constexpr): +@triton.jit(do_not_specialize=["T"]) +def parallel_nsa_bwd_kernel_dq( + q, + k, + v, + lse_slc, + delta_slc, + do_slc, + lse_swa, + delta_swa, + do_swa, + dq, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + B: tl.constexpr, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H if USE_OFFSETS: - i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + - 1).to(tl.int32) + i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + 1).to(tl.int32) bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) T = eos - bos else: @@ -449,27 +517,49 @@ def parallel_nsa_bwd_kernel_dq(q, k, v, lse_slc, delta_slc, do_slc, lse_swa, del tl.store(p_dq, (b_dq_slc + b_dq_swa).to(p_dq.dtype.element_ty), boundary_check=(0, 1)) -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor), -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) @triton.jit -def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices, - block_counts, offsets, token_indices, T, H: tl.constexpr, - HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr, - S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr, - BV: tl.constexpr, USE_OFFSETS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_fwd_kernel( + q, + k, + v, + o_slc, + o_swa, + lse_slc, + lse_swa, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H if USE_OFFSETS: - i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + - 1).to(tl.int32) + i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + 1).to(tl.int32) bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) T = eos - bos else: @@ -484,20 +574,18 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc else: NS = S - p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # the Q block is kept in the shared memory throughout the whole kernel # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), - (G, BV), (1, 0)) + p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_slc = tl.zeros([G, BV], dtype=tl.float32) - b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_slc = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_slc = tl.zeros([G], dtype=tl.float32) for i in range(NS): i_s = tl.load(block_indices + i).to(tl.int32) * BS @@ -510,7 +598,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1)) # [G, BS] b_s_slc = tl.dot(b_q, b_k_slc) - b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf')) + b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float("-inf")) # [G] b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc @@ -529,13 +617,12 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc tl.store(p_lse_slc, b_m_slc.to(p_lse_slc.dtype.element_ty)) if WS > 0: - p_o_swa = tl.make_block_ptr(o_swa + (bos + i_t) * HQ * V, (HQ, V), (V, 1), - (i_h * G, i_v * BV), (G, BV), (1, 0)) + p_o_swa = tl.make_block_ptr(o_swa + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_swa = lse_swa + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_swa = tl.zeros([G, BV], dtype=tl.float32) - b_m_swa = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_swa = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_swa = tl.zeros([G], dtype=tl.float32) for i_s in range(max(0, i_t - WS + 1), i_t + 1, BS): p_k_swa = tl.make_block_ptr(k, (K, T), (1, H * K), (0, i_s), (BK, BS), (0, 1)) @@ -546,7 +633,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_swa = tl.load(p_v_swa, boundary_check=(0, 1)) # [G, BS] b_s_swa = tl.dot(b_q, b_k_swa) - b_s_swa = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_swa, float('-inf')) + b_s_swa = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_swa, float("-inf")) # [G] b_m_swa, b_mp_swa = tl.maximum(b_m_swa, tl.max(b_s_swa, 1)), b_m_swa @@ -593,14 +680,8 @@ def parallel_nsa_block_mask( block_mask = torch.zeros(B, T, H, NS, dtype=torch.bool, device=block_indices.device) parallel_nsa_kernel_mask[(T, B, H * S)]( - block_indices=block_indices, - block_counts=block_counts, - block_mask=block_mask, - T=T, - H=H, - S=S, - BS=BS, - NS=NS) + block_indices=block_indices, block_counts=block_counts, block_mask=block_mask, T=T, H=H, S=S, BS=BS, NS=NS + ) return block_mask @@ -676,7 +757,8 @@ def parallel_nsa_bwd( BS=BS, WS=WS, BK=BK, - BV=BV) + BV=BV, + ) dq = dq.sum(0) if offsets is not None: @@ -719,14 +801,14 @@ def parallel_nsa_bwd( BS=BS, WS=WS, BK=BK, - BV=BV) + BV=BV, + ) dk = dk.sum(0) return dq, dk, dv @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -749,7 +831,8 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, window_size=window_size, scale=scale, offsets=offsets, - token_indices=token_indices) + token_indices=token_indices, + ) ctx.save_for_backward(q, k, v, o_slc, lse_slc, o_swa, lse_swa) ctx.block_indices = block_indices ctx.block_counts = block_counts @@ -781,22 +864,25 @@ def backward(ctx, do_slc, do_swa): window_size=ctx.window_size, scale=ctx.scale, offsets=ctx.offsets, - token_indices=ctx.token_indices) + token_indices=ctx.token_indices, + ) return dq.to(q), dk.to(k), dv.to(v), None, None, None, None, None, None, None, None -def parallel_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def parallel_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -836,51 +922,49 @@ def parallel_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, - window_size, scale, cu_seqlens) + o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: o = torch.addcmul(o_slc * g_slc.unsqueeze(-1), o_swa, g_swa.unsqueeze(-1)) else: o = o_slc * g_slc.unsqueeze(-1) if head_first: - o = rearrange(o, 'b t h d -> b h t d') + o = rearrange(o, "b t h d -> b h t d") return o if __name__ == "__main__": B, T, H, HQ, D, S, block_size, dtype = 2, 64, 1, 16, 32, 1, 32, torch.float16 torch.random.manual_seed(0) - q = torch.randn((B, T, HQ, D), dtype=dtype, device='cuda').requires_grad_(True) - k = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True) - v = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True) - g_slc = torch.ones((B, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - g_swa = torch.ones((B, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - do = torch.randn((B, T, HQ, D), dtype=dtype, device='cuda') - - block_indices = torch.full((B, T, H, S), T, dtype=torch.long, device='cuda') + q = torch.randn((B, T, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + k = torch.randn((B, T, H, D), dtype=dtype, device="cuda").requires_grad_(True) + v = torch.randn((B, T, H, D), dtype=dtype, device="cuda").requires_grad_(True) + g_slc = torch.ones((B, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.ones((B, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((B, T, HQ, D), dtype=dtype, device="cuda") + + block_indices = torch.full((B, T, H, S), T, dtype=torch.long, device="cuda") for b in range(B): for t in range(T): for h in range(H): i_i = torch.randperm(max(1, (t // block_size)))[:S] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (B, T, H), device='cuda') + block_counts = torch.randint(1, S + 1, (B, T, H), device="cuda") ref = naive_nsa( q=q, diff --git a/examples/deepseek_nsa/example_triton_nsa_fwd.py b/examples/deepseek_nsa/example_triton_nsa_fwd.py index 2c740013a7..c9ab28daaf 100644 --- a/examples/deepseek_nsa/example_triton_nsa_fwd.py +++ b/examples/deepseek_nsa/example_triton_nsa_fwd.py @@ -8,6 +8,7 @@ import triton.language as tl import fla + if parse(fla.__version__) < parse("0.2.1"): from fla.ops.common.utils import prepare_token_indices else: @@ -17,21 +18,44 @@ from einops import rearrange -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor), -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) @triton.jit -def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices, - block_counts, offsets, token_indices, T, H: tl.constexpr, - HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr, - S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr, - BV: tl.constexpr, USE_OFFSETS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_fwd_kernel( + q, + k, + v, + o_slc, + o_swa, + lse_slc, + lse_swa, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H @@ -46,20 +70,18 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc # else: NS = S - p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # the Q block is kept in the shared memory throughout the whole kernel # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), - (G, BV), (1, 0)) + p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_slc = tl.zeros([G, BV], dtype=tl.float32) - b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_slc = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_slc = tl.zeros([G], dtype=tl.float32) for i in range(NS): i_s = tl.load(block_indices + i).to(tl.int32) * BS @@ -72,7 +94,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1)) # [G, BS] b_s_slc = tl.dot(b_q, b_k_slc) - b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf')) + b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float("-inf")) # [G] b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc @@ -92,7 +114,6 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -105,8 +126,7 @@ def forward(ctx, q, k, v, block_indices, block_size, scale, offsets): # [[0, 0], [0, 1], [1, 0], [1, 1], [1, 2], [1, 3]] token_indices = prepare_token_indices(offsets) if offsets is not None else None - o, lse = parallel_nsa_fwd( - q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) + o, lse = parallel_nsa_fwd(q=q, k=k, v=v, block_indices=block_indices, block_size=block_size, scale=scale) ctx.save_for_backward(q, k, v, o, lse) ctx.block_indices = block_indices ctx.block_size = block_size @@ -177,7 +197,6 @@ def parallel_nsa_fwd( @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -200,7 +219,8 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, window_size=window_size, scale=scale, offsets=offsets, - token_indices=token_indices) + token_indices=token_indices, + ) ctx.save_for_backward(q, k, v, o_slc, lse_slc, o_swa, lse_swa) ctx.block_indices = block_indices ctx.block_counts = block_counts @@ -212,18 +232,20 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, return o_slc.to(q.dtype), o_swa.to(q.dtype) if o_swa is not None else o_swa -def parallel_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def parallel_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -263,51 +285,49 @@ def parallel_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, - window_size, scale, cu_seqlens) + o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: o = torch.addcmul(o_slc * g_slc.unsqueeze(-1), o_swa, g_swa.unsqueeze(-1)) else: o = o_slc * g_slc.unsqueeze(-1) if head_first: - o = rearrange(o, 'b t h d -> b h t d') + o = rearrange(o, "b t h d -> b h t d") return o if __name__ == "__main__": B, T, H, HQ, D, S, block_size, dtype = 2, 64, 1, 16, 32, 1, 32, torch.float16 torch.random.manual_seed(0) - q = torch.randn((B, T, HQ, D), dtype=dtype, device='cuda').requires_grad_(True) - k = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True) - v = torch.randn((B, T, H, D), dtype=dtype, device='cuda').requires_grad_(True) - g_slc = torch.ones((B, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - g_swa = torch.ones((B, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - do = torch.randn((B, T, HQ, D), dtype=dtype, device='cuda') - - block_indices = torch.full((B, T, H, S), T, dtype=torch.long, device='cuda') + q = torch.randn((B, T, HQ, D), dtype=dtype, device="cuda").requires_grad_(True) + k = torch.randn((B, T, H, D), dtype=dtype, device="cuda").requires_grad_(True) + v = torch.randn((B, T, H, D), dtype=dtype, device="cuda").requires_grad_(True) + g_slc = torch.ones((B, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.ones((B, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((B, T, HQ, D), dtype=dtype, device="cuda") + + block_indices = torch.full((B, T, H, S), T, dtype=torch.long, device="cuda") for b in range(B): for t in range(T): for h in range(H): i_i = torch.randperm(max(1, (t // block_size)))[:S] - block_indices[b, t, h, :len(i_i)] = i_i + block_indices[b, t, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (B, T, H), device='cuda') + block_counts = torch.randint(1, S + 1, (B, T, H), device="cuda") ref = naive_nsa( q=q, diff --git a/examples/deepseek_nsa/example_triton_nsa_fwd_varlen.py b/examples/deepseek_nsa/example_triton_nsa_fwd_varlen.py index 9ccbff6a4f..cb4eb6d7ba 100644 --- a/examples/deepseek_nsa/example_triton_nsa_fwd_varlen.py +++ b/examples/deepseek_nsa/example_triton_nsa_fwd_varlen.py @@ -8,6 +8,7 @@ import triton.language as tl import fla + if parse(fla.__version__) < parse("0.2.1"): from fla.ops.common.utils import prepare_token_indices else: @@ -17,27 +18,49 @@ from einops import rearrange -@triton.heuristics({ - 'USE_OFFSETS': lambda args: args['offsets'] is not None, - 'USE_BLOCK_COUNTS': lambda args: isinstance(args['block_counts'], torch.Tensor), -}) +@triton.heuristics( + { + "USE_OFFSETS": lambda args: args["offsets"] is not None, + "USE_BLOCK_COUNTS": lambda args: isinstance(args["block_counts"], torch.Tensor), + } +) @triton.autotune( configs=[triton.Config({}, num_warps=num_warps) for num_warps in [1, 2, 4, 8]], - key=['BS', 'BK', 'BV'], + key=["BS", "BK", "BV"], ) @triton.jit -def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, block_indices, - block_counts, offsets, token_indices, T, H: tl.constexpr, - HQ: tl.constexpr, G: tl.constexpr, K: tl.constexpr, V: tl.constexpr, - S: tl.constexpr, BS: tl.constexpr, WS: tl.constexpr, BK: tl.constexpr, - BV: tl.constexpr, USE_OFFSETS: tl.constexpr, - USE_BLOCK_COUNTS: tl.constexpr): +def parallel_nsa_fwd_kernel( + q, + k, + v, + o_slc, + o_swa, + lse_slc, + lse_swa, + scale, + block_indices, + block_counts, + offsets, + token_indices, + T, + H: tl.constexpr, + HQ: tl.constexpr, + G: tl.constexpr, + K: tl.constexpr, + V: tl.constexpr, + S: tl.constexpr, + BS: tl.constexpr, + WS: tl.constexpr, + BK: tl.constexpr, + BV: tl.constexpr, + USE_OFFSETS: tl.constexpr, + USE_BLOCK_COUNTS: tl.constexpr, +): i_t, i_v, i_bh = tl.program_id(0), tl.program_id(1), tl.program_id(2) i_b, i_h = i_bh // H, i_bh % H if USE_OFFSETS: - i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + - 1).to(tl.int32) + i_n, i_t = tl.load(token_indices + i_t * 2).to(tl.int32), tl.load(token_indices + i_t * 2 + 1).to(tl.int32) bos, eos = tl.load(offsets + i_n).to(tl.int32), tl.load(offsets + i_n + 1).to(tl.int32) T = eos - bos else: @@ -52,20 +75,18 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc else: NS = S - p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), - (1, 0)) + p_q = tl.make_block_ptr(q + (bos + i_t) * HQ * K, (HQ, K), (K, 1), (i_h * G, 0), (G, BK), (1, 0)) # the Q block is kept in the shared memory throughout the whole kernel # [G, BK] b_q = tl.load(p_q, boundary_check=(0, 1)) b_q = (b_q * scale).to(b_q.dtype) - p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), - (G, BV), (1, 0)) + p_o_slc = tl.make_block_ptr(o_slc + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_slc = lse_slc + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_slc = tl.zeros([G, BV], dtype=tl.float32) - b_m_slc = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_slc = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_slc = tl.zeros([G], dtype=tl.float32) for i in range(NS): i_s = tl.load(block_indices + i).to(tl.int32) * BS @@ -78,7 +99,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_slc = tl.load(p_v_slc, boundary_check=(0, 1)) # [G, BS] b_s_slc = tl.dot(b_q, b_k_slc) - b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float('-inf')) + b_s_slc = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_slc, float("-inf")) # [G] b_m_slc, b_mp_slc = tl.maximum(b_m_slc, tl.max(b_s_slc, 1)), b_m_slc @@ -97,13 +118,12 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc tl.store(p_lse_slc, b_m_slc.to(p_lse_slc.dtype.element_ty)) if WS > 0: - p_o_swa = tl.make_block_ptr(o_swa + (bos + i_t) * HQ * V, (HQ, V), (V, 1), - (i_h * G, i_v * BV), (G, BV), (1, 0)) + p_o_swa = tl.make_block_ptr(o_swa + (bos + i_t) * HQ * V, (HQ, V), (V, 1), (i_h * G, i_v * BV), (G, BV), (1, 0)) p_lse_swa = lse_swa + (bos + i_t) * HQ + i_h * G + tl.arange(0, G) # [G, BV] b_o_swa = tl.zeros([G, BV], dtype=tl.float32) - b_m_swa = tl.full([G], float('-inf'), dtype=tl.float32) + b_m_swa = tl.full([G], float("-inf"), dtype=tl.float32) b_acc_swa = tl.zeros([G], dtype=tl.float32) for i_s in range(max(0, i_t - WS + 1), i_t + 1, BS): p_k_swa = tl.make_block_ptr(k, (K, T), (1, H * K), (0, i_s), (BK, BS), (0, 1)) @@ -114,7 +134,7 @@ def parallel_nsa_fwd_kernel(q, k, v, o_slc, o_swa, lse_slc, lse_swa, scale, bloc b_v_swa = tl.load(p_v_swa, boundary_check=(0, 1)) # [G, BS] b_s_swa = tl.dot(b_q, b_k_swa) - b_s_swa = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_swa, float('-inf')) + b_s_swa = tl.where((i_t >= (i_s + tl.arange(0, BS)))[None, :], b_s_swa, float("-inf")) # [G] b_m_swa, b_mp_swa = tl.maximum(b_m_swa, tl.max(b_s_swa, 1)), b_m_swa @@ -196,7 +216,6 @@ def parallel_nsa_fwd( @torch.compile class ParallelNSAFunction(torch.autograd.Function): - @staticmethod @contiguous @autocast_custom_fwd @@ -219,7 +238,8 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, window_size=window_size, scale=scale, offsets=offsets, - token_indices=token_indices) + token_indices=token_indices, + ) ctx.save_for_backward(q, k, v, o_slc, lse_slc, o_swa, lse_swa) ctx.block_indices = block_indices ctx.block_counts = block_counts @@ -231,18 +251,20 @@ def forward(ctx, q, k, v, block_indices, block_counts, block_size, window_size, return o_slc.to(q.dtype), o_swa.to(q.dtype) if o_swa is not None else o_swa -def parallel_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def parallel_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -282,29 +304,27 @@ def parallel_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") assert q.shape[2] % (k.shape[2] * 16) == 0, "Group size must be a multiple of 16 in NSA" if isinstance(block_counts, int): block_indices = block_indices[:, :, :, :block_counts] block_counts = None - o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, - window_size, scale, cu_seqlens) + o_slc, o_swa = ParallelNSAFunction.apply(q, k, v, block_indices, block_counts, block_size, window_size, scale, cu_seqlens) if window_size > 0: o = torch.addcmul(o_slc * g_slc.unsqueeze(-1), o_swa, g_swa.unsqueeze(-1)) else: o = o_slc * g_slc.unsqueeze(-1) if head_first: - o = rearrange(o, 'b t h d -> b h t d') + o = rearrange(o, "b t h d -> b h t d") return o @@ -312,38 +332,35 @@ def parallel_nsa(q: torch.Tensor, N, T, H, HQ, D, S, block_size, dtype = 2, 64, 1, 16, 64, 1, 32, torch.float16 torch.manual_seed(42) # randomly split the sequence into N segments - offsets = torch.cat([ - torch.tensor([0], dtype=torch.long), - torch.arange(16, T)[torch.randperm(T - 1)[:N - 1]], - torch.tensor([T], dtype=torch.long) - ], 0).cuda().sort()[0] + offsets = ( + torch.cat( + [torch.tensor([0], dtype=torch.long), torch.arange(16, T)[torch.randperm(T - 1)[: N - 1]], torch.tensor([T], dtype=torch.long)], + 0, + ) + .cuda() + .sort()[0] + ) # offsets.shape is [N+1] # seq-first required for inputs with variable lengths - perm_q = torch.randperm(T, device='cuda') - perm_k = torch.randperm(T, device='cuda') - perm_v = torch.randperm(T, device='cuda') - q = torch.linspace( - 0, 1, steps=T, dtype=dtype, - device='cuda')[perm_q].view(1, T, 1, 1).expand(1, T, HQ, D).clone().requires_grad_(True) - k = torch.linspace( - 0, 1, steps=T, dtype=dtype, - device='cuda')[perm_k].view(1, T, 1, 1).expand(1, T, H, D).clone().requires_grad_(True) - v = torch.linspace( - 0, 1, steps=T, dtype=dtype, - device='cuda')[perm_v].view(1, T, 1, 1).expand(1, T, H, D).clone().requires_grad_(True) - g_slc = torch.rand((1, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - g_swa = torch.rand((1, T, HQ), dtype=dtype, device='cuda').requires_grad_(True) - do = torch.randn((1, T, HQ, D), dtype=dtype, device='cuda') + perm_q = torch.randperm(T, device="cuda") + perm_k = torch.randperm(T, device="cuda") + perm_v = torch.randperm(T, device="cuda") + q = torch.linspace(0, 1, steps=T, dtype=dtype, device="cuda")[perm_q].view(1, T, 1, 1).expand(1, T, HQ, D).clone().requires_grad_(True) + k = torch.linspace(0, 1, steps=T, dtype=dtype, device="cuda")[perm_k].view(1, T, 1, 1).expand(1, T, H, D).clone().requires_grad_(True) + v = torch.linspace(0, 1, steps=T, dtype=dtype, device="cuda")[perm_v].view(1, T, 1, 1).expand(1, T, H, D).clone().requires_grad_(True) + g_slc = torch.rand((1, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + g_swa = torch.rand((1, T, HQ), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((1, T, HQ, D), dtype=dtype, device="cuda") token_indices = prepare_token_indices(offsets).tolist() - block_indices = torch.full((1, T, H, S), T, dtype=torch.long, device='cuda') + block_indices = torch.full((1, T, H, S), T, dtype=torch.long, device="cuda") for i in range(T): _, t = token_indices[i] for h in range(H): i_i = torch.randperm(max(1, triton.cdiv(t, block_size)))[:S] - block_indices[0, i, h, :len(i_i)] = i_i + block_indices[0, i, h, : len(i_i)] = i_i block_indices = block_indices.sort(-1)[0] - block_counts = torch.randint(1, S + 1, (1, T, H), device='cuda') + block_counts = torch.randint(1, S + 1, (1, T, H), device="cuda") ref = naive_nsa( q=q, @@ -354,7 +371,8 @@ def parallel_nsa(q: torch.Tensor, block_indices=block_indices, block_counts=block_counts, block_size=block_size, - cu_seqlens=offsets) + cu_seqlens=offsets, + ) tri = parallel_nsa( q=q, @@ -365,7 +383,8 @@ def parallel_nsa(q: torch.Tensor, block_indices=block_indices, block_counts=block_counts, block_size=block_size, - cu_seqlens=offsets) + cu_seqlens=offsets, + ) print("tri", tri) print("ref", ref) diff --git a/examples/deepseek_nsa/reference.py b/examples/deepseek_nsa/reference.py index 958d0c19ee..58083108eb 100644 --- a/examples/deepseek_nsa/reference.py +++ b/examples/deepseek_nsa/reference.py @@ -6,18 +6,20 @@ from einops import rearrange, repeat -def naive_nsa(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - g_slc: torch.Tensor, - g_swa: torch.Tensor, - block_indices: torch.LongTensor, - block_counts: Optional[Union[torch.LongTensor, int]] = None, - block_size: int = 64, - window_size: int = 0, - scale: Optional[float] = None, - cu_seqlens: Optional[torch.LongTensor] = None, - head_first: bool = False) -> torch.Tensor: +def naive_nsa( + q: torch.Tensor, + k: torch.Tensor, + v: torch.Tensor, + g_slc: torch.Tensor, + g_swa: torch.Tensor, + block_indices: torch.LongTensor, + block_counts: Optional[Union[torch.LongTensor, int]] = None, + block_size: int = 64, + window_size: int = 0, + scale: Optional[float] = None, + cu_seqlens: Optional[torch.LongTensor] = None, + head_first: bool = False, +) -> torch.Tensor: r""" Args: q (torch.Tensor): @@ -57,26 +59,24 @@ def naive_nsa(q: torch.Tensor, Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ if scale is None: - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 if cu_seqlens is not None: assert q.shape[0] == 1, "batch size must be 1 when cu_seqlens are provided" if head_first: - raise RuntimeError( - "Sequences with variable lengths are not supported for head-first mode") + raise RuntimeError("Sequences with variable lengths are not supported for head-first mode") if head_first: - q, k, v, block_indices = map(lambda x: rearrange(x, 'b h t d -> b t h d'), - (q, k, v, block_indices)) - g_slc, g_swa = map(lambda x: rearrange(x, 'b h t -> b t h'), (g_slc, g_swa)) + q, k, v, block_indices = map(lambda x: rearrange(x, "b h t d -> b t h d"), (q, k, v, block_indices)) + g_slc, g_swa = map(lambda x: rearrange(x, "b h t -> b t h"), (g_slc, g_swa)) if isinstance(block_counts, torch.Tensor): - block_counts = rearrange(block_counts, 'b h t -> b t h') + block_counts = rearrange(block_counts, "b h t -> b t h") dtype = q.dtype G = q.shape[2] // k.shape[2] BS = block_size S = block_indices.shape[-1] - k, v, block_indices = (repeat(x, 'b t h d -> b t (h g) d', g=G) for x in (k, v, block_indices)) + k, v, block_indices = (repeat(x, "b t h d -> b t (h g) d", g=G) for x in (k, v, block_indices)) if isinstance(block_counts, torch.Tensor): - block_counts = repeat(block_counts, 'b t h -> b t (h g)', g=G) + block_counts = repeat(block_counts, "b t h -> b t (h g)", g=G) c = torch.arange(S).repeat_interleave(BS).unsqueeze(1).expand(-1, q.shape[2]).to(q.device) q, k, v = map(lambda x: x.float(), (q, k, v)) @@ -86,14 +86,11 @@ def naive_nsa(q: torch.Tensor, if cu_seqlens is None: varlen = False B, T = q.shape[:2] - cu_seqlens = torch.cat( - [block_indices.new_tensor(range(0, B * T, T)), - block_indices.new_tensor([B * T])]) + cu_seqlens = torch.cat([block_indices.new_tensor(range(0, B * T, T)), block_indices.new_tensor([B * T])]) for i in range(len(cu_seqlens) - 1): if not varlen: - q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = q[i], k[i], v[i], g_slc[i], g_swa[ - i], block_indices[i] + q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = q[i], k[i], v[i], g_slc[i], g_swa[i], block_indices[i] if isinstance(block_counts, torch.Tensor): s_b = block_counts[i] else: @@ -101,10 +98,10 @@ def naive_nsa(q: torch.Tensor, else: T = cu_seqlens[i + 1] - cu_seqlens[i] q_b, k_b, v_b, g_slc_b, g_swa_b, i_b = map( - lambda x: x[0][cu_seqlens[i]:cu_seqlens[i + 1]], - (q, k, v, g_slc, g_swa, block_indices)) + lambda x: x[0][cu_seqlens[i] : cu_seqlens[i + 1]], (q, k, v, g_slc, g_swa, block_indices) + ) if isinstance(block_counts, torch.Tensor): - s_b = block_counts[0][cu_seqlens[i]:cu_seqlens[i + 1]] + s_b = block_counts[0][cu_seqlens[i] : cu_seqlens[i + 1]] else: s_b = block_counts @@ -126,34 +123,28 @@ def naive_nsa(q: torch.Tensor, else: s_i = s_b # [S*BS, HQ, -1] - k_i_slc, v_i_slc = map( - lambda x: x.gather( - 0, - i_i.clamp(0, T - 1).unsqueeze(-1).expand(*i_i.shape, x.shape[-1])), (k_b, v_b)) + k_i_slc, v_i_slc = map(lambda x: x.gather(0, i_i.clamp(0, T - 1).unsqueeze(-1).expand(*i_i.shape, x.shape[-1])), (k_b, v_b)) # [S*BS, HQ] - attn_slc = torch.einsum('h d, n h d -> n h', q_i, k_i_slc).masked_fill( - torch.logical_or(i_i < 0, i_i > i_q) | - (c >= s_i if block_counts is not None else False), float('-inf')).softmax(0) + attn_slc = ( + torch.einsum("h d, n h d -> n h", q_i, k_i_slc) + .masked_fill(torch.logical_or(i_i < 0, i_i > i_q) | (c >= s_i if block_counts is not None else False), float("-inf")) + .softmax(0) + ) if not varlen: - o_slc[i, i_q] = torch.einsum('n h, n h v -> h v', attn_slc, - v_i_slc) * g_slc_i.unsqueeze(-1) + o_slc[i, i_q] = torch.einsum("n h, n h v -> h v", attn_slc, v_i_slc) * g_slc_i.unsqueeze(-1) else: - o_slc[0][cu_seqlens[i] + i_q] = torch.einsum('n h, n h v -> h v', attn_slc, - v_i_slc) * g_slc_i.unsqueeze(-1) + o_slc[0][cu_seqlens[i] + i_q] = torch.einsum("n h, n h v -> h v", attn_slc, v_i_slc) * g_slc_i.unsqueeze(-1) if window_size > 0: - k_i_swa, v_i_swa = map(lambda x: x[max(0, i_q - window_size + 1):i_q + 1], - (k_b, v_b)) - attn_swa = torch.einsum('h d, n h d -> n h', q_i, k_i_swa).softmax(0) + k_i_swa, v_i_swa = map(lambda x: x[max(0, i_q - window_size + 1) : i_q + 1], (k_b, v_b)) + attn_swa = torch.einsum("h d, n h d -> n h", q_i, k_i_swa).softmax(0) if not varlen: - o_swa[i, i_q] = torch.einsum('n h, n h v -> h v', attn_swa, - v_i_swa) * g_swa_i.unsqueeze(-1) + o_swa[i, i_q] = torch.einsum("n h, n h v -> h v", attn_swa, v_i_swa) * g_swa_i.unsqueeze(-1) else: - o_swa[0][cu_seqlens[i] + i_q] = torch.einsum('n h, n h v -> h v', attn_swa, - v_i_swa) * g_swa_i.unsqueeze(-1) + o_swa[0][cu_seqlens[i] + i_q] = torch.einsum("n h, n h v -> h v", attn_swa, v_i_swa) * g_swa_i.unsqueeze(-1) if head_first: - o_slc = rearrange(o_slc, 'b t h d -> b h t d') - o_swa = rearrange(o_swa, 'b t h d -> b h t d') + o_slc = rearrange(o_slc, "b t h d -> b h t d") + o_swa = rearrange(o_swa, "b t h d -> b h t d") return o_slc.to(dtype) + o_swa.to(dtype) if o_swa is not None else o_slc.to(dtype) @@ -187,7 +178,7 @@ def naive_nsa_simple( o (torch.Tensor): Outputs of shape `[B, T, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 dtype = q.dtype HQ = q.shape[2] @@ -197,8 +188,8 @@ def naive_nsa_simple( BS = block_size S = block_indices.shape[-1] SELECTED_BLOCKS_SIZE = S * BS - k, v, block_indices = (repeat(x, 'b t h d -> b t (h g) d', g=G) for x in (k, v, block_indices)) - block_counts = repeat(block_counts, 'b t h -> b t (h g)', g=G) + k, v, block_indices = (repeat(x, "b t h d -> b t (h g) d", g=G) for x in (k, v, block_indices)) + block_counts = repeat(block_counts, "b t h -> b t (h g)", g=G) c = torch.arange(S).repeat_interleave(BS).unsqueeze(1).expand(-1, q.shape[2]).to(q.device) q, k, v = map(lambda x: x.float(), (q, k, v)) o = torch.zeros_like(v) @@ -228,10 +219,10 @@ def naive_nsa_simple( v_i[t, h] = v_b[selected_block_index, h, :] # [S*BS, HQ] - attn = torch.einsum('h d, n h d -> n h', q_i, k_i) - attn = attn.masked_fill((i_i > i_q) | (c >= s_i), float('-inf')) + attn = torch.einsum("h d, n h d -> n h", q_i, k_i) + attn = attn.masked_fill((i_i > i_q) | (c >= s_i), float("-inf")) attn = torch.softmax(attn, dim=0) - o[i, i_q] = torch.einsum('n h, n h v -> h v', attn, v_i) + o[i, i_q] = torch.einsum("n h, n h v -> h v", attn, v_i) return o.to(dtype) @@ -265,7 +256,7 @@ def naive_nsa_simple_inference( o (torch.Tensor): Outputs of shape `[B, 1, HQ, V]` if `head_first=False` else `[B, HQ, T, V]`. """ - scale = k.shape[-1]**-0.5 + scale = k.shape[-1] ** -0.5 dtype = q.dtype HQ = q.shape[2] @@ -275,8 +266,8 @@ def naive_nsa_simple_inference( BS = block_size S = block_indices.shape[-1] SELECTED_BLOCKS_SIZE = S * BS - k, v, block_indices = (repeat(x, 'b t h d -> b t (h g) d', g=G) for x in (k, v, block_indices)) - block_counts = repeat(block_counts, 'b t h -> b t (h g)', g=G) + k, v, block_indices = (repeat(x, "b t h d -> b t (h g) d", g=G) for x in (k, v, block_indices)) + block_counts = repeat(block_counts, "b t h -> b t (h g)", g=G) c = torch.arange(S).repeat_interleave(BS).unsqueeze(1).expand(-1, q.shape[2]).to(q.device) q, k, v = map(lambda x: x.float(), (q, k, v)) o = torch.zeros_like(q) @@ -306,9 +297,9 @@ def naive_nsa_simple_inference( v_i[t, h] = v_b[selected_block_index, h, :] # [S*BS, HQ] - attn = torch.einsum('h d, n h d -> n h', q_i, k_i) - attn = attn.masked_fill((c >= s_i), float('-inf')) + attn = torch.einsum("h d, n h d -> n h", q_i, k_i) + attn = attn.masked_fill((c >= s_i), float("-inf")) attn = torch.softmax(attn, dim=0) - o[i, 0] = torch.einsum('n h, n h v -> h v', attn, v_i) + o[i, 0] = torch.einsum("n h, n h v -> h v", attn, v_i) return o.to(dtype) diff --git a/examples/deepseek_nsa/regression_example_tilelang_nsa.py b/examples/deepseek_nsa/regression_example_tilelang_nsa.py new file mode 100644 index 0000000000..1858f045a2 --- /dev/null +++ b/examples/deepseek_nsa/regression_example_tilelang_nsa.py @@ -0,0 +1,15 @@ +import tilelang.testing +import example_tilelang_nsa_fwd +import example_tilelang_nsa_decode + + +def regression_example_tilelang_nsa_fwd(): + tilelang.testing.process_func(example_tilelang_nsa_fwd.run_regression_perf) + + +def regression_example_tilelang_nsa_fwd_decode(): + tilelang.testing.process_func(example_tilelang_nsa_decode.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/deepseek_nsa/requirements.txt b/examples/deepseek_nsa/requirements.txt index 777c2ad4c8..e096dfd7d6 100644 --- a/examples/deepseek_nsa/requirements.txt +++ b/examples/deepseek_nsa/requirements.txt @@ -1 +1 @@ -git+https://github.com/fla-org/flash-linear-attention@c3bd56589033610264532b11f0972c69e4645f6e \ No newline at end of file +git+https://github.com/fla-org/flash-linear-attention@c3bd56589033610264532b11f0972c69e4645f6e diff --git a/examples/deepseek_v32/README.md b/examples/deepseek_v32/README.md index 8457745b0e..01a14b6b24 100644 --- a/examples/deepseek_v32/README.md +++ b/examples/deepseek_v32/README.md @@ -121,7 +121,7 @@ for i_i in T.Pipelined(NI, num_stages=num_stages): # ... compute attention over selected tokens ``` -This reduces compute from O(seq_len * seq_len_kv) to O(seq_len * topk). The causal mask is enforced by checking whether each index position is valid: +This reduces compute from O(seq_len *seq_len_kv) to O(seq_len* topk). The causal mask is enforced by checking whether each index position is valid: ```python for bi_i in T.Parallel(BI): @@ -193,10 +193,10 @@ for i_i in T.Pipelined(NI, num_stages=num_stages): # Load KV data for selected indices for bi_i, d_i in T.Parallel(BI, D): KV_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz, i_i * BI + bi_i], bz, d_i] - + # Recompute attention scores for backward T.gemm(Q_shared, KV_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) - + # Apply softmax gradient: dP = P * (dP_raw - Delta) for h_i, bi_i in T.Parallel(padded_H, BI): acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * (acc_dp[h_i, bi_i] - Delta[by, s_i, bz * padded_H + h_i]) * sm_scale @@ -204,7 +204,7 @@ for i_i in T.Pipelined(NI, num_stages=num_stages): The key gradient computations are: - **dQ = dP @ K** (query gradients) -- **dK = dP^T @ Q** (key gradients) +- **dK = dP^T @ Q** (key gradients) - **dV = P^T @ dO** (value gradients) **3. Atomic Sparse Updates**: Uses atomic operations for dKV accumulation: @@ -212,7 +212,7 @@ The key gradient computations are: ```python # Atomically update dKV at selected indices for bi_i, d_i in T.Parallel(BI // split_store, D // 4): - T.atomic_addx4(dKV[by, Indices[by, s_i, bz, i_i * BI + bi_i + s * (BI // split_store)], bz, d_i * 4], + T.atomic_addx4(dKV[by, Indices[by, s_i, bz, i_i * BI + bi_i + s * (BI // split_store)], bz, d_i * 4], acc_dkv_shared[bi_i, d_i * 4]) ``` diff --git a/examples/deepseek_v32/fp8_lighting_indexer.py b/examples/deepseek_v32/fp8_lighting_indexer.py index 21baa8fa85..03e88dd972 100644 --- a/examples/deepseek_v32/fp8_lighting_indexer.py +++ b/examples/deepseek_v32/fp8_lighting_indexer.py @@ -28,11 +28,11 @@ def validate_tensor_match(a, b, tolerance=1e-8, tensor_name="tensor", should_rai if should_raise: assert False if not torch.isclose( - a.masked_fill(a_finite, 0), - b.masked_fill(b_finite, 0), - rtol=0, - atol=0, - equal_nan=True, + a.masked_fill(a_finite, 0), + b.masked_fill(b_finite, 0), + rtol=0, + atol=0, + equal_nan=True, ).all(): display_error_message(f"{tensor_name} Error: nonfinite value mismatch") if should_raise: @@ -55,13 +55,10 @@ def get_configs(): threads=[128, 256], block_Q=[1, 2, 4], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] class SupplyProg: - def __init__(self): self.tensors_dict = {} @@ -88,7 +85,8 @@ def supply_prog(self, params): @tilelang.jit( pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - },) + }, +) def mqa_attn_return_logits( heads, index_dim, @@ -99,9 +97,9 @@ def mqa_attn_return_logits( ): if block_Q is None: block_Q = 128 // heads - dtype = "float8_e4m3" - accum_dtype = "float" - index_dtype = "int32" + dtype = T.float8_e4m3fn + accum_dtype = T.float32 + index_dtype = T.int32 seq_len = T.dynamic("seq_len") seq_len_kv = T.dynamic("seq_len_kv") @@ -113,46 +111,42 @@ def mqa_attn_return_logits( @T.prim_func def mqa_attn_return_logits_kernel( - IndexQ: T.Tensor(index_q_shape, dtype), # type: ignore - IndexK: T.Tensor(index_k_shape, dtype), # type: ignore - IndexKScale: T.Tensor(index_k_scale_shape, accum_dtype), # type: ignore - Logits: T.Tensor(logits_shape, accum_dtype), # type: ignore - Weights: T.Tensor([seq_len, heads], accum_dtype), # type: ignore - CuSeqLenKS: T.Tensor([seq_len], index_dtype), # type: ignore - CuSeqLenKE: T.Tensor([seq_len], index_dtype), # type: ignore + IndexQ: T.Tensor(index_q_shape, dtype), # type: ignore + IndexK: T.Tensor(index_k_shape, dtype), # type: ignore + IndexKScale: T.Tensor(index_k_scale_shape, accum_dtype), # type: ignore + Logits: T.Tensor(logits_shape, accum_dtype), # type: ignore + Weights: T.Tensor([seq_len, heads], accum_dtype), # type: ignore + CuSeqLenKS: T.Tensor([seq_len], index_dtype), # type: ignore + CuSeqLenKE: T.Tensor([seq_len], index_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_Q), threads=threads) as bx: - index_q_shared = T.alloc_shared([block_Q * heads, index_dim], dtype) index_k_shared = T.alloc_shared([block_N, index_dim], dtype) index_k_scale_fragment = T.alloc_fragment([block_N], accum_dtype) s = T.alloc_fragment([block_N, block_Q * heads], accum_dtype) - s_reshaped = T.alloc_fragment([block_N, block_Q, heads], accum_dtype) + s_reshaped = T.reshape(s, (block_N, block_Q, heads)) logits = T.alloc_fragment([block_N, block_Q], accum_dtype) weights = T.alloc_fragment([block_Q, heads], accum_dtype) seq_len_i = bx * block_Q - cu_k_s_min = T.alloc_local([1], index_dtype) - cu_k_e_max = T.alloc_local([1], index_dtype) + cu_k_s_min = T.alloc_var(index_dtype) + cu_k_e_max = T.alloc_var(index_dtype) - cu_k_s_min[0] = 2147483647 - cu_k_e_max[0] = -2147483648 + cu_k_s_min = 2147483647 + cu_k_e_max = -2147483648 for bq_i in T.serial(block_Q): - cu_k_s_min[0] = T.min(cu_k_s_min[0], T.min(CuSeqLenKS[seq_len_i + bq_i], - seq_len_kv)) + cu_k_s_min = T.min(cu_k_s_min, T.min(CuSeqLenKS[seq_len_i + bq_i], seq_len_kv)) for bq_i in T.serial(block_Q): - cu_k_e_max[0] = T.max(cu_k_e_max[0], T.min(CuSeqLenKE[seq_len_i + bq_i], - seq_len_kv)) + cu_k_e_max = T.max(cu_k_e_max, T.min(CuSeqLenKE[seq_len_i + bq_i], seq_len_kv)) T.copy(IndexQ[seq_len_i * heads, 0], index_q_shared) T.copy(Weights[seq_len_i, 0], weights) - for nbn_i in T.Pipelined( - T.ceildiv(cu_k_e_max[0] - cu_k_s_min[0], block_N), num_stages=num_stages): - T.copy(IndexK[cu_k_s_min[0] + nbn_i * block_N, 0], index_k_shared) - T.copy(IndexKScale[cu_k_s_min[0] + nbn_i * block_N], index_k_scale_fragment) + for nbn_i in T.Pipelined(T.ceildiv(cu_k_e_max - cu_k_s_min, block_N), num_stages=num_stages): + T.copy(IndexK[cu_k_s_min + nbn_i * block_N, 0], index_k_shared) + T.copy(IndexKScale[cu_k_s_min + nbn_i * block_N], index_k_scale_fragment) T.gemm( index_k_shared, @@ -164,15 +158,14 @@ def mqa_attn_return_logits_kernel( ) for bn_i, bq_i, h_i in T.Parallel(block_N, block_Q, heads): - s_reshaped[bn_i, bq_i, - h_i] = (T.max(s[bn_i, bq_i * heads + h_i], 0) * - weights[bq_i, h_i]) * index_k_scale_fragment[bn_i] + s_reshaped[bn_i, bq_i, h_i] = (T.max(s_reshaped[bn_i, bq_i, h_i], 0) * weights[bq_i, h_i]) * index_k_scale_fragment[ + bn_i + ] T.reduce_sum(s_reshaped, logits, dim=-1, clear=True) for bq_i, bn_i in T.Parallel(block_Q, block_N): - Logits[seq_len_i + bq_i, cu_k_s_min[0] + nbn_i * block_N + bn_i] = ( - logits[bn_i, bq_i]) + Logits[seq_len_i + bq_i, cu_k_s_min + nbn_i * block_N + bn_i] = logits[bn_i, bq_i] return mqa_attn_return_logits_kernel @@ -185,38 +178,30 @@ def clean_logits_( seq_len = T.dynamic("seq_len") seq_len_kv = T.dynamic("seq_len_kv") - dtype = "float" - indices_dtype = "int32" + dtype = T.float + indices_dtype = T.int32 @T.prim_func def clean_logits_kernel( - Logits: T.Tensor([seq_len, seq_len_kv], dtype), # type: ignore - CuSeqLenKS: T.Tensor([seq_len], indices_dtype), # type: ignore - CuSeqLenKE: T.Tensor([seq_len], indices_dtype), # type: ignore + Logits: T.Tensor([seq_len, seq_len_kv], dtype), # type: ignore + CuSeqLenKS: T.Tensor([seq_len], indices_dtype), # type: ignore + CuSeqLenKE: T.Tensor([seq_len], indices_dtype), # type: ignore ): with T.Kernel(seq_len, threads=threads) as bx: tx = T.thread_binding(0, threads, thread="threadIdx.x") - cu_k_s = T.alloc_local([1], indices_dtype) - cu_k_e = T.alloc_local([1], indices_dtype) - cu_k_s[0] = CuSeqLenKS[bx] - cu_k_e[0] = CuSeqLenKE[bx] + cu_k_s = CuSeqLenKS[bx] + cu_k_e = CuSeqLenKE[bx] for n_i in T.Pipelined(T.ceildiv(seq_len_kv, block_K)): for k_i in T.serial(block_K // threads): idx = n_i * block_K + k_i * threads + tx - if idx < cu_k_s[0] or idx >= cu_k_e[0]: + if idx < cu_k_s or idx >= cu_k_e: Logits[bx, idx] = -T.infinity(dtype) return clean_logits_kernel -def mqa_attn_return_logits_interface(q, - kv, - kv_scales, - weights, - cu_seqlen_ks, - cu_seqlen_ke, - clean_logits=True): +def mqa_attn_return_logits_interface(q, kv, kv_scales, weights, cu_seqlen_ks, cu_seqlen_ke, clean_logits=True): seq_len, heads, index_dim = q.shape seq_len_kv = kv.shape[0] @@ -238,57 +223,48 @@ def mqa_attn_return_logits_interface(q, return logits -def ref_fp8_mqa_logits(q: torch.Tensor, kv: torch.Tensor, weights: torch.Tensor, - cu_seqlen_ks: torch.Tensor, cu_seqlen_ke: torch.Tensor): +def ref_fp8_mqa_logits(q: torch.Tensor, kv: torch.Tensor, weights: torch.Tensor, cu_seqlen_ks: torch.Tensor, cu_seqlen_ke: torch.Tensor): k = kv q = q.float() k = k.float() seq_len_kv = kv.shape[0] - mask_lo = torch.arange(0, seq_len_kv, device='cuda')[None, :] >= cu_seqlen_ks[:, None] - mask_hi = torch.arange(0, seq_len_kv, device='cuda')[None, :] < cu_seqlen_ke[:, None] + mask_lo = torch.arange(0, seq_len_kv, device="cuda")[None, :] >= cu_seqlen_ks[:, None] + mask_hi = torch.arange(0, seq_len_kv, device="cuda")[None, :] < cu_seqlen_ke[:, None] mask = mask_lo & mask_hi - score = torch.einsum('mhd,nd->hmn', q, k) + score = torch.einsum("mhd,nd->hmn", q, k) logits = (score.relu() * weights.unsqueeze(-1).transpose(0, 1)).sum(dim=0) - logits = logits.masked_fill(~mask, float('-inf')) + logits = logits.masked_fill(~mask, float("-inf")) cost = mask.sum() return logits, cost def test_fp8_lighting_indexer(S=4096, SKV=8192, H=32, HKV=1, D=64, kv_stride=1): + # initial random seed to make the performance reproducible + torch.manual_seed(0) q = torch.randn(S, H, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16) kv = torch.randn(SKV, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16) weights = torch.randn(S, H, device="cuda", dtype=torch.float32) p = (torch.randn(S, SKV, device="cuda", dtype=torch.float32) * 4).softmax(dim=-1) - ks, ke = generate_random_cu_seqlens( - per_cp_seqlen=S, cp_size=4, cp_rank=3, kv_stride=kv_stride, average_q_len=2048) + ks, ke = generate_random_cu_seqlens(per_cp_seqlen=S, cp_size=4, cp_rank=3, kv_stride=kv_stride, average_q_len=2048) - logits_ref, cost_ref = ref_fp8_mqa_logits( - q=q, kv=kv, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) + logits_ref, cost_ref = ref_fp8_mqa_logits(q=q, kv=kv, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) q_fp8 = q.to(torch.float8_e4m3fn) kv_fp8, kv_scales = per_custom_dims_cast_to_fp8(kv, (0,), False) - logits_tl = mqa_attn_return_logits_interface( - q=q_fp8, kv=kv_fp8, kv_scales=kv_scales, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) - diff = validate_tensor_match( - logits_ref, logits_tl, tolerance=1e-14, tensor_name="logits", should_raise=False) + logits_tl = mqa_attn_return_logits_interface(q=q_fp8, kv=kv_fp8, kv_scales=kv_scales, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) + diff = validate_tensor_match(logits_ref, logits_tl, tolerance=1e-14, tensor_name="logits", should_raise=False) print(f"diff: {diff}") from tilelang.profiler import do_bench def logits_fn(): - return mqa_attn_return_logits_interface( - q=q_fp8, - kv=kv_fp8, - kv_scales=kv_scales, - weights=weights, - cu_seqlen_ks=ks, - cu_seqlen_ke=ke) + return mqa_attn_return_logits_interface(q=q_fp8, kv=kv_fp8, kv_scales=kv_scales, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CUDA]) as prof: logits_fn() @@ -302,5 +278,35 @@ def logits_fn(): print(f"cost_ref: {cost_ref}") +def run_regression_perf(S=4096, SKV=8192, H=32, HKV=1, D=64, kv_stride=1): + torch.manual_seed(0) + q = torch.randn(S, H, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16) + kv = torch.randn(SKV, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16) + weights = torch.randn(S, H, device="cuda", dtype=torch.float32) + p = (torch.randn(S, SKV, device="cuda", dtype=torch.float32) * 4).softmax(dim=-1) + + ks, ke = generate_random_cu_seqlens(per_cp_seqlen=S, cp_size=4, cp_rank=3, kv_stride=kv_stride, average_q_len=2048) + + logits_ref, cost_ref = ref_fp8_mqa_logits(q=q, kv=kv, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) + + q_fp8 = q.to(torch.float8_e4m3fn) + kv_fp8, kv_scales = per_custom_dims_cast_to_fp8(kv, (0,), False) + + logits_tl = mqa_attn_return_logits_interface(q=q_fp8, kv=kv_fp8, kv_scales=kv_scales, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) + diff = validate_tensor_match(logits_ref, logits_tl, tolerance=1e-14, tensor_name="logits", should_raise=False) + + from tilelang.profiler import do_bench + + def logits_fn(): + return mqa_attn_return_logits_interface(q=q_fp8, kv=kv_fp8, kv_scales=kv_scales, weights=weights, cu_seqlen_ks=ks, cu_seqlen_ke=ke) + + with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CUDA]) as prof: + logits_fn() + + print(prof.key_averages().table(sort_by="cuda_time_total", max_name_column_width=50)) + + return do_bench(logits_fn, backend="cupti") + + if __name__ == "__main__": test_fp8_lighting_indexer() diff --git a/examples/deepseek_v32/inference/README.md b/examples/deepseek_v32/inference/README.md index fe4cc21bba..60afe7ceb1 100644 --- a/examples/deepseek_v32/inference/README.md +++ b/examples/deepseek_v32/inference/README.md @@ -11,4 +11,4 @@ Launch the interactive chat interface and start exploring DeepSeek's capabilitie ```bash export CONFIG=config_671B_v3.2.json torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive -``` \ No newline at end of file +``` diff --git a/examples/deepseek_v32/inference/config_671B_v3.2.json b/examples/deepseek_v32/inference/config_671B_v3.2.json index be88f1cca2..375aa9aa2c 100644 --- a/examples/deepseek_v32/inference/config_671B_v3.2.json +++ b/examples/deepseek_v32/inference/config_671B_v3.2.json @@ -23,4 +23,4 @@ "index_n_heads": 64, "index_head_dim": 128, "index_topk": 2048 -} \ No newline at end of file +} diff --git a/examples/deepseek_v32/inference/convert.py b/examples/deepseek_v32/inference/convert.py index df7943918f..090be71455 100644 --- a/examples/deepseek_v32/inference/convert.py +++ b/examples/deepseek_v32/inference/convert.py @@ -42,7 +42,7 @@ def main(hf_ckpt_path, save_path, n_experts, mp): save_path (str): Path to the directory where the converted checkpoint files will be saved. n_experts (int): Total number of experts in the model. mp (int): Model parallelism factor. - + Returns: None """ diff --git a/examples/deepseek_v32/inference/kernel.py b/examples/deepseek_v32/inference/kernel.py index 2623435360..25abf15d59 100644 --- a/examples/deepseek_v32/inference/kernel.py +++ b/examples/deepseek_v32/inference/kernel.py @@ -11,21 +11,21 @@ tilelang.PassConfigKey.TL_DISABLE_FAST_MATH: True, } -FP8 = "float8_e4m3" -BF16 = "bfloat16" -FP32 = "float32" +FP8 = T.float8_e4m3fn +BF16 = T.bfloat16 +FP32 = T.float32 def fast_log2_ceil(x): - bits_x = T.reinterpret("uint32", x) + bits_x = T.reinterpret(T.uint32, x) exp_x = (bits_x >> 23) & 0xFF man_bits = bits_x & ((1 << 23) - 1) - return T.Cast("int32", exp_x - 127 + T.if_then_else(man_bits != 0, 1, 0)) + return T.Cast(T.int32, exp_x - 127 + T.if_then_else(man_bits != 0, 1, 0)) def fast_pow2(x): bits_x = (x + 127) << 23 - return T.reinterpret("float32", bits_x) + return T.reinterpret(T.float32, bits_x) def fast_round_scale(amax, fp8_max_inv): @@ -107,8 +107,8 @@ def act_quant(x: torch.Tensor, @tilelang.jit(pass_configs=pass_configs) -def fp8_gemm_kernel(N, K, out_dtype=BF16, accum_dtype="float32"): - assert out_dtype in [BF16, "float32"] +def fp8_gemm_kernel(N, K, out_dtype=BF16, accum_dtype=T.float32): + assert out_dtype in [BF16, T.float32] M = T.dynamic("M") group_size = 128 diff --git a/examples/deepseek_v32/inference/requirements.txt b/examples/deepseek_v32/inference/requirements.txt index 604fed552c..8c208a8b1d 100644 --- a/examples/deepseek_v32/inference/requirements.txt +++ b/examples/deepseek_v32/inference/requirements.txt @@ -2,4 +2,4 @@ torch transformers safetensors fast_hadamard_transform -tilelang==0.1.6 \ No newline at end of file +tilelang==0.1.6 diff --git a/examples/deepseek_v32/regression_tilelang_example_deepseek_v32.py b/examples/deepseek_v32/regression_tilelang_example_deepseek_v32.py new file mode 100644 index 0000000000..0610002a6b --- /dev/null +++ b/examples/deepseek_v32/regression_tilelang_example_deepseek_v32.py @@ -0,0 +1,30 @@ +import tilelang.testing +import fp8_lighting_indexer +import sparse_mla_bwd +import sparse_mla_fwd +import sparse_mla_fwd_pipelined +import topk_selector + + +def regression_topk_selector(): + tilelang.testing.process_func(topk_selector.run_regression_perf) + + +def regression_fp8_lighting_indexer(): + tilelang.testing.process_func(fp8_lighting_indexer.run_regression_perf, S=512, SKV=1024, H=32, HKV=1, D=64, kv_stride=1) + + +def regression_sparse_mla_fwd(): + tilelang.testing.process_func(sparse_mla_fwd.run_regression_perf, S=256, SKV=1024, H=64, HKV=1, DQK=576, DV=512, topk=256) + + +def regression_sparse_mla_fwd_pipelined(): + tilelang.testing.process_func(sparse_mla_fwd_pipelined.run_regression_perf, S=256, SKV=512, H=64, HKV=1, DQK=576, DV=512, topk=256) + + +def regression_sparse_mla_bwd(): + tilelang.testing.process_func(sparse_mla_bwd.run_regression_perf, S=256, SKV=512, H=64, HKV=1, DQKV=576, DV=512, topk=256) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/deepseek_v32/sparse_mla_bwd.py b/examples/deepseek_v32/sparse_mla_bwd.py index e7f9c60933..527de22b39 100644 --- a/examples/deepseek_v32/sparse_mla_bwd.py +++ b/examples/deepseek_v32/sparse_mla_bwd.py @@ -13,18 +13,18 @@ def preprocess( D, block_ND=32, num_stages=5, - dtype="bfloat16", - accum_dtype="float", + dtype=T.bfloat16, + accum_dtype=T.float32, ): - assert dtype == "bfloat16" - assert accum_dtype == "float" + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 shape = [B, S, H, D] @T.prim_func def preprocess_kernel( - O: T.Tensor(shape, dtype), - dO: T.Tensor(shape, dtype), - Delta: T.Tensor([B, S, H], accum_dtype), + O: T.Tensor(shape, dtype), + dO: T.Tensor(shape, dtype), + Delta: T.Tensor([B, S, H], accum_dtype), ): with T.Kernel(H, T.ceildiv(S, block_ND), B) as (bx, by, bz): o = T.alloc_fragment([block_ND, block_ND], accum_dtype) @@ -33,16 +33,12 @@ def preprocess_kernel( acc = T.alloc_fragment([block_ND, block_ND], accum_dtype) T.clear(acc) for k in T.Pipelined(T.ceildiv(D, block_ND), num_stages=num_stages): - T.copy( - O[bz, by * block_ND:(by + 1) * block_ND, bx, k * block_ND:(k + 1) * block_ND], - o) - T.copy( - dO[bz, by * block_ND:(by + 1) * block_ND, bx, k * block_ND:(k + 1) * block_ND], - do) + T.copy(O[bz, by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], o) + T.copy(dO[bz, by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], do) for i, j in T.Parallel(block_ND, block_ND): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, by * block_ND:(by + 1) * block_ND, bx]) + T.copy(delta, Delta[bz, by * block_ND : (by + 1) * block_ND, bx]) return preprocess_kernel @@ -56,22 +52,22 @@ def postprocess( kv_group=1, block_N=64, threads=128, - dtype="bfloat16", - accum_dtype="float", + dtype=T.bfloat16, + accum_dtype=T.float32, ): - assert dtype == "bfloat16" - assert accum_dtype == "float" + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 dkv_shape = [B, S_kv, kv_group, D + D_tail] @T.prim_func def postprocess_kernel( - dKV: T.Tensor(dkv_shape, accum_dtype), - dKV_out: T.Tensor(dkv_shape, dtype), + dKV: T.Tensor(dkv_shape, accum_dtype), + dKV_out: T.Tensor(dkv_shape, dtype), ): with T.Kernel(T.ceildiv(S_kv, block_N), kv_group, B, threads=threads) as (bx, by, bz): T.copy( - dKV[bz, bx * block_N:(bx + 1) * block_N, by, :], - dKV_out[bz, bx * block_N:(bx + 1) * block_N, by, :], + dKV[bz, bx * block_N : (bx + 1) * block_N, by, :], + dKV_out[bz, bx * block_N : (bx + 1) * block_N, by, :], ) return postprocess_kernel @@ -82,7 +78,9 @@ def postprocess_kernel( pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + tilelang.PassConfigKey.TL_ENABLE_AGGRESSIVE_SHARED_MEMORY_MERGE: True, + }, +) def bwd( B, S, @@ -97,18 +95,18 @@ def bwd( block_size=32, num_stages=0, threads=256, - indices_dtype="int32", - dtype="bfloat16", - accum_dtype="float", + indices_dtype=T.int32, + dtype=T.bfloat16, + accum_dtype=T.float32, ): - assert is_causal == True, 'non-casual is not supported now' - assert topk % block_size == 0, 'otherwise will load some index=0 thus causing wrong kv to be loaded' - assert dtype == "bfloat16" - assert accum_dtype == "float" - assert indices_dtype == "int32" + assert is_causal == True, "non-casual is not supported now" + assert topk % block_size == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + assert indices_dtype == T.int32 if sm_scale is None: - sm_scale = (D + D_tail)**(-0.5) + sm_scale = (D + D_tail) ** (-0.5) sm_scale_mul_reciprocal_log2 = sm_scale * 1.44269504 # log2(e) H_kv = H // kv_group @@ -118,12 +116,15 @@ def bwd( indices_shape = [B, S, kv_group, topk] delta_shape = [B, S, H] lse_shape = [B, S, H] - assert indices_dtype == "int32" - assert dtype == "bfloat16" - assert accum_dtype == "float" + assert indices_dtype == T.int32 + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 H = H_kv padded_H = max(tilelang.math.next_power_of_2(H_kv), 16) + block_H = min(64, padded_H) + assert padded_H % block_H == 0 + NH = padded_H // block_H BS = block_size NS = tilelang.cdiv(topk, block_size) @@ -131,122 +132,85 @@ def bwd( @T.prim_func def sparse_mla_bwd_kernel( - Q: T.Tensor(q_shape, dtype), - KV: T.Tensor(k_shape, dtype), - dO: T.Tensor(o_shape, dtype), - Indices: T.Tensor(indices_shape, indices_dtype), - Lse: T.Tensor(lse_shape, accum_dtype), - Delta: T.Tensor(delta_shape, accum_dtype), - dQ: T.Tensor(q_shape, dtype), - dKV: T.Tensor(k_shape, accum_dtype), + Q: T.Tensor(q_shape, dtype), + KV: T.Tensor(k_shape, dtype), + dO: T.Tensor(o_shape, dtype), + Indices: T.Tensor(indices_shape, indices_dtype), + Lse: T.Tensor(lse_shape, accum_dtype), + Delta: T.Tensor(delta_shape, accum_dtype), + dQ: T.Tensor(q_shape, dtype), + dKV: T.Tensor(k_shape, accum_dtype), ): - with T.Kernel(S, B, kv_group, threads=threads) as (s_i, by, bz): - Q_shared = T.alloc_shared([padded_H, D], dtype) - Q_tail_shared = T.alloc_shared([padded_H, D_tail], dtype) + with T.Kernel(S, B, kv_group * NH, threads=threads) as (s_i, by, bz): + Q_shared = T.alloc_shared([block_H, D], dtype) + Q_tail_shared = T.alloc_shared([block_H, D_tail], dtype) KV_shared = T.alloc_shared([BS, D], dtype) KV_tail_shared = T.alloc_shared([BS, D_tail], dtype) - dO_shared = T.alloc_shared([padded_H, D], dtype) + dO_shared = T.alloc_shared([block_H, D], dtype) mask = T.alloc_fragment([BS], "bool") - P_shared_cast = T.alloc_shared([padded_H, BS], dtype) - dP_shared_cast = T.alloc_shared([padded_H, BS], dtype) - dQ_shared = T.alloc_shared([padded_H, D], dtype) - dQ_tail_shared = T.alloc_shared([padded_H, D_tail], dtype) + P_shared_cast = T.alloc_shared([block_H, BS], dtype) + dP_shared_cast = T.alloc_shared([block_H, BS], dtype) + dQ_shared = T.alloc_shared([block_H, D], dtype) + dQ_tail_shared = T.alloc_shared([block_H, D_tail], dtype) - acc_p = T.alloc_fragment([padded_H, BS], accum_dtype) - acc_dp = T.alloc_fragment([padded_H, BS], accum_dtype) - acc_dq = T.alloc_fragment([padded_H, D], accum_dtype) - acc_dq_tail = T.alloc_fragment([padded_H, D_tail], accum_dtype) + acc_p = T.alloc_fragment([block_H, BS], accum_dtype) + acc_dp = T.alloc_fragment([block_H, BS], accum_dtype) + acc_dq = T.alloc_fragment([block_H, D], accum_dtype) + acc_dq_tail = T.alloc_fragment([block_H, D_tail], accum_dtype) acc_dkv = T.alloc_fragment([BS, D], accum_dtype) acc_dkv_tail = T.alloc_fragment([BS, D_tail], accum_dtype) - acc_dkv_shared = T.view(KV_shared, shape=[BS // split_store, D], dtype=accum_dtype) - acc_dkv_tail_shared = T.view( - KV_tail_shared, shape=[BS // split_store, D_tail], dtype=accum_dtype) + acc_dkv_shared = T.alloc_shared([BS // split_store, D], accum_dtype) + acc_dkv_tail_shared = T.alloc_shared([BS // split_store, D_tail], accum_dtype) max_kv_i = s_i - T.copy(Q[by, s_i, bz * padded_H:(bz + 1) * padded_H, :D], Q_shared) - T.copy(Q[by, s_i, bz * padded_H:(bz + 1) * padded_H, D:], Q_tail_shared) - T.copy(dO[by, s_i, bz * padded_H:(bz + 1) * padded_H, :D], dO_shared) + T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, :D], Q_shared) + T.copy(Q[by, s_i, bz * block_H : (bz + 1) * block_H, D:], Q_tail_shared) + T.copy(dO[by, s_i, bz * block_H : (bz + 1) * block_H, :D], dO_shared) T.clear(acc_dq) T.clear(acc_dq_tail) - T.annotate_layout({ - dQ_shared: tilelang.layout.make_swizzled_layout(dQ_shared), - dQ_tail_shared: tilelang.layout.make_swizzled_layout(dQ_tail_shared), - }) - # Process each block of indices for i_i in T.Pipelined(NS, num_stages=num_stages): # Check which indices are valid for bi_i in T.Parallel(BS): - mask[bi_i] = Indices[by, s_i, bz, i_i * BS + bi_i] <= max_kv_i + mask[bi_i] = Indices[by, s_i, bz // NH, i_i * BS + bi_i] <= max_kv_i # Compute attention scores - for h_i, bi_i in T.Parallel(padded_H, BS): + for h_i, bi_i in T.Parallel(block_H, BS): acc_p[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_p.dtype)) # Load KV, V for this block of indices for bi_i, d_i in T.Parallel(BS, D): - KV_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz, i_i * BS + bi_i], bz, d_i] + KV_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i], bz // NH, d_i] - T.gemm( - Q_shared, KV_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + T.gemm(Q_shared, KV_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) for bi_i, d_i in T.Parallel(BS, D_tail): - KV_tail_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz, i_i * BS + bi_i], bz, - D + d_i] - T.gemm( - Q_tail_shared, - KV_tail_shared, - acc_p, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol) - - for h_i, bi_i in T.Parallel(padded_H, BS): - acc_p[h_i, bi_i] = T.exp2(acc_p[h_i, bi_i] * sm_scale_mul_reciprocal_log2 - - Lse[by, s_i, bz * padded_H + h_i]) + KV_tail_shared[bi_i, d_i] = KV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i], bz // NH, D + d_i] + T.gemm(Q_tail_shared, KV_tail_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + + for h_i, bi_i in T.Parallel(block_H, BS): + acc_p[h_i, bi_i] = T.exp2(acc_p[h_i, bi_i] * sm_scale_mul_reciprocal_log2 - Lse[by, s_i, bz * block_H + h_i]) T.copy(acc_p, P_shared_cast) - T.gemm( - dO_shared, - KV_shared, - acc_dp, - transpose_B=True, - policy=T.GemmWarpPolicy.FullCol, - clear_accum=True) + T.gemm(dO_shared, KV_shared, acc_dp, transpose_B=True, policy=T.GemmWarpPolicy.FullCol, clear_accum=True) - for h_i, bi_i in T.Parallel(padded_H, BS): - acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * ( - acc_dp[h_i, bi_i] - Delta[by, s_i, bz * padded_H + h_i]) * sm_scale + for h_i, bi_i in T.Parallel(block_H, BS): + acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * (acc_dp[h_i, bi_i] - Delta[by, s_i, bz * block_H + h_i]) * sm_scale T.copy(acc_dp, dP_shared_cast) T.gemm(dP_shared_cast, KV_shared, acc_dq, policy=T.GemmWarpPolicy.FullCol) T.gemm(dP_shared_cast, KV_tail_shared, acc_dq_tail, policy=T.GemmWarpPolicy.FullCol) - T.gemm( - dP_shared_cast, - Q_shared, - acc_dkv, - transpose_A=True, - policy=T.GemmWarpPolicy.FullCol, - clear_accum=True) - T.gemm( - P_shared_cast, - dO_shared, - acc_dkv, - transpose_A=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(dP_shared_cast, Q_shared, acc_dkv, transpose_A=True, policy=T.GemmWarpPolicy.FullCol, clear_accum=True) + T.gemm(P_shared_cast, dO_shared, acc_dkv, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) T.clear(acc_dkv_tail) - T.gemm( - dP_shared_cast, - Q_tail_shared, - acc_dkv_tail, - transpose_A=True, - policy=T.GemmWarpPolicy.FullCol) + T.gemm(dP_shared_cast, Q_tail_shared, acc_dkv_tail, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) for s in range(split_store): for bi_i, d_i in T.Parallel(BS, D): @@ -255,41 +219,32 @@ def sparse_mla_bwd_kernel( for bi_i, d_i in T.Parallel(BS, D_tail): if bi_i < BS // split_store: - acc_dkv_tail_shared[bi_i, - d_i] = acc_dkv_tail[bi_i + s * (BS // split_store), - d_i] + acc_dkv_tail_shared[bi_i, d_i] = acc_dkv_tail[bi_i + s * (BS // split_store), d_i] for bi_i, d_i in T.Parallel(BS // split_store, D // 4): T.atomic_addx4( - dKV[by, Indices[by, s_i, bz, i_i * BS + bi_i + s * (BS // split_store)], - bz, d_i * 4], acc_dkv_shared[bi_i, d_i * 4]) + dKV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i + s * (BS // split_store)], bz // NH, d_i * 4], + acc_dkv_shared[bi_i, d_i * 4], + ) # Atomically update dKV, dKV_tail tensors for bi_i, d_i in T.Parallel(BS // split_store, D_tail // 4): T.atomic_addx4( - dKV[by, Indices[by, s_i, bz, i_i * BS + bi_i + s * (BS // split_store)], - bz, D + d_i * 4], acc_dkv_tail_shared[bi_i, d_i * 4]) + dKV[by, Indices[by, s_i, bz // NH, i_i * BS + bi_i + s * (BS // split_store)], bz // NH, D + d_i * 4], + acc_dkv_tail_shared[bi_i, d_i * 4], + ) # Store the accumulated dQ T.copy(acc_dq, dQ_shared) T.copy(acc_dq_tail, dQ_tail_shared) - T.copy(dQ_shared, dQ[by, s_i, bz * padded_H:(bz + 1) * padded_H, :D]) - T.copy(dQ_tail_shared, dQ[by, s_i, bz * padded_H:(bz + 1) * padded_H, D:]) + T.copy(dQ_shared, dQ[by, s_i, bz * block_H : (bz + 1) * block_H, :D]) + T.copy(dQ_tail_shared, dQ[by, s_i, bz * block_H : (bz + 1) * block_H, D:]) return sparse_mla_bwd_kernel -def sparse_mla_bwd(q, - kv, - o, - do, - indices, - lse, - sm_scale=None, - is_casual=True, - return_kernel=False, - delta=None): +def sparse_mla_bwd(q, kv, o, do, indices, lse, sm_scale=None, is_casual=True, return_kernel=False, delta=None): assert q.is_contiguous() assert kv.is_contiguous() assert indices.is_contiguous() @@ -322,6 +277,7 @@ def sparse_mla_bwd(q, def ref_sparse_mla_bwd_interface(q, kv, o, do, indices, lse, sm_scale=None, is_casual=True): from sparse_mla_fwd import ref_sparse_mla_fwd_interface + q = q.detach().clone() kv = kv.detach().clone() q.requires_grad = True @@ -331,30 +287,22 @@ def ref_sparse_mla_bwd_interface(q, kv, o, do, indices, lse, sm_scale=None, is_c return q.grad, kv.grad -def test_sparse_mla_bwd(B=1, - S=4096, - SKV=8192, - H=64, - HKV=1, - DQKV=576, - DV=512, - topk=2048, - dtype=torch.bfloat16, - check_correctness=True): +def test_sparse_mla_bwd(B=1, S=4096, SKV=8192, H=64, HKV=1, DQKV=576, DV=512, topk=2048, dtype=torch.bfloat16, check_correctness=True): # Prepare data - q = torch.randn((B, S, H, DQKV), dtype=dtype, device='cuda').requires_grad_(True) - kv = torch.randn((B, SKV, HKV, DQKV), dtype=dtype, device='cuda').requires_grad_(True) - do = torch.randn((B, S, H, DV), dtype=dtype, device='cuda') + q = torch.randn((B, S, H, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + kv = torch.randn((B, SKV, HKV, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((B, S, H, DV), dtype=dtype, device="cuda") - indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device='cuda') + indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device="cuda") for b in range(B): for t in range(S): for h in range(HKV): i_i = torch.randperm(max(1, t))[:topk] - indices[b, t, h, :len(i_i)] = i_i + indices[b, t, h, : len(i_i)] = i_i # Forward from sparse_mla_fwd import sparse_mla_fwd_interface + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices) tl_dq, tl_dkv = sparse_mla_bwd(q, kv, tl_out, do, indices, tl_lse) @@ -365,13 +313,15 @@ def test_sparse_mla_bwd(B=1, assert_tensors_similar(tl_dkv, ref_dkv, eps=1e-4, name="dkv") print("assert_tensors_similar passed") - per_token_flop = 2 * sum([ - H * DV * topk, - H * DQKV * topk, - H * DQKV * topk, - H * DQKV * topk, - H * DV * topk, - ]) + per_token_flop = 2 * sum( + [ + H * DV * topk, + H * DQKV * topk, + H * DQKV * topk, + H * DQKV * topk, + H * DV * topk, + ] + ) from tilelang.profiler import do_bench def fn(): @@ -379,20 +329,44 @@ def fn(): ms = do_bench(fn, rep=100, warmup=250) print(f"Average time: {ms:.3f} ms") - print(f'bwd io bandwidth = ', - (B * S * max(DQKV * 2, DQKV + DV) * topk * 2) / (ms * 1e-3) / 1e12) - print(f'bwd tflops = ', per_token_flop * S / (ms * 1e-3) / 1e12) + print(f"bwd io bandwidth = ", (B * S * max(DQKV * 2, DQKV + DV) * topk * 2) / (ms * 1e-3) / 1e12) + print(f"bwd tflops = ", per_token_flop * S / (ms * 1e-3) / 1e12) + + +def run_regression_perf(B=1, S=4096, SKV=8192, H=64, HKV=1, DQKV=576, DV=512, topk=2048, dtype=torch.bfloat16): + torch.manual_seed(42) + torch.cuda.manual_seed_all(42) + q = torch.randn((B, S, H, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + kv = torch.randn((B, SKV, HKV, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((B, S, H, DV), dtype=dtype, device="cuda") + + indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device="cuda") + for b in range(B): + for t in range(S): + for h in range(HKV): + i_i = torch.randperm(max(1, t))[:topk] + indices[b, t, h, : len(i_i)] = i_i + + from sparse_mla_fwd import sparse_mla_fwd_interface + + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices) + B, S, H, dim_plus_tail_dim = q.shape + _, S_kv, kv_group, _ = kv.shape + D = 512 + D_tail = dim_plus_tail_dim - D + topk = indices.shape[-1] + preprocess_kernel = preprocess(B, S, H, D) + bwd_kernel = bwd(B, S, S_kv, H, D, D_tail, topk, kv_group, None, True) + delta = preprocess_kernel(tl_out, do) + dkv = torch.zeros_like(kv, dtype=torch.float32) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + return bwd_kernel(q, kv, do, indices, tl_lse, delta, dkv) + + return do_bench(run_kernel_only, backend="cupti") if __name__ == "__main__": - test_sparse_mla_bwd( - B=1, - S=4096, - SKV=8192, - H=64, - HKV=1, - DQKV=576, - DV=512, - topk=2048, - dtype=torch.bfloat16, - check_correctness=True) + test_sparse_mla_bwd(B=1, S=4096, SKV=8192, H=64, HKV=1, DQKV=576, DV=512, topk=2048, dtype=torch.bfloat16, check_correctness=True) diff --git a/examples/deepseek_v32/sparse_mla_fwd.py b/examples/deepseek_v32/sparse_mla_fwd.py index a39c72c40f..2c8bf7fc74 100644 --- a/examples/deepseek_v32/sparse_mla_fwd.py +++ b/examples/deepseek_v32/sparse_mla_fwd.py @@ -25,15 +25,12 @@ def sparse_mla_fwd( num_stages=2, threads=256, ): - assert dim == tilelang.math.next_power_of_2( - dim), f"haven't check padding correctness yet, dim={dim}" - assert tail_dim == tilelang.math.next_power_of_2( - tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" + assert dim == tilelang.math.next_power_of_2(dim), f"haven't check padding correctness yet, dim={dim}" + assert tail_dim == tilelang.math.next_power_of_2(tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" assert is_causal == True, "non-casual is not supported" - assert (topk % - block_I == 0), "otherwise will load some index=0 thus causing wrong kv to be loaded" + assert topk % block_I == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" if sm_scale is None: - sm_scale = (1.0 / (dim + tail_dim))**0.5 * 1.44269504 # log2(e) + sm_scale = (1.0 / (dim + tail_dim)) ** 0.5 * 1.44269504 # log2(e) else: sm_scale = sm_scale * 1.44269504 # log2(e) @@ -47,17 +44,17 @@ def sparse_mla_fwd( o_shape = [batch, seq_len, heads, dim] indices_shape = [batch, seq_len, kv_group, topk] lse_shape = [batch, seq_len, heads] - indices_dtype = "int32" - dtype = "bfloat16" - accum_dtype = "float" + indices_dtype = T.int32 + dtype = T.bfloat16 + accum_dtype = T.float32 G = kv_group H = head_kv padded_H = max(tilelang.math.next_power_of_2(head_kv), 16) if padded_H != H: - assert ( - kv_group == 1 - ), "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + assert kv_group == 1, ( + "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + ) BI = block_I NI = tilelang.cdiv(topk, block_I) D = dim @@ -73,18 +70,17 @@ def sparse_mla_fwd( @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), # type: ignore - KV: T.Tensor(kv_shape, dtype), # type: ignore - Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore - Output: T.Tensor(o_shape, dtype), # type: ignore - Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + Output: T.Tensor(o_shape, dtype), # type: ignore + Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore ): - with T.Kernel( - seq_len * REPLICATE_H, batch, kv_group, threads=threads) as ( - bx, - by, - bz, - ): + with T.Kernel(seq_len * REPLICATE_H, batch, kv_group, threads=threads) as ( + bx, + by, + bz, + ): Q_shared = T.alloc_shared([H_per_block, D], dtype) Q_tail_shared = T.alloc_shared([H_per_block, D_tail], dtype) KV_shared = T.alloc_shared([BI, D], dtype) @@ -118,16 +114,13 @@ def main( T.copy(Q[b_i, s_i, H0:H1, D:], Q_tail_shared) for i_i in T.Pipelined(NI, num_stages=num_stages): - for bi_i in T.Parallel(BI): mask[bi_i] = Indices[b_i, s_i, g_i, i_i * BI + bi_i] <= max_kv_i for bi_i, d_i in T.Parallel(BI, D): - KV_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, - d_i] + KV_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, d_i] for bi_i, d_i in T.Parallel(BI, D_tail): - K_tail_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, - D + d_i] + K_tail_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, D + d_i] for h_i, bi_i in T.Parallel(H_per_block, BI): acc_s[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_s.dtype)) @@ -147,6 +140,8 @@ def main( ) T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(H_per_block): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(H_per_block): alpha[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(H_per_block, BI): @@ -174,15 +169,7 @@ def main( return main -def sparse_mla_fwd_interface(q, - kv, - indices, - sm_scale=None, - return_p_sum: bool = False, - d_v=512, - block_I=64, - num_stages=2, - threads=256): +def sparse_mla_fwd_interface(q, kv, indices, sm_scale=None, return_p_sum: bool = False, d_v=512, block_I=64, num_stages=2, threads=256): is_casual = True assert return_p_sum == False, "This kernel file is for fwd only" assert q.is_contiguous() and kv.is_contiguous() and indices.is_contiguous() @@ -199,16 +186,8 @@ def sparse_mla_fwd_interface(q, assert indices.shape == (batch, seq_len, kv_group, topk) kernel = sparse_mla_fwd( - heads, - dim, - tail_dim, - topk, - kv_group, - sm_scale, - is_casual, - block_I=block_I, - num_stages=num_stages, - threads=threads) + heads, dim, tail_dim, topk, kv_group, sm_scale, is_casual, block_I=block_I, num_stages=num_stages, threads=threads + ) out, lse = kernel(q, kv, indices) return out, lse @@ -228,14 +207,14 @@ def ref_sparse_mla_fwd_interface(q, kv, indices, sm_scale=None, is_casual=True): b, _, _, dim_v = v.shape g_index = g h_index = h // g - compressed_casual_mask = torch.arange( - 0, sq, dtype=torch.int32, device="cuda").view(-1, 1) >= torch.arange( - 1 - 1, sk * 1, 1, dtype=torch.int32, device="cuda").view(1, -1) + compressed_casual_mask = torch.arange(0, sq, dtype=torch.int32, device="cuda").view(-1, 1) >= torch.arange( + 1 - 1, sk * 1, 1, dtype=torch.int32, device="cuda" + ).view(1, -1) mask = q.new_zeros(b, g_index, sq, sk + 1, dtype=torch.bool).scatter(3, indices.long(), 1) mask = mask[..., :-1] mask = mask & compressed_casual_mask.view(1, 1, sq, sk) - mask[:, :, :1 - 1, 0] = True + mask[:, :, : 1 - 1, 0] = True mask = mask.view(b, g_index, 1, sq, sk) q = q.view(b, sq, g, -1, dim_q) @@ -250,19 +229,21 @@ def ref_sparse_mla_fwd_interface(q, kv, indices, sm_scale=None, is_casual=True): return o.to(torch.bfloat16) -def test_sparse_mla_fwd(B=1, - S=4096, - SKV=8192, - H=128, - HKV=1, - DQK=576, - DV=512, - topk=2048, - dtype=torch.bfloat16, - check_correctness=True, - block_I=64, - num_stages=2, - threads=256): +def test_sparse_mla_fwd( + B=1, + S=4096, + SKV=8192, + H=128, + HKV=1, + DQK=576, + DV=512, + topk=2048, + dtype=torch.bfloat16, + check_correctness=True, + block_I=64, + num_stages=2, + threads=256, +): torch.random.manual_seed(0) q = torch.randn((B, S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True) kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True) @@ -272,10 +253,9 @@ def test_sparse_mla_fwd(B=1, for t in range(S): for h in range(HKV): i_i = torch.randperm(max(1, t))[:topk] - indices[b, t, h, :len(i_i)] = i_i + indices[b, t, h, : len(i_i)] = i_i - tl_out, tl_lse = sparse_mla_fwd_interface( - q, kv, indices, block_I=block_I, num_stages=num_stages, threads=threads) + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices, block_I=block_I, num_stages=num_stages, threads=threads) if check_correctness: # otherwise may cause out of memory @@ -284,8 +264,7 @@ def test_sparse_mla_fwd(B=1, print("assert_tensors_similar passed") def fn(): - return sparse_mla_fwd_interface( - q, kv, indices, block_I=block_I, num_stages=num_stages, threads=threads) + return sparse_mla_fwd_interface(q, kv, indices, block_I=block_I, num_stages=num_stages, threads=threads) from tilelang.profiler import do_bench @@ -299,6 +278,36 @@ def fn(): print("fwd tflops = ", (B * S * (DQK + DV) * topk * 2 * H) / (ms * 1e-3) / 1e12) +def run_regression_perf( + B=1, S=4096, SKV=8192, H=128, HKV=1, DQK=576, DV=512, topk=2048, dtype=torch.bfloat16, block_I=64, num_stages=2, threads=256 +): + torch.random.manual_seed(0) + q = torch.randn((B, S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True) + kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True) + + indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device="cuda") + for b in range(B): + for t in range(S): + for h in range(HKV): + i_i = torch.randperm(max(1, t))[:topk] + indices[b, t, h, : len(i_i)] = i_i + + is_casual = True + _, _, heads, dim_plus_tail_dim = q.shape + _, _, kv_group, _ = kv.shape + dim = 512 + tail_dim = dim_plus_tail_dim - dim + _, _, _, topk = indices.shape + kernel = sparse_mla_fwd(heads, dim, tail_dim, topk, kv_group, None, is_casual, block_I=block_I, num_stages=num_stages, threads=threads) + + def run_kernel_only(): + kernel(q, kv, indices) + + from tilelang.profiler import do_bench + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": test_sparse_mla_fwd( B=1, @@ -313,4 +322,5 @@ def fn(): check_correctness=True, block_I=64, num_stages=2, - threads=256) + threads=256, + ) diff --git a/examples/deepseek_v32/sparse_mla_fwd_pipelined.py b/examples/deepseek_v32/sparse_mla_fwd_pipelined.py index 96dda7df57..7e664d11b4 100644 --- a/examples/deepseek_v32/sparse_mla_fwd_pipelined.py +++ b/examples/deepseek_v32/sparse_mla_fwd_pipelined.py @@ -9,10 +9,16 @@ @tilelang.jit( out_idx=[-2, -1], compile_flags=[ - "-O3", "-Wno-deprecated-declarations", "-U__CUDA_NO_HALF_OPERATORS__", - "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_HALF2_OPERATORS__", - "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda", - "--ptxas-options=-v,--register-usage-level=10", "-DNDEBUG" + "-O3", + "-Wno-deprecated-declarations", + "-U__CUDA_NO_HALF_OPERATORS__", + "-U__CUDA_NO_HALF_CONVERSIONS__", + "-U__CUDA_NO_HALF2_OPERATORS__", + "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", + "--expt-relaxed-constexpr", + "--expt-extended-lambda", + "--ptxas-options=-v,--register-usage-level=10", + "-DNDEBUG", ], ) def sparse_mla_fwd( @@ -32,14 +38,12 @@ def sparse_mla_fwd( num_stages=0, threads=384, ): - assert dim == tilelang.math.next_power_of_2( - dim), f"haven't check padding correctness yet, dim={dim}" - assert tail_dim == tilelang.math.next_power_of_2( - tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" - assert is_causal == True, 'non-casual is not supported' - assert topk % block_I == 0, 'otherwise will load some index=0 thus causing wrong kv to be loaded' + assert dim == tilelang.math.next_power_of_2(dim), f"haven't check padding correctness yet, dim={dim}" + assert tail_dim == tilelang.math.next_power_of_2(tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" + assert is_causal == True, "non-casual is not supported" + assert topk % block_I == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" if sm_scale is None: - sm_scale = (1.0 / (dim + tail_dim))**0.5 * 1.44269504 # log2(e) + sm_scale = (1.0 / (dim + tail_dim)) ** 0.5 * 1.44269504 # log2(e) else: sm_scale = sm_scale * 1.44269504 # log2(e) @@ -49,23 +53,25 @@ def sparse_mla_fwd( o_shape = [batch, seq_len, heads, dim] indices_shape = [batch, seq_len, kv_group, topk] lse_shape = [batch, seq_len, heads] - indices_dtype = "int32" - dtype = "bfloat16" - accum_dtype = "float" + indices_dtype = T.int32 + dtype = T.bfloat16 + accum_dtype = T.float32 G = kv_group H = head_kv padded_H = max(tilelang.math.next_power_of_2(head_kv), 16) if padded_H != H: - assert kv_group == 1, 'here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)' + assert kv_group == 1, ( + "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + ) BI = block_I NI = tilelang.cdiv(topk, block_I) - assert NI % 2 == 0, 'NI should be a multiple of 2' + assert NI % 2 == 0, "NI should be a multiple of 2" D = dim D_tail = tail_dim KV_stride = kv_stride if head_kv > 64: - assert head_kv % 64 == 0, 'head_kv should be a multiple of 64' + assert head_kv % 64 == 0, "head_kv should be a multiple of 64" REPLICATE_H = head_kv // 64 else: REPLICATE_H = 1 @@ -74,18 +80,14 @@ def sparse_mla_fwd( @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), # type: ignore - KV: T.Tensor(kv_shape, dtype), # type: ignore - Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore - q_start_index_s: T.Tensor(1, indices_dtype), - Output: T.Tensor(o_shape, dtype), # type: ignore - Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + q_start_index_s: T.Tensor(1, indices_dtype), + Output: T.Tensor(o_shape, dtype), # type: ignore + Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore ): - with T.Kernel( - (seq_len - kv_stride + 1 if CP0 else seq_len) * REPLICATE_H, - batch, - kv_group, - threads=threads) as (bx, by, bz): + with T.Kernel((seq_len - kv_stride + 1 if CP0 else seq_len) * REPLICATE_H, batch, kv_group, threads=threads) as (bx, by, bz): Q_shared_l = T.alloc_shared([H_per_block, D // 2], dtype) Q_shared_r = T.alloc_shared([H_per_block, D // 2], dtype) Q_tail_shared = T.alloc_shared([H_per_block, D_tail], dtype) @@ -110,7 +112,7 @@ def main( alpha_local = T.alloc_fragment([H_per_block], accum_dtype) m_i = T.alloc_fragment([H_per_block], accum_dtype) m_i_prev = T.alloc_fragment([H_per_block], accum_dtype) - indices_local = T.alloc_local([1], indices_dtype) + indices_local = T.alloc_var(indices_dtype) # TODO: Multi buffer bar_q = T.alloc_barrier(arrive_count=384) @@ -122,8 +124,7 @@ def main( bar_sScale_and_sS_free = T.alloc_barrier(arrive_count=256) b_i, g_i = by, bz - s_i = (bx + (KV_stride - 1 if CP0 else 0)) if REPLICATE_H == 1 else ( - bx // REPLICATE_H + (KV_stride - 1 if CP0 else 0)) + s_i = (bx + (KV_stride - 1 if CP0 else 0)) if REPLICATE_H == 1 else (bx // REPLICATE_H + (KV_stride - 1 if CP0 else 0)) q_i = q_start_index_s[0] + s_i max_kv_i = (q_i + 1 - KV_stride) // KV_stride @@ -132,26 +133,24 @@ def main( tx = T.get_thread_binding() - T.copy(Q[b_i, s_i, H0:H1, 0:D // 2], Q_shared_l) - T.copy(Q[b_i, s_i, H0:H1, D // 2:D], Q_shared_r) + T.copy(Q[b_i, s_i, H0:H1, 0 : D // 2], Q_shared_l) + T.copy(Q[b_i, s_i, H0:H1, D // 2 : D], Q_shared_r) T.copy(Q[b_i, s_i, H0:H1, D:], Q_tail_shared) T.barrier_arrive(bar_q) if tx < 128: T.set_max_nreg(240, 1) T.fill(sumexp, 0) - T.fill(m_i, -2**30) # avoid -inf - inf to cause nan + T.fill(m_i, -(2**30)) # avoid -inf - inf to cause nan T.fill(acc_o_l, 0) T.barrier_wait(bar_q, 0) for i_i in T.serial(T.ceildiv(NI, 2)): - # Buffer 0 T.barrier_wait(bar_k_0_ready[0], (i_i & 1)) for h_i, bi_i in T.Parallel(H_per_block, BI): - acc_s[h_i, bi_i] = T.if_then_else(is_kv_valid[bi_i], 0, - -T.infinity(acc_s.dtype)) + acc_s[h_i, bi_i] = T.if_then_else(is_kv_valid[bi_i], 0, -T.infinity(acc_s.dtype)) T.gemm(Q_shared_l, KV_shared_0_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_0_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_0, acc_s, transpose_B=True, wg_wait=-1) @@ -164,6 +163,8 @@ def main( T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(H_per_block): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(H_per_block): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(H_per_block, BI): @@ -185,8 +186,7 @@ def main( T.barrier_wait(bar_k_1_ready[0], (i_i & 1)) for h_i, bi_i in T.Parallel(H_per_block, BI): - acc_s[h_i, bi_i] = T.if_then_else(is_kv_valid[bi_i], 0, - -T.infinity(acc_s.dtype)) + acc_s[h_i, bi_i] = T.if_then_else(is_kv_valid[bi_i], 0, -T.infinity(acc_s.dtype)) T.gemm(Q_shared_l, KV_shared_1_l, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_shared_r, KV_shared_1_r, acc_s, transpose_B=True, wg_wait=-1) T.gemm(Q_tail_shared, K_tail_shared_1, acc_s, transpose_B=True, wg_wait=-1) @@ -198,6 +198,8 @@ def main( T.copy(m_i, m_i_prev) T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(H_per_block): + m_i[h_i] = T.max(m_i[h_i], m_i_prev[h_i]) for h_i in T.Parallel(H_per_block): alpha_local[h_i] = T.exp2((m_i_prev[h_i] - m_i[h_i]) * sm_scale) for h_i, bi_i in T.Parallel(H_per_block, BI): @@ -223,7 +225,7 @@ def main( for h_i in T.Parallel(H_per_block): sumexp[h_i] = T.log2(sumexp[h_i]) + m_i[h_i] * sm_scale T.copy(acc_o_l, O_shared_l) - T.copy(O_shared_l, Output[b_i, s_i, H0:H1, 0:D // 2]) + T.copy(O_shared_l, Output[b_i, s_i, H0:H1, 0 : D // 2]) elif tx >= 128 and tx < 256: T.set_max_nreg(168, 1) @@ -253,7 +255,7 @@ def main( acc_o_r[h_i, d_i] /= sum_exp_shared[h_i] T.copy(acc_o_r, O_shared_r) - T.copy(O_shared_r, Output[b_i, s_i, H0:H1, D // 2:D]) + T.copy(O_shared_r, Output[b_i, s_i, H0:H1, D // 2 : D]) elif tx >= 256: # producer T.set_max_nreg(80, 0) @@ -261,70 +263,58 @@ def main( # Buffer 0 T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - indices_local[0] = Indices[b_i, s_i, g_i, - (i_i * 2) * BI + r * 16 + (tx - 256) // 8] - is_kv_valid[r * 16 + (tx - 256) // 8] = indices_local[0] <= max_kv_i + indices_local = Indices[b_i, s_i, g_i, (i_i * 2) * BI + r * 16 + (tx - 256) // 8] + is_kv_valid[r * 16 + (tx - 256) // 8] = indices_local <= max_kv_i if is_kv_valid[r * 16 + (tx - 256) // 8]: with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_0_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_0_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, D // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_0_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_0_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, D // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, - D + (tx - 256) % 8 * 8 + v] + K_tail_shared_0[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, D + (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_0_ready[0]) # Buffer 1 T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1)) for r in T.serial(4): - indices_local[0] = Indices[b_i, s_i, g_i, - (i_i * 2 + 1) * BI + r * 16 + (tx - 256) // 8] - is_kv_valid[r * 16 + (tx - 256) // 8] = indices_local[0] <= max_kv_i + indices_local = Indices[b_i, s_i, g_i, (i_i * 2 + 1) * BI + r * 16 + (tx - 256) // 8] + is_kv_valid[r * 16 + (tx - 256) // 8] = indices_local <= max_kv_i if is_kv_valid[r * 16 + (tx - 256) // 8]: with T.attr("default", "async_scope", 1): for u in T.serial(4): for v in T.vectorized(8): - KV_shared_1_l[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, - 64 * u + (tx - 256) % 8 * 8 + v] - KV_shared_1_r[r * 16 + (tx - 256) // 8, - 64 * u + (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, D // 2 + - 64 * u + (tx - 256) % 8 * 8 + v] + KV_shared_1_l[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, 64 * u + (tx - 256) % 8 * 8 + v + ] + KV_shared_1_r[r * 16 + (tx - 256) // 8, 64 * u + (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, D // 2 + 64 * u + (tx - 256) % 8 * 8 + v + ] with T.attr("default", "async_scope", 1): for v in T.vectorized(8): - K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + - v] = KV[b_i, indices_local[0], g_i, - D + (tx - 256) % 8 * 8 + v] + K_tail_shared_1[r * 16 + (tx - 256) // 8, (tx - 256) % 8 * 8 + v] = KV[ + b_i, indices_local, g_i, D + (tx - 256) % 8 * 8 + v + ] T.cp_async_barrier_noinc(bar_k_1_ready[0]) return main -def sparse_mla_fwd_interface(q, - kv, - indices, - q_start_index_s, - kv_stride, - sm_scale=None, - is_casual=True, - return_kernel=False, - print_kernel=False): +def sparse_mla_fwd_interface( + q, kv, indices, q_start_index_s, kv_stride, sm_scale=None, is_casual=True, return_kernel=False, print_kernel=False +): assert q.is_contiguous() and kv.is_contiguous() and indices.is_contiguous() batch, seq_len, heads, dim_plus_tail_dim = q.shape _, seq_len_kv, kv_group, _ = kv.shape - assert dim_plus_tail_dim == 576, 'you should assign dim otherwise' + assert dim_plus_tail_dim == 576, "you should assign dim otherwise" dim = 512 assert kv.shape[-1] == dim_plus_tail_dim @@ -334,29 +324,23 @@ def sparse_mla_fwd_interface(q, assert indices.shape == (batch, seq_len, kv_group, topk) if q_start_index_s != 0: - assert q_start_index_s > kv_stride, "If it is because each cp has too short length, you should fix the logic involving CP0 (cp_rank == 0), to make sure q with pos < KV_Stride - 1 is masked (or you may just ignore how this is handled if nan in these q's Out would not effect others, which is reported to be likely to happen by wangding)" + assert q_start_index_s > kv_stride, ( + "If it is because each cp has too short length, you should fix the logic involving CP0 (cp_rank == 0), to make sure q with pos < KV_Stride - 1 is masked (or you may just ignore how this is handled if nan in these q's Out would not effect others, which is reported to be likely to happen by wangding)" + ) CP0 = q_start_index_s == 0 - kernel = sparse_mla_fwd(batch, seq_len, seq_len_kv, heads, dim, tail_dim, topk, kv_stride, - kv_group, sm_scale, is_casual, CP0) + kernel = sparse_mla_fwd(batch, seq_len, seq_len_kv, heads, dim, tail_dim, topk, kv_stride, kv_group, sm_scale, is_casual, CP0) if print_kernel: print(kernel.get_kernel_source()) - out, lse = kernel(q, kv, indices, - torch.tensor([q_start_index_s], dtype=torch.int32, device="cuda")) + out, lse = kernel(q, kv, indices, torch.tensor([q_start_index_s], dtype=torch.int32, device="cuda")) if return_kernel: return kernel if q_start_index_s == 0 and kv_stride > 1: - out[:, :kv_stride - 1, :, :] = 0 + out[:, : kv_stride - 1, :, :] = 0 return out, lse -def ref_sparse_mla_fwd_interface(q, - kv, - indices, - q_start_index_s, - kv_stride=4, - sm_scale=None, - is_casual=True): +def ref_sparse_mla_fwd_interface(q, kv, indices, q_start_index_s, kv_stride=4, sm_scale=None, is_casual=True): q = q.float() kv = kv.float() indices = indices.transpose(1, 2) @@ -365,7 +349,7 @@ def ref_sparse_mla_fwd_interface(q, if q_start_index_s is None: q_start_index_s = sk * kv_stride - sq - assert kv.shape[-1] == 576, 'you should assign dim otherwise' + assert kv.shape[-1] == 576, "you should assign dim otherwise" dim = 512 k = kv v = kv[..., :dim] @@ -374,15 +358,14 @@ def ref_sparse_mla_fwd_interface(q, num_kv_per_index = 1 g_index = g h_index = h // g - compressed_casual_mask = torch.arange( - q_start_index_s, sq + q_start_index_s, dtype=torch.int32, - device="cuda").view(-1, 1) >= torch.arange( - kv_stride - 1, sk * kv_stride, kv_stride, dtype=torch.int32, device="cuda").view(1, -1) + compressed_casual_mask = torch.arange(q_start_index_s, sq + q_start_index_s, dtype=torch.int32, device="cuda").view( + -1, 1 + ) >= torch.arange(kv_stride - 1, sk * kv_stride, kv_stride, dtype=torch.int32, device="cuda").view(1, -1) mask = q.new_zeros(b, g_index, sq, sk + 1, dtype=torch.bool).scatter(3, indices.long(), 1) mask = mask[..., :-1] mask = mask & compressed_casual_mask.view(1, 1, sq, sk) - mask[:, :, :kv_stride - 1, 0] = True + mask[:, :, : kv_stride - 1, 0] = True mask = mask.view(b, g_index, 1, sq, sk) q = q.view(b, sq, g, -1, dim_q) @@ -397,41 +380,32 @@ def ref_sparse_mla_fwd_interface(q, return o.to(torch.bfloat16) -def test_sparse_mla_fwd_pipelined(B=1, - S=4096, - SKV=8192, - H=128, - HKV=1, - DQK=576, - DV=512, - topk=2048, - dtype=torch.bfloat16, - q_start_s_index=1024, - check_correctness=True): +def test_sparse_mla_fwd_pipelined( + B=1, S=4096, SKV=8192, H=128, HKV=1, DQK=576, DV=512, topk=2048, dtype=torch.bfloat16, q_start_s_index=1024, check_correctness=True +): KV_stride = 1 torch.random.manual_seed(0) - q = torch.randn((B, S, H, DQK), dtype=dtype, device='cuda').requires_grad_(True) / 10 - kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device='cuda').requires_grad_(True) / 10 + q = torch.randn((B, S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True) / 10 + kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True) / 10 q_start_s_index_t = torch.tensor([q_start_s_index], dtype=torch.int32, device="cuda") q.clamp_(-10, 10) kv.clamp_(-10, 10) - indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device='cuda') + indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device="cuda") for b in range(B): for t in range(S): for h in range(HKV): i_i = torch.randperm(min(max(1, ((t + q_start_s_index) // KV_stride)), SKV))[:topk] - indices[b, t, h, :len(i_i)] = i_i + indices[b, t, h, : len(i_i)] = i_i - kernel = sparse_mla_fwd_interface( - q, kv, indices, q_start_s_index, KV_stride, return_kernel=True, print_kernel=True) + kernel = sparse_mla_fwd_interface(q, kv, indices, q_start_s_index, KV_stride, return_kernel=True, print_kernel=True) def fn(): out, lse = kernel(q, kv, indices, q_start_s_index_t) if q_start_s_index == 0 and KV_stride > 1: - out[:, :KV_stride - 1, :, :] = 0 + out[:, : KV_stride - 1, :, :] = 0 return out, lse tl_out, tl_lse = fn() @@ -442,14 +416,46 @@ def fn(): torch.testing.assert_close(tl_out, ref_out, rtol=1e-3, atol=1e-3) from tilelang.profiler import do_bench + ms = do_bench( fn, rep=10, warmup=10, ) print(f"Average time: {ms:.3f} ms") - print(f'fwd io bandwidth = ', (B * S * DQK * topk * 2) / (ms * 1e-3) / 1e12) - print(f'fwd tflops = ', (B * S * (DQK + DV) * topk * 2 * H) / (ms * 1e-3) / 1e12) + print(f"fwd io bandwidth = ", (B * S * DQK * topk * 2) / (ms * 1e-3) / 1e12) + print(f"fwd tflops = ", (B * S * (DQK + DV) * topk * 2 * H) / (ms * 1e-3) / 1e12) + + +def run_regression_perf(B=1, S=4096, SKV=8192, H=128, HKV=1, DQK=576, DV=512, topk=2048, dtype=torch.bfloat16, q_start_s_index=1024): + KV_stride = 1 + + torch.random.manual_seed(0) + q = torch.randn((B, S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True) / 10 + kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True) / 10 + q.clamp_(-10, 10) + kv.clamp_(-10, 10) + + indices = torch.full((B, S, HKV, topk), SKV, dtype=torch.int32, device="cuda") + for b in range(B): + for t in range(S): + for h in range(HKV): + i_i = torch.randperm(min(max(1, ((t + q_start_s_index) // KV_stride)), SKV))[:topk] + indices[b, t, h, : len(i_i)] = i_i + + batch, seq_len, heads, dim_plus_tail_dim = q.shape + _, seq_len_kv, kv_group, _ = kv.shape + dim = 512 + tail_dim = dim_plus_tail_dim - dim + CP0 = q_start_s_index == 0 + kernel = sparse_mla_fwd(batch, seq_len, seq_len_kv, heads, dim, tail_dim, topk, KV_stride, kv_group, None, True, CP0) + + def run_kernel_only(): + kernel(q, kv, indices, torch.tensor([q_start_s_index], dtype=torch.int32, device="cuda")) + + from tilelang.profiler import do_bench + + return do_bench(run_kernel_only, backend="cupti") if __name__ == "__main__": @@ -460,5 +466,4 @@ def fn(): B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 1024, 8192, 128, 1, 576, 512, 2048, torch.bfloat16 else: B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 4096, 8192, 128, 1, 576, 512, 2048, torch.bfloat16 - test_sparse_mla_fwd_pipelined( - B, S, SKV, H, HKV, DQK, DV, topk, dtype, check_correctness=args.test_correctness) + test_sparse_mla_fwd_pipelined(B, S, SKV, H, HKV, DQK, DV, topk, dtype, check_correctness=args.test_correctness) diff --git a/examples/deepseek_v32/test_tilelang_example_deepseek_v32.py b/examples/deepseek_v32/test_tilelang_example_deepseek_v32.py index 971a3206ce..983798f9f0 100644 --- a/examples/deepseek_v32/test_tilelang_example_deepseek_v32.py +++ b/examples/deepseek_v32/test_tilelang_example_deepseek_v32.py @@ -1,42 +1,43 @@ # ruff: noqa +import tilelang import tilelang.testing -from topk_selector import test_topk_selector -from fp8_lighting_indexer import test_fp8_lighting_indexer -from sparse_mla_fwd import test_sparse_mla_fwd -from sparse_mla_fwd_pipelined import test_sparse_mla_fwd_pipelined -from sparse_mla_bwd import test_sparse_mla_bwd +import topk_selector +import fp8_lighting_indexer +import sparse_mla_fwd +import sparse_mla_fwd_pipelined +import sparse_mla_bwd def test_example_topk_selector(): - test_topk_selector() + topk_selector.test_topk_selector() def test_example_fp8_lighting_indexer(): - test_fp8_lighting_indexer(S=1024, SKV=2048, H=32, HKV=1, D=64, kv_stride=1) + fp8_lighting_indexer.test_fp8_lighting_indexer(S=512, SKV=1024, H=32, HKV=1, D=64, kv_stride=1) @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_sparse_mla_fwd(): # small shapes for testing - test_sparse_mla_fwd( - S=256, SKV=1024, H=64, HKV=1, DQK=576, DV=512, topk=256, check_correctness=False) + sparse_mla_fwd.test_sparse_mla_fwd(S=256, SKV=1024, H=64, HKV=1, DQK=576, DV=512, topk=256, check_correctness=False) @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_sparse_mla_fwd_pipelined(): # small shapes for testing - test_sparse_mla_fwd_pipelined( - S=256, SKV=1024, H=64, HKV=1, DQK=576, DV=512, topk=256, check_correctness=False) + sparse_mla_fwd_pipelined.test_sparse_mla_fwd_pipelined(S=256, SKV=512, H=64, HKV=1, DQK=576, DV=512, topk=256, check_correctness=False) @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_sparse_mla_bwd(): - test_sparse_mla_bwd( - S=256, SKV=1024, H=64, HKV=1, DQKV=576, DV=512, topk=256, check_correctness=False) + sparse_mla_bwd.test_sparse_mla_bwd(S=256, SKV=512, H=64, HKV=1, DQKV=576, DV=512, topk=256, check_correctness=False) + sparse_mla_bwd.test_sparse_mla_bwd( + S=256, SKV=512, H=128, HKV=1, DQKV=576, DV=512, topk=256, check_correctness=False + ) # test for large H if __name__ == "__main__": diff --git a/examples/deepseek_v32/topk_selector.py b/examples/deepseek_v32/topk_selector.py index 4a4b432775..078eb26868 100644 --- a/examples/deepseek_v32/topk_selector.py +++ b/examples/deepseek_v32/topk_selector.py @@ -8,24 +8,24 @@ def convert_to_uint16(x): - hval = T.Cast("float16", x) - bits_uint = T.reinterpret("uint16", hval) + hval = T.Cast(T.float16, x) + bits_uint = T.reinterpret(T.uint16, hval) bits_uint = T.if_then_else(x < 0, ~bits_uint & (0xFFFF), bits_uint | (0x8000)) return bits_uint >> 8 def convert_to_uint32(x): - bits_uint = T.reinterpret("uint32", x) + bits_uint = T.reinterpret(T.uint32, x) bits_uint = T.if_then_else( x < 0, - ~bits_uint & T.Cast("uint32", (0xFFFFFFFF)), - bits_uint | T.Cast("uint32", (0x80000000)), + ~bits_uint & T.Cast(T.uint32, (0xFFFFFFFF)), + bits_uint | T.Cast(T.uint32, (0x80000000)), ) return bits_uint @tilelang.jit(pass_configs=pass_configs) -def tl_topk_impl(topk, in_dtype="float32", out_dtype="int32"): +def tl_topk_impl(topk, in_dtype=T.float32, out_dtype=T.int32): batch = T.dynamic("batch") seq_len = T.dynamic("seq_len") RADIX = 1 << 8 @@ -42,20 +42,20 @@ def tl_topk_kernel( with T.Kernel(batch, threads=BLOCK_SIZE) as (bx): tx = T.get_thread_binding() - s_threshold_bin_id = T.alloc_shared([1], "int32") - s_histogram = T.alloc_shared([RADIX + 1], "int32") - s_num_input = T.alloc_shared([2], "int32") - s_input_idx = T.alloc_shared([2, SMEM_INPUT_SIZE], "int32") - - l_threshold_bin_id = T.alloc_var("int32") - l_new_topk = T.alloc_var("int32") - l_num_input = T.alloc_var("int32") - l_bin_id32 = T.alloc_var("int32") - l_val = T.alloc_var("int32") - l_start_pos = T.alloc_var("int32") - l_start_idx = T.alloc_var("int32") - l_end_idx = T.alloc_var("int32") - l_out_pos = T.alloc_var("int32") + s_threshold_bin_id = T.alloc_shared([1], T.int32) + s_histogram = T.alloc_shared([RADIX + 1], T.int32) + s_num_input = T.alloc_shared([2], T.int32) + s_input_idx = T.alloc_shared([2, SMEM_INPUT_SIZE], T.int32) + + l_threshold_bin_id = T.alloc_var(T.int32) + l_new_topk = T.alloc_var(T.int32) + l_num_input = T.alloc_var(T.int32) + l_bin_id32 = T.alloc_var(T.int32) + l_val = T.alloc_var(T.int32) + l_start_pos = T.alloc_var(T.int32) + l_start_idx = T.alloc_var(T.int32) + l_end_idx = T.alloc_var(T.int32) + l_out_pos = T.alloc_var(T.int32) l_new_topk = topk l_start_idx = starts[bx] @@ -99,7 +99,7 @@ def tl_topk_kernel( input_idx = s * BLOCK_SIZE + tx if input_idx < l_end_idx and input_idx >= l_start_idx and input_idx < seq_len: bin_id = convert_to_uint16(input[bx, input_idx]) - l_bin_id32 = T.Cast("int32", bin_id) + l_bin_id32 = T.Cast(T.int32, bin_id) if l_bin_id32 > l_threshold_bin_id: # need a pos = T.atomic_add(s_histogram[bin_id32+1], 1) pos = T.atomic_add(s_histogram[l_bin_id32 + 1], 1, return_prev=True) @@ -127,9 +127,9 @@ def tl_topk_kernel( l_num_input = s_num_input[r_idx] for s in T.serial(T.ceildiv(l_num_input, BLOCK_SIZE)): if s * BLOCK_SIZE + tx < l_num_input: - l_bin_id32 = T.Cast("int32", (( - convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >> - (24 - round * 8)) & 0xFF)) + l_bin_id32 = T.Cast( + T.int32, ((convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >> (24 - round * 8)) & 0xFF) + ) T.atomic_add(s_histogram[l_bin_id32], 1) T.sync_threads() # cumsum @@ -156,23 +156,20 @@ def tl_topk_kernel( for s in T.serial(T.ceildiv(l_num_input, BLOCK_SIZE)): T.sync_threads() if s * BLOCK_SIZE + tx < l_num_input: - l_bin_id32 = T.Cast("int32", (( - convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >> - (24 - round * 8)) & 0xFF)) + l_bin_id32 = T.Cast( + T.int32, ((convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >> (24 - round * 8)) & 0xFF) + ) if l_bin_id32 > l_threshold_bin_id: - pos = T.atomic_add( - s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos + pos = T.atomic_add(s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos index[bx, pos] = s_input_idx[r_idx, s * BLOCK_SIZE + tx] elif l_bin_id32 == l_threshold_bin_id and l_new_topk > 0: if round == 3: - l_out_pos = T.atomic_add( - s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos + l_out_pos = T.atomic_add(s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos if l_out_pos < topk: index[bx, l_out_pos] = s_input_idx[r_idx, s * BLOCK_SIZE + tx] else: pos = T.atomic_add(s_num_input[r_idx ^ 1], 1, return_prev=True) - s_input_idx[r_idx ^ 1, pos] = s_input_idx[r_idx, - s * BLOCK_SIZE + tx] + s_input_idx[r_idx ^ 1, pos] = s_input_idx[r_idx, s * BLOCK_SIZE + tx] return tl_topk_kernel @@ -186,7 +183,6 @@ def tl_topk(input, starts, ends, topk): def test_topk_selector(batch=64, seq_len=32 * 1024, topk=2048): - batch = 64 seq_len = 32 * 1024 topk = 2048 @@ -212,8 +208,7 @@ def test_topk_selector(batch=64, seq_len=32 * 1024, topk=2048): set_ref = set(ref_np) set_trt = set(trt_np) intersection = set_ref & set_trt - print("selected/all:", len(intersection), "/", len(set_ref), "=", - len(intersection) / len(set_ref)) + print("selected/all:", len(intersection), "/", len(set_ref), "=", len(intersection) / len(set_ref)) # Performance test with CUDA events @@ -245,5 +240,35 @@ def test_topk_selector(batch=64, seq_len=32 * 1024, topk=2048): print(f"Average torch.topk time: {elapsed_time_ms / n_iters:.3f} ms") +def run_regression_perf(batch=64, seq_len=32 * 1024, topk=2048): + batch = 64 + seq_len = 32 * 1024 + topk = 2048 + torch.manual_seed(1) + input = torch.randn(batch, seq_len, dtype=torch.float32).cuda() + starts = torch.zeros(batch, dtype=torch.int32).cuda() + ends = torch.ones(batch, dtype=torch.int32).cuda() * seq_len + + indexes = tl_topk(input, starts, ends, topk) + + indexes_ref = torch.topk(input, topk, dim=-1)[1] + + for i in range(batch): + ref_np = indexes_ref[i].cpu().to(torch.int32).numpy() + trt_np = indexes[i].cpu().to(torch.int32).numpy() + + set_ref = set(ref_np) + set_trt = set(trt_np) + intersection = set_ref & set_trt + print("selected/all:", len(intersection), "/", len(set_ref), "=", len(intersection) / len(set_ref)) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + tl_topk(input, starts, ends, topk) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": test_topk_selector() diff --git a/examples/deepseek_v32/utils.py b/examples/deepseek_v32/utils.py index 2ea34b14a4..d7252e1711 100644 --- a/examples/deepseek_v32/utils.py +++ b/examples/deepseek_v32/utils.py @@ -23,8 +23,7 @@ def _is_equal(a, b): if isinstance(a, torch.Tensor): return a is b # Whitelist of types that are safe to compare by value for caching. - if isinstance(a, (int, float, str, bool, type(None))) and isinstance( - b, (int, float, str, bool, type(None))): + if isinstance(a, (int, float, str, bool, type(None))) and isinstance(b, (int, float, str, bool, type(None))): return a == b # For other types, we cannot guarantee a cheap and safe comparison, so we fail the cache check. return False @@ -58,9 +57,11 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: if len(args) == len(last_args) and len(kwargs) == len(last_kwargs): # For Tensors, check for object identity. For other types, check for equality. # Python caches small integers, so `is` works for them but not for large integers like 4096. - if all(_is_equal(a, b) for a, b in zip(args, last_args)) and \ - set(kwargs.keys()) == set(last_kwargs.keys()) and \ - all(_is_equal(v, last_kwargs[k]) for k, v in kwargs.items()): + if ( + all(_is_equal(a, b) for a, b in zip(args, last_args)) + and set(kwargs.keys()) == set(last_kwargs.keys()) + and all(_is_equal(v, last_kwargs[k]) for k, v in kwargs.items()) + ): return last_result result = fn(*args, **kwargs) @@ -79,73 +80,68 @@ def cal_seq_idx_from_cu_seqlens(cu_seqlens: torch.LongTensor, seq_len: int): @tensor_cache -def cal_seq_idx_for_q(cu_seqlens_qs: torch.LongTensor, cu_seqlens_qe: torch.LongTensor, - seq_len: int) -> torch.IntTensor: - seq_idx_for_q = torch.full((seq_len,), - len(cu_seqlens_qs), - dtype=torch.int32, - device=cu_seqlens_qs.device) +def cal_seq_idx_for_q(cu_seqlens_qs: torch.LongTensor, cu_seqlens_qe: torch.LongTensor, seq_len: int) -> torch.IntTensor: + seq_idx_for_q = torch.full((seq_len,), len(cu_seqlens_qs), dtype=torch.int32, device=cu_seqlens_qs.device) for i in range(len(cu_seqlens_qs)): - seq_idx_for_q[cu_seqlens_qs[i]:cu_seqlens_qe[i]] = i + seq_idx_for_q[cu_seqlens_qs[i] : cu_seqlens_qe[i]] = i return seq_idx_for_q @tensor_cache -def cal_cu_seqlen_ks_for_q(cu_seqlens_qs: torch.LongTensor, cu_seqlens_qe: torch.LongTensor, - cu_seqlens_ks: torch.LongTensor, seq_len: int) -> torch.IntTensor: +def cal_cu_seqlen_ks_for_q( + cu_seqlens_qs: torch.LongTensor, cu_seqlens_qe: torch.LongTensor, cu_seqlens_ks: torch.LongTensor, seq_len: int +) -> torch.IntTensor: cu_seqlen_ks_for_each_q = torch.gather( - input=torch.cat([ - cu_seqlens_ks, - torch.full((1,), - torch.iinfo(torch.int32).max, - dtype=torch.int32, - device=cu_seqlens_qs.device) - ]), + input=torch.cat([cu_seqlens_ks, torch.full((1,), torch.iinfo(torch.int32).max, dtype=torch.int32, device=cu_seqlens_qs.device)]), dim=0, - index=cal_seq_idx_for_q( - cu_seqlens_qs=cu_seqlens_qs, cu_seqlens_qe=cu_seqlens_qe, seq_len=seq_len).long()) + index=cal_seq_idx_for_q(cu_seqlens_qs=cu_seqlens_qs, cu_seqlens_qe=cu_seqlens_qe, seq_len=seq_len).long(), + ) return cu_seqlen_ks_for_each_q.int() @tensor_cache -def cal_cu_seqlen_ke_for_q(cu_seqlens_qs: torch.LongTensor, cu_seqlens_qe: torch.LongTensor, - cu_seqlens_ks: torch.LongTensor, cu_seqlens_ke: torch.LongTensor, - q_start_idxs: torch.LongTensor, seq_len: int, - kv_stride: int) -> torch.IntTensor: +def cal_cu_seqlen_ke_for_q( + cu_seqlens_qs: torch.LongTensor, + cu_seqlens_qe: torch.LongTensor, + cu_seqlens_ks: torch.LongTensor, + cu_seqlens_ke: torch.LongTensor, + q_start_idxs: torch.LongTensor, + seq_len: int, + kv_stride: int, +) -> torch.IntTensor: cu_seqlen_ke_for_each_q = torch.gather( - input=torch.cat( - [cu_seqlens_ke, - torch.zeros(1, dtype=torch.int32, device=cu_seqlens_qs.device)]), + input=torch.cat([cu_seqlens_ke, torch.zeros(1, dtype=torch.int32, device=cu_seqlens_qs.device)]), dim=0, - index=cal_seq_idx_for_q( - cu_seqlens_qs=cu_seqlens_qs, cu_seqlens_qe=cu_seqlens_qe, seq_len=seq_len).long()) - casual_cu_seqlen_ke_for_each_q = torch.zeros((seq_len,), - dtype=torch.int32, - device=cu_seqlens_qs.device) + index=cal_seq_idx_for_q(cu_seqlens_qs=cu_seqlens_qs, cu_seqlens_qe=cu_seqlens_qe, seq_len=seq_len).long(), + ) + casual_cu_seqlen_ke_for_each_q = torch.zeros((seq_len,), dtype=torch.int32, device=cu_seqlens_qs.device) for i in range(len(cu_seqlens_qs)): - casual_cu_seqlen_ke_for_each_q[cu_seqlens_qs[i]:cu_seqlens_qe[i]] = (torch.arange( - q_start_idxs[i], - q_start_idxs[i] + cu_seqlens_qe[i] - cu_seqlens_qs[i], - dtype=torch.int32, - device=cu_seqlens_qs.device) + 1) // kv_stride + cu_seqlens_ks[i] + casual_cu_seqlen_ke_for_each_q[cu_seqlens_qs[i] : cu_seqlens_qe[i]] = ( + torch.arange( + q_start_idxs[i], q_start_idxs[i] + cu_seqlens_qe[i] - cu_seqlens_qs[i], dtype=torch.int32, device=cu_seqlens_qs.device + ) + + 1 + ) // kv_stride + cu_seqlens_ks[i] cu_seqlen_ke_for_each_q = torch.minimum(casual_cu_seqlen_ke_for_each_q, cu_seqlen_ke_for_each_q) return cu_seqlen_ke_for_each_q.int() @tensor_cache -def cal_ks_ke_from_cu_seqlen_qk(cu_seqlens_q: torch.LongTensor, - cu_seqlens_k: torch.LongTensor = None, - offs_q: torch.LongTensor = None, - *, - seq_len: int, - kv_stride: int = 1, - cp_rank: int = 0, - cp_size: int = 1, - balanced_cp=False): - ''' +def cal_ks_ke_from_cu_seqlen_qk( + cu_seqlens_q: torch.LongTensor, + cu_seqlens_k: torch.LongTensor = None, + offs_q: torch.LongTensor = None, + *, + seq_len: int, + kv_stride: int = 1, + cp_rank: int = 0, + cp_size: int = 1, + balanced_cp=False, +): + """ seq_len: seq len per cp rank balanced cp slice assignment: 0 1 2 3 3 2 1 0 - ''' + """ n_seq = len(cu_seqlens_q) - 1 assert n_seq > 0 assert cu_seqlens_q.shape == (n_seq + 1,) @@ -170,10 +166,12 @@ def cal_ks_ke_from_cu_seqlen_qk(cu_seqlens_q: torch.LongTensor, def f(x: torch.Tensor): chunks = x.chunk(cp_size * 2) - return torch.cat([ - chunks[cp_rank], - chunks[cp_size - cp_rank - 1], - ]) + return torch.cat( + [ + chunks[cp_rank], + chunks[cp_size - cp_rank - 1], + ] + ) ks = f(ks) ke = f(ke) @@ -189,8 +187,7 @@ def ceil_to_ue8m0(x: torch.Tensor): return torch.pow(2.0, torch.ceil(torch.log2(x.abs()))) -def per_custom_dims_cast_to_fp8(x: torch.Tensor, dims: Tuple[int], - use_ue8m0: bool) -> Tuple[torch.Tensor, torch.Tensor]: +def per_custom_dims_cast_to_fp8(x: torch.Tensor, dims: Tuple[int], use_ue8m0: bool) -> Tuple[torch.Tensor, torch.Tensor]: excluded_dims = tuple([i for i in range(x.dim()) if i not in set(dims)]) x_amax = x.abs().float().amax(dim=excluded_dims, keepdim=True).clamp(1e-4) sf = x_amax / 448.0 @@ -239,14 +236,18 @@ def generate_random_cu_seqlens(per_cp_seqlen, cp_size=4, cp_rank=3, kv_stride=1, total_seqlen - (cp_rank + 1) * per_chunk_seqlen, total_seqlen - cp_rank * per_chunk_seqlen, ) - ks = torch.cat([ - cu_seqlens_ks_for_each_q[slice_short], - cu_seqlens_ks_for_each_q[slice_long], - ]) - ke = torch.cat([ - cu_seqlens_ke_for_each_q[slice_short], - cu_seqlens_ke_for_each_q[slice_long], - ]) + ks = torch.cat( + [ + cu_seqlens_ks_for_each_q[slice_short], + cu_seqlens_ks_for_each_q[slice_long], + ] + ) + ke = torch.cat( + [ + cu_seqlens_ke_for_each_q[slice_short], + cu_seqlens_ke_for_each_q[slice_long], + ] + ) assert len(ks) == len(ke) == per_cp_seqlen return ks, ke @@ -302,11 +303,9 @@ def assert_tensors_similar(x, y, eps=1e-8, name="tensor", raise_assert=True): raise_assert: Whether to raise assertion error on failure """ sim = calculate_tensor_similarity(x, y, name) - diff = 1. - sim + diff = 1.0 - sim if not (0 <= diff <= eps): - print( - f"\033[31mERROR: {name} similarity check failed, diff={diff:.2e} (threshold={eps:.2e})\033[0m" - ) + print(f"\033[31mERROR: {name} similarity check failed, diff={diff:.2e} (threshold={eps:.2e})\033[0m") if raise_assert: assert False # noqa: B011 @@ -316,11 +315,8 @@ def assert_tensors_similar(x, y, eps=1e-8, name="tensor", raise_assert=True): cu_seqlens = torch.randint(128, 4096, (1000,), dtype=torch.int32, device="cuda") last_idx = torch.where(cu_seqlens.cumsum(dim=0) >= seq_len)[0][0] cu_seqlens_cumsum = cu_seqlens[:last_idx].cumsum(dim=0) - cu_seqlens_qs = torch.cat( - [torch.zeros(1, dtype=torch.int32, device=cu_seqlens.device), cu_seqlens_cumsum]) - cu_seqlens_qe = torch.cat( - [cu_seqlens_cumsum, - torch.ones(1, dtype=torch.int32, device=cu_seqlens.device) * seq_len]) + cu_seqlens_qs = torch.cat([torch.zeros(1, dtype=torch.int32, device=cu_seqlens.device), cu_seqlens_cumsum]) + cu_seqlens_qe = torch.cat([cu_seqlens_cumsum, torch.ones(1, dtype=torch.int32, device=cu_seqlens.device) * seq_len]) from tilelang.profiler import do_bench diff --git a/examples/dequantize_gemm/README.md b/examples/dequantize_gemm/README.md index 0c6116775e..25ef617a21 100644 --- a/examples/dequantize_gemm/README.md +++ b/examples/dequantize_gemm/README.md @@ -19,7 +19,7 @@ def dequant_matmul( T.clear(Ct_local) for k in T.Pipelined( - T.ceildiv(K, block_K), + T.ceildiv(K, block_K), num_stages=num_stages ): T.copy(A[by * block_M, k * block_K], A_shared) diff --git a/examples/dequantize_gemm/dequantize_utils.py b/examples/dequantize_gemm/dequantize_utils.py index b14c0aee68..90a6265ffa 100644 --- a/examples/dequantize_gemm/dequantize_utils.py +++ b/examples/dequantize_gemm/dequantize_utils.py @@ -39,12 +39,10 @@ def torch_convert_bit_twiddling(tensor): res0 = val_concat_expanded & mask res1 = (val_concat_expanded << 3) & mask res2 = (val_concat_expanded << 6) & mask - res3 = ((val_concat_expanded << 1) & mask1) | ((val_concat_expanded >> 3) & mask2) | ( - (val_concat_expanded >> 7) & mask3) + res3 = ((val_concat_expanded << 1) & mask1) | ((val_concat_expanded >> 3) & mask2) | ((val_concat_expanded >> 7) & mask3) # Select the correct result based on position - bf16 = torch.where(pos == 0, res0, torch.where(pos == 1, res1, - torch.where(pos == 2, res2, res3))) + bf16 = torch.where(pos == 0, res0, torch.where(pos == 1, res1, torch.where(pos == 2, res2, res3))) # Convert to uint16 for .view(torch.bfloat16) bf16_uint16 = (bf16 & 0xFFFF).to(torch.uint16) @@ -110,7 +108,7 @@ def print_bit(name, val): val (torch.Tensor): A scalar PyTorch tensor (numeric) whose 32-bit binary representation will be shown. """ val_cpu = val.cpu().item() - binary_repr = f'{val_cpu:032b}' + binary_repr = f"{val_cpu:032b}" print(name, binary_repr) @@ -122,7 +120,7 @@ def calc_sim(x, y, name="tensor"): x, y = x.data.double(), y.data.double() denominator = (x * x + y * y).sum() if denominator == 0: - print_red_warning(f'{name} all zero') + print_red_warning(f"{name} all zero") return 1 sim = 2 * (x * y).sum() / denominator return sim @@ -132,21 +130,19 @@ def assert_similar(x, y, eps=1e-8, name="tensor", data="", raise_assert=True): x_mask = torch.isfinite(x) y_mask = torch.isfinite(y) if not torch.all(x_mask == y_mask): - print_red_warning(f'{name} Error: isfinite mask mismatch') + print_red_warning(f"{name} Error: isfinite mask mismatch") if raise_assert: raise AssertionError - if not torch.isclose( - x.masked_fill(x_mask, 0), y.masked_fill(y_mask, 0), rtol=0, atol=0, - equal_nan=True).all(): - print_red_warning(f'{name} Error: nonfinite value mismatch') + if not torch.isclose(x.masked_fill(x_mask, 0), y.masked_fill(y_mask, 0), rtol=0, atol=0, equal_nan=True).all(): + print_red_warning(f"{name} Error: nonfinite value mismatch") if raise_assert: raise AssertionError x = x.masked_fill(~x_mask, 0) y = y.masked_fill(~y_mask, 0) sim = calc_sim(x, y, name) - diff = (1. - sim).item() - print(f'{diff=}') + diff = (1.0 - sim).item() + print(f"{diff=}") if not (0 <= diff <= eps): - print_red_warning(f'{name} Error: {diff=}') + print_red_warning(f"{name} Error: {diff=}") if raise_assert: raise AssertionError diff --git a/examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py b/examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py index e30845b8d7..36b32c0a8a 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py +++ b/examples/dequantize_gemm/example_dequant_gemm_bf16_fp4_hopper.py @@ -24,6 +24,7 @@ def get_configs(): the parameter name to its chosen value. """ import itertools + iter_params = dict( block_M=[64, 128, 256], block_N=[64, 128, 256], @@ -32,65 +33,64 @@ def get_configs(): threads=[128, 256, 512], split=[1, 2], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] -@tilelang.autotune(configs=get_configs(),) +@tilelang.autotune( + configs=get_configs(), +) @tilelang.jit( out_idx=[-1], - pass_configs={ - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - }, + pass_configs={tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True}, ) -def matmul(M, - N, - K, - in_dtype, - out_dtype, - accum_dtype, - source_format='uint', - num_bits=4, - fast_dequant=True, - block_M=256, - block_N=128, - block_K=128, - num_stages=2, - threads=256, - split=1): +def matmul( + M, + N, + K, + in_dtype, + out_dtype, + accum_dtype, + source_format=T.uint32, + num_bits=4, + fast_dequant=True, + block_M=256, + block_N=128, + block_K=128, + num_stages=2, + threads=256, + split=1, +): + """ + Builds a parameterized TileLang/TIR matrix-multiplication kernel that dequantizes 4-bit FP inputs to BF16 on-the-fly and computes C = A @ B^T. + + This function returns a tiled, autotunable prim_func implementing a block-wise GEMM with shared-memory buffering and a pipelined K-loop. The kernel accepts: + - A: dense input of shape (M, K) with dtype `in_dtype`. + - B: packed quantized input of shape (N, QK) where QK = K / (8 / num_bits) stored as `uint8`. + - C: output of shape (M, N) with dtype `out_dtype`. + + The generated kernel supports two dequantization paths: + - fast_dequant (fast_dequant=True): calls an external mxfp dequantization intrinsic (twiddling-based) loaded from a C source returned by get_mxfp_intrin_group. + - simple dequant (fast_dequant=False): performs a pure-TIR FP4 -> BF16 conversion per element. + + Important behavior and requirements: + - num_bits (default 4) is the bit-width of the quantized elements; storage_dtype is uint8 and num_elems_per_byte = 8 // num_bits. + - QK = K // num_elems_per_byte and Block_QK = block_K // num_elems_per_byte determine B and shared-buffer shapes. + - Asserts that K % (block_K * split) == 0; K must be divisible by block_K * split for the tiling to be valid. + - When fast_dequant is True, a valid mxfp intrinsic group (C source and function name) must be available via tilelang.quantize.get_mxfp_intrin_group. + - The kernel launches a 2D grid over ceildiv(N, block_N) and ceildiv(M, block_M) and uses `threads` threads per block with `num_stages` pipeline stages. + + Parameters that alter kernel layout/behavior (brief): + - block_M, block_N, block_K: tile sizes for M, N, and K dimensions. + - num_stages: number of software pipeline stages for the K-loop. + - threads: number of threads used per kernel block. + - split: extra K-splitting factor; K must be divisible by block_K * split. + - source_format, num_bits: describe the quantized data layout passed to the mxfp intrinsics. + + Returns: + A TileLang/TIR prim_func (the compiled `main`) implementing the described dequantize-then-GEMM kernel. """ - Builds a parameterized TileLang/TIR matrix-multiplication kernel that dequantizes 4-bit FP inputs to BF16 on-the-fly and computes C = A @ B^T. - - This function returns a tiled, autotunable prim_func implementing a block-wise GEMM with shared-memory buffering and a pipelined K-loop. The kernel accepts: - - A: dense input of shape (M, K) with dtype `in_dtype`. - - B: packed quantized input of shape (N, QK) where QK = K / (8 / num_bits) stored as `uint8`. - - C: output of shape (M, N) with dtype `out_dtype`. - - The generated kernel supports two dequantization paths: - - fast_dequant (fast_dequant=True): calls an external mxfp dequantization intrinsic (twiddling-based) loaded from a C source returned by get_mxfp_intrin_group. - - simple dequant (fast_dequant=False): performs a pure-TIR FP4 -> BF16 conversion per element. - - Important behavior and requirements: - - num_bits (default 4) is the bit-width of the quantized elements; storage_dtype is uint8 and num_elems_per_byte = 8 // num_bits. - - QK = K // num_elems_per_byte and Block_QK = block_K // num_elems_per_byte determine B and shared-buffer shapes. - - Asserts that K % (block_K * split) == 0; K must be divisible by block_K * split for the tiling to be valid. - - When fast_dequant is True, a valid mxfp intrinsic group (C source and function name) must be available via tilelang.quantize.get_mxfp_intrin_group. - - The kernel launches a 2D grid over ceildiv(N, block_N) and ceildiv(M, block_M) and uses `threads` threads per block with `num_stages` pipeline stages. - - Parameters that alter kernel layout/behavior (brief): - - block_M, block_N, block_K: tile sizes for M, N, and K dimensions. - - num_stages: number of software pipeline stages for the K-loop. - - threads: number of threads used per kernel block. - - split: extra K-splitting factor; K must be divisible by block_K * split. - - source_format, num_bits: describe the quantized data layout passed to the mxfp intrinsics. - - Returns: - A TileLang/TIR prim_func (the compiled `main`) implementing the described dequantize-then-GEMM kernel. - """ num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 QK = K // num_elems_per_byte Block_QK = block_K // num_elems_per_byte @@ -121,7 +121,7 @@ def matmul(M, assert func_name is not None, "mxfp_intrin_info is not found" import_source = import_source - def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): + def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype=T.bfloat16): """ Create a TileLang macro that performs fast, twiddling-based dequantization from packed FP4 to BF16 using an external runtime plugin. @@ -131,13 +131,13 @@ def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): - Writes the dequantized BF16 values back to a shared dequantized buffer for use by the kernel. Notes and preconditions: - - Asserts that `in_dtype == "fp4"` and `out_dtype == "bfloat16"`. + - Asserts that `in_dtype == "fp4"` and `out_dtype == T.bfloat16`. - The generated macro depends on several surrounding-scope symbols (e.g., `import_source`, `func_name`, `block_K`, `Block_QK`, `threads`, `num_elems_per_byte`, `storage_dtype`, and `out_dtype`) and expects them to be defined consistently in the enclosing kernel. - The macro is optimized for block-wise, per-thread transactions sized to the target storage width (uses a MAX_TRANSACTION_SIZE_BITS constant) and uses local/register buffers sized accordingly. - The macro uses `T.import_source` to bring the external plugin into the module and `T.call_extern` to perform the high-throughput dequantization; callers must ensure the external function matches the expected calling convention and memory layout. """ assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] # Some variables for dequantization in each thread MAX_TRANSACTION_SIZE_BITS = 128 @@ -189,12 +189,11 @@ def fast_dequant_bf16_fp4_twiddling(B_shared, B_dequantize_shared): # Finally, store the dequantized data to shared memory. for v in T.vectorized(0, local_size): index = i * threads * local_size + tx * local_size + v - B_dequantize_shared[index // block_K, - index % block_K] = B_dequantize_local_thread[v] + B_dequantize_shared[index // block_K, index % block_K] = B_dequantize_local_thread[v] return fast_dequant_bf16_fp4_twiddling - def get_simple_dequant_func(in_dtype="fp4", out_dtype="bfloat16"): + def get_simple_dequant_func(in_dtype="fp4", out_dtype=T.bfloat16): """ Create a simple TIR dequantization macro that converts packed 4-bit FP (FP4) stored in uint8 into bfloat16. @@ -205,7 +204,7 @@ def get_simple_dequant_func(in_dtype="fp4", out_dtype="bfloat16"): - Writes the dequantized bfloat16 block into B_dequantize_shared. Constraints: - - Supports only in_dtype="fp4" and out_dtype="bfloat16". + - Supports only in_dtype="fp4" and out_dtype=T.bfloat16. - The helper assumes nbit == 4 and produces bfloat16 values. - The macro uses a fixed test-scale of 0 (no per-element scaling) as written. @@ -213,49 +212,49 @@ def get_simple_dequant_func(in_dtype="fp4", out_dtype="bfloat16"): A TIR macro function performing the described in-place block dequantization from packed uint8 FP4 to bfloat16. """ assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] - def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, - scale: tir.PrimExpr, dtype: str): + def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr, dtype: str): """ - Convert a 4-bit FP4 value packed in a uint8 byte into a bfloat16 value. - - This helper extracts the 4-bit field located at the bit position `pos` within the - byte `val`, interprets it as an FP4 (sign, exponent, mantissa) value, applies an - exponent `scale` offset to align it with bfloat16 exponent bias, clamps the - resulting exponent to 8 bits, and returns the assembled bfloat16 bit pattern. - - Parameters: - nbit (int): Number of bits in the packed element; must be 4. - val (tir.PrimExpr): A uint8 value containing packed FP4 elements. - pos (tir.PrimExpr): Index (0-based) of which FP4 nibble inside `val` to extract. - scale (tir.PrimExpr): Exponent offset applied when converting FP4 exponent to bfloat16. - dtype (str): Target dtype string; must be "bfloat16". - - Returns: - tir.PrimExpr: A bfloat16-typed PrimExpr containing the converted value. - - Notes: - - The function asserts `nbit == 4`, `dtype == "bfloat16"`, and that `val.dtype` is "uint8". - - The conversion uses a fixed mapping from FP4 exponent/mantissa layout into bfloat16 - bit fields and clamps the computed exponent to fit into 8 bits. + Convert a 4-bit FP4 value packed in a uint8 byte into a bfloat16 value. + + This helper extracts the 4-bit field located at the bit position `pos` within the + byte `val`, interprets it as an FP4 (sign, exponent, mantissa) value, applies an + exponent `scale` offset to align it with bfloat16 exponent bias, clamps the + resulting exponent to 8 bits, and returns the assembled bfloat16 bit pattern. + + Parameters: + nbit (int): Number of bits in the packed element; must be 4. + val (tir.PrimExpr): A uint8 value containing packed FP4 elements. + pos (tir.PrimExpr): Index (0-based) of which FP4 nibble inside `val` to extract. + scale (tir.PrimExpr): Exponent offset applied when converting FP4 exponent to bfloat16. + dtype (str): Target dtype string; must be T.bfloat16. + + Returns: + tir.PrimExpr: A bfloat16-typed PrimExpr containing the converted value. + + Notes: + - The function asserts `nbit == 4`, `dtype == T.bfloat16`, and that `val.dtype` is T.uint8. + - The conversion uses a fixed mapping from FP4 exponent/mantissa layout into bfloat16 + bit fields and clamps the computed exponent to fit into 8 bits. """ assert nbit == 4 - assert dtype == "bfloat16" - assert val.dtype == "uint8" - mask = tir.const((1 << nbit) - 1, "uint16") - f4 = (val >> (pos.astype("uint16") * tir.const(nbit, "uint16"))) & mask - s = f4 >> tir.const(3, "uint16") - e_f4 = (f4 & tir.const(6, "uint16")) >> tir.const(1, "uint16") + assert dtype == T.bfloat16 + assert val.dtype == T.uint8 + mask = tir.const((1 << nbit) - 1, T.uint16) + f4 = (val >> (pos.astype(T.uint16) * tir.const(nbit, T.uint16))) & mask + s = f4 >> tir.const(3, T.uint16) + e_f4 = (f4 & tir.const(6, T.uint16)) >> tir.const(1, T.uint16) # Exponential bias between f4 and bf16 is 2^(8-1) - 2^(2-1) = 126 - e_bf16 = e_f4 + tir.const(126, "uint16") + e_bf16 = e_f4 + tir.const(126, T.uint16) # Scale is the exponential part, within the representation of uint8 # To handle the overflow, we use the max function to limit the exponential part to 8 bits - e_bf16 = T.min(e_bf16 + scale, tir.const((1 << 8) - 1, "uint16")) - m_f4 = f4 & tir.const(1, "uint16") + e_bf16 = T.min(e_bf16 + scale, tir.const((1 << 8) - 1, T.uint16)) + m_f4 = f4 & tir.const(1, T.uint16) val_bf16 = tir.reinterpret( - "bfloat16", ((((s << tir.const(8, "uint16")) | e_bf16) << tir.const(7, "uint16")) - | (m_f4 << tir.const(6, "uint16"))).astype("uint16")) + T.bfloat16, + ((((s << tir.const(8, T.uint16)) | e_bf16) << tir.const(7, T.uint16)) | (m_f4 << tir.const(6, T.uint16))).astype(T.uint16), + ) return val_bf16 @T.macro @@ -292,32 +291,32 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared): @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + C: T.Tensor((M, N), out_dtype), ): """ - Kernel entry for the tiled, pipelined matmul used by the generated prim_func. - - This function implements a block-wise GEMM over a 2D grid (grid dims: ceildiv(N, block_N) x ceildiv(M, block_M)) with a thread block of `threads`. For each output block it: - - Allocates shared buffers for A, the packed/quantized B, and a dequantized B tile. - - Allocates a fragment accumulator (C_local) and a shared output tile (C_shared) with a swizzled layout. - - Pipelines over K in chunks of `block_K` for `num_stages` stages: - - Loads A and packed B tiles into shared memory. - - Dequantizes B into B_dequantize_shared using either the fast (twiddling/external) or the simple (pure-TIR) dequantization routine. - - Performs a GEMM accumulating into C_local with B transposed. - - Stores the accumulated block from C_local back to the global output C via C_shared. - - Parameters: - - A: input tile of shape (M, K) with dtype `in_dtype`. - - B: packed/quantized input of shape (N, QK) with storage dtype `storage_dtype` (quantized FP4 packing). - - C: output tensor of shape (M, N) with dtype `out_dtype`. - - Side effects: - - Writes the computed output block into the global tensor `C`. - - Uses and updates shared memory buffers and per-thread accumulators. - - No value is returned. + Kernel entry for the tiled, pipelined matmul used by the generated prim_func. + + This function implements a block-wise GEMM over a 2D grid (grid dims: ceildiv(N, block_N) x ceildiv(M, block_M)) with a thread block of `threads`. For each output block it: + - Allocates shared buffers for A, the packed/quantized B, and a dequantized B tile. + - Allocates a fragment accumulator (C_local) and a shared output tile (C_shared) with a swizzled layout. + - Pipelines over K in chunks of `block_K` for `num_stages` stages: + - Loads A and packed B tiles into shared memory. + - Dequantizes B into B_dequantize_shared using either the fast (twiddling/external) or the simple (pure-TIR) dequantization routine. + - Performs a GEMM accumulating into C_local with B transposed. + - Stores the accumulated block from C_local back to the global output C via C_shared. + + Parameters: + - A: input tile of shape (M, K) with dtype `in_dtype`. + - B: packed/quantized input of shape (N, QK) with storage dtype `storage_dtype` (quantized FP4 packing). + - C: output tensor of shape (M, N) with dtype `out_dtype`. + + Side effects: + - Writes the computed output block into the global tensor `C`. + - Uses and updates shared memory buffers and per-thread accumulators. + + No value is returned. """ with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -327,10 +326,6 @@ def main( C_local = T.alloc_fragment((block_M, block_N), accum_dtype) C_shared = T.alloc_shared((block_M, block_N), out_dtype) - T.annotate_layout({ - C_shared: tilelang.layout.make_swizzled_layout(C_shared), - }) - T.clear(C_local) for k in T.Pipelined(K // block_K, num_stages=num_stages): T.copy(A[by * block_M, k * block_K], A_shared) @@ -344,7 +339,7 @@ def main( T.gemm(A_shared, B_dequantize_shared, C_local, transpose_B=True) T.copy(C_local, C_shared) - T.copy(C_shared, C[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N]) + T.copy(C_shared, C[by * block_M : (by + 1) * block_M, bx * block_N : (bx + 1) * block_N]) return main @@ -363,7 +358,7 @@ def ref_program_twiddling(A, qB): Returns: torch.Tensor: Result matrix C with shape (M, N) in bfloat16. """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert_bit_twiddling(qB) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) @@ -383,7 +378,7 @@ def ref_program_simple(A, qB): Returns: torch.Tensor: Resulting matrix C in bfloat16 with shape (M, N). """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert(qB) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) @@ -409,16 +404,15 @@ def main(m=256, n=256, k=256, fast_dequant=True, tune=False): """ total_flops = 2 * m * n * k if tune: - kernel = matmul( - m, n, k, "bfloat16", "bfloat16", "float32", num_bits=4, fast_dequant=fast_dequant) + kernel = matmul(m, n, k, T.bfloat16, T.bfloat16, T.float32, num_bits=4, fast_dequant=fast_dequant) else: kernel = matmul( m, n, k, - "bfloat16", - "bfloat16", - "float32", + T.bfloat16, + T.bfloat16, + T.float32, num_bits=4, fast_dequant=fast_dequant, block_M=256, @@ -426,7 +420,8 @@ def main(m=256, n=256, k=256, fast_dequant=True, tune=False): block_K=128, num_stages=2, threads=256, - split=1) + split=1, + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Auto) if fast_dequant: profiler.assert_allclose(ref_program_twiddling, rtol=0.01, atol=0.01) @@ -437,6 +432,27 @@ def main(m=256, n=256, k=256, fast_dequant=True, tune=False): print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(m=4096, n=4096, k=4096, fast_dequant=True): + kernel = matmul( + m, + n, + k, + "bfloat16", + "bfloat16", + "float32", + num_bits=4, + fast_dequant=fast_dequant, + block_M=256, + block_N=128, + block_K=128, + num_stages=2, + threads=256, + split=1, + ) + profiler = kernel.get_profiler(tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main(256, 256, 256, True) main(256, 256, 256, False) diff --git a/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py b/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py index ac1417aebc..cc37c8bc42 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py +++ b/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper.py @@ -7,45 +7,45 @@ from dequantize_utils import torch_convert_bit_twiddling, torch_convert -def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr, - dtype: str): +def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr, dtype: str): """ - Convert a 4-bit field packed in a uint8 into a bfloat16 value, applying an exponent scale. + Convert a 4-bit field packed in a uint8 into a bfloat16 value, applying an exponent scale. - This helper extracts a 4-bit nibble from `val` at byte-nibble position `pos`, interprets its - bits as a sign/exponent/mantissa in the 4-bit custom FP4 layout, adjusts the exponent by - `scale` (clamped to an 8-bit range), and assembles the corresponding bfloat16 representation. + This helper extracts a 4-bit nibble from `val` at byte-nibble position `pos`, interprets its + bits as a sign/exponent/mantissa in the 4-bit custom FP4 layout, adjusts the exponent by + `scale` (clamped to an 8-bit range), and assembles the corresponding bfloat16 representation. - Parameters: - nbit (int): Number of bits in the packed field (must be 4). - val (tir.PrimExpr): Packed input value of dtype `uint8` containing one or more 4-bit fields. - pos (tir.PrimExpr): Index of the nibble within `val` (used to shift/extract the 4-bit field). - scale (tir.PrimExpr): Per-element exponent adjustment added to the extracted exponent (uint-like). - dtype (str): Destination dtype string (must be "bfloat16"). + Parameters: + nbit (int): Number of bits in the packed field (must be 4). + val (tir.PrimExpr): Packed input value of dtype `uint8` containing one or more 4-bit fields. + pos (tir.PrimExpr): Index of the nibble within `val` (used to shift/extract the 4-bit field). + scale (tir.PrimExpr): Per-element exponent adjustment added to the extracted exponent (uint-like). + dtype (str): Destination dtype string (must be T.bfloat16). - Returns: - tir.PrimExpr: The resulting value reinterpreted as `bfloat16`. + Returns: + tir.PrimExpr: The resulting value reinterpreted as `bfloat16`. - Notes: - - Preconditions are enforced via assertions: nbit == 4, dtype == "bfloat16", and val.dtype == "uint8". - - The function clamps the adjusted exponent to the 8-bit range before assembling the bfloat16 bit pattern. - """ + Notes: + - Preconditions are enforced via assertions: nbit == 4, dtype == T.bfloat16, and val.dtype == T.uint8. + - The function clamps the adjusted exponent to the 8-bit range before assembling the bfloat16 bit pattern. + """ assert nbit == 4 - assert dtype == "bfloat16" - assert val.dtype == "uint8" - mask = tir.const((1 << nbit) - 1, "uint16") - f4 = (val >> (pos.astype("uint16") * tir.const(nbit, "uint16"))) & mask - s = f4 >> tir.const(3, "uint16") - e_f4 = (f4 & tir.const(6, "uint16")) >> tir.const(1, "uint16") + assert dtype == T.bfloat16 + assert val.dtype == T.uint8 + mask = tir.const((1 << nbit) - 1, T.uint16) + f4 = (val >> (pos.astype(T.uint16) * tir.const(nbit, T.uint16))) & mask + s = f4 >> tir.const(3, T.uint16) + e_f4 = (f4 & tir.const(6, T.uint16)) >> tir.const(1, T.uint16) # Exponential bias between f4 and bf16 is 2^(8-1) - 2^(2-1) = 126 - e_bf16 = e_f4 + tir.const(126, "uint16") + e_bf16 = e_f4 + tir.const(126, T.uint16) # Scale is the exponential part, within the representation of uint8 # To handle the overflow, we may use the min function to limit the exponential part to 8 bits # e_bf16 = T.min(e_bf16 + scale, tir.const((1 << 8) - 1, "uint16")) - m_f4 = f4 & tir.const(1, "uint16") - val_bf16 = tir.reinterpret("bfloat16", - ((((s << tir.const(8, "uint16")) | e_bf16) << tir.const(7, "uint16")) - | (m_f4 << tir.const(6, "uint16"))).astype("uint16")) + m_f4 = f4 & tir.const(1, T.uint16) + val_bf16 = tir.reinterpret( + T.bfloat16, + ((((s << tir.const(8, T.uint16)) | e_bf16) << tir.const(7, T.uint16)) | (m_f4 << tir.const(6, T.uint16))).astype(T.uint16), + ) return val_bf16 @@ -65,6 +65,7 @@ def get_configs(): List[dict]: A list of configuration dictionaries covering all combinations. """ import itertools + iter_params = dict( block_M=[64, 128, 256], block_N=[64, 128, 256], @@ -73,70 +74,74 @@ def get_configs(): threads=[128, 256, 512], split=[1, 2], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] - - -@tilelang.autotune(configs=get_configs(),) -@tilelang.jit(out_idx=[-1],) -def matmul(M, - N, - K, - in_dtype, - out_dtype, - accum_dtype, - source_format='uint', - num_bits=4, - scale_size=32, - fast_dequant=True, - with_bias=False, - block_M=256, - block_N=128, - block_K=128, - num_stages=2, - threads=256, - split=1): + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] + + +@tilelang.autotune( + configs=get_configs(), +) +@tilelang.jit( + out_idx=[-1], +) +def matmul( + M, + N, + K, + in_dtype, + out_dtype, + accum_dtype, + source_format=T.uint32, + num_bits=4, + scale_size=32, + fast_dequant=True, + with_bias=False, + block_M=256, + block_N=128, + block_K=128, + num_stages=2, + threads=256, + split=1, +): """ - Construct and return a tiled matrix-multiply TIR kernel that multiplies A (shape MxK) by a quantized B (shape Nx(QK)) and writes an MxN output in out_dtype. - - The generated kernel accepts: - - A: dense matrix with element type `in_dtype`. - - B: packed quantized matrix stored as uint8 with `num_bits` bits per element (QK = K / (8/num_bits)). - - Scale: per-block scale/exponent information used to dequantize B. - The kernel dequantizes B to a working floating format (out_dtype/accum_dtype) using one of two paths: - - fast_dequant (True): uses an external, hardware/implementation-specific intrinsic group (twiddling) for batch dequantization. - - fast_dequant (False): uses a simple elementwise dequantization helper. - - Parameters: - M, N, K (int): matrix dimensions (A is MxK, result is MxN). K must be divisible by (block_K * split). - in_dtype (str): element type of A (e.g., "fp4" in this file). - out_dtype (str): output tensor element type (e.g., "bfloat16"). - accum_dtype (str): accumulation type used for the inner GEMM. - source_format (str, optional): format string passed to intrinsic selector (default "uint"). - num_bits (int, optional): number of bits per quantized element in B (default 4). - scale_size (int, optional): number of elements grouped per scale entry (default 32). - fast_dequant (bool, optional): choose the fast intrinsic dequantization path when available (default True). - block_M, block_N, block_K (int, optional): tile sizes for M, N, and K dimensions (defaults 256, 128, 128). - num_stages (int, optional): pipelining stages for K loop (default 2). - threads (int, optional): threads per block used by the kernel (default 256). - split (int, optional): split factor along K used by the scheduler (default 1). - with_bias (bool, optional): whether to add Bias to the output (default False). - - Returns: - A T.prim_func implementing the tiled, pipelined GEMM that: - - loads tiled blocks of A and packed B to shared memory, - - dequantizes B via the chosen path into a shared dequantized tile, - - performs a tiled GEMM accumulating into local fragments, - - writes the final MxN block to the global output tensor. + Construct and return a tiled matrix-multiply TIR kernel that multiplies A (shape MxK) by a quantized B (shape Nx(QK)) and writes an MxN output in out_dtype. - Notes: - - The function queries an intrinsic group to obtain a fast dequantization implementation when fast_dequant is enabled; that intrinsic must supply a valid C source and function name. - - The kernel layout uses swizzled shared-memory layouts for A, B, and the shared C tile. - - An assertion enforces that K % (block_K * split) == 0. + The generated kernel accepts: + - A: dense matrix with element type `in_dtype`. + - B: packed quantized matrix stored as uint8 with `num_bits` bits per element (QK = K / (8/num_bits)). + - Scale: per-block scale/exponent information used to dequantize B. + The kernel dequantizes B to a working floating format (out_dtype/accum_dtype) using one of two paths: + - fast_dequant (True): uses an external, hardware/implementation-specific intrinsic group (twiddling) for batch dequantization. + - fast_dequant (False): uses a simple elementwise dequantization helper. + + Parameters: + M, N, K (int): matrix dimensions (A is MxK, result is MxN). K must be divisible by (block_K * split). + in_dtype (str): element type of A (e.g., "fp4" in this file). + out_dtype (str): output tensor element type (e.g., T.bfloat16). + accum_dtype (str): accumulation type used for the inner GEMM. + source_format (str, optional): format string passed to intrinsic selector (default "uint"). + num_bits (int, optional): number of bits per quantized element in B (default 4). + scale_size (int, optional): number of elements grouped per scale entry (default 32). + fast_dequant (bool, optional): choose the fast intrinsic dequantization path when available (default True). + block_M, block_N, block_K (int, optional): tile sizes for M, N, and K dimensions (defaults 256, 128, 128). + num_stages (int, optional): pipelining stages for K loop (default 2). + threads (int, optional): threads per block used by the kernel (default 256). + split (int, optional): split factor along K used by the scheduler (default 1). + with_bias (bool, optional): whether to add Bias to the output (default False). + + Returns: + A T.prim_func implementing the tiled, pipelined GEMM that: + - loads tiled blocks of A and packed B to shared memory, + - dequantizes B via the chosen path into a shared dequantized tile, + - performs a tiled GEMM accumulating into local fragments, + - writes the final MxN block to the global output tensor. + + Notes: + - The function queries an intrinsic group to obtain a fast dequantization implementation when fast_dequant is enabled; that intrinsic must supply a valid C source and function name. + - The kernel layout uses swizzled shared-memory layouts for A, B, and the shared C tile. + - An assertion enforces that K % (block_K * split) == 0. """ num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 QK = K // num_elems_per_byte Block_QK = block_K // num_elems_per_byte A_shape = (M, K) @@ -150,6 +155,7 @@ def matmul(M, assert K % (block_K * split) == 0 from tilelang.quantize import get_mxfp_intrin_group + # fast_dequant_bf16_fp4_twiddling mxfp_intrin_info = get_mxfp_intrin_group( out_dtype=in_dtype, @@ -164,7 +170,7 @@ def matmul(M, assert func_name is not None, "mxfp_intrin_info is not found" import_source = import_source - def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): + def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype=T.bfloat16): """ Return a TileLang macro that performs fast dequantization of twiddled FP4-packed data into BF16. @@ -175,12 +181,12 @@ def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): - Writes the scaled BF16 results into B_dequantize_shared. Notes: - - This factory only supports in_dtype="fp4" and out_dtype="bfloat16". + - This factory only supports in_dtype="fp4" and out_dtype=T.bfloat16. - The macro depends on several names from the enclosing scope (e.g., import_source, func_name, DataType, num_elems_per_byte, storage_dtype, block_N, block_K, threads, scale_size); those must be defined and consistent with the kernel that will use the macro. - The macro issues a T.import_source and T.call_extern to invoke the external intrinsic; ensure the external implementation matching `func_name` is available at compilation/runtime. """ assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] # Some variables for dequantization in each thread MAX_TRANSACTION_SIZE_BITS = 128 @@ -252,24 +258,23 @@ def fast_dequant_bf16_fp4_twiddling(B_shared, B_dequantize_shared, Scale, k): for v in T.vectorized(0, local_size): index = i * threads * local_size + tx * local_size + v - B_dequantize_shared[index // block_K, - index % block_K] = B_dequantize_local_thread[v] + B_dequantize_shared[index // block_K, index % block_K] = B_dequantize_local_thread[v] return fast_dequant_bf16_fp4_twiddling - def get_simple_dequant_func(in_dtype="fp4", out_dtype="bfloat16"): + def get_simple_dequant_func(in_dtype="fp4", out_dtype=T.bfloat16): """ Create a simple (scalar) dequantization macro that converts 4-bit packed inputs to bfloat16. Returns a T.macro that, given shared-storage buffers B_shared, B_dequantize_shared, a Scale tensor, and block index k, unpacks 4-bit values from B_shared, converts each nibble to a bfloat16 value using _tir_u8_to_f4_to_bf16, applies the per-element exponential Scale, and writes the dequantized BF16 block into B_dequantize_shared. Notes: - - Only supports in_dtype="fp4" and out_dtype="bfloat16". + - Only supports in_dtype="fp4" and out_dtype=T.bfloat16. - The macro expects B_shared and B_dequantize_shared to have the shapes established in the enclosing scope (B_shared_shape, B_dequantize_shared_shape) and performs block-local copying into allocated fragments before elementwise conversion. - Scale holds the exponent-like scaling values indexed per output element as used by the conversion helper. """ assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] @T.macro def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale, k): @@ -301,33 +306,32 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale, k): B_local[i, j // num_elems_per_byte], j % num_elems_per_byte, Scale[ - bx * block_N + i, k * block_K // scale_size + j // - scale_size], # Scale is the exponential part, within the representation of uint8 + bx * block_N + i, k * block_K // scale_size + j // scale_size + ], # Scale is the exponential part, within the representation of uint8 dtype=out_dtype, - ) * T.shift_left( - 1, (Scale[bx * block_N + i, k * block_K // scale_size + j // scale_size])) + ) * T.shift_left(1, (Scale[bx * block_N + i, k * block_K // scale_size + j // scale_size])) T.copy(B_dequantize_local, B_dequantize_shared) return simple_dequant_bf16_fp4 @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - Scale: T.Tensor(Scale_shape, storage_dtype), - Bias: T.Tensor(Bias_shape, out_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + Scale: T.Tensor(Scale_shape, storage_dtype), + Bias: T.Tensor(Bias_shape, out_dtype), + C: T.Tensor((M, N), out_dtype), ): """ - Tiled, pipelined kernel entry that multiplies A with a quantized B (with per-block Scale) producing C. + Tiled, pipelined kernel entry that multiplies A with a quantized B (with per-block Scale) producing C. - This prim-level kernel implements a blocked, multi-threaded matmul: it loads tiles of A and the packed/quantized B into shared memory, dequantizes B (either via the fast intrinsic twiddling path or the simple per-element path), performs a block GEMM (with B transposed), and writes the accumulated block results into the output tensor C. The kernel allocates shared buffers for A, B, and the dequantized B, and a local fragment for accumulation; it runs over K in pipelined stages and expects the provided shapes and dtypes to match the tiling parameters used to build the function. + This prim-level kernel implements a blocked, multi-threaded matmul: it loads tiles of A and the packed/quantized B into shared memory, dequantizes B (either via the fast intrinsic twiddling path or the simple per-element path), performs a block GEMM (with B transposed), and writes the accumulated block results into the output tensor C. The kernel allocates shared buffers for A, B, and the dequantized B, and a local fragment for accumulation; it runs over K in pipelined stages and expects the provided shapes and dtypes to match the tiling parameters used to build the function. - Parameters are self-descriptive in the signature; notable behaviors: - - B is stored in a compact uint8-packed layout (num_bits per element) and is dequantized using Scale before GEMM. - - The selected dequantization path is controlled by the outer-scope flag `fast_dequant`. - - The GEMM uses transpose_B=True (i.e., multiplies A · B^T after dequantization). - - The function writes results in-place into C. + Parameters are self-descriptive in the signature; notable behaviors: + - B is stored in a compact uint8-packed layout (num_bits per element) and is dequantized using Scale before GEMM. + - The selected dequantization path is controlled by the outer-scope flag `fast_dequant`. + - The GEMM uses transpose_B=True (i.e., multiplies A · B^T after dequantization). + - The function writes results in-place into C. """ with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -337,23 +341,24 @@ def main( C_local = T.alloc_fragment((block_M, block_N), accum_dtype) C_shared = T.alloc_shared((block_M, block_N), out_dtype) - T.annotate_layout({ - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - C_shared: tilelang.layout.make_swizzled_layout(C_shared), - }) + T.annotate_layout( + { + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + } + ) if with_bias: - T.annotate_layout({ - Bias_shared: tilelang.layout.make_swizzled_layout(Bias_shared), - }) + T.annotate_layout( + { + Bias_shared: tilelang.layout.make_swizzled_layout(Bias_shared), + } + ) if threads == 512: T.disable_warp_group_reg_alloc() if with_bias: - T.copy(Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], - Bias_shared) + T.copy(Bias[by * block_M : (by + 1) * block_M, bx * block_N : (bx + 1) * block_N], Bias_shared) T.copy(Bias_shared, C_local) else: T.clear(C_local) @@ -368,7 +373,7 @@ def main( T.gemm(A_shared, B_dequantize_shared, C_local, transpose_B=True) T.copy(C_local, C_shared) - T.copy(C_shared, C[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N]) + T.copy(C_shared, C[by * block_M : (by + 1) * block_M, bx * block_N : (bx + 1) * block_N]) return main @@ -387,9 +392,9 @@ def ref_program_twiddling(A, qB, Scale, Bias=None): Returns: torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert_bit_twiddling(qB) - B *= 2**(Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) + B *= 2 ** (Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) return C @@ -410,9 +415,9 @@ def ref_program_twiddling_with_bias(A, qB, Scale, Bias): Returns: torch.Tensor: Resulting matrix C with shape (M, N) in bfloat16. """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert_bit_twiddling(qB) - B *= 2**(Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) + B *= 2 ** (Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias C = C.to(torch.__getattribute__(dtypeC)) return C @@ -434,9 +439,9 @@ def ref_program_simple(A, qB, Scale, Bias=None): No in-place modification is performed on inputs (a local floating copy of B is scaled). """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert(qB) - B *= 2**(Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) + B *= 2 ** (Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) return C @@ -462,9 +467,9 @@ def ref_program_simple_with_bias(A, qB, Scale, Bias): No in-place modification is performed on inputs (a local floating copy of B is scaled). """ - dtypeC = "bfloat16" + dtypeC = T.bfloat16 B = torch_convert(qB) - B *= 2**(Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) + B *= 2 ** (Scale[:, (torch.arange(B.shape[1], device=B.device) // 32)]) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias C = C.to(torch.__getattribute__(dtypeC)) return C @@ -491,24 +496,16 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, if tune: kernel = matmul( - m, - n, - k, - "bfloat16", - "bfloat16", - "float32", - num_bits=4, - scale_size=scale_size, - fast_dequant=fast_dequant, - with_bias=with_bias) + m, n, k, T.bfloat16, T.bfloat16, T.float32, num_bits=4, scale_size=scale_size, fast_dequant=fast_dequant, with_bias=with_bias + ) else: kernel = matmul( m, n, k, - "bfloat16", - "bfloat16", - "float32", + T.bfloat16, + T.bfloat16, + T.float32, num_bits=4, scale_size=scale_size, block_M=256, @@ -518,7 +515,8 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, threads=256, split=1, fast_dequant=fast_dequant, - with_bias=with_bias) + with_bias=with_bias, + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Auto) @@ -538,6 +536,29 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(m=4096, n=4096, k=4096, scale_size=32, fast_dequant=True, with_bias=False): + kernel = matmul( + m, + n, + k, + "bfloat16", + "bfloat16", + "float32", + num_bits=4, + scale_size=scale_size, + block_M=256, + block_N=128, + block_K=128, + num_stages=2, + threads=256, + split=1, + fast_dequant=fast_dequant, + with_bias=with_bias, + ) + profiler = kernel.get_profiler(tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": M, N, K = 256, 256, 256 scale_size = 32 diff --git a/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper_tma.py b/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper_tma.py index 7dad795971..12395df0ac 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper_tma.py +++ b/examples/dequantize_gemm/example_dequant_gemm_bf16_mxfp4_hopper_tma.py @@ -7,29 +7,28 @@ from dequantize_utils import torch_convert_bit_twiddling, torch_convert -def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr, - dtype: str): +def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale: tir.PrimExpr, dtype: str): """ - Convert a 4-bit field packed in a uint8 into a bfloat16 value, applying an exponent scale. + Convert a 4-bit field packed in a uint8 into a bfloat16 value, applying an exponent scale. - This helper extracts a 4-bit nibble from `val` at byte-nibble position `pos`, interprets its - bits as a sign/exponent/mantissa in the 4-bit custom FP4 layout, adjusts the exponent by - `scale` (clamped to an 8-bit range), and assembles the corresponding bfloat16 representation. + This helper extracts a 4-bit nibble from `val` at byte-nibble position `pos`, interprets its + bits as a sign/exponent/mantissa in the 4-bit custom FP4 layout, adjusts the exponent by + `scale` (clamped to an 8-bit range), and assembles the corresponding bfloat16 representation. - Parameters: - nbit (int): Number of bits in the packed field (must be 4). - val (tir.PrimExpr): Packed input value of dtype `uint8` containing one or more 4-bit fields. - pos (tir.PrimExpr): Index of the nibble within `val` (used to shift/extract the 4-bit field). - scale (tir.PrimExpr): Per-element exponent adjustment added to the extracted exponent (uint-like). - dtype (str): Destination dtype string (must be "bfloat16"). + Parameters: + nbit (int): Number of bits in the packed field (must be 4). + val (tir.PrimExpr): Packed input value of dtype `uint8` containing one or more 4-bit fields. + pos (tir.PrimExpr): Index of the nibble within `val` (used to shift/extract the 4-bit field). + scale (tir.PrimExpr): Per-element exponent adjustment added to the extracted exponent (uint-like). + dtype (str): Destination dtype string (must be "bfloat16"). - Returns: - tir.PrimExpr: The resulting value reinterpreted as `bfloat16`. + Returns: + tir.PrimExpr: The resulting value reinterpreted as `bfloat16`. - Notes: - - Preconditions are enforced via assertions: nbit == 4, dtype == "bfloat16", and val.dtype == "uint8". - - The function clamps the adjusted exponent to the 8-bit range before assembling the bfloat16 bit pattern. - """ + Notes: + - Preconditions are enforced via assertions: nbit == 4, dtype == "bfloat16", and val.dtype == "uint8". + - The function clamps the adjusted exponent to the 8-bit range before assembling the bfloat16 bit pattern. + """ assert nbit == 4 assert dtype == "bfloat16" assert val.dtype == "uint8" @@ -43,9 +42,10 @@ def _tir_u8_to_f4_to_bf16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, scale # To handle the overflow, we may use the min function to limit the exponential part to 8 bits # e_bf16 = T.min(e_bf16 + scale, tir.const((1 << 8) - 1, "uint16")) m_f4 = f4 & tir.const(1, "uint16") - val_bf16 = tir.reinterpret("bfloat16", - ((((s << tir.const(8, "uint16")) | e_bf16) << tir.const(7, "uint16")) - | (m_f4 << tir.const(6, "uint16"))).astype("uint16")) + val_bf16 = tir.reinterpret( + "bfloat16", + ((((s << tir.const(8, "uint16")) | e_bf16) << tir.const(7, "uint16")) | (m_f4 << tir.const(6, "uint16"))).astype("uint16"), + ) return val_bf16 @@ -65,6 +65,7 @@ def get_configs(): List[dict]: A list of configuration dictionaries covering all combinations. """ import itertools + iter_params = dict( block_M=[64, 128, 256], block_N=[64, 128, 256], @@ -73,67 +74,71 @@ def get_configs(): threads=[128, 256, 512], split=[1, 2], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] - - -@tilelang.autotune(configs=get_configs(),) -@tilelang.jit(out_idx=[-1],) -def matmul(M, - N, - K, - in_dtype, - out_dtype, - accum_dtype, - source_format='uint', - num_bits=4, - scale_size=32, - fast_dequant=True, - with_bias=False, - block_M=256, - block_N=128, - block_K=128, - num_stages=2, - threads=256, - split=1): + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] + + +@tilelang.autotune( + configs=get_configs(), +) +@tilelang.jit( + out_idx=[-1], +) +def matmul( + M, + N, + K, + in_dtype, + out_dtype, + accum_dtype, + source_format="uint", + num_bits=4, + scale_size=32, + fast_dequant=True, + with_bias=False, + block_M=256, + block_N=128, + block_K=128, + num_stages=2, + threads=256, + split=1, +): """ - Construct and return a tiled matrix-multiply TIR kernel that multiplies A (shape MxK) by a quantized B (shape Nx(QK)) and writes an MxN output in out_dtype. - - The generated kernel accepts: - - A: dense matrix with element type `in_dtype`. - - B: packed quantized matrix stored as uint8 with `num_bits` bits per element (QK = K / (8/num_bits)). - - Scale: per-block scale/exponent information used to dequantize B. - The kernel dequantizes B to a working floating format (out_dtype/accum_dtype) using one of two paths: - - fast_dequant (True): uses an external, hardware/implementation-specific intrinsic group (twiddling) for batch dequantization. - - fast_dequant (False): uses a simple elementwise dequantization helper. - - Parameters: - M, N, K (int): matrix dimensions (A is MxK, result is MxN). K must be divisible by (block_K * split). - in_dtype (str): element type of A (e.g., "fp4" in this file). - out_dtype (str): output tensor element type (e.g., "bfloat16"). - accum_dtype (str): accumulation type used for the inner GEMM. - source_format (str, optional): format string passed to intrinsic selector (default "uint"). - num_bits (int, optional): number of bits per quantized element in B (default 4). - scale_size (int, optional): number of elements grouped per scale entry (default 32). - fast_dequant (bool, optional): choose the fast intrinsic dequantization path when available (default True). - block_M, block_N, block_K (int, optional): tile sizes for M, N, and K dimensions (defaults 256, 128, 128). - num_stages (int, optional): pipelining stages for K loop (default 2). - threads (int, optional): threads per block used by the kernel (default 256). - split (int, optional): split factor along K used by the scheduler (default 1). - with_bias (bool, optional): whether to add Bias to the output (default False). - - Returns: - A T.prim_func implementing the tiled, pipelined GEMM that: - - loads tiled blocks of A and packed B to shared memory, - - dequantizes B via the chosen path into a shared dequantized tile, - - performs a tiled GEMM accumulating into local fragments, - - writes the final MxN block to the global output tensor. + Construct and return a tiled matrix-multiply TIR kernel that multiplies A (shape MxK) by a quantized B (shape Nx(QK)) and writes an MxN output in out_dtype. - Notes: - - The function queries an intrinsic group to obtain a fast dequantization implementation when fast_dequant is enabled; that intrinsic must supply a valid C source and function name. - - The kernel layout uses swizzled shared-memory layouts for A, B, and the shared C tile. - - An assertion enforces that K % (block_K * split) == 0. + The generated kernel accepts: + - A: dense matrix with element type `in_dtype`. + - B: packed quantized matrix stored as uint8 with `num_bits` bits per element (QK = K / (8/num_bits)). + - Scale: per-block scale/exponent information used to dequantize B. + The kernel dequantizes B to a working floating format (out_dtype/accum_dtype) using one of two paths: + - fast_dequant (True): uses an external, hardware/implementation-specific intrinsic group (twiddling) for batch dequantization. + - fast_dequant (False): uses a simple elementwise dequantization helper. + + Parameters: + M, N, K (int): matrix dimensions (A is MxK, result is MxN). K must be divisible by (block_K * split). + in_dtype (str): element type of A (e.g., "fp4" in this file). + out_dtype (str): output tensor element type (e.g., "bfloat16"). + accum_dtype (str): accumulation type used for the inner GEMM. + source_format (str, optional): format string passed to intrinsic selector (default "uint"). + num_bits (int, optional): number of bits per quantized element in B (default 4). + scale_size (int, optional): number of elements grouped per scale entry (default 32). + fast_dequant (bool, optional): choose the fast intrinsic dequantization path when available (default True). + block_M, block_N, block_K (int, optional): tile sizes for M, N, and K dimensions (defaults 256, 128, 128). + num_stages (int, optional): pipelining stages for K loop (default 2). + threads (int, optional): threads per block used by the kernel (default 256). + split (int, optional): split factor along K used by the scheduler (default 1). + with_bias (bool, optional): whether to add Bias to the output (default False). + + Returns: + A T.prim_func implementing the tiled, pipelined GEMM that: + - loads tiled blocks of A and packed B to shared memory, + - dequantizes B via the chosen path into a shared dequantized tile, + - performs a tiled GEMM accumulating into local fragments, + - writes the final MxN block to the global output tensor. + + Notes: + - The function queries an intrinsic group to obtain a fast dequantization implementation when fast_dequant is enabled; that intrinsic must supply a valid C source and function name. + - The kernel layout uses swizzled shared-memory layouts for A, B, and the shared C tile. + - An assertion enforces that K % (block_K * split) == 0. """ num_elems_per_byte = 8 // num_bits storage_dtype = "uint8" @@ -150,6 +155,7 @@ def matmul(M, assert K % (block_K * split) == 0 from tilelang.quantize import get_mxfp_intrin_group + # fast_dequant_bf16_fp4_twiddling mxfp_intrin_info = get_mxfp_intrin_group( out_dtype=in_dtype, @@ -252,8 +258,7 @@ def fast_dequant_bf16_fp4_twiddling(B_shared, B_dequantize_shared, Scale_shared, for v in T.vectorized(0, local_size): index = i * threads * local_size + tx * local_size + v - B_dequantize_shared[index // block_K, - index % block_K] = B_dequantize_local_thread[v] + B_dequantize_shared[index // block_K, index % block_K] = B_dequantize_local_thread[v] return fast_dequant_bf16_fp4_twiddling @@ -301,8 +306,8 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale_shared, k): B_local[i, j // num_elems_per_byte], j % num_elems_per_byte, Scale_shared[ - i, k * block_K // scale_size + j // - scale_size], # Scale is the exponential part, within the representation of uint8 + i, k * block_K // scale_size + j // scale_size + ], # Scale is the exponential part, within the representation of uint8 dtype=out_dtype, ) * T.shift_left(1, (Scale_shared[i, k * block_K // scale_size + j // scale_size])) T.copy(B_dequantize_local, B_dequantize_shared) @@ -311,22 +316,22 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale_shared, k): @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - Scale: T.Tensor(Scale_shape, storage_dtype), - Bias: T.Tensor(Bias_shape, out_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + Scale: T.Tensor(Scale_shape, storage_dtype), + Bias: T.Tensor(Bias_shape, out_dtype), + C: T.Tensor((M, N), out_dtype), ): """ - Tiled, pipelined kernel entry that multiplies A with a quantized B (with per-block Scale) producing C. + Tiled, pipelined kernel entry that multiplies A with a quantized B (with per-block Scale) producing C. - This prim-level kernel implements a blocked, multi-threaded matmul: it loads tiles of A and the packed/quantized B into shared memory, dequantizes B (either via the fast intrinsic twiddling path or the simple per-element path), performs a block GEMM (with B transposed), and writes the accumulated block results into the output tensor C. The kernel allocates shared buffers for A, B, and the dequantized B, and a local fragment for accumulation; it runs over K in pipelined stages and expects the provided shapes and dtypes to match the tiling parameters used to build the function. + This prim-level kernel implements a blocked, multi-threaded matmul: it loads tiles of A and the packed/quantized B into shared memory, dequantizes B (either via the fast intrinsic twiddling path or the simple per-element path), performs a block GEMM (with B transposed), and writes the accumulated block results into the output tensor C. The kernel allocates shared buffers for A, B, and the dequantized B, and a local fragment for accumulation; it runs over K in pipelined stages and expects the provided shapes and dtypes to match the tiling parameters used to build the function. - Parameters are self-descriptive in the signature; notable behaviors: - - B is stored in a compact uint8-packed layout (num_bits per element) and is dequantized using Scale before GEMM. - - The selected dequantization path is controlled by the outer-scope flag `fast_dequant`. - - The GEMM uses transpose_B=True (i.e., multiplies A · B^T after dequantization). - - The function writes results in-place into C. + Parameters are self-descriptive in the signature; notable behaviors: + - B is stored in a compact uint8-packed layout (num_bits per element) and is dequantized using Scale before GEMM. + - The selected dequantization path is controlled by the outer-scope flag `fast_dequant`. + - The GEMM uses transpose_B=True (i.e., multiplies A · B^T after dequantization). + - The function writes results in-place into C. """ with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -339,16 +344,20 @@ def main( # May use much more shared memory than necessary Scale_shared = T.alloc_shared((block_N, K // scale_size), storage_dtype) - T.annotate_layout({ - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - C_shared: tilelang.layout.make_swizzled_layout(C_shared), - }) + T.annotate_layout( + { + A_shared: tilelang.layout.make_swizzled_layout(A_shared), + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + C_shared: tilelang.layout.make_swizzled_layout(C_shared), + } + ) if with_bias: - T.annotate_layout({ - Bias_shared: tilelang.layout.make_swizzled_layout(Bias_shared), - }) + T.annotate_layout( + { + Bias_shared: tilelang.layout.make_swizzled_layout(Bias_shared), + } + ) if threads == 512: T.disable_warp_group_reg_alloc() @@ -357,26 +366,24 @@ def main( # T.copy(Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], # Bias_shared) # T.copy(Bias_shared, C_local) - T.copy(Bias[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N], - C_local) + T.copy(Bias[by * block_M : (by + 1) * block_M, bx * block_N : (bx + 1) * block_N], C_local) else: T.clear(C_local) # Use 1D TMA to load Scale - T.copy(Scale[bx * block_N:(bx + 1) * block_N, :], Scale_shared) + T.copy(Scale[bx * block_N : (bx + 1) * block_N, :], Scale_shared) for k in T.Pipelined(K // block_K, num_stages=num_stages): T.copy(A[by * block_M, k * block_K], A_shared) T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared) if fast_dequant: - get_fast_dequant_twiddling_func()(B_shared, B_dequantize_shared, Scale_shared, - k) + get_fast_dequant_twiddling_func()(B_shared, B_dequantize_shared, Scale_shared, k) else: get_simple_dequant_func()(B_shared, B_dequantize_shared, Scale_shared, k) T.gemm(A_shared, B_dequantize_shared, C_local, transpose_B=True) T.copy(C_local, C_shared) - T.copy(C_shared, C[by * block_M:(by + 1) * block_M, bx * block_N:(bx + 1) * block_N]) + T.copy(C_shared, C[by * block_M : (by + 1) * block_M, bx * block_N : (bx + 1) * block_N]) return main @@ -399,7 +406,7 @@ def ref_program_twiddling(A, qB, Scale, Bias=None): B = torch_convert_bit_twiddling(qB) for i in range(B.shape[0]): for j in range(B.shape[1]): - B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) + B[i][j] = B[i][j] * (2 ** (Scale[i][j // 32])) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) return C @@ -424,7 +431,7 @@ def ref_program_twiddling_with_bias(A, qB, Scale, Bias): B = torch_convert_bit_twiddling(qB) for i in range(B.shape[0]): for j in range(B.shape[1]): - B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) + B[i][j] = B[i][j] * (2 ** (Scale[i][j // 32])) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias C = C.to(torch.__getattribute__(dtypeC)) return C @@ -450,7 +457,7 @@ def ref_program_simple(A, qB, Scale, Bias=None): B = torch_convert(qB) for i in range(B.shape[0]): for j in range(B.shape[1]): - B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) + B[i][j] = B[i][j] * (2 ** (Scale[i][j // 32])) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) return C @@ -480,7 +487,7 @@ def ref_program_simple_with_bias(A, qB, Scale, Bias): B = torch_convert(qB) for i in range(B.shape[0]): for j in range(B.shape[1]): - B[i][j] = B[i][j] * (2**(Scale[i][j // 32])) + B[i][j] = B[i][j] * (2 ** (Scale[i][j // 32])) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) + Bias C = C.to(torch.__getattribute__(dtypeC)) return C @@ -507,16 +514,8 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, if tune: kernel = matmul( - m, - n, - k, - "bfloat16", - "bfloat16", - "float32", - num_bits=4, - scale_size=scale_size, - fast_dequant=fast_dequant, - with_bias=with_bias) + m, n, k, "bfloat16", "bfloat16", "float32", num_bits=4, scale_size=scale_size, fast_dequant=fast_dequant, with_bias=with_bias + ) else: kernel = matmul( m, @@ -534,7 +533,8 @@ def main(m=256, n=256, k=256, scale_size=32, fast_dequant=True, with_bias=False, threads=256, split=1, fast_dequant=fast_dequant, - with_bias=with_bias) + with_bias=with_bias, + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Auto) diff --git a/examples/dequantize_gemm/example_dequant_gemm_fine_grained.py b/examples/dequantize_gemm/example_dequant_gemm_fine_grained.py index 727d6d3b6f..37826874bc 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_fine_grained.py +++ b/examples/dequantize_gemm/example_dequant_gemm_fine_grained.py @@ -24,8 +24,9 @@ def matmul( num_bits=4, ): from tilelang.quantize import _tir_packed_to_unsigned_convert + num_elems_per_byte = 8 // num_bits - storage_dtype = "int8" + storage_dtype = T.int8 storage_nbit = int("".join(c for c in storage_dtype if c.isdigit())) storage_type = str("".join(c for c in storage_dtype if not c.isdigit())) A_shape = (M, K) @@ -39,9 +40,9 @@ def matmul( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -58,21 +59,19 @@ def main( T.copy(A[by * block_M, k * block_K], A_shared) T.copy(B[bx * block_N, k * block_K // num_elems_per_byte], B_shared) - for i in T.serial(block_N * block_K // num_elems_per_byte // - (threads * local_size_compressed)): + for i in T.serial(block_N * block_K // num_elems_per_byte // (threads * local_size_compressed)): for v in T.vectorized(0, local_size_compressed): index = i * threads * local_size_compressed + tx * local_size_compressed + v vi = index // (block_K // num_elems_per_byte) vj = index % (block_K // num_elems_per_byte) B_local[v] = B_shared[vi, vj] for v in T.serial(0, local_size): - B_dequantize_local[v] = _tir_packed_to_unsigned_convert( - storage_type, storage_nbit)( - num_bits, - B_local[v // num_elems_per_byte], - v % num_elems_per_byte, - dtype=in_dtype, - ) + B_dequantize_local[v] = _tir_packed_to_unsigned_convert(storage_type, storage_nbit)( + num_bits, + B_local[v // num_elems_per_byte], + v % num_elems_per_byte, + dtype=in_dtype, + ) for v in T.vectorized(0, local_size): index = i * threads * local_size + tx * local_size + v vi = index // block_K @@ -121,9 +120,7 @@ def run_gemm( def ref_program(A, qB): import torch - B = ( - torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, - dtype=torch.half).to(torch.half).to(A.device)) + B = torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, dtype=torch.half).to(torch.half).to(A.device) for i in range(B.shape[0]): for j in range(B.shape[1]): B[i][j] = ((qB[i][j // 2] >> (4 * (j % 2))) & 0xF).to(torch.half) @@ -146,25 +143,27 @@ def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( ): from tilelang.intrinsics.mma_layout import make_mma_swizzle_layout as make_swizzle_layout from tilelang.intrinsics.mma_macro_generator import ( - TensorCoreIntrinEmitterWithLadderTransform,) + TensorCoreIntrinEmitterWithLadderTransform, + ) from bitblas.gpu.intrin.lop3 import decode_i4_to_f16 + assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" num_bits = 4 num_elems_per_byte = 8 // num_bits - storage_dtype = "int8" + storage_dtype = T.int8 micro_size_x = micro_size_y = micro_size_k = 16 - if out_dtype == "int32": + if out_dtype == T.int32: micro_size_k = 32 # This is a debug config @@ -183,7 +182,7 @@ def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( block_M = block_row_warps * warp_row_tiles block_N = block_col_warps * warp_col_tiles - block_K = 32 if in_dtype == "float16" else 64 + block_K = 32 if in_dtype == T.float16 else 64 chunk = block_K // reduce_k is_smooth_a = False @@ -192,8 +191,7 @@ def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( pad_factor = 8 A_shape = (M, K) - B_shape = (N // micro_size_y, K // micro_size_k, micro_size_y, - micro_size_k // num_elems_per_byte) + B_shape = (N // micro_size_y, K // micro_size_k, micro_size_y, micro_size_k // num_elems_per_byte) A_shared_shape = (block_M, (block_K + pad_factor) if apply_pad_a else block_K) B_shared_shape = ( block_N // micro_size_y, @@ -228,7 +226,8 @@ def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( chunk=chunk, reduce_k=reduce_k, transform_kind_b=transform_b, - num_elems_per_byte=num_elems_per_byte) + num_elems_per_byte=num_elems_per_byte, + ) vec_load_qb = 16 if block_N * (block_K // reduce_k) // num_elems_per_byte // threads < vec_load_qb: @@ -236,14 +235,11 @@ def tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + C: T.Tensor((M, N), out_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads, - prelude=decode_i4_to_f16) as (bx, by): - + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads, prelude=decode_i4_to_f16) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, storage_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) @@ -255,40 +251,36 @@ def main( thread_binding = T.get_thread_binding(0) rk = T.get_thread_binding(1) - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + } + ) T.use_swizzle(panel_size=10) T.clear(C_local) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, (block_K // reduce_k)): vk = rk * (block_K // reduce_k) + k A_shared[i, vk] = A[by * block_M + i, ko * block_K + vk] # TODO(lei): Layout Inference Pass is not efficient to handle the four dims int8 load - for i in T.serial(block_N * (block_K // reduce_k) // num_elems_per_byte // - (threads * vec_load_qb)): + for i in T.serial(block_N * (block_K // reduce_k) // num_elems_per_byte // (threads * vec_load_qb)): for v in T.vectorized(0, vec_load_qb): t = thread_binding idx = i * threads * vec_load_qb * reduce_k + rk * threads * vec_load_qb + t * vec_load_qb + v vkk = idx % (micro_size_k // num_elems_per_byte) vjj = (idx // (micro_size_k // num_elems_per_byte)) % micro_size_y - vk = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y) % ( - block_K // micro_size_k) - vj = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y // - (block_K // micro_size_k)) % ( - block_N // micro_size_y) - B_shared[vj, vk, vjj, - vkk] = B[bx * (block_N // micro_size_y) + vj, - ko * (block_K // micro_size_k) + vk, vjj, vkk] + vk = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y) % (block_K // micro_size_k) + vj = (idx // (micro_size_k // num_elems_per_byte) // micro_size_y // (block_K // micro_size_k)) % ( + block_N // micro_size_y + ) + B_shared[vj, vk, vjj, vkk] = B[bx * (block_N // micro_size_y) + vj, ko * (block_K // micro_size_k) + vk, vjj, vkk] for ki in T.serial(0, (block_K // (micro_size_k * reduce_k))): - # Load A into fragment mma_emitter.ldmatrix_a( A_local, @@ -307,9 +299,13 @@ def main( for j in T.serial(warp_cols): local_size_b = mma_emitter.local_size_b - T.call_extern('handle', 'decode_i4u_to_f16', - T.address_of(B_local[j * local_size_b // num_elems_per_byte]), - T.address_of(B_dequantize_local[j * local_size_b]), 8) + T.call_extern( + "handle", + "decode_i4u_to_f16", + T.address_of(B_local[j * local_size_b // num_elems_per_byte]), + T.address_of(B_dequantize_local[j * local_size_b]), + 8, + ) mma_emitter.mma(A_local, B_dequantize_local, C_local) @@ -328,7 +324,8 @@ def main( reduced_accum_res[0], rk, dtype="handle", - )) + ) + ) if rk == 0: C_local[n] = reduced_accum_res[0] @@ -340,9 +337,9 @@ def main( for i, j in T.Parallel(block_M, (block_N // reduce_k)): vj = rk * (block_N // reduce_k) + j - C[by * block_M + i, - bx * block_N + vj] = C_shared[i // micro_size_x, vj // micro_size_y, - i % micro_size_x, vj % micro_size_y] + C[by * block_M + i, bx * block_N + vj] = C_shared[ + i // micro_size_x, vj // micro_size_y, i % micro_size_x, vj % micro_size_y + ] return main @@ -357,8 +354,8 @@ def assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correct transform_b, ): import bitblas - matmul = tl_matmul_with_ladder_weight_only_transform_block_reduce_int4( - M, N, K, in_dtype, out_dtype, accum_dtype, transform_b) + + matmul = tl_matmul_with_ladder_weight_only_transform_block_reduce_int4(M, N, K, in_dtype, out_dtype, accum_dtype, transform_b) kernel = tilelang.compile(matmul, out_idx=[2]) src_code = kernel.get_kernel_source() @@ -368,11 +365,10 @@ def assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correct assert src_code is not None num_bits = 4 num_elems_per_byte = 8 // num_bits - storage_dtype = "int8" + storage_dtype = T.int8 A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype)) - qB = torch.randint( - 0, 127, (N, K // num_elems_per_byte), device="cuda", dtype=getattr(torch, storage_dtype)) + qB = torch.randint(0, 127, (N, K // num_elems_per_byte), device="cuda", dtype=getattr(torch, storage_dtype)) C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype)) ladder_permutate_config = bitblas.ops.LadderPermutateConfig( @@ -407,9 +403,7 @@ def assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correct # Ensure that the latency is not None assert latency is not None - B = ( - torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, - dtype=torch.half).to(torch.half).to(A.device)) + B = torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, dtype=torch.half).to(torch.half).to(A.device) for i in range(B.shape[0]): for j in range(B.shape[1]): B[i][j] = ((qB[i][j // 2] >> (4 * (j % 2))) & 0xF).to(torch.half) @@ -423,14 +417,13 @@ def assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correct @tilelang.testing.requires_package("bitblas") def test_run_dequantize_gemm(): - run_gemm(256, 256, 256, "float16", "float16", "float16", 128, 128, 32, num_threads=128) - run_gemm(256, 256, 256, "int8", "int32", "int32", 128, 128, 32, num_threads=128) + run_gemm(256, 256, 256, T.float16, T.float16, T.float16, 128, 128, 32, num_threads=128) + run_gemm(256, 256, 256, T.int8, T.int32, T.int32, 128, 128, 32, num_threads=128) @tilelang.testing.requires_package("bitblas") def test_assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4(): - assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correctness( - 256, 1024, 512, "float16", "float16", "float16", 3) + assert_tl_matmul_with_ladder_weight_only_transform_block_reduce_int4_correctness(256, 1024, 512, T.float16, T.float16, T.float16, 3) def main(): diff --git a/examples/dequantize_gemm/example_dequant_gemm_fp4_hopper.py b/examples/dequantize_gemm/example_dequant_gemm_fp4_hopper.py index c5588d516c..2bdcbb0684 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_fp4_hopper.py +++ b/examples/dequantize_gemm/example_dequant_gemm_fp4_hopper.py @@ -9,30 +9,29 @@ def _tir_u8_to_f4_to_f16(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, dtype: str): assert nbit == 4 - assert dtype == "float16" - assert val.dtype == "uint8" + assert dtype == T.float16 + assert val.dtype == T.uint8 # e_f4 == 0 -> e_f16 = 0 # e_f4 != 0 -> e_f16 = e_f4 + ExponentialBias(f16, f4) = e_f4 + (2^4 - 2^1) = e_f4 + 14 # s1e2m1 - mask = tir.const((1 << nbit) - 1, "uint16") - f4 = (val >> (pos.astype("uint16") * tir.const(nbit, "uint16"))) & mask - s = f4 >> tir.const(3, "uint16") - e_f4 = (f4 & tir.const(6, "uint16")) >> tir.const(1, "uint16") - e_f16 = e_f4 + tir.const(14, "uint16") - m_f4 = f4 & tir.const(1, "uint16") + mask = tir.const((1 << nbit) - 1, T.uint16) + f4 = (val >> (pos.astype(T.uint16) * tir.const(nbit, T.uint16))) & mask + s = f4 >> tir.const(3, T.uint16) + e_f4 = (f4 & tir.const(6, T.uint16)) >> tir.const(1, T.uint16) + e_f16 = e_f4 + tir.const(14, T.uint16) + m_f4 = f4 & tir.const(1, T.uint16) m_f16 = m_f4 - val_f16 = tir.reinterpret("float16", - ((e_f16 | (s << tir.const(5, "uint16"))) << tir.const(10, "uint16") - | m_f16 << tir.const(9, "uint16")).astype("uint16")) - # return tir.Select(e_f4 == tir.const(0, "uint32"), tir.const(0, "float16"), val_f16) + val_f16 = tir.reinterpret( + T.float16, ((e_f16 | (s << tir.const(5, T.uint16))) << tir.const(10, T.uint16) | m_f16 << tir.const(9, T.uint16)).astype(T.uint16) + ) + # return tir.Select(e_f4 == tir.const(0, "uint32"), tir.const(0, T.float16), val_f16) return val_f16 def torch_convert(tensor): - def print_bit(name, val): val_cpu = val.cpu().item() - binary_repr = f'{val_cpu:032b}' + binary_repr = f"{val_cpu:032b}" print(name, binary_repr) def _convert(val, pos): @@ -61,15 +60,15 @@ def _convert(val, pos): @tilelang.jit(out_idx=[1]) def test_convert(N, K, block_N, block_K, in_dtype, num_bits=4, threads=128): num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 B_shape = (N, K // num_elems_per_byte) B_shared_shape = (block_N, block_K // num_elems_per_byte) B_dequantize_shared_shape = (block_N, block_K) @T.prim_func def main( - B: T.Tensor(B_shape, storage_dtype), - C: T.Tensor((N, K), in_dtype), + B: T.Tensor(B_shape, storage_dtype), + C: T.Tensor((N, K), in_dtype), ): with T.Kernel(T.ceildiv(N, block_N), threads=threads) as (bx): B_shared = T.alloc_shared(B_shared_shape, storage_dtype) @@ -99,7 +98,7 @@ def test_fp4_fp16_convert_close(): K, block_N, block_K, - "float16", + T.float16, ) B = torch.randint(0, 16, (N, K // 2), dtype=torch.uint8, device="cuda").to(torch.uint8) @@ -118,23 +117,15 @@ def get_configs(): splits = [1] _configs = list(itertools.product(block_M, block_N, block_K, num_stages, threads, splits)) - configs = [{ - 'block_M': c[0], - 'block_N': c[1], - 'block_K': c[2], - 'num_stages': c[3], - 'threads': c[4], - 'split': c[5] - } for c in _configs] + configs = [{"block_M": c[0], "block_N": c[1], "block_K": c[2], "num_stages": c[3], "threads": c[4], "split": c[5]} for c in _configs] return configs def matmul(M, N, K, in_dtype, out_dtype, accum_dtype, num_bits=4, tune=False): - @tilelang.jit(out_idx=[2]) def kernel_func(block_M, block_N, block_K, num_stages, threads, split=1): num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 A_shape = (M, K) B_shape = (N, K // num_elems_per_byte) A_shared_shape = (block_M, block_K) @@ -145,29 +136,24 @@ def kernel_func(block_M, block_N, block_K, num_stages, threads, split=1): @T.prim_func def main_split( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - Ct: T.Tensor((N, M), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + Ct: T.Tensor((N, M), out_dtype), ): - SplitC = T.alloc_buffer([ - split, (N + block_N - 1) // block_N * block_N, - (M + block_M - 1) // block_M * block_M - ], out_dtype) - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), split, - threads=threads) as (bx, by, bz): + SplitC = T.alloc_buffer([split, (N + block_N - 1) // block_N * block_N, (M + block_M - 1) // block_M * block_M], out_dtype) + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), split, threads=threads) as (bx, by, bz): A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, storage_dtype) B_local = T.alloc_fragment(B_shared_shape, storage_dtype) B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype) B_dequantize_prev_local = T.alloc_fragment(B_dequantize_shared_shape, in_dtype) Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype) - Ct_shared = T.alloc_shared((block_N, block_M), out_dtype) - T.annotate_layout({ - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared), - }) + T.annotate_layout( + { + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + } + ) T.clear(Ct_local) for k in T.Pipelined(K // (block_K * split), num_stages=num_stages): @@ -183,8 +169,7 @@ def main_split( ) T.copy(B_dequantize_local, B_dequantize_prev_local) T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True) - T.copy(Ct_local, SplitC[bz, bx * block_N:(bx + 1) * block_N, - by * block_M:(by + 1) * block_M]) + T.copy(Ct_local, SplitC[bz, bx * block_N : (bx + 1) * block_N, by * block_M : (by + 1) * block_M]) with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M)) as (bx, by): acc = T.alloc_fragment((block_N, block_M), out_dtype) T.clear(acc) @@ -195,12 +180,11 @@ def main_split( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - Ct: T.Tensor((N, M), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + Ct: T.Tensor((N, M), out_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, storage_dtype) B_local = T.alloc_fragment(B_shared_shape, storage_dtype) @@ -209,10 +193,11 @@ def main( Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype) Ct_shared = T.alloc_shared((block_N, block_M), out_dtype) - T.annotate_layout({ - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared), - }) + T.annotate_layout( + { + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + } + ) T.clear(Ct_local) for k in T.Pipelined(K // block_K, num_stages=num_stages): @@ -229,8 +214,7 @@ def main( T.copy(B_dequantize_local, B_dequantize_prev_local) T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True) T.copy(Ct_local, Ct_shared) - T.copy(Ct_shared, Ct[bx * block_N:(bx + 1) * block_N, - by * block_M:(by + 1) * block_M]) + T.copy(Ct_shared, Ct[bx * block_N : (bx + 1) * block_N, by * block_M : (by + 1) * block_M]) if split == 1: return main @@ -241,12 +225,7 @@ def main( @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit(out_idx=[2]) - def kernel(block_M=None, - block_N=None, - block_K=None, - num_stages=None, - threads=None, - split=None): + def kernel(block_M=None, block_N=None, block_K=None, num_stages=None, threads=None, split=None): return kernel_func(block_M, block_N, block_K, num_stages, threads, split).prim_func return kernel() @@ -259,7 +238,7 @@ def kernel(block_M, block_N, block_K, num_stages, threads, split=1): def ref_program(A, qB): - dtypeC = "float16" + dtypeC = T.float16 B = torch_convert(qB) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) @@ -269,10 +248,10 @@ def ref_program(A, qB): def main(m=256, n=256, k=256, tune=False): total_flops = 2 * m * n * k - if (not tune): - kernel = matmul( - m, n, k, "float16", "float16", "float32", num_bits=4, tune=tune)( - block_M=128, block_N=128, block_K=128, num_stages=2, threads=256, split=1) + if not tune: + kernel = matmul(m, n, k, T.float16, T.float16, T.float32, num_bits=4, tune=tune)( + block_M=128, block_N=128, block_K=128, num_stages=2, threads=256, split=1 + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Integer) profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) print("All checks pass.") @@ -283,7 +262,7 @@ def main(m=256, n=256, k=256, tune=False): print("Tile-lang: {:.2f} ms".format(latency)) print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) else: - best_result = matmul(m, n, k, "float16", "float16", "float32", num_bits=4, tune=tune) + best_result = matmul(m, n, k, T.float16, T.float16, T.float32, num_bits=4, tune=tune) best_latency = best_result.latency best_config = best_result.config print(f"Best latency: {best_latency}") @@ -291,12 +270,20 @@ def main(m=256, n=256, k=256, tune=False): print(f"Best config: {best_config}") +def run_regression_perf(m=4096, n=4096, k=4096): + kernel = matmul(m, n, k, "float16", "float16", "float32", num_bits=4, tune=False)( + block_M=128, block_N=128, block_K=128, num_stages=2, threads=256, split=1 + ) + profiler = kernel.get_profiler(tilelang.TensorSupplyType.Integer) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--m', type=int, default=256, help='M') - parser.add_argument('--n', type=int, default=256, help='N') - parser.add_argument('--k', type=int, default=256, help='K') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--m", type=int, default=256, help="M") + parser.add_argument("--n", type=int, default=256, help="N") + parser.add_argument("--k", type=int, default=256, help="K") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() M, N, K = args.m, args.n, args.k main(M, N, K, args.tune) diff --git a/examples/dequantize_gemm/example_dequant_gemm_w4a8.py b/examples/dequantize_gemm/example_dequant_gemm_w4a8.py index 52ee8216f5..b1f8b11328 100644 --- a/examples/dequantize_gemm/example_dequant_gemm_w4a8.py +++ b/examples/dequantize_gemm/example_dequant_gemm_w4a8.py @@ -9,15 +9,15 @@ def _tir_u8_to_i4_to_i8(nbit: int, val: tir.PrimExpr, pos: tir.PrimExpr, dtype: str): assert nbit == 4 - assert dtype == "int8" - assert val.dtype == "uint8" + assert dtype == T.int8 + assert val.dtype == T.uint8 - mask = tir.const((1 << nbit) - 1, "uint8") + mask = tir.const((1 << nbit) - 1, T.uint8) - i4 = (val >> (pos.astype("uint8") * tir.const(nbit, "uint8"))) & mask + i4 = (val >> (pos.astype(T.uint8) * tir.const(nbit, T.uint8))) & mask - i8_shifted = tir.reinterpret("int8", i4 << tir.const(4, "uint8")) - i8 = i8_shifted >> tir.const(4, "int8") + i8_shifted = tir.reinterpret(T.int8, i4 << tir.const(4, T.uint8)) + i8 = i8_shifted >> tir.const(4, T.int8) return i8 @@ -35,15 +35,15 @@ def get_configs(): @tilelang.jit(out_idx=[1]) def _convert_test(N, K, block_N, block_K, in_dtype, num_bits=4, threads=128): num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 B_shape = (N, K // num_elems_per_byte) B_shared_shape = (block_N, block_K // num_elems_per_byte) B_dequantize_shared_shape = (block_N, block_K) @T.prim_func def main( - B: T.Tensor(B_shape, storage_dtype), - C: T.Tensor((N, K), in_dtype), + B: T.Tensor(B_shape, storage_dtype), + C: T.Tensor((N, K), in_dtype), ): with T.Kernel(T.ceildiv(N, block_N), threads=threads) as (bx): B_shared = T.alloc_shared(B_shared_shape, storage_dtype) @@ -66,13 +66,12 @@ def main( def torch_convert(tensor): - def _convert(val, pos): assert val.dtype == torch.uint8 val = val.view(torch.int8) mask = (1 << 4) - 1 - i4_shifted = ((val >> (pos * 4)) & mask) - i4 = ((i4_shifted << 4) >> 4) + i4_shifted = (val >> (pos * 4)) & mask + i4 = (i4_shifted << 4) >> 4 return i4.view(torch.int8) @@ -86,7 +85,7 @@ def _convert(val, pos): def ref_program(A, qB): - dtypeC = "int32" + dtypeC = T.int32 B = torch_convert(qB) C = torch.matmul(A.to(torch.float), B.T.to(torch.float)) C = C.to(torch.__getattribute__(dtypeC)) @@ -94,11 +93,10 @@ def ref_program(A, qB): def matmul_int8xint4(M, N, K, in_dtype, out_dtype, accum_dtype, num_bits=4, tune=False): - @tilelang.jit(out_idx=[2]) def kernel_func(block_M, block_N, block_K, num_stages, threads): num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 A_shape = (M, K) B_shape = (N, K // num_elems_per_byte) A_shared_shape = (block_M, block_K) @@ -109,12 +107,11 @@ def kernel_func(block_M, block_N, block_K, num_stages, threads): @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, storage_dtype), - Ct: T.Tensor((N, M), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, storage_dtype), + Ct: T.Tensor((N, M), out_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, storage_dtype) B_local = T.alloc_fragment(B_shared_shape, storage_dtype) @@ -123,10 +120,11 @@ def main( Ct_local = T.alloc_fragment((block_N, block_M), accum_dtype) Ct_shared = T.alloc_shared((block_N, block_M), out_dtype) - T.annotate_layout({ - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - Ct_shared: tilelang.layout.make_swizzled_layout(Ct_shared), - }) + T.annotate_layout( + { + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + } + ) T.clear(Ct_local) for k in T.Pipelined(K // block_K, num_stages=num_stages): @@ -143,8 +141,7 @@ def main( T.copy(B_dequantize_local, B_dequantize_prev_local) T.gemm(B_dequantize_prev_local, A_shared, Ct_local, transpose_B=True) T.copy(Ct_local, Ct_shared) - T.copy(Ct_shared, Ct[bx * block_N:(bx + 1) * block_N, - by * block_M:(by + 1) * block_M]) + T.copy(Ct_shared, Ct[bx * block_N : (bx + 1) * block_N, by * block_M : (by + 1) * block_M]) return main @@ -167,10 +164,10 @@ def kernel(block_M, block_N, block_K, num_stages, threads): def main(m=128, n=256, k=256, tune=False): total_flops = 2 * m * n * k - if (not tune): - kernel = matmul_int8xint4( - m, n, k, "int8", "int32", "int32", num_bits=4, tune=tune)( - block_M=32, block_N=32, block_K=128, num_stages=1, threads=128) + if not tune: + kernel = matmul_int8xint4(m, n, k, T.int8, T.int32, T.int32, num_bits=4, tune=tune)( + block_M=32, block_N=32, block_K=128, num_stages=1, threads=128 + ) profiler = kernel.get_profiler() profiler.assert_allclose(ref_program, rtol=1e-2, atol=1e-2) print("All checks pass.") @@ -179,7 +176,7 @@ def main(m=128, n=256, k=256, tune=False): print(f"Tilelang: {latency} ms") else: - best_result = matmul_int8xint4(m, n, k, "int8", "int32", "int32", num_bits=4, tune=tune) + best_result = matmul_int8xint4(m, n, k, T.int8, T.int32, T.int32, num_bits=4, tune=tune) best_latency = best_result.latency best_config = best_result.config print(f"Bset latency: {best_latency}") @@ -187,6 +184,14 @@ def main(m=128, n=256, k=256, tune=False): print(f"Best tflops: {total_flops / best_latency * 1e-9}") +def run_regression_perf(m=4096, n=4096, k=4096): + kernel = matmul_int8xint4(m, n, k, "int8", "int32", "int32", num_bits=4, tune=False)( + block_M=32, block_N=32, block_K=128, num_stages=1, threads=128 + ) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--m", type=int, default=512, help="Matrix dimension M") diff --git a/examples/dequantize_gemm/example_dequant_gemv_fp16xint4.py b/examples/dequantize_gemm/example_dequant_gemv_fp16xint4.py index d3e90ec932..43e97f9309 100644 --- a/examples/dequantize_gemm/example_dequant_gemv_fp16xint4.py +++ b/examples/dequantize_gemm/example_dequant_gemv_fp16xint4.py @@ -4,7 +4,8 @@ import torch from tilelang import DataType from tilelang.quantize import ( - _tir_packed_int_to_int_convert,) + _tir_packed_int_to_int_convert, +) @tilelang.jit @@ -16,7 +17,7 @@ def dequantize_gemv( out_dtype: str, accum_dtype: str, num_bits: int = 4, - storage_dtype: str = "int8", + storage_dtype: T.dtype = T.int8, source_format: str = "uint", n_partition: int = 4, reduce_thread: int = 32, @@ -26,11 +27,10 @@ def dequantize_gemv( group_size: int = -1, with_scaling: bool = False, ) -> Callable[..., Any]: - assert n_partition is not None, "n_partition must be provided" assert reduce_thread is not None, ( - "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMV" - "sch_outer_reduction_with_config is not implemented") + "reduce_thread must be provided currently, as related bitblas.gpu.gemv.GEMVsch_outer_reduction_with_config is not implemented" + ) assert trans_A is False, "Dequantize only implement for trans_A=False currently" assert trans_B is True, "Dequantize only implement for trans_B=TRue currently" @@ -51,7 +51,7 @@ def dequantize_gemv( C_shape = (M, N) dp4a_size = 4 - use_dp4a = in_dtype == "int8" and accum_dtype == "int32" + use_dp4a = in_dtype == T.int8 and accum_dtype == T.int32 import_source: Optional[str] = None func_name: str = "" @@ -81,12 +81,12 @@ def main( C: T.Tensor[C_shape, out_dtype], ): with T.Kernel( - T.ceildiv(N, n_partition), - M, - threads=(reduce_thread, n_partition), + T.ceildiv(N, n_partition), + M, + threads=(reduce_thread, n_partition), ) as ( - bx, - by, + bx, + by, ): A_local = T.alloc_local((micro_size_k,), in_dtype) B_quant_local = T.alloc_local([micro_size_k_compressed], storage_dtype) @@ -107,8 +107,7 @@ def main( for v in T.vectorized(micro_size_k_compressed): B_quant_local[v] = B[ bx * n_partition + ni, - ko * (reduce_thread * micro_size_k_compressed) + - kr * micro_size_k_compressed + v, + ko * (reduce_thread * micro_size_k_compressed) + kr * micro_size_k_compressed + v, ] if fast_decoding: @@ -120,10 +119,9 @@ def main( ) else: for ki in T.serial(micro_size_k): - B_dequantize_local[ki] = _tir_packed_int_to_int_convert( - storage_type, - storage_nbit)(num_bits, B_quant_local[ki // num_elems_per_byte], - ki % num_elems_per_byte, in_dtype) + B_dequantize_local[ki] = _tir_packed_int_to_int_convert(storage_type, storage_nbit)( + num_bits, B_quant_local[ki // num_elems_per_byte], ki % num_elems_per_byte, in_dtype + ) if use_dp4a: for ki in T.serial(micro_size_k // dp4a_size): @@ -137,9 +135,9 @@ def main( accum_res[0] += A_local[ki] * B_dequantize_local[ki] with T.attr( - T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), - "reduce_scope", - T.reinterpret(T.uint64(0), dtype="handle"), + T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), + "reduce_scope", + T.reinterpret(T.uint64(0), dtype="handle"), ): T.evaluate( T.tvm_thread_allreduce( @@ -149,7 +147,8 @@ def main( reduced_accum_res[0], kr, dtype="handle", - )) + ) + ) if kr == 0: C[by, bx * n_partition + ni] = reduced_accum_res[0] @@ -160,11 +159,11 @@ def main() -> None: M = 1 N = 1024 K = 1024 - in_dtype = "float16" - out_dtype = "float16" - accum_dtype = "float16" + in_dtype = T.float16 + out_dtype = T.float16 + accum_dtype = T.float16 num_bits = 4 - storage_dtype = "int8" + storage_dtype = T.int8 source_format = "uint" n_partition = 4 reduce_thread = 32 @@ -174,26 +173,39 @@ def main() -> None: group_size = -1 with_scaling = False - kernel = dequantize_gemv(M, N, K, in_dtype, out_dtype, accum_dtype, num_bits, storage_dtype, - source_format, n_partition, reduce_thread, fast_decoding, trans_A, - trans_B, group_size, with_scaling) + kernel = dequantize_gemv( + M, + N, + K, + in_dtype, + out_dtype, + accum_dtype, + num_bits, + storage_dtype, + source_format, + n_partition, + reduce_thread, + fast_decoding, + trans_A, + trans_B, + group_size, + with_scaling, + ) storage_nbit = int("".join(c for c in storage_dtype if c.isdigit())) num_elems_per_byte = storage_nbit // num_bits A = torch.rand(M, K, dtype=getattr(torch, in_dtype)).cuda() - qB = torch.randint( - 0, 127, (N, K // num_elems_per_byte), dtype=getattr(torch, storage_dtype)).cuda() + qB = torch.randint(0, 127, (N, K // num_elems_per_byte), dtype=getattr(torch, storage_dtype)).cuda() C = torch.zeros(M, N, dtype=getattr(torch, accum_dtype)).cuda() if fast_decoding: from tilelang.quantize.utils import interleave_weight + qB = interleave_weight(qB, num_bits, in_dtype) kernel(A, qB, C) # int4 reference - B = ( - torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, - dtype=torch.half).to(torch.half).to(A.device)) + B = torch.zeros(qB.shape[0], qB.shape[1] * 8 // 4, dtype=torch.half).to(torch.half).to(A.device) for j in range(B.shape[1]): B[:, j] = ((qB[:, j // 2] >> (4 * (j % 2))) & 0xF).to(torch.half) @@ -205,5 +217,62 @@ def main() -> None: torch.testing.assert_close(C, ref_c, atol=1e3, rtol=1e-1) +def run_regression_perf(): + M = 1 + N = 8192 + K = 8192 + in_dtype = "float16" + out_dtype = "float16" + accum_dtype = "float16" + num_bits = 4 + storage_dtype = "int8" + source_format = "uint" + n_partition = 4 + reduce_thread = 32 + fast_decoding = True + trans_A = False + trans_B = True + group_size = -1 + with_scaling = False + + kernel = dequantize_gemv( + M, + N, + K, + in_dtype, + out_dtype, + accum_dtype, + num_bits, + storage_dtype, + source_format, + n_partition, + reduce_thread, + fast_decoding, + trans_A, + trans_B, + group_size, + with_scaling, + ) + + storage_nbit = int("".join(c for c in storage_dtype if c.isdigit())) + num_elems_per_byte = storage_nbit // num_bits + A = torch.rand(M, K, dtype=getattr(torch, in_dtype)).cuda() + qB = torch.randint(0, 127, (N, K // num_elems_per_byte), dtype=getattr(torch, storage_dtype)).cuda() + C = torch.zeros(M, N, dtype=getattr(torch, accum_dtype)).cuda() + + if fast_decoding: + from tilelang.quantize.utils import interleave_weight + + qB = interleave_weight(qB, num_bits, in_dtype) + kernel(A, qB, C) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(A, qB, C) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/dequantize_gemm/example_dequant_groupedgemm_bf16_mxfp4_hopper.py b/examples/dequantize_gemm/example_dequant_groupedgemm_bf16_mxfp4_hopper.py index c4cf5fb505..6ee595921b 100644 --- a/examples/dequantize_gemm/example_dequant_groupedgemm_bf16_mxfp4_hopper.py +++ b/examples/dequantize_gemm/example_dequant_groupedgemm_bf16_mxfp4_hopper.py @@ -25,6 +25,7 @@ def get_configs(): List[dict]: A list of configuration dictionaries covering all combinations. """ import itertools + iter_params = dict( block_M=[128], block_N=[64, 128, 256], @@ -33,33 +34,33 @@ def get_configs(): threads=[128, 256, 512], split=[1], ) - return [{ - k: v for k, v in zip(iter_params, values) - } for values in itertools.product(*iter_params.values())] + return [{k: v for k, v in zip(iter_params, values)} for values in itertools.product(*iter_params.values())] @tilelang.autotune(configs=get_configs()) @tilelang.jit(out_idx=[-1]) -def matmul(M, - N, - K, - topk, - E, - padding_M, - in_dtype, - out_dtype, - accum_dtype, - source_format='uint', - num_bits=4, - scale_size=32, - fast_dequant=True, - with_bias=False, - block_M=128, - block_N=256, - block_K=128, - num_stages=2, - threads=256, - split=1): +def matmul( + M, + N, + K, + topk, + E, + padding_M, + in_dtype, + out_dtype, + accum_dtype, + source_format=T.uint32, + num_bits=4, + scale_size=32, + fast_dequant=True, + with_bias=False, + block_M=128, + block_N=256, + block_K=128, + num_stages=2, + threads=256, + split=1, +): """ Construct and return a grouped (Mixture-of-Experts) matrix-multiply TIR kernel that multiplies A (shape MxK) by a quantized, expert-grouped B (shape ExNxQK) and writes an output of shape (M, topk, N) in out_dtype. @@ -82,8 +83,8 @@ def matmul(M, topk (int): number of experts selected per token. E (int): number of experts. padding_M (int): padded number of tokens after grouping and block alignment. - in_dtype (str): element type of A (e.g., "bfloat16"). - out_dtype (str): output tensor element type (e.g., "bfloat16"). + in_dtype (str): element type of A (e.g., T.bfloat16). + out_dtype (str): output tensor element type (e.g., T.bfloat16). accum_dtype (str): accumulation type used for the inner GEMM. source_format (str, optional): format string passed to intrinsic selector (default "uint"). num_bits (int, optional): number of bits per quantized element in B (default 4). @@ -110,16 +111,17 @@ def matmul(M, """ num_elems_per_byte = 8 // num_bits - storage_dtype = "uint8" + storage_dtype = T.uint8 QK = K // num_elems_per_byte Block_QK = block_K // num_elems_per_byte A_shared_shape = (block_M, block_K) B_shared_shape = (block_N, Block_QK) - Bias_shared_shape = (block_N) + Bias_shared_shape = block_N B_dequantize_shared_shape = (block_N, block_K) assert K % (block_K * split) == 0 from tilelang.quantize import get_mxfp_intrin_group + # fast_dequant_bf16_fp4_twiddling mxfp_intrin_info = get_mxfp_intrin_group( out_dtype=in_dtype, @@ -135,7 +137,7 @@ def matmul(M, import_source = import_source # the dequant part is the same as in dequant_gemm - def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): + def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype=T.bfloat16): """ Return a TileLang macro that performs fast dequantization of twiddled FP4-packed data into BF16. The returned macro has signature (B_shared, B_dequantize_shared, Scale, k) and: @@ -145,12 +147,12 @@ def get_fast_dequant_twiddling_func(in_dtype="fp4", out_dtype="bfloat16"): - Writes the scaled BF16 results into B_dequantize_shared. Notes: - - This factory only supports in_dtype="fp4" and out_dtype="bfloat16". + - This factory only supports in_dtype="fp4" and out_dtype=T.bfloat16. - The macro depends on several names from the enclosing scope (e.g., import_source, func_name, DataType, num_elems_per_byte, storage_dtype, block_N, block_K, threads, scale_size); those must be defined and consistent with the kernel that will use the macro. - The macro issues a T.import_source and T.call_extern to invoke the external intrinsic; ensure the external implementation matching `func_name` is available at compilation/runtime. """ assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] # Some variables for dequantization in each thread MAX_TRANSACTION_SIZE_BITS = 128 @@ -221,19 +223,16 @@ def fast_dequant_bf16_fp4_twiddling(B_shared, B_dequantize_shared, Scale_shared, for v in T.vectorized(0, local_size): index = i * threads * local_size + tx * local_size + v - B_dequantize_shared[index // block_K, - index % block_K] = B_dequantize_local_thread[v] + B_dequantize_shared[index // block_K, index % block_K] = B_dequantize_local_thread[v] return fast_dequant_bf16_fp4_twiddling - def get_simple_dequant_func(in_dtype="fp4", out_dtype="bfloat16"): - + def get_simple_dequant_func(in_dtype="fp4", out_dtype=T.bfloat16): assert in_dtype in ["fp4"] - assert out_dtype in ["bfloat16"] + assert out_dtype in [T.bfloat16] @T.macro def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale_shared, k): - B_local = T.alloc_fragment(B_shared_shape, storage_dtype) B_dequantize_local = T.alloc_fragment(B_dequantize_shared_shape, out_dtype) @@ -244,8 +243,8 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale_shared, k): B_local[i, j // num_elems_per_byte], j % num_elems_per_byte, Scale_shared[ - i, k * block_K // scale_size + j // - scale_size], # Scale is the exponential part, within the representation of uint8 + i, k * block_K // scale_size + j // scale_size + ], # Scale is the exponential part, within the representation of uint8 dtype=out_dtype, ) * T.shift_left(1, (Scale_shared[i, k * block_K // scale_size + j // scale_size])) T.copy(B_dequantize_local, B_dequantize_shared) @@ -254,19 +253,17 @@ def simple_dequant_bf16_fp4(B_shared, B_dequantize_shared, Scale_shared, k): @T.prim_func def main( - A: T.Tensor((M, K), in_dtype), - B: T.Tensor((E, N, QK), storage_dtype), - Scale: T.Tensor((E, N, K // scale_size), storage_dtype), - Bias: T.Tensor((E, N), out_dtype), - # Add fusedmoe tensors - topk_weights: T.Tensor((M * topk), out_dtype), - sorted_token_ids: T.Tensor((padding_M), "int32"), - expert_ids: T.Tensor((padding_M // block_M), "int32"), - C: T.Tensor((M, topk, N), out_dtype), + A: T.Tensor((M, K), in_dtype), + B: T.Tensor((E, N, QK), storage_dtype), + Scale: T.Tensor((E, N, K // scale_size), storage_dtype), + Bias: T.Tensor((E, N), out_dtype), + # Add fusedmoe tensors + topk_weights: T.Tensor((M * topk), out_dtype), + sorted_token_ids: T.Tensor((padding_M), T.int32), + expert_ids: T.Tensor((padding_M // block_M), T.int32), + C: T.Tensor((M, topk, N), out_dtype), ): - - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(padding_M, block_M), threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(padding_M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, storage_dtype) B_dequantize_shared = T.alloc_shared(B_dequantize_shared_shape, in_dtype) @@ -274,23 +271,23 @@ def main( C_local = T.alloc_fragment((block_M, block_N), accum_dtype) C_shared = T.alloc_shared((block_M, block_N), out_dtype) topk_weights_shared = T.alloc_shared((block_M), out_dtype) - sorted_token_ids_shared = T.alloc_shared((block_M), "int32") - expert_id = T.alloc_local((1), "int32") # the expert id for the current block + sorted_token_ids_shared = T.alloc_shared((block_M), T.int32) + expert_id = T.alloc_local((1), T.int32) # the expert id for the current block # To use 1D TMA, the last dim of Scale_shared must have stride=1 # May use much more shared memory than necessary Scale_shared = T.alloc_shared((block_N, K // scale_size), storage_dtype) - T.annotate_layout({ - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - B_shared: tilelang.layout.make_swizzled_layout(B_shared), - C_shared: tilelang.layout.make_swizzled_layout(C_shared), - }) + T.annotate_layout( + { + B_shared: tilelang.layout.make_swizzled_layout(B_shared), + } + ) T.use_swizzle(10) if threads == 512: T.disable_warp_group_reg_alloc() - T.copy(sorted_token_ids[by * block_M:(by + 1) * block_M], sorted_token_ids_shared) + T.copy(sorted_token_ids[by * block_M : (by + 1) * block_M], sorted_token_ids_shared) expert_id[0] = expert_ids[by] # Get the topk weights of each token in the current block @@ -300,11 +297,11 @@ def main( # Get bias and scale based on the expert id if with_bias: - T.copy(Bias[expert_id[0], bx * block_N:(bx + 1) * block_N], Bias_shared) + T.copy(Bias[expert_id[0], bx * block_N : (bx + 1) * block_N], Bias_shared) else: T.clear(Bias_shared) - T.copy(Scale[expert_id[0], bx * block_N:(bx + 1) * block_N, :], Scale_shared) + T.copy(Scale[expert_id[0], bx * block_N : (bx + 1) * block_N, :], Scale_shared) for i, j in T.Parallel(block_M, block_N): C_local[i, j] = Bias_shared[j] @@ -317,14 +314,13 @@ def main( base = copy_i * threads * 16 + tx * 16 if sorted_token_ids_shared[base // block_K] != -1: for copy_j in T.vectorized(16): - A_shared[base // block_K, base % block_K + - copy_j] = A[sorted_token_ids_shared[base // block_K] // topk, - k * block_K + base % block_K + copy_j] + A_shared[base // block_K, base % block_K + copy_j] = A[ + sorted_token_ids_shared[base // block_K] // topk, k * block_K + base % block_K + copy_j + ] T.copy(B[expert_id[0], bx * block_N, k * block_K // num_elems_per_byte], B_shared) if fast_dequant: - get_fast_dequant_twiddling_func()(B_shared, B_dequantize_shared, Scale_shared, - k) + get_fast_dequant_twiddling_func()(B_shared, B_dequantize_shared, Scale_shared, k) else: get_simple_dequant_func()(B_shared, B_dequantize_shared, Scale_shared, k) @@ -338,16 +334,17 @@ def main( base = copy_i * threads * 16 + tx * 16 if sorted_token_ids_shared[base // block_N] != -1: for copy_j in T.vectorized(16): - C[sorted_token_ids_shared[base // block_N] // topk, - sorted_token_ids_shared[base // block_N] % topk, bx * block_N + - base % block_N + copy_j] = C_shared[base // block_N, - base % block_N + copy_j] + C[ + sorted_token_ids_shared[base // block_N] // topk, + sorted_token_ids_shared[base // block_N] % topk, + bx * block_N + base % block_N + copy_j, + ] = C_shared[base // block_N, base % block_N + copy_j] return main def ref_moe(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, block_M=256): - dtypeC = "bfloat16" + dtypeC = T.bfloat16 M, K = A.shape E, N, QK = qB.shape topk = topk_weights.shape[0] // M @@ -355,7 +352,7 @@ def ref_moe(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, bloc assert scale_size == 32 # MXFP4 # Initialize output tensor - C = torch.ones((M, topk, N), dtype=getattr(torch, dtypeC), device='cuda') + C = torch.ones((M, topk, N), dtype=getattr(torch, dtypeC), device="cuda") # Iterate over sorted_token_ids for idx in range(len(sorted_token_ids)): # padding_M @@ -370,14 +367,11 @@ def ref_moe(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, bloc # Dequantize the expert weights B = torch_convert_bit_twiddling(qB[expert_id]) # shape: (N, K) - B *= 2**( - Scale[expert_id][:, (torch.arange(B.shape[1], device=B.device) // scale_size)].to( - torch.bfloat16)) + B *= 2 ** (Scale[expert_id][:, (torch.arange(B.shape[1], device=B.device) // scale_size)].to(torch.bfloat16)) # Compute the output for this token-expert pair # token_embedding @ B.T + bias - output = torch.matmul(token_embedding.to(torch.bfloat16), B.T.to( - torch.bfloat16)) + Bias[expert_id] + output = torch.matmul(token_embedding.to(torch.bfloat16), B.T.to(torch.bfloat16)) + Bias[expert_id] output = output.to(torch.__getattribute__(dtypeC)) # Apply the topk weight @@ -391,14 +385,12 @@ def ref_moe(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, bloc def get_data(m, n, k, qk, scale_size, topk, E, block_M): - A = torch.empty(m, k, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1) - qB = torch.randint( - 0, 256, (E, n, qk), dtype=torch.uint8, - device='cuda') # Quantized weight tensor for E experts. - Scale = torch.randint(0, 8, (E, n, k // scale_size), dtype=torch.uint8, device='cuda') - Bias = torch.empty(E, n, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1) - - weights = torch.empty(m, E, dtype=torch.bfloat16, device='cuda').uniform_(-1, 1) + A = torch.empty(m, k, dtype=torch.bfloat16, device="cuda").uniform_(-1, 1) + qB = torch.randint(0, 256, (E, n, qk), dtype=torch.uint8, device="cuda") # Quantized weight tensor for E experts. + Scale = torch.randint(0, 8, (E, n, k // scale_size), dtype=torch.uint8, device="cuda") + Bias = torch.empty(E, n, dtype=torch.bfloat16, device="cuda").uniform_(-1, 1) + + weights = torch.empty(m, E, dtype=torch.bfloat16, device="cuda").uniform_(-1, 1) # topk_weights: Router weights for the top-k experts for each token. # Shape: (m, topk) # tokens_experts: A flattened tensor of expert assignments for each token. @@ -420,10 +412,7 @@ def get_data(m, n, k, qk, scale_size, topk, E, block_M): pad_len = ((cnt + block_M - 1) // block_M) * block_M - cnt if pad_len > 0: # -1 for padding (`M` instead in vLLM moe_align_block_size()) - group_token_ids = torch.cat([ - group_token_ids, - torch.full((pad_len,), -1, dtype=group_token_ids.dtype, device='cuda') - ]) + group_token_ids = torch.cat([group_token_ids, torch.full((pad_len,), -1, dtype=group_token_ids.dtype, device="cuda")]) padded_token_ids.append(group_token_ids) expert_ids.extend([eid] * ((cnt + block_M - 1) // block_M)) start = end @@ -431,21 +420,13 @@ def get_data(m, n, k, qk, scale_size, topk, E, block_M): # sorted_token_ids: The final flattened and padded tensor of token indices. sorted_token_ids = torch.cat(padded_token_ids, dim=0).to(torch.int32) # (padding_M,) # expert_ids: The final tensor of expert IDs corresponding to `sorted_token_ids`. - expert_ids = torch.tensor(expert_ids, dtype=torch.int32, device='cuda') # （padding_M,） + expert_ids = torch.tensor(expert_ids, dtype=torch.int32, device="cuda") # （padding_M,） padding_M = sorted_token_ids.shape[0] # padding_M: token number after padding return A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M -def main(m=256, - n=256, - k=256, - scale_size=32, - topk=4, - E=32, - fast_dequant=True, - with_bias=False, - tune=False): +def main(m=256, n=256, k=256, scale_size=32, topk=4, E=32, fast_dequant=True, with_bias=False, tune=False): # Tunable parameters block_M, block_N, block_K = 128, 256, 128 # noqa: F841 num_stages = 1 # noqa: F841 @@ -456,8 +437,7 @@ def main(m=256, num_bits = 4 num_elems_per_byte = 8 // num_bits qk = k // num_elems_per_byte - A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M = get_data( - m, n, k, qk, scale_size, topk, E, block_M) + A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M = get_data(m, n, k, qk, scale_size, topk, E, block_M) if tune: with set_autotune_inputs([A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids]): @@ -469,9 +449,9 @@ def main(m=256, topk, E, padding_M, - "bfloat16", - "bfloat16", - "float32", + T.bfloat16, + T.bfloat16, + T.float32, num_bits=num_bits, scale_size=scale_size, fast_dequant=fast_dequant, @@ -485,9 +465,9 @@ def main(m=256, topk, E, padding_M, - "bfloat16", - "bfloat16", - "float32", + T.bfloat16, + T.bfloat16, + T.float32, num_bits=num_bits, scale_size=scale_size, fast_dequant=fast_dequant, @@ -510,14 +490,11 @@ def main(m=256, expert_ids, ) - print('Tilelang kernel run finished.') + print("Tilelang kernel run finished.") - ref_output = ref_moe( - A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, - block_M=block_M) # Maybe a little bit slow... + ref_output = ref_moe(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, block_M=block_M) # Maybe a little bit slow... - latency = tilelang.profiler.do_bench( - lambda: kernel(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids), warmup=100) + latency = tilelang.profiler.do_bench(lambda: kernel(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids), warmup=100) print("Tilelang: {:.2f} ms".format(latency)) print("Tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) @@ -525,32 +502,72 @@ def main(m=256, max_val = diff.max() max_idx = diff.argmax() print(f"max abs diff: {max_val} at index: {max_idx}") - assert_similar( - output, ref_output, name="output", - eps=2e-5) # We care about the similarity rather than abs. difference + assert_similar(output, ref_output, name="output", eps=2e-5) # We care about the similarity rather than abs. difference print("All checks pass. ✅") +def run_regression_perf(m=4096, n=4096, k=4096, scale_size=32, topk=4, E=32, fast_dequant=True, with_bias=False, tune=False): + block_M, block_N, block_K = 128, 256, 128 + num_stages = 1 + threads = 512 + split = 1 + num_bits = 4 + num_elems_per_byte = 8 // num_bits + qk = k // num_elems_per_byte + A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids, padding_M = get_data(m, n, k, qk, scale_size, topk, E, block_M) + + if tune: + with set_autotune_inputs([A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids]): + kernel = matmul( + m, + n, + k, + topk, + E, + padding_M, + "bfloat16", + "bfloat16", + "float32", + num_bits=num_bits, + scale_size=scale_size, + fast_dequant=fast_dequant, + with_bias=with_bias, + ) + else: + kernel = matmul( + m, + n, + k, + topk, + E, + padding_M, + "bfloat16", + "bfloat16", + "float32", + num_bits=num_bits, + scale_size=scale_size, + fast_dequant=fast_dequant, + with_bias=with_bias, + block_M=block_M, + block_N=block_N, + block_K=block_K, + num_stages=num_stages, + threads=threads, + split=split, + ) + + return tilelang.profiler.do_bench(lambda: kernel(A, qB, Scale, Bias, topk_weights, sorted_token_ids, expert_ids), backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - "--M", type=int, default=16384, help="M") # From gpt-oss-20b MoE's first gemm + parser.add_argument("--M", type=int, default=16384, help="M") # From gpt-oss-20b MoE's first gemm parser.add_argument("--N", type=int, default=5760, help="N") parser.add_argument("--K", type=int, default=2944, help="K") parser.add_argument("--scale_size", type=int, default=32, help="scale size") - parser.add_argument( - "--topk", type=int, default=4, help="topk") # experts activated for each token + parser.add_argument("--topk", type=int, default=4, help="topk") # experts activated for each token parser.add_argument("--E", type=int, default=32, help="E") # number of experts parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - main( - args.M, - args.N, - args.K, - args.scale_size, - topk=args.topk, - E=args.E, - fast_dequant=True, - with_bias=True, - tune=args.tune) + main(args.M, args.N, args.K, args.scale_size, topk=args.topk, E=args.E, fast_dequant=True, with_bias=True, tune=args.tune) diff --git a/examples/dequantize_gemm/regression_example_dequantize_gemm.py b/examples/dequantize_gemm/regression_example_dequantize_gemm.py new file mode 100644 index 0000000000..4ab03784ff --- /dev/null +++ b/examples/dequantize_gemm/regression_example_dequantize_gemm.py @@ -0,0 +1,35 @@ +import tilelang.testing +import example_dequant_gemm_bf16_fp4_hopper +import example_dequant_gemm_bf16_mxfp4_hopper +import example_dequant_gemm_fp4_hopper +import example_dequant_gemm_w4a8 +import example_dequant_gemv_fp16xint4 +import example_dequant_groupedgemm_bf16_mxfp4_hopper + + +def regression_example_dequant_gemv_fp16xint4(): + tilelang.testing.process_func(example_dequant_gemv_fp16xint4.run_regression_perf) + + +def regression_example_dequant_gemm_fp4_hopper(): + tilelang.testing.process_func(example_dequant_gemm_fp4_hopper.run_regression_perf) + + +def regression_example_dequant_gemm_bf16_fp4_hopper(): + tilelang.testing.process_func(example_dequant_gemm_bf16_fp4_hopper.run_regression_perf) + + +def regression_example_dequant_gemm_bf16_mxfp4_hopper(): + tilelang.testing.process_func(example_dequant_gemm_bf16_mxfp4_hopper.run_regression_perf) + + +def regression_example_dequant_groupedgemm_bf16_mxfp4_hopper(): + tilelang.testing.process_func(example_dequant_groupedgemm_bf16_mxfp4_hopper.run_regression_perf) + + +def regression_example_dequant_gemm_w4a8(): + tilelang.testing.process_func(example_dequant_gemm_w4a8.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/dequantize_gemm/test_example_dequantize_gemm.py b/examples/dequantize_gemm/test_example_dequantize_gemm.py index 01bc40e6c9..a2f777222b 100644 --- a/examples/dequantize_gemm/test_example_dequantize_gemm.py +++ b/examples/dequantize_gemm/test_example_dequantize_gemm.py @@ -3,7 +3,6 @@ import example_dequant_gemv_fp16xint4 import example_dequant_gemm_fp4_hopper import example_dequant_gemm_bf16_mxfp4_hopper -import example_dequant_gemm_bf16_mxfp4_hopper_tma import example_dequant_groupedgemm_bf16_mxfp4_hopper import example_dequant_gemm_w4a8 @@ -25,12 +24,6 @@ def test_example_dequant_gemm_bf16_mxfp4_hopper(): example_dequant_gemm_bf16_mxfp4_hopper.main() -@tilelang.testing.requires_cuda -@tilelang.testing.requires_cuda_compute_version_ge(9, 0) -def test_example_dequant_gemm_bf16_mxfp4_hopper_tma(): - example_dequant_gemm_bf16_mxfp4_hopper_tma.main() - - @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_dequant_groupedgemm_bf16_mxfp4_hopper(): diff --git a/examples/dequantize_gemm/utils.py b/examples/dequantize_gemm/utils.py index 7134ae6aa0..da9ddb9f85 100644 --- a/examples/dequantize_gemm/utils.py +++ b/examples/dequantize_gemm/utils.py @@ -34,8 +34,7 @@ def _convert(val0, val1, pos) -> torch.bfloat16: mask1 = 0b1000000000000000 mask2 = 0b0000000110000000 mask3 = 0b0000000001000000 - bf16 = ((val_concat << 1) & mask1) | ((val_concat >> 3) & mask2) | ( - (val_concat >> 7) & mask3) + bf16 = ((val_concat << 1) & mask1) | ((val_concat >> 3) & mask2) | ((val_concat >> 7) & mask3) bf16_new = torch.tensor([bf16], dtype=torch.uint16, device=val0.device).view(torch.bfloat16) # Add bias for change from fp4 to bf16 bf16_new = bf16_new.item() * (2**126) @@ -104,5 +103,5 @@ def print_bit(name, val): val (torch.Tensor): A scalar PyTorch tensor (numeric) whose 32-bit binary representation will be shown. """ val_cpu = val.cpu().item() - binary_repr = f'{val_cpu:032b}' + binary_repr = f"{val_cpu:032b}" print(name, binary_repr) diff --git a/examples/distributed/README.md b/examples/distributed/README.md index e73ae0fac3..48cf85488b 100644 --- a/examples/distributed/README.md +++ b/examples/distributed/README.md @@ -2,7 +2,7 @@ This directory contains examples demonstrating distributed computing capabilities using TileLang. -For example, +For example, ``` ./tilelang/distributed/launch.sh examples/distributed/example_allgather.py ``` @@ -11,7 +11,7 @@ For example, Before running the examples, you need to build NVSHMEM library for device-side code generation. -```bash +```bash export NVSHMEM_SRC="your_custom_nvshmem_dir" # default to 3rdparty/nvshmem_src cd tilelang/distributed source build_nvshmem.sh diff --git a/examples/distributed/deepseek_deepep/buffer.py b/examples/distributed/deepseek_deepep/buffer.py index f281f19e30..71f7f3faf5 100644 --- a/examples/distributed/deepseek_deepep/buffer.py +++ b/examples/distributed/deepseek_deepep/buffer.py @@ -1,4 +1,4 @@ -""" The interface for DeepEP. """ +"""The interface for DeepEP.""" import torch import torch.distributed as dist @@ -27,14 +27,16 @@ class EPBuffer: num_sms: int = 20 symm_heap_size: int = 2**30 # size of the symm heap for allocators - def __init__(self, - group: dist.ProcessGroup, - num_nvl_bytes: int, - num_topk: int, - num_experts: int, - hidden: int, - dispatch_cfg: Optional[Config] = None, - combine_cfg: Optional[Config] = None): + def __init__( + self, + group: dist.ProcessGroup, + num_nvl_bytes: int, + num_topk: int, + num_experts: int, + hidden: int, + dispatch_cfg: Optional[Config] = None, + combine_cfg: Optional[Config] = None, + ): """ Initialize the communication buffer. @@ -70,7 +72,8 @@ def __init__(self, is_distributed=True, local_rank=self.rank, num_local_ranks=self.num_ranks, - group=group) + group=group, + ) self._pre_alloc_symm_buffers() self._prepare_counters() @@ -87,81 +90,70 @@ def _pre_alloc_symm_buffers(self): def _pre_alloc_symm_buffers_intranode(self): # barrier signal is always zeroed after each usage, so we can pre-init here - barrier_signal = tilelang.tensor((self.num_ranks), - dtype=torch.int32, - device='cuda', - allocator=self._allocator).zero_() - - per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), - dtype=torch.int32, - device='cuda', - allocator=self._allocator) - per_expert_buffer = tilelang.tensor((self.num_ranks, self.num_local_experts), - dtype=torch.int32, - device='cuda', - allocator=self._allocator) - - channel_start_offset = tilelang.tensor([self.num_channels, self.num_ranks], - dtype=torch.int32, - device='cuda', - allocator=self._allocator) - channel_end_offset = tilelang.tensor([self.num_channels, self.num_ranks], - dtype=torch.int32, - device='cuda', - allocator=self._allocator) - channel_head_idx = tilelang.tensor([self.num_channels, self.num_ranks], - dtype=torch.int32, - device='cuda', - allocator=self._allocator) - channel_tail_idx = tilelang.tensor([self.num_channels, self.num_ranks], - dtype=torch.int32, - device='cuda', - allocator=self._allocator) + barrier_signal = tilelang.tensor((self.num_ranks), dtype=torch.int32, device="cuda", allocator=self._allocator).zero_() + + per_rank_buffer = tilelang.tensor((self.num_ranks, self.num_ranks), dtype=torch.int32, device="cuda", allocator=self._allocator) + per_expert_buffer = tilelang.tensor( + (self.num_ranks, self.num_local_experts), dtype=torch.int32, device="cuda", allocator=self._allocator + ) + + channel_start_offset = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device="cuda", allocator=self._allocator + ) + channel_end_offset = tilelang.tensor( + [self.num_channels, self.num_ranks], dtype=torch.int32, device="cuda", allocator=self._allocator + ) + channel_head_idx = tilelang.tensor([self.num_channels, self.num_ranks], dtype=torch.int32, device="cuda", allocator=self._allocator) + channel_tail_idx = tilelang.tensor([self.num_channels, self.num_ranks], dtype=torch.int32, device="cuda", allocator=self._allocator) # NOTE: for each #ranks, dispatch and combine cfg have the same num_max_nvl_chunked_recv_tokens, so we can use the same buffer here - channel_x_buffers = tilelang.tensor([ - self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, - self.hidden - ], - dtype=torch.bfloat16, - device='cuda', - allocator=self._allocator) + channel_x_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.hidden], + dtype=torch.bfloat16, + device="cuda", + allocator=self._allocator, + ) channel_src_idx_buffers = tilelang.tensor( [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens], dtype=torch.int32, - device='cuda', - allocator=self._allocator) - channel_topk_idx_buffers = tilelang.tensor([ - self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, - self.num_topk - ], - dtype=torch.int64, - device='cuda', - allocator=self._allocator) - channel_topk_weights_buffers = tilelang.tensor([ - self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, - self.num_topk - ], - dtype=torch.float32, - device='cuda', - allocator=self._allocator) - - self._symm_buffers = (barrier_signal, per_rank_buffer, per_expert_buffer, - channel_start_offset, channel_end_offset, channel_head_idx, - channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, - channel_topk_idx_buffers, channel_topk_weights_buffers) + device="cuda", + allocator=self._allocator, + ) + channel_topk_idx_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], + dtype=torch.int64, + device="cuda", + allocator=self._allocator, + ) + channel_topk_weights_buffers = tilelang.tensor( + [self.num_channels, self.num_ranks, self.dispatch_cfg.num_max_nvl_chunked_recv_tokens, self.num_topk], + dtype=torch.float32, + device="cuda", + allocator=self._allocator, + ) + + self._symm_buffers = ( + barrier_signal, + per_rank_buffer, + per_expert_buffer, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_idx_buffers, + channel_topk_weights_buffers, + ) def _pre_alloc_symm_buffers_internode(self): raise NotImplementedError("internode is not supported yet") def _prepare_counters(self): - self._moe_recv_counter, self._moe_recv_counter_mapped = create_mapped_tensor([1], - torch.int32) - self._moe_recv_expert_counter, self._moe_recv_expert_counter_mapped = create_mapped_tensor( - [self.num_local_experts], torch.int32) + self._moe_recv_counter, self._moe_recv_counter_mapped = create_mapped_tensor([1], torch.int32) + self._moe_recv_expert_counter, self._moe_recv_expert_counter_mapped = create_mapped_tensor([self.num_local_experts], torch.int32) if self.num_ranks > 8: # internode - self._moe_recv_rdma_counter, self._moe_recv_rdma_counter_mapped = create_mapped_tensor( - [1], torch.int32) + self._moe_recv_rdma_counter, self._moe_recv_rdma_counter_mapped = create_mapped_tensor([1], torch.int32) @staticmethod def set_num_sms(num_sms: int): @@ -204,19 +196,20 @@ def get_dispatch_layout(self, topk_idx: torch.Tensor): num_tokens_per_expert: `[num_experts]` with `torch.int`, the number of tokens to be sent to each expert. is_token_in_rank: `[num_tokens, num_ranks]` with `torch.bool`, whether a token be sent to a rank. """ - num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout( - topk_idx, self.num_experts, self.num_ranks) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = get_dispatch_layout(topk_idx, self.num_experts, self.num_ranks) return num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank - def dispatch(self, - x: torch.Tensor, - handle: Optional[Tuple] = None, - num_tokens_per_rank: Optional[torch.Tensor] = None, - is_token_in_rank: Optional[torch.Tensor] = None, - num_tokens_per_expert: Optional[torch.Tensor] = None, - topk_idx: Optional[torch.Tensor] = None, - topk_weights: Optional[torch.Tensor] = None, - expert_alignment: int = 1): + def dispatch( + self, + x: torch.Tensor, + handle: Optional[Tuple] = None, + num_tokens_per_rank: Optional[torch.Tensor] = None, + is_token_in_rank: Optional[torch.Tensor] = None, + num_tokens_per_expert: Optional[torch.Tensor] = None, + topk_idx: Optional[torch.Tensor] = None, + topk_weights: Optional[torch.Tensor] = None, + expert_alignment: int = 1, + ): """ Dispatch tokens to different ranks, both intranode and internode settings are supported. Intranode kernels require all the ranks should be visible via NVLink. @@ -273,11 +266,24 @@ def dispatch(self, else: assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = intranode_dispatch( - self.rank, self._allocator, self._symm_buffers, self._moe_recv_counter, - self._moe_recv_expert_counter, self._moe_recv_counter_mapped, - self._moe_recv_expert_counter_mapped, x, self.dispatch_cfg, handle, - num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, - topk_weights, expert_alignment, self.comm_stream) + self.rank, + self._allocator, + self._symm_buffers, + self._moe_recv_counter, + self._moe_recv_expert_counter, + self._moe_recv_counter_mapped, + self._moe_recv_expert_counter_mapped, + x, + self.dispatch_cfg, + handle, + num_tokens_per_rank, + is_token_in_rank, + num_tokens_per_expert, + topk_idx, + topk_weights, + expert_alignment, + self.comm_stream, + ) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): @@ -298,7 +304,7 @@ def combine(self, x: torch.Tensor, handle: Tuple, topk_weights: torch.Tensor): recv_x: the reduced token from its dispatched ranks. recv_topk_weights: the reduced top-k weights from its dispatch ranks. """ - recv_x, recv_topk_weights = intranode_combine(self.rank, self._allocator, - self._symm_buffers, x, self.combine_cfg, - handle, topk_weights, self.comm_stream) + recv_x, recv_topk_weights = intranode_combine( + self.rank, self._allocator, self._symm_buffers, x, self.combine_cfg, handle, topk_weights, self.comm_stream + ) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/deepep.md b/examples/distributed/deepseek_deepep/deepep.md index d3cea90dc4..620baf4283 100644 --- a/examples/distributed/deepseek_deepep/deepep.md +++ b/examples/distributed/deepseek_deepep/deepep.md @@ -20,14 +20,12 @@ The table below shows a latency and bandwidth comparison for DeepEP and TileScal | DeepEP | 1.0045 | 328.97 | 1.1552 | 287.14 | | TileScale | 1.0720 | 308.25 | 1.0809 | 306.86 | - # Intra-node Introduction This example implements DeepEP’s intra‑node (NVLink) dispatch/combine using TileScale kernels. z The intra‑node path lives under `intranode/` and provides a minimal public API that mirrors DeepEP’s behavior for NVLink‑connected ranks. - ## Overview - Scope: intra‑node (NVLink) only; all ranks must be within one node and NVLink‑visible. @@ -35,7 +33,6 @@ The intra‑node path lives under `intranode/` and provides a minimal public API - Datatypes: inputs are `torch.bfloat16`; routing `topk_idx` is `torch.int64`; `topk_weights` is `torch.float32`. - Channels: each channel uses 2 SMs (send/recv). With default `num_sms=20`, there are `num_channels=10`. - ## Public API (intranode) - `intranode.get_dispatch_layout(topk_idx, num_experts, num_ranks)` @@ -63,7 +60,6 @@ Convenience wrapper used by examples/tests: - Exposes the interface for the functions above via methods: `get_dispatch_layout`, `dispatch`, `combine`. - Manages TileScale allocator, symmetric buffers, and recommended kernel configs. - ## Core Data Structures and Handle - `rank_prefix_matrix` (num_ranks × num_ranks): cumulative per‑rank token counts; used to compute global offsets for receiver writes. @@ -82,7 +78,6 @@ Dispatch returns the handle: `(rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head)` which can be reused for cached re‑dispatch and is required by the combine stage. - ## Kernel Responsibilities (high level) - Layout @@ -97,14 +92,12 @@ which can be reused for cached re‑dispatch and is required by the combine stag - `cached_notify_combine_kernel`: recalculates `send_head` expectations and zeros `channel_head_idx`/`channel_tail_idx` for the combine round. - `combine_kernel`: senders return expert outputs; receivers reduce by sum per token. `recv_topk_weights` is the sum of returned weights per token. Requires `hidden % 8 == 0` for vectorized access on the receiver side. - ## Configuration and Tuning - `utils.Config` provides recommended values for `num_max_nvl_chunked_send_tokens` and `num_max_nvl_chunked_recv_tokens` per `num_ranks`. These control per‑round trunk sizes and receiver buffer depth per channel. - `EPBuffer.num_sms` controls total SMs assigned to high‑throughput kernels. Channels = `num_sms // 2` (one send SM + one recv SM per channel). - `expert_alignment` pads per‑local‑expert MoE receive counters up to the specified multiple, which can be used to size per‑expert workspace. - ## Execution Flow (non‑cached) 1) Prepare group and buffers @@ -138,7 +131,6 @@ which can be reused for cached re‑dispatch and is required by the combine stag 6) Cached re‑dispatch (optional) - For repeated communication with the same layout, pass `handle` back into `EPBuffer.dispatch(x, handle, ...)` to skip layout/notify work and return only `recv_x`. - ## Usage Quick start (intra‑node test): @@ -174,7 +166,6 @@ recv_x, recv_topk_idx, recv_topk_weights, per_expert_counts, handle = buf.dispat reduced_x, reduced_weights = buf.combine(expert_out, handle, recv_topk_weights) ``` - ## Notes and Limits - Intra‑node only: ranks must be NVLink‑visible; current code asserts `num_ranks <= 8` and `num_experts % num_ranks == 0`. @@ -184,7 +175,6 @@ reduced_x, reduced_weights = buf.combine(expert_out, handle, recv_topk_weights) - Ensure `topk_idx` is contiguous, 2D, and `torch.int64`. - Set `TILELANG_USE_DISTRIBUTED=1` to enable TileScale’s distributed runtime. - ## Files - `intranode/__init__.py` — re‑exports `get_dispatch_layout`, `intranode_dispatch`, `intranode_combine`. @@ -194,7 +184,6 @@ reduced_x, reduced_weights = buf.combine(expert_out, handle, recv_topk_weights) - `buffer.py` — EPBuffer wrapper: allocator and symmetric buffers, public methods. - `utils.py` — recommended configs and MoE counter helpers. - ## Implementation Notes - Negative offset encoding: senders write channel start/end offsets as `-value-1` so that a zero token count is distinguishable from an uninitialized `0`. diff --git a/examples/distributed/deepseek_deepep/deepep_utils.py b/examples/distributed/deepseek_deepep/deepep_utils.py index 1294acb316..2886402950 100644 --- a/examples/distributed/deepseek_deepep/deepep_utils.py +++ b/examples/distributed/deepseek_deepep/deepep_utils.py @@ -30,7 +30,7 @@ def __post_init__(self): # 1 sm for send, 1 sm for recv in each channel @staticmethod - def get_dispatch_config(num_ranks: int) -> 'Config': + def get_dispatch_config(num_ranks: int) -> "Config": """ Get a recommended dispatch config. @@ -56,11 +56,11 @@ def get_dispatch_config(num_ranks: int) -> 'Config': 144: Config(num_sms, 32, 720, 12, 128), 160: Config(num_sms, 28, 720, 12, 128), } - assert num_ranks in config_map, f'Unsupported number of EP ranks: {num_ranks}' + assert num_ranks in config_map, f"Unsupported number of EP ranks: {num_ranks}" return config_map[num_ranks] @staticmethod - def get_combine_config(num_ranks: int) -> 'Config': + def get_combine_config(num_ranks: int) -> "Config": """ Get a recommended combine config. @@ -86,33 +86,31 @@ def get_combine_config(num_ranks: int) -> 'Config': 144: Config(num_sms, 2, 720, 8, 128), 160: Config(num_sms, 2, 720, 8, 128), } - assert num_ranks in config_map, f'Unsupported number of EP ranks: {num_ranks}' + assert num_ranks in config_map, f"Unsupported number of EP ranks: {num_ranks}" return config_map[num_ranks] # Only necessary in inter-node cases -def set_rdma_env_args(num_qps_per_rank: int = 24, - allow_nvlink_for_low_latency_mode: bool = True, - allow_mnnvl: bool = False): - os.environ['NVSHMEM_DISABLE_P2P'] = '0' if allow_nvlink_for_low_latency_mode else '1' - os.environ['NVSHMEM_IB_ENABLE_IBGDA'] = '1' - os.environ['NVSHMEM_IBGDA_NUM_RC_PER_PE'] = f'{num_qps_per_rank}' +def set_rdma_env_args(num_qps_per_rank: int = 24, allow_nvlink_for_low_latency_mode: bool = True, allow_mnnvl: bool = False): + os.environ["NVSHMEM_DISABLE_P2P"] = "0" if allow_nvlink_for_low_latency_mode else "1" + os.environ["NVSHMEM_IB_ENABLE_IBGDA"] = "1" + os.environ["NVSHMEM_IBGDA_NUM_RC_PER_PE"] = f"{num_qps_per_rank}" # Make sure QP depth is always larger than the number of on-flight WRs, so that we can skip WQ slot check - nvshmem_qp_depth = int(os.environ.get('NVSHMEM_QP_DEPTH', '1024')) - os.environ['NVSHMEM_QP_DEPTH'] = str(nvshmem_qp_depth) + nvshmem_qp_depth = int(os.environ.get("NVSHMEM_QP_DEPTH", "1024")) + os.environ["NVSHMEM_QP_DEPTH"] = str(nvshmem_qp_depth) # Reduce gpu memory usage # 6 default teams + 1 extra team - os.environ['NVSHMEM_MAX_TEAMS'] = '7' + os.environ["NVSHMEM_MAX_TEAMS"] = "7" # Disable NVLink SHArP - os.environ['NVSHMEM_DISABLE_NVLS'] = '1' + os.environ["NVSHMEM_DISABLE_NVLS"] = "1" # NOTES: NVSHMEM initialization requires at least 256 MiB - os.environ['NVSHMEM_CUMEM_GRANULARITY'] = f'{2 ** 29}' + os.environ["NVSHMEM_CUMEM_GRANULARITY"] = f"{2**29}" if not allow_mnnvl: # Disable multi-node NVLink detection - os.environ['NVSHMEM_DISABLE_MNNVL'] = '1' + os.environ["NVSHMEM_DISABLE_MNNVL"] = "1" def unpack_bias(bias: Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]): @@ -147,10 +145,10 @@ def gen_inputs(num_tokens: int, hidden: int, num_topk: int, num_experts: int, nu assert num_topk <= num_experts, "num_topk must be less than or equal to num_experts" assert num_experts % num_ranks == 0, "num_experts must be divisible by num_ranks" - x = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device='cuda') - scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device='cuda').abs() + 1 + x = torch.randn((num_tokens, hidden), dtype=torch.bfloat16, device="cuda") + scores = torch.randn((num_tokens, num_experts), dtype=torch.float32, device="cuda").abs() + 1 topk_idx = torch.topk(scores, num_topk, dim=-1, largest=True, sorted=False)[1] - topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device='cuda') + topk_weights = torch.randn((num_tokens, num_topk), dtype=torch.float32, device="cuda") rank_idx = topk_idx // (num_experts // num_ranks) rank_idx.masked_fill_(topk_idx == -1, -1) inplace_unique(rank_idx, num_ranks) @@ -192,7 +190,7 @@ def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): # Flush L2 cache with 256 MB data torch.cuda.synchronize() - cache = torch.empty(int(256e6 // 4), dtype=torch.int, device='cuda') + cache = torch.empty(int(256e6 // 4), dtype=torch.int, device="cuda") # Warmup for _ in range(warmup): @@ -248,8 +246,5 @@ def ep_bench(fn, warmup: int = 50, rep: int = 50, post_fn=None): """ ep_ext = load_inline( - name="ep_ext", - cpp_sources=_src, - functions=["wait_for_counters_ready"], - extra_cflags=["-O3", "-march=native"], - verbose=False) + name="ep_ext", cpp_sources=_src, functions=["wait_for_counters_ready"], extra_cflags=["-O3", "-march=native"], verbose=False +) diff --git a/examples/distributed/deepseek_deepep/intranode/combine.py b/examples/distributed/deepseek_deepep/intranode/combine.py index 17c5f175c7..aa95b9339a 100644 --- a/examples/distributed/deepseek_deepep/intranode/combine.py +++ b/examples/distributed/deepseek_deepep/intranode/combine.py @@ -11,7 +11,7 @@ import tilelang.language as T tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) @@ -19,15 +19,15 @@ def cached_notify_combine_kernel(num_ranks, num_sms): num_channels = num_sms // 2 threads = max(128, 32 * num_ranks) - num_recv_tokens = T.dynamic('num_recv_tokens') + num_recv_tokens = T.dynamic("num_recv_tokens") @T.prim_func def cached_notify_combine_main( - send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), - ##### symm buffers ##### - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), - barrier_signal: T.Tensor((num_ranks,), 'int32'), + send_head: T.Tensor([num_recv_tokens, num_ranks], "int32"), + ##### symm buffers ##### + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + barrier_signal: T.Tensor((num_ranks,), "int32"), ): with T.Kernel(num_channels + 1, threads=threads) as bx: tx = T.get_thread_binding() @@ -48,17 +48,15 @@ def cached_notify_combine_main( token_start_idx = T.min(tokens_per_channel * channel_id, num_recv_tokens) token_end_idx = T.min(token_start_idx + tokens_per_channel, num_recv_tokens) - last_head = T.alloc_var('int32', init=2**25) # a heuristic large number - # todo: tilelang doesn't support reverse loop, we simulate this - for i in T.serial(0, token_end_idx - token_start_idx, 32): - token_idx_tail = token_end_idx - i - 1 + last_head = T.alloc_var("int32", init=2**25) # a heuristic large number + for token_idx_tail in T.serial(token_end_idx - 1, token_start_idx - 1, -32): token_idx = token_idx_tail - lane_id - current_head = T.alloc_var('int32') + current_head = T.alloc_var("int32") if token_idx >= token_start_idx: T.ld(send_head[token_idx, rank_id], current_head, nc=True) else: current_head = -1 - expected_head = T.alloc_var('int32') + expected_head = T.alloc_var("int32") expected_head = 0 for j in T.serial(T.min(32, token_idx_tail - token_start_idx + 1)): head = T.tvm_warp_shuffle(-1, current_head, j, 32, 32) @@ -74,31 +72,27 @@ def cached_notify_combine_main( def cached_notify_combine( - num_ranks, - num_sms, - ##### symm buffers ##### - send_head: torch.Tensor, - channel_head_idx: torch.Tensor, - channel_tail_idx: torch.Tensor, - barrier_signal: torch.Tensor, - allocator, - comm_stream=None): + num_ranks, + num_sms, + ##### symm buffers ##### + send_head: torch.Tensor, + channel_head_idx: torch.Tensor, + channel_tail_idx: torch.Tensor, + barrier_signal: torch.Tensor, + allocator, +): kernel = cached_notify_combine_kernel(num_ranks, num_sms) - kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) + kernel.initialize(allocator=allocator) - kernel( - send_head, - channel_head_idx, - channel_tail_idx, - barrier_signal, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel(send_head, channel_head_idx, channel_tail_idx, barrier_signal) # reduce runtime overhead -@tilelang.jit(pass_configs={ - "tl.disable_tma_lower": True, # use TMA later - "tl.disable_warp_specialized": True -}) +@tilelang.jit( + pass_configs={ + "tl.disable_tma_lower": True, # use TMA later + "tl.disable_warp_specialized": True, + } +) def combine_kernel( num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens @@ -106,10 +100,10 @@ def combine_kernel( hidden, num_topk, num_sms, - dtype: str = 'bfloat16', + dtype: str = "bfloat16", ): - num_tokens = T.dynamic('num_tokens') - num_recv_tokens = T.dynamic('num_recv_tokens') + num_tokens = T.dynamic("num_tokens") + num_recv_tokens = T.dynamic("num_recv_tokens") num_channels = num_sms // 2 threads = 768 # 24 warps @@ -140,12 +134,9 @@ def combine_main( # symm buffers channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # reuse, already zeroed - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], - dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], - "int32"), - channel_topk_weights_buffers: T.Tensor( - [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: tx = T.get_thread_binding() @@ -158,85 +149,85 @@ def combine_main( send_warp_id_in_rank = warp_id // num_ranks # get tasks - rank_offset = T.if_then_else(send_rank_id > 0, rank_prefix_matrix[send_rank_id - 1, - rank], 0) + rank_offset = T.if_then_else(send_rank_id > 0, rank_prefix_matrix[send_rank_id - 1, rank], 0) num_rank_tokens = rank_prefix_matrix[send_rank_id, rank] - rank_offset channel_offset = channel_prefix_matrix[send_rank_id, responsible_channel] - num_channel_tokens = T.if_then_else( - responsible_channel == num_channels - 1, num_rank_tokens, - channel_prefix_matrix[send_rank_id, responsible_channel + 1]) - channel_offset + num_channel_tokens = ( + T.if_then_else( + responsible_channel == num_channels - 1, + num_rank_tokens, + channel_prefix_matrix[send_rank_id, responsible_channel + 1], + ) + - channel_offset + ) token_start_idx = rank_offset + channel_offset token_end_idx = token_start_idx + num_channel_tokens # Iterate over all tokens and send by trunk - current_channel_tail_idx = T.alloc_var('int32') + current_channel_tail_idx = T.alloc_var("int32") current_channel_tail_idx = 0 - token_idx = T.alloc_var('int32') + token_idx = T.alloc_var("int32") token_idx = token_start_idx - with T.While(token_idx < token_end_idx): + while token_idx < token_end_idx: # Check destination queue emptiness, or wait a buffer to be released (rare cases) num_round_tokens = T.min(num_max_send_tokens, token_end_idx - token_idx) - if T.elect_one_sync(): + if T.shuffle_elect(32): T.wait_ge( channel_head_idx[responsible_channel, rank], current_channel_tail_idx + num_round_tokens - num_recv_buffer_tokens, - peer=send_rank_id) + peer=send_rank_id, + ) T.sync_warp() # Send by trunk for i in T.serial(send_warp_id_in_rank, num_round_tokens, warps_per_rank): # Get an empty slot - dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = T.alloc_var("int32") dst_slot_idx = (current_channel_tail_idx + i) % num_recv_buffer_tokens # 1. copy data T.put_warp( T.address_of(x[token_idx + i, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, - 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), hidden, dst_pe=send_rank_id, unroll_factor=4, - enable_aggressive_vectorize=True) + enable_aggressive_vectorize=True, + ) # 2. send src idx - idx = T.alloc_var('int32') - if T.elect_one_sync(): + idx = T.alloc_var("int32") + if T.shuffle_elect(32): T.ld(src_idx[token_idx + i], idx, nc=True) - T.st( - channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], - idx, - dst_pe=send_rank_id) + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], idx, dst_pe=send_rank_id) # 3. send topk_weights if num_topk > 0 and lane_id < num_topk: - weight = T.alloc_var('float32') + weight = T.alloc_var("float32") T.ld(topk_weights[token_idx + i, lane_id], weight, nc=True) T.st( - channel_topk_weights_buffers[responsible_channel, rank, - dst_slot_idx, lane_id], - weight, - dst_pe=send_rank_id) + channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight, dst_pe=send_rank_id + ) token_idx += num_round_tokens current_channel_tail_idx += num_round_tokens # move tail index T.sync_threads(send_rank_id, threads_per_rank) - if send_warp_id_in_rank == 0 and T.elect_one_sync(): + if T.shuffle_elect(96): T.st( channel_tail_idx[responsible_channel, rank], current_channel_tail_idx, - scope='sys', - sem='release', - dst_pe=send_rank_id) + scope="sys", + sem="release", + dst_pe=send_rank_id, + ) else: # receiver - #? Why we must need scope='shared', not 'shared.dynamic' here? - warp_channel_head_idx = T.alloc_shared([warps, num_ranks], 'int32', scope='shared') - shared_channel_tail_idx = T.alloc_shared( - [32], 'int32', scope='shared') #! workaround for illegal address - warp_retired = T.alloc_shared([warps], 'bool', scope='shared') + # ? Why we must need scope='shared', not 'shared.dynamic' here? + warp_channel_head_idx = T.alloc_shared([warps, num_ranks], "int32", scope="shared") + shared_channel_tail_idx = T.alloc_shared([32], "int32", scope="shared") #! workaround for illegal address + warp_retired = T.alloc_shared([warps], "bool", scope="shared") if tx < warps: warp_retired[tx] = False if lane_id < num_ranks: @@ -246,84 +237,66 @@ def combine_main( T.sync_threads() if tx < 32: # one warp for moving the queue head - last_head = T.alloc_var('int32') + last_head = T.alloc_var("int32") last_head = 0 - with T.While(lane_id < num_ranks): + while lane_id < num_ranks: # check retired - retired = T.alloc_var('bool') + retired = T.alloc_var("bool") retired = True for i in T.serial(1, warps): retired = retired and warp_retired[i] if retired: - T.loop_break() + break # Update queue tail - new_tail = T.alloc_var('int32') - T.ld( - channel_tail_idx[responsible_channel, lane_id], - new_tail, - sem="acquire", - scope="sys") + new_tail = T.alloc_var("int32") + T.ld(channel_tail_idx[responsible_channel, lane_id], new_tail, sem="acquire", scope="sys") # Use release semantics to ensure receiver warps see the update - T.st( - shared_channel_tail_idx[lane_id], new_tail, sem="release", - scope="cta") # todo: weaker sem pair + T.st(shared_channel_tail_idx[lane_id], new_tail, sem="release", scope="cta") # todo: weaker sem pair # Update minimum head - min_head = T.alloc_var('int32') + min_head = T.alloc_var("int32") min_head = 2**31 - 1 # int32 max for i in T.serial(1, warps): if not warp_retired[i]: min_head = T.min(min_head, warp_channel_head_idx[i, lane_id]) if min_head != 2**31 - 1 and min_head > last_head: last_head = min_head - T.st( - channel_head_idx[responsible_channel, lane_id], - min_head, - sem="relaxed", - scope="sys") + T.st(channel_head_idx[responsible_channel, lane_id], min_head, sem="relaxed", scope="sys") else: # other warps for reduction # All lanes will use data buffer, but only rank lane will use `head/tail/src_idx` # The same tokens as the dispatch process - num_tokens_per_channel = T.truncdiv(num_recv_tokens + num_channels - 1, - num_channels) + num_tokens_per_channel = T.truncdiv(num_recv_tokens + num_channels - 1, num_channels) # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var - token_start_idx = T.min(num_tokens_per_channel * responsible_channel, - num_recv_tokens) + token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_recv_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_recv_tokens) # Iterate over all tokens and combine - for token_idx in T.serial(token_start_idx + warp_id - 1, token_end_idx, - warps - 1): + for token_idx in T.serial(token_start_idx + warp_id - 1, token_end_idx, warps - 1): # Read expected head - expected_head = T.alloc_var('int32') + expected_head = T.alloc_var("int32") expected_head = -1 if lane_id < num_ranks: T.ld(send_head[token_idx, lane_id], expected_head, nc=True) - condvar = T.alloc_var('int32') + condvar = T.alloc_var("int32") T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") - with T.While(T.warp_any(condvar <= expected_head and expected_head >= 0)): - T.ld( - shared_channel_tail_idx[lane_id], - condvar, - sem="acquire", - scope="cta") - T.loop_continue() + while T.warp_any(condvar <= expected_head and expected_head >= 0): + T.ld(shared_channel_tail_idx[lane_id], condvar, sem="acquire", scope="cta") + continue # can we simplify this ? T.sync_warp() # Broadcast current heads - num_topk_ranks = T.alloc_var('int32') + num_topk_ranks = T.alloc_var("int32") num_topk_ranks = 0 - topk_ranks = T.alloc_local([num_ranks], 'int32') - slot_indices = T.alloc_local([num_ranks], 'int32') + topk_ranks = T.alloc_local([num_ranks], "int32") + slot_indices = T.alloc_local([num_ranks], "int32") for i in T.serial(num_ranks): expected_head_i = T.tvm_warp_shuffle(-1, expected_head, i, 32, 32) if expected_head_i >= 0: - slot_indices[ - num_topk_ranks] = expected_head_i % num_recv_buffer_tokens + slot_indices[num_topk_ranks] = expected_head_i % num_recv_buffer_tokens topk_ranks[num_topk_ranks] = i num_topk_ranks += 1 @@ -337,10 +310,10 @@ def combine_main( for j in T.serial(num_topk_ranks): for k in T.vectorized(8): T.ld( - channel_x_buffers[responsible_channel, topk_ranks[j], - slot_indices[j], i * 8 + k], + channel_x_buffers[responsible_channel, topk_ranks[j], slot_indices[j], i * 8 + k], recv_value[j, k], - nc=True) + nc=True, + ) # todo: support bias @@ -349,47 +322,52 @@ def combine_main( for k in T.vectorized(8): values[k] += recv_value[j, k] for j in T.vectorized(8): - recv_x[token_idx, - i * 8 + j] = values[j] # todo: further vectorize this + recv_x[token_idx, i * 8 + j] = values[j] # todo: further vectorize this # Reduce topk_weights if lane_id < num_topk: - weight_sum = T.alloc_var('float32') + weight_sum = T.alloc_var("float32") weight_sum = 0 for i in T.serial(num_topk_ranks): - weight = T.alloc_var('float32') + weight = T.alloc_var("float32") T.ld( - channel_topk_weights_buffers[responsible_channel, topk_ranks[i], - slot_indices[i], lane_id], + channel_topk_weights_buffers[responsible_channel, topk_ranks[i], slot_indices[i], lane_id], weight, - nc=True) + nc=True, + ) weight_sum += weight recv_topk_weights[token_idx, lane_id] = weight_sum # Update head if lane_id < num_ranks: warp_channel_head_idx[warp_id, lane_id] = T.if_then_else( - expected_head < 0, -expected_head - 1, expected_head + 1) + expected_head < 0, -expected_head - 1, expected_head + 1 + ) # Retired T.sync_warp() - if T.elect_one_sync(): + if T.shuffle_elect(32): warp_retired[warp_id] = True return combine_main -def intranode_combine(rank: int, - allocator, - symm_buffers, - x, - config, - handle, - topk_weights, - comm_stream=None): +def intranode_combine(rank: int, allocator, symm_buffers, x, config, handle, topk_weights, comm_stream=None): assert handle is not None rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, _, send_head = handle - barrier_signal, _, _, _, _, channel_head_idx, channel_tail_idx, channel_x_buffers, channel_src_idx_buffers, _, channel_topk_weights_buffers = symm_buffers + ( + barrier_signal, + _, + _, + _, + _, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + _, + channel_topk_weights_buffers, + ) = symm_buffers # acquire_shapes _, hidden = x.shape @@ -398,19 +376,12 @@ def intranode_combine(rank: int, num_recv_tokens = send_head.shape[0] # notify combine - cached_notify_combine( - num_ranks, - config.num_sms, - send_head, - channel_head_idx, - channel_tail_idx, - barrier_signal, - allocator, - comm_stream=comm_stream) + with torch.cuda.stream(comm_stream): + cached_notify_combine(num_ranks, config.num_sms, send_head, channel_head_idx, channel_tail_idx, barrier_signal, allocator) # combine - recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') - recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device='cuda') + recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device="cuda") + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device="cuda") kernel = combine_kernel( num_ranks, @@ -419,25 +390,26 @@ def intranode_combine(rank: int, hidden, num_topk, config.num_sms, - dtype='bfloat16') - kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel( - rank, - x, - topk_weights, - recv_src_idx, - recv_x, - recv_topk_weights, - rank_prefix_matrix, - recv_channel_prefix_matrix, - send_head, - channel_head_idx, - channel_tail_idx, - channel_x_buffers, - channel_src_idx_buffers, - channel_topk_weights_buffers, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + dtype="bfloat16", + ) + with torch.cuda.stream(comm_stream): + kernel.initialize(allocator=allocator) + kernel( + rank, + x, + topk_weights, + recv_src_idx, + recv_x, + recv_topk_weights, + rank_prefix_matrix, + recv_channel_prefix_matrix, + send_head, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_weights_buffers, + ) # reduce runtime overhead compute_stream = torch.cuda.current_stream() compute_stream.wait_stream(comm_stream) return recv_x, recv_topk_weights diff --git a/examples/distributed/deepseek_deepep/intranode/dispatch.py b/examples/distributed/deepseek_deepep/intranode/dispatch.py index 0811a4eb17..83912a0899 100644 --- a/examples/distributed/deepseek_deepep/intranode/dispatch.py +++ b/examples/distributed/deepseek_deepep/intranode/dispatch.py @@ -11,9 +11,10 @@ import tilelang.language as T from typing import Optional, Tuple from deepep_utils import Config, ep_ext # noqa: F403 +import tvm_ffi # tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log # notify_dispatch is responsible for: @@ -30,26 +31,26 @@ def notify_dispatch_kernel( num_local_experts = num_experts // num_ranks num_warps = threads // 32 - num_tokens = T.dynamic('num_tokens') + num_tokens = T.dynamic("num_tokens") @T.prim_func def notify_dispatch_main( - rank: T.int32, - num_tokens_per_rank: T.Tensor((num_ranks,), 'int32'), - num_tokens_per_expert: T.Tensor((num_experts,), 'int32'), - is_token_in_rank: T.Tensor((num_tokens, num_ranks), 'bool'), - moe_recv_counter_mapped: T.Tensor((1,), 'int32'), - moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), 'int32'), - per_rank_buffer: T.Tensor((num_ranks, num_ranks), 'int32'), - per_expert_buffer: T.Tensor((num_ranks, num_local_experts), 'int32'), - barrier_signal: T.Tensor((num_ranks,), 'int32'), - rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), 'int32'), - channel_prefix_matrix: T.Tensor((num_ranks, num_channels), 'int32'), - # 4 symm buffers to be zeroed - channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + rank: T.int32, + num_tokens_per_rank: T.Tensor((num_ranks,), "int32"), + num_tokens_per_expert: T.Tensor((num_experts,), "int32"), + is_token_in_rank: T.Tensor((num_tokens, num_ranks), "bool"), + moe_recv_counter_mapped: T.Tensor((1,), "int32"), + moe_recv_expert_counter_mapped: T.Tensor((num_local_experts,), "int32"), + per_rank_buffer: T.Tensor((num_ranks, num_ranks), "int32"), + per_expert_buffer: T.Tensor((num_ranks, num_local_experts), "int32"), + barrier_signal: T.Tensor((num_ranks,), "int32"), + rank_prefix_matrix: T.Tensor((num_ranks, num_ranks), "int32"), + channel_prefix_matrix: T.Tensor((num_ranks, num_channels), "int32"), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), ): with T.Kernel(num_ranks + 1, threads=threads) as bx: tx = T.get_thread_binding() @@ -64,10 +65,7 @@ def notify_dispatch_main( if tx < num_ranks: T.st(per_rank_buffer[rank, tx], num_tokens_per_rank[tx], dst_pe=tx) for i in T.serial(num_local_experts): - T.st( - per_expert_buffer[rank, i], - num_tokens_per_expert[tx * num_local_experts + i], - dst_pe=tx) + T.st(per_expert_buffer[rank, i], num_tokens_per_expert[tx * num_local_experts + i], dst_pe=tx) T.barrier_blocks(barrier_signal) @@ -80,7 +78,7 @@ def notify_dispatch_main( # Sum per-expert cnts if tx < num_local_experts: - sum = T.alloc_local([1], 'int32') + sum = T.alloc_local([1], "int32") sum[0] = 0 for i in T.serial(0, num_ranks): sum[0] += per_expert_buffer[i, tx] @@ -106,12 +104,12 @@ def notify_dispatch_main( # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var token_start_idx = T.min(num_tokens_per_channel * channel_id, num_tokens) token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) - cnt = T.alloc_var('int32') + cnt = T.alloc_var("int32") cnt = 0 for i in T.serial(token_start_idx + lane_id, token_end_idx, 32): cnt += is_token_in_rank[i, dst_rank] cnt = T.warp_reduce_sum(cnt) - if T.elect_one_sync(): + if T.shuffle_elect(32): channel_prefix_matrix[dst_rank, channel_id] = cnt T.sync_threads() @@ -149,7 +147,7 @@ def notify_dispatch( channel_tail_idx: torch.Tensor, # allocator allocator, - comm_stream=None, + comm_stream: torch.cuda.Stream = None, ): kernel = notify_dispatch_kernel( num_ranks, @@ -159,8 +157,8 @@ def notify_dispatch( ) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device='cuda') - channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device='cuda') + rank_prefix_matrix = torch.empty([num_ranks, num_ranks], dtype=torch.int32, device="cuda") + channel_prefix_matrix = torch.empty([num_ranks, num_channels], dtype=torch.int32, device="cuda") # clear buffers and counters moe_recv_counter.fill_(-1) @@ -182,27 +180,22 @@ def notify_dispatch( channel_end_offset, channel_head_idx, channel_tail_idx, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True # reduce runtime overhead ) - - num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready( - moe_recv_counter, moe_recv_expert_counter) + num_recv_tokens, num_recv_tokens_per_expert_list = ep_ext.wait_for_counters_ready(moe_recv_counter, moe_recv_expert_counter) return num_recv_tokens, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix # cached_notify_dispatch only needs to clear symm buffers @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) def cached_notify_dispatch_kernel(num_ranks: int, num_channels: int): - @T.prim_func def cached_notify_dispatch_main( - barrier_signal: T.Tensor((num_ranks,), 'int32'), - # 4 symm buffers to be zeroed - channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), - channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), - channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), + barrier_signal: T.Tensor((num_ranks,), "int32"), + # 4 symm buffers to be zeroed + channel_start_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_end_offset: T.Tensor([num_channels, num_ranks], "int32"), + channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), + channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), ): with T.Kernel(1, threads=128): T.sync_blocks(barrier_signal) @@ -232,22 +225,23 @@ def cached_notify_dispatch( comm_stream=None, ): kernel = cached_notify_dispatch_kernel(num_ranks, num_channels) - kernel.initialize( - allocator=allocator, stream=comm_stream.cuda_stream) # we still comm on barrier_signal - kernel( - barrier_signal, - channel_start_offset, - channel_end_offset, - channel_head_idx, - channel_tail_idx, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) + with torch.cuda.stream(comm_stream): + kernel( + barrier_signal, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + ) -@tilelang.jit(pass_configs={ - "tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True -}) +@tilelang.jit( + pass_configs={ + "tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True, + } +) def dispatch_kernel( num_ranks, num_max_send_tokens, # config.num_max_nvl_chunked_send_tokens @@ -256,7 +250,7 @@ def dispatch_kernel( num_topk, num_experts, num_sms, - dtype: str = 'bfloat16', + dtype: str = "bfloat16", ): threads = 768 # 24 warps TMABytesPerWarp = 8192 @@ -269,17 +263,17 @@ def dispatch_kernel( num_warps = threads // 32 # 24 num_warps_per_rank = num_warps // num_ranks # 3 - num_tokens = T.dynamic('num_tokens') - num_recv_tokens = T.dynamic('num_recv_tokens') + num_tokens = T.dynamic("num_tokens") + num_recv_tokens = T.dynamic("num_recv_tokens") @T.prim_func def dispatch_main( rank: T.int32, # output recv_x: T.Tensor((num_recv_tokens, hidden), dtype), - recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), - recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), 'int64'), - recv_topk_weights: T.Tensor((num_recv_tokens, num_topk), 'float'), + recv_src_idx: T.Tensor((num_recv_tokens,), "int32"), + recv_topk_idx: T.Tensor((num_recv_tokens, num_topk), "int64"), + recv_topk_weights: T.Tensor((num_recv_tokens, num_topk), "float"), recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), send_head: T.Tensor([num_tokens, num_ranks], "int32"), # input @@ -297,14 +291,10 @@ def dispatch_main( channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # channel data buffers, stored on the receiver side - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], - dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], - "int32"), - channel_topk_idx_buffers: T.Tensor( - [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int64"), - channel_topk_weights_buffers: T.Tensor( - [num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), + channel_topk_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "int64"), + channel_topk_weights_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_topk], "float32"), # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: @@ -318,65 +308,53 @@ def dispatch_main( # send offset by `-value-1` e.g. 0->-1, 1->-2 # this is for distinguishing zero tokens - if send_warp_id_in_rank == 0 and T.elect_one_sync(): - value = T.alloc_var('int32') - value = T.if_then_else( - responsible_channel > 0, channel_prefix_matrix[responsible_rank, - responsible_channel - 1], 0) - T.st( - channel_start_offset[responsible_channel, rank], - -value - 1, - scope='sys', - sem='relaxed', - dst_pe=responsible_rank) + if send_warp_id_in_rank == 0 and T.shuffle_elect(32): + value = T.alloc_var("int32") + value = T.if_then_else(responsible_channel > 0, channel_prefix_matrix[responsible_rank, responsible_channel - 1], 0) + T.st(channel_start_offset[responsible_channel, rank], -value - 1, scope="sys", sem="relaxed", dst_pe=responsible_rank) value = channel_prefix_matrix[responsible_rank, responsible_channel] - T.st( - channel_end_offset[responsible_channel, rank], - -value - 1, - scope='sys', - sem='relaxed', - dst_pe=responsible_rank) + T.st(channel_end_offset[responsible_channel, rank], -value - 1, scope="sys", sem="relaxed", dst_pe=responsible_rank) T.sync_warp() # get task num_tokens_per_channel = T.truncdiv(num_tokens + num_channels - 1, num_channels) # todo: this is a workaround, as TVM has a bug when calculating safe ceildiv for tir.Var - token_start_idx = T.alloc_var('int32') + token_start_idx = T.alloc_var("int32") token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) - token_end_idx = T.alloc_var('int32') + token_end_idx = T.alloc_var("int32") token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) # sender mainloop: iterate over all tokens and send by trunk - cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = T.alloc_var("int32") cached_channel_tail_idx = 0 - token_idx = T.alloc_var('int32') + token_idx = T.alloc_var("int32") token_idx = token_start_idx - with T.While(token_idx < token_end_idx): - if T.elect_one_sync(): + while token_idx < token_end_idx: + if T.shuffle_elect(32): T.wait_ge( channel_head_idx[responsible_channel, rank], num_max_send_tokens + cached_channel_tail_idx - num_recv_buffer_tokens, - responsible_rank) + responsible_rank, + ) T.sync_warp() - chunk_token_idx = T.alloc_var('int32') + chunk_token_idx = T.alloc_var("int32") chunk_token_idx = 0 while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: # for the same token, the warp assigned to save `send_head` may be different from the warp # assigned to send the following data - if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync( - ): + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.shuffle_elect(32): send_head[token_idx, responsible_rank] = T.if_then_else( - is_token_in_rank[token_idx, responsible_rank], - cached_channel_tail_idx, -1) + is_token_in_rank[token_idx, responsible_rank], cached_channel_tail_idx, -1 + ) # skip if not selected if not is_token_in_rank[token_idx, responsible_rank]: token_idx += 1 - T.loop_continue() + continue # selected, get an empty slot - dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = T.alloc_var("int32") dst_slot_idx = cached_channel_tail_idx % num_recv_buffer_tokens cached_channel_tail_idx += 1 if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: @@ -384,20 +362,16 @@ def dispatch_main( # 1. copy data T.put_warp( T.address_of(x[token_idx, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, - dst_slot_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), hidden, dst_pe=responsible_rank, unroll_factor=4, - enable_aggressive_vectorize=True) + enable_aggressive_vectorize=True, + ) # 2. copy src idx - if T.elect_one_sync(): - T.st( - channel_src_idx_buffers[responsible_channel, rank, - dst_slot_idx], - token_idx, - dst_pe=responsible_rank) + if T.shuffle_elect(32): + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, dst_pe=responsible_rank) # 3. copy `topk_idx` and `topk_weights` with transformed index if lane_id < num_topk: @@ -405,26 +379,26 @@ def dispatch_main( recv_expert_begin = responsible_rank * num_local_experts recv_expert_end = recv_expert_begin + num_local_experts - idx_value = T.alloc_var('int64') + idx_value = T.alloc_var("int64") T.ld(topk_idx[token_idx, lane_id], idx_value, nc=True) idx_value = T.if_then_else( - recv_expert_begin <= T.cast(idx_value, 'int32') < - recv_expert_end, idx_value - recv_expert_begin, -1) + recv_expert_begin <= T.cast(idx_value, "int32") < recv_expert_end, idx_value - recv_expert_begin, -1 + ) T.st( - channel_topk_idx_buffers[responsible_channel, rank, - dst_slot_idx, lane_id], + channel_topk_idx_buffers[responsible_channel, rank, dst_slot_idx, lane_id], idx_value, - dst_pe=responsible_rank) + dst_pe=responsible_rank, + ) # topk_weights - weight_value = T.alloc_var('float32') + weight_value = T.alloc_var("float32") T.ld(topk_weights[token_idx, lane_id], weight_value, nc=True) weight_value = T.if_then_else(idx_value >= 0, weight_value, 0) T.st( - channel_topk_weights_buffers[responsible_channel, rank, - dst_slot_idx, lane_id], + channel_topk_weights_buffers[responsible_channel, rank, dst_slot_idx, lane_id], weight_value, - dst_pe=responsible_rank) + dst_pe=responsible_rank, + ) # 4. copy scale (support fp8 later) @@ -434,36 +408,30 @@ def dispatch_main( # move tail index # here all warps should share the same new tail T.sync_threads(responsible_rank, num_threads_per_rank) - if send_warp_id_in_rank == 0 and T.elect_one_sync(): + if send_warp_id_in_rank == 0 and T.shuffle_elect(32): T.st( channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, - scope='sys', - sem='release', - dst_pe=responsible_rank) + scope="sys", + sem="release", + dst_pe=responsible_rank, + ) else: # receiver recv_thread_id_in_rank = tx % num_threads_per_rank recv_warp_id_in_rank = recv_thread_id_in_rank // 32 # calculate offset first - rank_offset = T.if_then_else(responsible_rank > 0, - rank_prefix_matrix[responsible_rank - 1, rank], 0) + rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank - 1, rank], 0) # receive channel offset - total_offset = T.alloc_var('int32') - num_tokens_to_recv = T.alloc_var('int32') - if T.elect_one_sync(): + total_offset = T.alloc_var("int32") + num_tokens_to_recv = T.alloc_var("int32") + if T.shuffle_elect(32): T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) - T.ld( - channel_start_offset[responsible_channel, responsible_rank], - total_offset, - sem='volatile') + T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem="volatile") T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) - T.ld( - channel_end_offset[responsible_channel, responsible_rank], - num_tokens_to_recv, - sem='volatile') + T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem="volatile") total_offset = -total_offset - 1 num_tokens_to_recv = -num_tokens_to_recv - 1 if recv_warp_id_in_rank == 0: @@ -474,24 +442,20 @@ def dispatch_main( num_tokens_to_recv = T.tvm_warp_shuffle(-1, num_tokens_to_recv, 0, 32, 32) # Shared tail indices for different warps - shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') + shared_channel_tail_idx = T.alloc_shared([num_ranks], "int32") - cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = T.alloc_var("int32") cached_channel_head_idx = 0 - cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = T.alloc_var("int32") cached_channel_tail_idx = 0 - with T.While(num_tokens_to_recv > 0): - with T.While(recv_thread_id_in_rank == 0): - T.ld( - channel_tail_idx[responsible_channel, responsible_rank], - cached_channel_tail_idx, - sem='acquire', - scope='sys') + while num_tokens_to_recv > 0: + while recv_thread_id_in_rank == 0: + T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem="acquire", scope="sys") # read to copy if cached_channel_head_idx != cached_channel_tail_idx: shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx - T.loop_break() + break # sync queue tail T.sync_threads(responsible_rank, num_threads_per_rank) @@ -500,48 +464,42 @@ def dispatch_main( # copy data # 1. recv x num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx - for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, - num_warps_per_rank): - token_idx_in_buffer = (cached_channel_head_idx + - chunk_idx) % num_recv_buffer_tokens + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens # T.copy(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, :], recv_x[total_offset+chunk_idx, :]) # todo: add ld_nc and st_na #! T.copy will cause layout inference error T.put_warp( - T.address_of(channel_x_buffers[responsible_channel, responsible_rank, - token_idx_in_buffer, 0]), + T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), T.address_of(recv_x[total_offset + chunk_idx, 0]), hidden, -1, 5, - enable_aggressive_vectorize=True) + enable_aggressive_vectorize=True, + ) # 2. recv src_idx - for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, - cached_channel_tail_idx, num_threads_per_rank): - local_src_idx = T.alloc_var('int32') + for chunk_idx in T.serial( + cached_channel_head_idx + recv_thread_id_in_rank, cached_channel_tail_idx, num_threads_per_rank + ): + local_src_idx = T.alloc_var("int32") T.ld( - channel_src_idx_buffers[responsible_channel, responsible_rank, - chunk_idx % num_recv_buffer_tokens], + channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, - nc=True) - recv_src_idx[total_offset + chunk_idx - - cached_channel_head_idx] = local_src_idx + nc=True, + ) + recv_src_idx[total_offset + chunk_idx - cached_channel_head_idx] = local_src_idx # 3. recv topk_idx and topk_weights - for idx in T.serial(recv_thread_id_in_rank, num_cur_recv_tokens * num_topk, - num_threads_per_rank): + for idx in T.serial(recv_thread_id_in_rank, num_cur_recv_tokens * num_topk, num_threads_per_rank): chunk_idx = idx // num_topk token_topk_idx = idx % num_topk - token_idx_in_buffer = (cached_channel_head_idx + - chunk_idx) % num_recv_buffer_tokens - recv_topk_idx[total_offset + chunk_idx, - token_topk_idx] = channel_topk_idx_buffers[ - responsible_channel, responsible_rank, - token_idx_in_buffer, token_topk_idx] - recv_topk_weights[total_offset + chunk_idx, - token_topk_idx] = channel_topk_weights_buffers[ - responsible_channel, responsible_rank, - token_idx_in_buffer, token_topk_idx] + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens + recv_topk_idx[total_offset + chunk_idx, token_topk_idx] = channel_topk_idx_buffers[ + responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx + ] + recv_topk_weights[total_offset + chunk_idx, token_topk_idx] = channel_topk_weights_buffers[ + responsible_channel, responsible_rank, token_idx_in_buffer, token_topk_idx + ] # 4. recv scale (support fp8 later) @@ -549,12 +507,8 @@ def dispatch_main( cached_channel_head_idx += num_cur_recv_tokens total_offset += num_cur_recv_tokens T.sync_threads(responsible_rank, num_threads_per_rank) - if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): - T.st( - channel_head_idx[responsible_channel, responsible_rank], - cached_channel_head_idx, - scope='sys', - sem='relaxed') + if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.shuffle_elect(32): + T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, scope="sys", sem="relaxed") # Exit num_tokens_to_recv -= num_cur_recv_tokens @@ -562,10 +516,12 @@ def dispatch_main( return dispatch_main -@tilelang.jit(pass_configs={ - "tl.disable_tma_lower": True, # enable TMA later - "tl.disable_warp_specialized": True -}) +@tilelang.jit( + pass_configs={ + "tl.disable_tma_lower": True, # enable TMA later + "tl.disable_warp_specialized": True, + } +) def cached_dispatch_kernel( num_ranks, num_tokens, @@ -573,7 +529,7 @@ def cached_dispatch_kernel( num_recv_buffer_tokens, # config.num_max_nvl_chunked_recv_tokens hidden, num_sms, - dtype: str = 'bfloat16', + dtype: str = "bfloat16", ): threads = 768 # 24 warps TMABytesPerWarp = 8192 @@ -585,14 +541,14 @@ def cached_dispatch_kernel( num_warps = threads // 32 # 24 num_warps_per_rank = num_warps // num_ranks # 3 - num_recv_tokens = T.dynamic('num_recv_tokens') + num_recv_tokens = T.dynamic("num_recv_tokens") @T.prim_func def cached_dispatch_main( rank: T.int32, # output recv_x: T.Tensor((num_recv_tokens, hidden), dtype), - recv_src_idx: T.Tensor((num_recv_tokens,), 'int32'), + recv_src_idx: T.Tensor((num_recv_tokens,), "int32"), recv_channel_offset: T.Tensor([num_ranks, num_channels], "int32"), send_head: T.Tensor([num_tokens, num_ranks], "int32"), # input @@ -608,10 +564,8 @@ def cached_dispatch_main( channel_head_idx: T.Tensor([num_channels, num_ranks], "int32"), channel_tail_idx: T.Tensor([num_channels, num_ranks], "int32"), # channel data buffers, stored on the receiver side - channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], - dtype), - channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], - "int32"), + channel_x_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, hidden], dtype), + channel_src_idx_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens], "int32"), # channel_x_scales_buffers: T.Tensor([num_channels, num_ranks, num_recv_buffer_tokens, num_scales], "float32"), ): with T.Kernel(num_sms, threads=threads) as bx: @@ -624,65 +578,52 @@ def cached_dispatch_main( # send offset by `-value-1` e.g. 0->-1, 1->-2 # this is for distinguishing zero tokens - if send_warp_id_in_rank == 0 and T.elect_one_sync(): - value = T.alloc_var('int32') - value = T.if_then_else( - responsible_channel > 0, channel_prefix_matrix[responsible_rank, - responsible_channel - 1], 0) - T.st( - channel_start_offset[responsible_channel, rank], - -value - 1, - scope='sys', - sem='relaxed', - dst_pe=responsible_rank) + if send_warp_id_in_rank == 0 and T.shuffle_elect(32): + value = T.alloc_var("int32") + value = T.if_then_else(responsible_channel > 0, channel_prefix_matrix[responsible_rank, responsible_channel - 1], 0) + T.st(channel_start_offset[responsible_channel, rank], -value - 1, scope="sys", sem="relaxed", dst_pe=responsible_rank) value = channel_prefix_matrix[responsible_rank, responsible_channel] - T.st( - channel_end_offset[responsible_channel, rank], - -value - 1, - scope='sys', - sem='relaxed', - dst_pe=responsible_rank) + T.st(channel_end_offset[responsible_channel, rank], -value - 1, scope="sys", sem="relaxed", dst_pe=responsible_rank) T.sync_warp() # get task - num_tokens_per_channel = T.alloc_var( - 'int32', init=T.ceildiv(num_tokens, num_channels)) - token_start_idx = T.alloc_var('int32') + num_tokens_per_channel = T.alloc_var("int32", init=T.ceildiv(num_tokens, num_channels)) + token_start_idx = T.alloc_var("int32") token_start_idx = T.min(num_tokens_per_channel * responsible_channel, num_tokens) - token_end_idx = T.alloc_var('int32') + token_end_idx = T.alloc_var("int32") token_end_idx = T.min(token_start_idx + num_tokens_per_channel, num_tokens) # sender mainloop: iterate over all tokens and send by trunk - cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = T.alloc_var("int32") cached_channel_tail_idx = 0 - token_idx = T.alloc_var('int32') + token_idx = T.alloc_var("int32") token_idx = token_start_idx - with T.While(token_idx < token_end_idx): - if T.elect_one_sync(): + while token_idx < token_end_idx: + if T.shuffle_elect(32): T.wait_ge( channel_head_idx[responsible_channel, rank], num_max_send_tokens + cached_channel_tail_idx - num_recv_buffer_tokens, - responsible_rank) + responsible_rank, + ) T.sync_warp() - chunk_token_idx = T.alloc_var('int32') + chunk_token_idx = T.alloc_var("int32") chunk_token_idx = 0 while chunk_token_idx < num_max_send_tokens and token_idx < token_end_idx: # for the same token, the warp assigned to save `send_head` may be different from the warp # assigned to send the following data - if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.elect_one_sync( - ): + if token_idx % num_warps_per_rank == send_warp_id_in_rank and T.shuffle_elect(32): send_head[token_idx, responsible_rank] = T.if_then_else( - is_token_in_rank[token_idx, responsible_rank], - cached_channel_tail_idx, -1) + is_token_in_rank[token_idx, responsible_rank], cached_channel_tail_idx, -1 + ) # skip if not selected if not is_token_in_rank[token_idx, responsible_rank]: token_idx += 1 - T.loop_continue() + continue # selected, get an empty slot - dst_slot_idx = T.alloc_var('int32') + dst_slot_idx = T.alloc_var("int32") dst_slot_idx = cached_channel_tail_idx % num_recv_buffer_tokens cached_channel_tail_idx += 1 if cached_channel_tail_idx % num_warps_per_rank == send_warp_id_in_rank: @@ -690,20 +631,16 @@ def cached_dispatch_main( # 1. copy data T.put_warp( T.address_of(x[token_idx, 0]), - T.address_of(channel_x_buffers[responsible_channel, rank, - dst_slot_idx, 0]), + T.address_of(channel_x_buffers[responsible_channel, rank, dst_slot_idx, 0]), hidden, dst_pe=responsible_rank, unroll_factor=4, - enable_aggressive_vectorize=True) + enable_aggressive_vectorize=True, + ) # 2. copy src idx - if T.elect_one_sync(): - T.st( - channel_src_idx_buffers[responsible_channel, rank, - dst_slot_idx], - token_idx, - dst_pe=responsible_rank) + if T.shuffle_elect(32): + T.st(channel_src_idx_buffers[responsible_channel, rank, dst_slot_idx], token_idx, dst_pe=responsible_rank) # 4. copy scale (support fp8 later) @@ -713,36 +650,30 @@ def cached_dispatch_main( # move tail index # here all warps should share the same new tail T.sync_threads(responsible_rank, num_threads_per_rank) - if send_warp_id_in_rank == 0 and T.elect_one_sync(): + if T.shuffle_elect(96): T.st( channel_tail_idx[responsible_channel, rank], cached_channel_tail_idx, - scope='sys', - sem='release', - dst_pe=responsible_rank) + scope="sys", + sem="release", + dst_pe=responsible_rank, + ) else: # receiver recv_thread_id_in_rank = tx % num_threads_per_rank recv_warp_id_in_rank = recv_thread_id_in_rank // 32 # calculate offset first - rank_offset = T.if_then_else(responsible_rank > 0, - rank_prefix_matrix[responsible_rank - 1, rank], 0) + rank_offset = T.if_then_else(responsible_rank > 0, rank_prefix_matrix[responsible_rank - 1, rank], 0) # receive channel offset - total_offset = T.alloc_var('int32') - num_tokens_to_recv = T.alloc_var('int32') - if T.elect_one_sync(): + total_offset = T.alloc_var("int32") + num_tokens_to_recv = T.alloc_var("int32") + if T.shuffle_elect(32): T.wait_ne(channel_start_offset[responsible_channel, responsible_rank], 0) - T.ld( - channel_start_offset[responsible_channel, responsible_rank], - total_offset, - sem='volatile') + T.ld(channel_start_offset[responsible_channel, responsible_rank], total_offset, sem="volatile") T.wait_ne(channel_end_offset[responsible_channel, responsible_rank], 0) - T.ld( - channel_end_offset[responsible_channel, responsible_rank], - num_tokens_to_recv, - sem='volatile') + T.ld(channel_end_offset[responsible_channel, responsible_rank], num_tokens_to_recv, sem="volatile") total_offset = -total_offset - 1 num_tokens_to_recv = -num_tokens_to_recv - 1 if recv_warp_id_in_rank == 0: @@ -753,24 +684,20 @@ def cached_dispatch_main( num_tokens_to_recv = T.tvm_warp_shuffle(-1, num_tokens_to_recv, 0, 32, 32) # Shared tail indices for different warps - shared_channel_tail_idx = T.alloc_shared([num_ranks], 'int32') + shared_channel_tail_idx = T.alloc_shared([num_ranks], "int32") - cached_channel_head_idx = T.alloc_var('int32') + cached_channel_head_idx = T.alloc_var("int32") cached_channel_head_idx = 0 - cached_channel_tail_idx = T.alloc_var('int32') + cached_channel_tail_idx = T.alloc_var("int32") cached_channel_tail_idx = 0 - with T.While(num_tokens_to_recv > 0): - with T.While(recv_thread_id_in_rank == 0): - T.ld( - channel_tail_idx[responsible_channel, responsible_rank], - cached_channel_tail_idx, - sem='acquire', - scope='sys') + while num_tokens_to_recv > 0: + while recv_thread_id_in_rank == 0: + T.ld(channel_tail_idx[responsible_channel, responsible_rank], cached_channel_tail_idx, sem="acquire", scope="sys") # read to copy if cached_channel_head_idx != cached_channel_tail_idx: shared_channel_tail_idx[responsible_rank] = cached_channel_tail_idx - T.loop_break() + break # sync queue tail T.sync_threads(responsible_rank, num_threads_per_rank) @@ -779,31 +706,29 @@ def cached_dispatch_main( # copy data # 1. recv x num_cur_recv_tokens = cached_channel_tail_idx - cached_channel_head_idx - for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, - num_warps_per_rank): - token_idx_in_buffer = (cached_channel_head_idx + - chunk_idx) % num_recv_buffer_tokens + for chunk_idx in T.serial(recv_warp_id_in_rank, num_cur_recv_tokens, num_warps_per_rank): + token_idx_in_buffer = (cached_channel_head_idx + chunk_idx) % num_recv_buffer_tokens #! T.copy will cause layout inference error T.put_warp( - T.address_of(channel_x_buffers[responsible_channel, responsible_rank, - token_idx_in_buffer, 0]), + T.address_of(channel_x_buffers[responsible_channel, responsible_rank, token_idx_in_buffer, 0]), T.address_of(recv_x[total_offset + chunk_idx, 0]), hidden, -1, 5, - enable_aggressive_vectorize=True) + enable_aggressive_vectorize=True, + ) # 2. recv src_idx - for chunk_idx in T.serial(cached_channel_head_idx + recv_thread_id_in_rank, - cached_channel_tail_idx, num_threads_per_rank): - local_src_idx = T.alloc_var('int32') + for chunk_idx in T.serial( + cached_channel_head_idx + recv_thread_id_in_rank, cached_channel_tail_idx, num_threads_per_rank + ): + local_src_idx = T.alloc_var("int32") T.ld( - channel_src_idx_buffers[responsible_channel, responsible_rank, - chunk_idx % num_recv_buffer_tokens], + channel_src_idx_buffers[responsible_channel, responsible_rank, chunk_idx % num_recv_buffer_tokens], local_src_idx, - nc=True) - recv_src_idx[total_offset + chunk_idx - - cached_channel_head_idx] = local_src_idx + nc=True, + ) + recv_src_idx[total_offset + chunk_idx - cached_channel_head_idx] = local_src_idx # 4. recv scale (support fp8 later) @@ -811,12 +736,8 @@ def cached_dispatch_main( cached_channel_head_idx += num_cur_recv_tokens total_offset += num_cur_recv_tokens T.sync_threads(responsible_rank, num_threads_per_rank) - if recv_warp_id_in_rank == num_warps_per_rank - 1 and T.elect_one_sync(): - T.st( - channel_head_idx[responsible_channel, responsible_rank], - cached_channel_head_idx, - scope='sys', - sem='relaxed') + if T.shuffle_elect(96): + T.st(channel_head_idx[responsible_channel, responsible_rank], cached_channel_head_idx, scope="sys", sem="relaxed") # Exit num_tokens_to_recv -= num_cur_recv_tokens @@ -848,8 +769,9 @@ def intranode_dispatch( # todo: support async functionality ): if handle is None: - assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, \ - "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" + assert num_tokens_per_rank is not None and is_token_in_rank is not None and num_tokens_per_expert is not None, ( + "num_tokens_per_rank, is_token_in_rank, and num_tokens_per_expert must be provided in non-cached mode" + ) else: rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle @@ -858,8 +780,19 @@ def intranode_dispatch( num_ranks = num_tokens_per_rank.shape[0] num_topk = topk_idx.shape[1] if handle is None else 0 - barrier_signal, per_rank_buffer, per_expert_buffer, channel_start_offset, channel_end_offset, channel_head_idx, channel_tail_idx, \ - channel_x_buffers, channel_src_idx_buffers, channel_topk_idx_buffers, channel_topk_weights_buffers = symm_buffers + ( + barrier_signal, + per_rank_buffer, + per_expert_buffer, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_idx_buffers, + channel_topk_weights_buffers, + ) = symm_buffers if handle is None: num_recv_tokens, num_recv_tokens_per_expert_list, rank_prefix_matrix, channel_prefix_matrix = notify_dispatch( @@ -895,76 +828,84 @@ def intranode_dispatch( channel_tail_idx, barrier_signal, allocator, - comm_stream=comm_stream) + comm_stream=comm_stream, + ) num_recv_tokens = recv_src_idx.size(0) - recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device='cuda') - recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device='cuda') + recv_x = torch.empty((num_recv_tokens, hidden), dtype=x.dtype, device="cuda") + recv_src_idx = torch.empty((num_recv_tokens,), dtype=torch.int32, device="cuda") if handle is None: - recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int64, device='cuda') - recv_topk_weights = torch.empty((num_recv_tokens, num_topk), - dtype=torch.float32, - device='cuda') - recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), - dtype=torch.int32, - device='cuda') - send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device='cuda') + recv_topk_idx = torch.empty((num_recv_tokens, num_topk), dtype=torch.int64, device="cuda") + recv_topk_weights = torch.empty((num_recv_tokens, num_topk), dtype=torch.float32, device="cuda") + recv_channel_prefix_matrix = torch.empty((num_ranks, config.num_channels), dtype=torch.int32, device="cuda") + send_head = torch.empty((num_tokens, num_ranks), dtype=torch.int32, device="cuda") # run dispatch if handle is None: - kernel = dispatch_kernel(num_ranks, config.num_max_nvl_chunked_send_tokens, - config.num_max_nvl_chunked_recv_tokens, hidden, num_topk, - num_experts, config.num_sms, 'bfloat16') - kernel.initialize(allocator=allocator) - kernel( - rank, - recv_x, - recv_src_idx, - recv_topk_idx, - recv_topk_weights, - recv_channel_prefix_matrix, - send_head, - x, - topk_idx, - topk_weights, - is_token_in_rank, - rank_prefix_matrix, - channel_prefix_matrix, - channel_start_offset, - channel_end_offset, - channel_head_idx, - channel_tail_idx, - channel_x_buffers, - channel_src_idx_buffers, - channel_topk_idx_buffers, - channel_topk_weights_buffers, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead - handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, - recv_src_idx, is_token_in_rank, send_head) + kernel = dispatch_kernel( + num_ranks, + config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, + hidden, + num_topk, + num_experts, + config.num_sms, + "bfloat16", + ) + kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) + with tvm_ffi.use_torch_stream(torch.cuda.stream(comm_stream)): + kernel( + rank, + recv_x, + recv_src_idx, + recv_topk_idx, + recv_topk_weights, + recv_channel_prefix_matrix, + send_head, + x, + topk_idx, + topk_weights, + is_token_in_rank, + rank_prefix_matrix, + channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + channel_topk_idx_buffers, + channel_topk_weights_buffers, + ) + handle = (rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head) return recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle else: - kernel = cached_dispatch_kernel(num_ranks, num_tokens, - config.num_max_nvl_chunked_send_tokens, - config.num_max_nvl_chunked_recv_tokens, hidden, - config.num_sms, 'bfloat16') + kernel = cached_dispatch_kernel( + num_ranks, + num_tokens, + config.num_max_nvl_chunked_send_tokens, + config.num_max_nvl_chunked_recv_tokens, + hidden, + config.num_sms, + "bfloat16", + ) kernel.initialize(allocator=allocator, stream=comm_stream.cuda_stream) - kernel( - rank, - recv_x, - recv_src_idx, - recv_channel_prefix_matrix, - send_head, - x, - is_token_in_rank, - rank_prefix_matrix, - channel_prefix_matrix, - channel_start_offset, - channel_end_offset, - channel_head_idx, - channel_tail_idx, - channel_x_buffers, - channel_src_idx_buffers, - stream=comm_stream.cuda_stream, - skip_tensor_validation=True) # reduce runtime overhead + with torch.cuda.stream(comm_stream): + kernel( + rank, + recv_x, + recv_src_idx, + recv_channel_prefix_matrix, + send_head, + x, + is_token_in_rank, + rank_prefix_matrix, + channel_prefix_matrix, + channel_start_offset, + channel_end_offset, + channel_head_idx, + channel_tail_idx, + channel_x_buffers, + channel_src_idx_buffers, + ) return recv_x diff --git a/examples/distributed/deepseek_deepep/intranode/example_intranode.py b/examples/distributed/deepseek_deepep/intranode/example_intranode.py index 8f555dfeea..41ea258349 100644 --- a/examples/distributed/deepseek_deepep/intranode/example_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/example_intranode.py @@ -13,7 +13,7 @@ from deepep_utils import gen_inputs, ep_bench # tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def test_intranode( @@ -37,170 +37,187 @@ def test_intranode( deepep_buffer = deep_ep.Buffer(group, num_nvl_bytes=2**30) # Generate inputs for testing - x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, - num_ranks) + x, topk_idx, topk_weights, rank_idx = gen_inputs(num_tokens, hidden, num_topk, num_experts, num_ranks) # 1. test get_dispatch_layout ref_num_tokens_per_rank, _, ref_num_tokens_per_expert, ref_is_token_in_rank, _ = deepep_buffer.get_dispatch_layout( - topk_idx, num_experts) - num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout( - topk_idx) + topk_idx, num_experts + ) + num_tokens_per_rank, num_tokens_per_expert, is_token_in_rank = ts_buffer.get_dispatch_layout(topk_idx) - assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), \ + assert torch.equal(num_tokens_per_expert, ref_num_tokens_per_expert), ( f"[rank {rank}] num_tokens_per_expert mismatch, max err: {(num_tokens_per_expert - ref_num_tokens_per_expert).abs().max()}" - assert torch.equal(is_token_in_rank, ref_is_token_in_rank), \ - f"[rank {rank}] is_token_in_rank mismatch" - assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), \ + ) + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), f"[rank {rank}] is_token_in_rank mismatch" + assert torch.equal(num_tokens_per_rank, ref_num_tokens_per_rank), ( f"[rank {rank}] num_tokens_per_rank mismatch, max err: {(num_tokens_per_rank - ref_num_tokens_per_rank).abs().max()}" + ) group.barrier() if rank == 0: - print('Check passed for get_dispatch_layout. ✅') + print("Check passed for get_dispatch_layout. ✅") # 2. test dispatch # ref - ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = \ - deepep_buffer.dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment) + ref_recv_x, ref_recv_topk_idx, ref_recv_topk_weights, ref_num_recv_tokens_per_expert_list, ref_handle, event = deepep_buffer.dispatch( + x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment + ) # ours if cached_dispatch: - recv_x = ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, - num_tokens_per_expert, None, None, expert_alignment) + recv_x = ts_buffer.dispatch( + x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment + ) else: recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( - x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, - topk_weights, expert_alignment) + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment + ) # check dispatch output - assert torch.equal( - recv_x, - ref_recv_x), f'[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}' + assert torch.equal(recv_x, ref_recv_x), f"[rank {rank}] recv_x mismatch, max err: {(recv_x - ref_recv_x).abs().max()}" if not cached_dispatch: - assert torch.equal( - recv_topk_idx, ref_recv_topk_idx - ), f'[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}' - assert torch.equal( - recv_topk_weights, ref_recv_topk_weights - ), f'[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}' - assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, f'[rank {rank}] num_recv_tokens_per_expert_list mismatch' + assert torch.equal(recv_topk_idx, ref_recv_topk_idx), ( + f"[rank {rank}] recv_topk_idx mismatch, max err: {(recv_topk_idx - ref_recv_topk_idx).abs().max()}" + ) + assert torch.equal(recv_topk_weights, ref_recv_topk_weights), ( + f"[rank {rank}] recv_topk_weights mismatch, max err: {(recv_topk_weights - ref_recv_topk_weights).abs().max()}" + ) + assert num_recv_tokens_per_expert_list == ref_num_recv_tokens_per_expert_list, ( + f"[rank {rank}] num_recv_tokens_per_expert_list mismatch" + ) # check handle rank_prefix_matrix, channel_prefix_matrix, recv_channel_prefix_matrix, recv_src_idx, is_token_in_rank, send_head = handle - ref_rank_prefix_matrix, ref_channel_prefix_matrix, ref_recv_channel_prefix_matrix, ref_recv_src_idx, ref_is_token_in_rank, ref_send_head = ref_handle - assert torch.equal( - rank_prefix_matrix, ref_rank_prefix_matrix - ), f'[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}' - assert torch.equal( - channel_prefix_matrix, ref_channel_prefix_matrix - ), f'[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}' - assert torch.equal( - recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix - ), f'[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}' - assert torch.equal( - recv_src_idx, ref_recv_src_idx - ), f'[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}' - assert torch.equal( - is_token_in_rank, ref_is_token_in_rank - ), f'[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}' - assert torch.equal( - send_head, ref_send_head - ), f'[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}' + ( + ref_rank_prefix_matrix, + ref_channel_prefix_matrix, + ref_recv_channel_prefix_matrix, + ref_recv_src_idx, + ref_is_token_in_rank, + ref_send_head, + ) = ref_handle + assert torch.equal(rank_prefix_matrix, ref_rank_prefix_matrix), ( + f"[rank {rank}] rank_prefix_matrix mismatch, max err: {(rank_prefix_matrix - ref_rank_prefix_matrix).abs().max()}" + ) + assert torch.equal(channel_prefix_matrix, ref_channel_prefix_matrix), ( + f"[rank {rank}] channel_prefix_matrix mismatch, max err: {(channel_prefix_matrix - ref_channel_prefix_matrix).abs().max()}" + ) + assert torch.equal(recv_channel_prefix_matrix, ref_recv_channel_prefix_matrix), ( + f"[rank {rank}] recv_channel_prefix_matrix mismatch, max err: {(recv_channel_prefix_matrix - ref_recv_channel_prefix_matrix).abs().max()}" + ) + assert torch.equal(recv_src_idx, ref_recv_src_idx), ( + f"[rank {rank}] recv_src_idx mismatch, max err: {(recv_src_idx - ref_recv_src_idx).abs().max()}" + ) + assert torch.equal(is_token_in_rank, ref_is_token_in_rank), ( + f"[rank {rank}] is_token_in_rank mismatch, max err: {(is_token_in_rank - ref_is_token_in_rank).abs().max()}" + ) + assert torch.equal(send_head, ref_send_head), ( + f"[rank {rank}] send_head mismatch, max err: {(send_head - ref_send_head).abs().max()}" + ) group.barrier() if rank == 0: - print(f'Check passed for {"cached" if cached_dispatch else "non-cached"} dispatch. ✅') + print(f"Check passed for {'cached' if cached_dispatch else 'non-cached'} dispatch. ✅") # 3. test combine - ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine( - recv_x, ref_handle, ref_recv_topk_weights) + ref_combined_x, ref_combined_topk_weights, _ = deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights) if cached_dispatch: # acquire handle first recv_x, recv_topk_idx, recv_topk_weights, num_recv_tokens_per_expert_list, handle = ts_buffer.dispatch( - x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, - topk_weights, expert_alignment) + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment + ) combined_x, combined_topk_weights = ts_buffer.combine(recv_x, handle, recv_topk_weights) - assert torch.equal( - combined_x, ref_combined_x - ), f'[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}' - assert torch.equal( - combined_topk_weights, ref_combined_topk_weights - ), f'[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}' + assert torch.equal(combined_x, ref_combined_x), ( + f"[rank {rank}] combined_x mismatch, max err: {(combined_x - ref_combined_x).abs().max()}" + ) + assert torch.equal(combined_topk_weights, ref_combined_topk_weights), ( + f"[rank {rank}] combined_topk_weights mismatch, max err: {(combined_topk_weights - ref_combined_topk_weights).abs().max()}" + ) group.barrier() if rank == 0: - print('Check passed for combine. ✅') + print("Check passed for combine. ✅") if rank == 0: - print('All checks passed for TileScale intranode DeepEP. ✅') + print("All checks passed for TileScale intranode DeepEP. ✅") # benchmark if rank == 0: - print( - f'========== Benchmarking {"cached" if cached_dispatch else "non-cached"} dispatch ==========' - ) + print(f"========== Benchmarking {'cached' if cached_dispatch else 'non-cached'} dispatch ==========") if not cached_dispatch: group.barrier() deepep_dispatch_time = ep_bench( - lambda: deepep_buffer. - dispatch(x, None, ref_num_tokens_per_rank, None, ref_is_token_in_rank, - ref_num_tokens_per_expert, topk_idx, topk_weights, expert_alignment), + lambda: deepep_buffer.dispatch( + x, + None, + ref_num_tokens_per_rank, + None, + ref_is_token_in_rank, + ref_num_tokens_per_expert, + topk_idx, + topk_weights, + expert_alignment, + ), warmup=50, - rep=50) - print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + rep=50, + ) + print(f"[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms") group.barrier() ts_dispatch_time = ep_bench( - lambda: ts_buffer. - dispatch(x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, - topk_idx, topk_weights, expert_alignment), + lambda: ts_buffer.dispatch( + x, None, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, topk_idx, topk_weights, expert_alignment + ), warmup=50, - rep=50) - print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + rep=50, + ) + print(f"[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms") group.barrier() else: group.barrier() deepep_dispatch_time = ep_bench( - lambda: deepep_buffer. - dispatch(x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, - ref_num_tokens_per_expert, None, None, expert_alignment), + lambda: deepep_buffer.dispatch( + x, ref_handle, ref_num_tokens_per_rank, None, ref_is_token_in_rank, ref_num_tokens_per_expert, None, None, expert_alignment + ), warmup=50, - rep=50) - print(f'[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms') + rep=50, + ) + print(f"[rank {rank}] DeepEP dispatch time: {deepep_dispatch_time:.4f}ms") group.barrier() ts_dispatch_time = ep_bench( - lambda: ts_buffer.dispatch(x, ref_handle, num_tokens_per_rank, is_token_in_rank, - num_tokens_per_expert, None, None, expert_alignment), + lambda: ts_buffer.dispatch( + x, ref_handle, num_tokens_per_rank, is_token_in_rank, num_tokens_per_expert, None, None, expert_alignment + ), warmup=50, - rep=50) - print(f'[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms') + rep=50, + ) + print(f"[rank {rank}] TileScale dispatch time: {ts_dispatch_time:.4f}ms") group.barrier() if rank == 0: - print('========== Benchmarking combine ==========') + print("========== Benchmarking combine ==========") group.barrier() - deepep_combine_time = ep_bench( - lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), warmup=50, rep=50) - print(f'[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms') + deepep_combine_time = ep_bench(lambda: deepep_buffer.combine(recv_x, ref_handle, ref_recv_topk_weights), warmup=50, rep=50) + print(f"[rank {rank}] DeepEP combine time: {deepep_combine_time:.4f}ms") group.barrier() - ts_combine_time = ep_bench( - lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), warmup=50, rep=50) - print(f'[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms') + ts_combine_time = ep_bench(lambda: ts_buffer.combine(recv_x, handle, recv_topk_weights), warmup=50, rep=50) + print(f"[rank {rank}] TileScale combine time: {ts_combine_time:.4f}ms") group.barrier() if rank == 0: - print('========== Benchmarking report ==========') + print("========== Benchmarking report ==========") dispatch_bf16_nvl_recv_bytes = recv_x.numel() * 2 combine_bf16_nvl_send_bytes = dispatch_bf16_nvl_recv_bytes if rank == 0: print( - f'DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)' + f"DeepEP dispatch time: {deepep_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / deepep_dispatch_time / 1e6:.2f} GB/s (NVL)" ) print( - f'TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)' + f"TileScale dispatch time: {ts_dispatch_time:.4f}ms, bandwidth: {dispatch_bf16_nvl_recv_bytes / ts_dispatch_time / 1e6:.2f} GB/s (NVL)" ) print( - f'DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)' + f"DeepEP combine time: {deepep_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / deepep_combine_time / 1e6:.2f} GB/s (NVL)" ) print( - f'TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)' + f"TileScale combine time: {ts_combine_time:.4f}ms, bandwidth: {combine_bf16_nvl_send_bytes / ts_combine_time / 1e6:.2f} GB/s (NVL)" ) @@ -227,12 +244,10 @@ def parse_args(): parser.add_argument("--num_ranks", type=int, default=8, help="Number of ranks") parser.add_argument("--num_tokens", type=int, default=4096, help="Number of tokens") parser.add_argument("--hidden", type=int, default=7168, help="Hidden size") - parser.add_argument( - "--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") + parser.add_argument("--num_topk", type=int, default=8, help="Number of top-k experts to select for each token") parser.add_argument("--num_experts", type=int, default=32, help="Number of experts") parser.add_argument("--expert_alignment", type=int, default=1, help="Expert alignment") - parser.add_argument( - "--cached", action="store_true", default=False, help="Whether to use cached dispatch") + parser.add_argument("--cached", action="store_true", default=False, help="Whether to use cached dispatch") return parser.parse_args() diff --git a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py index 97b67d1a44..c696297e11 100644 --- a/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py +++ b/examples/distributed/deepseek_deepep/intranode/get_dispatch_layout.py @@ -15,8 +15,8 @@ # TODO(wt): Add async functionality def get_dispatch_layout( - topk_idx: torch.Tensor, num_experts: int, - num_ranks: int) -> Tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor]: + topk_idx: torch.Tensor, num_experts: int, num_ranks: int +) -> Tuple[torch.Tensor, torch.Tensor | None, torch.Tensor, torch.Tensor]: """Calculate the layout required for later communication. Arguments: @@ -42,9 +42,9 @@ def get_dispatch_layout( # Allocate tensors # TODO(wt): Wait on previous events and allocate on comm stream when adding async functionality num_tokens, num_topk = topk_idx.shape - num_tokens_per_rank = torch.empty(num_ranks, dtype=torch.int32, device='cuda') - num_tokens_per_expert = torch.empty(num_experts, dtype=torch.int32, device='cuda') - is_token_in_rank = torch.empty((num_tokens, num_ranks), dtype=torch.bool, device='cuda') + num_tokens_per_rank = torch.empty(num_ranks, dtype=torch.int32, device="cuda") + num_tokens_per_expert = torch.empty(num_experts, dtype=torch.int32, device="cuda") + is_token_in_rank = torch.empty((num_tokens, num_ranks), dtype=torch.bool, device="cuda") # Launch the kernel kernel = get_dispatch_layout_kernel(num_topk, num_experts, num_ranks) @@ -72,14 +72,14 @@ def get_dispatch_layout_kernel( num_sms = T.ceildiv(num_experts, experts_per_sm) + T.ceildiv(num_ranks, ranks_per_sm) experts_per_rank = num_experts // num_ranks - num_tokens = T.dynamic('num_tokens') + num_tokens = T.dynamic("num_tokens") @T.prim_func def get_dispatch_layout_main( - topk_idx: T.Tensor([num_tokens, num_topk], "int64"), # type: ignore - num_tokens_per_rank: T.Tensor([num_ranks], "int32"), # type: ignore - num_tokens_per_expert: T.Tensor([num_experts], "int32"), # type: ignore - is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), # type: ignore + topk_idx: T.Tensor([num_tokens, num_topk], "int64"), # type: ignore + num_tokens_per_rank: T.Tensor([num_ranks], "int32"), # type: ignore + num_tokens_per_expert: T.Tensor([num_experts], "int32"), # type: ignore + is_token_in_rank: T.Tensor([num_tokens, num_ranks], "bool"), # type: ignore ): with T.Kernel(num_sms, threads=threads) as bx: tx = T.get_thread_binding() diff --git a/examples/distributed/deepseek_deepep/intranode/test_intranode.py b/examples/distributed/deepseek_deepep/intranode/test_intranode.py index 3177219969..c6f8a55c67 100644 --- a/examples/distributed/deepseek_deepep/intranode/test_intranode.py +++ b/examples/distributed/deepseek_deepep/intranode/test_intranode.py @@ -3,6 +3,7 @@ import example_intranode +@tilelang.testing.requires_distributed @tilelang.testing.requires_cuda def test_intranode(monkeypatch): monkeypatch.setattr("sys.argv", ["example_intranode.py"]) # optionally add testing params here diff --git a/examples/distributed/example_all_to_all.py b/examples/distributed/example_all_to_all.py index 328ebc86bc..dd0157c893 100644 --- a/examples/distributed/example_all_to_all.py +++ b/examples/distributed/example_all_to_all.py @@ -11,7 +11,6 @@ def all_to_all(PE_num, TOKEN_NUM, TOPK, HIDDEN, EXPERT_NUM, dtype="float16"): - EXPERTS_PER_RANK = EXPERT_NUM // PE_num @T.prim_func @@ -37,8 +36,8 @@ def main( m_end[0] = splits_cumsum[(peer + 1) * EXPERTS_PER_RANK] T.putmem_nbi_block( - T.address_of(data_dst[0, 0]), T.address_of(data_src[m_start[0], 0]), - (m_end[0] - m_start[0]) * HIDDEN * 2, peer) + T.address_of(data_dst[0, 0]), T.address_of(data_src[m_start[0], 0]), (m_end[0] - m_start[0]) * HIDDEN * 2, peer + ) T.fence() @@ -119,7 +118,7 @@ def splits_to_cumsum(splits: torch.Tensor): # print("split_cumsum:", split_cumsum) data_src = pynvshmem.nvshmem_create_tensor([args.M * args.topk, args.N], torch.float16) -data_src[:].copy_(ref_tensor[args.M * args.topk * RANK:args.M * args.topk * (RANK + 1), :]) +data_src[:].copy_(ref_tensor[args.M * args.topk * RANK : args.M * args.topk * (RANK + 1), :]) splits_cumsum = pynvshmem.nvshmem_create_tensor([args.G + 1], torch.int32) splits_cumsum[:].copy_(split_cumsum) diff --git a/examples/distributed/example_allgather.py b/examples/distributed/example_allgather.py index bc9cb3e1b2..56e8653913 100644 --- a/examples/distributed/example_allgather.py +++ b/examples/distributed/example_allgather.py @@ -13,8 +13,8 @@ def allgather(PE_num, M, N, dtype="float16", threads=128): @T.prim_func def a2a_split( - A: T.Tensor((M_per_rank, N), dtype), # type: ignore - B: T.Tensor((M, N), dtype), # type: ignore + A: T.Tensor((M_per_rank, N), dtype), # type: ignore + B: T.Tensor((M, N), dtype), # type: ignore ): # Each block is responsible for sending (block_M, N) to exact one rank. with T.Kernel(M_per_rank // block_M, PE_num - 1, threads=threads) as (bx, by): @@ -24,11 +24,9 @@ def a2a_split( A_shared = T.alloc_shared((block_M, N), dtype) local_base = bx * block_M global_base = M_per_rank * mype + local_base - T.copy(A[local_base:local_base + block_M, :], A_shared) + T.copy(A[local_base : local_base + block_M, :], A_shared) peer = (mype + by + 1) % npes - T.putmem_nbi_block( - T.address_of(B[global_base, 0]), T.address_of(A_shared[0, 0]), - block_M * N * dtype_map[dtype].itemsize, peer) + T.putmem_nbi_block(T.address_of(B[global_base, 0]), T.address_of(A_shared[0, 0]), block_M * N * dtype_map[dtype].itemsize, peer) return a2a_split @@ -37,8 +35,7 @@ def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--M", type=int, default=8192) parser.add_argument("--N", type=int, default=12288) - parser.add_argument( - "--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) + parser.add_argument("--dtype", type=str, default="float16", choices=["float16", "float32", "bfloat16"]) parser.add_argument("--threads", type=int, default=128, help="number of threads in a block") parser.add_argument("--print_source", action="store_true", help="print kernel source code") parser.add_argument("--warmup", type=int, default=1, help="number of warmup iterations") @@ -46,7 +43,7 @@ def parse_args(): return parser.parse_args() -if __name__ == '__main__': +if __name__ == "__main__": WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) assert WORLD_SIZE <= 8, "This benchmark is designed for intra-node communication" @@ -82,7 +79,7 @@ def tilelang_ag(): ag_buffer = pynvshmem.nvshmem_create_tensor([M_per_rank, N], torch_dtype) ag_buffer.copy_(local_data) out = pynvshmem.nvshmem_create_tensor([M, N], torch_dtype) - out[RANK * M_per_rank:(RANK + 1) * M_per_rank, :].copy_(local_data) + out[RANK * M_per_rank : (RANK + 1) * M_per_rank, :].copy_(local_data) kernel(ag_buffer, out) pynvshmem.nvshmem_barrier_all() # Ensure all ranks have completed return out diff --git a/examples/distributed/example_allgather_gemm.py b/examples/distributed/example_allgather_gemm.py index 96f95a7970..702f1264ad 100644 --- a/examples/distributed/example_allgather_gemm.py +++ b/examples/distributed/example_allgather_gemm.py @@ -8,16 +8,15 @@ def allgather_gemm(PE_num, M, N, K, block_M, block_N, block_K, dtype="float16"): - accum_dtype = "float" @T.prim_func def main( - A: T.Buffer((M, K), dtype), - A_ag: T.Buffer((M * PE_num, K), dtype), - B: T.Buffer((K, N), dtype), - signal: T.Buffer((PE_num,), "uint64"), - C: T.Buffer((M * PE_num, N), dtype), + A: T.Buffer((M, K), dtype), + A_ag: T.Buffer((M * PE_num, K), dtype), + B: T.Buffer((K, N), dtype), + signal: T.Buffer((PE_num,), "uint64"), + C: T.Buffer((M * PE_num, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -36,8 +35,14 @@ def main( for k in T.serial(PE_num - 1): peer[0] = (mype[0] + 1 + k) % npes[0] T.putmem_signal_nbi_block( - T.address_of(A_ag[mype[0] * M, 0]), T.address_of(A[0, 0]), - block_M * block_K * 2, T.address_of(signal[k]), k + 1, 9, peer[0]) + T.address_of(A_ag[mype[0] * M, 0]), + T.address_of(A[0, 0]), + block_M * block_K * 2, + T.address_of(signal[k]), + k + 1, + 9, + peer[0], + ) for k in T.serial(PE_num - 1): T.signal_wait_until(T.address_of(signal[k]), 0, k + 1) @@ -60,13 +65,7 @@ def main( WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP = init_distributed(return_tp_group=True) PE_num = WORLD_SIZE func = allgather_gemm(PE_num, M, N, K, block_M, block_N, block_K) -kernel = tilelang.compile( - func, - out_idx=-1, - pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) +kernel = tilelang.compile(func, out_idx=-1, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) # Get CUDA Source if RANK == 0: @@ -90,9 +89,9 @@ def ref_program(A, B): C_ref = ref_program(A_tensor, B_tensor) print("C_ref:", C_ref) -#profiler.init_distributed() +# profiler.init_distributed() A_local = pynvshmem.nvshmem_create_tensor([M, K], dtype) -A_local[:].copy_(A_tensor[M * RANK:M * (RANK + 1), :]) +A_local[:].copy_(A_tensor[M * RANK : M * (RANK + 1), :]) A_ag_local = pynvshmem.nvshmem_create_tensor([M * PE_num, K], dtype) A_ag_local.fill_(0) diff --git a/examples/distributed/example_allgather_gemm_overlapped.py b/examples/distributed/example_allgather_gemm_overlapped.py index cebf58ed1a..3094819671 100644 --- a/examples/distributed/example_allgather_gemm_overlapped.py +++ b/examples/distributed/example_allgather_gemm_overlapped.py @@ -12,6 +12,7 @@ cuda_python_version = importlib.metadata.version("cuda-python") from packaging import version + if version.parse(cuda_python_version) >= version.parse("12.8.0"): from cuda.bindings import driver as cuda else: @@ -19,14 +20,15 @@ from tilelang.distributed import perf_fn tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log @tilelang.jit(pass_configs={"tl.disable_warp_specialized": True, "tl.disable_tma_lower": True}) def set_signal_kernel(local_rank, num_local_ranks, threads): - @T.prim_func - def _set_signal_kernel(signal_buffer: T.Tensor((num_local_ranks), "uint32"),): + def _set_signal_kernel( + signal_buffer: T.Tensor((num_local_ranks), "uint32"), + ): with T.Kernel(1, threads=threads): tx = T.get_thread_binding(0) if tx < num_local_ranks: @@ -39,19 +41,9 @@ def _set_signal_kernel(signal_buffer: T.Tensor((num_local_ranks), "uint32"),): @tilelang.jit -def gemm_kernel(M, - N, - K, - local_rank, - num_local_rank, - block_M, - block_N, - block_K, - threads, - persistent=False, - dtype="float16", - accum_dtype="float"): - +def gemm_kernel( + M, N, K, local_rank, num_local_rank, block_M, block_N, block_K, threads, persistent=False, dtype="float16", accum_dtype="float" +): sm_num = driver.get_num_sms() m_blocks = T.ceildiv(M, block_M) n_blocks = T.ceildiv(N // num_local_rank, block_N) @@ -61,14 +53,12 @@ def gemm_kernel(M, @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N // num_local_rank), dtype), - signal_buffer: T.Tensor((num_local_rank), "uint32"), - C: T.Tensor((M, N // num_local_rank), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N // num_local_rank), dtype), + signal_buffer: T.Tensor((num_local_rank), "uint32"), + C: T.Tensor((M, N // num_local_rank), dtype), ): - with T.Kernel( - T.ceildiv(M, block_M) * T.ceildiv(N // num_local_rank, block_N), - threads=threads) as (bid): + with T.Kernel(T.ceildiv(M, block_M) * T.ceildiv(N // num_local_rank, block_N), threads=threads) as (bid): A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_K, block_N), dtype) C_shared = T.alloc_shared((block_M, block_N), dtype) @@ -103,10 +93,10 @@ def main( @T.prim_func def main_persistent( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N // num_local_rank), dtype), - signal_buffer: T.Tensor((num_local_rank), "uint32"), - C: T.Tensor((M, N // num_local_rank), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N // num_local_rank), dtype), + signal_buffer: T.Tensor((num_local_rank), "uint32"), + C: T.Tensor((M, N // num_local_rank), dtype), ): with T.Kernel(sm_num, threads=threads) as (bid): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -162,8 +152,8 @@ def cp_engine_producer_all_gather_full_mesh_pull( for src_rank in rank_orders: if src_rank == local_rank: continue - dst = ag_buffer[local_rank][src_rank * M_per_rank:(src_rank + 1) * M_per_rank, :] - src = ag_buffer[src_rank][src_rank * M_per_rank:(src_rank + 1) * M_per_rank, :] + dst = ag_buffer[local_rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] + src = ag_buffer[src_rank][src_rank * M_per_rank : (src_rank + 1) * M_per_rank, :] dst.copy_(src) (err,) = cuda.cuStreamWriteValue32( @@ -175,21 +165,33 @@ def cp_engine_producer_all_gather_full_mesh_pull( CUDA_CHECK(err) -def ag_gemm_op(A, B, C, ag_buffer, signal_buffer, M_per_rank, N, signal_target, local_rank, - local_world_size, set_signal_kernel, gemm_kernel, gemm_stream, ag_stream): - +def ag_gemm_op( + A, + B, + C, + ag_buffer, + signal_buffer, + M_per_rank, + N, + signal_target, + local_rank, + local_world_size, + set_signal_kernel, + gemm_kernel, + gemm_stream, + ag_stream, +): with torch.cuda.stream(gemm_stream): - set_signal_kernel(signal_buffer[local_rank], stream=gemm_stream.cuda_stream) + set_signal_kernel(signal_buffer[local_rank]) ag_stream.wait_stream(gemm_stream) - cp_engine_producer_all_gather_full_mesh_pull(ag_buffer, signal_buffer, M_per_rank, - signal_target, local_rank, local_world_size, - ag_stream) + cp_engine_producer_all_gather_full_mesh_pull( + ag_buffer, signal_buffer, M_per_rank, signal_target, local_rank, local_world_size, ag_stream + ) with torch.cuda.stream(gemm_stream): - gemm_kernel( - ag_buffer[local_rank], B, signal_buffer[local_rank], C, stream=gemm_stream.cuda_stream) + gemm_kernel(ag_buffer[local_rank], B, signal_buffer[local_rank], C) gemm_stream.wait_stream(ag_stream) current_stream = torch.cuda.current_stream() @@ -225,14 +227,9 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) assert rank == local_rank and num_ranks == num_local_ranks, "only support single node for now" allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) - gemm_func = gemm_kernel(M, N, K, local_rank, num_local_ranks, BLOCK_M, BLOCK_N, BLOCK_K, - threads, persistent) + size=2**30, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) + gemm_func = gemm_kernel(M, N, K, local_rank, num_local_ranks, BLOCK_M, BLOCK_N, BLOCK_K, threads, persistent) set_signal_func = set_signal_kernel( local_rank=local_rank, num_local_ranks=num_local_ranks, @@ -247,11 +244,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): B = tilelang.tensor((K, N_per_rank), dtype, allocator=allocator).normal_() C = tilelang.tensor((M, N_per_rank), dtype, allocator=allocator) ag_buffer = tilelang.tensor((M, K), dtype, allocator=allocator, return_peers=True) - A = ag_buffer[local_rank][M_per_rank * local_rank:M_per_rank * (local_rank + 1), :].normal_() - signal_buffer = tilelang.tensor((num_local_ranks,), - torch.uint32, - allocator=allocator, - return_peers=True) + A = ag_buffer[local_rank][M_per_rank * local_rank : M_per_rank * (local_rank + 1), :].normal_() + signal_buffer = tilelang.tensor((num_local_ranks,), torch.uint32, allocator=allocator, return_peers=True) gemm_stream = torch.cuda.Stream() ag_stream = torch.cuda.Stream(priority=-1) @@ -259,9 +253,22 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): dist.barrier() - tilelang_C = ag_gemm_op(A, B, C, ag_buffer, signal_buffer, M_per_rank, K, signal_target, - local_rank, num_local_ranks, set_signal_func, gemm_func, gemm_stream, - ag_stream) + tilelang_C = ag_gemm_op( + A, + B, + C, + ag_buffer, + signal_buffer, + M_per_rank, + K, + signal_target, + local_rank, + num_local_ranks, + set_signal_func, + gemm_func, + gemm_stream, + ag_stream, + ) torch_ag_buffer = torch.empty([M, K], dtype=dtype, device="cuda") torch_C = torch_ag_gemm(group, A, B, torch_ag_buffer) @@ -273,27 +280,38 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): print(f"torch_C: {torch_C}, tilelang_C: {tilelang_C}") _, tl_t = perf_fn( - lambda: - ag_gemm_op(A, B, C, ag_buffer, signal_buffer, M_per_rank, K, signal_target, local_rank, - num_local_ranks, set_signal_func, gemm_func, gemm_stream, ag_stream), + lambda: ag_gemm_op( + A, + B, + C, + ag_buffer, + signal_buffer, + M_per_rank, + K, + signal_target, + local_rank, + num_local_ranks, + set_signal_func, + gemm_func, + gemm_stream, + ag_stream, + ), warmup=5, - rep=10) - - print( - f"rank {local_rank} tilelang ag_gemm time: {tl_t:.2f} ms, TFLOPS: {2*M*N*K/1e9/(tl_t)/num_local_ranks:.2f}" + rep=10, ) + print(f"rank {local_rank} tilelang ag_gemm time: {tl_t:.2f} ms, TFLOPS: {2 * M * N * K / 1e9 / (tl_t) / num_local_ranks:.2f}") + dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=8192, help='M dimension') - parser.add_argument('--N', type=int, default=28672, help='N dimension') - parser.add_argument('--K', type=int, default=8192, help='K dimension') - parser.add_argument('--persistent', action='store_true', help='Use persistent kernel') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=8192, help="M dimension") + parser.add_argument("--N", type=int, default=28672, help="N dimension") + parser.add_argument("--K", type=int, default=8192, help="K dimension") + parser.add_argument("--persistent", action="store_true", help="Use persistent kernel") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/example_cannon.py b/examples/distributed/example_cannon.py index 649be6c4b2..ad25a41e7a 100644 --- a/examples/distributed/example_cannon.py +++ b/examples/distributed/example_cannon.py @@ -11,7 +11,6 @@ def cannon(MESH, M, N, K, block_M, block_N, block_K, dtype="float16", specialize=False): - M_local = T.ceildiv(M, MESH) N_local = T.ceildiv(N, MESH) K_local = T.ceildiv(K, MESH) @@ -22,13 +21,13 @@ def cannon(MESH, M, N, K, block_M, block_N, block_K, dtype="float16", specialize @T.prim_func def main( - A: T.Tensor((2, M_local, K_local), dtype), - B: T.Tensor((2, N_local, K_local), dtype), - A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - C: T.Tensor((M_local, N_local), dtype), + A: T.Tensor((2, M_local, K_local), dtype), + B: T.Tensor((2, N_local, K_local), dtype), + A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + C: T.Tensor((M_local, N_local), dtype), ): grid_size = T.min(sm_num, total_tiles) A_rows_per_block = T.ceildiv(M_local, grid_size) @@ -72,16 +71,23 @@ def main( T.address_of(A[(ko + 1) % 2, A_rows_per_block * block_id, 0]), T.address_of(A[ko % 2, A_rows_per_block * block_id, 0]), A_rows_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(A_signal_to[0]), 1, T.Amo.SIGNAL_ADD, a_peer_to[0]) + T.address_of(A_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + a_peer_to[0], + ) if block_id < T.ceildiv(N_local, B_cols_per_block): T.putmem_signal_nbi_block( T.address_of(B[(ko + 1) % 2, B_cols_per_block * block_id, 0]), T.address_of(B[ko % 2, B_cols_per_block * block_id, 0]), B_cols_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(B_signal_to[0]), 1, T.Amo.SIGNAL_ADD, b_peer_to[0]) + T.address_of(B_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + b_peer_to[0], + ) for w in T.serial(waves): - bx = (grid_size * w + block_id) // T.ceildiv(N_local, block_N) by = (grid_size * w + block_id) % T.ceildiv(N_local, block_N) @@ -122,13 +128,13 @@ def main( # TODO: fix correctness @T.prim_func def main_specialize( - A: T.Tensor((2, M_local, K_local), dtype), - B: T.Tensor((2, N_local, K_local), dtype), - A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - C: T.Tensor((M_local, N_local), dtype), + A: T.Tensor((2, M_local, K_local), dtype), + B: T.Tensor((2, N_local, K_local), dtype), + A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + C: T.Tensor((M_local, N_local), dtype), ): # 0-compute blocks: compute # compute_blocks-grid_size: copy @@ -172,21 +178,26 @@ def main_specialize( total_tiles * ko, ) T.putmem_signal_nbi_block( - T.address_of(A[(ko + 1) % 2, A_rows_per_block * (block_id - compute_blocks), - 0]), + T.address_of(A[(ko + 1) % 2, A_rows_per_block * (block_id - compute_blocks), 0]), T.address_of(A[ko % 2, A_rows_per_block * (block_id - compute_blocks), 0]), A_rows_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(A_signal_to[0]), 1, T.Amo.SIGNAL_ADD, a_peer_to[0]) + T.address_of(A_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + a_peer_to[0], + ) T.putmem_signal_nbi_block( - T.address_of(B[(ko + 1) % 2, B_cols_per_block * (block_id - compute_blocks), - 0]), + T.address_of(B[(ko + 1) % 2, B_cols_per_block * (block_id - compute_blocks), 0]), T.address_of(B[ko % 2, B_cols_per_block * (block_id - compute_blocks), 0]), B_cols_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(B_signal_to[0]), 1, T.Amo.SIGNAL_ADD, b_peer_to[0]) + T.address_of(B_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + b_peer_to[0], + ) if block_id < compute_blocks: for w in T.serial(waves): - bx = (compute_blocks * w + block_id) // T.ceildiv(N_local, block_N) by = (compute_blocks * w + block_id) % T.ceildiv(N_local, block_N) @@ -256,11 +267,7 @@ def parse_args(): K_local = math.ceil(K / MESH) func = cannon(MESH, M, N, K, block_M, block_N, block_K, args.dtype, specialize) - kernel = tilelang.compile( - func, pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) # Get CUDA Source if RANK == 0: @@ -281,11 +288,9 @@ def parse_args(): b_scatter_list = [] for r in range(WORLD_SIZE): rr, cc = divmod(r, MESH) - c_tile = C[M_local * rr:M_local * (rr + 1), N_local * cc:N_local * (cc + 1)] - a_tile = A[M_local * rr:M_local * (rr + 1), - K_local * ((cc + rr) % MESH):K_local * ((cc + rr) % MESH + 1)] - b_tile = B[N_local * cc:N_local * (cc + 1), - K_local * ((cc + rr) % MESH):K_local * ((cc + rr) % MESH + 1)] + c_tile = C[M_local * rr : M_local * (rr + 1), N_local * cc : N_local * (cc + 1)] + a_tile = A[M_local * rr : M_local * (rr + 1), K_local * ((cc + rr) % MESH) : K_local * ((cc + rr) % MESH + 1)] + b_tile = B[N_local * cc : N_local * (cc + 1), K_local * ((cc + rr) % MESH) : K_local * ((cc + rr) % MESH + 1)] c_scatter_list.append(c_tile.contiguous()) a_scatter_list.append(a_tile.contiguous()) @@ -320,7 +325,7 @@ def parse_args(): dist.barrier() if r == RANK: if torch.allclose(C_tilelang, ref, rtol=1e-2, atol=1e-2): - print('-' * 100) + print("-" * 100) print(f"[Rank {RANK}] ✅ Tilelang and Torch match") else: abs_error = torch.abs(C_tilelang - ref) @@ -330,7 +335,7 @@ def parse_args(): max_rel_error = rel_error.max().item() mismatch_ratio = (abs_error > (1e-2 + 1e-2 * torch.abs(ref))).float().mean().item() - print('-' * 100) + print("-" * 100) print(f"[Rank {RANK}] ❌ Tilelang and Torch mismatch") print(f"[Rank {RANK}] ref:\n{ref}") print(f"[Rank {RANK}] tilelang:\n{C_tilelang}") @@ -381,8 +386,7 @@ def reduce_local_time(local_time): total_flops = 2 * M * N * K -avg_time = reduce_local_time( - bench(kernel, A, B, A_signal_to, A_signal_from, B_signal_to, B_signal_from, C_tilelang)) +avg_time = reduce_local_time(bench(kernel, A, B, A_signal_to, A_signal_from, B_signal_to, B_signal_from, C_tilelang)) if RANK == 0: print(f"avg time of RANK {RANK}: {avg_time} ms") diff --git a/examples/distributed/example_gemm_rs_overlapped.py b/examples/distributed/example_gemm_rs_overlapped.py index 4fb1c6d434..27c2278bdf 100644 --- a/examples/distributed/example_gemm_rs_overlapped.py +++ b/examples/distributed/example_gemm_rs_overlapped.py @@ -14,19 +14,9 @@ @tilelang.jit -def gemm_kernel(M, - N, - K, - local_rank, - num_local_rank, - block_M, - block_N, - block_K, - threads, - persistent=False, - dtype="float16", - accum_dtype="float"): - +def gemm_kernel( + M, N, K, local_rank, num_local_rank, block_M, block_N, block_K, threads, persistent=False, dtype="float16", accum_dtype="float" +): M_per_rank = T.ceildiv(M, num_local_rank) GROUP_SIZE_M = 8 @@ -41,11 +31,11 @@ def swizzle_2d(tile_id, num_pid_m, num_pid_n): @T.prim_func def main( - A: T.Tensor((M, K // num_local_rank), dtype), - B: T.Tensor((K // num_local_rank, N), dtype), - scatter_signal_buf: T.Tensor((num_local_rank), "uint32"), - counter_signal_buf: T.Tensor((num_local_rank), "uint32"), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K // num_local_rank), dtype), + B: T.Tensor((K // num_local_rank, N), dtype), + scatter_signal_buf: T.Tensor((num_local_rank), "uint32"), + counter_signal_buf: T.Tensor((num_local_rank), "uint32"), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(M, block_M) * T.ceildiv(N, block_N), threads=threads) as (bid): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -87,27 +77,12 @@ def main( return main -def gemm_rs_op(A, - B, - C, - output, - ctx, - gemm_kernel, - gemm_stream, - rs_stream, - local_rank, - print_source=False): - +def gemm_rs_op(A, B, C, output, ctx, gemm_kernel, gemm_stream, rs_stream, local_rank, print_source=False): current_stream = torch.cuda.current_stream() rs_stream.wait_stream(gemm_stream) - gemm_kernel( - A, - B, - ctx.scatter_signal_bufs[local_rank], - ctx.counter_bufs[local_rank], - C, - stream=gemm_stream.cuda_stream) + with torch.cuda.stream(gemm_stream): + gemm_kernel(A, B, ctx.scatter_signal_bufs[local_rank], ctx.counter_bufs[local_rank], C) if print_source and local_rank == 1: print(gemm_kernel.get_kernel_source()) @@ -155,14 +130,9 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) assert rank == local_rank and num_ranks == num_local_ranks, "only support single node for now" allocator = tilelang.get_allocator( - size=2**30, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) - gemm_func = gemm_kernel(M, N, K, local_rank, num_local_ranks, BLOCK_M, BLOCK_N, BLOCK_K, - threads, persistent) + size=2**30, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) + gemm_func = gemm_kernel(M, N, K, local_rank, num_local_ranks, BLOCK_M, BLOCK_N, BLOCK_K, threads, persistent) gemm_func.initialize(allocator=allocator) A = tilelang.tensor((M, K_per_rank), dtype, allocator=allocator).normal_() / 10 @@ -172,20 +142,12 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): gemm_stream = torch.cuda.Stream() rs_stream = torch.cuda.Stream(priority=-1) ctx = create_reduce_scater_2d_ctx( - M, - N, - local_rank, - num_local_ranks, - num_local_ranks, - dtype, - allocator, - overlap_with_gemm=True, - num_reduction_sms=15) + M, N, local_rank, num_local_ranks, num_local_ranks, dtype, allocator, overlap_with_gemm=True, num_reduction_sms=15 + ) dist.barrier() - tilelang_out = gemm_rs_op( - A, B, C, output, ctx, gemm_func, gemm_stream, rs_stream, local_rank, print_source=True) + tilelang_out = gemm_rs_op(A, B, C, output, ctx, gemm_func, gemm_stream, rs_stream, local_rank, print_source=True) torch_out = torch_gemm_rs(group, A, B, None, num_local_ranks) atol = 1e-2 @@ -196,26 +158,20 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): print(f"rank {local_rank} check failed.❌") print(f"torch_out: {torch_out}, tilelang_out: {tilelang_out}") - _, tl_t = perf_fn( - lambda: gemm_rs_op(A, B, C, output, ctx, gemm_func, gemm_stream, rs_stream, local_rank), - warmup=5, - rep=5) + _, tl_t = perf_fn(lambda: gemm_rs_op(A, B, C, output, ctx, gemm_func, gemm_stream, rs_stream, local_rank), warmup=5, rep=5) - print( - f"rank {local_rank} tilelang gemm_rs time: {tl_t:.2f} ms, TFLOPS: {2*M*N*K/1e9/(tl_t)/num_local_ranks:.2f}" - ) + print(f"rank {local_rank} tilelang gemm_rs time: {tl_t:.2f} ms, TFLOPS: {2 * M * N * K / 1e9 / (tl_t) / num_local_ranks:.2f}") dist.destroy_process_group() if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=8192, help='M dimension') - parser.add_argument('--N', type=int, default=8192, help='N dimension') - parser.add_argument('--K', type=int, default=29568, help='K dimension') - parser.add_argument('--persistent', action='store_true', help='Use persistent kernel') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=8192, help="M dimension") + parser.add_argument("--N", type=int, default=8192, help="N dimension") + parser.add_argument("--K", type=int, default=29568, help="K dimension") + parser.add_argument("--persistent", action="store_true", help="Use persistent kernel") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/example_nvshmem.py b/examples/distributed/example_nvshmem.py index 6499a46484..8f8de69ed5 100644 --- a/examples/distributed/example_nvshmem.py +++ b/examples/distributed/example_nvshmem.py @@ -29,11 +29,10 @@ def tilelang_callback_cuda_postproc(code, _): def dist_test(M, N, block_M, block_N, dtype="int16"): - @T.prim_func def main( - A: T.Buffer((M, N), dtype), - B: T.Buffer((M, N), dtype), + A: T.Buffer((M, N), dtype), + B: T.Buffer((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_N), dtype) diff --git a/examples/distributed/example_overlapping_allgather.py b/examples/distributed/example_overlapping_allgather.py index 13c3e6dac6..281e07deed 100644 --- a/examples/distributed/example_overlapping_allgather.py +++ b/examples/distributed/example_overlapping_allgather.py @@ -19,28 +19,24 @@ def internode_gather(M, local_world_size, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") rank[0] = (T.get_pe() + local_world_size) % (2 * local_world_size) # 2 nodes - T.putmem_nbi_block( - T.address_of(dst[bx * block_M]), T.address_of(src[bx * block_M]), block_M * 4, - rank[0]) + T.putmem_nbi_block(T.address_of(dst[bx * block_M]), T.address_of(src[bx * block_M]), block_M * 4, rank[0]) return main def intranode_gather(M, world_size, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M * world_size), "float32"), - src: T.Tensor((M * 2), "float32"), + dst: T.Tensor((M * world_size), "float32"), + src: T.Tensor((M * 2), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -68,24 +64,19 @@ def main( return main -if __name__ == '__main__': +if __name__ == "__main__": tilelang.disable_cache() M = 2 K = 12288 - #for 2 node(16 GPUs), world_size=16,rank is 0-15,local rank is 0-7 - WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP, LC_GROUP = init_distributed( - return_tp_group=True, return_lc_group=True) - local_world_size = int(os.environ.get('LOCAL_WORLD_SIZE', 1)) + # for 2 node(16 GPUs), world_size=16,rank is 0-15,local rank is 0-7 + WORLD_SIZE, RANK, LOCAL_RANK, TP_GROUP, LC_GROUP = init_distributed(return_tp_group=True, return_lc_group=True) + local_world_size = int(os.environ.get("LOCAL_WORLD_SIZE", 1)) LOCAL_RANK = int(os.environ.get("LOCAL_RANK", 0)) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=LOCAL_RANK, - num_local_ranks=local_world_size, - group=LC_GROUP) + size=2**25, device="cuda", is_distributed=True, local_rank=LOCAL_RANK, num_local_ranks=local_world_size, group=LC_GROUP + ) print(local_world_size, LOCAL_RANK) # Each rank sends the local_tensor to ranks of other nodes with the same local_rank @@ -99,7 +90,7 @@ def main( print(interkernel.get_kernel_source()) src = pynvshmem.nvshmem_create_tensor([M], torch.float32) dst = pynvshmem.nvshmem_create_tensor([M], torch.float32) - input_data = torch.ones([M], dtype=torch.float32, device='cuda') * RANK + input_data = torch.ones([M], dtype=torch.float32, device="cuda") * RANK src.copy_(input_data) pynvshmem.nvshmem_barrier_all() @@ -119,20 +110,14 @@ def main( src_intra = tilelang.tensor((M * 2), torch.float32, allocator=allocator).normal_() dst_intra = tilelang.tensor((M * WORLD_SIZE), torch.float32, allocator=allocator) if RANK < WORLD_SIZE / 2: - cudart.cudaMemcpy(src_intra.data_ptr(), src.data_ptr(), M * 4, - cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) - cudart.cudaMemcpy(src_intra.data_ptr() + M * 4, dst.data_ptr(), M * 4, - cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) + cudart.cudaMemcpy(src_intra.data_ptr(), src.data_ptr(), M * 4, cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) + cudart.cudaMemcpy(src_intra.data_ptr() + M * 4, dst.data_ptr(), M * 4, cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) else: - cudart.cudaMemcpy(src_intra.data_ptr(), dst.data_ptr(), M * 4, - cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) - cudart.cudaMemcpy(src_intra.data_ptr() + M * 4, src.data_ptr(), M * 4, - cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) + cudart.cudaMemcpy(src_intra.data_ptr(), dst.data_ptr(), M * 4, cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) + cudart.cudaMemcpy(src_intra.data_ptr() + M * 4, src.data_ptr(), M * 4, cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice) env.USE_NVSHMEM = False - intrakernel = tilelang.compile( - intranode_gather(M, WORLD_SIZE, M, 128), - pass_configs={tilelang.PassConfigKey.TL_DISABLE_RDC: True}) + intrakernel = tilelang.compile(intranode_gather(M, WORLD_SIZE, M, 128), pass_configs={tilelang.PassConfigKey.TL_DISABLE_RDC: True}) intrakernel.initialize(allocator=allocator) if LOCAL_RANK == 0: print(intrakernel.get_kernel_source()) diff --git a/examples/distributed/example_post_attn_all2all_transpose.py b/examples/distributed/example_post_attn_all2all_transpose.py index e17c55ad99..de2c43671e 100644 --- a/examples/distributed/example_post_attn_all2all_transpose.py +++ b/examples/distributed/example_post_attn_all2all_transpose.py @@ -2,6 +2,7 @@ import torch.distributed as dist import pynvshmem import tilelang +import tilelang.testing import tilelang.language as T from tilelang.distributed import init_distributed, dtype_map import argparse @@ -43,21 +44,14 @@ def torch_reverse_all_to_all_transpose_reference(data_src, group): # Step 2: Prepare output list for all_to_all output_list = [] for _ in range(world_size): - recv_data = torch.empty( - batch_size, - heads_per_pe, - seq_per_pe, - head_dim, - dtype=data_src.dtype, - device=data_src.device) + recv_data = torch.empty(batch_size, heads_per_pe, seq_per_pe, head_dim, dtype=data_src.dtype, device=data_src.device) output_list.append(recv_data) # Step 3: Execute all_to_all dist.all_to_all(output_list, input_list, group=group) # Step 4: Reorganize received data - result = torch.empty( - batch_size, seq_per_pe, num_heads, head_dim, dtype=data_src.dtype, device=data_src.device) + result = torch.empty(batch_size, seq_per_pe, num_heads, head_dim, dtype=data_src.dtype, device=data_src.device) for pe_idx in range(world_size): head_start = pe_idx * heads_per_pe @@ -69,12 +63,7 @@ def torch_reverse_all_to_all_transpose_reference(data_src, group): return result -def sequence_parallel_reverse_all_to_all_transpose(PE_num, - BATCH_SIZE, - NUM_HEADS, - SEQ_LEN, - HEAD_DIM, - dtype="float16"): +def sequence_parallel_reverse_all_to_all_transpose(PE_num, BATCH_SIZE, NUM_HEADS, SEQ_LEN, HEAD_DIM, dtype="float16"): """ Reverse All-to-All: Convert from head parallel to sequence parallel Input: [BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM] @@ -88,9 +77,9 @@ def sequence_parallel_reverse_all_to_all_transpose(PE_num, @T.prim_func def main( - data_src: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), - data_dst: T.Tensor((BATCH_SIZE, SEQ_PER_PE, NUM_HEADS, HEAD_DIM), dtype), - signal: T.Tensor((PE_num,), "uint64"), + data_src: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), + data_dst: T.Tensor((BATCH_SIZE, SEQ_PER_PE, NUM_HEADS, HEAD_DIM), dtype), + signal: T.Tensor((PE_num,), "uint64"), ): with T.Kernel(NUM_BLOCKS_X, PE_num, threads=128) as (bx, target_pe): tx = T.thread_binding(128, thread="threadIdx.x") @@ -118,8 +107,10 @@ def main( T.putmem_nbi_block( T.address_of(data_dst[batch_idx, seq_idx, dst_head_idx, 0]), - T.address_of(data_src[batch_idx, head_idx, src_seq_idx, 0]), transfer_size, - target_pe) + T.address_of(data_src[batch_idx, head_idx, src_seq_idx, 0]), + transfer_size, + target_pe, + ) T.fence() @@ -129,7 +120,8 @@ def main( T.address_of(signal[mype[0]]), 1, # Signal the number of head chunks processed T.Amo.SIGNAL_ADD, - target_pe) + target_pe, + ) T.fence() # Wait for all blocks to complete all head transfers T.signal_wait_until(T.address_of(signal[target_pe]), T.CmpType.EQ, NUM_BLOCKS_X) @@ -177,6 +169,7 @@ def parse_args(): return parser.parse_args() +@tilelang.testing.requires_distributed def test_reverse_transpose_all_to_all_with_golden_reference(): args = parse_args() @@ -203,13 +196,8 @@ def test_reverse_transpose_all_to_all_with_golden_reference(): print("Converting from HEAD_PARALLEL to SEQUENCE_PARALLEL") # Compile TileLang kernel - func = sequence_parallel_reverse_all_to_all_transpose(PE_num, args.batch_size, args.num_heads, - args.seq_len, args.head_dim, args.dtype) - kernel = tilelang.compile( - func, pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) + func = sequence_parallel_reverse_all_to_all_transpose(PE_num, args.batch_size, args.num_heads, args.seq_len, args.head_dim, args.dtype) + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) if RANK == 0: print("\nTileLang Kernel Source:") @@ -219,9 +207,7 @@ def test_reverse_transpose_all_to_all_with_golden_reference(): dtype_torch = dtype_map[args.dtype] # Create input data: [BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM] - head parallel format - input_data = torch.rand([args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], - dtype=dtype_torch, - device='cuda') + input_data = torch.rand([args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype=dtype_torch, device="cuda") print(f"PE {RANK} Input shape: {input_data.shape}") print(f"PE {RANK} Input sample: {input_data[0, 0, 0, :3]}") @@ -235,10 +221,8 @@ def test_reverse_transpose_all_to_all_with_golden_reference(): # === Test 2: TileLang NVSHMEM Implementation === def tilelang_reverse_all_to_all(): # Create NVSHMEM tensors - data_src = pynvshmem.nvshmem_create_tensor( - [args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) - data_dst = pynvshmem.nvshmem_create_tensor( - [args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype_torch) + data_src = pynvshmem.nvshmem_create_tensor([args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) + data_dst = pynvshmem.nvshmem_create_tensor([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype_torch) signal = pynvshmem.nvshmem_create_tensor([PE_num], torch.uint64) # Initialize data @@ -268,6 +252,7 @@ def tilelang_reverse_all_to_all(): dist.destroy_process_group() +@tilelang.testing.requires_distributed def test_roundtrip_consistency(): """Test that forward + reverse all-to-all gives back original data""" args = parse_args() @@ -285,9 +270,7 @@ def test_roundtrip_consistency(): SEQ_PER_PE = args.seq_len // WORLD_SIZE # Create original data in sequence parallel format - original_data = torch.rand([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], - dtype=dtype_torch, - device='cuda') + original_data = torch.rand([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype=dtype_torch, device="cuda") # Forward: sequence parallel -> head parallel head_parallel_data = torch_sequence_all_to_all_transpose_reference(original_data, TP_GROUP) diff --git a/examples/distributed/example_pre_attn_all2all.py b/examples/distributed/example_pre_attn_all2all.py index 53884f5b0e..cb85a9389b 100644 --- a/examples/distributed/example_pre_attn_all2all.py +++ b/examples/distributed/example_pre_attn_all2all.py @@ -2,6 +2,7 @@ import torch.distributed as dist import pynvshmem import tilelang +import tilelang.testing import tilelang.language as T from tilelang.distributed import init_distributed, dtype_map import argparse @@ -44,13 +45,7 @@ def torch_sequence_all_to_all_reference(data_src, group): output_list = [] for _ in range(world_size): # Receive [BATCH_SIZE, HEADS_PER_PE, SEQ_PER_PE, HEAD_DIM] from each PE - recv_data = torch.empty( - batch_size, - heads_per_pe, - seq_per_pe, - head_dim, - dtype=data_src.dtype, - device=data_src.device) + recv_data = torch.empty(batch_size, heads_per_pe, seq_per_pe, head_dim, dtype=data_src.dtype, device=data_src.device) output_list.append(recv_data) # Step 3: Execute all_to_all @@ -59,8 +54,7 @@ def torch_sequence_all_to_all_reference(data_src, group): # Step 4: Reorganize received data # output_list[pe_idx] contains data from PE pe_idx # Need to arrange by sequence dimension - result = torch.empty( - batch_size, heads_per_pe, seq_len, head_dim, dtype=data_src.dtype, device=data_src.device) + result = torch.empty(batch_size, heads_per_pe, seq_len, head_dim, dtype=data_src.dtype, device=data_src.device) for pe_idx in range(world_size): seq_start = pe_idx * seq_per_pe @@ -86,12 +80,12 @@ def sequence_parallel_all_to_all(PE_num, BATCH_SIZE, NUM_HEADS, SEQ_LEN, HEAD_DI @T.prim_func def main( - # Input: [BATCH_SIZE, NUM_HEADS, SEQ_PER_PE, HEAD_DIM] - data_src: T.Tensor((BATCH_SIZE, NUM_HEADS, SEQ_PER_PE, HEAD_DIM), dtype), - # Output: [BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM] - data_dst: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), - # Sync signals - signal: T.Tensor((PE_num,), "uint64"), + # Input: [BATCH_SIZE, NUM_HEADS, SEQ_PER_PE, HEAD_DIM] + data_src: T.Tensor((BATCH_SIZE, NUM_HEADS, SEQ_PER_PE, HEAD_DIM), dtype), + # Output: [BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM] + data_dst: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), + # Sync signals + signal: T.Tensor((PE_num,), "uint64"), ): # Grid: (batch*head, target_pe) with T.Kernel(NUM_BLOCKS_X, PE_num, threads=128) as (bx, target_pe): @@ -116,7 +110,10 @@ def main( # Single block transfer for entire [SEQ_PER_PE, HEAD_DIM] data T.putmem_nbi_block( T.address_of(data_dst[batch_idx, head_idx, dst_seq_start, 0]), - T.address_of(data_src[batch_idx, src_head_idx, 0, 0]), transfer_size, target_pe) + T.address_of(data_src[batch_idx, src_head_idx, 0, 0]), + transfer_size, + target_pe, + ) # Memory fence T.fence() @@ -127,7 +124,8 @@ def main( T.address_of(signal[mype[0]]), 1, 10, # NVSHMEM_SIGNAL_ADD - target_pe) + target_pe, + ) T.fence() for k in T.serial(PE_num): T.signal_wait_until(T.address_of(signal[k]), 0, NUM_BLOCKS_X) @@ -165,8 +163,7 @@ def parse_args(): parser = argparse.ArgumentParser() parser.add_argument("--batch_size", type=int, default=2, help="Batch size") parser.add_argument("--seq_len", type=int, default=256, help="Sequence length") - parser.add_argument( - "--num_heads", type=int, default=16, help="Number of attention heads,combine QKV") + parser.add_argument("--num_heads", type=int, default=16, help="Number of attention heads,combine QKV") parser.add_argument("--head_dim", type=int, default=64, help="Head dimension") parser.add_argument("--dtype", default="float16", help="Data type") parser.add_argument("--warmup", type=int, default=3, help="Warmup iterations") @@ -175,6 +172,7 @@ def parse_args(): return parser.parse_args() +@tilelang.testing.requires_distributed def test_all_to_all_with_golden_reference(): args = parse_args() @@ -200,13 +198,8 @@ def test_all_to_all_with_golden_reference(): print(f"Heads per PE: {HEADS_PER_PE}") # Compile TileLang kernel - func = sequence_parallel_all_to_all(PE_num, args.batch_size, args.num_heads, args.seq_len, - args.head_dim, args.dtype) - kernel = tilelang.compile( - func, pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) + func = sequence_parallel_all_to_all(PE_num, args.batch_size, args.num_heads, args.seq_len, args.head_dim, args.dtype) + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) if RANK == 0: print("\nTileLang Kernel Source:") @@ -216,9 +209,7 @@ def test_all_to_all_with_golden_reference(): dtype_torch = dtype_map[args.dtype] # Create input data (same for both implementations) - input_data = torch.rand([args.batch_size, args.num_heads, SEQ_PER_PE, args.head_dim], - dtype=dtype_torch, - device='cuda') + input_data = torch.rand([args.batch_size, args.num_heads, SEQ_PER_PE, args.head_dim], dtype=dtype_torch, device="cuda") print(f"PE {RANK} Input shape: {input_data.shape}") print(f"PE {RANK} Input sample: {input_data[0, 0, 0, :3]}") @@ -233,10 +224,8 @@ def test_all_to_all_with_golden_reference(): # === Test 2: TileLang NVSHMEM Implementation === def tilelang_all_to_all(): # Create NVSHMEM tensors - data_src = pynvshmem.nvshmem_create_tensor( - [args.batch_size, args.num_heads, SEQ_PER_PE, args.head_dim], dtype_torch) - data_dst = pynvshmem.nvshmem_create_tensor( - [args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) + data_src = pynvshmem.nvshmem_create_tensor([args.batch_size, args.num_heads, SEQ_PER_PE, args.head_dim], dtype_torch) + data_dst = pynvshmem.nvshmem_create_tensor([args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) signal = pynvshmem.nvshmem_create_tensor([PE_num], torch.uint64) # Initialize data @@ -246,7 +235,7 @@ def tilelang_all_to_all(): # Execute kernel kernel(data_src, data_dst, signal) - #pynvshmem.nvshmem_barrier_all() + # pynvshmem.nvshmem_barrier_all() return data_dst diff --git a/examples/distributed/example_pre_attn_all2all_transpose.py b/examples/distributed/example_pre_attn_all2all_transpose.py index f5c4b9fc3b..80f6ef6b79 100644 --- a/examples/distributed/example_pre_attn_all2all_transpose.py +++ b/examples/distributed/example_pre_attn_all2all_transpose.py @@ -2,6 +2,7 @@ import torch.distributed as dist import pynvshmem import tilelang +import tilelang.testing import tilelang.language as T from tilelang.distributed import init_distributed, dtype_map import argparse @@ -41,21 +42,14 @@ def torch_sequence_all_to_all_transpose_reference(data_src, group): # Step 2: Prepare output list for all_to_all output_list = [] for _ in range(world_size): - recv_data = torch.empty( - batch_size, - seq_per_pe, - heads_per_pe, - head_dim, - dtype=data_src.dtype, - device=data_src.device) + recv_data = torch.empty(batch_size, seq_per_pe, heads_per_pe, head_dim, dtype=data_src.dtype, device=data_src.device) output_list.append(recv_data) # Step 3: Execute all_to_all dist.all_to_all(output_list, input_list, group=group) # Step 4: Reorganize received data with transpose - result = torch.empty( - batch_size, heads_per_pe, seq_len, head_dim, dtype=data_src.dtype, device=data_src.device) + result = torch.empty(batch_size, heads_per_pe, seq_len, head_dim, dtype=data_src.dtype, device=data_src.device) for pe_idx in range(world_size): seq_start = pe_idx * seq_per_pe @@ -67,12 +61,7 @@ def torch_sequence_all_to_all_transpose_reference(data_src, group): return result -def sequence_parallel_all_to_all_transpose(PE_num, - BATCH_SIZE, - NUM_HEADS, - SEQ_LEN, - HEAD_DIM, - dtype="float16"): +def sequence_parallel_all_to_all_transpose(PE_num, BATCH_SIZE, NUM_HEADS, SEQ_LEN, HEAD_DIM, dtype="float16"): """ Coarse-grained version with proper transpose handling Each block handles one (batch, head) combination and processes all sequence positions @@ -85,9 +74,9 @@ def sequence_parallel_all_to_all_transpose(PE_num, @T.prim_func def main( - data_src: T.Tensor((BATCH_SIZE, SEQ_PER_PE, NUM_HEADS, HEAD_DIM), dtype), - data_dst: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), - signal: T.Tensor((PE_num,), "uint64"), + data_src: T.Tensor((BATCH_SIZE, SEQ_PER_PE, NUM_HEADS, HEAD_DIM), dtype), + data_dst: T.Tensor((BATCH_SIZE, HEADS_PER_PE, SEQ_LEN, HEAD_DIM), dtype), + signal: T.Tensor((PE_num,), "uint64"), ): with T.Kernel(NUM_BLOCKS_X, PE_num, threads=128) as (bx, target_pe): tx = T.thread_binding(128, thread="threadIdx.x") @@ -115,8 +104,10 @@ def main( T.putmem_nbi_block( T.address_of(data_dst[batch_idx, head_idx, dst_seq_idx, 0]), - T.address_of(data_src[batch_idx, seq_idx, src_head_idx, 0]), transfer_size, - target_pe) + T.address_of(data_src[batch_idx, seq_idx, src_head_idx, 0]), + transfer_size, + target_pe, + ) T.fence() @@ -126,7 +117,8 @@ def main( T.address_of(signal[mype[0]]), 1, # Signal the number of sequence positions processed T.Amo.SIGNAL_ADD, - target_pe) + target_pe, + ) T.fence() # Wait for all blocks to complete all sequence positions T.signal_wait_until(T.address_of(signal[target_pe]), T.CmpType.EQ, NUM_BLOCKS_X) @@ -173,6 +165,7 @@ def parse_args(): return parser.parse_args() +@tilelang.testing.requires_distributed def test_transpose_all_to_all_with_golden_reference(): args = parse_args() @@ -198,13 +191,8 @@ def test_transpose_all_to_all_with_golden_reference(): print(f"Heads per PE: {HEADS_PER_PE}") # Compile TileLang kernel - func = sequence_parallel_all_to_all_transpose(PE_num, args.batch_size, args.num_heads, - args.seq_len, args.head_dim, args.dtype) - kernel = tilelang.compile( - func, pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) + func = sequence_parallel_all_to_all_transpose(PE_num, args.batch_size, args.num_heads, args.seq_len, args.head_dim, args.dtype) + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) if RANK == 0: print("\nTileLang Kernel Source:") @@ -214,9 +202,7 @@ def test_transpose_all_to_all_with_golden_reference(): dtype_torch = dtype_map[args.dtype] # Create input data: [BATCH_SIZE, SEQ_PER_PE, NUM_HEADS, HEAD_DIM] - random like example - input_data = torch.rand([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], - dtype=dtype_torch, - device='cuda') + input_data = torch.rand([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype=dtype_torch, device="cuda") print(f"PE {RANK} Input shape: {input_data.shape}") print(f"PE {RANK} Input sample: {input_data[0, 0, 0, :3]}") @@ -230,10 +216,8 @@ def test_transpose_all_to_all_with_golden_reference(): # === Test 2: TileLang NVSHMEM Implementation === def tilelang_all_to_all(): # Create NVSHMEM tensors - data_src = pynvshmem.nvshmem_create_tensor( - [args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype_torch) - data_dst = pynvshmem.nvshmem_create_tensor( - [args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) + data_src = pynvshmem.nvshmem_create_tensor([args.batch_size, SEQ_PER_PE, args.num_heads, args.head_dim], dtype_torch) + data_dst = pynvshmem.nvshmem_create_tensor([args.batch_size, HEADS_PER_PE, args.seq_len, args.head_dim], dtype_torch) signal = pynvshmem.nvshmem_create_tensor([PE_num], torch.uint64) # Initialize data diff --git a/examples/distributed/example_simple_shift.py b/examples/distributed/example_simple_shift.py index a837c4b8d9..b1e69d9604 100644 --- a/examples/distributed/example_simple_shift.py +++ b/examples/distributed/example_simple_shift.py @@ -5,11 +5,10 @@ def simple_shift(M, N, block_M, block_N, dtype="float16"): - @T.prim_func def main( - A: T.Buffer((M, N), dtype), - B: T.Buffer((M, N), dtype), + A: T.Buffer((M, N), dtype), + B: T.Buffer((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): mype = T.alloc_local([1], "int32") @@ -19,8 +18,7 @@ def main( npes[0] = T.get_pe_num() peer[0] = (mype[0] + 1) % npes[0] - T.putmem_nbi_block( - T.address_of(B[0, 0]), T.address_of(A[0, 0]), block_M * block_N * 2, peer[0]) + T.putmem_nbi_block(T.address_of(B[0, 0]), T.address_of(A[0, 0]), block_M * block_N * 2, peer[0]) return main @@ -28,6 +26,7 @@ def main( WORLD_SIZE, RANK, LOCAL_RANK = init_distributed() func = simple_shift(128, 128, 128, 128) +# Auto-selects cython backend when TILELANG_USE_DISTRIBUTED=1 is set kernel = tilelang.compile(func, out_idx=-1) # Get CUDA Source diff --git a/examples/distributed/example_sp_ag_attention_intra_node.py b/examples/distributed/example_sp_ag_attention_intra_node.py index c4d120fea4..5b893e4f2d 100644 --- a/examples/distributed/example_sp_ag_attention_intra_node.py +++ b/examples/distributed/example_sp_ag_attention_intra_node.py @@ -17,7 +17,6 @@ class FusedSequenceParallelAttn(torch.nn.Module): - def __init__( self, pg: torch.distributed.ProcessGroup, @@ -47,8 +46,9 @@ def __init__( self.max_seqlen_k = max_seqlen_k self.head_dim = head_dim - assert (max_seqlen_q % self.world_size == 0 and max_seqlen_q % self.world_size - == 0), f"sequence length should be multiple of world_size({self.world_size})" + assert max_seqlen_q % self.world_size == 0 and max_seqlen_q % self.world_size == 0, ( + f"sequence length should be multiple of world_size({self.world_size})" + ) self.max_q_shard_len = self.max_seqlen_q // self.world_size self.input_dtype = input_dtype @@ -101,7 +101,6 @@ def forward(self, q_shard, k_shards, v_shards, cu_seqlens_q, cu_seqlens_k, print class TorchSequenceParallelAttn(torch.nn.Module): - def __init__( self, pg: torch.distributed.ProcessGroup, @@ -138,8 +137,9 @@ def __init__( self.max_q_shard_len = max_seqlen_q // self.world_size self.max_kv_shard_ken = max_seqlen_q // self.world_size - assert (max_seqlen_q % self.world_size == 0 and max_seqlen_q % self.world_size - == 0), f"sequence length should be multiple of world_size({self.world_size})" + assert max_seqlen_q % self.world_size == 0 and max_seqlen_q % self.world_size == 0, ( + f"sequence length should be multiple of world_size({self.world_size})" + ) self.ag_k_buffer: torch.Tensor = torch.empty( self.batch_size * self.max_seqlen_k, @@ -161,9 +161,9 @@ def forward(self, q_shard, k_shard, v_shard, cu_seqlens_q, cu_seqlens_k): def _gen_mask(offset, q_shard_len, kv_len): if self.is_causal: mask = torch.zeros((q_shard_len, kv_len), dtype=torch.bool, device=self.device) - mask[:, :offset + q_shard_len] = True + mask[:, : offset + q_shard_len] = True if offset < kv_len: - mask[:, offset:offset + q_shard_len].tril_() + mask[:, offset : offset + q_shard_len].tril_() return mask return None @@ -186,37 +186,27 @@ def _gen_mask(offset, q_shard_len, kv_len): half_q_shard_len = q_shard_len // 2 half_kv_shard_len = kv_shard_len // 2 - q0_shard = q_shard[cu_seqlens_q_start:cu_seqlens_q_start + - half_q_shard_len, :, :].clone() - q1_shard = q_shard[cu_seqlens_q_start + - half_q_shard_len:cu_seqlens_q_end, :, :].clone() - - q0_shard_permute = torch.permute( - q0_shard.reshape(1, half_q_shard_len, q_head, head_dim), - (0, 2, 1, 3)).contiguous() - q1_shard_permute = torch.permute( - q1_shard.reshape(1, half_q_shard_len, q_head, head_dim), - (0, 2, 1, 3)).contiguous() - - k0_shard = k_shard[cu_seqlens_k_start:cu_seqlens_k_start + - half_kv_shard_len, :, :].clone() - k1_shard = k_shard[cu_seqlens_k_start + - half_kv_shard_len:cu_seqlens_k_end, :, :].clone() - v0_shard = v_shard[cu_seqlens_k_start:cu_seqlens_k_start + - half_kv_shard_len, :, :].clone() - v1_shard = v_shard[cu_seqlens_k_start + - half_kv_shard_len:cu_seqlens_k_end, :, :].clone() - - buffer_size = (half_kv_shard_len * kv_head * head_dim * self.world_size) - - ag_k0 = self.ag_k_buffer.reshape(-1)[:buffer_size].reshape( - half_kv_shard_len * self.world_size, kv_head, head_dim) - ag_k1 = self.ag_k_buffer.reshape(-1)[buffer_size:2 * buffer_size].reshape( - half_kv_shard_len * self.world_size, kv_head, head_dim) - ag_v0 = self.ag_v_buffer.reshape(-1)[:buffer_size].reshape( - half_kv_shard_len * self.world_size, kv_head, head_dim) - ag_v1 = self.ag_v_buffer.reshape(-1)[buffer_size:2 * buffer_size].reshape( - half_kv_shard_len * self.world_size, kv_head, head_dim) + q0_shard = q_shard[cu_seqlens_q_start : cu_seqlens_q_start + half_q_shard_len, :, :].clone() + q1_shard = q_shard[cu_seqlens_q_start + half_q_shard_len : cu_seqlens_q_end, :, :].clone() + + q0_shard_permute = torch.permute(q0_shard.reshape(1, half_q_shard_len, q_head, head_dim), (0, 2, 1, 3)).contiguous() + q1_shard_permute = torch.permute(q1_shard.reshape(1, half_q_shard_len, q_head, head_dim), (0, 2, 1, 3)).contiguous() + + k0_shard = k_shard[cu_seqlens_k_start : cu_seqlens_k_start + half_kv_shard_len, :, :].clone() + k1_shard = k_shard[cu_seqlens_k_start + half_kv_shard_len : cu_seqlens_k_end, :, :].clone() + v0_shard = v_shard[cu_seqlens_k_start : cu_seqlens_k_start + half_kv_shard_len, :, :].clone() + v1_shard = v_shard[cu_seqlens_k_start + half_kv_shard_len : cu_seqlens_k_end, :, :].clone() + + buffer_size = half_kv_shard_len * kv_head * head_dim * self.world_size + + ag_k0 = self.ag_k_buffer.reshape(-1)[:buffer_size].reshape(half_kv_shard_len * self.world_size, kv_head, head_dim) + ag_k1 = self.ag_k_buffer.reshape(-1)[buffer_size : 2 * buffer_size].reshape( + half_kv_shard_len * self.world_size, kv_head, head_dim + ) + ag_v0 = self.ag_v_buffer.reshape(-1)[:buffer_size].reshape(half_kv_shard_len * self.world_size, kv_head, head_dim) + ag_v1 = self.ag_v_buffer.reshape(-1)[buffer_size : 2 * buffer_size].reshape( + half_kv_shard_len * self.world_size, kv_head, head_dim + ) torch.distributed.all_gather_into_tensor( ag_k0, k0_shard, @@ -238,19 +228,15 @@ def _gen_mask(offset, q_shard_len, kv_len): group=self.pg, ) ag_k1 = ag_k1.reshape(self.world_size, half_kv_shard_len, kv_head, head_dim) - ag_k1 = torch.flip(ag_k1, [0]).reshape(self.world_size * half_kv_shard_len, kv_head, - head_dim) + ag_k1 = torch.flip(ag_k1, [0]).reshape(self.world_size * half_kv_shard_len, kv_head, head_dim) ag_k = torch.cat((ag_k0, ag_k1), dim=0) - ag_k = torch.permute(ag_k.reshape(1, kv_len, kv_head, head_dim), - (0, 2, 1, 3)).contiguous() + ag_k = torch.permute(ag_k.reshape(1, kv_len, kv_head, head_dim), (0, 2, 1, 3)).contiguous() ag_k = ag_k.repeat_interleave(q_head // kv_head, -3) ag_v1 = ag_v1.reshape(self.world_size, half_kv_shard_len, kv_head, head_dim) - ag_v1 = torch.flip(ag_v1, [0]).reshape(self.world_size * half_kv_shard_len, kv_head, - head_dim) + ag_v1 = torch.flip(ag_v1, [0]).reshape(self.world_size * half_kv_shard_len, kv_head, head_dim) ag_v = torch.cat((ag_v0, ag_v1), dim=0) - ag_v = torch.permute(ag_v.reshape(1, kv_len, kv_head, head_dim), - (0, 2, 1, 3)).contiguous() + ag_v = torch.permute(ag_v.reshape(1, kv_len, kv_head, head_dim), (0, 2, 1, 3)).contiguous() ag_v = ag_v.repeat_interleave(q_head // kv_head, -3) offset_q0 = half_q_shard_len * self.rank @@ -258,16 +244,12 @@ def _gen_mask(offset, q_shard_len, kv_len): prefix = kv_len - q_len mask0 = _gen_mask(prefix + offset_q0, half_q_shard_len, kv_len) mask1 = _gen_mask(prefix + offset_q1, half_q_shard_len, kv_len) - out0 = torch.nn.functional.scaled_dot_product_attention( - q0_shard_permute, ag_k, ag_v, attn_mask=mask0) - out1 = torch.nn.functional.scaled_dot_product_attention( - q1_shard_permute, ag_k, ag_v, attn_mask=mask1) + out0 = torch.nn.functional.scaled_dot_product_attention(q0_shard_permute, ag_k, ag_v, attn_mask=mask0) + out1 = torch.nn.functional.scaled_dot_product_attention(q1_shard_permute, ag_k, ag_v, attn_mask=mask1) out = torch.cat((out0, out1), dim=2) # [1, q_head, q_shard_len, head_dim] else: cu_q_shard = q_shard[cu_seqlens_q_start:cu_seqlens_q_end, :, :].clone() - cu_q_shard_permute = torch.permute( - cu_q_shard.reshape(1, q_shard_len, q_head, head_dim), - (0, 2, 1, 3)).contiguous() + cu_q_shard_permute = torch.permute(cu_q_shard.reshape(1, q_shard_len, q_head, head_dim), (0, 2, 1, 3)).contiguous() total_size = kv_len * kv_head * head_dim ag_k = self.ag_k_buffer.reshape(-1)[:total_size].reshape(kv_len, kv_head, head_dim) @@ -284,19 +266,17 @@ def _gen_mask(offset, q_shard_len, kv_len): cu_v_shard, group=self.pg, ) - ag_k = torch.permute(ag_k.reshape(1, kv_len, kv_head, head_dim), - (0, 2, 1, 3)).contiguous() + ag_k = torch.permute(ag_k.reshape(1, kv_len, kv_head, head_dim), (0, 2, 1, 3)).contiguous() ag_k = ag_k.repeat_interleave(q_head // kv_head, -3) - ag_v = torch.permute(ag_v.reshape(1, kv_len, kv_head, head_dim), - (0, 2, 1, 3)).contiguous() + ag_v = torch.permute(ag_v.reshape(1, kv_len, kv_head, head_dim), (0, 2, 1, 3)).contiguous() ag_v = ag_v.repeat_interleave(q_head // kv_head, -3) offset = self.rank * q_shard_len prefix = kv_len - q_len mask = _gen_mask(prefix + offset, q_shard_len, kv_len) out = torch.nn.functional.scaled_dot_product_attention( - cu_q_shard_permute, ag_k, ag_v, - attn_mask=mask) # [1, q_head, q_shard_len, head_dim] + cu_q_shard_permute, ag_k, ag_v, attn_mask=mask + ) # [1, q_head, q_shard_len, head_dim] out = torch.permute(out.reshape(q_head, q_shard_len, head_dim), (1, 0, 2)).contiguous() out_list.append(out) @@ -327,29 +307,20 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) assert rank == local_rank and num_ranks == num_local_ranks, "only support single node for now" allocator = tilelang.get_allocator( - size=2**30, - device=device, - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**30, device=device, is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) cu_seqlens_q = torch.tensor(cu_seqlens_q_list, dtype=torch.int32, device=device) cu_seqlens_q = cu_seqlens_q // num_local_ranks cu_seqlens_k = torch.tensor(cu_seqlens_k_list, dtype=torch.int32, device=device) - q_shard = tilelang.tensor((cu_seqlens_q[-1], q_head, head_dim), - dtype=dtype, - allocator=allocator).normal_( - mean=0.0, std=0.5) - k_shards = tilelang.tensor((cu_seqlens_k[-1] // num_local_ranks, kv_head, head_dim), - dtype=dtype, - allocator=allocator, - return_peers=True) - v_shards = tilelang.tensor((cu_seqlens_k[-1] // num_local_ranks, kv_head, head_dim), - dtype=dtype, - allocator=allocator, - return_peers=True) + q_shard = tilelang.tensor((cu_seqlens_q[-1], q_head, head_dim), dtype=dtype, allocator=allocator).normal_(mean=0.0, std=0.5) + k_shards = tilelang.tensor( + (cu_seqlens_k[-1] // num_local_ranks, kv_head, head_dim), dtype=dtype, allocator=allocator, return_peers=True + ) + v_shards = tilelang.tensor( + (cu_seqlens_k[-1] // num_local_ranks, kv_head, head_dim), dtype=dtype, allocator=allocator, return_peers=True + ) k_shards[local_rank].normal_(mean=0.0, std=0.5) v_shards[local_rank].normal_(mean=0.0, std=0.5) @@ -386,12 +357,10 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): enable_zig_zag, ) - tilescale_out = tilescale_module( - q_shard, k_shards, v_shards, cu_seqlens_q, cu_seqlens_k, print_source=True) + tilescale_out = tilescale_module(q_shard, k_shards, v_shards, cu_seqlens_q, cu_seqlens_k, print_source=True) print(f"tilescale_out: {tilescale_out.shape}") - torch_out = torch_module(q_shard, k_shards[local_rank], v_shards[local_rank], cu_seqlens_q, - cu_seqlens_k) + torch_out = torch_module(q_shard, k_shards[local_rank], v_shards[local_rank], cu_seqlens_q, cu_seqlens_k) print(f"torch_out: {torch_out.shape}") atol = 1e-2 @@ -402,10 +371,7 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): print(f"rank {local_rank} check failed.❌") print(f"torch_out: {torch_out}, tilelang_out: {tilescale_out}") - _, tl_t = perf_fn( - lambda: tilescale_module(q_shard, k_shards, v_shards, cu_seqlens_q, cu_seqlens_k), - warmup=5, - rep=5) + _, tl_t = perf_fn(lambda: tilescale_module(q_shard, k_shards, v_shards, cu_seqlens_q, cu_seqlens_k), warmup=5, rep=5) print(f"rank {local_rank} tilescale time: {tl_t:.2f} ms") @@ -414,20 +380,16 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=1, help='Number of processes to spawn (default: 2)') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") parser.add_argument("--batch_size", type=int, default=2, help="batch size") parser.add_argument("--q_head", type=int, default=32, help="num q heads") parser.add_argument("--kv_head", type=int, default=8, help="num kv heads") parser.add_argument("--max_seqlen_q", type=int, default=8192, help="max sequence length of q") - parser.add_argument( - "--max_seqlen_k", type=int, default=12288, help="max sequence length of k/v") + parser.add_argument("--max_seqlen_k", type=int, default=12288, help="max sequence length of k/v") parser.add_argument("--head_dim", type=int, default=128, help="head dim") - parser.add_argument( - "--seqlens_q", type=int, nargs='+', default=[4096, 8192], help="sequence lengths of q") - parser.add_argument( - "--seqlens_k", type=int, nargs='+', default=[6144, 12288], help="sequence lengths of k/v") - parser.add_argument('--is_causal', action='store_true', help='causal') + parser.add_argument("--seqlens_q", type=int, nargs="+", default=[4096, 8192], help="sequence lengths of q") + parser.add_argument("--seqlens_k", type=int, nargs="+", default=[6144, 12288], help="sequence lengths of k/v") + parser.add_argument("--is_causal", action="store_true", help="causal") parser.add_argument( "--zig-zag", "--no-zig-zag", diff --git a/examples/distributed/example_summa.py b/examples/distributed/example_summa.py index 168517c09d..640a31de6b 100644 --- a/examples/distributed/example_summa.py +++ b/examples/distributed/example_summa.py @@ -11,7 +11,6 @@ def summa(MESH, M, N, K, block_M, block_N, block_K, dtype="float16"): - M_local = T.ceildiv(M, MESH) N_local = T.ceildiv(N, MESH) K_local = T.ceildiv(K, MESH) @@ -22,13 +21,13 @@ def summa(MESH, M, N, K, block_M, block_N, block_K, dtype="float16"): @T.prim_func def main( - A: T.Tensor((2, M_local, K_local), dtype), - B: T.Tensor((2, N_local, K_local), dtype), - A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), - B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), - C: T.Tensor((M_local, N_local), dtype), + A: T.Tensor((2, M_local, K_local), dtype), + B: T.Tensor((2, N_local, K_local), dtype), + A_signal_to: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + A_signal_from: T.Tensor((T.ceildiv(M, block_M),), "uint64"), + B_signal_to: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + B_signal_from: T.Tensor((T.ceildiv(N, block_N),), "uint64"), + C: T.Tensor((M_local, N_local), dtype), ): grid_size = T.min(sm_num, total_tiles) A_rows_per_block = T.ceildiv(M_local, grid_size) @@ -63,8 +62,11 @@ def main( T.address_of(A[(ko + 1) % 2, A_rows_per_block * block_id, 0]), T.address_of(A[ko % 2, A_rows_per_block * block_id, 0]), A_rows_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(A_signal_to[0]), 1, T.Amo.SIGNAL_ADD, - pe_mn * MESH + peer_k) + T.address_of(A_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + pe_mn * MESH + peer_k, + ) # broadcast B if pe_k == ko: @@ -80,8 +82,11 @@ def main( T.address_of(B[(ko + 1) % 2, B_cols_per_block * block_id, 0]), T.address_of(B[ko % 2, B_cols_per_block * block_id, 0]), B_cols_per_block * K_local * dtype_map[dtype].itemsize, - T.address_of(B_signal_to[0]), 1, T.Amo.SIGNAL_ADD, - pe_mn * MESH + peer_k) + T.address_of(B_signal_to[0]), + 1, + T.Amo.SIGNAL_ADD, + pe_mn * MESH + peer_k, + ) # TODO: check if __syncthreads() is needed T.signal_wait_until( @@ -96,7 +101,6 @@ def main( ) for w in T.serial(waves): - bx = (grid_size * w + block_id) // T.ceildiv(N_local, block_N) by = (grid_size * w + block_id) % T.ceildiv(N_local, block_N) @@ -158,11 +162,7 @@ def parse_args(): K_local = math.ceil(K / MESH) func = summa(MESH, M, N, K, block_M, block_N, block_K, args.dtype) - kernel = tilelang.compile( - func, pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) + kernel = tilelang.compile(func, pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) # Get CUDA Source if RANK == 0: @@ -183,9 +183,9 @@ def parse_args(): b_scatter_list = [] for r in range(WORLD_SIZE): rr, cc = divmod(r, MESH) - c_tile = C[M_local * rr:M_local * (rr + 1), N_local * cc:N_local * (cc + 1)] - a_tile = A[M_local * rr:M_local * (rr + 1), K_local * cc:K_local * (cc + 1)] - b_tile = B[N_local * cc:N_local * (cc + 1), K_local * rr:K_local * (rr + 1)] + c_tile = C[M_local * rr : M_local * (rr + 1), N_local * cc : N_local * (cc + 1)] + a_tile = A[M_local * rr : M_local * (rr + 1), K_local * cc : K_local * (cc + 1)] + b_tile = B[N_local * cc : N_local * (cc + 1), K_local * rr : K_local * (rr + 1)] c_scatter_list.append(c_tile.contiguous()) a_scatter_list.append(a_tile.contiguous()) @@ -220,7 +220,7 @@ def parse_args(): dist.barrier() if r == RANK: if torch.allclose(C_tilelang, ref, rtol=1e-2, atol=1e-2): - print('-' * 100) + print("-" * 100) print(f"[Rank {RANK}] ✅ Tilelang and Torch match") else: abs_error = torch.abs(C_tilelang - ref) @@ -230,7 +230,7 @@ def parse_args(): max_rel_error = rel_error.max().item() mismatch_ratio = (abs_error > (1e-2 + 1e-2 * torch.abs(ref))).float().mean().item() - print('-' * 100) + print("-" * 100) print(f"[Rank {RANK}] ❌ Tilelang and Torch mismatch") print(f"[Rank {RANK}] ref:\n{ref}") print(f"[Rank {RANK}] tilelang:\n{C_tilelang}") @@ -281,8 +281,7 @@ def reduce_local_time(local_time): total_flops = 2 * M * N * K -avg_time = reduce_local_time( - bench(kernel, A, B, A_signal_to, A_signal_from, B_signal_to, B_signal_from, C_tilelang)) +avg_time = reduce_local_time(bench(kernel, A, B, A_signal_to, A_signal_from, B_signal_to, B_signal_from, C_tilelang)) if RANK == 0: print(f"avg time of RANK {RANK}: {avg_time} ms") diff --git a/examples/distributed/gemm_rs_utils.py b/examples/distributed/gemm_rs_utils.py index 2d81414676..0a6634c393 100644 --- a/examples/distributed/gemm_rs_utils.py +++ b/examples/distributed/gemm_rs_utils.py @@ -79,16 +79,13 @@ def __post_init__(self): for buf in self.signal_bufs: assert buf.shape[0] >= 2 * self.world_size - self.scatter_signal_bufs = [buf[:self.world_size] for buf in self.signal_bufs] - self.rs_per_node_signal_bufs = [ - buf[self.world_size:self.world_size * 2] for buf in self.signal_bufs - ] + self.scatter_signal_bufs = [buf[: self.world_size] for buf in self.signal_bufs] + self.rs_per_node_signal_bufs = [buf[self.world_size : self.world_size * 2] for buf in self.signal_bufs] for node_id in range(self.nnodes): self.scatter_signal_buf_list_for_each_node.append( - self.scatter_signal_bufs[self.local_rank][node_id * - self.local_world_size:(node_id + 1) * - self.local_world_size]) + self.scatter_signal_bufs[self.local_rank][node_id * self.local_world_size : (node_id + 1) * self.local_world_size] + ) def reset_barriers(self) -> int: # self.scatter_signal_bufs[self.local_rank].fill_(0) @@ -101,9 +98,7 @@ def get_scatter_bufs_and_signal_for_each_node(self, input, node_id): M_per_node = M_per_rank * self.local_world_size M_start = node_id * M_per_node M_end = M_start + M_per_node - scatter_bufs_intra_node = [ - self.scatter_bufs[i][M_start:M_end] for i in range(self.local_world_size) - ] + scatter_bufs_intra_node = [self.scatter_bufs[i][M_start:M_end] for i in range(self.local_world_size)] return scatter_bufs_intra_node, self.scatter_signal_buf_list_for_each_node[node_id] @property @@ -131,36 +126,32 @@ def scatter_signal_buf(self) -> torch.Tensor: return self.scatter_signal_bufs[self.local_rank] -def create_reduce_scater_2d_ctx(max_M, - N, - rank, - world_size, - local_world_size, - dtype, - overlap_with_gemm=True, - num_reduction_sms=15) -> ReduceScatter2DContext: +def create_reduce_scater_2d_ctx( + max_M, N, rank, world_size, local_world_size, dtype, overlap_with_gemm=True, num_reduction_sms=15 +) -> ReduceScatter2DContext: """ - for num_reduction_sms: tunable param, 16 are enough for H800 - For H800, we overlap local reduce and inter-node p2p with intra-node scatter. - The reduction kernel bandwidth is not a bottleneck if it exceeds 450GB, so only a few SMs are needed. - For machines with higher intra_node bandwidth(e.g. H100), we may need to increase the number of SMs or redesign overlapping. + for num_reduction_sms: tunable param, 16 are enough for H800 + For H800, we overlap local reduce and inter-node p2p with intra-node scatter. + The reduction kernel bandwidth is not a bottleneck if it exceeds 450GB, so only a few SMs are needed. + For machines with higher intra_node bandwidth(e.g. H100), we may need to increase the number of SMs or redesign overlapping. """ assert world_size % local_world_size == 0 assert max_M % world_size == 0 scatter_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([max_M, N], dtype) - rs_per_node_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node( - [max_M // local_world_size, N], dtype) + rs_per_node_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([max_M // local_world_size, N], dtype) - p2p_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([max_M // local_world_size, N], - dtype) + p2p_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([max_M // local_world_size, N], dtype) # signal_buf: scatter_signal | rs_per_node_signal num_signal_bufs = 2 - signal_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([ - world_size * num_signal_bufs, - ], SIGNAL_DTYPE) + signal_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node( + [ + world_size * num_signal_bufs, + ], + SIGNAL_DTYPE, + ) # TODO: implement barrier_all_on_stream # barrier_all_on_stream(None, torch.cuda.current_stream()) @@ -187,7 +178,8 @@ def create_reduce_scater_2d_ctx(max_M, p2p_stream=p2p_stream, num_sync_sms=num_sync_sms, num_p2p_sms=num_p2p_sms, - num_reduction_sms=num_reduction_sms) + num_reduction_sms=num_reduction_sms, + ) return ctx @@ -211,14 +203,7 @@ class GEMMReduceScatterTensorParallelContext: GROUP_M: int = 8 stages: int = 3 - def update(self, - rs_stream, - output_dtype=None, - BLOCK_M=128, - BLOCK_N=256, - BLOCK_K=64, - GROUP_M=8, - stages=3): + def update(self, rs_stream, output_dtype=None, BLOCK_M=128, BLOCK_N=256, BLOCK_K=64, GROUP_M=8, stages=3): self.rs_stream = rs_stream self.output_dtype = output_dtype self.BLOCK_M = BLOCK_M @@ -233,20 +218,10 @@ def get_gemm_out_buf(self, input): return self.gemm_out_bufs[local_rank][:M] -def create_gemm_rs_context(max_M, - N, - rank, - world_size, - local_world_size, - output_dtype, - rs_stream, - BLOCK_M=128, - BLOCK_N=256, - BLOCK_K=64, - GROUP_M=8, - stages=3) -> GEMMReduceScatterTensorParallelContext: - rs_ctx = create_reduce_scater_2d_ctx( - max_M, N, rank, world_size, local_world_size, output_dtype, overlap_with_gemm=True) +def create_gemm_rs_context( + max_M, N, rank, world_size, local_world_size, output_dtype, rs_stream, BLOCK_M=128, BLOCK_N=256, BLOCK_K=64, GROUP_M=8, stages=3 +) -> GEMMReduceScatterTensorParallelContext: + rs_ctx = create_reduce_scater_2d_ctx(max_M, N, rank, world_size, local_world_size, output_dtype, overlap_with_gemm=True) NUM_SMS = torch.cuda.get_device_properties("cuda").multi_processor_count num_gemm_sms = NUM_SMS - rs_ctx.num_rs_sms gemm_out_bufs = pynvshmem.nvshmem_create_tensor_list_intra_node([max_M, N], output_dtype) @@ -260,5 +235,6 @@ def create_gemm_rs_context(max_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K, GROUP_M=GROUP_M, - stages=stages) + stages=stages, + ) return ctx diff --git a/examples/distributed/primitives/example_get_block.py b/examples/distributed/primitives/example_get_block.py index 9039fbf6cc..369e810324 100644 --- a/examples/distributed/primitives/example_get_block.py +++ b/examples/distributed/primitives/example_get_block.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def get_kernel(M, num_rank, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -42,12 +41,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) kernel = tilelang.compile(get_kernel(M, num_ranks, BLOCK_M, threads)) kernel.initialize(allocator=allocator) if local_rank == 0: @@ -78,9 +73,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=65536, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=65536, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/example_get_warp.py b/examples/distributed/primitives/example_get_warp.py index 49b1fc02a0..80d34d2ce5 100644 --- a/examples/distributed/primitives/example_get_warp.py +++ b/examples/distributed/primitives/example_get_warp.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def get_kernel(M, num_rank, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -31,7 +30,8 @@ def main( dst=T.address_of(dst[warp_start]), size=warp_copy_size, src_pe=rank[0] ^ 1, - unroll_factor=4) + unroll_factor=4, + ) T.fence_sys() return main @@ -45,12 +45,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) kernel = tilelang.compile(get_kernel(M, num_ranks, BLOCK_M, threads)) kernel.initialize(allocator=allocator) if local_rank == 0: @@ -81,9 +77,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=65536, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=65536, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/example_put_block.py b/examples/distributed/primitives/example_put_block.py index 19e22b1ce3..3b59c6c56d 100644 --- a/examples/distributed/primitives/example_put_block.py +++ b/examples/distributed/primitives/example_put_block.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def kernel_(M, num_rank, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -41,12 +40,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) kernel = tilelang.compile(kernel_(M, num_ranks, BLOCK_M, threads)) kernel.initialize(allocator=allocator) if local_rank == 0: @@ -77,9 +72,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=65536, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=65536, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/example_put_warp.py b/examples/distributed/primitives/example_put_warp.py index a0351f6bf6..4d397bc9d0 100644 --- a/examples/distributed/primitives/example_put_warp.py +++ b/examples/distributed/primitives/example_put_warp.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def kernel_(M, num_rank, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "bfloat16"), - src: T.Tensor((M), "bfloat16"), + dst: T.Tensor((M), "bfloat16"), + src: T.Tensor((M), "bfloat16"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -31,7 +30,8 @@ def main( dst=T.address_of(dst[warp_start]), size=warp_copy_size, dst_pe=rank[0] ^ 1, - unroll_factor=4) + unroll_factor=4, + ) return main @@ -44,12 +44,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) kernel = tilelang.compile(kernel_(M, num_ranks, BLOCK_M, threads)) kernel.initialize(allocator=allocator) if local_rank == 0: @@ -80,9 +76,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=65536, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=65536, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/example_remote_st.py b/examples/distributed/primitives/example_remote_st.py index 251e5e08b3..05f95f50d7 100644 --- a/examples/distributed/primitives/example_remote_st.py +++ b/examples/distributed/primitives/example_remote_st.py @@ -8,15 +8,14 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def kernel_(M, num_rank, block_M, threads): - @T.prim_func def main( - dst: T.Tensor((M), "float32"), - src: T.Tensor((M), "float32"), + dst: T.Tensor((M), "float32"), + src: T.Tensor((M), "float32"), ): with T.Kernel(T.ceildiv(M, block_M), threads=threads) as (bx): rank = T.alloc_local([1], "uint64") @@ -36,12 +35,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) kernel = tilelang.compile(kernel_(M, num_ranks, BLOCK_M, threads)) kernel.initialize(allocator=allocator) if local_rank == 0: @@ -72,9 +67,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=1024, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=1024, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/example_sync.py b/examples/distributed/primitives/example_sync.py index fa5949a3fa..eba17c442b 100644 --- a/examples/distributed/primitives/example_sync.py +++ b/examples/distributed/primitives/example_sync.py @@ -7,7 +7,7 @@ from tilelang.distributed import init_dist tilelang.disable_cache() -os.environ['NCCL_DEBUG'] = 'WARN' # silence NCCL log +os.environ["NCCL_DEBUG"] = "WARN" # silence NCCL log def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): @@ -16,12 +16,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): rank, num_ranks, group = init_dist(local_rank, num_local_ranks) allocator = tilelang.get_allocator( - size=2**25, - device="cuda", - is_distributed=True, - local_rank=local_rank, - num_local_ranks=num_local_ranks, - group=group) + size=2**25, device="cuda", is_distributed=True, local_rank=local_rank, num_local_ranks=num_local_ranks, group=group + ) dst = tilelang.tensor((M), torch.float32, allocator=allocator) srcs = tilelang.tensor((M), torch.float32, allocator=allocator, return_peers=True) @@ -39,9 +35,8 @@ def main(local_rank: int, num_local_ranks: int, args: argparse.Namespace): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--num-processes', type=int, default=2, help='Number of processes to spawn (default: 2)') - parser.add_argument('--M', type=int, default=65536, help='M dimension') + parser.add_argument("--num-processes", type=int, default=2, help="Number of processes to spawn (default: 2)") + parser.add_argument("--M", type=int, default=65536, help="M dimension") args = parser.parse_args() num_processes = args.num_processes diff --git a/examples/distributed/primitives/test_get_block.py b/examples/distributed/primitives/test_get_block.py index 6675965b0c..63c52435ac 100644 --- a/examples/distributed/primitives/test_get_block.py +++ b/examples/distributed/primitives/test_get_block.py @@ -5,6 +5,7 @@ import example_get_block +@tilelang.testing.requires_distributed @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_get_block(): diff --git a/examples/distributed/primitives/test_get_warp.py b/examples/distributed/primitives/test_get_warp.py index c482fa394b..a542361fa4 100644 --- a/examples/distributed/primitives/test_get_warp.py +++ b/examples/distributed/primitives/test_get_warp.py @@ -5,6 +5,7 @@ import example_get_warp +@tilelang.testing.requires_distributed @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_get_warp(): diff --git a/examples/distributed/primitives/test_put_block.py b/examples/distributed/primitives/test_put_block.py index 83ef08fb26..2e31de6275 100644 --- a/examples/distributed/primitives/test_put_block.py +++ b/examples/distributed/primitives/test_put_block.py @@ -5,6 +5,7 @@ import example_put_block +@tilelang.testing.requires_distributed @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_put_block(): diff --git a/examples/distributed/primitives/test_put_warp.py b/examples/distributed/primitives/test_put_warp.py index de4cc14761..3b289cd27d 100644 --- a/examples/distributed/primitives/test_put_warp.py +++ b/examples/distributed/primitives/test_put_warp.py @@ -5,6 +5,7 @@ import example_put_warp +@tilelang.testing.requires_distributed @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_put_warp(): diff --git a/examples/distributed/reduce_scatter.py b/examples/distributed/reduce_scatter.py index fcb8e997f4..6ddc5707e9 100644 --- a/examples/distributed/reduce_scatter.py +++ b/examples/distributed/reduce_scatter.py @@ -72,16 +72,13 @@ def __post_init__(self): for buf in self.signal_bufs: assert buf.shape[0] >= 2 * self.world_size - self.scatter_signal_bufs = [buf[:self.world_size] for buf in self.signal_bufs] - self.rs_per_node_signal_bufs = [ - buf[self.world_size:self.world_size * 2] for buf in self.signal_bufs - ] + self.scatter_signal_bufs = [buf[: self.world_size] for buf in self.signal_bufs] + self.rs_per_node_signal_bufs = [buf[self.world_size : self.world_size * 2] for buf in self.signal_bufs] for node_id in range(self.nnodes): self.scatter_signal_buf_list_for_each_node.append( - self.scatter_signal_bufs[self.local_rank][node_id * - self.local_world_size:(node_id + 1) * - self.local_world_size]) + self.scatter_signal_bufs[self.local_rank][node_id * self.local_world_size : (node_id + 1) * self.local_world_size] + ) def reset_barriers(self): self.signal_bufs[self.local_rank].fill_(0) @@ -93,9 +90,7 @@ def get_scatter_bufs_and_signal_for_each_node(self, input, node_id): M_per_node = M_per_rank * self.local_world_size M_start = node_id * M_per_node M_end = M_start + M_per_node - scatter_bufs_intra_node = [ - self.scatter_bufs[i][M_start:M_end] for i in range(self.local_world_size) - ] + scatter_bufs_intra_node = [self.scatter_bufs[i][M_start:M_end] for i in range(self.local_world_size)] return scatter_bufs_intra_node, self.scatter_signal_buf_list_for_each_node[node_id] @property @@ -123,50 +118,29 @@ def scatter_signal_buf(self) -> torch.Tensor: return self.scatter_signal_bufs[self.local_rank] -def create_reduce_scater_2d_ctx(max_M, - N, - rank, - world_size, - local_world_size, - dtype, - allocator, - overlap_with_gemm=True, - num_reduction_sms=15) -> ReduceScatter2DContext: +def create_reduce_scater_2d_ctx( + max_M, N, rank, world_size, local_world_size, dtype, allocator, overlap_with_gemm=True, num_reduction_sms=15 +) -> ReduceScatter2DContext: """ - for num_reduction_sms: tunable param, 16 are enough for H800 - For H800, we overlap local reduce and inter-node p2p with intra-node scatter. - The reduction kernel bandwidth is not a bottleneck if it exceeds 450GB, so only a few SMs are needed. - For machines with higher intra_node bandwidth(e.g. H100), we may need to increase the number of SMs or redesign overlapping. + for num_reduction_sms: tunable param, 16 are enough for H800 + For H800, we overlap local reduce and inter-node p2p with intra-node scatter. + The reduction kernel bandwidth is not a bottleneck if it exceeds 450GB, so only a few SMs are needed. + For machines with higher intra_node bandwidth(e.g. H100), we may need to increase the number of SMs or redesign overlapping. """ assert world_size % local_world_size == 0 assert max_M % world_size == 0 scatter_bufs = tilelang.tensor((max_M, N), dtype, allocator=allocator, return_peers=True) - rs_per_node_bufs = tilelang.tensor((max_M // local_world_size, N), - dtype, - allocator=allocator, - return_peers=True) - p2p_bufs = tilelang.tensor((max_M // local_world_size, N), - dtype, - allocator=allocator, - return_peers=True) + rs_per_node_bufs = tilelang.tensor((max_M // local_world_size, N), dtype, allocator=allocator, return_peers=True) + p2p_bufs = tilelang.tensor((max_M // local_world_size, N), dtype, allocator=allocator, return_peers=True) # signal_buf: scatter_signal | rs_per_node_signal num_signal_bufs = 2 - signal_bufs = tilelang.tensor((world_size * num_signal_bufs), - dtype=torch.uint32, - allocator=allocator, - return_peers=True) - symm_barriers = tilelang.tensor((local_world_size,), - torch.int32, - allocator=allocator, - return_peers=True) + signal_bufs = tilelang.tensor((world_size * num_signal_bufs), dtype=torch.uint32, allocator=allocator, return_peers=True) + symm_barriers = tilelang.tensor((local_world_size,), torch.int32, allocator=allocator, return_peers=True) symm_barriers[rank] = 0 - counter_signal_buf = tilelang.tensor((local_world_size), - dtype=torch.uint32, - allocator=allocator, - return_peers=True) + counter_signal_buf = tilelang.tensor((local_world_size), dtype=torch.uint32, allocator=allocator, return_peers=True) dist.barrier() @@ -191,29 +165,21 @@ def create_reduce_scater_2d_ctx(max_M, reduction_stream=reduction_stream, num_sync_sms=num_sync_sms, num_p2p_sms=num_p2p_sms, - num_reduction_sms=num_reduction_sms) + num_reduction_sms=num_reduction_sms, + ) return ctx @tilelang.jit -def kernel_ring_reduce_tma(M_per_rank, - N, - block_M, - block_N, - begin_idx, - num_splits, - threads, - persistent=False, - dtype="float16", - accum_dtype="float"): - +def kernel_ring_reduce_tma( + M_per_rank, N, block_M, block_N, begin_idx, num_splits, threads, persistent=False, dtype="float16", accum_dtype="float" +): @T.prim_func def _kernel_ring_reduce_tma( - C: T.Tensor((M_per_rank * num_splits, N), dtype), - output: T.Tensor((M_per_rank, N), dtype), + C: T.Tensor((M_per_rank * num_splits, N), dtype), + output: T.Tensor((M_per_rank, N), dtype), ): - with T.Kernel( - T.ceildiv(M_per_rank, block_M), T.ceildiv(N, block_N), threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(M_per_rank, block_M), T.ceildiv(N, block_N), threads=threads) as (bx, by): data_shared = T.alloc_shared((block_M, block_N), dtype) init_shared = T.alloc_shared((block_M, block_N), dtype) data_local = T.alloc_fragment((block_M, block_N), dtype) @@ -233,10 +199,7 @@ def _kernel_ring_reduce_tma( return _kernel_ring_reduce_tma -def _wait_eq_cuda(signal_tensor: torch.Tensor, - signal: int, - stream: Optional[torch.cuda.Stream] = None, - require_i64=False): +def _wait_eq_cuda(signal_tensor: torch.Tensor, signal: int, stream: Optional[torch.cuda.Stream] = None, require_i64=False): stream = stream or torch.cuda.current_stream() if signal_tensor.dtype in (torch.int32, torch.uint32): (err,) = cuda.cuStreamWaitValue32( @@ -258,11 +221,13 @@ def _wait_eq_cuda(signal_tensor: torch.Tensor, raise Exception(f"Unsupported signal dtype {signal_tensor.dtype}") -def intra_node_scatter(input_intra_node, - scatter_bufs_intra_node: List[torch.Tensor], - scatter_signal_buf_intra_node: torch.Tensor, - local_rank, - overlap_with_gemm=True): +def intra_node_scatter( + input_intra_node, + scatter_bufs_intra_node: List[torch.Tensor], + scatter_signal_buf_intra_node: torch.Tensor, + local_rank, + overlap_with_gemm=True, +): M, N = input_intra_node.shape local_world_size = len(scatter_bufs_intra_node) M_per_rank = M // local_world_size @@ -275,10 +240,8 @@ def intra_node_scatter(input_intra_node, # print(f"scatter_signal_buf_intra_node[remote_local_rank]: {scatter_signal_buf_intra_node[remote_local_rank]}") if overlap_with_gemm: _wait_eq_cuda(scatter_signal_buf_intra_node[remote_local_rank], 1, stream) - src = input_intra_node[remote_local_rank * M_per_rank:(remote_local_rank + 1) * - M_per_rank, :] - dst = scatter_bufs_intra_node[remote_local_rank][local_rank * M_per_rank:(local_rank + 1) * - M_per_rank, :] + src = input_intra_node[remote_local_rank * M_per_rank : (remote_local_rank + 1) * M_per_rank, :] + dst = scatter_bufs_intra_node[remote_local_rank][local_rank * M_per_rank : (local_rank + 1) * M_per_rank, :] with torch.cuda.stream(stream): dst.copy_(src) @@ -292,21 +255,15 @@ def ring_reduce_tma( ): total_M, N = input.shape M_per_split = total_M // num_splits - assert output.shape[ - 0] == M_per_split and total_M % num_splits == 0, f"{output.shape}, {total_M}, {num_splits}" + assert output.shape[0] == M_per_split and total_M % num_splits == 0, f"{output.shape}, {total_M}, {num_splits}" def alloc_fn(size, alignment, stream): return torch.empty(size, device="cuda", dtype=torch.int8) if num_sms == -1: ring_reduce_tma_func = kernel_ring_reduce_tma( - M_per_split, - N, - block_M=64, - block_N=64, - begin_idx=begin_idx, - num_splits=num_splits, - threads=128) + M_per_split, N, block_M=64, block_N=64, begin_idx=begin_idx, num_splits=num_splits, threads=128 + ) # if begin_idx == 0: # print(ring_reduce_tma_func.get_kernel_source()) ring_reduce_tma_func(input, output, stream=torch.cuda.current_stream().cuda_stream) @@ -345,9 +302,7 @@ def ring_reduce( raise NotImplementedError("Only Hopper ring reduce is implemented now.") -def reduce_scatter_for_each_node(input: torch.Tensor, - ctx: ReduceScatter2DContext, - output: Optional[torch.Tensor] = None): +def reduce_scatter_for_each_node(input: torch.Tensor, ctx: ReduceScatter2DContext, output: Optional[torch.Tensor] = None): world_size = ctx.world_size local_world_size = ctx.local_world_size local_rank = ctx.local_rank @@ -364,18 +319,14 @@ def reduce_scatter_for_each_node(input: torch.Tensor, stream = torch.cuda.current_stream() for n in range(0, nnodes): cur_node_id = (node_id + n + 1) % nnodes - input_intra_node = input[cur_node_id * M_per_node:(cur_node_id + 1) * M_per_node] - scatter_bufs_intra_node, scatter_signal_buf_intra_node = ctx.get_scatter_bufs_and_signal_for_each_node( - input, cur_node_id) + input_intra_node = input[cur_node_id * M_per_node : (cur_node_id + 1) * M_per_node] + scatter_bufs_intra_node, scatter_signal_buf_intra_node = ctx.get_scatter_bufs_and_signal_for_each_node(input, cur_node_id) intra_node_scatter( - input_intra_node, - scatter_bufs_intra_node, - scatter_signal_buf_intra_node, - local_rank, - overlap_with_gemm=ctx.overlap_with_gemm) + input_intra_node, scatter_bufs_intra_node, scatter_signal_buf_intra_node, local_rank, overlap_with_gemm=ctx.overlap_with_gemm + ) # ring reduce intra node - rs_buf_cur_node = rs_per_node_buf[M_per_rank * cur_node_id:(cur_node_id + 1) * M_per_rank] + rs_buf_cur_node = rs_per_node_buf[M_per_rank * cur_node_id : (cur_node_id + 1) * M_per_rank] # nvshmem_barrier_all_on_stream(stream) reduction_stream.wait_stream(stream) with torch.cuda.stream(reduction_stream): @@ -385,7 +336,8 @@ def reduce_scatter_for_each_node(input: torch.Tensor, reduce_out_buf, local_rank, local_world_size, - num_sms=-1 if n == nnodes - 1 else num_reduction_sms) + num_sms=-1 if n == nnodes - 1 else num_reduction_sms, + ) # inter node p2p if nnodes > 1: @@ -408,12 +360,10 @@ def reduce_scatter_for_each_node(input: torch.Tensor, stream.wait_stream(reduction_stream) if nnodes == 1: return output - return p2p_buf[:M_per_rank * nnodes] + return p2p_buf[: M_per_rank * nnodes] -def reduce_scatter_multi_node(input: torch.Tensor, - ctx: ReduceScatter2DContext, - output: Optional[torch.Tensor] = None): +def reduce_scatter_multi_node(input: torch.Tensor, ctx: ReduceScatter2DContext, output: Optional[torch.Tensor] = None): """ A hierarchical reduce-scatter implementation that overlaps the intra-node scatter with the local reduce and the inter-node p2p(after reduce). It also provides a rank-wise @@ -443,9 +393,7 @@ def reduce_scatter_multi_node(input: torch.Tensor, return output -def reduce_scatter_2d_op(input: torch.Tensor, - ctx: ReduceScatter2DContext, - output: Optional[torch.Tensor] = None): +def reduce_scatter_2d_op(input: torch.Tensor, ctx: ReduceScatter2DContext, output: Optional[torch.Tensor] = None): M, N = input.shape assert input.dtype == ctx.dtype assert ctx.max_M >= M and ctx.N == N diff --git a/examples/distributed/sp_ag_attention_intra_node.py b/examples/distributed/sp_ag_attention_intra_node.py index 421f133931..b66684ae6e 100644 --- a/examples/distributed/sp_ag_attention_intra_node.py +++ b/examples/distributed/sp_ag_attention_intra_node.py @@ -10,10 +10,13 @@ @tilelang.jit -def barrier_all_blocks_sys_kernel(num_local_rank,): - +def barrier_all_blocks_sys_kernel( + num_local_rank, +): @T.prim_func - def main(barrier: T.Tensor((num_local_rank), "int32"),): + def main( + barrier: T.Tensor((num_local_rank), "int32"), + ): with T.Kernel(1, threads=32): T.barrier_blocks(barrier) @@ -25,28 +28,36 @@ def main(barrier: T.Tensor((num_local_rank), "int32"),): tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, compile_flags=[ - "-O3", "-Wno-deprecated-declarations", "-U__CUDA_NO_HALF_OPERATORS__", - "-U__CUDA_NO_HALF_CONVERSIONS__", "-U__CUDA_NO_HALF2_OPERATORS__", - "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", "--expt-relaxed-constexpr", "--expt-extended-lambda", - "--ptxas-options=-v,--register-usage-level=10", "-DNDEBUG" + "-O3", + "-Wno-deprecated-declarations", + "-U__CUDA_NO_HALF_OPERATORS__", + "-U__CUDA_NO_HALF_CONVERSIONS__", + "-U__CUDA_NO_HALF2_OPERATORS__", + "-U__CUDA_NO_BFLOAT16_CONVERSIONS__", + "--expt-relaxed-constexpr", + "--expt-extended-lambda", + "--ptxas-options=-v,--register-usage-level=10", + "-DNDEBUG", ], ) -def flashattn(batch_size, - groups, - UQ, - UKV, - heads, - dim, - is_causal, - enable_zig_zag, - enable_specialized, - rank, - num_ranks, - block_M=64, - block_N=64, - num_stages=1, - threads=128): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) +def flashattn( + batch_size, + groups, + UQ, + UKV, + heads, + dim, + is_causal, + enable_zig_zag, + enable_specialized, + rank, + num_ranks, + block_M=64, + block_N=64, + num_stages=1, + threads=128, +): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [UQ, heads, dim] kv_shape = [UKV, head_kv, dim] @@ -83,8 +94,7 @@ def inner( global_offset_q: T.int32, kv_len_per_sp_block: T.int32, ): - T.copy(Q_unpad[q_start_idx + bx * block_M:q_start_idx + (bx + 1) * block_M, head_idx, :], - Q_shared) + T.copy(Q_unpad[q_start_idx + bx * block_M : q_start_idx + (bx + 1) * block_M, head_idx, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) @@ -92,30 +102,30 @@ def inner( prefix_len = k_current_seqlen - q_current_seqlen * num_ranks loop_range = ( - T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) - if is_causal else T.ceildiv(k_current_seqlen, block_N)) + T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(k_current_seqlen, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): sp_block_idx = (k * block_N) // kv_len_per_sp_block - wait_rank = ( - sp_block_idx if sp_block_idx < num_ranks else 2 * num_ranks - sp_block_idx - 1) - kv_load_offset = ((k * block_N) % kv_len_per_sp_block + - sp_block_idx // num_ranks * kv_len_per_sp_block + wait_rank * - (k_current_seqlen // num_ranks)) - T.copy( - K_unpad[k_start_idx + kv_load_offset:k_start_idx + kv_load_offset + block_N, - kv_head_idx, :], K_shared) + wait_rank = sp_block_idx if sp_block_idx < num_ranks else 2 * num_ranks - sp_block_idx - 1 + kv_load_offset = ( + (k * block_N) % kv_len_per_sp_block + + sp_block_idx // num_ranks * kv_len_per_sp_block + + wait_rank * (k_current_seqlen // num_ranks) + ) + T.copy(K_unpad[k_start_idx + kv_load_offset : k_start_idx + kv_load_offset + block_N, kv_head_idx, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else( - (prefix_len + global_offset_q + bx * block_M + i < k * block_N + j) or - (bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), -1e9, 0) + (prefix_len + global_offset_q + bx * block_M + i < k * block_N + j) + or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), + -1e9, + 0, + ) else: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), -1e9, 0) + acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), -1e9, 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @@ -138,9 +148,7 @@ def inner( for i, j in T.Parallel(block_M, dim): acc_o[i, j] *= scores_scale[i] - T.copy( - V_unpad[v_start_idx + kv_load_offset:v_start_idx + kv_load_offset + block_N, - kv_head_idx, :], V_shared) + T.copy(V_unpad[v_start_idx + kv_load_offset : v_start_idx + kv_load_offset + block_N, kv_head_idx, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) @@ -154,17 +162,15 @@ def inner( @T.prim_func def main( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(kv_shape, dtype), - V_unpad: T.Tensor(kv_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(kv_shape, dtype), + V_unpad: T.Tensor(kv_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), + cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, - threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -194,24 +200,46 @@ def main( global_offset_q = q_current_seqlen * rank kv_len_per_sp_block = k_current_seqlen // num_ranks - inner(Q_unpad, K_unpad, V_unpad, Output_unpad, Q_shared, K_shared, V_shared, O_shared, - acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum, q_start_idx, k_start_idx, v_start_idx, q_current_seqlen, k_current_seqlen, - bx, head_idx, kv_head_idx, global_offset_q, kv_len_per_sp_block) + inner( + Q_unpad, + K_unpad, + V_unpad, + Output_unpad, + Q_shared, + K_shared, + V_shared, + O_shared, + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + scores_scale, + scores_sum, + logsum, + q_start_idx, + k_start_idx, + v_start_idx, + q_current_seqlen, + k_current_seqlen, + bx, + head_idx, + kv_head_idx, + global_offset_q, + kv_len_per_sp_block, + ) @T.prim_func def main_zigzag( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(kv_shape, dtype), - V_unpad: T.Tensor(kv_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(kv_shape, dtype), + V_unpad: T.Tensor(kv_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), + cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, - threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -239,27 +267,51 @@ def main_zigzag( k_current_seqlen = k_end_idx - k_start_idx half_q_shard_len = q_current_seqlen // 2 - global_offset_q = rank * half_q_shard_len if bx * block_M < half_q_shard_len else \ - q_current_seqlen * num_ranks - (rank + 2) * half_q_shard_len + global_offset_q = ( + rank * half_q_shard_len if bx * block_M < half_q_shard_len else q_current_seqlen * num_ranks - (rank + 2) * half_q_shard_len + ) kv_len_per_sp_block = k_current_seqlen // (2 * num_ranks) - inner(Q_unpad, K_unpad, V_unpad, Output_unpad, Q_shared, K_shared, V_shared, O_shared, - acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum, q_start_idx, k_start_idx, v_start_idx, q_current_seqlen, k_current_seqlen, - bx, head_idx, kv_head_idx, global_offset_q, kv_len_per_sp_block) + inner( + Q_unpad, + K_unpad, + V_unpad, + Output_unpad, + Q_shared, + K_shared, + V_shared, + O_shared, + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + scores_scale, + scores_sum, + logsum, + q_start_idx, + k_start_idx, + v_start_idx, + q_current_seqlen, + k_current_seqlen, + bx, + head_idx, + kv_head_idx, + global_offset_q, + kv_len_per_sp_block, + ) @T.prim_func def main_specialized( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(kv_shape, dtype), - V_unpad: T.Tensor(kv_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(kv_shape, dtype), + V_unpad: T.Tensor(kv_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), + cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=384) as (bx_, by, bz): + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=384) as (bx_, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -279,10 +331,12 @@ def main_specialized( bar_k_release = T.alloc_barrier(arrive_count=256) bar_v_release = T.alloc_barrier(arrive_count=256) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), - }) + T.annotate_layout( + { + O_shared: tilelang.layout.make_swizzled_layout(O_shared), + Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), + } + ) batch_idx = bz head_idx = by @@ -311,7 +365,9 @@ def main_specialized( prefix_len = k_current_seqlen - q_current_seqlen * num_ranks loop_range = ( T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) - if is_causal else T.ceildiv(k_current_seqlen, block_N)) + if is_causal + else T.ceildiv(k_current_seqlen, block_N) + ) T.barrier_wait(bar_q_ready, 0) for k in T.serial(loop_range): @@ -319,21 +375,18 @@ def main_specialized( for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else( (prefix_len + global_offset_q + bx * block_M + i < k * block_N + j) - or (bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), -1e9, 0) + or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), + -1e9, + 0, + ) else: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -1e9, 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), -1e9, 0 + ) T.barrier_wait(bar_k_ready, k % 2) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.barrier_arrive(bar_k_release) T.copy(scores_max, scores_max_prev) @@ -371,35 +424,30 @@ def main_specialized( prefix_len = k_current_seqlen - q_current_seqlen * num_ranks loop_range = ( T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) - if is_causal else T.ceildiv(k_current_seqlen, block_N)) - T.copy( - Q_unpad[q_start_idx + bx * block_M:q_start_idx + (bx + 1) * block_M, - head_idx, :], Q_shared) + if is_causal + else T.ceildiv(k_current_seqlen, block_N) + ) + T.copy(Q_unpad[q_start_idx + bx * block_M : q_start_idx + (bx + 1) * block_M, head_idx, :], Q_shared) T.barrier_arrive(bar_q_ready) for k in T.serial(loop_range): T.barrier_wait(bar_k_release, (k + 1) % 2) - T.copy( - K_unpad[k_start_idx + (k * block_N):k_start_idx + (k * block_N) + block_N, - kv_head_idx, :], K_shared) + T.copy(K_unpad[k_start_idx + (k * block_N) : k_start_idx + (k * block_N) + block_N, kv_head_idx, :], K_shared) T.barrier_arrive(bar_k_ready) T.barrier_wait(bar_v_release, (k + 1) % 2) - T.copy( - V_unpad[v_start_idx + (k * block_N):v_start_idx + (k * block_N) + block_N, - kv_head_idx, :], V_shared) + T.copy(V_unpad[v_start_idx + (k * block_N) : v_start_idx + (k * block_N) + block_N, kv_head_idx, :], V_shared) T.barrier_arrive(bar_v_ready) @T.prim_func def main_specialized_zigzag( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(kv_shape, dtype), - V_unpad: T.Tensor(kv_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(kv_shape, dtype), + V_unpad: T.Tensor(kv_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), + cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=384) as (bx_, by, bz): + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=384) as (bx_, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -420,10 +468,12 @@ def main_specialized_zigzag( bar_k_release = T.alloc_barrier(arrive_count=256) bar_v_release = T.alloc_barrier(arrive_count=256) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), - }) + T.annotate_layout( + { + O_shared: tilelang.layout.make_swizzled_layout(O_shared), + Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), + } + ) batch_idx = bz head_idx = by @@ -441,8 +491,9 @@ def main_specialized_zigzag( bx = T.ceildiv(max_seqlen_q, block_M) - bx_ - 1 half_q_shard_len = q_current_seqlen // 2 - global_offset_q = rank * half_q_shard_len if bx * block_M < half_q_shard_len else \ - q_current_seqlen * num_ranks - (rank + 2) * half_q_shard_len + global_offset_q = ( + rank * half_q_shard_len if bx * block_M < half_q_shard_len else q_current_seqlen * num_ranks - (rank + 2) * half_q_shard_len + ) kv_len_per_sp_block = k_current_seqlen // (2 * num_ranks) tid = T.get_thread_binding(0) @@ -455,7 +506,9 @@ def main_specialized_zigzag( prefix_len = k_current_seqlen - q_current_seqlen * num_ranks loop_range = ( T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) - if is_causal else T.ceildiv(k_current_seqlen, block_N)) + if is_causal + else T.ceildiv(k_current_seqlen, block_N) + ) T.barrier_wait(bar_q_ready, 0) for k in T.serial(loop_range): @@ -463,21 +516,18 @@ def main_specialized_zigzag( for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else( (prefix_len + global_offset_q + bx * block_M + i < k * block_N + j) - or (bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), -1e9, 0) + or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), + -1e9, + 0, + ) else: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -1e9, 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i >= q_current_seqlen or k * block_N + j >= k_current_seqlen), -1e9, 0 + ) T.barrier_wait(bar_k_ready, k % 2) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.barrier_arrive(bar_k_release) T.copy(scores_max, scores_max_prev) @@ -515,28 +565,24 @@ def main_specialized_zigzag( prefix_len = k_current_seqlen - q_current_seqlen * num_ranks loop_range = ( T.ceildiv(prefix_len + global_offset_q + (bx + 1) * block_M, block_N) - if is_causal else T.ceildiv(k_current_seqlen, block_N)) - T.copy( - Q_unpad[q_start_idx + bx * block_M:q_start_idx + (bx + 1) * block_M, - head_idx, :], Q_shared) + if is_causal + else T.ceildiv(k_current_seqlen, block_N) + ) + T.copy(Q_unpad[q_start_idx + bx * block_M : q_start_idx + (bx + 1) * block_M, head_idx, :], Q_shared) T.barrier_arrive(bar_q_ready) for k in T.serial(loop_range): sp_block_idx = (k * block_N) // kv_len_per_sp_block - wait_rank = ( - sp_block_idx if sp_block_idx < num_ranks else 2 * num_ranks - sp_block_idx - - 1) - kv_load_offset = ((k * block_N) % kv_len_per_sp_block + - sp_block_idx // num_ranks * kv_len_per_sp_block + wait_rank * - (k_current_seqlen // num_ranks)) + wait_rank = sp_block_idx if sp_block_idx < num_ranks else 2 * num_ranks - sp_block_idx - 1 + kv_load_offset = ( + (k * block_N) % kv_len_per_sp_block + + sp_block_idx // num_ranks * kv_len_per_sp_block + + wait_rank * (k_current_seqlen // num_ranks) + ) T.barrier_wait(bar_k_release, (k + 1) % 2) - T.copy( - K_unpad[k_start_idx + kv_load_offset:k_start_idx + kv_load_offset + block_N, - kv_head_idx, :], K_shared) + T.copy(K_unpad[k_start_idx + kv_load_offset : k_start_idx + kv_load_offset + block_N, kv_head_idx, :], K_shared) T.barrier_arrive(bar_k_ready) T.barrier_wait(bar_v_release, (k + 1) % 2) - T.copy( - V_unpad[v_start_idx + kv_load_offset:v_start_idx + kv_load_offset + block_N, - kv_head_idx, :], V_shared) + T.copy(V_unpad[v_start_idx + kv_load_offset : v_start_idx + kv_load_offset + block_N, kv_head_idx, :], V_shared) T.barrier_arrive(bar_v_ready) if enable_specialized: @@ -571,16 +617,14 @@ def create_sp_ag_attention_context_intra_node( device, allocator, ): - ag_k_buffers = tilelang.tensor((batch_size * max_seqlen_k, kv_head, head_dim), - dtype=input_dtype, - allocator=allocator, - return_peers=True) + ag_k_buffers = tilelang.tensor( + (batch_size * max_seqlen_k, kv_head, head_dim), dtype=input_dtype, allocator=allocator, return_peers=True + ) ag_k_buffer = ag_k_buffers[rank] - ag_v_buffers = tilelang.tensor((batch_size * max_seqlen_k, kv_head, head_dim), - dtype=input_dtype, - allocator=allocator, - return_peers=True) + ag_v_buffers = tilelang.tensor( + (batch_size * max_seqlen_k, kv_head, head_dim), dtype=input_dtype, allocator=allocator, return_peers=True + ) ag_v_buffer = ag_v_buffers[rank] attn_output_buffer = torch.empty( @@ -603,14 +647,16 @@ def create_sp_ag_attention_context_intra_node( ag_v_buffer=ag_v_buffer, attn_output_buffer=attn_output_buffer, ag_stream=ag_stream, - barrier=barrier) + barrier=barrier, + ) return ctx def barrier_all_on_stream(barrier: torch.Tensor, stream: torch.cuda.Stream, world_size: int): barrier_all_blocks_sys_func = barrier_all_blocks_sys_kernel(world_size) - barrier_all_blocks_sys_func(barrier, stream=stream.cuda_stream) + with torch.cuda.stream(stream): + barrier_all_blocks_sys_func(barrier) def cp_engine_producer_kv_all_gather( @@ -681,12 +727,12 @@ def _cp_engine_copy_data(dst_ptr, src_ptr, cp_size, stream): for offset in range(1, world_size): src_rank = (rank + offset) % world_size - k_src_ptr = (k_shards[src_rank].data_ptr() + byte_start // world_size) - k_dst_ptr = (k_buffers[rank].data_ptr() + byte_start + src_rank * byte_per_rank) + k_src_ptr = k_shards[src_rank].data_ptr() + byte_start // world_size + k_dst_ptr = k_buffers[rank].data_ptr() + byte_start + src_rank * byte_per_rank _cp_engine_copy_data(k_dst_ptr, k_src_ptr, cp_size, ag_stream) - v_src_ptr = (v_shards[src_rank].data_ptr() + byte_start // world_size) - v_dst_ptr = (v_buffers[rank].data_ptr() + byte_start + src_rank * byte_per_rank) + v_src_ptr = v_shards[src_rank].data_ptr() + byte_start // world_size + v_dst_ptr = v_buffers[rank].data_ptr() + byte_start + src_rank * byte_per_rank _cp_engine_copy_data(v_dst_ptr, v_src_ptr, cp_size, ag_stream) barrier_all_on_stream(barrier, ag_stream, world_size) @@ -710,7 +756,6 @@ def fused_sp_ag_attn_intra_node( enable_specialized: bool = False, print_source: bool = False, ): - BLOCK_M = 128 BLOCK_N = 128 num_stages = 2 @@ -764,20 +809,14 @@ def fused_sp_ag_attn_intra_node( block_M=BLOCK_M, block_N=BLOCK_N, num_stages=num_stages, - threads=threads) + threads=threads, + ) if rank == 0 and print_source: print(kernel.get_kernel_source()) - kernel( - q_shard, - ag_k, - ag_v, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - output, - stream=compute_stream.cuda_stream) + with torch.cuda.stream(compute_stream): + kernel(q_shard, ag_k, ag_v, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, output) compute_stream.wait_stream(ctx.ag_stream) barrier_all_on_stream(ctx.barrier, compute_stream, world_size) diff --git a/examples/distributed/triton_sp.py b/examples/distributed/triton_sp.py index d8236259ba..1b99a5fac3 100644 --- a/examples/distributed/triton_sp.py +++ b/examples/distributed/triton_sp.py @@ -97,8 +97,7 @@ def store_v4_b32_cond(ptr, val0, val1, val2, val3, mask, _semantic=None): } """, constraints=("=r,l,r,r,r,r,r"), # no use output - args=[ptr, val0, val1, val2, val3, - mask.to(tl.int32, _semantic=_semantic)], + args=[ptr, val0, val1, val2, val3, mask.to(tl.int32, _semantic=_semantic)], dtype=tl.int32, is_pure=False, pack=1, @@ -125,7 +124,7 @@ def _matmul_launch_metadata(grid, kernel, args): bytes_per_elem = args["c_ptr"].element_size() else: bytes_per_elem = 1 if args["FP8_OUTPUT"] else 2 - ret[f"flops{bytes_per_elem * 8}"] = 2. * M * N * K + ret[f"flops{bytes_per_elem * 8}"] = 2.0 * M * N * K ret["bytes"] = bytes_per_elem * (M * K + N * K + M * N) return ret @@ -138,13 +137,12 @@ def _kernel_consumer_gemm_persistent_repr(proxy): c_dtype = proxy.signature["c_ptr"].lstrip("*") BM, BN, BK = constexprs["BLOCK_SIZE_M"], constexprs["BLOCK_SIZE_N"], constexprs["BLOCK_SIZE_K"] - return f"cutlass_triton3x_sm{cap_major}{cap_minor}_a2a_consumer_gemm_persistent_tensorop_{a_dtype}_{b_dtype}_{c_dtype}_{BM}x{BN}x{BK}_ntn" + return ( + f"cutlass_triton3x_sm{cap_major}{cap_minor}_a2a_consumer_gemm_persistent_tensorop_{a_dtype}_{b_dtype}_{c_dtype}_{BM}x{BN}x{BK}_ntn" + ) -@triton.jit( - do_not_specialize=["sp_rank"], - launch_metadata=_matmul_launch_metadata, - repr=_kernel_consumer_gemm_persistent_repr) +@triton.jit(do_not_specialize=["sp_rank"], launch_metadata=_matmul_launch_metadata, repr=_kernel_consumer_gemm_persistent_repr) def matmul_kernel_descriptor_persistent( a_ptr, b_ptr, @@ -176,13 +174,10 @@ def matmul_kernel_descriptor_persistent( tl.static_assert(K % sp_size == 0, f"K {K} must be divisible by sp_size {sp_size}") K_per_sp_rank: tl.constexpr = K // sp_size - tl.static_assert( - K_per_sp_rank % BLOCK_SIZE_K == 0, - f"K_per_sp_rank {K_per_sp_rank} must be divisible by BLOCK_SIZE_K {BLOCK_SIZE_K}") + tl.static_assert(K_per_sp_rank % BLOCK_SIZE_K == 0, f"K_per_sp_rank {K_per_sp_rank} must be divisible by BLOCK_SIZE_K {BLOCK_SIZE_K}") k_tiles: tl.constexpr = K // BLOCK_SIZE_K - tl.static_assert(A2A_TILE_N % BLOCK_SIZE_K == 0, - f"A2A_TILE_N {A2A_TILE_N} must be divisible by BLOCK_SIZE_N {BLOCK_SIZE_K}") + tl.static_assert(A2A_TILE_N % BLOCK_SIZE_K == 0, f"A2A_TILE_N {A2A_TILE_N} must be divisible by BLOCK_SIZE_N {BLOCK_SIZE_K}") NUM_K_PER_TILE: tl.constexpr = A2A_TILE_N // BLOCK_SIZE_K # This is used for k-swizzle # k_tiles_per_rank: tl.constexpr = K_per_sp_rank // BLOCK_SIZE_K @@ -212,10 +207,8 @@ def matmul_kernel_descriptor_persistent( tile_id_c = start_pid - NUM_GEMM_SMS num_pid_in_group = GROUP_SIZE_M * num_pid_n - for tile_id in tl.range( - start_pid, num_tiles, NUM_GEMM_SMS, flatten=False, warp_specialize=WARP_SPECIALIZE): - pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, - NUM_GEMM_SMS) + for tile_id in tl.range(start_pid, num_tiles, NUM_GEMM_SMS, flatten=False, warp_specialize=WARP_SPECIALIZE): + pid_m, pid_n = _compute_pid(tile_id, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_GEMM_SMS) offs_am = pid_m * BLOCK_SIZE_M offs_bn = pid_n * BLOCK_SIZE_N @@ -235,12 +228,12 @@ def matmul_kernel_descriptor_persistent( if ki % NUM_K_PER_TILE == 0: for chunk_id in range(chunk_beg, chunk_end + 1): token = dl.wait( - gemm_barrier_ptr + chunk_id * (k_tiles // NUM_K_PER_TILE) + - ki // NUM_K_PER_TILE, + gemm_barrier_ptr + chunk_id * (k_tiles // NUM_K_PER_TILE) + ki // NUM_K_PER_TILE, 1, scope="gpu", semantic="acquire", - waitValue=1) + waitValue=1, + ) a_desc = dl.consume_token(a_desc, token) offs_k = ki * BLOCK_SIZE_K a = a_desc.load([offs_am, offs_k]) @@ -248,15 +241,13 @@ def matmul_kernel_descriptor_persistent( accumulator = tl.dot(a, b.T, accumulator) tile_id_c += NUM_GEMM_SMS - pid_m, pid_n = _compute_pid(tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, - NUM_GEMM_SMS) + pid_m, pid_n = _compute_pid(tile_id_c, num_pid_in_group, num_pid_m, GROUP_SIZE_M, NUM_GEMM_SMS) offs_cm = pid_m * BLOCK_SIZE_M offs_cn = pid_n * BLOCK_SIZE_N if HAS_BIAS: offs_bias_n = tl.arange(0, BLOCK_SIZE_N) - bias_data = tl.load( - bias_ptr + offs_cn + offs_bias_n, mask=(offs_cn + offs_bias_n < N)).to(tl.float32) + bias_data = tl.load(bias_ptr + offs_cn + offs_bias_n, mask=(offs_cn + offs_bias_n < N)).to(tl.float32) accumulator = accumulator + bias_data[None, :] if EPILOGUE_SUBTILE: @@ -272,15 +263,7 @@ def matmul_kernel_descriptor_persistent( c_desc.store([offs_cm, offs_cn], c) -def matmul_descriptor_persistent(sp_rank, - sp_size, - a, - b, - bias, - c, - gemm_barrier, - gemm_config: triton.Config, - warp_specialize: bool = False): +def matmul_descriptor_persistent(sp_rank, sp_size, a, b, bias, c, gemm_barrier, gemm_config: triton.Config, warp_specialize: bool = False): # Check constraints. assert a.shape[1] == b.shape[1], "Incompatible dimensions" # b is transposed assert a.dtype == b.dtype, "Incompatible dtypes" @@ -295,8 +278,7 @@ def alloc_fn(size: int, alignment: int, stream: Optional[int]): triton.set_allocator(alloc_fn) def grid(META): - return (min(META["NUM_GEMM_SMS"], - triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"])),) + return (min(META["NUM_GEMM_SMS"], triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"])),) matmul_kernel_descriptor_persistent[grid]( a, @@ -350,8 +332,7 @@ def kernel_all2all_push_intra_node_nvl( if FUSE_SYNC: tl.static_assert(SUPPORT_ATOMIC, "FUSE_SYNC requires SUPPORT_ATOMIC to be True") - barrier_all_intra_node_atomic_cas_block(sp_rank, rank, sp_size, - intra_node_sync_buf_ptr + pid * sp_size) + barrier_all_intra_node_atomic_cas_block(sp_rank, rank, sp_size, intra_node_sync_buf_ptr + pid * sp_size) for i in tl.static_range(sp_size + 1): tl.store(cum_seqlen_gpu_ptr + i, cum_seqlen_cpu_tuple[i]) @@ -363,13 +344,11 @@ def kernel_all2all_push_intra_node_nvl( offs_n = tl.arange(0, BLOCK_N // VEC) if sp_size <= NUM_COMM_SM: - tl.static_assert(NUM_COMM_SM % sp_size == 0, - f"NUM_COMM_SM {NUM_COMM_SM} must be divisible by sp_size {sp_size}") + tl.static_assert(NUM_COMM_SM % sp_size == 0, f"NUM_COMM_SM {NUM_COMM_SM} must be divisible by sp_size {sp_size}") NUM_SM_PER_SP: tl.constexpr = NUM_COMM_SM // sp_size NUM_SP_PER_SM: tl.constexpr = 1 else: - tl.static_assert(sp_size % NUM_COMM_SM == 0, - f"sp_size {sp_size} must be divisible by NUM_COMM_SM {NUM_COMM_SM}") + tl.static_assert(sp_size % NUM_COMM_SM == 0, f"sp_size {sp_size} must be divisible by NUM_COMM_SM {NUM_COMM_SM}") NUM_SM_PER_SP: tl.constexpr = 1 NUM_SP_PER_SM: tl.constexpr = sp_size // NUM_COMM_SM @@ -384,8 +363,8 @@ def kernel_all2all_push_intra_node_nvl( remote_seq_len = seq_end - seq_beg num_tile_m = tl.cdiv(remote_seq_len, BLOCK_M) tl.static_assert( - local_head * head_dim % BLOCK_N == 0, - f"local_head * head_dim {local_head * head_dim} must be divisible by BLOCK_N {BLOCK_N}") + local_head * head_dim % BLOCK_N == 0, f"local_head * head_dim {local_head * head_dim} must be divisible by BLOCK_N {BLOCK_N}" + ) num_tile_n = local_head * head_dim // BLOCK_N for tile_id_m_outer_n_tail in range(0, tl.cdiv(num_tile_m, GROUP_SIZE_M) * num_tile_n): @@ -398,32 +377,32 @@ def kernel_all2all_push_intra_node_nvl( attn_mask_m = attn_offs_m < seq_end attn_offs_n = tile_id_n_tail * BLOCK_N + offs_n * VEC data0, data1, data2, data3 = load_v4_b32_cond( - attn_out_ptr + attn_offs_m[:, None] * local_head * head_dim + - attn_offs_n[None, :], - mask=attn_mask_m[:, None]) + attn_out_ptr + attn_offs_m[:, None] * local_head * head_dim + attn_offs_n[None, :], mask=attn_mask_m[:, None] + ) out_offs_m = tile_id_m_tail * BLOCK_M + offs_m out_mask_m = out_offs_m < remote_seq_len out_offs_n = sp_rank * local_head * head_dim + tile_id_n_tail * BLOCK_N + offs_n * VEC store_v4_b32_cond( - remote_a2a_out_ptr + out_offs_m[:, None] * global_head * head_dim + - out_offs_n[None, :], + remote_a2a_out_ptr + out_offs_m[:, None] * global_head * head_dim + out_offs_n[None, :], data0, data1, data2, data3, - mask=out_mask_m[:, None]) + mask=out_mask_m[:, None], + ) if not SKIP_BARRIER: __syncthreads() - notify_barrier_ptr = remote_barrier_ptr + tile_id_m_tail * num_tile_n * sp_size + sp_rank * num_tile_n + tile_id_n_tail + notify_barrier_ptr = ( + remote_barrier_ptr + tile_id_m_tail * num_tile_n * sp_size + sp_rank * num_tile_n + tile_id_n_tail + ) thread_idx = tid(0) if thread_idx == 0: st(notify_barrier_ptr, 1, scope="sys", semantic="release") class SpUlysessOAll2AllGemmKernel: - def __init__( self, world_group: torch.distributed.ProcessGroup, @@ -492,14 +471,13 @@ def finalize(self): def init_symm_buffer(self): max_local_seq = self.max_seqlen // self.sp_size self._comm_output_buffer = nvshmem_create_tensor( - [self.max_num_comm_buf, self.max_batch, max_local_seq, self.num_head * self.head_dim], - self.input_dtype) + [self.max_num_comm_buf, self.max_batch, max_local_seq, self.num_head * self.head_dim], self.input_dtype + ) self._barrier_buffer = nvshmem_create_tensor( - [triton.cdiv(self.max_batch * self.max_seqlen, self.BLOCK_SIZE_M) * self.num_head], - torch.int32) + [triton.cdiv(self.max_batch * self.max_seqlen, self.BLOCK_SIZE_M) * self.num_head], torch.int32 + ) self._barrier_buffer.zero_() - self._intra_node_sync_buffer = nvshmem_create_tensor([self.sp_size * self.max_sms], - torch.int32) + self._intra_node_sync_buffer = nvshmem_create_tensor([self.sp_size * self.max_sms], torch.int32) self._intra_node_sync_buffer.zero_() self._sp_group_sync_buffer = nvshmem_create_tensor([self.world_size], torch.int32) self._sp_group_sync_buffer.zero_() @@ -525,30 +503,31 @@ def sp_group_barrier_all_intra_node(self, stream=None): stream = torch.cuda.current_stream() if stream is None else stream sp_local_rank = self.local_rank % self.sp_size with torch.cuda.stream(stream): - barrier_all_intra_node_atomic_cas_block[(1,)](sp_local_rank, self.rank, self.sp_size, - self._sp_group_sync_buffer) + barrier_all_intra_node_atomic_cas_block[(1,)](sp_local_rank, self.rank, self.sp_size, self._sp_group_sync_buffer) def reset_cusum_seq_lens(self, local_seqlen, seq_lens_cpu=None): if seq_lens_cpu is None: seq_lens_cpu = [local_seqlen] * self.sp_size else: seq_lens_cpu = seq_lens_cpu.tolist() - assert local_seqlen == seq_lens_cpu[ - self.local_rank % self. - sp_size], f"local_seqlen {local_seqlen} != seq_lens_cpu[{self.local_rank % self.sp_size}]={seq_lens_cpu[self.local_rank % self.sp_size]}" + assert local_seqlen == seq_lens_cpu[self.local_rank % self.sp_size], ( + f"local_seqlen {local_seqlen} != seq_lens_cpu[{self.local_rank % self.sp_size}]={seq_lens_cpu[self.local_rank % self.sp_size]}" + ) cum_seqlen_cpu = [0] + list(itertools.accumulate(seq_lens_cpu)) self._cum_seq_len_cpu_tuple = tuple(cum_seqlen_cpu) - def forward(self, - inputs: torch.Tensor, - weight: torch.Tensor, - seq_lens_cpu: Optional[torch.Tensor] = None, - bias: Optional[torch.Tensor] = None, - output: Optional[torch.Tensor] = None, - a2a_output: Optional[torch.Tensor] = None, - transpose_weight: bool = False, - num_comm_sms: int = -1, - sm_margin: int = 0): + def forward( + self, + inputs: torch.Tensor, + weight: torch.Tensor, + seq_lens_cpu: Optional[torch.Tensor] = None, + bias: Optional[torch.Tensor] = None, + output: Optional[torch.Tensor] = None, + a2a_output: Optional[torch.Tensor] = None, + transpose_weight: bool = False, + num_comm_sms: int = -1, + sm_margin: int = 0, + ): if num_comm_sms == -1: num_comm_sms = self.world_size assert num_comm_sms >= 0, "num_comm_sms must be non-negative" @@ -582,7 +561,7 @@ def forward(self, self.reset_cusum_seq_lens(local_seqlen=local_seq_len, seq_lens_cpu=seq_lens_cpu) - gemm_input_a = self._comm_output_buffer.view(-1)[:M * K].view([M, K]) + gemm_input_a = self._comm_output_buffer.view(-1)[: M * K].view([M, K]) cur_stream = torch.cuda.current_stream() @@ -618,46 +597,42 @@ def forward(self, ) if output is None: - output = torch.empty([bs, local_seq_len, N], - device=inputs.device, - dtype=self.output_dtype) + output = torch.empty([bs, local_seq_len, N], device=inputs.device, dtype=self.output_dtype) assert len(output.shape) == 3, f"output must be 4D tensor, got {len(output)}D" - assert output.shape[ - 0] == bs, f"output batch size {output.shape[0]} must be equal to input batch size {bs}" - assert output.shape[ - 1] == local_seq_len, f"output seq_len {output.shape[1]} must be equal to local_seq_len {local_seq_len}" - assert output.shape[ - 2] == N, f"output head {output.shape[2]} must be equal to output size {N}" + assert output.shape[0] == bs, f"output batch size {output.shape[0]} must be equal to input batch size {bs}" + assert output.shape[1] == local_seq_len, f"output seq_len {output.shape[1]} must be equal to local_seq_len {local_seq_len}" + assert output.shape[2] == N, f"output head {output.shape[2]} must be equal to output size {N}" assert output.is_contiguous(), f"output must be contiguous, got {output.shape}" - assert self.max_gemm_sms - num_comm_sms - sm_margin > 0, f"max_gemm_sms {self.max_gemm_sms} - num_comm_sms {num_comm_sms} - sm_margin {sm_margin} must be greater than 0" + assert self.max_gemm_sms - num_comm_sms - sm_margin > 0, ( + f"max_gemm_sms {self.max_gemm_sms} - num_comm_sms {num_comm_sms} - sm_margin {sm_margin} must be greater than 0" + ) gemm_config = triton.Config( { - 'BLOCK_SIZE_M': self.BLOCK_SIZE_M, - 'BLOCK_SIZE_N': self.BLOCK_SIZE_N, - 'BLOCK_SIZE_K': self.BLOCK_SIZE_K, - 'GROUP_SIZE_M': self.GROUP_SIZE_M, - 'A2A_TILE_M': self.A2A_TILE_M, - 'A2A_TILE_N': self.A2A_TILE_N, - 'NUM_GEMM_SMS': self.max_gemm_sms - num_comm_sms - sm_margin + "BLOCK_SIZE_M": self.BLOCK_SIZE_M, + "BLOCK_SIZE_N": self.BLOCK_SIZE_N, + "BLOCK_SIZE_K": self.BLOCK_SIZE_K, + "GROUP_SIZE_M": self.GROUP_SIZE_M, + "A2A_TILE_M": self.A2A_TILE_M, + "A2A_TILE_N": self.A2A_TILE_N, + "NUM_GEMM_SMS": self.max_gemm_sms - num_comm_sms - sm_margin, }, num_stages=self.num_stages, - num_warps=self.num_warps) + num_warps=self.num_warps, + ) with torch.cuda.stream(self.compute_stream): - matmul_descriptor_persistent(self.sp_rank, self.sp_size, gemm_input_a, weight, bias, - output, self._barrier_buffer, gemm_config, - self.warp_specialize) + matmul_descriptor_persistent( + self.sp_rank, self.sp_size, gemm_input_a, weight, bias, output, self._barrier_buffer, gemm_config, self.warp_specialize + ) if a2a_output is not None: - assert a2a_output.shape == ( - bs, local_seq_len, local_head * self.sp_size, head_dim - ), f"a2a_output shape {a2a_output.shape} must be equal to (bs, local_seq_len, local_head * self.sp_size, head_dim) ({bs}, {local_seq_len}, {local_head * self.sp_size}, {head_dim})" - assert a2a_output.is_contiguous( - ), f"a2a_output must be contiguous, got {a2a_output.shape}" - a2a_output.copy_( - gemm_input_a.view(bs, local_seq_len, local_head * self.sp_size * head_dim)) + assert a2a_output.shape == (bs, local_seq_len, local_head * self.sp_size, head_dim), ( + f"a2a_output shape {a2a_output.shape} must be equal to (bs, local_seq_len, local_head * self.sp_size, head_dim) ({bs}, {local_seq_len}, {local_head * self.sp_size}, {head_dim})" + ) + assert a2a_output.is_contiguous(), f"a2a_output must be contiguous, got {a2a_output.shape}" + a2a_output.copy_(gemm_input_a.view(bs, local_seq_len, local_head * self.sp_size * head_dim)) ret = (output, a2a_output) else: ret = (output,) @@ -701,7 +676,7 @@ def post_attn_a2a( self.reset_cusum_seq_lens(local_seqlen=local_seq_len, seq_lens_cpu=seq_lens_cpu) assert comm_buf_idx < self.max_num_comm_buf, f"comm_buf_idx {comm_buf_idx} must be less than num_comm_buf {self.max_num_comm_buf}" - gemm_input_a = self._comm_output_buffer[comm_buf_idx].view(-1)[:M * K].view([M, K]) + gemm_input_a = self._comm_output_buffer[comm_buf_idx].view(-1)[: M * K].view([M, K]) cur_stream = torch.cuda.current_stream() diff --git a/examples/dsa_sparse_finetune/dsa.py b/examples/dsa_sparse_finetune/dsa.py new file mode 100644 index 0000000000..9fae8e5e3d --- /dev/null +++ b/examples/dsa_sparse_finetune/dsa.py @@ -0,0 +1,223 @@ +from typing import Optional +import torch +import torch.nn.functional as F +from indexer_topk_reducesum import indexer_topk_reducesum_interface +from indexer_bwd import indexer_bwd_interface +from sparse_mla_fwd import sparse_mla_fwd_interface +from sparse_mla_bwd import sparse_mla_bwd +from sparse_mla_topk_reducesum import sparse_mla_topk_reducesum_interface +from einops import einsum, repeat +from utils import get_abs_err, get_err_ratio + + +class RegsiterLossFunction(torch.autograd.Function): + @staticmethod + def forward(ctx, x, loss): + ctx.save_for_backward(loss) + return x + + @staticmethod + def backward(ctx, grad): + loss = ctx.saved_tensors + return grad, torch.ones(1, dtype=loss[0].dtype, device=loss[0].device) + + +register_loss = RegsiterLossFunction.apply + + +def ref_deepseek_sparse_attention_innner( + q: torch.Tensor, + kv: torch.Tensor, + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + topk: int, + dim_v: int, + sm_scale: Optional[float] = None, + index_sm_scale: Optional[float] = None, +): + dtype = q.dtype + q, kv, index_q, index_k, weights = map(lambda x: x.to(torch.float32), (q, kv, index_q, index_k, weights)) + + index_sm_scale = index_q.shape[-1] ** -0.5 + b, s = index_q.shape[:2] + + # tl_topk_indices = tl_topk_indices.to(torch.int64) + # tl_topk_indices[tl_topk_indices == -1] = s + + casual_mask = (torch.arange(s)[:, None] >= torch.arange(s)[None, :]).to(q.device) + index_logits = einsum(index_q, index_k, "b s1 h k, b s2 k -> b s1 h s2") + index_logits = F.relu(index_logits) + index_logits = (index_logits * weights.unsqueeze(-1)).sum(dim=-2, dtype=torch.float32) * index_sm_scale + index_logits = torch.where(casual_mask, index_logits, float("-inf")) + topk_indices = torch.topk(index_logits, k=topk, dim=-1).indices + topk_logits = torch.gather(F.pad(index_logits, (0, 1), value=float("-inf")), dim=-1, index=topk_indices) + topk_score = F.log_softmax(topk_logits, dim=-1, dtype=torch.float32) + index_topk_score = topk_score + + if sm_scale is None: + sm_scale = kv.shape[-1] ** -0.5 + + h = q.shape[-2] + index_mask = torch.zeros((b, s, s + 1), dtype=torch.bool, device="cuda").scatter_( + dim=-1, index=topk_indices, src=torch.ones_like(topk_indices, dtype=torch.bool) + )[:, :, :-1] + mask = repeat(casual_mask & index_mask, "b s1 s2 -> b s1 h s2", h=h) + k, v = kv, kv[..., :dim_v] + logits = einsum(q, k, "b s1 h d, b s2 d -> b s1 h s2") * sm_scale + logits = torch.where(mask, logits, float("-inf")) + attn_score = F.softmax(logits, dim=-1, dtype=torch.float32) + o = einsum(attn_score, v, "b s1 h s2, b s2 d -> b s1 h d") + + attn_score = attn_score.sum(dim=-2) # [b, s1, s2] + attn_topk_score = torch.gather(F.pad(attn_score, (0, 1)), dim=-1, index=topk_indices) + attn_topk_score = attn_topk_score / attn_topk_score.sum(dim=-1, keepdim=True) + + loss = F.kl_div(index_topk_score.clip(-100, 0), attn_topk_score.detach().log().clip(-100, 0), log_target=True, reduction="sum") + o = register_loss(o, loss) + + return o.to(dtype), topk_indices + + +def ref_deepseek_sparse_attention( + q: torch.Tensor, + kv: torch.Tensor, + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + offsets: torch.Tensor, + topk: int, + dim_v: int, + sm_scale: Optional[float] = None, + index_sm_scale: Optional[float] = None, +): + all_o, all_topk_indices = [], [] + for i in range(offsets.shape[0] - 1): + o, topk_indices = ref_deepseek_sparse_attention_innner( + q[None, offsets[i] : offsets[i + 1]], + kv[None, offsets[i] : offsets[i + 1]], + index_q[None, offsets[i] : offsets[i + 1]], + index_k[None, offsets[i] : offsets[i + 1]], + weights[None, offsets[i] : offsets[i + 1]], + topk, + dim_v, + sm_scale, + index_sm_scale, + ) + all_o.append(o.squeeze(0)) + all_topk_indices.append(topk_indices.squeeze(0)) + o = torch.cat(all_o, dim=0) + topk_indices = torch.cat(all_topk_indices, dim=0) + return o, topk_indices + + +class DSAFunction(torch.autograd.Function): + @staticmethod + def forward( + ctx, + q: torch.Tensor, + kv: torch.Tensor, + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + offsets: torch.Tensor, + topk: int, + dim_v: int, + sm_scale: Optional[float] = None, + ): + # topk_indices, index_score = ref_index_score(index_q, weights, index_k, topk) + topk_indices, index_score = indexer_topk_reducesum_interface(index_q, weights, index_k, topk, offsets) + o, lse = sparse_mla_fwd_interface(q, kv.unsqueeze(-2), topk_indices.unsqueeze(-2), offsets, sm_scale=sm_scale, d_v=dim_v) + ctx.save_for_backward(q, kv, index_q, index_k, weights, topk_indices, index_score, o, lse, offsets) + ctx.topk = topk + ctx.dim_v = dim_v + ctx.sm_scale = sm_scale + return o, topk_indices + + @staticmethod + def backward( + ctx, + do: torch.Tensor, + _1: torch.Tensor, + ): + q, kv, index_q, index_k, weights, topk_indices, index_score, o, lse, offsets = ctx.saved_tensors + attn_score = sparse_mla_topk_reducesum_interface( + q, kv.unsqueeze(-2), topk_indices.unsqueeze(-2), lse, offsets, dim_v=ctx.dim_v + ).squeeze(-2) + dq, dkv = sparse_mla_bwd(q, kv.unsqueeze(-2), o, do, topk_indices.unsqueeze(-2), lse, offsets, sm_scale=ctx.sm_scale) + dindex_q, dweights, dindex_k = indexer_bwd_interface(index_q, weights, index_k, attn_score, index_score, topk_indices, offsets) + return dq, dkv.squeeze(-2), dindex_q, dindex_k, dweights, None, None, None, None + + +def deepseek_sparse_attention( + q: torch.Tensor, + kv: torch.Tensor, + index_q: torch.Tensor, + index_k: torch.Tensor, + weights: torch.Tensor, + offsets: torch.Tensor, + topk: int, + dim_v: int, + sm_scale: Optional[float] = None, +): + return DSAFunction.apply(q, kv, index_q, index_k, weights, offsets, topk, dim_v, sm_scale) + + +def test_kernel( + B=1, + S=2048, + H=16, + D=512, + tail_D=64, + index_D=128, + topk=64, +): + torch.manual_seed(42) + q = torch.randn((S, H, D + tail_D)).cuda().bfloat16().requires_grad_() + kv = torch.randn((S, D + tail_D)).cuda().bfloat16().requires_grad_() + index_q = torch.randn((S, H, index_D)).cuda().bfloat16().requires_grad_() + weights = torch.randn((S, H)).cuda().bfloat16().requires_grad_() + index_k = torch.randn((S, index_D)).cuda().bfloat16().requires_grad_() + do = torch.randn((S, H, D)).cuda().bfloat16().requires_grad_() + offsets = torch.tensor([0, S // 2, S], dtype=torch.int32).cuda() + + o, topk_indices = deepseek_sparse_attention(q, kv, index_q, index_k, weights, offsets, topk, D) + o.backward(do) + q_grad, q.grad = q.grad, None + kv_grad, kv.grad = kv.grad, None + index_q_grad, index_q.grad = index_q.grad, None + index_k_grad, index_k.grad = index_k.grad, None + weights_grad, weights.grad = weights.grad, None + + ref_o, ref_topk_indices = ref_deepseek_sparse_attention(q, kv, index_q, index_k, weights, offsets, topk, D) + ref_o.backward(do) + ref_q_grad, q.grad = q.grad, None + ref_kv_grad, kv.grad = kv.grad, None + ref_index_q_grad, index_q.grad = index_q.grad, None + ref_index_k_grad, index_k.grad = index_k.grad, None + ref_weights_grad, weights.grad = weights.grad, None + + print(f"o err: {get_abs_err(o, ref_o):.6f} ratio: {get_err_ratio(o, ref_o):.6f}") + print(f"q.grad err: {get_abs_err(q_grad, ref_q_grad):.6f} ratio: {get_err_ratio(q_grad, ref_q_grad):.6f}") + print(f"kv.grad err: {get_abs_err(kv_grad, ref_kv_grad):.6f} ratio: {get_err_ratio(kv_grad, ref_kv_grad):.6f}") + print( + f"index_q.grad err: {get_abs_err(index_q_grad[:, :64, :], ref_index_q_grad[:, :64, :]):.6f} ratio: {get_err_ratio(index_q_grad[:, :64, :], ref_index_q_grad[:, :64, :]):.6f}" + ) + print(f"index_k.grad err: {get_abs_err(index_k_grad, ref_index_k_grad):.6f} ratio: {get_err_ratio(index_k_grad, ref_index_k_grad):.6f}") + print(f"weights.grad err: {get_abs_err(weights_grad, ref_weights_grad):.6f} ratio: {get_err_ratio(weights_grad, ref_weights_grad):.6f}") + + intersections = [] + for j in range(S): + ref_np = ref_topk_indices[j].cpu().to(torch.int32).numpy() + trt_np = topk_indices[j].cpu().to(torch.int32).numpy() + + mask = trt_np != -1 + + set_ref = set(ref_np[mask]) + set_trt = set(trt_np[mask]) + intersection = set_ref & set_trt + intersections.append(len(intersection) / len(set_ref)) + print("average intersections: {:.4f}".format(sum(intersections) / len(intersections))) + + +test_kernel() diff --git a/examples/dsa_sparse_finetune/index.py b/examples/dsa_sparse_finetune/index.py new file mode 100644 index 0000000000..5e48004110 --- /dev/null +++ b/examples/dsa_sparse_finetune/index.py @@ -0,0 +1,82 @@ +# Modified from: https://github.com/fla-org/flash-linear-attention/blob/main/fla/ops/utils/index.py +import torch +import torch.nn.functional as F +import functools +from typing import Callable, Any + + +def tensor_cache( + fn: Callable[..., torch.Tensor], +) -> Callable[..., torch.Tensor]: + """ + A decorator that caches the most recent result of a function with tensor inputs. + + This decorator will store the output of the decorated function for the most recent set of input tensors. + If the function is called again with the same input tensors, it will return the cached result. + + + Args: + fn (Callable[..., torch.Tensor]): + The function to be decorated. It should take tensor inputs and return tensor outputs. + + Returns: + Callable[..., torch.Tensor]: + A wrapped version of the input function with single-entry caching. + """ + last_args: tuple | None = None + last_kwargs: dict | None = None + last_result: Any = None + + @functools.wraps(fn) + def wrapper(*args: Any, **kwargs: Any) -> Any: + nonlocal last_args, last_kwargs, last_result + + if ( + (last_args is not None and last_kwargs is not None) + and (len(args) == len(last_args) and len(kwargs) == len(last_kwargs)) + and all(a is b for a, b in zip(args, last_args, strict=False)) + and all(k in last_kwargs and v is last_kwargs[k] for k, v in kwargs.items()) + ): + return last_result + + result = fn(*args, **kwargs) + last_args, last_kwargs, last_result = args, kwargs, result + return result + + return wrapper + + +@tensor_cache +def prepare_lens(cu_seqlens: torch.LongTensor) -> torch.LongTensor: + return torch.diff(cu_seqlens) + + +@tensor_cache +def prepare_cu_seqlens_from_lens( + lens: torch.LongTensor, + dtype: torch.dtype | None = torch.int32, +) -> torch.LongTensor: + return F.pad(lens.cumsum(dim=0, dtype=dtype), (1, 0)) + + +@tensor_cache +def prepare_lens_from_cu_seqlens( + cu_seqlens: torch.LongTensor, +) -> torch.LongTensor: + return torch.diff(cu_seqlens) + + +@tensor_cache +def prepare_position_ids(cu_seqlens: torch.LongTensor) -> torch.LongTensor: + return torch.cat([torch.arange(n, dtype=cu_seqlens.dtype, device=cu_seqlens.device) for n in prepare_lens(cu_seqlens).unbind()]) + + +@tensor_cache +def prepare_sequence_ids(cu_seqlens: torch.LongTensor) -> torch.LongTensor: + return prepare_position_ids(cu_seqlens).eq(0).cumsum(0) - 1 + + +@tensor_cache +def prepare_token_indices(cu_seqlens: torch.LongTensor) -> torch.LongTensor: + position_ids = prepare_position_ids(cu_seqlens) + return torch.stack([prepare_sequence_ids(cu_seqlens), position_ids], 1).to(cu_seqlens) diff --git a/examples/dsa_sparse_finetune/indexer_bwd.py b/examples/dsa_sparse_finetune/indexer_bwd.py new file mode 100644 index 0000000000..68508ad4e4 --- /dev/null +++ b/examples/dsa_sparse_finetune/indexer_bwd.py @@ -0,0 +1,254 @@ +import torch +import torch.nn.functional as F +from einops import einsum, repeat + +import tilelang as tl +import tilelang.language as T +from typing import Optional +from index import prepare_token_indices + +from utils import get_abs_err, get_err_ratio + +BF16 = T.bfloat16 +FP32 = T.float32 +INT32 = T.int32 + +pass_configs = { + tl.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tl.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, +} + + +@tl.jit(pass_configs=pass_configs) +def tl_indexer_bwd_impl( + heads: int, + dim: int, + topk: int, + sm_scale: Optional[float] = None, + block_I: int = 32, + num_stages: int = 0, + num_threads: int = 128, +): + assert num_stages == 0 + assert topk == tl.math.next_power_of_2(topk) + assert topk % block_I == 0 + assert heads <= 64 and heads % 8 == 0 + batch_plus_one = T.symbolic("batch_plus_one") + seq_len = T.symbolic("seq_len") + dtype: str = BF16 + accum_dtype: str = FP32 + index_q_shape = [seq_len, heads, dim] + weights_shape = [seq_len, heads] + index_k_shape = [seq_len, dim] + shape_p = [seq_len, topk] + topk_indices_shape = [seq_len, topk] + offsets_shape = [batch_plus_one] + token_indices_shape = [seq_len, 2] + if sm_scale is None: + sm_scale = dim**-0.5 + + @T.prim_func + def tl_indexer_bwd_kernel( + IndexQ: T.Tensor(index_q_shape, dtype), + Weights: T.Tensor(weights_shape, dtype), + IndexK: T.Tensor(index_k_shape, dtype), + dIndexQ: T.Tensor(index_q_shape, dtype), + dWeights: T.Tensor(weights_shape, dtype), + dIndexK: T.Tensor(index_k_shape, dtype), + AttnScore: T.Tensor(shape_p, FP32), + IndexScore: T.Tensor(shape_p, FP32), + TopkIndices: T.Tensor(topk_indices_shape, INT32), + Offsets: T.Tensor(offsets_shape, INT32), + TokenIndices: T.Tensor(token_indices_shape, INT32), + ): + with T.Kernel(seq_len, threads=num_threads) as (bx): + i_b, i_t = TokenIndices[bx, 0], TokenIndices[bx, 1] + bos = Offsets[i_b] + num_blocks = T.ceildiv(topk, block_I) + + index_q_shared = T.alloc_shared([heads, dim], dtype=dtype) + weights_shared = T.alloc_shared([heads], dtype=dtype) + + d_index_q_frag = T.alloc_fragment([heads, dim], dtype=accum_dtype) + d_weights_frag = T.alloc_fragment([heads], dtype=accum_dtype) + + T.copy(IndexQ[bos + i_t, :, :], index_q_shared) + T.copy(Weights[bos + i_t, :], weights_shared) + T.fill(d_index_q_frag, 0) + T.fill(d_weights_frag, 0) + + for i, j in T.Parallel(heads, dim): + index_q_shared[i, j] = index_q_shared[i, j] * sm_scale + + for bi_i in T.Pipelined(num_blocks, num_stages=num_stages): + i_st = bi_i * block_I + i_ed = (bi_i + 1) * block_I + + indices_shared = T.alloc_shared([block_I], dtype=INT32) + T.copy(TopkIndices[bos + i_t, i_st:i_ed], indices_shared) + + index_k_shared = T.alloc_shared([block_I, dim], dtype=dtype) + for i, j in T.Parallel(block_I, dim): + pos = indices_shared[i] + index_k_shared[i, j] = T.if_then_else((pos > -1) & (pos <= i_t), IndexK[bos + pos, j], 0) + + attn_score_shared = T.alloc_shared([block_I], dtype=accum_dtype) + index_score_shared = T.alloc_shared([block_I], dtype=accum_dtype) + for i in T.Parallel(block_I): + attn_score_shared[i] = AttnScore[bos + i_t, i_st + i] + index_score_shared[i] = IndexScore[bos + i_t, i_st + i] + + logits = T.alloc_fragment((block_I, heads), accum_dtype) + T.gemm( + index_k_shared, + index_q_shared, + logits, + transpose_A=False, + transpose_B=True, + clear_accum=True, + ) + for i, j in T.Parallel(block_I, heads): + logits[i, j] = T.max(logits[i, j], 0) + + # dw + d_weights_i = T.alloc_fragment((block_I, heads), accum_dtype) + for i, j in T.Parallel(block_I, heads): + d_weights_i[i, j] = (index_score_shared[i] - attn_score_shared[i]) * logits[i, j] + T.reduce_sum(d_weights_i, d_weights_frag, dim=0, clear=False) + + d_logits_qk = T.alloc_shared((block_I, heads), accum_dtype) + d_logits_qk_cast1 = T.alloc_fragment((block_I, heads), dtype) + d_logits_qk_cast2 = T.alloc_fragment((block_I, heads), dtype) + + for i, j in T.Parallel(block_I, heads): + d_relu = T.alloc_var(accum_dtype) + if logits[i, j] > 0: + d_relu = 1.0 + else: + d_relu = 0.0 + d_logits_qk[i, j] = (index_score_shared[i] - attn_score_shared[i]) * d_relu * weights_shared[j] + + # dq + T.copy(d_logits_qk, d_logits_qk_cast1) + T.gemm( + d_logits_qk_cast1, # [BS, HQ] + index_k_shared, # [BS, K] + d_index_q_frag, # [HQ, K] + transpose_A=True, + transpose_B=False, + clear_accum=False, + ) + + # dk + T.copy(d_logits_qk, d_logits_qk_cast2) + d_index_k_frag = T.alloc_fragment([block_I, dim], dtype=accum_dtype) + T.gemm( + d_logits_qk_cast2, # [BS, HQ] + index_q_shared, # [HQ, K] + d_index_k_frag, # [BS, K] + transpose_A=False, + transpose_B=False, + clear_accum=True, + ) + + for i, j in T.Parallel(block_I, dim): + pos = indices_shared[i] + if (pos > -1) & (pos <= i_t): + T.atomic_add(dIndexK[bos + pos, j], d_index_k_frag[i, j]) + + for i, j in T.Parallel(heads, dim): + d_index_q_frag[i, j] = d_index_q_frag[i, j] * sm_scale + + T.copy(d_index_q_frag, dIndexQ[bos + i_t, :, :]) + T.copy(d_weights_frag, dWeights[bos + i_t, :]) + + return tl_indexer_bwd_kernel + + +def indexer_bwd_interface( + q: torch.Tensor, + weights: torch.Tensor, + k: torch.Tensor, + attn_score: torch.Tensor, + index_score: torch.Tensor, + topk_indices: torch.Tensor, + offsets: torch.Tensor, +): + _, heads, dim, topk = *q.shape, topk_indices.shape[-1] + token_indices = prepare_token_indices(offsets) + dq = torch.zeros_like(q) + dweights = torch.zeros_like(weights) + dk = torch.zeros_like(k) + kernel = tl_indexer_bwd_impl(heads, dim, topk) + kernel(q, weights, k, dq, dweights, dk, attn_score, index_score, topk_indices, offsets, token_indices) + return dq, dweights, dk + + +def ref_indexer_bwd( + Q: torch.Tensor, Weights: torch.Tensor, K: torch.Tensor, TopkIndices: torch.Tensor, AttnScore: torch.Tensor, offsets: torch.Tensor +) -> torch.Tensor: + Q.requires_grad_(True) + Weights.requires_grad_(True) + K.requires_grad_(True) + softmax_scale = Q.shape[-1] ** -0.5 + all_loss = [] + all_log_topk_prob = [] + for i in range(offsets.shape[0] - 1): + assert (offsets[i + 1] - offsets[i]).item() >= TopkIndices.shape[-1] + q = Q[offsets[i] : offsets[i + 1]] + weights = Weights[offsets[i] : offsets[i + 1]] + k = K[offsets[i] : offsets[i + 1]] + topk_indices = TopkIndices[offsets[i] : offsets[i + 1]] + attn_score = AttnScore[offsets[i] : offsets[i + 1]] + s = q.shape[0] + mask = (torch.arange(s)[:, None] >= torch.arange(s)[None, :]).to(q.device) + logits = einsum(q, k, "s1 h k, s2 k -> s1 h s2") * softmax_scale + logits = F.relu(logits) + score = (logits * weights.unsqueeze(-1)).sum(dim=-2, dtype=torch.float32) + score = torch.where(mask, score, float("-inf")) + topk_value = torch.gather(score, dim=-1, index=topk_indices.to(torch.int64)) + log_topk_prob = F.log_softmax(topk_value, dim=-1, dtype=torch.float32) + loss = F.kl_div(log_topk_prob.clip(-100, 0), attn_score.log().clip(-100, 0), log_target=True, reduction="sum") + all_loss.append(loss) + all_log_topk_prob.append(log_topk_prob) + loss = torch.stack(all_loss).sum() + loss.backward() + log_topk_prob = torch.cat(all_log_topk_prob, dim=0) + return log_topk_prob.exp(), Q.grad, Weights.grad, K.grad + + +def test_kernel( + B=1, + S=2048, + H=16, + D=128, + topk=64, +): + torch.manual_seed(42) + q = torch.randn((S, H, D)).cuda().bfloat16() + w = torch.randn((S, H)).cuda().bfloat16() + k = torch.randn((S, D)).cuda().bfloat16() + offsets = torch.tensor([0, 1023, S], dtype=torch.int32).cuda() + + all_attn_score = [] + for i in range(offsets.shape[0] - 1): + seq_len = (offsets[i + 1] - offsets[i]).item() + mask = (torch.arange(seq_len)[:, None] >= torch.arange(topk)[None, :]).to(q.device) + logits = torch.ones(seq_len, topk).cuda() + logits = torch.where(mask, logits, float("-inf")) + attn_score = F.softmax(logits, dim=-1, dtype=torch.float32) + all_attn_score.append(attn_score) + attn_score = torch.cat(all_attn_score, dim=0) + + topk_indices = repeat(torch.arange(topk, dtype=torch.int32).cuda(), "k -> s k", s=S).contiguous() + index_score, ref_dq, ref_dw, ref_dk = ref_indexer_bwd(q, w, k, topk_indices, attn_score, offsets) + + dq, dw, dk = indexer_bwd_interface(q, w, k, attn_score, index_score, topk_indices, offsets) + + print(f"dq err: {get_abs_err(dq, ref_dq):.6f} ratio: {get_err_ratio(dq, ref_dq):.6f}") + print(f"dq err: {get_abs_err(dw, ref_dw):.6f} ratio: {get_err_ratio(dw, ref_dw):.6f}") + print(f"dq err: {get_abs_err(dk, ref_dk):.6f} ratio: {get_err_ratio(dk, ref_dk):.6f}") + + +if __name__ == "__main__": + test_kernel() diff --git a/examples/dsa_sparse_finetune/indexer_topk_reducesum.py b/examples/dsa_sparse_finetune/indexer_topk_reducesum.py new file mode 100644 index 0000000000..d76eb02724 --- /dev/null +++ b/examples/dsa_sparse_finetune/indexer_topk_reducesum.py @@ -0,0 +1,273 @@ +import math +import torch +import torch.nn.functional as F +from einops import einsum + +import tilelang as tl +import tilelang.language as T +from typing import Optional +from index import prepare_token_indices + +from utils import get_abs_err, get_err_ratio + +BF16 = T.bfloat16 +FP32 = T.float32 +INT32 = T.int32 + +pass_configs = { + tl.PassConfigKey.TL_DISABLE_THREAD_STORAGE_SYNC: True, + tl.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tl.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, +} + + +@tl.jit(pass_configs=pass_configs) +def tl_indexer_topk_reducesum_impl( + heads: int, + dim: int, + topk: int, + sm_scale: Optional[float] = None, + block_K: int = 32, + dtype: str = FP32, + num_stages: int = 0, + num_threads: int = 128, +): + assert topk == tl.math.next_power_of_2(topk) + assert topk % block_K == 0 + assert heads <= 64 and heads % 8 == 0 + assert num_stages == 0 + batch_plus_one = T.symbolic("batch_plus_one") + seq_len = T.symbolic("seq_len") + + index_q_shape = [seq_len, heads, dim] + weights_shape = [seq_len, heads] + index_k_shape = [seq_len, dim] + topk_indices_shape = [seq_len, topk] + offsets_shape = [batch_plus_one] + token_indices_shape = [seq_len, 2] + + N = 2 * topk + num_iters = int(round(math.log2(N))) + if sm_scale is None: + sm_scale = dim**-0.5 + + @T.macro + def bitonic_sort( + topk_index_shared: T.SharedBuffer([N], dtype=INT32), + topk_value_shared: T.SharedBuffer([N], dtype=FP32), + ): + T.sync_threads() + for i1 in T.serial(num_iters): + for i2 in T.serial(i1 + 1): + for i in T.Parallel(N): + ascending = (i & (1 << (i1 + 1))) != 0 + j = i ^ (1 << (i1 - i2)) + if i < j and ( + (ascending and topk_value_shared[i] > topk_value_shared[j]) + or (not ascending and topk_value_shared[i] < topk_value_shared[j]) + ): + val = topk_value_shared[i] + topk_value_shared[i] = topk_value_shared[j] + topk_value_shared[j] = val + idx = topk_index_shared[i] + topk_index_shared[i] = topk_index_shared[j] + topk_index_shared[j] = idx + T.sync_threads() + + @T.prim_func + def tl_indexer_topk_reducesum_kernel( + IndexQ: T.Tensor(index_q_shape, dtype), + Weights: T.Tensor(weights_shape, dtype), + IndexK: T.Tensor(index_k_shape, dtype), + TopkIndices: T.Tensor(topk_indices_shape, INT32), + ReduceSum: T.Tensor(topk_indices_shape, FP32), + Offsets: T.Tensor(offsets_shape, INT32), + TokenIndices: T.Tensor(token_indices_shape, INT32), + ): + with T.Kernel(seq_len, threads=num_threads) as (bx): + i_b, i_t = TokenIndices[bx, 0], TokenIndices[bx, 1] + bos, eos = Offsets[i_b], Offsets[i_b + 1] + num_blocks = T.ceildiv(i_t + 1, block_K) + + topk_index_shared = T.alloc_shared([N], dtype=INT32) + topk_value_shared = T.alloc_shared([N], dtype=FP32) + + T.fill(topk_index_shared, -1) + T.fill(topk_value_shared, float("-inf")) + T.sync_threads() + + index_q_shared = T.alloc_shared([heads, dim], dtype=dtype) + T.copy(IndexQ[bos + i_t, :, :], index_q_shared) + T.sync_threads() + + weights_frag = T.alloc_shared([heads], dtype=dtype) + T.copy(Weights[bos + i_t, :], weights_frag) + T.sync_threads() + + for i, j in T.Parallel(heads, dim): + index_q_shared[i, j] = index_q_shared[i, j] * sm_scale + T.sync_threads() + + for bk_i in T.Pipelined(num_blocks, num_stages=num_stages): + k_st = bk_i * block_K + k_ed = T.min((bk_i + 1) * block_K, eos - bos) + + index_k_shared = T.alloc_shared([block_K, dim], dtype=dtype) + for i, j in T.Parallel(block_K, dim): + index_k_shared[i, j] = T.if_then_else(k_st + i < k_ed, IndexK[bos + k_st + i, j], 0) + T.sync_threads() + + logits = T.alloc_fragment((block_K, heads), FP32) + T.gemm( + index_k_shared, + index_q_shared, + logits, + transpose_A=False, + transpose_B=True, + clear_accum=True, + ) + T.sync_threads() + + for i, j in T.Parallel(block_K, heads): + logits[i, j] = T.max(logits[i, j], 0) * weights_frag[j] + T.sync_threads() + + logits_sum = T.alloc_fragment(block_K, FP32) + T.reduce_sum(logits, logits_sum, dim=1) + T.sync_threads() + + offset = T.alloc_var(INT32) + if k_st >= topk: + offset = topk + (k_st % topk) + else: + offset = k_st + T.sync_threads() + for i in T.Parallel(block_K): + if k_st + i > i_t: + logits_sum[i] = float("-inf") + j = offset + i + topk_index_shared[j] = k_st + i + topk_value_shared[j] = logits_sum[i] + T.sync_threads() + + if k_ed > topk and k_ed % topk == 0: + bitonic_sort(topk_index_shared, topk_value_shared) + + bitonic_sort(topk_index_shared, topk_value_shared) + + logits_max_frag = T.alloc_fragment([1], dtype=FP32) + logits_frag = T.alloc_fragment([topk], dtype=FP32) + reducesum_shared = T.alloc_shared([topk], dtype=FP32) + + T.copy(topk_value_shared[:topk], logits_frag) + T.sync_threads() + + T.reduce_max(logits_frag, logits_max_frag, dim=-1) + T.sync_threads() + + for i in T.Parallel(topk): + logits_frag[i] = T.exp(logits_frag[i] - logits_max_frag[0]) + T.sync_threads() + + lse_frag = T.alloc_fragment([1], dtype=FP32) + T.reduce_sum(logits_frag, lse_frag) + T.sync_threads() + + for i in T.Parallel(topk): + reducesum_shared[i] = logits_frag[i] / lse_frag[0] + T.sync_threads() + + # for i in T.Parallel(topk): + # reducesum_shared[i] = logits_frag[i] + # T.sync_threads() + + for i in T.Parallel(topk): + if topk_index_shared[i] > i_t: + topk_index_shared[i] = -1 + T.sync_threads() + + T.copy(topk_index_shared[:topk], TopkIndices[bos + i_t, :]) + T.copy(reducesum_shared[:topk], ReduceSum[bos + i_t, :]) + + return tl_indexer_topk_reducesum_kernel + + +def indexer_topk_reducesum_interface( + q: torch.Tensor, + weights: torch.Tensor, + k: torch.Tensor, + topk: int, + offsets: torch.Tensor, + dtype: str = BF16, +): + seq_len, heads, dim = q.shape + kernel = tl_indexer_topk_reducesum_impl(heads=heads, dim=dim, topk=topk, dtype=dtype) + token_indices = prepare_token_indices(offsets) + topk_indices = torch.zeros((seq_len, topk), device=q.device, dtype=torch.int32) + topk_score = torch.zeros((seq_len, topk), device=q.device, dtype=torch.float32) + kernel(q, weights, k, topk_indices, topk_score, offsets, token_indices) + return topk_indices, topk_score + + +def ref_index_score(Q: torch.Tensor, Weights: torch.Tensor, K: torch.Tensor, topk: int, offsets: torch.Tensor) -> torch.Tensor: + all_topk_indices = [] + all_topk_score = [] + for i in range(offsets.shape[0] - 1): + assert (offsets[i + 1] - offsets[i]).item() >= topk + q = Q[offsets[i] : offsets[i + 1]] + weights = Weights[offsets[i] : offsets[i + 1]] + k = K[offsets[i] : offsets[i + 1]] + softmax_scale = q.shape[-1] ** -0.5 + s = q.shape[0] + mask = (torch.arange(s)[:, None] >= torch.arange(s)[None, :]).to(q.device) + logits = einsum(q, k, "s1 h k, s2 k -> s1 h s2") + logits = F.relu(logits) + logits = (logits * weights.unsqueeze(-1)).sum(dim=-2, dtype=torch.float32) * softmax_scale + logits = torch.where(mask, logits, float("-inf")) + topk_logits, topk_indices = torch.topk(logits, k=topk, dim=-1) + topk_score = F.softmax(topk_logits, dim=-1, dtype=torch.float32) + all_topk_indices.append(topk_indices) + all_topk_score.append(topk_score) + topk_indices = torch.cat(all_topk_indices, dim=0) + topk_score = torch.cat(all_topk_score, dim=0) + return topk_indices, topk_score + + +def test_kernel( + B=1, + S=2048, + H=64, + D=128, + topk=64, +): + torch.manual_seed(42) + + q = torch.randn((S, H, D)).cuda().bfloat16() + weights = torch.randn((S, H)).cuda().bfloat16() + k = torch.randn((S, D)).cuda().bfloat16() + offsets = torch.tensor([0, S], dtype=torch.int32).cuda() + + ref_topk_indices, ref_topk_score = ref_index_score(q, weights, k, topk, offsets) + + topk_indices, topk_score = indexer_topk_reducesum_interface(q, weights, k, topk, offsets) + + for j in range(S): + ref_np = ref_topk_indices[j].cpu().to(torch.int32).numpy() + trt_np = topk_indices[j].cpu().to(torch.int32).numpy() + + ref_np_val = ref_topk_score[j] + trt_np_val = topk_score[j] + + mask = (ref_np_val > 0).cpu().numpy() + + set_ref = set(ref_np[mask]) + set_trt = set(trt_np[mask]) + intersection = set_ref & set_trt + + print("idx:", j, "selected/all:", len(intersection), "/", len(set_ref), "=", len(intersection) / len(set_ref)) + + print(f"err: {get_abs_err(ref_np_val, trt_np_val):.6f} ratio: {get_err_ratio(ref_np_val, trt_np_val):.6f}") + + +if __name__ == "__main__": + test_kernel() diff --git a/examples/dsa_sparse_finetune/sparse_mla_bwd.py b/examples/dsa_sparse_finetune/sparse_mla_bwd.py new file mode 100644 index 0000000000..53e5f8bfea --- /dev/null +++ b/examples/dsa_sparse_finetune/sparse_mla_bwd.py @@ -0,0 +1,347 @@ +# ruff: noqa +import tilelang +from tilelang import language as T +import torch +from index import prepare_token_indices + +from utils import assert_tensors_similar + + +@tilelang.jit(out_idx=[-1]) +def preprocess( + H, + D, + block_ND=32, + num_stages=5, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + + S = T.symbolic("S") + + shape = [S, H, D] + + @T.prim_func + def preprocess_kernel( + O: T.Tensor(shape, dtype), + dO: T.Tensor(shape, dtype), + Delta: T.Tensor([S, H], accum_dtype), + ): + with T.Kernel(H, T.ceildiv(S, block_ND)) as (bx, by): + o = T.alloc_fragment([block_ND, block_ND], accum_dtype) + do = T.alloc_fragment([block_ND, block_ND], accum_dtype) + delta = T.alloc_fragment([block_ND], accum_dtype) + acc = T.alloc_fragment([block_ND, block_ND], accum_dtype) + T.clear(acc) + for k in T.Pipelined(T.ceildiv(D, block_ND), num_stages=num_stages): + T.copy(O[by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], o) + T.copy(dO[by * block_ND : (by + 1) * block_ND, bx, k * block_ND : (k + 1) * block_ND], do) + for i, j in T.Parallel(block_ND, block_ND): + acc[i, j] += o[i, j] * do[i, j] + T.reduce_sum(acc, delta, 1) + T.copy(delta, Delta[by * block_ND : (by + 1) * block_ND, bx]) + + return preprocess_kernel + + +@tilelang.jit(out_idx=[-1]) +def postprocess( + D, + D_tail, + kv_group=1, + block_N=64, + threads=128, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + S_kv = T.symbolic("S_kv") + + dkv_shape = [S_kv, kv_group, D + D_tail] + + @T.prim_func + def postprocess_kernel( + dKV: T.Tensor(dkv_shape, accum_dtype), + dKV_out: T.Tensor(dkv_shape, dtype), + ): + with T.Kernel(T.ceildiv(S_kv, block_N), kv_group, threads=threads) as (bx, by): + T.copy( + dKV[bx * block_N : (bx + 1) * block_N, by, :], + dKV_out[bx * block_N : (bx + 1) * block_N, by, :], + ) + + return postprocess_kernel + + +@tilelang.jit( + out_idx=[-2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, +) +def bwd( + H, + D, + D_tail, + topk, + kv_group=1, + sm_scale=None, + is_causal=True, + block_size=32, + num_stages=0, + threads=128, + indices_dtype=T.int32, + dtype=T.bfloat16, + accum_dtype=T.float32, +): + assert is_causal == True, "non-casual is not supported now" + assert topk % block_size == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + assert indices_dtype == T.int32 + + if sm_scale is None: + sm_scale = (D + D_tail) ** (-0.5) + + B_plus_one = T.symbolic("B_plus_one") + S = T.symbolic("S") + + H_kv = H // kv_group + q_shape = [S, H, D + D_tail] + k_shape = [S, kv_group, D + D_tail] + o_shape = [S, H, D] + indices_shape = [S, kv_group, topk] + delta_shape = [S, H] + lse_shape = [S, H] + offsets_shape = [B_plus_one] + token_indices_shape = [S, 2] + assert indices_dtype == T.int32 + assert dtype == T.bfloat16 + assert accum_dtype == T.float32 + + H = H_kv + padded_H = max(tilelang.math.next_power_of_2(H_kv), 16) + BS = block_size + NS = tilelang.cdiv(topk, block_size) + + split_store = 2 + + @T.prim_func + def sparse_mla_bwd_kernel( + Q: T.Tensor(q_shape, dtype), + KV: T.Tensor(k_shape, dtype), + dO: T.Tensor(o_shape, dtype), + Indices: T.Tensor(indices_shape, indices_dtype), + Lse: T.Tensor(lse_shape, accum_dtype), + Delta: T.Tensor(delta_shape, accum_dtype), + Offsets: T.Tensor(offsets_shape, indices_dtype), + TokenIndices: T.Tensor(token_indices_shape, indices_dtype), + dQ: T.Tensor(q_shape, dtype), + dKV: T.Tensor(k_shape, accum_dtype), + ): + with T.Kernel(S, kv_group, threads=threads) as (b_s_i, bz): + Q_shared = T.alloc_shared([padded_H, D], dtype) + Q_tail_shared = T.alloc_shared([padded_H, D_tail], dtype) + KV_shared = T.alloc_shared([BS, D], dtype) + KV_tail_shared = T.alloc_shared([BS, D_tail], dtype) + dO_shared = T.alloc_shared([padded_H, D], dtype) + mask = T.alloc_fragment([BS], "bool") + + P_shared_cast = T.alloc_shared([padded_H, BS], dtype) + dP_shared_cast = T.alloc_shared([padded_H, BS], dtype) + dQ_shared = T.alloc_shared([padded_H, D], dtype) + dQ_tail_shared = T.alloc_shared([padded_H, D_tail], dtype) + + acc_p = T.alloc_fragment([padded_H, BS], accum_dtype) + acc_dp = T.alloc_fragment([padded_H, BS], accum_dtype) + acc_dq = T.alloc_fragment([padded_H, D], accum_dtype) + acc_dq_tail = T.alloc_fragment([padded_H, D_tail], accum_dtype) + acc_dkv = T.alloc_fragment([BS, D], accum_dtype) + acc_dkv_tail = T.alloc_fragment([BS, D_tail], accum_dtype) + acc_dkv_shared = T.view(KV_shared, shape=[BS // split_store, D], dtype=accum_dtype) + acc_dkv_tail_shared = T.view(KV_tail_shared, shape=[BS // split_store, D_tail], dtype=accum_dtype) + + b_i, s_i = TokenIndices[b_s_i, 0], TokenIndices[b_s_i, 1] + bos, eos = Offsets[b_i], Offsets[b_i + 1] + + max_kv_i = s_i + + T.copy(Q[bos + s_i, bz * padded_H : (bz + 1) * padded_H, :D], Q_shared) + T.copy(Q[bos + s_i, bz * padded_H : (bz + 1) * padded_H, D:], Q_tail_shared) + T.copy(dO[bos + s_i, bz * padded_H : (bz + 1) * padded_H, :D], dO_shared) + + T.clear(acc_dq) + T.clear(acc_dq_tail) + + # Process each block of indices + for i_i in T.Pipelined(NS, num_stages=num_stages): + # Check which indices are valid + for bi_i in T.Parallel(BS): + mask[bi_i] = (Indices[bos + s_i, bz, i_i * BS + bi_i] <= max_kv_i) & (Indices[bos + s_i, bz, i_i * BS + bi_i] != -1) + + # Compute attention scores + for h_i, bi_i in T.Parallel(padded_H, BS): + acc_p[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_p.dtype)) + + # Load KV, V for this block of indices + for bi_i, d_i in T.Parallel(BS, D): + KV_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, bz, i_i * BS + bi_i], bz, d_i] + + T.gemm(Q_shared, KV_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + + for bi_i, d_i in T.Parallel(BS, D_tail): + KV_tail_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, bz, i_i * BS + bi_i], bz, D + d_i] + T.gemm(Q_tail_shared, KV_tail_shared, acc_p, transpose_B=True, policy=T.GemmWarpPolicy.FullCol) + + for h_i, bi_i in T.Parallel(padded_H, BS): + acc_p[h_i, bi_i] = T.exp(acc_p[h_i, bi_i] * sm_scale - Lse[bos + s_i, bz * padded_H + h_i]) + + T.copy(acc_p, P_shared_cast) + + T.gemm(dO_shared, KV_shared, acc_dp, transpose_B=True, policy=T.GemmWarpPolicy.FullCol, clear_accum=True) + + for h_i, bi_i in T.Parallel(padded_H, BS): + acc_dp[h_i, bi_i] = acc_p[h_i, bi_i] * (acc_dp[h_i, bi_i] - Delta[bos + s_i, bz * padded_H + h_i]) * sm_scale + + T.copy(acc_dp, dP_shared_cast) + T.gemm(dP_shared_cast, KV_shared, acc_dq, policy=T.GemmWarpPolicy.FullCol) + T.gemm(dP_shared_cast, KV_tail_shared, acc_dq_tail, policy=T.GemmWarpPolicy.FullCol) + + T.gemm(dP_shared_cast, Q_shared, acc_dkv, transpose_A=True, policy=T.GemmWarpPolicy.FullCol, clear_accum=True) + T.gemm(P_shared_cast, dO_shared, acc_dkv, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) + + T.clear(acc_dkv_tail) + T.gemm(dP_shared_cast, Q_tail_shared, acc_dkv_tail, transpose_A=True, policy=T.GemmWarpPolicy.FullCol) + + for s in range(split_store): + for bi_i, d_i in T.Parallel(BS, D): + if bi_i < BS // split_store: + acc_dkv_shared[bi_i, d_i] = acc_dkv[bi_i + s * (BS // split_store), d_i] + + for bi_i, d_i in T.Parallel(BS, D_tail): + if bi_i < BS // split_store: + acc_dkv_tail_shared[bi_i, d_i] = acc_dkv_tail[bi_i + s * (BS // split_store), d_i] + + for bi_i, d_i in T.Parallel(BS // split_store, D // 4): + T.atomic_addx4( + dKV[bos + Indices[bos + s_i, bz, i_i * BS + bi_i + s * (BS // split_store)], bz, d_i * 4], + acc_dkv_shared[bi_i, d_i * 4], + ) + + # Atomically update dKV, dKV_tail tensors + for bi_i, d_i in T.Parallel(BS // split_store, D_tail // 4): + T.atomic_addx4( + dKV[bos + Indices[bos + s_i, bz, i_i * BS + bi_i + s * (BS // split_store)], bz, D + d_i * 4], + acc_dkv_tail_shared[bi_i, d_i * 4], + ) + + # Store the accumulated dQ + T.copy(acc_dq, dQ_shared) + T.copy(acc_dq_tail, dQ_tail_shared) + + T.copy(dQ_shared, dQ[bos + s_i, bz * padded_H : (bz + 1) * padded_H, :D]) + T.copy(dQ_tail_shared, dQ[bos + s_i, bz * padded_H : (bz + 1) * padded_H, D:]) + + return sparse_mla_bwd_kernel + + +def sparse_mla_bwd(q, kv, o, do, indices, lse, offsets, sm_scale=None, is_casual=True, return_kernel=False, delta=None): + assert q.is_contiguous() + assert kv.is_contiguous() + assert indices.is_contiguous() + assert lse.is_contiguous() + S, H, dim_plus_tail_dim = q.shape + S_kv, kv_group, _ = kv.shape + assert kv.shape[-1] == dim_plus_tail_dim + assert S == S_kv + # dim should be assigned + D = 512 + + D_tail = dim_plus_tail_dim - D + topk = indices.shape[-1] + assert indices.shape == (S, kv_group, topk) + assert lse.shape == (S, H) + + token_indices = prepare_token_indices(offsets) + + # Get kernels + preprocess_kernel = preprocess(H, D) + bwd_kernel = bwd(H, D, D_tail, topk, kv_group, sm_scale, is_casual) + postprocess_kernel = postprocess(D, D_tail, kv_group) + + if delta is None: + delta = preprocess_kernel(o, do) + dkv = torch.zeros_like(kv, dtype=torch.float32) + dq = bwd_kernel(q, kv, do, indices, lse, delta, offsets, token_indices, dkv) + dkv = postprocess_kernel(dkv) + + return dq, dkv + + +def ref_sparse_mla_bwd_interface(q, kv, o, do, indices, lse, offsets, sm_scale=None, is_casual=True): + from sparse_mla_fwd import ref_sparse_mla_fwd_interface + + q = q.detach().clone() + kv = kv.detach().clone() + q.requires_grad = True + kv.requires_grad = True + o = ref_sparse_mla_fwd_interface(q, kv, indices, offsets, sm_scale, is_casual) + o.backward(do) + return q.grad, kv.grad + + +def test_sparse_mla_bwd(B=1, S=2048, H=64, HKV=1, DQKV=576, DV=512, topk=512, dtype=torch.bfloat16, check_correctness=True): + # Prepare data + q = torch.randn((S, H, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + kv = torch.randn((S, HKV, DQKV), dtype=dtype, device="cuda").requires_grad_(True) + do = torch.randn((S, H, DV), dtype=dtype, device="cuda") + offsets = torch.tensor([0, S], dtype=torch.int32, device="cuda") + + indices = torch.full((S, HKV, topk), S, dtype=torch.int32, device="cuda") + for i in range(offsets.shape[0] - 1): + seq_len = (offsets[i + 1] - offsets[i]).item() + assert seq_len >= topk + for t in range(seq_len): + for h in range(HKV): + i_i = torch.randperm(max(1, t))[:topk] + indices[offsets[i] + t, h, : len(i_i)] = i_i + + # Forward + from sparse_mla_fwd import sparse_mla_fwd_interface + + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices, offsets) + + tl_dq, tl_dkv = sparse_mla_bwd(q, kv, tl_out, do, indices, tl_lse, offsets) + ref_dq, ref_dkv = ref_sparse_mla_bwd_interface(q, kv, None, do, indices, None, offsets) + + if check_correctness: + assert_tensors_similar(tl_dq, ref_dq, eps=1e-4, name="dq") + assert_tensors_similar(tl_dkv, ref_dkv, eps=1e-4, name="dkv") + print("assert_tensors_similar passed") + + per_token_flop = 2 * sum( + [ + H * DV * topk, + H * DQKV * topk, + H * DQKV * topk, + H * DQKV * topk, + H * DV * topk, + ] + ) + from tilelang.profiler import do_bench + + def fn(): + return sparse_mla_bwd(q, kv, tl_out, do, indices, tl_lse, offsets) + + ms = do_bench(fn, rep=100, warmup=250) + print(f"Average time: {ms:.3f} ms") + print(f"bwd io bandwidth = ", (B * S * max(DQKV * 2, DQKV + DV) * topk * 2) / (ms * 1e-3) / 1e12) + print(f"bwd tflops = ", per_token_flop * S / (ms * 1e-3) / 1e12) + + +if __name__ == "__main__": + test_sparse_mla_bwd(B=1, S=2048, H=64, HKV=1, DQKV=576, DV=512, topk=512, dtype=torch.bfloat16, check_correctness=True) diff --git a/examples/dsa_sparse_finetune/sparse_mla_fwd.py b/examples/dsa_sparse_finetune/sparse_mla_fwd.py new file mode 100644 index 0000000000..d875236952 --- /dev/null +++ b/examples/dsa_sparse_finetune/sparse_mla_fwd.py @@ -0,0 +1,310 @@ +# ruff: noqa +import torch +import tilelang +from tilelang import language as T +from index import prepare_token_indices + +from utils import assert_tensors_similar + + +@tilelang.jit( + out_idx=[-2, -1], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, +) +def sparse_mla_fwd( + heads, + dim, + tail_dim, + topk, + kv_group=1, + sm_scale=None, + is_causal=True, + CP0=True, + block_I=32, + num_stages=2, + threads=128, +): + assert dim == tilelang.math.next_power_of_2(dim), f"haven't check padding correctness yet, dim={dim}" + assert tail_dim == tilelang.math.next_power_of_2(tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" + assert is_causal == True, "non-casual is not supported" + assert topk % block_I == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + if sm_scale is None: + sm_scale = (1.0 / (dim + tail_dim)) ** 0.5 + else: + sm_scale = sm_scale + + batch_plus_one = T.symbolic("batch_plus_one") + seq_len = T.symbolic("seq_len") + + head_kv = heads // kv_group + q_shape = [seq_len, heads, dim + tail_dim] + kv_shape = [seq_len, kv_group, dim + tail_dim] + o_shape = [seq_len, heads, dim] + indices_shape = [seq_len, kv_group, topk] + lse_shape = [seq_len, heads] + offsets_shape = [batch_plus_one] + token_indices_shape = [seq_len, 2] + indices_dtype = T.int32 + dtype = T.bfloat16 + accum_dtype = T.float32 + + G = kv_group + H = head_kv + padded_H = max(tilelang.math.next_power_of_2(head_kv), 16) + if padded_H != H: + assert kv_group == 1, ( + "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + ) + BI = block_I + NI = tilelang.cdiv(topk, block_I) + D = dim + D_tail = tail_dim + + if head_kv > 64: + assert head_kv % 64 == 0, "head_kv should be a multiple of 64" + REPLICATE_H = head_kv // 64 + else: + REPLICATE_H = 1 + + H_per_block = padded_H if REPLICATE_H == 1 else 64 + + @T.prim_func + def main( + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + Offsets: T.Tensor(offsets_shape, indices_dtype), # type: ignore + TokenIndices: T.Tensor(token_indices_shape, indices_dtype), # type: ignore + Output: T.Tensor(o_shape, dtype), # type: ignore + Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore + ): + with T.Kernel(seq_len * REPLICATE_H, kv_group, threads=threads) as ( + bx, + by, + ): + Q_shared = T.alloc_shared([H_per_block, D], dtype) + Q_tail_shared = T.alloc_shared([H_per_block, D_tail], dtype) + KV_shared = T.alloc_shared([BI, D], dtype) + K_tail_shared = T.alloc_shared([BI, D_tail], dtype) + mask = T.alloc_fragment([BI], "bool") + + acc_o = T.alloc_fragment([H_per_block, D], accum_dtype) + acc_s = T.alloc_fragment([H_per_block, BI], accum_dtype) + S_shared = T.alloc_shared([H_per_block, BI], dtype) + sumexp = T.alloc_fragment([H_per_block], accum_dtype) + sumexp_i = T.alloc_fragment([H_per_block], accum_dtype) + alpha = T.alloc_fragment([H_per_block], accum_dtype) + m_i = T.alloc_fragment([H_per_block], accum_dtype) + m_i_prev = T.alloc_fragment([H_per_block], accum_dtype) + + T.fill(acc_o, 0) + T.fill(sumexp, 0) + T.fill(m_i, -(2**30)) # avoid -inf - inf to cause nan + + b_s_i = bx if REPLICATE_H == 1 else (bx // REPLICATE_H) + b_i, s_i = TokenIndices[b_s_i, 0], TokenIndices[b_s_i, 1] + bos, eos = Offsets[b_i], Offsets[b_i + 1] + g_i = by + q_i = s_i + max_kv_i = q_i + + H0 = g_i * padded_H + (0 if REPLICATE_H == 1 else (bx % REPLICATE_H) * 64) + H1 = H0 + H_per_block + + T.copy(Q[bos + s_i, H0:H1, :D], Q_shared) + T.copy(Q[bos + s_i, H0:H1, D:], Q_tail_shared) + + for i_i in T.Pipelined(NI, num_stages=num_stages): + for bi_i in T.Parallel(BI): + mask[bi_i] = (Indices[bos + s_i, g_i, i_i * BI + bi_i] <= max_kv_i) & (Indices[bos + s_i, g_i, i_i * BI + bi_i] != -1) + + for bi_i, d_i in T.Parallel(BI, D): + KV_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, g_i, i_i * BI + bi_i], g_i, d_i] + for bi_i, d_i in T.Parallel(BI, D_tail): + K_tail_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, g_i, i_i * BI + bi_i], g_i, D + d_i] + + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_s.dtype)) + T.gemm( + Q_shared, + KV_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.gemm( + Q_tail_shared, + K_tail_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.copy(m_i, m_i_prev) + T.reduce_max(acc_s, m_i, dim=1, clear=False) + for h_i in T.Parallel(H_per_block): + alpha[h_i] = T.exp((m_i_prev[h_i] - m_i[h_i]) * sm_scale) + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.exp(acc_s[h_i, bi_i] * sm_scale - m_i[h_i] * sm_scale) + T.reduce_sum(acc_s, sumexp_i, dim=1) # is this a accumulate operator? + for h_i in T.Parallel(H_per_block): + sumexp[h_i] = sumexp[h_i] * alpha[h_i] + sumexp_i[h_i] + for h_i, d_i in T.Parallel(H_per_block, D): + acc_o[h_i, d_i] = acc_o[h_i, d_i] * alpha[h_i] + + T.copy(acc_s, S_shared) + T.gemm(S_shared, KV_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + + # Rescale + for h_i, d_i in T.Parallel(H_per_block, D): + acc_o[h_i, d_i] /= sumexp[h_i] + for h_i in T.Parallel(H_per_block): + sumexp[h_i] = T.log(sumexp[h_i]) + m_i[h_i] * sm_scale + + T.copy(acc_o, Output[bos + s_i, H0:H1, :]) + T.copy(sumexp, Lse[bos + s_i, H0:H1]) + + return main + + +def sparse_mla_fwd_interface( + q, kv, indices, offsets, sm_scale=None, return_p_sum: bool = False, d_v=512, block_I=32, num_stages=2, threads=128 +): + is_casual = True + assert return_p_sum == False, "This kernel file is for fwd only" + assert q.is_contiguous() and kv.is_contiguous() and indices.is_contiguous() + seq_len, heads, dim_plus_tail_dim = q.shape + seq_len_kv, kv_group, _ = kv.shape + assert seq_len == seq_len_kv + + assert dim_plus_tail_dim == 576, "you should assign dim otherwise" + dim = d_v + + assert kv.shape[-1] == dim_plus_tail_dim + tail_dim = dim_plus_tail_dim - dim + _, _, topk = indices.shape + assert indices.shape == (seq_len, kv_group, topk) + + token_indices = prepare_token_indices(offsets) + + kernel = sparse_mla_fwd( + heads, dim, tail_dim, topk, kv_group, sm_scale, is_casual, block_I=block_I, num_stages=num_stages, threads=threads + ) + out, lse = kernel(q, kv, indices, offsets, token_indices) + return out, lse + + +def ref_sparse_mla_fwd_interface(Q, KV, Indices, offsets, sm_scale=None, is_casual=True): + Q = Q.float() + KV = KV.float() + all_o = [] + for i in range(offsets.shape[0] - 1): + q = Q[None, offsets[i] : offsets[i + 1]] + kv = KV[None, offsets[i] : offsets[i + 1]] + indices = Indices[None, offsets[i] : offsets[i + 1]].clone() + + indices = indices.transpose(1, 2) + b, sq, h, dim_q = q.shape + b, sk, g, _ = kv.shape + + assert kv.shape[-1] == 576, "you should assign dim otherwise" + dim = 512 + k = kv + v = kv[..., :dim] + + b, _, _, dim_v = v.shape + g_index = g + h_index = h // g + compressed_casual_mask = torch.arange(0, sq, dtype=torch.int32, device="cuda").view(-1, 1) >= torch.arange( + 1 - 1, sk * 1, 1, dtype=torch.int32, device="cuda" + ).view(1, -1) + + indices[indices > sk] = sk + mask = q.new_zeros(b, g_index, sq, sk + 1, dtype=torch.bool).scatter(3, indices.long(), 1) + mask = mask[..., :-1] + mask = mask & compressed_casual_mask.view(1, 1, sq, sk) + mask[:, :, : 1 - 1, 0] = True + mask = mask.view(b, g_index, 1, sq, sk) + + q = q.view(b, sq, g, -1, dim_q) + score = torch.einsum("bmghd,bngd->bghmn", q, k) + sm_scale = dim_q**-0.5 if sm_scale is None else sm_scale + score = score.masked_fill(~mask, float("-inf")).mul(sm_scale) + p = score.softmax(dim=-1) + p = p.view(b, g_index, h_index, -1, sq, sk) + p = p.view(b, g, -1, sq, sk) + o = torch.einsum("bghmn,bngd->bmghd", p.type(v.dtype), v) + o = o.reshape(b, sq, h, dim_v) + all_o.append(o.squeeze(0)) + o = torch.cat(all_o, dim=0) + return o.to(torch.bfloat16) + + +def test_sparse_mla_fwd( + B=1, + S=4096, + H=128, + HKV=1, + DQK=576, + DV=512, + topk=2048, + dtype=torch.bfloat16, + check_correctness=True, + block_I=64, + num_stages=2, + threads=256, +): + torch.random.manual_seed(0) + q = torch.randn((S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True) + kv = torch.randn((S, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True) + offsets = torch.tensor([0, S // 2 - 1, S], dtype=torch.int32, device="cuda") + + indices = torch.full((S, HKV, topk), S, dtype=torch.int32, device="cuda") + for i in range(offsets.shape[0] - 1): + seq_len = (offsets[i + 1] - offsets[i]).item() + assert seq_len >= topk + for t in range(seq_len): + for h in range(HKV): + i_i = torch.randperm(max(1, t))[:topk] + indices[offsets[i] + t, h, : len(i_i)] = i_i + + tl_out, tl_lse = sparse_mla_fwd_interface(q, kv, indices, offsets, block_I=block_I, num_stages=num_stages, threads=threads) + + if check_correctness: + # otherwise may cause out of memory + ref_out = ref_sparse_mla_fwd_interface(q, kv, indices, offsets) + assert_tensors_similar(tl_out, ref_out, eps=1e-2, name="out") + print("assert_tensors_similar passed") + + def fn(): + return sparse_mla_fwd_interface(q, kv, indices, offsets, block_I=block_I, num_stages=num_stages, threads=threads) + + from tilelang.profiler import do_bench + + ms = do_bench( + fn, + rep=100, + warmup=250, + ) + print(f"Average time: {ms:.3f} ms") + print("fwd io bandwidth = ", (B * S * DQK * topk * 2) / (ms * 1e-3) / 1e12) + print("fwd tflops = ", (B * S * (DQK + DV) * topk * 2 * H) / (ms * 1e-3) / 1e12) + + +if __name__ == "__main__": + test_sparse_mla_fwd( + B=1, + S=4096, + H=128, + HKV=1, + DQK=576, + DV=512, + topk=1024, + dtype=torch.bfloat16, + check_correctness=True, + block_I=64, + num_stages=2, + threads=256, + ) diff --git a/examples/dsa_sparse_finetune/sparse_mla_topk_reducesum.py b/examples/dsa_sparse_finetune/sparse_mla_topk_reducesum.py new file mode 100644 index 0000000000..a03bc74f51 --- /dev/null +++ b/examples/dsa_sparse_finetune/sparse_mla_topk_reducesum.py @@ -0,0 +1,226 @@ +# ruff: noqa +import torch +import torch.nn as nn +import torch.nn.functional as F +import tilelang +from tilelang import language as T +from einops import repeat, rearrange, einsum +from index import prepare_token_indices +from utils import get_abs_err, get_err_ratio + +BF16 = T.bfloat16 +FP32 = T.float32 +INT32 = T.int32 + +pass_configs = { + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, +} + + +@tilelang.jit(pass_configs=pass_configs) +def tl_sparse_mla_topk_reducesum_impl( + heads, + dim, + tail_dim, + topk, + kv_group=1, + sm_scale=None, + block_I=32, + num_stages=2, + threads=128, +): + assert dim == tilelang.math.next_power_of_2(dim), f"haven't check padding correctness yet, dim={dim}" + assert tail_dim == tilelang.math.next_power_of_2(tail_dim), f"haven't check padding correctness yet, dim={tail_dim}" + assert topk % block_I == 0, "otherwise will load some index=0 thus causing wrong kv to be loaded" + if sm_scale is None: + sm_scale = (1.0 / (dim + tail_dim)) ** 0.5 + + batch_plus_one = T.symbolic("batch_plus_one") + seq_len = T.symbolic("seq_len") + seq_len_kv = T.symbolic("seq_len_kv") + + head_kv = heads // kv_group + indices_dtype = T.int32 + dtype = T.bfloat16 + accum_dtype = T.float32 + + G = kv_group + H = head_kv + padded_H = max(tilelang.math.next_power_of_2(head_kv), 16) + if padded_H != H: + assert kv_group == 1, ( + "here we solve the H padding automatically, other wise you should handle Q copy and Output copy with your mask (when kv_group == 1, use g_i * padded_H:(g_i+1) * padded_H would be handled automatically)" + ) + BI = block_I + NI = tilelang.cdiv(topk, block_I) + D = dim + D_tail = tail_dim + + if head_kv > 64: + assert head_kv % 64 == 0, "head_kv should be a multiple of 64" + REPLICATE_H = head_kv // 64 + else: + REPLICATE_H = 1 + + H_per_block = padded_H if REPLICATE_H == 1 else 64 + + q_shape = [seq_len, heads, dim + tail_dim] + kv_shape = [seq_len_kv, kv_group, dim + tail_dim] + indices_shape = [seq_len, kv_group, topk] + lse_shape = [seq_len, heads] + reducesum_shape = [seq_len, kv_group, REPLICATE_H, topk] + offsets_shape = [batch_plus_one] + token_indices_shape = [seq_len, 2] + + @T.prim_func + def tl_sparse_mla_topk_reducesum_kernel( + Q: T.Tensor(q_shape, dtype), # type: ignore + KV: T.Tensor(kv_shape, dtype), # type: ignore + Indices: T.Tensor(indices_shape, indices_dtype), # type: ignore + Lse: T.Tensor(lse_shape, accum_dtype), # type: ignore + Offsets: T.Tensor(offsets_shape, indices_dtype), # type: ignore + TokenIndices: T.Tensor(token_indices_shape, indices_dtype), # type: ignore + ReduceSum: T.Tensor(reducesum_shape, accum_dtype), # type: ignore + ): + with T.Kernel(seq_len * REPLICATE_H, kv_group, threads=threads) as ( + bx, + by, + ): + Q_shared = T.alloc_shared([H_per_block, D], dtype) + Q_tail_shared = T.alloc_shared([H_per_block, D_tail], dtype) + KV_shared = T.alloc_shared([BI, D], dtype) + K_tail_shared = T.alloc_shared([BI, D_tail], dtype) + mask = T.alloc_fragment([BI], "bool") + + acc_s = T.alloc_fragment([H_per_block, BI], accum_dtype) + reducesum = T.alloc_fragment([BI], accum_dtype) + lse = T.alloc_fragment([H_per_block], accum_dtype) + + T.fill(lse, 0) + + b_s_i = bx if REPLICATE_H == 1 else (bx // REPLICATE_H) + b_i, s_i = TokenIndices[b_s_i, 0], TokenIndices[b_s_i, 1] + bos, eos = Offsets[b_i], Offsets[b_i + 1] + r_i = bx % REPLICATE_H + g_i = by + q_i = s_i + max_kv_i = q_i + + H0 = g_i * padded_H + (0 if REPLICATE_H == 1 else (bx % REPLICATE_H) * 64) + H1 = H0 + H_per_block + + T.copy(Q[bos + s_i, H0:H1, :D], Q_shared) + T.copy(Q[bos + s_i, H0:H1, D:], Q_tail_shared) + T.copy(Lse[bos + s_i, H0:H1], lse) + + for i_i in T.Pipelined(NI, num_stages=num_stages): + for bi_i in T.Parallel(BI): + mask[bi_i] = (Indices[bos + s_i, g_i, i_i * BI + bi_i] <= max_kv_i) & (Indices[bos + s_i, g_i, i_i * BI + bi_i] != -1) + + for bi_i, d_i in T.Parallel(BI, D): + KV_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, g_i, i_i * BI + bi_i], g_i, d_i] + for bi_i, d_i in T.Parallel(BI, D_tail): + K_tail_shared[bi_i, d_i] = KV[bos + Indices[bos + s_i, g_i, i_i * BI + bi_i], g_i, D + d_i] + + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.if_then_else(mask[bi_i], 0, -T.infinity(acc_s.dtype)) + T.gemm( + Q_shared, + KV_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + T.gemm( + Q_tail_shared, + K_tail_shared, + acc_s, + transpose_B=True, + policy=T.GemmWarpPolicy.FullRow, + ) + for h_i, bi_i in T.Parallel(H_per_block, BI): + acc_s[h_i, bi_i] = T.exp(acc_s[h_i, bi_i] * sm_scale - lse[h_i]) + T.reduce_sum(acc_s, reducesum, dim=0) + T.copy(reducesum, ReduceSum[bos + s_i, g_i, r_i, i_i * BI : i_i * BI + BI]) + + return tl_sparse_mla_topk_reducesum_kernel + + +def sparse_mla_topk_reducesum_interface( + q: torch.Tensor, + kv: torch.Tensor, + topk_indices: torch.Tensor, + lse: torch.Tensor, + offsets: torch.Tensor, + dim_v: int, +): + assert kv.shape[-2] == 1 + seq_len, heads, dim_plus_tail_dim, topk = *q.shape, topk_indices.shape[-1] + REPLICATE_H = max(heads // 64, 1) + tail_dim = dim_plus_tail_dim - dim_v + token_indices = prepare_token_indices(offsets) + + reducesum = torch.zeros([seq_len, 1, REPLICATE_H, topk], dtype=torch.float32, device=q.device) + kernel = tl_sparse_mla_topk_reducesum_impl(heads=heads, dim=dim_v, tail_dim=tail_dim, topk=topk) + kernel(q, kv, topk_indices, lse, offsets, token_indices, reducesum) + reducesum = reducesum.sum(dim=-2) # [batch, seq_len, 1, RH, topk] -> [batch, seq_len, 1, topk] + attn_score = reducesum / reducesum.sum(dim=-1, keepdim=True) + + return attn_score + + +def ref_mla_topk_softmax(Q: torch.Tensor, K: torch.Tensor, TopkIndices: torch.Tensor, offsets: torch.Tensor): + # q: [batch, seq_len, heads, dim] + # k: [batch, seq_len, dim] + sm_scale = Q.shape[-1] ** -0.5 + all_lse = [] + all_topk_score = [] + for i in range(offsets.shape[0] - 1): + q = Q[offsets[i] : offsets[i + 1]] + k = K[offsets[i] : offsets[i + 1]] + topk_indices = TopkIndices[offsets[i] : offsets[i + 1]] + seq_len = q.shape[0] + mask = (torch.arange(seq_len)[:, None] >= torch.arange(seq_len)[None, :]).unsqueeze(-2).cuda() + logits = einsum(q, k, "s1 h d, s2 d -> s1 h s2") * sm_scale + logits = torch.where(mask, logits, float("-inf")) + score = F.softmax(logits, dim=-1, dtype=torch.float32) + score_sum = score.sum(dim=-2) + topk_score = torch.gather(score_sum, dim=-1, index=topk_indices.to(torch.int64)) + topk_score = topk_score / topk_score.sum(dim=-1, keepdim=True) + max_logits = logits.amax(dim=-1).to(torch.float32) + lse = torch.log((logits - max_logits.unsqueeze(-1).to(torch.float32)).exp().sum(dim=-1)) + max_logits + all_lse.append(lse) + all_topk_score.append(topk_score) + lse = torch.cat(all_lse, dim=0) + topk_score = torch.cat(all_topk_score, dim=0) + return lse, topk_score + + +def test_kernel( + B=1, + S=2048, + H=16, + D=512, + tail_D=64, + topk=128, +): + torch.manual_seed(42) + + q = torch.randn((S, H, D + tail_D)).cuda().bfloat16() + kv = torch.randn((S, D + tail_D)).cuda().bfloat16() + offsets = torch.tensor([0, 1023, S], dtype=torch.int32).cuda() + + topk_indices = repeat(torch.arange(topk, dtype=torch.int32).cuda(), "k -> s k", s=S).contiguous() + + lse, ref_attn_score = ref_mla_topk_softmax(q, kv, topk_indices, offsets) + + kv = kv.unsqueeze(-2) + topk_indices = topk_indices.unsqueeze(-2) + + attn_score = sparse_mla_topk_reducesum_interface(q, kv, topk_indices, lse, offsets, dim_v=D).squeeze(-2) + print(f"attn_score err: {get_abs_err(attn_score, ref_attn_score):.6f} ratio: {get_err_ratio(attn_score, ref_attn_score):.6f}") + + +if __name__ == "__main__": + test_kernel() diff --git a/examples/dsa_sparse_finetune/utils.py b/examples/dsa_sparse_finetune/utils.py new file mode 100644 index 0000000000..96afd064dc --- /dev/null +++ b/examples/dsa_sparse_finetune/utils.py @@ -0,0 +1,73 @@ +import torch + + +def get_abs_err(y, x): + x = x.to(torch.float32) + y = y.to(torch.float32) + return (x - y).flatten().abs().max().item() + + +def get_err_ratio(y, x): + x = x.to(torch.float32) + y = y.to(torch.float32) + err = (x - y).flatten().square().mean().sqrt().item() + base = (x).flatten().square().mean().sqrt().item() + return err / base + + +def calculate_tensor_similarity(x, y, name="tensor"): + """ + Calculate similarity between two tensors using a normalized dot product metric. + + Unlike torch.testing.assert_close which uses absolute/relative tolerance based on + element-wise differences, this function computes a global similarity score: + sim = 2 * / (||x||^2 + ||y||^2) + + This metric is scale-invariant and measures the cosine-like similarity normalized + by the magnitude of both tensors. It returns 1 for identical tensors and values + closer to 0 for dissimilar ones. This is particularly useful for comparing tensors + with varying magnitudes where relative errors matter more than absolute differences. + + Args: + x: First tensor to compare + y: Second tensor to compare + name: Name of the tensor for logging purposes + + Returns: + Similarity score in range [0, 1] where 1 means identical + """ + x, y = x.data.double(), y.data.double() + denominator = (x * x + y * y).sum() + if denominator == 0: + print(f"\033[33mWARNING: {name} all zero\033[0m") + return 1 + sim = 2 * (x * y).sum() / denominator + return sim + + +def assert_tensors_similar(x, y, eps=1e-8, name="tensor", raise_assert=True): + """ + Assert that two tensors are similar using a global similarity metric. + + Key differences from torch.testing.assert_close: + - torch.testing.assert_close: Uses element-wise comparison with rtol/atol, checking + that |x - y| <= atol + rtol * |y| for each element. It's sensitive to outliers + and requires all elements to satisfy the tolerance. + - assert_tensors_similar: Uses a single global similarity score (1 - sim) where sim is the + normalized dot product. It's more robust to outliers and focuses on overall + tensor similarity rather than element-wise precision. This is better suited for + comparing large tensors where a few outlier elements shouldn't fail the test. + + Args: + x: First tensor to compare + y: Second tensor to compare + eps: Maximum allowed difference (1 - similarity), default 1e-8 + name: Name of the tensor for error messages + raise_assert: Whether to raise assertion error on failure + """ + sim = calculate_tensor_similarity(x, y, name) + diff = 1.0 - sim + if not (0 <= diff <= eps): + print(f"\033[31mERROR: {name} similarity check failed, diff={diff:.2e} (threshold={eps:.2e})\033[0m") + if raise_assert: + assert False # noqa: B011 diff --git a/examples/dynamic_shape/example_dynamic.py b/examples/dynamic_shape/example_dynamic.py index be018c8b70..e338d76ca1 100644 --- a/examples/dynamic_shape/example_dynamic.py +++ b/examples/dynamic_shape/example_dynamic.py @@ -1,10 +1,9 @@ import tilelang import tilelang.language as T import tilelang.testing -from tilelang import tvm as tvm -@tilelang.jit(pass_configs={"tl.disable_dynamic_tail_split": True, "tl.dynamic_alignment": 8}) +@tilelang.jit def matmul_dynamic_mnk( block_M, block_N, @@ -17,9 +16,9 @@ def matmul_dynamic_mnk( num_stages, threads, ): - M = tvm.te.var("m") - N = tvm.te.var("n") - K = tvm.te.var("k") + M = T.dynamic("m") + N = T.dynamic("n") + K = T.dynamic("k") A_shape = (K, M) if trans_A else (M, K) B_shape = (N, K) if trans_B else (K, N) @@ -29,9 +28,9 @@ def matmul_dynamic_mnk( @T.prim_func def dynamic_matmul( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -53,15 +52,14 @@ def dynamic_matmul( return dynamic_matmul -def matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, - accum_dtype, num_stages, threads): +def matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads): print( f"M: {M}, N: {N}, K: {K}, block_M: {block_M}, block_N: {block_N}, block_K: {block_K}, trans_A: {trans_A}, trans_B: {trans_B}, in_dtype: {in_dtype}, out_dtype: {out_dtype}, accum_dtype: {accum_dtype}, num_stages: {num_stages}, threads: {threads}" ) - kernel = matmul_dynamic_mnk(block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, - accum_dtype, num_stages, threads) + kernel = matmul_dynamic_mnk(block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads) import torch + if trans_A: A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype)) else: @@ -103,8 +101,30 @@ def main(M=16384, N=16384, K=16384): accum_dtype = "float32" num_stages = 3 threads = 128 - matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, - accum_dtype, num_stages, threads) + matmul_dynamic(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads) + + +def run_regression_perf(M=4096, N=4096, K=4096): + block_M, block_N, block_K = 128, 128, 32 + trans_A, trans_B = False, False + in_dtype, out_dtype = "float16", "float16" + accum_dtype = "float32" + num_stages = 3 + threads = 128 + kernel = matmul_dynamic_mnk(block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads) + import torch + + if trans_A: + A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype)) + else: + A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype)) + if trans_B: + B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype)) + else: + B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype)) + C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype)) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(input_tensors=[A, B, C], backend="cupti") if __name__ == "__main__": diff --git a/examples/dynamic_shape/regression_example_dynamic.py b/examples/dynamic_shape/regression_example_dynamic.py new file mode 100644 index 0000000000..958695990d --- /dev/null +++ b/examples/dynamic_shape/regression_example_dynamic.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_dynamic + + +def regression_example_dynamic(): + tilelang.testing.process_func(example_dynamic.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/elementwise/example_elementwise_add.py b/examples/elementwise/example_elementwise_add.py index bc9bb4df5b..32da940155 100644 --- a/examples/elementwise/example_elementwise_add.py +++ b/examples/elementwise/example_elementwise_add.py @@ -3,19 +3,25 @@ import torch import tilelang import tilelang.language as T -from tilelang.autotuner import AutoTuner def ref_program(x, y): return x + y +def get_configs(): + block_M = [64, 128, 256] + block_N = [64, 128, 256] + threads = [64, 128, 256] + configs = list(itertools.product(block_M, block_N, threads)) + return [{"block_M": bm, "block_N": bn, "threads": th} for bm, bn, th in configs] + + +@tilelang.autotune(configs=get_configs()) @tilelang.jit(out_idx=[-1]) def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads): - @T.prim_func - def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor( - (M, N), out_dtype)): + def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor((M, N), out_dtype)): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_N), in_dtype) B_shared = T.alloc_shared((block_M, block_N), in_dtype) @@ -24,7 +30,7 @@ def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T. T.copy(A[by * block_M, bx * block_N], A_shared) T.copy(B[by * block_M, bx * block_N], B_shared) - for (local_y, local_x) in T.Parallel(block_M, block_N): + for local_y, local_x in T.Parallel(block_M, block_N): C_local[local_y, local_x] = A_shared[local_y, local_x] + B_shared[local_y, local_x] T.copy(C_local, C_shared) T.copy(C_shared, C[by * block_M, bx * block_N]) @@ -32,53 +38,40 @@ def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T. return elem_add -def get_configs(M, N): - block_M = [64, 128, 256] - block_N = [64, 128, 256] - threads = [64, 128, 256] - configs = list(itertools.product(block_M, block_N, threads)) - return [{"block_M": bm, "block_N": bn, "threads": th} for bm, bn, th in configs] - - -def get_best_config(M, N): +def main(M=1024, N=1024, use_autotune=False): + a = torch.randn(M, N, dtype=torch.float32, device="cuda") + b = torch.randn(M, N, dtype=torch.float32, device="cuda") - def kernel(block_M=None, block_N=None, threads=None): - return elementwise_add(M, N, block_M, block_N, "float32", "float32", threads) + if use_autotune: + kernel = elementwise_add(M, N, in_dtype=T.float32, out_dtype=T.float32) + else: + # Default config + config = {"block_M": 32, "block_N": 32, "threads": 128} + kernel = elementwise_add(M, N, **config, in_dtype=T.float32, out_dtype=T.float32) - autotuner = AutoTuner.from_kernel( - kernel=kernel, configs=get_configs(M, N)).set_compile_args( - out_idx=[-1], - target="cuda", - ).set_profile_args( - supply_type=tilelang.TensorSupplyType.Auto, - ref_prog=ref_program, - skip_check=False, - ) - return autotuner.run(warmup=3, rep=20) + out = kernel(a, b) + torch.testing.assert_close(out, ref_program(a, b), rtol=1e-2, atol=1e-2) -def main(): +def run_regression_perf(): parser = argparse.ArgumentParser() - parser.add_argument("--m", type=int, default=1024) - parser.add_argument("--n", type=int, default=1024) - parser.add_argument("--use_autotune", action="store_true", default=False) + parser.add_argument("--m", type=int, default=4096) + parser.add_argument("--n", type=int, default=4096) args, _ = parser.parse_known_args() M, N = args.m, args.n - a = torch.randn(M, N, dtype=torch.float32, device="cuda") b = torch.randn(M, N, dtype=torch.float32, device="cuda") + config = {"block_M": 32, "block_N": 32, "threads": 128} + kernel = elementwise_add(M, N, **config, in_dtype="float32", out_dtype="float32") + from tilelang.profiler import do_bench - if args.use_autotune: - result = get_best_config(M, N) - kernel = result.kernel - else: - # Default config - config = {"block_M": 32, "block_N": 32, "threads": 128} - kernel = elementwise_add(M, N, **config, in_dtype="float32", out_dtype="float32") - - out = kernel(a, b) - torch.testing.assert_close(out, ref_program(a, b), rtol=1e-2, atol=1e-2) + return do_bench(lambda: kernel(a, b), backend="cupti") if __name__ == "__main__": - main() + parser = argparse.ArgumentParser() + parser.add_argument("--m", type=int, default=1024) + parser.add_argument("--n", type=int, default=1024) + parser.add_argument("--use_autotune", action="store_true", default=False) + args, _ = parser.parse_known_args() + main(args.m, args.n, args.use_autotune) diff --git a/examples/elementwise/example_elementwise_add_tma_1d.py b/examples/elementwise/example_elementwise_add_tma_1d.py index 0467eba881..501e1f00d7 100644 --- a/examples/elementwise/example_elementwise_add_tma_1d.py +++ b/examples/elementwise/example_elementwise_add_tma_1d.py @@ -10,10 +10,8 @@ def ref_program(x, y): @tilelang.jit(out_idx=[-1]) def elementwise_add(M, N, block_M, block_N, in_dtype, out_dtype, threads): - @T.prim_func - def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor( - (M, N), out_dtype)): + def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T.Tensor((M, N), out_dtype)): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_N), in_dtype) B_shared = T.alloc_shared((block_M, block_N), in_dtype) @@ -22,7 +20,7 @@ def elem_add(A: T.Tensor((M, N), in_dtype), B: T.Tensor((M, N), in_dtype), C: T. T.copy(A[by * block_M, bx * block_N], A_shared) T.copy(B[by * block_M, bx * block_N], B_shared) - for (local_y, local_x) in T.Parallel(block_M, block_N): + for local_y, local_x in T.Parallel(block_M, block_N): C_local[local_y, local_x] = A_shared[local_y, local_x] + B_shared[local_y, local_x] T.copy(C_local, C_shared) T.copy(C_shared, C[by * block_M, bx * block_N]) diff --git a/examples/elementwise/regression_example_elementwise.py b/examples/elementwise/regression_example_elementwise.py new file mode 100644 index 0000000000..261202a568 --- /dev/null +++ b/examples/elementwise/regression_example_elementwise.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_elementwise_add + + +def regression_example_elementwise_add(): + tilelang.testing.process_func(example_elementwise_add.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/elementwise/test_example_elementwise.py b/examples/elementwise/test_example_elementwise.py index ff0b45a0a5..24f675cd6a 100644 --- a/examples/elementwise/test_example_elementwise.py +++ b/examples/elementwise/test_example_elementwise.py @@ -1,14 +1,13 @@ import tilelang.testing import example_elementwise_add -import example_elementwise_add_tma_1d def test_example_elementwise_add(): example_elementwise_add.main() -def test_example_elementwise_add_tma_1d(): - example_elementwise_add_tma_1d.main() +def test_example_elementwise_add_autotune(): + example_elementwise_add.main(use_autotune=True) if __name__ == "__main__": diff --git a/examples/flash_attention/README.md b/examples/flash_attention/README.md index be11a8dc64..355ed73258 100644 --- a/examples/flash_attention/README.md +++ b/examples/flash_attention/README.md @@ -34,8 +34,6 @@ def flash_attention( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - # Annotate layout for Q_shared, e.g., use a swizzled layout to optimize memory access - T.annotate_layout({Q_shared: tl.layout.make_swizzled_layout(Q_shared)}) # Copy a block of Q from global memory to Q_shared T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) @@ -77,6 +75,8 @@ def flash_attention( # Compute the maximum value per row on dimension 1 (block_N) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # Compute the factor by which we need to rescale previous partial sums for i in T.Parallel(block_M): @@ -106,4 +106,4 @@ def flash_attention( # Write back the final output block from acc_o to the Output buffer T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) -``` \ No newline at end of file +``` diff --git a/examples/flash_attention/bert_padding.py b/examples/flash_attention/bert_padding.py index 7058fd773d..15c4097ce7 100644 --- a/examples/flash_attention/bert_padding.py +++ b/examples/flash_attention/bert_padding.py @@ -6,7 +6,6 @@ class IndexFirstAxis(torch.autograd.Function): - @staticmethod def forward(ctx, input, indices): ctx.save_for_backward(indices) @@ -15,9 +14,7 @@ def forward(ctx, input, indices): second_dim = other_shape.numel() # TD [2022-03-04] For some reason torch.gather is a bit faster than indexing. # return input[indices] - return torch.gather( - rearrange(input, "b ... -> b (...)"), 0, - repeat(indices, "z -> z d", d=second_dim)).reshape(-1, *other_shape) + return torch.gather(rearrange(input, "b ... -> b (...)"), 0, repeat(indices, "z -> z d", d=second_dim)).reshape(-1, *other_shape) @staticmethod def backward(ctx, grad_output): @@ -40,14 +37,12 @@ def backward(ctx, grad_output): class IndexPutFirstAxis(torch.autograd.Function): - @staticmethod def forward(ctx, values, indices, first_axis_dim): ctx.save_for_backward(indices) assert indices.ndim == 1 assert values.ndim >= 2 - output = torch.zeros( - first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype) + output = torch.zeros(first_axis_dim, *values.shape[1:], device=values.device, dtype=values.dtype) # TD [2022-03-04] For some reason torch.scatter is a bit faster than indexing. output[indices] = values # output.scatter_(0, repeat(indices, 'z -> z d', d=values.shape[1]), values) @@ -66,7 +61,6 @@ def backward(ctx, grad_output): class IndexFirstAxisResidual(torch.autograd.Function): - @staticmethod def forward(ctx, input, indices): ctx.save_for_backward(indices) @@ -128,7 +122,7 @@ def unpad_input_for_concatenated_sequences(hidden_states, attention_mask_in_leng """ Supports concatenating short samples in one sequence. The attention_mask_in_length is utilized to mask other short samples. It helps efficient training of variant lengths-based samples (e.g., the supervised fine-tuning task in large language model). The motivation for this function is explained [here](https://github.com/Dao-AILab/flash-attention/issues/432#issuecomment-1668822286). - + For example, if batch = 3 and seqlen = 6, the attention_mask_in_length is: ``` [ @@ -177,9 +171,7 @@ def unpad_input_for_concatenated_sequences(hidden_states, attention_mask_in_leng """ length = attention_mask_in_length.sum(dim=-1) seqlen = attention_mask_in_length.size(-1) - attention_mask_2d = torch.arange( - seqlen, device=length.device, dtype=length.dtype).expand(len(length), - seqlen) < length.unsqueeze(1) + attention_mask_2d = torch.arange(seqlen, device=length.device, dtype=length.dtype).expand(len(length), seqlen) < length.unsqueeze(1) real_indices_idx = torch.nonzero(attention_mask_in_length.flatten(), as_tuple=False).flatten() seqlens_in_batch = attention_mask_in_length.flatten()[real_indices_idx] indices = torch.nonzero(attention_mask_2d.flatten(), as_tuple=False).flatten() diff --git a/examples/flash_attention/example_gqa_bwd.py b/examples/flash_attention/example_gqa_bwd.py index 907a121d26..801927faf4 100644 --- a/examples/flash_attention/example_gqa_bwd.py +++ b/examples/flash_attention/example_gqa_bwd.py @@ -6,25 +6,27 @@ @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, groups=1): - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=256) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -39,26 +41,25 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by // groups, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared) + T.copy(V[bz, k * block_N : (k + 1) * block_N, by // groups, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim_v): @@ -72,29 +73,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim_v): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim_v): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim_v] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -103,81 +106,74 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim_v, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep def make_dq_layout(dQ): # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment - return T.Layout(dQ.shape, - lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) + return T.Layout(dQ.shape, lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) @tilelang.jit( - out_idx=[1], pass_configs={ + out_idx=[1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_postprocess(batch, heads, seq_len, dim_qk): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim_qk] blk = 64 @T.prim_func def flash_bwd_post( - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(shape, dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) T.copy( - dQ[bz, bx * blk:(bx + 1) * blk, by, :], - dQ_out[bz, bx * blk:(bx + 1) * blk, by, :], + dQ[bz, bx * blk : (bx + 1) * blk, by, :], + dQ_out[bz, bx * blk : (bx + 1) * blk, by, :], ) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_atomic_add(batch, - heads, - seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_atomic_add(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -197,35 +193,35 @@ def flash_bwd( dk_shared = T.alloc_shared([block_M, dim_qk], accum_dtype) dv_shared = T.alloc_shared([block_M, dim_v], accum_dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -237,49 +233,41 @@ def flash_bwd( for i, j in T.Parallel(block_N, dim_qk): T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j]) T.copy(dv, dv_shared) - T.atomic_add(dV[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dv_shared) + T.atomic_add(dV[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dv_shared) T.copy(dk, dk_shared) - T.atomic_add(dK[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dk_shared) + T.atomic_add(dK[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dk_shared) return flash_bwd -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_split(batch, - heads, - seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_split(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] dk_shape = [groups, batch, seq_len, head_kv, dim_qk] # sum after kernel dv_shape = [groups, batch, seq_len, head_kv, dim_v] # sum after kernel - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(dk_shape, dtype), # type: ignore - dV: T.Tensor(dv_shape, dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(dk_shape, dtype), # type: ignore + dV: T.Tensor(dv_shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -299,37 +287,35 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim_v], dtype) dk_shared = T.alloc_shared([block_M, dim_qk], dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -342,16 +328,15 @@ def flash_bwd( T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j]) T.copy(dv, dv_shared) - T.copy(dv_shared, dV[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :]) + T.copy(dv_shared, dV[bx % groups, bz, by * block_M : (by + 1) * block_M, bx // groups, :]) T.copy(dk, dk_shared) - T.copy(dk, dK[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :]) + T.copy(dk, dK[bx % groups, bz, by * block_M : (by + 1) * block_M, bx // groups, :]) return flash_bwd @torch.compile class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): BATCH, N_CTX, H, D_HEAD_QK = q.shape @@ -369,7 +354,10 @@ def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): def backward(ctx, do): q, k, v, o, lse = ctx.saved_tensors BATCH, N_CTX, H, D_HEAD_QK = q.shape - HEAD_KV, D_HEAD_V, = v.shape[-2], v.shape[-1] + ( + HEAD_KV, + D_HEAD_V, + ) = v.shape[-2], v.shape[-1] groups = H // HEAD_KV def maybe_contiguous(x): @@ -386,17 +374,8 @@ def maybe_contiguous(x): if ctx.use_atomic: kernel = flashattn_bwd_atomic_add( - BATCH, - H, - N_CTX, - D_HEAD_QK, - D_HEAD_V, - ctx.causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=groups) + BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N, threads=256, num_stages=2, groups=groups + ) shape_q = [BATCH, N_CTX, H, D_HEAD_QK] shape_k = [BATCH, N_CTX, HEAD_KV, D_HEAD_QK] shape_v = [BATCH, N_CTX, HEAD_KV, D_HEAD_V] @@ -409,17 +388,8 @@ def maybe_contiguous(x): dv = dv.to(torch.float16) else: kernel = flashattn_bwd_split( - BATCH, - H, - N_CTX, - D_HEAD_QK, - D_HEAD_V, - ctx.causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=groups) + BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N, threads=256, num_stages=2, groups=groups + ) shape_q = [BATCH, N_CTX, H, D_HEAD_QK] shape_k = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_QK] # sum after kernel shape_v = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_V] # sum after kernel @@ -441,53 +411,45 @@ def ref_program(Q, K, V, is_causal, groups=1): # K: [B, T, HK, D_QK] # V: [B, T, HV, D_V] # HQ = HKV * groups - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim_qk = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim_qk, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output -def main(BATCH: int = 1, - H: int = 32, - N_CTX: int = 256, - D_HEAD_QK: int = 192, - D_HEAD_V: int = 128, - groups: int = 16, - causal: bool = False, - use_atomic: bool = True): +def main( + BATCH: int = 1, + H: int = 32, + N_CTX: int = 256, + D_HEAD_QK: int = 192, + D_HEAD_V: int = 128, + groups: int = 16, + causal: bool = False, + use_atomic: bool = True, +): flops_per_qk = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_QK flops_per_v = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_V total_flops = 3 * flops_per_qk + 2 * flops_per_v if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() head_kv = H // groups - K = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - V = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - dO = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + K = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + V = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + dO = torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() O = attention(Q, K, V, causal, groups, use_atomic) O.backward(dO, retain_graph=True) dQ, Q.grad = Q.grad.clone(), None @@ -504,7 +466,7 @@ def main(BATCH: int = 1, torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2) - print('All checks passed.✅') + print("All checks passed.✅") def run(): O_ref.backward(dO, retain_graph=True) @@ -522,19 +484,61 @@ def run1(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(): + BATCH = 1 + H = 32 + N_CTX = 256 + D_HEAD_QK = 192 + D_HEAD_V = 128 + groups = 16 + causal = False + device = "cuda" + torch.manual_seed(42) + head_kv = H // groups + Q = torch.randn(BATCH, N_CTX, H, D_HEAD_QK, device=device, dtype=torch.half) + K = torch.randn(BATCH, N_CTX, head_kv, D_HEAD_QK, device=device, dtype=torch.half) + V = torch.randn(BATCH, N_CTX, head_kv, D_HEAD_V, device=device, dtype=torch.half) + O = torch.randn(BATCH, N_CTX, H, D_HEAD_V, device=device, dtype=torch.half) + dO = torch.randn(BATCH, N_CTX, H, D_HEAD_V, device=device, dtype=torch.half) + lse = torch.zeros(BATCH, H, N_CTX, device=device, dtype=torch.float32) + with torch.no_grad(): + mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD_V) + kernel = flashattn_bwd_split( + BATCH, + H, + N_CTX, + D_HEAD_QK, + D_HEAD_V, + causal, + block_M=128, + block_N=32, + threads=256, + num_stages=2, + groups=groups, + ) + dQ = torch.zeros_like(Q, dtype=torch.float32) + dK = torch.zeros(groups, BATCH, N_CTX, head_kv, D_HEAD_QK, device=device, dtype=torch.float16) + dV = torch.zeros(groups, BATCH, N_CTX, head_kv, D_HEAD_V, device=device, dtype=torch.float16) + Delta = mod_prep(O, dO) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, dO, lse, Delta, dQ, dK, dV) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head_qk', type=int, default=192, help='Head dimension for Q/K') - parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V') - parser.add_argument('--causal', action='store_true', help='Causal flag') - parser.add_argument('--groups', type=int, default=16, help='groups') - parser.add_argument( - '--use_atomic', action='store_true', default=False, help='Use atomic add for dK/dV') - parser.add_argument( - '--use_split', action='store_true', default=False, help='Use split for dK/dV') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head_qk", type=int, default=192, help="Head dimension for Q/K") + parser.add_argument("--d_head_v", type=int, default=128, help="Head dimension for V") + parser.add_argument("--causal", action="store_true", help="Causal flag") + parser.add_argument("--groups", type=int, default=16, help="groups") + parser.add_argument("--use_atomic", action="store_true", default=False, help="Use atomic add for dK/dV") + parser.add_argument("--use_split", action="store_true", default=False, help="Use split for dK/dV") args = parser.parse_args() # Handle backward compatibility and logic @@ -546,5 +550,4 @@ def run1(): # Default: use atomic use_atomic = True - main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, - use_atomic) + main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, use_atomic) diff --git a/examples/flash_attention/example_gqa_bwd_tma_reduce.py b/examples/flash_attention/example_gqa_bwd_tma_reduce.py index b0732eb5a6..fea547b6e6 100644 --- a/examples/flash_attention/example_gqa_bwd_tma_reduce.py +++ b/examples/flash_attention/example_gqa_bwd_tma_reduce.py @@ -5,27 +5,31 @@ from tilelang.contrib import nvcc import argparse +tilelang.disable_cache() + @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, groups=1): - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=256) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -40,26 +44,27 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) - T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + # Warning: in causal/varlen/unaligned seqlen scenarios, the -inf will cause undefined behavior in exp ops + # We should set it to negative large number instead + T.fill(scores_max, T.Cast(accum_dtype, -1e30)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by // groups, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, T.Cast(accum_dtype, -1e30)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared) + T.copy(V[bz, k * block_N : (k + 1) * block_N, by // groups, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim_v): @@ -73,29 +78,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim_v): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim_v): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim_v] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -104,12 +111,12 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim_v, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep @@ -120,12 +127,14 @@ def make_dq_layout(dQ): @tilelang.jit( - out_idx=[3, 4, 5], pass_configs={ + out_idx=[3, 4, 5], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_postprocess(batch, heads, head_kv, seq_len, dim_qk, dim_v): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] @@ -133,64 +142,55 @@ def flashattn_bwd_postprocess(batch, heads, head_kv, seq_len, dim_qk, dim_v): @T.prim_func def flash_bwd_post( - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(q_shape, dtype), # type: ignore - dK_out: T.Tensor(k_shape, dtype), # type: ignore - dV_out: T.Tensor(v_shape, dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(q_shape, dtype), # type: ignore + dK_out: T.Tensor(k_shape, dtype), # type: ignore + dV_out: T.Tensor(v_shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) - T.copy(dQ[bz, bx * blk:(bx + 1) * blk, by, :], dQ_out[bz, bx * blk:(bx + 1) * blk, - by, :]) + T.copy(dQ[bz, bx * blk : (bx + 1) * blk, by, :], dQ_out[bz, bx * blk : (bx + 1) * blk, by, :]) with T.Kernel(T.ceildiv(seq_len, blk), head_kv, batch, threads=128) as (bx, by, bz): - T.annotate_layout({ - dK: make_dq_layout(dK), - dV: make_dq_layout(dV), - }) - T.copy(dK[bz, bx * blk:(bx + 1) * blk, by, :], dK_out[bz, bx * blk:(bx + 1) * blk, - by, :]) - T.copy(dV[bz, bx * blk:(bx + 1) * blk, by, :], dV_out[bz, bx * blk:(bx + 1) * blk, - by, :]) + T.annotate_layout( + { + dK: make_dq_layout(dK), + dV: make_dq_layout(dV), + } + ) + T.copy(dK[bz, bx * blk : (bx + 1) * blk, by, :], dK_out[bz, bx * blk : (bx + 1) * blk, by, :]) + T.copy(dV[bz, bx * blk : (bx + 1) * blk, by, :], dV_out[bz, bx * blk : (bx + 1) * blk, by, :]) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_atomic_add(batch, - heads, - seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_atomic_add(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -211,37 +211,37 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim_v], accum_dtype) dq_shared = T.alloc_shared([block_N, dim_qk], accum_dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - dK: make_dq_layout(dK), - dV: make_dq_layout(dV), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + dK: make_dq_layout(dK), + dV: make_dq_layout(dV), + } + ) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -251,53 +251,43 @@ def flash_bwd( T.clear(dq) T.gemm(dsT_shared, K_shared, dq, transpose_A=True) T.copy(dq, dq_shared) - T.atomic_add(dQ[bz, k * block_N:(k + 1) * block_N, bx, :], dq_shared, use_tma=True) + T.atomic_add(dQ[bz, k * block_N : (k + 1) * block_N, bx, :], dq_shared, use_tma=True) T.copy(dv, dv_shared) - T.atomic_add( - dV[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dv_shared, use_tma=True) + T.atomic_add(dV[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dv_shared, use_tma=True) T.copy(dk, dk_shared) - T.atomic_add( - dK[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dk_shared, use_tma=True) + T.atomic_add(dK[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dk_shared, use_tma=True) return flash_bwd -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_split(batch, - heads, - seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_split_novarlen(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] dk_shape = [groups, batch, seq_len, head_kv, dim_qk] # sum after kernel dv_shape = [groups, batch, seq_len, head_kv, dim_v] # sum after kernel - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(dk_shape, dtype), # type: ignore - dV: T.Tensor(dv_shape, dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(dk_shape, dtype), # type: ignore + dV: T.Tensor(dv_shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -317,37 +307,35 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim_v], dtype) dk_shared = T.alloc_shared([block_M, dim_qk], dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -360,16 +348,15 @@ def flash_bwd( T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j]) T.copy(dv, dv_shared) - T.copy(dv_shared, dV[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :]) + T.copy(dv_shared, dV[bx % groups, bz, by * block_M : (by + 1) * block_M, bx // groups, :]) T.copy(dk, dk_shared) - T.copy(dk, dK[bx % groups, bz, by * block_M:(by + 1) * block_M, bx // groups, :]) + T.copy(dk, dK[bx % groups, bz, by * block_M : (by + 1) * block_M, bx // groups, :]) return flash_bwd @torch.compile class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): BATCH, N_CTX, H, D_HEAD_QK = q.shape @@ -387,7 +374,10 @@ def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): def backward(ctx, do): q, k, v, o, lse = ctx.saved_tensors BATCH, N_CTX, H, D_HEAD_QK = q.shape - HEAD_KV, D_HEAD_V, = v.shape[-2], v.shape[-1] + ( + HEAD_KV, + D_HEAD_V, + ) = v.shape[-2], v.shape[-1] groups = H // HEAD_KV def maybe_contiguous(x): @@ -404,17 +394,8 @@ def maybe_contiguous(x): if ctx.use_atomic: kernel = flashattn_bwd_atomic_add( - BATCH, - H, - N_CTX, - D_HEAD_QK, - D_HEAD_V, - ctx.causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=groups) + BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N, threads=256, num_stages=2, groups=groups + ) shape_q = [BATCH, N_CTX, H, D_HEAD_QK] shape_k = [BATCH, N_CTX, HEAD_KV, D_HEAD_QK] shape_v = [BATCH, N_CTX, HEAD_KV, D_HEAD_V] @@ -424,18 +405,9 @@ def maybe_contiguous(x): kernel(q, k, v, do, lse, delta, dq, dk, dv) dq, dk, dv = mod_post(dq, dk, dv) else: - kernel = flashattn_bwd_split( - BATCH, - H, - N_CTX, - D_HEAD_QK, - D_HEAD_V, - ctx.causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=groups) + kernel = flashattn_bwd_split_novarlen( + BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N, threads=256, num_stages=2, groups=groups + ) shape_q = [BATCH, N_CTX, H, D_HEAD_QK] shape_k = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_QK] # sum after kernel shape_v = [groups, BATCH, N_CTX, HEAD_KV, D_HEAD_V] # sum after kernel @@ -443,8 +415,7 @@ def maybe_contiguous(x): dk = torch.empty(shape_k, dtype=torch.float16, device=q.device) dv = torch.empty(shape_v, dtype=torch.float16, device=q.device) kernel(q, k, v, do, lse, delta, dq, dk, dv) - dq, _, _ = mod_post(dq, torch.zeros_like(k, dtype=torch.float32), - torch.zeros_like(v, dtype=torch.float32)) + dq, _, _ = mod_post(dq, torch.zeros_like(k, dtype=torch.float32), torch.zeros_like(v, dtype=torch.float32)) dk, dv = dk.sum(0), dv.sum(0) return dq, dk, dv, None, None, None @@ -458,53 +429,45 @@ def ref_program(Q, K, V, is_causal, groups=1): # K: [B, T, HK, D_QK] # V: [B, T, HV, D_V] # HQ = HKV * groups - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim_qk = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim_qk, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output -def main(BATCH: int = 1, - H: int = 32, - N_CTX: int = 256, - D_HEAD_QK: int = 192, - D_HEAD_V: int = 128, - groups: int = 16, - causal: bool = False, - use_atomic: bool = True): +def main( + BATCH: int = 1, + H: int = 32, + N_CTX: int = 256, + D_HEAD_QK: int = 192, + D_HEAD_V: int = 128, + groups: int = 16, + causal: bool = False, + use_atomic: bool = True, +): flops_per_qk = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_QK flops_per_v = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_V total_flops = 3 * flops_per_qk + 2 * flops_per_v if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() head_kv = H // groups - K = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - V = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - dO = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + K = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + V = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + dO = torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() O = attention(Q, K, V, causal, groups, use_atomic) O.backward(dO, retain_graph=True) dQ, Q.grad = Q.grad.clone(), None @@ -521,7 +484,7 @@ def main(BATCH: int = 1, torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2) - print('All checks passed.✅') + print("All checks passed.✅") def run(): O_ref.backward(dO, retain_graph=True) @@ -544,17 +507,15 @@ def run1(): print(f"Detected GPU compute capability: {arch}") assert float(arch) >= 9.0, "This example only supports GPU with compute capability >= 9.0" parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head_qk', type=int, default=192, help='Head dimension for Q/K') - parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V') - parser.add_argument('--causal', action='store_true', help='Causal flag') - parser.add_argument('--groups', type=int, default=16, help='groups') - parser.add_argument( - '--use_atomic', action='store_true', default=False, help='Use atomic add for dK/dV') - parser.add_argument( - '--use_split', action='store_true', default=False, help='Use split for dK/dV') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head_qk", type=int, default=192, help="Head dimension for Q/K") + parser.add_argument("--d_head_v", type=int, default=128, help="Head dimension for V") + parser.add_argument("--causal", action="store_true", help="Causal flag") + parser.add_argument("--groups", type=int, default=16, help="groups") + parser.add_argument("--use_atomic", action="store_true", default=False, help="Use atomic add for dK/dV") + parser.add_argument("--use_split", action="store_true", default=False, help="Use split for dK/dV") args = parser.parse_args() # Handle backward compatibility and logic @@ -566,5 +527,4 @@ def run1(): # Default: use atomic use_atomic = True - main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, - use_atomic) + main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, use_atomic) diff --git a/examples/flash_attention/example_gqa_bwd_tma_reduce_varlen.py b/examples/flash_attention/example_gqa_bwd_tma_reduce_varlen.py index 82d3637682..a9f45e077d 100644 --- a/examples/flash_attention/example_gqa_bwd_tma_reduce_varlen.py +++ b/examples/flash_attention/example_gqa_bwd_tma_reduce_varlen.py @@ -7,56 +7,44 @@ from einops import rearrange, repeat from bert_padding import pad_input, unpad_input -torch.manual_seed(1) - def generate_random_padding_mask(max_seqlen, batch_size, device, mode="random"): assert mode in ["full", "random", "third"] if mode == "full": lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32) elif mode == "random": - lengths = torch.randint( - max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device) + lengths = torch.randint(max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device) elif mode == "third": lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (batch_size, 1), device=device) - padding_mask = ( - repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths) + padding_mask = repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths return padding_mask @tilelang.jit( - out_idx=[5, 6], pass_configs={ + out_idx=[5, 6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_fwd(batch, - total_q, - total_kv, - heads, - max_seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - groups=1): - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn_fwd(batch, total_q, total_kv, N_CTX, heads, max_seq_len, dim_qk, dim_v, is_causal, block_M, block_N, groups=1): + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [total_q, heads, dim_qk] k_shape = [total_kv, head_kv, dim_qk] v_shape = [total_kv, head_kv, dim_v] o_shape = [total_q, heads, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - cu_seqlens_q: T.Tensor([batch + 1], "int32"), # type: ignore - cu_seqlens_k: T.Tensor([batch + 1], "int32"), # type: ignore - Output: T.Tensor(o_shape, dtype), # type: ignore - lse: T.Tensor([total_q, heads], accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + cu_seqlens_q: T.Tensor([batch + 1], T.int32), # type: ignore + cu_seqlens_k: T.Tensor([batch + 1], T.int32), # type: ignore + Output: T.Tensor(o_shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(max_seq_len, block_M), heads, batch, threads=256) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -78,8 +66,6 @@ def flash_fwd( q_current_seqlen = q_end_idx - q_start_idx k_current_seqlen = k_end_idx - k_start_idx - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - for i, d in T.Parallel(block_M, dim_qk): if bx * block_M + i < q_current_seqlen: Q_shared[i, d] = Q[q_start_idx + bx * block_M + i, by, d] @@ -88,7 +74,9 @@ def flash_fwd( T.fill(acc_o, 0.0) T.fill(logsum, 0.0) - T.fill(scores_max, -T.infinity(accum_dtype)) + # Warning: in causal/varlen/unaligned seqlen scenarios, the -inf will cause undefined behavior in exp ops + # We should set it to negative large number instead + T.fill(scores_max, T.Cast(accum_dtype, -1e30)) loop_range = T.ceildiv(k_current_seqlen, block_N) for k in T.Pipelined(loop_range, num_stages=1): for i, d in T.Parallel(block_N, dim_qk): @@ -99,15 +87,17 @@ def flash_fwd( if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= k * block_N + j) and - (bx * block_M + i < q_current_seqlen and - k * block_N + j < k_current_seqlen), 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i >= k * block_N + j) + and (bx * block_M + i < q_current_seqlen and k * block_N + j < k_current_seqlen), + 0, + T.Cast(accum_dtype, -1e30), + ) else: for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.if_then_else( - bx * block_M + i < q_current_seqlen and - k * block_N + j < k_current_seqlen, 0, -T.infinity(acc_s.dtype)) + bx * block_M + i < q_current_seqlen and k * block_N + j < k_current_seqlen, 0, T.Cast(accum_dtype, -1e30) + ) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) for i, d in T.Parallel(block_N, dim_v): if k * block_N + i < k_current_seqlen: @@ -116,6 +106,8 @@ def flash_fwd( V_shared[i, d] = 0.0 T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim_v): @@ -137,27 +129,29 @@ def flash_fwd( for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale if bx * block_M + i < q_current_seqlen: - lse[q_start_idx + bx * block_M + i, by] = logsum[i] + lse[bz, by, bx * block_M + i] = logsum[i] return flash_fwd @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn_bwd_preprocess(batch, heads, total_q, max_seq_len, dim_v): - dtype = "float16" - accum_dtype = "float" + }, +) +def flashattn_bwd_preprocess(batch, heads, total_q, N_CTX, max_seq_len, dim_v): + dtype = T.float16 + accum_dtype = T.float32 shape = [total_q, heads, dim_v] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - cu_seqlens_q: T.Tensor([batch + 1], "int32"), # type: ignore - Delta: T.Tensor([total_q, heads], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + cu_seqlens_q: T.Tensor([batch + 1], T.int32), # type: ignore + Delta: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(max_seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -185,23 +179,25 @@ def flash_bwd_prep( for i in T.Parallel(blk): if by * blk + i < q_current_seqlen: - Delta[q_start_idx + by * blk + i, bx] = delta[i] + Delta[bz, bx, by * blk + i] = delta[i] return flash_bwd_prep def make_dq_layout(dQ): - # bshd -> bhld to use tma reduction instruction - return T.Layout(dQ.shape, lambda b, l, h, d: [b, h, l, d]) + # bshd -> bhsd to use tma reduction instruction + return T.Layout(dQ.shape, lambda l, h, d: [h, l, d]) @tilelang.jit( - out_idx=[3, 4, 5], pass_configs={ + out_idx=[3, 4, 5], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_postprocess(total_q, total_kv, heads, head_kv, dim_qk, dim_v): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 q_shape = [total_q, heads, dim_qk] k_shape = [total_kv, head_kv, dim_qk] v_shape = [total_kv, head_kv, dim_v] @@ -209,69 +205,62 @@ def flashattn_bwd_postprocess(total_q, total_kv, heads, head_kv, dim_qk, dim_v): @T.prim_func def flash_bwd_post( - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(q_shape, dtype), # type: ignore - dK_out: T.Tensor(k_shape, dtype), # type: ignore - dV_out: T.Tensor(v_shape, dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(q_shape, dtype), # type: ignore + dK_out: T.Tensor(k_shape, dtype), # type: ignore + dV_out: T.Tensor(v_shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(total_q, blk), heads, threads=128) as (bx, by): - # T.annotate_layout({dQ: make_dq_layout(dQ)}) - T.copy(dQ[bx * blk:(bx + 1) * blk, by, :], dQ_out[bx * blk:(bx + 1) * blk, by, :]) + T.annotate_layout({dQ: make_dq_layout(dQ)}) + T.copy(dQ[bx * blk : (bx + 1) * blk, by, :], dQ_out[bx * blk : (bx + 1) * blk, by, :]) with T.Kernel(T.ceildiv(total_kv, blk), head_kv, threads=128) as (bx, by): - # T.annotate_layout({ - # dK: make_dq_layout(dK), - # dV: make_dq_layout(dV), - # }) - T.copy(dK[bx * blk:(bx + 1) * blk, by, :], dK_out[bx * blk:(bx + 1) * blk, by, :]) - T.copy(dV[bx * blk:(bx + 1) * blk, by, :], dV_out[bx * blk:(bx + 1) * blk, by, :]) + T.annotate_layout( + { + dK: make_dq_layout(dK), + dV: make_dq_layout(dV), + } + ) + T.copy(dK[bx * blk : (bx + 1) * blk, by, :], dK_out[bx * blk : (bx + 1) * blk, by, :]) + T.copy(dV[bx * blk : (bx + 1) * blk, by, :], dV_out[bx * blk : (bx + 1) * blk, by, :]) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_atomic_add(batch, - total_q, - total_kv, - heads, - max_seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_atomic_add( + batch, total_q, total_kv, N_CTX, heads, max_seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1 +): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [total_q, heads, dim_qk] k_shape = [total_kv, head_kv, dim_qk] v_shape = [total_kv, head_kv, dim_v] do_shape = [total_q, heads, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor(do_shape, dtype), # type: ignore - lse: T.Tensor([total_q, heads], accum_dtype), # type: ignore - Delta: T.Tensor([total_q, heads], accum_dtype), # type: ignore - cu_seqlens_q: T.Tensor([batch + 1], "int32"), # type: ignore - cu_seqlens_k: T.Tensor([batch + 1], "int32"), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor(do_shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore + cu_seqlens_q: T.Tensor([batch + 1], T.int32), # type: ignore + cu_seqlens_k: T.Tensor([batch + 1], T.int32), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore ): - with T.Kernel( - heads, T.ceildiv(max_seq_len, block_M), batch, threads=threads) as (bx, by, bz): + with T.Kernel(heads, T.ceildiv(max_seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) dsT_shared = T.alloc_shared([block_M, block_N], dtype) q = T.alloc_shared([block_N, dim_qk], dtype) @@ -286,6 +275,9 @@ def flash_bwd( dv = T.alloc_fragment([block_M, dim_v], accum_dtype) dk = T.alloc_fragment([block_M, dim_qk], accum_dtype) dq = T.alloc_fragment([block_N, dim_qk], accum_dtype) + dv_shared = T.alloc_shared([block_M, dim_v], accum_dtype) + dk_shared = T.alloc_shared([block_M, dim_qk], accum_dtype) + dq_shared = T.alloc_shared([block_N, dim_qk], accum_dtype) q_start_idx = cu_seqlens_q[bz] k_start_idx = cu_seqlens_k[bz] @@ -294,71 +286,53 @@ def flash_bwd( q_current_seqlen = q_end_idx - q_start_idx k_current_seqlen = k_end_idx - k_start_idx - T.annotate_layout({ - # dQ: make_dq_layout(dQ), - # dK: make_dq_layout(dK), - # dV: make_dq_layout(dV), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + dK: make_dq_layout(dK), + dV: make_dq_layout(dV), + } + ) - for i, d in T.Parallel(block_M, dim_qk): - if by * block_M + i < k_current_seqlen: - K_shared[i, d] = K[k_start_idx + by * block_M + i, bx // groups, d] - V_shared[i, d] = V[k_start_idx + by * block_M + i, bx // groups, d] - else: - K_shared[i, d] = 0.0 - V_shared[i, d] = 0.0 + T.copy(K[k_start_idx + by * block_M : k_start_idx + (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[k_start_idx + by * block_M : k_start_idx + (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) - loop_st = (T.floordiv(by * block_M, block_N) if is_causal else 0) + loop_st = T.min(T.floordiv(by * block_M, block_N), T.floordiv(q_current_seqlen, block_N)) if is_causal else 0 loop_ed = T.ceildiv(q_current_seqlen, block_N) for k_base in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - for i, d in T.Parallel(block_N, dim_qk): - if k_base * block_N + i < q_current_seqlen: - q[i, d] = Q[q_start_idx + k_base * block_N + i, bx, d] - else: - q[i, d] = 0.0 + T.copy(Q[q_start_idx + k_base * block_N : q_start_idx + (k_base + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - for i in T.Parallel(block_N): - if k_base * block_N + i < q_current_seqlen: - lse_shared[i] = lse[q_start_idx + k_base * block_N + i, bx] - else: - lse_shared[i] = 0.0 + T.copy(lse[bz, bx, k_base * block_N : (k_base + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else((by * block_M + i <= k_base * block_N + j) and - (by * block_M + i < k_current_seqlen and - k_base * block_N + j < q_current_seqlen), - qkT[i, j], 0) + qkT[i, j] = T.if_then_else( + (by * block_M + i <= k_base * block_N + j) + and (by * block_M + i < k_current_seqlen and k_base * block_N + j < q_current_seqlen), + qkT[i, j], + 0, + ) else: for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.if_then_else( - by * block_M + i < k_current_seqlen and - k_base * block_N + j < q_current_seqlen, qkT[i, j], 0) + by * block_M + i < k_current_seqlen and k_base * block_N + j < q_current_seqlen, qkT[i, j], 0 + ) - for i, d in T.Parallel(block_N, dim_v): - if k_base * block_N + i < q_current_seqlen: - do[i, d] = dO[q_start_idx + k_base * block_N + i, bx, d] - else: - do[i, d] = 0.0 + T.copy(dO[q_start_idx + k_base * block_N : q_start_idx + (k_base + 1) * block_N, bx, :], do) T.clear(dsT) # dsT: (block_kv, block_q) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - for i in T.Parallel(block_N): - if k_base * block_N + i < q_current_seqlen: - delta[i] = Delta[q_start_idx + k_base * block_N + i, bx] - else: - delta[i] = 0.0 + T.copy(Delta[bz, bx, k_base * block_N : (k_base + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale T.gemm(dsT_cast, q, dk, policy=T.GemmWarpPolicy.FullRow) @@ -366,44 +340,42 @@ def flash_bwd( T.copy(dsT_cast, dsT_shared) T.clear(dq) T.gemm(dsT_shared, K_shared, dq, transpose_A=True) + T.copy(dq, dq_shared) T.atomic_add( - dQ[q_start_idx + k_base * block_N:q_start_idx + k_base * block_N + block_N, - bx, :], - dq, - memory_order="release") + dQ[q_start_idx + k_base * block_N : q_start_idx + k_base * block_N + block_N, bx, :], + dq_shared, + memory_order="relaxed", + use_tma=True, + ) + T.copy(dv, dv_shared) T.atomic_add( - dV[k_start_idx + by * block_M:k_start_idx + by * block_M + block_M, - bx // groups, :], - dv, - memory_order="release") + dV[k_start_idx + by * block_M : k_start_idx + by * block_M + block_M, bx // groups, :], + dv_shared, + memory_order="relaxed", + use_tma=True, + ) + T.copy(dk, dk_shared) T.atomic_add( - dK[k_start_idx + by * block_M:k_start_idx + by * block_M + block_M, - bx // groups, :], - dk, - memory_order="release") + dK[k_start_idx + by * block_M : k_start_idx + by * block_M + block_M, bx // groups, :], + dk_shared, + memory_order="relaxed", + use_tma=True, + ) return flash_bwd -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd_split(batch, - total_q, - total_kv, - heads, - max_seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd_split( + batch, total_q, total_kv, N_CTX, heads, max_seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1 +): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [total_q, heads, dim_qk] k_shape = [total_kv, head_kv, dim_qk] @@ -411,25 +383,24 @@ def flashattn_bwd_split(batch, do_shape = [total_q, heads, dim_v] dk_shape = [groups, total_kv, head_kv, dim_qk] # sum after kernel dv_shape = [groups, total_kv, head_kv, dim_v] # sum after kernel - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor(do_shape, dtype), # type: ignore - lse: T.Tensor([total_q, heads], accum_dtype), # type: ignore - Delta: T.Tensor([total_q, heads], accum_dtype), # type: ignore - cu_seqlens_q: T.Tensor([batch + 1], "int32"), # type: ignore - cu_seqlens_k: T.Tensor([batch + 1], "int32"), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(dk_shape, dtype), # type: ignore - dV: T.Tensor(dv_shape, dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor(do_shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, N_CTX], accum_dtype), # type: ignore + cu_seqlens_q: T.Tensor([batch + 1], T.int32), # type: ignore + cu_seqlens_k: T.Tensor([batch + 1], T.int32), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(dk_shape, dtype), # type: ignore + dV: T.Tensor(dv_shape, dtype), # type: ignore ): - with T.Kernel( - heads, T.ceildiv(max_seq_len, block_M), batch, threads=threads) as (bx, by, bz): + with T.Kernel(heads, T.ceildiv(max_seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) dsT_shared = T.alloc_shared([block_M, block_N], dtype) q = T.alloc_shared([block_N, dim_qk], dtype) @@ -454,67 +425,52 @@ def flash_bwd( q_current_seqlen = q_end_idx - q_start_idx k_current_seqlen = k_end_idx - k_start_idx - T.annotate_layout({ - # dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) - for i, d in T.Parallel(block_M, dim_qk): - if by * block_M + i < k_current_seqlen: - K_shared[i, d] = K[k_start_idx + by * block_M + i, bx // groups, d] - V_shared[i, d] = V[k_start_idx + by * block_M + i, bx // groups, d] - else: - K_shared[i, d] = 0.0 - V_shared[i, d] = 0.0 + T.copy(K[k_start_idx + by * block_M : k_start_idx + (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[k_start_idx + by * block_M : k_start_idx + (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) - loop_st = (T.floordiv(by * block_M, block_N) if is_causal else 0) + loop_st = T.min(T.floordiv(by * block_M, block_N), T.floordiv(q_current_seqlen, block_N)) if is_causal else 0 loop_ed = T.ceildiv(q_current_seqlen, block_N) for k_base in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - for i, d in T.Parallel(block_N, dim_qk): - if k_base * block_N + i < q_current_seqlen: - q[i, d] = Q[q_start_idx + k_base * block_N + i, bx, d] - else: - q[i, d] = 0.0 + # Note: The padding zero of varlen should be considered in T.copy + T.copy(Q[q_start_idx + k_base * block_N : q_start_idx + (k_base + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - for i, d in T.Parallel(block_N, dim_v): - if k_base * block_N + i < q_current_seqlen: - do[i, d] = dO[q_start_idx + k_base * block_N + i, bx, d] - else: - do[i, d] = 0.0 + + T.copy(dO[q_start_idx + k_base * block_N : q_start_idx + (k_base + 1) * block_N, bx, :], do) + T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - for i in T.Parallel(block_N): - if k_base * block_N + i < q_current_seqlen: - lse_shared[i] = lse[q_start_idx + k_base * block_N + i, bx] - else: - lse_shared[i] = 0.0 + + T.copy(lse[bz, bx, k_base * block_N : (k_base + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else((by * block_M + i <= k_base * block_N + j) and - (by * block_M + i < k_current_seqlen and - k_base * block_N + j < q_current_seqlen), - qkT[i, j], 0) + qkT[i, j] = T.if_then_else( + (by * block_M + i <= k_base * block_N + j) + and (by * block_M + i < k_current_seqlen and k_base * block_N + j < q_current_seqlen), + qkT[i, j], + 0, + ) else: for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.if_then_else( - by * block_M + i < k_current_seqlen and - k_base * block_N + j < q_current_seqlen, qkT[i, j], 0) + by * block_M + i < k_current_seqlen and k_base * block_N + j < q_current_seqlen, qkT[i, j], 0 + ) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - for i in T.Parallel(block_N): - if k_base * block_N + i < q_current_seqlen: - delta[i] = Delta[q_start_idx + k_base * block_N + i, bx] - else: - delta[i] = 0.0 + + T.copy(Delta[bz, bx, k_base * block_N : (k_base + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -525,57 +481,38 @@ def flash_bwd( T.gemm(dsT_shared, K_shared, dq, transpose_A=True) for i, j in T.Parallel(block_N, dim_qk): if k_base * block_N + i < q_current_seqlen: - T.atomic_add(dQ[q_start_idx + k_base * block_N + i, bx, j], dq[i, j]) + T.atomic_add(dQ[q_start_idx + k_base * block_N + i, bx, j], dq[i, j], memory_order="relaxed") T.copy(dv, dv_shared) - for i, d in T.Parallel(block_M, dim_v): - if by * block_M + i < k_current_seqlen: - dV[bx % groups, k_start_idx + by * block_M + i, bx // groups, d] = dv[i, d] + T.copy(dv_shared, dV[bx % groups, k_start_idx + by * block_M : k_start_idx + by * block_M + block_M, bx // groups, :]) T.copy(dk, dk_shared) - for i, d in T.Parallel(block_M, dim_qk): - if by * block_M + i < k_current_seqlen: - dK[bx % groups, k_start_idx + by * block_M + i, bx // groups, d] = dk[i, d] + T.copy(dk_shared, dK[bx % groups, k_start_idx + by * block_M : k_start_idx + by * block_M + block_M, bx // groups, :]) return flash_bwd @torch.compile class _attention(torch.autograd.Function): - @staticmethod - def forward(ctx, - q, - k, - v, - seqlens_q, - seqlens_k, - cu_seqlens_q, - cu_seqlens_k, - max_seqlen_q, - max_seqlen_k, - causal, - groups=1, - use_atomic=True): + def forward( + ctx, q, k, v, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, causal, groups=1, use_atomic=True + ): BATCH, N_CTX, H, D_HEAD_QK = q.shape D_HEAD_V = v.shape[-1] block_M = 128 block_N = 64 - q_unpad, indices_q, _, _ = unpad_input( - q, (torch.arange(N_CTX, device=q.device).unsqueeze(0) < seqlens_q.unsqueeze(1))) - k_unpad, indices_k, _, _ = unpad_input( - k, (torch.arange(N_CTX, device=k.device).unsqueeze(0) < seqlens_k.unsqueeze(1))) - v_unpad, _, _, _ = unpad_input( - v, (torch.arange(N_CTX, device=v.device).unsqueeze(0) < seqlens_k.unsqueeze(1))) + q_unpad, indices_q, _, _ = unpad_input(q, (torch.arange(N_CTX, device=q.device).unsqueeze(0) < seqlens_q.unsqueeze(1))) + k_unpad, indices_k, _, _ = unpad_input(k, (torch.arange(N_CTX, device=k.device).unsqueeze(0) < seqlens_k.unsqueeze(1))) + v_unpad, _, _, _ = unpad_input(v, (torch.arange(N_CTX, device=v.device).unsqueeze(0) < seqlens_k.unsqueeze(1))) total_q = q_unpad.shape[0] total_kv = k_unpad.shape[0] - mod = flashattn_fwd(BATCH, total_q, total_kv, H, max_seqlen_q, D_HEAD_QK, D_HEAD_V, causal, - block_M, block_N, groups) + mod = flashattn_fwd(BATCH, total_q, total_kv, N_CTX, H, max_seqlen_q, D_HEAD_QK, D_HEAD_V, causal, block_M, block_N, groups) o_unpad, lse = mod(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k) o = pad_input(o_unpad, indices_q, BATCH, N_CTX) - ctx.save_for_backward(q_unpad, k_unpad, v_unpad, o_unpad, lse, seqlens_q, seqlens_k, - cu_seqlens_q, cu_seqlens_k) + ctx.save_for_backward(q_unpad, k_unpad, v_unpad, o_unpad, lse, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k) + ctx.batch = BATCH ctx.causal = causal ctx.use_atomic = use_atomic ctx.max_seqlen_q = max_seqlen_q @@ -587,9 +524,9 @@ def forward(ctx, @staticmethod def backward(ctx, do): N_CTX = do.shape[1] - q, k, v, o, lse, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k = ctx.saved_tensors - do_unpad, _, _, _ = unpad_input( - do, (torch.arange(N_CTX, device=do.device).unsqueeze(0) < seqlens_q.unsqueeze(1))) + q, k, v, o, lse_clone, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k = ctx.saved_tensors + # lse_clone = lse.clone() + do_unpad, _, _, _ = unpad_input(do, (torch.arange(N_CTX, device=do.device).unsqueeze(0) < seqlens_q.unsqueeze(1))) total_q, H, D_HEAD_QK = q.shape total_kv, HEAD_KV, D_HEAD_V = v.shape groups = H // HEAD_KV @@ -603,7 +540,7 @@ def maybe_contiguous(x): do, q, k, v, o = [maybe_contiguous(x) for x in (do_unpad, q, k, v, o)] block_M = 128 block_N = 32 - mod_prep = flashattn_bwd_preprocess(BATCH, H, total_q, ctx.max_seqlen_q, D_HEAD_V) + mod_prep = flashattn_bwd_preprocess(BATCH, H, total_q, N_CTX, ctx.max_seqlen_q, D_HEAD_V) mod_post = flashattn_bwd_postprocess(total_q, total_kv, H, HEAD_KV, D_HEAD_QK, D_HEAD_V) delta = mod_prep(o, do, cu_seqlens_q) @@ -612,6 +549,7 @@ def maybe_contiguous(x): BATCH, total_q, total_kv, + N_CTX, H, ctx.max_seqlen_q, D_HEAD_QK, @@ -621,17 +559,19 @@ def maybe_contiguous(x): block_N, threads=256, num_stages=2, - groups=groups) + groups=groups, + ) dq = torch.zeros_like(q, dtype=torch.float32) dk = torch.zeros_like(k, dtype=torch.float32) dv = torch.zeros_like(v, dtype=torch.float32) - kernel(q, k, v, do, lse, delta, cu_seqlens_q, cu_seqlens_k, dq, dk, dv) + kernel(q, k, v, do, lse_clone, delta, cu_seqlens_q, cu_seqlens_k, dq, dk, dv) dq, dk, dv = mod_post(dq, dk, dv) else: kernel = flashattn_bwd_split( BATCH, total_q, total_kv, + N_CTX, H, ctx.max_seqlen_q, D_HEAD_QK, @@ -641,13 +581,13 @@ def maybe_contiguous(x): block_N, threads=256, num_stages=2, - groups=groups) + groups=groups, + ) dq = torch.zeros_like(q, dtype=torch.float32) dk = torch.empty(groups, *k.shape, dtype=torch.float16, device=q.device) dv = torch.empty(groups, *v.shape, dtype=torch.float16, device=q.device) - kernel(q, k, v, do, lse, delta, cu_seqlens_q, cu_seqlens_k, dq, dk, dv) - dq, _, _ = mod_post(dq, torch.zeros_like(k, dtype=torch.float32), - torch.zeros_like(v, dtype=torch.float32)) + kernel(q, k, v, do, lse_clone, delta, cu_seqlens_q, cu_seqlens_k, dq, dk, dv) + dq, _, _ = mod_post(dq, torch.zeros_like(k, dtype=torch.float32), torch.zeros_like(v, dtype=torch.float32)) dk, dv = dk.sum(0), dv.sum(0) dq = pad_input(dq, ctx.indices_q, BATCH, N_CTX) @@ -666,15 +606,13 @@ def ref_program(Q, K, V, padding_mask, is_causal, groups=1): # HQ = HKV * groups # To handle precision issue Q, K, V = Q.float(), K.float(), V.float() - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim_qk = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim_qk, dtype=scores.dtype)) if padding_mask is not None: scores.masked_fill_(rearrange(~padding_mask, "b s -> b 1 1 s"), float("-inf")) @@ -682,41 +620,35 @@ def ref_program(Q, K, V, padding_mask, is_causal, groups=1): seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) if padding_mask is not None: output.masked_fill_(rearrange(~padding_mask, "b s -> b s 1 1"), 0.0) return output -def main(BATCH: int = 1, - H: int = 32, - N_CTX: int = 256, - D_HEAD_QK: int = 192, - D_HEAD_V: int = 128, - groups: int = 16, - causal: bool = False, - use_atomic: bool = True): +def main( + BATCH: int = 1, + H: int = 32, + N_CTX: int = 256, + D_HEAD_QK: int = 192, + D_HEAD_V: int = 128, + groups: int = 16, + causal: bool = False, + use_atomic: bool = True, +): flops_per_qk = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_QK flops_per_v = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_V total_flops = 3 * flops_per_qk + 2 * flops_per_v if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() head_kv = H // groups - K = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - V = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - dO = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + K = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + V = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + dO = torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() padding_mask = generate_random_padding_mask(N_CTX, BATCH, "cuda", mode="random") seqlens_q = padding_mask.sum(dim=-1, dtype=torch.int32) cu_seqlens_q = F.pad(torch.cumsum(seqlens_q, dim=0, dtype=torch.int32), (1, 0)) @@ -725,8 +657,7 @@ def main(BATCH: int = 1, # In training backward pass, seqlens_k should be the same as seqlens_q seqlens_k, cu_seqlens_k, max_seqlen_k = seqlens_q, cu_seqlens_q, max_seqlen_q - O = attention(Q, K, V, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, - max_seqlen_k, causal, groups, use_atomic) + O = attention(Q, K, V, seqlens_q, seqlens_k, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, causal, groups, use_atomic) O.backward(dO, retain_graph=True) dQ, Q.grad = Q.grad.clone(), None dK, K.grad = K.grad.clone(), None @@ -738,12 +669,6 @@ def main(BATCH: int = 1, dK_ref, K.grad = K.grad.clone(), None dV_ref, V.grad = V.grad.clone(), None - torch.testing.assert_close(O, O_ref.half(), rtol=1e-2, atol=1e-2) - torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2) - torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2) - torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2) - print('All checks passed.✅') - def run(): O_ref.backward(dO, retain_graph=True) @@ -759,24 +684,85 @@ def run1(): print("tilelang: {:.2f} ms".format(latency)) print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) + torch.testing.assert_close(O, O_ref.half(), rtol=1e-2, atol=1e-2) + torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2) + torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2) + torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2) + print("All checks passed.✅") + print( + "Note: this varlen kernel performance is as good as the non-varlen kernel shown in Nsight-Compute. As you may observe that the TFLOPS is a bit lower, that's because the unpad operation is included in the above benchmark." + ) + + +def run_regression_perf(): + BATCH = 1 + H = 32 + N_CTX = 256 + D_HEAD_QK = 192 + D_HEAD_V = 128 + groups = 16 + causal = False + device = "cuda" + torch.manual_seed(42) + total_q = BATCH * N_CTX + total_kv = BATCH * N_CTX + head_kv = H // groups + Q = torch.randn(total_q, H, D_HEAD_QK, device=device, dtype=torch.half) + K = torch.randn(total_kv, head_kv, D_HEAD_QK, device=device, dtype=torch.half) + V = torch.randn(total_kv, head_kv, D_HEAD_V, device=device, dtype=torch.half) + O = torch.randn(total_q, H, D_HEAD_V, device=device, dtype=torch.half) + dO = torch.randn(total_q, H, D_HEAD_V, device=device, dtype=torch.half) + cu_seqlens_q = torch.arange(0, (BATCH + 1) * N_CTX, N_CTX, device=device, dtype=torch.int32) + cu_seqlens_k = cu_seqlens_q + max_seqlen_q = N_CTX + lse = torch.zeros(BATCH, H, N_CTX, device=device, dtype=torch.float32) + with torch.no_grad(): + mod_prep = flashattn_bwd_preprocess(BATCH, H, total_q, N_CTX, max_seqlen_q, D_HEAD_V) + kernel = flashattn_bwd_split( + BATCH, + total_q, + total_kv, + N_CTX, + H, + max_seqlen_q, + D_HEAD_QK, + D_HEAD_V, + causal, + block_M=128, + block_N=32, + threads=256, + num_stages=2, + groups=groups, + ) + dQ = torch.zeros_like(Q, dtype=torch.float32) + dK = torch.zeros(groups, total_kv, head_kv, D_HEAD_QK, device=device, dtype=torch.float16) + dV = torch.zeros(groups, total_kv, head_kv, D_HEAD_V, device=device, dtype=torch.float16) + Delta = mod_prep(O, dO, cu_seqlens_q) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, dO, lse, Delta, cu_seqlens_q, cu_seqlens_k, dQ, dK, dV) + + return do_bench(run_kernel_only, backend="cupti") + if __name__ == "__main__": arch = nvcc.get_target_compute_version() print(f"Detected GPU compute capability: {arch}") assert float(arch) >= 9.0, "This example only supports GPU with compute capability >= 9.0" parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head_qk', type=int, default=192, help='Head dimension for Q/K') - parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V') - parser.add_argument('--causal', action='store_true', help='Causal flag') - parser.add_argument('--groups', type=int, default=16, help='groups') - parser.add_argument( - '--use_atomic', action='store_true', default=False, help='Use atomic add for dK/dV') - parser.add_argument( - '--use_split', action='store_true', default=False, help='Use split for dK/dV') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head_qk", type=int, default=192, help="Head dimension for Q/K") + parser.add_argument("--d_head_v", type=int, default=128, help="Head dimension for V") + parser.add_argument("--causal", action="store_true", help="Causal flag") + parser.add_argument("--groups", type=int, default=16, help="groups") + parser.add_argument("--use_atomic", action="store_true", default=False, help="Use atomic add for dK/dV") + parser.add_argument("--use_split", action="store_true", default=False, help="Use split for dK/dV") args = parser.parse_args() + # Can be set to True/False for testing + args.causal = True # Handle backward compatibility and logic if args.use_split: @@ -787,5 +773,4 @@ def run1(): # Default: use atomic use_atomic = True - main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, - use_atomic) + main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal, use_atomic) diff --git a/examples/flash_attention/example_gqa_bwd_wgmma_pipelined.py b/examples/flash_attention/example_gqa_bwd_wgmma_pipelined.py index ed07e7d9d3..2da64472c0 100644 --- a/examples/flash_attention/example_gqa_bwd_wgmma_pipelined.py +++ b/examples/flash_attention/example_gqa_bwd_wgmma_pipelined.py @@ -6,25 +6,27 @@ @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, groups=1): - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + Output: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=256) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -39,26 +41,25 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by // groups, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared) + T.copy(V[bz, k * block_N : (k + 1) * block_N, by // groups, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim_v): @@ -72,29 +73,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim_v): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim_v): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim_v] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -103,50 +106,42 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim_v, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) -def flashattn_bwd(batch, - heads, - seq_len, - dim_qk, - dim_v, - is_causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=1): - sm_scale = (1.0 / dim_qk)**0.5 - scale = (1.0 / dim_qk)**0.5 * 1.44269504 # log2(e) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) +def flashattn_bwd(batch, heads, seq_len, dim_qk, dim_v, is_causal, block_M, block_N, threads=256, num_stages=2, groups=1): + sm_scale = (1.0 / dim_qk) ** 0.5 + scale = (1.0 / dim_qk) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim_qk] k_shape = [batch, seq_len, head_kv, dim_qk] v_shape = [batch, seq_len, head_kv, dim_v] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(q_shape, dtype), # type: ignore - K: T.Tensor(k_shape, dtype), # type: ignore - V: T.Tensor(v_shape, dtype), # type: ignore - dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(q_shape, accum_dtype), # type: ignore - dK: T.Tensor(k_shape, accum_dtype), # type: ignore - dV: T.Tensor(v_shape, accum_dtype), # type: ignore + Q: T.Tensor(q_shape, dtype), # type: ignore + K: T.Tensor(k_shape, dtype), # type: ignore + V: T.Tensor(v_shape, dtype), # type: ignore + dO: T.Tensor([batch, seq_len, heads, dim_v], dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(q_shape, accum_dtype), # type: ignore + dK: T.Tensor(k_shape, accum_dtype), # type: ignore + dV: T.Tensor(v_shape, accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=threads) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim_qk], dtype) @@ -167,45 +162,30 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim_v], accum_dtype) dq_shared = T.alloc_shared([block_N, dim_qk], accum_dtype) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dq_shared: tilelang.layout.make_swizzled_layout(dq_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - }) - - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx // groups, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx // groups, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx // groups, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx // groups, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=num_stages): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) - T.gemm( - K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) - T.gemm( - V_shared, - do, - dsT, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow, - wg_wait=-1) + T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) T.wait_wgmma(1) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -217,18 +197,17 @@ def flash_bwd( T.gemm(dsT_shared, K_shared, dq, transpose_A=True, wg_wait=1) T.wait_wgmma(0) T.copy(dq, dq_shared) - T.atomic_add(dQ[bz, k * block_N:(k + 1) * block_N, bx, :], dq_shared) + T.atomic_add(dQ[bz, k * block_N : (k + 1) * block_N, bx, :], dq_shared) T.copy(dv, dv_shared) - T.atomic_add(dV[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dv_shared) + T.atomic_add(dV[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dv_shared) T.copy(dk, dk_shared) - T.atomic_add(dK[bz, by * block_M:(by + 1) * block_M, bx // groups, :], dk_shared) + T.atomic_add(dK[bz, by * block_M : (by + 1) * block_M, bx // groups, :], dk_shared) return flash_bwd @torch.compile class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): BATCH, N_CTX, H, D_HEAD_QK = q.shape @@ -246,7 +225,10 @@ def forward(ctx, q, k, v, causal, groups=1, use_atomic=True): def backward(ctx, do): q, k, v, o, lse = ctx.saved_tensors BATCH, N_CTX, H, D_HEAD_QK = q.shape - HEAD_KV, D_HEAD_V, = v.shape[-2], v.shape[-1] + ( + HEAD_KV, + D_HEAD_V, + ) = v.shape[-2], v.shape[-1] groups = H // HEAD_KV def maybe_contiguous(x): @@ -260,18 +242,7 @@ def maybe_contiguous(x): mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD_V) delta = mod_prep(o, do) - kernel = flashattn_bwd( - BATCH, - H, - N_CTX, - D_HEAD_QK, - D_HEAD_V, - ctx.causal, - block_M, - block_N, - threads=256, - num_stages=2, - groups=groups) + kernel = flashattn_bwd(BATCH, H, N_CTX, D_HEAD_QK, D_HEAD_V, ctx.causal, block_M, block_N, threads=256, num_stages=2, groups=groups) shape_q = [BATCH, N_CTX, H, D_HEAD_QK] shape_k = [BATCH, N_CTX, HEAD_KV, D_HEAD_QK] shape_v = [BATCH, N_CTX, HEAD_KV, D_HEAD_V] @@ -294,52 +265,36 @@ def ref_program(Q, K, V, is_causal, groups=1): # K: [B, T, HK, D_QK] # V: [B, T, HV, D_V] # HQ = HKV * groups - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim_qk = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim_qk, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output -def main(BATCH: int = 1, - H: int = 32, - N_CTX: int = 256, - D_HEAD_QK: int = 192, - D_HEAD_V: int = 128, - groups: int = 16, - causal: bool = False): +def main(BATCH: int = 1, H: int = 32, N_CTX: int = 256, D_HEAD_QK: int = 192, D_HEAD_V: int = 128, groups: int = 16, causal: bool = False): flops_per_qk = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_QK flops_per_v = 2.0 * BATCH * H * N_CTX * N_CTX * D_HEAD_V total_flops = 3 * flops_per_qk + 2 * flops_per_v if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() head_kv = H // groups - K = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - V = ( - torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) - dO = ( - torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + K = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + V = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + dO = torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() O = attention(Q, K, V, causal, groups) O.backward(dO, retain_graph=True) dQ, Q.grad = Q.grad.clone(), None @@ -356,7 +311,7 @@ def main(BATCH: int = 1, torch.testing.assert_close(dV, dV_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dK, dK_ref, rtol=1e-2, atol=1e-2) torch.testing.assert_close(dQ, dQ_ref, rtol=1e-2, atol=1e-2) - print('All checks passed.✅') + print("All checks passed.✅") def run(): O_ref.backward(dO, retain_graph=True) @@ -374,15 +329,34 @@ def run1(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf( + BATCH: int = 1, H: int = 32, N_CTX: int = 256, D_HEAD_QK: int = 192, D_HEAD_V: int = 128, groups: int = 16, causal: bool = False +): + Q = torch.empty(BATCH, N_CTX, H, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + + head_kv = H // groups + K = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_QK, dtype=torch.half, device="cuda").normal_().requires_grad_() + V = torch.empty(BATCH, N_CTX, head_kv, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + dO = torch.empty(BATCH, N_CTX, H, D_HEAD_V, dtype=torch.half, device="cuda").normal_().requires_grad_() + O = attention(Q, K, V, causal, groups) + + def run1(): + O.backward(dO, retain_graph=True) + + from tilelang.profiler import do_bench + + return do_bench(run1, warmup=500, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head_qk', type=int, default=192, help='Head dimension for Q/K') - parser.add_argument('--d_head_v', type=int, default=128, help='Head dimension for V') - parser.add_argument('--causal', action='store_true', help='Causal flag') - parser.add_argument('--groups', type=int, default=16, help='groups') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head_qk", type=int, default=192, help="Head dimension for Q/K") + parser.add_argument("--d_head_v", type=int, default=128, help="Head dimension for V") + parser.add_argument("--causal", action="store_true", help="Causal flag") + parser.add_argument("--groups", type=int, default=16, help="groups") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head_qk, args.d_head_v, args.groups, args.causal) diff --git a/examples/flash_attention/example_gqa_fwd_bshd.py b/examples/flash_attention/example_gqa_fwd_bshd.py index 4d9d06a4fc..e884a81588 100644 --- a/examples/flash_attention/example_gqa_fwd_bshd.py +++ b/examples/flash_attention/example_gqa_fwd_bshd.py @@ -9,7 +9,6 @@ class FlashAttentionTuneSpace: - def __init__( self, block_sizes=(64, 128, 256), @@ -40,7 +39,7 @@ def get_configs(user_config=None): warp_M = block_M // warp_count warp_N = block_N // warp_count - if (warp_M % config.warp_alignment != 0 or warp_N % config.warp_alignment != 0): + if warp_M % config.warp_alignment != 0 or warp_N % config.warp_alignment != 0: continue shared_mem = 2 * config.dtype_bytes * config.dim * (block_M + block_N) @@ -48,114 +47,38 @@ def get_configs(user_config=None): continue for num_stages in config.num_stages_range: - valid_configs.append({ - "block_M": block_M, - "block_N": block_N, - "num_stages": num_stages, - "threads": threads, - }) + valid_configs.append( + { + "block_M": block_M, + "block_N": block_N, + "num_stages": num_stages, + "threads": threads, + } + ) return valid_configs @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, - heads, - seq_len, - dim, - is_causal, - groups=1, - block_M=64, - block_N=64, - num_stages=0, - threads=128): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch, heads, seq_len, dim, is_causal, groups=1, block_M=64, block_N=64, num_stages=0, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] - dtype = "float16" - accum_dtype = "float" - - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -171,25 +94,49 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by // groups, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, k * block_N : (k + 1) * block_N, by // groups, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(O_shared, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) return main @@ -199,50 +146,34 @@ def ref_program(Q, K, V, is_causal, groups=1): # K: [B, T, HK, D] # V: [B, T, HV, D] # HQ = HKV * groups - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output -def main(batch: int = 1, - heads: int = 64, - seq_len: int = 4096, - dim: int = 128, - is_causal: bool = False, - groups: int = 16, - tune: bool = False): +def main( + batch: int = 1, heads: int = 64, seq_len: int = 4096, dim: int = 128, is_causal: bool = False, groups: int = 16, tune: bool = False +): flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim total_flops = 2 * flops_per_matmul if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_len, - dim, - is_causal, - groups=groups, - block_M=64, - block_N=64, - num_stages=2, - threads=128) + if not tune: + kernel = flashattn(batch, heads, seq_len, dim, is_causal, groups=groups, block_M=64, block_N=64, num_stages=2, threads=128) ref_program_processed = partial(ref_program, is_causal=is_causal, groups=groups) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) @@ -264,14 +195,22 @@ def main(batch: int = 1, print(f"Ref latency: {ref_latency}") +def run_regression_perf( + batch: int = 1, heads: int = 64, seq_len: int = 4096, dim: int = 128, is_causal: bool = False, groups: int = 16, tune: bool = False +): + kernel = flashattn(batch, heads, seq_len, dim, is_causal, groups=groups, block_M=64, block_N=64, num_stages=2, threads=128) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='heads') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') - parser.add_argument('--groups', type=int, default=16, help='groups') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="heads") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--tune", action="store_true", help="tune configs") + parser.add_argument("--groups", type=int, default=16, help="groups") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups, args.tune) diff --git a/examples/flash_attention/example_gqa_fwd_bshd_wgmma_pipelined.py b/examples/flash_attention/example_gqa_fwd_bshd_wgmma_pipelined.py index 1c1fc12d2a..73a725d9f9 100644 --- a/examples/flash_attention/example_gqa_fwd_bshd_wgmma_pipelined.py +++ b/examples/flash_attention/example_gqa_fwd_bshd_wgmma_pipelined.py @@ -24,9 +24,11 @@ def get_configs(): rep=10, ) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn( batch, heads, @@ -39,90 +41,19 @@ def flashattn( num_stages=0, threads=128, ): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [batch, seq_len, heads, dim] kv_shape = [batch, seq_len, head_kv, dim] - dtype = "float16" - accum_dtype = "float" - - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by // groups, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, k * block_N:(k + 1) * block_N, by // groups, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -138,30 +69,55 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined( - loop_range, - num_stages=num_stages, - order=[-1, 0, 3, 1, -1, 2], - stage=[-1, 0, 0, 1, -1, 1], - group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + loop_range, + num_stages=num_stages, + order=[-1, 0, 3, 1, -1, 2], + stage=[-1, 0, 0, 1, -1, 1], + group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10, 11], [12], [13], [14]], + ): + T.copy(K[bz, k * block_N : (k + 1) * block_N, by // groups, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, k * block_N : (k + 1) * block_N, by // groups, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(O_shared, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) return main @@ -171,23 +127,21 @@ def ref_program(Q, K, V, is_causal, groups=1): # K: [B, T, HK, D] # V: [B, T, HV, D] # HQ = HKV * groups - assert Q.size(2) == K.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" - assert Q.size(2) == V.size( - 2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" + assert Q.size(2) == K.size(2) * groups, f"Q.size(2): {Q.size(2)}, K.size(2): {K.size(2)}, groups: {groups}" + assert Q.size(2) == V.size(2) * groups, f"Q.size(2): {Q.size(2)}, V.size(2): {V.size(2)}, groups: {groups}" dim = Q.size(-1) K = K.repeat_interleave(groups, dim=2) V = V.repeat_interleave(groups, dim=2) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -205,18 +159,8 @@ def main( if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_len, - dim, - is_causal, - groups=groups, - block_M=128, - block_N=128, - num_stages=2, - threads=256) + if not tune: + kernel = flashattn(batch, heads, seq_len, dim, is_causal, groups=groups, block_M=128, block_N=128, num_stages=2, threads=256) ref_program_processed = partial(ref_program, is_causal=is_causal, groups=groups) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) @@ -238,14 +182,28 @@ def main( print(f"Ref latency: {ref_latency}") +def run_regression_perf( + batch: int = 1, + heads: int = 64, + seq_len: int = 4096, + dim: int = 128, + is_causal: bool = False, + groups: int = 16, +): + kernel = flashattn(batch, heads, seq_len, dim, is_causal, groups=groups, block_M=128, block_N=128, num_stages=2, threads=256) + + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='heads') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') - parser.add_argument('--groups', type=int, default=16, help='groups') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="heads") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--tune", action="store_true", help="tune configs") + parser.add_argument("--groups", type=int, default=16, help="groups") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.groups, args.tune) diff --git a/examples/flash_attention/example_gqa_fwd_varlen.py b/examples/flash_attention/example_gqa_fwd_varlen.py index 37e81ebb33..0e8e21c43d 100644 --- a/examples/flash_attention/example_gqa_fwd_varlen.py +++ b/examples/flash_attention/example_gqa_fwd_varlen.py @@ -4,80 +4,36 @@ import tilelang import tilelang.language as T import tilelang.testing -from einops import rearrange, repeat from tilelang.profiler import do_bench from varlen_utils import generate_random_padding_mask, generate_qkv -def attention_ref( - q, - k, - v, - query_padding_mask=None, - key_padding_mask=None, - causal=False, - window_size=(-1, -1), - upcast=True, -): - if causal: - window_size = (window_size[0], 0) - dtype_og = q.dtype - if upcast: - q, k, v = q.float(), k.float(), v.float() - dim = q.shape[-1] - scale = (1.0 / dim)**0.5 - k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2]) - v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2]) - scores = torch.einsum("bthd,bshd->bhts", q, k) - if key_padding_mask is not None: - scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")) - scores = scores * scale - attention = torch.softmax(scores, dim=-1).to(v.dtype) - - if query_padding_mask is not None: - attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0) - output = torch.einsum("bhts,bshd->bthd", attention, v) - if query_padding_mask is not None: - output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0) - return output.to(dtype=dtype_og), attention.to(dtype=dtype_og) - - @tilelang.jit( - out_idx=[6], pass_configs={ + out_idx=[6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch_size, - groups, - UQ, - UKV, - heads, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=1, - threads=128): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch_size, groups, UQ, UKV, heads, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) head_kv = heads // groups q_shape = [UQ, heads, dim] kv_shape = [UKV, head_kv, dim] o_shape = [UQ, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(kv_shape, dtype), - V_unpad: T.Tensor(kv_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(kv_shape, dtype), + V_unpad: T.Tensor(kv_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], T.int32), + cu_seqlens_k: T.Tensor([batch_size + 1], T.int32), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, - threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -96,54 +52,51 @@ def main( kv_head_idx = head_idx // groups q_start_idx = cu_seqlens_q[batch_idx] - k_start_idx = cu_seqlens_k[batch_idx] - v_start_idx = cu_seqlens_k[batch_idx] + kv_start_idx = cu_seqlens_k[batch_idx] q_end_idx = cu_seqlens_q[batch_idx + 1] k_end_idx = cu_seqlens_k[batch_idx + 1] - v_end_idx = cu_seqlens_k[batch_idx + 1] q_current_seqlen = q_end_idx - q_start_idx - k_current_seqlen = k_end_idx - k_start_idx - v_current_seqlen = v_end_idx - v_start_idx + kv_current_seqlen = k_end_idx - kv_start_idx - T.copy( - Q_unpad[q_start_idx + bx * block_M:q_start_idx + (bx + 1) * block_M, head_idx, :], - Q_shared) - for i, d in T.Parallel(block_M, dim): - if bx * block_M + i >= q_current_seqlen: - Q_shared[i, d] = 0 + T.copy(Q_unpad[q_start_idx + bx * block_M : q_start_idx + (bx + 1) * block_M, head_idx, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv(k_current_seqlen, block_N) + offset = kv_current_seqlen - q_current_seqlen # always align on the right + max_visible_k_idx = offset + (bx + 1) * block_M + loop_range = ( + T.min(T.ceildiv(max_visible_k_idx, block_N), T.ceildiv(kv_current_seqlen, block_N)) + if is_causal + else T.ceildiv(kv_current_seqlen, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): - T.copy( - K_unpad[k_start_idx + k * block_N:k_start_idx + (k + 1) * block_N, - kv_head_idx, :], K_shared) - for i, d in T.Parallel(block_N, dim): - if k * block_N + i >= k_current_seqlen: - K_shared[i, d] = 0 + T.copy(K_unpad[kv_start_idx + k * block_N : kv_start_idx + (k + 1) * block_N, kv_head_idx, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= k * block_N + j) and - (bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -T.infinity(acc_s.dtype), 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i + offset < k * block_N + j) + or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen), + -1e9, + 0, + ) else: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -T.infinity(acc_s.dtype), 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen), -1e9, 0 + ) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) @@ -157,19 +110,15 @@ def main( for i, j in T.Parallel(block_M, dim): acc_o[i, j] *= scores_scale[i] - T.copy( - V_unpad[v_start_idx + k * block_N:v_start_idx + (k + 1) * block_N, - kv_head_idx, :], V_shared) - for i, d in T.Parallel(block_N, dim): - if k * block_N + i >= v_current_seqlen: - V_shared[i, d] = 0 + T.copy(V_unpad[kv_start_idx + k * block_N : kv_start_idx + (k + 1) * block_N, kv_head_idx, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_M, dim): - acc_o[i, j] /= logsum[i] - T.copy(acc_o, O_shared) + # When sq > skv, some tokens can see nothing + acc_o[i, j] = 0 if is_causal and bx * block_M + i + offset < 0 else acc_o[i, j] / logsum[i] + T.copy(acc_o, O_shared) for i, d in T.Parallel(block_M, dim): if bx * block_M + i < q_current_seqlen: Output_unpad[q_start_idx + bx * block_M + i, head_idx, d] = O_shared[i, d] @@ -177,13 +126,9 @@ def main( return main -def main(batch: int = 1, - heads: int = 64, - q_seqlen: int = 2048, - k_seqlen: int = 2048, - dim: int = 128, - groups: int = 16, - is_causal: bool = False): +def main( + batch: int = 1, heads: int = 64, q_seqlen: int = 2048, k_seqlen: int = 2048, dim: int = 128, groups: int = 16, is_causal: bool = False +): assert heads % groups == 0, "heads must be divisible by groups" flops_per_matmul = 2.0 * batch * heads * q_seqlen * k_seqlen * dim @@ -191,8 +136,7 @@ def main(batch: int = 1, tilelang.testing.set_random_seed(0) - causal = False - if causal: + if is_causal: total_flops *= 0.5 tilelang.testing.set_random_seed(0) @@ -201,9 +145,9 @@ def main(batch: int = 1, device = torch.device("cuda") head_kv = heads // groups - q = torch.randn(batch, q_seqlen, heads, dim, dtype=dtype, device=device, requires_grad=True) - k = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device, requires_grad=True) - v = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device, requires_grad=True) + q = torch.randn(batch, q_seqlen, heads, dim, dtype=dtype, device=device) + k = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device) + v = torch.randn(batch, k_seqlen, head_kv, dim, dtype=dtype, device=device) query_padding_mask = generate_random_padding_mask(q_seqlen, batch, device, mode="random") key_padding_mask = generate_random_padding_mask(k_seqlen, batch, device, mode="random") @@ -222,53 +166,46 @@ def main(batch: int = 1, output_pad_fn, _, _, - ) = generate_qkv( - q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) + ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) UQ = q_unpad.shape[0] UKV = k_unpad.shape[0] - kernel = flashattn( - batch, - groups, - UQ, - UKV, - heads, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=1, - threads=128) + kernel = flashattn(batch, groups, UQ, UKV, heads, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) out_unpad = kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q) out = output_pad_fn(out_unpad) - out_ref, _ = attention_ref( - q, - k, - v, - query_padding_mask=query_padding_mask, - key_padding_mask=key_padding_mask, + import flash_attn + + fa_out_unpad = flash_attn.flash_attn_varlen_func( + q_unpad, + k_unpad, + v_unpad, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + 0.0, causal=is_causal, ) - torch.testing.assert_close(out, out_ref, rtol=1e-2, atol=1e-2) + fa_out = output_pad_fn(fa_out_unpad) + torch.testing.assert_close(out, fa_out, rtol=1e-2, atol=1e-2) + print("All checks passed.✅") - latency = do_bench( - lambda: kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q)) + latency = do_bench(lambda: kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q), _n_warmup=5, _n_repeat=5) print("Tile-lang: {:.2f} ms".format(latency)) print("Tile-lang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='query heads') - parser.add_argument('--groups', type=int, default=16, help='groups') - parser.add_argument('--q_seqlen', type=int, default=2048, help='query sequence length') - parser.add_argument('--k_seqlen', type=int, default=2048, help='key/value sequence length') - parser.add_argument('--dim', type=int, default=128, help='head dim') - parser.add_argument('--is_causal', action='store_true', help='causal attention') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="query heads") + parser.add_argument("--groups", type=int, default=16, help="groups") + parser.add_argument("--q_seqlen", type=int, default=2048, help="query sequence length") + parser.add_argument("--k_seqlen", type=int, default=2048, help="key/value sequence length") + parser.add_argument("--dim", type=int, default=128, help="head dim") + parser.add_argument("--is_causal", action="store_true", help="causal attention") args = parser.parse_args() - main(args.batch, args.heads, args.q_seqlen, args.k_seqlen, args.dim, args.groups, - args.is_causal) + main(args.batch, args.heads, args.q_seqlen, args.k_seqlen, args.dim, args.groups, args.is_causal) diff --git a/examples/flash_attention/example_mha_bwd_bhsd.py b/examples/flash_attention/example_mha_bwd_bhsd.py index 1595ae7646..34e8fefc51 100644 --- a/examples/flash_attention/example_mha_bwd_bhsd.py +++ b/examples/flash_attention/example_mha_bwd_bhsd.py @@ -7,22 +7,24 @@ @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - Output: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + Output: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=128) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -38,29 +40,28 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) # T.copy(Q_shared, Q_local) # for i, j in T.Parallel(block_M, dim): # Q_local[i, j] *= scale - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): @@ -74,29 +75,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(acc_o, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -105,68 +108,71 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, bx, by * blk:(by + 1) * blk, k * blk:(k + 1) * blk], do) + T.copy(O[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, bx, by * blk : (by + 1) * blk, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep def make_dq_layout(dQ): # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment - return T.Layout(dQ.shape, - lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) + return T.Layout(dQ.shape, lambda b, h, l, d: [b, h, l // 8, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) @tilelang.jit( - out_idx=[1], pass_configs={ + out_idx=[1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_postprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, heads, seq_len, dim] blk = 64 @T.prim_func def flash_bwd_post( - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(shape, dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) T.copy( - dQ[bz, by, bx * blk:(bx + 1) * blk, :], - dQ_out[bz, by, bx * blk:(bx + 1) * blk, :], + dQ[bz, by, bx * blk : (bx + 1) * blk, :], + dQ_out[bz, by, bx * blk : (bx + 1) * blk, :], ) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def flashattn_bwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - sm_scale = (1.0 / dim)**0.5 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + sm_scale = (1.0 / dim) ** 0.5 + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, heads, seq_len, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dK: T.Tensor(shape, dtype), # type: ignore - dV: T.Tensor(shape, dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dK: T.Tensor(shape, dtype), # type: ignore + dV: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=128) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim], dtype) @@ -190,36 +196,36 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim], dtype) dk_shared = T.alloc_shared([block_M, dim], dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - }) - T.copy(K[bz, bx, by * block_M:(by + 1) * block_M, :], K_shared) - T.copy(V[bz, bx, by * block_M:(by + 1) * block_M, :], V_shared) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) + T.copy(K[bz, bx, by * block_M : (by + 1) * block_M, :], K_shared) + T.copy(V[bz, bx, by * block_M : (by + 1) * block_M, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=2): - T.copy(Q[bz, bx, k * block_N:(k + 1) * block_N, :], q) + T.copy(Q[bz, bx, k * block_N : (k + 1) * block_N, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, bx, k * block_N:(k + 1) * block_N, :], do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + # We don't need to handle OOB positions for non-causal cases, + # since OOB values won't affect other positions here. + T.copy(dO[bz, bx, k * block_N : (k + 1) * block_N, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -232,14 +238,13 @@ def flash_bwd( T.atomic_add(dQ[bz, bx, k * block_N + i, j], dq[i, j]) T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, dV[bz, bx, by * block_M:(by + 1) * block_M, :]) - T.copy(dk_shared, dK[bz, bx, by * block_M:(by + 1) * block_M, :]) + T.copy(dv_shared, dV[bz, bx, by * block_M : (by + 1) * block_M, :]) + T.copy(dk_shared, dK[bz, bx, by * block_M : (by + 1) * block_M, :]) return flash_bwd class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal): BATCH, H, N_CTX, D_HEAD = q.shape @@ -281,15 +286,15 @@ def maybe_contiguous(x): def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bhqd,bhkd->bhqk', Q, K) + scores = torch.einsum("bhqd,bhkd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(2) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bhkd->bhqd', attention_weights, V) + output = torch.einsum("bhqk,bhkd->bhqd", attention_weights, V) return output @@ -304,9 +309,7 @@ def main( total_flops = 5 * flops_per_matmul if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, H, N_CTX, D_HEAD, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, H, N_CTX, D_HEAD, dtype=torch.half, device="cuda").normal_().requires_grad_() K = torch.empty_like(Q).normal_().requires_grad_() V = torch.empty_like(Q).normal_().requires_grad_() dO = torch.randn_like(Q) @@ -345,12 +348,43 @@ def run1(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(): + BATCH = 1 + H = 16 + N_CTX = 512 + D_HEAD = 64 + causal = False + device = "cuda" + torch.manual_seed(0) + block_M = 64 + block_N = 64 if D_HEAD <= 64 else 32 + Q = torch.randn(BATCH, H, N_CTX, D_HEAD, device=device, dtype=torch.half) + K = torch.randn_like(Q) + V = torch.randn_like(Q) + O = torch.randn_like(Q) + dO = torch.randn_like(Q) + lse = torch.zeros(BATCH, H, N_CTX, device=device, dtype=torch.float32) + with torch.no_grad(): + mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD) + kernel = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, causal, block_M, block_N) + dQ = torch.zeros(BATCH, H, N_CTX, D_HEAD, device=device, dtype=torch.float32) + dK = torch.zeros(BATCH, H, N_CTX, D_HEAD, device=device, dtype=torch.float16) + dV = torch.zeros(BATCH, H, N_CTX, D_HEAD, device=device, dtype=torch.float16) + Delta = mod_prep(O, dO) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, dO, lse, Delta, dQ, dK, dV) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head', type=int, default=64, help='Head dimension') - parser.add_argument('--causal', type=bool, default=False, help='Causal flag') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head", type=int, default=64, help="Head dimension") + parser.add_argument("--causal", type=bool, default=False, help="Causal flag") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head, args.causal) diff --git a/examples/flash_attention/example_mha_bwd.py b/examples/flash_attention/example_mha_bwd_bshd.py similarity index 65% rename from examples/flash_attention/example_mha_bwd.py rename to examples/flash_attention/example_mha_bwd_bshd.py index 543c2c0e75..fc8328fa4a 100644 --- a/examples/flash_attention/example_mha_bwd.py +++ b/examples/flash_attention/example_mha_bwd_bshd.py @@ -7,22 +7,24 @@ @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - Output: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + Output: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=128) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -38,25 +40,25 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by, :], K_shared) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, k * block_N:(k + 1) * block_N, by, :], V_shared) + T.copy(V[bz, k * block_N : (k + 1) * block_N, by, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): @@ -70,29 +72,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -101,68 +105,71 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep def make_dq_layout(dQ): # atomicAdd can not be vectorized, so we need to reorder dq to match the 8x8 gemm fragment - return T.Layout(dQ.shape, - lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) + return T.Layout(dQ.shape, lambda b, l, h, d: [b, l // 8, h, d // 8, (d % 2), 4 * (l % 8) + (d % 8) // 2]) @tilelang.jit( - out_idx=[1], pass_configs={ + out_idx=[1], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_postprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim] blk = 64 @T.prim_func def flash_bwd_post( - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dQ_out: T.Tensor(shape, dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dQ_out: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, blk), heads, batch, threads=128) as (bx, by, bz): T.annotate_layout({dQ: make_dq_layout(dQ)}) T.copy( - dQ[bz, bx * blk:(bx + 1) * blk, by, :], - dQ_out[bz, bx * blk:(bx + 1) * blk, by, :], + dQ[bz, bx * blk : (bx + 1) * blk, by, :], + dQ_out[bz, bx * blk : (bx + 1) * blk, by, :], ) return flash_bwd_post -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def flashattn_bwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - sm_scale = (1.0 / dim)**0.5 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + sm_scale = (1.0 / dim) ** 0.5 + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dK: T.Tensor(shape, dtype), # type: ignore - dV: T.Tensor(shape, dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dK: T.Tensor(shape, dtype), # type: ignore + dV: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=128) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim], dtype) @@ -186,33 +193,36 @@ def flash_bwd( dv_shared = T.alloc_shared([block_M, dim], dtype) dk_shared = T.alloc_shared([block_M, dim], dtype) - T.annotate_layout({ - dQ: make_dq_layout(dQ), - }) - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx, :], V_shared) + T.annotate_layout( + { + dQ: make_dq_layout(dQ), + } + ) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=2): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + # We don't need to handle OOB positions for non-causal cases, + # since OOB values won't affect other positions here. + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -225,14 +235,13 @@ def flash_bwd( T.atomic_add(dQ[bz, k * block_N + i, bx, j], dq[i, j]) T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, dV[bz, by * block_M:(by + 1) * block_M, bx, :]) - T.copy(dk_shared, dK[bz, by * block_M:(by + 1) * block_M, bx, :]) + T.copy(dv_shared, dV[bz, by * block_M : (by + 1) * block_M, bx, :]) + T.copy(dk_shared, dK[bz, by * block_M : (by + 1) * block_M, bx, :]) return flash_bwd class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal): BATCH, N_CTX, H, D_HEAD = q.shape @@ -274,15 +283,15 @@ def maybe_contiguous(x): def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -297,9 +306,7 @@ def main( total_flops = 5 * flops_per_matmul if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD, dtype=torch.half, device="cuda").normal_().requires_grad_() K = torch.empty_like(Q).normal_().requires_grad_() V = torch.empty_like(Q).normal_().requires_grad_() dO = torch.randn_like(Q) @@ -336,12 +343,43 @@ def run1(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(): + BATCH = 1 + H = 16 + N_CTX = 512 + D_HEAD = 64 + causal = False + device = "cuda" + torch.manual_seed(42) + block_M = 64 + block_N = 64 if D_HEAD <= 64 else 32 + Q = torch.randn(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.half) + K = torch.randn_like(Q) + V = torch.randn_like(Q) + O = torch.randn_like(Q) + dO = torch.randn_like(Q) + lse = torch.zeros(BATCH, H, N_CTX, device=device, dtype=torch.float32) + with torch.no_grad(): + mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD) + kernel = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, causal, block_M, block_N) + dQ = torch.zeros(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.float32) + dK = torch.zeros(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.float16) + dV = torch.zeros(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.float16) + Delta = mod_prep(O, dO) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, dO, lse, Delta, dQ, dK, dV) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head', type=int, default=64, help='Head dimension') - parser.add_argument('--causal', type=bool, default=False, help='Causal flag') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head", type=int, default=64, help="Head dimension") + parser.add_argument("--causal", type=bool, default=False, help="Causal flag") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head, args.causal) diff --git a/examples/flash_attention/example_mha_bwd_wgmma_pipelined.py b/examples/flash_attention/example_mha_bwd_bshd_wgmma_pipelined.py similarity index 64% rename from examples/flash_attention/example_mha_bwd_wgmma_pipelined.py rename to examples/flash_attention/example_mha_bwd_bshd_wgmma_pipelined.py index 7ad417ef55..c0fe4e33d2 100644 --- a/examples/flash_attention/example_mha_bwd_wgmma_pipelined.py +++ b/examples/flash_attention/example_mha_bwd_bshd_wgmma_pipelined.py @@ -7,22 +7,24 @@ @tilelang.jit( - out_idx=[3, 4], pass_configs={ + out_idx=[3, 4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_fwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_fwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - Output: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + Output: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=128) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -37,27 +39,26 @@ def flash_fwd( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({Q_shared: tilelang.layout.make_swizzled_layout(Q_shared)}) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = ( - T.ceildiv( - (bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N)) + loop_range = T.ceildiv((bx + 1) * block_M, block_N) if is_causal else T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_range, num_stages=1): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by, :], K_shared) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by, :], K_shared) if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: - T.clear(acc_s) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - T.copy(V[bz, k * block_N:(k + 1) * block_N, by, :], V_shared) + T.copy(V[bz, k * block_N : (k + 1) * block_N, by, :], V_shared) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): @@ -71,29 +72,31 @@ def flash_fwd( logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] - T.copy(acc_o, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(acc_o, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, lse[bz, by, bx * block_M:(bx + 1) * block_M]) + T.copy(logsum, lse[bz, by, bx * block_M : (bx + 1) * block_M]) return flash_fwd @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def flashattn_bwd_preprocess(batch, heads, seq_len, dim): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 shape = [batch, seq_len, heads, dim] blk = 32 @T.prim_func def flash_bwd_prep( - O: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + O: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, blk), batch) as (bx, by, bz): o = T.alloc_fragment([blk, blk], dtype) @@ -102,37 +105,39 @@ def flash_bwd_prep( delta = T.alloc_fragment([blk], accum_dtype) T.clear(acc) for k in range(T.ceildiv(dim, blk)): - T.copy(O[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], o) - T.copy(dO[bz, by * blk:(by + 1) * blk, bx, k * blk:(k + 1) * blk], do) + T.copy(O[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], o) + T.copy(dO[bz, by * blk : (by + 1) * blk, bx, k * blk : (k + 1) * blk], do) for i, j in T.Parallel(blk, blk): acc[i, j] += o[i, j] * do[i, j] T.reduce_sum(acc, delta, 1) - T.copy(delta, Delta[bz, bx, by * blk:(by + 1) * blk]) + T.copy(delta, Delta[bz, bx, by * blk : (by + 1) * blk]) return flash_bwd_prep -@tilelang.jit(pass_configs={ - tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, -}) +@tilelang.jit( + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + } +) def flashattn_bwd(batch, heads, seq_len, dim, is_causal, block_M, block_N): - sm_scale = (1.0 / dim)**0.5 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + sm_scale = (1.0 / dim) ** 0.5 + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def flash_bwd( - Q: T.Tensor(shape, dtype), # type: ignore - K: T.Tensor(shape, dtype), # type: ignore - V: T.Tensor(shape, dtype), # type: ignore - dO: T.Tensor(shape, dtype), # type: ignore - lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore - dQ: T.Tensor(shape, accum_dtype), # type: ignore - dK: T.Tensor(shape, dtype), # type: ignore - dV: T.Tensor(shape, dtype), # type: ignore + Q: T.Tensor(shape, dtype), # type: ignore + K: T.Tensor(shape, dtype), # type: ignore + V: T.Tensor(shape, dtype), # type: ignore + dO: T.Tensor(shape, dtype), # type: ignore + lse: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + Delta: T.Tensor([batch, heads, seq_len], accum_dtype), # type: ignore + dQ: T.Tensor(shape, accum_dtype), # type: ignore + dK: T.Tensor(shape, dtype), # type: ignore + dV: T.Tensor(shape, dtype), # type: ignore ): with T.Kernel(heads, T.ceildiv(seq_len, block_M), batch, threads=256) as (bx, by, bz): K_shared = T.alloc_shared([block_M, dim], dtype) @@ -157,47 +162,34 @@ def flash_bwd( dk_shared = T.alloc_shared([block_M, dim], dtype) dq_shared = T.alloc_shared([block_N, dim], accum_dtype) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - dq_shared: tilelang.layout.make_swizzled_layout(dq_shared), - }) - - T.copy(K[bz, by * block_M:(by + 1) * block_M, bx, :], K_shared) - T.copy(V[bz, by * block_M:(by + 1) * block_M, bx, :], V_shared) + T.copy(K[bz, by * block_M : (by + 1) * block_M, bx, :], K_shared) + T.copy(V[bz, by * block_M : (by + 1) * block_M, bx, :], V_shared) T.clear(dv) T.clear(dk) loop_st = T.floordiv(by * block_M, block_N) if is_causal else 0 loop_ed = T.ceildiv(seq_len, block_N) for k in T.Pipelined(loop_st, loop_ed, num_stages=2): - T.copy(Q[bz, k * block_N:(k + 1) * block_N, bx, :], q) + T.copy(Q[bz, k * block_N : (k + 1) * block_N, bx, :], q) T.clear(qkT) - T.gemm( - K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) - T.copy(dO[bz, k * block_N:(k + 1) * block_N, bx, :], do) + T.gemm(K_shared, q, qkT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) + T.copy(dO[bz, k * block_N : (k + 1) * block_N, bx, :], do) T.clear(dsT) - T.gemm( - V_shared, - do, - dsT, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow, - wg_wait=-1) + T.gemm(V_shared, do, dsT, transpose_B=True, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) T.wait_wgmma(1) - T.copy(lse[bz, bx, k * block_N:(k + 1) * block_N], lse_shared) + T.copy(lse[bz, bx, k * block_N : (k + 1) * block_N], lse_shared) for i, j in T.Parallel(block_M, block_N): qkT[i, j] = T.exp2(qkT[i, j] * scale - lse_shared[j]) if is_causal: for i, j in T.Parallel(block_M, block_N): - qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], - 0) + qkT[i, j] = T.if_then_else(by * block_M + i <= k * block_N + j, qkT[i, j], 0) + # We don't need to handle OOB positions for non-causal cases, + # since OOB values won't affect other positions here. T.wait_wgmma(0) T.copy(qkT, qkT_cast) T.gemm(qkT_cast, do, dv, policy=T.GemmWarpPolicy.FullRow, wg_wait=-1) - T.copy(Delta[bz, bx, k * block_N:(k + 1) * block_N], delta) + T.copy(Delta[bz, bx, k * block_N : (k + 1) * block_N], delta) for i, j in T.Parallel(block_M, block_N): dsT_cast[i, j] = qkT[i, j] * (dsT[i, j] - delta[j]) * sm_scale @@ -208,17 +200,16 @@ def flash_bwd( T.gemm(dsT_shared, K_shared, dq, transpose_A=True, wg_wait=1) T.wait_wgmma(0) T.copy(dq, dq_shared) - T.atomic_add(dQ[bz, k * block_N:(k + 1) * block_N, bx, :], dq_shared) + T.atomic_add(dQ[bz, k * block_N : (k + 1) * block_N, bx, :], dq_shared) T.copy(dv, dv_shared) T.copy(dk, dk_shared) - T.copy(dv_shared, dV[bz, by * block_M:(by + 1) * block_M, bx, :]) - T.copy(dk_shared, dK[bz, by * block_M:(by + 1) * block_M, bx, :]) + T.copy(dv_shared, dV[bz, by * block_M : (by + 1) * block_M, bx, :]) + T.copy(dk_shared, dK[bz, by * block_M : (by + 1) * block_M, bx, :]) return flash_bwd class _attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, causal): BATCH, N_CTX, H, D_HEAD = q.shape @@ -260,15 +251,15 @@ def maybe_contiguous(x): def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -283,9 +274,7 @@ def main( total_flops = 5 * flops_per_matmul if causal: total_flops *= 0.5 - Q = ( - torch.empty(BATCH, N_CTX, H, D_HEAD, dtype=torch.half, - device="cuda").normal_().requires_grad_()) + Q = torch.empty(BATCH, N_CTX, H, D_HEAD, dtype=torch.half, device="cuda").normal_().requires_grad_() K = torch.empty_like(Q).normal_().requires_grad_() V = torch.empty_like(Q).normal_().requires_grad_() dO = torch.randn_like(Q) @@ -305,7 +294,7 @@ def main( assert torch.allclose(dV, dV_ref, rtol=1e-2, atol=1e-2) assert torch.allclose(dK, dK_ref, rtol=1e-2, atol=1e-2) assert torch.allclose(dQ, dQ_ref, rtol=1e-2, atol=1e-2) - print('All checks passed.✅') + print("All checks passed.✅") def run(): O_ref.backward(dO, retain_graph=True) @@ -321,12 +310,44 @@ def run1(): print("tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(): + BATCH = 1 + H = 32 + N_CTX = 256 + D_HEAD = 64 + causal = False + device = "cuda" + torch.manual_seed(0) + block_M = 128 + block_N = 128 if D_HEAD <= 64 else 32 + Q = torch.randn(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.half) + K = torch.randn_like(Q) + V = torch.randn_like(Q) + O = torch.randn_like(Q) + dO = torch.randn_like(Q) + lse = torch.zeros(BATCH, H, N_CTX, device=device, dtype=torch.float32) + with torch.no_grad(): + mod_prep = flashattn_bwd_preprocess(BATCH, H, N_CTX, D_HEAD) + kernel = flashattn_bwd(BATCH, H, N_CTX, D_HEAD, causal, block_M, block_N) + dQ = torch.zeros(BATCH, N_CTX, H, D_HEAD, device=device, dtype=torch.float32) + dK = torch.zeros_like(Q, dtype=torch.float16) + dV = torch.zeros_like(Q, dtype=torch.float16) + Delta = mod_prep(O, dO) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(Q, K, V, dO, lse, Delta, dQ, dK, dV) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='Batch size') - parser.add_argument('--h', type=int, default=32, help='Number of heads') - parser.add_argument('--n_ctx', type=int, default=1024, help='Context size') - parser.add_argument('--d_head', type=int, default=64, help='Head dimension') - parser.add_argument('--causal', type=bool, default=False, help='Causal flag') + parser.add_argument("--batch", type=int, default=8, help="Batch size") + parser.add_argument("--h", type=int, default=32, help="Number of heads") + parser.add_argument("--n_ctx", type=int, default=1024, help="Context size") + parser.add_argument("--d_head", type=int, default=64, help="Head dimension") + parser.add_argument("--causal", type=bool, default=False, help="Causal flag") args = parser.parse_args() main(args.batch, args.h, args.n_ctx, args.d_head, args.causal) diff --git a/examples/flash_attention/example_mha_fwd_bhsd.py b/examples/flash_attention/example_mha_fwd_bhsd.py index f07f7a618c..4007365418 100644 --- a/examples/flash_attention/example_mha_fwd_bhsd.py +++ b/examples/flash_attention/example_mha_fwd_bhsd.py @@ -15,107 +15,27 @@ def get_configs(): @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, - heads, - seq_q, - seq_kv, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=1, - threads=128): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - q_idx = bx * block_M + i + past_len - k_idx = k * block_N + j - acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -131,43 +51,69 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min( - T.ceildiv(seq_kv, block_N), T.ceildiv( - (bx + 1) * block_M + - past_len, block_N)) if is_causal else T.ceildiv(seq_kv, block_N)) + T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + if is_causal + else T.ceildiv(seq_kv, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_kv, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bhqd,bhkd->bhqk', Q, K) + scores = torch.einsum("bhqd,bhkd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_q = Q.size(2) seq_kv = K.size(2) mask = torch.tril(torch.ones(seq_q, seq_kv, device=scores.device), seq_kv - seq_q) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bhkd->bhqd', attention_weights, V) + output = torch.einsum("bhqk,bhkd->bhqd", attention_weights, V) return output @@ -185,18 +131,8 @@ def main( if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_q, - seq_kv, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=1, - threads=128) + if not tune: + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128) ref_program_processed = partial(ref_program, is_causal=is_causal) profiler = kernel.get_profiler() @@ -219,14 +155,28 @@ def main( print(f"Ref latency: {ref_latency}") +def run_regression_perf( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 64, + is_causal: bool = False, + tune: bool = False, +): + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=1, help='heads') - parser.add_argument('--seq_q', type=int, default=256, help='query sequence length') - parser.add_argument('--seq_kv', type=int, default=256, help='key/value sequence length') - parser.add_argument('--dim', type=int, default=64, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=1, help="heads") + parser.add_argument("--seq_q", type=int, default=256, help="query sequence length") + parser.add_argument("--seq_kv", type=int, default=256, help="key/value sequence length") + parser.add_argument("--dim", type=int, default=64, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal", default=False) + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.is_causal, args.tune) diff --git a/examples/flash_attention/example_mha_fwd_bhsd_wgmma_pipelined.py b/examples/flash_attention/example_mha_fwd_bhsd_wgmma_pipelined.py index 26167b34b7..90514f7627 100644 --- a/examples/flash_attention/example_mha_fwd_bhsd_wgmma_pipelined.py +++ b/examples/flash_attention/example_mha_fwd_bhsd_wgmma_pipelined.py @@ -15,107 +15,27 @@ def get_configs(): @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, - heads, - seq_q, - seq_kv, - dim, - is_causal, - block_M=128, - block_N=128, - num_stages=2, - threads=256): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 past_len = seq_kv - seq_q assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" - @T.macro - def MMA0( - K: T.Tensor(kv_shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - q_idx = bx * block_M + i + past_len - k_idx = k * block_N + j - acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(kv_shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -131,48 +51,75 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min( - T.ceildiv(seq_kv, block_N), T.ceildiv( - (bx + 1) * block_M + - past_len, block_N)) if is_causal else T.ceildiv(seq_kv, block_N)) + T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + if is_causal + else T.ceildiv(seq_kv, block_N) + ) for k in T.Pipelined( - loop_range, - num_stages=num_stages, - order=[-1, 0, 3, 1, -1, 2], - stage=[-1, 0, 0, 1, -1, 1], - group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + loop_range, + num_stages=num_stages, + order=[-1, 0, 3, 1, -1, 2], + stage=[-1, 0, 0, 1, -1, 1], + group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10, 11], [12], [13], [14]], + ): + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_kv, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bhqd,bhkd->bhqk', Q, K) + scores = torch.einsum("bhqd,bhkd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_q = Q.size(2) seq_kv = K.size(2) mask = torch.tril(torch.ones(seq_q, seq_kv, device=scores.device), seq_kv - seq_q) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bhkd->bhqd', attention_weights, V) + output = torch.einsum("bhqk,bhkd->bhqd", attention_weights, V) return output @@ -190,18 +137,8 @@ def main( if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_q, - seq_kv, - dim, - is_causal, - block_M=128, - block_N=128, - num_stages=2, - threads=256) + if not tune: + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) ref_program_processed = partial(ref_program, is_causal=is_causal) profiler = kernel.get_profiler() @@ -224,14 +161,28 @@ def main( print(f"Ref latency: {ref_latency}") +def run_regression_perf( + batch: int = 1, + heads: int = 32, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 128, + is_causal: bool = False, + tune: bool = False, +): + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_q', type=int, default=4096, help='query sequence length') - parser.add_argument('--seq_kv', type=int, default=4096, help='key/value sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_q", type=int, default=4096, help="query sequence length") + parser.add_argument("--seq_kv", type=int, default=4096, help="key/value sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.is_causal, args.tune) diff --git a/examples/flash_attention/example_mha_fwd_bshd.py b/examples/flash_attention/example_mha_fwd_bshd.py index 6a1f707e57..e584971c0b 100644 --- a/examples/flash_attention/example_mha_fwd_bshd.py +++ b/examples/flash_attention/example_mha_fwd_bshd.py @@ -15,100 +15,23 @@ def get_configs(): @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, - heads, - seq_len, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=1, - threads=128): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch, heads, seq_len, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" - - @T.macro - def MMA0( - K: T.Tensor(shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, k * block_N:(k + 1) * block_N, by, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q: T.Tensor(shape, dtype), - K: T.Tensor(shape, dtype), - V: T.Tensor(shape, dtype), - Output: T.Tensor(shape, dtype), + Q: T.Tensor(shape, dtype), + K: T.Tensor(shape, dtype), + V: T.Tensor(shape, dtype), + Output: T.Tensor(shape, dtype), ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -124,40 +47,64 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + T.copy(K[bz, k * block_N : (k + 1) * block_N, by, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, k * block_N : (k + 1) * block_N, by, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(O_shared, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) return main def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -174,17 +121,8 @@ def main( if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_len, - dim, - is_causal, - block_M=128, - block_N=128, - num_stages=1, - threads=128) + if not tune: + kernel = flashattn(batch, heads, seq_len, dim, is_causal, block_M=128, block_N=128, num_stages=1, threads=128) ref_program_processed = partial(ref_program, is_causal=is_causal) profiler = kernel.get_profiler() profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) @@ -206,13 +144,19 @@ def main( print(f"Ref latency: {ref_latency}") +def run_regression_perf(batch: int = 8, heads: int = 32, seq_len: int = 4096, dim: int = 128, is_causal: bool = False): + kernel = flashattn(batch, heads, seq_len, dim, is_causal, block_M=128, block_N=128, num_stages=1, threads=128) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.tune) diff --git a/examples/flash_attention/example_mha_fwd_bshd_wgmma_pipelined.py b/examples/flash_attention/example_mha_fwd_bshd_wgmma_pipelined.py index 3928db4c3b..d6e1490c9a 100644 --- a/examples/flash_attention/example_mha_fwd_bshd_wgmma_pipelined.py +++ b/examples/flash_attention/example_mha_fwd_bshd_wgmma_pipelined.py @@ -15,100 +15,23 @@ def get_configs(): @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit( - out_idx=[3], pass_configs={ + out_idx=[3], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch, - heads, - seq_len, - dim, - is_causal, - block_M=128, - block_N=128, - num_stages=2, - threads=256): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch, heads, seq_len, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape = [batch, seq_len, heads, dim] - dtype = "float16" - accum_dtype = "float" - - @T.macro - def MMA0( - K: T.Tensor(shape, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - bx: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(K[bz, k * block_N:(k + 1) * block_N, by, :], K_shared) - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( - V: T.Tensor(shape, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - by: T.int32, - bz: T.int32, - ): - T.copy(V[bz, k * block_N:(k + 1) * block_N, by, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q: T.Tensor(shape, dtype), - K: T.Tensor(shape, dtype), - V: T.Tensor(shape, dtype), - Output: T.Tensor(shape, dtype), + Q: T.Tensor(shape, dtype), + K: T.Tensor(shape, dtype), + V: T.Tensor(shape, dtype), + Output: T.Tensor(shape, dtype), ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -124,45 +47,70 @@ def main( scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - T.copy(Q[bz, bx * block_M:(bx + 1) * block_M, by, :], Q_shared) + T.copy(Q[bz, bx * block_M : (bx + 1) * block_M, by, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = ( - T.min(T.ceildiv(seq_len, block_N), T.ceildiv( - (bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N)) + T.min(T.ceildiv(seq_len, block_N), T.ceildiv((bx + 1) * block_M, block_N)) if is_causal else T.ceildiv(seq_len, block_N) + ) for k in T.Pipelined( - loop_range, - num_stages=num_stages, - order=[-1, 0, 3, 1, -1, 2], - stage=[-1, 0, 0, 1, -1, 1], - group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]): - MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + loop_range, + num_stages=num_stages, + order=[-1, 0, 3, 1, -1, 2], + stage=[-1, 0, 0, 1, -1, 1], + group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10, 11], [12], [13], [14]], + ): + T.copy(K[bz, k * block_N : (k + 1) * block_N, by, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j >= seq_len, -T.infinity(acc_s.dtype), 0) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, k * block_N : (k + 1) * block_N, by, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :]) + T.copy(O_shared, Output[bz, bx * block_M : (bx + 1) * block_M, by, :]) return main def ref_program(Q, K, V, is_causal): dim = Q.size(-1) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) if is_causal: seq_len = Q.size(1) mask = torch.tril(torch.ones(seq_len, seq_len, device=scores.device)) mask = mask.unsqueeze(0).unsqueeze(0) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -179,17 +127,8 @@ def main( if is_causal: total_flops *= 0.5 - if (not tune): - kernel = flashattn( - batch, - heads, - seq_len, - dim, - is_causal, - block_M=128, - block_N=128, - num_stages=2, - threads=256) + if not tune: + kernel = flashattn(batch, heads, seq_len, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) ref_program_processed = partial(ref_program, is_causal=is_causal) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) @@ -211,13 +150,19 @@ def main( print(f"Ref latency: {ref_latency}") +def run_regression_perf(batch: int = 8, heads: int = 32, seq_len: int = 4096, dim: int = 128, is_causal: bool = False): + kernel = flashattn(batch, heads, seq_len, dim, is_causal, block_M=128, block_N=128, num_stages=2, threads=256) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--is_causal', action='store_true', help='causal') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", help="causal") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.tune) diff --git a/examples/flash_attention/example_mha_fwd_varlen.py b/examples/flash_attention/example_mha_fwd_varlen.py index f381e900af..0f3610b110 100644 --- a/examples/flash_attention/example_mha_fwd_varlen.py +++ b/examples/flash_attention/example_mha_fwd_varlen.py @@ -4,109 +4,51 @@ import tilelang.language as T import tilelang.testing import argparse +from tilelang.profiler import do_bench +from tilelang.autotuner import set_autotune_inputs, autotune import torch -from einops import rearrange, repeat from varlen_utils import generate_random_padding_mask, generate_qkv +import itertools -def attention_ref( - q, - k, - v, - query_padding_mask=None, - key_padding_mask=None, - causal=False, - window_size=(-1, -1), # -1 means infinite window size - upcast=True, -): - """ - Arguments: - q: (batch_size, seqlen_q, nheads, head_dim) - k: (batch_size, seqlen_k, nheads_k, head_dim) - v: (batch_size, seqlen_k, nheads_k, head_dim) - query_padding_mask: (batch_size, seqlen_q) - key_padding_mask: (batch_size, seqlen_k) - attn_bias: broadcastable to (batch_size, nheads, seqlen_q, seqlen_k) - dropout_p: float - dropout_mask: (batch_size, nheads, seqlen_q, seqlen_k) - causal: whether to apply causal masking - window_size: (int, int), left and right window size - upcast: whether to cast all inputs to fp32, do all computation in fp32, then cast - output back to fp16/bf16. - reorder_ops: whether to change the order of operations (scaling k instead of scaling q, etc.) - without changing the math. This is to estimate the numerical error from operation - reordering. - Output: - output: (batch_size, seqlen_q, nheads, head_dim) - attention: (batch_size, nheads, seqlen_q, seqlen_k), softmax after dropout - """ - if causal: - window_size = (window_size[0], 0) - dtype_og = q.dtype - if upcast: - q, k, v = q.float(), k.float(), v.float() - dim = q.shape[-1] - scale = (1.0 / dim)**0.5 # log2(e) - k = repeat(k, "b s h d -> b s (h g) d", g=q.shape[2] // k.shape[2]) - v = repeat(v, "b s h d -> b s (h g) d", g=q.shape[2] // v.shape[2]) - scores = torch.einsum("bthd,bshd->bhts", q, k) - if key_padding_mask is not None: - scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), float("-inf")) - # scores.masked_fill_(rearrange(~key_padding_mask, "b s -> b 1 1 s"), 0) - scores = scores * scale - attention = torch.softmax(scores, dim=-1).to(v.dtype) - - # We want to mask here so that the attention matrix doesn't have any NaNs - # Otherwise we'll get NaN in dV - if query_padding_mask is not None: - attention = attention.masked_fill(rearrange(~query_padding_mask, "b s -> b 1 s 1"), 0.0) - output = torch.einsum("bhts,bshd->bthd", attention, v) - if query_padding_mask is not None: - output.masked_fill_(rearrange(~query_padding_mask, "b s -> b s 1 1"), 0.0) - return output.to(dtype=dtype_og), attention.to(dtype=dtype_og) +def get_configs(): + iter_params = dict(block_M=[64, 128], block_N=[64, 128], num_stages=[0, 1, 2, 3], threads=[128, 256]) + return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] +@autotune(configs=get_configs()) @tilelang.jit( - out_idx=[6], pass_configs={ + out_idx=[6], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) -def flashattn(batch_size, - UQ, - UKV, - heads, - dim, - is_causal, - block_M=64, - block_N=64, - num_stages=0, - threads=32): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + }, +) +def flashattn(batch_size, UQ, UKV, heads, dim, is_causal, block_M=64, block_N=64, num_stages=1, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) q_shape = [UQ, heads, dim] k_shape = [UKV, heads, dim] v_shape = [UKV, heads, dim] o_shape = [UQ, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 @T.prim_func def main( - Q_unpad: T.Tensor(q_shape, dtype), - K_unpad: T.Tensor(k_shape, dtype), - V_unpad: T.Tensor(v_shape, dtype), - cu_seqlens_q: T.Tensor([batch_size + 1], "int32"), - cu_seqlens_k: T.Tensor([batch_size + 1], "int32"), - max_seqlen_q: T.int32, - Output_unpad: T.Tensor(o_shape, dtype), + Q_unpad: T.Tensor(q_shape, dtype), + K_unpad: T.Tensor(k_shape, dtype), + V_unpad: T.Tensor(v_shape, dtype), + cu_seqlens_q: T.Tensor([batch_size + 1], T.int32), + cu_seqlens_k: T.Tensor([batch_size + 1], T.int32), + max_seqlen_q: T.int32, + Output_unpad: T.Tensor(o_shape, dtype), ): - with T.Kernel( - T.ceildiv(max_seqlen_q, block_M), heads, batch_size, - threads=threads) as (bx, by, bz): - Q_shared = T.alloc_shared([block_M, dim], dtype, "shared") - K_shared = T.alloc_shared([block_N, dim], dtype, "shared") - V_shared = T.alloc_shared([block_N, dim], dtype, "shared") - O_shared = T.alloc_shared([block_M, dim], dtype, "shared") + with T.Kernel(T.ceildiv(max_seqlen_q, block_M), heads, batch_size, threads=threads) as (bx, by, bz): + Q_shared = T.alloc_shared([block_M, dim], dtype) + K_shared = T.alloc_shared([block_N, dim], dtype) + V_shared = T.alloc_shared([block_N, dim], dtype) + O_shared = T.alloc_shared([block_M, dim], dtype) acc_s = T.alloc_fragment([block_M, block_N], accum_dtype) acc_s_cast = T.alloc_fragment([block_M, block_N], dtype) acc_o = T.alloc_fragment([block_M, dim], accum_dtype) @@ -120,46 +62,46 @@ def main( head_idx = by q_start_idx = cu_seqlens_q[batch_idx] - k_start_idx = cu_seqlens_k[batch_idx] - v_start_idx = cu_seqlens_k[batch_idx] + kv_start_idx = cu_seqlens_k[batch_idx] q_end_idx = cu_seqlens_q[batch_idx + 1] - k_end_idx = cu_seqlens_k[batch_idx + 1] - v_end_idx = cu_seqlens_k[batch_idx + 1] + kv_end_idx = cu_seqlens_k[batch_idx + 1] q_current_seqlen = q_end_idx - q_start_idx - k_current_seqlen = k_end_idx - k_start_idx - v_current_seqlen = v_end_idx - v_start_idx + kv_current_seqlen = kv_end_idx - kv_start_idx - for i, d in T.Parallel(block_M, dim): - if bx * block_M + i < q_current_seqlen: - Q_shared[i, d] = Q_unpad[q_start_idx + bx * block_M + i, head_idx, d] - else: - Q_shared[i, d] = 0 + T.copy( + Q_unpad[q_start_idx + bx * block_M : q_start_idx + bx * block_M + block_M, head_idx, :], Q_shared + ) # OOB positions will be handled below T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - loop_range = T.ceildiv(k_current_seqlen, block_N) + offset = kv_current_seqlen - q_current_seqlen # always align on the right + loop_range = ( + T.min(T.ceildiv(offset + (bx + 1) * block_M, block_N), T.ceildiv(kv_current_seqlen, block_N)) + if is_causal + else T.ceildiv(kv_current_seqlen, block_N) + ) for k in T.Pipelined(loop_range, num_stages=num_stages): # Q * K - for i, d in T.Parallel(block_N, dim): - if k * block_N + i < k_current_seqlen: - K_shared[i, d] = K_unpad[k_start_idx + k * block_N + i, head_idx, d] - else: - K_shared[i, d] = 0 + T.copy( + K_unpad[kv_start_idx + k * block_N : kv_start_idx + k * block_N + block_N, head_idx, :], K_shared + ) # OOB positions will be handled below if is_causal: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= k * block_N + j) and - (bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -T.infinity(acc_s.dtype), 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i + offset < k * block_N + j) + or (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen), + -1e9, + 0, + ) else: for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else((bx * block_M + i >= q_current_seqlen or - k * block_N + j >= k_current_seqlen), - -T.infinity(acc_s.dtype), 0) + acc_s[i, j] = T.if_then_else( + (bx * block_M + i >= q_current_seqlen or k * block_N + j >= kv_current_seqlen), -1e9, 0 + ) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) @@ -167,6 +109,8 @@ def main( T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) # To do causal softmax, we need to set the scores_max to 0 if it is -inf # This process is called Check_inf in FlashAttention3 code, and it only need to be done # in the first ceil_div(kBlockM, kBlockN) steps. @@ -189,18 +133,17 @@ def main( acc_o[i, j] *= scores_scale[i] # V * softmax(Q * K) - for i, d in T.grid(block_N, dim): - if k * block_N + i < v_current_seqlen: - V_shared[i, d] = V_unpad[v_start_idx + k * block_N + i, head_idx, d] - else: - V_shared[i, d] = 0 + T.copy( + V_unpad[kv_start_idx + k * block_N : kv_start_idx + k * block_N + block_N, head_idx, :], V_shared + ) # OOB positions' weights are 0 T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_M, dim): - acc_o[i, j] /= logsum[i] - T.copy(acc_o, O_shared) + # When sq > skv, some tokens can see nothing + acc_o[i, j] = 0 if is_causal and bx * block_M + i + offset < 0 else acc_o[i, j] / logsum[i] + T.copy(acc_o, O_shared) for i, d in T.Parallel(block_M, dim): if bx * block_M + i < q_current_seqlen: Output_unpad[q_start_idx + bx * block_M + i, head_idx, d] = O_shared[i, d] @@ -208,19 +151,17 @@ def main( return main -def main(batch: int = 8, heads: int = 64, seq_len: int = 2048, dim: int = 128): +def main(batch: int = 8, heads: int = 64, seq_len: int = 2048, dim: int = 128, causal: bool = False, tune: bool = False): flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim total_flops = 2 * flops_per_matmul tilelang.testing.set_random_seed(0) - causal = False if causal: total_flops *= 0.5 dtype = torch.float16 device = torch.device("cuda") - window_size = (-1, -1) q = torch.randn(batch, seq_len, heads, dim, dtype=dtype, requires_grad=True).to(device) k = torch.randn(batch, seq_len, heads, dim, dtype=dtype, requires_grad=True).to(device) @@ -240,30 +181,23 @@ def main(batch: int = 8, heads: int = 64, seq_len: int = 2048, dim: int = 128): k, v, output_pad_fn, - dq_pad_fn, - dk_pad_fn, - ) = generate_qkv( - q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) + _, + _, + ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) UQ = q_unpad.shape[0] # unpadded query length - UK = k_unpad.shape[0] # unpadded key length UKV = k_unpad.shape[0] # unpadded query key length - kernel = flashattn(batch, UQ, UKV, heads, dim, causal) + if tune: + with set_autotune_inputs(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q): + kernel = flashattn(batch, UQ, UKV, heads, dim, causal) + else: + kernel = flashattn(batch, UQ, UKV, heads, dim, causal, block_M=64, block_N=64, num_stages=1, threads=128) + # NOTE: (128, 128, 2or3, 256) is recommended for Hopper out_unpad = kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q) out = output_pad_fn(out_unpad) - out_ref, _ = attention_ref( - q, - k, - v, - query_padding_mask, - key_padding_mask, - causal=causal, - ) - torch.testing.assert_close(out, out_ref, rtol=1e-2, atol=1e-2) - import flash_attn fla_out_unpad = flash_attn.flash_attn_varlen_func( @@ -282,13 +216,67 @@ def main(batch: int = 8, heads: int = 64, seq_len: int = 2048, dim: int = 128): print("All checks passed.✅") + # benchmark + t = do_bench(lambda: kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q)) + print(f"Tilelang time: {t} ms") + print(f"Tilelang: {total_flops / t * 1e-9} TFlops") + t = do_bench( + lambda: flash_attn.flash_attn_varlen_func( + q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, 0.0, causal=causal + ) + ) + print(f"FA2 time: {t} ms") + print(f"FA2: {total_flops / t * 1e-9} TFlops") + + +def run_regression_perf(batch: int = 8, heads: int = 64, seq_len: int = 2048, dim: int = 128, causal: bool = False): + flops_per_matmul = 2.0 * batch * heads * seq_len * seq_len * dim + total_flops = 2 * flops_per_matmul + tilelang.testing.set_random_seed(0) + if causal: + total_flops *= 0.5 + dtype = torch.float16 + device = torch.device("cuda") + q = torch.randn(batch, seq_len, heads, dim, dtype=dtype, requires_grad=True).to(device) + k = torch.randn(batch, seq_len, heads, dim, dtype=dtype, requires_grad=True).to(device) + v = torch.randn(batch, seq_len, heads, dim, dtype=dtype, requires_grad=True).to(device) + query_padding_mask = generate_random_padding_mask(seq_len, batch, device, mode="random") + key_padding_mask = generate_random_padding_mask(seq_len, batch, device, mode="random") + ( + q_unpad, + k_unpad, + v_unpad, + cu_seqlens_q, + cu_seqlens_k, + max_seqlen_q, + max_seqlen_k, + q, + k, + v, + output_pad_fn, + dq_pad_fn, + dk_pad_fn, + ) = generate_qkv(q, k, v, query_padding_mask, key_padding_mask, kvpacked=False) + UQ = q_unpad.shape[0] + UKV = k_unpad.shape[0] + kernel = flashattn(batch, UQ, UKV, heads, dim, causal, block_M=128, block_N=128, num_stages=2, threads=256) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(q_unpad, k_unpad, v_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q) + + return do_bench(run_kernel_only, backend="cupti") + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=64, help='heads') - parser.add_argument('--seq_len', type=int, default=2048, help='sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=64, help="heads") + parser.add_argument("--seq_len", type=int, default=2048, help="sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--is_causal", action="store_true", default=False, help="causal attention") + parser.add_argument("--tune", action="store_true", default=False, help="tune the kernel") args = parser.parse_args() - main(args.batch, args.heads, args.seq_len, args.dim) + main(args.batch, args.heads, args.seq_len, args.dim, args.is_causal, args.tune) diff --git a/examples/flash_attention/regression_example_flash_attention.py b/examples/flash_attention/regression_example_flash_attention.py new file mode 100644 index 0000000000..8710bbb6e2 --- /dev/null +++ b/examples/flash_attention/regression_example_flash_attention.py @@ -0,0 +1,74 @@ +import tilelang.testing +import example_gqa_fwd_bshd +import example_gqa_fwd_bshd_wgmma_pipelined +import example_mha_fwd_bhsd +import example_mha_fwd_bhsd_wgmma_pipelined +import example_mha_fwd_bshd +import example_mha_fwd_bshd_wgmma_pipelined +import example_mha_fwd_varlen +import example_gqa_bwd_tma_reduce_varlen +import example_gqa_bwd +import example_gqa_bwd_wgmma_pipelined +import example_mha_bwd_bshd +import example_mha_bwd_bhsd +import example_mha_bwd_bshd_wgmma_pipelined + + +def regression_example_gqa_bwd_tma_reduce_varlen(): + tilelang.testing.process_func(example_gqa_bwd_tma_reduce_varlen.run_regression_perf) + + +def regression_example_gqa_bwd(): + tilelang.testing.process_func(example_gqa_bwd.run_regression_perf) + + +def regression_example_gqa_bwd_wgmma_pipelined(): + tilelang.testing.process_func(example_gqa_bwd_wgmma_pipelined.run_regression_perf) + + +def regression_example_mha_bwd_bshd(): + tilelang.testing.process_func(example_mha_bwd_bshd.run_regression_perf) + + +def regression_example_mha_bwd_bhsd(): + tilelang.testing.process_func(example_mha_bwd_bhsd.run_regression_perf) + + +def regression_example_mha_bwd_bshd_wgmma_pipelined(): + tilelang.testing.process_func(example_mha_bwd_bshd_wgmma_pipelined.run_regression_perf) + + +def regression_example_gqa_fwd_bshd_wgmma_pipelined(): + tilelang.testing.process_func( + example_gqa_fwd_bshd_wgmma_pipelined.run_regression_perf, batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16 + ) + + +def regression_example_gqa_fwd_bshd(): + tilelang.testing.process_func( + example_gqa_fwd_bshd.run_regression_perf, batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16 + ) + + +def regression_example_mha_fwd_bhsd_wgmma_pipelined(): + tilelang.testing.process_func(example_mha_fwd_bhsd_wgmma_pipelined.run_regression_perf) + + +def regression_example_mha_fwd_bhsd(): + tilelang.testing.process_func(example_mha_fwd_bhsd.run_regression_perf) + + +def regression_example_mha_fwd_bshd_wgmma_pipelined(): + tilelang.testing.process_func(example_mha_fwd_bshd_wgmma_pipelined.run_regression_perf, batch=1, heads=32, seq_len=256) + + +def regression_example_mha_fwd_bshd(): + tilelang.testing.process_func(example_mha_fwd_bshd.run_regression_perf, batch=1, seq_len=256) + + +def regression_example_mha_fwd_varlen(): + tilelang.testing.process_func(example_mha_fwd_varlen.run_regression_perf, batch=4, heads=16, seq_len=512, dim=64) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/flash_attention/test_example_flash_attention.py b/examples/flash_attention/test_example_flash_attention.py index 8a58f3b6aa..a74bf071b9 100644 --- a/examples/flash_attention/test_example_flash_attention.py +++ b/examples/flash_attention/test_example_flash_attention.py @@ -2,7 +2,7 @@ import example_gqa_bwd import example_gqa_bwd_wgmma_pipelined -import example_mha_bwd +import example_mha_bwd_bshd import example_mha_bwd_bhsd import example_mha_fwd_bhsd_wgmma_pipelined import example_gqa_fwd_bshd @@ -10,8 +10,15 @@ import example_gqa_fwd_bshd_wgmma_pipelined import example_mha_fwd_bshd_wgmma_pipelined import example_mha_fwd_varlen -import example_mha_bwd_wgmma_pipelined +import example_mha_bwd_bshd_wgmma_pipelined import example_mha_fwd_bhsd +import example_gqa_bwd_tma_reduce_varlen +import example_gqa_fwd_varlen + + +@tilelang.testing.requires_cuda +def test_example_gqa_bwd_tma_reduce_varlen(): + example_gqa_bwd_tma_reduce_varlen.main() @tilelang.testing.requires_cuda @@ -27,31 +34,41 @@ def test_example_gqa_bwd_wgmma_pipelined(): @tilelang.testing.requires_cuda def test_example_mha_bwd(): - example_mha_bwd.main(BATCH=1) + example_mha_bwd_bshd.main( + BATCH=1, + H=16, + N_CTX=512, + D_HEAD=64, + causal=False, + ) @tilelang.testing.requires_cuda def test_example_mha_bwd_bhsd(): - example_mha_bwd_bhsd.main(BATCH=1) + example_mha_bwd_bhsd.main( + BATCH=1, + H=16, + N_CTX=512, + D_HEAD=64, + causal=False, + ) @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_mha_bwd_wgmma_pipelined(): - example_mha_bwd_wgmma_pipelined.main(BATCH=1) + example_mha_bwd_bshd_wgmma_pipelined.main(BATCH=1, H=32, N_CTX=256, D_HEAD=64, causal=False) @tilelang.testing.requires_cuda @tilelang.testing.requires_cuda_compute_version_ge(9, 0) def test_example_gqa_fwd_bshd_wgmma_pipelined(): - example_gqa_fwd_bshd_wgmma_pipelined.main( - batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16, tune=False) + example_gqa_fwd_bshd_wgmma_pipelined.main(batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16, tune=False) @tilelang.testing.requires_cuda def test_example_gqa_fwd_bshd(): - example_gqa_fwd_bshd.main( - batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16, tune=False) + example_gqa_fwd_bshd.main(batch=1, heads=16, seq_len=1024, dim=128, is_causal=False, groups=16, tune=False) @tilelang.testing.requires_cuda @@ -78,7 +95,14 @@ def test_example_mha_fwd_bshd(): @tilelang.testing.requires_cuda def test_example_mha_fwd_varlen(): - example_mha_fwd_varlen.main() + example_mha_fwd_varlen.main(batch=4, heads=16, seq_len=512, dim=64, causal=False) + example_mha_fwd_varlen.main(batch=4, heads=16, seq_len=512, dim=64, causal=True) + + +@tilelang.testing.requires_cuda +def test_example_gqa_fwd_varlen(): + example_gqa_fwd_varlen.main(batch=4, heads=16, q_seqlen=512, k_seqlen=512, dim=64, is_causal=False) + example_gqa_fwd_varlen.main(batch=4, heads=16, q_seqlen=512, k_seqlen=512, dim=64, is_causal=True) if __name__ == "__main__": diff --git a/examples/flash_attention/varlen_utils.py b/examples/flash_attention/varlen_utils.py index 4301215d55..43e21cc3b8 100644 --- a/examples/flash_attention/varlen_utils.py +++ b/examples/flash_attention/varlen_utils.py @@ -9,22 +9,14 @@ def generate_random_padding_mask(max_seqlen, batch_size, device, mode="random"): if mode == "full": lengths = torch.full((batch_size, 1), max_seqlen, device=device, dtype=torch.int32) elif mode == "random": - lengths = torch.randint( - max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device) + lengths = torch.randint(max(1, max_seqlen - 20), max_seqlen + 1, (batch_size, 1), device=device) elif mode == "third": lengths = torch.randint(max_seqlen // 3, max_seqlen + 1, (batch_size, 1), device=device) - padding_mask = ( - repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths) + padding_mask = repeat(torch.arange(max_seqlen, device=device), "s -> b s", b=batch_size) < lengths return padding_mask -def generate_qkv(q, - k, - v, - query_padding_mask=None, - key_padding_mask=None, - kvpacked=False, - qkvpacked=False): +def generate_qkv(q, k, v, query_padding_mask=None, key_padding_mask=None, kvpacked=False, qkvpacked=False): """ Arguments: q: (batch_size, seqlen_q, nheads, d) @@ -39,15 +31,12 @@ def generate_qkv(q, if query_padding_mask is not None: q_unpad, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, query_padding_mask) - output_pad_fn = lambda output_unpad: pad_input(output_unpad, indices_q, batch_size, seqlen_q - ) + output_pad_fn = lambda output_unpad: pad_input(output_unpad, indices_q, batch_size, seqlen_q) else: q_unpad = rearrange(q, "b s h d -> (b s) h d") - cu_seqlens_q = torch.arange( - 0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q_unpad.device) + cu_seqlens_q = torch.arange(0, (batch_size + 1) * seqlen_q, step=seqlen_q, dtype=torch.int32, device=q_unpad.device) max_seqlen_q = seqlen_q - output_pad_fn = lambda output_unpad: rearrange( - output_unpad, "(b s) h d -> b s h d", b=batch_size) + output_pad_fn = lambda output_unpad: rearrange(output_unpad, "(b s) h d -> b s h d", b=batch_size) if key_padding_mask is not None: k_unpad, indices_k, cu_seqlens_k, max_seqlen_k = unpad_input(k, key_padding_mask) @@ -55,8 +44,7 @@ def generate_qkv(q, else: k_unpad = rearrange(k, "b s h d -> (b s) h d") v_unpad = rearrange(v, "b s h d -> (b s) h d") - cu_seqlens_k = torch.arange( - 0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=k_unpad.device) + cu_seqlens_k = torch.arange(0, (batch_size + 1) * seqlen_k, step=seqlen_k, dtype=torch.int32, device=k_unpad.device) max_seqlen_k = seqlen_k if qkvpacked: @@ -67,8 +55,7 @@ def generate_qkv(q, if query_padding_mask is not None: dqkv_pad_fn = lambda dqkv_unpad: pad_input(dqkv_unpad, indices_q, batch_size, seqlen_q) else: - dqkv_pad_fn = lambda dqkv_unpad: rearrange( - dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size) + dqkv_pad_fn = lambda dqkv_unpad: rearrange(dqkv_unpad, "(b s) t h d -> b s t h d", b=batch_size) return ( qkv_unpad.detach().requires_grad_(), cu_seqlens_q, @@ -84,8 +71,7 @@ def generate_qkv(q, if key_padding_mask is not None: dkv_pad_fn = lambda dkv_unpad: pad_input(dkv_unpad, indices_k, batch_size, seqlen_k) else: - dkv_pad_fn = lambda dkv_unpad: rearrange( - dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size) + dkv_pad_fn = lambda dkv_unpad: rearrange(dkv_unpad, "(b s) t h d -> b s t h d", b=batch_size) return ( q_unpad.detach().requires_grad_(), kv_unpad.detach().requires_grad_(), diff --git a/examples/flash_decoding/example_gqa_decode.py b/examples/flash_decoding/example_gqa_decode.py index 5f946d8b5c..9e6f360178 100644 --- a/examples/flash_decoding/example_gqa_decode.py +++ b/examples/flash_decoding/example_gqa_decode.py @@ -15,18 +15,12 @@ def get_configs(): block_N = [64, 128] block_H = [64] - num_split = [2, 4, 8] + num_split = [1, 2, 4, 8] num_stages = [1, 2, 3] threads = [128] _configs = list(itertools.product(block_N, block_H, num_split, num_stages, threads)) - configs = [{ - 'block_N': c[0], - 'block_H': c[1], - 'num_split': c[2], - 'num_stages': c[3], - 'threads': c[4] - } for c in _configs] + configs = [{"block_N": c[0], "block_H": c[1], "num_split": c[2], "num_stages": c[3], "threads": c[4]} for c in _configs] return configs @@ -40,45 +34,44 @@ def get_heuristic_config() -> Tuple[Dict, int]: sm_version = sm_major * 10 + sm_minor print(f"CUDA device capability: {sm_version}") if sm_version == 89: - cfg = dict(block_N=128, block_H=64, num_split=16, num_stages=0, threads=128) + cfg = dict(block_N=128, block_H=64, num_split=1, num_stages=0, threads=128) else: - cfg = dict(block_N=128, block_H=64, num_split=16, num_stages=2, threads=128) + cfg = dict(block_N=128, block_H=64, num_split=8, num_stages=2, threads=128) return cfg, sm_version # TODO(lei): fix warp specialized and tma lower pass def get_pass_configs(): - return { - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - } + return {tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True} @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit(out_idx=[6], pass_configs=get_pass_configs()) -def flashattn(batch, heads, groups, seqlen_kv, dim, block_N, block_H, num_split, num_stages, - threads): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) +def flashattn(batch, heads, groups, seqlen_kv, dim, block_N, block_H, num_split, num_stages, threads): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape_q = [batch, heads, dim] shape_k = [batch, seqlen_kv, groups, dim] shape_v = [batch, seqlen_kv, groups, dim] shape_o = [batch, heads, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // groups part_shape = [batch, heads, num_split, dim] valid_block_H = min(block_H, kv_group_num) valid_block_N = min(block_N, seqlen_kv // num_split) - @T.macro - def flash_attn( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), - Output: T.Tensor([batch, heads, dim], dtype), + @T.prim_func + def flashattn_gqa_decode_split( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor(part_shape, dtype), + Output: T.Tensor(shape_o, dtype), ): + # split with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) @@ -96,25 +89,43 @@ def flash_attn( bid = bx hid = by + sid = bz cur_kv_head = hid // (kv_group_num // valid_block_H) - T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared) + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = T.ceildiv((seqlen_kv // num_split), block_N) + for k in T.Pipelined(loop_range, num_stages=num_stages): - T.copy(K[bid, k * block_N:(k + 1) * block_N, cur_kv_head, :], K_shared) - T.copy(mask[bid, k * block_N:(k + 1) * block_N, cur_kv_head], mask_local) + T.copy( + K[ + bid, + (seqlen_kv // num_split) * sid + k * valid_block_N : (seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, + cur_kv_head, + :, + ], + K_shared, + ) + T.copy( + mask[ + bid, + (seqlen_kv // num_split) * sid + k * valid_block_N : (seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, + cur_kv_head, + ], + mask_local, + ) T.clear(acc_s) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, block_N): - acc_s[i, j] = T.if_then_else(mask_local[j] != 0, acc_s[i, j], - -T.infinity(accum_dtype)) + acc_s[i, j] = T.if_then_else((mask_local[j] != 0) & (j < seqlen_kv // num_split), acc_s[i, j], -T.infinity(accum_dtype)) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): @@ -125,23 +136,66 @@ def flash_attn( T.copy(acc_s, acc_s_cast) for i, j in T.Parallel(block_H, dim): acc_o[i, j] *= scores_scale[i] - T.copy(V[bid, k * block_N:(k + 1) * block_N, cur_kv_head, :], V_shared) + T.copy( + V[ + bid, + (seqlen_kv // num_split) * sid + k * valid_block_N : (seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, + cur_kv_head, + :, + ], + V_shared, + ) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] for i in T.Parallel(block_H): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + + for i in T.Parallel(block_H): + if i < valid_block_H: + glse[bid, hid * valid_block_H + i, sid] = logsum[i] T.copy(acc_o[:valid_block_H, :], O_shared) - T.copy(O_shared, Output[bid, hid * valid_block_H:(hid + 1) * valid_block_H, :]) - - @T.macro - def flash_attn_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor(part_shape, dtype), + T.copy(O_shared, Output_partial[bid, hid * valid_block_H : (hid + 1) * valid_block_H, sid, :]) + + # combine + with T.Kernel(heads, batch, threads=128) as (by, bz): + po_local = T.alloc_fragment([dim], dtype) + o_accum_local = T.alloc_fragment([dim], accum_dtype) + lse_local = T.alloc_fragment([num_split, 128], dtype) + lse_logsum_local = T.alloc_fragment([128], accum_dtype) + lse_max_local = T.alloc_fragment([128], accum_dtype) + scale_local = T.alloc_fragment([128], accum_dtype) + + T.clear(lse_logsum_local) + T.clear(o_accum_local) + for k, j in T.Parallel(num_split, 128): + lse_local[k, j] = glse[bz, by, k] + T.reduce_max(lse_local, lse_max_local, dim=0, clear=True) + for k in T.serial(num_split): + for j in T.Parallel(128): + lse_logsum_local[j] += T.exp2(lse_local[k, j] - lse_max_local[j]) + for j in T.Parallel(128): + lse_logsum_local[j] = T.log2(lse_logsum_local[j]) + lse_max_local[j] + for k in T.serial(num_split): + for i in T.Parallel(dim): + po_local[i] = Output_partial[bz, by, k, i] + for j in T.Parallel(128): + scale_local[j] = T.exp2(lse_local[k, j] - lse_logsum_local[j]) + # Note: Pay attention to dim and the number of threads in Parallel + for i in T.Parallel(dim): + o_accum_local[i] += po_local[i] * scale_local[i] + for i in T.Parallel(dim): + Output[bz, by, i] = o_accum_local[i] + + @T.prim_func + def flashattn_gqa_decode_no_split( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor(part_shape, dtype), + Output: T.Tensor(shape_o, dtype), ): with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_H, dim], dtype) @@ -160,34 +214,26 @@ def flash_attn_split( bid = bx hid = by - sid = bz cur_kv_head = hid // (kv_group_num // valid_block_H) - T.copy(Q[bid, hid * valid_block_H:hid * valid_block_H + block_H, :], Q_shared) + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) loop_range = T.ceildiv((seqlen_kv // num_split), block_N) - for k in T.Pipelined(loop_range, num_stages=num_stages): - T.copy( - K[bid, (seqlen_kv // num_split) * sid + - k * valid_block_N:(seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, - cur_kv_head, :], K_shared) - T.copy( - mask[bid, (seqlen_kv // num_split) * sid + - k * valid_block_N:(seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, - cur_kv_head], mask_local) + T.copy(K[bid, k * block_N : (k + 1) * block_N, cur_kv_head, :], K_shared) + T.copy(mask[bid, k * block_N : (k + 1) * block_N, cur_kv_head], mask_local) T.clear(acc_s) T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, block_N): - acc_s[i, - j] = T.if_then_else((mask_local[j] != 0) & (j < seqlen_kv // num_split), - acc_s[i, j], -T.infinity(accum_dtype)) + acc_s[i, j] = T.if_then_else(mask_local[j] != 0, acc_s[i, j], -T.infinity(accum_dtype)) T.copy(scores_max, scores_max_prev) T.fill(scores_max, -T.infinity(accum_dtype)) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_H): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_H): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_H, block_N): @@ -198,88 +244,14 @@ def flash_attn_split( T.copy(acc_s, acc_s_cast) for i, j in T.Parallel(block_H, dim): acc_o[i, j] *= scores_scale[i] - T.copy( - V[bid, (seqlen_kv // num_split) * sid + - k * valid_block_N:(seqlen_kv // num_split) * sid + (k + 1) * valid_block_N, - cur_kv_head, :], V_shared) + T.copy(V[bid, k * block_N : (k + 1) * block_N, cur_kv_head, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_H, dim): acc_o[i, j] /= logsum[i] for i in T.Parallel(block_H): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - - for i in T.Parallel(block_H): - if i < valid_block_H: - glse[bid, hid * valid_block_H + i, sid] = logsum[i] T.copy(acc_o[:valid_block_H, :], O_shared) - T.copy(O_shared, Output_partial[bid, hid * valid_block_H:(hid + 1) * valid_block_H, - sid, :]) - - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor(part_shape, dtype), - Output: T.Tensor(shape_o, dtype), - ): - with T.Kernel(heads, batch, threads=128) as (by, bz): - po_local = T.alloc_fragment([dim], dtype) - o_accum_local = T.alloc_fragment([dim], accum_dtype) - lse_local = T.alloc_fragment([num_split, 128], dtype) - lse_local_split = T.alloc_local([1], accum_dtype) - lse_logsum_local = T.alloc_local([1], accum_dtype) - lse_max_local = T.alloc_fragment([128], accum_dtype) - scale_local = T.alloc_local([1], accum_dtype) - - T.annotate_layout({ - lse_logsum_local: T.Fragment(lse_logsum_local.shape, forward_thread_fn=lambda i: i), - lse_max_local: T.Fragment(lse_max_local.shape, forward_thread_fn=lambda i: i), - # lse_local: (local_id, thread_id) - lse_local: T.Fragment(lse_local.shape, forward_fn=lambda i, j: (j, i)), - }) - - T.clear(lse_logsum_local) - T.clear(o_accum_local) - for k, j in T.Parallel(num_split, 128): - lse_local[k, j] = glse[bz, by, k] - T.reduce_max(lse_local, lse_max_local, dim=0, clear=True) - for k in T.Pipelined(num_split, num_stages=1): - lse_local_split[0] = glse[bz, by, k] - lse_logsum_local[0] += T.exp2(lse_local_split[0] - lse_max_local[0]) - lse_logsum_local[0] = T.log2(lse_logsum_local[0]) + lse_max_local[0] - for k in T.serial(num_split): - for i in T.Parallel(dim): - po_local[i] = Output_partial[bz, by, k, i] - lse_local_split[0] = glse[bz, by, k] - scale_local[0] = T.exp2(lse_local_split[0] - lse_logsum_local[0]) - for i in T.Parallel(dim): - o_accum_local[i] += po_local[i] * scale_local[0] - for i in T.Parallel(dim): - Output[bz, by, i] = o_accum_local[i] - - @T.prim_func - def flashattn_gqa_decode_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor(part_shape, dtype), - Output: T.Tensor(shape_o, dtype), - ): - flash_attn_split(Q, K, V, mask, glse, Output_partial) - combine(glse, Output_partial, Output) - - @T.prim_func - def flashattn_gqa_decode_no_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_k, dtype), - V: T.Tensor(shape_v, dtype), - mask: T.Tensor([batch, seqlen_kv, groups], "uint8"), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor(part_shape, dtype), - Output: T.Tensor(shape_o, dtype), - ): - flash_attn(Q, K, V, mask, Output) + T.copy(O_shared, Output[bid, hid * valid_block_H : (hid + 1) * valid_block_H, :]) if num_split > 1: return flashattn_gqa_decode_split @@ -300,27 +272,21 @@ def ref_program(query, key, value, mask, glse, Output_partial): dim = query.shape[-1] num_head_groups = query.shape[1] // key.shape[2] scale = dim**0.5 - key = rearrange(key, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] - value = rearrange(value, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + key = rearrange(key, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] + value = rearrange(value, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - query = rearrange( - query, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + query = rearrange(query, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] if mask is not None: - mask = rearrange(mask, 'b s h -> b h s') + mask = rearrange(mask, "b s h -> b h s") mask = mask.unsqueeze(1) - scores = scores.masked_fill(mask == 0, float('-inf')) + scores = scores.masked_fill(mask == 0, float("-inf")) - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, value, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, value, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out @@ -334,16 +300,12 @@ def flash_split_ref(Q, K, V, mask): seqlen_kv = K.size(1) num_head_groups = nheads // groups - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) acc_s = torch.empty((batch, num_head_groups, groups, block_N), device="cuda", dtype=torch.float) - acc_s_cast = torch.empty((batch, num_head_groups, groups, block_N), - device="cuda", - dtype=torch.float16) + acc_s_cast = torch.empty((batch, num_head_groups, groups, block_N), device="cuda", dtype=torch.float16) acc_o = torch.empty((batch, num_head_groups, groups, dim), device="cuda", dtype=torch.float) scores_max = torch.empty((batch, num_head_groups, groups), device="cuda", dtype=torch.float) - scores_max_prev = torch.empty((batch, num_head_groups, groups), - device="cuda", - dtype=torch.float) + scores_max_prev = torch.empty((batch, num_head_groups, groups), device="cuda", dtype=torch.float) scores_scale = torch.empty((batch, num_head_groups, groups), device="cuda", dtype=torch.float) scores_sum = torch.empty((batch, num_head_groups, groups), device="cuda", dtype=torch.float) logsum = torch.empty((batch, num_head_groups, groups), device="cuda", dtype=torch.float) @@ -351,25 +313,25 @@ def flash_split_ref(Q, K, V, mask): glogsum = torch.empty((num_split, batch, nheads), device="cuda", dtype=torch.float) Q_ = Q * scale - Q_ = rearrange(Q_, 'b (h g) d -> b g h d', g=num_head_groups) + Q_ = rearrange(Q_, "b (h g) d -> b g h d", g=num_head_groups) for ks in range(num_split): acc_o.fill_(0) logsum.fill_(0) - scores_max.fill_(float('-inf')) - scores_max_prev.fill_(float('-inf')) + scores_max.fill_(float("-inf")) + scores_max_prev.fill_(float("-inf")) for i in range(int((seqlen_kv // num_split) / block_N)): acc_s.fill_(0) - acc_s = torch.einsum('bghd,bkhd->bghk', Q_, - K[:, (seqlen_kv // num_split) * ks + - i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) # [batch, nheads, block_N] + acc_s = torch.einsum( + "bghd,bkhd->bghk", + Q_, + K[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) # [batch, nheads, block_N] if mask is not None: - mask_local = mask[:, (seqlen_kv // num_split) * ks + - i * block_N:(seqlen_kv // num_split) * ks + (i + 1) * block_N, :] - mask_local = rearrange(mask_local, 'b s h -> b h s') + mask_local = mask[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :] + mask_local = rearrange(mask_local, "b s h -> b h s") mask_local = mask_local.unsqueeze(1) - acc_s = acc_s.masked_fill(mask_local == 0, float('-inf')) + acc_s = acc_s.masked_fill(mask_local == 0, float("-inf")) scores_max_prev = scores_max scores_max = acc_s.max(dim=-1, keepdim=False).values # [batch, nheads] scores_scale = torch.exp2(scores_max_prev - scores_max) # [batch, nheads] @@ -377,15 +339,16 @@ def flash_split_ref(Q, K, V, mask): acc_s = torch.exp2(acc_s - scores_max[:, :, :, None]) acc_s_cast = acc_s.to(torch.float16) # [batch, nheads, block_N] acc_o += torch.einsum( - 'bghk,bkhd->bghd', acc_s_cast, - V[:, (seqlen_kv // num_split) * ks + i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) + "bghk,bkhd->bghd", + acc_s_cast, + V[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) scores_sum = acc_s.sum(dim=-1, keepdim=False) logsum = logsum * scores_scale + scores_sum - acc_o_out = rearrange(acc_o, 'b g h d->b (h g) d') - logsum_out = rearrange(logsum, 'b g h->b (h g)') + acc_o_out = rearrange(acc_o, "b g h d->b (h g) d") + logsum_out = rearrange(logsum, "b g h->b (h g)") acc_o_out /= logsum_out[:, :, None] - logsum_out = torch.log2(logsum_out) + rearrange(scores_max, 'b g h->b (h g)') + logsum_out = torch.log2(logsum_out) + rearrange(scores_max, "b g h->b (h g)") gacc_o[ks, :, :, :] = acc_o_out glogsum[ks, :, :] = logsum_out @@ -421,7 +384,7 @@ def calc_sim(x, y, name="tensor"): x, y = x.data.double(), y.data.double() denominator = (x * x + y * y).sum() if denominator == 0: - print_red_warning(f'{name} all zero') + print_red_warning(f"{name} all zero") return 1 sim = 2 * (x * y).sum() / denominator return sim @@ -429,28 +392,23 @@ def calc_sim(x, y, name="tensor"): def assert_similar(x, y, eps=1e-2, name="tensor", assert_=False, print_=True): sim = calc_sim(x, y, name) - diff = 1. - sim + diff = 1.0 - sim if not (0 <= diff <= eps): - print_red_warning(f'{name} Error: {diff}') + print_red_warning(f"{name} Error: {diff}") if assert_: - raise AssertionError(f'{name} Error: {diff}') + raise AssertionError(f"{name} Error: {diff}") else: if print_: - print(f'passed: {name} diff={diff}') + print(f"passed: {name} diff={diff}") -def main(batch: int = 1, - heads: int = 32, - groups: int = 8, - kv_seqlen: int = 8192, - dim: int = 128, - tune: bool = False): +def main(batch: int = 1, heads: int = 32, groups: int = 8, kv_seqlen: int = 8192, dim: int = 128, tune: bool = False): batch, heads, groups, kv_seqlen, dim = batch, heads, groups, kv_seqlen, dim qk_flops = 2 * batch * heads * kv_seqlen * dim pv_flops = 2 * batch * heads * kv_seqlen * dim total_flops = qk_flops + pv_flops - if (not tune): + if not tune: config, sm_version = get_heuristic_config() kernel = flashattn(batch, heads, groups, kv_seqlen, dim, **config) profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) @@ -459,8 +417,9 @@ def main(batch: int = 1, k = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16) v = torch.randn(batch, kv_seqlen, groups, dim, device="cuda", dtype=torch.float16) mask = torch.randint(0, 2, (batch, kv_seqlen, groups), device="cuda", dtype=torch.uint8) - glse = torch.empty(batch, heads, 16, device="cuda", dtype=torch.float16) - Output_partial = torch.empty(batch, heads, 16, dim, device="cuda", dtype=torch.float16) + split = config["num_split"] + glse = torch.empty(batch, heads, split, device="cuda", dtype=torch.float16) + Output_partial = torch.empty(batch, heads, split, dim, device="cuda", dtype=torch.float16) o = kernel(q, k, v, mask, glse, Output_partial) o_ref = ref_program(q, k, v, mask, glse, Output_partial) o_ref_split = ref_split_program(q, k, v, mask, glse, Output_partial) @@ -469,7 +428,7 @@ def main(batch: int = 1, print(o_ref) assert_similar(o, o_ref, name="o_ref") - assert_similar(o_ref_split, o_ref, name="o_ref_split") + assert_similar(o, o_ref_split, name="o_ref_split") print("All checks pass.") latency = profiler.do_bench(ref_program, warmup=500) @@ -489,13 +448,21 @@ def main(batch: int = 1, print(f"Ref latency: {ref_latency}") +def run_regression_perf(batch: int = 1, heads: int = 32, groups: int = 8, kv_seqlen: int = 8192, dim: int = 128): + batch, heads, groups, kv_seqlen, dim = batch, heads, groups, kv_seqlen, dim + config, _ = get_heuristic_config() + kernel = flashattn(batch, heads, groups, kv_seqlen, dim, **config) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=32, help='heads') - parser.add_argument('--groups', type=int, default=8, help='groups') - parser.add_argument('--kv_seqlen', type=int, default=8192, help='kv sequence length') - parser.add_argument('--dim', type=int, default=128, help='dim') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=1, help="batch size") + parser.add_argument("--heads", type=int, default=32, help="heads") + parser.add_argument("--groups", type=int, default=8, help="groups") + parser.add_argument("--kv_seqlen", type=int, default=8192, help="kv sequence length") + parser.add_argument("--dim", type=int, default=128, help="dim") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() main(args.batch, args.heads, args.groups, args.kv_seqlen, args.dim, args.tune) diff --git a/examples/flash_decoding/example_gqa_decode_varlen_logits.py b/examples/flash_decoding/example_gqa_decode_varlen_logits.py new file mode 100644 index 0000000000..30acd879e6 --- /dev/null +++ b/examples/flash_decoding/example_gqa_decode_varlen_logits.py @@ -0,0 +1,785 @@ +import torch +import triton +import triton.language as tl +import math +import argparse +import tilelang +import tilelang.language as T + +torch.manual_seed(0) + + +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +@triton.jit +def _fwd_inner( + q, + k_ptrs, + v_ptrs, + s_ptrs, + m_i, + l_i, + acc, + offs_h, + mask_h, + offs_n, + seqlen, + softmax_scale, + lo, + hi, + stride_kt, + stride_vt, + stride_sh, + stride_sn, + BLOCK_N: tl.constexpr, +): + """Inner loop computation for attention""" + + for blk_idx in tl.range(lo, hi): + start_n = blk_idx * BLOCK_N + k = tl.load(k_ptrs + start_n * stride_kt, mask=offs_n[None, :] + start_n < seqlen) + v = tl.load(v_ptrs + start_n * stride_vt, mask=offs_n[:, None] + start_n < seqlen) + + qk = tl.dot(q, k) + qk *= softmax_scale + qk += tl.where(offs_n[None, :] + start_n < seqlen, 0, -1.0e9) + + row_max = tl.max(qk, 1) + tl.store(s_ptrs + offs_h * stride_sh + blk_idx * stride_sn, row_max, mask=mask_h) + + m_ij = tl.maximum(m_i, row_max) + qk -= m_ij[:, None] + p = tl.math.exp(qk) + l_ij = tl.sum(p, 1) + alpha = tl.math.exp(m_i - m_ij) + l_i = l_i * alpha + l_ij + m_i = m_ij + acc *= alpha[:, None] + p = p.to(v.type.element_ty) + acc += tl.dot(p, v) + + return m_i, l_i, acc + + +@triton.autotune( + configs=[triton.Config({}, num_warps=num_warps, num_stages=num_stages) for num_warps in [4, 8] for num_stages in [2, 4]], + key=["gqa_group_size", "BLOCK_N", "BLOCK_D", "BLOCK_H"], +) +@triton.jit +def _fwd_kernel_varlen( + Q, # [token_q = b, h_q, dim] + K, # [token_k, h_kv, dim] + V, + O, + S, + s_aux, + softmax_scale, + cu_seqlens_k, + stride_qt, + stride_qh, + stride_qd, + stride_kt, + stride_kh, + stride_kd, + stride_vt, + stride_vh, + stride_vd, + stride_ot, + stride_oh, + stride_od, + stride_sb, + stride_sh, + stride_sn, # bmask shape [b, q_h, seq/BLOCK_N] + gqa_group_size: tl.constexpr, + BLOCK_H: tl.constexpr, + BLOCK_N: tl.constexpr, + BLOCK_D: tl.constexpr, +): + off_z = tl.program_id(0) + off_h_for_kv = tl.program_id(1) + off_h_q = off_h_for_kv * gqa_group_size + + cu_k_start = tl.load(cu_seqlens_k + off_z) + cu_k_end = tl.load(cu_seqlens_k + off_z + 1) + + seqlen_k = cu_k_end - cu_k_start + + offs_h = tl.arange(0, BLOCK_H) + offs_n = tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_D) + + Q_ptrs = Q + off_z * stride_qt + off_h_q * stride_qh + K_ptrs = K + (cu_k_start) * stride_kt + off_h_for_kv * stride_kh + V_ptrs = V + (cu_k_start) * stride_vt + off_h_for_kv * stride_vh + O_ptrs = O + off_z * stride_ot + off_h_q * stride_oh + S_ptrs = S + off_z * stride_sb + off_h_q * stride_sh + + mask_h = offs_h < gqa_group_size + q = tl.load(Q_ptrs + offs_d[None, :] * stride_qd + offs_h[:, None] * stride_qh, mask=mask_h[:, None]) + + if s_aux is not None: + sink = tl.load(s_aux + off_h_q + offs_h, mask=mask_h).to(tl.float32) + l_i = tl.zeros([BLOCK_H], dtype=tl.float32) + m_i = tl.zeros([BLOCK_H], dtype=tl.float32) + sink + else: + l_i = tl.full([BLOCK_H], 1.0, dtype=tl.float32) + m_i = tl.full([BLOCK_H], float("-inf"), dtype=tl.float32) + + acc = tl.zeros([BLOCK_H, BLOCK_D], dtype=tl.float32) + + k_ptrs = K_ptrs + offs_n[None, :] * stride_kt + offs_d[:, None] * stride_kd + v_ptrs = V_ptrs + offs_n[:, None] * stride_vt + offs_d[None, :] * stride_vd + + lo, hi = 0, tl.cdiv(seqlen_k, BLOCK_N) + m_i, l_i, acc = _fwd_inner( + q, + k_ptrs, + v_ptrs, + S_ptrs, + m_i, + l_i, + acc, + offs_h, + mask_h, + offs_n, + seqlen_k, + softmax_scale, + lo, + hi, + stride_kt, + stride_vt, + stride_sh, + stride_sn, + BLOCK_N, + ) + + if s_aux is not None: + sink = tl.math.exp(sink - m_i) + l_i = l_i + sink + acc = acc / l_i[:, None] + + else: + l_recip = 1 / l_i[:, None] + acc = acc * l_recip + + for blk_idx in tl.range(lo, hi): + s = tl.load(S_ptrs + offs_h * stride_sh + blk_idx * stride_sn, mask=mask_h) + s = tl.exp(s - m_i) / l_i + tl.store(S_ptrs + offs_h * stride_sh + blk_idx * stride_sn, s, mask=mask_h) + + acc = acc.to(O.dtype.element_ty) + + tl.store(O_ptrs + offs_h[:, None] * stride_oh + offs_d[None, :] * stride_od, acc, mask=mask_h[:, None]) + + +def get_configs(): + import itertools + + block_N = [64, 128] + block_H = [64] + num_split = [1] + num_stages = [1, 2, 3] + threads = [128] + _configs = list(itertools.product(block_N, block_H, num_split, num_stages, threads)) + + configs = [{"block_N": c[0], "block_H": c[1], "num_split": c[2], "num_stages": c[3], "threads": c[4]} for c in _configs] + return configs + + +@tilelang.jit(out_idx=[-2, -1]) +def flashattn( + batch, heads, k_heads, max_seqlen_kv, total_seqlen_k, dim, has_sink, block_N=128, block_H=64, num_split=1, num_stages=1, threads=128 +): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + shape_q = [batch, heads, dim] + shape_k = [total_seqlen_k, k_heads, dim] + shape_v = [total_seqlen_k, k_heads, dim] + shape_o = [batch, heads, dim] + shape_s = [batch, heads, math.ceil(max_seqlen_kv / block_N)] + dtype = T.float16 + accum_dtype = T.float32 + kv_group_num = heads // k_heads + + valid_block_H = min(block_H, kv_group_num) + # TODO: check if max_seqlen_kv is correct for varlen case + + @T.prim_func + def flashattn_gqa_decode_no_split( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + cu_seqlens_k: T.Tensor([batch + 1], T.int32), + s_aux: T.Tensor([heads], T.float32), + Output: T.Tensor(shape_o, dtype), + S: T.Tensor(shape_s, dtype), + ): + with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): + Q_shared = T.alloc_shared([block_H, dim], dtype) + K_shared = T.alloc_shared([block_N, dim], dtype) + V_shared = T.alloc_shared([block_N, dim], dtype) + O_shared = T.alloc_shared([valid_block_H, dim], dtype) + acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) + acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) + acc_o = T.alloc_fragment([block_H, dim], accum_dtype) + scores_max = T.alloc_fragment([block_H], accum_dtype) + scores_max_prev = T.alloc_fragment([block_H], accum_dtype) + scores_scale = T.alloc_fragment([block_H], accum_dtype) + scores_sum = T.alloc_fragment([block_H], accum_dtype) + logsum = T.alloc_fragment([block_H], accum_dtype) + S_shared = T.alloc_shared([block_H, math.ceil(max_seqlen_kv / block_N)], dtype) + # S_fragment = T.alloc_fragment([block_H, math.ceil(max_seqlen_kv / block_N)], accum_dtype) + s_aux_shared = T.alloc_shared([block_H], T.float32) + + bid = bx + hid = by + cur_kv_head = hid // (kv_group_num // valid_block_H) + + cur_start_k = cu_seqlens_k[bid] + cur_end_k = cu_seqlens_k[bid + 1] + cur_seqlen_k = cur_end_k - cur_start_k + + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) + T.fill(acc_o, 0) + T.fill(logsum, 0) + T.fill(scores_max, -T.infinity(accum_dtype)) + + # loop_range = T.ceildiv((seqlen_kv // num_split), block_N) + loop_range = T.ceildiv((cur_seqlen_k // num_split), block_N) + for k in T.Pipelined(loop_range, num_stages=num_stages): + T.copy(K[cur_start_k + k * block_N : cur_start_k + (k + 1) * block_N, cur_kv_head, :], K_shared) + T.clear(acc_s) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_H, block_N): + # acc_s[i, j] = T.if_then_else(mask_local[j] != 0 and k * block_N + j < cur_seqlen_k, acc_s[i, j], + # -T.infinity(accum_dtype)) + acc_s[i, j] = T.if_then_else(k * block_N + j < cur_seqlen_k, acc_s[i, j], -T.infinity(accum_dtype)) + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + # scores_max_prev is m_i + # scores_max is row_max->m_ij in triton + T.copy(scores_max, S_shared[:, k]) + # scores_scale is alpha in triton + for i in T.Parallel(block_H): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_H, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + # scores_sum is l_ij in triton + # logsum is l_i in triton + for i in T.Parallel(block_H): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + for i, j in T.Parallel(block_H, dim): + acc_o[i, j] *= scores_scale[i] + T.copy(V[cur_start_k + k * block_N : cur_start_k + (k + 1) * block_N, cur_kv_head, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + + if has_sink: + T.copy(s_aux[hid * valid_block_H : hid * valid_block_H + block_H], s_aux_shared) + for i in T.Parallel(block_H): + logsum[i] += s_aux_shared[i] + for i, j in T.Parallel(block_H, dim): + acc_o[i, j] /= logsum[i] + for h, k in T.Parallel(block_H, math.ceil(max_seqlen_kv / block_N)): + S_shared[h, k] = T.exp2((S_shared[h, k] - scores_max[h]) * scale) / logsum[h] + # T.copy(S_shared, S_fragment) + # for h, k in T.Parallel(block_H, math.ceil(max_seqlen_kv / block_N)): + # S_fragment[h, k] = T.exp2((S_fragment[h, k] - scores_max[h]) * scale) / logsum[h] + for i in T.Parallel(block_H): + logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + T.copy(acc_o[:valid_block_H, :], O_shared) + T.copy(O_shared, Output[bid, hid * valid_block_H : (hid + 1) * valid_block_H, :]) + # T.copy(S_fragment, S_shared) + T.copy(S_shared[:valid_block_H, :], S[bid, hid * valid_block_H : (hid + 1) * valid_block_H, :]) + + # TODO: split version + return flashattn_gqa_decode_no_split + + +def flash_attn_with_attn_pool_decode_tilelang( + Q: torch.Tensor, ## [tq = b, q_h, q_dim] + K: torch.Tensor, ## [tk, k_h, k_dim] + V: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_k: int, + real_max_k_seqlen: int, + num_split: int, + softmax_scale: float, + s_aux: torch.Tensor = None, + block_size: int = 64, + use_per_kv_head_sparse_index: bool = False, + tl_kernel=None, +): + num_tokens, q_h, head_size = Q.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = K.size(1) + + assert Q.dim() == K.dim() == 3 + assert Q.size(2) == K.size(2) + assert cu_seqlens_k.dim() == 1 + assert head_size in {64, 128, 256} + assert Q.is_contiguous() + # assert K.is_contiguous() + # assert V.is_contiguous() + + gqa_group_size = q_h // k_h + + O_tl = torch.zeros_like(Q) + S_tl = torch.zeros((batch, q_h, math.ceil(real_max_k_seqlen / block_size)), dtype=Q.dtype, device=Q.device) + O_tl, S_tl = tl_kernel(Q, K, V, cu_seqlens_k, s_aux) + + if use_per_kv_head_sparse_index: + S_tl = torch.max_pool2d(S_tl, kernel_size=(gqa_group_size, 1), stride=(gqa_group_size, 1)) + else: + S_tl = torch.max_pool2d(S_tl, kernel_size=(q_h, 1), stride=(q_h, 1)) + + return O_tl, S_tl + + +def flash_attn_with_attn_pool_decode( + Q: torch.Tensor, ## [tq = b, q_h, q_dim] + K: torch.Tensor, ## [tk, k_h, k_dim] + V: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_k: int, + real_max_k_seqlen: int, + num_split: int, + softmax_scale: float, + s_aux: torch.Tensor = None, + block_size: int = 64, + use_per_kv_head_sparse_index: bool = False, +): + num_tokens, q_h, head_size = Q.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = K.size(1) + + assert Q.dim() == K.dim() == 3 + assert Q.size(2) == K.size(2) + assert cu_seqlens_k.dim() == 1 + assert head_size in {64, 128, 256} + assert Q.is_contiguous() + # assert K.is_contiguous() + # assert V.is_contiguous() + + gqa_group_size = q_h // k_h + + BLOCK_D = head_size + BLOCK_N = block_size + BLOCK_H = 64 + + O = torch.zeros_like(Q) + S = torch.zeros((batch, q_h, math.ceil(max_seqlen_k / block_size)), dtype=Q.dtype, device=Q.device) + + def grid(META): + return (batch, k_h) + + with torch.cuda.device(Q.device.index): + _fwd_kernel_varlen[grid]( + Q, + K, + V, + O, + S, + s_aux, + softmax_scale, + cu_seqlens_k, + *Q.stride(), + *K.stride(), + *V.stride(), + *O.stride(), + *S.stride(), + gqa_group_size, + BLOCK_H=BLOCK_H, + BLOCK_N=BLOCK_N, + BLOCK_D=BLOCK_D, + ) + + if use_per_kv_head_sparse_index: + S = torch.max_pool2d(S, kernel_size=(gqa_group_size, 1), stride=(gqa_group_size, 1)) + else: + S = torch.max_pool2d(S, kernel_size=(q_h, 1), stride=(q_h, 1)) + + return O, S + + +def test_varlen_decode_main(args): + """Test decode kernel with variable sequence lengths""" + batch_size = args.batch_size + q_heads = args.q_heads + kv_heads = args.kv_heads + max_k_seqlen = args.k_seqlen # Use as max sequence length + real_max_k_seqlen = args.k_seqlen + head_size = args.head_size + block_size = args.block_size + dtype = torch.bfloat16 if args.dtype == T.bfloat16 else torch.float16 + + print(f"Testing decode kernel with variable sequence lengths (max_k_seqlen={max_k_seqlen})") + + # Generate sink values if needed + sink = None + if args.test_sink: + sink = torch.randn(q_heads, device="cuda", dtype=torch.float32) * 0.1 # Small sink values + print(f"Using sink attention with sink values: {sink}") + + # Generate variable length k sequences + k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,)) + print(f"k_seqlens: {k_seqlens}") + + # Generate cumulative sequence lengths for k + cu_seqlens_k = torch.zeros(batch_size + 1, device="cuda", dtype=torch.int32) + total_k_tokens = 0 + for i in range(batch_size): + cu_seqlens_k[i] = total_k_tokens + total_k_tokens += k_seqlens[i] + cu_seqlens_k[batch_size] = total_k_tokens + + print(f"cu_seqlens_k: {cu_seqlens_k}") + + # Generate tensors - Q is [batch_size, q_heads, head_size] for decode + q_decode = torch.randn(batch_size, q_heads, head_size, device="cuda", dtype=dtype) + k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + + softmax_scale = 1.0 / math.sqrt(head_size) + max_seqlen_k = int(k_seqlens.max()) + + print(f"Actual max_seqlen_k: {max_seqlen_k}") + print(f"q_decode shape: {q_decode.shape}") + print(f"k_varlen shape: {k_varlen.shape}") + print(f"v_varlen shape: {v_varlen.shape}") + + num_tokens, q_h, head_size = q_decode.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = k_varlen.size(1) + tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size, args.test_sink) + + # Test our decode kernel + O_triton, S_triton = flash_attn_with_attn_pool_decode( + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + real_max_k_seqlen, + args.num_split, + softmax_scale, + s_aux=sink, + block_size=block_size, + ) + O_tilelang, S_tilelang = flash_attn_with_attn_pool_decode_tilelang( + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + real_max_k_seqlen, + args.num_split, + softmax_scale, + s_aux=sink, + block_size=block_size, + tl_kernel=tl_kernel, + ) + for i in range(batch_size): + S_tilelang[i, :, math.ceil((cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item()) / block_size) :] = 0 + + # Create torch reference - pad tensors for comparison + k_padded_list = [] + v_padded_list = [] + + for i in range(batch_size): + actual_k_len = k_seqlens[i] + + # Extract and pad k, v for this batch + k_start = cu_seqlens_k[i] + k_end = cu_seqlens_k[i + 1] + + # Pad to max_seqlen_k + k_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device="cuda", dtype=dtype) + v_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device="cuda", dtype=dtype) + + k_padded[:actual_k_len] = k_varlen[k_start:k_end] + v_padded[:actual_k_len] = v_varlen[k_start:k_end] + + k_padded_list.append(k_padded) + v_padded_list.append(v_padded) + + # Stack to create batched tensors [b, max_seqlen, kv_heads, head_size] + k_padded_batched = torch.stack(k_padded_list, dim=0).transpose(1, 2) # [b, kv_heads, max_seqlen, head_size] + v_padded_batched = torch.stack(v_padded_list, dim=0).transpose(1, 2) # [b, kv_heads, max_seqlen, head_size] + + # Expand q to match kv heads: [b, q_heads, 1, head_size] + q_expanded = q_decode.unsqueeze(2) # [b, q_heads, 1, head_size] + + print(f"q_expanded shape: {q_expanded.shape}") + print(f"k_padded_batched shape: {k_padded_batched.shape}") + print(f"v_padded_batched shape: {v_padded_batched.shape}") + + # Compute torch reference + k_repeat = repeat_kv(k_padded_batched, q_heads // kv_heads) # [b, q_heads, max_seqlen, head_size] + v_repeat = repeat_kv(v_padded_batched, q_heads // kv_heads) # [b, q_heads, max_seqlen, head_size] + + if sink is None: + # Standard attention computation: [b, q_heads, 1, head_size] @ [b, q_heads, head_size, max_seqlen] + attn_score = torch.matmul(q_expanded, k_repeat.transpose(-2, -1)) * softmax_scale # [b, q_heads, 1, max_seqlen] + + # Apply sequence length masking + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_score[i, :, :, actual_k_len:] = float("-inf") + + attn_weights = attn_score.softmax(dim=-1) # [b, q_heads, 1, max_seqlen] + + # Mask out invalid positions + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_weights[i, :, :, actual_k_len:] = 0.0 + + # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size] + O_torch = torch.matmul(attn_weights, v_repeat) # [b, q_heads, 1, head_size] + else: + # s_aux attention + logits = torch.matmul(q_expanded, k_repeat.transpose(-2, -1)) * softmax_scale # [b, q_heads, 1, max_seqlen] + + # Apply sequence length masking + for i in range(batch_size): + actual_k_len = k_seqlens[i] + logits[i, :, :, actual_k_len:] = float("-inf") + + sink_expanded = sink.view(1, q_heads, 1, 1) # [1, q_heads, 1, 1] + logits_max = torch.max(logits, dim=-1, keepdim=True).values + logits_or_sinks_max = torch.maximum(logits_max, sink_expanded) + sinks = torch.exp(sink_expanded - logits_or_sinks_max) + unnormalized_scores = torch.exp(logits - logits_or_sinks_max) + normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks + attn_weights = unnormalized_scores / normalizer + + # Mask out invalid positions + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_weights[i, :, :, actual_k_len:] = 0.0 + + # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size] + O_torch = torch.matmul(attn_weights.to(v_repeat.dtype), v_repeat) # [b, q_heads, 1, head_size] + + O_torch = O_torch.squeeze(2) # [b, q_heads, head_size] + + # Compute attention score pooling for S + attn_score_pooled = torch.max_pool2d( + attn_weights.squeeze(2), # [b, q_heads, max_seqlen] + kernel_size=(q_heads, block_size), + stride=(q_heads, block_size), + ceil_mode=True, + ).to(dtype=torch.float16) # [b, 1, ceil(max_seqlen/block_size)] + + print(f"O_triton shape: {O_triton.shape}") + print(f"O_tilelang shape: {O_tilelang.shape}") + print(f"O_torch shape: {O_torch.shape}") + print(f"S_triton shape: {S_triton.shape}") + print(f"S_tilelang shape: {S_tilelang.shape}") + print(f"attn_score_pooled shape: {attn_score_pooled.shape}") + + # Compare results + max_diff_o = torch.max(torch.abs(O_triton - O_torch)) + max_diff_o_tl = torch.max(torch.abs(O_tilelang - O_torch)) + print(f"Max difference in O: {max_diff_o.item()}") + print(f"Max difference in O_tilelang: {max_diff_o_tl.item()}") + + max_diff_s = torch.max(torch.abs(S_triton - attn_score_pooled)) + max_diff_s_tl = torch.max( + torch.abs( + S_tilelang[:, :, : math.ceil(max_seqlen_k / block_size)] - attn_score_pooled[:, :, : math.ceil(max_seqlen_k / block_size)] + ) + ) + print(f"Max difference in S: {max_diff_s.item()}") + print(f"Max difference in S_tilelang: {max_diff_s_tl.item()}") + + assert torch.allclose(O_triton, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o.item()}" + assert torch.allclose(S_triton, attn_score_pooled, atol=1e-2, rtol=1e-2), f"Score mismatch: {max_diff_s.item()}" + assert torch.allclose(O_tilelang, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o_tl.item()}" + assert torch.allclose( + S_tilelang[:, :, : math.ceil(max_seqlen_k / block_size)], + attn_score_pooled[:, :, : math.ceil(max_seqlen_k / block_size)], + atol=1e-2, + rtol=1e-2, + ), f"Score mismatch: {max_diff_s_tl.item()}" + + print("✅ All tests passed!") + + +def do_bench(fn, *args, warmup=10, rep=10, **kwargs): + """ + Do benchmark for a function. + """ + start_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)] + end_event = [torch.cuda.Event(enable_timing=True) for i in range(rep)] + for _ in range(warmup): + fn(*args, **kwargs) + + torch.cuda.synchronize() + for i in range(rep): + start_event[i].record() + fn(*args, **kwargs) + end_event[i].record() + torch.cuda.synchronize() + + # Record clocks + times = torch.tensor( + [s.elapsed_time(e) for s, e in zip(start_event, end_event)], + dtype=torch.float, + ) + + return times.mean().item() + + +def speed_benchmark_decode_comparison(args): + """Speed benchmark for decode kernel""" + batch_size = args.batch_size + q_heads = args.q_heads + kv_heads = args.kv_heads + max_k_seqlen = args.k_seqlen + head_size = args.head_size + block_size = args.block_size + dtype = torch.bfloat16 if args.dtype == T.bfloat16 else torch.float16 + + print("\n=== Decode Speed Benchmark Comparison ===") + print("Configuration:") + print(f" Batch size: {batch_size}") + print(f" Q heads: {q_heads}, KV heads: {kv_heads}") + print(f" Max K sequence length: {max_k_seqlen}") + print(f" Head size: {head_size}") + print(f" Block size: {block_size}") + print(f" Data type: {dtype}") + print(f" Variable lengths: {args.test_varlen}") + print(f" s_aux attention: {args.test_sink}") + print() + + # Generate input data + if args.test_varlen: + k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,)) + else: + k_seqlens = torch.full((batch_size,), max_k_seqlen, dtype=int) + + # Generate cumulative sequence lengths for k + cu_seqlens_k = torch.zeros(batch_size + 1, device="cuda", dtype=torch.int32) + total_k_tokens = 0 + for i in range(batch_size): + cu_seqlens_k[i] = total_k_tokens + total_k_tokens += k_seqlens[i] + cu_seqlens_k[batch_size] = total_k_tokens + + # Generate tensors + q_decode = torch.randn(batch_size, q_heads, head_size, device="cuda", dtype=dtype) + k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + + softmax_scale = 1.0 / math.sqrt(head_size) + max_seqlen_k = int(k_seqlens.max()) + + # Generate sink values if needed + sink = None + if args.test_sink: + sink = torch.randn(q_heads, device="cuda", dtype=torch.float32) * 0.1 # Small sink values + print(" Using sink attention with sink values") + + print("Setup complete:") + print(f" Total K tokens: {total_k_tokens}") + print(f" Actual max K seq len: {max_seqlen_k}") + if args.test_varlen: + print(f" K sequence lengths: {k_seqlens.tolist()}") + + # Warmup + num_tokens, q_h, head_size = q_decode.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = k_varlen.size(1) + tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size, args.test_sink) + + # Benchmark + print("⚡ Benchmarking Tilelang kernel (100 iterations)...") + tilelang_time = do_bench( + flash_attn_with_attn_pool_decode_tilelang, + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + args.k_seqlen, + 1, + softmax_scale, + sink, + block_size, + False, + tl_kernel, + ) + print(f"Average decode kernel time Tilelang: {tilelang_time:.3f} ms") + + # Benchmark + print("⚡ Benchmarking Triton kernel (100 iterations)...") + triton_time = do_bench( + flash_attn_with_attn_pool_decode, + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + args.k_seqlen, + 1, + softmax_scale, + sink, + block_size, + ) + print(f"Average decode kernel time Triton: {triton_time:.3f} ms") + + print(f"Speedup: {(triton_time / tilelang_time):.3f}") + + +def main(): + args = argparse.Namespace( + batch_size=1, + q_heads=32, + kv_heads=8, + k_seqlen=8192, + head_size=128, + block_size=128, + dtype=T.float16, + ) + args.test_sink = True + args.test_varlen = True + args.dtype = T.float16 + args.num_split = 1 + test_varlen_decode_main(args) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flash Attention Decode with Attention Pooling") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size") + parser.add_argument("--q_heads", type=int, default=32, help="Number of query heads") + parser.add_argument("--kv_heads", type=int, default=8, help="Number of key-value heads") + parser.add_argument("--k_seqlen", type=int, default=8192, help="Key sequence length") + parser.add_argument("--head_size", type=int, default=128, choices=[64, 128, 256], help="Head dimension") + parser.add_argument("--block_size", type=int, default=128, help="Block size for computation") + parser.add_argument("--dtype", type=str, default=T.bfloat16, choices=[T.float16, T.bfloat16], help="Data type") + parser.add_argument("--test_varlen", action="store_true", help="Test with truly variable sequence lengths") + parser.add_argument("--test_sink", action="store_true", help="Test with sink attention mechanism") + parser.add_argument("--benchmark", action="store_true", help="Run speed benchmark") + parser.add_argument("--num_split", type=int, default=1, choices=[1, 16], help="Number of splits") + args = parser.parse_args() + args.test_sink = True + args.test_varlen = True + args.dtype = T.float16 + args.num_split = 1 + + if args.benchmark: + speed_benchmark_decode_comparison(args) + else: + test_varlen_decode_main(args) diff --git a/examples/flash_decoding/example_gqa_decode_varlen_logits_paged.py b/examples/flash_decoding/example_gqa_decode_varlen_logits_paged.py new file mode 100644 index 0000000000..87748512d8 --- /dev/null +++ b/examples/flash_decoding/example_gqa_decode_varlen_logits_paged.py @@ -0,0 +1,550 @@ +import torch +import math +import argparse +import tilelang +import tilelang.language as T +from example_gqa_decode_varlen_logits import flash_attn_with_attn_pool_decode, repeat_kv, do_bench + +torch.manual_seed(0) + + +def get_configs(): + import itertools + + block_N = [64, 128] + block_H = [64] + num_split = [1] + num_stages = [1, 2, 3] + threads = [128] + _configs = list(itertools.product(block_N, block_H, num_split, num_stages, threads)) + + configs = [{"block_N": c[0], "block_H": c[1], "num_split": c[2], "num_stages": c[3], "threads": c[4]} for c in _configs] + return configs + + +# @autotune(configs=get_configs(), warmup=10, rep=10) +@tilelang.jit(out_idx=[-2, -1]) +def flashattn( + batch, + heads, + k_heads, + max_seqlen_kv, + total_seqlen_k, + dim, + has_sink, + page_block_size, + block_N=128, + block_H=64, + num_split=1, + num_stages=1, + threads=128, +): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + shape_q = [batch, heads, dim] + shape_k = [total_seqlen_k, k_heads, dim] + shape_v = [total_seqlen_k, k_heads, dim] + shape_o = [batch, heads, dim] + shape_s = [batch, heads, math.ceil(max_seqlen_kv / block_N)] + dtype = T.float16 + accum_dtype = T.float32 + kv_group_num = heads // k_heads + assert page_block_size >= block_N and page_block_size % block_N == 0, ( + "page_block_size must be larger than block_N and a multiple of block_N" + ) + + valid_block_H = min(block_H, kv_group_num) + # TODO: check if max_seqlen_kv is correct for varlen case + + @T.prim_func + def flashattn_gqa_decode_no_split( + Q: T.Tensor(shape_q, dtype), + K: T.Tensor(shape_k, dtype), + V: T.Tensor(shape_v, dtype), + cu_seqlens_k: T.Tensor([batch + 1], T.int32), + s_aux: T.Tensor([heads], T.float32), + BLOCK_TABLE: T.Tensor([batch, math.ceil(max_seqlen_kv / page_block_size)], T.int32), + Output: T.Tensor(shape_o, dtype), + S: T.Tensor(shape_s, dtype), + ): + with T.Kernel(batch, heads // valid_block_H, num_split, threads=threads) as (bx, by, bz): + Q_shared = T.alloc_shared([block_H, dim], dtype) + K_shared = T.alloc_shared([block_N, dim], dtype) + V_shared = T.alloc_shared([block_N, dim], dtype) + O_shared = T.alloc_shared([valid_block_H, dim], dtype) + acc_s = T.alloc_fragment([block_H, block_N], accum_dtype) + acc_s_cast = T.alloc_fragment([block_H, block_N], dtype) + acc_o = T.alloc_fragment([block_H, dim], accum_dtype) + scores_max = T.alloc_fragment([block_H], accum_dtype) + scores_max_prev = T.alloc_fragment([block_H], accum_dtype) + scores_scale = T.alloc_fragment([block_H], accum_dtype) + scores_sum = T.alloc_fragment([block_H], accum_dtype) + logsum = T.alloc_fragment([block_H], accum_dtype) + S_shared = T.alloc_shared([block_H, math.ceil(max_seqlen_kv / block_N)], dtype) + s_aux_shared = T.alloc_shared([block_H], T.float32) + + bid = bx + hid = by + cur_kv_head = hid // (kv_group_num // valid_block_H) + + cur_start_k = cu_seqlens_k[bid] + cur_end_k = cu_seqlens_k[bid + 1] + cur_seqlen_k = cur_end_k - cur_start_k + + T.copy(Q[bid, hid * valid_block_H : hid * valid_block_H + block_H, :], Q_shared) + T.fill(acc_o, 0) + T.fill(logsum, 0) + T.fill(scores_max, -T.infinity(accum_dtype)) + + # loop_range = T.ceildiv((seqlen_kv // num_split), block_N) + loop_range = T.ceildiv((cur_seqlen_k // num_split), block_N) + for k in T.Pipelined(loop_range, num_stages=num_stages): + k_start = BLOCK_TABLE[bid, (k * block_N) // page_block_size] * page_block_size + (k * block_N) % page_block_size + T.copy(K[cur_start_k + k_start : cur_start_k + k_start + block_N, cur_kv_head, :], K_shared) + T.clear(acc_s) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_H, block_N): + acc_s[i, j] = T.if_then_else(k * block_N + j < cur_seqlen_k, acc_s[i, j], -T.infinity(accum_dtype)) + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + # scores_max_prev is m_i + # scores_max is row_max->m_ij in triton + T.copy(scores_max, S_shared[:, k]) + # scores_scale is alpha in triton + for i in T.Parallel(block_H): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_H, block_N): + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + # scores_sum is l_ij in triton + # logsum is l_i in triton + for i in T.Parallel(block_H): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + for i, j in T.Parallel(block_H, dim): + acc_o[i, j] *= scores_scale[i] + v_start = BLOCK_TABLE[bid, (k * block_N) // page_block_size] * page_block_size + (k * block_N) % page_block_size + T.copy(V[cur_start_k + v_start : cur_start_k + v_start + block_N, cur_kv_head, :], V_shared) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + + if has_sink: + T.copy(s_aux[hid * valid_block_H : hid * valid_block_H + block_H], s_aux_shared) + for i in T.Parallel(block_H): + logsum[i] += s_aux_shared[i] + for i, j in T.Parallel(block_H, dim): + acc_o[i, j] /= logsum[i] + for h, k in T.Parallel(block_H, math.ceil(max_seqlen_kv / block_N)): + S_shared[h, k] = T.exp2((S_shared[h, k] - scores_max[h]) * scale) / logsum[h] + for i in T.Parallel(block_H): + logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale + T.copy(acc_o[:valid_block_H, :], O_shared) + T.copy(O_shared, Output[bid, hid * valid_block_H : (hid + 1) * valid_block_H, :]) + T.copy(S_shared[:valid_block_H, :], S[bid, hid * valid_block_H : (hid + 1) * valid_block_H, :]) + + # TODO: split version + return flashattn_gqa_decode_no_split + + +def flash_attn_with_attn_pool_decode_tilelang( + Q: torch.Tensor, ## [tq = b, q_h, q_dim] + K: torch.Tensor, ## [tk, k_h, k_dim] + V: torch.Tensor, + cu_seqlens_k: torch.Tensor, + max_seqlen_k: int, + real_max_k_seqlen: int, + num_split: int, + softmax_scale: float, + s_aux: torch.Tensor = None, + block_size: int = 64, + use_per_kv_head_sparse_index: bool = False, + tl_kernel=None, + block_table: torch.Tensor = None, +): + num_tokens, q_h, head_size = Q.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = K.size(1) + + assert Q.dim() == K.dim() == 3 + assert Q.size(2) == K.size(2) + assert cu_seqlens_k.dim() == 1 + assert head_size in {64, 128, 256} + assert Q.is_contiguous() + assert K.is_contiguous() + assert V.is_contiguous() + + gqa_group_size = q_h // k_h + + O_tl = torch.zeros_like(Q) + S_tl = torch.zeros((batch, q_h, math.ceil(real_max_k_seqlen / block_size)), dtype=Q.dtype, device=Q.device) + O_tl, S_tl = tl_kernel(Q, K, V, cu_seqlens_k, s_aux, block_table) + + if use_per_kv_head_sparse_index: + S_tl = torch.max_pool2d(S_tl, kernel_size=(gqa_group_size, 1), stride=(gqa_group_size, 1)) + else: + S_tl = torch.max_pool2d(S_tl, kernel_size=(q_h, 1), stride=(q_h, 1)) + + return O_tl, S_tl + + +def test_varlen_decode_main(args): + """Test decode kernel with variable sequence lengths""" + batch_size = args.batch_size + q_heads = args.q_heads + kv_heads = args.kv_heads + max_k_seqlen = args.k_seqlen # Use as max sequence length + real_max_k_seqlen = args.k_seqlen + head_size = args.head_size + block_size = args.block_size + page_block_size = args.page_block_size + dtype = torch.bfloat16 if args.dtype == T.bfloat16 else torch.float16 + + print(f"Testing decode kernel with variable sequence lengths (max_k_seqlen={max_k_seqlen})") + + # Generate sink values if needed + sink = None + if args.test_sink: + sink = torch.randn(q_heads, device="cuda", dtype=torch.float32) * 0.1 # Small sink values + print(f"Using sink attention with sink values: {sink}") + + # Generate variable length k sequences + k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,)) + print(f"k_seqlens: {k_seqlens}") + + # Generate cumulative sequence lengths for k + cu_seqlens_k = torch.zeros(batch_size + 1, device="cuda", dtype=torch.int32) + total_k_tokens = 0 + for i in range(batch_size): + cu_seqlens_k[i] = total_k_tokens + total_k_tokens += k_seqlens[i] + cu_seqlens_k[batch_size] = total_k_tokens + + print(f"cu_seqlens_k: {cu_seqlens_k}") + + # Generate tensors - Q is [batch_size, q_heads, head_size] for decode + q_decode = torch.randn(batch_size, q_heads, head_size, device="cuda", dtype=dtype) + k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + + softmax_scale = 1.0 / math.sqrt(head_size) + max_seqlen_k = int(k_seqlens.max()) + + print(f"Actual max_seqlen_k: {max_seqlen_k}") + print(f"q_decode shape: {q_decode.shape}") + print(f"k_varlen shape: {k_varlen.shape}") + print(f"v_varlen shape: {v_varlen.shape}") + + num_tokens, q_h, head_size = q_decode.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = k_varlen.size(1) + tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size, args.test_sink, page_block_size) + + block_table = torch.zeros(batch, math.ceil(real_max_k_seqlen / page_block_size), device="cuda", dtype=torch.int32) + block_cnt = 0 + for i in range(batch): + cur_seqlen = cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item() + for j in range(math.ceil(cur_seqlen / page_block_size)): + block_table[i, j] = block_cnt + block_cnt += 1 + block_cnt = 0 + + # Test our decode kernel + O_triton, S_triton = flash_attn_with_attn_pool_decode( + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + real_max_k_seqlen, + args.num_split, + softmax_scale, + s_aux=sink, + block_size=block_size, + ) + O_tilelang, S_tilelang = flash_attn_with_attn_pool_decode_tilelang( + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + real_max_k_seqlen, + args.num_split, + softmax_scale, + s_aux=sink, + block_size=block_size, + tl_kernel=tl_kernel, + block_table=block_table, + ) + for i in range(batch_size): + S_tilelang[i, :, math.ceil((cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item()) / block_size) :] = 0 + + # Create torch reference - pad tensors for comparison + k_padded_list = [] + v_padded_list = [] + + for i in range(batch_size): + actual_k_len = k_seqlens[i] + + # Extract and pad k, v for this batch + k_start = cu_seqlens_k[i] + k_end = cu_seqlens_k[i + 1] + + # Pad to max_seqlen_k + k_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device="cuda", dtype=dtype) + v_padded = torch.zeros(max_seqlen_k, kv_heads, head_size, device="cuda", dtype=dtype) + + k_padded[:actual_k_len] = k_varlen[k_start:k_end] + v_padded[:actual_k_len] = v_varlen[k_start:k_end] + + k_padded_list.append(k_padded) + v_padded_list.append(v_padded) + + # Stack to create batched tensors [b, max_seqlen, kv_heads, head_size] + k_padded_batched = torch.stack(k_padded_list, dim=0).transpose(1, 2) # [b, kv_heads, max_seqlen, head_size] + v_padded_batched = torch.stack(v_padded_list, dim=0).transpose(1, 2) # [b, kv_heads, max_seqlen, head_size] + + # Expand q to match kv heads: [b, q_heads, 1, head_size] + q_expanded = q_decode.unsqueeze(2) # [b, q_heads, 1, head_size] + + print(f"q_expanded shape: {q_expanded.shape}") + print(f"k_padded_batched shape: {k_padded_batched.shape}") + print(f"v_padded_batched shape: {v_padded_batched.shape}") + + # Compute torch reference + k_repeat = repeat_kv(k_padded_batched, q_heads // kv_heads) # [b, q_heads, max_seqlen, head_size] + v_repeat = repeat_kv(v_padded_batched, q_heads // kv_heads) # [b, q_heads, max_seqlen, head_size] + + if sink is None: + # Standard attention computation: [b, q_heads, 1, head_size] @ [b, q_heads, head_size, max_seqlen] + attn_score = torch.matmul(q_expanded, k_repeat.transpose(-2, -1)) * softmax_scale # [b, q_heads, 1, max_seqlen] + + # Apply sequence length masking + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_score[i, :, :, actual_k_len:] = float("-inf") + + attn_weights = attn_score.softmax(dim=-1) # [b, q_heads, 1, max_seqlen] + + # Mask out invalid positions + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_weights[i, :, :, actual_k_len:] = 0.0 + + # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size] + O_torch = torch.matmul(attn_weights, v_repeat) # [b, q_heads, 1, head_size] + else: + # s_aux attention + logits = torch.matmul(q_expanded, k_repeat.transpose(-2, -1)) * softmax_scale # [b, q_heads, 1, max_seqlen] + + # Apply sequence length masking + for i in range(batch_size): + actual_k_len = k_seqlens[i] + logits[i, :, :, actual_k_len:] = float("-inf") + + sink_expanded = sink.view(1, q_heads, 1, 1) # [1, q_heads, 1, 1] + logits_max = torch.max(logits, dim=-1, keepdim=True).values + logits_or_sinks_max = torch.maximum(logits_max, sink_expanded) + sinks = torch.exp(sink_expanded - logits_or_sinks_max) + unnormalized_scores = torch.exp(logits - logits_or_sinks_max) + normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks + attn_weights = unnormalized_scores / normalizer + + # Mask out invalid positions + for i in range(batch_size): + actual_k_len = k_seqlens[i] + attn_weights[i, :, :, actual_k_len:] = 0.0 + + # Compute output: [b, q_heads, 1, max_seqlen] @ [b, q_heads, max_seqlen, head_size] + O_torch = torch.matmul(attn_weights.to(v_repeat.dtype), v_repeat) # [b, q_heads, 1, head_size] + + O_torch = O_torch.squeeze(2) # [b, q_heads, head_size] + + # Compute attention score pooling for S + attn_score_pooled = torch.max_pool2d( + attn_weights.squeeze(2), # [b, q_heads, max_seqlen] + kernel_size=(q_heads, block_size), + stride=(q_heads, block_size), + ceil_mode=True, + ).to(dtype=torch.float16) # [b, 1, ceil(max_seqlen/block_size)] + + print(f"O_triton shape: {O_triton.shape}") + print(f"O_tilelang shape: {O_tilelang.shape}") + print(f"O_torch shape: {O_torch.shape}") + print(f"S_triton shape: {S_triton.shape}") + print(f"S_tilelang shape: {S_tilelang.shape}") + print(f"attn_score_pooled shape: {attn_score_pooled.shape}") + + # Compare results + max_diff_o = torch.max(torch.abs(O_triton - O_torch)) + max_diff_o_tl = torch.max(torch.abs(O_tilelang - O_torch)) + print(f"Max difference in O: {max_diff_o.item()}") + print(f"Max difference in O_tilelang: {max_diff_o_tl.item()}") + + max_diff_s = torch.max(torch.abs(S_triton - attn_score_pooled)) + max_diff_s_tl = torch.max(torch.abs(S_tilelang[:, :, : math.ceil(max_seqlen_k / block_size)] - attn_score_pooled)) + print(f"Max difference in S: {max_diff_s.item()}") + print(f"Max difference in S_tilelang: {max_diff_s_tl.item()}") + + assert torch.allclose(O_triton, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o.item()}" + assert torch.allclose(S_triton, attn_score_pooled, atol=1e-2, rtol=1e-2), f"Score mismatch: {max_diff_s.item()}" + assert torch.allclose(O_tilelang, O_torch, atol=1e-2, rtol=1e-2), f"Output mismatch: {max_diff_o_tl.item()}" + assert torch.allclose(S_tilelang[:, :, : math.ceil(max_seqlen_k / block_size)], attn_score_pooled, atol=1e-2, rtol=1e-2), ( + f"Score mismatch: {max_diff_s_tl.item()}" + ) + + print("✅ All tests passed!") + + +def speed_benchmark_decode_comparison(args): + """Speed benchmark for decode kernel""" + batch_size = args.batch_size + q_heads = args.q_heads + kv_heads = args.kv_heads + max_k_seqlen = args.k_seqlen + real_max_k_seqlen = args.k_seqlen + head_size = args.head_size + block_size = args.block_size + page_block_size = args.page_block_size + dtype = torch.bfloat16 if args.dtype == T.bfloat16 else torch.float16 + + print("\n=== Decode Speed Benchmark Comparison ===") + print("Configuration:") + print(f" Batch size: {batch_size}") + print(f" Q heads: {q_heads}, KV heads: {kv_heads}") + print(f" Max K sequence length: {max_k_seqlen}") + print(f" Head size: {head_size}") + print(f" Block size: {block_size}") + print(f" Data type: {dtype}") + print(f" Variable lengths: {args.test_varlen}") + print(f" s_aux attention: {args.test_sink}") + print() + + # Generate input data + if args.test_varlen: + k_seqlens = torch.randint(max_k_seqlen // 4, max_k_seqlen + 1, size=(batch_size,)) + else: + k_seqlens = torch.full((batch_size,), max_k_seqlen, dtype=int) + + # Generate cumulative sequence lengths for k + cu_seqlens_k = torch.zeros(batch_size + 1, device="cuda", dtype=torch.int32) + total_k_tokens = 0 + for i in range(batch_size): + cu_seqlens_k[i] = total_k_tokens + total_k_tokens += k_seqlens[i] + cu_seqlens_k[batch_size] = total_k_tokens + + # Generate tensors + q_decode = torch.randn(batch_size, q_heads, head_size, device="cuda", dtype=dtype) + k_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + v_varlen = torch.randn(total_k_tokens, kv_heads, head_size, device="cuda", dtype=dtype) + + softmax_scale = 1.0 / math.sqrt(head_size) + max_seqlen_k = int(k_seqlens.max()) + + # Generate sink values if needed + sink = None + if args.test_sink: + sink = torch.randn(q_heads, device="cuda", dtype=torch.float32) * 0.1 # Small sink values + print(" Using sink attention with sink values") + + print("Setup complete:") + print(f" Total K tokens: {total_k_tokens}") + print(f" Actual max K seq len: {max_seqlen_k}") + if args.test_varlen: + print(f" K sequence lengths: {k_seqlens.tolist()}") + + # Warmup + num_tokens, q_h, head_size = q_decode.shape + batch = cu_seqlens_k.size(0) - 1 + k_h = k_varlen.size(1) + tl_kernel = flashattn(batch, q_h, k_h, args.k_seqlen, cu_seqlens_k[-1].item(), head_size, args.test_sink, page_block_size) + + block_table = torch.zeros(batch, math.ceil(real_max_k_seqlen / page_block_size), device="cuda", dtype=torch.int32) + block_cnt = 0 + for i in range(batch): + cur_seqlen = cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item() + for j in range(math.ceil(cur_seqlen / page_block_size)): + block_table[i, j] = block_cnt + block_cnt += 1 + block_cnt = 0 + + # Benchmark + print("⚡ Benchmarking Tilelang kernel (100 iterations)...") + tilelang_time = do_bench( + flash_attn_with_attn_pool_decode_tilelang, + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + args.k_seqlen, + 1, + softmax_scale, + sink, + block_size, + False, + tl_kernel, + block_table, + ) + print(f"Average decode kernel time Tilelang: {tilelang_time:.3f} ms") + + # Benchmark + print("⚡ Benchmarking Triton kernel (100 iterations)...") + triton_time = do_bench( + flash_attn_with_attn_pool_decode, + q_decode, + k_varlen, + v_varlen, + cu_seqlens_k, + max_seqlen_k, + args.k_seqlen, + 1, + softmax_scale, + sink, + block_size, + ) + print(f"Average decode kernel time Triton: {triton_time:.3f} ms") + print(f"Speedup: {(triton_time / tilelang_time):.3f}") + + +def main(): + args = argparse.Namespace( + batch_size=1, + q_heads=32, + kv_heads=8, + k_seqlen=8192, + head_size=128, + block_size=128, + dtype=T.float16, + ) + args.test_sink = True + args.test_varlen = True + args.dtype = T.float16 + args.num_split = 1 + args.page_block_size = 128 + test_varlen_decode_main(args) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flash Attention Decode with Attention Pooling") + parser.add_argument("--batch_size", type=int, default=1, help="Batch size") + parser.add_argument("--q_heads", type=int, default=32, help="Number of query heads") + parser.add_argument("--kv_heads", type=int, default=8, help="Number of key-value heads") + parser.add_argument("--k_seqlen", type=int, default=8192, help="Key sequence length") + parser.add_argument("--head_size", type=int, default=128, choices=[64, 128, 256], help="Head dimension") + parser.add_argument("--block_size", type=int, default=128, help="Block size for computation") + parser.add_argument("--dtype", type=str, default=T.bfloat16, choices=[T.float16, T.bfloat16], help="Data type") + parser.add_argument("--test_varlen", action="store_true", help="Test with truly variable sequence lengths") + parser.add_argument("--test_sink", action="store_true", help="Test with sink attention mechanism") + parser.add_argument("--benchmark", action="store_true", help="Run speed benchmark") + parser.add_argument("--num_split", type=int, default=1, choices=[1, 16], help="Number of splits") + parser.add_argument("--page_block_size", type=int, default=128, help="Page block size") + args = parser.parse_args() + args.test_sink = True + args.test_varlen = True + args.dtype = T.float16 + args.num_split = 1 + + if args.benchmark: + speed_benchmark_decode_comparison(args) + else: + test_varlen_decode_main(args) diff --git a/examples/flash_decoding/example_mha_inference.py b/examples/flash_decoding/example_mha_inference.py index b4285a64fb..24a90c57b5 100644 --- a/examples/flash_decoding/example_mha_inference.py +++ b/examples/flash_decoding/example_mha_inference.py @@ -10,102 +10,24 @@ @tilelang.jit(out_idx=[5]) def flashattn(batch, heads, seqlen_q, seqlen_kv, dim, is_causal, block_M, block_N): - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) shape_q = [batch, seqlen_q, heads, dim] shape_kv = [batch, seqlen_kv, heads, dim] part_shape = [batch, seqlen_q, heads, num_split, dim] - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 - @T.macro - def MMA0( + @T.prim_func + def flashattn_mha_inference( + Q: T.Tensor(shape_q, dtype), K: T.Tensor(shape_kv, dtype), - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - k: T.int32, - mid: T.int32, - hid: T.int32, - bid: T.int32, - sid: T.int32, - ): - T.copy( - K[bid, (seqlen_kv // num_split) * sid + k * block_N:(seqlen_kv // num_split) * sid + - (k + 1) * block_N, hid, :], K_shared) - # TODO: Handle causal split case - if is_causal: - for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(mid * block_M + i >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) - else: - T.clear(acc_s) - T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def MMA1( V: T.Tensor(shape_kv, dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - k: T.int32, - hid: T.int32, - bid: T.int32, - sid: T.int32, - ): - T.copy( - V[bid, (seqlen_kv // num_split) * sid + k * block_N:(seqlen_kv // num_split) * sid + - (k + 1) * block_N, hid, :], V_shared) - T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), + glse: T.Tensor([batch, heads, num_split, seqlen_q], dtype), + Output_partial: T.Tensor(part_shape, dtype), # [batch, seqlen_q, heads, num_split, dim] + Output: T.Tensor(shape_q, dtype), ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - - @T.macro - def flash_attn_split( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_kv, dtype), - V: T.Tensor(shape_kv, dtype), - glse: T.Tensor([batch, heads, num_split, seqlen_q], dtype), - Output_partial: T.Tensor(part_shape, dtype), - ): - with T.Kernel( - T.ceildiv(seqlen_q, block_M), heads * batch, num_split, - threads=128) as (bx, by, bz): + # split + with T.Kernel(T.ceildiv(seqlen_q, block_M), heads * batch, num_split, threads=128) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([block_N, dim], dtype) V_shared = T.alloc_shared([block_N, dim], dtype) @@ -126,43 +48,73 @@ def flash_attn_split( # NOTE(wt): tma barrier has some problems with padded dimensions (seq_q here) currently # disable relevant tma copy and use SIMT as fallback for now - T.copy(Q[bid, mid * block_M:(mid + 1) * block_M, hid, :], Q_shared, disable_tma=True) + T.copy(Q[bid, mid * block_M : (mid + 1) * block_M, hid, :], Q_shared, disable_tma=True) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) # TODO: Handle causal split case loop_range = ( - T.min(T.ceildiv(seqlen_kv, block_N), T.ceildiv( - (mid + 1) * block_M, block_N)) if is_causal else T.ceildiv( - (seqlen_kv // num_split), block_N)) + T.min(T.ceildiv(seqlen_kv, block_N), T.ceildiv((mid + 1) * block_M, block_N)) + if is_causal + else T.ceildiv((seqlen_kv // num_split), block_N) + ) for k in T.Pipelined(loop_range, num_stages=2): - MMA0(K, Q_shared, K_shared, acc_s, k, mid, hid, bid, sid) - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, - logsum) - Rescale(acc_o, scores_scale) - MMA1(V, V_shared, acc_s_cast, acc_o, k, hid, bid, sid) + T.copy( + K[bid, (seqlen_kv // num_split) * sid + k * block_N : (seqlen_kv // num_split) * sid + (k + 1) * block_N, hid, :], + K_shared, + ) + # TODO: Handle causal split case + if is_causal: + for i, j in T.Parallel(block_M, block_N): + acc_s[i, j] = T.if_then_else(mid * block_M + i >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) + else: + T.clear(acc_s) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # in the first ceil_div(kBlockM, kBlockN) steps. + # for i in T.Parallel(block_M): + # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy( + V[bid, (seqlen_kv // num_split) * sid + k * block_N : (seqlen_kv // num_split) * sid + (k + 1) * block_N, hid, :], + V_shared, + ) + T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] for i in T.Parallel(block_M): logsum[i] = T.log2(logsum[i]) + scores_max[i] * scale - T.copy(logsum, glse[bid, hid, sid, mid * block_M:(mid + 1) * block_M]) + T.copy(logsum, glse[bid, hid, sid, mid * block_M : (mid + 1) * block_M]) T.copy(acc_o, O_shared) - T.copy( - O_shared, - Output_partial[bid, mid * block_M:(mid + 1) * block_M, hid, sid, :], - disable_tma=True) - - @T.macro - def combine( - glse: T.Tensor([batch, heads, num_split, seqlen_q], dtype), - Output_partial: T.Tensor(part_shape, dtype), - Output: T.Tensor(shape_q, dtype), - ): + T.copy(O_shared, Output_partial[bid, mid * block_M : (mid + 1) * block_M, hid, sid, :], disable_tma=True) + + # combine with T.Kernel(T.ceildiv(seqlen_q, block_M), heads, batch, threads=128) as (bx, by, bz): po_local = T.alloc_fragment([block_M, dim], dtype) - po_shared = T.alloc_shared([block_M, dim], dtype) o_accum_local = T.alloc_fragment([block_M, dim], accum_dtype) o_shared = T.alloc_shared([block_M, dim], dtype) lse_local = T.alloc_fragment([num_split, block_M], dtype) @@ -171,20 +123,17 @@ def combine( lse_max_local = T.alloc_fragment([block_M], accum_dtype) scale_local = T.alloc_fragment([block_M], accum_dtype) - T.annotate_layout({ - o_accum_local: T.Fragment(o_accum_local.shape, forward_thread_fn=lambda i, j: i), - o_shared: tilelang.layout.make_swizzled_layout(o_shared), - po_shared: tilelang.layout.make_swizzled_layout(po_shared), - }) - T.clear(lse_logsum_local) T.clear(o_accum_local) - T.copy(glse[ - bz, - by, - :, - bx * block_M:(bx + 1) * block_M, - ], lse_local) + T.copy( + glse[ + bz, + by, + :, + bx * block_M : (bx + 1) * block_M, + ], + lse_local, + ) T.reduce_max(lse_local, lse_max_local, dim=0, clear=False) for k in T.Pipelined(num_split): T.copy(lse_local[k, :], lse_local_split) @@ -193,11 +142,7 @@ def combine( for i in T.Parallel(block_M): lse_logsum_local[i] = T.log2(lse_logsum_local[i]) + lse_max_local[i] for k in T.Pipelined(num_split, num_stages=2): - T.copy( - Output_partial[bz, bx * block_M:(bx + 1) * block_M, by, k, :], - po_shared, - disable_tma=True) - T.copy(po_shared, po_local) + T.copy(Output_partial[bz, bx * block_M : (bx + 1) * block_M, by, k, :], po_local) for i in T.Parallel(block_M): lse_local_split[i] = lse_local[k, i] for i in T.Parallel(block_M): @@ -205,19 +150,7 @@ def combine( for i, j in T.Parallel(block_M, dim): o_accum_local[i, j] += po_local[i, j] * scale_local[i] T.copy(o_accum_local, o_shared) - T.copy(o_shared, Output[bz, bx * block_M:(bx + 1) * block_M, by, :], disable_tma=True) - - @T.prim_func - def flashattn_mha_inference( - Q: T.Tensor(shape_q, dtype), - K: T.Tensor(shape_kv, dtype), - V: T.Tensor(shape_kv, dtype), - glse: T.Tensor([batch, heads, num_split, seqlen_q], dtype), - Output_partial: T.Tensor(part_shape, dtype), # [batch, seqlen_q, heads, num_split, dim] - Output: T.Tensor(shape_q, dtype), - ): - flash_attn_split(Q, K, V, glse, Output_partial) - combine(glse, Output_partial, Output) + T.copy(o_shared, Output[bz, bx * block_M : (bx + 1) * block_M, by, :], disable_tma=True) return flashattn_mha_inference @@ -225,10 +158,10 @@ def flashattn_mha_inference( def ref_program(Q, K, V, glse, Output_partial, causal): assert causal is False dim = Q.size(-1) - scores = torch.einsum('bqhd,bkhd->bhqk', Q, K) + scores = torch.einsum("bqhd,bkhd->bhqk", Q, K) scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) attention_weights = F.softmax(scores, dim=-1) - output = torch.einsum('bhqk,bkhd->bqhd', attention_weights, V) + output = torch.einsum("bhqk,bkhd->bqhd", attention_weights, V) return output @@ -256,7 +189,7 @@ def flash_split_ref(Q, K, V, causal): block_N = 128 seqlen_kv = K.size(1) - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) acc_s = torch.empty((batch, nheads, block_M, block_N), device="cuda", dtype=torch.float) acc_s_cast = torch.empty((batch, nheads, block_M, block_N), device="cuda", dtype=torch.float16) acc_o = torch.empty((batch, block_M, nheads, dim), device="cuda", dtype=torch.float) @@ -273,14 +206,15 @@ def flash_split_ref(Q, K, V, causal): for ks in range(num_split): acc_o.fill_(0) logsum.fill_(0) - scores_max.fill_(float('-inf')) - scores_max_prev.fill_(float('-inf')) + scores_max.fill_(float("-inf")) + scores_max_prev.fill_(float("-inf")) for i in range(int((seqlen_kv // num_split) / block_N)): acc_s.fill_(0) - acc_s = torch.einsum('bqhd,bkhd->bhqk', Q_, - K[:, (seqlen_kv // num_split) * ks + - i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) # [batch, seqlen, nheads, block_N] + acc_s = torch.einsum( + "bqhd,bkhd->bhqk", + Q_, + K[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) # [batch, seqlen, nheads, block_N] scores_max_prev = scores_max scores_max = acc_s.max(dim=-1, keepdim=False).values # [blockM] scores_scale = torch.exp2(scores_max_prev - scores_max) @@ -288,9 +222,10 @@ def flash_split_ref(Q, K, V, causal): acc_s = torch.exp2(acc_s - scores_max[:, :, :, None]) acc_s_cast = acc_s.to(torch.float16) acc_o += torch.einsum( - 'bhqk,bkhd->bqhd', acc_s_cast, - V[:, (seqlen_kv // num_split) * ks + i * block_N:(seqlen_kv // num_split) * ks + - (i + 1) * block_N, :, :]) + "bhqk,bkhd->bqhd", + acc_s_cast, + V[:, (seqlen_kv // num_split) * ks + i * block_N : (seqlen_kv // num_split) * ks + (i + 1) * block_N, :, :], + ) scores_sum = acc_s.sum(dim=-1, keepdim=False) logsum = logsum * scores_scale + scores_sum acc_o /= logsum[:, :, :, None].transpose(1, 2) @@ -298,13 +233,10 @@ def flash_split_ref(Q, K, V, causal): gacc_o[ks, :, :, :, :] = acc_o glogsum[ks, :, :, :] = logsum - return glogsum.to(torch.float16).permute(1, 2, 0, - 3), gacc_o.to(torch.float16).permute(1, 2, 3, 0, 4) + return glogsum.to(torch.float16).permute(1, 2, 0, 3), gacc_o.to(torch.float16).permute(1, 2, 3, 0, 4) -def main(): - BATCH, H, Q_CTX, KV_CTX, D_HEAD = 1, 32, 128, 8192, 128 - causal = False +def main(BATCH=1, H=32, Q_CTX=128, KV_CTX=8192, D_HEAD=128, causal=False): flops_per_matmul = 2.0 * BATCH * H * Q_CTX * KV_CTX * D_HEAD total_flops = 2 * flops_per_matmul if causal: @@ -325,5 +257,13 @@ def main(): print("{:.2f} TFlops".format(total_flops / latency * 1e-9)) +def run_regression_perf(BATCH=1, H=32, Q_CTX=128, KV_CTX=8192, D_HEAD=128, causal=False): + BLOCK_M = 128 + BLOCK_N = 64 + kernel = flashattn(BATCH, H, Q_CTX, KV_CTX, D_HEAD, causal, BLOCK_M, BLOCK_N) + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/flash_decoding/regression_example_flash_decoding.py b/examples/flash_decoding/regression_example_flash_decoding.py new file mode 100644 index 0000000000..476bceb34c --- /dev/null +++ b/examples/flash_decoding/regression_example_flash_decoding.py @@ -0,0 +1,17 @@ +import tilelang.testing +import example_gqa_decode +import example_mha_inference + + +def regression_example_gqa_decode(): + tilelang.testing.process_func(example_gqa_decode.run_regression_perf) + + +def regression_example_mha_inference(): + tilelang.testing.process_func( + example_mha_inference.run_regression_perf, BATCH=1, H=32, Q_CTX=128, KV_CTX=2048, D_HEAD=128, causal=False + ) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/flash_decoding/test_example_flash_decoding.py b/examples/flash_decoding/test_example_flash_decoding.py index a6ec1c68e1..a02a920974 100644 --- a/examples/flash_decoding/test_example_flash_decoding.py +++ b/examples/flash_decoding/test_example_flash_decoding.py @@ -2,6 +2,8 @@ import example_gqa_decode import example_mha_inference +import example_gqa_decode_varlen_logits +import example_gqa_decode_varlen_logits_paged # TODO(lei): fix the correctness of gqa decode on sm90 @@ -12,7 +14,15 @@ def test_example_example_gqa_decode(): def test_example_example_mha_inference(): - example_mha_inference.main() + example_mha_inference.main(BATCH=1, H=32, Q_CTX=128, KV_CTX=2048, D_HEAD=128, causal=False) + + +def test_example_example_gqa_decode_varlen_logits(): + example_gqa_decode_varlen_logits.main() + + +def test_example_example_gqa_decode_varlen_logits_paged(): + example_gqa_decode_varlen_logits_paged.main() if __name__ == "__main__": diff --git a/examples/fusedmoe/example_fusedmoe_tilelang.py b/examples/fusedmoe/example_fusedmoe_tilelang.py index a8d6849659..5c236dd802 100644 --- a/examples/fusedmoe/example_fusedmoe_tilelang.py +++ b/examples/fusedmoe/example_fusedmoe_tilelang.py @@ -9,17 +9,18 @@ @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) -def moe_forward_tilelang_shared(d_hidden, - d_expert, - n_shared_experts, - dtype, - num_tokens, - block_token=128, - block_dhidden=128, - block_dexpert=128, - threads=256, - num_stages=1): - +def moe_forward_tilelang_shared( + d_hidden, + d_expert, + n_shared_experts, + dtype, + num_tokens, + block_token=128, + block_dhidden=128, + block_dexpert=128, + threads=256, + num_stages=1, +): scale = 1.44269504 # log2(e) # Parameters @@ -32,21 +33,19 @@ def moe_forward_tilelang_shared(d_hidden, shared_W_up_shape = (dexpert, dhidden) shared_W_down_shape = (dhidden, dexpert) - accum_type = "float32" + accum_type = T.float32 @T.prim_func def kernel_shared( - input: T.Tensor(input_shape, dtype), # type: ignore - shared_W_gate: T.Tensor(shared_W_gate_shape, dtype), # type: ignore - shared_W_up: T.Tensor(shared_W_up_shape, dtype), # type: ignore - shared_W_down: T.Tensor(shared_W_down_shape, dtype), # type: ignore - up_logits: T.Tensor((num_tokens, dexpert), dtype), # type: ignore - output: T.Tensor(input_shape, dtype), # type: ignore + input: T.Tensor(input_shape, dtype), # type: ignore + shared_W_gate: T.Tensor(shared_W_gate_shape, dtype), # type: ignore + shared_W_up: T.Tensor(shared_W_up_shape, dtype), # type: ignore + shared_W_down: T.Tensor(shared_W_down_shape, dtype), # type: ignore + up_logits: T.Tensor((num_tokens, dexpert), dtype), # type: ignore + output: T.Tensor(input_shape, dtype), # type: ignore ): # Step 1: Compute gate and up logits - with T.Kernel( - T.ceildiv(num_tokens, block_token), T.ceildiv(dexpert, block_dexpert), - threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(num_tokens, block_token), T.ceildiv(dexpert, block_dexpert), threads=threads) as (bx, by): # Split the block to shared experts and routed experts input_shared = T.alloc_fragment((block_token, block_dhidden), dtype=dtype) W_gate_shared = T.alloc_shared((block_dexpert, block_dhidden), dtype=dtype) @@ -70,16 +69,13 @@ def kernel_shared( # Fuse with SiLU and element-wise product for i, j in T.Parallel(block_token, block_dexpert): - gate_logits_local[i, j] = gate_logits_local[i, j] * ( - 1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale))) + gate_logits_local[i, j] = gate_logits_local[i, j] * (1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale))) up_logits_local[i, j] = up_logits_local[i, j] * gate_logits_local[i, j] T.copy(up_logits_local, up_logits[bx * block_token, by * block_dexpert]) # Step 2: Compute down logits - with T.Kernel( - T.ceildiv(num_tokens, block_token), T.ceildiv(dhidden, block_dhidden), - threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(num_tokens, block_token), T.ceildiv(dhidden, block_dhidden), threads=threads) as (bx, by): up_logits_shared = T.alloc_fragment((block_token, block_dexpert), dtype=dtype) W_down_shared = T.alloc_shared((block_dhidden, block_dexpert), dtype=dtype) output_local = T.alloc_fragment((block_token, block_dhidden), dtype=accum_type) @@ -98,20 +94,21 @@ def kernel_shared( @tilelang.jit(pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) -def moe_forward_tilelang_routed(d_hidden, - d_expert, - n_routed_experts, - dtype, - group_sum, - group_count, - block_token=128, - block_dhidden=128, - block_dexpert=128, - threads=256, - num_stages=1, - k_pack=1, - coalesced_width=None): - +def moe_forward_tilelang_routed( + d_hidden, + d_expert, + n_routed_experts, + dtype, + group_sum, + group_count, + block_token=128, + block_dhidden=128, + block_dexpert=128, + threads=256, + num_stages=1, + k_pack=1, + coalesced_width=None, +): scale = 1.44269504 # log2(e) # Parameters @@ -124,7 +121,7 @@ def moe_forward_tilelang_routed(d_hidden, # group_count = len(group_sizes_list) # M = sum([(group_size + block_token - 1) // block_token for group_size in group_sizes_list]) M = math.ceil(group_sum / block_token) + group_count - accum_dtype = "float32" + accum_dtype = T.float32 # Tensors: Note that input shape is reshape to (bs * seq_len * n_experts_per_token, dhidden) for grouped gemm input_shape = (group_sum, dhidden) @@ -132,22 +129,22 @@ def moe_forward_tilelang_routed(d_hidden, routed_expert_gate_shape = (n_routed_experts, dexpert, dhidden) routed_expert_up_shape = (n_routed_experts, dexpert, dhidden) routed_expert_down_shape = (n_routed_experts, dhidden, dexpert) - routed_expert_weights_shape = (group_sum) - group_sizes_shape = (n_routed_experts) + routed_expert_weights_shape = group_sum + group_sizes_shape = n_routed_experts @T.prim_func def kernel( - input: T.Tensor(input_shape, dtype), # type: ignore - routed_expert_gate: T.Tensor(routed_expert_gate_shape, dtype), # type: ignore - routed_expert_up: T.Tensor(routed_expert_up_shape, dtype), # type: ignore - routed_expert_down: T.Tensor(routed_expert_down_shape, dtype), # type: ignore - routed_expert_weights: T.Tensor(routed_expert_weights_shape, dtype), # type: ignore - group_sizes: T.Tensor(group_sizes_shape, "int32"), # type: ignore - group_offsets: T.Tensor(group_sizes_shape, "int32"), # type: ignore - group_padded_offsets: T.Tensor(group_sizes_shape, "int32"), # type: ignore - group_idx_for_bx: T.Tensor((M,), "int32"), # type: ignore - up_logits: T.Tensor(intermediate_shape, dtype), # type: ignore - output: T.Tensor(input_shape, dtype), # type: ignore + input: T.Tensor(input_shape, dtype), # type: ignore + routed_expert_gate: T.Tensor(routed_expert_gate_shape, dtype), # type: ignore + routed_expert_up: T.Tensor(routed_expert_up_shape, dtype), # type: ignore + routed_expert_down: T.Tensor(routed_expert_down_shape, dtype), # type: ignore + routed_expert_weights: T.Tensor(routed_expert_weights_shape, dtype), # type: ignore + group_sizes: T.Tensor(group_sizes_shape, T.int32), # type: ignore + group_offsets: T.Tensor(group_sizes_shape, T.int32), # type: ignore + group_padded_offsets: T.Tensor(group_sizes_shape, T.int32), # type: ignore + group_idx_for_bx: T.Tensor((M,), T.int32), # type: ignore + up_logits: T.Tensor(intermediate_shape, dtype), # type: ignore + output: T.Tensor(input_shape, dtype), # type: ignore ): # Step 1: Compute gate and up logits with T.Kernel(M, T.ceildiv(dexpert, block_dexpert), threads=threads) as (bx, by): @@ -158,58 +155,44 @@ def kernel( gate_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_dtype) up_logits_local = T.alloc_fragment((block_token, block_dexpert), dtype=accum_dtype) - cur_group_idx = T.alloc_local([1], "int32") - cur_group_size = T.alloc_local([1], "int32") - T.use_swizzle(10, enable=True) m_start_padded = bx * block_token - cur_group_idx[0] = group_idx_for_bx[bx] + cur_group_idx = group_idx_for_bx[bx] - cur_group_size[0] = group_sizes[cur_group_idx[0]] - m_start = m_start_padded - group_padded_offsets[cur_group_idx[0]] + group_offsets[ - cur_group_idx[0]] - actual_rows = T.max( - 0, - T.min(block_token, cur_group_size[0] - - (m_start_padded - group_padded_offsets[cur_group_idx[0]]))) + cur_group_size = group_sizes[cur_group_idx] + m_start = m_start_padded - group_padded_offsets[cur_group_idx] + group_offsets[cur_group_idx] + actual_rows = T.max(0, T.min(block_token, cur_group_size - (m_start_padded - group_padded_offsets[cur_group_idx]))) T.clear(gate_logits_local) T.clear(up_logits_local) for k in T.Pipelined(T.ceildiv(dhidden, block_dhidden), num_stages=num_stages): T.copy( - input[m_start:m_start + block_token, k * block_dhidden:(k + 1) * block_dhidden], + input[m_start : m_start + block_token, k * block_dhidden : (k + 1) * block_dhidden], input_shared, - coalesced_width=coalesced_width) + coalesced_width=coalesced_width, + ) T.copy( - routed_expert_gate[cur_group_idx[0], - by * block_dexpert:(by + 1) * block_dexpert, - k * block_dhidden:(k + 1) * block_dhidden], + routed_expert_gate[ + cur_group_idx, by * block_dexpert : (by + 1) * block_dexpert, k * block_dhidden : (k + 1) * block_dhidden + ], routed_expert_gate_shared, - coalesced_width=coalesced_width) - T.gemm( - input_shared, - routed_expert_gate_shared, - gate_logits_local, - k_pack=k_pack, - transpose_B=True) + coalesced_width=coalesced_width, + ) + T.gemm(input_shared, routed_expert_gate_shared, gate_logits_local, k_pack=k_pack, transpose_B=True) T.copy( - routed_expert_up[cur_group_idx[0], by * block_dexpert:(by + 1) * block_dexpert, - k * block_dhidden:(k + 1) * block_dhidden], - routed_expert_up_shared, - coalesced_width=coalesced_width) - T.gemm( - input_shared, + routed_expert_up[ + cur_group_idx, by * block_dexpert : (by + 1) * block_dexpert, k * block_dhidden : (k + 1) * block_dhidden + ], routed_expert_up_shared, - up_logits_local, - k_pack=k_pack, - transpose_B=True) + coalesced_width=coalesced_width, + ) + T.gemm(input_shared, routed_expert_up_shared, up_logits_local, k_pack=k_pack, transpose_B=True) for i, j in T.Parallel(block_token, block_dexpert): - gate_logits_local[i, j] = gate_logits_local[i, j] * ( - 1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale))) + gate_logits_local[i, j] = gate_logits_local[i, j] * (1.0 / (1.0 + T.exp2(-gate_logits_local[i, j] * scale))) up_logits_local[i, j] = up_logits_local[i, j] * gate_logits_local[i, j] for i, j in T.Parallel(block_token, block_dexpert): @@ -222,60 +205,42 @@ def kernel( routed_expert_down_shared = T.alloc_shared((block_dhidden, block_dexpert), dtype=dtype) output_local = T.alloc_fragment((block_token, block_dhidden), dtype=accum_dtype) - cur_group_idx = T.alloc_local([1], "int32") - cur_group_size = T.alloc_local([1], "int32") - T.use_swizzle(10, enable=True) m_start_padded = bx * block_token - cur_group_idx[0] = group_idx_for_bx[bx] + cur_group_idx = group_idx_for_bx[bx] - cur_group_size[0] = group_sizes[cur_group_idx[0]] - m_start = m_start_padded - group_padded_offsets[cur_group_idx[0]] + group_offsets[ - cur_group_idx[0]] - actual_rows = T.max( - 0, - T.min(block_token, cur_group_size[0] - - (m_start_padded - group_padded_offsets[cur_group_idx[0]]))) + cur_group_size = group_sizes[cur_group_idx] + m_start = m_start_padded - group_padded_offsets[cur_group_idx] + group_offsets[cur_group_idx] + actual_rows = T.max(0, T.min(block_token, cur_group_size - (m_start_padded - group_padded_offsets[cur_group_idx]))) T.clear(output_local) for k in T.Pipelined(T.ceildiv(dexpert, block_dexpert), num_stages=num_stages): T.copy( - up_logits[m_start:m_start + block_token, - k * block_dexpert:(k + 1) * block_dexpert], + up_logits[m_start : m_start + block_token, k * block_dexpert : (k + 1) * block_dexpert], up_logits_shared, - coalesced_width=coalesced_width) + coalesced_width=coalesced_width, + ) T.copy( - routed_expert_down[cur_group_idx[0], - by * block_dhidden:(by + 1) * block_dhidden, - k * block_dexpert:(k + 1) * block_dexpert], - routed_expert_down_shared, - coalesced_width=coalesced_width) - T.gemm( - up_logits_shared, + routed_expert_down[ + cur_group_idx, by * block_dhidden : (by + 1) * block_dhidden, k * block_dexpert : (k + 1) * block_dexpert + ], routed_expert_down_shared, - output_local, - k_pack=k_pack, - transpose_B=True) + coalesced_width=coalesced_width, + ) + T.gemm(up_logits_shared, routed_expert_down_shared, output_local, k_pack=k_pack, transpose_B=True) for i, j in T.Parallel(block_token, block_dhidden): if i < actual_rows: - output[m_start + i, by * block_dhidden + - j] = output_local[i, j] * routed_expert_weights[m_start + i] + output[m_start + i, by * block_dhidden + j] = output_local[i, j] * routed_expert_weights[m_start + i] return kernel class Expert(nn.Module): - - def __init__(self, - config: Dict, - gate: torch.Tensor, - up: torch.Tensor, - down: torch.Tensor, - d_expert: Optional[int] = None): + def __init__(self, config: Dict, gate: torch.Tensor, up: torch.Tensor, down: torch.Tensor, d_expert: Optional[int] = None): super().__init__() self.config = config self.act_fn = nn.SiLU() @@ -294,14 +259,13 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class MoEGate(nn.Module): - def __init__(self, config: Dict, weights: Dict): super().__init__() self.top_k: int = config["n_experts_per_token"] self.num_experts: int = config["n_routed_experts"] self.d_hidden: int = config["d_hidden"] - self.W_g_weight = weights['router.weight'].t() + self.W_g_weight = weights["router.weight"].t() def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: logits = x @ self.W_g_weight @@ -312,76 +276,69 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: class MoE(nn.Module): - - def __init__(self, - config: Dict, - shared_kernel: tilelang.JITKernel, - routed_kernel: tilelang.JITKernel, - weights: Dict, - padding_M: int = 128): + def __init__( + self, config: Dict, shared_kernel: tilelang.JITKernel, routed_kernel: tilelang.JITKernel, weights: Dict, padding_M: int = 128 + ): super().__init__() self.config = config self.shared_kernel = shared_kernel self.routed_kernel = routed_kernel self.padding_M = padding_M - self.experts = nn.ModuleList([ - Expert( - config, - gate=weights[f'experts.{i}.0.weight'], - up=weights[f'experts.{i}.1.weight'], - down=weights[f'experts.{i}.2.weight']) for i in range(config["n_routed_experts"]) - ]) + self.experts = nn.ModuleList( + [ + Expert( + config, + gate=weights[f"experts.{i}.0.weight"], + up=weights[f"experts.{i}.1.weight"], + down=weights[f"experts.{i}.2.weight"], + ) + for i in range(config["n_routed_experts"]) + ] + ) self.device = torch.device("cuda") self.gating_network = MoEGate(config, weights).to(self.device) shared_expert_dim = config["d_expert"] * config["n_shared_experts"] self.shared_expert = Expert( config=config, - gate=weights['shared_experts.0.weight'], - up=weights['shared_experts.1.weight'], - down=weights['shared_experts.2.weight'], - d_expert=shared_expert_dim).to(self.device) + gate=weights["shared_experts.0.weight"], + up=weights["shared_experts.1.weight"], + down=weights["shared_experts.2.weight"], + d_expert=shared_expert_dim, + ).to(self.device) self.expert_cache = torch.zeros( - (config["batch_size"] * config["seq_len"], config["d_hidden"]), - dtype=torch.float16, - device=self.device) - self.stacked_expert_w_gate = torch.stack([expert.W_gate_weight for expert in self.experts], - dim=0) - self.stacked_expert_w_up = torch.stack([expert.W_up_weight for expert in self.experts], - dim=0) - self.stacked_expert_w_down = torch.stack([expert.W_down_weight for expert in self.experts], - dim=0) + (config["batch_size"] * config["seq_len"], config["d_hidden"]), dtype=torch.float16, device=self.device + ) + self.stacked_expert_w_gate = torch.stack([expert.W_gate_weight for expert in self.experts], dim=0) + self.stacked_expert_w_up = torch.stack([expert.W_up_weight for expert in self.experts], dim=0) + self.stacked_expert_w_down = torch.stack([expert.W_down_weight for expert in self.experts], dim=0) self.stacked_expert_tokens = torch.empty( - (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], - self.config["d_hidden"]), + (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], self.config["d_hidden"]), dtype=torch.float16, - device=self.device) + device=self.device, + ) self.stacked_expert_weights = torch.empty( - (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"]), - dtype=torch.float16, - device=self.device) + (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"]), dtype=torch.float16, device=self.device + ) self.stacked_expert_tokens_idxs = torch.empty( - (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"]), - dtype=torch.int64, - device=self.device) + (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"]), dtype=torch.int64, device=self.device + ) self.up_logits_shared = torch.empty( - (config["batch_size"] * config["seq_len"], self.config["d_expert"]), - dtype=torch.float16, - device=self.device) + (config["batch_size"] * config["seq_len"], self.config["d_expert"]), dtype=torch.float16, device=self.device + ) self.expert_output_shared = torch.empty( - (config["batch_size"] * config["seq_len"], self.config["d_hidden"]), - dtype=torch.float16, - device=self.device) + (config["batch_size"] * config["seq_len"], self.config["d_hidden"]), dtype=torch.float16, device=self.device + ) self.up_logits_routed = torch.empty( - (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], - self.config["d_expert"]), + (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], self.config["d_expert"]), dtype=torch.float16, - device=self.device) + device=self.device, + ) self.expert_output_routed = torch.empty( - (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], - self.config["d_hidden"]), + (config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], self.config["d_hidden"]), dtype=torch.float16, - device=self.device) + device=self.device, + ) @torch.no_grad() def forward(self, x: torch.Tensor) -> torch.Tensor: @@ -413,22 +370,20 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: self.stacked_expert_tokens[start_idx:end_idx] = expert_tokens self.stacked_expert_tokens_idxs[start_idx:end_idx] = exp_token_idxs - self.stacked_expert_weights[start_idx:end_idx] = flat_expert_weights[ - idxs[start_idx:end_idx]] + self.stacked_expert_weights[start_idx:end_idx] = flat_expert_weights[idxs[start_idx:end_idx]] group_sizes = torch.tensor(counts, dtype=torch.int32, device=self.device) - group_offset = torch.tensor( - tokens_per_expert - counts, dtype=torch.int32, device=self.device) + group_offset = torch.tensor(tokens_per_expert - counts, dtype=torch.int32, device=self.device) group_padded_offsets = [0 for _ in range(len(group_sizes))] for i in range(1, len(group_sizes)): - group_padded_offsets[i] = group_padded_offsets[i - 1] + math.ceil( - (counts[i - 1] + 1) / self.padding_M) * self.padding_M + group_padded_offsets[i] = group_padded_offsets[i - 1] + math.ceil((counts[i - 1] + 1) / self.padding_M) * self.padding_M block_token = 128 - M = math.ceil( - self.config["batch_size"] * self.config["seq_len"] * - self.config["n_experts_per_token"] / block_token) + self.config["n_routed_experts"] + M = ( + math.ceil(self.config["batch_size"] * self.config["seq_len"] * self.config["n_experts_per_token"] / block_token) + + self.config["n_routed_experts"] + ) group_idx_for_bx = [0 for _ in range(M)] for bx in range(M): @@ -437,8 +392,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: if m_start_padded >= group_padded_offsets[i]: group_idx_for_bx[bx] = i - group_padded_offsets = torch.tensor( - group_padded_offsets, dtype=torch.int32, device=self.device) + group_padded_offsets = torch.tensor(group_padded_offsets, dtype=torch.int32, device=self.device) group_idx_for_bx = torch.tensor(group_idx_for_bx, dtype=torch.int32, device=self.device) # Multi-stream execution @@ -448,11 +402,19 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: with torch.cuda.stream(routed_stream): # Tilelang version: Grouped GEMM - self.routed_kernel(self.stacked_expert_tokens, self.stacked_expert_w_gate, - self.stacked_expert_w_up, self.stacked_expert_w_down, - self.stacked_expert_weights, group_sizes, group_offset, - group_padded_offsets, group_idx_for_bx, self.up_logits_routed, - self.expert_output_routed) + self.routed_kernel( + self.stacked_expert_tokens, + self.stacked_expert_w_gate, + self.stacked_expert_w_up, + self.stacked_expert_w_down, + self.stacked_expert_weights, + group_sizes, + group_offset, + group_padded_offsets, + group_idx_for_bx, + self.up_logits_routed, + self.expert_output_routed, + ) # Scatter reduce self.expert_cache = torch.scatter_reduce( @@ -460,14 +422,19 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: 0, self.stacked_expert_tokens_idxs.view(-1, 1).repeat(1, x_flat.shape[-1]), self.expert_output_routed, - reduce='sum') + reduce="sum", + ) routed_output = self.expert_cache.view(*orig_shape) with torch.cuda.stream(shared_stream): - - self.shared_kernel(x_flat, self.shared_expert.W_gate_weight, - self.shared_expert.W_up_weight, self.shared_expert.W_down_weight, - self.up_logits_shared, self.expert_output_shared) + self.shared_kernel( + x_flat, + self.shared_expert.W_gate_weight, + self.shared_expert.W_up_weight, + self.shared_expert.W_down_weight, + self.up_logits_shared, + self.expert_output_shared, + ) shared_output = self.expert_output_shared.view(*orig_shape) torch.cuda.synchronize() @@ -491,14 +458,15 @@ def custom_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor: """ input_tensor, weights, config = data - dtype_str = "float16" + dtype_str = T.float16 shared_kernel = moe_forward_tilelang_shared( config["d_hidden"], config["d_expert"], config["n_shared_experts"], dtype=dtype_str, - num_tokens=config["batch_size"] * config["seq_len"]) + num_tokens=config["batch_size"] * config["seq_len"], + ) routed_kernel = moe_forward_tilelang_routed( config["d_hidden"], config["d_expert"], @@ -512,7 +480,8 @@ def custom_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor: threads=256, num_stages=1, k_pack=1, - coalesced_width=2) + coalesced_width=2, + ) moe = MoE(config, shared_kernel, routed_kernel, weights, padding_M=128) @@ -521,13 +490,7 @@ def custom_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor: return output -def main(d_hidden=7168, - d_expert=2048, - n_routed_experts=8, - n_shared_experts=1, - n_experts_per_token=4, - batch_size=1, - seq_len=8192): +def main(d_hidden=7168, d_expert=2048, n_routed_experts=8, n_shared_experts=1, n_experts_per_token=4, batch_size=1, seq_len=8192): config = { "dhidden": d_hidden, "dexpert": d_expert, @@ -536,7 +499,7 @@ def main(d_hidden=7168, "nexpertspertoken": n_experts_per_token, "bs": batch_size, "seqlen": seq_len, - "seed": 81394 + "seed": 81394, } data = generate_input(**config) @@ -551,5 +514,121 @@ def main(d_hidden=7168, print("✅ Tilelang and Torch match") +def run_regression_perf( + d_hidden=7168, d_expert=2048, n_routed_experts=8, n_shared_experts=1, n_experts_per_token=4, batch_size=1, seq_len=8192 +): + config = { + "dhidden": d_hidden, + "dexpert": d_expert, + "nroutedexperts": n_routed_experts, + "nsharedexperts": n_shared_experts, + "nexpertspertoken": n_experts_per_token, + "bs": batch_size, + "seqlen": seq_len, + "seed": 81394, + } + from tilelang.profiler import do_bench + + data = generate_input(**config) + + x, weights, config = data + + dtype_str = "float16" + + shared_kernel = moe_forward_tilelang_shared( + config["d_hidden"], + config["d_expert"], + config["n_shared_experts"], + dtype=dtype_str, + num_tokens=config["batch_size"] * config["seq_len"], + ) + routed_kernel = moe_forward_tilelang_routed( + config["d_hidden"], + config["d_expert"], + config["n_routed_experts"], + dtype=dtype_str, + group_sum=config["batch_size"] * config["seq_len"] * config["n_experts_per_token"], + group_count=config["n_routed_experts"], + block_token=128, + block_dhidden=128, + block_dexpert=128, + threads=256, + num_stages=1, + k_pack=1, + coalesced_width=2, + ) + + moe = MoE(config, shared_kernel, routed_kernel, weights, padding_M=128) + batch_size, seq_len, hidden_dim = x.shape + expert_indices, expert_scores = moe.gating_network(x) + flat_expert_indices = expert_indices.view(-1) + flat_expert_weights = expert_scores.view(-1) + x_flat = x.view(-1, hidden_dim) + idxs = flat_expert_indices.argsort() + counts = flat_expert_indices.bincount().cpu().numpy() + tokens_per_expert = counts.cumsum() + num_per_tok = moe.config["n_experts_per_token"] + token_idxs = idxs // num_per_tok + for expert_id, end_idx in enumerate(tokens_per_expert): + start_idx = 0 if expert_id == 0 else tokens_per_expert[expert_id - 1] + if start_idx == end_idx: + continue + exp_token_idxs = token_idxs[start_idx:end_idx] + expert_tokens = x_flat[exp_token_idxs] + moe.stacked_expert_tokens[start_idx:end_idx] = expert_tokens + moe.stacked_expert_tokens_idxs[start_idx:end_idx] = exp_token_idxs + moe.stacked_expert_weights[start_idx:end_idx] = flat_expert_weights[idxs[start_idx:end_idx]] + group_sizes = torch.tensor(counts, dtype=torch.int32, device=moe.device) + group_offset = torch.tensor(tokens_per_expert - counts, dtype=torch.int32, device=moe.device) + group_padded_offsets = [0 for _ in range(len(group_sizes))] + for i in range(1, len(group_sizes)): + group_padded_offsets[i] = group_padded_offsets[i - 1] + math.ceil((counts[i - 1] + 1) / moe.padding_M) * moe.padding_M + block_token = 128 + M = ( + math.ceil(moe.config["batch_size"] * moe.config["seq_len"] * moe.config["n_experts_per_token"] / block_token) + + moe.config["n_routed_experts"] + ) + group_idx_for_bx = [0 for _ in range(M)] + for bx in range(M): + m_start_padded = bx * block_token + for i in range(moe.config["n_routed_experts"]): + if m_start_padded >= group_padded_offsets[i]: + group_idx_for_bx[bx] = i + group_padded_offsets = torch.tensor(group_padded_offsets, dtype=torch.int32, device=moe.device) + group_idx_for_bx = torch.tensor(group_idx_for_bx, dtype=torch.int32, device=moe.device) + + def run_shared_kernel_only(): + moe.routed_kernel( + moe.stacked_expert_tokens, + moe.stacked_expert_w_gate, + moe.stacked_expert_w_up, + moe.stacked_expert_w_down, + moe.stacked_expert_weights, + group_sizes, + group_offset, + group_padded_offsets, + group_idx_for_bx, + moe.up_logits_routed, + moe.expert_output_routed, + ) + + def run_routed_kernel_only(): + moe.routed_kernel( + moe.stacked_expert_tokens, + moe.stacked_expert_w_gate, + moe.stacked_expert_w_up, + moe.stacked_expert_w_down, + moe.stacked_expert_weights, + group_sizes, + group_offset, + group_padded_offsets, + group_idx_for_bx, + moe.up_logits_routed, + moe.expert_output_routed, + ) + + return do_bench(run_routed_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/fusedmoe/example_fusedmoe_torch.py b/examples/fusedmoe/example_fusedmoe_torch.py index 00219c6e94..6b6322aff7 100644 --- a/examples/fusedmoe/example_fusedmoe_torch.py +++ b/examples/fusedmoe/example_fusedmoe_torch.py @@ -6,7 +6,6 @@ # Reference code in PyTorch class ExpertTorch(nn.Module): - def __init__(self, config: Dict, d_expert: Optional[int] = None): super().__init__() self.config = config @@ -25,7 +24,6 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: class MoEGateTorch(nn.Module): - def __init__(self, config: Dict): super().__init__() self.top_k: int = config["n_experts_per_token"] @@ -43,12 +41,10 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: class MoETorch(nn.Module): - def __init__(self, config: Dict): super().__init__() self.config = config - self.experts = nn.ModuleList( - [ExpertTorch(config) for _ in range(config["n_routed_experts"])]) + self.experts = nn.ModuleList([ExpertTorch(config) for _ in range(config["n_routed_experts"])]) self.gating_network = MoEGateTorch(config) shared_expert_dim = config["d_expert"] * config["n_shared_experts"] self.shared_expert = ExpertTorch(config=config, d_expert=shared_expert_dim) @@ -67,8 +63,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor: return routed_output + shared_output @torch.no_grad() - def moe_infer(self, x: torch.Tensor, flat_expert_indices: torch.Tensor, - flat_expert_weights: torch.Tensor) -> torch.Tensor: + def moe_infer(self, x: torch.Tensor, flat_expert_indices: torch.Tensor, flat_expert_weights: torch.Tensor) -> torch.Tensor: expert_cache = torch.zeros_like(x) # test_expert_cache = torch.zeros((x.shape[0] * self.config["n_experts_per_token"], self.config["d_hidden"])) # test_expert_tokens = torch.zeros((x.shape[0] * self.config["n_experts_per_token"], self.config["d_hidden"])) @@ -91,8 +86,7 @@ def moe_infer(self, x: torch.Tensor, flat_expert_indices: torch.Tensor, expert_out = expert(expert_tokens) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) - expert_cache.scatter_reduce_( - 0, exp_token_idxs.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') + expert_cache.scatter_reduce_(0, exp_token_idxs.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce="sum") return expert_cache @@ -116,21 +110,21 @@ def ref_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor: moe = MoETorch(config) # Fill in the given weights of the model - moe.gating_network.W_g.weight = nn.Parameter(weights['router.weight']) + moe.gating_network.W_g.weight = nn.Parameter(weights["router.weight"]) for i in range(num_experts): - gate_proj_weight = weights[f'experts.{i}.0.weight'] - up_proj_weight = weights[f'experts.{i}.1.weight'] - down_proj_weight = weights[f'experts.{i}.2.weight'] + gate_proj_weight = weights[f"experts.{i}.0.weight"] + up_proj_weight = weights[f"experts.{i}.1.weight"] + down_proj_weight = weights[f"experts.{i}.2.weight"] # Transpose weights to match expected shape for nn.Linear moe.experts[i].W_gate.weight = nn.Parameter(gate_proj_weight.t()) moe.experts[i].W_up.weight = nn.Parameter(up_proj_weight.t()) moe.experts[i].W_down.weight = nn.Parameter(down_proj_weight.t()) - moe.shared_expert.W_gate.weight = nn.Parameter(weights['shared_experts.0.weight'].t()) - moe.shared_expert.W_up.weight = nn.Parameter(weights['shared_experts.1.weight'].t()) - moe.shared_expert.W_down.weight = nn.Parameter(weights['shared_experts.2.weight'].t()) + moe.shared_expert.W_gate.weight = nn.Parameter(weights["shared_experts.0.weight"].t()) + moe.shared_expert.W_up.weight = nn.Parameter(weights["shared_experts.1.weight"].t()) + moe.shared_expert.W_down.weight = nn.Parameter(weights["shared_experts.2.weight"].t()) output = moe(input_tensor) @@ -140,10 +134,9 @@ def ref_kernel(data: Tuple[torch.Tensor, Dict, Dict]) -> torch.Tensor: # Input generation for the reference code -def generate_input(dhidden: int, dexpert: int, nroutedexperts: int, nsharedexperts: int, - nexpertspertoken: int, bs: int, seqlen: int, - seed: int) -> Tuple[torch.Tensor, Dict, Dict]: - +def generate_input( + dhidden: int, dexpert: int, nroutedexperts: int, nsharedexperts: int, nexpertspertoken: int, bs: int, seqlen: int, seed: int +) -> Tuple[torch.Tensor, Dict, Dict]: # Really dumb but for now _ isn't parsing correctly. d_hidden = dhidden d_expert = dexpert @@ -163,50 +156,40 @@ def generate_input(dhidden: int, dexpert: int, nroutedexperts: int, nsharedexper "seq_len": seq_len, } - gen = torch.Generator(device='cuda') + gen = torch.Generator(device="cuda") gen.manual_seed(seed) num_experts = n_routed_experts expert_dim = d_expert weights = {} - input_tensor = torch.randn((batch_size, seq_len, d_hidden), - device='cuda', - dtype=torch.float16, - generator=gen).contiguous() + input_tensor = torch.randn((batch_size, seq_len, d_hidden), device="cuda", dtype=torch.float16, generator=gen).contiguous() # Initialize router weights - weights['router.weight'] = torch.randn( - (num_experts, d_hidden), device="cuda", dtype=torch.float16, - generator=gen) / math.sqrt(d_hidden) + weights["router.weight"] = torch.randn((num_experts, d_hidden), device="cuda", dtype=torch.float16, generator=gen) / math.sqrt(d_hidden) for i in range(num_experts): - weights[f'experts.{i}.0.weight'] = torch.randn( - (d_hidden, expert_dim), device='cuda', dtype=torch.float16, - generator=gen) / math.sqrt(expert_dim) - - weights[f'experts.{i}.1.weight'] = torch.randn( - (d_hidden, expert_dim), device='cuda', dtype=torch.float16, - generator=gen) / math.sqrt(expert_dim) - - weights[f'experts.{i}.2.weight'] = torch.randn( - (expert_dim, d_hidden), device='cuda', dtype=torch.float16, - generator=gen) / math.sqrt(d_hidden) - - weights['shared_experts.0.weight'] = torch.randn( - (d_hidden, expert_dim * n_shared_experts), - device='cuda', - dtype=torch.float16, - generator=gen) / math.sqrt(expert_dim * n_shared_experts) - weights['shared_experts.1.weight'] = torch.randn( - (d_hidden, expert_dim * n_shared_experts), - device='cuda', - dtype=torch.float16, - generator=gen) / math.sqrt(expert_dim * n_shared_experts) - weights['shared_experts.2.weight'] = torch.randn((expert_dim * n_shared_experts, d_hidden), - device='cuda', - dtype=torch.float16, - generator=gen) / math.sqrt(d_hidden) + weights[f"experts.{i}.0.weight"] = torch.randn( + (d_hidden, expert_dim), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(expert_dim) + + weights[f"experts.{i}.1.weight"] = torch.randn( + (d_hidden, expert_dim), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(expert_dim) + + weights[f"experts.{i}.2.weight"] = torch.randn( + (expert_dim, d_hidden), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(d_hidden) + + weights["shared_experts.0.weight"] = torch.randn( + (d_hidden, expert_dim * n_shared_experts), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(expert_dim * n_shared_experts) + weights["shared_experts.1.weight"] = torch.randn( + (d_hidden, expert_dim * n_shared_experts), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(expert_dim * n_shared_experts) + weights["shared_experts.2.weight"] = torch.randn( + (expert_dim * n_shared_experts, d_hidden), device="cuda", dtype=torch.float16, generator=gen + ) / math.sqrt(d_hidden) return (input_tensor, weights, config) diff --git a/examples/fusedmoe/regression_example_fusedmoe.py b/examples/fusedmoe/regression_example_fusedmoe.py new file mode 100644 index 0000000000..ac0f18aaeb --- /dev/null +++ b/examples/fusedmoe/regression_example_fusedmoe.py @@ -0,0 +1,19 @@ +import tilelang.testing +import example_fusedmoe_tilelang + + +def regression_example_fusedmoe_tilelang(): + tilelang.testing.process_func( + example_fusedmoe_tilelang.run_regression_perf, + d_hidden=1024, + d_expert=256, + n_routed_experts=8, + n_shared_experts=1, + n_experts_per_token=4, + batch_size=1, + seq_len=1024, + ) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/fusedmoe/test_example_fusedmoe.py b/examples/fusedmoe/test_example_fusedmoe.py index 806aff49ee..ba8415895d 100644 --- a/examples/fusedmoe/test_example_fusedmoe.py +++ b/examples/fusedmoe/test_example_fusedmoe.py @@ -4,13 +4,8 @@ def test_example_fusedmoe_tilelang(): example_fusedmoe_tilelang.main( - d_hidden=1024, - d_expert=256, - n_routed_experts=8, - n_shared_experts=1, - n_experts_per_token=4, - batch_size=1, - seq_len=1024) + d_hidden=1024, d_expert=256, n_routed_experts=8, n_shared_experts=1, n_experts_per_token=4, batch_size=1, seq_len=1024 + ) if __name__ == "__main__": diff --git a/examples/gdn/example_chunk_delta_bwd.py b/examples/gdn/example_chunk_delta_bwd.py index 518b0ee21a..4230df525e 100644 --- a/examples/gdn/example_chunk_delta_bwd.py +++ b/examples/gdn/example_chunk_delta_bwd.py @@ -12,6 +12,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__, flush=True) from fla.ops.common.chunk_delta_h import chunk_gated_delta_rule_bwd_dhu except ImportError: @@ -24,7 +25,7 @@ torch.random.manual_seed(0) # torch.set_printoptions(profile="full") -from utils import * +from test_utils import assert_similar def prepare_input( @@ -49,6 +50,7 @@ def prepare_input( G = F.logsigmoid(G) try: from fla.ops.utils.cumsum import chunk_local_cumsum + G = chunk_local_cumsum(G, chunk_size) except ImportError: print("fla not found, skip cumsum") @@ -125,8 +127,11 @@ def torch_chunk_gated_delta_rule_bwd_dhu( DV = dv.shape[-1] block_S = 64 BS = S // block_S - dh, dh0, dv2 = torch.empty((B, BS, H, DK, DV), dtype=output_dtype), torch.empty( - (B, H, DK, DV), dtype=state_dtype), torch.empty((B, S, H, DV), dtype=output_dtype) + dh, dh0, dv2 = ( + torch.empty((B, BS, H, DK, DV), dtype=output_dtype), + torch.empty((B, H, DK, DV), dtype=state_dtype), + torch.empty((B, S, H, DV), dtype=output_dtype), + ) dh_tmp = torch.empty((B, H, DK, DV), dtype=accum_dtype) dv_tmp = torch.empty((B, S, H, DV), dtype=accum_dtype) Q_tmp = torch.empty((B, S, H, DK), dtype=accum_dtype) @@ -138,34 +143,30 @@ def torch_chunk_gated_delta_rule_bwd_dhu( for i_s in range(BS - 1, -1, -1): dh[:, i_s, :, :, :] = dh_tmp - dv_tmp = torch.matmul(K[:, i_s * block_S:(i_s + 1) * block_S, :, :].permute(0, 2, 1, 3), - dh_tmp.to(K.dtype)).permute(0, 2, 1, 3) + dv_tmp = torch.matmul(K[:, i_s * block_S : (i_s + 1) * block_S, :, :].permute(0, 2, 1, 3), dh_tmp.to(K.dtype)).permute(0, 2, 1, 3) if use_g: for i_bh in range(B * H): i_b, i_h = i_bh // H, i_bh % H for i_s2 in range(block_S): - if G[i_b, i_s * block_S + block_S - 1, i_h] - G[i_b, i_s * block_S + i_s2, - i_h] <= 0: - dv_tmp[i_b, i_s2, - i_h, :] *= torch.exp(G[i_b, i_s * block_S + block_S - 1, i_h] - - G[i_b, i_s * block_S + i_s2, i_h]) + if G[i_b, i_s * block_S + block_S - 1, i_h] - G[i_b, i_s * block_S + i_s2, i_h] <= 0: + dv_tmp[i_b, i_s2, i_h, :] *= torch.exp(G[i_b, i_s * block_S + block_S - 1, i_h] - G[i_b, i_s * block_S + i_s2, i_h]) else: dv_tmp[i_b, i_s2, i_h, :] = 0 - dv_tmp += dv[:, i_s * block_S:(i_s + 1) * block_S, :, :] - dv2[:, i_s * block_S:(i_s + 1) * block_S, :, :] = dv_tmp + dv_tmp += dv[:, i_s * block_S : (i_s + 1) * block_S, :, :] + dv2[:, i_s * block_S : (i_s + 1) * block_S, :, :] = dv_tmp if use_g: G_last = G[:, i_s * block_S + block_S - 1, :] for i_bh in range(B * H): i_b, i_h = i_bh // H, i_bh % H dh_tmp[i_b, i_h, :, :] *= torch.exp(G_last[i_b, i_h]) - Q_tmp = Q[:, i_s * block_S:(i_s + 1) * block_S, :, :] + Q_tmp = Q[:, i_s * block_S : (i_s + 1) * block_S, :, :] for i_s2 in range(block_S): for i_k in range(DK): Q_tmp[:, i_s2, :, i_k] *= torch.exp(G[:, i_s * block_S + i_s2, :]) Q_tmp *= scale - W_tmp = W[:, i_s * block_S:(i_s + 1) * block_S, :, :] - dO_tmp = dO[:, i_s * block_S:(i_s + 1) * block_S, :, :] + W_tmp = W[:, i_s * block_S : (i_s + 1) * block_S, :, :] + dO_tmp = dO[:, i_s * block_S : (i_s + 1) * block_S, :, :] torch.backends.cuda.matmul.allow_tf32 = True dh_tmp += torch.matmul(Q_tmp.permute(0, 2, 3, 1), dO_tmp.permute(0, 2, 1, 3)) @@ -223,25 +224,24 @@ def tilelang_chunk_gated_delta_rule_bwd_dhu( @T.prim_func def kernel( - # Input - Q: T.Tensor(Q_shape, dtype=input_dtype), - K: T.Tensor(K_shape, dtype=input_dtype), - W: T.Tensor(W_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - h0: T.Tensor(h0_shape, dtype=input_dtype), - dht: T.Tensor(dht_shape, dtype=input_dtype), - dO: T.Tensor(dO_shape, dtype=input_dtype), - dv: T.Tensor(dv_shape, dtype=input_dtype), - # Output - dh: T.Tensor(dh_shape, dtype=output_dtype), - dh0: T.Tensor(dh0_shape, dtype=state_dtype), - dv2: T.Tensor(dv2_shape, dtype=output_dtype), + # Input + Q: T.Tensor(Q_shape, dtype=input_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + W: T.Tensor(W_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + h0: T.Tensor(h0_shape, dtype=input_dtype), + dht: T.Tensor(dht_shape, dtype=input_dtype), + dO: T.Tensor(dO_shape, dtype=input_dtype), + dv: T.Tensor(dv_shape, dtype=input_dtype), + # Output + dh: T.Tensor(dh_shape, dtype=output_dtype), + dh0: T.Tensor(dh0_shape, dtype=state_dtype), + dv2: T.Tensor(dv2_shape, dtype=output_dtype), ): with T.Kernel(T.ceildiv(DV, block_DV), B * H, threads=threads) as (bv, bbh): bb, bh = bbh // H, bbh % H b_dh_shared = T.alloc_shared((DK, block_DV), dtype=output_dtype) - b_dh_shared_fp32 = T.alloc_shared((DK, block_DV), dtype=state_dtype) b_dh_fragment = T.alloc_fragment((DK, block_DV), dtype=accum_dtype) b_dh_fragment_1 = T.alloc_fragment((DK, block_DV), dtype=accum_dtype) b_dh_fragment_2 = T.alloc_fragment((DK, block_DV), dtype=accum_dtype) @@ -249,17 +249,14 @@ def kernel( dv_fragment = T.alloc_fragment((block_S, block_DV), dtype=accum_dtype) dv_fragment_2 = T.alloc_fragment((block_S, block_DV), dtype=accum_dtype) dO_shared = T.alloc_shared((block_S, block_DV), dtype=input_dtype) - dO_shared_t = T.alloc_shared((block_DV, block_S), dtype="float32") - dO_fragment = T.alloc_fragment((block_S, block_DV), dtype="float32") - dO_fragment_t = T.alloc_fragment((block_DV, block_S), dtype="float32") + dO_shared_t = T.alloc_shared((block_DV, block_S), dtype=T.float32) + dO_fragment = T.alloc_fragment((block_S, block_DV), dtype=T.float32) + dO_fragment_t = T.alloc_fragment((block_DV, block_S), dtype=T.float32) K_shared = T.alloc_shared((block_S, DK), dtype=input_dtype) Q_shared = T.alloc_shared((block_S, DK), dtype=input_dtype) - Q_shared_fp32 = T.alloc_shared((block_S, DK), dtype="float32") W_shared = T.alloc_shared((block_S, DK), dtype=input_dtype) - G_last_local = T.alloc_local((1), dtype=gate_dtype) - G_last_local_exp = T.alloc_local((1), dtype=gate_dtype) G_shared = T.alloc_shared((block_S), dtype=gate_dtype, scope="shared") G_fragment = T.alloc_fragment((block_S), dtype=gate_dtype) G_fragment_post = T.alloc_fragment((block_S), dtype=gate_dtype) @@ -269,20 +266,15 @@ def kernel( T.use_swizzle(10) - T.annotate_layout({ - b_dh_shared: tilelang.layout.make_swizzled_layout(b_dh_shared), - b_dh_shared_fp32: tilelang.layout.make_swizzled_layout(b_dh_shared_fp32), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - dO_shared: tilelang.layout.make_swizzled_layout(dO_shared), - dO_shared_t: tilelang.layout.make_swizzled_layout(dO_shared_t), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), - Q_shared_fp32: tilelang.layout.make_swizzled_layout(Q_shared_fp32), - W_shared: tilelang.layout.make_swizzled_layout(W_shared), - }) + T.annotate_layout( + { + dO_shared: tilelang.layout.make_swizzled_layout(dO_shared), + Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), + } + ) if use_final_state_gradient: - T.copy(dht[bb, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV], b_dh_shared) + T.copy(dht[bb, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV], b_dh_shared) T.copy(b_dh_shared, b_dh_fragment) else: T.clear(b_dh_fragment) @@ -293,57 +285,45 @@ def kernel( # Store the updated dh T.copy(b_dh_fragment, b_dh_shared) - T.copy(b_dh_shared, dh[bb, i_s_inv, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV]) + T.copy(b_dh_shared, dh[bb, i_s_inv, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV]) # Update dv - T.copy(K[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, 0:DK], K_shared) + T.copy(K[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, 0:DK], K_shared) T.gemm(K_shared, b_dh_shared, dv_fragment, clear_accum=True) if use_g: - T.copy( - G[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh], - G_shared, - disable_tma=True) + T.copy(G[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh], G_shared, disable_tma=True) T.copy(G_shared, G_fragment) - G_last_local[0] = G_shared[block_S - 1] - G_last_local_exp[0] = T.exp(G_last_local[0]) + G_last_local = G_shared[block_S - 1] + G_last_local_exp = T.exp(G_last_local) for i_s2 in T.Parallel(block_S): - G_fragment_post[i_s2] = T.exp(G_last_local[0] - G_fragment[i_s2]) + G_fragment_post[i_s2] = T.exp(G_last_local - G_fragment[i_s2]) for i_s2, i_v in T.Parallel(block_S, block_DV): - # with T.If(G_last_local[0] - G_shared[i_s2] <= 0): - with T.If(G_last_local[0] - G_fragment[i_s2] <= 0): - with T.Then(): - dv_fragment[i_s2, - i_v] = dv_fragment[i_s2, i_v] * G_fragment_post[i_s2] - with T.Else(): - dv_fragment[i_s2, i_v] = 0 - - T.copy( - dv[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, - bv * block_DV:(bv + 1) * block_DV], dv_shared) + dv_fragment[i_s2, i_v] = ( + dv_fragment[i_s2, i_v] * G_fragment_post[i_s2] if G_last_local - G_fragment[i_s2] <= 0 else 0 + ) + + T.copy(dv[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV], dv_shared) T.copy(dv_shared, dv_fragment_2) for i_s2, i_v in T.Parallel(block_S, block_DV): dv_fragment[i_s2, i_v] = dv_fragment[i_s2, i_v] + dv_fragment_2[i_s2, i_v] # Store the updated dv T.copy(dv_fragment, dv_shared) - T.copy( - dv_shared, dv2[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, - bv * block_DV:(bv + 1) * block_DV]) + T.copy(dv_shared, dv2[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV]) # Update dh - T.copy(Q[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, 0:DK], Q_shared) - T.copy(W[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, 0:DK], W_shared) + T.copy(Q[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, 0:DK], Q_shared) + T.copy(W[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, 0:DK], W_shared) T.clear(Q_fragment) if use_g: for i_k, i_v in T.Parallel(DK, block_DV): - b_dh_fragment[i_k, i_v] *= G_last_local_exp[0] + b_dh_fragment[i_k, i_v] *= G_last_local_exp T.copy(Q_shared, Q_fragment) for i_s2 in T.Parallel(block_S): G_fragment_exp[i_s2] = T.exp(G_shared[i_s2]) for i_s2, i_k in T.Parallel(block_S, DK): - # Q_fragment[i_s2, i_k] = Q_fragment[i_s2, i_k] * T.exp(G_shared[i_s2]) * scale Q_fragment[i_s2, i_k] = Q_fragment[i_s2, i_k] * G_fragment_exp[i_s2] * scale else: T.copy(Q_shared, Q_fragment) @@ -353,9 +333,7 @@ def kernel( for i_s2, i_k in T.Parallel(block_S, DK): Q_fragment_t[i_k, i_s2] = Q_fragment[i_s2, i_k] - T.copy( - dO[bb, i_s_inv * block_S:(i_s_inv + 1) * block_S, bh, - bv * block_DV:(bv + 1) * block_DV], dO_shared) + T.copy(dO[bb, i_s_inv * block_S : (i_s_inv + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV], dO_shared) T.copy(dO_shared, dO_fragment) for i_s2, i_v in T.Parallel(block_S, block_DV): dO_fragment_t[i_v, i_s2] = dO_fragment[i_s2, i_v] @@ -369,7 +347,7 @@ def kernel( b_dh_fragment[i_k, i_v] += b_dh_fragment_1[i_k, i_v] - b_dh_fragment_2[i_k, i_v] if use_initial_state: - T.copy(b_dh_fragment, dh0[bb, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV]) + T.copy(b_dh_fragment, dh0[bb, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV]) return kernel @@ -444,44 +422,61 @@ def run_test( num_stages=0, use_torch=False, ): - Q, K, W, G, h0, dht, dO, dv = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - dh_ref, dh0_ref, dv2_ref = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - dh_tilelang, dh0_tilelang, dv2_tilelang = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) + Q, K, W, G, h0, dht, dO, dv = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) + dh_ref, dh0_ref, dv2_ref = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype) + ) + dh_tilelang, dh0_tilelang, dv2_tilelang = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype) + ) # fla ref print("fla running...", flush=True) if use_g: - dh_ref, dh0_ref, dv2_ref = chunk_gated_delta_rule_bwd_dhu(Q, K, W, G, h0, dht, dO, dv, - scale) + dh_ref, dh0_ref, dv2_ref = chunk_gated_delta_rule_bwd_dhu(Q, K, W, G, h0, dht, dO, dv, scale) else: G = G.fill_(0) - dh_ref, dh0_ref, dv2_ref = chunk_gated_delta_rule_bwd_dhu(Q, K, W, G, h0, dht, dO, dv, - scale) + dh_ref, dh0_ref, dv2_ref = chunk_gated_delta_rule_bwd_dhu(Q, K, W, G, h0, dht, dO, dv, scale) # tilelang print("tilelang running...", flush=True) - kernel = tilelang_chunk_gated_delta_rule_bwd_dhu(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, - chunk_size, scale, use_g, use_initial_state, - use_final_state_gradient, block_DV, threads, - num_stages) + kernel = tilelang_chunk_gated_delta_rule_bwd_dhu( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + scale, + use_g, + use_initial_state, + use_final_state_gradient, + block_DV, + threads, + num_stages, + ) # kernel = tilelang.compile(program) print(kernel.get_kernel_source()) dh_tilelang, dh0_tilelang, dv2_tilelang = kernel(Q, K, W, G, h0, dht, dO, dv) - fla_time = do_bench( - chunk_gated_delta_rule_bwd_dhu, Q, K, W, G, h0, dht, dO, dv, scale, chunk_size=chunk_size) + fla_time = do_bench(chunk_gated_delta_rule_bwd_dhu, Q, K, W, G, h0, dht, dO, dv, scale, chunk_size=chunk_size) tilelang_time = do_bench(kernel, Q, K, W, G, h0, dht, dO, dv) print(f"fla time: {fla_time} ms") @@ -496,19 +491,47 @@ def run_test( print("torch running...", flush=True) if use_g: dh_ref_torch, dh0_ref_torch, dv2_ref_torch = torch_chunk_gated_delta_rule_bwd_dhu( - Q, K, W, G, h0, dht, dO, dv, scale, use_g, use_initial_state, - use_final_state_gradient, getattr(torch, input_dtype), getattr(torch, output_dtype), - getattr(torch, accum_dtype), getattr(torch, - gate_dtype), getattr(torch, state_dtype)) + Q, + K, + W, + G, + h0, + dht, + dO, + dv, + scale, + use_g, + use_initial_state, + use_final_state_gradient, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) dh_ref_torch = dh_ref_torch.cuda() dh0_ref_torch = dh0_ref_torch.cuda() dv2_ref_torch = dv2_ref_torch.cuda() else: dh_ref_torch, dh0_ref_torch, dv2_ref_torch = torch_chunk_gated_delta_rule_bwd_dhu( - Q, K, W, None, h0, dht, dO, dv, scale, use_g, use_initial_state, - use_final_state_gradient, getattr(torch, input_dtype), getattr(torch, output_dtype), - getattr(torch, accum_dtype), getattr(torch, - gate_dtype), getattr(torch, state_dtype)) + Q, + K, + W, + None, + h0, + dht, + dO, + dv, + scale, + use_g, + use_initial_state, + use_final_state_gradient, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) dh_ref_torch = dh_ref_torch.cuda() dh0_ref_torch = dh0_ref_torch.cuda() dv2_ref_torch = dv2_ref_torch.cuda() @@ -554,11 +577,11 @@ def main(): H=8, DK=DK, DV=128, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", - gate_dtype="float32", - state_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, + gate_dtype=T.float32, + state_dtype=T.float32, chunk_size=64, scale=DK**-0.5, use_g=True, diff --git a/examples/gdn/example_chunk_delta_h.py b/examples/gdn/example_chunk_delta_h.py index 4d6b657ffc..2ee84e7bf6 100644 --- a/examples/gdn/example_chunk_delta_h.py +++ b/examples/gdn/example_chunk_delta_h.py @@ -3,12 +3,14 @@ import sys # noqa: F401 import tilelang import tilelang.language as T +from tilelang.autotuner import autotune # Add your fla repository path to sys.path # Currently we use the fla repository from the flash-linear-attention project at commit id f03cb3ae # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.common.chunk_delta_h import chunk_gated_delta_rule_fwd_h except ImportError: @@ -19,7 +21,7 @@ import torch.nn.functional as F from tilelang.engine.callback import register_cuda_postproc_callback # noqa: F401 -from utils import * +from test_utils import assert_similar # (zhengju) We can slightly modify the generated cuda code from tilelang lowering # in the debug folder to make the performance better. To enable this callback, @@ -55,6 +57,7 @@ def prepare_input( G = F.logsigmoid(G) try: from fla.ops.utils.cumsum import chunk_local_cumsum + G = chunk_local_cumsum(G, chunk_size) except ImportError: print("fla not found, skip cumsum") @@ -80,7 +83,21 @@ def prepare_output( return h, final_state, V_new -@tilelang.jit(out_idx=[-3, -2, -1]) +def get_configs(): + import itertools + + block_DK = [32, 64, 128] + block_DV = [32, 64, 128] + threads = [128, 256] + num_stages = [1, 2, 3] + _configs = list(itertools.product(block_DK, block_DV, threads, num_stages)) + + configs = [{"block_DK": c[0], "block_DV": c[1], "threads": c[2], "num_stages": c[3]} for c in _configs] + return configs + + +@autotune(configs=get_configs(), warmup=3, rep=5) +@tilelang.jit(out_idx=[-3, -2, -1], pass_configs={tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True}) def tilelang_chunk_gated_delta_rule_fwd_h( # task config B, @@ -94,15 +111,15 @@ def tilelang_chunk_gated_delta_rule_fwd_h( gate_dtype, state_dtype, chunk_size, - use_g=True, - use_initial_state=True, - store_final_state=True, - save_new_value=True, + use_g, + use_initial_state, + store_final_state, + save_new_value, # kernel config block_DK=64, - block_DV=64, - threads=256, - num_stages=0, + block_DV=32, + threads=128, + num_stages=1, ): block_S = chunk_size BS = S // block_S @@ -118,14 +135,14 @@ def tilelang_chunk_gated_delta_rule_fwd_h( @T.prim_func def kernel( - K: T.Tensor(K_shape, dtype=input_dtype), - W: T.Tensor(W_shape, dtype=input_dtype), - U: T.Tensor(U_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - initial_state: T.Tensor(initial_state_shape, dtype=input_dtype), - h: T.Tensor(h_shape, dtype=output_dtype), - final_state: T.Tensor(final_state_shape, dtype=state_dtype), - V_new: T.Tensor(V_shape, dtype=output_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + W: T.Tensor(W_shape, dtype=input_dtype), + U: T.Tensor(U_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + initial_state: T.Tensor(initial_state_shape, dtype=input_dtype), + h: T.Tensor(h_shape, dtype=output_dtype), + final_state: T.Tensor(final_state_shape, dtype=state_dtype), + V_new: T.Tensor(V_shape, dtype=output_dtype), ): with T.Kernel(T.ceildiv(DV, block_DV), B * H, threads=threads) as (bv, bbh): bb, bh = bbh // H, bbh % H @@ -139,39 +156,35 @@ def kernel( V_new_fragment = T.alloc_fragment((block_S, block_DV), dtype=accum_dtype) V_new_shared = T.alloc_shared((block_S, block_DV), dtype=output_dtype) K_shared = T.alloc_shared((block_S, DK), dtype=input_dtype) - G_last_local = T.alloc_local((1), dtype=gate_dtype) + G_last_local = T.alloc_var(T.float32) G_shared = T.alloc_shared((block_S, block_DV), dtype=gate_dtype) G_fragment = T.alloc_fragment((block_S, block_DV), dtype=gate_dtype) - T.annotate_layout({ - b_h_shared: tilelang.layout.make_swizzled_layout(b_h_shared), - U_shared: tilelang.layout.make_swizzled_layout(U_shared), - W_shared: tilelang.layout.make_swizzled_layout(W_shared), - V_new_shared: tilelang.layout.make_swizzled_layout(V_new_shared), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - G_shared: tilelang.layout.make_swizzled_layout(G_shared), - }) + T.annotate_layout( + { + U_shared: tilelang.layout.make_swizzled_layout(U_shared), + G_shared: tilelang.layout.make_swizzled_layout(G_shared), + } + ) T.use_swizzle(10) if use_initial_state: - T.copy(initial_state[bb, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV], b_h_shared) + T.copy(initial_state[bb, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV], b_h_shared) T.copy(b_h_shared, b_h_fragment) else: T.clear(b_h_fragment) for i_s in T.Pipelined(T.ceildiv(S, block_S), num_stages=num_stages): # Store previous result to the hidden tensor, like the epilogue - T.copy(b_h_shared, h[bb, i_s, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV]) + T.copy(b_h_shared, h[bb, i_s, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV]) # Recurrence - T.copy(W[bb, i_s * block_S:(i_s + 1) * block_S, bh, 0:DK], W_shared) + T.copy(W[bb, i_s * block_S : (i_s + 1) * block_S, bh, 0:DK], W_shared) T.gemm(W_shared, b_h_shared, V_new_fragment, clear_accum=True) # U - W * S - T.copy( - U[bb, i_s * block_S:(i_s + 1) * block_S, bh, bv * block_DV:(bv + 1) * block_DV], - U_shared) + T.copy(U[bb, i_s * block_S : (i_s + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV], U_shared) T.copy(U_shared, U_fragment) for i_s2, i_v in T.Parallel(block_S, block_DV): V_new_fragment[i_s2, i_v] = -V_new_fragment[i_s2, i_v] + U_fragment[i_s2, i_v] @@ -179,27 +192,24 @@ def kernel( # Save V_new if save_new_value: T.copy(V_new_fragment, dst=V_new_shared) - T.copy( - V_new_shared, V_new[bb, i_s * block_S:(i_s + 1) * block_S, bh, - bv * block_DV:(bv + 1) * block_DV]) + T.copy(V_new_shared, V_new[bb, i_s * block_S : (i_s + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV]) - T.copy(K[bb, i_s * block_S:(i_s + 1) * block_S, bh, 0:DK], K_shared) + T.copy(K[bb, i_s * block_S : (i_s + 1) * block_S, bh, 0:DK], K_shared) # use_g if use_g: - G_last_local[0] = G[bb, (i_s + 1) * block_S - 1, bh] + G_last_local = G[bb, (i_s + 1) * block_S - 1, bh] for i_s2, i_v in T.Parallel(block_S, block_DV): G_shared[i_s2, i_v] = G[bb, i_s * block_S + i_s2, bh] T.copy(G_shared, G_fragment) for i_s2, i_v in T.Parallel(block_S, block_DV): - with T.If(G_last_local[0] - G_fragment[i_s2, i_v] <= 0): - with T.Then(): - V_new_fragment[i_s2, i_v] = V_new_fragment[i_s2, i_v] * T.exp( - G_last_local[0] - G_fragment[i_s2, i_v]) - with T.Else(): - V_new_fragment[i_s2, i_v] = 0 - G_last_local[0] = T.exp(G_last_local[0]) + V_new_fragment[i_s2, i_v] = ( + V_new_fragment[i_s2, i_v] * T.exp2((G_last_local - G_fragment[i_s2, i_v]) * 1.442695) + if G_last_local - G_fragment[i_s2, i_v] <= 0 + else 0 + ) + G_last_local = T.exp2(G_last_local * 1.442695) for i_k, i_v in T.Parallel(DK, block_DV): - b_h_fragment[i_k, i_v] *= G_last_local[0] + b_h_fragment[i_k, i_v] *= G_last_local # Update intermediate results T.copy(V_new_fragment, V_new_shared) @@ -209,7 +219,7 @@ def kernel( # Save final state if store_final_state: - T.copy(b_h_fragment, final_state[bb, bh, 0:DK, bv * block_DV:(bv + 1) * block_DV]) + T.copy(b_h_fragment, final_state[bb, bh, 0:DK, bv * block_DV : (bv + 1) * block_DV]) return kernel @@ -260,47 +270,77 @@ def run_test( threads=128, num_stages=0, ): - K, W, U, G, initial_state = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype)) - h_ref, final_state_ref, V_new_ref = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, state_dtype)) - h_tilelang, final_state_tilelang, V_new_tilelang = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, state_dtype)) + K, W, U, G, initial_state = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + ) + h_ref, final_state_ref, V_new_ref = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, state_dtype) + ) + h_tilelang, final_state_tilelang, V_new_tilelang = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, state_dtype) + ) # fla ref - h_ref, V_new_ref, final_state_ref = chunk_gated_delta_rule_fwd_h(K, W, U, G, initial_state, - store_final_state, chunk_size, - save_new_value) + h_ref, V_new_ref, final_state_ref = chunk_gated_delta_rule_fwd_h( + k=K, + w=W, + u=U, + g=G, + initial_state=initial_state, + output_final_state=store_final_state, + chunk_size=chunk_size, + save_new_value=save_new_value, + ) # tilelang - kernel = tilelang_chunk_gated_delta_rule_fwd_h(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, chunk_size, - use_g, use_initial_state, store_final_state, - save_new_value, block_DK, block_DV, threads, - num_stages) + kernel = tilelang_chunk_gated_delta_rule_fwd_h( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + use_g, + use_initial_state, + store_final_state, + save_new_value, + ) h_tilelang, final_state_tilelang, V_new_tilelang = kernel(K, W, U, G, initial_state) # (zhengju) If you want to print the generated cuda code, you can uncomment the following line # print("CUDA Code:\n", kernel.get_kernel_source()) - fla_time = do_bench(chunk_gated_delta_rule_fwd_h, K, W, U, G, initial_state, store_final_state, - chunk_size, save_new_value) + fla_time = do_bench( + chunk_gated_delta_rule_fwd_h, + k=K, + w=W, + u=U, + g=G, + initial_state=initial_state, + output_final_state=store_final_state, + chunk_size=chunk_size, + save_new_value=save_new_value, + ) tilelang_time = do_bench(kernel, K, W, U, G, initial_state) # check correctness try: h_ref_fp32 = h_ref.to(torch.float32) h_tilelang_fp32 = h_tilelang.to(torch.float32) - assert_similar( - h_ref_fp32, - h_tilelang_fp32, - eps=1e-5, - name="tilelang chunk gated delta rule fwd h", - raise_assert=False) + assert_similar(h_ref_fp32, h_tilelang_fp32, eps=1e-5, name="tilelang chunk gated delta rule fwd h", raise_assert=False) print("tilelang chunk gated delta rule fwd h passed √") except Exception as e: print("tilelang chunk gated delta rule fwd h failed ✗") @@ -314,7 +354,8 @@ def run_test( final_state_tilelang_fp32, eps=1e-5, name="tilelang chunk gated delta rule fwd final_state", - raise_assert=False) + raise_assert=False, + ) print("tilelang chunk gated delta rule fwd final_state passed √") except Exception as e: print("tilelang chunk gated delta rule fwd final_state failed ✗") @@ -323,12 +364,7 @@ def run_test( try: V_new_ref_fp32 = V_new_ref.to(torch.float32) V_new_tilelang_fp32 = V_new_tilelang.to(torch.float32) - assert_similar( - V_new_ref_fp32, - V_new_tilelang_fp32, - eps=1e-5, - name="tilelang chunk gated delta rule fwd V_new", - raise_assert=False) + assert_similar(V_new_ref_fp32, V_new_tilelang_fp32, eps=1e-5, name="tilelang chunk gated delta rule fwd V_new", raise_assert=False) print("tilelang chunk gated delta rule fwd V_new passed √") except Exception as e: print("tilelang chunk gated delta rule fwd V_new failed ✗") @@ -345,20 +381,20 @@ def main(): H=32, DK=128, DV=128, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", - gate_dtype="float32", - state_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, + gate_dtype=T.float32, + state_dtype=T.float32, chunk_size=64, use_g=True, - use_initial_state=True, + use_initial_state=False, store_final_state=True, save_new_value=True, - block_DK=64, + block_DK=32, block_DV=32, threads=128, - num_stages=1, + num_stages=2, ) diff --git a/examples/gdn/example_chunk_o.py b/examples/gdn/example_chunk_o.py index 1c084be705..a4d7281f55 100644 --- a/examples/gdn/example_chunk_o.py +++ b/examples/gdn/example_chunk_o.py @@ -9,6 +9,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.common.chunk_o import chunk_fwd_o except ImportError: @@ -87,16 +88,14 @@ def tilelang_chunk_fwd_o( @T.prim_func def kernel( - Q: T.Tensor(Q_shape, dtype=input_dtype), - K: T.Tensor(K_shape, dtype=input_dtype), - V: T.Tensor(V_shape, dtype=input_dtype), - HIDDEN: T.Tensor(H_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - O: T.Tensor(O_shape, dtype=output_dtype), + Q: T.Tensor(Q_shape, dtype=input_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + V: T.Tensor(V_shape, dtype=input_dtype), + HIDDEN: T.Tensor(H_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + O: T.Tensor(O_shape, dtype=output_dtype), ): - with T.Kernel( - T.ceildiv(DV, block_DV), T.ceildiv(S, block_S), B * H, - threads=threads) as (bv, bs, bbh): + with T.Kernel(T.ceildiv(DV, block_DV), T.ceildiv(S, block_S), B * H, threads=threads) as (bv, bs, bbh): bb, bh = bbh // H, bbh % H Q_shared = T.alloc_shared((block_S, block_DK), dtype=input_dtype) K_shared = T.alloc_shared((block_S, block_DK), dtype=input_dtype) @@ -109,28 +108,13 @@ def kernel( G_shared = T.alloc_shared((block_S,), dtype=gate_dtype, scope="shared") G_diff_local = T.alloc_fragment((block_S, block_S), dtype=gate_dtype) - T.annotate_layout({ - Q_shared: tilelang.layout.make_swizzled_layout(Q_shared), - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - V_shared: tilelang.layout.make_swizzled_layout(V_shared), - H_shared: tilelang.layout.make_swizzled_layout(H_shared), - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - }) - T.clear(A_fragment) T.clear(O_fragment) T.disable_warp_group_reg_alloc() for i_k in T.Pipelined(T.ceildiv(DK, block_DK), num_stages=num_stages): - T.copy( - Q[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - Q_shared) - T.copy( - K[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - K_shared) - T.copy( - HIDDEN[bb, bs, bh, i_k * block_DK:(i_k + 1) * block_DK, - bv * block_DV:(bv + 1) * block_DV], H_shared) + T.copy(Q[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], Q_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], K_shared) + T.copy(HIDDEN[bb, bs, bh, i_k * block_DK : (i_k + 1) * block_DK, bv * block_DV : (bv + 1) * block_DV], H_shared) T.gemm(Q_shared, H_shared, O_fragment) T.gemm(Q_shared, K_shared, A_fragment, transpose_B=True) @@ -145,8 +129,7 @@ def kernel( for i_s1, i_s2 in T.Parallel(block_S, block_S): with T.If(G_diff_local[i_s1, i_s2] <= 0): with T.Then(): - A_fragment[i_s1, i_s2] = A_fragment[i_s1, i_s2] * T.exp( - G_diff_local[i_s1, i_s2]) + A_fragment[i_s1, i_s2] = A_fragment[i_s1, i_s2] * T.exp(G_diff_local[i_s1, i_s2]) with T.Else(): A_fragment[i_s1, i_s2] = 0 @@ -155,8 +138,7 @@ def kernel( with T.Then(): A_fragment[i_s1, i_s2] = 0 - T.copy(V[bb, bs * block_S:(bs + 1) * block_S, bh, bv * block_DV:(bv + 1) * block_DV], - V_shared) + T.copy(V[bb, bs * block_S : (bs + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV], V_shared) T.copy(A_fragment, A_shared) T.gemm(A_shared, V_shared, O_fragment) @@ -164,8 +146,7 @@ def kernel( O_fragment[i_s, i_v] = O_fragment[i_s, i_v] * scale T.copy(O_fragment, O_shared) - T.copy(O_shared, O[bb, bs * block_S:(bs + 1) * block_S, bh, - bv * block_DV:(bv + 1) * block_DV]) + T.copy(O_shared, O[bb, bs * block_S : (bs + 1) * block_S, bh, bv * block_DV : (bv + 1) * block_DV]) return kernel @@ -191,8 +172,9 @@ def run_test( output_dtype_torch = getattr(torch, output_dtype) accum_dtype_torch = getattr(torch, accum_dtype) gate_dtype_torch = getattr(torch, gate_dtype) - Q, K, V, HIDDEN, G = prepare_input(B, S, H, DK, DV, chunk_size, input_dtype_torch, - output_dtype_torch, accum_dtype_torch, gate_dtype_torch) + Q, K, V, HIDDEN, G = prepare_input( + B, S, H, DK, DV, chunk_size, input_dtype_torch, output_dtype_torch, accum_dtype_torch, gate_dtype_torch + ) scale = 1.0 / DK**0.5 O_ref = prepare_output(B, S, H, DK, DV, chunk_size, output_dtype_torch) @@ -200,9 +182,25 @@ def run_test( block_S = chunk_size O_tilelang = prepare_output(B, S, H, DK, DV, chunk_size, output_dtype_torch) - kernel = tilelang_chunk_fwd_o(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, chunk_size, scale, use_g, block_S, block_DK, block_DV, - threads, num_stages) + kernel = tilelang_chunk_fwd_o( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + chunk_size, + scale, + use_g, + block_S, + block_DK, + block_DV, + threads, + num_stages, + ) O_tilelang = kernel(Q, K, V, HIDDEN, G) try: @@ -221,10 +219,10 @@ def main(): DK=128, DV=128, chunk_size=64, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", - gate_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, + gate_dtype=T.float32, use_g=True, block_DK=128, block_DV=128, diff --git a/examples/gdn/example_chunk_o_bwd.py b/examples/gdn/example_chunk_o_bwd.py index 76b4792df2..e589818f4c 100644 --- a/examples/gdn/example_chunk_o_bwd.py +++ b/examples/gdn/example_chunk_o_bwd.py @@ -7,13 +7,12 @@ import tilelang.language as T from tilelang.engine.callback import register_cuda_postproc_callback # noqa: F401 -print(tilelang.__file__) - # Add your fla repository path to sys.path # Currently we use the fla repository from the flash-linear-attention project at commit id f03cb3ae # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.common.chunk_o import chunk_bwd_dqkwg except ImportError: @@ -21,7 +20,7 @@ fla = None import torch -from utils import * +from test_utils import assert_similar torch.random.manual_seed(0) # torch.set_printoptions(profile="full") @@ -110,10 +109,8 @@ def prepare_output( @tilelang.jit( out_idx=[-4, -3, -2, -1], - pass_configs={ - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - }) + pass_configs={tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True}, +) def tilelang_chunk_o_bwd_dqkwg( # task config B, @@ -157,25 +154,23 @@ def tilelang_chunk_o_bwd_dqkwg( @T.prim_func def kernel( - # input - Q: T.Tensor(Q_shape, dtype=input_dtype), - K: T.Tensor(K_shape, dtype=input_dtype), - V: T.Tensor(V_shape, dtype=input_dtype), - h: T.Tensor(h_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - dO: T.Tensor(dO_shape, dtype=input_dtype), - dh: T.Tensor(dh_shape, dtype=input_dtype), - dv: T.Tensor(dv_shape, dtype=input_dtype), - W: T.Tensor(W_shape, dtype=input_dtype), - # output - dq: T.Tensor(dq_shape, dtype=output_dtype), - dk: T.Tensor(dk_shape, dtype=output_dtype), - dw: T.Tensor(dw_shape, dtype=output_dtype), - dg: T.Tensor(dg_shape, dtype=gate_dtype), + # input + Q: T.Tensor(Q_shape, dtype=input_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + V: T.Tensor(V_shape, dtype=input_dtype), + h: T.Tensor(h_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + dO: T.Tensor(dO_shape, dtype=input_dtype), + dh: T.Tensor(dh_shape, dtype=input_dtype), + dv: T.Tensor(dv_shape, dtype=input_dtype), + W: T.Tensor(W_shape, dtype=input_dtype), + # output + dq: T.Tensor(dq_shape, dtype=output_dtype), + dk: T.Tensor(dk_shape, dtype=output_dtype), + dw: T.Tensor(dw_shape, dtype=output_dtype), + dg: T.Tensor(dg_shape, dtype=gate_dtype), ): - with T.Kernel( - T.ceildiv(DK, block_DK), T.ceildiv(S, block_S), B * H, - threads=threads) as (bk, bs, bbh): + with T.Kernel(T.ceildiv(DK, block_DK), T.ceildiv(S, block_S), B * H, threads=threads) as (bk, bs, bbh): bb, bh = bbh // H, bbh % H V_shared = T.alloc_shared((block_S, block_DV), dtype=input_dtype) @@ -204,27 +199,27 @@ def kernel( dg_fragment = T.alloc_fragment((block_S,), dtype=gate_dtype) dg_fragment_2 = T.alloc_fragment((block_S,), dtype=gate_dtype) dg_fragment_final = T.alloc_fragment((block_S,), dtype=gate_dtype) - dg_last_local = T.alloc_local((2,), dtype=gate_dtype) + dg_last_local_0 = T.alloc_var(dtype=gate_dtype) + dg_last_local_1 = T.alloc_var(dtype=gate_dtype) + G_last_local = T.alloc_var(dtype=gate_dtype) + dg_last_fragment = T.alloc_fragment((block_DV * block_DK), dtype=gate_dtype) dg_last_fragment_scalar = T.alloc_fragment((1,), dtype=gate_dtype) dg_last_fragment_2 = T.alloc_fragment((block_S * block_DK), dtype=gate_dtype) dg_last_fragment_scalar_2 = T.alloc_fragment((1,), dtype=gate_dtype) - G_shared = T.alloc_shared((block_S, block_DK), dtype=gate_dtype, scope="shared") - G_last_local = T.alloc_local((1,), dtype=gate_dtype) + G_shared = T.alloc_shared((block_S, block_DK), dtype=gate_dtype) T.use_swizzle(10) - T.annotate_layout({ - V_shared: tilelang.layout.make_swizzled_layout(V_shared), - dO_shared: tilelang.layout.make_swizzled_layout(dO_shared), - h_shared: tilelang.layout.make_swizzled_layout(h_shared), - dh_shared: tilelang.layout.make_swizzled_layout(dh_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared), - q_shared: tilelang.layout.make_swizzled_layout(q_shared), - k_shared: tilelang.layout.make_swizzled_layout(k_shared), - }) - - T.clear(dg_last_local) + T.annotate_layout( + { + q_shared: tilelang.layout.make_swizzled_layout(q_shared), + k_shared: tilelang.layout.make_swizzled_layout(k_shared), + } + ) + + T.clear(dg_last_local_0) + T.clear(dg_last_local_1) T.clear(G_last_local) T.clear(G_shared) T.clear(q_fragment) @@ -237,18 +232,10 @@ def kernel( T.clear(dw_fragment) for i_v in T.Pipelined(T.ceildiv(DV, block_DV), num_stages=num_stages): - T.copy( - V[bb, bs * block_S:(bs + 1) * block_S, bh, i_v * block_DV:(i_v + 1) * block_DV], - V_shared) - T.copy( - dO[bb, bs * block_S:(bs + 1) * block_S, bh, - i_v * block_DV:(i_v + 1) * block_DV], dO_shared) - T.copy( - h[bb, bs, bh, bk * block_DK:(bk + 1) * block_DK, - i_v * block_DV:(i_v + 1) * block_DV], h_shared) - T.copy( - dh[bb, bs, bh, bk * block_DK:(bk + 1) * block_DK, - i_v * block_DV:(i_v + 1) * block_DV], dh_shared) + T.copy(V[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], V_shared) + T.copy(dO[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], dO_shared) + T.copy(h[bb, bs, bh, bk * block_DK : (bk + 1) * block_DK, i_v * block_DV : (i_v + 1) * block_DV], h_shared) + T.copy(dh[bb, bs, bh, bk * block_DK : (bk + 1) * block_DK, i_v * block_DV : (i_v + 1) * block_DV], dh_shared) if use_g: T.clear(dg_last_fragment_scalar) @@ -256,32 +243,25 @@ def kernel( # for i_kv in T.Parallel(block_DK * block_DV): # dg_last_fragment[i_kv] = h_shared[i_kv // block_DV, i_kv % block_DV] * dh_shared[i_kv // block_DV, i_kv % block_DV] for i_kv in T.Parallel(block_DK * block_DV): - i_k, i_v = i_kv // block_DV, i_kv % block_DV - dg_last_fragment[i_kv] = h_shared[i_k, i_v] * dh_shared[i_k, i_v] + dg_last_fragment[i_kv] = h_shared[i_kv // block_DV, i_kv % block_DV] * dh_shared[i_kv // block_DV, i_kv % block_DV] T.reduce_sum(dg_last_fragment, dg_last_fragment_scalar, dim=-1, clear=False) - dg_last_local[0] += dg_last_fragment_scalar[0] + dg_last_local_0 = dg_last_local_0 + dg_last_fragment_scalar[0] T.gemm(dO_shared, V_shared, ds_fragment, transpose_B=True) T.gemm(dO_shared, h_shared, dq_fragment, transpose_B=True) T.gemm(V_shared, dh_shared, dk_fragment, transpose_B=True) if use_dw: - T.copy( - dv[bb, bs * block_S:(bs + 1) * block_S, bh, - i_v * block_DV:(i_v + 1) * block_DV], dv_shared) + T.copy(dv[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], dv_shared) T.gemm(dv_shared, h_shared, dw_fragment, transpose_B=True) if use_dw: for i_s, i_k in T.Parallel(block_S, block_DK): dw_fragment[i_s, i_k] = -dw_fragment[i_s, i_k] - T.copy( - dw_fragment, dw[bb, bs * block_S:(bs + 1) * block_S, bh, - bk * block_DK:(bk + 1) * block_DK]) - - T.copy(Q[bb, bs * block_S:(bs + 1) * block_S, bh, bk * block_DK:(bk + 1) * block_DK], - q_shared) - T.copy(K[bb, bs * block_S:(bs + 1) * block_S, bh, bk * block_DK:(bk + 1) * block_DK], - k_shared) + T.copy(dw_fragment, dw[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK]) + + T.copy(Q[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK], q_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK], k_shared) T.copy(q_shared, q_fragment) T.copy(k_shared, k_fragment) @@ -290,13 +270,12 @@ def kernel( T.clear(dg_fragment_2) for i_s, i_k in T.Parallel(block_S, block_DK): G_shared[i_s, i_k] = G[bb, bs * block_S + i_s, bh] - G_last_local[0] = G[bb, bs * block_S + block_S - 1, bh] + dg_last_local_0 = G[bb, bs * block_S + block_S - 1, bh] # Use gmem directly instead of local register - dg_last_local[0] = dg_last_local[0] * T.exp(G[bb, bs * block_S + block_S - 1, bh]) + dg_last_local_0 = dg_last_local_0 * T.exp(G[bb, bs * block_S + block_S - 1, bh]) for i_s, i_k in T.Parallel(block_S, block_DK): - dq_fragment[i_s, i_k] = dq_fragment[i_s, i_k] * T.exp(G[bb, bs * block_S + i_s, - bh]) * scale + dq_fragment[i_s, i_k] = dq_fragment[i_s, i_k] * T.exp(G[bb, bs * block_S + i_s, bh]) * scale T.clear(dg_fragment_reduce_tmp) for i_s, i_k in T.Parallel(block_S, block_DK): dg_fragment_reduce_tmp[i_s, i_k] = dq_fragment[i_s, i_k] * q_shared[i_s, i_k] @@ -304,12 +283,11 @@ def kernel( T.reduce_sum(dg_fragment_reduce_tmp, dg_fragment, dim=-1, clear=False) for i_s, i_k in T.Parallel(block_S, block_DK): - with T.If(G_last_local[0] - G[bb, bs * block_S + i_s, bh] <= 0): - with T.Then(): - dk_fragment[i_s, i_k] = dk_fragment[i_s, i_k] * T.exp( - G_last_local[0] - G[bb, bs * block_S + i_s, bh]) - with T.Else(): - dk_fragment[i_s, i_k] = 0 + dk_fragment[i_s, i_k] = ( + dk_fragment[i_s, i_k] * T.exp(G_last_local - G[bb, bs * block_S + i_s, bh]) + if G_last_local - G[bb, bs * block_S + i_s, bh] <= 0 + else 0 + ) T.clear(dg_fragment_reduce_tmp) for i_s, i_k in T.Parallel(block_S, block_DK): dg_fragment_reduce_tmp[i_s, i_k] = dk_fragment[i_s, i_k] * (-k_shared[i_s, i_k]) @@ -323,24 +301,20 @@ def kernel( i_s, i_k = i_sk // block_DK, i_sk % block_DK dg_last_fragment_2[i_sk] = dk_shared[i_s, i_k] * k_shared[i_s, i_k] T.reduce_sum(dg_last_fragment_2, dg_last_fragment_scalar_2, dim=-1, clear=False) - dg_last_local[1] = dg_last_fragment_scalar_2[0] + dg_last_local_1 = dg_last_fragment_scalar_2[0] for i_s1, i_s2 in T.Parallel(block_S, block_S): - with T.If(i_s1 >= i_s2 and - G[bb, bs * block_S + i_s1, bh] - G[bb, bs * block_S + i_s2, bh] <= 0): - with T.Then(): - ds_fragment[i_s1, i_s2] = ds_fragment[ - i_s1, i_s2] * T.exp(G[bb, bs * block_S + i_s1, bh] - - G[bb, bs * block_S + i_s2, bh]) * scale - with T.Else(): - ds_fragment[i_s1, i_s2] = 0 + ds_fragment[i_s1, i_s2] = ( + (ds_fragment[i_s1, i_s2] * T.exp(G[bb, bs * block_S + i_s1, bh] - G[bb, bs * block_S + i_s2, bh]) * scale) + if G[bb, bs * block_S + i_s1, bh] - G[bb, bs * block_S + i_s2, bh] <= 0 + else 0 + ) T.clear(ds_fragment_positive) T.clear(ds_fragment_positive_transpose) T.gemm(q_shared, k_shared, ds_fragment_positive, transpose_B=True) for i_s1, i_s2 in T.Parallel(block_S, block_S): - ds_fragment_positive[ - i_s1, i_s2] = ds_fragment[i_s1, i_s2] * ds_fragment_positive[i_s1, i_s2] + ds_fragment_positive[i_s1, i_s2] = ds_fragment[i_s1, i_s2] * ds_fragment_positive[i_s1, i_s2] # FIXME: The reduce_sum statement with clear=True will cause an error of warp specialized pass T.reduce_sum(ds_fragment_positive, dg_fragment, dim=1, clear=False) @@ -362,25 +336,16 @@ def kernel( T.gemm(ds_shared, q_shared, dk_fragment, transpose_A=True) for i_s in T.Parallel(block_S): - with T.If(i_s >= block_S - 1): # noqa: SIM117 - with T.Then(): - dg_fragment_final[ - i_s] = dg_fragment_final[i_s] + dg_last_local[0] + dg_last_local[1] - - T.copy( - dq_fragment, dq[bb, bs * block_S:(bs + 1) * block_S, bh, - bk * block_DK:(bk + 1) * block_DK]) - T.copy( - dk_fragment, dk[bb, bs * block_S:(bs + 1) * block_S, bh, - bk * block_DK:(bk + 1) * block_DK]) + dg_fragment_final[i_s] = dg_fragment_final[i_s] + dg_last_local_0 + dg_last_local_1 + + T.copy(dq_fragment, dq[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK]) + T.copy(dk_fragment, dk[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK]) for i_s in T.Parallel(block_S): dg[bk, bb, bs * block_S + i_s, bh] = dg_fragment_final[i_s] else: for i_s1, i_s2 in T.Parallel(block_S, block_S): - with T.If(i_s1 < i_s2): # noqa: SIM117 - with T.Then(): - ds_fragment[i_s1, i_s2] = 0 + ds_fragment[i_s1, i_s2] = 0 if i_s1 < i_s2 else ds_fragment[i_s1, i_s2] T.clear(dk_fragment_2) T.copy(ds_fragment, ds_shared) T.gemm(ds_shared, k_shared, dq_fragment) @@ -388,12 +353,8 @@ def kernel( for i_s, i_k in T.Parallel(block_S, block_DK): dq_fragment[i_s, i_k] = dq_fragment[i_s, i_k] * scale dk_fragment[i_s, i_k] = dk_fragment[i_s, i_k] + dk_fragment_2[i_s, i_k] * scale - T.copy( - dq_fragment, dq[bb, bs * block_S:(bs + 1) * block_S, bh, - bk * block_DK:(bk + 1) * block_DK]) - T.copy( - dk_fragment, dk[bb, bs * block_S:(bs + 1) * block_S, bh, - bk * block_DK:(bk + 1) * block_DK]) + T.copy(dq_fragment, dq[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK]) + T.copy(dk_fragment, dk[bb, bs * block_S : (bs + 1) * block_S, bh, bk * block_DK : (bk + 1) * block_DK]) return kernel @@ -443,33 +404,53 @@ def run_test( threads=256, num_stages=0, ): - Q, K, V, h, G, dO, dh, dv, W = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - dq_ref, dk_ref, dw_ref, dg_ref = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype), block_DK) + Q, K, V, h, G, dO, dh, dv, W = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) + dq_ref, dk_ref, dw_ref, dg_ref = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype), block_DK + ) dq_tilelang, dk_tilelang, dw_tilelang, dg_tilelang = prepare_output( - B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), - getattr(torch, state_dtype), block_DK) + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype), block_DK + ) # ref if use_g: - dq_ref, dk_ref, dw_ref, dg_ref = chunk_bwd_dqkwg( - Q, K, V, G, dO, h, dh, dv, W, chunk_size=chunk_size, scale=scale) + dq_ref, dk_ref, dw_ref, dg_ref = chunk_bwd_dqkwg(Q, K, V, G, dO, h, dh, dv, W, chunk_size=chunk_size, scale=scale) else: - dq_ref, dk_ref, dw_ref, dg_ref = chunk_bwd_dqkwg( - Q, K, V, None, dO, h, dh, dv, W, chunk_size=chunk_size, scale=scale) + dq_ref, dk_ref, dw_ref, dg_ref = chunk_bwd_dqkwg(Q, K, V, None, dO, h, dh, dv, W, chunk_size=chunk_size, scale=scale) # tilelang - kernel = tilelang_chunk_o_bwd_dqkwg(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, state_dtype, chunk_size, scale, use_g, use_dw, - block_DK, block_DV, threads, num_stages) - print(kernel.get_kernel_source()) + kernel = tilelang_chunk_o_bwd_dqkwg( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + scale, + use_g, + use_dw, + block_DK, + block_DV, + threads, + num_stages, + ) dq_tilelang, dk_tilelang, dw_tilelang, dg_tilelang = kernel(Q, K, V, h, G, dO, dh, dv, W) if use_g: @@ -516,11 +497,11 @@ def main(): H=8, DK=DK, DV=DV, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", - gate_dtype="float32", - state_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, + gate_dtype=T.float32, + state_dtype=T.float32, chunk_size=64, scale=DK**-0.5, # scale=1, diff --git a/examples/gdn/example_chunk_scaled_dot_kkt.py b/examples/gdn/example_chunk_scaled_dot_kkt.py index d07a4776a2..8c7a4d573b 100644 --- a/examples/gdn/example_chunk_scaled_dot_kkt.py +++ b/examples/gdn/example_chunk_scaled_dot_kkt.py @@ -9,6 +9,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.common.chunk_scaled_dot_kkt import chunk_scaled_dot_kkt_fwd except ImportError: @@ -56,9 +57,9 @@ def tilelang_chunk_scaled_dot_kkt_fwd( H, DK, chunk_size=64, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, use_g=True, # kernel config block_S=64, @@ -75,10 +76,10 @@ def tilelang_chunk_scaled_dot_kkt_fwd( @T.prim_func def kernel( - K: T.Tensor(K_shape, dtype=input_dtype), - Beta: T.Tensor(Beta_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=accum_dtype), - A: T.Tensor(output_shape, dtype=output_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + Beta: T.Tensor(Beta_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=accum_dtype), + A: T.Tensor(output_shape, dtype=output_dtype), ): with T.Kernel(T.ceildiv(S, block_S), B * H, threads=threads) as (bs, bbh): bb, bh = bbh // H, bbh % H @@ -93,20 +94,13 @@ def kernel( G_shared = T.alloc_shared((block_S,), dtype=accum_dtype, scope="shared") G_diff_local = T.alloc_fragment((block_S, block_S), dtype=accum_dtype) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - }) - T.fill(A_fragment, 0) T.disable_warp_group_reg_alloc() for i_s in T.Parallel(block_S): Beta_shared[i_s] = Beta[bb, bs * block_S + i_s, bh] for i_k in T.Pipelined(T.ceildiv(DK, block_DK), num_stages=num_stages): - T.copy( - K[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - K_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], K_shared) for i_s, i_k2 in T.Parallel(block_S, block_DK): Beta_K_fragment[i_s, i_k2] = K_shared[i_s, i_k2] * Beta_shared[i_s] T.gemm(Beta_K_fragment, K_shared, A_fragment, transpose_B=True) @@ -119,8 +113,7 @@ def kernel( for i_s1, i_s2 in T.Parallel(block_S, block_S): with T.If(G_diff_local[i_s1, i_s2] <= 0 and i_s1 > i_s2): with T.Then(): - A_fragment[i_s1, i_s2] = A_fragment[i_s1, i_s2] * T.exp( - G_diff_local[i_s1, i_s2]) + A_fragment[i_s1, i_s2] = A_fragment[i_s1, i_s2] * T.exp(G_diff_local[i_s1, i_s2]) with T.Else(): A_fragment[i_s1, i_s2] = 0 else: @@ -130,7 +123,7 @@ def kernel( A_fragment[i_s1, i_s2] = 0 T.copy(A_fragment, A_shared) - T.copy(A_shared, A[bb, bs * block_S:(bs + 1) * block_S, bh, :]) + T.copy(A_shared, A[bb, bs * block_S : (bs + 1) * block_S, bh, :]) return kernel @@ -149,24 +142,21 @@ def run_test( threads, num_stages, ): - K, Beta, G = prepare_input(B, S, H, DK, getattr(torch, input_dtype), - getattr(torch, output_dtype), getattr(torch, accum_dtype)) + K, Beta, G = prepare_input(B, S, H, DK, getattr(torch, input_dtype), getattr(torch, output_dtype), getattr(torch, accum_dtype)) A_ref = prepare_output(B, S, H, chunk_size, getattr(torch, output_dtype)) A_tilelang = prepare_output(B, S, H, chunk_size, getattr(torch, output_dtype)) # reference if use_g: - A_ref = chunk_scaled_dot_kkt_fwd( - K, Beta, G, chunk_size=chunk_size, output_dtype=getattr(torch, output_dtype)) + A_ref = chunk_scaled_dot_kkt_fwd(K, Beta, G, chunk_size=chunk_size, output_dtype=getattr(torch, output_dtype)) else: - A_ref = chunk_scaled_dot_kkt_fwd( - K, Beta, None, chunk_size=chunk_size, output_dtype=getattr(torch, output_dtype)) + A_ref = chunk_scaled_dot_kkt_fwd(K, Beta, None, chunk_size=chunk_size, output_dtype=getattr(torch, output_dtype)) # tilelang block_S = chunk_size - kernel = tilelang_chunk_scaled_dot_kkt_fwd(B, S, H, DK, chunk_size, input_dtype, output_dtype, - accum_dtype, use_g, block_S, block_DK, threads, - num_stages) + kernel = tilelang_chunk_scaled_dot_kkt_fwd( + B, S, H, DK, chunk_size, input_dtype, output_dtype, accum_dtype, use_g, block_S, block_DK, threads, num_stages + ) A_tilelang = kernel(K, Beta, G) try: @@ -186,13 +176,14 @@ def main(): H=32, DK=128, chunk_size=64, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, use_g=True, block_DK=64, threads=128, - num_stages=2) + num_stages=2, + ) if __name__ == "__main__": diff --git a/examples/gdn/example_cumsum.py b/examples/gdn/example_cumsum.py index 9896c7ecf7..0760b49645 100644 --- a/examples/gdn/example_cumsum.py +++ b/examples/gdn/example_cumsum.py @@ -10,6 +10,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.utils.cumsum import chunk_local_cumsum_scalar except ImportError: @@ -20,11 +21,8 @@ @tilelang.jit( - out_idx=[-1], - pass_configs={ - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - }) + out_idx=[-1], pass_configs={tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True} +) def tilelang_chunk_local_cumsum_scalar( # task config B, @@ -34,43 +32,43 @@ def tilelang_chunk_local_cumsum_scalar( is_varlen=False, head_first=False, reverse=False, - input_dtype="float16", - output_dtype="float32", + input_dtype=T.float16, + output_dtype=T.float32, # kernel config block_S=64, threads=256, use_fragment=False, ): G_shape = (B, H, S) if head_first else (B, S, H) - assert chunk_size == 2**(chunk_size.bit_length() - 1), "chunk_size must be a power of 2" + assert chunk_size == 2 ** (chunk_size.bit_length() - 1), "chunk_size must be a power of 2" assert chunk_size == block_S, "chunk_size must be equal to block_S" @T.prim_func def kernel( - G: T.Tensor(G_shape, dtype=input_dtype), - G_new: T.Tensor(G_shape, dtype=output_dtype), + G: T.Tensor(G_shape, dtype=input_dtype), + G_new: T.Tensor(G_shape, dtype=output_dtype), ): with T.Kernel(T.ceildiv(S, block_S), B * H, threads=threads) as (bs, bbh): bb, bh = bbh // H, bbh % H G_shared = T.alloc_shared((1, block_S), dtype=output_dtype, scope="shared") if head_first: - T.copy(G[bb, bh, bs * block_S:(bs + 1) * block_S], G_shared) + T.copy(G[bb, bh, bs * block_S : (bs + 1) * block_S], G_shared) else: - T.copy(G[bb, bs * block_S:(bs + 1) * block_S, bh], G_shared) + T.copy(G[bb, bs * block_S : (bs + 1) * block_S, bh], G_shared) if use_fragment: G_fragment = T.alloc_fragment((1, block_S), dtype=output_dtype, scope="shared") T.copy(G_shared, G_fragment) T.cumsum(G_fragment, dim=1, reverse=reverse) if head_first: - T.copy(G_fragment, G_new[bb, bh, bs * block_S:(bs + 1) * block_S]) + T.copy(G_fragment, G_new[bb, bh, bs * block_S : (bs + 1) * block_S]) else: - T.copy(G_fragment, G_new[bb, bs * block_S:(bs + 1) * block_S, bh]) + T.copy(G_fragment, G_new[bb, bs * block_S : (bs + 1) * block_S, bh]) else: T.cumsum(G_shared, dim=1, reverse=reverse) if head_first: - T.copy(G_shared, G_new[bb, bh, bs * block_S:(bs + 1) * block_S]) + T.copy(G_shared, G_new[bb, bh, bs * block_S : (bs + 1) * block_S]) else: - T.copy(G_shared, G_new[bb, bs * block_S:(bs + 1) * block_S, bh]) + T.copy(G_shared, G_new[bb, bs * block_S : (bs + 1) * block_S, bh]) return kernel @@ -113,11 +111,8 @@ def run_test( # reference cumsum G_new_ref = chunk_local_cumsum_scalar( - g=G, - chunk_size=chunk_size, - reverse=reverse, - head_first=head_first, - output_dtype=getattr(torch, output_dtype)) + g=G, chunk_size=chunk_size, reverse=reverse, head_first=head_first, output_dtype=getattr(torch, output_dtype) + ) # tilelang cumsum block_S = chunk_size @@ -159,10 +154,11 @@ def main(): chunk_size=64, reverse=True, head_first=False, - input_dtype="float32", - output_dtype="float32", + input_dtype=T.float32, + output_dtype=T.float32, threads=256, - use_fragment=False) + use_fragment=False, + ) if __name__ == "__main__": diff --git a/examples/gdn/example_wy_fast.py b/examples/gdn/example_wy_fast.py index 0a0983a82f..d36dcf9b72 100644 --- a/examples/gdn/example_wy_fast.py +++ b/examples/gdn/example_wy_fast.py @@ -9,6 +9,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.gated_delta_rule.wy_fast import recompute_w_u_fwd except ImportError: @@ -73,13 +74,13 @@ def tilelang_recompute_w_u_fwd( @T.prim_func def kernel( - K: T.Tensor(K_shape, dtype=input_dtype), - V: T.Tensor(V_shape, dtype=input_dtype), - Beta: T.Tensor(Beta_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - A: T.Tensor(A_shape, dtype=output_dtype), - W: T.Tensor(K_shape, dtype=output_dtype), - U: T.Tensor(V_shape, dtype=output_dtype), + K: T.Tensor(K_shape, dtype=input_dtype), + V: T.Tensor(V_shape, dtype=input_dtype), + Beta: T.Tensor(Beta_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + A: T.Tensor(A_shape, dtype=output_dtype), + W: T.Tensor(K_shape, dtype=output_dtype), + U: T.Tensor(V_shape, dtype=output_dtype), ): with T.Kernel(T.ceildiv(S, block_S), B * H, threads=threads) as (bs, bbh): bb, bh = bbh // H, bbh % H @@ -95,49 +96,37 @@ def kernel( W_Beta_shared = T.alloc_shared((block_S, block_DK), dtype=input_dtype) U_Beta_shared = T.alloc_shared((block_S, block_DV), dtype=input_dtype) - T.annotate_layout({ - K_shared: tilelang.layout.make_swizzled_layout(K_shared), - V_shared: tilelang.layout.make_swizzled_layout(V_shared), - A_shared: tilelang.layout.make_swizzled_layout(A_shared), - W_shared: tilelang.layout.make_swizzled_layout(W_shared), - U_shared: tilelang.layout.make_swizzled_layout(U_shared), - W_Beta_shared: tilelang.layout.make_swizzled_layout(W_Beta_shared), - U_Beta_shared: tilelang.layout.make_swizzled_layout(U_Beta_shared), - }) + T.annotate_layout( + { + K_shared: tilelang.layout.make_swizzled_layout(K_shared), + V_shared: tilelang.layout.make_swizzled_layout(V_shared), + } + ) T.disable_warp_group_reg_alloc() for i_s in T.Parallel(block_S): Beta_shared[i_s] = Beta[bb, bs * block_S + i_s, bh] G_shared[i_s] = T.exp(G[bb, bs * block_S + i_s, bh]) - T.copy(A[bb, bs * block_S:(bs + 1) * block_S, bh, :], A_shared) + T.copy(A[bb, bs * block_S : (bs + 1) * block_S, bh, :], A_shared) for i_v in T.Pipelined(T.ceildiv(DV, block_DV), num_stages=num_stages): - T.copy( - V[bb, bs * block_S:(bs + 1) * block_S, bh, i_v * block_DV:(i_v + 1) * block_DV], - V_shared) + T.copy(V[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], V_shared) for i_s, i_v2 in T.Parallel(block_S, block_DV): U_Beta_shared[i_s, i_v2] = V_shared[i_s, i_v2] * Beta_shared[i_s] T.gemm(A_shared, U_Beta_shared, U_fragment, clear_accum=True) # First copy to smem, then copy to gmem to reduce U2RU instructions T.copy(U_fragment, U_shared) - T.copy( - U_shared, U[bb, bs * block_S:(bs + 1) * block_S, bh, - i_v * block_DV:(i_v + 1) * block_DV]) + T.copy(U_shared, U[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV]) for i_k in T.Pipelined(T.ceildiv(DK, block_DK), num_stages=num_stages): - T.copy( - K[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - K_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], K_shared) for i_s, i_k2 in T.Parallel(block_S, block_DK): - W_Beta_shared[i_s, - i_k2] = K_shared[i_s, i_k2] * Beta_shared[i_s] * G_shared[i_s] + W_Beta_shared[i_s, i_k2] = K_shared[i_s, i_k2] * Beta_shared[i_s] * G_shared[i_s] T.gemm(A_shared, W_Beta_shared, W_fragment, clear_accum=True) # First copy to smem, then copy to gmem to reduce U2RU instructions T.copy(W_fragment, W_shared) - T.copy( - W_shared, W[bb, bs * block_S:(bs + 1) * block_S, bh, - i_k * block_DK:(i_k + 1) * block_DK]) + T.copy(W_shared, W[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK]) return kernel @@ -159,15 +148,8 @@ def run_test( num_stages, ): K, V, Beta, G, A = prepare_input( - B, - S, - H, - DK, - DV, - chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - gate_dtype=getattr(torch, gate_dtype)) + B, S, H, DK, DV, chunk_size, getattr(torch, input_dtype), getattr(torch, output_dtype), gate_dtype=getattr(torch, gate_dtype) + ) W_ref, U_ref = prepare_output(B, S, H, DK, DV, getattr(torch, output_dtype)) W_tilelang, U_tilelang = prepare_output(B, S, H, DK, DV, getattr(torch, output_dtype)) @@ -191,7 +173,8 @@ def run_test( block_DK=block_DK, block_DV=block_DV, threads=threads, - num_stages=num_stages) + num_stages=num_stages, + ) print(kernel.get_kernel_source()) W_tilelang, U_tilelang = kernel(K, V, Beta, G, A) @@ -217,14 +200,15 @@ def main(): DK=128, DV=128, chunk_size=64, - input_dtype="bfloat16", - output_dtype="bfloat16", - gate_dtype="float32", - accum_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + gate_dtype=T.float32, + accum_dtype=T.float32, block_DK=64, block_DV=32, threads=128, - num_stages=3) + num_stages=3, + ) if __name__ == "__main__": diff --git a/examples/gdn/example_wy_fast_bwd_split.py b/examples/gdn/example_wy_fast_bwd_split.py index 618a82b4c8..de8afc2b77 100644 --- a/examples/gdn/example_wy_fast_bwd_split.py +++ b/examples/gdn/example_wy_fast_bwd_split.py @@ -10,6 +10,7 @@ # sys.path.insert(0, "/home/tzj/flash-linear-attention") try: import fla + print(fla.__file__) from fla.ops.gated_delta_rule.wy_fast import bwd_prepare_wy_repr except ImportError: @@ -93,10 +94,8 @@ def prepare_output( @tilelang.jit( out_idx=[-5, -4, -3, -2, -1], - pass_configs={ - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - }) + pass_configs={tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True}, +) def tilelang_wy_fast_bwd( # task config B, @@ -135,20 +134,20 @@ def tilelang_wy_fast_bwd( @T.prim_func def kernel( - # input - K: T.Tensor(K_shape, dtype=input_dtype), - V: T.Tensor(V_shape, dtype=input_dtype), - Beta: T.Tensor(Beta_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - A: T.Tensor(A_shape, dtype=input_dtype), - dw: T.Tensor(dw_shape, dtype=input_dtype), - du: T.Tensor(du_shape, dtype=input_dtype), - # output - dA: T.Tensor(dA_shape, dtype=input_dtype), - dk: T.Tensor(dk_shape, dtype=output_dtype), - dv: T.Tensor(dv_shape, dtype=output_dtype), - dbeta: T.Tensor(dbeta_shape, dtype=output_dtype), - dg: T.Tensor(dg_shape, dtype=gate_dtype), + # input + K: T.Tensor(K_shape, dtype=input_dtype), + V: T.Tensor(V_shape, dtype=input_dtype), + Beta: T.Tensor(Beta_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + A: T.Tensor(A_shape, dtype=input_dtype), + dw: T.Tensor(dw_shape, dtype=input_dtype), + du: T.Tensor(du_shape, dtype=input_dtype), + # output + dA: T.Tensor(dA_shape, dtype=input_dtype), + dk: T.Tensor(dk_shape, dtype=output_dtype), + dv: T.Tensor(dv_shape, dtype=output_dtype), + dbeta: T.Tensor(dbeta_shape, dtype=output_dtype), + dg: T.Tensor(dg_shape, dtype=gate_dtype), ): with T.Kernel(T.ceildiv(S, block_S), B * H, threads=threads) as (bs, bbh): bb, bh = bbh // H, bbh % H @@ -187,7 +186,7 @@ def kernel( T.clear(dbeta_fragment_v) T.clear(dg_fragment) - T.copy(A[bb, bs * block_S:(bs + 1) * block_S, bh, :], A_shared) + T.copy(A[bb, bs * block_S : (bs + 1) * block_S, bh, :], A_shared) for i_s in T.Parallel(block_S): Beta_shared[i_s] = Beta[bb, bs * block_S + i_s, bh] G_shared[i_s] = G[bb, bs * block_S + i_s, bh] @@ -195,51 +194,37 @@ def kernel( # Update dk for i_k in T.Pipelined(T.ceildiv(DK, block_DK), num_stages=num_stages): - T.copy( - K[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - K_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], K_shared) for i_s, i_k2 in T.Parallel(block_S, block_DK): - K_shared_beta_g[i_s, - i_k2] = K_shared[i_s, - i_k2] * Beta_shared[i_s] * G_shared_exp[i_s] - T.copy( - dw[bb, bs * block_S:(bs + 1) * block_S, bh, - i_k * block_DK:(i_k + 1) * block_DK], dw_shared) + K_shared_beta_g[i_s, i_k2] = K_shared[i_s, i_k2] * Beta_shared[i_s] * G_shared_exp[i_s] + T.copy(dw[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], dw_shared) T.gemm(dw_shared, K_shared_beta_g, dA_fragment, transpose_B=True) T.gemm(A_shared, dw_shared, dk_fragment_beta_g, clear_accum=True, transpose_A=True) for i_s, i_k2 in T.Parallel(block_S, block_DK): - dk_fragment[ - i_s, - i_k2] = dk_fragment_beta_g[i_s, i_k2] * Beta_shared[i_s] * G_shared_exp[i_s] + dk_fragment[i_s, i_k2] = dk_fragment_beta_g[i_s, i_k2] * Beta_shared[i_s] * G_shared_exp[i_s] # for i_s, i_k2 in T.Parallel(block_S, block_DK): # dbeta_fragment[i_s] = dbeta_fragment[i_s] + dk_fragment_beta_g[i_s, i_k2] * K_shared[i_s, i_k2] * G_shared_exp[i_s] for i_s, i_k2 in T.Parallel(block_S, block_DK): - dbeta_fragment_reduce_tmpk[i_s, i_k2] = dk_fragment_beta_g[ - i_s, i_k2] * K_shared[i_s, i_k2] * G_shared_exp[i_s] + dbeta_fragment_reduce_tmpk[i_s, i_k2] = dk_fragment_beta_g[i_s, i_k2] * K_shared[i_s, i_k2] * G_shared_exp[i_s] T.reduce_sum(dbeta_fragment_reduce_tmpk, dbeta_fragment_k, dim=1, clear=False) # for i_s, i_k2 in T.Parallel(block_S, block_DK): # dg_fragment[i_s] = dg_fragment[i_s] + dk_fragment_beta_g[i_s, i_k2] * K_shared[i_s, i_k2] * G_shared_exp[i_s] * Beta_shared[i_s] for i_s, i_k2 in T.Parallel(block_S, block_DK): - dg_fragment_reduce_tmp[i_s, i_k2] = dk_fragment_beta_g[i_s, i_k2] * K_shared[ - i_s, i_k2] * G_shared_exp[i_s] * Beta_shared[i_s] + dg_fragment_reduce_tmp[i_s, i_k2] = ( + dk_fragment_beta_g[i_s, i_k2] * K_shared[i_s, i_k2] * G_shared_exp[i_s] * Beta_shared[i_s] + ) T.reduce_sum(dg_fragment_reduce_tmp, dg_fragment, dim=1, clear=False) # correct dk - T.copy( - dk_fragment, dk[bb, bs * block_S:(bs + 1) * block_S, bh, - i_k * block_DK:(i_k + 1) * block_DK]) + T.copy(dk_fragment, dk[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK]) # Update dv for i_v in T.Pipelined(T.ceildiv(DV, block_DV), num_stages=num_stages): - T.copy( - V[bb, bs * block_S:(bs + 1) * block_S, bh, i_v * block_DV:(i_v + 1) * block_DV], - V_shared) + T.copy(V[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], V_shared) for i_s, i_v2 in T.Parallel(block_S, block_DV): V_shared_beta[i_s, i_v2] = V_shared[i_s, i_v2] * Beta_shared[i_s] - T.copy( - du[bb, bs * block_S:(bs + 1) * block_S, bh, - i_v * block_DV:(i_v + 1) * block_DV], du_shared) + T.copy(du[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV], du_shared) T.gemm(du_shared, V_shared_beta, dA_fragment, transpose_B=True) T.gemm(A_shared, du_shared, dv_fragment_beta, clear_accum=True, transpose_A=True) for i_s, i_v2 in T.Parallel(block_S, block_DV): @@ -247,30 +232,22 @@ def kernel( # for i_s, i_v2 in T.Parallel(block_S, block_DV): # dbeta_fragment[i_s] = dbeta_fragment[i_s] + dv_fragment_beta[i_s, i_v2] * V_shared[i_s, i_v2] for i_s, i_v2 in T.Parallel(block_S, block_DV): - dbeta_fragment_reduce_tmpv[i_s, - i_v2] = dv_fragment_beta[i_s, i_v2] * V_shared[i_s, - i_v2] + dbeta_fragment_reduce_tmpv[i_s, i_v2] = dv_fragment_beta[i_s, i_v2] * V_shared[i_s, i_v2] T.reduce_sum(dbeta_fragment_reduce_tmpv, dbeta_fragment_v, dim=1, clear=False) - T.copy( - dv_fragment, dv[bb, bs * block_S:(bs + 1) * block_S, bh, - i_v * block_DV:(i_v + 1) * block_DV]) + T.copy(dv_fragment, dv[bb, bs * block_S : (bs + 1) * block_S, bh, i_v * block_DV : (i_v + 1) * block_DV]) # Temporary store dbeta, dg and dA for i_s in T.Parallel(block_S): dbeta[bb, bs * block_S + i_s, bh] = dbeta_fragment_k[i_s] + dbeta_fragment_v[i_s] dg[bb, bs * block_S + i_s, bh] = dg_fragment[i_s] # correct dA - T.copy(dA_fragment, dA[bb, bs * block_S:(bs + 1) * block_S, bh, :]) + T.copy(dA_fragment, dA[bb, bs * block_S : (bs + 1) * block_S, bh, :]) return kernel -@tilelang.jit( - pass_configs={ - tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True - }) +@tilelang.jit(pass_configs={tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True}) def tilelang_wy_fast_bwd_split( # task config B, @@ -308,20 +285,20 @@ def tilelang_wy_fast_bwd_split( @T.prim_func def kernel( - # input - K: T.Tensor(K_shape, dtype=input_dtype), - V: T.Tensor(V_shape, dtype=input_dtype), - Beta: T.Tensor(Beta_shape, dtype=input_dtype), - G: T.Tensor(G_shape, dtype=gate_dtype), - A: T.Tensor(A_shape, dtype=input_dtype), - dw: T.Tensor(dw_shape, dtype=input_dtype), - du: T.Tensor(du_shape, dtype=input_dtype), - dA: T.Tensor(dA_shape, dtype=input_dtype), - dk: T.Tensor(dk_shape, dtype=output_dtype), - dv: T.Tensor(dv_shape, dtype=output_dtype), - dbeta_k: T.Tensor(dbeta_shape, dtype=output_dtype), - dg_A_positive: T.Tensor(dA_shape, dtype=gate_dtype), - dg_A_negative: T.Tensor(dA_shape, dtype=gate_dtype), + # input + K: T.Tensor(K_shape, dtype=input_dtype), + V: T.Tensor(V_shape, dtype=input_dtype), + Beta: T.Tensor(Beta_shape, dtype=input_dtype), + G: T.Tensor(G_shape, dtype=gate_dtype), + A: T.Tensor(A_shape, dtype=input_dtype), + dw: T.Tensor(dw_shape, dtype=input_dtype), + du: T.Tensor(du_shape, dtype=input_dtype), + dA: T.Tensor(dA_shape, dtype=input_dtype), + dk: T.Tensor(dk_shape, dtype=output_dtype), + dv: T.Tensor(dv_shape, dtype=output_dtype), + dbeta_k: T.Tensor(dbeta_shape, dtype=output_dtype), + dg_A_positive: T.Tensor(dA_shape, dtype=gate_dtype), + dg_A_negative: T.Tensor(dA_shape, dtype=gate_dtype), ): with T.Kernel(T.ceildiv(S, block_S), B * H, threads=threads) as (bs, bbh): bb, bh = bbh // H, bbh % H @@ -350,7 +327,7 @@ def kernel( T.clear(dA_A_fragment_1) T.clear(dA_A_fragment_2) - T.copy(A[bb, bs * block_S:(bs + 1) * block_S, bh, :], A_shared) + T.copy(A[bb, bs * block_S : (bs + 1) * block_S, bh, :], A_shared) for i_s in T.Parallel(block_S): Beta_shared[i_s] = Beta[bb, bs * block_S + i_s, bh] G_shared[i_s] = G[bb, bs * block_S + i_s, bh] @@ -361,7 +338,7 @@ def kernel( # for i_s in T.Parallel(block_S): # dbeta_fragment[i_s] = dbeta[bb, bs * block_S + i_s, bh] # dg_fragment[i_s] = dg[bb, bs * block_S + i_s, bh] - T.copy(dA[bb, bs * block_S:(bs + 1) * block_S, bh, :], dA_shared) + T.copy(dA[bb, bs * block_S : (bs + 1) * block_S, bh, :], dA_shared) # T.copy(dA_shared, dA[bb, bs * block_S:(bs + 1) * block_S, bh, :]) # Update dA @@ -385,8 +362,7 @@ def kernel( for i_s1, i_s2 in T.Parallel(block_S, block_S): with T.If(G[bb, bs * block_S + i_s1, bh] - G[bb, bs * block_S + i_s2, bh] <= 0): with T.Then(): - dA_fragment[i_s1, i_s2] *= T.exp(G[bb, bs * block_S + i_s1, bh] - - G[bb, bs * block_S + i_s2, bh]) + dA_fragment[i_s1, i_s2] *= T.exp(G[bb, bs * block_S + i_s1, bh] - G[bb, bs * block_S + i_s2, bh]) with T.Else(): dA_fragment[i_s1, i_s2] = 0 T.copy(dA_fragment, dA_shared) @@ -397,12 +373,8 @@ def kernel( # Update dk using previous dk T.clear(A_fragment) for i_k in T.Pipelined(T.ceildiv(DK, block_DK), num_stages=num_stages): - T.copy( - K[bb, bs * block_S:(bs + 1) * block_S, bh, i_k * block_DK:(i_k + 1) * block_DK], - K_shared) - T.copy( - dk[bb, bs * block_S:(bs + 1) * block_S, bh, - i_k * block_DK:(i_k + 1) * block_DK], dk_shared) + T.copy(K[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], K_shared) + T.copy(dk[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK], dk_shared) T.copy(dk_shared, dk_fragment) for i_s, i_k2 in T.Parallel(block_S, block_DK): K_shared_beta[i_s, i_k2] = K_shared[i_s, i_k2] * Beta_shared[i_s] @@ -411,18 +383,14 @@ def kernel( # for i_s, i_k2 in T.Parallel(block_S, block_DK): # dbeta_fragment[i_s] = dbeta_fragment[i_s] + dk_fragment_beta[i_s, i_k2] * K_shared[i_s, i_k2] for i_s, i_k2 in T.Parallel(block_S, block_DK): - dbeta_fragment_reduce_tmpk[i_s, - i_k2] = dk_fragment_beta[i_s, i_k2] * K_shared[i_s, - i_k2] + dbeta_fragment_reduce_tmpk[i_s, i_k2] = dk_fragment_beta[i_s, i_k2] * K_shared[i_s, i_k2] T.reduce_sum(dbeta_fragment_reduce_tmpk, dbeta_fragment_k, dim=1, clear=False) T.gemm(dA_shared, K_shared_beta, dk_fragment, transpose_A=True) for i_s, i_k2 in T.Parallel(block_S, block_DK): dk_shared_beta[i_s, i_k2] = dk_fragment_beta[i_s, i_k2] * Beta_shared[i_s] for i_s, i_k2 in T.Parallel(block_S, block_DK): dk_fragment[i_s, i_k2] = dk_fragment[i_s, i_k2] + dk_shared_beta[i_s, i_k2] - T.copy( - dk_fragment, dk[bb, bs * block_S:(bs + 1) * block_S, bh, - i_k * block_DK:(i_k + 1) * block_DK]) + T.copy(dk_fragment, dk[bb, bs * block_S : (bs + 1) * block_S, bh, i_k * block_DK : (i_k + 1) * block_DK]) # Update dg and dbeta T.copy(A_fragment, A_shared) @@ -460,19 +428,25 @@ def run_test( threads=128, num_stages=0, ): - K, V, Beta, G, A, dw, du = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, - accum_dtype), getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - dk_ref, dv_ref, dbeta_ref, dg_ref = prepare_output(B, S, H, DK, DV, chunk_size, - getattr(torch, output_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) + K, V, Beta, G, A, dw, du = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) + dk_ref, dv_ref, dbeta_ref, dg_ref = prepare_output( + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype) + ) dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = prepare_output( - B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), - getattr(torch, state_dtype)) + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype) + ) BS = chunk_size dA_tilelang = torch.empty(B, S, H, BS, dtype=getattr(torch, input_dtype)).cuda() dbeta_tilelang_k = torch.empty(B, S, H, dtype=getattr(torch, output_dtype)).cuda() @@ -480,28 +454,55 @@ def run_test( dg_tilelang_A_negative = torch.empty(B, S, H, BS, dtype=getattr(torch, gate_dtype)).cuda() # ref - dk_ref, dv_ref, dbeta_ref, dg_ref = bwd_prepare_wy_repr( - K, V, G, Beta, A, dw, du, cu_seqlens=None) + dk_ref, dv_ref, dbeta_ref, dg_ref = bwd_prepare_wy_repr(K, V, G, Beta, A, dw, du, cu_seqlens=None) # tilelang - kernel = tilelang_wy_fast_bwd(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, state_dtype, chunk_size, block_DK, block_DV, threads, - num_stages) - dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = kernel( - K, V, Beta, G, A, dw, du) + kernel = tilelang_wy_fast_bwd( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + block_DK, + block_DV, + threads, + num_stages, + ) + dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = kernel(K, V, Beta, G, A, dw, du) torch.cuda.synchronize() - kernel_split = tilelang_wy_fast_bwd_split(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, chunk_size, - block_DK, block_DV, threads, num_stages) - kernel_split(K, V, Beta, G, A, dw, du, dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang_k, - dg_tilelang_A_positive, dg_tilelang_A_negative) + kernel_split = tilelang_wy_fast_bwd_split( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + block_DK, + block_DV, + threads, + num_stages, + ) + kernel_split( + K, V, Beta, G, A, dw, du, dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang_k, dg_tilelang_A_positive, dg_tilelang_A_negative + ) torch.cuda.synchronize() dbeta_tilelang = dbeta_tilelang_k + dbeta_tilelang - dg_tilelang = dg_tilelang + dg_tilelang_A_positive.sum(dim=-1) - dg_tilelang_A_negative.sum( - dim=-1) + dg_tilelang = dg_tilelang + dg_tilelang_A_positive.sum(dim=-1) - dg_tilelang_A_negative.sum(dim=-1) + + from test_utils import assert_similar - from utils import assert_similar assert_similar(dk_ref, dk_tilelang, eps=1e-5, name="dk", raise_assert=False) assert_similar(dv_ref, dv_tilelang, eps=1e-5, name="dv", raise_assert=False) assert_similar(dbeta_ref, dbeta_tilelang, eps=1e-5, name="dbeta", raise_assert=False) @@ -517,11 +518,11 @@ def main(): H=8, DK=DK, DV=DV, - input_dtype="bfloat16", - output_dtype="bfloat16", - accum_dtype="float32", - gate_dtype="float32", - state_dtype="float32", + input_dtype=T.bfloat16, + output_dtype=T.bfloat16, + accum_dtype=T.float32, + gate_dtype=T.float32, + state_dtype=T.float32, chunk_size=64, block_DK=32, block_DV=32, diff --git a/examples/gdn/test_example_gdn_compilation.py b/examples/gdn/test_example_gdn_compilation.py index e184dbcace..6f9fa5d2f7 100644 --- a/examples/gdn/test_example_gdn_compilation.py +++ b/examples/gdn/test_example_gdn_compilation.py @@ -1,16 +1,16 @@ -import tilelang.testing import torch +from tilelang import language as T B = 1 S = 1024 # small but for test only. H = 32 DK = 128 DV = 128 -input_dtype = "bfloat16" -output_dtype = "bfloat16" -accum_dtype = "float32" -gate_dtype = "float32" -state_dtype = "float32" +input_dtype = T.bfloat16 +output_dtype = T.bfloat16 +accum_dtype = T.float32 +gate_dtype = T.float32 +state_dtype = T.float32 chunk_size = 64 use_g = True use_initial_state = True @@ -20,21 +20,15 @@ block_DK = 64 block_DV = 32 threads = 128 -num_stages = 1 +num_stages = 0 def test_example_wy_fast_compilation(): from example_wy_fast import tilelang_recompute_w_u_fwd, prepare_input + K, V, Beta, G, A = prepare_input( - B, - S, - H, - DK, - DV, - chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - gate_dtype=getattr(torch, gate_dtype)) + B, S, H, DK, DV, chunk_size, getattr(torch, input_dtype), getattr(torch, output_dtype), gate_dtype=getattr(torch, gate_dtype) + ) # tilelang block_S = chunk_size kernel = tilelang_recompute_w_u_fwd( @@ -52,22 +46,31 @@ def test_example_wy_fast_compilation(): block_DK=block_DK, block_DV=block_DV, threads=threads, - num_stages=num_stages) + num_stages=num_stages, + ) print(kernel.get_kernel_source()) W_tilelang, U_tilelang = kernel(K, V, Beta, G, A) def test_example_wy_fast_bwd_split_compilation(): from example_wy_fast_bwd_split import tilelang_wy_fast_bwd, tilelang_wy_fast_bwd_split, prepare_input, prepare_output - K, V, Beta, G, A, dw, du = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, - accum_dtype), getattr(torch, gate_dtype), - getattr(torch, state_dtype)) + + K, V, Beta, G, A, dw, du = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = prepare_output( - B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), - getattr(torch, state_dtype)) + B, S, H, DK, DV, chunk_size, getattr(torch, output_dtype), getattr(torch, gate_dtype), getattr(torch, state_dtype) + ) BS = chunk_size dA_tilelang = torch.empty(B, S, H, BS, dtype=getattr(torch, input_dtype)).cuda() dbeta_tilelang_k = torch.empty(B, S, H, dtype=getattr(torch, output_dtype)).cuda() @@ -75,67 +78,146 @@ def test_example_wy_fast_bwd_split_compilation(): dg_tilelang_A_negative = torch.empty(B, S, H, BS, dtype=getattr(torch, gate_dtype)).cuda() # tilelang - kernel = tilelang_wy_fast_bwd(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, state_dtype, chunk_size, block_DK, block_DV, threads, - num_stages) - dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = kernel( - K, V, Beta, G, A, dw, du) + kernel = tilelang_wy_fast_bwd( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + block_DK, + block_DV, + threads, + num_stages, + ) + dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang, dg_tilelang = kernel(K, V, Beta, G, A, dw, du) torch.cuda.synchronize() - kernel_split = tilelang_wy_fast_bwd_split(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, chunk_size, - block_DK, block_DV, threads, num_stages) - kernel_split(K, V, Beta, G, A, dw, du, dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang_k, - dg_tilelang_A_positive, dg_tilelang_A_negative) + kernel_split = tilelang_wy_fast_bwd_split( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + block_DK, + block_DV, + threads, + num_stages, + ) + kernel_split( + K, V, Beta, G, A, dw, du, dA_tilelang, dk_tilelang, dv_tilelang, dbeta_tilelang_k, dg_tilelang_A_positive, dg_tilelang_A_negative + ) torch.cuda.synchronize() dbeta_tilelang = dbeta_tilelang_k + dbeta_tilelang - dg_tilelang = dg_tilelang + dg_tilelang_A_positive.sum(dim=-1) - dg_tilelang_A_negative.sum( - dim=-1) + dg_tilelang = dg_tilelang + dg_tilelang_A_positive.sum(dim=-1) - dg_tilelang_A_negative.sum(dim=-1) def test_example_chunk_o_compilation(): from example_chunk_o import tilelang_chunk_fwd_o, prepare_input - Q, K, V, HIDDEN, G = prepare_input(B, S, H, DK, DV, chunk_size, getattr(torch, input_dtype), - getattr(torch, output_dtype), getattr(torch, accum_dtype), - getattr(torch, gate_dtype)) + + Q, K, V, HIDDEN, G = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + ) scale = 1.0 / DK**0.5 block_S = chunk_size - kernel = tilelang_chunk_fwd_o(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, chunk_size, scale, use_g, block_S, block_DK, block_DV, - threads, num_stages) + kernel = tilelang_chunk_fwd_o( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + chunk_size, + scale, + use_g, + block_S, + block_DK, + block_DV, + threads, + num_stages, + ) O_tilelang = kernel(Q, K, V, HIDDEN, G) # noqa: F841 def test_example_chunk_o_bwd_compilation(): from example_chunk_o_bwd import tilelang_chunk_o_bwd_dqkwg, prepare_input - Q, K, V, h, G, dO, dh, dv, W = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - kernel = tilelang_chunk_o_bwd_dqkwg(B, S, H, DK, DV, input_dtype, output_dtype, accum_dtype, - gate_dtype, state_dtype, chunk_size, 1.0, use_g, True, - block_DK, block_DV, threads, num_stages) - dq_tilelang, dk_tilelang, dw_tilelang, dg_tilelang = kernel(Q, K, V, h, G, dO, dh, dv, - W) # noqa: F841 + + Q, K, V, h, G, dO, dh, dv, W = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) + kernel = tilelang_chunk_o_bwd_dqkwg( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + 1.0, + use_g, + True, + block_DK, + block_DV, + threads, + num_stages, + ) + + dq_tilelang, dk_tilelang, dw_tilelang, dg_tilelang = kernel(Q, K, V, h, G, dO, dh, dv, W) # noqa: F841 if use_g: dg_tilelang = dg_tilelang.sum(dim=0) def test_example_chunk_scaled_dot_kkt_compilation(): from example_chunk_scaled_dot_kkt import tilelang_chunk_scaled_dot_kkt_fwd, prepare_input - K, Beta, G = prepare_input(B, S, H, DK, getattr(torch, input_dtype), - getattr(torch, output_dtype), getattr(torch, accum_dtype)) + + K, Beta, G = prepare_input(B, S, H, DK, getattr(torch, input_dtype), getattr(torch, output_dtype), getattr(torch, accum_dtype)) block_S = chunk_size - kernel = tilelang_chunk_scaled_dot_kkt_fwd(B, S, H, DK, chunk_size, input_dtype, output_dtype, - accum_dtype, use_g, block_S, block_DK, threads, - num_stages) + kernel = tilelang_chunk_scaled_dot_kkt_fwd( + B, S, H, DK, chunk_size, input_dtype, output_dtype, accum_dtype, use_g, block_S, block_DK, threads, num_stages + ) A_tilelang = kernel(K, Beta, G) # noqa: F841 def test_example_cumsum_compilation(): from example_cumsum import tilelang_chunk_local_cumsum_scalar, prepare_cumsum_input, prepare_cumsum_output + G = prepare_cumsum_input(B, S, H, getattr(torch, gate_dtype)) G_new_tilelang = prepare_cumsum_output(B, S, H, getattr(torch, gate_dtype)) block_S = chunk_size @@ -157,35 +239,82 @@ def test_example_cumsum_compilation(): def test_example_chunk_delta_h_compilation(): from example_chunk_delta_h import tilelang_chunk_gated_delta_rule_fwd_h, prepare_input - K, W, U, G, initial_state = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype)) - kernel = tilelang_chunk_gated_delta_rule_fwd_h(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, chunk_size, - use_g, use_initial_state, store_final_state, - save_new_value, block_DK, block_DV, threads, - num_stages) - h_tilelang, final_state_tilelang, V_new_tilelang = kernel(K, W, U, G, - initial_state) # noqa: F841 + + K, W, U, G, initial_state = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + ) + kernel = tilelang_chunk_gated_delta_rule_fwd_h( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + use_g, + use_initial_state, + store_final_state, + save_new_value, + block_DK, + block_DV, + threads, + num_stages, + ) + h_tilelang, final_state_tilelang, V_new_tilelang = kernel(K, W, U, G, initial_state) # noqa: F841 def test_example_chunk_delta_bwd_compilation(): from example_chunk_delta_bwd import tilelang_chunk_gated_delta_rule_bwd_dhu, prepare_input - Q, K, W, G, h0, dht, dO, dv = prepare_input(B, S, H, DK, DV, chunk_size, - getattr(torch, input_dtype), - getattr(torch, output_dtype), - getattr(torch, accum_dtype), - getattr(torch, gate_dtype), - getattr(torch, state_dtype)) - kernel = tilelang_chunk_gated_delta_rule_bwd_dhu(B, S, H, DK, DV, input_dtype, output_dtype, - accum_dtype, gate_dtype, state_dtype, - chunk_size, 1.0, use_g, use_initial_state, - use_final_state_gradient, block_DV, threads, - num_stages) + + Q, K, W, G, h0, dht, dO, dv = prepare_input( + B, + S, + H, + DK, + DV, + chunk_size, + getattr(torch, input_dtype), + getattr(torch, output_dtype), + getattr(torch, accum_dtype), + getattr(torch, gate_dtype), + getattr(torch, state_dtype), + ) + kernel = tilelang_chunk_gated_delta_rule_bwd_dhu( + B, + S, + H, + DK, + DV, + input_dtype, + output_dtype, + accum_dtype, + gate_dtype, + state_dtype, + chunk_size, + 1.0, + use_g, + use_initial_state, + use_final_state_gradient, + block_DV, + threads, + num_stages, + ) dh_tilelang, dh0_tilelang, dv2_tilelang = kernel(Q, K, W, G, h0, dht, dO, dv) # noqa: F841 if __name__ == "__main__": - tilelang.testing.main() + # tilelang.testing.main() + test_example_chunk_delta_bwd_compilation() diff --git a/examples/gdn/test_utils.py b/examples/gdn/test_utils.py new file mode 100644 index 0000000000..3588551ce3 --- /dev/null +++ b/examples/gdn/test_utils.py @@ -0,0 +1,38 @@ +import torch + + +def print_red_warning(message): + print(f"\033[31mWARNING: {message}\033[0m") + + +def calc_sim(x, y, name="tensor"): + x, y = x.data.double(), y.data.double() + denominator = (x * x + y * y).sum() + if denominator == 0: + print_red_warning(f"{name} all zero") + return 1 + sim = 2 * (x * y).sum() / denominator + return sim + + +def assert_similar(x, y, eps=1e-8, name="tensor", data="", raise_assert=True): + x_mask = torch.isfinite(x) + y_mask = torch.isfinite(y) + if not torch.all(x_mask == y_mask): + print_red_warning(f"{name} Error: isfinite mask mismatch") + if raise_assert: + raise AssertionError + if not torch.isclose(x.masked_fill(x_mask, 0), y.masked_fill(y_mask, 0), rtol=0, atol=0, equal_nan=True).all(): + print_red_warning(f"{name} Error: nonfinite value mismatch") + if raise_assert: + raise AssertionError + x = x.masked_fill(~x_mask, 0) + y = y.masked_fill(~y_mask, 0) + sim = calc_sim(x, y, name) + diff = 1.0 - sim + if not (0 <= diff <= eps): + print_red_warning(f"{name} Error: {diff}") + if raise_assert: + raise AssertionError + else: + print(f"{name} {data} passed") diff --git a/examples/gdn/utils.py b/examples/gdn/utils.py index 37f8d8e69f..3588551ce3 100644 --- a/examples/gdn/utils.py +++ b/examples/gdn/utils.py @@ -9,7 +9,7 @@ def calc_sim(x, y, name="tensor"): x, y = x.data.double(), y.data.double() denominator = (x * x + y * y).sum() if denominator == 0: - print_red_warning(f'{name} all zero') + print_red_warning(f"{name} all zero") return 1 sim = 2 * (x * y).sum() / denominator return sim @@ -19,21 +19,19 @@ def assert_similar(x, y, eps=1e-8, name="tensor", data="", raise_assert=True): x_mask = torch.isfinite(x) y_mask = torch.isfinite(y) if not torch.all(x_mask == y_mask): - print_red_warning(f'{name} Error: isfinite mask mismatch') + print_red_warning(f"{name} Error: isfinite mask mismatch") if raise_assert: raise AssertionError - if not torch.isclose( - x.masked_fill(x_mask, 0), y.masked_fill(y_mask, 0), rtol=0, atol=0, - equal_nan=True).all(): - print_red_warning(f'{name} Error: nonfinite value mismatch') + if not torch.isclose(x.masked_fill(x_mask, 0), y.masked_fill(y_mask, 0), rtol=0, atol=0, equal_nan=True).all(): + print_red_warning(f"{name} Error: nonfinite value mismatch") if raise_assert: raise AssertionError x = x.masked_fill(~x_mask, 0) y = y.masked_fill(~y_mask, 0) sim = calc_sim(x, y, name) - diff = 1. - sim + diff = 1.0 - sim if not (0 <= diff <= eps): - print_red_warning(f'{name} Error: {diff}') + print_red_warning(f"{name} Error: {diff}") if raise_assert: raise AssertionError else: diff --git a/examples/gemm/README.md b/examples/gemm/README.md index 059d08c842..9ab7fb6614 100644 --- a/examples/gemm/README.md +++ b/examples/gemm/README.md @@ -4,20 +4,23 @@ TileLang is a domain-specific language designed to simplify the process of writi ## Table of Contents -1. [Getting Started](#getting-started) -2. [Simple GEMM Example](#simple-gemm-example) - - [Code Walkthrough](#code-walkthrough) - - [Compiling and Profiling](#compiling-and-profiling) -3. [Advanced GEMM Features](#advanced-gemm-features) - - [Custom Memory Layout / Swizzling](#custom-memory-layout--swizzling) - - [Parallel Copy and Auto-Pipelining](#parallel-copy-and-auto-pipelining) - - [Rasterization for L2 Cache Locality](#rasterization-for-l2-cache-locality) -4. [Enhanced GEMM Example with Annotations](#enhanced-gemm-example-with-annotations) -5. [Verifying Correctness](#verifying-correctness) -6. [Fine-grained MMA Computations](#fine-grained-mma-computations) - - [Example Workflow](#example-workflow) - - [Summary](#summary) -7. [References](#references) +- [Table of Contents](#table-of-contents) +- [Getting Started](#getting-started) + - [Prerequisites](#prerequisites) + - [Installation](#installation) +- [Simple GEMM Example](#simple-gemm-example) + - [Code Walkthrough](#code-walkthrough) + - [Compiling and Profiling](#compiling-and-profiling) +- [Advanced GEMM Features](#advanced-gemm-features) + - [Custom Memory Layout / Swizzling](#custom-memory-layout--swizzling) + - [Parallel Copy and Auto-Pipelining](#parallel-copy-and-auto-pipelining) + - [Rasterization for L2 Cache Locality](#rasterization-for-l2-cache-locality) +- [Enhanced GEMM Example with Annotations](#enhanced-gemm-example-with-annotations) +- [Verifying Correctness](#verifying-correctness) +- [Fine-grained MMA Computations](#fine-grained-mma-computations) + - [Example Workflow](#example-workflow) + - [Summary](#summary) +- [References](#references) --- @@ -25,10 +28,10 @@ TileLang is a domain-specific language designed to simplify the process of writi ### Prerequisites -- **Python 3.8+** -- **NVIDIA GPU** with a recent CUDA toolkit installed +- **Python 3.8+** +- **NVIDIA GPU** with a recent CUDA toolkit installed - **PyTorch** (optional, for easy correctness verification) -- **tilelang** +- **tilelang** - **bitblas** (optional; used for swizzle layout utilities in the advanced examples) ### Installation @@ -50,7 +53,7 @@ import tilelang from tilelang import Profiler import tilelang.language as T -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float): @T.prim_func def main( A: T.Tensor((M, K), dtype), @@ -87,26 +90,26 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo ### Code Walkthrough -1. **Define the Kernel Launch Configuration:** +1. **Define the Kernel Launch Configuration:** ```python with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): ``` This creates a grid of blocks (ceildiv(N, block_N) in x-dimension, ceildiv(M, block_M) in y-dimension), each with 128 threads. -2. **Shared Memory Allocation:** +2. **Shared Memory Allocation:** ```python A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_K, block_N), dtype) ``` Tiles of \(A\) and \(B\) are loaded into these shared memory buffers for faster access. -3. **Local Fragment Accumulation:** +3. **Local Fragment Accumulation:** ```python C_local = T.alloc_fragment((block_M, block_N), accum_dtype) ``` Partial results are stored in registers (or local memory) to reduce writes to global memory. -4. **Pipelined Loading and GEMM:** +4. **Pipelined Loading and GEMM:** ```python for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3): T.copy(...) @@ -114,7 +117,7 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo ``` Loads blocks of \(A\) and \(B\) in a pipelined fashion (up to 3 stages). This exploits overlap of data transfer and computation. -5. **Copy Out the Results:** +5. **Copy Out the Results:** ```python T.copy(C_local, C[by * block_M, bx * block_N]) ``` @@ -173,7 +176,7 @@ import tilelang.language as T # that helps align data for MMA (Matrix Multiply-Accumulate) operations. from tilelang.intrinsics import make_mma_swizzle_layout as make_swizzle_layout -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float): @T.prim_func def main( A: T.Tensor((M, K), dtype), @@ -216,10 +219,10 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="flo return main ``` -**Key Differences vs. Basic Example** -1. **`T.annotate_layout(...)`**: Annotates how data should be organized in shared memory (swizzling). -2. **`T.use_swizzle(...)`**: Enables swizzle-based rasterization. -3. **Parallel Copy Loop** with `T.Parallel(...)`: Distributes global-to-shared copy across all threads, potentially vectorizing load/store instructions. +**Key Differences vs. Basic Example** +1. **`T.annotate_layout(...)`**: Annotates how data should be organized in shared memory (swizzling). +2. **`T.use_swizzle(...)`**: Enables swizzle-based rasterization. +3. **Parallel Copy Loop** with `T.Parallel(...)`: Distributes global-to-shared copy across all threads, potentially vectorizing load/store instructions. --- @@ -247,7 +250,7 @@ print("Results match!") ## Fine-grained MMA Computations -For advanced users who require full control over warp-level matrix multiplication operations, TileLang allows you to specify fine-grained MMA (Matrix Multiply-Accumulate) computations in a manner similar to writing raw CUDA. While higher-level abstractions like `T.gemm(...)` or automatic MMA emitters are sufficient for many use cases, specialized workloads (for example, dequantize gemm may require fine-grained layout transformation on shared to register stage) may benefit from explicitly controlling each MMA instruction, the data layout, and the synchronization points. +For advanced users who require full control over warp-level matrix multiplication operations, TileLang allows you to specify fine-grained MMA (Matrix Multiply-Accumulate) computations in a manner similar to writing raw CUDA. While higher-level abstractions like `T.gemm(...)` or automatic MMA emitters are sufficient for many use cases, specialized workloads (for example, dequantize gemm may require fine-grained layout transformation on shared to register stage) may benefit from explicitly controlling each MMA instruction, the data layout, and the synchronization points. ### Example Workflow @@ -262,18 +265,18 @@ def tl_matmul( accum_dtype, ): assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - if out_dtype == "int32": + if out_dtype == T.int32: micro_size_k = 32 # This is a debug config @@ -394,10 +397,10 @@ def tl_matmul( ] ``` -1. **Set Up Tile Sizes and Thread Bindings** +1. **Set Up Tile Sizes and Thread Bindings** Just like in CUDA, you will typically start by defining how many warps or threads per block you want and how your matrix is subdivided. In TileLang, this is done via `T.Kernel(...)` and `T.thread_binding(...),` which ensure that the correct number of threads are active, and each thread is bound to a specific role (e.g., warp ID or lane ID). -2. **Allocate Warp-local Fragments** +2. **Allocate Warp-local Fragments** Instead of using a single shared buffer for partial sums, you allocate local buffers (register fragments) to hold sub-blocks of matrices \(A\) and \(B\). In TileLang, this is done with something like: ```python A_local = T.alloc_local((warp_rows * local_size_a), in_dtype) @@ -406,7 +409,7 @@ def tl_matmul( ``` Each of these `local` allocations represents a region of per-thread storage, which collectively forms the warp’s register tiles. -3. **Load Data via `ldmatrix`** +3. **Load Data via `ldmatrix`** Fine-grained loading instructions allow you to specify exactly how data moves from shared memory to the warp-level fragments. In the example below, `mma_emitter.ldmatrix_a()` and `.ldmatrix_b()` are higher-level wrappers around warp-synchronous intrinsics. You can write your own load logic as well: ```python for ki in T.serial(0, (block_K // micro_size_k)): @@ -418,7 +421,7 @@ def tl_matmul( ``` Internally, these calls orchestrate how each thread in the warp issues the correct load instructions, performs address calculations, and stores the data into registers. -4. **Perform the MMA Instruction** +4. **Perform the MMA Instruction** After loading sub-tiles (fragments), the warp executes the `mma` instruction. This operation is essentially: \[ C_{\text{local}} \;+=\; A_{\text{local}} \;\times\; B_{\text{local}} @@ -429,7 +432,7 @@ def tl_matmul( ``` Under the hood, this translates into Tensor Core instructions (e.g., `wmma.mma.sync` in PTX), which process multiple data elements per warp in parallel. -5. **Store Results via `stmatrix`** +5. **Store Results via `stmatrix`** Finally, you write the results from the warp-level fragments back to shared memory or global memory. This step might happen multiple times in a loop or just once at the end. The code snippet: ```python mma_emitter.stmatrix(C_local, C_shared) @@ -444,6 +447,6 @@ By combining warp-synchronous intrinsics (`ldmatrix`, `mma`, `stmatrix`) with ma ## References -- [NVIDIA CUTLASS Library](https://github.com/NVIDIA/cutlass): A collection of high-performance CUDA C++ template abstractions for GEMM. -- [NVIDIA CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html): Official documentation for CUDA. +- [NVIDIA CUTLASS Library](https://github.com/NVIDIA/cutlass): A collection of high-performance CUDA C++ template abstractions for GEMM. +- [NVIDIA CUDA Programming Guide](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html): Official documentation for CUDA. - [PyTorch Documentation](https://pytorch.org/docs): For verifying correctness via CPU or GPU-based matmul. diff --git a/examples/gemm/example_gemm.py b/examples/gemm/example_gemm.py index f18cd388a7..dfa4311217 100644 --- a/examples/gemm/example_gemm.py +++ b/examples/gemm/example_gemm.py @@ -3,13 +3,12 @@ @tilelang.jit(out_idx=[-1]) -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def gemm( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -58,5 +57,11 @@ def main(): print(f"tilelang Latency: {latency}ms") +def run_regression_perf(): + kernel = matmul(1024, 1024, 1024, 128, 128, 32) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/gemm/example_gemm_autotune.py b/examples/gemm/example_gemm_autotune.py index 661ef1276d..016d448a4c 100644 --- a/examples/gemm/example_gemm_autotune.py +++ b/examples/gemm/example_gemm_autotune.py @@ -51,9 +51,9 @@ def get_configs(M, N, K, with_roller=False, topk=20): M=M, N=N, K=K, - in_dtype="float16", - out_dtype="float16", - accum_dtype="float", + in_dtype=T.float16, + out_dtype=T.float16, + accum_dtype=T.float32, ).with_arch(arch) func = carve_template.equivalent_function() @@ -90,7 +90,8 @@ def get_configs(M, N, K, with_roller=False, topk=20): num_stages, thread_num, enable_rasterization, - )) + ) + ) configs = [ { @@ -100,13 +101,13 @@ def get_configs(M, N, K, with_roller=False, topk=20): "num_stages": c[3], "thread_num": c[4], "enable_rasteration": c[5], # keep param name for backward-compat - } for c in _configs + } + for c in _configs ] return configs def get_best_config(M, N, K, with_roller=False): - def kernel( block_M=None, block_N=None, @@ -115,17 +116,16 @@ def kernel( thread_num=None, enable_rasteration=None, ): - dtype = "bfloat16" - accum_dtype = "float" + dtype = T.bfloat16 + accum_dtype = T.float32 @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_N, block_K), dtype) C_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -146,15 +146,18 @@ def main( return main - autotuner = AutoTuner.from_kernel( - kernel=kernel, configs=get_configs(M, N, K, with_roller)).set_compile_args( + autotuner = ( + AutoTuner.from_kernel(kernel=kernel, configs=get_configs(M, N, K, with_roller)) + .set_compile_args( out_idx=[-1], target="auto", - ).set_profile_args( + ) + .set_profile_args( supply_type=tl.TensorSupplyType.Integer, ref_prog=ref_program, skip_check=False, ) + ) return autotuner.run(warmup=3, rep=20) @@ -167,52 +170,20 @@ def get_heuristic_config() -> dict: sm_version = sm_major * 10 + sm_minor print(f"CUDA device capability: {sm_version}") if sm_version in {80}: - return { - "block_M": 128, - "block_N": 256, - "block_K": 32, - "num_stages": 2, - "thread_num": 128, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 2, "thread_num": 128, "enable_rasteration": True} elif sm_version in {90}: - return { - "block_M": 128, - "block_N": 256, - "block_K": 64, - "num_stages": 3, - "thread_num": 256, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 64, "num_stages": 3, "thread_num": 256, "enable_rasteration": True} else: - return { - "block_M": 128, - "block_N": 256, - "block_K": 32, - "num_stages": 0, - "thread_num": 128, - "enable_rasteration": True - } + return {"block_M": 128, "block_N": 256, "block_K": 32, "num_stages": 0, "thread_num": 128, "enable_rasteration": True} @tl.jit(out_idx=[-1]) -def matmul(M, - N, - K, - block_M, - block_N, - block_K, - num_stages, - thread_num, - enable_rasteration, - dtype="float16", - accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, num_stages, thread_num, enable_rasteration, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def gemm_autotune( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -236,11 +207,7 @@ def gemm_autotune( return gemm_autotune -def main(M: int = 4096, - N: int = 4096, - K: int = 4096, - use_autotune: bool = False, - with_roller: bool = False): +def main(M: int = 4096, N: int = 4096, K: int = 4096, use_autotune: bool = False, with_roller: bool = False): use_autotune = True if use_autotune: result = get_best_config(M, N, K, with_roller) @@ -261,20 +228,19 @@ def main(M: int = 4096, print(f"Ref TFlops: {2 * M * N * K / ref_latency * 1e-9}") +def run_regression_perf(M: int = 4096, N: int = 4096, K: int = 4096): + config = get_heuristic_config() + kernel = matmul(M, N, K, **config) + profiler = kernel.get_profiler(tensor_supply_type=tl.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") parser.add_argument("--m", type=int, default=4096, help="Matrix dimension M") parser.add_argument("--n", type=int, default=4096, help="Matrix dimension N") parser.add_argument("--k", type=int, default=4096, help="Matrix dimension K") - parser.add_argument( - "--use_autotune", - action="store_true", - default=False, - help="Whether to use autotune for matmul configs") - parser.add_argument( - "--with_roller", - action="store_true", - default=False, - help="Whether to enable BitBLAS roller for search space") + parser.add_argument("--use_autotune", action="store_true", default=False, help="Whether to use autotune for matmul configs") + parser.add_argument("--with_roller", action="store_true", default=False, help="Whether to enable BitBLAS roller for search space") args = parser.parse_args() main(args.m, args.n, args.k, args.use_autotune, args.with_roller) diff --git a/examples/gemm/example_gemm_intrinsics.py b/examples/gemm/example_gemm_intrinsics.py index 5c014ce3a4..d4bc9480ff 100644 --- a/examples/gemm/example_gemm_intrinsics.py +++ b/examples/gemm/example_gemm_intrinsics.py @@ -4,7 +4,8 @@ import tilelang.language as T from tilelang.intrinsics import get_swizzle_layout from tilelang.intrinsics.mma_macro_generator import ( - TensorCoreIntrinEmitter,) + TensorCoreIntrinEmitter, +) from tilelang.transform import simplify_prim_func @@ -34,18 +35,18 @@ def tl_matmul( accum_dtype, ): assert in_dtype in [ - "float16", - "int8", + T.float16, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - if out_dtype == "int32": + if out_dtype == T.int32: micro_size_k = 32 # This is a debug config @@ -53,7 +54,7 @@ def tl_matmul( block_col_warps = 2 warp_row_tiles = 64 warp_col_tiles = 64 - # chunk = 32 if in_dtype == "float16" else 64 + # chunk = 32 if in_dtype == T.float16 else 64 chunk = 32 shared_scope = "shared.dyn" @@ -99,12 +100,11 @@ def tl_matmul( @T.prim_func def gemm_intrinsics( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) @@ -112,10 +112,12 @@ def gemm_intrinsics( B_local = T.alloc_local((warp_cols * local_size_b), in_dtype) C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype) - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - B_shared: make_swizzle_layout(B_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + B_shared: make_swizzle_layout(B_shared), + } + ) # Improve L2 Cache T.use_swizzle(panel_size=10) @@ -123,7 +125,6 @@ def gemm_intrinsics( T.clear(C_local) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, block_K): A_shared[i, k] = A[by * block_M + i, ko * block_K + k] @@ -133,7 +134,6 @@ def gemm_intrinsics( B_shared[j, k] = B[bx * block_N + j, ko * block_K + k] for ki in T.serial(0, (block_K // micro_size_k)): - # Load A into fragment mma_emitter.ldmatrix_a(A_local, A_shared, ki) @@ -163,7 +163,7 @@ def ref_program(A, B): def main(M=4096, N=4096, K=4096): - in_dtype, out_dtype, accum_dtype = "float16", "float16", "float32" + in_dtype, out_dtype, accum_dtype = T.float16, T.float16, T.float32 kernel = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype) src_code = kernel.get_kernel_source() # src_code is the generated cuda source @@ -181,5 +181,12 @@ def main(M=4096, N=4096, K=4096): profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2) +def run_regression_perf(M=4096, N=4096, K=4096): + in_dtype, out_dtype, accum_dtype = "float16", "float16", "float32" + kernel = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype) + profiler = kernel.get_profiler() + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main(M=4096, N=4096, K=4096) diff --git a/examples/gemm/example_gemm_persistent.py b/examples/gemm/example_gemm_persistent.py index a2a7122d39..ad3d556ede 100644 --- a/examples/gemm/example_gemm_persistent.py +++ b/examples/gemm/example_gemm_persistent.py @@ -5,22 +5,12 @@ @tilelang.jit(out_idx=[-1]) -def matmul_non_persistent(M, - N, - K, - block_M, - block_N, - block_K, - threads, - num_stages, - dtype="float16", - accum_dtype="float"): - +def matmul_non_persistent(M, N, K, block_M, block_N, block_K, threads, num_stages, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -43,18 +33,9 @@ def main( @tilelang.jit(out_idx=[-1]) -def matmul_persistent(M, - N, - K, - block_M, - block_N, - block_K, - threads, - num_stages, - dtype="float16", - accum_dtype="float", - use_persistent_primitive=True): - +def matmul_persistent( + M, N, K, block_M, block_N, block_K, threads, num_stages, dtype=T.float16, accum_dtype=T.float32, use_persistent_primitive=True +): sm_num = driver.get_num_sms() m_blocks = T.ceildiv(M, block_M) n_blocks = T.ceildiv(N, block_N) @@ -63,9 +44,9 @@ def matmul_persistent(M, @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(sm_num, threads=threads) as (block_id): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -90,9 +71,9 @@ def main( @T.prim_func def main_persistent_primitive( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(sm_num, threads=threads) as (block_id): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -100,8 +81,7 @@ def main_persistent_primitive( C_local = T.alloc_fragment((block_M, block_N), accum_dtype) C_shared = T.alloc_shared((block_M, block_N), dtype) - for bx, by in T.Persistent( - [T.ceildiv(M, block_M), T.ceildiv(N, block_N)], sm_num, block_id): + for bx, by in T.Persistent([T.ceildiv(M, block_M), T.ceildiv(N, block_N)], sm_num, block_id): T.clear(C_local) for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(A[bx * block_M, k * block_K], A_shared) @@ -128,18 +108,15 @@ def main(M=4096, N=4096, K=4096): num_stages = 3 persistent_kernel = matmul_persistent(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, threads, num_stages) - persistent_profiler = persistent_kernel.get_profiler( - tensor_supply_type=tilelang.TensorSupplyType.Randn) + persistent_profiler = persistent_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) persistent_profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) print("Persistent GEMM: All check passed.") persistent_latency = persistent_profiler.do_bench(warmup=500) print(f"Persistent GEMM Latency: {persistent_latency} ms") print(f"Persistent GEMM TFlops: {total_flops / persistent_latency * 1e-9} TFlops") - non_persistent_kernel = matmul_non_persistent(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, threads, - num_stages) - non_persistent_profiler = non_persistent_kernel.get_profiler( - tensor_supply_type=tilelang.TensorSupplyType.Randn) + non_persistent_kernel = matmul_non_persistent(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, threads, num_stages) + non_persistent_profiler = non_persistent_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) non_persistent_profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) print("Non-Persistent GEMM: All check passed.") non_persistent_latency = non_persistent_profiler.do_bench(warmup=500) @@ -149,11 +126,22 @@ def main(M=4096, N=4096, K=4096): print(f"Persistent GEMM Speedup: {non_persistent_latency / persistent_latency}") +def run_regression_perf(M=4096, N=4096, K=4096): + BLOCK_M = 128 + BLOCK_N = 256 + BLOCK_K = 64 + threads = 256 + num_stages = 3 + persistent_kernel = matmul_persistent(M, N, K, BLOCK_M, BLOCK_N, BLOCK_K, threads, num_stages) + persistent_profiler = persistent_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Randn) + return persistent_profiler.do_bench(backend="cupti") + + if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--M', type=int, default=8192, help='M dimension') - parser.add_argument('--N', type=int, default=8192, help='N dimension') - parser.add_argument('--K', type=int, default=8192, help='K dimension') + parser.add_argument("--M", type=int, default=8192, help="M dimension") + parser.add_argument("--N", type=int, default=8192, help="N dimension") + parser.add_argument("--K", type=int, default=8192, help="K dimension") args = parser.parse_args() M, N, K = args.M, args.N, args.K main(M, N, K) diff --git a/examples/gemm/example_gemm_schedule.py b/examples/gemm/example_gemm_schedule.py index f4727412b7..17dbcc5688 100644 --- a/examples/gemm/example_gemm_schedule.py +++ b/examples/gemm/example_gemm_schedule.py @@ -3,13 +3,12 @@ @tilelang.jit(out_idx=[-1]) -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def gemm_schedule( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -65,5 +64,19 @@ def main(): print(kernel.get_kernel_source()) +def run_regression_perf(): + kernel = matmul(1024, 1024, 1024, 128, 128, 32) + import torch + + a = torch.randn(1024, 1024).cuda().half() + b = torch.randn(1024, 1024).cuda().half() + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(a, b) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/gemm/regression_example_gemm.py b/examples/gemm/regression_example_gemm.py new file mode 100644 index 0000000000..3583cf16ac --- /dev/null +++ b/examples/gemm/regression_example_gemm.py @@ -0,0 +1,25 @@ +import tilelang.testing +import example_gemm +import example_gemm_autotune +import example_gemm_intrinsics +import example_gemm_schedule + + +def regression_example_gemm_autotune(): + tilelang.testing.process_func(example_gemm_autotune.run_regression_perf, M=1024, N=1024, K=1024) + + +def regression_example_gemm_intrinsics(): + tilelang.testing.process_func(example_gemm_intrinsics.run_regression_perf, M=1024, N=1024, K=1024) + + +def regression_example_gemm_schedule(): + tilelang.testing.process_func(example_gemm_schedule.run_regression_perf) + + +def regression_example_gemm(): + tilelang.testing.process_func(example_gemm.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/gemm_fp8/README.md b/examples/gemm_fp8/README.md index 9d7011a064..2b3dc9560f 100644 --- a/examples/gemm_fp8/README.md +++ b/examples/gemm_fp8/README.md @@ -1 +1 @@ -**Notes**: Now we only support fp8 with mma instructions instead of `T.gemm`, because the cutlass version of tilelang is too old, we should update the cutlass version in future. \ No newline at end of file +**Notes**: Now we only support fp8 with mma instructions instead of `T.gemm`, because the cutlass version of tilelang is too old, we should update the cutlass version in future. diff --git a/examples/gemm_fp8/example_tilelang_gemm_amd.py b/examples/gemm_fp8/example_tilelang_gemm_amd.py index 0e6ace7571..93f8c4980c 100644 --- a/examples/gemm_fp8/example_tilelang_gemm_amd.py +++ b/examples/gemm_fp8/example_tilelang_gemm_amd.py @@ -17,10 +17,8 @@ def supply_prog(args): a_param, b_param = args M, K = a_param.shape N, _ = b_param.shape - a = (torch.randn(M, K, dtype=torch.float16, device='cuda') * - 0.01).to(dtype=torch.float8_e4m3fnuz) - b = (torch.randn(N, K, dtype=torch.float16, device='cuda') * - 0.01).to(dtype=torch.float8_e4m3fnuz) + a = (torch.randn(M, K, dtype=torch.float16, device="cuda") * 0.01).to(dtype=torch.float8_e4m3fnuz) + b = (torch.randn(N, K, dtype=torch.float16, device="cuda") * 0.01).to(dtype=torch.float8_e4m3fnuz) return [a, b] @@ -35,40 +33,36 @@ def get_configs(): valid_configs = [] - for m, n, k, stages, t, kp, gemm_type in itertools.product(block_Ms, block_Ns, block_Ks, - num_stages, num_threads, k_packs, - gemm_types): - valid_configs.append({ - "block_M": m, - "block_N": n, - "block_K": k, - "num_stages": stages, - "num_threads": t, - "k_pack": kp, - "gemm_type": gemm_type, - }) + for m, n, k, stages, t, kp, gemm_type in itertools.product(block_Ms, block_Ns, block_Ks, num_stages, num_threads, k_packs, gemm_types): + valid_configs.append( + { + "block_M": m, + "block_N": n, + "block_K": k, + "num_stages": stages, + "num_threads": t, + "k_pack": kp, + "gemm_type": gemm_type, + } + ) return valid_configs @tilelang.autotune( - configs=get_configs(), - cache_input_tensors=True, - ref_prog=ref_program, - manual_check_prog=manual_check_prog, - supply_prog=supply_prog) + configs=get_configs(), cache_input_tensors=True, ref_prog=ref_program, manual_check_prog=manual_check_prog, supply_prog=supply_prog +) @tilelang.jit(out_idx=[-1]) def fp8_matmul(M, N, K, block_M, block_N, block_K, num_stages, num_threads, k_pack, gemm_type): - dtype = "float8_e4m3fnuz" - accum_dtype = "float" + dtype = T.float8_e4m3fnuz + accum_dtype = T.float32 @T.prim_func def gemm_fp8_rs( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), accum_dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), accum_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by): A_local = T.alloc_fragment((block_M, block_K), dtype) B_shared = T.alloc_shared((block_N, block_K), dtype) C_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -77,24 +71,17 @@ def gemm_fp8_rs( for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(A[by * block_M, k * block_K], A_local) T.copy(B[bx * block_N, k * block_K], B_shared) - T.gemm( - A_local, - B_shared, - C_local, - transpose_B=True, - k_pack=k_pack, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(A_local, B_shared, C_local, transpose_B=True, k_pack=k_pack, policy=T.GemmWarpPolicy.FullRow) T.copy(C_local, C[by * block_M, bx * block_N]) @T.prim_func def gemm_fp8_ss( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), accum_dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), accum_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=num_threads) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_N, block_K), dtype) C_local = T.alloc_fragment((block_M, block_N), accum_dtype) @@ -103,13 +90,7 @@ def gemm_fp8_ss( for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(A[by * block_M, k * block_K], A_shared) T.copy(B[bx * block_N, k * block_K], B_shared) - T.gemm( - A_shared, - B_shared, - C_local, - transpose_B=True, - k_pack=k_pack, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(A_shared, B_shared, C_local, transpose_B=True, k_pack=k_pack, policy=T.GemmWarpPolicy.FullRow) T.copy(C_local, C[by * block_M, bx * block_N]) @@ -123,10 +104,8 @@ def gemm_fp8_ss( def test_gemm_fp8(M, N, K): kernel = fp8_matmul(M, N, K) - a = (torch.randn(M, K, dtype=torch.float16, device='cuda') * - 0.01).to(dtype=torch.float8_e4m3fnuz) - b = (torch.randn(N, K, dtype=torch.float16, device='cuda') * - 0.01).to(dtype=torch.float8_e4m3fnuz) + a = (torch.randn(M, K, dtype=torch.float16, device="cuda") * 0.01).to(dtype=torch.float8_e4m3fnuz) + b = (torch.randn(N, K, dtype=torch.float16, device="cuda") * 0.01).to(dtype=torch.float8_e4m3fnuz) c = kernel(a, b) ref_c = ref_program(a, b) torch_assert_close(c, ref_c, rtol=1e-2, atol=1e-2) diff --git a/examples/gemm_fp8/example_tilelang_gemm_fp8.py b/examples/gemm_fp8/example_tilelang_gemm_fp8.py index a403ed068a..0869979756 100644 --- a/examples/gemm_fp8/example_tilelang_gemm_fp8.py +++ b/examples/gemm_fp8/example_tilelang_gemm_fp8.py @@ -1,7 +1,6 @@ import torch import tilelang import tilelang.language as T -from tilelang.utils.tensor import map_torch_type def calc_diff(x, y): @@ -12,13 +11,12 @@ def calc_diff(x, y): @tilelang.jit(out_idx=[-1]) -def matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype=T.float32): @T.prim_func def gemm_fp8( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -37,12 +35,12 @@ def gemm_fp8( def test_gemm_fp8(M, N, K, dtype): - torch_dtype = map_torch_type(dtype) + torch_dtype = T.dtype(dtype).as_torch() kernel = matmul(M, N, K, 128, 128, 64, dtype) - a = torch.randn(M, K, dtype=torch.float16, device='cuda').to(dtype=torch_dtype) - b = torch.randn(N, K, dtype=torch.float16, device='cuda').to(dtype=torch_dtype) + a = torch.randn(M, K, dtype=torch.float16, device="cuda").to(dtype=torch_dtype) + b = torch.randn(N, K, dtype=torch.float16, device="cuda").to(dtype=torch_dtype) c = kernel(a, b) @@ -57,8 +55,21 @@ def test_gemm_fp8(M, N, K, dtype): def main(): - test_gemm_fp8(1024, 1024, 1024, 'float8_e4m3') - test_gemm_fp8(1024, 1024, 1024, 'float8_e5m2') + test_gemm_fp8(1024, 1024, 1024, T.float8_e4m3fn) + test_gemm_fp8(1024, 1024, 1024, T.float8_e5m2) + + +def run_regression_perf(): + M, N, K = 4096, 4096, 4096 + dtype = "float8_e4m3" + kernel_e4m3 = matmul(M, N, K, 128, 128, 64, dtype) + profiler_e4m3 = kernel_e4m3.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e4m3 = profiler_e4m3.do_bench(backend="cupti") + dtype = "float8_e5m2" + kernel_e5m2 = matmul(M, N, K, 128, 128, 64, dtype) + profiler_e5m2 = kernel_e5m2.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e5m2 = profiler_e5m2.do_bench(backend="cupti") + return (latency_e4m3 + latency_e5m2) / 2 if __name__ == "__main__": diff --git a/examples/gemm_fp8/example_tilelang_gemm_fp8_2xAcc.py b/examples/gemm_fp8/example_tilelang_gemm_fp8_2xAcc.py index 1d9207aff2..a702e8ae0a 100644 --- a/examples/gemm_fp8/example_tilelang_gemm_fp8_2xAcc.py +++ b/examples/gemm_fp8/example_tilelang_gemm_fp8_2xAcc.py @@ -1,11 +1,10 @@ import torch import tilelang import tilelang.language as T -from tilelang.utils.tensor import map_torch_type @tilelang.jit(out_idx=[-1]) -def matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype="float"): +def matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype=T.float32): # for fp8 gemm, do one promote after 4 wgmma inst, i.e. block_K = 128. # if block_K < 128, promote after 128/block_K iters. # if block_K > 128, promote after every iter. @@ -13,9 +12,9 @@ def matmul(M, N, K, block_M, block_N, block_K, dtype, accum_dtype="float"): @T.prim_func def gemm_fp8_2xAcc( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), accum_dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), accum_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): A_shared = T.alloc_shared((block_M, block_K), dtype) @@ -55,18 +54,18 @@ def calc_diff(x, y): def test_gemm_fp8(M, N, K, dtype): - torch_dtype = map_torch_type(dtype) + torch_dtype = T.dtype(dtype).as_torch() kernel = matmul(M, N, K, 128, 128, 64, dtype) - a = torch.rand(M, K, dtype=torch.float16, device='cuda') + a = torch.rand(M, K, dtype=torch.float16, device="cuda") a = (100 * (2 * a - 1)).to(dtype=torch_dtype) - b = torch.rand(N, K, dtype=torch.float16, device='cuda') + b = torch.rand(N, K, dtype=torch.float16, device="cuda") b = (100 * (2 * b - 1)).to(dtype=torch_dtype) c = kernel(a, b) - ref_c = (a.float() @ b.float().T) + ref_c = a.float() @ b.float().T diff = calc_diff(c, ref_c) print(f"diff: {diff}") @@ -74,8 +73,21 @@ def test_gemm_fp8(M, N, K, dtype): def main(): - test_gemm_fp8(1024, 1024, 8192, 'float8_e4m3') - test_gemm_fp8(1024, 1024, 8192, 'float8_e5m2') + test_gemm_fp8(1024, 1024, 8192, T.float8_e4m3fn) + test_gemm_fp8(1024, 1024, 8192, T.float8_e5m2) + + +def run_regression_perf(): + M, N, K = 1024, 1024, 8192 + dtype = "float8_e4m3" + kernel_e4m3 = matmul(M, N, K, 128, 128, 64, dtype) + profiler_e4m3 = kernel_e4m3.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e4m3 = profiler_e4m3.do_bench(backend="cupti") + dtype = "float8_e5m2" + kernel_e5m2 = matmul(M, N, K, 128, 128, 64, dtype) + profiler_e5m2 = kernel_e5m2.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e5m2 = profiler_e5m2.do_bench(backend="cupti") + return (latency_e4m3 + latency_e5m2) / 2 if __name__ == "__main__": diff --git a/examples/gemm_fp8/example_tilelang_gemm_fp8_intrinsic.py b/examples/gemm_fp8/example_tilelang_gemm_fp8_intrinsic.py index ed44aab695..762885ec38 100644 --- a/examples/gemm_fp8/example_tilelang_gemm_fp8_intrinsic.py +++ b/examples/gemm_fp8/example_tilelang_gemm_fp8_intrinsic.py @@ -5,7 +5,8 @@ import tilelang.language as T from tilelang.intrinsics import get_swizzle_layout from tilelang.intrinsics.mma_macro_generator import ( - TensorCoreIntrinEmitter,) + TensorCoreIntrinEmitter, +) from tilelang.transform import simplify_prim_func from tilelang.utils.tensor import map_torch_type @@ -38,21 +39,26 @@ def tl_matmul( accum_dtype, ): assert in_dtype in [ - "float16", - "float8_e4m3", - "float8_e5m2", - "int8", + T.float16, + T.float8_e4m3fn, + T.float8_e5m2, + T.int8, ], "Currently only float16 and int8 are supported" assert out_dtype in [ - "float16", - "float32", - "int32", + T.float16, + T.float32, + T.int32, ], "Currently only float16, float32 and int32 are supported" micro_size_x = micro_size_y = micro_size_k = 16 - is_float8 = in_dtype in ["float8_e4m3", "float8_e5m2"] - if out_dtype == "int32" or is_float8: + is_float8 = in_dtype in [ + T.float8_e4m3fn, + T.float8_e5m2, + T.float8_e4m3fn, + T.float8_e5m2fnuz, + ] + if out_dtype == T.int32 or is_float8: micro_size_k = 32 # This is a debug config @@ -60,7 +66,7 @@ def tl_matmul( block_col_warps = 2 warp_row_tiles = 32 warp_col_tiles = 32 - chunk = 32 if in_dtype == "float16" else 64 + chunk = 32 if in_dtype == T.float16 else 64 shared_scope = "shared.dyn" # Pipeline Stage @@ -105,12 +111,11 @@ def tl_matmul( @T.prim_func def gemm_fp8_intrinsic( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): - A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope) B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope) C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope) @@ -118,10 +123,12 @@ def gemm_fp8_intrinsic( B_local = T.alloc_local((warp_cols * local_size_b), in_dtype) C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype) - T.annotate_layout({ - A_shared: make_swizzle_layout(A_shared), - B_shared: make_swizzle_layout(B_shared), - }) + T.annotate_layout( + { + A_shared: make_swizzle_layout(A_shared), + B_shared: make_swizzle_layout(B_shared), + } + ) # Improve L2 Cache T.use_swizzle(panel_size=10) @@ -129,7 +136,6 @@ def gemm_fp8_intrinsic( T.clear(C_local) for ko in T.Pipelined((K // block_K), num_stages=stage): - # Load A into shared memory for i, k in T.Parallel(block_M, block_K): A_shared[i, k] = A[by * block_M + i, ko * block_K + k] @@ -139,7 +145,6 @@ def gemm_fp8_intrinsic( B_shared[j, k] = B[bx * block_N + j, ko * block_K + k] for ki in T.serial(0, (block_K // micro_size_k)): - # Load A into fragment mma_emitter.ldmatrix_a( A_local, @@ -215,8 +220,22 @@ def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype): def main(): - assert_tl_matmul_correctness(128, 128, 128, "float8_e4m3", "float32", "float32") - assert_tl_matmul_correctness(128, 128, 128, "float8_e5m2", "float32", "float32") + assert_tl_matmul_correctness(128, 128, 128, T.float8_e4m3fn, T.float32, T.float32) + assert_tl_matmul_correctness(128, 128, 128, T.float8_e5m2, T.float32, T.float32) + + +def run_regression_perf(): + M, N, K = 4096, 4096, 4096 + out_dtype, accum_dtype = "float32", "float32" + in_dtype = T.float8_e4m3fn + kernel_e4m3 = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype) + profiler_e4m3 = kernel_e4m3.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e4m3 = profiler_e4m3.do_bench(backend="cupti") + in_dtype = T.float8_e5m2 + kernel_e5m2 = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype) + profiler_e5m2 = kernel_e5m2.get_profiler(tilelang.TensorSupplyType.Integer) + latency_e5m2 = profiler_e5m2.do_bench(backend="cupti") + return (latency_e4m3 + latency_e5m2) / 2 if __name__ == "__main__": diff --git a/examples/gemm_fp8/example_tilelang_gemm_fp8_sm100.py b/examples/gemm_fp8/example_tilelang_gemm_fp8_sm100.py new file mode 100644 index 0000000000..aa7e8b3608 --- /dev/null +++ b/examples/gemm_fp8/example_tilelang_gemm_fp8_sm100.py @@ -0,0 +1,124 @@ +import torch +import tilelang +import tilelang.language as T +from tilelang.utils.tensor import map_torch_type + + +def matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype) + B_shared = T.alloc_shared(B_shared_shape, in_dtype) + C_tmem = T.alloc_tmem([block_M, block_N], accum_dtype) + mbar = T.alloc_barrier(1) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + C_shared = T.alloc_shared((block_M, block_N), out_dtype) + + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + T.copy(A[by * block_M, k * block_K], A_shared) + T.copy(B[bx * block_N, k * block_K], B_shared) + T.gemm_v2( + A_shared, + B_shared, + C_tmem, + trans_A, + trans_B, + mbar=mbar, + wg_wait=-1, + clear_accum=(k == 0), + ) + T.mbarrier_wait_parity(mbar, k % 2) + + T.copy(C_tmem, C_local) + T.copy(C_local, C_shared) + + T.copy(C_shared, C[by * block_M, bx * block_N]) + + return main + + +def calc_diff(x, y): + x, y = x.double(), y.double() + denominator = (x * x + y * y).sum() + sim = 2 * (x * y).sum() / denominator + return 1 - sim + + +M, N, K = 4096, 4096, 8192 +block_M, block_N, block_K = 64, 256, 32 +trans_A, trans_B = False, True +num_stages = 2 +threads = 256 +for tvm_fp8_dtype in [T.float8_e4m3fn, T.float8_e5m2]: + for tvm_acc_dtype in [T.float16, T.float32]: # , torch.float16]: + torch_fp8_dtype = map_torch_type(tvm_fp8_dtype) + torch_acc_dtype = map_torch_type(tvm_acc_dtype) + print(f"running {tvm_fp8_dtype} -> {tvm_acc_dtype}") + in_dtype, out_dtype, accum_dtype = tvm_fp8_dtype, tvm_acc_dtype, tvm_acc_dtype + + func = matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, + ) + jit_kernel = tilelang.compile( + func, + out_idx=[2], + target="cuda", + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + tilelang.PassConfigKey.TL_ENABLE_PTXAS_VERBOSE_OUTPUT: True, + }, + ) + # jit_kernel.export_ptx("./dump.ptx") + # jit_kernel.export_sources("./dump.cu") + + a = torch.randn(M, K, device="cuda", dtype=torch.float16).to(torch_fp8_dtype) + b = torch.randn(N, K, device="cuda", dtype=torch.float16).to(torch_fp8_dtype) + + c = jit_kernel(a, b) + ref_c = (a.to(torch.half) @ b.T.to(torch.half)).float() + c = c.float() + diff = calc_diff(c, ref_c) + # assert diff < 1e-3, f"{diff}" + print(f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] diff = {diff}") + + profiler = jit_kernel.get_profiler() + latency = profiler.do_bench() + print(f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] Latency: {latency} ms") + print(f"[{tvm_fp8_dtype} -> {tvm_acc_dtype}] Flops: {2 * M * N * K / (latency / 1e3) / 1e12} TFLOPS") diff --git a/examples/gemm_fp8/regression_example_gemm_fp8.py b/examples/gemm_fp8/regression_example_gemm_fp8.py new file mode 100644 index 0000000000..3ba2f4f274 --- /dev/null +++ b/examples/gemm_fp8/regression_example_gemm_fp8.py @@ -0,0 +1,20 @@ +import tilelang.testing +import example_tilelang_gemm_fp8 +import example_tilelang_gemm_fp8_2xAcc +import example_tilelang_gemm_fp8_intrinsic + + +def regression_example_tilelang_gemm_fp8_2xAcc(): + tilelang.testing.process_func(example_tilelang_gemm_fp8_2xAcc.run_regression_perf) + + +def regression_example_tilelang_gemm_fp8_intrinsic(): + tilelang.testing.process_func(example_tilelang_gemm_fp8_intrinsic.run_regression_perf) + + +def regression_example_tilelang_gemm_fp8(): + tilelang.testing.process_func(example_tilelang_gemm_fp8.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/gemm_fp8/test_example_gemm_fp8.py b/examples/gemm_fp8/test_example_gemm_fp8.py index 19a9ee00a7..8a60d0e020 100644 --- a/examples/gemm_fp8/test_example_gemm_fp8.py +++ b/examples/gemm_fp8/test_example_gemm_fp8.py @@ -1,17 +1,30 @@ +import pytest +import torch import tilelang.testing import example_tilelang_gemm_fp8_2xAcc import example_tilelang_gemm_fp8_intrinsic import example_tilelang_gemm_fp8 +def requires_sm89(): + """FP8 tensor core MMA requires SM89 (Ada Lovelace) or higher.""" + major, minor = torch.cuda.get_device_capability() + return pytest.mark.skipif( + major < 9 and not (major == 8 and minor >= 9), reason="FP8 tensor core MMA requires SM89 or higher (Ada Lovelace/Hopper)" + ) + + +@requires_sm89() def test_example_tilelang_gemm_fp8_2xAcc(): example_tilelang_gemm_fp8_2xAcc.main() +@requires_sm89() def test_example_tilelang_gemm_fp8_intrinsic(): example_tilelang_gemm_fp8_intrinsic.main() +@requires_sm89() def test_example_tilelang_gemm_fp8(): example_tilelang_gemm_fp8.main() diff --git a/examples/gemm_sm100/README.md b/examples/gemm_sm100/README.md index 73dd76c308..d630d2d0d3 100644 --- a/examples/gemm_sm100/README.md +++ b/examples/gemm_sm100/README.md @@ -40,19 +40,19 @@ import tilelang.language as T @T.prim_func def main( - A: T.Tensor((M, K), "bfloat16"), - B: T.Tensor((N, K), "bfloat16"), - C: T.Tensor((M, N), "bfloat16"), + A: T.Tensor((M, K), T.bfloat16), + B: T.Tensor((N, K), T.bfloat16), + C: T.Tensor((M, N), T.bfloat16), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=256) as (bx, by): # 1. Allocate memory buffers - A_shared = T.alloc_shared((block_M, block_K), "bfloat16") # A matrix shared memory - B_shared = T.alloc_shared((block_N, block_K), "bfloat16") # B matrix shared memory - C_tmem = T.alloc_tmem([block_M, block_N], "float") # TCGEN5MMA output to Tensor Memory + A_shared = T.alloc_shared((block_M, block_K), T.bfloat16) # A matrix shared memory + B_shared = T.alloc_shared((block_N, block_K), T.bfloat16) # B matrix shared memory + C_tmem = T.alloc_tmem([block_M, block_N], T.float) # TCGEN5MMA output to Tensor Memory mbar = T.alloc_barrier(1) # mbarrier synchronization primitive - C_local = T.alloc_fragment((block_M, block_N), "float") # Register storage - C_shared = T.alloc_shared((block_M, block_N), "bfloat16") # Output shared memory + C_local = T.alloc_fragment((block_M, block_N), T.float) # Register storage + C_shared = T.alloc_shared((block_M, block_N), T.bfloat16) # Output shared memory # 2. Main computation loop for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1): @@ -103,4 +103,3 @@ latency = profiler.do_bench() print(f"Latency: {latency} ms") print(f"Performance: {2 * M * N * K / (latency/1e3) / 1e12:.2f} TFLOPS") ``` - diff --git a/examples/gemm_sm100/gemm_mma.py b/examples/gemm_sm100/gemm_mma.py index a58e5a7c00..226e33c01e 100644 --- a/examples/gemm_sm100/gemm_mma.py +++ b/examples/gemm_sm100/gemm_mma.py @@ -4,13 +4,12 @@ # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=256) as (bx, by): @@ -62,7 +61,8 @@ def main( pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + }, +) print(jit_kernel.get_kernel_source()) # 3. Test the kernel in Python with PyTorch data import torch diff --git a/examples/gemm_sm100/gemm_tcgen5mma.py b/examples/gemm_sm100/gemm_tcgen5mma.py index 9008c7ef52..523a94fea6 100644 --- a/examples/gemm_sm100/gemm_tcgen5mma.py +++ b/examples/gemm_sm100/gemm_tcgen5mma.py @@ -25,9 +25,9 @@ def matmul( @T.prim_func def main( - A: T.Tensor(A_shape, in_dtype), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) @@ -40,15 +40,7 @@ def main( for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(A[by * block_M, k * block_K], A_shared) T.copy(B[bx * block_N, k * block_K], B_shared) - T.gemm( - A_shared, - B_shared, - C_tmem, - trans_A, - trans_B, - mbar=mbar, - wg_wait=-1, - clear_accum=k == 0) + T.gemm(A_shared, B_shared, C_tmem, trans_A, trans_B, mbar=mbar, wg_wait=-1, clear_accum=k == 0) T.mbarrier_wait_parity(mbar, k % 2) T.copy(C_tmem, C_local) @@ -62,12 +54,11 @@ def main( M, N, K = 4096, 4096, 8192 block_M, block_N, block_K = 128, 256, 128 trans_A, trans_B = False, True -in_dtype, out_dtype, accum_dtype = "bfloat16", "bfloat16", "float" +in_dtype, out_dtype, accum_dtype = T.bfloat16, T.bfloat16, T.float num_stages = 2 threads = 256 -func = matmul(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, - accum_dtype, num_stages, threads) +func = matmul(M, N, K, block_M, block_N, block_K, trans_A, trans_B, in_dtype, out_dtype, accum_dtype, num_stages, threads) jit_kernel = tilelang.compile( func, out_idx=[2], @@ -75,7 +66,8 @@ def main( pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + }, +) print(jit_kernel.get_kernel_source()) @@ -88,4 +80,4 @@ def main( profiler = jit_kernel.get_profiler() latency = profiler.do_bench() print(f"Latency: {latency} ms") -print(f"Flops: {2 * M * N * K / (latency/1e3) / 1e12} TFLOPS") +print(f"Flops: {2 * M * N * K / (latency / 1e3) / 1e12} TFLOPS") diff --git a/examples/gemm_sp/example_custom_compress.py b/examples/gemm_sp/example_custom_compress.py new file mode 100644 index 0000000000..0544b82557 --- /dev/null +++ b/examples/gemm_sp/example_custom_compress.py @@ -0,0 +1,337 @@ +import argparse + +import tilelang +import tilelang.language as T + +from tilelang.layout import make_cutlass_metadata_layout +from tilelang.utils.sparse import randn_semi_sparse +from tilelang.utils.tensor import torch_assert_close + +from triton.testing import do_bench + +import torch + +torch.manual_seed(42) + +DEFAULT_CONFIG = { # take best config from autotune script + "4090": { + T.float: { + "block_M": 128, + "block_N": 64, + "block_K": 64, + "num_stages": 1, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + T.float16: { + "block_M": 256, + "block_N": 128, + "block_K": 64, + "num_stages": 2, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + }, + "h20": { + T.float: { + "block_M": 128, + "block_N": 64, + "block_K": 128, + "num_stages": 3, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + T.float16: { + "block_M": 128, + "block_N": 64, + "block_K": 128, + "num_stages": 3, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + }, +} + +ARCH_INFO = {"8.0": (16, "int16"), "8.9": (16, "int16"), "9.0": (8, "uint8")} + + +@tilelang.jit(out_idx=[-1]) +def matmul_sp_fp16_custom_compress( + M, N, K, accum_dtype, block_M, block_N, block_K, num_stages, thread_num, policy, enable_rasterization, use_cutlass_layout +): + e_factor, e_dtype = (16, T.int16) + + @T.prim_func + def gemm_sp_fp16_custom_compress( + A_sparse: T.Tensor((M, K // 2), T.float16), + E: T.Tensor((M, K // e_factor), e_dtype), + B: T.Tensor((K, N), T.float16), + C: T.Tensor((M, N), accum_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K // 2), T.float16) + E_shared = T.alloc_shared((block_M, block_K // e_factor), e_dtype) + B_shared = T.alloc_shared((block_K, block_N), T.float16) + C_shared = T.alloc_shared((block_M, block_N), accum_dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + if use_cutlass_layout: + T.annotate_layout( + { + E: make_cutlass_metadata_layout(E, mma_dtype=T.float16, arch="8.0", block_k=block_K), + E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=T.float16, arch="8.0", block_k=block_K), + } + ) + T.clear(C_local) + T.disable_warp_group_reg_alloc() + T.use_swizzle(panel_size=10, enable=enable_rasterization) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + T.copy(A_sparse[by * block_M, k * block_K // 2], A_shared) + T.copy(E[by * block_M, k * block_K // e_factor], E_shared) + T.copy(B[k * block_K, bx * block_N], B_shared) + T.gemm_sp_v2(A_shared, E_shared, B_shared, C_local, False, False, policy=policy) + + T.copy(C_local, C_shared) + T.copy(C_shared, C[by * block_M, bx * block_N]) + + return gemm_sp_fp16_custom_compress + + +def torch_compress(dense): + """ + A naive compression function, where each 4-bit meta matches 4 elements in original matrix in row major layout. + """ + if dense.dim() != 2: + raise RuntimeError(f"Expected 2-dimensional dense tensor, got {dense.dim()}-dimensional tensor") + + m, k = dense.shape + + meta_dtype = torch.int8 + if dense.dtype == torch.int8: + meta_dtype = torch.int32 + elif dense.dtype in [torch.half, torch.bfloat16, torch.float]: + meta_dtype = torch.int16 + else: + raise RuntimeError(f"Invalid datatype {dense.dtype} of dense matrix") + quadbits_per_meta_elem = meta_dtype.itemsize * 8 // 4 + if quadbits_per_meta_elem not in (4, 8): + raise RuntimeError("Invalid number of elements per meta element calculated") + + if meta_dtype == torch.int32: + if m % 16 != 0: + raise RuntimeError(f"Number of rows of dense matrix {m} must be divisible by 16") + else: + if m % 32 != 0: + raise RuntimeError(f"Number of rows of dense matrix {m} must be divisible by 32") + if k % (4 * quadbits_per_meta_elem) != 0: + raise RuntimeError(f"Number of columns of dense matrix {k} must be divisible by {4 * quadbits_per_meta_elem}") + + if dense.dtype != torch.float: + ksparse = 4 + dense_4 = dense.view(-1, k // ksparse, ksparse) + m0, m1, _m2, m3 = (dense_4 != 0).unbind(-1) + else: + ksparse = 2 + dense_2 = dense.view(-1, k // ksparse, ksparse) + m0, _m2 = m1, m3 = (dense_2 != 0).unbind(-1) + meta_ncols = k // (ksparse * quadbits_per_meta_elem) + + # Encoding quadruples of True/False values as follows: + # [True, True, False, False] -> 0b0100 + # [True, False, True, False] -> 0b1000 + # [False, True, True, False] -> 0b1001 + # [True, False, False, True ] -> 0b1100 + # [False, True, False, True ] -> 0b1101 + # [False, False, True, True ] -> 0b1110 + # Thus, lower two bits in the encoding are index of the True value + # at the lowest index in the quadruple, and the higher two bits in + # the encoding are index of the other True value in the quadruple. + # In case there are less than two True values, than False value or + # values at some index or indices are considered True for the + # encoding. In case there are more than two True values, then the + # excess True value(s) at some indices are considered False for + # the encoding. The exact encodings used for these cases are as + # follows: + # [False, False, False, False] -> 0b1110 + # [False, False, False, True ] -> 0b1110 + # [False, False, True, False] -> 0b1110 + # [False, True, False, False] -> 0b1001 + # [False, True, True, True ] -> 0b1101 + # [True, False, False, False] -> 0b1000 + # [True, False, True, True ] -> 0b1100 + # [True, True, False, True ] -> 0b0100 + # [True, True, True, False] -> 0b0100 + # [True, True, True, True ] -> 0b0100 + # These particular encodings are chosen, with the help of Espresso + # logic minimizer software, for the purpose of minimization of + # corresponding Boolean functions, that translate non-zero flags + # into encoding bits. Note also possible choices for the first + # and last of these encodings were limited only to (0b0100, + # 0b1110), in order to produce valid encodings for 1:2 sparsity + # case. + + expr0 = m0 & m1 + expr1 = ~m0 & m1 + expr2 = ~m0 & ~m1 + bit0 = expr1 + bit1 = expr2 + bit2 = expr0 | expr2 | m3 + bit3 = expr1 | ~m1 + idxs0 = bit0 | (bit1.to(torch.int64) << 1) + idxs1 = bit2 | (bit3.to(torch.int64) << 1) + + if dense.dtype != torch.float: + sparse0 = dense_4.gather(-1, idxs0.unsqueeze(-1)) # type: ignore[possibly-undefined] + sparse1 = dense_4.gather(-1, idxs1.unsqueeze(-1)) + sparse = torch.stack((sparse0, sparse1), dim=-1).view(m, k // 2) + else: + sparse = dense_2.gather(-1, idxs0.unsqueeze(-1) // 2).view(m, k // 2) # type: ignore[possibly-undefined] + + meta_4 = idxs0 | (idxs1 << 2) + meta_n = meta_4.view((-1, meta_ncols, quadbits_per_meta_elem)).to(meta_dtype) + + if quadbits_per_meta_elem == 4: + meta = meta_n[:, :, 0] | (meta_n[:, :, 1] << 4) | (meta_n[:, :, 2] << 8) | (meta_n[:, :, 3] << 12) + elif quadbits_per_meta_elem == 8: + meta = ( + meta_n[:, :, 0] + | (meta_n[:, :, 1] << 4) + | (meta_n[:, :, 2] << 8) + | (meta_n[:, :, 3] << 12) + | (meta_n[:, :, 4] << 16) + | (meta_n[:, :, 5] << 20) + | (meta_n[:, :, 6] << 24) + | (meta_n[:, :, 7] << 28) + ) + + return (sparse, meta) + + +def decode_metadata(meta: torch.Tensor) -> torch.Tensor: + assert meta.dtype is torch.int16 + groups_per_meta = 16 // 4 # 4 groups per uint16 + out = [] + for g in range(groups_per_meta): + group_bits = (meta >> (g * 4)) & 0xF + idx0 = group_bits & 0x3 + idx1 = (group_bits >> 2) & 0x3 + out.append(torch.stack([idx0, idx1], dim=-1)) + return torch.concat(out, dim=-1).view(meta.shape[0], -1) + + +@tilelang.jit( + out_idx=[1, 2], + pass_configs={ + tilelang.PassConfigKey.TIR_DISABLE_VECTORIZE: True, + }, +) +def compress_kernel(M, K, block_M, block_K, dtype, use_cutlass_layout): + e_factor, e_dtype = ARCH_INFO["8.0"] + e_K = K // e_factor + elem, group = 2, 4 + + assert M % block_M == 0, "M must be divisible by block_M" + assert K % block_K == 0, "K must be divisible by block_K" + assert K % e_factor == 0, "K must be divisible by e_factor" + assert block_K % e_factor == 0, "block_K must be divisible by e_factor" + + @T.prim_func + def kernel( + A: T.Tensor((M, K), dtype), + A_sp: T.Tensor((M, K // 2), dtype), + E: T.Tensor((M, e_K), e_dtype), + ): + with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(K, block_K), threads=block_M) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + A_sp_shared = T.alloc_shared((block_M, block_K // 2), dtype) + E_shared = T.alloc_shared((block_M, block_K // e_factor), e_dtype) + if use_cutlass_layout: + T.annotate_layout( + { + E: make_cutlass_metadata_layout(E, mma_dtype=T.float16, arch="8.0", block_k=block_K), + E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=T.float16, arch="8.0", block_k=block_K), + } + ) + T.clear(A_sp_shared) + T.clear(E_shared) + # TODO: alloc_var seems buggy here + non_zero_cnt = T.alloc_local((1,), dtype=T.uint8) + non_zero_elt_log_idx = T.alloc_local((elem,), dtype=T.uint8) + T.copy(A[bx * block_M, by * block_K], A_shared) + for tm in T.Parallel(block_M): + for g_i in range(0, block_K // group): + a_k = g_i * group + non_zero_cnt[0] = 0 + for i in range(elem): + non_zero_elt_log_idx[i] = 0 + for i in range(group): + val = A_shared[tm, a_k + i] + if val != 0.0: + non_zero_elt_log_idx[non_zero_cnt[0]] = i + A_sp_shared[tm, a_k // 2 + non_zero_cnt[0]] = val + non_zero_cnt[0] += 1 + # TODO: use T.device_assert(non_zero_cnt <= 2) after rebasing main + if non_zero_cnt[0] == 1 and non_zero_elt_log_idx[0] == 3: + non_zero_elt_log_idx[0] = 0 + non_zero_elt_log_idx[1] = 3 + A_sp_shared[tm, a_k // 2 + 1] = A_sp_shared[tm, a_k // 2] + A_sp_shared[tm, a_k // 2] = 0.0 + elif non_zero_cnt[0] == 1: + A_sp_shared[tm, a_k // 2 + 1] = 0 + non_zero_elt_log_idx[1] = 3 + for i in T.serial(elem): + val = non_zero_elt_log_idx[i] + E_shared[tm, a_k // e_factor] |= T.shift_left(val, 4 * (g_i % (e_factor // group)) + 2 * i) + T.copy(A_sp_shared, A_sp[bx * block_M, by * block_K // 2]) + T.copy(E_shared, E[bx * block_M, by * block_K // e_factor]) + + return kernel + + +def main(m=16384, n=16384, k=16384, use_cutlass_layout=False, use_torch_compressor=False, accum_dtype=None, cfg="4090"): + if accum_dtype is None: + accum_dtype = T.float + kernel = matmul_sp_fp16_custom_compress(m, n, k, accum_dtype, **DEFAULT_CONFIG[cfg][accum_dtype], use_cutlass_layout=use_cutlass_layout) + + a = randn_semi_sparse(m, k, device="cuda", dtype=torch.half) + b = torch.randn(k, n, device="cuda", dtype=torch.half) + + if use_torch_compressor: + assert not use_cutlass_layout, "torch sparse must be used with naive layout" + a_sparse, e = torch_compress(a) + else: + a_sparse, e = compress_kernel(m, k, 32, 32, T.float16, use_cutlass_layout=use_cutlass_layout)(a) + + c = kernel(a_sparse, e, b) + + ref_c = a @ b + + assert not c.isnan().any(), "Reference result contains NaNs, please report an issue" + torch_assert_close(c, ref_c.to(c.dtype), rtol=1e-3, atol=1e-3) + print(f"Precision check passed. Max diff: {(c - ref_c).abs().max()}, Mean diff: {(c - ref_c).abs().mean()}") + + latency = do_bench(lambda: kernel(a_sparse, e, b)) + ref_latency = do_bench(lambda: a @ b) + + total_flops = 2 * m * n * k + tflops = total_flops / latency / 1e9 + ref_tflops = total_flops / ref_latency / 1e9 + print(f"Sparse TFLOPS: {tflops:.2f}, Latency: {latency / 1e3} s") + print(f"Reference TFLOPS: {ref_tflops:.2f}, Latency: {ref_latency / 1e3:} s") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") + parser.add_argument("--m", type=int, default=16384, help="Matrix dimension M") + parser.add_argument("--n", type=int, default=16384, help="Matrix dimension N") + parser.add_argument("--k", type=int, default=16384, help="Matrix dimension K") + parser.add_argument("--use_cutlass_layout", action="store_true", help="Use cutlass layout for E tensor") + parser.add_argument("--use_torch_compressor", action="store_true", help="Use torch sparse for reference") + parser.add_argument("--accum_dtype", type=str, default="float", choices=["float", "float16"], help="Accumulation datatype") + parser.add_argument("--cfg", type=str, choices=["4090"], default="4090") + args = parser.parse_args() + accum_dtype = T.float if args.accum_dtype == "float" else T.float16 + main(args.m, args.n, args.k, args.use_cutlass_layout, args.use_torch_compressor, accum_dtype, args.cfg) diff --git a/examples/gemm_sp/example_gemm_sp.py b/examples/gemm_sp/example_gemm_sp.py index 505f2b8837..8163c84cc8 100644 --- a/examples/gemm_sp/example_gemm_sp.py +++ b/examples/gemm_sp/example_gemm_sp.py @@ -1,11 +1,9 @@ -# Copyright (c) Tile-AI Corporation. -# Licensed under the MIT License. import argparse import tilelang import tilelang.language as T -from tilelang.layout import make_metadata_layout +from tilelang.layout import make_cutlass_metadata_layout from tilelang.utils.sparse import compress, randn_semi_sparse from tilelang.contrib import nvcc from triton.testing import do_bench @@ -14,86 +12,79 @@ arch = nvcc.get_target_compute_version() -ARCH_INFO = {"8.0": (16, "int16"), "8.9": (16, "int16"), "9.0": (8, "uint8")} - -default_config = { # take best config from autotune script +DEFAULT_CONFIG = { # take best config from autotune script "4090": { - 'float': { - 'block_M': 128, - 'block_N': 64, - 'block_K': 64, - 'num_stages': 1, - 'thread_num': 128, - 'policy': T.GemmWarpPolicy.Square, - 'enable_rasterization': True + T.float: { + "block_M": 128, + "block_N": 64, + "block_K": 64, + "num_stages": 1, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + T.float16: { + "block_M": 256, + "block_N": 128, + "block_K": 64, + "num_stages": 2, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, }, - 'float16': { - 'block_M': 256, - 'block_N': 128, - 'block_K': 64, - 'num_stages': 2, - 'thread_num': 128, - 'policy': T.GemmWarpPolicy.Square, - 'enable_rasterization': True - } }, "h20": { - 'float': { - 'block_M': 128, - 'block_N': 64, - 'block_K': 128, - 'num_stages': 3, - 'thread_num': 128, - 'policy': T.GemmWarpPolicy.Square, - 'enable_rasterization': True + T.float: { + "block_M": 128, + "block_N": 64, + "block_K": 128, + "num_stages": 3, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, }, - 'float16': { - 'block_M': 128, - 'block_N': 64, - 'block_K': 128, - 'num_stages': 3, - 'thread_num': 128, - 'policy': T.GemmWarpPolicy.Square, - 'enable_rasterization': True - } - } + T.float16: { + "block_M": 128, + "block_N": 64, + "block_K": 128, + "num_stages": 3, + "thread_num": 128, + "policy": T.GemmWarpPolicy.Square, + "enable_rasterization": True, + }, + }, } +ARCH_INFO = {"8.0": (16, "int16"), "8.9": (16, "int16"), "9.0": (8, "uint8")} + @tilelang.jit(out_idx=[-1]) -def matmul_sp_fp16(M, N, K, accum_dtype, block_M, block_N, block_K, num_stages, thread_num, policy, - enable_rasterization): +def matmul_sp_fp16(M, N, K, accum_dtype, block_M, block_N, block_K, num_stages, thread_num, policy, enable_rasterization): e_factor, e_dtype = ARCH_INFO[arch] @T.prim_func def gemm_sp_fp16( - A_sparse: T.Tensor((M, K // 2), 'float16'), - E: T.Tensor((M, K // e_factor), e_dtype), - B: T.Tensor((K, N), 'float16'), - C: T.Tensor((M, N), accum_dtype), + A_sparse: T.Tensor((M, K // 2), T.float16), + E: T.Tensor((M, K // e_factor), e_dtype), + B: T.Tensor((K, N), T.float16), + C: T.Tensor((M, N), accum_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=thread_num) as (bx, by): - A_shared = T.alloc_shared((block_M, block_K // 2), 'float16') + A_shared = T.alloc_shared((block_M, block_K // 2), T.float16) E_shared = T.alloc_shared((block_M, block_K // e_factor), e_dtype) - B_shared = T.alloc_shared((block_K, block_N), 'float16') + B_shared = T.alloc_shared((block_K, block_N), T.float16) C_shared = T.alloc_shared((block_M, block_N), accum_dtype) C_local = T.alloc_fragment((block_M, block_N), accum_dtype) T.clear(C_local) T.disable_warp_group_reg_alloc() T.use_swizzle(panel_size=10, enable=enable_rasterization) - T.annotate_layout({ - E: - make_metadata_layout( - E, mma_dtype="float16", backend="cutlass", block_k=block_K, arch=arch), - E_shared: - make_metadata_layout( - E_shared, - mma_dtype="float16", - backend="cutlass", - block_k=block_K, - arch=arch), - }) + T.annotate_layout( + { + E: make_cutlass_metadata_layout(E, mma_dtype=T.float16, block_k=block_K, arch=arch), + E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=T.float16, block_k=block_K, arch=arch), + } + ) for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(A_sparse[by * block_M, k * block_K // 2], A_shared) T.copy(E[by * block_M, k * block_K // e_factor], E_shared) @@ -106,30 +97,15 @@ def gemm_sp_fp16( return gemm_sp_fp16 -def main(): - parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") - parser.add_argument("--m", type=int, default=16384, help="Matrix dimension M") - parser.add_argument("--n", type=int, default=16384, help="Matrix dimension N") - parser.add_argument("--k", type=int, default=16384, help="Matrix dimension K") - parser.add_argument( - "--accum_dtype", - type=str, - default="float", - choices=["float", "float16"], - help="Accumulation datatype") - parser.add_argument("--cfg", type=str, choices=["4090", "h20"], required=True) - args = parser.parse_args() - kernel = matmul_sp_fp16(args.m, args.n, args.k, args.accum_dtype, - **default_config[args.cfg][args.accum_dtype]) +def main(m=16384, n=16384, k=16384, accum_dtype=None, cfg="4090"): + if accum_dtype is None: + accum_dtype = T.float + kernel = matmul_sp_fp16(m, n, k, accum_dtype, **DEFAULT_CONFIG[cfg][accum_dtype]) - a = randn_semi_sparse(args.m, args.k, device='cuda', dtype=torch.half) - b = torch.randn(args.k, args.n, device='cuda', dtype=torch.half) + a = randn_semi_sparse(m, k, device="cuda", dtype=torch.half) + b = torch.randn(k, n, device="cuda", dtype=torch.half) - a_sparse, e = compress( - a, - transposed=False, - block_k=default_config[args.cfg][args.accum_dtype]['block_K'], - arch=arch) + a_sparse, e = compress(a, transposed=False, block_k=DEFAULT_CONFIG[cfg][accum_dtype]["block_K"], arch=arch) c = kernel(a_sparse, e, b) ref_c = a @ b @@ -141,12 +117,20 @@ def main(): latency = do_bench(lambda: kernel(a_sparse, e, b)) ref_latency = do_bench(lambda: a @ b) - total_flops = 2 * args.m * args.n * args.k + total_flops = 2 * m * n * k tflops = total_flops / latency / 1e9 ref_tflops = total_flops / ref_latency / 1e9 - print(f"Sparse TFLOPS: {tflops:.2f}, Latency: {latency/1e3} s") - print(f"Reference TFLOPS: {ref_tflops:.2f}, Latency: {ref_latency/1e3:} s") + print(f"Sparse TFLOPS: {tflops:.2f}, Latency: {latency / 1e3} s") + print(f"Reference TFLOPS: {ref_tflops:.2f}, Latency: {ref_latency / 1e3:} s") if __name__ == "__main__": - main() + parser = argparse.ArgumentParser(description="Autotuned MatMul Benchmark") + parser.add_argument("--m", type=int, default=16384, help="Matrix dimension M") + parser.add_argument("--n", type=int, default=16384, help="Matrix dimension N") + parser.add_argument("--k", type=int, default=16384, help="Matrix dimension K") + parser.add_argument("--accum_dtype", type=str, default="float", choices=["float", "float16"], help="Accumulation datatype") + parser.add_argument("--cfg", type=str, choices=["4090", "h20"], default="4090") + args = parser.parse_args() + accum_dtype = T.float if args.accum_dtype == "float" else T.float16 + main(args.m, args.n, args.k, accum_dtype, args.cfg) diff --git a/examples/gemm_sp/test_example_gemm_sp.py b/examples/gemm_sp/test_example_gemm_sp.py new file mode 100644 index 0000000000..fe26df1449 --- /dev/null +++ b/examples/gemm_sp/test_example_gemm_sp.py @@ -0,0 +1,16 @@ +import tilelang.testing + +import example_custom_compress +import example_gemm_sp + + +def test_example_custom_compress(): + example_custom_compress.main() + + +def test_example_gemm_sp(): + example_gemm_sp.main() + + +if __name__ == "__main__": + tilelang.testing.main() diff --git a/examples/gemm_splitk/example_tilelang_gemm_splitk.py b/examples/gemm_splitk/example_tilelang_gemm_splitk.py index c966697118..64ffade8e9 100644 --- a/examples/gemm_splitk/example_tilelang_gemm_splitk.py +++ b/examples/gemm_splitk/example_tilelang_gemm_splitk.py @@ -3,27 +3,16 @@ @tilelang.jit -def matmul(M, - N, - K, - block_M, - block_N, - block_K, - split_k, - dtype="float16", - accum_dtype="float", - out_dtype="float32"): - +def matmul(M, N, K, block_M, block_N, block_K, split_k, dtype=T.float16, accum_dtype=T.float32, out_dtype=T.float32): splitK = K // split_k @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), out_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz): A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_K, block_N), dtype) C_shared = T.alloc_shared((block_M, block_N), out_dtype) @@ -67,5 +56,28 @@ def main(): torch.testing.assert_close(c, ref_c.to(c.dtype), rtol=1e-2, atol=1e-2) +def run_regression_perf(): + M = 4096 + N = 4096 + K = 4096 + block_M = 128 + block_N = 128 + block_K = 32 + split_k = 4 + kernel = matmul(M, N, K, block_M, block_N, block_K, split_k) + import torch + + torch.random.manual_seed(42) + a = torch.randn(M, K).cuda().half() + b = torch.randn(K, N).cuda().half() + c = torch.zeros(M, N).cuda().float() + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(a, b, c) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/gemm_splitk/example_tilelang_gemm_splitk_vectorize_atomicadd.py b/examples/gemm_splitk/example_tilelang_gemm_splitk_vectorize_atomicadd.py index 145d622edf..3d33478cf2 100644 --- a/examples/gemm_splitk/example_tilelang_gemm_splitk_vectorize_atomicadd.py +++ b/examples/gemm_splitk/example_tilelang_gemm_splitk_vectorize_atomicadd.py @@ -3,27 +3,16 @@ @tilelang.jit -def matmul(M, - N, - K, - block_M, - block_N, - block_K, - split_k, - dtype="float16", - accum_dtype="float", - out_dtype="float32"): - +def matmul(M, N, K, block_M, block_N, block_K, split_k, dtype=T.float16, accum_dtype=T.float32, out_dtype=T.float32): splitK = K // split_k @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((M, N), out_dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((M, N), out_dtype), ): - with T.Kernel( - T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), split_k, threads=128) as (bx, by, bz): A_shared = T.alloc_shared((block_M, block_K), dtype) B_shared = T.alloc_shared((block_K, block_N), dtype) C_shared = T.alloc_shared((block_M, block_N), out_dtype) @@ -66,5 +55,29 @@ def main(): torch.testing.assert_close(c, ref_c.to(c.dtype), rtol=1e-2, atol=1e-2) +def run_regression_perf(): + M = 4096 + N = 4096 + K = 4096 + block_M = 128 + block_N = 128 + block_K = 32 + split_k = 4 + + kernel = matmul(M, N, K, block_M, block_N, block_K, split_k) + import torch + + torch.random.manual_seed(42) + a = torch.randn(M, K).cuda().half() + b = torch.randn(K, N).cuda().half() + c = torch.zeros(M, N).cuda().float() + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(a, b, c) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/gemm_splitk/regression_example_gemm_splitk.py b/examples/gemm_splitk/regression_example_gemm_splitk.py new file mode 100644 index 0000000000..c76b7e55c6 --- /dev/null +++ b/examples/gemm_splitk/regression_example_gemm_splitk.py @@ -0,0 +1,15 @@ +import tilelang.testing +import example_tilelang_gemm_splitk +import example_tilelang_gemm_splitk_vectorize_atomicadd + + +def regression_example_tilelang_gemm_splitk(): + tilelang.testing.process_func(example_tilelang_gemm_splitk.run_regression_perf) + + +def regression_example_tilelang_gemm_splitk_vectorize_atomicadd(): + tilelang.testing.process_func(example_tilelang_gemm_splitk_vectorize_atomicadd.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/gemm_streamk/example_tilelang_gemm_streamk.py b/examples/gemm_streamk/example_tilelang_gemm_streamk.py index 31cf40647c..b2e8e93690 100644 --- a/examples/gemm_streamk/example_tilelang_gemm_streamk.py +++ b/examples/gemm_streamk/example_tilelang_gemm_streamk.py @@ -39,7 +39,7 @@ def cdiv(a, b): # Two-tile SK + DP streamk_tiles = total_tiles % streamk_programs -if (total_tiles - streamk_tiles > streamk_programs): # (total_tiles // total_programs > 1) +if total_tiles - streamk_tiles > streamk_programs: # (total_tiles // total_programs > 1) streamk_tiles += streamk_programs blocking_tiles = total_tiles - streamk_tiles @@ -77,95 +77,71 @@ def tl_matmul_streamk( A_shared_shape = (block_M, block_K) if not trans_A else (block_K, block_M) B_shared_shape = (block_K, block_N) if not trans_B else (block_N, block_K) - @T.macro - def compute_first_wave( - pid: T.int32, - A_buf: T.Tensor, - A_buf_shared: T.SharedBuffer, - B_buf: T.Tensor, - B_buf_shared: T.SharedBuffer, - C: T.Tensor, - C_local: T.LocalBuffer, - ): - start_iter = T.alloc_fragment((1,), "int32", "local") - end_iter = T.alloc_fragment((1,), "int32", "local") - - start_iter[0] = pid * streamk_full_tiles + T.min(pid, streamk_partial_tiles) - last_iter = (pid + 1) * streamk_full_tiles + T.min(pid + 1, streamk_partial_tiles) - - while start_iter[0] < last_iter: - end_iter[0] = T.min( - start_iter[0] + (iters_per_tile - (start_iter[0] % iters_per_tile)), - last_iter, - ) - - tile_id = start_iter[0] // iters_per_tile - remain_iters = start_iter[0] % iters_per_tile - pid_m = tile_id // T.ceildiv(N, block_N) - pid_n = tile_id % T.ceildiv(N, block_N) - - T.clear(C_local) - for k in T.Pipelined(end_iter[0] - start_iter[0], num_stages=num_stages): - T.copy( - A_buf[pid_m * block_M, (k + (start_iter[0] % iters_per_tile)) * block_K], - A_buf_shared, - ) - T.copy( - B_buf[pid_n * block_N, (k + (start_iter[0] % iters_per_tile)) * block_K], - B_buf_shared, - ) - T.gemm(A_buf_shared, B_buf_shared, C_local, transpose_B=trans_B) - - # last iteration of the tile always happens before its start on another SM - if remain_iters == 0 and (end_iter[0] % iters_per_tile == 0): - T.copy(C_local, C[pid_m * block_M, pid_n * block_N]) - else: - for i, j in T.Parallel(block_M, block_N): - T.atomic_add(C[pid_m * block_M + i, pid_n * block_N + j], C_local[i, j]) - - start_iter[0] = end_iter[0] - - @T.macro - def compute_full_tiles( - pid: T.int32, - A_buf: T.Tensor, - A_shared: T.SharedBuffer, - B_buf: T.Tensor, - B_shared: T.SharedBuffer, - C: T.Tensor, - C_local: T.LocalBuffer, - ): - - for p in T.serial(sm_patition_factor): - tile_id = pid + streamk_tiles + p * total_sm - pid_m = tile_id // T.ceildiv(N, block_N) - pid_n = tile_id % T.ceildiv(N, block_N) - T.clear(C_local) - - for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1): - T.copy(A_buf[pid_m * block_M, k * block_K], A_shared) - T.copy(B_buf[pid_n * block_N, k * block_K], B_shared) - T.gemm(A_shared, B_shared, C_local, transpose_B=trans_B) - T.copy(C_local, C[pid_m * block_M, pid_n * block_N]) - @T.prim_func def main( - A: T.Tensor(A_shape, dtypeAB), - B: T.Tensor(B_shape, dtypeAB), - C: T.Tensor((M, N), dtypeC), + A: T.Tensor(A_shape, dtypeAB), + B: T.Tensor(B_shape, dtypeAB), + C: T.Tensor((M, N), dtypeC), ): with T.Kernel(streamk_programs, threads=threads) as pid: - A_shared = T.alloc_shared(A_shared_shape, dtypeAB) B_shared = T.alloc_shared(B_shared_shape, dtypeAB) A_shared_full_tiles = T.alloc_shared(A_shared_shape, dtypeAB) B_shared_full_tiles = T.alloc_shared(B_shared_shape, dtypeAB) C_local = T.alloc_fragment((block_M, block_N), accum_dtype) - compute_first_wave(pid, A, A_shared, B, B_shared, C, C_local) + # compute first wave + start_iter = T.alloc_fragment((1,), T.int32, "local") + end_iter = T.alloc_fragment((1,), T.int32, "local") + + start_iter[0] = pid * streamk_full_tiles + T.min(pid, streamk_partial_tiles) + last_iter = (pid + 1) * streamk_full_tiles + T.min(pid + 1, streamk_partial_tiles) + while start_iter[0] < last_iter: + end_iter[0] = T.min( + start_iter[0] + (iters_per_tile - (start_iter[0] % iters_per_tile)), + last_iter, + ) + + tile_id = start_iter[0] // iters_per_tile + remain_iters = start_iter[0] % iters_per_tile + pid_m = tile_id // T.ceildiv(N, block_N) + pid_n = tile_id % T.ceildiv(N, block_N) + + T.clear(C_local) + for k in T.Pipelined(end_iter[0] - start_iter[0], num_stages=num_stages): + T.copy( + A[pid_m * block_M, (k + (start_iter[0] % iters_per_tile)) * block_K], + A_shared, + ) + T.copy( + B[pid_n * block_N, (k + (start_iter[0] % iters_per_tile)) * block_K], + B_shared, + ) + T.gemm(A_shared, B_shared, C_local, transpose_B=trans_B) + + # last iteration of the tile always happens before its start on another SM + if remain_iters == 0 and (end_iter[0] % iters_per_tile == 0): + T.copy(C_local, C[pid_m * block_M, pid_n * block_N]) + else: + for i, j in T.Parallel(block_M, block_N): + T.atomic_add(C[pid_m * block_M + i, pid_n * block_N + j], C_local[i, j]) + + start_iter[0] = end_iter[0] + + # compute full tiles if sm_patition_factor > 0: - compute_full_tiles(pid, A, A_shared_full_tiles, B, B_shared_full_tiles, C, C_local) + for p in T.serial(sm_patition_factor): + tile_id = pid + streamk_tiles + p * total_sm + pid_m = tile_id // T.ceildiv(N, block_N) + pid_n = tile_id % T.ceildiv(N, block_N) + T.clear(C_local) + + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=1): + T.copy(A[pid_m * block_M, k * block_K], A_shared_full_tiles) + T.copy(B[pid_n * block_N, k * block_K], B_shared_full_tiles) + T.gemm(A_shared_full_tiles, B_shared_full_tiles, C_local, transpose_B=trans_B) + T.copy(C_local, C[pid_m * block_M, pid_n * block_N]) return main @@ -181,9 +157,9 @@ def main(): BLOCK_SIZE_K, False, True, - "float16", - "float16", - "float32", + T.float16, + T.float16, + T.float32, 2, 64, ) @@ -201,5 +177,30 @@ def main(): torch.testing.assert_close(C, b_c, rtol=1e-2, atol=1e-2) +def run_regression_perf(): + kernel = tl_matmul_streamk( + m, + n, + k, + streamk_tiles, + BLOCK_SIZE_M, + BLOCK_SIZE_N, + BLOCK_SIZE_K, + False, + True, + "float16", + "float16", + "float32", + 2, + 64, + ) + b_c = torch.zeros((m, n), device="cuda", dtype=torch.float16) + torch.cuda.synchronize() + + from tilelang.profiler import do_bench + + return do_bench(lambda: kernel(A, B, b_c), backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/gemm_streamk/test_example_tilelang_gemm_splitk.py b/examples/gemm_streamk/test_example_tilelang_gemm_streamk.py similarity index 100% rename from examples/gemm_streamk/test_example_tilelang_gemm_splitk.py rename to examples/gemm_streamk/test_example_tilelang_gemm_streamk.py diff --git a/examples/gemv/example_gemv.py b/examples/gemv/example_gemv.py index 4e43dcd9ad..8ca77a2e89 100644 --- a/examples/gemv/example_gemv.py +++ b/examples/gemv/example_gemv.py @@ -17,15 +17,14 @@ def naive_gemv( K: int, BLOCK_N: int, BLOCK_K: int, - dtype: str = "float16", - accum_dtype: str = "float", + dtype: T.dtype = T.float16, + accum_dtype: T.dtype = T.float, ): - @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N)) as bn: tn = T.get_thread_binding(0) # tn = threadIdx.x @@ -38,8 +37,7 @@ def main( A_shared[tk] = A[bk * BLOCK_K + tk] B_shared[tn, tk] = B[bn * BLOCK_N + tn, bk * BLOCK_K + tk] for tk in T.serial(BLOCK_K): - C_reg[0] += A_shared[tk].astype(accum_dtype) * B_shared[tn, - tk].astype(accum_dtype) + C_reg[0] += A_shared[tk].astype(accum_dtype) * B_shared[tn, tk].astype(accum_dtype) C[bn * BLOCK_N + tn] = C_reg[0] return main @@ -51,15 +49,14 @@ def naive_splitk_gemv( K: int, BLOCK_N: int, BLOCK_K: int, - dtype: str = "float16", - accum_dtype: str = "float", + dtype: T.dtype = T.float16, + accum_dtype: T.dtype = T.float, ): - @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, BLOCK_K)) as bn: tn = T.get_thread_binding(0) @@ -88,16 +85,16 @@ def splitk_gemv( BLOCK_N: int, BLOCK_K: int, reduce_threads: int, - dtype: str = "float16", - accum_dtype: str = "float", + dtype: T.dtype = T.float16, + accum_dtype: T.dtype = T.float, ): TILE_K = T.ceildiv(BLOCK_K, reduce_threads) @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn: tn = T.get_thread_binding(0) @@ -127,8 +124,8 @@ def splitk_gemv_vectorized( K: int, BLOCK_N: int, reduce_threads: int, - dtype: str = "float16", - accum_dtype: str = "float", + dtype: T.dtype = T.float16, + accum_dtype: T.dtype = T.float, ): MAX_TRANSACTION_SIZE_IN_BITS = 128 TILE_K = MAX_TRANSACTION_SIZE_IN_BITS // DataType(dtype).bits @@ -136,9 +133,9 @@ def splitk_gemv_vectorized( @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn: tn = T.get_thread_binding(0) @@ -168,8 +165,8 @@ def splitk_gemv_vectorized_tvm( K: int, BLOCK_N: int, reduce_threads: int, - dtype: str = "float16", - accum_dtype: str = "float", + dtype: T.dtype = T.float16, + accum_dtype: T.dtype = T.float, ): MAX_TRANSACTION_SIZE_IN_BITS = 128 TILE_K = MAX_TRANSACTION_SIZE_IN_BITS // DataType(dtype).bits @@ -177,9 +174,9 @@ def splitk_gemv_vectorized_tvm( @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn: tn = T.get_thread_binding(0) @@ -197,9 +194,9 @@ def main( C_accum[0] += A_local[k].astype(accum_dtype) * B_local[k].astype(accum_dtype) C_reduced = T.alloc_local((1,), accum_dtype) with T.attr( - T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), - "reduce_scope", - T.reinterpret(T.uint64(0), dtype="handle"), + T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), + "reduce_scope", + T.reinterpret(T.uint64(0), dtype="handle"), ): T.evaluate( T.tvm_thread_allreduce( @@ -209,7 +206,8 @@ def main( C_reduced[0], tk, dtype="handle", - )) + ) + ) C[bn * BLOCK_N + tn] = C_reduced[0] @@ -218,10 +216,8 @@ def main( def get_block_template_configs(): iter_params = dict( - block_M=[2, 4, 8, 32, 64, 128], - block_N=[2, 4, 8, 32, 64, 128], - num_stages=[0, 1, 2, 3, 4], - threads=[32, 64, 128, 256]) + block_M=[2, 4, 8, 32, 64, 128], block_N=[2, 4, 8, 32, 64, 128], num_stages=[0, 1, 2, 3, 4], threads=[32, 64, 128, 256] + ) return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] @@ -237,18 +233,11 @@ def get_block_template_configs(): }, out_idx=[2], ) -def gemv_alloc_reducer(M, - N, - block_M=128, - block_N=128, - num_stages=2, - threads=256, - dtype: str = "float16", - accum_dtype: str = "float"): - +def gemv_alloc_reducer( + M, N, block_M=128, block_N=128, num_stages=2, threads=256, dtype: T.dtype = T.float16, accum_dtype: T.dtype = T.float +): @T.prim_func - def main(a: T.Tensor((M, N), dtype), x: T.Tensor(N, dtype), o: T.Tensor(M, - dtype)): # type: ignore + def main(a: T.Tensor((M, N), dtype), x: T.Tensor(N, dtype), o: T.Tensor(M, dtype)): # type: ignore with T.Kernel(T.ceildiv(M, block_M), threads=threads) as i0_m: o_reducer = T.alloc_reducer(block_M, accum_dtype, replication="all") T.clear(o_reducer) @@ -287,17 +276,17 @@ def get_autotuned_kernel( BLOCK_N=None, reduce_threads=None, ): - dtype = "float16" - accum_dtype = "float" + dtype = T.float16 + accum_dtype = T.float32 MAX_TRANSACTION_SIZE_IN_BITS = 128 TILE_K = MAX_TRANSACTION_SIZE_IN_BITS // DataType(dtype).bits BLOCK_K = reduce_threads * TILE_K @T.prim_func def main( - A: T.Tensor((K,), dtype), - B: T.Tensor((N, K), dtype), - C: T.Tensor((N,), dtype), + A: T.Tensor((K,), dtype), + B: T.Tensor((N, K), dtype), + C: T.Tensor((N,), dtype), ): with T.Kernel(T.ceildiv(N, BLOCK_N), threads=(BLOCK_N, reduce_threads)) as bn: tn = T.get_thread_binding(0) @@ -315,9 +304,9 @@ def main( C_accum[0] += A_local[k].astype(accum_dtype) * B_local[k].astype(accum_dtype) C_reduced = T.alloc_local((1,), accum_dtype) with T.attr( - T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), - "reduce_scope", - T.reinterpret(T.uint64(0), dtype="handle"), + T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]), + "reduce_scope", + T.reinterpret(T.uint64(0), dtype="handle"), ): T.evaluate( T.tvm_thread_allreduce( @@ -327,21 +316,22 @@ def main( C_reduced[0], tk, dtype="handle", - )) + ) + ) C[bn * BLOCK_N + tn] = C_reduced[0] return main -def check_correctness_and_bench(kernel, N, K, bench_ref=True): +def check_correctness_and_bench(kernel, N, K, do_bench=True): profiler = kernel.get_profiler() profiler.assert_allclose(lambda x, y: x @ y.T, atol=1e-2, rtol=1e-2) - if bench_ref: + if do_bench: latency = profiler.do_bench(lambda x, y: x @ y.T, warmup=50) print(f"Torch Latency: {latency} ms") - latency = profiler.do_bench(kernel, warmup=50) - print(f"TileLang Latency: {latency} ms\n") + latency = profiler.do_bench(kernel, warmup=50) + print(f"TileLang Latency: {latency} ms\n") def main(do_bench: bool = True): @@ -350,16 +340,16 @@ def main(do_bench: bool = True): parser.add_argument("--k", type=int, default=1024, help="Matrix dimension K") args, _ = parser.parse_known_args() N, K = args.n, args.k - check_correctness_and_bench(naive_gemv(N, K, 128, 128), N, K) - check_correctness_and_bench(naive_splitk_gemv(N, K, 32, 32), N, K) - check_correctness_and_bench(splitk_gemv(N, K, 32, 32, 32), N, K) - check_correctness_and_bench(splitk_gemv_vectorized(N, K, 2, 32), N, K) - check_correctness_and_bench(splitk_gemv_vectorized_tvm(N, K, 2, 32), N, K) - check_correctness_and_bench(gemv_alloc_reducer(N, K, block_M=128, block_N=128), N, K) + check_correctness_and_bench(naive_gemv(N, K, 128, 128), N, K, do_bench=do_bench) + check_correctness_and_bench(naive_splitk_gemv(N, K, 32, 32), N, K, do_bench=do_bench) + check_correctness_and_bench(splitk_gemv(N, K, 32, 32, 32), N, K, do_bench=do_bench) + check_correctness_and_bench(splitk_gemv_vectorized(N, K, 2, 32), N, K, do_bench=do_bench) + check_correctness_and_bench(splitk_gemv_vectorized_tvm(N, K, 2, 32), N, K, do_bench=do_bench) + check_correctness_and_bench(gemv_alloc_reducer(N, K, block_M=128, block_N=128), N, K, do_bench=do_bench) print("Test passed!") - if not do_bench: + if do_bench: best_result = get_autotuned_kernel(N, K) best_config = best_result.config kernel = splitk_gemv_vectorized_tvm(N, K, **best_config) @@ -374,5 +364,23 @@ def main(do_bench: bool = True): print(f"TileLang BlockReduce Latency: {tilelang_tile_latency} ms\n") +def run_regression_perf(): + N, K = 4096, 4096 + latency = 0.0 + kernel_list = [ + naive_gemv(N, K, 128, 128), + naive_splitk_gemv(N, K, 32, 32), + splitk_gemv(N, K, 32, 32, 32), + splitk_gemv_vectorized(N, K, 2, 32), + splitk_gemv_vectorized_tvm(N, K, 2, 32), + gemv_alloc_reducer(N, K, block_M=128, block_N=128), + ] + for kernel in kernel_list: + profiler = kernel.get_profiler() + # Benchmark the TileLang kernel itself, not the PyTorch reference. + latency += profiler.do_bench(backend="cupti") + return latency / len(kernel_list) + + if __name__ == "__main__": main() diff --git a/examples/gemv/regression_example_gemv.py b/examples/gemv/regression_example_gemv.py new file mode 100644 index 0000000000..dd6f1d39fd --- /dev/null +++ b/examples/gemv/regression_example_gemv.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_gemv + + +def regression_example_gemv(): + tilelang.testing.process_func(example_gemv.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/gemv/test_example_gemv.py b/examples/gemv/test_example_gemv.py index 3881ca7693..323337a7a6 100644 --- a/examples/gemv/test_example_gemv.py +++ b/examples/gemv/test_example_gemv.py @@ -1,5 +1,3 @@ -import tilelang.testing - import example_gemv @@ -8,4 +6,4 @@ def test_example_gemv(): if __name__ == "__main__": - tilelang.testing.main() + test_example_gemv() diff --git a/examples/grouped_gemm/example_grouped_gemm_bwd.py b/examples/grouped_gemm/example_grouped_gemm_bwd.py index ac8da7e2c3..49cce0d1dd 100644 --- a/examples/grouped_gemm/example_grouped_gemm_bwd.py +++ b/examples/grouped_gemm/example_grouped_gemm_bwd.py @@ -5,78 +5,55 @@ import tilelang.language as T -@tilelang.jit( - out_idx=[2], pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) -def grouped_gemm_fwd(batch_sum, - batch_count, - K, - N, - block_M, - block_N, - block_K, - num_stages=2, - threads=128, - dtype="float16"): +@tilelang.jit(out_idx=[2], pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) +def grouped_gemm_fwd(batch_sum, batch_count, K, N, block_M, block_N, block_K, num_stages=2, threads=128, dtype=T.float16): """ args: a (torch.Tensor): Input tensor of shape (M, K). b (torch.Tensor): Input tensor of shape (G, K, N). """ - accum_dtype = "float32" + accum_dtype = T.float32 @T.prim_func def kernel( - A: T.Tensor([batch_sum, K], dtype), # type: ignore - B: T.Tensor([batch_count, K, N], dtype), # type: ignore - C: T.Tensor([batch_sum, N], dtype), # type: ignore - batch_sizes: T.Tensor([batch_count], "int32"), # type: ignore - batch_offsets: T.Tensor([batch_count], "int32"), # type: ignore - batch_padded_offsets: T.Tensor([batch_count], "int32"), # type: ignore + A: T.Tensor([batch_sum, K], dtype), # type: ignore + B: T.Tensor([batch_count, K, N], dtype), # type: ignore + C: T.Tensor([batch_sum, N], dtype), # type: ignore + batch_sizes: T.Tensor([batch_count], T.int32), # type: ignore + batch_offsets: T.Tensor([batch_count], T.int32), # type: ignore + batch_padded_offsets: T.Tensor([batch_count], T.int32), # type: ignore ): - - with T.Kernel( - T.ceildiv(batch_sum, block_M) + batch_count, T.ceildiv(N, block_N), - threads=threads) as (bx, by): + with T.Kernel(T.ceildiv(batch_sum, block_M) + batch_count, T.ceildiv(N, block_N), threads=threads) as (bx, by): A_shared = T.alloc_shared([block_M, block_K], dtype) B_shared = T.alloc_shared([block_K, block_N], dtype) C_local = T.alloc_fragment([block_M, block_N], accum_dtype) - cur_batch_idx = T.alloc_local([1], "int32") - cur_batch_size = T.alloc_local([1], "int32") + cur_batch_idx = T.alloc_var(dtype=T.int32) + cur_batch_size = T.alloc_var(dtype=T.int32) m_start_padded = bx * block_M for i in range(batch_count): - in_cur_batch_idx = (m_start_padded >= batch_padded_offsets[i]) - cur_batch_idx[0] = T.if_then_else(in_cur_batch_idx, i, cur_batch_idx[0]) + in_cur_batch_idx = m_start_padded >= batch_padded_offsets[i] + cur_batch_idx = T.if_then_else(in_cur_batch_idx, i, cur_batch_idx) - cur_batch_size[0] = batch_sizes[cur_batch_idx[0]] - m_start = m_start_padded - batch_padded_offsets[cur_batch_idx[0]] + batch_offsets[ - cur_batch_idx[0]] - actual_rows = T.max( - 0, - T.min(block_M, - cur_batch_size[0] + batch_padded_offsets[cur_batch_idx[0]] - m_start_padded)) + cur_batch_size = batch_sizes[cur_batch_idx] + m_start = m_start_padded - batch_padded_offsets[cur_batch_idx] + batch_offsets[cur_batch_idx] + actual_rows = T.max(0, T.min(block_M, cur_batch_size + batch_padded_offsets[cur_batch_idx] - m_start_padded)) T.clear(C_local) for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): - T.copy(A[m_start:m_start + block_M, k * block_K:(k + 1) * block_K], A_shared) - T.copy( - B[cur_batch_idx[0], k * block_K:(k + 1) * block_K, - by * block_N:(by + 1) * block_N], B_shared) + T.copy(A[m_start : m_start + block_M, k * block_K : (k + 1) * block_K], A_shared) + T.copy(B[cur_batch_idx, k * block_K : (k + 1) * block_K, by * block_N : (by + 1) * block_N], B_shared) T.gemm(A_shared, B_shared, C_local) for i, j in T.Parallel(block_M, block_N): - with T.If(i < actual_rows), T.Then(): + if i < actual_rows: C[m_start + i, by * block_N + j] = C_local[i, j] return kernel class _GroupedGEMM(torch.autograd.Function): - @staticmethod def forward(ctx, a, b, batch_sizes): block_M = 64 @@ -99,15 +76,11 @@ def forward(ctx, a, b, batch_sizes): for i in range(batch_count - 1): batch_offsets_list.append(batch_offsets_list[-1] + batch_sizes[i]) for i in range(batch_count - 1): - batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + - math.ceil((batch_sizes[i] + 1) / padding_M) * - padding_M) + batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + math.ceil((batch_sizes[i] + 1) / padding_M) * padding_M) batch_offsets = torch.tensor(batch_offsets_list, device=a.device, dtype=torch.int32) - batch_padded_offsets = torch.tensor( - batch_padded_offsets_list, device=a.device, dtype=torch.int32) + batch_padded_offsets = torch.tensor(batch_padded_offsets_list, device=a.device, dtype=torch.int32) - kernel = grouped_gemm_fwd(batch_sum, batch_count, K, N, block_M, block_N, block_K, - num_stages, threads) + kernel = grouped_gemm_fwd(batch_sum, batch_count, K, N, block_M, block_N, block_K, num_stages, threads) o = kernel(a, b, batch_sizes, batch_offsets, batch_padded_offsets) ctx.save_for_backward(a, b, batch_sizes, batch_offsets) @@ -135,8 +108,7 @@ def maybe_contiguous(x): return x A, B, batch_sizes = [maybe_contiguous(x) for x in (A, B, batch_sizes)] - kernel = grouped_gemm_bwd(ctx.batch_sum, ctx.batch_count, M, N, block_M, block_N, block_K, - num_stages, threads) + kernel = grouped_gemm_bwd(ctx.batch_sum, ctx.batch_count, M, N, block_M, block_N, block_K, num_stages, threads) dB = kernel(A, grad_output, batch_sizes, batch_offsets) return None, dB, None @@ -172,9 +144,7 @@ def construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype): for i in range(batch_count - 1): batch_offsets_list.append(batch_offsets_list[-1] + batch_sizes_list[i]) for i in range(batch_count - 1): - batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + - math.ceil((batch_sizes_list[i] + 1) / padding_M) * - padding_M) + batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + math.ceil((batch_sizes_list[i] + 1) / padding_M) * padding_M) A = torch.randn(batch_sum, K, device=device, dtype=dtype) B = torch.randn(batch_count, K, M, device=device, dtype=dtype) C = torch.empty(batch_sum, M, device=device, dtype=dtype) @@ -187,40 +157,24 @@ def construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype): return A, B, C, batch_sizes, batch_offsets, batch_padded_offsets -@tilelang.jit( - out_idx=[2], pass_configs={ - "tl.disable_tma_lower": True, - "tl.disable_warp_specialized": True - }) -def grouped_gemm_bwd(batch_sum, - batch_count, - M, - N, - block_M, - block_N, - block_K, - num_stages=2, - threads=128, - dtype="float16"): +@tilelang.jit(out_idx=[2], pass_configs={"tl.disable_tma_lower": True, "tl.disable_warp_specialized": True}) +def grouped_gemm_bwd(batch_sum, batch_count, M, N, block_M, block_N, block_K, num_stages=2, threads=128, dtype=T.float16): """ args: a (torch.Tensor): Input tensor of shape (M, K). b (torch.Tensor): Input tensor of shape (G, K, N). """ - accum_dtype = "float32" + accum_dtype = T.float32 @T.prim_func def kernel( - A: T.Tensor([batch_sum, M], dtype), # type: ignore - B: T.Tensor([batch_sum, N], dtype), # type: ignore - C: T.Tensor([batch_count, M, N], dtype), # type: ignore - batch_sizes: T.Tensor([batch_count], "int32"), # type: ignore - batch_offsets: T.Tensor([batch_count], "int32"), # type: ignore + A: T.Tensor([batch_sum, M], dtype), # type: ignore + B: T.Tensor([batch_sum, N], dtype), # type: ignore + C: T.Tensor([batch_count, M, N], dtype), # type: ignore + batch_sizes: T.Tensor([batch_count], T.int32), # type: ignore + batch_offsets: T.Tensor([batch_count], T.int32), # type: ignore ): - - with T.Kernel( - T.ceildiv(M, block_M), T.ceildiv(N, block_N), batch_count, - threads=threads) as (bx, by, bz): + with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), batch_count, threads=threads) as (bx, by, bz): A_shared = T.alloc_shared([block_K, block_M], dtype) B_shared = T.alloc_shared([block_K, block_N], dtype) C_local = T.alloc_fragment([block_M, block_N], accum_dtype) @@ -228,13 +182,9 @@ def kernel( T.clear(C_local) for k in T.Pipelined(T.ceildiv(batch_sizes[bz], block_K), num_stages=num_stages): for i, j in T.Parallel(block_K, block_M): - A_shared[i, j] = T.if_then_else( - i < batch_sizes[bz], A[batch_offsets[bz] + k * block_K + i, - bx * block_M + j], 0) + A_shared[i, j] = T.if_then_else(i < batch_sizes[bz], A[batch_offsets[bz] + k * block_K + i, bx * block_M + j], 0) for i, j in T.Parallel(block_K, block_N): - B_shared[i, j] = T.if_then_else( - i < batch_sizes[bz], B[batch_offsets[bz] + k * block_K + i, - by * block_N + j], 0) + B_shared[i, j] = T.if_then_else(i < batch_sizes[bz], B[batch_offsets[bz] + k * block_K + i, by * block_N + j], 0) T.gemm(A_shared, B_shared, C_local, transpose_A=True) T.copy(C_local, C[bz, bx * block_M, by * block_N]) @@ -242,23 +192,12 @@ def kernel( return kernel -def run_tilelang_grouped_gemm(batch_sizes_list, - K, - M, - block_M, - block_N, - block_K, - trans_b, - num_stages=2, - threads=128, - profile=False): - +def run_tilelang_grouped_gemm(batch_sizes_list, K, M, block_M, block_N, block_K, trans_b, num_stages=2, threads=128, profile=False): padding_M = block_M device = torch.device("cuda") dtype = torch.float16 - A, B, C, batch_sizes, batch_offsets, batch_padded_offsets = construct_inputs( - batch_sizes_list, K, M, False, padding_M, device, dtype) + A, B, C, batch_sizes, batch_offsets, batch_padded_offsets = construct_inputs(batch_sizes_list, K, M, False, padding_M, device, dtype) A.requires_grad_(False) B.requires_grad_(True) @@ -273,10 +212,7 @@ def run_tilelang_grouped_gemm(batch_sizes_list, O.backward(dO, retain_graph=True) dB, B.grad = B.grad.clone(), None - if ( - torch.allclose(O, O_ref, rtol=1e-2, atol=1e-2) and \ - torch.allclose(dB, dB_ref, rtol=1e-2, atol=1e-2) - ): + if torch.allclose(O, O_ref, rtol=1e-2, atol=1e-2) and torch.allclose(dB, dB_ref, rtol=1e-2, atol=1e-2): print("✅ Tilelang and Torch match") else: print("❌ Tilelang and Torch mismatch") @@ -284,12 +220,11 @@ def run_tilelang_grouped_gemm(batch_sizes_list, if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--batch_sizes', type=str, default="64, 128", help='comma-separated batch sizes') - parser.add_argument('--K', type=int, default=8192, help='reduce dim') - parser.add_argument('--M', type=int, default=8192, help='output dim') - parser.add_argument('--trans_b', action="store_true", help="transpose B") - parser.add_argument('--profile', action="store_true", help="profile") + parser.add_argument("--batch_sizes", type=str, default="64, 128", help="comma-separated batch sizes") + parser.add_argument("--K", type=int, default=8192, help="reduce dim") + parser.add_argument("--M", type=int, default=8192, help="output dim") + parser.add_argument("--trans_b", action="store_true", help="transpose B") + parser.add_argument("--profile", action="store_true", help="profile") args = parser.parse_args() batch_sizes_list = [int(x) for x in args.batch_sizes.split(",")] @@ -301,14 +236,4 @@ def run_tilelang_grouped_gemm(batch_sizes_list, num_stages = 2 threads = 256 - run_tilelang_grouped_gemm( - batch_sizes_list, - K, - M, - block_M, - block_N, - block_K, - trans_b, - num_stages, - threads, - profile=args.profile) + run_tilelang_grouped_gemm(batch_sizes_list, K, M, block_M, block_N, block_K, trans_b, num_stages, threads, profile=args.profile) diff --git a/examples/grouped_gemm/example_grouped_gemm_fwd.py b/examples/grouped_gemm/example_grouped_gemm_fwd.py index 9b58e3a21c..b714727415 100644 --- a/examples/grouped_gemm/example_grouped_gemm_fwd.py +++ b/examples/grouped_gemm/example_grouped_gemm_fwd.py @@ -18,8 +18,7 @@ def torch_gmm(a, b, batch_sizes, batch_offsets_tensor, trans_b=False): torch.Tensor: Resulting tensor after grouped matrix multiplication. """ assert a.shape[0] == sum(batch_sizes), "Sum of batch_sizes must equal the first dimension of a" - assert b.shape[0] == len( - batch_sizes), "The first dimension of b must match the length of batch_sizes" + assert b.shape[0] == len(batch_sizes), "The first dimension of b must match the length of batch_sizes" # Initialize output tensor output = torch.empty((sum(batch_sizes), b.shape[2]), device=a.device, dtype=a.dtype) @@ -38,15 +37,7 @@ def torch_gmm(a, b, batch_sizes, batch_offsets_tensor, trans_b=False): @tilelang.jit(out_idx=[2]) -def grouped_gemm(batch_sizes_list, - K, - N, - block_M, - block_N, - block_K, - num_stages=2, - threads=128, - dtype="float16"): +def grouped_gemm(batch_sizes_list, K, N, block_M, block_N, block_K, num_stages=2, threads=128, dtype=T.float16): """ args: a (torch.Tensor): Input tensor of shape (M, K). @@ -54,50 +45,43 @@ def grouped_gemm(batch_sizes_list, """ batch_sum = sum(batch_sizes_list) batch_count = len(batch_sizes_list) - accum_dtype = "float32" + accum_dtype = T.float32 total_m_blocks = sum((size + block_M - 1) // block_M for size in batch_sizes_list) @T.prim_func def kernel( - A: T.Tensor([batch_sum, K], dtype), # type: ignore - B: T.Tensor([batch_count, K, N], dtype), # type: ignore - C: T.Tensor([batch_sum, N], dtype), # type: ignore - batch_sizes: T.Tensor([batch_count], "int32"), # type: ignore - batch_offsets: T.Tensor([batch_count], "int32"), # type: ignore - batch_padded_offsets: T.Tensor([batch_count], "int32"), # type: ignore + A: T.Tensor([batch_sum, K], dtype), # type: ignore + B: T.Tensor([batch_count, K, N], dtype), # type: ignore + C: T.Tensor([batch_sum, N], dtype), # type: ignore + batch_sizes: T.Tensor([batch_count], T.int32), # type: ignore + batch_offsets: T.Tensor([batch_count], T.int32), # type: ignore + batch_padded_offsets: T.Tensor([batch_count], T.int32), # type: ignore ): - with T.Kernel(total_m_blocks, T.ceildiv(N, block_N), threads=threads) as (bx, by): A_shared = T.alloc_shared([block_M, block_K], dtype) B_shared = T.alloc_shared([block_K, block_N], dtype) C_local = T.alloc_fragment([block_M, block_N], accum_dtype) - cur_batch_idx = T.alloc_local([1], "int32") - cur_batch_size = T.alloc_local([1], "int32") + cur_batch_idx = T.alloc_var(dtype=T.int32) + cur_batch_size = T.alloc_var(dtype=T.int32) m_start_padded = bx * block_M for i in range(batch_count): - in_cur_batch_idx = (m_start_padded >= batch_padded_offsets[i]) - cur_batch_idx[0] = T.if_then_else(in_cur_batch_idx, i, cur_batch_idx[0]) + in_cur_batch_idx = m_start_padded >= batch_padded_offsets[i] + cur_batch_idx = T.if_then_else(in_cur_batch_idx, i, cur_batch_idx) - cur_batch_size[0] = batch_sizes[cur_batch_idx[0]] - m_start = m_start_padded - batch_padded_offsets[cur_batch_idx[0]] + batch_offsets[ - cur_batch_idx[0]] - actual_rows = T.max( - 0, - T.min(block_M, - cur_batch_size[0] + batch_padded_offsets[cur_batch_idx[0]] - m_start_padded)) + cur_batch_size = batch_sizes[cur_batch_idx] + m_start = m_start_padded - batch_padded_offsets[cur_batch_idx] + batch_offsets[cur_batch_idx] + actual_rows = T.max(0, T.min(block_M, cur_batch_size + batch_padded_offsets[cur_batch_idx] - m_start_padded)) T.clear(C_local) for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): - T.copy(A[m_start:m_start + block_M, k * block_K:(k + 1) * block_K], A_shared) - T.copy( - B[cur_batch_idx[0], k * block_K:(k + 1) * block_K, - by * block_N:(by + 1) * block_N], B_shared) + T.copy(A[m_start : m_start + block_M, k * block_K : (k + 1) * block_K], A_shared) + T.copy(B[cur_batch_idx, k * block_K : (k + 1) * block_K, by * block_N : (by + 1) * block_N], B_shared) T.gemm(A_shared, B_shared, C_local) for i, j in T.Parallel(block_M, block_N): - with T.If(i < actual_rows), T.Then(): + if i < actual_rows: C[m_start + i, by * block_N + j] = C_local[i, j] return kernel @@ -111,8 +95,7 @@ def construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype): for i in range(batch_count - 1): batch_offsets_list.append(batch_offsets_list[-1] + batch_sizes_list[i]) for i in range(batch_count - 1): - batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + - math.ceil((batch_sizes_list[i]) / padding_M) * padding_M) + batch_padded_offsets_list.append(batch_padded_offsets_list[-1] + math.ceil((batch_sizes_list[i]) / padding_M) * padding_M) A = torch.randn(batch_sum, K, device=device, dtype=dtype) B = torch.randn(batch_count, K, M, device=device, dtype=dtype) C = torch.empty(batch_sum, M, device=device, dtype=dtype) @@ -125,27 +108,16 @@ def construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype): return A, B, C, batch_sizes, batch_offsets, batch_padded_offsets -def run_tilelang_grouped_gemm(batch_sizes_list, - K, - M, - block_M, - block_N, - block_K, - trans_b, - num_stages=2, - threads=128, - profile=False): +def run_tilelang_grouped_gemm(batch_sizes_list, K, M, block_M, block_N, block_K, trans_b, num_stages=2, threads=128, profile=False): padding_M = block_M batch_sum = sum(batch_sizes_list) - kernel = grouped_gemm( - tuple(batch_sizes_list), K, M, block_M, block_N, block_K, num_stages, threads) + kernel = grouped_gemm(tuple(batch_sizes_list), K, M, block_M, block_N, block_K, num_stages, threads) # print(kernel.get_kernel_source()) device = torch.device("cuda") dtype = torch.float16 - A, B, C, batch_sizes, batch_offsets, batch_padded_offsets = construct_inputs( - batch_sizes_list, K, M, trans_b, padding_M, device, dtype) + A, B, C, batch_sizes, batch_offsets, batch_padded_offsets = construct_inputs(batch_sizes_list, K, M, trans_b, padding_M, device, dtype) out = kernel(A, B, batch_sizes, batch_offsets, batch_padded_offsets) ref_output = torch_gmm(A, B, batch_sizes, batch_offsets, trans_b) # print(out) @@ -157,8 +129,7 @@ def run_tilelang_grouped_gemm(batch_sizes_list, if profile: profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) - latency = profiler.do_bench( - warmup=500, input_tensors=[A, B, batch_sizes, batch_offsets, batch_padded_offsets]) + latency = profiler.do_bench(warmup=500, input_tensors=[A, B, batch_sizes, batch_offsets, batch_padded_offsets]) print(f"Latency: {latency} ms") print(f"TFlops: {batch_sum * K * M * 2 / latency * 1e-9} TFlops") @@ -173,12 +144,11 @@ def test_grouped_gemm(): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument( - '--batch_sizes', type=str, default="64, 128", help='comma-separated batch sizes') - parser.add_argument('--K', type=int, default=8192, help='reduce dim') - parser.add_argument('--M', type=int, default=8192, help='output dim') - parser.add_argument('--trans_b', action="store_true", help="transpose B") - parser.add_argument('--profile', action="store_true", help="profile") + parser.add_argument("--batch_sizes", type=str, default="64, 128", help="comma-separated batch sizes") + parser.add_argument("--K", type=int, default=8192, help="reduce dim") + parser.add_argument("--M", type=int, default=8192, help="output dim") + parser.add_argument("--trans_b", action="store_true", help="transpose B") + parser.add_argument("--profile", action="store_true", help="profile") args = parser.parse_args() batch_sizes_list = [int(x) for x in args.batch_sizes.split(",")] @@ -190,14 +160,4 @@ def test_grouped_gemm(): num_stages = 2 threads = 256 - run_tilelang_grouped_gemm( - batch_sizes_list, - K, - M, - block_M, - block_N, - block_K, - trans_b, - num_stages, - threads, - profile=args.profile) + run_tilelang_grouped_gemm(batch_sizes_list, K, M, block_M, block_N, block_K, trans_b, num_stages, threads, profile=args.profile) diff --git a/examples/hadamard_transform/example_hadamard.py b/examples/hadamard_transform/example_hadamard.py index 531d468918..65f463b71b 100644 --- a/examples/hadamard_transform/example_hadamard.py +++ b/examples/hadamard_transform/example_hadamard.py @@ -17,7 +17,7 @@ def is_pow_of_2(n): def hadamard(b, n, dtype): assert is_pow_of_2(n), "n must be a power of 2" assert 2 <= n <= 32768, "n must be in [2, 32768]" - elem_size = {'float32': 4, 'float16': 2, 'bfloat16': 2}[dtype] + elem_size = {T.float32: 4, T.float16: 2, T.bfloat16: 2}[dtype] logN = int(math.log2(n)) threads = [0, 1, 1, 1, 2, 4, 8, 16, 32, 32, 128, 256, 256, 256, 256, 256][logN] @@ -40,23 +40,21 @@ def hadamard(b, n, dtype): # print(f'{exchange_round=}') @T.macro - def warp_shfl(local: T.Tensor((thread_elem,), dtype), buf: T.Tensor((thread_elem,), dtype), - round: int): + def warp_shfl(local: T.Tensor((thread_elem,), dtype), buf: T.Tensor((thread_elem,), dtype), round: int): tx = T.get_thread_binding(0) for i in T.serial(round): tx_stride = 1 << i another_tx = tx ^ tx_stride - sign = ( - tx >> i - ) & 1 # get i-th lowest bit of tx, which determines the operation type for shared[tx, :] + sign = (tx >> i) & 1 # get i-th lowest bit of tx, which determines the operation type for shared[tx, :] for j in T.Pipelined(thread_elem, num_stages=1): buf[j] = T.tvm_warp_shuffle( - 0xffffffff, # mask of all threads + 0xFFFFFFFF, # mask of all threads local[j], another_tx % warp_size, warp_size, - warp_size) + warp_size, + ) local[j] = T.if_then_else(sign == 0, local[j] + buf[j], buf[j] - local[j]) @T.prim_func @@ -78,10 +76,8 @@ def main(A: T.Tensor((b, n), dtype), B: T.Tensor((b, n), dtype)): for j in T.serial(chunknum): chunkbase = j * chunksize for k in T.serial(chunksize // 2): - local[chunkbase + - k] = local[chunkbase + k] + local[chunkbase + k + chunksize // 2] - local[chunkbase + k + chunksize // - 2] = local[chunkbase + k] - 2 * local[chunkbase + k + chunksize // 2] + local[chunkbase + k] = local[chunkbase + k] + local[chunkbase + k + chunksize // 2] + local[chunkbase + k + chunksize // 2] = local[chunkbase + k] - 2 * local[chunkbase + k + chunksize // 2] # 3. Hadamard inside warp, n<=512 # In warp level, we rely on warp shuffle to exchange data inside each warp, without using shared memory @@ -131,28 +127,27 @@ def ref_program(x: torch.Tensor): assert x.ndim == 2 dim = x.shape[-1] assert is_pow_of_2(dim) - return F.linear( - x, torch.tensor(scipy.linalg.hadamard(dim, dtype=float), dtype=x.dtype, device=x.device)) + return F.linear(x, torch.tensor(scipy.linalg.hadamard(dim, dtype=float), dtype=x.dtype, device=x.device)) def main(): parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=64, help='Batch size') - parser.add_argument('--dim', type=int, default=32768, help='Dimension') + parser.add_argument("--batch", type=int, default=64, help="Batch size") + parser.add_argument("--dim", type=int, default=32768, help="Dimension") args = parser.parse_args() B, D = args.batch, args.dim - x = torch.randn((B, D), device='cuda') - kernel = hadamard(B, D, 'float32') + x = torch.randn((B, D), device="cuda") + kernel = hadamard(B, D, T.float32) y = kernel(x) y_ref = ref_program(x) torch.testing.assert_close(y, y_ref, atol=1e-2, rtol=1e-2) - print('All tests passed.') + print("All tests passed.") profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) latency = profiler.do_bench(warmup=100) print("Tile-lang: {:.2f} ms".format(latency)) -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/lazy_jit/lazyjit.en.ipynb b/examples/lazy_jit/lazyjit.en.ipynb new file mode 100644 index 0000000000..5b5df8e6a7 --- /dev/null +++ b/examples/lazy_jit/lazyjit.en.ipynb @@ -0,0 +1,977 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "5e0deecc", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "sys.path.insert(0, str(Path.cwd().parent.parent.absolute()))\n", + "import tilelang\n", + "import torch\n", + "import tilelang.language as T" + ] + }, + { + "cell_type": "markdown", + "id": "1ca2c56d", + "metadata": {}, + "source": [ + "# Tilelang Lazy JIT" + ] + }, + { + "cell_type": "markdown", + "id": "156e7370", + "metadata": {}, + "source": [ + "## Tensor Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "b070c109", + "metadata": {}, + "source": [ + "Tilelang Lazy JIT merges JIT kernel generation and invocation into a single workflow.\n", + "\n", + "The function signature looks similar to Triton, but we add many enhancements; the most important one is allowing rich Tensor annotations:\n", + "\n", + "* If a Tensor has complex shape constraints, we can move its annotation into the function body.\n", + "* Use `T.const` or `T.dynamic` to create shape variables, then annotate complex Tensors with `T.Tensor`.\n", + "* Use `T.empty` to declare return tensors." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "60bf8954", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm(\n", + " A,\n", + " B,\n", + " out_dtype: T.dtype = T.float32,\n", + " block_M: int = 128,\n", + " block_N: int = 128,\n", + " block_K: int = 32,\n", + "):\n", + " M, N, K = T.const(\"M, N, K\")\n", + "\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + "\n", + " C = T.empty((M, N), out_dtype)\n", + "\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " A_shared = T.alloc_shared((block_M, block_K), A.dtype)\n", + " B_shared = T.alloc_shared((block_K, block_N), B.dtype)\n", + " C_local = T.alloc_fragment((block_M, block_N), out_dtype)\n", + " T.clear(C_local)\n", + " for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):\n", + " T.copy(A[bx * block_M, k * block_K], A_shared)\n", + " T.copy(B[k * block_K, by * block_N], B_shared)\n", + " T.gemm(A_shared, B_shared, C_local)\n", + " T.copy(C_local, C[bx * block_M, by * block_N])\n", + " return C" + ] + }, + { + "cell_type": "markdown", + "id": "28f868fe", + "metadata": {}, + "source": [ + "Calling the function with Tensors directly triggers the full JIT compile-and-run pipeline:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ee13394a", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm(A, B)\n", + "\n", + "# check output is correct\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "c6705091", + "metadata": {}, + "source": [ + "Changing the call arguments may trigger a recompilation when compilation parameters change:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8aab5b7", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 1024, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm(A, B, block_M=64, block_N=64)" + ] + }, + { + "cell_type": "markdown", + "id": "ce6b7391", + "metadata": {}, + "source": [ + "You can also explicitly call the `compile` method to build the kernel.\n", + "\n", + "1. `ker.compile` compiles the kernel\n", + "2. `ker.get_tir` retrieves the TIR\n", + "3. `ker.par_compile` compiles in parallel" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f3cf3a2d", + "metadata": {}, + "outputs": [], + "source": [ + "kernel = gemm.compile(A, B, block_M=64, block_N=64)\n", + "C = kernel(A, B)" + ] + }, + { + "cell_type": "markdown", + "id": "921761b5", + "metadata": {}, + "source": [ + "## More Tensor Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "4539e54e", + "metadata": {}, + "source": [ + "### Use macros to separate implementation" + ] + }, + { + "cell_type": "markdown", + "id": "ad96ba65", + "metadata": {}, + "source": [ + "Next, we implement a simple GEMM in several different ways. For convenience, we first write a macro that contains the core GEMM logic:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "171d4fe6", + "metadata": {}, + "outputs": [], + "source": [ + "@T.macro\n", + "def gemm_impl(A, B, C, M, N, K, block_M, block_N, block_K):\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " A_shared = T.alloc_shared((block_M, block_K), A.dtype)\n", + " B_shared = T.alloc_shared((block_K, block_N), B.dtype)\n", + " C_local = T.alloc_fragment((block_M, block_N), C.dtype)\n", + " T.clear(C_local)\n", + " for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):\n", + " T.copy(A[bx * block_M, k * block_K], A_shared)\n", + " T.copy(B[k * block_K, by * block_N], B_shared)\n", + " T.gemm(A_shared, B_shared, C_local)\n", + " T.copy(C_local, C[bx * block_M, by * block_N])" + ] + }, + { + "cell_type": "markdown", + "id": "446a1acd", + "metadata": {}, + "source": [ + "### Use `T.dynamic` to mark dynamic shapes\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6a38aa95", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_dyn_K(A, B):\n", + " M, N, K = T.dynamic(\"M, N, K\")\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, 128, 128, 32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fe6cfdc8", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_dyn_K(A, B)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "2ee97bf7", + "metadata": {}, + "source": [ + "### Use `T.StridedTensor` to annotate tensors with strides\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9dde1dae", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def as_contingious(A):\n", + " M, N, dM, dN = T.dynamic(\"M, N, dM, dN\")\n", + " A: T.StridedTensor[[M, N], [dM, dN], T.float32]\n", + " B = T.empty((M, N), A.dtype)\n", + " block_M = 128\n", + " block_N = 128\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " T.copy(\n", + " A[bx * block_M : (bx + 1) * block_M, by * block_N : (by + 1) * block_N],\n", + " B[bx * block_M : (bx + 1) * block_M, by * block_N : (by + 1) * block_N],\n", + " )\n", + " return B" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dec2c0a7", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 1024, device=\"cuda\")\n", + "B = as_contingious(A.T)\n", + "B_ref = A.T.contiguous()\n", + "torch.testing.assert_close(B, B_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "f5fb20d6", + "metadata": {}, + "source": [ + "## More Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "890df0a2", + "metadata": {}, + "source": [ + "### Use parameters directly as annotations" + ] + }, + { + "cell_type": "markdown", + "id": "e9a47d42", + "metadata": {}, + "source": [ + "You can directly use function parameters in the annotations." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0fc17af6", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_ptr(\n", + " A,\n", + " B,\n", + " M,\n", + " N,\n", + " K,\n", + "):\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, block_M=128, block_N=128, block_K=32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8e52a554", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_ptr(A, B, 1024, 256, 512)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "6b19ef90", + "metadata": {}, + "source": [ + "### Annotations for runtime variables" + ] + }, + { + "cell_type": "markdown", + "id": "bba5f27f", + "metadata": {}, + "source": [ + "Runtime variables work the same; if the function annotation becomes too long, you can move it into the function body." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c1e7598a", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_ptr_dyn(A, B, M, N, K):\n", + " M: T.int32\n", + " N: T.int32\n", + " K: T.int32\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, block_M=128, block_N=128, block_K=32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9e9a4c88", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_ptr_dyn(A, B, 1024, 256, 512)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "81427765", + "metadata": {}, + "source": [ + "### Constraints for constants" + ] + }, + { + "cell_type": "markdown", + "id": "4d6b084b", + "metadata": {}, + "source": [ + "A constant annotation created by `T.const` must be used directly at least once, otherwise an error is raised." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c90dd24f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constexpr variable `M` is not used in any buffer shape or stride.\n", + "At least one **DIRECT** usage is required. Please check:\n", + "(1) the variable is not used\n", + "(2) all uses are indirect, e.g. M * 2, M * 3. (you can replace them with separate constexpr variables)\n", + "Buffer shapes: {A: [M * 2, M * 3]}\n", + "Buffer strides: {A: [M * 3, 1]}\n" + ] + } + ], + "source": [ + "@tilelang.lazy_jit\n", + "def example_wrong_kernel(A):\n", + " M = T.const(\"M\")\n", + " A: T.Tensor[[M * 2, M * 3], T.float32]\n", + " with T.Kernel(1) as _:\n", + " A[0, 0]\n", + "\n", + "\n", + "try:\n", + " A = torch.randn(64, 96, dtype=torch.float32, device=\"cuda\")\n", + " example_wrong_kernel(A)\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "e07e762b", + "metadata": {}, + "source": [ + "### Dynamic dimensions" + ] + }, + { + "cell_type": "markdown", + "id": "f48e5d7a", + "metadata": {}, + "source": [ + "If you want certain parameters in a Tensor annotation to change, it is recommended to switch to the `T.ptr` + `T.match_buffer` style." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1d050321", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@tilelang.lazy_jit\n", + "def dyn_annot(\n", + " A: T.ptr, # 1. T.ptr type annotation\n", + " is_2d=False,\n", + "):\n", + " if is_2d:\n", + " M, N = T.const(\"M, N\")\n", + " # 2. dynamic shape annotation inside function body\n", + " A = T.match_buffer(A, [M, N], T.float32)\n", + " with T.Kernel(1) as _:\n", + " A[0, 0]\n", + " else:\n", + " L = T.const(\"L\")\n", + " A = T.match_buffer(A, [L], T.float32)\n", + " with T.Kernel(1) as _:\n", + " A[0]\n", + "\n", + "\n", + "A = torch.randn(64, 96, dtype=torch.float32, device=\"cuda\")\n", + "dyn_annot(A, is_2d=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2e9f1bb3", + "metadata": {}, + "source": [ + "### Default arguments" + ] + }, + { + "cell_type": "markdown", + "id": "f7fc9917", + "metadata": {}, + "source": [ + "Scalar annotations like `T.float32` can carry default values." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "42ec86a1", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def add_one(X, data: T.float32 = 1):\n", + " M, N = T.const(\"M, N\")\n", + " X: T.Tensor[[M, N], T.float32]\n", + " Y = T.empty((M, N), T.float32)\n", + " with T.Kernel(T.ceildiv(M, 128), threads=128) as bx:\n", + " for i, j in T.Parallel(128, N):\n", + " Y[bx * 128 + i, j] = X[bx * 128 + i, j] + data\n", + " return Y" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d49e1120", + "metadata": {}, + "outputs": [], + "source": [ + "X = torch.randn(1024, 1024, dtype=torch.float32, device=\"cuda\")\n", + "Y = add_one(X)\n", + "torch.testing.assert_close(Y, X + 1)" + ] + }, + { + "cell_type": "markdown", + "id": "a02baedc", + "metadata": {}, + "source": [ + "## Overhead of argument matching" + ] + }, + { + "cell_type": "markdown", + "id": "860a2972", + "metadata": {}, + "source": [ + "LazyJIT has very small overhead; each additional constant annotation costs about 200 ns.\n", + "* 200 ns is roughly the cost of an FFI call that reads parameters from a `torch.Tensor`'s shape/stride." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc676e33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kernel call : 7.68 us\n", + "Parse cache key: 0.41 us\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "A = torch.randn(128, 128, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(128, 128, dtype=torch.float16, device=\"cuda\")\n", + "\n", + "\n", + "@tilelang.lazy_jit\n", + "def dummy_kernel(A, B):\n", + " M, N = T.const(\"M, N\")\n", + " A: T.Tensor[[M, N], T.float16]\n", + " B: T.Tensor[[M, N], T.float16]\n", + " with T.Kernel(1) as _:\n", + " pass\n", + "\n", + "\n", + "# compile it first\n", + "dummy_kernel(A, B)\n", + "\n", + "\n", + "def eval_overhead(f):\n", + " start = time.perf_counter_ns()\n", + " for _ in range(10000):\n", + " f()\n", + " stop = time.perf_counter_ns()\n", + " return (stop - start) / 10000 / 1000\n", + "\n", + "\n", + "kernel_call_overhead = eval_overhead(lambda: dummy_kernel(A, B))\n", + "parse_cache_key_overhead = eval_overhead(lambda: dummy_kernel.parse_cache_key(A, B))\n", + "\n", + "print(f\"Kernel call : {kernel_call_overhead:.2f} us\")\n", + "print(f\"Parse cache key: {parse_cache_key_overhead:.2f} us\")" + ] + }, + { + "cell_type": "markdown", + "id": "39166cb4", + "metadata": {}, + "source": [ + "## Compilation and parallel compilation" + ] + }, + { + "cell_type": "markdown", + "id": "8c6fbe08", + "metadata": {}, + "source": [ + "Both `lazyjit` and the original `jit` support parallel compilation.\n", + "\n", + "To avoid wasting memory on temporary `torch.Tensor` objects, you can use `T.Tensor` to create placeholders." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7222e57b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8a4e4eb3cd4445bda6e8693da31ef3b8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Elaborating: 0%| | 0/8 [00:00,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from itertools import product\n", + "\n", + "\n", + "def get_configs():\n", + " return [\n", + " {\n", + " \"A\": T.Tensor((1024, 1024), T.float32),\n", + " \"B\": T.Tensor((1024, 1024), T.float32),\n", + " \"block_M\": block_M,\n", + " \"block_N\": block_N,\n", + " \"block_K\": block_K,\n", + " }\n", + " for block_M, block_N, block_K in product([32, 64], repeat=3)\n", + " ]\n", + "\n", + "\n", + "gemm.par_compile(get_configs())" + ] + }, + { + "cell_type": "markdown", + "id": "5160d2cc", + "metadata": {}, + "source": [ + "## More convenient macros" + ] + }, + { + "cell_type": "markdown", + "id": "be44afc4", + "metadata": {}, + "source": [ + "tilelang's macros have been improved:\n", + "\n", + "1. Allow using `T.Ref` as an annotation, similar to C++ references.\n", + "2. Allow returning multiple values.\n", + "3. Allow nesting and recursion." + ] + }, + { + "cell_type": "markdown", + "id": "79575972", + "metadata": {}, + "source": [ + "### Passing references with `T.Ref`\n", + "\n", + "A `T.Ref` reference can point to a scalar variable or to an element of a buffer." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "90eaa6e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def foo(x_handle: T.handle):\n", + " x = T.match_buffer(x_handle, (2,), strides=(1,))\n", + " # with T.block(\"root\"):\n", + " bx = T.launch_thread(\"blockIdx.x\", 1)\n", + " tx = T.launch_thread(\"threadIdx.x\", 128)\n", + " ty = T.launch_thread(\"threadIdx.y\", 1)\n", + " tz = T.launch_thread(\"threadIdx.z\", 1)\n", + " with T.block(\"tilelang_root\"):\n", + " T.reads()\n", + " idx = T.Buffer((1,), \"int32\", scope=\"local.var\")\n", + " T.writes(x[T.min(1, idx[0]):T.min(1, idx[0]) + (T.max(1, idx[0]) + 1 - T.min(1, idx[0]))])\n", + " T.block_attr({\"tl.local_var_init\": {idx.data: 0}})\n", + " idx = T.alloc_buffer((1,), \"int32\", data=idx.data, scope=\"local.var\")\n", + " x[1] = T.float32(1.0)\n", + " _tmp: T.int32 = idx[0]\n", + " x[_tmp] = T.float32(1.0)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@T.macro\n", + "def macro_with_ref(x: T.Ref):\n", + " x = 1 # noqa: F841\n", + "\n", + "\n", + "@T.prim_func\n", + "def foo(x: T.Tensor((2,))):\n", + " with T.Kernel(1) as _:\n", + " # Supports constant indices\n", + " macro_with_ref(x[1])\n", + "\n", + " # Also supports variable indices\n", + " idx = T.alloc_var(T.int32, 0)\n", + " macro_with_ref(x[idx])\n", + "\n", + "\n", + "foo" + ] + }, + { + "cell_type": "markdown", + "id": "7bb447a2", + "metadata": {}, + "source": [ + "### Pass macros as arguments\n", + "\n", + "You can pass a macro as a function argument." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dc7bb779", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def element_wise(A, fn):\n", + " N = T.dynamic(\"N\")\n", + " A: T.Tensor[[N], T.float32]\n", + " B = T.empty((N,), dtype=A.dtype)\n", + " block_N = 128\n", + " with T.Kernel(T.ceildiv(N, block_N), threads=128) as bx:\n", + " for i in T.Parallel(block_N):\n", + " idx = bx * block_N + i\n", + " B[idx] = fn(A[idx])\n", + " return B\n", + "\n", + "\n", + "@T.macro\n", + "def add_one(x):\n", + " return x + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a89fdb44", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, device=\"cuda\")\n", + "B = element_wise(A, add_one)\n", + "B_ref = A + 1\n", + "torch.testing.assert_close(B, B_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "ef6e403a", + "metadata": {}, + "source": [ + "### Recursive macros\n", + "\n", + "You may not need this often, but macros can be recursive as long as the termination condition is known at compile time." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7703cab5", + "metadata": {}, + "outputs": [], + "source": [ + "@T.macro\n", + "def n31(x, var: T.Ref):\n", + " if x == 1:\n", + " pass\n", + " elif x % 2 == 0:\n", + " var = var // 2\n", + " n31(x // 2, var)\n", + " else:\n", + " var = var * 3 + 1\n", + " n31(x * 3 + 1, var)\n", + "\n", + "\n", + "@tilelang.lazy_jit\n", + "def foo(A: T.Tensor[[1], T.int32], n: int):\n", + " with T.Kernel(1) as _:\n", + " n31(n, A[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "542ddd4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([18], device='cuda:0', dtype=torch.int32)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = torch.tensor([100], dtype=torch.int32, device=\"cuda\")\n", + "foo(A, 5)\n", + "A" + ] + }, + { + "cell_type": "markdown", + "id": "dc30c2d2", + "metadata": {}, + "source": [ + "### Macros returning multiple values" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d5a2388f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def foo():\n", + " # with T.block(\"root\"):\n", + " x = T.launch_thread(\"blockIdx.x\", 32)\n", + " tx = T.launch_thread(\"threadIdx.x\", 128)\n", + " ty = T.launch_thread(\"threadIdx.y\", 1)\n", + " tz = T.launch_thread(\"threadIdx.z\", 1)\n", + " with T.block(\"tilelang_root\"):\n", + " T.reads()\n", + " T.writes()\n", + " s: T.int32 = T.sin(x)\n", + " c: T.int32 = T.cos(x)\n", + " a: T.int32 = s + c\n", + " b: T.int32 = s - c\n", + " T.evaluate(0)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@T.macro\n", + "def sincos(x):\n", + " return T.sin(x), T.cos(x)\n", + "\n", + "\n", + "@T.prim_func\n", + "def foo():\n", + " with T.Kernel(32) as x:\n", + " s, c = sincos(x)\n", + " a = s + c # noqa: F841\n", + " b = s - c # noqa: F841\n", + "\n", + "\n", + "foo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd83fea7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tilelang-dev_0", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/lazy_jit/lazyjit.zh.ipynb b/examples/lazy_jit/lazyjit.zh.ipynb new file mode 100644 index 0000000000..387aff461d --- /dev/null +++ b/examples/lazy_jit/lazyjit.zh.ipynb @@ -0,0 +1,977 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "5e0deecc", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "sys.path.insert(0, str(Path.cwd().parent.parent.absolute()))\n", + "import tilelang\n", + "import torch\n", + "import tilelang.language as T" + ] + }, + { + "cell_type": "markdown", + "id": "1ca2c56d", + "metadata": {}, + "source": [ + "# Tilelang Lazy JIT" + ] + }, + { + "cell_type": "markdown", + "id": "156e7370", + "metadata": {}, + "source": [ + "## Tensor Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "b070c109", + "metadata": {}, + "source": [ + "Tilelang Lazy JIT 将 jit 生成和调用的逻辑合并到一起\n", + "\n", + "函数签名的写法与 triton 相似，但做了大量增强，最主要的增强是允许对 Tensor 的标注：\n", + "\n", + "* 如果一个 Tensor 有复杂的 shape 约束，我们可以把它的标注移动到函数内部\n", + "* 通过 `T.const` 或 `T.dynamic` 来建立一些 shape 变量，然后用 `T.Tensor` 标注复杂的 Tensor\n", + "* 用 `T.empty` 来声明返回值" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "60bf8954", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm(\n", + " A,\n", + " B,\n", + " out_dtype: T.dtype = T.float32,\n", + " block_M: int = 128,\n", + " block_N: int = 128,\n", + " block_K: int = 32,\n", + "):\n", + " M, N, K = T.const(\"M, N, K\")\n", + "\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + "\n", + " C = T.empty((M, N), out_dtype)\n", + "\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " A_shared = T.alloc_shared((block_M, block_K), A.dtype)\n", + " B_shared = T.alloc_shared((block_K, block_N), B.dtype)\n", + " C_local = T.alloc_fragment((block_M, block_N), out_dtype)\n", + " T.clear(C_local)\n", + " for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):\n", + " T.copy(A[bx * block_M, k * block_K], A_shared)\n", + " T.copy(B[k * block_K, by * block_N], B_shared)\n", + " T.gemm(A_shared, B_shared, C_local)\n", + " T.copy(C_local, C[bx * block_M, by * block_N])\n", + " return C" + ] + }, + { + "cell_type": "markdown", + "id": "28f868fe", + "metadata": {}, + "source": [ + "直接将 Tensor 作为参数调用，即可触发完整的 jit 编译运行流程：" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ee13394a", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm(A, B)\n", + "\n", + "# check output is correct\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "c6705091", + "metadata": {}, + "source": [ + "更改调用的参数，如果编译器参数发生了变化，会触发重新编译：" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d8aab5b7", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 1024, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm(A, B, block_M=64, block_N=64)" + ] + }, + { + "cell_type": "markdown", + "id": "ce6b7391", + "metadata": {}, + "source": [ + "你也可以手动调用 compile 函数编译 kernel\n", + "\n", + "1. `ker.compile` 编译 kernel\n", + "2. `ker.get_tir` 获取 tir\n", + "3. `ker.par_compile` 并行编译" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "f3cf3a2d", + "metadata": {}, + "outputs": [], + "source": [ + "kernel = gemm.compile(A, B, block_M=64, block_N=64)\n", + "C = kernel(A, B)" + ] + }, + { + "cell_type": "markdown", + "id": "921761b5", + "metadata": {}, + "source": [ + "## More Tensor Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "4539e54e", + "metadata": {}, + "source": [ + "### 用 macro 来分离实现" + ] + }, + { + "cell_type": "markdown", + "id": "ad96ba65", + "metadata": {}, + "source": [ + "接下来，我们会用各种方式来实现一个简单的 gemm，为了方便，我们先写一个 macro 把 gemm 的主要逻辑写出来：" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "171d4fe6", + "metadata": {}, + "outputs": [], + "source": [ + "@T.macro\n", + "def gemm_impl(A, B, C, M, N, K, block_M, block_N, block_K):\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " A_shared = T.alloc_shared((block_M, block_K), A.dtype)\n", + " B_shared = T.alloc_shared((block_K, block_N), B.dtype)\n", + " C_local = T.alloc_fragment((block_M, block_N), C.dtype)\n", + " T.clear(C_local)\n", + " for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3):\n", + " T.copy(A[bx * block_M, k * block_K], A_shared)\n", + " T.copy(B[k * block_K, by * block_N], B_shared)\n", + " T.gemm(A_shared, B_shared, C_local)\n", + " T.copy(C_local, C[bx * block_M, by * block_N])" + ] + }, + { + "cell_type": "markdown", + "id": "446a1acd", + "metadata": {}, + "source": [ + "### 用 T.dynamic 标记动态 Shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6a38aa95", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_dyn_K(A, B):\n", + " M, N, K = T.dynamic(\"M, N, K\")\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, 128, 128, 32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fe6cfdc8", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_dyn_K(A, B)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "2ee97bf7", + "metadata": {}, + "source": [ + "### 用 T.StridedTensor 标记带 stride 的 Tensor\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "9dde1dae", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def as_contingious(A):\n", + " M, N, dM, dN = T.dynamic(\"M, N, dM, dN\")\n", + " A: T.StridedTensor[[M, N], [dM, dN], T.float32]\n", + " B = T.empty((M, N), A.dtype)\n", + " block_M = 128\n", + " block_N = 128\n", + " with T.Kernel(T.ceildiv(M, block_M), T.ceildiv(N, block_N), threads=128) as (bx, by):\n", + " T.copy(\n", + " A[bx * block_M : (bx + 1) * block_M, by * block_N : (by + 1) * block_N],\n", + " B[bx * block_M : (bx + 1) * block_M, by * block_N : (by + 1) * block_N],\n", + " )\n", + " return B" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "dec2c0a7", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 1024, device=\"cuda\")\n", + "B = as_contingious(A.T)\n", + "B_ref = A.T.contiguous()\n", + "torch.testing.assert_close(B, B_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "f5fb20d6", + "metadata": {}, + "source": [ + "## More Annotation" + ] + }, + { + "cell_type": "markdown", + "id": "890df0a2", + "metadata": {}, + "source": [ + "### 直接用参数当 annotation" + ] + }, + { + "cell_type": "markdown", + "id": "e9a47d42", + "metadata": {}, + "source": [ + "可以直接把函数参数写到 annotation 里面" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0fc17af6", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_ptr(\n", + " A,\n", + " B,\n", + " M,\n", + " N,\n", + " K,\n", + "):\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, block_M=128, block_N=128, block_K=32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "8e52a554", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_ptr(A, B, 1024, 256, 512)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "6b19ef90", + "metadata": {}, + "source": [ + "### 对运行时变量的 annotation" + ] + }, + { + "cell_type": "markdown", + "id": "bba5f27f", + "metadata": {}, + "source": [ + "运行时变量也是一样，如果嫌函数 annotation 太长，可以放到函数体里面" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c1e7598a", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def gemm_ptr_dyn(A, B, M, N, K):\n", + " M: T.int32\n", + " N: T.int32\n", + " K: T.int32\n", + " A: T.Tensor[[M, K], T.float16]\n", + " B: T.Tensor[[K, N], T.float16]\n", + " C = T.empty((M, N), T.float32)\n", + " gemm_impl(A, B, C, M, N, K, block_M=128, block_N=128, block_K=32)\n", + " return C" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "9e9a4c88", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, 512, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(512, 256, dtype=torch.float16, device=\"cuda\")\n", + "C = gemm_ptr_dyn(A, B, 1024, 256, 512)\n", + "C_ref = (A @ B).float()\n", + "torch.testing.assert_close(C, C_ref, rtol=1e-2, atol=1e-2)" + ] + }, + { + "cell_type": "markdown", + "id": "81427765", + "metadata": {}, + "source": [ + "### 常量的约束" + ] + }, + { + "cell_type": "markdown", + "id": "4d6b084b", + "metadata": {}, + "source": [ + "`T.const` 创建的常量 annotation 只要要被直接使用一次，否则会报错" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "c90dd24f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Constexpr variable `M` is not used in any buffer shape or stride.\n", + "At least one **DIRECT** usage is required. Please check:\n", + "(1) the variable is not used\n", + "(2) all uses are indirect, e.g. M * 2, M * 3. (you can replace them with separate constexpr variables)\n", + "Buffer shapes: {A: [M * 2, M * 3]}\n", + "Buffer strides: {A: [M * 3, 1]}\n" + ] + } + ], + "source": [ + "@tilelang.lazy_jit\n", + "def example_wrong_kernel(A):\n", + " M = T.const(\"M\")\n", + " A: T.Tensor[[M * 2, M * 3], T.float32]\n", + " with T.Kernel(1) as _:\n", + " A[0, 0]\n", + "\n", + "\n", + "try:\n", + " A = torch.randn(64, 96, dtype=torch.float32, device=\"cuda\")\n", + " example_wrong_kernel(A)\n", + "except Exception as e:\n", + " print(e)" + ] + }, + { + "cell_type": "markdown", + "id": "e07e762b", + "metadata": {}, + "source": [ + "### 动态维度的" + ] + }, + { + "cell_type": "markdown", + "id": "f48e5d7a", + "metadata": {}, + "source": [ + "如果想要 Tensor 的 annotation 类型某个参数变化，建议改成 T.ptr + T.match_buffer 格式。" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1d050321", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@tilelang.lazy_jit\n", + "def dyn_annot(\n", + " A: T.ptr, # 1. T.ptr type annotation\n", + " is_2d=False,\n", + "):\n", + " if is_2d:\n", + " M, N = T.const(\"M, N\")\n", + " # 2. dynamic shape annotation inside function body\n", + " A = T.match_buffer(A, [M, N], T.float32)\n", + " with T.Kernel(1) as _:\n", + " A[0, 0]\n", + " else:\n", + " L = T.const(\"L\")\n", + " A = T.match_buffer(A, [L], T.float32)\n", + " with T.Kernel(1) as _:\n", + " A[0]\n", + "\n", + "\n", + "A = torch.randn(64, 96, dtype=torch.float32, device=\"cuda\")\n", + "dyn_annot(A, is_2d=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2e9f1bb3", + "metadata": {}, + "source": [ + "### 带默认参数的" + ] + }, + { + "cell_type": "markdown", + "id": "f7fc9917", + "metadata": {}, + "source": [ + "类似 `T.float32` 标注的标量可以带默认参数" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "42ec86a1", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def add_one(X, data: T.float32 = 1):\n", + " M, N = T.const(\"M, N\")\n", + " X: T.Tensor[[M, N], T.float32]\n", + " Y = T.empty((M, N), T.float32)\n", + " with T.Kernel(T.ceildiv(M, 128), threads=128) as bx:\n", + " for i, j in T.Parallel(128, N):\n", + " Y[bx * 128 + i, j] = X[bx * 128 + i, j] + data\n", + " return Y" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d49e1120", + "metadata": {}, + "outputs": [], + "source": [ + "X = torch.randn(1024, 1024, dtype=torch.float32, device=\"cuda\")\n", + "Y = add_one(X)\n", + "torch.testing.assert_close(Y, X + 1)" + ] + }, + { + "cell_type": "markdown", + "id": "a02baedc", + "metadata": {}, + "source": [ + "## 参数匹配的 Overhead" + ] + }, + { + "cell_type": "markdown", + "id": "860a2972", + "metadata": {}, + "source": [ + "LazyJIT overhead 很小，每个 constant 添加约 200ns 的 overhead\n", + "* 200ns 大约是从 torch.Tensor 的 shape/stride 中拿参数的 ffi call 的代价" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc676e33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Kernel call : 7.68 us\n", + "Parse cache key: 0.41 us\n" + ] + } + ], + "source": [ + "import time\n", + "\n", + "A = torch.randn(128, 128, dtype=torch.float16, device=\"cuda\")\n", + "B = torch.randn(128, 128, dtype=torch.float16, device=\"cuda\")\n", + "\n", + "\n", + "@tilelang.lazy_jit\n", + "def dummy_kernel(A, B):\n", + " M, N = T.const(\"M, N\")\n", + " A: T.Tensor[[M, N], T.float16]\n", + " B: T.Tensor[[M, N], T.float16]\n", + " with T.Kernel(1) as _:\n", + " pass\n", + "\n", + "\n", + "# compile it first\n", + "dummy_kernel(A, B)\n", + "\n", + "\n", + "def eval_overhead(f):\n", + " start = time.perf_counter_ns()\n", + " for _ in range(10000):\n", + " f()\n", + " stop = time.perf_counter_ns()\n", + " return (stop - start) / 10000 / 1000\n", + "\n", + "\n", + "kernel_call_overhead = eval_overhead(lambda: dummy_kernel(A, B))\n", + "parse_cache_key_overhead = eval_overhead(lambda: dummy_kernel.parse_cache_key(A, B))\n", + "\n", + "print(f\"Kernel call : {kernel_call_overhead:.2f} us\")\n", + "print(f\"Parse cache key: {parse_cache_key_overhead:.2f} us\")" + ] + }, + { + "cell_type": "markdown", + "id": "39166cb4", + "metadata": {}, + "source": [ + "## 编译与并行编译" + ] + }, + { + "cell_type": "markdown", + "id": "8c6fbe08", + "metadata": {}, + "source": [ + "lazyjit 和原来的 jit 都支持并行编译\n", + "\n", + "为了防止 torch.tensor 白白浪费内存，可以使用 T.Tensor 来创建 placeholder" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "7222e57b", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8a4e4eb3cd4445bda6e8693da31ef3b8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Elaborating: 0%| | 0/8 [00:00,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from itertools import product\n", + "\n", + "\n", + "def get_configs():\n", + " return [\n", + " {\n", + " \"A\": T.Tensor((1024, 1024), T.float32),\n", + " \"B\": T.Tensor((1024, 1024), T.float32),\n", + " \"block_M\": block_M,\n", + " \"block_N\": block_N,\n", + " \"block_K\": block_K,\n", + " }\n", + " for block_M, block_N, block_K in product([32, 64], repeat=3)\n", + " ]\n", + "\n", + "\n", + "gemm.par_compile(get_configs())" + ] + }, + { + "cell_type": "markdown", + "id": "5160d2cc", + "metadata": {}, + "source": [ + "## 更便利的 Macro" + ] + }, + { + "cell_type": "markdown", + "id": "be44afc4", + "metadata": {}, + "source": [ + "tilelang 的 macro 现在已经升级：\n", + "\n", + "1. 允许用 `T.Ref` 作为 annotation，这类似与 C++ 的引用传递\n", + "2. 允许返回多个值\n", + "3. 允许嵌套，递归" + ] + }, + { + "cell_type": "markdown", + "id": "79575972", + "metadata": {}, + "source": [ + "### T.Ref 传递引用\n", + "\n", + "T.Ref 传递的引用可以 var 也可以是 Buffer 的索引" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "90eaa6e5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def foo(x_handle: T.handle):\n", + " x = T.match_buffer(x_handle, (2,), strides=(1,))\n", + " # with T.block(\"root\"):\n", + " bx = T.launch_thread(\"blockIdx.x\", 1)\n", + " tx = T.launch_thread(\"threadIdx.x\", 128)\n", + " ty = T.launch_thread(\"threadIdx.y\", 1)\n", + " tz = T.launch_thread(\"threadIdx.z\", 1)\n", + " with T.block(\"tilelang_root\"):\n", + " T.reads()\n", + " idx = T.Buffer((1,), \"int32\", scope=\"local.var\")\n", + " T.writes(x[T.min(1, idx[0]):T.min(1, idx[0]) + (T.max(1, idx[0]) + 1 - T.min(1, idx[0]))])\n", + " T.block_attr({\"tl.local_var_init\": {idx.data: 0}})\n", + " idx = T.alloc_buffer((1,), \"int32\", data=idx.data, scope=\"local.var\")\n", + " x[1] = T.float32(1.0)\n", + " _tmp: T.int32 = idx[0]\n", + " x[_tmp] = T.float32(1.0)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@T.macro\n", + "def macro_with_ref(x: T.Ref):\n", + " x = 1 # noqa: F841\n", + "\n", + "\n", + "@T.prim_func\n", + "def foo(x: T.Tensor((2,))):\n", + " with T.Kernel(1) as _:\n", + " # 支持常量 index\n", + " macro_with_ref(x[1])\n", + "\n", + " # 也支持变量 index\n", + " idx = T.alloc_var(T.int32, 0)\n", + " macro_with_ref(x[idx])\n", + "\n", + "\n", + "foo" + ] + }, + { + "cell_type": "markdown", + "id": "7bb447a2", + "metadata": {}, + "source": [ + "### 当作参数传递\n", + "\n", + "你可以把 macro 当做参数传递" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "dc7bb779", + "metadata": {}, + "outputs": [], + "source": [ + "@tilelang.lazy_jit\n", + "def element_wise(A, fn):\n", + " N = T.dynamic(\"N\")\n", + " A: T.Tensor[[N], T.float32]\n", + " B = T.empty((N,), dtype=A.dtype)\n", + " block_N = 128\n", + " with T.Kernel(T.ceildiv(N, block_N), threads=128) as bx:\n", + " for i in T.Parallel(block_N):\n", + " idx = bx * block_N + i\n", + " B[idx] = fn(A[idx])\n", + " return B\n", + "\n", + "\n", + "@T.macro\n", + "def add_one(x):\n", + " return x + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "a89fdb44", + "metadata": {}, + "outputs": [], + "source": [ + "A = torch.randn(1024, device=\"cuda\")\n", + "B = element_wise(A, add_one)\n", + "B_ref = A + 1\n", + "torch.testing.assert_close(B, B_ref)" + ] + }, + { + "cell_type": "markdown", + "id": "ef6e403a", + "metadata": {}, + "source": [ + "### Macro 递归\n", + "\n", + "虽然不知道有没有这种需求，但 macro 是可以递归的，终止条件要求编译期间确定" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "7703cab5", + "metadata": {}, + "outputs": [], + "source": [ + "@T.macro\n", + "def n31(x, var: T.Ref):\n", + " if x == 1:\n", + " pass\n", + " elif x % 2 == 0:\n", + " var = var // 2\n", + " n31(x // 2, var)\n", + " else:\n", + " var = var * 3 + 1\n", + " n31(x * 3 + 1, var)\n", + "\n", + "\n", + "@tilelang.lazy_jit\n", + "def foo(A: T.Tensor[[1], T.int32], n: int):\n", + " with T.Kernel(1) as _:\n", + " n31(n, A[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "542ddd4e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([18], device='cuda:0', dtype=torch.int32)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "A = torch.tensor([100], dtype=torch.int32, device=\"cuda\")\n", + "foo(A, 5)\n", + "A" + ] + }, + { + "cell_type": "markdown", + "id": "dc30c2d2", + "metadata": {}, + "source": [ + "### Macro 返回多个值" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "d5a2388f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "# from tvm.script import tir as T\n", + "\n", + "@T.prim_func\n", + "def foo():\n", + " # with T.block(\"root\"):\n", + " x = T.launch_thread(\"blockIdx.x\", 32)\n", + " tx = T.launch_thread(\"threadIdx.x\", 128)\n", + " ty = T.launch_thread(\"threadIdx.y\", 1)\n", + " tz = T.launch_thread(\"threadIdx.z\", 1)\n", + " with T.block(\"tilelang_root\"):\n", + " T.reads()\n", + " T.writes()\n", + " s: T.int32 = T.sin(x)\n", + " c: T.int32 = T.cos(x)\n", + " a: T.int32 = s + c\n", + " b: T.int32 = s - c\n", + " T.evaluate(0)" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "@T.macro\n", + "def sincos(x):\n", + " return T.sin(x), T.cos(x)\n", + "\n", + "\n", + "@T.prim_func\n", + "def foo():\n", + " with T.Kernel(32) as x:\n", + " s, c = sincos(x)\n", + " a = s + c # noqa: F841\n", + " b = s - c # noqa: F841\n", + "\n", + "\n", + "foo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dd83fea7", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "tilelang-dev_0", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/linear_attention/example_linear_attn_bwd.py b/examples/linear_attention/example_linear_attn_bwd.py index 568bcc55f0..82ae1d982a 100644 --- a/examples/linear_attention/example_linear_attn_bwd.py +++ b/examples/linear_attention/example_linear_attn_bwd.py @@ -13,20 +13,20 @@ pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + } +) def tl_fused_chunk_bwd_kernel( B, S, H, DK, DV, - dtype: str = 'float16', + dtype: T.dtype = T.float16, scale: float = None, ) -> torch.Tensor: - if scale is None: scale = DK**-0.5 - accum_dtype = 'float' + accum_dtype = T.float32 chunk_size = 64 BK = BV = 64 # Set to 128 can be faster, but has some numerical differences with FLA @@ -37,13 +37,13 @@ def tl_fused_chunk_bwd_kernel( @T.prim_func def fused_chunk_linear_attn_bwd( - Q: T.Tensor([B, S, H, DK], dtype), # type: ignore - K: T.Tensor([B, S, H, DK], dtype), # type: ignore - V: T.Tensor([B, S, H, DV], dtype), # type: ignore - dO: T.Tensor([B, S, H, DV], dtype), # type: ignore - dQ: T.Tensor([B, S, H, DK], accum_dtype), # type: ignore - dK: T.Tensor([B, S, H, DK], accum_dtype), # type: ignore - dV: T.Tensor([B, S, H, DV], accum_dtype), # type: ignore + Q: T.Tensor([B, S, H, DK], dtype), # type: ignore + K: T.Tensor([B, S, H, DK], dtype), # type: ignore + V: T.Tensor([B, S, H, DV], dtype), # type: ignore + dO: T.Tensor([B, S, H, DV], dtype), # type: ignore + dQ: T.Tensor([B, S, H, DK], accum_dtype), # type: ignore + dK: T.Tensor([B, S, H, DK], accum_dtype), # type: ignore + dV: T.Tensor([B, S, H, DV], accum_dtype), # type: ignore ): with T.Kernel(NV, NK, B * H) as (i_v, i_k, i_bh): i_b = i_bh // H @@ -66,11 +66,6 @@ def fused_chunk_linear_attn_bwd( dh = T.alloc_fragment([BK, BV], accum_dtype) dh_shared = T.alloc_shared([BK, BV], dtype) - T.annotate_layout({ - dq_shared: tilelang.layout.make_swizzled_layout(dq_shared), - dk_shared: tilelang.layout.make_swizzled_layout(dk_shared), - dv_shared: tilelang.layout.make_swizzled_layout(dv_shared) - }) T.use_swizzle(10) T.clear(h) @@ -78,10 +73,9 @@ def fused_chunk_linear_attn_bwd( # Calculate dQ for i in T.Pipelined(0, NT): - T.copy(K[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], k) - T.copy(V[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], v) - T.copy(dO[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], - do) + T.copy(K[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], k) + T.copy(V[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], v) + T.copy(dO[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], do) T.gemm(do, v, ds, transpose_B=True, clear_accum=True) for row, col in T.Parallel(chunk_size, chunk_size): @@ -94,29 +88,19 @@ def fused_chunk_linear_attn_bwd( for row, col in T.Parallel(chunk_size, BK): dq[row, col] *= scale T.copy(dq, dq_shared) - T.atomic_add( - dQ[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], - dq_shared) + T.atomic_add(dQ[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], dq_shared) # Calculate dK, dV (reversely) for i in T.Pipelined(1, NT + 1): start = NT - i for row, col in T.Parallel(chunk_size, BK): q[row, col] = Q[i_b, start * chunk_size + row, i_h, i_k * BK + col] * scale - T.copy( - K[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, - i_k * BK:(i_k + 1) * BK], k) - T.copy( - V[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, - i_v * BV:(i_v + 1) * BV], v) - T.copy( - dO[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, - i_v * BV:(i_v + 1) * BV], do) + T.copy(K[i_b, start * chunk_size : (start + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], k) + T.copy(V[i_b, start * chunk_size : (start + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], v) + T.copy(dO[i_b, start * chunk_size : (start + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], do) # Calculate dk - T.gemm( - v, do, ds, transpose_B=True, clear_accum=True - ) # ds here actually means `s`, but we simply reuse the buffer `ds` + T.gemm(v, do, ds, transpose_B=True, clear_accum=True) # ds here actually means `s`, but we simply reuse the buffer `ds` for row, col in T.Parallel(chunk_size, chunk_size): ds_shared[row, col] = T.if_then_else(row <= col, ds[row, col], 0) T.gemm(ds_shared, q, dk, clear_accum=True) @@ -134,13 +118,9 @@ def fused_chunk_linear_attn_bwd( T.gemm(q, do, dh, transpose_A=True) T.copy(dk, dk_shared) - T.atomic_add( - dK[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, - i_k * BK:(i_k + 1) * BK], dk_shared) + T.atomic_add(dK[i_b, start * chunk_size : (start + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], dk_shared) T.copy(dv, dv_shared) - T.atomic_add( - dV[i_b, start * chunk_size:(start + 1) * chunk_size, i_h, - i_v * BV:(i_v + 1) * BV], dv_shared) + T.atomic_add(dV[i_b, start * chunk_size : (start + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], dv_shared) return fused_chunk_linear_attn_bwd @@ -155,34 +135,31 @@ def tl_fused_chunk_bwd(Q, K, V, dO): return dQ.to(torch.float16), dK.to(torch.float16), dV.to(torch.float16) -def ref_program(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - scale: Optional[float] = None) -> Tuple[torch.Tensor, torch.Tensor]: +def ref_program(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, scale: Optional[float] = None) -> Tuple[torch.Tensor, torch.Tensor]: q, k, v = q.float(), k.float(), v.float() if scale is None: - scale = q.shape[-1]**-0.5 + scale = q.shape[-1] ** -0.5 chunk_size = 64 - q = rearrange(q, 'b (n c) h d -> b h n c d', c=chunk_size) * scale - k = rearrange(k, 'b (n c) h d -> b h n c d', c=chunk_size) - v = rearrange(v, 'b (n c) h d -> b h n c d', c=chunk_size) + q = rearrange(q, "b (n c) h d -> b h n c d", c=chunk_size) * scale + k = rearrange(k, "b (n c) h d -> b h n c d", c=chunk_size) + v = rearrange(v, "b (n c) h d -> b h n c d", c=chunk_size) kv = k.transpose(-1, -2) @ v kv = kv.cumsum(2) h = kv[:, :, -1, :, :] kv = torch.cat([torch.zeros_like(kv[:, :, :1]), kv[:, :, :-1]], dim=2) inter = q @ kv - intra = ((q @ k.transpose(-1, -2)).masked_fill_( - torch.triu(torch.ones(chunk_size, chunk_size, dtype=bool, device=q.device), diagonal=1), - 0)) @ v + intra = ( + (q @ k.transpose(-1, -2)).masked_fill_(torch.triu(torch.ones(chunk_size, chunk_size, dtype=bool, device=q.device), diagonal=1), 0) + ) @ v o = inter + intra - return rearrange(o, 'b h n c d -> b (n c) h d'), h + return rearrange(o, "b h n c d -> b (n c) h d"), h def main(B=1, S=1024, H=16, D=128): - q = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) - k = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) - v = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16, requires_grad=True) - do = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) + q = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + k = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + v = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + do = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) # qk norm is necessary for linear attn q = l2norm_fwd(q)[0].requires_grad_(True) @@ -193,30 +170,42 @@ def main(B=1, S=1024, H=16, D=128): o_ref, _ = ref_program(q, k, v) o_ref.backward(do, retain_graph=True) - assert torch.allclose( - dq, q.grad, atol=1e-2, rtol=1e-2), f'dq max err: {(dq - q.grad).abs().max()}' - assert torch.allclose( - dk, k.grad, atol=1e-2, rtol=1e-2), f'dk max err: {(dk - k.grad).abs().max()}' - assert torch.allclose( - dv, v.grad, atol=1e-2, rtol=1e-2), f'dv max err: {(dv - v.grad).abs().max()}' - print('Passed all tests!✅') + assert torch.allclose(dq, q.grad, atol=1e-2, rtol=1e-2), f"dq max err: {(dq - q.grad).abs().max()}" + assert torch.allclose(dk, k.grad, atol=1e-2, rtol=1e-2), f"dk max err: {(dk - k.grad).abs().max()}" + assert torch.allclose(dv, v.grad, atol=1e-2, rtol=1e-2), f"dv max err: {(dv - v.grad).abs().max()}" + print("Passed all tests!✅") # Benchmark q.grad = k.grad = v.grad = None o_ref, _ = fused_chunk_linear_attn(q, k, v, output_final_state=True, normalize=False) - t1 = do_bench(lambda: o_ref.backward(do, retain_graph=True), backend='cupti') - t2 = do_bench(lambda: tl_fused_chunk_bwd(q, k, v, do), backend='cupti') - print(f'Triton latency: {t1:.3f} ms') - print(f'TileLang latency: {t2:.3f} ms') - print(f'Speedup: {t1/t2:.3f}x') + t1 = do_bench(lambda: o_ref.backward(do, retain_graph=True), backend="cupti") + t2 = do_bench(lambda: tl_fused_chunk_bwd(q, k, v, do), backend="cupti") + print(f"Triton latency: {t1:.3f} ms") + print(f"TileLang latency: {t2:.3f} ms") + print(f"Speedup: {t1 / t2:.3f}x") + + +def run_regression_perf(B=1, S=1024, H=16, D=128): + q = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + k = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + v = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16, requires_grad=True) + do = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + q = l2norm_fwd(q)[0].requires_grad_(True) + k = l2norm_fwd(k)[0].requires_grad_(True) + kernel = tl_fused_chunk_bwd_kernel(B, S, H, D, D) + dQ = torch.zeros_like(q, dtype=torch.float32) + dK = torch.zeros_like(k, dtype=torch.float32) + dV = torch.zeros_like(v, dtype=torch.float32) + kernel(q, k, v, do, dQ, dK, dV) + return do_bench(lambda: kernel(q, k, v, do, dQ, dK, dV), backend="cupti") -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--B', type=int, default=8, help='Batch size') - parser.add_argument('--S', type=int, default=1024, help='Seq len') - parser.add_argument('--H', type=int, default=32, help='Num heads') - parser.add_argument('--D', type=int, default=128, help='Head dim') + parser.add_argument("--B", type=int, default=8, help="Batch size") + parser.add_argument("--S", type=int, default=1024, help="Seq len") + parser.add_argument("--H", type=int, default=32, help="Num heads") + parser.add_argument("--D", type=int, default=128, help="Head dim") args = parser.parse_args() main(args.B, args.S, args.H, args.D) diff --git a/examples/linear_attention/example_linear_attn_fwd.py b/examples/linear_attention/example_linear_attn_fwd.py index cbf352bbc8..cdfd5cb721 100644 --- a/examples/linear_attention/example_linear_attn_fwd.py +++ b/examples/linear_attention/example_linear_attn_fwd.py @@ -14,20 +14,20 @@ pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, - }) + }, +) def tl_fused_chunk_fwd_kernel( B, S, H, DK, DV, - dtype: str = 'float16', + dtype: T.dtype = T.float16, scale: float = None, ) -> torch.Tensor: - if scale is None: scale = DK**-0.5 - accum_dtype = 'float' + accum_dtype = T.float32 chunk_size = 64 BK = BV = 64 # Set to 128 can be faster, but has some numerical differences with FLA @@ -38,11 +38,12 @@ def tl_fused_chunk_fwd_kernel( @T.prim_func def fused_chunk_linear_attn_fwd( - Q: T.Tensor([B, S, H, DK], dtype), # type: ignore - K: T.Tensor([B, S, H, DK], dtype), # type: ignore - V: T.Tensor([B, S, H, DV], dtype), # type: ignore - O: T.Tensor([B, S, H, DV], accum_dtype), # type: ignore - final_state: T.Tensor([B, H, DK, DV], accum_dtype)): # type: ignore + Q: T.Tensor([B, S, H, DK], dtype), # type: ignore + K: T.Tensor([B, S, H, DK], dtype), # type: ignore + V: T.Tensor([B, S, H, DV], dtype), # type: ignore + O: T.Tensor([B, S, H, DV], accum_dtype), # type: ignore + final_state: T.Tensor([B, H, DK, DV], accum_dtype), + ): # type: ignore with T.Kernel(NV, NK, B * H) as (i_v, i_k, i_bh): i_b = i_bh // H i_h = i_bh % H @@ -57,7 +58,6 @@ def fused_chunk_linear_attn_fwd( o = T.alloc_fragment([chunk_size, BV], accum_dtype) o_shared = T.alloc_shared([chunk_size, BV], accum_dtype) - T.annotate_layout({o_shared: tilelang.layout.make_swizzled_layout(o_shared)}) T.use_swizzle(10) T.clear(h) @@ -65,8 +65,8 @@ def fused_chunk_linear_attn_fwd( for i in T.Pipelined(0, NT): for row, col in T.Parallel(chunk_size, BK): q[row, col] = Q[i_b, i * chunk_size + row, i_h, i_k * BK + col] * scale - T.copy(K[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], k) - T.copy(V[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], v) + T.copy(K[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], k) + T.copy(V[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], v) T.gemm(q, k, s, clear_accum=True, transpose_B=True) for row, col in T.Parallel(chunk_size, chunk_size): @@ -77,13 +77,10 @@ def fused_chunk_linear_attn_fwd( T.gemm(k, v, h, transpose_A=True) T.gemm(q, h_shared, o) T.copy(o, o_shared) - T.atomic_add( - O[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], - o_shared) - #TODO: consider using vectorized atomic add or tma reduce for sm90 + T.atomic_add(O[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], o_shared) # Output final state - T.copy(h, final_state[i_b, i_h, i_k * BK:(i_k + 1) * BK, i_v * BV:(i_v + 1) * BV]) + T.copy(h, final_state[i_b, i_h, i_k * BK : (i_k + 1) * BK, i_v * BV : (i_v + 1) * BV]) return fused_chunk_linear_attn_fwd @@ -91,38 +88,36 @@ def fused_chunk_linear_attn_fwd( def tl_fused_chunk_fwd(q, k, v): B, S, H, D = q.shape kernel = tl_fused_chunk_fwd_kernel(B, S, H, D, D) - o = torch.zeros((B, S, H, D), device='cuda', dtype=torch.float32) + print(kernel.get_kernel_source()) + o = torch.zeros((B, S, H, D), device="cuda", dtype=torch.float32) h = kernel(q, k, v, o) return o, h -def ref_program(q: torch.Tensor, - k: torch.Tensor, - v: torch.Tensor, - scale: Optional[float] = None) -> Tuple[torch.Tensor, torch.Tensor]: +def ref_program(q: torch.Tensor, k: torch.Tensor, v: torch.Tensor, scale: Optional[float] = None) -> Tuple[torch.Tensor, torch.Tensor]: q, k, v = q.float(), k.float(), v.float() if scale is None: - scale = q.shape[-1]**-0.5 + scale = q.shape[-1] ** -0.5 chunk_size = 64 - q = rearrange(q, 'b (n c) h d -> b h n c d', c=chunk_size) * scale - k = rearrange(k, 'b (n c) h d -> b h n c d', c=chunk_size) - v = rearrange(v, 'b (n c) h d -> b h n c d', c=chunk_size) + q = rearrange(q, "b (n c) h d -> b h n c d", c=chunk_size) * scale + k = rearrange(k, "b (n c) h d -> b h n c d", c=chunk_size) + v = rearrange(v, "b (n c) h d -> b h n c d", c=chunk_size) kv = k.transpose(-1, -2) @ v kv = kv.cumsum(2) h = kv[:, :, -1, :, :] kv = torch.cat([torch.zeros_like(kv[:, :, :1]), kv[:, :, :-1]], dim=2) inter = q @ kv - intra = ((q @ k.transpose(-1, -2)).masked_fill_( - torch.triu(torch.ones(chunk_size, chunk_size, dtype=bool, device=q.device), diagonal=1), - 0)) @ v + intra = ( + (q @ k.transpose(-1, -2)).masked_fill_(torch.triu(torch.ones(chunk_size, chunk_size, dtype=bool, device=q.device), diagonal=1), 0) + ) @ v o = inter + intra - return rearrange(o, 'b h n c d -> b (n c) h d'), h + return rearrange(o, "b h n c d -> b (n c) h d"), h def main(B=1, S=512, H=16, D=128): - q = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) - k = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) - v = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) + q = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + k = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + v = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) # qk norm is necessary for linear attn q, _ = l2norm_fwd(q) @@ -131,25 +126,35 @@ def main(B=1, S=512, H=16, D=128): o, h = tl_fused_chunk_fwd(q, k, v) o_ref, h_ref = ref_program(q, k, v) - assert torch.allclose(o, o_ref, atol=1e-2, rtol=1e-2), f'o max err: {(o - o_ref).abs().max()}' - assert torch.allclose(h, h_ref, atol=1e-2, rtol=1e-2), f'h max err: {(h - h_ref).abs().max()}' - print('Passed all tests!✅') + assert torch.allclose(o, o_ref, atol=1e-2, rtol=1e-2), f"o max err: {(o - o_ref).abs().max()}" + assert torch.allclose(h, h_ref, atol=1e-2, rtol=1e-2), f"h max err: {(h - h_ref).abs().max()}" + print("Passed all tests!✅") + + t1 = do_bench(lambda: fused_chunk_linear_attn(q, k, v, output_final_state=True, normalize=False), backend="cupti") + t2 = do_bench(lambda: tl_fused_chunk_fwd(q, k, v), backend="cupti") + print(f"Triton latency: {t1:.3f} ms") + print(f"TileLang latency: {t2:.3f} ms") + print(f"Speedup: {t1 / t2:.3f}x") - t1 = do_bench( - lambda: fused_chunk_linear_attn(q, k, v, output_final_state=True, normalize=False), - backend='cupti') - t2 = do_bench(lambda: tl_fused_chunk_fwd(q, k, v), backend='cupti') - print(f'Triton latency: {t1:.3f} ms') - print(f'TileLang latency: {t2:.3f} ms') - print(f'Speedup: {t1/t2:.3f}x') + +def run_regression_perf(B=1, S=512, H=16, D=128): + q = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + k = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + v = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + q, _ = l2norm_fwd(q) + k, _ = l2norm_fwd(k) + B, S, H, D = q.shape + kernel = tl_fused_chunk_fwd_kernel(B, S, H, D, D) + o = torch.zeros((B, S, H, D), device="cuda", dtype=torch.float32) + return do_bench(lambda: kernel(q, k, v, o), backend="cupti") -if __name__ == '__main__': +if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--B', type=int, default=8, help='Batch size') - parser.add_argument('--S', type=int, default=1024, help='Seq len') - parser.add_argument('--H', type=int, default=32, help='Num heads') - parser.add_argument('--D', type=int, default=128, help='Head dim') + parser.add_argument("--B", type=int, default=8, help="Batch size") + parser.add_argument("--S", type=int, default=1024, help="Seq len") + parser.add_argument("--H", type=int, default=32, help="Num heads") + parser.add_argument("--D", type=int, default=128, help="Head dim") args = parser.parse_args() main(args.B, args.S, args.H, args.D) diff --git a/examples/linear_attention/example_mamba_chunk_scan.py b/examples/linear_attention/example_mamba_chunk_scan.py index add49052db..88a9b75bc2 100644 --- a/examples/linear_attention/example_mamba_chunk_scan.py +++ b/examples/linear_attention/example_mamba_chunk_scan.py @@ -9,6 +9,7 @@ def chunk_scan_triton(cb, x, dt, dA_cumsum, C, states, D): from mamba_ssm.ops.triton.ssd_chunk_scan import _chunk_scan_fwd + out, _ = _chunk_scan_fwd(cb, x, dt, dA_cumsum, C, states, D) return out @@ -43,14 +44,15 @@ def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D): dt_segment_sum = dA_cumsum[:, :, :, :, None] - dA_cumsum[:, :, :, None, :] decay = torch.exp(dt_segment_sum) scores_decay = cb * rearrange(decay, "b h c l s -> b c h l s") - causal_mask = torch.tril( - torch.ones(chunk_size, chunk_size, device=x.device, dtype=bool), diagonal=0) + causal_mask = torch.tril(torch.ones(chunk_size, chunk_size, device=x.device, dtype=bool), diagonal=0) scores_decay = scores_decay.masked_fill(~causal_mask, 0) - out = torch.einsum('bchls,bhcs,bcshp->bclhp', scores_decay.to(x.dtype), dt.to(x.dtype), - rearrange(x, "b (c s) h p -> b c s h p", c=nchunks)) + out = torch.einsum( + "bchls,bhcs,bcshp->bclhp", scores_decay.to(x.dtype), dt.to(x.dtype), rearrange(x, "b (c s) h p -> b c s h p", c=nchunks) + ) state_decay_out = torch.exp(rearrange(dA_cumsum, "b h c l -> b c l h 1")) - out_prev = torch.einsum('bclhn,bchpn->bclhp', rearrange( - C, "b (c l) h n -> b c l h n", c=nchunks), prev_states.to(C.dtype)) * state_decay_out + out_prev = ( + torch.einsum("bclhn,bchpn->bclhp", rearrange(C, "b (c l) h n -> b c l h n", c=nchunks), prev_states.to(C.dtype)) * state_decay_out + ) out = out + out_prev out = rearrange(out, "b c l h p -> b (c l) h p") if D is not None: @@ -61,12 +63,7 @@ def ref_program(cb, x, dt, dA_cumsum, C, prev_states, D): def get_configs(): - iter_params = dict( - block_M=[64, 128, 256], - block_N=[32, 64], - block_K=[64, 128, 256], - block_Dstate=[128], - num_stages=[1, 2, 3, 4, 5]) + iter_params = dict(block_M=[64, 128, 256], block_N=[32, 64], block_K=[64, 128, 256], block_Dstate=[128], num_stages=[1, 2, 3, 4, 5]) return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] @@ -77,56 +74,58 @@ def get_configs(): tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, }, ) -def chunk_scan_fwd(batch, - seqlen, - chunk_size, - ngroups, - nheads, - headdim, - dstate, - block_M=64, - block_N=64, - block_K=64, - block_Dstate=128, - num_stages=2, - threads=128): - dtype = "float16" - accum_dtype = "float" +def chunk_scan_fwd( + batch, + seqlen, + chunk_size, + ngroups, + nheads, + headdim, + dstate, + block_M=64, + block_N=64, + block_K=64, + block_Dstate=128, + num_stages=2, + threads=128, +): + dtype = T.float16 + accum_dtype = T.float32 nchunks = T.ceildiv(seqlen, chunk_size) p = 1.44269504 @T.prim_func def main( - cb: T.Tensor((batch, nchunks, ngroups, chunk_size, chunk_size), dtype), # type: ignore - x: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore - dt: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore - dA_cumsum: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore - C: T.Tensor((batch, seqlen, ngroups, dstate), dtype), # type: ignore - prev_states: T.Tensor((batch, nchunks, nheads, headdim, dstate), dtype), # type: ignore - D: T.Tensor((nheads), dtype), # type: ignore - Output: T.Tensor((batch, seqlen, nheads, headdim), dtype) # type: ignore + cb: T.Tensor((batch, nchunks, ngroups, chunk_size, chunk_size), dtype), # type: ignore + x: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore + dt: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore + dA_cumsum: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), # type: ignore + C: T.Tensor((batch, seqlen, ngroups, dstate), dtype), # type: ignore + prev_states: T.Tensor((batch, nchunks, nheads, headdim, dstate), dtype), # type: ignore + D: T.Tensor((nheads), dtype), # type: ignore + Output: T.Tensor((batch, seqlen, nheads, headdim), dtype), # type: ignore ): - with T.Kernel( - nheads, - T.ceildiv(chunk_size, block_M) * T.ceildiv(headdim, block_N), - batch * nchunks, - threads=threads) as (bz, bx, by): + with T.Kernel(nheads, T.ceildiv(chunk_size, block_M) * T.ceildiv(headdim, block_N), batch * nchunks, threads=threads) as ( + bz, + bx, + by, + ): acc_o = T.alloc_fragment((block_M, block_N), accum_dtype) acc_o_shared = T.alloc_shared((block_M, block_N), dtype) - cb_shared = T.alloc_shared((block_M, block_K), dtype, scope="shared.dyn") + cb_shared = T.alloc_shared((block_M, block_K), dtype) cb_local = T.alloc_fragment((block_M, block_K), dtype) - dA_cs_k_shared = T.alloc_shared((block_K), dtype, scope="shared") + dA_cs_k_shared = T.alloc_shared((block_K), dtype) dA_cs_k_local = T.alloc_fragment((block_K), accum_dtype) dA_cs_m_local = T.alloc_fragment((block_M), accum_dtype) - dt_shared = T.alloc_shared((block_K), dtype, scope="shared") + dt_shared = T.alloc_shared((block_K), dtype) dt_local = T.alloc_fragment((block_K), accum_dtype) x_shared = T.alloc_shared((block_K, block_N), dtype, scope="shared.dyn") - dA_cs_m_shared = T.alloc_shared((block_M), dtype, scope="shared") + dA_cs_m_shared = T.alloc_shared((block_M), dtype) scale_m_local = T.alloc_fragment((block_M), accum_dtype) C_shared = T.alloc_shared((block_M, block_Dstate), dtype) prev_state_shared = T.alloc_shared((block_N, block_Dstate), dtype) D_local = T.alloc_fragment((1), accum_dtype) - x_residual_shared = T.alloc_shared((block_M, block_N), dtype, scope="shared.dyn") + x_residual_shared = T.alloc_shared((block_M, block_N), dtype) x_residual_local = T.alloc_fragment((block_M, block_N), accum_dtype) batch_idx = by % batch @@ -136,27 +135,31 @@ def main( m_idx = bx // T.ceildiv(headdim, block_N) n_idx = bx % T.ceildiv(headdim, block_N) - T.annotate_layout({ - acc_o_shared: tilelang.layout.make_swizzled_layout(acc_o_shared), - cb_shared: tilelang.layout.make_swizzled_layout(cb_shared), - x_residual_shared: tilelang.layout.make_swizzled_layout(x_residual_shared) - }) + T.annotate_layout( + { + cb_shared: tilelang.layout.make_swizzled_layout(cb_shared), + x_residual_shared: tilelang.layout.make_swizzled_layout(x_residual_shared), + } + ) T.no_set_max_nreg() - T.copy(dA_cumsum[batch_idx, bz, chunk_idx, m_idx * block_M:(m_idx + 1) * block_M], - dA_cs_m_shared) + T.copy(dA_cumsum[batch_idx, bz, chunk_idx, m_idx * block_M : (m_idx + 1) * block_M], dA_cs_m_shared) T.copy(dA_cs_m_shared, dA_cs_m_local) T.clear(acc_o) for i in T.Parallel(block_M): scale_m_local[i] = T.exp2(dA_cs_m_local[i] * p) T.copy( - C[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz // (nheads // ngroups), 0:block_Dstate], C_shared) - T.copy( - prev_states[batch_idx, chunk_idx, bz, n_idx * block_N:(n_idx + 1) * block_N, - 0:block_Dstate], prev_state_shared) + C[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz // (nheads // ngroups), + 0:block_Dstate, + ], + C_shared, + ) + T.copy(prev_states[batch_idx, chunk_idx, bz, n_idx * block_N : (n_idx + 1) * block_N, 0:block_Dstate], prev_state_shared) T.gemm(C_shared, prev_state_shared, acc_o, transpose_B=True) for i, j in T.Parallel(block_M, block_N): acc_o[i, j] *= scale_m_local[i] @@ -165,34 +168,47 @@ def main( for k in T.Pipelined(loop_range, num_stages=num_stages): T.copy( - cb[batch_idx, chunk_idx, bz // (nheads // ngroups), - m_idx * block_M:(m_idx + 1) * block_M, k * block_K:(k + 1) * block_K], - cb_shared) + cb[ + batch_idx, + chunk_idx, + bz // (nheads // ngroups), + m_idx * block_M : (m_idx + 1) * block_M, + k * block_K : (k + 1) * block_K, + ], + cb_shared, + ) T.copy(cb_shared, cb_local) - T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], - dA_cs_k_shared) + T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dA_cs_k_shared) T.copy(dA_cs_k_shared, dA_cs_k_local) for i, j in T.Parallel(block_M, block_K): - cb_local[i, - j] = cb_local[i, - j] * T.exp2(dA_cs_m_local[i] * p - dA_cs_k_local[j] * p) - T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared) + cb_local[i, j] = cb_local[i, j] * T.exp2(dA_cs_m_local[i] * p - dA_cs_k_local[j] * p) + T.copy(dt[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dt_shared) T.copy(dt_shared, dt_local) for i, j in T.Parallel(block_M, block_K): cb_local[i, j] *= dt_local[j] for i, j in T.Parallel(block_M, block_K): - cb_local[i, j] = T.if_then_else(m_idx * block_M + i >= k * block_K + j, - cb_local[i, j], 0) + cb_local[i, j] = T.if_then_else(m_idx * block_M + i >= k * block_K + j, cb_local[i, j], 0) T.copy( - x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size + - (k + 1) * block_K, bz, n_idx * block_N:(n_idx + 1) * block_N], x_shared) + x[ + batch_idx, + chunk_idx * chunk_size + k * block_K : chunk_idx * chunk_size + (k + 1) * block_K, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + x_shared, + ) T.gemm(cb_local, x_shared, acc_o) D_local[0] = D[bz] T.copy( - x[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N], - x_residual_shared) + x[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + x_residual_shared, + ) T.copy(x_residual_shared, x_residual_local) for i, j in T.Parallel(block_M, block_N): acc_o[i, j] += x_residual_local[i, j] * D_local[0] @@ -200,27 +216,40 @@ def main( T.copy(acc_o, acc_o_shared) T.copy( acc_o_shared, - Output[batch_idx, chunk_idx * chunk_size + m_idx * block_M:chunk_idx * chunk_size + - (m_idx + 1) * block_M, bz, n_idx * block_N:(n_idx + 1) * block_N]) + Output[ + batch_idx, + chunk_idx * chunk_size + m_idx * block_M : chunk_idx * chunk_size + (m_idx + 1) * block_M, + bz, + n_idx * block_N : (n_idx + 1) * block_N, + ], + ) return main if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=80, help='heads') - parser.add_argument('--groups', type=int, default=1, help='groups') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--chunk_size', type=int, default=256, help='chunk size') - parser.add_argument('--dim', type=int, default=64, help='dim') - parser.add_argument('--dstate', type=int, default=128, help='dstate') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=80, help="heads") + parser.add_argument("--groups", type=int, default=1, help="groups") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--chunk_size", type=int, default=256, help="chunk size") + parser.add_argument("--dim", type=int, default=64, help="dim") + parser.add_argument("--dstate", type=int, default=128, help="dstate") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - batch, heads, groups, seq_len, chunk_size, dim, dstate = args.batch, args.heads, args.groups, args.seq_len, args.chunk_size, args.dim, args.dstate + batch, heads, groups, seq_len, chunk_size, dim, dstate = ( + args.batch, + args.heads, + args.groups, + args.seq_len, + args.chunk_size, + args.dim, + args.dstate, + ) total_flops = 2 * batch * seq_len * chunk_size * heads * dim * 0.5 + 2 * batch * seq_len * heads * dim * dstate - if (not args.tune): + if not args.tune: kernel = chunk_scan_fwd( batch, seq_len, @@ -234,7 +263,8 @@ def main( block_K=64, block_Dstate=128, num_stages=2, - threads=128) + threads=128, + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Normal) profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) print("All checks pass.") diff --git a/examples/linear_attention/example_mamba_chunk_state.py b/examples/linear_attention/example_mamba_chunk_state.py index ad3df0df81..96126889bd 100644 --- a/examples/linear_attention/example_mamba_chunk_state.py +++ b/examples/linear_attention/example_mamba_chunk_state.py @@ -10,6 +10,7 @@ def chunk_state_triton(B, x, dt, dA_cumsum): from mamba_ssm.ops.triton.ssd_chunk_state import _chunk_state_fwd + return _chunk_state_fwd(B, x, dt, dA_cumsum, states_in_fp32=False) @@ -41,46 +42,33 @@ def ref_program(B, x, dt, dA_cumsum): x = rearrange(x, "b (c l) h p -> b c l h p", l=chunk_size) B = rearrange(B, "b (c l) ... -> b c l ...", l=chunk_size) decay_states = torch.exp((dA_cumsum[:, :, :, -1:] - dA_cumsum)) - return torch.einsum("bclhn,bhcl,bhcl,bclhp->bchpn", B.to(x.dtype), decay_states.to(x.dtype), - dt.to(x.dtype), x) + return torch.einsum("bclhn,bhcl,bhcl,bclhp->bchpn", B.to(x.dtype), decay_states.to(x.dtype), dt.to(x.dtype), x) def get_configs(): - iter_params = dict( - block_M=[64, 128], block_N=[32, 64, 128], block_K=[32, 64], num_stages=[1, 2, 3, 4, 5]) + iter_params = dict(block_M=[64, 128], block_N=[32, 64, 128], block_K=[32, 64], num_stages=[1, 2, 3, 4, 5]) return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] @autotune(configs=get_configs(), warmup=10, rep=10) @tilelang.jit(out_idx=[4]) -def chunk_state_fwd(batch, - seqlen, - chunk_size, - ngroups, - nheads, - headdim, - dstate, - block_M=64, - block_N=64, - block_K=64, - num_stages=2, - threads=128): - dtype = "float16" - accum_dtype = "float" +def chunk_state_fwd( + batch, seqlen, chunk_size, ngroups, nheads, headdim, dstate, block_M=64, block_N=64, block_K=64, num_stages=2, threads=128 +): + dtype = T.float16 + accum_dtype = T.float32 nchunks = T.ceildiv(seqlen, chunk_size) p = 1.44269504 @T.prim_func - def main(B: T.Tensor((batch, seqlen, ngroups, dstate), dtype), x: T.Tensor( - (batch, seqlen, nheads, headdim), dtype), dt: T.Tensor( - (batch, nheads, nchunks, chunk_size), dtype), dA_cumsum: T.Tensor( - (batch, nheads, nchunks, chunk_size), dtype), Output: T.Tensor( - (batch, nchunks, nheads, headdim, dstate), dtype)): - with T.Kernel( - nheads, - T.ceildiv(headdim, block_M) * T.ceildiv(dstate, block_N), - batch * nchunks, - threads=threads) as (bz, bx, by): + def main( + B: T.Tensor((batch, seqlen, ngroups, dstate), dtype), + x: T.Tensor((batch, seqlen, nheads, headdim), dtype), + dt: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), + dA_cumsum: T.Tensor((batch, nheads, nchunks, chunk_size), dtype), + Output: T.Tensor((batch, nchunks, nheads, headdim, dstate), dtype), + ): + with T.Kernel(nheads, T.ceildiv(headdim, block_M) * T.ceildiv(dstate, block_N), batch * nchunks, threads=threads) as (bz, bx, by): x_shared = T.alloc_shared((block_K, block_M), dtype) x_local = T.alloc_fragment((block_K, block_M), dtype) xt_local = T.alloc_fragment((block_M, block_K), dtype) @@ -101,20 +89,22 @@ def main(B: T.Tensor((batch, seqlen, ngroups, dstate), dtype), x: T.Tensor( m_idx = bx // T.ceildiv(dstate, block_N) n_idx = bx % T.ceildiv(dstate, block_N) - T.annotate_layout({ - x_shared: tilelang.layout.make_swizzled_layout(x_shared), - acc_o_shared: tilelang.layout.make_swizzled_layout(acc_o_shared) - }) + T.annotate_layout({x_shared: tilelang.layout.make_swizzled_layout(x_shared)}) dA_cs_last[0] = dA_cumsum[batch_idx, bz, chunk_idx, chunk_size - 1] T.clear(acc_o) for k in T.Pipelined(loop_range, num_stages=num_stages): T.copy( - x[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size + - (k + 1) * block_K, bz, m_idx * block_M:(m_idx + 1) * block_M], x_shared) - T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], - dA_cumsum_shared) - T.copy(dt[batch_idx, bz, chunk_idx, k * block_K:(k + 1) * block_K], dt_shared) + x[ + batch_idx, + chunk_idx * chunk_size + k * block_K : chunk_idx * chunk_size + (k + 1) * block_K, + bz, + m_idx * block_M : (m_idx + 1) * block_M, + ], + x_shared, + ) + T.copy(dA_cumsum[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dA_cumsum_shared) + T.copy(dt[batch_idx, bz, chunk_idx, k * block_K : (k + 1) * block_K], dt_shared) T.copy(dA_cumsum_shared, dA_cumsum_local) T.copy(dt_shared, dt_local) for i in T.Parallel(block_K): @@ -123,47 +113,50 @@ def main(B: T.Tensor((batch, seqlen, ngroups, dstate), dtype), x: T.Tensor( for i, j in T.Parallel(block_M, block_K): xt_local[i, j] = x_local[j, i] * scale[j] T.copy( - B[batch_idx, chunk_idx * chunk_size + k * block_K:chunk_idx * chunk_size + - (k + 1) * block_K, bz // (nheads // ngroups), - n_idx * block_N:(n_idx + 1) * block_N], B_shared) + B[ + batch_idx, + chunk_idx * chunk_size + k * block_K : chunk_idx * chunk_size + (k + 1) * block_K, + bz // (nheads // ngroups), + n_idx * block_N : (n_idx + 1) * block_N, + ], + B_shared, + ) T.gemm(xt_local, B_shared, acc_o) T.copy(acc_o, acc_o_shared) T.copy( acc_o_shared, - Output[batch_idx, chunk_idx, bz, m_idx * block_M:(m_idx + 1) * block_M, - n_idx * block_N:(n_idx + 1) * block_N]) + Output[batch_idx, chunk_idx, bz, m_idx * block_M : (m_idx + 1) * block_M, n_idx * block_N : (n_idx + 1) * block_N], + ) return main if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=8, help='batch size') - parser.add_argument('--heads', type=int, default=80, help='heads') - parser.add_argument('--groups', type=int, default=1, help='groups') - parser.add_argument('--seq_len', type=int, default=4096, help='sequence length') - parser.add_argument('--chunk_size', type=int, default=256, help='chunk size') - parser.add_argument('--dim', type=int, default=64, help='dim') - parser.add_argument('--dstate', type=int, default=128, help='dstate') - parser.add_argument('--tune', action='store_true', help='tune configs') + parser.add_argument("--batch", type=int, default=8, help="batch size") + parser.add_argument("--heads", type=int, default=80, help="heads") + parser.add_argument("--groups", type=int, default=1, help="groups") + parser.add_argument("--seq_len", type=int, default=4096, help="sequence length") + parser.add_argument("--chunk_size", type=int, default=256, help="chunk size") + parser.add_argument("--dim", type=int, default=64, help="dim") + parser.add_argument("--dstate", type=int, default=128, help="dstate") + parser.add_argument("--tune", action="store_true", help="tune configs") args = parser.parse_args() - batch, heads, groups, seq_len, chunk_size, dim, dstate = args.batch, args.heads, args.groups, args.seq_len, args.chunk_size, args.dim, args.dstate + batch, heads, groups, seq_len, chunk_size, dim, dstate = ( + args.batch, + args.heads, + args.groups, + args.seq_len, + args.chunk_size, + args.dim, + args.dstate, + ) total_flops = 2 * batch * seq_len * heads * dim * dstate - if (not args.tune): + if not args.tune: kernel = chunk_state_fwd( - batch, - seq_len, - chunk_size, - groups, - heads, - dim, - dstate, - block_M=64, - block_N=128, - block_K=64, - num_stages=4, - threads=128) + batch, seq_len, chunk_size, groups, heads, dim, dstate, block_M=64, block_N=128, block_K=64, num_stages=4, threads=128 + ) profiler = kernel.get_profiler(tilelang.TensorSupplyType.Normal) profiler.assert_allclose(ref_program, rtol=0.01, atol=0.01) print("All checks pass.") diff --git a/examples/linear_attention/example_retention_fwd.py b/examples/linear_attention/example_retention_fwd.py index 66012e0c1e..f45e383889 100644 --- a/examples/linear_attention/example_retention_fwd.py +++ b/examples/linear_attention/example_retention_fwd.py @@ -13,13 +13,12 @@ def chunk_retention_fwd_kernel( H, DK, DV, - dtype: str = 'float16', + dtype: T.dtype = T.float16, scale: float = None, ) -> torch.Tensor: - if scale is None: scale = DK**-0.5 - accum_dtype = 'float' + accum_dtype = T.float32 chunk_size = 64 BK = BV = 64 # Set to 128 can be faster, but has some numerical differences with FLA @@ -30,16 +29,16 @@ def chunk_retention_fwd_kernel( @T.prim_func def chunk_retention_fwd( - Q: T.Tensor([B, S, H, DK], dtype), # type: ignore - K: T.Tensor([B, S, H, DK], dtype), # type: ignore - V: T.Tensor([B, S, H, DV], dtype), # type: ignore - O: T.Tensor([NK, B, S, H, DV], dtype), # type: ignore + Q: T.Tensor([B, S, H, DK], dtype), # type: ignore + K: T.Tensor([B, S, H, DK], dtype), # type: ignore + V: T.Tensor([B, S, H, DV], dtype), # type: ignore + O: T.Tensor([NK, B, S, H, DV], dtype), # type: ignore ): with T.Kernel(NV, NK, B * H) as (i_v, i_k, i_bh): i_b = i_bh // H i_h = i_bh % H - log_decay = T.alloc_var('float32') - log_decay = T.log2(1 - T.exp2(-5. - 1. * i_h)) # Head-specific log decay + log_decay = T.alloc_var(T.float32) + log_decay = T.log2(1 - T.exp2(-5.0 - 1.0 * i_h)) # Head-specific log decay q = T.alloc_shared([chunk_size, BK], dtype) k = T.alloc_shared([chunk_size, BK], dtype) @@ -51,26 +50,17 @@ def chunk_retention_fwd( o = T.alloc_fragment([chunk_size, BV], accum_dtype) T.clear(h) - T.annotate_layout({ - q: tl.layout.make_swizzled_layout(q), - k: tl.layout.make_swizzled_layout(k), - v: tl.layout.make_swizzled_layout(v), - h_shared: tl.layout.make_swizzled_layout(h_shared), - s_shared: tl.layout.make_swizzled_layout(s_shared), - }) T.use_swizzle(10) for i in T.Pipelined(0, NT): for row, col in T.Parallel(chunk_size, BK): q[row, col] = Q[i_b, i * chunk_size + row, i_h, i_k * BK + col] * scale - T.copy(K[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_k * BK:(i_k + 1) * BK], k) - T.copy(V[i_b, i * chunk_size:(i + 1) * chunk_size, i_h, i_v * BV:(i_v + 1) * BV], v) + T.copy(K[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_k * BK : (i_k + 1) * BK], k) + T.copy(V[i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV], v) T.gemm(q, k, s, clear_accum=True, transpose_B=True) for row, col in T.Parallel(chunk_size, chunk_size): - s_shared[row, - col] = T.if_then_else(row >= col, s[row, col] * T.exp2( - (row - col) * log_decay), 0) + s_shared[row, col] = T.if_then_else(row >= col, s[row, col] * T.exp2((row - col) * log_decay), 0) T.copy(h, h_shared) T.gemm(q, h_shared, o, clear_accum=True) @@ -82,9 +72,7 @@ def chunk_retention_fwd( v[row, col] = v[row, col] * T.exp2((chunk_size - row - 1) * log_decay) for row, col in T.Parallel(BK, BV): h[row, col] = T.exp2(chunk_size * log_decay) * h[row, col] - T.copy( - o, O[i_k, i_b, i * chunk_size:(i + 1) * chunk_size, i_h, - i_v * BV:(i_v + 1) * BV]) + T.copy(o, O[i_k, i_b, i * chunk_size : (i + 1) * chunk_size, i_h, i_v * BV : (i_v + 1) * BV]) T.gemm(k, v, h, transpose_A=True) return chunk_retention_fwd @@ -96,24 +84,24 @@ def postprocess(o): def main(): parser = argparse.ArgumentParser() - parser.add_argument('--B', type=int, default=8, help='Batch size') - parser.add_argument('--S', type=int, default=4096, help='Seq len') - parser.add_argument('--H', type=int, default=32, help='Num heads') - parser.add_argument('--D', type=int, default=128, help='Head dim') + parser.add_argument("--B", type=int, default=8, help="Batch size") + parser.add_argument("--S", type=int, default=4096, help="Seq len") + parser.add_argument("--H", type=int, default=32, help="Num heads") + parser.add_argument("--D", type=int, default=128, help="Head dim") args = parser.parse_args() B, S, H, D = args.B, args.S, args.H, args.D total_flops = 2.0 * B * S * S * H * D # causal - q = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) - k = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) - v = torch.randn((B, S, H, D), device='cuda', dtype=torch.float16) + q = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + k = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) + v = torch.randn((B, S, H, D), device="cuda", dtype=torch.float16) kernel = chunk_retention_fwd_kernel(B, S, H, D, D) t = do_bench(lambda: postprocess(kernel(q, k, v)), warmup=25, rep=100) - print(f'Tilelang latency: {t:.3f} ms') - print(f'Tilelang TFLOPs: {total_flops/t * 1e-9}') + print(f"Tilelang latency: {t:.3f} ms") + print(f"Tilelang TFLOPs: {total_flops / t * 1e-9}") -if __name__ == '__main__': +if __name__ == "__main__": main() diff --git a/examples/linear_attention/regression_linear_attn.py b/examples/linear_attention/regression_linear_attn.py new file mode 100644 index 0000000000..ced8540870 --- /dev/null +++ b/examples/linear_attention/regression_linear_attn.py @@ -0,0 +1,15 @@ +import tilelang.testing +import example_linear_attn_bwd +import example_linear_attn_fwd + + +def regression_example_linear_attn_fwd(): + tilelang.testing.process_func(example_linear_attn_fwd.run_regression_perf) + + +def regression_example_linear_attn_bwd(): + tilelang.testing.process_func(example_linear_attn_bwd.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/minference/example_vertical_slash_sparse_attn.py b/examples/minference/example_vertical_slash_sparse_attn.py index ebf8513a1b..91af8b454a 100644 --- a/examples/minference/example_vertical_slash_sparse_attn.py +++ b/examples/minference/example_vertical_slash_sparse_attn.py @@ -15,12 +15,11 @@ @tilelang.jit(out_idx=[3]) def _tl_vs_sparse_flashattn(batch, heads, seq_len, dim, vertical_size, slash_size): - block_M = 64 block_N = 64 num_stages = 2 threads = 128 - scale = (1.0 / dim)**0.5 * 1.44269504 + scale = (1.0 / dim) ** 0.5 * 1.44269504 shape = [batch, heads, seq_len, dim] seq_blocks = (seq_len + block_M - 1) // block_M @@ -30,15 +29,13 @@ def _tl_vs_sparse_flashattn(batch, heads, seq_len, dim, vertical_size, slash_siz offset_shape = count_shape + [slash_size] index_shape = count_shape + [vertical_size] - vertical_size_round, slash_size_round = tilelang.next_power_of_2( - vertical_size), tilelang.next_power_of_2(slash_size) + vertical_size_round, slash_size_round = tilelang.next_power_of_2(vertical_size), tilelang.next_power_of_2(slash_size) - dtype = "float16" - accum_dtype = "float" - int_dtype = "int32" + dtype = T.float16 + accum_dtype = T.float32 + int_dtype = T.int32 def kernel_func(block_M, block_N, num_stages, threads): - @T.macro def Prefetch( K: T.Tensor(shape, dtype), @@ -53,32 +50,30 @@ def Prefetch( ): with T.attr("default", "async_scope", 1): for i, j in T.Parallel(block_N, dim): - K_shared[i, j] = T.if_then_else(k + i < column_count, - K[bz, by, column_index[k + i], j], 0) + K_shared[i, j] = T.if_then_else(k + i < column_count, K[bz, by, column_index[k + i], j], 0) with T.attr("default", "async_scope", 1): for i, j in T.Parallel(block_N, dim): - V_shared[i, j] = T.if_then_else(k + i < column_count, - V[bz, by, column_index[k + i], j], 0) + V_shared[i, j] = T.if_then_else(k + i < column_count, V[bz, by, column_index[k + i], j], 0) T.ptx_commit_group() @T.macro def Compute( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - k: T.int32, - column_count: T.int32, - Q_shared: T.SharedBuffer([block_M, dim], dtype), - K_shared: T.SharedBuffer([block_N, dim], dtype), - V_shared: T.SharedBuffer([block_N, dim], dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - count: T.int32, + acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), + acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), + acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), + scores_max: T.FragmentBuffer([block_M], accum_dtype), + scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), + k: T.int32, + column_count: T.int32, + Q_shared: T.SharedBuffer([block_M, dim], dtype), + K_shared: T.SharedBuffer([block_N, dim], dtype), + V_shared: T.SharedBuffer([block_N, dim], dtype), + scores_scale: T.FragmentBuffer([block_M], accum_dtype), + scores_sum: T.FragmentBuffer([block_M], accum_dtype), + logsum: T.FragmentBuffer([block_M], accum_dtype), + count: T.int32, ): T.ptx_wait_group(count) for i, j in T.Parallel(block_M, block_N): @@ -87,6 +82,8 @@ def Compute( T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) @@ -106,17 +103,16 @@ def Compute( @T.prim_func def vs_sparse_flashattn_ws( - Q: T.Tensor(shape, dtype), - K: T.Tensor(shape, dtype), - V: T.Tensor(shape, dtype), - Output: T.Tensor(shape, dtype), - BlockCount: T.Tensor(count_shape, int_dtype), - BlockOffset: T.Tensor(offset_shape, int_dtype), - ColumnCount: T.Tensor(count_shape, int_dtype), - ColumnIndex: T.Tensor(index_shape, int_dtype), + Q: T.Tensor(shape, dtype), + K: T.Tensor(shape, dtype), + V: T.Tensor(shape, dtype), + Output: T.Tensor(shape, dtype), + BlockCount: T.Tensor(count_shape, int_dtype), + BlockOffset: T.Tensor(offset_shape, int_dtype), + ColumnCount: T.Tensor(count_shape, int_dtype), + ColumnIndex: T.Tensor(index_shape, int_dtype), ): with T.Kernel(T.ceildiv(seq_len, block_M), heads, batch, threads=256) as (bc, by, bz): - bx = T.ceildiv(seq_len, block_M) - 1 - bc Q_shared = T.alloc_shared([block_M, dim], dtype) K_shared = T.alloc_shared([2, block_N, dim], dtype) @@ -134,19 +130,15 @@ def vs_sparse_flashattn_ws( scores_scale = T.alloc_fragment([block_M], accum_dtype) scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - block_count = T.alloc_local([1], int_dtype) + block_count = T.alloc_var(dtype=int_dtype) block_offset = T.alloc_shared([slash_size_round], int_dtype, scope="shared") - column_count = T.alloc_local([1], int_dtype) + column_count = T.alloc_var(dtype=int_dtype) column_index = T.alloc_shared([vertical_size_round], int_dtype, scope="shared") T.create_list_of_mbarrier([128] * 9) - T.annotate_layout({ - O_shared: tilelang.layout.make_swizzled_layout(O_shared), - }) - - block_count[0] = BlockCount[bz, by, bx] - column_count[0] = ColumnCount[bz, by, bx] + block_count = BlockCount[bz, by, bx] + column_count = ColumnCount[bz, by, bx] for vi in T.Parallel(slash_size_round): if vi < slash_size: @@ -160,15 +152,15 @@ def vs_sparse_flashattn_ws( if tid >= 128: T.annotate_producer_reg_dealloc() - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.mbarrier_arrive(mbarrier=8) - for bi in T.serial(block_count[0]): + for bi in T.serial(block_count): k = block_offset[bi] T.mbarrier_wait_parity(mbarrier=bi % 2 + 4, parity=(((bi & 3) >> 1) ^ 1)) - T.copy(K[bz, by, k:k + block_N, :], K_shared[bi % 2, :, :]) + T.copy(K[bz, by, k : k + block_N, :], K_shared[bi % 2, :, :]) T.mbarrier_arrive(mbarrier=bi % 2) T.mbarrier_wait_parity(mbarrier=bi % 2 + 6, parity=(((bi & 3) >> 1) ^ 1)) - T.copy(V[bz, by, k:k + block_N, :], V_shared[bi % 2, :, :]) + T.copy(V[bz, by, k : k + block_N, :], V_shared[bi % 2, :, :]) T.mbarrier_arrive(mbarrier=bi % 2 + 2) else: T.annotate_consumer_reg_alloc() @@ -176,40 +168,31 @@ def vs_sparse_flashattn_ws( T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) T.mbarrier_wait_parity(mbarrier=8, parity=0) - for bi in T.serial(block_count[0]): + for bi in T.serial(block_count): k = block_offset[bi] for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else(bx * block_M + i >= k + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i >= k + j, 0, -T.infinity(acc_s.dtype)) T.mbarrier_wait_parity(mbarrier=bi % 2, parity=((bi & 3) >> 1)) - T.gemm( - Q_shared, - K_shared[bi % 2, :, :], - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) + T.gemm(Q_shared, K_shared[bi % 2, :, :], acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) T.mbarrier_arrive(mbarrier=bi % 2 + 4) T.copy(scores_max, scores_max_prev) T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - - scores_max[i] * scale) + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, block_N): acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) for i, j in T.Parallel(block_M, dim): acc_o[i, j] = acc_o[i, j] * scores_scale[i] T.copy(acc_s, acc_s_cast) - T.mbarrier_wait_parity(mbarrier=bi % 2 + 2, parity=(((bi & 3) >> 1))) - T.gemm( - acc_s_cast, - V_shared[bi % 2, :, :], - acc_o, - policy=T.GemmWarpPolicy.FullRow) + T.mbarrier_wait_parity(mbarrier=bi % 2 + 2, parity=((bi & 3) >> 1)) + T.gemm(acc_s_cast, V_shared[bi % 2, :, :], acc_o, policy=T.GemmWarpPolicy.FullRow) T.mbarrier_arrive(mbarrier=bi % 2 + 6) @@ -218,39 +201,86 @@ def vs_sparse_flashattn_ws( for i in T.Parallel(block_M): logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - if column_count[0] != 0: - Prefetch(K, V, K_shared_1, V_shared_1, column_index, column_count[0], 0, bz, - by) - for bi in T.serial(T.ceildiv(column_count[0], block_N) - 1): + if column_count != 0: + Prefetch(K, V, K_shared_1, V_shared_1, column_index, column_count, 0, bz, by) + for bi in T.serial(T.ceildiv(column_count, block_N) - 1): k = bi * block_N if bi % 2 == 0: - Prefetch(K, V, K_shared_2, V_shared_2, column_index, - column_count[0], k + block_N, bz, by) - - Compute(acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, k, - column_count[0], Q_shared, K_shared_1, V_shared_1, - scores_scale, scores_sum, logsum, 1) + Prefetch(K, V, K_shared_2, V_shared_2, column_index, column_count, k + block_N, bz, by) + + Compute( + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + k, + column_count, + Q_shared, + K_shared_1, + V_shared_1, + scores_scale, + scores_sum, + logsum, + 1, + ) else: - Prefetch(K, V, K_shared_1, V_shared_1, column_index, - column_count[0], k + block_N, bz, by) - - Compute(acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, k, - column_count[0], Q_shared, K_shared_2, V_shared_2, - scores_scale, scores_sum, logsum, 1) - if T.ceildiv(column_count[0], block_N) % 2 == 0: - Compute(acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, - T.ceildiv(column_count[0], block_N) * block_N - block_N, - column_count[0], Q_shared, K_shared_2, V_shared_2, scores_scale, - scores_sum, logsum, 0) + Prefetch(K, V, K_shared_1, V_shared_1, column_index, column_count, k + block_N, bz, by) + + Compute( + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + k, + column_count, + Q_shared, + K_shared_2, + V_shared_2, + scores_scale, + scores_sum, + logsum, + 1, + ) + if T.ceildiv(column_count, block_N) % 2 == 0: + Compute( + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + T.ceildiv(column_count, block_N) * block_N - block_N, + column_count, + Q_shared, + K_shared_2, + V_shared_2, + scores_scale, + scores_sum, + logsum, + 0, + ) else: - Compute(acc_s, acc_s_cast, acc_o, scores_max, scores_max_prev, - T.ceildiv(column_count[0], block_N) * block_N - block_N, - column_count[0], Q_shared, K_shared_1, V_shared_1, scores_scale, - scores_sum, logsum, 0) + Compute( + acc_s, + acc_s_cast, + acc_o, + scores_max, + scores_max_prev, + T.ceildiv(column_count, block_N) * block_N - block_N, + column_count, + Q_shared, + K_shared_1, + V_shared_1, + scores_scale, + scores_sum, + logsum, + 0, + ) for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return vs_sparse_flashattn_ws @@ -466,11 +496,8 @@ def vertical_slash_sparse_attention( import os current_dir = os.path.dirname(os.path.abspath(__file__)) - sources = [ - os.path.join(current_dir, 'ops', 'kernels.cpp'), - os.path.join(current_dir, 'ops', 'vertical_slash_index.cu') - ] - ops = load(name='convert', sources=sources, verbose=False) + sources = [os.path.join(current_dir, "ops", "kernels.cpp"), os.path.join(current_dir, "ops", "vertical_slash_index.cu")] + ops = load(name="convert", sources=sources, verbose=False) convert_vertical_slash_indexes = ops.convert_vertical_slash_indexes batch_size, num_heads, context_size, head_dim = query.shape pad = (block_size_M - context_size) & (block_size_M - 1) @@ -481,15 +508,13 @@ def vertical_slash_sparse_attention( value = torch.nn.functional.pad(value, [0, 0, 0, pad, 0, 0, 0, 0]) if head_dim not in [16, 32, 64, 128, 256, 512]: - target_dim = 2**math.ceil(math.log2(head_dim)) - head_dim + target_dim = 2 ** math.ceil(math.log2(head_dim)) - head_dim query = torch.nn.functional.pad(query, [0, target_dim, 0, 0, 0, 0, 0, 0]) key = torch.nn.functional.pad(key, [0, target_dim, 0, 0, 0, 0, 0, 0]) value = torch.nn.functional.pad(value, [0, target_dim, 0, 0, 0, 0, 0, 0]) - v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort( - dim=-1, descending=False)[0] - s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort( - dim=-1, descending=True)[0] + v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0] + s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0] seqlens = torch.tensor([context_size] * query.shape[0], dtype=torch.int32, device=query.device) sm_scale = head_dim**-0.5 @@ -502,8 +527,7 @@ def vertical_slash_sparse_attention( block_size_N, ) - tl_kernel = _tl_vs_sparse_flashattn(batch_size, num_heads, context_size, head_dim, - v_idx.shape[2], s_idx.shape[2]) + tl_kernel = _tl_vs_sparse_flashattn(batch_size, num_heads, context_size, head_dim, v_idx.shape[2], s_idx.shape[2]) def run(is_triton: bool = True): if is_triton: @@ -521,8 +545,7 @@ def run(is_triton: bool = True): block_size_N, ) else: - out = tl_kernel(query, key, value, block_count, block_offset, column_count, - column_index) + out = tl_kernel(query, key, value, block_count, block_offset, column_count, column_index) return out[..., :context_size, :head_dim] return run @@ -532,8 +555,7 @@ def sum_all_diagonal_matrix(mat: torch.tensor): b, h, n, m = mat.shape zero_mat = torch.zeros((b, h, n, n)).to(mat.device) # Zero matrix used for padding mat_padded = torch.cat((zero_mat, mat, zero_mat), -1) # pads the matrix on left and right - mat_strided = mat_padded.as_strided( - (1, 1, n, n + m), (1, n * (2 * n + m), 2 * n + m + 1, 1)) # Change the strides + mat_strided = mat_padded.as_strided((1, 1, n, n + m), (1, n * (2 * n + m), 2 * n + m + 1, 1)) # Change the strides sum_diags = torch.sum(mat_strided, 2) # Sums the resulting matrix's columns return sum_diags[:, :, 1:] @@ -555,24 +577,23 @@ def main(argv=None): vertical_size, slash_size = args.vertical_size, args.slash_size torch.manual_seed(0) - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) q_len = SEQ_LEN vertical_size, slash_size = min(q_len, vertical_size), min(q_len, slash_size) last_q = 64 - qk = torch.einsum('bhmk, bhnk -> bhmn', q[:, :, -last_q:, :], k) + qk = torch.einsum("bhmk, bhnk -> bhmn", q[:, :, -last_q:, :], k) arange = torch.arange(last_q, device="cuda") - qk[:, :, :, -last_q:] = torch.where(arange[None, None, :, None] >= arange[None, None, None, :], - qk[:, :, :, -last_q:], -torch.inf) + qk[:, :, :, -last_q:] = torch.where(arange[None, None, :, None] >= arange[None, None, None, :], qk[:, :, :, -last_q:], -torch.inf) qk = torch.nn.functional.softmax(qk, dim=-1, dtype=torch.float32) vertical = qk.sum(-2, keepdim=True) vertical[..., :30] = torch.inf vertical_topk = torch.topk(vertical, vertical_size, -1).indices - slash = sum_all_diagonal_matrix(qk)[..., :-last_q + 1] + slash = sum_all_diagonal_matrix(qk)[..., : -last_q + 1] slash[..., -30:] = torch.inf slash = (q_len - 1) - torch.topk(slash, slash_size, -1).indices @@ -592,5 +613,78 @@ def main(argv=None): print(f"speedup: {triton_time / tilelang_time:.2f}x") +def run_regression_perf(argv=None): + parser = argparse.ArgumentParser() + parser.add_argument("--batch", type=int, default=1) + parser.add_argument("--heads", type=int, default=1) + parser.add_argument("--seq_len", type=int, default=16384) + parser.add_argument("--head_dim", type=int, default=64) + parser.add_argument("--vertical_size", type=int, default=1000) + parser.add_argument("--slash_size", type=int, default=200) + args = parser.parse_args(argv) + BATCH, N_HEADS, SEQ_LEN, D_HEAD = args.batch, args.heads, args.seq_len, args.head_dim + vertical_size, slash_size = args.vertical_size, args.slash_size + torch.manual_seed(0) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + q_len = SEQ_LEN + vertical_size, slash_size = min(q_len, vertical_size), min(q_len, slash_size) + last_q = 64 + qk = torch.einsum("bhmk, bhnk -> bhmn", q[:, :, -last_q:, :], k) + arange = torch.arange(last_q, device="cuda") + qk[:, :, :, -last_q:] = torch.where(arange[None, None, :, None] >= arange[None, None, None, :], qk[:, :, :, -last_q:], -torch.inf) + qk = torch.nn.functional.softmax(qk, dim=-1, dtype=torch.float32) + vertical = qk.sum(-2, keepdim=True) + vertical[..., :30] = torch.inf + vertical_topk = torch.topk(vertical, vertical_size, -1).indices + slash = sum_all_diagonal_matrix(qk)[..., : -last_q + 1] + slash[..., -30:] = torch.inf + slash = (q_len - 1) - torch.topk(slash, slash_size, -1).indices + block_size_M = 64 + block_size_N = 64 + query, key, value = q, k, v + v_idx, s_idx = vertical_topk, slash + batch_size, num_heads, context_size, head_dim = query.shape + v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0] + s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0] + from torch.utils.cpp_extension import load + import os + + current_dir = os.path.dirname(os.path.abspath(__file__)) + sources = [os.path.join(current_dir, "ops", "kernels.cpp"), os.path.join(current_dir, "ops", "vertical_slash_index.cu")] + ops = load(name="convert", sources=sources, verbose=False) + convert_vertical_slash_indexes = ops.convert_vertical_slash_indexes + batch_size, num_heads, context_size, head_dim = query.shape + pad = (block_size_M - context_size) & (block_size_M - 1) + if pad == block_size_M: + pad = 0 + query = torch.nn.functional.pad(query, [0, 0, 0, pad, 0, 0, 0, 0]) + key = torch.nn.functional.pad(key, [0, 0, 0, pad, 0, 0, 0, 0]) + value = torch.nn.functional.pad(value, [0, 0, 0, pad, 0, 0, 0, 0]) + if head_dim not in [16, 32, 64, 128, 256, 512]: + target_dim = 2 ** math.ceil(math.log2(head_dim)) - head_dim + query = torch.nn.functional.pad(query, [0, target_dim, 0, 0, 0, 0, 0, 0]) + key = torch.nn.functional.pad(key, [0, target_dim, 0, 0, 0, 0, 0, 0]) + value = torch.nn.functional.pad(value, [0, target_dim, 0, 0, 0, 0, 0, 0]) + v_idx = v_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=False)[0] + s_idx = s_idx.to(torch.int32).reshape((batch_size, num_heads, -1)).sort(dim=-1, descending=True)[0] + seqlens = torch.tensor([context_size] * query.shape[0], dtype=torch.int32, device=query.device) + block_count, block_offset, column_count, column_index = convert_vertical_slash_indexes( + seqlens, + v_idx, + s_idx, + context_size, + block_size_M, + block_size_N, + ) + tl_kernel = _tl_vs_sparse_flashattn(batch_size, num_heads, context_size, head_dim, vertical_topk.shape[-1], slash.shape[-1]) + + def run_kernel_only(): + tl_kernel(query, key, value, block_count, block_offset, column_count, column_index) + + return do_bench(run_kernel_only, backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/minference/regression_vs_sparse_attn.py b/examples/minference/regression_vs_sparse_attn.py new file mode 100644 index 0000000000..32fdfa9e80 --- /dev/null +++ b/examples/minference/regression_vs_sparse_attn.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_vertical_slash_sparse_attn + + +def regression_example_vertical_slash_sparse_attn(): + tilelang.testing.process_func(example_vertical_slash_sparse_attn.run_regression_perf, argv=[]) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/norm/rms_norm.py b/examples/norm/rms_norm.py index 25bac50fca..57bccc1a0f 100644 --- a/examples/norm/rms_norm.py +++ b/examples/norm/rms_norm.py @@ -4,7 +4,7 @@ def rms_norm_splitk(M, N, blk_m, blk_k): - dtype = "float" + dtype = T.float @T.prim_func def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): @@ -21,7 +21,7 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): A_local[i, j] += A_shared[i, j] * A_shared[i, j] T.reduce_sum(A_local, A_powsum, dim=1) for i in T.Parallel(blk_m): - A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12 + A_powsum[i] = T.rsqrt(A_powsum[i] / N + 1e-12) for k in range(num_k_step): # reverse, better cache hit rate @@ -35,7 +35,7 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): @tilelang.jit(out_idx=[-1], pass_configs={"tl.disable_tma_lower": True}) def rms_norm(M, N, blk_m): - dtype = "float" + dtype = T.float @T.prim_func def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): @@ -45,16 +45,16 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): A_local = T.alloc_fragment((blk_m, N), dtype) A_powsum = T.alloc_fragment((blk_m,), dtype) - T.copy(A[bx * blk_m:(bx + 1) * blk_m, :], A_shared) + T.copy(A[bx * blk_m : (bx + 1) * blk_m, :], A_shared) T.copy(A_shared, A_local) for i, j in T.Parallel(blk_m, N): A_pow_local[i, j] = A_local[i, j] * A_local[i, j] T.reduce_sum(A_pow_local, A_powsum, dim=1) for i in T.Parallel(blk_m): - A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12 + A_powsum[i] = T.rsqrt(A_powsum[i] / N + 1e-12) for i, j in T.Parallel(blk_m, N): A_local[i, j] *= A_powsum[i] - T.copy(A_local, B[bx * blk_m:(bx + 1) * blk_m, :]) + T.copy(A_local, B[bx * blk_m : (bx + 1) * blk_m, :]) return main diff --git a/examples/norm/test_rms_norm.py b/examples/norm/test_rms_norm.py index 8cc4135318..53db03d98c 100644 --- a/examples/norm/test_rms_norm.py +++ b/examples/norm/test_rms_norm.py @@ -5,7 +5,7 @@ def rms_norm_splitk(M, N, blk_m, blk_k): - dtype = "float" + dtype = T.float @T.prim_func def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): @@ -22,7 +22,7 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): A_local[i, j] += A_shared[i, j] * A_shared[i, j] T.reduce_sum(A_local, A_powsum, dim=1) for i in T.Parallel(blk_m): - A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12 + A_powsum[i] = T.rsqrt(A_powsum[i] / N + 1e-12) for k in range(num_k_step): # reverse, better cache hit rate @@ -35,7 +35,7 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): def rms_norm(M, N, blk_m): - dtype = "float" + dtype = T.float @T.prim_func def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): @@ -45,16 +45,16 @@ def main(A: T.Tensor((M, N), dtype), B: T.Tensor((M, N), dtype)): A_local = T.alloc_fragment((blk_m, N), dtype) A_powsum = T.alloc_fragment((blk_m,), dtype) - T.copy(A[bx * blk_m:(bx + 1) * blk_m, :], A_shared) + T.copy(A[bx * blk_m : (bx + 1) * blk_m, :], A_shared) T.copy(A_shared, A_local) for i, j in T.Parallel(blk_m, N): A_pow_local[i, j] = A_local[i, j] * A_local[i, j] T.reduce_sum(A_pow_local, A_powsum, dim=1) for i in T.Parallel(blk_m): - A_powsum[i] = T.rsqrt(A_powsum[i] / N) + 1e-12 + A_powsum[i] = T.rsqrt(A_powsum[i] / N + 1e-12) for i, j in T.Parallel(blk_m, N): A_local[i, j] *= A_powsum[i] - T.copy(A_local, B[bx * blk_m:(bx + 1) * blk_m, :]) + T.copy(A_local, B[bx * blk_m : (bx + 1) * blk_m, :]) return main diff --git a/examples/online_softmax/online_softmax.py b/examples/online_softmax/online_softmax.py index 432482d063..811870e441 100644 --- a/examples/online_softmax/online_softmax.py +++ b/examples/online_softmax/online_softmax.py @@ -9,19 +9,19 @@ def softmax_kernel( M, N, - dtype: str = "float16", + dtype: T.dtype = T.float16, ) -> "Callable": BN = min(tl.next_power_of_2(N), 8192) NN = tl.cdiv(N, BN) - accum_dtype = "float" + accum_dtype = T.float32 scale = 1.44269504 # log2(e) @T.prim_func def main( - X: T.Tensor([M, N], dtype), - Y: T.Tensor([M, N], dtype), + X: T.Tensor([M, N], dtype), + Y: T.Tensor([M, N], dtype), ): with T.Kernel(M, threads=128) as (i_m): x = T.alloc_fragment([BN], dtype) @@ -33,7 +33,7 @@ def main( T.fill(lse, -T.infinity(accum_dtype)) for i_n in T.Pipelined(0, NN): - T.copy(X[i_m, i_n * BN:(i_n + 1) * BN], x) + T.copy(X[i_m, i_n * BN : (i_n + 1) * BN], x) T.reduce_max(x, max_x, dim=0, clear=True) @@ -45,12 +45,12 @@ def main( lse[0] = max_x[0] * scale + T.log2(T.exp2(lse[0] - max_x[0] * scale) + sum_exp_x[0]) for i_n in T.Pipelined(0, NN): - T.copy(X[i_m, i_n * BN:(i_n + 1) * BN], x) + T.copy(X[i_m, i_n * BN : (i_n + 1) * BN], x) for j in T.Parallel(BN): y[j] = T.exp2(x[j] * scale - lse[0]) - T.copy(y, Y[i_m, i_n * BN:(i_n + 1) * BN]) + T.copy(y, Y[i_m, i_n * BN : (i_n + 1) * BN]) return main @@ -69,4 +69,4 @@ def main( t2 = do_bench(lambda: kernel(X), warmup=25, rep=100) print(f"torch latency: {t1:.3f} ms") print(f"TileLang latency: {t2:.3f} ms") -print(f"Speedup: {t1/t2:.3f}x") +print(f"Speedup: {t1 / t2:.3f}x") diff --git a/examples/plot_layout/README.md b/examples/plot_layout/README.md index a65d771c20..8204e93d80 100644 --- a/examples/plot_layout/README.md +++ b/examples/plot_layout/README.md @@ -10,7 +10,7 @@ from typing import Literal, Callable from tilelang.intrinsics.utils import get_mma_micro_size from tilelang.tools import plot_layout -def make_mma_load_base_layout(dtype: str = "float16", +def make_mma_load_base_layout(dtype: str = T.float16, matrix: Literal["A", "B"] = "A", transposed: bool = False) -> T.Fragment: """ @@ -69,7 +69,7 @@ def make_mma_load_base_layout(dtype: str = "float16", micro_size_s, _, micro_size_r = get_mma_micro_size(dtype) transform_func = transform_func - inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype="int32") + inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype=T.int32) def forward_thread(i: int, j: int) -> int: """ @@ -94,7 +94,7 @@ def make_mma_load_base_layout(dtype: str = "float16", # Create a 16×16 matrix layout for ldmatrix operations -base_layout = make_mma_load_base_layout(dtype="float16", matrix="A", transposed=False) +base_layout = make_mma_load_base_layout(dtype=T.float16, matrix="A", transposed=False) # Print the layout structure (optional for debugging) print(base_layout) diff --git a/examples/plot_layout/fragment_mfma_load_a.py b/examples/plot_layout/fragment_mfma_load_a.py new file mode 100644 index 0000000000..d45cc227bc --- /dev/null +++ b/examples/plot_layout/fragment_mfma_load_a.py @@ -0,0 +1,127 @@ +import tilelang.language as T +from typing import Literal, Callable +from tvm.tir import IndexMap +from tilelang.intrinsics.utils import get_mma_micro_size + +from tilelang.intrinsics.mfma_layout import ( + shared_16x4_to_local_64x1_layout_A, + shared_16x16_to_local_64x4_layout_A, + shared_16x32_to_local_64x8_layout_A, + shared_16x64_to_local_64x16_layout_A, +) + + +def make_mfma_load_base_layout( + dtype: T.dtype = T.float16, matrix: Literal["A", "B"] = "A", k_dim: int = 16, transposed: bool = False +) -> T.Fragment: + """ + Create a layout function for storing MFMA results into a fragment buffer. + This layout is used in conjunction with `inverse_mfma_store_layout` to + map fragment indices to threads and local indices. + + Parameters + ---------- + dtype : str + The data type of the matrix. + matrix : Literal["A", "B"] + The mfma operand to be loaded. + k_dim : int + The k dimension of the mfma. + transposed : bool + Whether the matrix is transposed, by default False. + + Returns + ------- + T.Fragment + Describes how threads and indices in fragment are laid out. + + """ + + assert matrix in ["A", "B"], "matrix should be either A or B" + # s represents spatial axis + # r represents reduction axis + # sr represents the two dims are spatial + reduction + # rs represents the two dims are reduction + spatial + transform_func_sr_a: Callable = None + transform_func_sr_b: Callable = None + + if k_dim == 4: + transform_func_sr_a = shared_16x4_to_local_64x1_layout_A + transform_func_sr_b = shared_16x4_to_local_64x1_layout_A + elif k_dim == 16: + transform_func_sr_a = shared_16x16_to_local_64x4_layout_A + transform_func_sr_b = shared_16x16_to_local_64x4_layout_A + elif k_dim == 32: + transform_func_sr_a = shared_16x32_to_local_64x8_layout_A + transform_func_sr_b = shared_16x32_to_local_64x8_layout_A + elif k_dim == 64: + transform_func_sr_a = shared_16x64_to_local_64x16_layout_A + transform_func_sr_b = shared_16x64_to_local_64x16_layout_A + else: + raise ValueError("k_dim must be 4 or 16 or 32 or 64 currently") + + is_sr_conditions = [False] + is_sr_conditions.append(matrix == "A" and not transposed) + is_sr_conditions.append(matrix == "B" and transposed) + is_sr_axis_order = any(is_sr_conditions) + + micro_size_x, micro_size_y, micro_size_k = get_mma_micro_size(dtype) + + # the layout of mma.sync is row.col. + # so the b matrix expected a transposed basic layout + transform_func: Callable = None + if matrix == "A": + transform_func = transform_func_sr_a if is_sr_axis_order else lambda i, j: transform_func_sr_a(j, i) + micro_size_s, micro_size_r = micro_size_x, micro_size_k + elif matrix == "B": + transform_func = transform_func_sr_b if is_sr_axis_order else lambda i, j: transform_func_sr_b(j, i) + micro_size_s, micro_size_r = micro_size_k, micro_size_y + else: + raise ValueError(f"Unsupported matrix {matrix}") + + inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype=T.int32) + + def forward_thread(i: int, j: int) -> int: + """ + Given the row index `i` and column index `j` in the fragment, + """ + lane_id, _ = inverse_mma_load_layout.map_indices([i, j]) + return lane_id + + def forward_index(i: int, j: int) -> int: + """ + Given the row index `i` and column index `j` in the fragment, + """ + _, local_id = inverse_mma_load_layout.map_indices([i, j]) + return local_id + + base_fragment = T.Fragment( + [micro_size_s, micro_size_r] if is_sr_axis_order else [micro_size_r, micro_size_s], + forward_thread_fn=forward_thread, + forward_index_fn=forward_index, + ) + return base_fragment + + +block_rows = 2 +block_cols = 2 +warp_rows = 2 +warp_cols = 2 +chunk = 2 + +from tilelang.tools import plot_layout + +# ldmatrix layout 16x16 +base_layout = make_mfma_load_base_layout(dtype=T.float16, matrix="A", transposed=False) +print(base_layout) +plot_layout(base_layout, name="base_layout") + +# warp layout 32x32 +warp_layout = base_layout.repeat([warp_rows, warp_cols], repeat_on_thread=False, lower_dim_first=False) +print(warp_layout) +plot_layout(warp_layout, name="warp_layout") + +# block layout 64x32 +block_layout = warp_layout.repeat([block_rows, 1], repeat_on_thread=True, lower_dim_first=True).replicate(block_cols) +print(block_layout) +plot_layout(block_layout, name="block_layout") diff --git a/examples/plot_layout/fragment_mma_load_a.py b/examples/plot_layout/fragment_mma_load_a.py index 9888994483..df4a0b8870 100644 --- a/examples/plot_layout/fragment_mma_load_a.py +++ b/examples/plot_layout/fragment_mma_load_a.py @@ -5,9 +5,7 @@ from tilelang.intrinsics.utils import get_mma_micro_size -def make_mma_load_base_layout(dtype: str = "float16", - matrix: Literal["A", "B"] = "A", - transposed: bool = False) -> T.Fragment: +def make_mma_load_base_layout(dtype: T.dtype = T.float16, matrix: Literal["A", "B"] = "A", transposed: bool = False) -> T.Fragment: """ Create a layout function for storing MMA results into a fragment buffer. This layout is used in conjunction with `inverse_mma_store_layout` to @@ -36,6 +34,7 @@ def make_mma_load_base_layout(dtype: str = "float16", shared_16x16_to_mma_32x8_layout_sr_b, shared_16x32_to_mma_32x16_layout_sr_b, ) + assert matrix in ["A", "B"], "matrix should be either A or B" dtype_bits = DataType(dtype).bits # s represents spatial axis @@ -67,17 +66,15 @@ def make_mma_load_base_layout(dtype: str = "float16", # so the b matrix expected a transposed basic layout transform_func: Callable = None if matrix == "A": - transform_func = transform_func_sr_a if is_sr_axis_order else lambda i, j: transform_func_sr_a( - j, i) + transform_func = transform_func_sr_a if is_sr_axis_order else lambda i, j: transform_func_sr_a(j, i) micro_size_s, micro_size_r = micro_size_x, micro_size_k elif matrix == "B": - transform_func = transform_func_sr_b if is_sr_axis_order else lambda i, j: transform_func_sr_b( - j, i) + transform_func = transform_func_sr_b if is_sr_axis_order else lambda i, j: transform_func_sr_b(j, i) micro_size_s, micro_size_r = micro_size_k, micro_size_y else: raise ValueError(f"Unsupported matrix {matrix}") - inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype="int32") + inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype=T.int32) def forward_thread(i: int, j: int) -> int: """ @@ -110,7 +107,7 @@ def forward_index(i: int, j: int) -> int: from tilelang.tools import plot_layout # ldmatrix layout 16x16 -base_layout = make_mma_load_base_layout(dtype="float16", matrix="A", transposed=False) +base_layout = make_mma_load_base_layout(dtype=T.float16, matrix="A", transposed=False) print(base_layout) plot_layout(base_layout, name="base_layout") diff --git a/examples/quickstart.py b/examples/quickstart.py index 42514ee39e..e99fc0dbce 100644 --- a/examples/quickstart.py +++ b/examples/quickstart.py @@ -6,13 +6,12 @@ # target currently can be "cuda" or "hip" or "cpu". # if not specified, it will be inferred from the input tensors during compile time @tilelang.jit -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def matmul_relu_kernel( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): @@ -55,10 +54,9 @@ def matmul_relu_kernel( block_N = 128 block_K = 32 -# 1. Define the kernel (matmul) and compile/lower it into an executable module +# Define the kernel (matmul) and compile/lower it into an executable module matmul_relu_kernel = matmul(M, N, K, block_M, block_N, block_K) - -# 3. Test the kernel in Python with PyTorch data +# Test the kernel in Python with PyTorch data import torch # Create random input tensors on the GPU @@ -78,7 +76,7 @@ def matmul_relu_kernel( print("Kernel output matches PyTorch reference.") # 4. Retrieve and inspect the generated CUDA source (optional) -# cuda_source = jit_kernel.get_kernel_source() +# cuda_source = matmul_relu_kernel.get_kernel_source() # print("Generated CUDA kernel:\n", cuda_source) # 5.Profile latency with kernel diff --git a/examples/rand/rand_uint.py b/examples/rand/rand_uint.py new file mode 100644 index 0000000000..466a51b7a3 --- /dev/null +++ b/examples/rand/rand_uint.py @@ -0,0 +1,57 @@ +import tilelang +import tilelang.language as T +import torch +import triton +import triton.language as tl + + +@tilelang.jit +def tilelang_rand_1d(M=1024, seed=42): + num_per_thread = 128 + threads = 1 + blk_M = num_per_thread * threads + + @T.prim_func + def rand_kernel(A: T.Tensor((M,), "uint32")): + with T.Kernel(T.ceildiv(M, threads * num_per_thread), threads=threads) as bx: + tx = T.get_thread_binding() + T.rng_init(seed, 0, bx * blk_M + tx * num_per_thread) + for i, j in T.Parallel(threads, num_per_thread): + offsets = (bx * threads + i) * num_per_thread + idx = offsets + j + if idx < M: + A[idx] = T.rng_rand() + + return rand_kernel + + +@triton.jit +def triton_rand_1d(X, M, elements_per_thread, seed): + pid = tl.program_id(0) + offset = pid * elements_per_thread + tl.arange(0, elements_per_thread) + + r0, r1, r2, r3 = tl.randint4x(seed, offset) + + base_idx = offset * 4 + tl.store(X + base_idx, r0, mask=base_idx < M) + tl.store(X + base_idx + 1, r1, mask=(base_idx + 1) < M) + tl.store(X + base_idx + 2, r2, mask=(base_idx + 2) < M) + tl.store(X + base_idx + 3, r3, mask=(base_idx + 3) < M) + + +def test_rand_1d(M, seed): + kernel = tilelang_rand_1d(M, seed) + tilelang_result = torch.empty(M, dtype=torch.uint32, device="cuda") + kernel(tilelang_result) + + triton_result = torch.empty(M, dtype=torch.uint32, device="cuda") + grid = (triton.cdiv(M, 128),) + triton_rand_1d[grid](triton_result, tl.constexpr(M), tl.constexpr(128 // 4), seed) + + torch.testing.assert_close(tilelang_result, triton_result) + + +if __name__ == "__main__": + test_rand_1d(1024, 42) + test_rand_1d(512, 123) + test_rand_1d(128, 0) diff --git a/examples/seer_attention/block_sparse_attn_tilelang.py b/examples/seer_attention/block_sparse_attn_tilelang.py index dcd581c6b9..0a3c3a6e37 100644 --- a/examples/seer_attention/block_sparse_attn_tilelang.py +++ b/examples/seer_attention/block_sparse_attn_tilelang.py @@ -10,10 +10,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -30,70 +27,33 @@ def get_sparse_attn_mask_from_threshold(x, threshold, use_dense_for_last_block=F @tilelang.jit( - out_idx=[4], pass_configs={ + out_idx=[4], + pass_configs={ tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, - }) + }, +) def blocksparse_flashattn(batch, heads, seq_q, seq_kv, dim, downsample_len, is_causal): block_M = 64 block_N = 64 num_stages = 0 threads = 128 - scale = (1.0 / dim)**0.5 * 1.44269504 # log2(e) + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) q_shape = [batch, heads, seq_q, dim] kv_shape = [batch, heads, seq_kv, dim] block_mask_shape = [batch, heads, downsample_len, downsample_len] - dtype = "float16" - accum_dtype = "float" - block_mask_dtype = "int8" + dtype = T.float16 + accum_dtype = T.float32 + block_mask_dtype = T.int8 def kernel_func(block_M, block_N, num_stages, threads): - - @T.macro - def Softmax( - acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), - acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), - scores_max: T.FragmentBuffer([block_M], accum_dtype), - scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - scores_sum: T.FragmentBuffer([block_M], accum_dtype), - logsum: T.FragmentBuffer([block_M], accum_dtype), - ): - T.copy(scores_max, scores_max_prev) - T.fill(scores_max, -T.infinity(accum_dtype)) - T.reduce_max(acc_s, scores_max, dim=1, clear=False) - # To do causal softmax, we need to set the scores_max to 0 if it is -inf - # This process is called Check_inf in FlashAttention3 code, and it only need to be done - # in the first ceil_div(kBlockM, kBlockN) steps. - # for i in T.Parallel(block_M): - # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) - for i in T.Parallel(block_M): - scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) - for i, j in T.Parallel(block_M, block_N): - # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - - # max * log_2(e)) This allows the compiler to use the ffma - # instruction instead of fadd and fmul separately. - acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) - T.reduce_sum(acc_s, scores_sum, dim=1) - for i in T.Parallel(block_M): - logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] - T.copy(acc_s, acc_s_cast) - - @T.macro - def Rescale( - acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), - scores_scale: T.FragmentBuffer([block_M], accum_dtype), - ): - for i, j in T.Parallel(block_M, dim): - acc_o[i, j] *= scores_scale[i] - @T.prim_func def main( - Q: T.Tensor(q_shape, dtype), - K: T.Tensor(kv_shape, dtype), - V: T.Tensor(kv_shape, dtype), - BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), - Output: T.Tensor(q_shape, dtype), + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + BlockSparseMask: T.Tensor(block_mask_shape, block_mask_dtype), + Output: T.Tensor(q_shape, dtype), ): with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): Q_shared = T.alloc_shared([block_M, dim], dtype) @@ -108,47 +68,61 @@ def main( scores_scale = T.alloc_fragment([block_M], accum_dtype) scores_sum = T.alloc_fragment([block_M], accum_dtype) logsum = T.alloc_fragment([block_M], accum_dtype) - block_mask = T.alloc_local([downsample_len], block_mask_dtype) + block_mask = T.alloc_fragment([downsample_len], block_mask_dtype) - T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared) + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) T.fill(acc_o, 0) T.fill(logsum, 0) T.fill(scores_max, -T.infinity(accum_dtype)) - for vj in T.serial(downsample_len): - block_mask[vj] = BlockSparseMask[bz, by, bx, vj] + T.copy(BlockSparseMask[bz, by, bx, :], block_mask) loop_range = T.ceildiv(seq_kv, block_N) for k in T.Pipelined(loop_range, num_stages=num_stages): if block_mask[k] != 0: - T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared) + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) if is_causal: past_len = seq_kv - seq_q for i, j in T.Parallel(block_M, block_N): - acc_s[i, j] = T.if_then_else( - bx * block_M + i + past_len >= k * block_N + j, 0, - -T.infinity(acc_s.dtype)) + acc_s[i, j] = T.if_then_else(bx * block_M + i + past_len >= k * block_N + j, 0, -T.infinity(acc_s.dtype)) else: T.clear(acc_s) - T.gemm( - Q_shared, - K_shared, - acc_s, - transpose_B=True, - policy=T.GemmWarpPolicy.FullRow) - - Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, - scores_sum, logsum) - Rescale(acc_o, scores_scale) - T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared) + T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + for i in T.Parallel(block_M): + scores_max[i] = T.max(scores_max[i], scores_max_prev[i]) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # in the first ceil_div(kBlockM, kBlockN) steps. + # for i in T.Parallel(block_M): + # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) for i, j in T.Parallel(block_M, dim): acc_o[i, j] /= logsum[i] T.copy(acc_o, O_shared) - T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :]) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) return main @@ -163,44 +137,40 @@ def test_topk_sparse_attention(): torch.manual_seed(0) # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) downsample_factor = BLOCK downsample_len = math.ceil(SEQ_LEN / downsample_factor) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.float16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.float16) x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) # Run tilelang kernel - kernel = blocksparse_flashattn( - BATCH, N_HEADS, SEQ_LEN, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) + kernel = blocksparse_flashattn(BATCH, N_HEADS, SEQ_LEN, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) tilelang_output = kernel(q, k, v, block_mask.to(torch.int8)) # Compute reference # Expand block mask to full attention matrix - full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')) + full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")) full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) # Apply causal # PyTorch reference implementation - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale - attn = attn.masked_fill(~full_mask, float('-inf')) + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale + attn = attn.masked_fill(~full_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) print("ref_output", ref_output) print("tilelang_output", tilelang_output) # Verify accuracy - assert torch.allclose(tilelang_output, ref_output, atol=1e-2, rtol=1e-2), \ - "TileLang output doesn't match reference" + assert torch.allclose(tilelang_output, ref_output, atol=1e-2, rtol=1e-2), "TileLang output doesn't match reference" print("Pass topk sparse attention test with qlen == klen") @@ -213,42 +183,40 @@ def test_topk_sparse_attention_qlen_lt_klen(): torch.manual_seed(0) # Create inputs. - q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device='cuda', dtype=torch.float16) - k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.float16) - v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.float16) + q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.float16) sm_scale = 1.0 / (D_HEAD**0.5) downsample_factor = BLOCK downsample_len = math.ceil(K_LEN / downsample_factor) # number of blocks along one dimension - x_ds = torch.randn( - BATCH, N_HEADS, downsample_len, downsample_len, device='cuda', dtype=torch.float16) + x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="cuda", dtype=torch.float16) # Force the first column to be high so that the first block is always selected. x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) - kernel = blocksparse_flashattn( - BATCH, N_HEADS, Q_LEN, K_LEN, D_HEAD, downsample_len, is_causal=True) + kernel = blocksparse_flashattn(BATCH, N_HEADS, Q_LEN, K_LEN, D_HEAD, downsample_len, is_causal=True) print(kernel.get_kernel_source()) tilelang_output = kernel(q, k, v, block_mask.to(torch.int8)) past_len = K_LEN - Q_LEN - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale - full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')).bool() + full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")).bool() full_mask_full = full_mask_full[..., :K_LEN, :K_LEN] effective_mask = full_mask_full[..., past_len:K_LEN, :] # shape: (B, H, Q_LEN, K_LEN) i_global = torch.arange(past_len, K_LEN, device=k.device).unsqueeze(1) # shape: (Q_LEN, 1) j_global = torch.arange(K_LEN, device=k.device).unsqueeze(0) # shape: (1, K_LEN) - causal_mask = (j_global <= i_global) # shape: (Q_LEN, K_LEN) + causal_mask = j_global <= i_global # shape: (Q_LEN, K_LEN) final_mask = effective_mask & causal_mask # shape: (B, H, Q_LEN, K_LEN) - attn = attn.masked_fill(~final_mask, float('-inf')) + attn = attn.masked_fill(~final_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) print("ref_output", ref_output) print("tilelang_output", tilelang_output) @@ -264,5 +232,56 @@ def main(): test_topk_sparse_attention_qlen_lt_klen() +def run_regression_perf(): + BATCH, N_HEADS, SEQ_LEN, D_HEAD = 4, 2, 256, 64 + TOPK = 2 + BLOCK = 64 + torch.manual_seed(0) + + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.float16) + + downsample_factor = BLOCK + downsample_len = math.ceil(SEQ_LEN / downsample_factor) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.float16) + x_ds[:, :, :, 0] = 100 + block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) + + kernel = blocksparse_flashattn(BATCH, N_HEADS, SEQ_LEN, SEQ_LEN, D_HEAD, downsample_len, is_causal=True) + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(q, k, v, block_mask.to(torch.int8)) + + latency_1 = do_bench(run_kernel_only, backend="cupti") + + BATCH, N_HEADS = 1, 1 + Q_LEN, K_LEN, D_HEAD = 128, 256, 64 + TOPK = 1 + BLOCK = 64 + torch.manual_seed(0) + + q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="cuda", dtype=torch.float16) + k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.float16) + v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.float16) + + downsample_factor = BLOCK + downsample_len = math.ceil(K_LEN / downsample_factor) + x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="cuda", dtype=torch.float16) + x_ds[:, :, :, 0] = 100 + block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) + + kernel = blocksparse_flashattn(BATCH, N_HEADS, Q_LEN, K_LEN, D_HEAD, downsample_len, is_causal=True) + print(kernel.get_kernel_source()) + + def run_kernel_only2(): + kernel(q, k, v, block_mask.to(torch.int8)) + + latency_2 = do_bench(run_kernel_only2, backend="cupti") + + return (latency_1 + latency_2) / 2 + + if __name__ == "__main__": main() diff --git a/examples/seer_attention/block_sparse_attn_triton.py b/examples/seer_attention/block_sparse_attn_triton.py index ed33cc1e2a..b4cc3cd00c 100644 --- a/examples/seer_attention/block_sparse_attn_triton.py +++ b/examples/seer_attention/block_sparse_attn_triton.py @@ -15,10 +15,7 @@ def get_sparse_attn_mask_from_topk(x, topk, use_dense_for_last_block=False): bsz, num_head, downsample_len, _ = x.shape # N_CTX = downsample_len * BLOCK sparse_index = torch.topk(x, topk, dim=-1).indices - dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], - False, - dtype=torch.bool, - device=x.device) + dense_mask = torch.full([bsz, num_head, downsample_len, downsample_len], False, dtype=torch.bool, device=x.device) dense_mask.scatter_(-1, sparse_index, True) if use_dense_for_last_block: dense_mask[:, :, -2:, :] = True @@ -54,7 +51,6 @@ def _fwd_kernel_inner( BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, ): - mask_val = tl.load(block_mask_ptr + k_block_col_idx * stride_bmask_n) if mask_val == True: @@ -69,7 +65,7 @@ def _fwd_kernel_inner( qk *= sm_scale # the following is needed only when LAST_K_BLOCK or BLOCK_M < BLOCK_N - qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, float('-inf')) + qk += tl.where(offs_m[:, None] + past_len >= (start_n + offs_n[None, :]), 0, float("-inf")) m_ij = tl.maximum(m_i, tl.max(qk, 1)) qk -= m_ij[:, None] @@ -149,7 +145,7 @@ def _fwd_kernel( v_ptrs = V + off_v mask_ptrs = block_mask_ptr + start_m * stride_bmm - m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf') + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") l_i = tl.zeros([BLOCK_M], dtype=tl.float32) acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) @@ -185,24 +181,12 @@ def _fwd_kernel( acc = acc * l_recip acc = acc.to(Out.dtype.element_ty) - off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[ - None, :] * stride_od + off_o = off_z * stride_oz + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :] * stride_od out_ptrs = Out + off_o tl.store(out_ptrs, acc, mask=offs_m[:, None] < N_CTX) -def _forward(ctx, - q, - k, - v, - block_sparse_mask, - sm_scale, - BLOCK_M=64, - BLOCK_N=64, - num_warps=None, - num_stages=1, - out=None): - +def _forward(ctx, q, k, v, block_sparse_mask, sm_scale, BLOCK_M=64, BLOCK_N=64, num_warps=None, num_stages=1, out=None): assert q.shape[-1] == k.shape[-1] == v.shape[-1] assert k.shape[2] == v.shape[2] o = out if out is not None else torch.empty_like(q).contiguous() @@ -247,7 +231,6 @@ def _forward(ctx, class _sparse_attention(torch.autograd.Function): - @staticmethod def forward(ctx, q, k, v, block_sparse_dense, sm_scale): # shape constraints @@ -271,9 +254,9 @@ def test_topk_sparse_attention(): torch.manual_seed(0) # Create inputs - q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) + q = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + k = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + v = torch.randn(BATCH, N_HEADS, SEQ_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) sm_scale = 1.0 / (D_HEAD**0.5) # Create sparse mask (downsampled to block level) @@ -281,9 +264,7 @@ def test_topk_sparse_attention(): downsample_len = math.ceil(SEQ_LEN / downsample_factor) print("downsample_len", downsample_len) - x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], - device='cuda', - dtype=torch.bfloat16) + x_ds = torch.randn([BATCH, N_HEADS, downsample_len, downsample_len], device="cuda", dtype=torch.bfloat16) x_ds[:, :, :, 0] = 100 print("x_ds.shape", x_ds.shape) block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) @@ -295,22 +276,21 @@ def test_topk_sparse_attention(): # Compute reference # Expand block mask to full attention matrix - full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')) + full_mask = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")) full_mask = full_mask[..., :SEQ_LEN, :SEQ_LEN].bool() full_mask = full_mask & torch.tril(torch.ones_like(full_mask)) # Apply causal # PyTorch reference implementation - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale - attn = attn.masked_fill(~full_mask, float('-inf')) + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale + attn = attn.masked_fill(~full_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) # print("ref_output", ref_output) # print("triton_output", triton_output) # Verify accuracy - assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), \ - "Triton output doesn't match reference" + assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference" print("Pass topk sparse attention test with qlen == klen") @@ -322,16 +302,15 @@ def test_topk_sparse_attention_qlt_kl(): torch.manual_seed(0) # Create inputs. - q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) - v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device='cuda', dtype=torch.bfloat16) + q = torch.randn(BATCH, N_HEADS, Q_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + k = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) + v = torch.randn(BATCH, N_HEADS, K_LEN, D_HEAD, device="cuda", dtype=torch.bfloat16) # softmax scale sm_scale = 1.0 / (D_HEAD**0.5) downsample_factor = BLOCK downsample_len = math.ceil(K_LEN / downsample_factor) # number of blocks along one dimension - x_ds = torch.randn( - BATCH, N_HEADS, downsample_len, downsample_len, device='cuda', dtype=torch.bfloat16) + x_ds = torch.randn(BATCH, N_HEADS, downsample_len, downsample_len, device="cuda", dtype=torch.bfloat16) # Force the first column to be high so that the first block is always selected. x_ds[:, :, :, 0] = 100 block_mask = get_sparse_attn_mask_from_topk(x_ds, topk=TOPK) @@ -340,26 +319,25 @@ def test_topk_sparse_attention_qlt_kl(): past_len = K_LEN - Q_LEN - attn = torch.einsum('bhsd,bhtd->bhst', q, k) * sm_scale + attn = torch.einsum("bhsd,bhtd->bhst", q, k) * sm_scale - full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device='cuda')).bool() + full_mask_full = torch.kron(block_mask.float(), torch.ones(BLOCK, BLOCK, device="cuda")).bool() full_mask_full = full_mask_full[..., :K_LEN, :K_LEN] effective_mask = full_mask_full[..., past_len:K_LEN, :] # shape: (B, H, Q_LEN, K_LEN) i_global = torch.arange(past_len, K_LEN, device=k.device).unsqueeze(1) # shape: (Q_LEN, 1) j_global = torch.arange(K_LEN, device=k.device).unsqueeze(0) # shape: (1, K_LEN) - causal_mask = (j_global <= i_global) # shape: (Q_LEN, K_LEN) + causal_mask = j_global <= i_global # shape: (Q_LEN, K_LEN) final_mask = effective_mask & causal_mask # shape: (B, H, Q_LEN, K_LEN) - attn = attn.masked_fill(~final_mask, float('-inf')) + attn = attn.masked_fill(~final_mask, float("-inf")) attn = F.softmax(attn, dim=-1) - ref_output = torch.einsum('bhst,bhtd->bhsd', attn, v) + ref_output = torch.einsum("bhst,bhtd->bhsd", attn, v) # Verify accuracy. - assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), \ - "Triton output doesn't match reference when qlen < klen" + assert torch.allclose(triton_output, ref_output, atol=1e-2, rtol=1e-2), "Triton output doesn't match reference when qlen < klen" print("Pass topk sparse attention test with qlen < klen") diff --git a/examples/seer_attention/regression_block_sparse_attn_tilelang.py b/examples/seer_attention/regression_block_sparse_attn_tilelang.py new file mode 100644 index 0000000000..86d7b3b282 --- /dev/null +++ b/examples/seer_attention/regression_block_sparse_attn_tilelang.py @@ -0,0 +1,10 @@ +import tilelang.testing +import block_sparse_attn_tilelang + + +def regression_block_sparse_attn_tilelang(): + tilelang.testing.process_func(block_sparse_attn_tilelang.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/sparse_tensorcore/regression_example_sparse_tensorcore.py b/examples/sparse_tensorcore/regression_example_sparse_tensorcore.py new file mode 100644 index 0000000000..1167c1603c --- /dev/null +++ b/examples/sparse_tensorcore/regression_example_sparse_tensorcore.py @@ -0,0 +1,11 @@ +import tilelang.testing +import tilelang +import tilelang_example_sparse_tensorcore + + +def regression_example_sparse_tensorcore(): + tilelang.testing.process_func(tilelang_example_sparse_tensorcore.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/sparse_tensorcore/tilelang_example_sparse_tensorcore.py b/examples/sparse_tensorcore/tilelang_example_sparse_tensorcore.py index 59c79c283b..f33832afff 100644 --- a/examples/sparse_tensorcore/tilelang_example_sparse_tensorcore.py +++ b/examples/sparse_tensorcore/tilelang_example_sparse_tensorcore.py @@ -1,7 +1,8 @@ import torch import tilelang from tilelang.utils.sparse import compress_sm90 -from tilelang.layout import make_metadata_layout +from tilelang.layout import make_cutlass_metadata_layout +from tilelang import language as T import tilelang.testing @@ -24,32 +25,24 @@ def matmul_sp( A_shared_shape = (block_M, block_K // 2) B_shared_shape = (block_K, block_N) - import tilelang.language as T - @T.prim_func def main( - A_sparse: T.Tensor(A_sparse_shape, in_dtype), - E: T.Tensor((M, K // 8), 'uint8'), - B: T.Tensor(B_shape, in_dtype), - C: T.Tensor((M, N), out_dtype), + A_sparse: T.Tensor(A_sparse_shape, in_dtype), + E: T.Tensor((M, K // 8), "uint8"), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), ): with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): A_shared = T.alloc_shared(A_shared_shape, in_dtype) B_shared = T.alloc_shared(B_shared_shape, in_dtype) - E_shared = T.alloc_shared((block_M, block_K // 8), 'uint8') + E_shared = T.alloc_shared((block_M, block_K // 8), "uint8") C_local = T.alloc_fragment((block_M, block_N), accum_dtype) - T.annotate_layout({ - E: - make_metadata_layout( - E, mma_dtype="float16", arch="9.0", backend="cutlass", block_k=block_K), - E_shared: - make_metadata_layout( - E_shared, - mma_dtype="float16", - arch="9.0", - backend="cutlass", - block_k=block_K), - }) + T.annotate_layout( + { + E: make_cutlass_metadata_layout(E, mma_dtype=T.float16, arch="9.0", block_k=block_K), + E_shared: make_cutlass_metadata_layout(E_shared, mma_dtype=T.float16, arch="9.0", block_k=block_K), + } + ) T.clear(C_local) for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): T.copy(E[by * block_M, k * block_K // 8], E_shared) @@ -61,7 +54,7 @@ def main( return main -def generate_2_to_4_sparse_tensor(shape, dtype=torch.float32, device='cpu'): +def generate_2_to_4_sparse_tensor(shape, dtype=torch.float32, device="cpu"): if shape[-1] % 4 != 0: raise ValueError("Last dimension must be divisible by 4 for 2:4 sparsity.") @@ -106,9 +99,9 @@ def run_gemm_sp( num_threads, ) - A = generate_2_to_4_sparse_tensor((M, K), dtype=torch.float16, device='cuda') + A = generate_2_to_4_sparse_tensor((M, K), dtype=torch.float16, device="cuda") A_sparse, E = compress_sm90(A, block_k=block_K, transposed=False) - B = torch.randn((K, N), device='cuda', dtype=torch.float16) + B = torch.randn((K, N), device="cuda", dtype=torch.float16) C_sp = kernel(A_sparse, E, B).half() C = torch.matmul(A, B) @@ -117,7 +110,46 @@ def run_gemm_sp( def main(): - run_gemm_sp(512, 1024, 768, "float16", "float16", "float32", 128, 128, 128, 2, 128) + run_gemm_sp(512, 1024, 768, T.float16, T.float16, T.float32, 128, 128, 128, 2, 128) + + +def run_regression_perf(): + M, N, K, block_M, block_N, block_K, in_dtype, out_dtype, accum_dtype, num_stages, num_threads = ( + 512, + 1024, + 768, + 128, + 128, + 128, + "float16", + "float16", + "float32", + 2, + 128, + ) + kernel = matmul_sp( + M, + N, + K, + block_M, + block_N, + block_K, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + num_threads, + ) + A = generate_2_to_4_sparse_tensor((M, K), dtype=torch.float16, device="cuda") + A_sparse, E = compress_sm90(A, block_k=block_K, transposed=False) + B = torch.randn((K, N), device="cuda", dtype=torch.float16) + + from tilelang.profiler import do_bench + + def run_kernel_only(): + kernel(A_sparse, E, B) + + return do_bench(run_kernel_only, backend="cupti") if __name__ == "__main__": diff --git a/examples/topk/example_topk.py b/examples/topk/example_topk.py index 0ca19fb18d..ed5ba0d4a5 100644 --- a/examples/topk/example_topk.py +++ b/examples/topk/example_topk.py @@ -22,19 +22,19 @@ def tl_topk( blk_m, threads=128, ): - dtype = "float32" + dtype = T.float32 @T.prim_func def topk_kernel( - logits: T.Tensor([M, N], dtype), - topk_gates: T.Tensor([M, topk], dtype), - topk_indices: T.Tensor([M, topk], "int32"), + logits: T.Tensor([M, N], dtype), + topk_gates: T.Tensor([M, topk], dtype), + topk_indices: T.Tensor([M, topk], T.int32), ): with T.Kernel(T.ceildiv(M, blk_m), threads=threads) as bx: logits_frag = T.alloc_fragment([blk_m, N], dtype=dtype) max_val = T.alloc_fragment([blk_m], dtype=dtype) - expand_max_idx = T.alloc_fragment([blk_m, N], "int32") - max_idx = T.alloc_fragment([blk_m], "int32") + expand_max_idx = T.alloc_fragment([blk_m, N], T.int32) + max_idx = T.alloc_fragment([blk_m], T.int32) T.copy(logits[bx * blk_m, 0], logits_frag) @@ -43,15 +43,12 @@ def topk_kernel( T.reduce_max(logits_frag, max_val, dim=1, clear=True) for i, j in T.Parallel(blk_m, N): - expand_max_idx[i, j] = T.if_then_else(max_val[i] == logits_frag[i, j], j, - expand_max_idx[i, j]) + expand_max_idx[i, j] = T.if_then_else(max_val[i] == logits_frag[i, j], j, expand_max_idx[i, j]) T.reduce_max(expand_max_idx, max_idx, dim=1, clear=True) for i, j in T.Parallel(blk_m, N): - - logits_frag[i, j] = T.if_then_else(max_val[i] == logits_frag[i, j], -10000.0, - logits_frag[i, j]) + logits_frag[i, j] = T.if_then_else(max_val[i] == logits_frag[i, j], -10000.0, logits_frag[i, j]) for i in T.Parallel(blk_m): topk_gates[bx * blk_m + i, k] = max_val[i] @@ -61,7 +58,6 @@ def topk_kernel( def ref_program(logits, top_k): - top_k_gates, top_k_indices = logits.topk(top_k, dim=1) return top_k_gates, top_k_indices.to(torch.int32) @@ -93,5 +89,29 @@ def main(argv=None): print(f"Tilelang latency: {tilelang_latency}") +def run_regression_perf(argv=None): + parser = argparse.ArgumentParser() + parser.add_argument("--M", type=int, default=320, help="num_tokens") + parser.add_argument("--N", type=int, default=128, help="num_experts") + parser.add_argument("--topk", type=int, default=6, help="topk") + parser.add_argument("--blk_m", type=int, default=64, help="blk_m") + # In benchmark mode, ignore process-wide sys.argv unless an explicit argv is provided. + args = parser.parse_args(argv or []) + M, N, topk, blk_m = args.M, args.N, args.topk, args.blk_m + + logits = torch.rand((M, N), device="cuda", dtype=torch.float32) + + kernel = tl_topk(M=M, N=N, topk=topk, blk_m=blk_m) + tl_gates, tl_indices = kernel(logits) + + torch_gates, torch_indices = ref_program(logits, topk) + + torch.testing.assert_close(tl_gates, torch_gates) + torch.testing.assert_close(tl_indices, torch_indices) + + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Auto) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/topk/regression_topk_tilelang.py b/examples/topk/regression_topk_tilelang.py new file mode 100644 index 0000000000..f59d866e8a --- /dev/null +++ b/examples/topk/regression_topk_tilelang.py @@ -0,0 +1,10 @@ +import tilelang.testing +import example_topk + + +def regression_example_topk(): + tilelang.testing.process_func(example_topk.run_regression_perf) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/examples/visual_layout_inference/visual_layout_inference.py b/examples/visual_layout_inference/visual_layout_inference.py new file mode 100644 index 0000000000..8fa1eaf854 --- /dev/null +++ b/examples/visual_layout_inference/visual_layout_inference.py @@ -0,0 +1,61 @@ +import tilelang +import tilelang.language as T + + +# use pass_configs to enable layout visualization +@tilelang.jit( + out_idx=[-1], + pass_configs={ + tilelang.PassConfigKey.TL_LAYOUT_VISUALIZATION_ENABLE: True, + tilelang.PassConfigKey.TL_LAYOUT_VISUALIZATION_FORMATS: "svg", + }, +) +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): + @T.prim_func + def gemm( + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + B_shared = T.alloc_shared((block_K, block_N), dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=3): + T.copy(A[by * block_M, k * block_K], A_shared) + T.copy(B[k * block_K, bx * block_N], B_shared) + T.gemm(A_shared, B_shared, C_local) + + T.copy(C_local, C[by * block_M, bx * block_N]) + + return gemm + + +def main(): + kernel = matmul(128, 128, 128, 32, 32, 32) + + import torch + + a = torch.randn(128, 128).cuda().half() + b = torch.randn(128, 128).cuda().half() + + c = kernel(a, b) + + ref_c = a @ b + + torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) + print("All check passed.") + + # print the layout visualization result and save figures to ./tmp. + """ + C_local inferenced layout: + Shape: [32, 32] -> [8] + Thread: _j // 16 * 64 + _i // 16 * 32 + _i % 8 * 4 + _j % 8 // 2 + Index: [_j % 16 // 8 * 4 + _i % 16 // 8 * 2 + _j % 2] + """ + + +if __name__ == "__main__": + main() diff --git a/examples/warp_specialize/example_warp_specialize_flashmla.py b/examples/warp_specialize/example_warp_specialize_flashmla.py index 4a8f41ee4f..155a459707 100644 --- a/examples/warp_specialize/example_warp_specialize_flashmla.py +++ b/examples/warp_specialize/example_warp_specialize_flashmla.py @@ -9,21 +9,23 @@ @tilelang.jit(out_idx=[6]) def flashattn(batch, heads, kv_head_num, seqlen_kv, dim, pe_dim, block_N, block_H, num_split): - scale = (1.0 / (dim + pe_dim))**0.5 * 1.44269504 # log2(e) - dtype = "float16" - accum_dtype = "float" + scale = (1.0 / (dim + pe_dim)) ** 0.5 * 1.44269504 # log2(e) + dtype = T.float16 + accum_dtype = T.float32 kv_group_num = heads // kv_head_num VALID_BLOCK_H = min(block_H, kv_group_num) assert kv_head_num == 1, "kv_head_num must be 1" h_dim = dim // 2 - @T.macro - def flash_attn( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), + @T.prim_func + def main_no_split( + Q: T.Tensor([batch, heads, dim], dtype), + Q_pe: T.Tensor([batch, heads, pe_dim], dtype), + KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), + K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), + glse: T.Tensor([batch, heads, num_split], dtype), + Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), + Output: T.Tensor([batch, heads, dim], dtype), ): with T.Kernel(heads // min(block_H, kv_group_num), batch, threads=256) as (hid, bid): # smem_sQ @@ -81,11 +83,6 @@ def flash_attn( cur_kv_head = hid // (kv_group_num // block_H) - T.annotate_layout({ - O_shared_l: tilelang.layout.make_swizzled_layout(O_shared_l), - O_shared_r: tilelang.layout.make_swizzled_layout(O_shared_r), - }) - # barriers_Q q_shared_ready_barrier = T.alloc_barrier(arrive_count=256) @@ -108,9 +105,9 @@ def flash_attn( tx = T.get_thread_binding() - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :h_dim], Q_shared_l) - T.copy(Q[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, h_dim:], Q_shared_r) - T.copy(Q_pe[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :h_dim], Q_shared_l) + T.copy(Q[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, h_dim:], Q_shared_r) + T.copy(Q_pe[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :], Q_pe_shared) T.barrier_arrive(q_shared_ready_barrier) T.barrier_wait(q_shared_ready_barrier, 0) @@ -123,25 +120,18 @@ def flash_attn( T.fill(acc_o_l, 0) T.fill(logsum_0, 0) - T.copy(KV[bid, block_N:2 * block_N, cur_kv_head, :h_dim], KV_shared_1_l) + T.copy(KV[bid, block_N : 2 * block_N, cur_kv_head, :h_dim], KV_shared_1_l) T.barrier_arrive(kv_shared_1_l_is_ready) - T.copy(KV[bid, block_N:2 * block_N, cur_kv_head, h_dim:], KV_shared_1_r) + T.copy(KV[bid, block_N : 2 * block_N, cur_kv_head, h_dim:], KV_shared_1_r) T.barrier_arrive(kv_shared_1_r_is_ready) - T.copy(K_pe[bid, block_N:2 * block_N, cur_kv_head, :], K_pe_shared_1) + T.copy(K_pe[bid, block_N : 2 * block_N, cur_kv_head, :], K_pe_shared_1) T.barrier_arrive(kv_shared_1_pe_is_ready) for k in T.serial(loop_range): - T.barrier_wait(kv_shared_0_l_is_ready, k % 2) - T.gemm( - Q_shared_l, - KV_shared_0_l, - acc_s_0, - transpose_B=True, - clear_accum=True, - wg_wait=-1) + T.gemm(Q_shared_l, KV_shared_0_l, acc_s_0, transpose_B=True, clear_accum=True, wg_wait=-1) T.barrier_wait(kv_shared_0_r_is_ready, k % 2) T.gemm(Q_shared_r, KV_shared_0_r, acc_s_0, transpose_B=True, wg_wait=-1) @@ -161,8 +151,7 @@ def flash_attn( for i, j in T.Parallel(block_H, block_N): acc_s_0[i, j] = T.exp2(acc_s_0[i, j] * scale - scores_max[i] * scale) for i in T.Parallel(block_H): - scores_scale_0[i] = T.exp2(scores_max_prev_0[i] * scale - - scores_max[i] * scale) + scores_scale_0[i] = T.exp2(scores_max_prev_0[i] * scale - scores_max[i] * scale) T.reduce_sum(acc_s_0, scores_sum_0, dim=1) @@ -182,9 +171,7 @@ def flash_attn( T.barrier_wait(scale_1_ready_barrier, k % 2) if k < loop_range - 1: - T.copy( - KV[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, - cur_kv_head, :h_dim], KV_shared_0_l) + T.copy(KV[bid, (2 * k + 2) * block_N : (2 * k + 3) * block_N, cur_kv_head, :h_dim], KV_shared_0_l) T.barrier_arrive(kv_shared_0_l_is_ready) # Step 11. @@ -204,15 +191,10 @@ def flash_attn( T.gemm(SP1_shared, KV_shared_1_l, acc_o_l) if k < loop_range - 1: - - T.copy( - KV[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, - cur_kv_head, :h_dim], KV_shared_1_l) + T.copy(KV[bid, (2 * k + 3) * block_N : (2 * k + 4) * block_N, cur_kv_head, :h_dim], KV_shared_1_l) T.barrier_arrive(kv_shared_1_l_is_ready) - T.copy( - K_pe[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, cur_kv_head, :], - K_pe_shared_1) + T.copy(K_pe[bid, (2 * k + 3) * block_N : (2 * k + 4) * block_N, cur_kv_head, :], K_pe_shared_1) T.barrier_arrive(kv_shared_1_pe_is_ready) T.copy(logsum_0, logsum) @@ -221,8 +203,7 @@ def flash_attn( for i, j in T.Parallel(block_H, h_dim): acc_o_l[i, j] /= logsum[i] T.copy(acc_o_l, O_shared_l) - T.copy(O_shared_l, Output[bid, - hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, :h_dim]) + T.copy(O_shared_l, Output[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, :h_dim]) else: T.copy(Q_pe_shared, Q_pe_local_1) @@ -237,16 +218,9 @@ def flash_attn( T.barrier_arrive(kv_shared_0_pe_is_ready) for k in T.serial(loop_range): - # Step 2. T.barrier_wait(kv_shared_1_l_is_ready, k % 2) - T.gemm( - Q_shared_l, - KV_shared_1_l, - acc_s_1, - transpose_B=True, - clear_accum=True, - wg_wait=-1) + T.gemm(Q_shared_l, KV_shared_1_l, acc_s_1, transpose_B=True, clear_accum=True, wg_wait=-1) T.barrier_wait(kv_shared_1_r_is_ready, k % 2) T.gemm(Q_shared_r, KV_shared_1_r, acc_s_1, transpose_B=True, wg_wait=-1) @@ -265,8 +239,7 @@ def flash_attn( T.copy(scores_max_1, scores_max) for i in T.Parallel(block_H): - scores_scale_1[i] = T.exp2(scores_max_prev_1[i] * scale - - scores_max[i] * scale) + scores_scale_1[i] = T.exp2(scores_max_prev_1[i] * scale - scores_max[i] * scale) # Step 8. for i, j in T.Parallel(block_H, block_N): @@ -279,8 +252,7 @@ def flash_attn( acc_o_r[i, j] = acc_o_r[i, j] * (scores_scale_0[i] * scores_scale_1[i]) for i in T.Parallel(block_H): - logsum_1[i] = logsum_1[i] * scores_scale_1[i] * scores_scale_0[ - i] + scores_sum_1[i] + logsum_1[i] = logsum_1[i] * scores_scale_1[i] * scores_scale_0[i] + scores_sum_1[i] T.barrier_arrive(scale_1_ready_barrier) @@ -291,9 +263,7 @@ def flash_attn( T.barrier_arrive(s_shared_ready_barrier) if k < loop_range - 1: - T.copy( - KV[bid, (2 * k + 3) * block_N:(2 * k + 4) * block_N, cur_kv_head, - h_dim:], KV_shared_1_r) + T.copy(KV[bid, (2 * k + 3) * block_N : (2 * k + 4) * block_N, cur_kv_head, h_dim:], KV_shared_1_r) T.barrier_arrive(kv_shared_1_r_is_ready) T.barrier_wait(p0_1_1_ready_barrier, k % 2) @@ -301,15 +271,10 @@ def flash_attn( T.gemm(SP0_shared, KV_shared_0_r, acc_o_r) if k < loop_range - 1: - - T.copy( - KV[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, cur_kv_head, - h_dim:], KV_shared_0_r) + T.copy(KV[bid, (2 * k + 2) * block_N : (2 * k + 3) * block_N, cur_kv_head, h_dim:], KV_shared_0_r) T.barrier_arrive(kv_shared_0_r_is_ready) - T.copy( - K_pe[bid, (2 * k + 2) * block_N:(2 * k + 3) * block_N, cur_kv_head, :], - K_pe_shared_0) + T.copy(K_pe[bid, (2 * k + 2) * block_N : (2 * k + 3) * block_N, cur_kv_head, :], K_pe_shared_0) T.barrier_arrive(kv_shared_0_pe_is_ready) T.barrier_wait(lse_0_ready_barrier, 0) @@ -319,20 +284,7 @@ def flash_attn( for i, j in T.Parallel(block_H, h_dim): acc_o_r[i, j] /= logsum[i] T.copy(acc_o_r, O_shared_r) - T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H:(hid + 1) * VALID_BLOCK_H, - h_dim:]) - - @T.prim_func - def main_no_split( - Q: T.Tensor([batch, heads, dim], dtype), - Q_pe: T.Tensor([batch, heads, pe_dim], dtype), - KV: T.Tensor([batch, seqlen_kv, kv_head_num, dim], dtype), - K_pe: T.Tensor([batch, seqlen_kv, kv_head_num, pe_dim], dtype), - glse: T.Tensor([batch, heads, num_split], dtype), - Output_partial: T.Tensor([batch, heads, num_split, dim], dtype), - Output: T.Tensor([batch, heads, dim], dtype), - ): - flash_attn(Q, Q_pe, KV, K_pe, Output) + T.copy(O_shared_r, Output[bid, hid * VALID_BLOCK_H : (hid + 1) * VALID_BLOCK_H, h_dim:]) return main_no_split @@ -352,31 +304,24 @@ def ref_program(q, q_pe, kv, k_pe, glse, Output_partial): dim = q.shape[-1] pe_dim = q_pe.shape[-1] num_head_groups = q.shape[1] // kv.shape[2] - scale = (dim + pe_dim)**0.5 - q = rearrange( - q, 'b (h g) d -> b g h d', g=num_head_groups) # [batch_size, num_head_groups, groups, dim] + scale = (dim + pe_dim) ** 0.5 + q = rearrange(q, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, dim] - q_pe = rearrange( - q_pe, 'b (h g) d -> b g h d', - g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] + q_pe = rearrange(q_pe, "b (h g) d -> b g h d", g=num_head_groups) # [batch_size, num_head_groups, groups, pe_dim] - kv = rearrange(kv, 'b n h d -> b h n d') # [batch_size, groups, seqlen_kv, dim] + kv = rearrange(kv, "b n h d -> b h n d") # [batch_size, groups, seqlen_kv, dim] - k_pe = rearrange(k_pe, 'b n h d -> b h n d') # [batch_size, num_head_groups, groups, pe_dim] + k_pe = rearrange(k_pe, "b n h d -> b h n d") # [batch_size, num_head_groups, groups, pe_dim] query = torch.concat([q, q_pe], dim=-1) key = torch.concat([kv, k_pe], dim=-1) - scores = einsum( - query, key, - 'b g h d, b h s d -> b g h s') # [batch_size, num_head_groups, groups, seqlen_kv] + scores = einsum(query, key, "b g h d, b h s d -> b g h s") # [batch_size, num_head_groups, groups, seqlen_kv] - attention = F.softmax( - scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] + attention = F.softmax(scores / scale, dim=-1) # [batch_size, num_head_groups, groups, seqlen_kv] - out = einsum(attention, kv, - 'b g h s, b h s d -> b g h d') # [batch_size, num_head_groups, groups, dim] - out = rearrange(out, 'b g h d -> b (h g) d') # [batch_size, heads, dim] + out = einsum(attention, kv, "b g h s, b h s d -> b g h d") # [batch_size, num_head_groups, groups, dim] + out = rearrange(out, "b g h d -> b (h g) d") # [batch_size, heads, dim] return out @@ -399,12 +344,12 @@ def main(batch=1, heads=64, kv_heads=1, kv_ctx=1024, dim=512, pe_dim=64): if __name__ == "__main__": parser = argparse.ArgumentParser() - parser.add_argument('--batch', type=int, default=1, help='batch size') - parser.add_argument('--heads', type=int, default=128, help='q heads number') - parser.add_argument('--kv_heads', type=int, default=1, help='kv heads number') - parser.add_argument('--kv_ctx', type=int, default=8192, help='kv context length') - parser.add_argument('--dim', type=int, default=512, help='head dim') - parser.add_argument('--pe_dim', type=int, default=64, help='pe head dim') + parser.add_argument("--batch", type=int, default=132, help="batch size") + parser.add_argument("--heads", type=int, default=128, help="q heads number") + parser.add_argument("--kv_heads", type=int, default=1, help="kv heads number") + parser.add_argument("--kv_ctx", type=int, default=8192, help="kv context length") + parser.add_argument("--dim", type=int, default=512, help="head dim") + parser.add_argument("--pe_dim", type=int, default=64, help="pe head dim") args = parser.parse_args() batch, heads, kv_heads, kv_ctx, dim, pe_dim = args.batch, args.heads, args.kv_heads, args.kv_ctx, args.dim, args.pe_dim main(batch, heads, kv_heads, kv_ctx, dim, pe_dim) diff --git a/examples/warp_specialize/example_warp_specialize_gemm_barrierpipe_stage2.py b/examples/warp_specialize/example_warp_specialize_gemm_barrierpipe_stage2.py index 3f552795ee..1672dbfb80 100644 --- a/examples/warp_specialize/example_warp_specialize_gemm_barrierpipe_stage2.py +++ b/examples/warp_specialize/example_warp_specialize_gemm_barrierpipe_stage2.py @@ -1,12 +1,13 @@ import tilelang import tilelang.language as T +tilelang.disable_cache() + # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit @tilelang.jit(out_idx=[2]) -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): num_stages = 2 mbarrier_list = [128, 128] * num_stages @@ -30,19 +31,13 @@ def main( for ko in range(T.ceildiv(K, block_K)): with T.ws(1): - T.mbarrier_wait_parity( - mbarrier=ko % num_stages + num_stages, - parity=((ko // num_stages) % num_stages) ^ 1) - T.copy(A[by * block_M:(by + 1) * block_M, ko * block_K:(ko + 1) * block_K], - A_shared[ko % num_stages, :, :]) - T.copy(B[ko * block_K:(ko + 1) * block_K, bx * block_N:(bx + 1) * block_N], - B_shared[ko % num_stages, :, :]) + T.mbarrier_wait_parity(mbarrier=ko % num_stages + num_stages, parity=((ko // num_stages) % num_stages) ^ 1) + T.copy(A[by * block_M : (by + 1) * block_M, ko * block_K : (ko + 1) * block_K], A_shared[ko % num_stages, :, :]) + T.copy(B[ko * block_K : (ko + 1) * block_K, bx * block_N : (bx + 1) * block_N], B_shared[ko % num_stages, :, :]) T.mbarrier_arrive(mbarrier=ko % num_stages) with T.ws(0): - T.mbarrier_wait_parity( - mbarrier=ko % num_stages, parity=(ko // num_stages) % num_stages) - T.gemm(A_shared[ko % num_stages, :, :], B_shared[ko % num_stages, :, :], - C_local) + T.mbarrier_wait_parity(mbarrier=ko % num_stages, parity=(ko // num_stages) % num_stages) + T.gemm(A_shared[ko % num_stages, :, :], B_shared[ko % num_stages, :, :], C_local) T.mbarrier_arrive(mbarrier=ko % num_stages + num_stages) with T.ws(0): @@ -52,11 +47,14 @@ def main( def main(M=16384, N=16384, K=16384): + tilelang.disable_cache() block_M = 128 block_N = 128 block_K = 64 jit_kernel = matmul(M, N, K, block_M, block_N, block_K) + print(jit_kernel.get_kernel_source()) + import torch a = torch.randn(M, K, device="cuda", dtype=torch.float16) @@ -84,5 +82,15 @@ def main(M=16384, N=16384, K=16384): print(f"Latency: {latency} ms") +def run_regression_perf(M=16384, N=16384, K=16384): + tilelang.disable_cache() + block_M = 128 + block_N = 128 + block_K = 64 + jit_kernel = matmul(M, N, K, block_M, block_N, block_K) + profiler = jit_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/warp_specialize/example_warp_specialize_gemm_copy_0_gemm_1.py b/examples/warp_specialize/example_warp_specialize_gemm_copy_0_gemm_1.py index 9ba9f68160..b582ee74cc 100644 --- a/examples/warp_specialize/example_warp_specialize_gemm_copy_0_gemm_1.py +++ b/examples/warp_specialize/example_warp_specialize_gemm_copy_0_gemm_1.py @@ -5,20 +5,12 @@ # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit @tilelang.jit(out_idx=[2]) -def matmul_warp_specialize_copy_0_gemm_1(M, - N, - K, - block_M, - block_N, - block_K, - dtype="float16", - accum_dtype="float"): - +def matmul_warp_specialize_copy_0_gemm_1(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=256) as (bx, by): @@ -82,5 +74,27 @@ def main(M=1024, N=1024, K=1024): print(f"Latency: {latency} ms") +def run_regression_perf(M=4096, N=4096, K=4096): + block_M = 128 + block_N = 128 + block_K = 64 + + jit_kernel = matmul_warp_specialize_copy_0_gemm_1(M, N, K, block_M, block_N, block_K) + + import torch + + a = torch.randn(M, K, device="cuda", dtype=torch.float16) + b = torch.randn(K, N, device="cuda", dtype=torch.float16) + + c = jit_kernel(a, b) + ref_c = a @ b + + torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) + + profiler = jit_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/warp_specialize/example_warp_specialize_gemm_copy_1_gemm_0.py b/examples/warp_specialize/example_warp_specialize_gemm_copy_1_gemm_0.py index faaf48c648..d6d243bb01 100644 --- a/examples/warp_specialize/example_warp_specialize_gemm_copy_1_gemm_0.py +++ b/examples/warp_specialize/example_warp_specialize_gemm_copy_1_gemm_0.py @@ -5,20 +5,12 @@ # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit @tilelang.jit(out_idx=[2]) -def matmul_warp_specialize_copy_1_gemm_0(M, - N, - K, - block_M, - block_N, - block_K, - dtype="float16", - accum_dtype="float"): - +def matmul_warp_specialize_copy_1_gemm_0(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=256) as (bx, by): @@ -83,5 +75,28 @@ def main(M=16384, N=16384, K=16384): print(f"Latency: {latency} ms") +def run_regression_perf(M=16384, N=16384, K=16384): + block_M = 128 + block_N = 128 + block_K = 64 + + jit_kernel = matmul_warp_specialize_copy_1_gemm_0(M, N, K, block_M, block_N, block_K) + + import torch + + a = torch.randn(M, K, device="cuda", dtype=torch.float16) + b = torch.randn(K, N, device="cuda", dtype=torch.float16) + + c = jit_kernel(a, b) + + ref_c = a @ b + + torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) + + profiler = jit_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/warp_specialize/example_warp_specialize_gemm_copy_gemm_0_1.py b/examples/warp_specialize/example_warp_specialize_gemm_copy_gemm_0_1.py index c91274540f..5468aa6eac 100644 --- a/examples/warp_specialize/example_warp_specialize_gemm_copy_gemm_0_1.py +++ b/examples/warp_specialize/example_warp_specialize_gemm_copy_gemm_0_1.py @@ -5,26 +5,20 @@ # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit @tilelang.jit( - out_idx=[2], pass_configs={ + out_idx=[2], + pass_configs={ tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, - }) -def matmul_warp_specialize_copy_1_gemm_0(M, - N, - K, - block_M, - block_N, - block_K, - dtype="float16", - accum_dtype="float"): - + }, +) +def matmul_warp_specialize_copy_1_gemm_0(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): warp_group_num = 2 threads = 128 * warp_group_num @T.prim_func def main( - A: T.Tensor((M, K), dtype), - B: T.Tensor((K, N), dtype), - C: T.Tensor((M, N), dtype), + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), ): # Initialize Kernel Context with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): diff --git a/examples/warp_specialize/example_warp_specialize_gemm_softpipe_stage2.py b/examples/warp_specialize/example_warp_specialize_gemm_softpipe_stage2.py index 3b1d867198..54566b785d 100644 --- a/examples/warp_specialize/example_warp_specialize_gemm_softpipe_stage2.py +++ b/examples/warp_specialize/example_warp_specialize_gemm_softpipe_stage2.py @@ -5,8 +5,7 @@ # add decorator @tilelang.jit if you want to return a torch function # @tilelang.jit @tilelang.jit(out_idx=[2]) -def matmul(M, N, K, block_M, block_N, block_K, dtype="float16", accum_dtype="float"): - +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): @T.prim_func def main( A: T.Tensor[(M, K), dtype], @@ -79,5 +78,28 @@ def main(M=16384, N=16384, K=16384): print(f"Latency: {latency} ms") +def run_regression_perf(M=16384, N=16384, K=16384): + block_M = 128 + block_N = 128 + block_K = 64 + + jit_kernel = matmul(M, N, K, block_M, block_N, block_K) + + import torch + + a = torch.randn(M, K, device="cuda", dtype=torch.float16) + b = torch.randn(K, N, device="cuda", dtype=torch.float16) + + c = jit_kernel(a, b) + + ref_c = a @ b + + torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) + + profiler = jit_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + return profiler.do_bench(backend="cupti") + + if __name__ == "__main__": main() diff --git a/examples/warp_specialize/regression_example_warp_specialize.py b/examples/warp_specialize/regression_example_warp_specialize.py new file mode 100644 index 0000000000..d5cd17d486 --- /dev/null +++ b/examples/warp_specialize/regression_example_warp_specialize.py @@ -0,0 +1,25 @@ +import tilelang.testing +import example_warp_specialize_gemm_barrierpipe_stage2 +import example_warp_specialize_gemm_copy_0_gemm_1 +import example_warp_specialize_gemm_copy_1_gemm_0 +import example_warp_specialize_gemm_softpipe_stage2 + + +def regression_example_warp_specialize_gemm_barrierpipe_stage2(): + tilelang.testing.process_func(example_warp_specialize_gemm_barrierpipe_stage2.run_regression_perf, M=1024, N=1024, K=1024) + + +def regression_example_warp_specialize_gemm_copy_0_gemm_1(): + tilelang.testing.process_func(example_warp_specialize_gemm_copy_0_gemm_1.run_regression_perf, M=1024, N=1024, K=1024) + + +def regression_example_warp_specialize_gemm_copy_1_gemm_0(): + tilelang.testing.process_func(example_warp_specialize_gemm_copy_1_gemm_0.run_regression_perf, M=1024, N=1024, K=1024) + + +def regression_example_warp_specialize_gemm_softpipe_stage2(): + tilelang.testing.process_func(example_warp_specialize_gemm_softpipe_stage2.run_regression_perf, M=1024, N=1024, K=1024) + + +if __name__ == "__main__": + tilelang.testing.regression() diff --git a/format.sh b/format.sh index 8f127433cf..3cc4390dbe 100755 --- a/format.sh +++ b/format.sh @@ -9,7 +9,7 @@ # bash format.sh --all # # -# YAPF + Clang formatter (if installed). This script formats all changed files from the last mergebase. +# Ruff (format) + Clang formatter (if installed). This script formats all changed files from the last mergebase. # You are encouraged to run this locally before pushing changes for review. # Cause the script to exit if a single command fails @@ -29,10 +29,7 @@ ALL_FILES='' ONLY_CHANGED='' FILES=() if (($# == 0)); then - if [[ -n "$(git status --porcelain --ignore-submodules --untracked-files=no)" ]]; then - echo "Detected uncommitted changes. Please commit or stash them before running $0." >&2 - exit 1 - fi + # Default: allow dirty workspace; run on changed files (committed + worktree) ONLY_CHANGED='true' else while (($# > 0)); do @@ -78,14 +75,17 @@ if [[ -n "${ALL_FILES}" ]]; then echo "Checking all files..." >&2 elif [[ -n "${ONLY_CHANGED}" ]]; then MERGE_BASE="$(get_merge_base)" - echo "Checking changed files compared to merge base (${MERGE_BASE})..." >&2 + echo "Checking changed files vs merge base (${MERGE_BASE}) and working tree..." >&2 elif [[ "${#FILES[@]}" -gt 0 ]]; then echo "Checking specified files: ${FILES[*]}..." >&2 fi +# Some systems set pip's default to --user, which breaks isolated virtualenvs. +export PIP_USER=0 + # If pre-commit is not installed, install it. if ! python3 -m pre_commit --version &>/dev/null; then - python3 -m pip install pre-commit + python3 -m pip install pre-commit --user fi echo 'tile-lang pre-commit: Check Start' @@ -93,7 +93,17 @@ echo 'tile-lang pre-commit: Check Start' if [[ -n "${ALL_FILES}" ]]; then python3 -m pre_commit run --all-files elif [[ -n "${ONLY_CHANGED}" ]]; then - python3 -m pre_commit run --from-ref "${MERGE_BASE}" --to-ref HEAD + # Collect changed files (committed since merge-base + current worktree) + CHANGED_FILES="$(git diff --name-only --diff-filter=ACM "${MERGE_BASE}" 2>/dev/null || true)" + if [[ -n "${CHANGED_FILES}" ]]; then + echo "Running pre-commit on changed files:" + echo "${CHANGED_FILES}" + # Convert newline-separated files to space-separated and run pre-commit once + CHANGED_FILES_SPACE="$(echo "${CHANGED_FILES}" | tr '\n' ' ')" + python3 -m pre_commit run --files ${CHANGED_FILES_SPACE} + else + echo "No files changed relative to merge base and worktree. Skipping pre-commit." + fi elif [[ "${#FILES[@]}" -gt 0 ]]; then python3 -m pre_commit run --files "${FILES[@]}" fi @@ -105,7 +115,7 @@ echo 'tile-lang clang-tidy: Check Start' if [[ -x "$(command -v run-clang-tidy)" ]]; then # Check if clang-tidy is available if [[ ! -x "$(command -v clang-tidy)" ]]; then - python3 -m pip install --upgrade --requirements "${ROOT}/requirements-lint.txt" + python3 -m pip install --upgrade --requirements "${ROOT}/requirements-lint.txt" --user fi # Get clang-tidy version CLANG_TIDY_VERSION="$(clang-tidy --version | head -n1 | awk '{print $4}')" diff --git a/images/MatmulExample.svg b/images/MatmulExample.svg index 6e20daf554..294e8f6310 100644 --- a/images/MatmulExample.svg +++ b/images/MatmulExample.svg @@ -1 +1 @@ -A_shared=T.alloc_shared((block_M,block_K))B_shared=T.alloc_shared((block_K,block_N))C_local=T.alloc_fragment((block_M,block_N),accum_dtype)importtilelang.languageasTdefMatmul(A:T.Buffer,B:T.Buffer,C:T.Buffer):withT.Kernel(ceildiv(N,block_N),ceildiv(M,block_M),threads=128)as(bx,by):T.clear(C_local)forkinT.Pipelined(ceildiv(K,block_K),num_stages=3):T.copy(A[by*block_M,k*block_K],A_shared)T.copy(B[k*block_K,bx*block_N],B_shared)T.gemm(A_shared,B_shared,C_local)Kernel Context InitializationBuffer AllocationRegisterInitialize Accumulate Buffer with ZeroMain Loop with Pipeline AnnotationT.copy(C_local,C[by*block_M,bx*block_N])Write Back to Global MemoryCopy Data from Global to Shared MemoryGEMMSharedMemoryGlobal MemoryShared MemoryRegister Files(a) Efficient GEMM with Multi-Level Tiling on GPUs(b) Describing Tiled GPU GEMM with TileLang \ No newline at end of file +A_shared=T.alloc_shared((block_M,block_K))B_shared=T.alloc_shared((block_K,block_N))C_local=T.alloc_fragment((block_M,block_N),accum_dtype)importtilelang.languageasTdefMatmul(A:T.Buffer,B:T.Buffer,C:T.Buffer):withT.Kernel(ceildiv(N,block_N),ceildiv(M,block_M),threads=128)as(bx,by):T.clear(C_local)forkinT.Pipelined(ceildiv(K,block_K),num_stages=3):T.copy(A[by*block_M,k*block_K],A_shared)T.copy(B[k*block_K,bx*block_N],B_shared)T.gemm(A_shared,B_shared,C_local)Kernel Context InitializationBuffer AllocationRegisterInitialize Accumulate Buffer with ZeroMain Loop with Pipeline AnnotationT.copy(C_local,C[by*block_M,bx*block_N])Write Back to Global MemoryCopy Data from Global to Shared MemoryGEMMSharedMemoryGlobal MemoryShared MemoryRegister Files(a) Efficient GEMM with Multi-Level Tiling on GPUs(b) Describing Tiled GPU GEMM with TileLang diff --git a/images/logo-row.svg b/images/logo-row.svg index 633243f3a9..e73244b743 100644 --- a/images/logo-row.svg +++ b/images/logo-row.svg @@ -1 +1 @@ - \ No newline at end of file + diff --git a/maint/gemm_v2/correctness_evaluation.py b/maint/gemm_v2/correctness_evaluation.py new file mode 100644 index 0000000000..44441cdeb7 --- /dev/null +++ b/maint/gemm_v2/correctness_evaluation.py @@ -0,0 +1,739 @@ +# pytest correctness_evaluation.py -n 32 +import pytest +from tilelang import tvm as tvm +import tilelang.testing +from tilelang import language as T +import torch + + +def matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.gemm(A_shared, B_shared, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def _compile_and_check( + program, + trans_A, + trans_B, + in_dtype, + out_dtype, +): + kernel = tilelang.compile( + program, + out_idx=[2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + # tilelang.PassConfigKey.TIR_USE_ASYNC_COPY: False, + }, + ) + + print(kernel.get_kernel_source()) + + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + def ref_program(A, B): + if trans_A: + A = A.T + if trans_B: + B = B.T + if in_dtype == T.float32: + A = (A.view(torch.int32) - 0x1000).view(torch.float32) + B = (B.view(torch.int32) - 0x1000).view(torch.float32) + C = torch.matmul(A.to(torch.float), B.to(torch.float)) + C = C.to(torch.__getattribute__(out_dtype)) + return C + + profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2) + print("assert_allclose") + + +def run_gemm( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=2, + num_threads=128, +): + if block_N >= 256 or block_M >= 256 or block_K >= 256: + num_stages = 0 + program = matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +def matmul_rs( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + A_frag_shape = A_shared_shape + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + A_frag = T.alloc_fragment(A_frag_shape, in_dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.copy(A_shared, A_frag) + T.gemm_v2(A_frag, B_shared, C_local, trans_A, trans_B) + # T.gemm(A_frag, B_shared, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def run_gemm_rs( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=2, + num_threads=128, +): + if block_N >= 256 or block_M >= 256 or block_K >= 256: + num_stages = 0 + program = matmul_rs( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +def matmul_sr( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + B_frag_shape = B_shared_shape + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + B_frag = T.alloc_fragment(B_frag_shape, in_dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.copy(B_shared, B_frag) + T.gemm_v2(A_shared, B_frag, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def run_gemm_sr( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=2, + num_threads=128, +): + if block_N >= 256 or block_M >= 256 or block_K >= 256: + num_stages = 0 + program = matmul_sr( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +def matmul_rr( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + A_frag_shape = A_shared_shape + B_frag_shape = B_shared_shape + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + A_frag = T.alloc_fragment(A_frag_shape, in_dtype) + B_frag = T.alloc_fragment(B_frag_shape, in_dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.copy(A_shared, A_frag) + T.copy(B_shared, B_frag) + T.gemm_v2(A_frag, B_frag, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def run_gemm_rr( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=2, + num_threads=128, +): + if block_N >= 256 or block_M >= 256 or block_K >= 256: + num_stages = 0 + program = matmul_rr( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +M_VALUES = [64, 128, 256] +N_VALUES = [16, 32, 64, 128, 256, 512] +K_VALUES = [16, 32, 64, 128] +K_VALUES_8Bit = [32, 64, 128] +FALSE_TRUE_CASES = ( + [ + pytest.param( + k, + T.float16, + T.float16, + T.float16, + id=f"K{k}-float16-float16-float16", + ) + for k in K_VALUES + ] + + [ + pytest.param( + k, + T.int8, + T.int32, + T.int32, + id="K32-int8-int32-int32", + ) + for k in K_VALUES_8Bit + ] + + [ + pytest.param( + k, + T.float8_e5m2, + T.float32, + T.float32, + id="K32-float8_e5m2-float32-float32", + ) + for k in K_VALUES_8Bit + ] + + [ + pytest.param( + k, + T.float8_e4m3fn, + T.float32, + T.float32, + id="K32-float8_e4m3-float32-float32", + ) + for k in K_VALUES_8Bit + ] +) + + +def _ensure_torch_dtypes(*dtype_names): + import torch + + for name in set(dtype_names): + if not hasattr(torch, name): + pytest.skip(f"Torch does not expose dtype {name}") + + +def run_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + run_gemm_rs(m, n, k * 3, False, True, in_dtype, out_dtype, accum_dtype, m, n, k) + + +def run_gemm_rs_false_false(m, n, k): + run_gemm_rs(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_rs_true_false(m, n, k): + run_gemm_rs(m, n, k * 3, True, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_rs_true_true(m, n, k): + run_gemm_rs(m, n, k * 3, True, True, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_sr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + run_gemm_sr(m, n, k * 3, False, True, in_dtype, out_dtype, accum_dtype, m, n, k) + + +def run_gemm_sr_false_false(m, n, k): + run_gemm_sr(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_sr_true_false(m, n, k): + run_gemm_sr(m, n, k * 3, True, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_sr_true_true(m, n, k): + run_gemm_sr(m, n, k * 3, True, True, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_rr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + run_gemm_rr(m, n, k * 3, False, True, in_dtype, out_dtype, accum_dtype, m, n, k) + + +def run_gemm_rr_false_false(m, n, k): + run_gemm_rr(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_rr_true_false(m, n, k): + run_gemm_rr(m, n, k * 3, True, False, T.float16, T.float16, T.float16, m, n, k) + + +def run_gemm_rr_true_true(m, n, k): + run_gemm_rr(m, n, k * 3, True, True, T.float16, T.float16, T.float16, m, n, k) + + +TRANS_CASES = [ + pytest.param(False, False, id="nn"), + pytest.param(False, True, id="nt"), + pytest.param(True, False, id="tn"), + pytest.param(True, True, id="tt"), +] + + +@pytest.fixture(scope="module", autouse=True) +def _setup_tilelang_environment(): + tilelang.disable_cache() + tilelang.testing.set_random_seed(42) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + import torch + + required_torch_attrs = { + in_dtype, + out_dtype, + accum_dtype, + } + for attr in required_torch_attrs: + if not hasattr(torch, attr): + pytest.skip(f"Torch does not expose dtype {attr}") + run_gemm( + m, + n, + k * 3, + False, + True, + in_dtype, + out_dtype, + accum_dtype, + m, + n, + k, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_false_false(m, n, k): + run_gemm( + m, + n, + k * 3, + False, + False, + T.float16, + T.float16, + T.float16, + m, + n, + k, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_true_false(m, n, k): + run_gemm( + m, + n, + k * 3, + True, + False, + T.float16, + T.float16, + T.float16, + m, + n, + k, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_true_true(m, n, k): + run_gemm( + m, + n, + k * 3, + True, + True, + T.float16, + T.float16, + T.float16, + m, + n, + k, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + _ensure_torch_dtypes(in_dtype, out_dtype, accum_dtype) + run_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rs_false_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rs_false_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rs_true_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rs_true_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rs_true_true(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rs_true_true(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_sr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + _ensure_torch_dtypes(in_dtype, out_dtype, accum_dtype) + run_gemm_sr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_sr_false_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_sr_false_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_sr_true_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_sr_true_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_sr_true_true(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_sr_true_true(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_rr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + _ensure_torch_dtypes(in_dtype, out_dtype, accum_dtype) + run_gemm_rr_false_true(m, n, k, in_dtype, out_dtype, accum_dtype) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rr_false_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rr_false_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rr_true_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rr_true_false(m, n, k) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rr_true_true(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rr_true_true(m, n, k) + + +if __name__ == "__main__": + tilelang.testing.main() + + # # Test Pass + # for m in [64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm(m, n, k * 3, False, True, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") + + # # Test Pass + # for m in [64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # print(f"======================= Test {m} {n} {k} False False =============================") + # run_gemm(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") + + # # Test Pass + # for m in [64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # print(f"======================= Test {m} {n} {k} True False =============================") + # run_gemm(m, n, k * 3, True, False, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m}, {n} {k} Pass") + # print(f"Test {n} Pass") + + # # Test Pass + # for m in [64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # print(f"======================= Test {m} {n} {k} True True =============================") + # run_gemm(m, n, k * 3, True, True, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m}, {n} {k} Pass") + # print(f"Test {n} Pass") + + # Test Pass + # for m in [64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm_rs(m, n, k * 3, False, True, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") + + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # run_gemm_rs(64, n, k, False, False, T.float16, T.float16, T.float16, 64, n, k, 0, 256) + # print(f"Test {64} {n} {k} Pass") + + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # run_gemm(64, n, k, False, False, T.float16, T.float16, T.float16, 64, n, k, 0, 256) + # print(f"Test {64} {n} {k} Pass") diff --git a/maint/gemm_v2/correctness_evaluation_sm70.py b/maint/gemm_v2/correctness_evaluation_sm70.py new file mode 100644 index 0000000000..606d102611 --- /dev/null +++ b/maint/gemm_v2/correctness_evaluation_sm70.py @@ -0,0 +1,350 @@ +# pytest maint/gemm_v2/correctness_evaluation_sm70.py -n 32 +import pytest +from tilelang import tvm as tvm +import tilelang.testing +from tilelang import language as T + + +def matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + # T.gemm(A_shared, B_shared, C_local, trans_A, trans_B) + T.gemm_v2(A_shared, B_shared, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def _compile_and_check( + program, + trans_A, + trans_B, + in_dtype, + out_dtype, +): + kernel = tilelang.compile( + program, + out_idx=[2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + # tilelang.PassConfigKey.TIR_USE_ASYNC_COPY: False, + }, + ) + + print(kernel.get_kernel_source()) + + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + def ref_program(A, B): + import torch + + if trans_A: + A = A.T + if trans_B: + B = B.T + if in_dtype == T.float32: + A = (A.view(torch.int32) - 0x1000).view(torch.float32) + B = (B.view(torch.int32) - 0x1000).view(torch.float32) + C = torch.matmul(A.to(torch.float), B.to(torch.float)) + C = C.to(torch.__getattribute__(out_dtype)) + return C + + profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2) + print("assert_allclose") + + +def run_gemm( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=3, + num_threads=128, +): + program = matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +def matmul_rs( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + A_frag_shape = A_shared_shape + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope="shared.dyn") + B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope="shared.dyn") + A_frag = T.alloc_fragment(A_frag_shape, in_dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + T.clear(C_local) + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + if trans_A: + T.copy(A[k * block_K, by * block_M], A_shared) + else: + T.copy(A[by * block_M, k * block_K], A_shared) + if trans_B: + T.copy(B[bx * block_N, k * block_K], B_shared) + else: + T.copy(B[k * block_K, bx * block_N], B_shared) + T.copy(A_shared, A_frag) + T.gemm_v2(A_frag, B_shared, C_local, trans_A, trans_B) + # T.gemm(A_frag, B_shared, C_local, trans_A, trans_B) + T.copy(C_local, C[by * block_M, bx * block_N]) + + return main + + +def run_gemm_rs( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=3, + num_threads=128, +): + program = matmul_rs( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +M_VALUES = [64, 128] +N_VALUES = [32, 64, 128] +K_VALUES = [16, 32, 64] +FALSE_TRUE_CASES = [ + pytest.param( + k, + T.float16, + T.float16, + T.float16, + id=f"K{k}-float16-float16-float16", + ) + for k in K_VALUES +] + [ + pytest.param( + k, + T.float16, + T.float16, + T.float32, + id=f"K{k}-float16-float16-float32", + ) + for k in K_VALUES +] + + +def _ensure_torch_dtypes(*dtype_names): + import torch + + for name in set(dtype_names): + if not hasattr(torch, name): + pytest.skip(f"Torch does not expose dtype {name}") + + +def run_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + run_gemm_rs(m, n, k * 3, False, True, in_dtype, out_dtype, accum_dtype, m, n, k, 2, 128) + + +def run_gemm_rs_false_false(m, n, k): + run_gemm_rs(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k, 2, 128) + + +TRANS_CASES = [ + pytest.param(False, False, id="nn"), + pytest.param(False, True, id="nt"), + pytest.param(True, False, id="tn"), + pytest.param(True, True, id="tt"), +] + + +@pytest.fixture(scope="module", autouse=True) +def _setup_tilelang_environment(): + tilelang.disable_cache() + tilelang.testing.set_random_seed(42) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + import torch + + required_torch_attrs = { + in_dtype, + out_dtype, + accum_dtype, + } + for attr in required_torch_attrs: + if not hasattr(torch, attr): + pytest.skip(f"Torch does not expose dtype {attr}") + run_gemm( + m, + n, + k * 3, + False, + True, + in_dtype, + out_dtype, + accum_dtype, + m, + n, + k, + 2, + 128, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_false_false(m, n, k): + run_gemm( + m, + n, + k * 3, + False, + False, + T.float16, + T.float16, + T.float16, + m, + n, + k, + 2, + 128, + ) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + _ensure_torch_dtypes(in_dtype, out_dtype, accum_dtype) + run_gemm_rs_false_true(m, n, k, in_dtype, out_dtype, accum_dtype) + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k", K_VALUES, ids=lambda v: f"K{v}") +def test_gemm_rs_false_false(m, n, k): + _ensure_torch_dtypes(T.float16) + run_gemm_rs_false_false(m, n, k) + + +if __name__ == "__main__": + tilelang.testing.main() + + # # Test Pass + # for m in [64, 128]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64]: + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm(m, n, k * 3, False, True, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") + + # # Test Pass + # for m in [64, 128]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64]: + # print(f"======================= Test {m} {n} {k} False False =============================") + # run_gemm(m, n, k * 3, False, False, T.float16, T.float16, T.float16, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") diff --git a/maint/gemm_v2/correctness_evaluation_tcgen05.py b/maint/gemm_v2/correctness_evaluation_tcgen05.py new file mode 100644 index 0000000000..8d9728182b --- /dev/null +++ b/maint/gemm_v2/correctness_evaluation_tcgen05.py @@ -0,0 +1,218 @@ +# pytest correctness_evaluation.py -n 32 +import pytest +from tilelang import tvm as tvm +import tilelang.testing +import tilelang.language as T + + +def matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + accum_dtype, + num_stages, + threads, +): + A_shape = (K, M) if trans_A else (M, K) + B_shape = (N, K) if trans_B else (K, N) + A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K) + B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N) + + @T.prim_func + def main( + A: T.Tensor(A_shape, in_dtype), + B: T.Tensor(B_shape, in_dtype), + C: T.Tensor((M, N), out_dtype), + ): + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by): + A_shared = T.alloc_shared(A_shared_shape, in_dtype) + B_shared = T.alloc_shared(B_shared_shape, in_dtype) + C_tmem = T.alloc_tmem([block_M, block_N], accum_dtype) + mbar = T.alloc_barrier(1) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + C_shared = T.alloc_shared((block_M, block_N), out_dtype) + + for k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages): + T.copy(A[by * block_M, k * block_K], A_shared) + T.copy(B[bx * block_N, k * block_K], B_shared) + T.gemm(A_shared, B_shared, C_tmem, trans_A, trans_B, mbar=mbar, wg_wait=-1, clear_accum=k == 0) + T.mbarrier_wait_parity(mbar, k % 2) + + T.copy(C_tmem, C_local) + T.copy(C_local, C_shared) + + T.copy(C_shared, C[by * block_M, bx * block_N]) + + return main + + +def _compile_and_check( + program, + trans_A, + trans_B, + in_dtype, + out_dtype, +): + kernel = tilelang.compile( + program, + out_idx=[2], + pass_configs={ + tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True, + tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True, + }, + ) + + print(kernel.get_kernel_source()) + + profiler = kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + + def ref_program(A, B): + import torch + + if trans_A: + A = A.T + if trans_B: + B = B.T + if in_dtype == T.float32: + A = (A.view(torch.int32) - 0x1000).view(torch.float32) + B = (B.view(torch.int32) - 0x1000).view(torch.float32) + C = torch.matmul(A.to(torch.float), B.to(torch.float)) + C = C.to(torch.__getattribute__(out_dtype)) + return C + + profiler.assert_allclose(ref_program, atol=1e-2, rtol=1e-2) + print("assert_allclose") + + +def run_gemm( + M, + N, + K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + block_M, + block_N, + block_K, + num_stages=2, + num_threads=128, +): + if block_N >= 256 or block_M >= 256 or block_K >= 256: + num_stages = 0 + program = matmul( + M, + N, + K, + block_M, + block_N, + block_K, + trans_A, + trans_B, + in_dtype, + out_dtype, + dtypeAccum, + num_stages, + num_threads, + ) + + _compile_and_check(program, trans_A, trans_B, in_dtype, out_dtype) + + +M_VALUES = [32, 64, 128, 256] +N_VALUES = [64, 128, 256, 512] +K_VALUES = [16, 32, 64, 128] +K_VALUES_8Bit = [32, 64, 128] +FALSE_TRUE_CASES = [ + pytest.param( + k, + T.float16, + T.float32, + T.float32, + id=f"K{k}-float16-float-float", + ) + for k in K_VALUES +] + [ + pytest.param( + k, + T.float8_e5m2, + T.float32, + T.float32, + id="K32-float8_e5m2-float32-float32", + ) + for k in K_VALUES_8Bit +] + +TRANS_CASES = [ + pytest.param(False, True, id="nt"), +] + + +@pytest.mark.parametrize("m", M_VALUES, ids=lambda v: f"M{v}") +@pytest.mark.parametrize("n", N_VALUES, ids=lambda v: f"N{v}") +@pytest.mark.parametrize("k,in_dtype,out_dtype,accum_dtype", FALSE_TRUE_CASES) +def test_gemm_false_true(m, n, k, in_dtype, out_dtype, accum_dtype): + import torch + + required_torch_attrs = { + in_dtype, + out_dtype, + accum_dtype, + } + for attr in required_torch_attrs: + if not hasattr(torch, attr): + pytest.skip(f"Torch does not expose dtype {attr}") + run_gemm( + m, + n, + k * 3, + False, + True, + in_dtype, + out_dtype, + accum_dtype, + m, + n, + k, + ) + + +if __name__ == "__main__": + tilelang.testing.main() + + # # Test Pass + # for m in [32, 64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [16, 32, 64, 128]: + # if m in [32, 64] and (n not in [64, 128, 256]): + # continue + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm(m, n, k * 3, False, True, T.float16, T.float, T.float, m, n, k, 2, 128) + # print(f"Test {m} {n} {k} Pass") + + # # Test Pass + # for m in [32, 64, 128, 256]: + # for n in [32, 64, 128]: + # for k in [16, 32, 64, 128]: + # if m in [32, 64] and (n not in [64, 128, 256]): + # continue + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm(m, n, k * 3, False, True, T.float16, T.float, T.float, m, n, k, 2, 256) + # print(f"Test {m} {n} {k} Pass") + + # # Test Pass + # for m in [32, 64, 128, 256]: + # for n in [16, 32, 64, 128]: + # for k in [32, 64, 128]: + # if m in [32, 64] and (n not in [64, 128, 256]): + # continue + # print(f"======================= Test {m} {n} {k} False True =============================") + # run_gemm(m, n, k * 3, False, True, T.float8_e5m2, T.float, T.float, m, n, k, 2, 128) diff --git a/maint/gemm_v2/latency.py b/maint/gemm_v2/latency.py new file mode 100644 index 0000000000..b7b2a2af95 --- /dev/null +++ b/maint/gemm_v2/latency.py @@ -0,0 +1,98 @@ +import tilelang +import tilelang.language as T +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--use_v2", action="store_true") +args = parser.parse_args() + +use_v2 = args.use_v2 + + +# @tilelang.jit(target="cuda") +# target currently can be "cuda" or "hip" or "cpu". +# if not specified, it will be inferred from the input tensors during compile time +@tilelang.jit +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): + @T.prim_func + def matmul_relu_kernel( + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), + ): + # Initialize Kernel Context + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + B_shared = T.alloc_shared((block_K, block_N), dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + + # Enable rasterization for better L2 cache locality (Optional) + # T.use_swizzle(panel_size=10, enable=True) + + # Clear local accumulation + T.clear(C_local) + + for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3): + # Copy tile of A + # This is a sugar syntax for parallelized copy + T.copy(A[by * block_M, ko * block_K], A_shared) + + # Copy tile of B + T.copy(B[ko * block_K, bx * block_N], B_shared) + + # Perform a tile-level GEMM on the shared buffers + # Currently we dispatch to the cute/hip on Nvidia/AMD GPUs + if use_v2: + T.gemm_v2(A_shared, B_shared, C_local) + else: + T.gemm_v1(A_shared, B_shared, C_local) + + # relu + for i, j in T.Parallel(block_M, block_N): + C_local[i, j] = T.max(C_local[i, j], 0) + + # Copy result back to global memory + T.copy(C_local, C[by * block_M, bx * block_N]) + + return matmul_relu_kernel + + +M = 16384 # M = T.dynamic("m") if you want to use dynamic shape +N = 16384 +K = 16384 +block_M = 128 +block_N = 128 +block_K = 32 + +# 1. Define the kernel (matmul) and compile/lower it into an executable module +matmul_relu_kernel = matmul(M, N, K, block_M, block_N, block_K) + +# 3. Test the kernel in Python with PyTorch data +import torch + +# Create random input tensors on the GPU +a = torch.randn(M, K, device="cuda", dtype=torch.float16) +b = torch.randn(K, N, device="cuda", dtype=torch.float16) +c = torch.empty(M, N, device="cuda", dtype=torch.float16) + +# Run the kernel through the Profiler +matmul_relu_kernel(a, b, c) + +print(c) +# Reference multiplication using PyTorch +ref_c = torch.relu(a @ b) + +# Validate correctness +torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) +print("Kernel output matches PyTorch reference.") + +# 4. Retrieve and inspect the generated CUDA source (optional) +# cuda_source = jit_kernel.get_kernel_source() +# print("Generated CUDA kernel:\n", cuda_source) + +# 5.Profile latency with kernel +profiler = matmul_relu_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + +latency = profiler.do_bench() + +print(f"Latency: {latency} ms") diff --git a/maint/gemm_v2/latency_gemm.py b/maint/gemm_v2/latency_gemm.py new file mode 100644 index 0000000000..5f0450e023 --- /dev/null +++ b/maint/gemm_v2/latency_gemm.py @@ -0,0 +1,98 @@ +import tilelang +import tilelang.language as T +import argparse + +parser = argparse.ArgumentParser() +parser.add_argument("--use_v2", action="store_true") +args = parser.parse_args() + +use_v2 = args.use_v2 + + +# @tilelang.jit(target="cuda") +# target currently can be "cuda" or "hip" or "cpu". +# if not specified, it will be inferred from the input tensors during compile time +@tilelang.jit +def matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32): + @T.prim_func + def matmul_relu_kernel( + A: T.Tensor((M, K), dtype), + B: T.Tensor((K, N), dtype), + C: T.Tensor((M, N), dtype), + ): + # Initialize Kernel Context + with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by): + A_shared = T.alloc_shared((block_M, block_K), dtype) + B_shared = T.alloc_shared((block_K, block_N), dtype) + C_local = T.alloc_fragment((block_M, block_N), accum_dtype) + + # Enable rasterization for better L2 cache locality (Optional) + # T.use_swizzle(panel_size=10, enable=True) + + # Clear local accumulation + T.clear(C_local) + + for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=3): + # Copy tile of A + # This is a sugar syntax for parallelized copy + T.copy(A[by * block_M, ko * block_K], A_shared) + + # Copy tile of B + T.copy(B[ko * block_K, bx * block_N], B_shared) + + # Perform a tile-level GEMM on the shared buffers + # Currently we dispatch to the cute/hip on Nvidia/AMD GPUs + if use_v2: + T.gemm_v2(A_shared, B_shared, C_local) + else: + T.gemm_v1(A_shared, B_shared, C_local) + + # relu + for i, j in T.Parallel(block_M, block_N): + C_local[i, j] = T.max(C_local[i, j], 0) + + # Copy result back to global memory + T.copy(C_local, C[by * block_M, bx * block_N]) + + return matmul_relu_kernel + + +M = 16384 # M = T.dynamic("m") if you want to use dynamic shape +N = 16384 +K = 16384 +block_M = 128 +block_N = 128 +block_K = 64 + +# 1. Define the kernel (matmul) and compile/lower it into an executable module +matmul_relu_kernel = matmul(M, N, K, block_M, block_N, block_K) + +# 3. Test the kernel in Python with PyTorch data +import torch + +# Create random input tensors on the GPU +a = torch.randn(M, K, device="cuda", dtype=torch.float16) +b = torch.randn(K, N, device="cuda", dtype=torch.float16) +c = torch.empty(M, N, device="cuda", dtype=torch.float16) + +# Run the kernel through the Profiler +matmul_relu_kernel(a, b, c) + +print(c) +# Reference multiplication using PyTorch +ref_c = torch.relu(a @ b) + +# Validate correctness +torch.testing.assert_close(c, ref_c, rtol=1e-2, atol=1e-2) +print("Kernel output matches PyTorch reference.") + +# 4. Retrieve and inspect the generated CUDA source (optional) +# cuda_source = jit_kernel.get_kernel_source() +# print("Generated CUDA kernel:\n", cuda_source) + +# 5.Profile latency with kernel +profiler = matmul_relu_kernel.get_profiler(tensor_supply_type=tilelang.TensorSupplyType.Normal) + +latency = profiler.do_bench() + +print(f"Latency: {latency} ms") diff --git a/maint/gemm_v2/latency_mha_fwd_bhsd.py b/maint/gemm_v2/latency_mha_fwd_bhsd.py new file mode 100644 index 0000000000..7a83d7cec8 --- /dev/null +++ b/maint/gemm_v2/latency_mha_fwd_bhsd.py @@ -0,0 +1,228 @@ +import torch +import torch.nn.functional as F +import tilelang +from tilelang.autotuner import * +import tilelang.language as T +import itertools +import argparse +from functools import partial + +parser = argparse.ArgumentParser() +parser.add_argument("--batch", type=int, default=128, help="batch size") +parser.add_argument("--heads", type=int, default=16, help="heads") +parser.add_argument("--seq_q", type=int, default=1024, help="query sequence length") +parser.add_argument("--seq_kv", type=int, default=1024, help="key/value sequence length") +parser.add_argument("--dim", type=int, default=256, help="dim") +parser.add_argument("--is_causal", action="store_true", help="causal") +parser.add_argument("--tune", action="store_true", help="tune configs") +parser.add_argument("--use_v2", action="store_true") + +args = parser.parse_args() + +use_v2 = args.use_v2 + + +def get_configs(): + iter_params = dict(block_M=[128], block_N=[128], num_stages=[2], threads=[256]) + return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())] + + +@autotune(configs=get_configs(), warmup=10, rep=10) +@tilelang.jit( + out_idx=[3], + pass_configs={ + tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True, + }, +) +def flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=0, threads=128): + scale = (1.0 / dim) ** 0.5 * 1.44269504 # log2(e) + q_shape = [batch, heads, seq_q, dim] + kv_shape = [batch, heads, seq_kv, dim] + dtype = T.float16 + accum_dtype = T.float32 + + past_len = seq_kv - seq_q + assert past_len >= 0, "seq_kv must be greater than or equal to seq_q" + + @T.macro + def MMA0( + K: T.Tensor(kv_shape, dtype), + Q_shared: T.SharedBuffer([block_M, dim], dtype), + K_shared: T.SharedBuffer([block_N, dim], dtype), + acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), + k: T.int32, + bx: T.int32, + by: T.int32, + bz: T.int32, + ): + T.copy(K[bz, by, k * block_N : (k + 1) * block_N, :], K_shared) + if is_causal: + for i, j in T.Parallel(block_M, block_N): + q_idx = bx * block_M + i + past_len + k_idx = k * block_N + j + acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype)) + else: + T.clear(acc_s) + if use_v2: + T.gemm_v2(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + else: + T.gemm_v1(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow) + + @T.macro + def MMA1( + V: T.Tensor(kv_shape, dtype), + V_shared: T.SharedBuffer([block_N, dim], dtype), + acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), + acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), + k: T.int32, + by: T.int32, + bz: T.int32, + ): + T.copy(V[bz, by, k * block_N : (k + 1) * block_N, :], V_shared) + # T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + if use_v2: + T.gemm_v2(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + else: + T.gemm_v1(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow) + + @T.macro + def Softmax( + acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype), + acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype), + scores_max: T.FragmentBuffer([block_M], accum_dtype), + scores_max_prev: T.FragmentBuffer([block_M], accum_dtype), + scores_scale: T.FragmentBuffer([block_M], accum_dtype), + scores_sum: T.FragmentBuffer([block_M], accum_dtype), + logsum: T.FragmentBuffer([block_M], accum_dtype), + ): + T.copy(scores_max, scores_max_prev) + T.fill(scores_max, -T.infinity(accum_dtype)) + T.reduce_max(acc_s, scores_max, dim=1, clear=False) + # To do causal softmax, we need to set the scores_max to 0 if it is -inf + # This process is called Check_inf in FlashAttention3 code, and it only need to be done + # in the first ceil_div(kBlockM, kBlockN) steps. + # for i in T.Parallel(block_M): + # scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0, scores_max[i]) + for i in T.Parallel(block_M): + scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale) + + for i, j in T.Parallel(block_M, block_N): + # Instead of computing exp(x - max), we compute exp2(x * log_2(e) - + # max * log_2(e)) This allows the compiler to use the ffma + # instruction instead of fadd and fmul separately. + acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale) + T.reduce_sum(acc_s, scores_sum, dim=1) + for i in T.Parallel(block_M): + logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i] + T.copy(acc_s, acc_s_cast) + + @T.macro + def Rescale( + acc_o: T.FragmentBuffer([block_M, dim], accum_dtype), + scores_scale: T.FragmentBuffer([block_M], accum_dtype), + ): + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] *= scores_scale[i] + + @T.prim_func + def main( + Q: T.Tensor(q_shape, dtype), + K: T.Tensor(kv_shape, dtype), + V: T.Tensor(kv_shape, dtype), + Output: T.Tensor(q_shape, dtype), + ): + with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz): + Q_shared = T.alloc_shared([block_M, dim], dtype) + K_shared = T.alloc_shared([block_N, dim], dtype) + V_shared = T.alloc_shared([block_N, dim], dtype) + O_shared = T.alloc_shared([block_M, dim], dtype) + acc_s = T.alloc_fragment([block_M, block_N], accum_dtype) + acc_s_cast = T.alloc_fragment([block_M, block_N], dtype) + acc_o = T.alloc_fragment([block_M, dim], accum_dtype) + scores_max = T.alloc_fragment([block_M], accum_dtype) + scores_max_prev = T.alloc_fragment([block_M], accum_dtype) + scores_scale = T.alloc_fragment([block_M], accum_dtype) + scores_sum = T.alloc_fragment([block_M], accum_dtype) + logsum = T.alloc_fragment([block_M], accum_dtype) + + T.copy(Q[bz, by, bx * block_M : (bx + 1) * block_M, :], Q_shared) + T.fill(acc_o, 0) + T.fill(logsum, 0) + T.fill(scores_max, -T.infinity(accum_dtype)) + + loop_range = ( + T.min(T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N)) + if is_causal + else T.ceildiv(seq_kv, block_N) + ) + + for k in T.Pipelined(loop_range, num_stages=num_stages): + MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz) + Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum, logsum) + Rescale(acc_o, scores_scale) + MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz) + for i, j in T.Parallel(block_M, dim): + acc_o[i, j] /= logsum[i] + T.copy(acc_o, O_shared) + T.copy(O_shared, Output[bz, by, bx * block_M : (bx + 1) * block_M, :]) + + return main + + +def ref_program(Q, K, V, is_causal): + dim = Q.size(-1) + scores = torch.einsum("bhqd,bhkd->bhqk", Q, K) + scores = scores / torch.sqrt(torch.tensor(dim, dtype=scores.dtype)) + if is_causal: + seq_q = Q.size(2) + seq_kv = K.size(2) + mask = torch.tril(torch.ones(seq_q, seq_kv, device=scores.device), seq_kv - seq_q) + mask = mask.unsqueeze(0).unsqueeze(0) + scores = scores.masked_fill(mask == 0, float("-inf")) + attention_weights = F.softmax(scores, dim=-1) + output = torch.einsum("bhqk,bhkd->bhqd", attention_weights, V) + return output + + +def main( + batch: int = 1, + heads: int = 1, + seq_q: int = 256, + seq_kv: int = 256, + dim: int = 64, + is_causal: bool = False, + tune: bool = False, +): + flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim + total_flops = 2 * flops_per_matmul + if is_causal: + total_flops *= 0.5 + + if not tune: + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal, block_M=64, block_N=64, num_stages=0, threads=128) + print(kernel.get_kernel_source()) + ref_program_processed = partial(ref_program, is_causal=is_causal) + + profiler = kernel.get_profiler() + profiler.assert_allclose(ref_program_processed, rtol=0.01, atol=0.01) + print("All checks pass.") + latency = profiler.do_bench(ref_program_processed, warmup=500) + print(f"Ref: {latency:.2f} ms") + print(f"Ref: {total_flops / latency * 1e-9:.2f} TFlops") + latency = profiler.do_bench(warmup=500) + print(f"Tile-lang: {latency:.2f} ms") + print(f"Tile-lang: {total_flops / latency * 1e-9:.2f} TFlops") + else: + kernel = flashattn(batch, heads, seq_q, seq_kv, dim, is_causal) + best_latency = kernel.latency + best_config = kernel.config + ref_latency = kernel.ref_latency + print(f"Best latency: {best_latency}") + print(f"Best TFlops: {total_flops / best_latency * 1e-9}") + print(f"Best config: {best_config}") + print(f"Ref latency: {ref_latency}") + + +if __name__ == "__main__": + tilelang.disable_cache() + main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.is_causal, args.tune) diff --git a/maint/host_checks/01_num_args_mismatch.py b/maint/host_checks/01_num_args_mismatch.py new file mode 100644 index 0000000000..9528652eea --- /dev/null +++ b/maint/host_checks/01_num_args_mismatch.py @@ -0,0 +1,22 @@ +"""Reproduce: Argument count mismatch. + +Note: The adapter-level wrapper expects only inputs (A, B) because C is marked as output. +Calling with the wrong number of inputs raises a ValueError before host entry. +""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 256 + fn = build_matmul_kernel(M, N, K, target="cuda") + + a = torch.empty((M, K), device="cuda", dtype=torch.float16) + # Missing b + # Expected: ValueError with message about expected vs. actual inputs + fn(a) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/02_pointer_type_error.py b/maint/host_checks/02_pointer_type_error.py new file mode 100644 index 0000000000..188a4f8cc0 --- /dev/null +++ b/maint/host_checks/02_pointer_type_error.py @@ -0,0 +1,23 @@ +"""Reproduce: Pointer-type argument expected but scalar provided. + +We pass an integer for A; wrapper forwards it to the host where a pointer is expected. +Expected: error like "Expect buffer A_handle to be pointer or tensor" (exact name depends on kernel param). +""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 256 + fn = build_matmul_kernel(M, N, K, target="cuda") + + # Wrong type for A (int instead of tensor) + a = 1 + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/03_ndim_mismatch.py b/maint/host_checks/03_ndim_mismatch.py new file mode 100644 index 0000000000..76637e8ded --- /dev/null +++ b/maint/host_checks/03_ndim_mismatch.py @@ -0,0 +1,19 @@ +"""Reproduce: ndim (rank) mismatch for A.""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 128 + fn = build_matmul_kernel(M, N, K, target="cuda") + + # A has rank 3 instead of 2 + a = torch.empty((M, K, 1), device="cuda", dtype=torch.float16) + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/04_dtype_mismatch.py b/maint/host_checks/04_dtype_mismatch.py new file mode 100644 index 0000000000..f3554c1d6a --- /dev/null +++ b/maint/host_checks/04_dtype_mismatch.py @@ -0,0 +1,19 @@ +"""Reproduce: dtype mismatch for A (float32 vs expected float16).""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 128 + fn = build_matmul_kernel(M, N, K, target="cuda") + print(fn.get_host_source()) + + a = torch.empty((M, K), device="cuda", dtype=torch.float32) # should be float16 + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/05_shape_mismatch.py b/maint/host_checks/05_shape_mismatch.py new file mode 100644 index 0000000000..a482481765 --- /dev/null +++ b/maint/host_checks/05_shape_mismatch.py @@ -0,0 +1,19 @@ +"""Reproduce: shape constant/symbol mismatch on A.""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 128 + fn = build_matmul_kernel(M, N, K, target="cuda") + + # A's second dimension is wrong (K+1 instead of K) + a = torch.empty((M, K + 1), device="cuda", dtype=torch.float16) + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/06_strides_mismatch.py b/maint/host_checks/06_strides_mismatch.py new file mode 100644 index 0000000000..7e523cd64e --- /dev/null +++ b/maint/host_checks/06_strides_mismatch.py @@ -0,0 +1,19 @@ +"""Reproduce: strides check failure (non-contiguous A via transpose).""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 128 + fn = build_matmul_kernel(M, N, K, target="cuda") + + a = torch.empty((M, K), device="cuda", dtype=torch.float16) + a_nc = a.t() # non-contiguous after transpose + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a_nc, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/07_device_type_mismatch.py b/maint/host_checks/07_device_type_mismatch.py new file mode 100644 index 0000000000..af8e5efd5d --- /dev/null +++ b/maint/host_checks/07_device_type_mismatch.py @@ -0,0 +1,18 @@ +"""Reproduce: device_type mismatch by passing CPU tensors to a CUDA kernel.""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 64 + fn = build_matmul_kernel(M, N, K, target="cuda") + + a = torch.empty((M, K), device="cpu", dtype=torch.float16) + b = torch.empty((K, N), device="cpu", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/08_device_id_mismatch.py b/maint/host_checks/08_device_id_mismatch.py new file mode 100644 index 0000000000..280aca1570 --- /dev/null +++ b/maint/host_checks/08_device_id_mismatch.py @@ -0,0 +1,25 @@ +"""Reproduce: device_id mismatch (requires >=2 CUDA devices).""" + +import torch +from common import build_matmul_kernel + + +def main(): + if not torch.cuda.is_available(): + raise RuntimeError("CUDA is not available") + if torch.cuda.device_count() < 2: + print("[SKIP] Need at least 2 CUDA devices to reproduce device_id mismatch.") + return + + M = N = K = 64 + fn = build_matmul_kernel(M, N, K, target="cuda") + + a = torch.empty((M, K), device="cuda:0", dtype=torch.float16) + b = torch.empty((K, N), device="cuda:1", dtype=torch.float16) + # Output device is derived by the adapter; mismatch occurs in host checks + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/09_null_data_pointer.py b/maint/host_checks/09_null_data_pointer.py new file mode 100644 index 0000000000..09f5de1aff --- /dev/null +++ b/maint/host_checks/09_null_data_pointer.py @@ -0,0 +1,26 @@ +"""Reproduce: NULL data pointer (advanced). + +Passing None for a tensor argument will be forwarded through the adapter. Depending on +FFI handling, this commonly triggers a pointer-type assertion (e.g., "Expect buffer to be pointer or tensor") +or a host-side non-NULL pointer check. + +Note: Constructing a true DLTensor with NULL data in PyTorch is not typical; this script +demonstrates passing None, which still reproduces the intended class of failure. +""" + +import torch +from common import build_matmul_kernel + + +def main(): + M = N = K = 64 + fn = build_matmul_kernel(M, N, K, target="cuda") + + a = None # attempt to pass a null-like pointer + b = torch.empty((K, N), device="cuda", dtype=torch.float16) + + fn(a, b) + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/10_scalar_type_mismatch.py b/maint/host_checks/10_scalar_type_mismatch.py new file mode 100644 index 0000000000..4f2c90b8d1 --- /dev/null +++ b/maint/host_checks/10_scalar_type_mismatch.py @@ -0,0 +1,15 @@ +"""Reproduce: scalar parameter type mismatch (int/bool).""" + +from common import build_scalar_check_kernel + + +def main(): + fn = build_scalar_check_kernel(target="cuda") + + # Wrong types + fn(1.0, True) # x should be int -> Expect arg[0] to be int + fn(1, 2.5) # flag should be bool -> Expect arg[1] to be boolean + + +if __name__ == "__main__": + main() diff --git a/maint/host_checks/README.md b/maint/host_checks/README.md new file mode 100644 index 0000000000..ac23d6fd2a --- /dev/null +++ b/maint/host_checks/README.md @@ -0,0 +1,21 @@ +# Host-Side Check Repro Scripts + +This folder contains standalone scripts that deliberately trigger host-side (and adapter-side) validation errors described in `docs/compiler_internals/tensor_checks.md`. Each script can be run directly and will reproduce the corresponding error with a minimal example. + +Prerequisites +- CUDA-capable environment (most scripts compile a CUDA-targeted kernel) +- Python packages: torch, tilelang + +Usage +- Run any script, e.g.: + - `python 01_num_args_mismatch.py` + - `python 02_pointer_type_error.py` + - ... up to `10_scalar_type_mismatch.py` + +- Or run all at once with a summary: + - `python run_all.py` + - Logs per test are saved under `logs/` as `