[fusheng-ji]

Evidence location: the measurement outputs of this PR (JSON, figures, audit files) are not part of its diff. They are on the fork's pr_assets branch under pr495/ (links in this description are fixed to that commit). Paths such as reports/experiments/... below name where a runner writes its output.

Status

• Open question for @Flink-ddd and the reviewers: keep this kernel, or reuse an existing one? flash-attn's Triton cross entropy, the kernel verl's logprobs_from_logits calls, is batch-invariant, uses the same memory as this PR's kernel, and is faster: 6.2 vs 27.2 ms forward + backward at 32768 rows, and 3.7 vs 12.5 ms for the backward alone. This kernel's remaining differences are a smaller dlogits error (max 1.1e-3 vs 2.6e-3 of the largest gradient; 99.9986% vs 99.990% of elements correctly rounded) and that it needs no Triton or flash-attn dependency. Under the reuse rule from RFC [RFC][MiniMax-H3][CUDA/ROCm] WS1/WS2 kernel roadmap, ablation matrix and integration plan #420 that would point to reusing flash-attn's kernel, or vendoring it (BSD-3). That adds a dependency, so it's your call. Details are under Prior art.
• Added: existing implementations in the evidence (9cec6d0 runner, de63477 evidence): Liger, FLA and flash-attn cross entropy, plus a dense batch-size sweep. These replace the earlier torch-only comparison (f338e29, 4b81603).

Summary

Addresses #174 (long-sequence logp memory safety and workspace sizing) for the generic CUDA fused logp op (FusedLogpGenericOp / _FusedLogpAutograd in rl_engine/kernels/ops/cuda/loss/logp.py).

The previous backward materialized FP32 softmax and an FP32 gradient over the full [N, V] logits, about 10 bytes of transient memory per logit. At 32k response tokens × 152k vocab, forward + backward peaked at 46 GiB. This is where 8k–32k CoT training runs out of memory.

The PR has two commits:

1. Bounded row-chunked FP32 backward (fused_logp_backward_chunked). The VJP is computed over row chunks of at most FUSED_LOGP_BWD_CHUNK_ELEMS = 2^27 FP32 elements, so the transient workspace is ~1 GiB regardless of N. The output is bitwise identical to the previous implementation for any chunk size. This path is kept as the fallback for _C builds that do not have the new kernel.
2. Fused CUDA backward kernel (_C.fused_logp_backward). It computes dlogits = grad * (one_hot - softmax) with one block per row and writes it directly in the logits dtype, so the backward allocates nothing beyond the returned gradient.
   • Its max / sum-exp passes visit each thread's elements in the same order as fused_logp_forward_kernel, with the same 256-thread block and the same blockReduceMax / blockReduceSum. The softmax used in the backward is therefore bitwise consistent with the forward logp.
   • Loads are batched 8 at a time (kFusedLogpBackwardUnroll) for memory-level parallelism. Accumulation order does not change: the unrolled kernel is bitwise identical to the non-unrolled version on all checked cases.
   • It is deterministic and batch-invariant, with one block per row and a fixed reduction tree.
   • _FusedLogpAutograd.backward uses the kernel when the backend provides it and otherwise uses the chunked path.

Both paths also fix target handling at the boundary; see the behavior changes below.

Not touched: the forward kernels, the SM90 fused_logp_sm90 path, linear_logp, and the PyTorch / Triton / Ascend logp ops.

Prior art & reuse decision

Each implementation computes the selected-token logp and its gradient from BF16 logits; for the cross-entropy kernels, logp = -loss of a per-token cross entropy. Batch invariance was checked bitwise: eight rows computed alone vs at the front, middle and back of batches of every size 1–9 and 2^k−1 / 2^k / 2^k+1 up to 2048, over three seeds, for both logp and dlogits.

Implementation	Batch-invariant (logp / dlogits)	Peak memory, 32k rows	Forward + backward, 32k rows	Notes
torch log_softmax + gather	yes / yes	55.6 GiB	44.0 ms	builds the FP32 [N, V] softmax, which is what #174 is about
vLLM batch-invariant log_softmax	yes / no backward	n/a	n/a	inference only
Liger cross_entropy (reduction="none")	yes / yes	9.3 GiB	15.8 ms	writes the gradient into the logits; 74.3% of dlogits correctly rounded
FLA fused_cross_entropy	no (92 / 92 of 2232 comparisons differ)	9.3 GiB	7.1 ms	a few percent of rows change by one FP32 ulp with the batch size
flash-attn Triton cross_entropy_loss (verl's path)	yes / yes	9.3 GiB	6.2 ms	99.990% of dlogits correctly rounded; returns FP32 logp
this PR's fused kernel	yes / yes	9.3 GiB	27.2 ms	99.9986% of dlogits correctly rounded; no new dependency

Decision: open; see the question under Status. This PR's kernel is not the only batch-invariant backward that fits long sequences: flash-attn's and Liger's do too, and flash-attn's is 4.4× faster end to end and 3.4× faster on the backward alone. The PR keeps its own kernel until the reviewers decide. If the decision is to reuse, the chunked fallback and the boundary-target fixes below still apply.

Comparison charts

B200, 32768 rows, V = 151936, BF16	torch log_softmax + gather	chunked fallback	fused kernel	Liger CE	FLA CE	flash-attn CE (verl)
forward + backward	44.0 ms	59.4 ms	27.2 ms	15.8 ms	7.1 ms	6.2 ms
backward only	19.3 ms	44.8 ms	12.5 ms	11.3 ms	3.7 ms	3.7 ms
peak memory (incl. returned grad)	55.6 GiB	10.3 GiB	9.3 GiB	9.3 GiB	9.3 GiB	9.3 GiB
dlogits max error / max, vs FP64	2.6e-3	1.1e-3	1.1e-3	1.1e-3	2.6e-3	2.6e-3
dlogits correctly rounded	99.994%	99.9995%	99.9986%	74.3%	99.990%	99.990%
batch-size sweep, logp / dlogits differing (of 2232)	0 / 0	0 / 0	0 / 0	0 / 0	92 / 92	0 / 0

The FP64 reference is fed the same upstream gradient each path receives. docs/usage/evidence/fused-logp-backward-b200/report.json was written by benchmarks/fused_logp_backward_evidence.py --flash-attn-src <checkout> from a clean tree at 574d3ef on an otherwise idle B200 (torch 2.13.0+cu130, triton 3.7.1, liger-kernel 0.8.4, fla-core 0.5.2, flash-attention 94e22c9). The previous VJP (57.1 ms, 46.4 GiB) is still in the report and the figure.

Behavior changes

• Out-of-range targets. The forward kernel already returns a constant 0 for targets outside [0, V), such as the ignore index -100. The old backward indexed probs[rows, labels] directly. Python wrapped -100 into a real vocab column, giving a nonzero gradient inconsistent with the forward, and targets >= V raised an indexing error. Those rows now get an exactly zero gradient.
• Gradient numerics for valid targets. With the fused kernel, valid-target gradients are no longer bit-for-bit equal to the old torch.softmax-based VJP, because the softmax now comes from the forward kernel's own reductions.
  • Measured max abs difference against the chunked/old path: ~1e-8 for BF16, ~8e-6 for FP16, ~2e-6 for FP32.
  • The FP32 difference comes from the extension being built with --use_fast_math, so expf is the fast intrinsic. The forward kernel has the same property.
  • Against an FP64 reference, BF16 and FP16 error is identical to the old path.
  • On builds without the kernel, the chunked fallback is bitwise identical to the old path.

Results

Single B200, V = 151936, BF16 logits, median of 5. All variants call the same _C.fused_logp forward through _FusedLogpAutograd; only the backward differs. Peak memory is measured from before the step and includes the returned BF16 gradient (2.3 / 4.6 / 9.3 GiB).

Forward + backward

N rows	Before	Chunked fallback	Fused kernel
8192	11.59 GiB / 14.5 ms	3.32 GiB / 15.0 ms	2.32 GiB / 7.0 ms
16384	23.18 GiB / 28.7 ms	5.64 GiB / 29.8 ms	4.64 GiB / 13.7 ms
32768	46.37 GiB / 57.1 ms	10.27 GiB / 59.3 ms	9.27 GiB / 27.2 ms

Backward only

N rows	Before	Chunked fallback	Fused kernel
8192	10.6 ms	11.2 ms	3.2 ms
16384	21.2 ms	22.3 ms	6.2 ms
32768	42.2 ms	44.4 ms	12.2 ms

The fused backward reads each row 3 times and writes it once. At 32k rows it runs about 2.4× above the HBM floor (~5 ms at ~8 TB/s). A single-pass online max/sum would remove one read, but it would break the bitwise consistency with the forward's two-pass reduction, so this PR does not do it.

The chunk size of the fallback was chosen from this sweep at N = 32768:

chunk_elems	rows/chunk	transient	time
2^22	27	0.03 GiB	114.8 ms
2^24	110	0.13 GiB	54.8 ms
2^26	441	0.50 GiB	48.8 ms
2^27	883	1.00 GiB	44.4 ms
2^28	1766	2.00 GiB	43.3 ms

Tests

New tests/test_fused_logp_chunked_backward.py (46 tests).

Chunked fallback:

• bitwise equal to the previous VJP for FP32/BF16 at chunk sizes of 1/3/7/64 rows (uneven tails included), on CPU and CUDA
• agreement with the autograd log_softmax + gather gradient
• zero rows for out-of-range targets (-100, V, V+5) while other rows stay bitwise unchanged

Fused kernel:

• agreement with an FP64 reference for FP32/BF16/FP16 at V = 1, 255, 1031 and 151936
• bitwise run-to-run determinism, and batch invariance across row subsets
• out-of-range targets and an -inf-masked vocab tail: zero gradient, all finite
• input validation (grad dtype, target dtype, length mismatch) and empty input

Dispatch:

• FusedLogpGenericOp autograd uses the fused kernel
• a backend without fused_logp_backward falls back to the chunked path

Workspace at 8k / 16k / 32k rows × 151936 vocab:

• fused: zero bytes beyond the output
• chunked: within the bound
• skipped when free device memory is insufficient

pytest -q tests/test_fused_logp_chunked_backward.py
# 46 passed

pytest -q tests/test_fused_logp_chunked_backward.py tests/test_logp.py \
  tests/test_deterministic_logp.py tests/test_batch_invariant_logp.py \
  tests/test_op_accuracy.py tests/test_rl_kernel_loss_step.py tests/test_logprob_comparison.py \
  tests/test_gradient_invariance.py tests/test_ws1_gtest_gpu.py \
  tests/test_ws1_chain_integration.py tests/test_tolerance_contract.py
# 327 passed, 34 skipped

All 34 skips need hardware not present (SM90-only kernels, Hopper-only comparisons, Ascend), plus one CUDA-dispatch guard in test_logp.py.

Environment: 1× NVIDIA B200, CUDA 13.0, PyTorch 2.13.0+cu130, Python 3.12. The extension was built with TORCH_CUDA_ARCH_LIST=10.0 python setup.py build_ext --inplace. Formatting was checked with black 24.4.2 and isort 5.13.2 (--line-length=100), as pinned in .pre-commit-config.yaml.

Not verified:

• ROCm: not compiled and not run. fused_logp_backward is registered next to fused_logp and is also built for ROCm; it only uses the existing HIP-compatible helpers (fused_logp_shfl_down_32, blockReduce*).
• Hopper (SM90) and Ampere: not run. The kernel uses no architecture-specific features.

Benchmark script (forward + backward)

import torch
import rl_engine.kernels.ops.cuda.loss.logp as m
from rl_engine.kernels.ops.base import _C

def original_vjp(logits, labels, g, dt):
    probs = torch.softmax(logits.float(), dim=-1)
    rows = torch.arange(logits.size(0), device=logits.device)
    probs[rows, labels] -= 1.0
    return (-g.reshape(-1, 1).float() * probs).to(dt)

class ForwardOnly:  # a _C build without fused_logp_backward
    fused_logp = staticmethod(_C.fused_logp)

chunked = m.fused_logp_backward_chunked
V = 151936
for n in (8192, 16384, 32768):
    x0 = torch.randn(n, V, device="cuda", dtype=torch.bfloat16)
    y = torch.randint(0, V, (n,), device="cuda")
    for name, backend, vjp in (("before", ForwardOnly(), original_vjp),
                               ("chunked", ForwardOnly(), chunked),
                               ("fused", _C, chunked)):
        m.fused_logp_backward_chunked = vjp
        def step():
            x = x0.detach().requires_grad_(True)
            m._FusedLogpAutograd.apply(x, y, backend).float().sum().backward()
            return x.grad
        step()  # warm-up
        torch.cuda.synchronize(); torch.cuda.reset_peak_memory_stats()
        base = torch.cuda.memory_allocated()
        s, e = torch.cuda.Event(True), torch.cuda.Event(True); ts = []
        for _ in range(5):
            s.record(); g = step(); e.record(); torch.cuda.synchronize()
            ts.append(s.elapsed_time(e)); del g
        peak = (torch.cuda.max_memory_allocated() - base) / 2**30
        print(n, name, f"peak={peak:.2f}GiB", f"t={sorted(ts)[2]:.1f}ms")
    m.fused_logp_backward_chunked = chunked
    del x0; torch.cuda.empty_cache()

Relation to other work

#234 also edits the backward in rl_engine/kernels/ops/cuda/loss/logp.py and csrc/fused_logp_kernel.cu. It currently conflicts with main; whichever lands second will need a rebase.

Summary by CodeRabbit

• New Features
  • Added fused CUDA support for log-probability gradient computation, with a memory-conscious fallback when fused support is unavailable.
  • Invalid target IDs now produce zero gradients in either computation path.
• Bug Fixes
  • Improved handling of long sequences by avoiding a full-tensor softmax workspace during backward computation.
• Documentation
  • Added benchmark results comparing gradient accuracy, performance, memory use, and consistency across batch sizes.

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📝 Walkthrough

Adds and exposes a CUDA backward kernel for fused log-probability. The autograd path uses the kernel when available and otherwise computes gradients in bounded row chunks. Tests cover both paths, input validation, gradient values, and workspace limits. Benchmark tools and evidence report accuracy, row invariance, timing, and memory use.

Changes: Fused log-probability backward

Layer / File(s)	Summary
CUDA kernel and backend binding csrc/fused_logp_kernel.cu, csrc/ops.cpp, rl_engine/_C.pyi	Adds and exposes fused_logp_backward. The CUDA entry point validates inputs, handles empty batches, and launches a per-row kernel that computes gradients for valid targets and zeros gradients for invalid targets.
Python dispatch, fallback, and validation rl_engine/kernels/ops/cuda/loss/logp.py, tests/test_fused_logp_chunked_backward.py	The autograd path uses the fused operation when available and otherwise uses an FP32 row-chunked fallback. Tests compare both paths with references and check invalid targets, validation, and workspace bounds.
Benchmark evidence and plots benchmarks/fused_logp_backward_evidence.py, benchmarks/plot_fused_logp_backward_evidence.py, docs/operators/fused-logp.md, docs/usage/evidence/fused-logp-backward-b200/report.json	Adds benchmark and plotting scripts. The report and operator documentation include accuracy, row-invariance, timing, and memory measurements for seven implementations.

Priority: ➖ Normal

Estimated code review effort: 3 (Moderate) | ~25 minutes

Change: Feature

Sequence Diagram(s)

sequenceDiagram
  participant AutogradBridge
  participant BackendExtension
  participant CudaKernel
  AutogradBridge->>BackendExtension: call fused_logp_backward when available
  BackendExtension->>CudaKernel: launch backward kernel
  CudaKernel-->>BackendExtension: return logits gradients
  BackendExtension-->>AutogradBridge: return logits gradients

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Merge Risk: ⚪ Minimal · up to 5969a

The invalid-label gradient fix is in place, and no merge-blocking issue remains in the inspected change.

Security architecture risk: 🔵 Low · up to efcac

The new backward path admits exceptionally large vocabulary sizes that can overflow its loop indices, potentially causing invalid GPU memory access or stalled GPU work. Ordinary vocabulary sizes do not trigger this case. Reaching it requires control of tensor dimensions and substantial GPU memory; remote or cross-tenant exposure has not been established.

Retained concerns

• Medium · security · inferred: The new public backward API admits dimensions outside the safe domain of its signed loop arithmetic. With the default launch configuration and a valid target, V=2147482624 passes validation, but base += tile exceeds INT_MAX. Overflow can invalidate the loop termination and memory bounds, potentially faulting or stalling the caller's CUDA workload. This is newly exposed backward behavior, not merely the unchanged forward limit. It requires an exceptionally large tensor; one BF16 row plus its returned gradient needs approximately 8 GiB, and attacker-controlled production reachability is unproven.

Security review details

Security Blast Radius

• inferred — The demonstrated exposure is callers able to submit tensors to the native method or existing generic autograd path. The index-overflow concern could affect GPU work sharing the caller's CUDA context. Supplied evidence does not establish remote shape control, cross-tenant access, privilege escalation, or exposure of other services or data stores.

Security Findings and Attack Paths

• inferred — The source-derived failure path is caller-controlled vocabulary dimension, acceptance by the INT_MAX admission check, then signed overflow in the unrolled valid-target loop. This can undermine bounds and termination despite valid tensor ranks, lengths, devices, and target IDs. The path requires substantial memory and was not executed; it is an inferred concern, not a verified exploit.

Trust Boundaries and Controls

• observed — Host validation checks tensor compatibility, and invalid targets are handled before target indexing. Device equality does not itself select the active CUDA device: the new wrapper launches on the current stream without an explicit device guard. The existing forward uses the same convention, so this observation alone does not establish newly expanded multi-device exposure.

Resilience and Maintainability Implications

• observed — Per-invocation saved tensors and fresh output allocation avoid shared writable gradient buffers. The native wrapper checks launch errors. Tests specify repeated execution, batch invariance, malformed inputs, and empty batches; they do not establish overlapping-call isolation or containment of asynchronous execution failures.

Hardening Proposals

• proposed — Align the admitted dimension range with the kernel arithmetic by using wider loop indices or a bound that includes unrolled increments and offsets. Validate this boundary independently of ordinary numerical-comparison tests.

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Review comments at @rl_engine/kernels/ops/cuda/loss/logp.py:
- Line 50: After scaling `probs` in the loss backward path, explicitly zero rows
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[Flink-ddd]

Please add a device guard and a multi-GPU regression test for fused_logp_backward, and fix the int32 loop overflow near INT_MAX.

Please also rerun the accuracy comparison with identical effective upstream gradients, the current BF16 and FP32 paths use different reference gradients. I'd favor reusing flash-attn if it meets our train and rollout bitwise contract.

Since this backward is also enabled on ROCm, please add build/runtime validation or keep that path disabled there until validated.

[fusheng-ji]

Please add a device guard and a multi-GPU regression test for fused_logp_backward, and fix the int32 loop overflow near INT_MAX.

Please also rerun the accuracy comparison with identical effective upstream gradients, the current BF16 and FP32 paths use different reference gradients. I'd favor reusing flash-attn if it meets our train and rollout bitwise contract.

Since this backward is also enabled on ROCm, please add build/runtime validation or keep that path disabled there until validated.

Hi @Flink-ddd. Thanks for your time and the review. The fixes and additional experiments are pushed through 7b095615.

• Device guard: Added CUDAGuard before copies, allocations, and launch, using the logits device’s current stream. The two-GPU regression fails against the old extension and passes with the fix.
• Integer overflow: Changed vocabulary loop counters and unrolled offsets to int64_t, including forward loops. The boundary regression reproduces the old overflow near INT_MAX under UBSan.
• ROCm: Disabled the fused backward kernel and binding on HIP. Python dispatch also bypasses stale ROCm extensions exposing the symbol and uses the bounded chunked fallback.
• Accuracy: Reran all implementations with identical effective upstream gradients and one common FP64 reference.

For accuracy, the upstream gradient is quantized to BF16 once and promoted losslessly to FP32. The runner asserts that each output boundary preserves those values. Inputs are BF16 logits, with 257 rows and vocabulary size 151936.

Implementation	Maximum absolute gradient error vs FP64	Correctly rounded BF16 gradients
PyTorch log_softmax + gather	0.00321335	99.992161%
Previous full FP32 VJP	0.00321335	99.999498%
Chunked fallback	0.00321335	99.999498%
Fused CUDA kernel	0.00321335	99.998632%
Liger CE	0.00321335	74.264804%
FLA CE	0.00321335	99.991864%
flash-attn CE	0.00321335	99.989418%

The earlier maximum-error advantage claim is withdrawn. It resulted from comparing different effective upstream gradients.

I also completed the previously missing actual Vime/vLLM WS2 provider experiment. It calls the genuine vLLM Sampler.forward and both integration providers, observing their actual logp/LSE outputs without replacing numerical kernels. Each topology covers 48 configurations: three seeds, BF16/FP32, temperatures 1.0/0.7, padding/support masks, permutations, chunking, and active-row compaction. TP=2 uses two physical B200s with NCCL.

For the coordinated CE experiment, the same pinned flash-attn implementation scores training shards and the full rollout vocabulary using the actual sampler-selected IDs. Differences between CE and the existing implementation are recorded as diagnostics; the replacement gate compares CE training against CE rollout.

Same-batch training → rollout scoring	Differing logp rows	Differing LSE rows	Maximum absolute difference
Existing WS2: TP1 → TP1	0/1200	0/1200	0
Existing WS2: TP2 → TP1	0/1200	0/1200	0
Coordinated CE: TP1 → TP1	0/1200	0/1200	0
Coordinated CE: TP2 → TP1	282/1200	282/1200	1.907e-6

The rollout adapter scores the full gathered vocabulary at TP=1. Both TP=2 ranks reproduce the reported counts; they are replicated checks of the same configurations.

CE passes the tested same-topology training/no_grad, permutation, chunking, and compaction checks. However, the tested distributed CE composition does not satisfy the cross-TP bitwise contract, so I retained the existing forward implementation.

The experiment also exposed an existing WS2 reference-provider limitation: both providers agree within the same layout, but changing the layout produces logp/LSE differences:

• Single-row chunks: 22/1200 rows per output.
• Seven-row chunks: 27/1200.
• Active-row compaction: 6/456.

An independent PyTorch-only reproduction isolated the local tile’s torch.sum: its accumulation order changes with the row count. These baseline failures are explicitly recorded; this experiment does not claim that the existing WS2 provider passes the complete layout contract.

Validation includes 12 passing new provider regression tests. The earlier checks had 279 passes and 34 skips; one additional Triton failure was reproduced on the unchanged baseline. Vime WS2 top-p replay is explicitly rejected and remains unvalidated.

The [provider report, complete raw data, and reproduction commands] document these results. This is a provider-level experiment on frozen logits, without an end-to-end model run.