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Hybrid recurrent cache - #13979
Hybrid recurrent cache#13979
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These dynamic casts should not be necessary. Instead you need a new llm_graph_context::build_attn_inp_kv_hybrid_recurrent() method, similar to llm_graph_context::build_attn_inp_kv_unified_iswa().
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I'm working through this now and a couple of questions are coming up:
- Would it be best to combine
build_inp_s_copywithbuild_attn_inp_kvfor hybrid so that models call just one "build inputs" function, or keep them separate for simplicity? - For the
build_attnmethods, each has a correspondingllm_graph_input_attn_*class. Thebuild_inp_s_*methods don't have this pattern which would make this a bit harder to have code reuse. Are there plans to refactor that further @compilade? - In the
mamba2branch,s_maskseems to be totally removed. I'd prefer not to do all of the boilerplate for duplicatingbuild_inp_s_maskfor the hybrid recurrent case if that's definitely going to be going away. Is there any reason that might stick around past the merge ofmamba2?
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Answering out of order, but it should still make sense:
2. For the
build_attnmethods, each has a correspondingllm_graph_input_attn_*class. Thebuild_inp_s_*methods don't have this pattern
They do follow this pattern, see
Line 190 in 7e00e60
(this is on the current master)
I think you might mean the build_attn_* methods also return instances of llm_graph_input_attn_*?
That seems to be directly related to llm_graph_context::build_attn() which has multiple implementations which differ by the type of the first argument (e.g. for llm_graph_input_attn_kv_unified, llm_graph_input_attn_no_cache, etc.)
Are there plans to refactor that further @compilade?
Not really, outside of removing s_mask (and related functions and classes) as part of #13834.
- Would it be best to combine
build_inp_s_copywithbuild_attn_inp_kvfor hybrid so that models call just one "build inputs" function, or keep them separate for simplicity?
Personally, I think it would be simpler to keep them separate, because they are fundamentally different (one is intended to be used by build_copy_mask_state (renamed to build_recurrent_state in #13834), while the other is used by build_attn), and they are pretty much independent, even in hybrid models (at least for Jamba, the recurrent and self-attention layers are mostly independent on that front).
I don't see how build_attn would ever need s_copy.
build_inp_s_copy and build_inp_attn_kv_* are called once at the beginning of the graph, while build_attn and build_recurrent_state are called once per layer (where applicable, and so usually different layers for both).
3. Is there any reason [
s_mask] might stick around past the merge ofmamba2?
No reason to keep it, s_mask will be removed. Its functionality is redundant with s_copy, and otherwise prevents minimizing unnecessary state copies. It was used to clear the states, but the same can be done through inp_s_copy and clearing by copying a zero-ed state (which is the rs_z'th state in the mamba2 branch (and #13834)).
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Thanks, that's super helpful! I was missing the distinction between build_attn_inp and build_attn which makes perfect sense.
Personally, I think it would be simpler to keep them separate
I agree on my personal gut feeling, so I'll go with this and see how it feels once complete.
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Ok, I think this feels a lot cleaner now. For build_inp_s_copy, I opted to add an optional parameter so that the caller can take ownership of casting the cache state rather than duplicating the function into build_inp_s_copy_hybrid. That felt a little cleaner w.r.t. code reuse, but I'm happy to do a separate method if that's preferred.
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It looks like there's one more place that will need changing in build_copy_mask_state (renamed to build_recurrent_state on mamba2). Similar to build_inp_s_copy, I think the cleanest way to do this for code reuse is to add an optional parameter that, if unset, will use the current logic of casting mstate.
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Yes, but that will be fixed in #13834
(Noting here in case this gets merged first so that I don't forget to update the comment)
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Should I strip out this TODO at this point?
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@ggerganov I've noticed that the
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I've tried adding retry logic for all requests in 39a93b3 to work around the race between |
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The changes to the server tests should not be needed. Let's revert the commit for now and I'll investigate. |
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@ggerganov Thanks, it looks like those changes didn't fix the failures anyway, so definitely not the right fix. I've reset them out and can open an issue with details of what I see locally |
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Issue for follow up on |
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I've rebased on #13834. Drafting for now until it's merged |
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That was quick! Undrafting now that #13834 is merged |
It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
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Ok, renames/refactors done. 🤞 on Ci! |
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Successfully rebased Granite 4 and verified that things are working as implemented here, including the refactor of the |
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
| // TODO: move this implementation to llama_memory_recurrent. | ||
| // this is analogous to llama_kv_cache_unified::cpy_k / cpy_v | ||
| // when moving, avoid passing `ggml_cgraph` - only pass `ggml_context`. would likely need to split the | ||
| // implementation in 2 separate methods. the goal is to avoid calling `ggml_build_forward_expand` in | ||
| // `llama_memory_recurrent` |
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the goal is to avoid calling
ggml_build_forward_expand
I'm not sure that would be convenient; build_rs when used with avoid_copies pretty much has to have a way to order the writes.
Splitting the function in two would require handling states_extra at call sites, while for now it's transparently handled within build_rs.
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I see. It's ok to pass the ggml_cgraph as argument in that case.
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org/llama.cpp#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org/llama.cpp#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org/llama.cpp#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* feat: Add llama_model_is_hybrid API call Also, split llama_model_is_recurrent into llm_arch_is_recurrent in llama-arch with llama_model_is_recurrent delegating to llm_arch_is_recurrent. The same split is done for hybird. This is needed because there are places where the llama_model has not yet been initialized but we need to check if the model is recurrent (specifically for the per-layer recurrent check array in hparams). Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add c++ side constants for attention layer indices hparam Branch: GraniteFour * feat: Add support for distinguishing recurrent vs non-recurrent layers in hparams Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Auto-fill hparams.recurrent_layer_arr based on whether the model is recurrent Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: rename *_is_hybrid -> *_is_hybrid_recurrent The implementation of the hybrid cache intentionally does not specify the types of the child caches, so there was a naming mismatch with these predicate functions that used "hybrid" to imply "hybrid recurrent." Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Add layer filter to recurrent cache Branch: HybridCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer sizing everywhere in kv caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: First pass at llama_kv_cache_hybrid_recurrent This follows the pattern in iswa where the two child caches are held explicitly to support the case where a model requires a single attention cache and a single recurrent cache where each layer uses exactly one of the caches. This is a rewrite of the more generic approach in the original hybrid cache PR: ggml-org#13276 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Construct hybrid recurrent cache for hybrid recurrent models This includes a refactor of the create_memory logic to avoid needing to use the arch enum explicitly unless a model needs explicit cache instantiation logic beyond the standard logic for recurrent, hybrid, unified, and iswa. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix wrong bool condition for split equal in hybrid cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix shift logic to defer to unified cache Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Support hybrid recurrent in llama-graph NOTE: I intentionally did not add support for s_mask since it will be going away soon Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix logic for initializing inputs and attn layers for hybrid caches Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update recurrent cache for changes to remove intermediate kv_cache interface Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix status for init_update sig for recurrent cache state Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Add missing padding to n_ctx for hybrid cache construction Branch: GraniteFour Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Update clear signature for data argument after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove errant virtual destructor leftover from previous impl attempt Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Use per-layer n_embd_k/v_s calls for mamba (1) layers Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_s from unified cache No longer needed now that unified isn't also supporting recurrent ggml-org#13979 (comment) Branch: HybridRecurrentCache * refactor: Remove layer index from n_embd_k/v_s Now that it's not used at all in the unified cache, we don't need to use the layer index to zero it out for attention layers. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Remove n_embd_k/v_gqa from recurrent cache This is no longer needed now that there are separate implementations ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Allow custom layer filters for hybrid recurrent This should help support architectures like Falcon H1 where there is overlap between layers that need attention and recurrent caches. ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove logits_all after rebase Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Remove llama_model_is_hybrid_Recurrent public API ggml-org#13979 (comment) Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use llama_memory_state_ptr for child states in hybrid memory state Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * feat: Overhaul build_recurrent_state / build_inp_s_copy to match attention pattern https://github.com/ggml-org/llama.cpp/pull/13979/files#r2141701738 This is a big overhaul to bring consistency between how inputs and per- layer components are created for attention layers and recurrent layers. The main changes are: - Rename class llm_graph_input_s_copy -> llm_graph_input_rs - Add a corresponding llm_graph_input_rs_hybrid_recurrent - Rename build_inp_s_copy -> build_rs_inp_recurrent - Add a corresponding build_rs_inp_hybrid_recurrent - Rename build_recurrent_state -> build_rs to match build_attn w/ llm_graph_input_rs android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a corresponding overload of build_rs w/ llm_graph_input_rs_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input - Add a llm_graph_input_attn_kv_hybrid_recurrent analogous to llm_graph_input_attn_kv_unified - Add a build_attn override that takes llm_graph_input_attn_kv_hybrid_recurrent android-build AUTHORS bamba-9b-2.2T.gguf bamba-9b-2.2T.q4_k_m.gguf broken.log build build-rel build-xcframework.sh build.android build.android.bak ci cmake CMakeLists.txt CMakePresets.json CODEOWNERS common common.o CONTRIBUTING.md convert_hf_to_gguf_update.py convert_hf_to_gguf.py convert_llama_ggml_to_gguf.py convert_lora_to_gguf.py debug.log docs examples flake.lock flake.nix ggml ggml-alloc.o ggml-backend.o ggml-metal.o ggml-model-BF16.gguf ggml-model-Q4_K_M.gguf ggml-quants.o ggml.o gguf-py grammar-parser.o grammars include LICENSE licenses llama.log llama.o llamacpp_trace.log main.log Makefile media models mypy.ini pocs poetry.lock prompts pyproject.toml pyrightconfig.json q4_k_m_boot.log q8_0_boot.log quant.log quant2.log README.md requirements requirements.txt sampling.o scripts SECURITY.md src test-grammar-output.tmp test-json-schema-input.tmp tests tools vendor working.log as the first input This makes the two paradigms fully consistent. The main drawback is the code duplication in the build_attn and build_rs implementations where the only difference between implementations is how they cast the memory state. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * fix: Fix resize vs reserve and skip null tensors in size computation https://github.com/ggml-org/llama.cpp/pull/13979/files#r2149469788 Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-Authored-By: @younesbelkada * fix: Fix initialization of child states Since initially writing this PR, the logic in the child state types changed such that using the "init full" signature and keeping the ubatches on the parent struct no longer worked. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Use a common build_recurrent_state method that is cache-agnostic This reduces the code duplication between the different build_rs impls and also retains a similar signature to the previous build_recurrent_state method while standardizing on the input-dispatched build_rs implementation. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * recurrent : rework graph inputs + add TODOs ggml-ci * refactor: Make status and child states const in hybrid and iswa Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: Rename llama_kv_cache_[recurrent|hybrid_recurrent] to remove kv cache This removes the notion of "kv" from the interface names for these memory types. There are still many references to kv in the implementation of the recurrent memory which will need further adjustment. Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor!: Rename all k/v related values for recurrent/hybrid to r/s Anywhere that "kv_<state|cell|size|etc>" is used, I've used the more generic "mem_" prefix. The specifics of "k" (key) translate to "r" (recurrent state) and "v" (value) translate to "s" (state-space embedding states). Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refacor: _recurrent -> _recr for brevity It just _happens_ to have the same number of letters as _attn! Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for ref Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * refactor: recurrent_layer() -> is_recurrent() Branch: HybridRecurrentCache Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> * style: Fix spacing for size_s_bytes declaration Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --------- Signed-off-by: Gabe Goodhart <ghart@us.ibm.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This is a re-opened version of #13904 after #13746 was merged
Description
This PR introduces the
llama_kv_cache_hybrid_recurrentcache implementation. It follows the pattern ofllama_kv_cache_unified_iswaby holding two child cache instances and implementing the interface logic such that it manages both correctly for the appropriate layers.Changes
The main change in this PR is the addition of
llama_kv_cache_hybrid_recurrentinllama-kv-cache-hybrid-recurrent.*. In addition to this, the PR does the following:llama_model_is_hybrid_recurrentpublic API (akin tollama_model_is_recurrent)LLM_KV_ATTENTION_LAYER_INDICESas an hparam to hold the indices of the layers that should use attention (versus recurrent)iswa, but that mechanism also isn't particularly extensible. It might be more appropriate to have a generic mechanism for indicating the type of caching to use for each layer, but that would start to approach the generic hybrid implementation that I originally attempted which ended up being too abstract (feat: Hybrid unified/recurrent cache #13276).llm_graph_contextthat need a specific type of cache to use getters (get_state_unified/get_state_recurrent) that will properly handlellama_kv_cache_hybrid_recurrentn_embd_k_s/n_embd_v_slayer-dependent and use layer indices when calling them in the existing cache implementationsllama_kv_cache_recurrentllama_model::create_memoryto usellm_arch_is_recurrentandllm_arch_is_hybrid_recurrentrather than relying on adding models to theswitchstatement which was redundant with the implementation of these functionsedits by @ggerganov below:
Notes so I don't forget