* 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: https://github.com/ggml-org/llama.cpp/pull/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
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140761069
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
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140825128
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.
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2140748922
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
https://github.com/ggml-org/llama.cpp/pull/13979#discussion_r2141728423
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>
* Add Arcee AFM support
* Add draft update code
* Fix linter and update URL, may still not be final
* Update src/llama-model.cpp
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
* Remote accidental blank line
---------
Co-authored-by: Xuan-Son Nguyen <thichthat@gmail.com>
Adds:
* Dots1Model to convert_hf_to_gguf.py
* Computation graph code to llama-model.cpp
* Chat template to llama-chat.cpp to detect this model's template.
---
The model is called "dots.llm1" (I decided to shorten it to dots1 or
DOTS1 in the code generally) architecture.
The only models that exist as of writing of this commit that follow this
architecture are "dots.llm1.inst" and "dots.llm1.base" from here:
* https://huggingface.co/rednote-hilab/dots.llm1.inst
* https://huggingface.co/rednote-hilab/dots.llm1.base
The model architecture is a combination of Qwen and Deepseek parts, as
seen here:
ffe12627b4/src/transformers/models/dots1/modular_dots1.py
* kv-cache : avoid modifying recurrent cells when setting inputs
* kv-cache : remove inp_s_mask
It was replaced with equivalent and simpler functionality
with rs_z (the first zeroed state) and the already-existing inp_s_copy.
* kv-cache : fix non-consecutive token pos warning for recurrent models
The problem was apparently caused by how the tail cells were swapped.
* graph : simplify logic for recurrent state copies
* kv-cache : use cell without src refs for rs_z in recurrent cache
* llama-graph : fix recurrent state copy
The `state_copy` shuffle assumes everything is moved at once,
which is not true when `states_extra` is copied back to the cache
before copying the range of states between `head` and `head + n_seqs`.
This is only a problem if any of the cells in [`head`, `head + n_seqs`)
have an `src` in [`head + n_seqs`, `head + n_kv`),
which does happen when `n_ubatch > 1` in the `llama-parallel` example.
Changing the order of the operations avoids the potential overwrite
before use, although when copies are avoided (like with Mamba2),
this will require further changes.
* llama-graph : rename n_state to state_size in build_recurrent_state
This naming should reduce confusion between the state size
and the number of states.
* kv-cache : simplify the "struct llama_kv_cache" interface
ggml-ci
* kv-cache : revert the (n_swa + n_ubatch) change (for next PR)
ggml-ci
* kv-cache : some comments
ggml-ci
* context : fix graph reserve for multiple sequences
ggml-ci
* kv-cache : fix typo [no ci]
* kv-cache : fix find_slot() logic for free slots
ggml-ci
* llama : add TODO for deprecating the defrag API in the future
* kv-cache : improve find_slot() using min/max seq pos info
ggml-ci
* llama : handle aborts and compute errors
ggml-ci
* memory : extract state into llama_memory_state
ggml-ci
* kv-cache : add comments
ggml-ci
* server : update batching logic to reset n_batch on successful decode
* server : upon full re-processing, remove the sequence from the cache
* kv-cache : add TODO for doing split_equal when split_simple fails
ggml-ci
* add distilbert
* small fixes
* add note for LLM_ARCH_DISTIL_BERT
* Use MODEL_ARCH.BERT for DistilBert
---------
Co-authored-by: dinhhuy <huy.dinh@brains-tech.co.jp>
This matches how others do it, but will still avoid the extra
initialization when rope is disabled.
Branch: GraniteFour
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: Add GGUF conversion for granitemoeshared
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: hparam and arch plumbing for granitemoeshared
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Split MoE fused tensors for shared experts in conversion
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* feat: First WIP cut at model arch in cpp
The hparam and architecture plumbing should be correct, but the
implementation of the shared experts seems to still be broken.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Cleaner (maybe more correct?) splitting for gate/up
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix the input to the shared experts
I had misread that the shared experts take the inputs _before_ the standard
MoE layer and was feeding the output of the MoE to the shared experts.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Avoid architecture-specific checks for Granite MoE Shared
This is a cleaner way that will allow more flexibility in architecture
strings going forward.
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* refactor: Split granite architectures out of llm_build_llama
This helps de-clutter the llama-family graph construction and allows
granite to diverge further (in preparation for Granite 4).
NOTE: I removed the granite scale factors from llm_build_deci because they
appear to only be there as copy-paste from llm_build_llama. The HF config
does not seem to set those values:
https://huggingface.co/Deci/DeciLM-7B/blob/main/config.json
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Fix compiler warning about uninitialized inp_pos
This should not have been reachable, but it warns on some compliers
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Consoladate GraniteMoEShared into GraniteMoE for conversion
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* fix: Consolidate GraniteMoEShared into GraniteMoE on the c++ side
Branch: GraniteMoEShared
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
* llama/ggml: add LLM training support
more compact progress bar
llama_save_model_to_file
llama_opt_param_filter
ggml_graph_dup force_grads
refactor ggml_opt, fix test-opt
* remove logits_all
* refactor CUDA implementation for ACC
* reset graph at beginning of opt period
* Nomic Embed Text V2 with Mixture-of-Experts (MoE) architecture
- Adds MoE-based embedding model supporting multilingual embeddings.
- Selects architecture variant based on hyperparameter detection (MoE layers).
- Removes unnecessary subclass initialization checks for clarity.
https://www.nomic.ai/blog/posts/nomic-embed-text-v2
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* fix tokenizer
* don't rename this tensor
---------
Co-authored-by: Jared Van Bortel <jared@nomic.ai>
* graph : make mla compatible with FA
* metal : add exp FA kernels for DeepSeek models
ggml-ci
* llama : minor naming updates
ggml-ci
* ggml : disable FA for DS head sizes
* tests : add FA tests for MLA shapes
ggml-ci
* Merged using squash to remove all noise commit messages
* Force flash attention off for `LLM_ARCH_DEEPSEEK2` - embedding too large
* Removed 3 conts (2x RoPE and 1x RMS-norm)
* Changed to use `<cmath>` instead of `<math.h>`
* Reverted removal of the 3 conts
* Used `reshape` in `llm_graph_context::build_attn_mha()`
* Use `k_pe = ggml_reshape`
* Removed the 3 conts again
* Removed the 3D views of `wk_b` and `wv_b`, and just save and 3D in GGUF
* Removed MQA optimisation from `build_attn_mha()` as no gains now
* Simplified `is_mla` branch in `llm_build_deepseek2()`
* Removed `build_attn_mla` and added `nullptr` to all `build_atnn` calls
* Fixed call to `build_attn` in `llm_build_t5_enc`
