Mistral Small 2506 models using Pixtral vision encoder were running out
of GPU memory when processing images larger than 1024x1024 pixels due to
exponential memory growth from unlimited image size.
This fix applies the same 1024x1024 limit used by Qwen2VL models to
prevent OOM issues while maintaining compatibility with existing models.
* Add support for VK_EXT_debug_utils to add labels to Vulkan objects. In step 1 compute pipelines are getting labeled.
* remove #ifdef for debug utils and add queue marker.
* Add header and namespace to use enqueue_functions extension
* Convert submit and parallel_for to use new extension in convert.cpp
* Convert submit and parallel_for to use extension in ggml-sycl.cpp
* Convert submit and parallel_for to use extension in gla.cpp
* Convert submit and parallel_for in mmq.cpp
* Convert submit and parallel_for in mmvq.cpp
* Convert submit and parallel_for in remaining files
* Convert all simple parallel_for to nd_launch from enqueue_functions
extension
* Wrapping extension in general function
Create a general function that enable the enqueue_functions extension if
it is enable in the compiler, otherwise call the general SYCL function
to launch kernels.
---------
Signed-off-by: nscipione <nicolo.scipione@codeplay.com>
* Add PowerPC feature detection and scoring
* ggml-cpu: Implement GGML_CPU_ALL_VARIANTS for PowerPC
* ggml-cpu: Delay some initializations until function is called
When using GGML_BACKEND_DL=ON, these initializations might use
instructions that are not supported by the current CPU.
---------
Co-authored-by: Diego Devesa <slarengh@gmail.com>
Add no_warmup parameter to cmd_params struct and command-line parsing to allow users to skip warmup runs before benchmarking.
- Add no_warmup boolean field to cmd_params struct
- Add --no-warmup command-line argument parsing
- Add help text documentation for the new flag
- Wrap existing warmup logic in conditional check
- Maintain full backward compatibility (warmup enabled by default)
Addresses #14224
* 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>
