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* TEMPORARY: Update the llama.cpp upstream to my fork's Granite Four branch
This will be redone once my branch is merged upstream in llama.cpp
* feat: Update all patches
There are a number that are no longer needed at all:
- 0003-embeddings: Embeddings entirely overhauled on master
- 0008-ensure-KV-cache-is-fully-defragmented: KV caching entirely
overhauled on master
- 0019-metal-add-mean-kernel-14267: Merged upstream
- 0020-CUDA-add-mean-operation-14313: Merged upstream
* feat: Sync llama.cpp and ggml
* fix: Update rsync-filter for all moved/new/removed files
* fix: Add files missing from sync
* fix: Update ggml rsync-filter for new ggml-cpu/arch subdirs
* fix: Add ggml files missing from sync
* fix: Narrow llama.cpp rsync-filter to not include mtmd main tool cpp files
* fix: Remove mtmd main cpp files
* fix: Add missing include in sampling_ext.cpp
* fix: Update llama.go to use mtmd instead of clip/llava
* fix: Add patch for mtmd_input_text
* chore: Ignore *.patched in the patch directory
* fix: Fix support for arch-specific ggml-cpu source files with new arrangement
In https://github.com/ggml-org/llama.cpp/pull/13892, all arch-specific
implementations were split out into a nested tree structure under
ggml-cpu/arch. This conflicts with standard CGO layout where all
arch-specific source files are expected to live in the same directory as
the parent go module and use suffixes based on GOOS and GOARCH. As such,
there were really two options for getting this to work:
1. Add a patch on top of the GGML sync to rearrange the files to match the
GO layout convention
2. Use CGO directives to conditionally include the nested source files in
the compilation units
This commit does (2) in order to minimize the set of changes needed on top
of the upstream file layout. To get this to work, there are two key things
needed:
1. In cpu.go, #cgo directives are added to explicitly set __${GOARCH}__ in
the preprocessor directives
2. In arch-impls.c|cpp, use an #ifdef | #elif defined | #endif chain to
explicitly include the .c|.cpp files for the given architecture from the
nested directory
* fix: Use mtmd_helper to correctly load the bitmap for the image
* fix: Apply patch for mtmd_text_input
* fix: Add missing stb to llama.cpp rsync-filter
* fix: Add sync'ed stb vendored header
* fix: Use c++17 and include vendor for go wrapper modules
* fix: Update patch 0015 for upstream implementation of uuid
* feat: Bump to the latest tip of the branch
* fix: Update patches for bump
* feat: Bump back to the cenral repo and point at the latest master
This includes granite 4 and a number of other model architectures!
* fix: Revert changes to ggml export GPU UUID patch
* fix: Add patch for GGML_VERSION and GGML_COMMIT constants
* feat: Sync all patched code
* build: Include cmake/common.cmake in ggml sync
* build: Add top-level include for GNUINstallDirs in CMakeLists.txt
This is used to populate CMAKE_INSTALL_BINDIR
* fix: Add a patch to avoid power throttling API on non-msvc windows builds
* fix: Sync patch changes for ggml-cpu.c
* feat: Bump llama.cpp to 4a4f42
This picks up support for Kimi K2 and PLaMO-2
* feat: Sync llama.cpp
* fix: Handle multi-chunk image encodings from mtmd
* fix: Re-number patches after merge with `main`
* feat: Bump to 41e78c in the makefile
* fix: Fix Solar and argsort/copy patches after bump
* fix: Remove Gemma3n CUDA Graphs patch
It was implemented upstream:
https://github.com/ggml-org/llama.cpp/pull/14741
* feat: Sync llama.cpp / ggml after latest bump
* build: Remove unnecessary CFLAGS definitions in cpu.go
* fix: Remove unnecessary additions in the rsync-filter
* fix: Remove unused vendored code for chat template parsing
* Revert "fix: Remove Gemma3n CUDA Graphs patch"
This reverts commit d724caced3ce21f08924d4b7801f94ce6638f6ea.
* fix: Update 0020 CUDA Graphs for gemma3n to keep both llama.cpp and ollama fixes
https://github.com/ollama/ollama/pull/11195#issuecomment-3137312394
* fix: Sync ggml-cuda.cu after keeping both style cuda graph fixes for gemma3n
* unwind mxfp4 patch
Prepare to bump ggml with their impl for mxfp4
* bump
* fix windows build error
* Convert tensors at load time
Repack the mxfp4 tensors as ggmls kernels expect them to be.
* convert mlp bf16 to f32
* buffer the conversion better
* reshape earlier
* openai swiglu
* add ids
* split qkv, gate_up
* fix nested alt tags
* fast attention
* remove debug messages
* fix lint
* remove redundant test
* remap values only if source/target are different
* add back i32->i32 copy
* refactor cpu quants
* clean up vendor
* update patch instructions
* clean up patches
* remove webgpu
* update mem
* also handle gpt-oss
* revert convert changes
---------
Signed-off-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Gabe Goodhart <ghart@us.ibm.com>
Co-authored-by: Daniel Hiltgen <daniel@ollama.com>
117 lines
4.0 KiB
C++
Vendored
117 lines
4.0 KiB
C++
Vendored
#pragma once
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#include "llama.h"
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#include <memory>
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struct llama_ubatch;
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class llama_batch_allocr;
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class llama_io_write_i;
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class llama_io_read_i;
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struct llama_memory_params {
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// kv cache
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ggml_type type_k;
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ggml_type type_v;
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// use full-size SWA cache
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bool swa_full;
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};
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enum llama_memory_status {
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LLAMA_MEMORY_STATUS_SUCCESS = 0,
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LLAMA_MEMORY_STATUS_NO_UPDATE,
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LLAMA_MEMORY_STATUS_FAILED_PREPARE,
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LLAMA_MEMORY_STATUS_FAILED_COMPUTE,
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};
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// helper function for combining the status of two memory contexts
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// useful for implementing hybrid memory types (e.g. iSWA)
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llama_memory_status llama_memory_status_combine(llama_memory_status s0, llama_memory_status s1);
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// helper function for checking if a memory status indicates a failure
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bool llama_memory_status_is_fail(llama_memory_status status);
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// the interface for managing the memory context during batch processing
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// this interface is implemented per memory type. see:
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// - llama_kv_cache_unified_context
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// - llama_kv_cache_unified_iswa_context
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// ...
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//
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// the only method that should mutate the memory and the memory context is llama_memory_i::apply()
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struct llama_memory_context_i {
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virtual ~llama_memory_context_i() = default;
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// consume the current ubatch from the context and proceed to the next one
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// return false if we are done
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virtual bool next() = 0;
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// apply the memory state for the current ubatch to the memory object
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// return false on failure
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virtual bool apply() = 0;
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// get the current ubatch
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virtual const llama_ubatch & get_ubatch() const = 0;
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// get the status of the memory context - used for error handling and checking if any updates would be applied
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virtual llama_memory_status get_status() const = 0;
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};
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using llama_memory_context_ptr = std::unique_ptr<llama_memory_context_i>;
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// general concept of LLM memory
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// the KV cache is a type of LLM memory, but there can be other types
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struct llama_memory_i {
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virtual ~llama_memory_i() = default;
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// split the input batch into a set of ubatches and verify that they can fit into the cache
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// return a context object containing the ubatches and memory state required to process them
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// check the llama_memory_context_i::get_status() for the result
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virtual llama_memory_context_ptr init_batch(
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llama_batch_allocr & balloc,
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uint32_t n_ubatch,
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bool embd_all) = 0;
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// simulate full cache, used for allocating worst-case compute buffers
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virtual llama_memory_context_ptr init_full() = 0;
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// prepare for any pending memory updates, such as shifts, defrags, etc.
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// status == LLAMA_MEMORY_STATUS_NO_UPDATE if there is nothing to update
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virtual llama_memory_context_ptr init_update(llama_context * lctx, bool optimize) = 0;
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// getters
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virtual bool get_can_shift() const = 0;
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//
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// ops
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//
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// if data == true, the data buffers will also be cleared together with the metadata
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virtual void clear(bool data) = 0;
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virtual bool seq_rm (llama_seq_id seq_id, llama_pos p0, llama_pos p1) = 0;
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virtual void seq_cp (llama_seq_id seq_id_src, llama_seq_id seq_id_dst, llama_pos p0, llama_pos p1) = 0;
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virtual void seq_keep(llama_seq_id seq_id) = 0;
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virtual void seq_add (llama_seq_id seq_id, llama_pos p0, llama_pos p1, llama_pos shift) = 0;
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virtual void seq_div (llama_seq_id seq_id, llama_pos p0, llama_pos p1, int d) = 0;
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virtual llama_pos seq_pos_min(llama_seq_id seq_id) const = 0;
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virtual llama_pos seq_pos_max(llama_seq_id seq_id) const = 0;
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//
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// state write/read
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//
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virtual void state_write(llama_io_write_i & io, llama_seq_id seq_id = -1) const = 0;
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virtual void state_read (llama_io_read_i & io, llama_seq_id seq_id = -1) = 0;
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};
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using llama_memory_ptr = std::unique_ptr<llama_memory_i>;
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// TODO: temporary until the llama_kv_cache is removed from the public API
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struct llama_kv_cache : public llama_memory_i {
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virtual ~llama_kv_cache() = default;
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};
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