- 13 Oct, 2025 1 commit
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Gabe Goodhart authored
Llama cpp bump (df1b612): granite docling / mamba2 optimizations / multimodal encoding fixes (#12552) * feat: Bump llama.cpp to df1b612 Branch: LlamaCPPBump-GraniteDocling Signed-off-by:
Gabe Goodhart <ghart@us.ibm.com> * fix(mtmd): Correctly encode text chunks during mtmd tokenization There can be text chunks that appear interspersed with the image embeddings that contain template delimiter tokens for some models. These need to be correctly translated to text tokens. Branch: LlamaCPPBump-GraniteDocling Signed-off-by:
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- 14 Aug, 2025 1 commit
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Michael Yang authored
* 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:Daniel Hiltgen <daniel@ollama.com>
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- 14 May, 2025 1 commit
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Michael Yang authored
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- 14 Feb, 2025 1 commit
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Jesse Gross authored
This provides integration with the new Ollama engine (58245413 next ollama runner (#7913)) and the rest of the Ollama infrastructure such as the runner and Ollama server. In addition, it also builds out the KV cache infrastructure to support requirements of how Ollama runs models such as: - Parallel processing - Memory management for defragmentation and shifting - Multi-modal modals Both old and new engines continue to be supported. By default, only the old engine is used. To enable the new engine: Start the server with the OLLAMA_NEW_ENGINE environment variable set: OLLAMA_NEW_ENGINE=1 ./ollama serve Start a model that is supported by the Ollama engine. This one is Llama 3.1 8b Q4_K_M: ./ollama run jessegross/llama3.1
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- 10 Dec, 2024 1 commit
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Daniel Hiltgen authored
* llama: wire up builtin runner This adds a new entrypoint into the ollama CLI to run the cgo built runner. On Mac arm64, this will have GPU support, but on all other platforms it will be the lowest common denominator CPU build. After we fully transition to the new Go runners more tech-debt can be removed and we can stop building the "default" runner via make and rely on the builtin always. * build: Make target improvements Add a few new targets and help for building locally. This also adjusts the runner lookup to favor local builds, then runners relative to the executable, and finally payloads. * Support customized CPU flags for runners This implements a simplified custom CPU flags pattern for the runners. When built without overrides, the runner name contains the vector flag we check for (AVX) to ensure we don't try to run on unsupported systems and crash. If the user builds a customized set, we omit the naming scheme and don't check for compatibility. This avoids checking requirements at runtime, so that logic has been removed as well. This can be used to build GPU runners with no vector flags, or CPU/GPU runners with additional flags (e.g. AVX512) enabled. * Use relative paths If the user checks out the repo in a path that contains spaces, make gets really confused so use relative paths for everything in-repo to avoid breakage. * Remove payloads from main binary * install: clean up prior libraries This removes support for v0.3.6 and older versions (before the tar bundle) and ensures we clean up prior libraries before extracting the bundle(s). Without this change, runners and dependent libraries could leak when we update and lead to subtle runtime errors.
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- 08 Nov, 2024 1 commit
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Jesse Gross authored
If we get a request with a zero length image, it will result in an out-of-bounds error when we pass the data to the image encoder.
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- 02 Nov, 2024 2 commits
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Jesse Gross authored
Check for NULL return values from llama.cpp in more places and convert them into Go errors, which should make debugging easier in the future rather than having hidden surprises in our data structures.
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Jesse Gross authored
Mllama has large embeddings (100 MB per image) and each embedding is represented as 1 token when passed to llama.cpp. Batches are pre- allocated for the size of the tokens times the batch size, so this results in allocations of over 50 GB at the default batch size. On some systems, these mallocs will fail. Since an image is represented as a single token and mllama doesn't support more than 1 image per request, we only need to allocate a batch size of 1, which is much more reasonable. In addition, for non-multimodal models, we don't need to allocate the embedding batches at all. Fixes #7464
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- 31 Oct, 2024 1 commit
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Jesse Gross authored
Currently if an input has embeddings at any point then we will set cross attention to true from the beginning. This means that any tokens before the embeddings are sent will incorrectly have cross attention layers applied. This only sets cross attention when we have an embedding, either previously in this sequence or in the cache. It also makes cross attention capable of supporting parallelism at the runner level, though the mllama implementation doesn't support that yet.
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- 30 Oct, 2024 1 commit
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Jesse Gross authored
-Update mllama to take the cross attention state as embeddings in a batch, more similar to how Llava handles it. This improves integration with the input cache. -Pass locations in a prompt for embeddings using tags similar to Llava. -Abstract interface to vision models so the main runner accesses Clip and Mllama similarly Co-authored-by:Michael Yang <mxyng@pm.me>
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