From 482211438dd671224a7f176b7480b4ded424212c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 12 Dec 2025 17:43:58 +0100 Subject: [PATCH 01/40] CUDA: fix overflow in MMA kernel without stream-k (#17939) --- ggml/src/ggml-cuda/fattn-common.cuh | 6 +++--- ggml/src/ggml-cuda/fattn-mma-f16.cuh | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml/src/ggml-cuda/fattn-common.cuh b/ggml/src/ggml-cuda/fattn-common.cuh index 2750117aa9..8dc82a9d3b 100644 --- a/ggml/src/ggml-cuda/fattn-common.cuh +++ b/ggml/src/ggml-cuda/fattn-common.cuh @@ -642,8 +642,8 @@ static __global__ void flash_attn_stream_k_fixup( const int iter_k = (ne11 + (nbatch_fa - 1)) / nbatch_fa; const int iter_j = (ne01 + (ncols1 - 1)) / ncols1; - const int kbc0 = (bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; - const int kbc0_stop = (bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc0 = int64_t(bidx0 + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc0_stop = int64_t(bidx0 + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; const bool did_not_have_any_data = kbc0 == kbc0_stop; const bool wrote_beginning_of_tile = kbc0 % iter_k == 0; @@ -679,7 +679,7 @@ static __global__ void flash_attn_stream_k_fixup( int bidx = bidx0 - 1; int kbc_stop = kbc0; while(true) { - const int kbc = bidx*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc = int64_t(bidx)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; if (kbc == kbc_stop) { // Did not have any data. bidx--; kbc_stop = kbc; diff --git a/ggml/src/ggml-cuda/fattn-mma-f16.cuh b/ggml/src/ggml-cuda/fattn-mma-f16.cuh index d51537f7d0..7bd1044c19 100644 --- a/ggml/src/ggml-cuda/fattn-mma-f16.cuh +++ b/ggml/src/ggml-cuda/fattn-mma-f16.cuh @@ -1380,8 +1380,8 @@ static __global__ void flash_attn_ext_f16( const int iter_j = (ne01.z + (ncols1 - 1)) / ncols1; // kbc == k block continuous, current index in continuous ijk space. - int kbc = (blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; - const int kbc_stop = (blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + int kbc = int64_t(blockIdx.x + 0)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; + const int kbc_stop = int64_t(blockIdx.x + 1)*(iter_k*iter_j*(ne02/ncols2)*ne03) / gridDim.x; // If the seams of 2 CUDA blocks fall within an output tile their results need to be combined. // For this we need to track both the block that starts the tile (needs_fixup) and the block that finishes the tile (is_fixup). @@ -1401,7 +1401,7 @@ static __global__ void flash_attn_ext_f16( const float2 * Q_f2 = (const float2 *) (Q + nb03*sequence + nb02* head0); const half2 * K_h2 = (const half2 *) (K + nb13*sequence + nb12*(head0 / gqa_ratio)); const half * mask_h = ncols2 == 1 && !mask ? nullptr : - (const half *) (mask + nb33*(sequence % ne33)); + (const half *) (mask + nb33*(sequence % ne33)); float2 * dstk = ((float2 *) dst) + (sequence*ne01.z*ne02 + head0) * (DV/2); const half2 * V_h2 = mla ? K_h2 + (DKQ/2 - DV/2) : (const half2 *) (V + nb23*sequence + nb22*(head0 / gqa_ratio)); From b7f5f46e03edbe73abb0784e27faa20efb8a42d5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Fri, 12 Dec 2025 19:39:23 +0100 Subject: [PATCH 02/40] docker : include legacy llama-completion binary (#17964) --- .devops/cann.Dockerfile | 2 +- .devops/cpu.Dockerfile | 2 +- .devops/cuda.Dockerfile | 2 +- .devops/intel.Dockerfile | 2 +- .devops/musa.Dockerfile | 2 +- .devops/rocm.Dockerfile | 2 +- .devops/s390x.Dockerfile | 2 +- .devops/tools.sh | 8 ++++++-- .devops/vulkan.Dockerfile | 2 +- 9 files changed, 14 insertions(+), 10 deletions(-) diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile index cd8f87b2ea..9d27e00fb1 100644 --- a/.devops/cann.Dockerfile +++ b/.devops/cann.Dockerfile @@ -111,7 +111,7 @@ ENTRYPOINT ["/app/tools.sh"] # ============================================================================== FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app ENTRYPOINT [ "/app/llama-cli" ] diff --git a/.devops/cpu.Dockerfile b/.devops/cpu.Dockerfile index 6e16ecda44..b9e84ab986 100644 --- a/.devops/cpu.Dockerfile +++ b/.devops/cpu.Dockerfile @@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/cuda.Dockerfile b/.devops/cuda.Dockerfile index 54f793d0a3..fed5863157 100644 --- a/.devops/cuda.Dockerfile +++ b/.devops/cuda.Dockerfile @@ -74,7 +74,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/intel.Dockerfile b/.devops/intel.Dockerfile index d1a8fbed4c..adebf08229 100644 --- a/.devops/intel.Dockerfile +++ b/.devops/intel.Dockerfile @@ -73,7 +73,7 @@ ENTRYPOINT ["/app/tools.sh"] FROM base AS light COPY --from=build /app/lib/ /app -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/musa.Dockerfile b/.devops/musa.Dockerfile index faa3500e61..34d6ad9f40 100644 --- a/.devops/musa.Dockerfile +++ b/.devops/musa.Dockerfile @@ -81,7 +81,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/rocm.Dockerfile b/.devops/rocm.Dockerfile index d6bf28b105..53c3ed8d88 100644 --- a/.devops/rocm.Dockerfile +++ b/.devops/rocm.Dockerfile @@ -94,7 +94,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app diff --git a/.devops/s390x.Dockerfile b/.devops/s390x.Dockerfile index b7c9457680..1e66f061d5 100644 --- a/.devops/s390x.Dockerfile +++ b/.devops/s390x.Dockerfile @@ -105,7 +105,7 @@ WORKDIR /llama.cpp/bin # Copy llama.cpp binaries and libraries COPY --from=collector /llama.cpp/bin/*.so /llama.cpp/bin -COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin +COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin/llama-completion /llama.cpp/bin ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ] diff --git a/.devops/tools.sh b/.devops/tools.sh index 8a3a693400..cc5ee17dfd 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -13,6 +13,8 @@ elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then exec ./llama-quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then exec ./llama-cli "$@" +elif [[ "$arg1" == '--run-legacy' || "$arg1" == '-l' ]]; then + exec ./llama-completion "$@" elif [[ "$arg1" == '--bench' || "$arg1" == '-b' ]]; then exec ./llama-bench "$@" elif [[ "$arg1" == '--perplexity' || "$arg1" == '-p' ]]; then @@ -32,8 +34,10 @@ elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then else echo "Unknown command: $arg1" echo "Available commands: " - echo " --run (-r): Run a model previously converted into ggml" - echo " ex: -m /models/7B/ggml-model-q4_0.bin -p \"Building a website can be done in 10 simple steps:\" -n 512" + echo " --run (-r): Run a model (chat) previously converted into ggml" + echo " ex: -m /models/7B/ggml-model-q4_0.bin" + echo " --run-legacy (-l): Run a model (legacy completion) previously converted into ggml" + echo " ex: -m /models/7B/ggml-model-q4_0.bin -no-cnv -p \"Building a website can be done in 10 simple steps:\" -n 512" echo " --bench (-b): Benchmark the performance of the inference for various parameters." echo " ex: -m model.gguf" echo " --perplexity (-p): Measure the perplexity of a model over a given text." diff --git a/.devops/vulkan.Dockerfile b/.devops/vulkan.Dockerfile index fd7195c5be..b37b4f277d 100644 --- a/.devops/vulkan.Dockerfile +++ b/.devops/vulkan.Dockerfile @@ -68,7 +68,7 @@ ENTRYPOINT ["/app/tools.sh"] ### Light, CLI only FROM base AS light -COPY --from=build /app/full/llama-cli /app +COPY --from=build /app/full/llama-cli /app/full/llama-completion /app WORKDIR /app From a8c7f33d792c7ba8ecb889752f80945373e2ddfc Mon Sep 17 00:00:00 2001 From: jiahao su Date: Sat, 13 Dec 2025 03:43:00 +0800 Subject: [PATCH 03/40] ci : change the cann version and the container pull method (#17953) fix error format Update build.yml Remove unnecessary zip files fix update --- .devops/cann.Dockerfile | 2 +- .github/workflows/build.yml | 49 +++++++++++++++++----- .github/workflows/release.yml | 79 +++++++++++++++++++++++++++++++++++ 3 files changed, 119 insertions(+), 11 deletions(-) diff --git a/.devops/cann.Dockerfile b/.devops/cann.Dockerfile index 9d27e00fb1..83182c9700 100644 --- a/.devops/cann.Dockerfile +++ b/.devops/cann.Dockerfile @@ -4,7 +4,7 @@ # Define the CANN base image for easier version updates later ARG CHIP_TYPE=910b -ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc1.alpha001-${CHIP_TYPE}-openeuler22.03-py3.11 +ARG CANN_BASE_IMAGE=quay.io/ascend/cann:8.3.rc2-${CHIP_TYPE}-openeuler24.03-py3.11 # ============================================================================== # BUILD STAGE diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 383427f36f..9fe1401df4 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -1400,25 +1400,54 @@ jobs: chip_type: ['910b', '310p'] build: ['Release'] runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} - container: ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc1.alpha001-910b-openeuler22.03-py3.11' || '8.2.rc1-310p-openeuler22.03-py3.11' }} steps: - name: Checkout uses: actions/checkout@v4 + with: + fetch-depth: 0 - - name: Dependencies + - name: Free up disk space + uses: ggml-org/free-disk-space@v1.3.1 + with: + tool-cache: true + + - name: Set container image + id: cann-image run: | - yum update -y - yum install -y git gcc gcc-c++ make cmake libcurl-devel + image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" + echo "image=${image}" >> "${GITHUB_OUTPUT}" + + - name: Pull container image + run: docker pull "${{ steps.cann-image.outputs.image }}" - name: Build + env: + BUILD_TYPE: ${{ matrix.build }} + SOC_TYPE: ascend${{ matrix.chip_type }} run: | - export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + HOST_UID=$(id -u) + HOST_GID=$(id -g) - cmake -S . -B build \ - -DCMAKE_BUILD_TYPE=${{ matrix.build }} \ - -DGGML_CANN=on \ - -DSOC_TYPE=ascend${{ matrix.chip_type }} - cmake --build build -j $(nproc) + docker run --rm \ + -v "${PWD}:/workspace" \ + -w /workspace \ + -e SOC_TYPE=${SOC_TYPE} \ + -e BUILD_TYPE=${BUILD_TYPE} \ + "${{ steps.cann-image.outputs.image }}" \ + bash -lc ' + set -e + yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel + yum clean all && rm -rf /var/cache/yum + git config --global --add safe.directory "/workspace" + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${SOC_TYPE} + cmake --build build -j $(nproc) + + chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build + ' # TODO: simplify the following workflows using a matrix # TODO: run lighter CI on PRs and the full CI only on master (if needed) diff --git a/.github/workflows/release.yml b/.github/workflows/release.yml index 77aec20c11..446cae9f84 100644 --- a/.github/workflows/release.yml +++ b/.github/workflows/release.yml @@ -731,6 +731,78 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz name: llama-${{ steps.tag.outputs.name }}-xcframework.tar.gz + + openEuler-cann: + strategy: + matrix: + arch: [x86, aarch64] + chip_type: ['910b', '310p'] + build: ['Release'] + runs-on: ${{ matrix.arch == 'aarch64' && 'ubuntu-24.04-arm' || 'ubuntu-24.04' }} + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 + + - name: Free up disk space + uses: ggml-org/free-disk-space@v1.3.1 + with: + tool-cache: true + + - name: Set container image + id: cann-image + run: | + image="ascendai/cann:${{ matrix.chip_type == '910b' && '8.3.rc2-910b-openeuler24.03-py3.11' || '8.3.rc2-310p-openeuler24.03-py3.11' }}" + echo "image=${image}" >> "${GITHUB_OUTPUT}" + + - name: Pull container image + run: docker pull "${{ steps.cann-image.outputs.image }}" + + - name: Build + env: + BUILD_TYPE: ${{ matrix.build }} + SOC_TYPE: ascend${{ matrix.chip_type }} + run: | + HOST_UID=$(id -u) + HOST_GID=$(id -g) + + docker run --rm \ + -v "${PWD}:/workspace" \ + -w /workspace \ + -e SOC_TYPE=${SOC_TYPE} \ + -e BUILD_TYPE=${BUILD_TYPE} \ + "${{ steps.cann-image.outputs.image }}" \ + bash -lc ' + set -e + yum install -y --setopt=install_weak_deps=False --setopt=tsflags=nodocs git gcc gcc-c++ make cmake libcurl-devel + yum clean all && rm -rf /var/cache/yum + git config --global --add safe.directory "/workspace" + export LD_LIBRARY_PATH=${ASCEND_TOOLKIT_HOME}/lib64:${ASCEND_TOOLKIT_HOME}/$(uname -m)-linux/devlib/:${LD_LIBRARY_PATH} + cmake -S . -B build \ + -DCMAKE_BUILD_TYPE=${BUILD_TYPE} \ + -DGGML_CANN=on \ + -DSOC_TYPE=${SOC_TYPE} + cmake --build build -j $(nproc) + + chown -R '"${HOST_UID}"':'"${HOST_GID}"' /workspace/build + ' + + - name: Determine tag name + id: tag + uses: ./.github/actions/get-tag-name + + - name: Pack artifacts + run: | + cp LICENSE ./build/bin/ + tar -czvf llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz --transform "s,./,llama-${{ steps.tag.outputs.name }}/," -C ./build/bin . + + - name: Upload artifacts (tar) + uses: actions/upload-artifact@v4 + with: + path: llama-${{ steps.tag.outputs.name }}-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz + name: llama-bin-${{ matrix.chip_type }}-openEuler-${{ matrix.arch }}.tar.gz + release: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} @@ -752,6 +824,7 @@ jobs: - macOS-arm64 - macOS-x64 - ios-xcode-build + - openEuler-cann steps: - name: Clone @@ -844,6 +917,12 @@ jobs: - [Windows x64 (SYCL)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip) - [Windows x64 (HIP)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-win-hip-radeon-x64.zip) + **openEuler:** + - [openEuler x86 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-x86.tar.gz) + - [openEuler x86 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-x86.tar.gz) + - [openEuler aarch64 (310p)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-310p-openEuler-aarch64.tar.gz) + - [openEuler aarch64 (910b)](https://github.com/ggml-org/llama.cpp/releases/download/${{ steps.tag.outputs.name }}/llama-${{ steps.tag.outputs.name }}-bin-910b-openEuler-aarch64.tar.gz) + - name: Upload release id: upload_release uses: actions/github-script@v3 From e39a2ce66d0a61915f22097e5453e291618b3518 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 12 Dec 2025 21:14:48 +0100 Subject: [PATCH 04/40] clip: move model cgraphs into their own files (#17965) * clip: move model cgraphs into their own files * more explicit enums * fix linux build * fix naming * missing headers * nits: add comments for contributors --- tools/mtmd/CMakeLists.txt | 18 +- tools/mtmd/clip-graph.h | 115 ++ tools/mtmd/clip-impl.h | 6 + tools/mtmd/clip-model.h | 279 +++ tools/mtmd/clip.cpp | 2725 ++++++----------------------- tools/mtmd/models/cogvlm.cpp | 98 ++ tools/mtmd/models/internvl.cpp | 69 + tools/mtmd/models/kimivl.cpp | 63 + tools/mtmd/models/llama4.cpp | 96 + tools/mtmd/models/llava.cpp | 374 ++++ tools/mtmd/models/minicpmv.cpp | 114 ++ tools/mtmd/models/models.h | 58 + tools/mtmd/models/pixtral.cpp | 86 + tools/mtmd/models/qwen2vl.cpp | 183 ++ tools/mtmd/models/qwen3vl.cpp | 191 ++ tools/mtmd/models/siglip.cpp | 81 + tools/mtmd/models/whisper-enc.cpp | 107 ++ tools/mtmd/mtmd.h | 5 + 18 files changed, 2433 insertions(+), 2235 deletions(-) create mode 100644 tools/mtmd/clip-graph.h create mode 100644 tools/mtmd/clip-model.h create mode 100644 tools/mtmd/models/cogvlm.cpp create mode 100644 tools/mtmd/models/internvl.cpp create mode 100644 tools/mtmd/models/kimivl.cpp create mode 100644 tools/mtmd/models/llama4.cpp create mode 100644 tools/mtmd/models/llava.cpp create mode 100644 tools/mtmd/models/minicpmv.cpp create mode 100644 tools/mtmd/models/models.h create mode 100644 tools/mtmd/models/pixtral.cpp create mode 100644 tools/mtmd/models/qwen2vl.cpp create mode 100644 tools/mtmd/models/qwen3vl.cpp create mode 100644 tools/mtmd/models/siglip.cpp create mode 100644 tools/mtmd/models/whisper-enc.cpp diff --git a/tools/mtmd/CMakeLists.txt b/tools/mtmd/CMakeLists.txt index a104ea1c0e..3ee42036fd 100644 --- a/tools/mtmd/CMakeLists.txt +++ b/tools/mtmd/CMakeLists.txt @@ -6,11 +6,25 @@ add_library(mtmd mtmd.cpp mtmd-audio.cpp mtmd.h + mtmd-helper.cpp + mtmd-helper.h clip.cpp clip.h clip-impl.h - mtmd-helper.cpp - mtmd-helper.h + clip-model.h + clip-graph.h + models/models.h + models/cogvlm.cpp + models/internvl.cpp + models/kimivl.cpp + models/llama4.cpp + models/llava.cpp + models/minicpmv.cpp + models/pixtral.cpp + models/qwen2vl.cpp + models/qwen3vl.cpp + models/siglip.cpp + models/whisper-enc.cpp ) set_target_properties(mtmd PROPERTIES diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h new file mode 100644 index 0000000000..6d303b4e48 --- /dev/null +++ b/tools/mtmd/clip-graph.h @@ -0,0 +1,115 @@ +#pragma once + +#include "ggml.h" +#include "ggml-cpp.h" +#include "clip.h" +#include "clip-impl.h" +#include "clip-model.h" + +#include +#include + +struct clip_graph { + const clip_model & model; + const clip_hparams & hparams; + projector_type proj_type; + + // we only support single image per batch + const clip_image_f32 & img; + + const int patch_size; + const int n_patches_x; + const int n_patches_y; + const int n_patches; + const int n_embd; + const int n_head; + const int d_head; + const int n_layer; + const int n_mmproj_embd; + const float eps; + const float kq_scale; + const clip_flash_attn_type flash_attn_type; + + // for debugging + const bool debug_graph; + std::vector & debug_print_tensors; + + ggml_context_ptr ctx0_ptr; + ggml_context * ctx0; + ggml_cgraph * gf; + + clip_graph(clip_ctx * ctx, const clip_image_f32 & img); + + virtual ~clip_graph() = default; + virtual ggml_cgraph * build() = 0; + + // + // utility functions + // + void cb(ggml_tensor * cur0, const char * name, int il) const; + + // siglip2 naflex + ggml_tensor * resize_position_embeddings(); + + // build vision transformer (ViT) cgraph + // this function should cover most of the models + // if your model has specific features, you should probably duplicate this function + ggml_tensor * build_vit( + ggml_tensor * inp, + int64_t n_pos, + norm_type norm_t, + ffn_op_type ffn_t, + ggml_tensor * learned_pos_embd, + std::function add_pos); + + // build the input after conv2d (inp_raw --> patches) + // returns tensor with shape [n_embd, n_patches] + ggml_tensor * build_inp(); + + ggml_tensor * build_inp_raw(int channels = 3); + + ggml_tensor * build_norm( + ggml_tensor * cur, + ggml_tensor * mw, + ggml_tensor * mb, + norm_type type, + float norm_eps, + int il) const; + + ggml_tensor * build_ffn( + ggml_tensor * cur, + ggml_tensor * up, + ggml_tensor * up_b, + ggml_tensor * gate, + ggml_tensor * gate_b, + ggml_tensor * down, + ggml_tensor * down_b, + ffn_op_type type_op, + int il) const; + + ggml_tensor * build_attn( + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_mask, + float kq_scale, + int il) const; + + // implementation of the 2D RoPE without adding a new op in ggml + // this is not efficient (use double the memory), but works on all backends + // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 + ggml_tensor * build_rope_2d( + ggml_context * ctx0, + ggml_tensor * cur, + ggml_tensor * pos_a, // first half + ggml_tensor * pos_b, // second half + const float freq_base, + const bool interleave_freq + ); + + // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) + // support dynamic resolution + ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor); +}; diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index e5563eabfc..1726823ec6 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -1,3 +1,5 @@ +#pragma once + #include "ggml.h" #include "gguf.h" #include "clip.h" @@ -134,6 +136,10 @@ // align x to upper multiple of n #define CLIP_ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) +// forward declaration +// TODO: improve this later +struct clip_ctx; + enum projector_type { PROJECTOR_TYPE_MLP, PROJECTOR_TYPE_MLP_NORM, diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h new file mode 100644 index 0000000000..51bcce1ebb --- /dev/null +++ b/tools/mtmd/clip-model.h @@ -0,0 +1,279 @@ +#pragma once + +#include "ggml.h" +#include "clip.h" +#include "clip-impl.h" + +#include +#include +#include +#include + +enum ffn_op_type { + FFN_GELU, + FFN_GELU_ERF, + FFN_SILU, + FFN_GELU_QUICK, +}; + +enum norm_type { + NORM_TYPE_NORMAL, + NORM_TYPE_RMS, +}; + +enum patch_merge_type { + PATCH_MERGE_FLAT, + PATCH_MERGE_SPATIAL_UNPAD, +}; + +struct clip_hparams { + int32_t image_size = 0; + int32_t patch_size = 0; + int32_t n_embd = 0; + int32_t n_ff = 0; + int32_t projection_dim = 0; + int32_t n_head = 0; + int32_t n_layer = 0; + // idefics3 + int32_t image_longest_edge = 0; + int32_t image_min_pixels = -1; + int32_t image_max_pixels = -1; + int32_t n_merge = 0; // number of patch merges **per-side** + + float image_mean[3]; + float image_std[3]; + + // for models using dynamic image size, we need to have a smaller image size to warmup + // otherwise, user will get OOM everytime they load the model + int32_t warmup_image_size = 0; + int32_t warmup_audio_size = 3000; + + ffn_op_type ffn_op = FFN_GELU; + + patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; + + float eps = 1e-6; + float rope_theta = 0.0; + + std::vector image_res_candidates; // for llava-uhd style models + int32_t image_crop_resolution; + std::unordered_set vision_feature_layer; + int32_t attn_window_size = 0; + int32_t n_wa_pattern = 0; + + // audio + int32_t n_mel_bins = 0; // whisper preprocessor + int32_t proj_stack_factor = 0; // ultravox + + // legacy + bool has_llava_projector = false; + int minicpmv_version = 0; + int32_t minicpmv_query_num = 0; // MiniCPM-V query number + + // custom value provided by user, can be undefined if not set + int32_t custom_image_min_tokens = -1; + int32_t custom_image_max_tokens = -1; + + void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) { + const int cur_merge = n_merge == 0 ? 1 : n_merge; + const int patch_area = patch_size * patch_size * cur_merge * cur_merge; + image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area; + image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area; + warmup_image_size = static_cast(std::sqrt(image_max_pixels)); + } + + void set_warmup_n_tokens(int n_tokens) { + int n_tok_per_side = static_cast(std::sqrt(n_tokens)); + GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n"); + const int cur_merge = n_merge == 0 ? 1 : n_merge; + warmup_image_size = n_tok_per_side * patch_size * cur_merge; + // TODO: support warmup size for custom token numbers + } +}; + +struct clip_layer { + // attention + ggml_tensor * k_w = nullptr; + ggml_tensor * k_b = nullptr; + ggml_tensor * q_w = nullptr; + ggml_tensor * q_b = nullptr; + ggml_tensor * v_w = nullptr; + ggml_tensor * v_b = nullptr; + ggml_tensor * qkv_w = nullptr; + ggml_tensor * qkv_b = nullptr; + + ggml_tensor * o_w = nullptr; + ggml_tensor * o_b = nullptr; + + ggml_tensor * k_norm = nullptr; + ggml_tensor * q_norm = nullptr; + + // layernorm 1 + ggml_tensor * ln_1_w = nullptr; + ggml_tensor * ln_1_b = nullptr; + + ggml_tensor * ff_up_w = nullptr; + ggml_tensor * ff_up_b = nullptr; + ggml_tensor * ff_gate_w = nullptr; + ggml_tensor * ff_gate_b = nullptr; + ggml_tensor * ff_down_w = nullptr; + ggml_tensor * ff_down_b = nullptr; + + // layernorm 2 + ggml_tensor * ln_2_w = nullptr; + ggml_tensor * ln_2_b = nullptr; + + // layer scale (no bias) + ggml_tensor * ls_1_w = nullptr; + ggml_tensor * ls_2_w = nullptr; + + // qwen3vl deepstack merger + ggml_tensor * deepstack_norm_w = nullptr; + ggml_tensor * deepstack_norm_b = nullptr; + ggml_tensor * deepstack_fc1_w = nullptr; + ggml_tensor * deepstack_fc1_b = nullptr; + ggml_tensor * deepstack_fc2_w = nullptr; + ggml_tensor * deepstack_fc2_b = nullptr; + + bool has_deepstack() const { + return deepstack_fc1_w != nullptr; + } +}; + +struct clip_model { + clip_modality modality = CLIP_MODALITY_VISION; + projector_type proj_type = PROJECTOR_TYPE_MLP; + clip_hparams hparams; + + // embeddings + ggml_tensor * class_embedding = nullptr; + ggml_tensor * patch_embeddings_0 = nullptr; + ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) + ggml_tensor * patch_bias = nullptr; + ggml_tensor * position_embeddings = nullptr; + + ggml_tensor * pre_ln_w = nullptr; + ggml_tensor * pre_ln_b = nullptr; + + std::vector layers; + + int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer + + ggml_tensor * post_ln_w; + ggml_tensor * post_ln_b; + + ggml_tensor * projection; // TODO: rename it to fc (fully connected layer) + ggml_tensor * mm_fc_w; + ggml_tensor * mm_fc_b; + + // LLaVA projection + ggml_tensor * mm_input_norm_w = nullptr; + ggml_tensor * mm_input_norm_b = nullptr; + ggml_tensor * mm_0_w = nullptr; + ggml_tensor * mm_0_b = nullptr; + ggml_tensor * mm_2_w = nullptr; + ggml_tensor * mm_2_b = nullptr; + + ggml_tensor * image_newline = nullptr; + + // Yi type models with mlp+normalization projection + ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 + ggml_tensor * mm_1_b = nullptr; + ggml_tensor * mm_3_w = nullptr; + ggml_tensor * mm_3_b = nullptr; + ggml_tensor * mm_4_w = nullptr; + ggml_tensor * mm_4_b = nullptr; + + // GLMV-Edge projection + ggml_tensor * mm_model_adapter_conv_w = nullptr; + ggml_tensor * mm_model_adapter_conv_b = nullptr; + + // MobileVLM projection + ggml_tensor * mm_model_mlp_1_w = nullptr; + ggml_tensor * mm_model_mlp_1_b = nullptr; + ggml_tensor * mm_model_mlp_3_w = nullptr; + ggml_tensor * mm_model_mlp_3_b = nullptr; + ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; + ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; + ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; + ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; + + // MobileVLM_V2 projection + ggml_tensor * mm_model_mlp_0_w = nullptr; + ggml_tensor * mm_model_mlp_0_b = nullptr; + ggml_tensor * mm_model_mlp_2_w = nullptr; + ggml_tensor * mm_model_mlp_2_b = nullptr; + ggml_tensor * mm_model_peg_0_w = nullptr; + ggml_tensor * mm_model_peg_0_b = nullptr; + + // MINICPMV projection + ggml_tensor * mm_model_pos_embed_k = nullptr; + ggml_tensor * mm_model_query = nullptr; + ggml_tensor * mm_model_proj = nullptr; + ggml_tensor * mm_model_kv_proj = nullptr; + ggml_tensor * mm_model_attn_q_w = nullptr; + ggml_tensor * mm_model_attn_q_b = nullptr; + ggml_tensor * mm_model_attn_k_w = nullptr; + ggml_tensor * mm_model_attn_k_b = nullptr; + ggml_tensor * mm_model_attn_v_w = nullptr; + ggml_tensor * mm_model_attn_v_b = nullptr; + ggml_tensor * mm_model_attn_o_w = nullptr; + ggml_tensor * mm_model_attn_o_b = nullptr; + ggml_tensor * mm_model_ln_q_w = nullptr; + ggml_tensor * mm_model_ln_q_b = nullptr; + ggml_tensor * mm_model_ln_kv_w = nullptr; + ggml_tensor * mm_model_ln_kv_b = nullptr; + ggml_tensor * mm_model_ln_post_w = nullptr; + ggml_tensor * mm_model_ln_post_b = nullptr; + + // gemma3 + ggml_tensor * mm_input_proj_w = nullptr; + ggml_tensor * mm_soft_emb_norm_w = nullptr; + + // pixtral + ggml_tensor * token_embd_img_break = nullptr; + ggml_tensor * mm_patch_merger_w = nullptr; + + // ultravox / whisper encoder + ggml_tensor * conv1d_1_w = nullptr; + ggml_tensor * conv1d_1_b = nullptr; + ggml_tensor * conv1d_2_w = nullptr; + ggml_tensor * conv1d_2_b = nullptr; + ggml_tensor * mm_norm_pre_w = nullptr; + ggml_tensor * mm_norm_mid_w = nullptr; + + // cogvlm + ggml_tensor * mm_post_fc_norm_w = nullptr; + ggml_tensor * mm_post_fc_norm_b = nullptr; + ggml_tensor * mm_h_to_4h_w = nullptr; + ggml_tensor * mm_gate_w = nullptr; + ggml_tensor * mm_4h_to_h_w = nullptr; + ggml_tensor * mm_boi = nullptr; + ggml_tensor * mm_eoi = nullptr; + + bool audio_has_avgpool() const { + return proj_type == PROJECTOR_TYPE_QWEN2A + || proj_type == PROJECTOR_TYPE_VOXTRAL; + } + + bool audio_has_stack_frames() const { + return proj_type == PROJECTOR_TYPE_ULTRAVOX + || proj_type == PROJECTOR_TYPE_VOXTRAL; + } +}; diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 7360e8e09d..bb922e30b4 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1,9 +1,9 @@ -// NOTE: This is modified from clip.cpp only for LLaVA, -// so there might be still unnecessary artifacts hanging around -// I'll gradually clean and extend it -// Note: Even when using identical normalized image inputs (see normalize_image_u8_to_f32()) we have a significant difference in resulting embeddings compared to pytorch #include "clip.h" #include "clip-impl.h" +#include "clip-model.h" +#include "clip-graph.h" +#include "models/models.h" + #include "ggml.h" #include "ggml-cpp.h" #include "ggml-alloc.h" @@ -26,18 +26,6 @@ struct clip_logger_state g_logger_state = {clip_log_callback_default, NULL}; -enum ffn_op_type { - FFN_GELU, - FFN_GELU_ERF, - FFN_SILU, - FFN_GELU_QUICK, -}; - -enum norm_type { - NORM_TYPE_NORMAL, - NORM_TYPE_RMS, -}; - //#define CLIP_DEBUG_FUNCTIONS #ifdef CLIP_DEBUG_FUNCTIONS @@ -149,267 +137,6 @@ static void clip_image_convert_f32_to_u8(const clip_image_f32& src, clip_image_u #endif -// -// clip layers -// - -enum patch_merge_type { - PATCH_MERGE_FLAT, - PATCH_MERGE_SPATIAL_UNPAD, -}; - -struct clip_hparams { - int32_t image_size = 0; - int32_t patch_size = 0; - int32_t n_embd = 0; - int32_t n_ff = 0; - int32_t projection_dim = 0; - int32_t n_head = 0; - int32_t n_layer = 0; - // idefics3 - int32_t image_longest_edge = 0; - int32_t image_min_pixels = -1; - int32_t image_max_pixels = -1; - int32_t n_merge = 0; // number of patch merges **per-side** - - float image_mean[3]; - float image_std[3]; - - // for models using dynamic image size, we need to have a smaller image size to warmup - // otherwise, user will get OOM everytime they load the model - int32_t warmup_image_size = 0; - int32_t warmup_audio_size = 3000; - - ffn_op_type ffn_op = FFN_GELU; - - patch_merge_type mm_patch_merge_type = PATCH_MERGE_FLAT; - - float eps = 1e-6; - float rope_theta = 0.0; - - std::vector image_res_candidates; // for llava-uhd style models - int32_t image_crop_resolution; - std::unordered_set vision_feature_layer; - int32_t attn_window_size = 0; - int32_t n_wa_pattern = 0; - - // audio - int32_t n_mel_bins = 0; // whisper preprocessor - int32_t proj_stack_factor = 0; // ultravox - - // legacy - bool has_llava_projector = false; - int minicpmv_version = 0; - int32_t minicpmv_query_num = 0; // MiniCPM-V query number - - // custom value provided by user, can be undefined if not set - int32_t custom_image_min_tokens = -1; - int32_t custom_image_max_tokens = -1; - - void set_limit_image_tokens(int n_tokens_min, int n_tokens_max) { - const int cur_merge = n_merge == 0 ? 1 : n_merge; - const int patch_area = patch_size * patch_size * cur_merge * cur_merge; - image_min_pixels = (custom_image_min_tokens > 0 ? custom_image_min_tokens : n_tokens_min) * patch_area; - image_max_pixels = (custom_image_max_tokens > 0 ? custom_image_max_tokens : n_tokens_max) * patch_area; - warmup_image_size = static_cast(std::sqrt(image_max_pixels)); - } - - void set_warmup_n_tokens(int n_tokens) { - int n_tok_per_side = static_cast(std::sqrt(n_tokens)); - GGML_ASSERT(n_tok_per_side * n_tok_per_side == n_tokens && "n_tokens must be n*n"); - const int cur_merge = n_merge == 0 ? 1 : n_merge; - warmup_image_size = n_tok_per_side * patch_size * cur_merge; - // TODO: support warmup size for custom token numbers - } -}; - -struct clip_layer { - // attention - ggml_tensor * k_w = nullptr; - ggml_tensor * k_b = nullptr; - ggml_tensor * q_w = nullptr; - ggml_tensor * q_b = nullptr; - ggml_tensor * v_w = nullptr; - ggml_tensor * v_b = nullptr; - ggml_tensor * qkv_w = nullptr; - ggml_tensor * qkv_b = nullptr; - - ggml_tensor * o_w = nullptr; - ggml_tensor * o_b = nullptr; - - ggml_tensor * k_norm = nullptr; - ggml_tensor * q_norm = nullptr; - - // layernorm 1 - ggml_tensor * ln_1_w = nullptr; - ggml_tensor * ln_1_b = nullptr; - - ggml_tensor * ff_up_w = nullptr; - ggml_tensor * ff_up_b = nullptr; - ggml_tensor * ff_gate_w = nullptr; - ggml_tensor * ff_gate_b = nullptr; - ggml_tensor * ff_down_w = nullptr; - ggml_tensor * ff_down_b = nullptr; - - // layernorm 2 - ggml_tensor * ln_2_w = nullptr; - ggml_tensor * ln_2_b = nullptr; - - // layer scale (no bias) - ggml_tensor * ls_1_w = nullptr; - ggml_tensor * ls_2_w = nullptr; - - // qwen3vl deepstack merger - ggml_tensor * deepstack_norm_w = nullptr; - ggml_tensor * deepstack_norm_b = nullptr; - ggml_tensor * deepstack_fc1_w = nullptr; - ggml_tensor * deepstack_fc1_b = nullptr; - ggml_tensor * deepstack_fc2_w = nullptr; - ggml_tensor * deepstack_fc2_b = nullptr; - - bool has_deepstack() const { - return deepstack_fc1_w != nullptr; - } -}; - -struct clip_model { - clip_modality modality = CLIP_MODALITY_VISION; - projector_type proj_type = PROJECTOR_TYPE_MLP; - clip_hparams hparams; - - // embeddings - ggml_tensor * class_embedding = nullptr; - ggml_tensor * patch_embeddings_0 = nullptr; - ggml_tensor * patch_embeddings_1 = nullptr; // second Conv2D kernel when we decouple Conv3D along temproal dimension (Qwen2VL) - ggml_tensor * patch_bias = nullptr; - ggml_tensor * position_embeddings = nullptr; - - ggml_tensor * pre_ln_w = nullptr; - ggml_tensor * pre_ln_b = nullptr; - - std::vector layers; - - int32_t n_deepstack_layers = 0; // used by Qwen3-VL, calculated from clip_layer - - ggml_tensor * post_ln_w; - ggml_tensor * post_ln_b; - - ggml_tensor * projection; // TODO: rename it to fc (fully connected layer) - ggml_tensor * mm_fc_w; - ggml_tensor * mm_fc_b; - - // LLaVA projection - ggml_tensor * mm_input_norm_w = nullptr; - ggml_tensor * mm_input_norm_b = nullptr; - ggml_tensor * mm_0_w = nullptr; - ggml_tensor * mm_0_b = nullptr; - ggml_tensor * mm_2_w = nullptr; - ggml_tensor * mm_2_b = nullptr; - - ggml_tensor * image_newline = nullptr; - - // Yi type models with mlp+normalization projection - ggml_tensor * mm_1_w = nullptr; // Yi type models have 0, 1, 3, 4 - ggml_tensor * mm_1_b = nullptr; - ggml_tensor * mm_3_w = nullptr; - ggml_tensor * mm_3_b = nullptr; - ggml_tensor * mm_4_w = nullptr; - ggml_tensor * mm_4_b = nullptr; - - // GLMV-Edge projection - ggml_tensor * mm_model_adapter_conv_w = nullptr; - ggml_tensor * mm_model_adapter_conv_b = nullptr; - - // MobileVLM projection - ggml_tensor * mm_model_mlp_1_w = nullptr; - ggml_tensor * mm_model_mlp_1_b = nullptr; - ggml_tensor * mm_model_mlp_3_w = nullptr; - ggml_tensor * mm_model_mlp_3_b = nullptr; - ggml_tensor * mm_model_block_1_block_0_0_w = nullptr; - ggml_tensor * mm_model_block_1_block_0_1_w = nullptr; - ggml_tensor * mm_model_block_1_block_0_1_b = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc1_w = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc1_b = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc2_w = nullptr; - ggml_tensor * mm_model_block_1_block_1_fc2_b = nullptr; - ggml_tensor * mm_model_block_1_block_2_0_w = nullptr; - ggml_tensor * mm_model_block_1_block_2_1_w = nullptr; - ggml_tensor * mm_model_block_1_block_2_1_b = nullptr; - ggml_tensor * mm_model_block_2_block_0_0_w = nullptr; - ggml_tensor * mm_model_block_2_block_0_1_w = nullptr; - ggml_tensor * mm_model_block_2_block_0_1_b = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc1_w = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc1_b = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc2_w = nullptr; - ggml_tensor * mm_model_block_2_block_1_fc2_b = nullptr; - ggml_tensor * mm_model_block_2_block_2_0_w = nullptr; - ggml_tensor * mm_model_block_2_block_2_1_w = nullptr; - ggml_tensor * mm_model_block_2_block_2_1_b = nullptr; - - // MobileVLM_V2 projection - ggml_tensor * mm_model_mlp_0_w = nullptr; - ggml_tensor * mm_model_mlp_0_b = nullptr; - ggml_tensor * mm_model_mlp_2_w = nullptr; - ggml_tensor * mm_model_mlp_2_b = nullptr; - ggml_tensor * mm_model_peg_0_w = nullptr; - ggml_tensor * mm_model_peg_0_b = nullptr; - - // MINICPMV projection - ggml_tensor * mm_model_pos_embed_k = nullptr; - ggml_tensor * mm_model_query = nullptr; - ggml_tensor * mm_model_proj = nullptr; - ggml_tensor * mm_model_kv_proj = nullptr; - ggml_tensor * mm_model_attn_q_w = nullptr; - ggml_tensor * mm_model_attn_q_b = nullptr; - ggml_tensor * mm_model_attn_k_w = nullptr; - ggml_tensor * mm_model_attn_k_b = nullptr; - ggml_tensor * mm_model_attn_v_w = nullptr; - ggml_tensor * mm_model_attn_v_b = nullptr; - ggml_tensor * mm_model_attn_o_w = nullptr; - ggml_tensor * mm_model_attn_o_b = nullptr; - ggml_tensor * mm_model_ln_q_w = nullptr; - ggml_tensor * mm_model_ln_q_b = nullptr; - ggml_tensor * mm_model_ln_kv_w = nullptr; - ggml_tensor * mm_model_ln_kv_b = nullptr; - ggml_tensor * mm_model_ln_post_w = nullptr; - ggml_tensor * mm_model_ln_post_b = nullptr; - - // gemma3 - ggml_tensor * mm_input_proj_w = nullptr; - ggml_tensor * mm_soft_emb_norm_w = nullptr; - - // pixtral - ggml_tensor * token_embd_img_break = nullptr; - ggml_tensor * mm_patch_merger_w = nullptr; - - // ultravox / whisper encoder - ggml_tensor * conv1d_1_w = nullptr; - ggml_tensor * conv1d_1_b = nullptr; - ggml_tensor * conv1d_2_w = nullptr; - ggml_tensor * conv1d_2_b = nullptr; - ggml_tensor * mm_norm_pre_w = nullptr; - ggml_tensor * mm_norm_mid_w = nullptr; - - // cogvlm - ggml_tensor * mm_post_fc_norm_w = nullptr; - ggml_tensor * mm_post_fc_norm_b = nullptr; - ggml_tensor * mm_h_to_4h_w = nullptr; - ggml_tensor * mm_gate_w = nullptr; - ggml_tensor * mm_4h_to_h_w = nullptr; - ggml_tensor * mm_boi = nullptr; - ggml_tensor * mm_eoi = nullptr; - - bool audio_has_avgpool() const { - return proj_type == PROJECTOR_TYPE_QWEN2A - || proj_type == PROJECTOR_TYPE_VOXTRAL; - } - - bool audio_has_stack_frames() const { - return proj_type == PROJECTOR_TYPE_ULTRAVOX - || proj_type == PROJECTOR_TYPE_VOXTRAL; - } -}; - struct clip_ctx { clip_model model; @@ -492,2081 +219,613 @@ struct clip_ctx { } }; -struct clip_graph { - clip_ctx * ctx; - const clip_model & model; - const clip_hparams & hparams; +// +// clip_graph +// - // we only support single image per batch - const clip_image_f32 & img; +clip_graph::clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : + model(ctx->model), + hparams(model.hparams), + proj_type(ctx->proj_type()), + img(img), + patch_size(hparams.patch_size), + n_patches_x(img.nx / patch_size), + n_patches_y(img.ny / patch_size), + n_patches(n_patches_x * n_patches_y), + n_embd(hparams.n_embd), + n_head(hparams.n_head), + d_head(n_embd / n_head), + n_layer(hparams.n_layer), + n_mmproj_embd(clip_n_mmproj_embd(ctx)), + eps(hparams.eps), + kq_scale(1.0f / sqrtf((float)d_head)), + flash_attn_type(ctx->flash_attn_type), + debug_graph(ctx->debug_graph), + debug_print_tensors(ctx->debug_print_tensors) { + struct ggml_init_params params = { + /*.mem_size =*/ ctx->buf_compute_meta.size(), + /*.mem_buffer =*/ ctx->buf_compute_meta.data(), + /*.no_alloc =*/ true, + }; + ctx0_ptr.reset(ggml_init(params)); + ctx0 = ctx0_ptr.get(); + gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false); +} - const int patch_size; - const int n_patches_x; - const int n_patches_y; - const int n_patches; - const int n_embd; - const int n_head; - const int d_head; - const int n_layer; - const float eps; - const float kq_scale; - - ggml_context_ptr ctx0_ptr; - ggml_context * ctx0; - ggml_cgraph * gf; - - clip_graph(clip_ctx * ctx, const clip_image_f32 & img) : - ctx(ctx), - model(ctx->model), - hparams(model.hparams), - img(img), - patch_size(hparams.patch_size), - n_patches_x(img.nx / patch_size), - n_patches_y(img.ny / patch_size), - n_patches(n_patches_x * n_patches_y), - n_embd(hparams.n_embd), - n_head(hparams.n_head), - d_head(n_embd / n_head), - n_layer(hparams.n_layer), - eps(hparams.eps), - kq_scale(1.0f / sqrtf((float)d_head)) { - struct ggml_init_params params = { - /*.mem_size =*/ ctx->buf_compute_meta.size(), - /*.mem_buffer =*/ ctx->buf_compute_meta.data(), - /*.no_alloc =*/ true, - }; - ctx0_ptr.reset(ggml_init(params)); - ctx0 = ctx0_ptr.get(); - gf = ggml_new_graph_custom(ctx0, ctx->max_nodes, false); - } - - ggml_cgraph * build_siglip() { - ggml_tensor * inp = build_inp(); - - ggml_tensor * learned_pos_embd = model.position_embeddings; - if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) { - learned_pos_embd = resize_position_embeddings(); - } - - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - nullptr); - - if (ctx->proj_type() == PROJECTOR_TYPE_GEMMA3) { - const int batch_size = 1; - GGML_ASSERT(n_patches_x == n_patches_y); - const int patches_per_image = n_patches_x; - const int kernel_size = hparams.n_merge; - - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size); - - // doing a pool2d to reduce the number of output tokens - cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); - cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size); - cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); - - // apply norm before projection - cur = ggml_rms_norm(ctx0, cur, eps); - cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w); - - // apply projection - cur = ggml_mul_mat(ctx0, - ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), - cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_IDEFICS3) { - // pixel_shuffle - // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - cur = ggml_mul_mat(ctx0, model.projection, cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_LFM2) { - // pixel unshuffle block - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - - // projection - cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm - cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); - cur = ggml_add(ctx0, cur, model.mm_input_norm_b); - - cur = build_ffn(cur, - model.mm_1_w, model.mm_1_b, - nullptr, nullptr, - model.mm_2_w, model.mm_2_b, - FFN_GELU, - -1); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_JANUS_PRO) { - cur = build_ffn(cur, - model.mm_0_w, model.mm_0_b, - nullptr, nullptr, - model.mm_1_w, model.mm_1_b, - hparams.ffn_op, - -1); - - } else { - GGML_ABORT("SigLIP: Unsupported projector type"); - } - - // build the graph +void clip_graph::cb(ggml_tensor * cur0, const char * name, int il) const { + if (debug_graph) { + ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0)); + std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name; + ggml_set_name(cur, cur_name.c_str()); + ggml_set_output(cur); ggml_build_forward_expand(gf, cur); + debug_print_tensors.push_back(cur); + } +} - return gf; +// siglip2 naflex +ggml_tensor * clip_graph::resize_position_embeddings() { + ggml_tensor * pos_embd = model.position_embeddings; + const int height = img.ny / patch_size; + const int width = img.nx / patch_size; + const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS; + const int n_per_side = (int)std::sqrt(pos_embd->ne[1]); + + GGML_ASSERT(pos_embd); + + if (height == n_per_side && width == n_per_side) { + return pos_embd; } - ggml_cgraph * build_pixtral() { - const int n_merge = hparams.n_merge; + pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side); // -> (n_embd, n_per_side, n_per_side) + pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3); // -> (n_per_side, n_per_side, n_embd) + pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd) + pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3); // -> (n_embd, width, height) + pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); // -> (n_embd, width * height) - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); + return pos_embd; +} - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true); - }; - - ggml_tensor * inp = build_inp(); - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_RMS, - hparams.ffn_op, - nullptr, // no learned pos embd - add_pos); - - // mistral small 3.1 patch merger - // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 - if (model.mm_patch_merger_w) { - GGML_ASSERT(hparams.n_merge > 0); - - cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); - - // reshape image tokens to 2D grid - cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); - cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] - cur = ggml_cont(ctx0, cur); - - // torch.nn.functional.unfold is just an im2col under the hood - // we just need a dummy kernel to make it work - ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); - cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); - - // project to n_embd - cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); - cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); - } - - // LlavaMultiModalProjector (always using GELU activation) - { - cur = build_ffn(cur, - model.mm_1_w, model.mm_1_b, - nullptr, nullptr, - model.mm_2_w, model.mm_2_b, - FFN_GELU, - -1); - } - - // arrangement of the [IMG_BREAK] token - if (model.token_embd_img_break) { - // not efficient, but works - // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] - // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension - // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] - - const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; - const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; - const int p_total = p_x * p_y; - const int n_embd_text = cur->ne[0]; - const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row - - ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y); - ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y); - tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor - tok = ggml_add(ctx0, tok, model.token_embd_img_break); - tmp = ggml_concat(ctx0, tmp, tok, 1); - cur = ggml_view_2d(ctx0, tmp, - n_embd_text, n_tokens_output, - ggml_row_size(tmp->type, n_embd_text), 0); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // Qwen2VL and Qwen2.5VL use M-RoPE - ggml_cgraph * build_qwen2vl() { - GGML_ASSERT(model.patch_bias == nullptr); - GGML_ASSERT(model.class_embedding == nullptr); - - const int batch_size = 1; - const bool use_window_attn = hparams.n_wa_pattern > 0; - const int n_wa_pattern = hparams.n_wa_pattern; - const int n_pos = n_patches; - const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position - - norm_type norm_t = ctx->proj_type() == PROJECTOR_TYPE_QWEN25VL - ? NORM_TYPE_RMS // qwen 2.5 vl - : NORM_TYPE_NORMAL; // qwen 2 vl - - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - GGML_ASSERT(img.nx % (patch_size * 2) == 0); - GGML_ASSERT(img.ny % (patch_size * 2) == 0); - - // second conv dimension - { - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - - inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_cont_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); - inp = ggml_cont_3d( - ctx0, inp, - n_embd, n_patches_x * n_patches_y, batch_size); - } - - ggml_tensor * inpL = inp; - ggml_tensor * window_mask = nullptr; - ggml_tensor * window_idx = nullptr; - ggml_tensor * inv_window_idx = nullptr; - - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - } - - if (use_window_attn) { - // handle window attention inputs - inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); - ggml_set_name(inv_window_idx, "inv_window_idx"); - ggml_set_input(inv_window_idx); - // mask for window attention - window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); - ggml_set_name(window_mask, "window_mask"); - ggml_set_input(window_mask); - - // if flash attn is used, we need to pad the mask and cast to f16 - if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { - window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16); - } - - // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size] - GGML_ASSERT(batch_size == 1); - inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4); - inpL = ggml_get_rows(ctx0, inpL, inv_window_idx); - inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size); - } - - // loop over layers - for (int il = 0; il < n_layer; il++) { - const auto & layer = model.layers[il]; - const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; - - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "ln1", il); - - // self-attention - { - ggml_tensor * Qcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b); - ggml_tensor * Kcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b); - ggml_tensor * Vcur = ggml_add(ctx0, - ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b); - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - // apply M-RoPE - Qcur = ggml_rope_multi( - ctx0, Qcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - Kcur = ggml_rope_multi( - ctx0, Kcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - - cb(Qcur, "Qcur_rope", il); - cb(Kcur, "Kcur_rope", il); - - ggml_tensor * attn_mask = full_attn ? nullptr : window_mask; - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, attn_mask, kq_scale, il); - cb(cur, "attn_out", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); - } - - // multimodal projection - ggml_tensor * embeddings = inpL; - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); - embeddings = build_ffn(embeddings, - model.mm_0_w, model.mm_0_b, - nullptr, nullptr, - model.mm_1_w, model.mm_1_b, - FFN_GELU, - -1); - - if (use_window_attn) { - window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); - ggml_set_name(window_idx, "window_idx"); - ggml_set_input(window_idx); - - // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size] - GGML_ASSERT(batch_size == 1); - embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4); - embeddings = ggml_get_rows(ctx0, embeddings, window_idx); - embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - // Qwen3VL - ggml_cgraph * build_qwen3vl() { - GGML_ASSERT(model.patch_bias != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - GGML_ASSERT(model.class_embedding == nullptr); - - const int batch_size = 1; - const int n_pos = n_patches; - const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position - - norm_type norm_t = NORM_TYPE_NORMAL; - - int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; - - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - - GGML_ASSERT(img.nx % (patch_size * 2) == 0); - GGML_ASSERT(img.ny % (patch_size * 2) == 0); - - // second conv dimension - { - auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_add(ctx0, inp, inp_1); - - inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] - inp = ggml_cont_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - inp = ggml_reshape_4d( - ctx0, inp, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); - inp = ggml_cont_3d( - ctx0, inp, - n_embd, n_patches_x * n_patches_y, batch_size); - } - - // add patch bias - if (model.patch_bias != nullptr) { - inp = ggml_add(ctx0, inp, model.patch_bias); - cb(inp, "patch_bias", -1); - } - - // calculate absolute position embedding and apply - ggml_tensor * learned_pos_embd = resize_position_embeddings(); - learned_pos_embd = ggml_cont_4d( - ctx0, learned_pos_embd, - n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); - learned_pos_embd = ggml_reshape_4d( - ctx0, learned_pos_embd, - n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); - learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3); - learned_pos_embd = ggml_cont_3d( - ctx0, learned_pos_embd, - n_embd, n_patches_x * n_patches_y, batch_size); +// build vision transformer (ViT) cgraph +// this function should cover most of the models +// if your model has specific features, you should probably duplicate this function +ggml_tensor * clip_graph::build_vit( + ggml_tensor * inp, + int64_t n_pos, + norm_type norm_t, + ffn_op_type ffn_t, + ggml_tensor * learned_pos_embd, + std::function add_pos + ) { + if (learned_pos_embd) { inp = ggml_add(ctx0, inp, learned_pos_embd); - cb(inp, "inp_pos_emb", -1); + cb(inp, "pos_embed", -1); + } - ggml_tensor * inpL = inp; + ggml_tensor * inpL = inp; - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + cb(inpL, "pre_ln", -1); + } - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - } + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size] - ggml_tensor * deepstack_features = nullptr; - const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "layer_inp_normed", il); - // loop over layers - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "ln1", il); - - // self-attention - { + // self-attention + { + ggml_tensor * Qcur = nullptr; + ggml_tensor * Kcur = nullptr; + ggml_tensor * Vcur = nullptr; + if (layer.qkv_w != nullptr) { + // fused qkv cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); - cur = ggml_add(ctx0, cur, layer.qkv_b); - - ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ 0); - - ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, n_embd)); - - ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, 2 * n_embd)); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - // apply M-RoPE - Qcur = ggml_rope_multi( - ctx0, Qcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - Kcur = ggml_rope_multi( - ctx0, Kcur, positions, nullptr, - d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); - - cb(Qcur, "Qcur_rope", il); - cb(Kcur, "Kcur_rope", il); - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - if (layer.has_deepstack()) { - ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size); - feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il); - feat = build_ffn(feat, - layer.deepstack_fc1_w, layer.deepstack_fc1_b, - nullptr, nullptr, - layer.deepstack_fc2_w, layer.deepstack_fc2_b, - ffn_op_type::FFN_GELU, il); - - if(!deepstack_features) { - deepstack_features = feat; - } else { - // concat along the feature dimension - deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0); + if (layer.qkv_b != nullptr) { + cur = ggml_add(ctx0, cur, layer.qkv_b); } - } - inpL = cur; - } + Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ 0); - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); - } + Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, n_embd)); - // multimodal projection - ggml_tensor * embeddings = inpL; - embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); + Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, 2 * n_embd)); - embeddings = build_ffn(embeddings, - model.mm_0_w, model.mm_0_b, - nullptr, nullptr, - model.mm_1_w, model.mm_1_b, - ffn_op_type::FFN_GELU, -1); + // TODO: q/k norm requires row size == n_embd, while here it's d_head + // we can add support in the future if needed + GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr); - embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - ggml_cgraph * build_minicpmv() { - GGML_ASSERT(model.class_embedding == nullptr); - const int n_pos = n_patches; - const int n_embd_proj = clip_n_mmproj_embd(ctx); - - // position embeddings for the projector (not for ViT) - // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70 - // base frequency omega - ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4); - ggml_set_name(omega, "omega"); - ggml_set_input(omega); - - // 2D input positions (using float for sinusoidal embeddings) - ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - // for selecting learned pos embd, used by ViT - struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); - - ggml_tensor * inp = build_inp(); - ggml_tensor * embeddings = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - nullptr); - - // resampler projector (it is just another transformer) - - ggml_tensor * q = model.mm_model_query; - ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); - - // norm - q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1); - v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1); - - // calculate sinusoidal pos embd - ggml_tensor * pos_embed = nullptr; - { - // outer product - ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows - ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w); - ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h); - // sin and cos - ggml_tensor * pos_embd_x = ggml_concat( - ctx0, - ggml_sin(ctx0, theta_x), - ggml_cos(ctx0, theta_x), - 0 // concat on first dim - ); - ggml_tensor * pos_embd_y = ggml_concat( - ctx0, - ggml_sin(ctx0, theta_y), - ggml_cos(ctx0, theta_y), - 0 // concat on first dim - ); - pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0); - } - - // k = v + pos_embed - ggml_tensor * k = ggml_add(ctx0, v, pos_embed); - - // attention - { - const int d_head = 128; - int n_head = n_embd_proj/d_head; - // Use actual config value if available, otherwise fall back to hardcoded values - int num_query = ctx->model.hparams.minicpmv_query_num; - ggml_tensor * Q = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), - model.mm_model_attn_q_b); - ggml_tensor * K = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), - model.mm_model_attn_k_b); - ggml_tensor * V = ggml_add(ctx0, - ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), - model.mm_model_attn_v_b); - - Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query); - K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos); - V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos); - - cb(Q, "resampler_Q", -1); - cb(K, "resampler_K", -1); - cb(V, "resampler_V", -1); - - float resampler_kq_scale = 1.0f/ sqrtf(float(d_head)); - embeddings = build_attn( - model.mm_model_attn_o_w, - model.mm_model_attn_o_b, - Q, K, V, nullptr, resampler_kq_scale, -1); - cb(embeddings, "resampler_attn_out", -1); - } - // layernorm - embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1); - - // projection - embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - - ggml_cgraph * build_internvl() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; - ggml_tensor * inp = build_inp(); - - // add CLS token - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - // The larger models use a different ViT, which uses RMS norm instead of layer norm - // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188 - norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45) - ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B) - : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models) - - ggml_tensor * cur = build_vit( - inp, n_pos, - norm_t, - hparams.ffn_op, - model.position_embeddings, - nullptr); - - // remove CLS token - cur = ggml_view_2d(ctx0, cur, - n_embd, n_patches, - ggml_row_size(cur->type, n_embd), 0); - - // pixel shuffle - { - const int scale_factor = model.hparams.n_merge; - const int bsz = 1; // batch size, always 1 for now since we don't support batching - const int height = n_patches_y; - const int width = n_patches_x; - GGML_ASSERT(scale_factor > 0); - cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_cont_4d(ctx0, cur, - n_embd * scale_factor * scale_factor, - height / scale_factor, - width / scale_factor, - bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - // flatten to 2D - cur = ggml_cont_2d(ctx0, cur, - n_embd * scale_factor * scale_factor, - cur->ne[1] * cur->ne[2]); - } - - // projector (always using GELU activation) - { - // projector LayerNorm uses pytorch's default eps = 1e-5 - // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79 - cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1); - cur = build_ffn(cur, - model.mm_1_w, model.mm_1_b, - nullptr, nullptr, - model.mm_3_w, model.mm_3_b, - FFN_GELU, - -1); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - ggml_cgraph * build_llama4() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; // +1 for [CLS] - - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - ggml_tensor * inp = build_inp_raw(); - - // Llama4UnfoldConvolution - { - ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0, - patch_size, patch_size, 3, n_embd); - inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type); - inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp); - inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches); - cb(inp, "patch_conv", -1); - } - - // add CLS token - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - // build ViT with 2D position embeddings - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - // first half is X axis and second half is Y axis - // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312 - // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441 - return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); - }; - ggml_tensor * cur = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - model.position_embeddings, - add_pos); - - // remove CLS token - cur = ggml_view_2d(ctx0, cur, - n_embd, n_patches, - ggml_row_size(cur->type, n_embd), 0); - - // pixel shuffle - // based on Llama4VisionPixelShuffleMLP - // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151 - { - const int scale_factor = model.hparams.n_merge; - const int bsz = 1; // batch size, always 1 for now since we don't support batching - GGML_ASSERT(scale_factor > 0); - GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images - cur = ggml_reshape_4d(ctx0, cur, - n_embd * scale_factor, - n_patches_x / scale_factor, - n_patches_y, - bsz); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - cur = ggml_cont_4d(ctx0, cur, - n_embd * scale_factor * scale_factor, - n_patches_x / scale_factor, - n_patches_y / scale_factor, - bsz); - //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - // flatten to 2D - cur = ggml_cont_2d(ctx0, cur, - n_embd * scale_factor * scale_factor, - n_patches / scale_factor / scale_factor); - cb(cur, "pixel_shuffle", -1); - } - - // based on Llama4VisionMLP2 (always uses GELU activation, no bias) - { - cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur); - cur = ggml_gelu(ctx0, cur); - cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur); - cur = ggml_gelu(ctx0, cur); - cb(cur, "adapter_mlp", -1); - } - - // Llama4MultiModalProjector - cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); - cb(cur, "projected", -1); - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - ggml_cgraph * build_kimivl() { - // 2D input positions - ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_h, "pos_h"); - ggml_set_input(pos_h); - - ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(pos_w, "pos_w"); - ggml_set_input(pos_w); - - ggml_tensor * learned_pos_embd = resize_position_embeddings(); - - // build ViT with 2D position embeddings - auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { - // first half is X axis and second half is Y axis - return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); - }; - - ggml_tensor * inp = build_inp(); - ggml_tensor * cur = build_vit( - inp, n_patches, - NORM_TYPE_NORMAL, - hparams.ffn_op, - learned_pos_embd, - add_pos); - - cb(cur, "vit_out", -1); - - { - // patch_merger - const int scale_factor = model.hparams.n_merge; - cur = build_patch_merge_permute(cur, scale_factor); - - // projection norm - int proj_inp_dim = cur->ne[0]; - cur = ggml_view_2d(ctx0, cur, - n_embd, cur->ne[1] * scale_factor * scale_factor, - ggml_row_size(cur->type, n_embd), 0); - cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm - cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); - cur = ggml_add(ctx0, cur, model.mm_input_norm_b); - cur = ggml_view_2d(ctx0, cur, - proj_inp_dim, cur->ne[1] / scale_factor / scale_factor, - ggml_row_size(cur->type, proj_inp_dim), 0); - cb(cur, "proj_inp_normed", -1); - - // projection mlp - cur = build_ffn(cur, - model.mm_1_w, model.mm_1_b, - nullptr, nullptr, - model.mm_2_w, model.mm_2_b, - FFN_GELU, - -1); - cb(cur, "proj_out", -1); - } - - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // this graph is used by llava, granite and glm - // due to having embedding_stack (used by granite), we cannot reuse build_vit - ggml_cgraph * build_llava() { - const int batch_size = 1; - const int n_pos = n_patches + (model.class_embedding ? 1 : 0); - - GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported"); - - // Calculate the deepest feature layer based on hparams and projector type - int max_feature_layer = n_layer; - { - // Get the index of the second to last layer; this is the default for models that have a llava projector - int il_last = hparams.n_layer - 1; - int deepest_feature_layer = -1; - - if (ctx->proj_type() == PROJECTOR_TYPE_MINICPMV || ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) { - il_last += 1; - } - - // If we set explicit vision feature layers, only go up to the deepest one - // NOTE: only used by granite-vision models for now - for (const auto & feature_layer : hparams.vision_feature_layer) { - if (feature_layer > deepest_feature_layer) { - deepest_feature_layer = feature_layer; - } - } - max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer; - } - - ggml_tensor * inp = build_inp(); - - // concat class_embeddings and patch_embeddings - if (model.class_embedding) { - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - } - - ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); - ggml_set_name(positions, "positions"); - ggml_set_input(positions); - - inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions)); - - ggml_tensor * inpL = inp; - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1); - cb(inpL, "pre_ln", -1); - } - - std::vector embedding_stack; - const auto & vision_feature_layer = hparams.vision_feature_layer; - - // loop over layers - for (int il = 0; il < max_feature_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // If this is an embedding feature layer, save the output. - // NOTE: 0 index here refers to the input to the encoder. - if (vision_feature_layer.find(il) != vision_feature_layer.end()) { - embedding_stack.push_back(cur); - } - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "layer_inp_normed", il); - - // self-attention - { - ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + } else { + // separate q, k, v + Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); if (layer.q_b) { Qcur = ggml_add(ctx0, Qcur, layer.q_b); } - ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); if (layer.k_b) { Kcur = ggml_add(ctx0, Kcur, layer.k_b); } - ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); if (layer.v_b) { Vcur = ggml_add(ctx0, Vcur, layer.v_b); } + if (layer.q_norm) { + Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il); + cb(Qcur, "Qcur_norm", il); + } + + if (layer.k_norm) { + Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il); + cb(Kcur, "Kcur_norm", il); + } + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); } - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); - } - - ggml_tensor * embeddings = inpL; - - // process vision feature layers (used by granite) - { - // final layer is a vision feature layer - if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) { - embedding_stack.push_back(inpL); - } - - // If feature layers are explicitly set, stack them (if we have multiple) - if (!embedding_stack.empty()) { - embeddings = embedding_stack[0]; - for (size_t i = 1; i < embedding_stack.size(); i++) { - embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); - } - } - } - - // llava projector (also used by granite) - if (ctx->model.hparams.has_llava_projector) { - embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); - - ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); - ggml_set_name(patches, "patches"); - ggml_set_input(patches); - - // shape [1, 576, 1024] - // ne is whcn, ne = [1024, 576, 1, 1] - embeddings = ggml_get_rows(ctx0, embeddings, patches); - - // print_tensor_info(embeddings, "embeddings"); - - // llava projector - if (ctx->proj_type() == PROJECTOR_TYPE_MLP) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - - embeddings = ggml_gelu(ctx0, embeddings); - if (model.mm_2_w) { - embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); - } - } - else if (ctx->proj_type() == PROJECTOR_TYPE_MLP_NORM) { - embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); - // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); - // First LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), - model.mm_1_b); - - // GELU activation - embeddings = ggml_gelu(ctx0, embeddings); - - // Second linear layer - embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); - embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); - - // Second LayerNorm - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), - model.mm_4_b); - } - else if (ctx->proj_type() == PROJECTOR_TYPE_LDP) { - // MobileVLM projector - int n_patch = 24; - ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); - mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); - mlp_1 = ggml_gelu(ctx0, mlp_1); - ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); - mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); - // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] - - // block 1 - ggml_tensor * block_1 = nullptr; - { - // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] - mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3); - mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); - // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); - - // layer norm - // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - - // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // hardswish - ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); - - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); - // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); - - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); - - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); - - // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] - // residual - block_1 = ggml_add(ctx0, mlp_3, block_1); - } - - // block_2 - { - // stride = 2 - block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); - - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // layer norm - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); - // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] - // hardswish - ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); - - // not sure the parameters is right for globalAvgPooling - block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); - // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - // pointwise conv - block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); - block_1 = ggml_relu(ctx0, block_1); - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); - block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); - block_1 = ggml_hardsigmoid(ctx0, block_1); - - // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] - block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); - block_1 = ggml_mul(ctx0, block_1_hw, block_1); - - int w = block_1->ne[0], h = block_1->ne[1]; - block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); - block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); - // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] - block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); - block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); - - - // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] - block_1 = ggml_norm(ctx0, block_1, eps); - block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); - block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); - // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] - } - embeddings = block_1; - } - else if (ctx->proj_type() == PROJECTOR_TYPE_LDPV2) - { - int n_patch = 24; - ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); - mlp_0 = ggml_gelu(ctx0, mlp_0); - ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); - mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); - // mlp_2 ne = [2048, 576, 1, 1] - // // AVG Pool Layer 2*2, strides = 2 - mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3); - // mlp_2 ne = [576, 2048, 1, 1] - mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); - // mlp_2 ne [24, 24, 2048, 1] - mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); - // weight ne = [3, 3, 2048, 1] - ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); - peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); - mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); - peg_0 = ggml_add(ctx0, peg_0, mlp_2); - peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); - embeddings = peg_0; - } - else { - GGML_ABORT("fatal error"); - } - } - - // glm projector - else if (ctx->proj_type() == PROJECTOR_TYPE_GLM_EDGE) { - size_t gridsz = (size_t)sqrt(embeddings->ne[1]); - embeddings = ggml_permute(ctx0,embeddings,1,0,2,3); - embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); - embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); - embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); - embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); - embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); - // GLU - { - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); - embeddings = ggml_norm(ctx0, embeddings, eps); - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); - embeddings = ggml_gelu_inplace(ctx0, embeddings); - ggml_tensor * x = embeddings; - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); - x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); - embeddings = ggml_swiglu_split(ctx0, embeddings, x); - embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); - } - // arrangement of BOI/EOI token embeddings - // note: these embeddings are not present in text model, hence we cannot process them as text tokens - // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 - { - embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI - embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI - } - } - - else { - GGML_ABORT("llava: unknown projector type"); - } - - // build the graph - ggml_build_forward_expand(gf, embeddings); - - return gf; - } - // whisper encoder with custom projector - ggml_cgraph * build_whisper_enc() { - const int n_frames = img.nx; - const int n_pos = n_frames / 2; - GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos); - - ggml_tensor * inp = build_inp_raw(1); - - // conv1d block - { - // convolution + gelu - ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); - cur = ggml_add(ctx0, cur, model.conv1d_1_b); - - cur = ggml_gelu_erf(ctx0, cur); - - cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); - cur = ggml_add(ctx0, cur, model.conv1d_2_b); - - cur = ggml_gelu_erf(ctx0, cur); - // transpose - inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); - cb(inp, "after_conv1d", -1); - } - - // sanity check (only check one layer, but it should be the same for all) - GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b); - GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b); - GGML_ASSERT(model.layers[0].q_b); - GGML_ASSERT(model.layers[0].v_b); - GGML_ASSERT(!model.layers[0].k_b); // no bias for k - GGML_ASSERT(model.post_ln_w && model.post_ln_b); - - ggml_tensor * pos_embd_selected = ggml_view_2d( - ctx0, model.position_embeddings, - model.position_embeddings->ne[0], n_pos, - model.position_embeddings->nb[1], 0 - ); - ggml_tensor * cur = build_vit( - inp, n_pos, - NORM_TYPE_NORMAL, - hparams.ffn_op, - pos_embd_selected, - nullptr); - - cb(cur, "after_transformer", -1); - - if (model.audio_has_stack_frames()) { - // StackAudioFrames - // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py - int64_t stride = n_embd * hparams.proj_stack_factor; - int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride); - int64_t pad = padded_len - ggml_nelements(cur); - if (pad > 0) { - cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0); - cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); - } - cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, - ggml_row_size(cur->type, stride), 0); - cb(cur, "after_stacked", -1); - } - - if (ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX) { - // UltravoxProjector - // pre-norm - cur = ggml_rms_norm(ctx0, cur, 1e-6); - cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); - - // ffn in - cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); - - // swiglu - // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half - cur = ggml_swiglu_swapped(ctx0, cur); - - // mid-norm - cur = ggml_rms_norm(ctx0, cur, 1e-6); - cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w); - - // ffn out - cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_QWEN2A) { - // projector - cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur); - cur = ggml_add(ctx0, cur, model.mm_fc_b); - - } else if (ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL) { - // projector - cur = build_ffn(cur, - model.mm_1_w, model.mm_1_b, - nullptr, nullptr, - model.mm_2_w, model.mm_2_b, - FFN_GELU_ERF, - -1); - - } else { - GGML_ABORT("%s: unknown projector type", __func__); - } - - cb(cur, "projected", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - - // cogvlm vision encoder - ggml_cgraph * build_cogvlm() { - GGML_ASSERT(model.class_embedding != nullptr); - GGML_ASSERT(model.position_embeddings != nullptr); - - const int n_pos = n_patches + 1; // +1 for [CLS] - - // build input and concatenate class embedding - ggml_tensor * inp = build_inp(); - inp = ggml_concat(ctx0, inp, model.class_embedding, 1); - - inp = ggml_add(ctx0, inp, model.position_embeddings); - cb(inp, "inp_pos", -1); - - ggml_tensor * inpL = inp; - - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; - - cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); - - cur = ggml_add(ctx0, cur, layer.qkv_b); - - ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], 0); - ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], n_embd * sizeof(float)); - ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), - cur->nb[1], 2 * n_embd * sizeof(float)); - cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); + if (add_pos) { + Qcur = add_pos(Qcur, layer); + Kcur = add_pos(Kcur, layer); + cb(Qcur, "Qcur_pos", il); + cb(Kcur, "Kcur_pos", il); + } + cur = build_attn(layer.o_w, layer.o_b, Qcur, Kcur, Vcur, nullptr, kq_scale, il); cb(cur, "attn_out", il); - - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "attn_post_norm", il); - - cur = ggml_add(ctx0, cur, inpL); - inpL = cur; - - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - hparams.ffn_op, il); - - cb(cur, "ffn_out", il); - - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); - cb(cur, "ffn_post_norm", il); - - cur = ggml_add(ctx0, cur, inpL); - cb(cur, "layer_out", il); - inpL = cur; - } - // remove CLS token (like build_llama4 does) - ggml_tensor * cur = ggml_view_2d(ctx0, inpL, - n_embd, n_patches, - ggml_row_size(inpL->type, n_embd), 0); + if (layer.ls_1_w) { + cur = ggml_mul(ctx0, cur, layer.ls_1_w); + cb(cur, "attn_out_scaled", il); + } - // Multiply with mm_model_proj - cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); - // Apply layernorm, weight, bias - cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1); + inpL = cur; // inpL = residual, cur = hidden_states - // Apply GELU - cur = ggml_gelu_inplace(ctx0, cur); + cb(cur, "ffn_inp", il); - // Branch 1: multiply with mm_h_to_4h_w - ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur); + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); - // Branch 2: multiply with mm_gate_w - ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur); + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + ffn_t, il); - // Apply silu - gate = ggml_swiglu_split(ctx0, gate, h_to_4h); + cb(cur, "ffn_out", il); - // Apply mm_4h_to_h_w - cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate); + if (layer.ls_2_w) { + cur = ggml_mul(ctx0, cur, layer.ls_2_w); + cb(cur, "ffn_out_scaled", il); + } - // Concatenate with boi and eoi - cur = ggml_concat(ctx0, model.mm_boi, cur, 1); - cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); - // build the graph - ggml_build_forward_expand(gf, cur); - - return gf; + inpL = cur; } -private: - // - // utility functions - // - - void cb(ggml_tensor * cur0, const char * name, int il) const { - if (ctx->debug_graph) { - ggml_tensor * cur = ggml_cpy(ctx0, cur0, ggml_dup_tensor(ctx0, cur0)); - std::string cur_name = il >= 0 ? std::string(name) + "_" + std::to_string(il) : name; - ggml_set_name(cur, cur_name.c_str()); - ggml_set_output(cur); - ggml_build_forward_expand(gf, cur); - ctx->debug_print_tensors.push_back(cur); - } + if (model.audio_has_avgpool()) { + ggml_tensor * cur = inpL; + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont(ctx0, cur); + cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0); + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont(ctx0, cur); + inpL = cur; } - // siglip2 naflex - ggml_tensor * resize_position_embeddings() { - ggml_tensor * pos_embd = model.position_embeddings; - const int height = img.ny / patch_size; - const int width = img.nx / patch_size; - const uint32_t mode = GGML_SCALE_MODE_BILINEAR | GGML_SCALE_FLAG_ANTIALIAS; - const int n_per_side = (int)std::sqrt(pos_embd->ne[1]); - - GGML_ASSERT(pos_embd); - - if (height == n_per_side && width == n_per_side) { - return pos_embd; - } - - pos_embd = ggml_reshape_3d(ctx0, pos_embd, n_embd, n_per_side, n_per_side); // -> (n_embd, n_per_side, n_per_side) - pos_embd = ggml_permute(ctx0, pos_embd, 2, 0, 1, 3); // -> (n_per_side, n_per_side, n_embd) - pos_embd = ggml_interpolate(ctx0, pos_embd, width, height, n_embd, 1, mode); // -> (width, height, n_embd) - pos_embd = ggml_permute(ctx0, pos_embd, 1, 2, 0, 3); // -> (n_embd, width, height) - pos_embd = ggml_cont_2d(ctx0, pos_embd, n_embd, width * height); // -> (n_embd, width * height) - - return pos_embd; + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); } + return inpL; +} - // build vision transformer (ViT) cgraph - // this function should cover most of the models - // if your model has specific features, you should probably duplicate this function - ggml_tensor * build_vit( - ggml_tensor * inp, - int64_t n_pos, - norm_type norm_t, - ffn_op_type ffn_t, - ggml_tensor * learned_pos_embd, - std::function add_pos - ) { - if (learned_pos_embd) { - inp = ggml_add(ctx0, inp, learned_pos_embd); - cb(inp, "pos_embed", -1); - } - - ggml_tensor * inpL = inp; - - // pre-layernorm - if (model.pre_ln_w) { - inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); - cb(inpL, "pre_ln", -1); - } - - // loop over layers - for (int il = 0; il < n_layer; il++) { - auto & layer = model.layers[il]; - ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states - - // layernorm1 - cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); - cb(cur, "layer_inp_normed", il); - - // self-attention - { - ggml_tensor * Qcur = nullptr; - ggml_tensor * Kcur = nullptr; - ggml_tensor * Vcur = nullptr; - if (layer.qkv_w != nullptr) { - // fused qkv - cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); - if (layer.qkv_b != nullptr) { - cur = ggml_add(ctx0, cur, layer.qkv_b); - } - - Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ 0); - - Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, n_embd)); - - Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, - /* nb1 */ ggml_row_size(cur->type, d_head), - /* nb2 */ cur->nb[1], - /* offset */ ggml_row_size(cur->type, 2 * n_embd)); - - // TODO: q/k norm requires row size == n_embd, while here it's d_head - // we can add support in the future if needed - GGML_ASSERT(layer.q_norm == nullptr && layer.k_norm == nullptr); - - } else { - // separate q, k, v - Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); - if (layer.q_b) { - Qcur = ggml_add(ctx0, Qcur, layer.q_b); - } - - Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); - if (layer.k_b) { - Kcur = ggml_add(ctx0, Kcur, layer.k_b); - } - - Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); - if (layer.v_b) { - Vcur = ggml_add(ctx0, Vcur, layer.v_b); - } - - if (layer.q_norm) { - Qcur = build_norm(Qcur, layer.q_norm, NULL, norm_t, eps, il); - cb(Qcur, "Qcur_norm", il); - } - - if (layer.k_norm) { - Kcur = build_norm(Kcur, layer.k_norm, NULL, norm_t, eps, il); - cb(Kcur, "Kcur_norm", il); - } - - Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); - Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); - Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); - } - - cb(Qcur, "Qcur", il); - cb(Kcur, "Kcur", il); - cb(Vcur, "Vcur", il); - - if (add_pos) { - Qcur = add_pos(Qcur, layer); - Kcur = add_pos(Kcur, layer); - cb(Qcur, "Qcur_pos", il); - cb(Kcur, "Kcur_pos", il); - } - - cur = build_attn(layer.o_w, layer.o_b, - Qcur, Kcur, Vcur, nullptr, kq_scale, il); - cb(cur, "attn_out", il); - } - - if (layer.ls_1_w) { - cur = ggml_mul(ctx0, cur, layer.ls_1_w); - cb(cur, "attn_out_scaled", il); - } - - // re-add the layer input, e.g., residual - cur = ggml_add(ctx0, cur, inpL); - - inpL = cur; // inpL = residual, cur = hidden_states - - cb(cur, "ffn_inp", il); - - // layernorm2 - cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); - cb(cur, "ffn_inp_normed", il); - - // ffn - cur = build_ffn(cur, - layer.ff_up_w, layer.ff_up_b, - layer.ff_gate_w, layer.ff_gate_b, - layer.ff_down_w, layer.ff_down_b, - ffn_t, il); - - cb(cur, "ffn_out", il); - - if (layer.ls_2_w) { - cur = ggml_mul(ctx0, cur, layer.ls_2_w); - cb(cur, "ffn_out_scaled", il); - } - - // residual 2 - cur = ggml_add(ctx0, inpL, cur); - cb(cur, "layer_out", il); - - inpL = cur; - } - - if (ctx->model.audio_has_avgpool()) { - ggml_tensor * cur = inpL; - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont(ctx0, cur); - cur = ggml_pool_1d(ctx0, cur, GGML_OP_POOL_AVG, 2, 2, 0); - cur = ggml_transpose(ctx0, cur); - cur = ggml_cont(ctx0, cur); - inpL = cur; - } - - // post-layernorm - if (model.post_ln_w) { - inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, -1); - } - return inpL; +// build the input after conv2d (inp_raw --> patches) +// returns tensor with shape [n_embd, n_patches] +ggml_tensor * clip_graph::build_inp() { + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); + inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); + if (model.patch_bias) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); } + return inp; +} - // build the input after conv2d (inp_raw --> patches) - // returns tensor with shape [n_embd, n_patches] - ggml_tensor * build_inp() { - ggml_tensor * inp_raw = build_inp_raw(); - ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); - inp = ggml_reshape_2d(ctx0, inp, n_patches, n_embd); - inp = ggml_cont(ctx0, ggml_transpose(ctx0, inp)); - if (model.patch_bias) { - inp = ggml_add(ctx0, inp, model.patch_bias); - cb(inp, "patch_bias", -1); - } - return inp; - } +ggml_tensor * clip_graph::build_inp_raw(int channels) { + ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels); + ggml_set_name(inp_raw, "inp_raw"); + ggml_set_input(inp_raw); + return inp_raw; +} - ggml_tensor * build_inp_raw(int channels = 3) { - ggml_tensor * inp_raw = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, img.nx, img.ny, channels); - ggml_set_name(inp_raw, "inp_raw"); - ggml_set_input(inp_raw); - return inp_raw; - } - - ggml_tensor * build_norm( - ggml_tensor * cur, - ggml_tensor * mw, - ggml_tensor * mb, - norm_type type, - float norm_eps, - int il) const { - - cur = type == NORM_TYPE_RMS - ? ggml_rms_norm(ctx0, cur, norm_eps) - : ggml_norm(ctx0, cur, norm_eps); - - if (mw || mb) { - cb(cur, "norm", il); - } - - if (mw) { - cur = ggml_mul(ctx0, cur, mw); - if (mb) { - cb(cur, "norm_w", il); - } - } - - if (mb) { - cur = ggml_add(ctx0, cur, mb); - } - - return cur; - } - - ggml_tensor * build_ffn( - ggml_tensor * cur, - ggml_tensor * up, - ggml_tensor * up_b, - ggml_tensor * gate, - ggml_tensor * gate_b, - ggml_tensor * down, - ggml_tensor * down_b, - ffn_op_type type_op, - int il) const { - - ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur; - cb(tmp, "ffn_up", il); - - if (up_b) { - tmp = ggml_add(ctx0, tmp, up_b); - cb(tmp, "ffn_up_b", il); - } - - if (gate) { - cur = ggml_mul_mat(ctx0, gate, cur); - cb(cur, "ffn_gate", il); - - if (gate_b) { - cur = ggml_add(ctx0, cur, gate_b); - cb(cur, "ffn_gate_b", il); - } - } else { - cur = tmp; - } - - // we only support parallel ffn for now - switch (type_op) { - case FFN_SILU: - if (gate) { - cur = ggml_swiglu_split(ctx0, cur, tmp); - cb(cur, "ffn_swiglu", il); - } else { - cur = ggml_silu(ctx0, cur); - cb(cur, "ffn_silu", il); - } break; - case FFN_GELU: - if (gate) { - cur = ggml_geglu_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu", il); - } else { - cur = ggml_gelu(ctx0, cur); - cb(cur, "ffn_gelu", il); - } break; - case FFN_GELU_ERF: - if (gate) { - cur = ggml_geglu_erf_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu_erf", il); - } else { - cur = ggml_gelu_erf(ctx0, cur); - cb(cur, "ffn_gelu_erf", il); - } break; - case FFN_GELU_QUICK: - if (gate) { - cur = ggml_geglu_quick_split(ctx0, cur, tmp); - cb(cur, "ffn_geglu_quick", il); - } else { - cur = ggml_gelu_quick(ctx0, cur); - cb(cur, "ffn_gelu_quick", il); - } break; - } - - if (down) { - cur = ggml_mul_mat(ctx0, down, cur); - } - - if (down_b) { - cb(cur, "ffn_down", il); - } - - if (down_b) { - cur = ggml_add(ctx0, cur, down_b); - } - - return cur; - } - - ggml_tensor * build_attn( - ggml_tensor * wo, - ggml_tensor * wo_b, - ggml_tensor * q_cur, - ggml_tensor * k_cur, - ggml_tensor * v_cur, - ggml_tensor * kq_mask, - float kq_scale, - int il) const { - // these nodes are added to the graph together so that they are not reordered - // by doing so, the number of splits in the graph is reduced - ggml_build_forward_expand(gf, q_cur); - ggml_build_forward_expand(gf, k_cur); - ggml_build_forward_expand(gf, v_cur); - - ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3); - //cb(q, "q", il); - - ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3); - //cb(k, "k", il); - - ggml_tensor * cur; - - if (ctx->flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { - ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3); - - k = ggml_cast(ctx0, k, GGML_TYPE_F16); - v = ggml_cast(ctx0, v, GGML_TYPE_F16); - - cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f); - ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); - - cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]); - - } else { - ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); - v = ggml_cont(ctx0, v); - - const auto n_tokens = q->ne[1]; - const auto n_head = q->ne[2]; - - ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); - // F32 may not needed for vision encoders? - // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - - kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); - - ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); - cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); - cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens); - } - - cb(cur, "kqv_out", il); - - if (wo) { - cur = ggml_mul_mat(ctx0, wo, cur); - } - - if (wo_b) { - cur = ggml_add(ctx0, cur, wo_b); - } - - return cur; - } - - // implementation of the 2D RoPE without adding a new op in ggml - // this is not efficient (use double the memory), but works on all backends - // TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 - static ggml_tensor * build_rope_2d( - ggml_context * ctx0, +ggml_tensor * clip_graph::build_norm( ggml_tensor * cur, - ggml_tensor * pos_a, // first half - ggml_tensor * pos_b, // second half - const float freq_base, - const bool interleave_freq - ) { - const int64_t n_dim = cur->ne[0]; - const int64_t n_head = cur->ne[1]; - const int64_t n_pos = cur->ne[2]; + ggml_tensor * mw, + ggml_tensor * mb, + norm_type type, + float norm_eps, + int il) const { - // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) - // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 - // first half of cur will use 1e-0, 1e-2 (even) - // second half of cur will use 1e-1, 1e-3 (odd) - // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even - // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) - // then for the second half, we use freq_scale to shift the inv_freq - // ^ why? replace (2i) with (2i+1) in the above equation - const float freq_scale_odd = interleave_freq - ? std::pow(freq_base, (float)-2/n_dim) - : 1.0; + cur = type == NORM_TYPE_RMS + ? ggml_rms_norm(ctx0, cur, norm_eps) + : ggml_norm(ctx0, cur, norm_eps); - // first half - ggml_tensor * first; - { - first = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - 0); - first = ggml_rope_ext( - ctx0, - first, - pos_a, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - 1.0f, 0.0f, 1.0f, 0.0f, 0.0f - ); - } - - // second half - ggml_tensor * second; - { - second = ggml_view_3d(ctx0, cur, - n_dim/2, n_head, n_pos, - ggml_row_size(cur->type, n_dim), - ggml_row_size(cur->type, n_dim*n_head), - n_dim/2 * ggml_element_size(cur)); - second = ggml_rope_ext( - ctx0, - second, - pos_b, // positions - nullptr, // freq factors - n_dim/2, // n_dims - 0, 0, freq_base, - freq_scale_odd, - 0.0f, 1.0f, 0.0f, 0.0f - ); - } - - cur = ggml_concat(ctx0, first, second, 0); - return cur; + if (mw || mb) { + cb(cur, "norm", il); } - // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) - // support dynamic resolution - ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor) { - GGML_ASSERT(scale_factor > 1); - - const int n_embd = cur->ne[0]; - int width = img.nx / patch_size; - int height = img.ny / patch_size; - - // pad width and height to factor - const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width; - const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height; - cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height); - if (pad_width || pad_height) { - cur = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0); - width += pad_width; - height += pad_height; + if (mw) { + cur = ggml_mul(ctx0, cur, mw); + if (mb) { + cb(cur, "norm_w", il); } - - // unshuffle h - cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - - // unshuffle w - cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor); - cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); - - cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); - cb(cur, "pixel_shuffle", -1); - - return cur; } -}; + if (mb) { + cur = ggml_add(ctx0, cur, mb); + } + + return cur; +} + +ggml_tensor * clip_graph::build_ffn( + ggml_tensor * cur, + ggml_tensor * up, + ggml_tensor * up_b, + ggml_tensor * gate, + ggml_tensor * gate_b, + ggml_tensor * down, + ggml_tensor * down_b, + ffn_op_type type_op, + int il) const { + + ggml_tensor * tmp = up ? ggml_mul_mat(ctx0, up, cur) : cur; + cb(tmp, "ffn_up", il); + + if (up_b) { + tmp = ggml_add(ctx0, tmp, up_b); + cb(tmp, "ffn_up_b", il); + } + + if (gate) { + cur = ggml_mul_mat(ctx0, gate, cur); + cb(cur, "ffn_gate", il); + + if (gate_b) { + cur = ggml_add(ctx0, cur, gate_b); + cb(cur, "ffn_gate_b", il); + } + } else { + cur = tmp; + } + + // we only support parallel ffn for now + switch (type_op) { + case FFN_SILU: + if (gate) { + cur = ggml_swiglu_split(ctx0, cur, tmp); + cb(cur, "ffn_swiglu", il); + } else { + cur = ggml_silu(ctx0, cur); + cb(cur, "ffn_silu", il); + } break; + case FFN_GELU: + if (gate) { + cur = ggml_geglu_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu", il); + } else { + cur = ggml_gelu(ctx0, cur); + cb(cur, "ffn_gelu", il); + } break; + case FFN_GELU_ERF: + if (gate) { + cur = ggml_geglu_erf_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu_erf", il); + } else { + cur = ggml_gelu_erf(ctx0, cur); + cb(cur, "ffn_gelu_erf", il); + } break; + case FFN_GELU_QUICK: + if (gate) { + cur = ggml_geglu_quick_split(ctx0, cur, tmp); + cb(cur, "ffn_geglu_quick", il); + } else { + cur = ggml_gelu_quick(ctx0, cur); + cb(cur, "ffn_gelu_quick", il); + } break; + } + + if (down) { + cur = ggml_mul_mat(ctx0, down, cur); + } + + if (down_b) { + cb(cur, "ffn_down", il); + } + + if (down_b) { + cur = ggml_add(ctx0, cur, down_b); + } + + return cur; +} + +ggml_tensor * clip_graph::build_attn( + ggml_tensor * wo, + ggml_tensor * wo_b, + ggml_tensor * q_cur, + ggml_tensor * k_cur, + ggml_tensor * v_cur, + ggml_tensor * kq_mask, + float kq_scale, + int il) const { + // these nodes are added to the graph together so that they are not reordered + // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(gf, q_cur); + ggml_build_forward_expand(gf, k_cur); + ggml_build_forward_expand(gf, v_cur); + + ggml_tensor * q = ggml_permute(ctx0, q_cur, 0, 2, 1, 3); + //cb(q, "q", il); + + ggml_tensor * k = ggml_permute(ctx0, k_cur, 0, 2, 1, 3); + //cb(k, "k", il); + + ggml_tensor * cur; + + if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { + ggml_tensor * v = ggml_permute(ctx0, v_cur, 0, 2, 1, 3); + + k = ggml_cast(ctx0, k, GGML_TYPE_F16); + v = ggml_cast(ctx0, v, GGML_TYPE_F16); + + cur = ggml_flash_attn_ext(ctx0, q, k, v, kq_mask, kq_scale, 0.0f, 0.0f); + ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); + + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0]*cur->ne[1], cur->ne[2]*cur->ne[3]); + + } else { + ggml_tensor * v = ggml_permute(ctx0, v_cur, 1, 2, 0, 3); + v = ggml_cont(ctx0, v); + + const auto n_tokens = q->ne[1]; + const auto n_head = q->ne[2]; + + ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); + // F32 may not needed for vision encoders? + // ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + + kq = ggml_soft_max_ext(ctx0, kq, kq_mask, kq_scale, 0.0f); + + ggml_tensor * kqv = ggml_mul_mat(ctx0, v, kq); + cur = ggml_permute(ctx0, kqv, 0, 2, 1, 3); + cur = ggml_cont_2d(ctx0, cur, cur->ne[0]*n_head, n_tokens); + } + + cb(cur, "kqv_out", il); + + if (wo) { + cur = ggml_mul_mat(ctx0, wo, cur); + } + + if (wo_b) { + cur = ggml_add(ctx0, cur, wo_b); + } + + return cur; +} + +// implementation of the 2D RoPE without adding a new op in ggml +// this is not efficient (use double the memory), but works on all backends +// TODO: there was a more efficient which relies on ggml_view and ggml_rope_ext_inplace, but the rope inplace does not work well with non-contiguous tensors ; we should fix that and revert back to the original implementation in https://github.com/ggml-org/llama.cpp/pull/13065 +ggml_tensor * clip_graph::build_rope_2d( + ggml_context * ctx0, + ggml_tensor * cur, + ggml_tensor * pos_a, // first half + ggml_tensor * pos_b, // second half + const float freq_base, + const bool interleave_freq +) { + const int64_t n_dim = cur->ne[0]; + const int64_t n_head = cur->ne[1]; + const int64_t n_pos = cur->ne[2]; + + // for example, if we have cur tensor of shape (n_dim=8, n_head, n_pos) + // we will have a list of 4 inv_freq: 1e-0, 1e-1, 1e-2, 1e-3 + // first half of cur will use 1e-0, 1e-2 (even) + // second half of cur will use 1e-1, 1e-3 (odd) + // the trick here is to rotate just half of n_dim, so inv_freq will automatically be even + // ^ don't ask me why, it's math! -2(2i) / n_dim == -2i / (n_dim/2) + // then for the second half, we use freq_scale to shift the inv_freq + // ^ why? replace (2i) with (2i+1) in the above equation + const float freq_scale_odd = interleave_freq + ? std::pow(freq_base, (float)-2/n_dim) + : 1.0; + + // first half + ggml_tensor * first; + { + first = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + 0); + first = ggml_rope_ext( + ctx0, + first, + pos_a, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + 1.0f, 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + // second half + ggml_tensor * second; + { + second = ggml_view_3d(ctx0, cur, + n_dim/2, n_head, n_pos, + ggml_row_size(cur->type, n_dim), + ggml_row_size(cur->type, n_dim*n_head), + n_dim/2 * ggml_element_size(cur)); + second = ggml_rope_ext( + ctx0, + second, + pos_b, // positions + nullptr, // freq factors + n_dim/2, // n_dims + 0, 0, freq_base, + freq_scale_odd, + 0.0f, 1.0f, 0.0f, 0.0f + ); + } + + cur = ggml_concat(ctx0, first, second, 0); + return cur; +} + +// aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) +// support dynamic resolution +ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) { + GGML_ASSERT(scale_factor > 1); + + const int n_embd = cur->ne[0]; + int width = img.nx / patch_size; + int height = img.ny / patch_size; + + // pad width and height to factor + const int64_t pad_width = CLIP_ALIGN(width, scale_factor) - width; + const int64_t pad_height = CLIP_ALIGN(height, scale_factor) - height; + cur = ggml_reshape_3d(ctx0, cur, n_embd, width, height); + if (pad_width || pad_height) { + cur = ggml_pad(ctx0, cur, 0, pad_width, pad_height, 0); + width += pad_width; + height += pad_height; + } + + // unshuffle h + cur = ggml_reshape_3d(ctx0, cur, n_embd * scale_factor, width / scale_factor, height); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + + // unshuffle w + cur = ggml_cont_3d(ctx0, cur, n_embd * scale_factor * scale_factor, height / scale_factor, width / scale_factor); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + + cur = ggml_cont_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cb(cur, "pixel_shuffle", -1); + + return cur; +} static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32_batch & imgs) { GGML_ASSERT(imgs.entries.size() == 1 && "n_batch > 1 is not supported"); - clip_graph graph(ctx, *imgs.entries[0]); - ggml_cgraph * res; + const clip_image_f32 & img = *imgs.entries[0]; + std::unique_ptr builder; switch (ctx->proj_type()) { case PROJECTOR_TYPE_GEMMA3: case PROJECTOR_TYPE_IDEFICS3: case PROJECTOR_TYPE_LFM2: + case PROJECTOR_TYPE_JANUS_PRO: { - res = graph.build_siglip(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_PIXTRAL: case PROJECTOR_TYPE_LIGHTONOCR: { - res = graph.build_pixtral(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_QWEN2VL: case PROJECTOR_TYPE_QWEN25VL: { - res = graph.build_qwen2vl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_QWEN3VL: { - res = graph.build_qwen3vl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_MINICPMV: { - res = graph.build_minicpmv(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_INTERNVL: { - res = graph.build_internvl(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_LLAMA4: { - res = graph.build_llama4(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_VOXTRAL: case PROJECTOR_TYPE_QWEN2A: { - res = graph.build_whisper_enc(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_KIMIVL: { - res = graph.build_kimivl(); - } break; - case PROJECTOR_TYPE_JANUS_PRO: - { - res = graph.build_siglip(); + builder = std::make_unique(ctx, img); } break; case PROJECTOR_TYPE_COGVLM: { - res = graph.build_cogvlm(); + builder = std::make_unique(ctx, img); + } break; + case PROJECTOR_TYPE_MLP: + case PROJECTOR_TYPE_MLP_NORM: + case PROJECTOR_TYPE_LDP: + case PROJECTOR_TYPE_LDPV2: + case PROJECTOR_TYPE_GLM_EDGE: + { + builder = std::make_unique(ctx, img); } break; default: - { - res = graph.build_llava(); - } break; + GGML_ABORT("missing cgraph builder"); } - return res; + + return builder->build(); } +// +// clip_model_loader +// + struct clip_model_loader { ggml_context_ptr ctx_meta; gguf_context_ptr ctx_gguf; diff --git a/tools/mtmd/models/cogvlm.cpp b/tools/mtmd/models/cogvlm.cpp new file mode 100644 index 0000000000..d5b739c687 --- /dev/null +++ b/tools/mtmd/models/cogvlm.cpp @@ -0,0 +1,98 @@ +#include "models.h" + +ggml_cgraph * clip_graph_cogvlm::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; // +1 for [CLS] + + // build input and concatenate class embedding + ggml_tensor * inp = build_inp(); + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + inp = ggml_add(ctx0, inp, model.position_embeddings); + cb(inp, "inp_pos", -1); + + ggml_tensor * inpL = inp; + + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; + + cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); + + cur = ggml_add(ctx0, cur, layer.qkv_b); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], 0); + ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], n_embd * sizeof(float)); + ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, d_head*sizeof(float), + cur->nb[1], 2 * n_embd * sizeof(float)); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "attn_post_norm", il); + + cur = ggml_add(ctx0, cur, inpL); + inpL = cur; + + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "ffn_post_norm", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "layer_out", il); + inpL = cur; + + } + + // remove CLS token (like build_llama4 does) + ggml_tensor * cur = ggml_view_2d(ctx0, inpL, + n_embd, n_patches, + ggml_row_size(inpL->type, n_embd), 0); + + // Multiply with mm_model_proj + cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); + + // Apply layernorm, weight, bias + cur = build_norm(cur, model.mm_post_fc_norm_w, model.mm_post_fc_norm_b, NORM_TYPE_NORMAL, 1e-5, -1); + + // Apply GELU + cur = ggml_gelu_inplace(ctx0, cur); + + // Branch 1: multiply with mm_h_to_4h_w + ggml_tensor * h_to_4h = ggml_mul_mat(ctx0, model.mm_h_to_4h_w, cur); + + // Branch 2: multiply with mm_gate_w + ggml_tensor * gate = ggml_mul_mat(ctx0, model.mm_gate_w, cur); + + // Apply silu + gate = ggml_swiglu_split(ctx0, gate, h_to_4h); + + // Apply mm_4h_to_h_w + cur = ggml_mul_mat(ctx0, model.mm_4h_to_h_w, gate); + + // Concatenate with boi and eoi + cur = ggml_concat(ctx0, model.mm_boi, cur, 1); + cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/internvl.cpp b/tools/mtmd/models/internvl.cpp new file mode 100644 index 0000000000..9aded3b97c --- /dev/null +++ b/tools/mtmd/models/internvl.cpp @@ -0,0 +1,69 @@ +#include "models.h" + +ggml_cgraph * clip_graph_internvl::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; + ggml_tensor * inp = build_inp(); + + // add CLS token + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + // The larger models use a different ViT, which uses RMS norm instead of layer norm + // ref: https://github.com/ggml-org/llama.cpp/pull/13443#issuecomment-2869786188 + norm_type norm_t = (hparams.n_embd == 3200 && hparams.n_layer == 45) + ? NORM_TYPE_RMS // 6B ViT (Used by InternVL 2.5/3 - 26B, 38B, 78B) + : NORM_TYPE_NORMAL; // 300M ViT (Used by all smaller InternVL models) + + ggml_tensor * cur = build_vit( + inp, n_pos, + norm_t, + hparams.ffn_op, + model.position_embeddings, + nullptr); + + // remove CLS token + cur = ggml_view_2d(ctx0, cur, + n_embd, n_patches, + ggml_row_size(cur->type, n_embd), 0); + + // pixel shuffle + { + const int scale_factor = model.hparams.n_merge; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + const int height = n_patches_y; + const int width = n_patches_x; + GGML_ASSERT(scale_factor > 0); + cur = ggml_reshape_4d(ctx0, cur, n_embd * scale_factor, height / scale_factor, width, bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_cont_4d(ctx0, cur, + n_embd * scale_factor * scale_factor, + height / scale_factor, + width / scale_factor, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // flatten to 2D + cur = ggml_cont_2d(ctx0, cur, + n_embd * scale_factor * scale_factor, + cur->ne[1] * cur->ne[2]); + } + + // projector (always using GELU activation) + { + // projector LayerNorm uses pytorch's default eps = 1e-5 + // ref: https://huggingface.co/OpenGVLab/InternVL3-8B-Instruct/blob/a34d3e4e129a5856abfd6aa6de79776484caa14e/modeling_internvl_chat.py#L79 + cur = build_norm(cur, model.mm_0_w, model.mm_0_b, NORM_TYPE_NORMAL, 1e-5, -1); + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_3_w, model.mm_3_b, + FFN_GELU, + -1); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/kimivl.cpp b/tools/mtmd/models/kimivl.cpp new file mode 100644 index 0000000000..0a06f5090e --- /dev/null +++ b/tools/mtmd/models/kimivl.cpp @@ -0,0 +1,63 @@ +#include "models.h" + +ggml_cgraph * clip_graph_kimivl::build() { + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * learned_pos_embd = resize_position_embeddings(); + + // build ViT with 2D position embeddings + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + // first half is X axis and second half is Y axis + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + add_pos); + + cb(cur, "vit_out", -1); + + { + // patch_merger + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + // projection norm + int proj_inp_dim = cur->ne[0]; + cur = ggml_view_2d(ctx0, cur, + n_embd, cur->ne[1] * scale_factor * scale_factor, + ggml_row_size(cur->type, n_embd), 0); + cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm + cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); + cur = ggml_add(ctx0, cur, model.mm_input_norm_b); + cur = ggml_view_2d(ctx0, cur, + proj_inp_dim, cur->ne[1] / scale_factor / scale_factor, + ggml_row_size(cur->type, proj_inp_dim), 0); + cb(cur, "proj_inp_normed", -1); + + // projection mlp + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + cb(cur, "proj_out", -1); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/llama4.cpp b/tools/mtmd/models/llama4.cpp new file mode 100644 index 0000000000..30d1df5bcd --- /dev/null +++ b/tools/mtmd/models/llama4.cpp @@ -0,0 +1,96 @@ +#include "models.h" + +ggml_cgraph * clip_graph_llama4::build() { + GGML_ASSERT(model.class_embedding != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + + const int n_pos = n_patches + 1; // +1 for [CLS] + + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + ggml_tensor * inp = build_inp_raw(); + + // Llama4UnfoldConvolution + { + ggml_tensor * kernel = ggml_reshape_4d(ctx0, model.patch_embeddings_0, + patch_size, patch_size, 3, n_embd); + inp = ggml_im2col(ctx0, kernel, inp, patch_size, patch_size, 0, 0, 1, 1, true, inp->type); + inp = ggml_mul_mat(ctx0, model.patch_embeddings_0, inp); + inp = ggml_reshape_2d(ctx0, inp, n_embd, n_patches); + cb(inp, "patch_conv", -1); + } + + // add CLS token + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + + // build ViT with 2D position embeddings + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + // first half is X axis and second half is Y axis + // ref: https://github.com/huggingface/transformers/blob/40a493c7ed4f19f08eadb0639cf26d49bfa5e180/src/transformers/models/llama4/modeling_llama4.py#L1312 + // ref: https://github.com/Blaizzy/mlx-vlm/blob/a57156aa87b33cca6e5ee6cfc14dd4ef8f611be6/mlx_vlm/models/llama4/vision.py#L441 + return build_rope_2d(ctx0, cur, pos_w, pos_h, hparams.rope_theta, false); + }; + ggml_tensor * cur = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + model.position_embeddings, + add_pos); + + // remove CLS token + cur = ggml_view_2d(ctx0, cur, + n_embd, n_patches, + ggml_row_size(cur->type, n_embd), 0); + + // pixel shuffle + // based on Llama4VisionPixelShuffleMLP + // https://github.com/huggingface/transformers/blob/2932f318a20d9e54cc7aea052e040164d85de7d6/src/transformers/models/llama4/modeling_llama4.py#L1151 + { + const int scale_factor = model.hparams.n_merge; + const int bsz = 1; // batch size, always 1 for now since we don't support batching + GGML_ASSERT(scale_factor > 0); + GGML_ASSERT(n_patches_x == n_patches_y); // llama4 only supports square images + cur = ggml_reshape_4d(ctx0, cur, + n_embd * scale_factor, + n_patches_x / scale_factor, + n_patches_y, + bsz); + cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + cur = ggml_cont_4d(ctx0, cur, + n_embd * scale_factor * scale_factor, + n_patches_x / scale_factor, + n_patches_y / scale_factor, + bsz); + //cur = ggml_permute(ctx0, cur, 0, 2, 1, 3); + // flatten to 2D + cur = ggml_cont_2d(ctx0, cur, + n_embd * scale_factor * scale_factor, + n_patches / scale_factor / scale_factor); + cb(cur, "pixel_shuffle", -1); + } + + // based on Llama4VisionMLP2 (always uses GELU activation, no bias) + { + cur = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, cur); + cur = ggml_gelu(ctx0, cur); + cur = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, cur); + cur = ggml_gelu(ctx0, cur); + cb(cur, "adapter_mlp", -1); + } + + // Llama4MultiModalProjector + cur = ggml_mul_mat(ctx0, model.mm_model_proj, cur); + cb(cur, "projected", -1); + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/llava.cpp b/tools/mtmd/models/llava.cpp new file mode 100644 index 0000000000..0bfb5f05f6 --- /dev/null +++ b/tools/mtmd/models/llava.cpp @@ -0,0 +1,374 @@ +#include "models.h" + +// this graph is used by llava, granite and glm +// due to having embedding_stack (used by granite), we cannot reuse build_vit +ggml_cgraph * clip_graph_llava::build() { + const int batch_size = 1; + const int n_pos = n_patches + (model.class_embedding ? 1 : 0); + + GGML_ASSERT(n_patches_x == n_patches_y && "only square images supported"); + + // Calculate the deepest feature layer based on hparams and projector type + int max_feature_layer = n_layer; + { + // Get the index of the second to last layer; this is the default for models that have a llava projector + int il_last = hparams.n_layer - 1; + int deepest_feature_layer = -1; + + if (proj_type == PROJECTOR_TYPE_MINICPMV || proj_type == PROJECTOR_TYPE_GLM_EDGE) { + il_last += 1; + } + + // If we set explicit vision feature layers, only go up to the deepest one + // NOTE: only used by granite-vision models for now + for (const auto & feature_layer : hparams.vision_feature_layer) { + if (feature_layer > deepest_feature_layer) { + deepest_feature_layer = feature_layer; + } + } + max_feature_layer = deepest_feature_layer < 0 ? il_last : deepest_feature_layer; + } + + ggml_tensor * inp = build_inp(); + + // concat class_embeddings and patch_embeddings + if (model.class_embedding) { + inp = ggml_concat(ctx0, inp, model.class_embedding, 1); + } + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + inp = ggml_add(ctx0, inp, ggml_get_rows(ctx0, model.position_embeddings, positions)); + + ggml_tensor * inpL = inp; + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, NORM_TYPE_NORMAL, eps, -1); + cb(inpL, "pre_ln", -1); + } + + std::vector embedding_stack; + const auto & vision_feature_layer = hparams.vision_feature_layer; + + // loop over layers + for (int il = 0; il < max_feature_layer; il++) { + auto & layer = model.layers[il]; + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // If this is an embedding feature layer, save the output. + // NOTE: 0 index here refers to the input to the encoder. + if (vision_feature_layer.find(il) != vision_feature_layer.end()) { + embedding_stack.push_back(cur); + } + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "layer_inp_normed", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_mul_mat(ctx0, layer.q_w, cur); + if (layer.q_b) { + Qcur = ggml_add(ctx0, Qcur, layer.q_b); + } + + ggml_tensor * Kcur = ggml_mul_mat(ctx0, layer.k_w, cur); + if (layer.k_b) { + Kcur = ggml_add(ctx0, Kcur, layer.k_b); + } + + ggml_tensor * Vcur = ggml_mul_mat(ctx0, layer.v_w, cur); + if (layer.v_b) { + Vcur = ggml_add(ctx0, Vcur, layer.v_b); + } + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_pos); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_pos); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_pos); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, NORM_TYPE_NORMAL, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, NORM_TYPE_NORMAL, eps, -1); + } + + ggml_tensor * embeddings = inpL; + + // process vision feature layers (used by granite) + { + // final layer is a vision feature layer + if (vision_feature_layer.find(max_feature_layer) != vision_feature_layer.end()) { + embedding_stack.push_back(inpL); + } + + // If feature layers are explicitly set, stack them (if we have multiple) + if (!embedding_stack.empty()) { + embeddings = embedding_stack[0]; + for (size_t i = 1; i < embedding_stack.size(); i++) { + embeddings = ggml_concat(ctx0, embeddings, embedding_stack[i], 0); + } + } + } + + // llava projector (also used by granite) + if (hparams.has_llava_projector) { + embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); + + ggml_tensor * patches = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(patches, "patches"); + ggml_set_input(patches); + + // shape [1, 576, 1024] + // ne is whcn, ne = [1024, 576, 1, 1] + embeddings = ggml_get_rows(ctx0, embeddings, patches); + + // print_tensor_info(embeddings, "embeddings"); + + // llava projector + if (proj_type == PROJECTOR_TYPE_MLP) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + + embeddings = ggml_gelu(ctx0, embeddings); + if (model.mm_2_w) { + embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + } + } + else if (proj_type == PROJECTOR_TYPE_MLP_NORM) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); + // First LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), + model.mm_1_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); + + // Second LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), + model.mm_4_b); + } + else if (proj_type == PROJECTOR_TYPE_LDP) { + // MobileVLM projector + int n_patch = 24; + ggml_tensor * mlp_1 = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w, embeddings); + mlp_1 = ggml_add(ctx0, mlp_1, model.mm_model_mlp_1_b); + mlp_1 = ggml_gelu(ctx0, mlp_1); + ggml_tensor * mlp_3 = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, mlp_1); + mlp_3 = ggml_add(ctx0, mlp_3, model.mm_model_mlp_3_b); + // mlp_3 shape = [1, 576, 2048], ne = [2048, 576, 1, 1] + + // block 1 + ggml_tensor * block_1 = nullptr; + { + // transpose from [1, 576, 2048] --> [1, 2048, 576] --> [1, 2048, 24, 24] + mlp_3 = ggml_permute(ctx0, mlp_3, 1, 0, 2, 3); + mlp_3 = ggml_cont_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); + // stride = 1, padding = 1, bias is nullptr + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); + + // layer norm + // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_0_1_w), model.mm_model_block_1_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + + // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_1_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + // block_1_hw shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1], block_1 shape = [1, 2048], ne = [2048, 1, 1, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_1_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + // block_1 shape = [1, 24, 24, 2048], ne = [2048, 24, 24, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_1_block_2_1_w), model.mm_model_block_1_block_2_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] + // residual + block_1 = ggml_add(ctx0, mlp_3, block_1); + } + + // block_2 + { + // stride = 2 + block_1 = ggml_conv_2d_dw(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); + + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // layer norm + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 2, 0, 3)); + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_0_1_w), model.mm_model_block_2_block_0_1_b); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 2, 0, 1, 3)); + // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] + // hardswish + ggml_tensor * block_1_hw = ggml_hardswish(ctx0, block_1); + + // not sure the parameters is right for globalAvgPooling + block_1 = ggml_pool_2d(ctx0, block_1_hw, GGML_OP_POOL_AVG, block_1_hw->ne[0], block_1_hw->ne[1], block_1_hw->ne[0], block_1_hw->ne[1], 0, 0); + // block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + // pointwise conv + block_1 = ggml_reshape_2d(ctx0, block_1, block_1->ne[0]*block_1->ne[1]*block_1->ne[2], block_1->ne[3]); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc1_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc1_b); + block_1 = ggml_relu(ctx0, block_1); + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_1_fc2_w, block_1); + block_1 = ggml_add(ctx0, block_1, model.mm_model_block_2_block_1_fc2_b); + block_1 = ggml_hardsigmoid(ctx0, block_1); + + // block_1_hw shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1], block_1 shape = [1, 2048, 1, 1], ne = [1, 1, 2048, 1] + block_1 = ggml_reshape_4d(ctx0, block_1, 1, 1, block_1->ne[0], block_1->ne[1]); + block_1 = ggml_mul(ctx0, block_1_hw, block_1); + + int w = block_1->ne[0], h = block_1->ne[1]; + block_1 = ggml_reshape_3d(ctx0, block_1, w*h, block_1->ne[2], block_1->ne[3]); + block_1 = ggml_cont(ctx0, ggml_permute(ctx0, block_1, 1, 0, 2, 3)); + // block_1 shape = [1, 24*24, 2048], ne = [24*24, 2048, 1] + block_1 = ggml_mul_mat(ctx0, model.mm_model_block_2_block_2_0_w, block_1); + block_1 = ggml_reshape_4d(ctx0, block_1, block_1->ne[0], w, h, block_1->ne[3]); + + + // block_1 shape = [1, 12, 12, 2048], ne = [2048, 12, 12, 1] + block_1 = ggml_norm(ctx0, block_1, eps); + block_1 = ggml_add(ctx0, ggml_mul(ctx0, block_1, model.mm_model_block_2_block_2_1_w), model.mm_model_block_2_block_2_1_b); + block_1 = ggml_reshape_3d(ctx0, block_1, block_1->ne[0], block_1->ne[1] * block_1->ne[2], block_1->ne[3]); + // block_1 shape = [1, 144, 2048], ne = [2048, 144, 1] + } + embeddings = block_1; + } + else if (proj_type == PROJECTOR_TYPE_LDPV2) + { + int n_patch = 24; + ggml_tensor * mlp_0 = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + mlp_0 = ggml_add(ctx0, mlp_0, model.mm_model_mlp_0_b); + mlp_0 = ggml_gelu(ctx0, mlp_0); + ggml_tensor * mlp_2 = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, mlp_0); + mlp_2 = ggml_add(ctx0, mlp_2, model.mm_model_mlp_2_b); + // mlp_2 ne = [2048, 576, 1, 1] + // // AVG Pool Layer 2*2, strides = 2 + mlp_2 = ggml_permute(ctx0, mlp_2, 1, 0, 2, 3); + // mlp_2 ne = [576, 2048, 1, 1] + mlp_2 = ggml_cont_4d(ctx0, mlp_2, n_patch, n_patch, mlp_2->ne[1], mlp_2->ne[2]); + // mlp_2 ne [24, 24, 2048, 1] + mlp_2 = ggml_pool_2d(ctx0, mlp_2, GGML_OP_POOL_AVG, 2, 2, 2, 2, 0, 0); + // weight ne = [3, 3, 2048, 1] + ggml_tensor * peg_0 = ggml_conv_2d_dw(ctx0, model.mm_model_peg_0_w, mlp_2, 1, 1, 1, 1, 1, 1); + peg_0 = ggml_cont(ctx0, ggml_permute(ctx0, peg_0, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, model.mm_model_peg_0_b); + mlp_2 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_2, 1, 2, 0, 3)); + peg_0 = ggml_add(ctx0, peg_0, mlp_2); + peg_0 = ggml_reshape_3d(ctx0, peg_0, peg_0->ne[0], peg_0->ne[1] * peg_0->ne[2], peg_0->ne[3]); + embeddings = peg_0; + } + else { + GGML_ABORT("fatal error"); + } + } + + // glm projector + else if (proj_type == PROJECTOR_TYPE_GLM_EDGE) { + size_t gridsz = (size_t)sqrt(embeddings->ne[1]); + embeddings = ggml_permute(ctx0,embeddings,1,0,2,3); + embeddings = ggml_cont_3d(ctx0, embeddings, gridsz, gridsz, embeddings->ne[1]); + embeddings = ggml_conv_2d(ctx0, model.mm_model_adapter_conv_w, embeddings, 2, 2, 0, 0, 1, 1); + embeddings = ggml_reshape_3d(ctx0, embeddings,embeddings->ne[0]*embeddings->ne[1] , embeddings->ne[2], batch_size); + embeddings = ggml_cont(ctx0, ggml_permute(ctx0,embeddings, 1, 0, 2, 3)); + embeddings = ggml_add(ctx0, embeddings, model.mm_model_adapter_conv_b); + // GLU + { + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_0_w, embeddings); + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_model_ln_q_w), model.mm_model_ln_q_b); + embeddings = ggml_gelu_inplace(ctx0, embeddings); + ggml_tensor * x = embeddings; + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_2_w, embeddings); + x = ggml_mul_mat(ctx0, model.mm_model_mlp_1_w,x); + embeddings = ggml_swiglu_split(ctx0, embeddings, x); + embeddings = ggml_mul_mat(ctx0, model.mm_model_mlp_3_w, embeddings); + } + // arrangement of BOI/EOI token embeddings + // note: these embeddings are not present in text model, hence we cannot process them as text tokens + // see: https://huggingface.co/THUDM/glm-edge-v-2b/blob/main/siglip.py#L53 + { + embeddings = ggml_concat(ctx0, model.mm_boi, embeddings, 1); // BOI + embeddings = ggml_concat(ctx0, embeddings, model.mm_eoi, 1); // EOI + } + } + + else { + GGML_ABORT("llava: unknown projector type"); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/minicpmv.cpp b/tools/mtmd/models/minicpmv.cpp new file mode 100644 index 0000000000..3594ea29fa --- /dev/null +++ b/tools/mtmd/models/minicpmv.cpp @@ -0,0 +1,114 @@ +#include "models.h" + +ggml_cgraph * clip_graph_minicpmv::build() { + GGML_ASSERT(model.class_embedding == nullptr); + const int n_pos = n_patches; + const int n_embd_proj = n_mmproj_embd; + + // position embeddings for the projector (not for ViT) + // see: https://huggingface.co/openbmb/MiniCPM-o-2_6/blob/main/resampler.py#L70 + // base frequency omega + ggml_tensor * omega = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_embd_proj / 4); + ggml_set_name(omega, "omega"); + ggml_set_input(omega); + + // 2D input positions (using float for sinusoidal embeddings) + ggml_tensor * pos_h = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + ggml_tensor * pos_w = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, 1, n_pos); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + // for selecting learned pos embd, used by ViT + struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + ggml_tensor * learned_pos_embd = ggml_get_rows(ctx0, model.position_embeddings, positions); + + ggml_tensor * inp = build_inp(); + ggml_tensor * embeddings = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + nullptr); + + // resampler projector (it is just another transformer) + + ggml_tensor * q = model.mm_model_query; + ggml_tensor * v = ggml_mul_mat(ctx0, model.mm_model_kv_proj, embeddings); + + // norm + q = build_norm(q, model.mm_model_ln_q_w, model.mm_model_ln_q_b, NORM_TYPE_NORMAL, eps, -1); + v = build_norm(v, model.mm_model_ln_kv_w, model.mm_model_ln_kv_b, NORM_TYPE_NORMAL, eps, -1); + + // calculate sinusoidal pos embd + ggml_tensor * pos_embed = nullptr; + { + // outer product + ggml_tensor * omega_b = ggml_repeat_4d(ctx0, omega, omega->ne[0], n_pos, 1, 1); // n_pos rows + ggml_tensor * theta_x = ggml_mul(ctx0, omega_b, pos_w); + ggml_tensor * theta_y = ggml_mul(ctx0, omega_b, pos_h); + // sin and cos + ggml_tensor * pos_embd_x = ggml_concat( + ctx0, + ggml_sin(ctx0, theta_x), + ggml_cos(ctx0, theta_x), + 0 // concat on first dim + ); + ggml_tensor * pos_embd_y = ggml_concat( + ctx0, + ggml_sin(ctx0, theta_y), + ggml_cos(ctx0, theta_y), + 0 // concat on first dim + ); + pos_embed = ggml_concat(ctx0, pos_embd_x, pos_embd_y, 0); + } + + // k = v + pos_embed + ggml_tensor * k = ggml_add(ctx0, v, pos_embed); + + // attention + { + const int d_head = 128; + int n_head = n_embd_proj/d_head; + // Use actual config value if available, otherwise fall back to hardcoded values + int num_query = hparams.minicpmv_query_num; + ggml_tensor * Q = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_q_w, q), + model.mm_model_attn_q_b); + ggml_tensor * K = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_k_w, k), + model.mm_model_attn_k_b); + ggml_tensor * V = ggml_add(ctx0, + ggml_mul_mat(ctx0, model.mm_model_attn_v_w, v), + model.mm_model_attn_v_b); + + Q = ggml_reshape_3d(ctx0, Q, d_head, n_head, num_query); + K = ggml_reshape_3d(ctx0, K, d_head, n_head, n_pos); + V = ggml_reshape_3d(ctx0, V, d_head, n_head, n_pos); + + cb(Q, "resampler_Q", -1); + cb(K, "resampler_K", -1); + cb(V, "resampler_V", -1); + + float resampler_kq_scale = 1.0f/ sqrtf(float(d_head)); + embeddings = build_attn( + model.mm_model_attn_o_w, + model.mm_model_attn_o_b, + Q, K, V, nullptr, resampler_kq_scale, -1); + cb(embeddings, "resampler_attn_out", -1); + } + // layernorm + embeddings = build_norm(embeddings, model.mm_model_ln_post_w, model.mm_model_ln_post_b, NORM_TYPE_NORMAL, eps, -1); + + // projection + embeddings = ggml_mul_mat(ctx0, model.mm_model_proj, embeddings); + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/models.h b/tools/mtmd/models/models.h new file mode 100644 index 0000000000..4b35da259c --- /dev/null +++ b/tools/mtmd/models/models.h @@ -0,0 +1,58 @@ +#pragma once + +#include "../clip-graph.h" + +struct clip_graph_siglip : clip_graph { + clip_graph_siglip(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_pixtral : clip_graph { + clip_graph_pixtral(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_qwen2vl : clip_graph { + clip_graph_qwen2vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_qwen3vl : clip_graph { + clip_graph_qwen3vl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_minicpmv : clip_graph { + clip_graph_minicpmv(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_internvl : clip_graph { + clip_graph_internvl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_llama4 : clip_graph { + clip_graph_llama4(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_kimivl : clip_graph { + clip_graph_kimivl(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_cogvlm : clip_graph { + clip_graph_cogvlm(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_llava : clip_graph { + clip_graph_llava(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; + +struct clip_graph_whisper_enc : clip_graph { + clip_graph_whisper_enc(clip_ctx * ctx, const clip_image_f32 & img) : clip_graph(ctx, img) {} + ggml_cgraph * build() override; +}; diff --git a/tools/mtmd/models/pixtral.cpp b/tools/mtmd/models/pixtral.cpp new file mode 100644 index 0000000000..a849210b53 --- /dev/null +++ b/tools/mtmd/models/pixtral.cpp @@ -0,0 +1,86 @@ +#include "models.h" + +ggml_cgraph * clip_graph_pixtral::build() { + const int n_merge = hparams.n_merge; + + // 2D input positions + ggml_tensor * pos_h = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_h, "pos_h"); + ggml_set_input(pos_h); + + ggml_tensor * pos_w = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_patches); + ggml_set_name(pos_w, "pos_w"); + ggml_set_input(pos_w); + + auto add_pos = [&](ggml_tensor * cur, const clip_layer &) { + return build_rope_2d(ctx0, cur, pos_h, pos_w, hparams.rope_theta, true); + }; + + ggml_tensor * inp = build_inp(); + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_RMS, + hparams.ffn_op, + nullptr, // no learned pos embd + add_pos); + + // mistral small 3.1 patch merger + // ref: https://github.com/huggingface/transformers/blob/7a3e208892c06a5e278144eaf38c8599a42f53e7/src/transformers/models/mistral3/modeling_mistral3.py#L67 + if (model.mm_patch_merger_w) { + GGML_ASSERT(hparams.n_merge > 0); + + cur = ggml_mul(ctx0, ggml_rms_norm(ctx0, cur, eps), model.mm_input_norm_w); + + // reshape image tokens to 2D grid + cur = ggml_reshape_3d(ctx0, cur, n_embd, n_patches_x, n_patches_y); + cur = ggml_permute(ctx0, cur, 2, 0, 1, 3); // [x, y, n_embd] + cur = ggml_cont(ctx0, cur); + + // torch.nn.functional.unfold is just an im2col under the hood + // we just need a dummy kernel to make it work + ggml_tensor * kernel = ggml_view_3d(ctx0, cur, n_merge, n_merge, cur->ne[2], 0, 0, 0); + cur = ggml_im2col(ctx0, kernel, cur, n_merge, n_merge, 0, 0, 1, 1, true, inp->type); + + // project to n_embd + cur = ggml_reshape_2d(ctx0, cur, cur->ne[0], cur->ne[1] * cur->ne[2]); + cur = ggml_mul_mat(ctx0, model.mm_patch_merger_w, cur); + } + + // LlavaMultiModalProjector (always using GELU activation) + { + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + } + + // arrangement of the [IMG_BREAK] token + if (model.token_embd_img_break) { + // not efficient, but works + // the trick is to view the embeddings as a 3D tensor with shape [n_embd, n_patches_per_row, n_rows] + // and then concatenate the [IMG_BREAK] token to the end of each row, aka n_patches_per_row dimension + // after the concatenation, we have a tensor with shape [n_embd, n_patches_per_row + 1, n_rows] + + const int p_y = n_merge > 0 ? n_patches_y / n_merge : n_patches_y; + const int p_x = n_merge > 0 ? n_patches_x / n_merge : n_patches_x; + const int p_total = p_x * p_y; + const int n_embd_text = cur->ne[0]; + const int n_tokens_output = p_total + p_y - 1; // one [IMG_BREAK] per row, except the last row + + ggml_tensor * tmp = ggml_reshape_3d(ctx0, cur, n_embd_text, p_x, p_y); + ggml_tensor * tok = ggml_new_tensor_3d(ctx0, tmp->type, n_embd_text, 1, p_y); + tok = ggml_scale(ctx0, tok, 0.0); // clear the tensor + tok = ggml_add(ctx0, tok, model.token_embd_img_break); + tmp = ggml_concat(ctx0, tmp, tok, 1); + cur = ggml_view_2d(ctx0, tmp, + n_embd_text, n_tokens_output, + ggml_row_size(tmp->type, n_embd_text), 0); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/qwen2vl.cpp b/tools/mtmd/models/qwen2vl.cpp new file mode 100644 index 0000000000..85f158bb1c --- /dev/null +++ b/tools/mtmd/models/qwen2vl.cpp @@ -0,0 +1,183 @@ +#include "models.h" + +ggml_cgraph * clip_graph_qwen2vl::build() { + GGML_ASSERT(model.patch_bias == nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + + const int batch_size = 1; + const bool use_window_attn = hparams.n_wa_pattern > 0; + const int n_wa_pattern = hparams.n_wa_pattern; + const int n_pos = n_patches; + const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position + + norm_type norm_t = proj_type == PROJECTOR_TYPE_QWEN25VL + ? NORM_TYPE_RMS // qwen 2.5 vl + : NORM_TYPE_NORMAL; // qwen 2 vl + + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + // second conv dimension + { + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_cont_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d( + ctx0, inp, + n_embd, n_patches_x * n_patches_y, batch_size); + } + + ggml_tensor * inpL = inp; + ggml_tensor * window_mask = nullptr; + ggml_tensor * window_idx = nullptr; + ggml_tensor * inv_window_idx = nullptr; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + } + + if (use_window_attn) { + // handle window attention inputs + inv_window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(inv_window_idx, "inv_window_idx"); + ggml_set_input(inv_window_idx); + // mask for window attention + window_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_pos, n_pos); + ggml_set_name(window_mask, "window_mask"); + ggml_set_input(window_mask); + + // if flash attn is used, we need to pad the mask and cast to f16 + if (flash_attn_type == CLIP_FLASH_ATTN_TYPE_ENABLED) { + window_mask = ggml_cast(ctx0, window_mask, GGML_TYPE_F16); + } + + // inpL shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + inpL = ggml_reshape_2d(ctx0, inpL, n_embd * 4, n_patches_x * n_patches_y * batch_size / 4); + inpL = ggml_get_rows(ctx0, inpL, inv_window_idx); + inpL = ggml_reshape_3d(ctx0, inpL, n_embd, n_patches_x * n_patches_y, batch_size); + } + + // loop over layers + for (int il = 0; il < n_layer; il++) { + const auto & layer = model.layers[il]; + const bool full_attn = use_window_attn ? (il + 1) % n_wa_pattern == 0 : true; + + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "ln1", il); + + // self-attention + { + ggml_tensor * Qcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.q_w, cur), layer.q_b); + ggml_tensor * Kcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.k_w, cur), layer.k_b); + ggml_tensor * Vcur = ggml_add(ctx0, + ggml_mul_mat(ctx0, layer.v_w, cur), layer.v_b); + + Qcur = ggml_reshape_3d(ctx0, Qcur, d_head, n_head, n_patches); + Kcur = ggml_reshape_3d(ctx0, Kcur, d_head, n_head, n_patches); + Vcur = ggml_reshape_3d(ctx0, Vcur, d_head, n_head, n_patches); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + // apply M-RoPE + Qcur = ggml_rope_multi( + ctx0, Qcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Kcur = ggml_rope_multi( + ctx0, Kcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + ggml_tensor * attn_mask = full_attn ? nullptr : window_mask; + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, attn_mask, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); + } + + // multimodal projection + ggml_tensor * embeddings = inpL; + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); + embeddings = build_ffn(embeddings, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + FFN_GELU, + -1); + + if (use_window_attn) { + window_idx = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_pos / 4); + ggml_set_name(window_idx, "window_idx"); + ggml_set_input(window_idx); + + // embeddings shape: [n_embd, n_patches_x * n_patches_y, batch_size] + GGML_ASSERT(batch_size == 1); + embeddings = ggml_reshape_2d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4); + embeddings = ggml_get_rows(ctx0, embeddings, window_idx); + embeddings = ggml_reshape_3d(ctx0, embeddings, hparams.projection_dim, n_patches_x * n_patches_y / 4, batch_size); + } + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/qwen3vl.cpp b/tools/mtmd/models/qwen3vl.cpp new file mode 100644 index 0000000000..35a42cb84d --- /dev/null +++ b/tools/mtmd/models/qwen3vl.cpp @@ -0,0 +1,191 @@ +#include "models.h" + +ggml_cgraph * clip_graph_qwen3vl::build() { + GGML_ASSERT(model.patch_bias != nullptr); + GGML_ASSERT(model.position_embeddings != nullptr); + GGML_ASSERT(model.class_embedding == nullptr); + + const int batch_size = 1; + const int n_pos = n_patches; + const int num_position_ids = n_pos * 4; // m-rope requires 4 dim per position + + norm_type norm_t = NORM_TYPE_NORMAL; + + int mrope_sections[4] = {d_head/4, d_head/4, d_head/4, d_head/4}; + + ggml_tensor * inp_raw = build_inp_raw(); + ggml_tensor * inp = ggml_conv_2d(ctx0, model.patch_embeddings_0, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + + GGML_ASSERT(img.nx % (patch_size * 2) == 0); + GGML_ASSERT(img.ny % (patch_size * 2) == 0); + + // second conv dimension + { + auto inp_1 = ggml_conv_2d(ctx0, model.patch_embeddings_1, inp_raw, patch_size, patch_size, 0, 0, 1, 1); + inp = ggml_add(ctx0, inp, inp_1); + + inp = ggml_permute(ctx0, inp, 1, 2, 0, 3); // [w, h, c, b] -> [c, w, h, b] + inp = ggml_cont_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + inp = ggml_reshape_4d( + ctx0, inp, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + inp = ggml_permute(ctx0, inp, 0, 2, 1, 3); + inp = ggml_cont_3d( + ctx0, inp, + n_embd, n_patches_x * n_patches_y, batch_size); + } + + // add patch bias + if (model.patch_bias != nullptr) { + inp = ggml_add(ctx0, inp, model.patch_bias); + cb(inp, "patch_bias", -1); + } + + // calculate absolute position embedding and apply + ggml_tensor * learned_pos_embd = resize_position_embeddings(); + learned_pos_embd = ggml_cont_4d( + ctx0, learned_pos_embd, + n_embd * 2, n_patches_x / 2, n_patches_y, batch_size); + learned_pos_embd = ggml_reshape_4d( + ctx0, learned_pos_embd, + n_embd * 2, n_patches_x / 2, 2, batch_size * (n_patches_y / 2)); + learned_pos_embd = ggml_permute(ctx0, learned_pos_embd, 0, 2, 1, 3); + learned_pos_embd = ggml_cont_3d( + ctx0, learned_pos_embd, + n_embd, n_patches_x * n_patches_y, batch_size); + inp = ggml_add(ctx0, inp, learned_pos_embd); + cb(inp, "inp_pos_emb", -1); + + ggml_tensor * inpL = inp; + + ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_position_ids); + ggml_set_name(positions, "positions"); + ggml_set_input(positions); + + // pre-layernorm + if (model.pre_ln_w) { + inpL = build_norm(inpL, model.pre_ln_w, model.pre_ln_b, norm_t, eps, -1); + } + + // deepstack features (stack along the feature dimension), [n_embd * len(deepstack_layers), n_patches_x * n_patches_y, batch_size] + ggml_tensor * deepstack_features = nullptr; + const int merge_factor = hparams.n_merge > 0 ? hparams.n_merge * hparams.n_merge : 4; // default 2x2=4 for qwen3vl + + // loop over layers + for (int il = 0; il < n_layer; il++) { + auto & layer = model.layers[il]; + + ggml_tensor * cur = inpL; // inpL = residual, cur = hidden_states + + // layernorm1 + cur = build_norm(cur, layer.ln_1_w, layer.ln_1_b, norm_t, eps, il); + cb(cur, "ln1", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, layer.qkv_w, cur); + cur = ggml_add(ctx0, cur, layer.qkv_b); + + ggml_tensor * Qcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ 0); + + ggml_tensor * Kcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, n_embd)); + + ggml_tensor * Vcur = ggml_view_3d(ctx0, cur, d_head, n_head, n_pos, + /* nb1 */ ggml_row_size(cur->type, d_head), + /* nb2 */ cur->nb[1], + /* offset */ ggml_row_size(cur->type, 2 * n_embd)); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + // apply M-RoPE + Qcur = ggml_rope_multi( + ctx0, Qcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + Kcur = ggml_rope_multi( + ctx0, Kcur, positions, nullptr, + d_head/2, mrope_sections, GGML_ROPE_TYPE_VISION, 32768, 10000, 1, 0, 1, 32, 1); + + cb(Qcur, "Qcur_rope", il); + cb(Kcur, "Kcur_rope", il); + + cur = build_attn(layer.o_w, layer.o_b, + Qcur, Kcur, Vcur, nullptr, kq_scale, il); + cb(cur, "attn_out", il); + } + + // re-add the layer input, e.g., residual + cur = ggml_add(ctx0, cur, inpL); + + inpL = cur; // inpL = residual, cur = hidden_states + + cb(cur, "ffn_inp", il); + + // layernorm2 + cur = build_norm(cur, layer.ln_2_w, layer.ln_2_b, norm_t, eps, il); + cb(cur, "ffn_inp_normed", il); + + // ffn + cur = build_ffn(cur, + layer.ff_up_w, layer.ff_up_b, + layer.ff_gate_w, layer.ff_gate_b, + layer.ff_down_w, layer.ff_down_b, + hparams.ffn_op, il); + + cb(cur, "ffn_out", il); + + // residual 2 + cur = ggml_add(ctx0, inpL, cur); + cb(cur, "layer_out", il); + + if (layer.has_deepstack()) { + ggml_tensor * feat = ggml_reshape_3d(ctx0, cur, n_embd * merge_factor, n_pos / merge_factor, batch_size); + feat = build_norm(feat, layer.deepstack_norm_w, layer.deepstack_norm_b, norm_t, eps, il); + feat = build_ffn(feat, + layer.deepstack_fc1_w, layer.deepstack_fc1_b, + nullptr, nullptr, + layer.deepstack_fc2_w, layer.deepstack_fc2_b, + ffn_op_type::FFN_GELU, il); + + if(!deepstack_features) { + deepstack_features = feat; + } else { + // concat along the feature dimension + deepstack_features = ggml_concat(ctx0, deepstack_features, feat, 0); + } + } + + inpL = cur; + } + + // post-layernorm + if (model.post_ln_w) { + inpL = build_norm(inpL, model.post_ln_w, model.post_ln_b, norm_t, eps, n_layer); + } + + // multimodal projection + ggml_tensor * embeddings = inpL; + embeddings = ggml_reshape_3d(ctx0, embeddings, n_embd * 4, n_pos / 4, batch_size); + + embeddings = build_ffn(embeddings, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + ffn_op_type::FFN_GELU, -1); + + embeddings = ggml_concat(ctx0, embeddings, deepstack_features, 0); // concat along the feature dimension + + // build the graph + ggml_build_forward_expand(gf, embeddings); + + return gf; +} diff --git a/tools/mtmd/models/siglip.cpp b/tools/mtmd/models/siglip.cpp new file mode 100644 index 0000000000..ef094cfd0e --- /dev/null +++ b/tools/mtmd/models/siglip.cpp @@ -0,0 +1,81 @@ +#include "models.h" + +ggml_cgraph * clip_graph_siglip::build() { + ggml_tensor * inp = build_inp(); + + ggml_tensor * learned_pos_embd = model.position_embeddings; + if (proj_type == PROJECTOR_TYPE_LFM2) { + learned_pos_embd = resize_position_embeddings(); + } + + ggml_tensor * cur = build_vit( + inp, n_patches, + NORM_TYPE_NORMAL, + hparams.ffn_op, + learned_pos_embd, + nullptr); + + if (proj_type == PROJECTOR_TYPE_GEMMA3) { + const int batch_size = 1; + GGML_ASSERT(n_patches_x == n_patches_y); + const int patches_per_image = n_patches_x; + const int kernel_size = hparams.n_merge; + + cur = ggml_transpose(ctx0, cur); + cur = ggml_cont_4d(ctx0, cur, patches_per_image, patches_per_image, n_embd, batch_size); + + // doing a pool2d to reduce the number of output tokens + cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_AVG, kernel_size, kernel_size, kernel_size, kernel_size, 0, 0); + cur = ggml_reshape_3d(ctx0, cur, cur->ne[0] * cur->ne[0], n_embd, batch_size); + cur = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + + // apply norm before projection + cur = ggml_rms_norm(ctx0, cur, eps); + cur = ggml_mul(ctx0, cur, model.mm_soft_emb_norm_w); + + // apply projection + cur = ggml_mul_mat(ctx0, + ggml_cont(ctx0, ggml_transpose(ctx0, model.mm_input_proj_w)), + cur); + + } else if (proj_type == PROJECTOR_TYPE_IDEFICS3) { + // pixel_shuffle + // https://github.com/huggingface/transformers/blob/0a950e0bbe1ed58d5401a6b547af19f15f0c195e/src/transformers/models/idefics3/modeling_idefics3.py#L578 + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + cur = ggml_mul_mat(ctx0, model.projection, cur); + + } else if (proj_type == PROJECTOR_TYPE_LFM2) { + // pixel unshuffle block + const int scale_factor = model.hparams.n_merge; + cur = build_patch_merge_permute(cur, scale_factor); + + // projection + cur = ggml_norm(ctx0, cur, 1e-5); // default nn.LayerNorm + cur = ggml_mul(ctx0, cur, model.mm_input_norm_w); + cur = ggml_add(ctx0, cur, model.mm_input_norm_b); + + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU, + -1); + + } else if (proj_type == PROJECTOR_TYPE_JANUS_PRO) { + cur = build_ffn(cur, + model.mm_0_w, model.mm_0_b, + nullptr, nullptr, + model.mm_1_w, model.mm_1_b, + hparams.ffn_op, + -1); + + } else { + GGML_ABORT("SigLIP: Unsupported projector type"); + } + + // build the graph + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/models/whisper-enc.cpp b/tools/mtmd/models/whisper-enc.cpp new file mode 100644 index 0000000000..07d378b095 --- /dev/null +++ b/tools/mtmd/models/whisper-enc.cpp @@ -0,0 +1,107 @@ +#include "models.h" + +ggml_cgraph * clip_graph_whisper_enc::build() { + const int n_frames = img.nx; + const int n_pos = n_frames / 2; + GGML_ASSERT(model.position_embeddings->ne[1] >= n_pos); + + ggml_tensor * inp = build_inp_raw(1); + + // conv1d block + { + // convolution + gelu + ggml_tensor * cur = ggml_conv_1d_ph(ctx0, model.conv1d_1_w, inp, 1, 1); + cur = ggml_add(ctx0, cur, model.conv1d_1_b); + + cur = ggml_gelu_erf(ctx0, cur); + + cur = ggml_conv_1d_ph(ctx0, model.conv1d_2_w, cur, 2, 1); + cur = ggml_add(ctx0, cur, model.conv1d_2_b); + + cur = ggml_gelu_erf(ctx0, cur); + // transpose + inp = ggml_cont(ctx0, ggml_transpose(ctx0, cur)); + cb(inp, "after_conv1d", -1); + } + + // sanity check (only check one layer, but it should be the same for all) + GGML_ASSERT(model.layers[0].ln_1_w && model.layers[0].ln_1_b); + GGML_ASSERT(model.layers[0].ln_2_w && model.layers[0].ln_2_b); + GGML_ASSERT(model.layers[0].q_b); + GGML_ASSERT(model.layers[0].v_b); + GGML_ASSERT(!model.layers[0].k_b); // no bias for k + GGML_ASSERT(model.post_ln_w && model.post_ln_b); + + ggml_tensor * pos_embd_selected = ggml_view_2d( + ctx0, model.position_embeddings, + model.position_embeddings->ne[0], n_pos, + model.position_embeddings->nb[1], 0 + ); + ggml_tensor * cur = build_vit( + inp, n_pos, + NORM_TYPE_NORMAL, + hparams.ffn_op, + pos_embd_selected, + nullptr); + + cb(cur, "after_transformer", -1); + + if (model.audio_has_stack_frames()) { + // StackAudioFrames + // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py + int64_t stride = n_embd * hparams.proj_stack_factor; + int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride); + int64_t pad = padded_len - ggml_nelements(cur); + if (pad > 0) { + cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0); + cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); + } + cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, + ggml_row_size(cur->type, stride), 0); + cb(cur, "after_stacked", -1); + } + + if (proj_type == PROJECTOR_TYPE_ULTRAVOX) { + // UltravoxProjector + // pre-norm + cur = ggml_rms_norm(ctx0, cur, 1e-6); + cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); + + // ffn in + cur = ggml_mul_mat(ctx0, model.mm_1_w, cur); + + // swiglu + // see SwiGLU in ultravox_model.py, the second half passed through is silu, not the first half + cur = ggml_swiglu_swapped(ctx0, cur); + + // mid-norm + cur = ggml_rms_norm(ctx0, cur, 1e-6); + cur = ggml_mul(ctx0, cur, model.mm_norm_mid_w); + + // ffn out + cur = ggml_mul_mat(ctx0, model.mm_2_w, cur); + + } else if (proj_type == PROJECTOR_TYPE_QWEN2A) { + // projector + cur = ggml_mul_mat(ctx0, model.mm_fc_w, cur); + cur = ggml_add(ctx0, cur, model.mm_fc_b); + + } else if (proj_type == PROJECTOR_TYPE_VOXTRAL) { + // projector + cur = build_ffn(cur, + model.mm_1_w, model.mm_1_b, + nullptr, nullptr, + model.mm_2_w, model.mm_2_b, + FFN_GELU_ERF, + -1); + + } else { + GGML_ABORT("%s: unknown projector type", __func__); + } + + cb(cur, "projected", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; +} diff --git a/tools/mtmd/mtmd.h b/tools/mtmd/mtmd.h index b3df24c299..9f7e861e92 100644 --- a/tools/mtmd/mtmd.h +++ b/tools/mtmd/mtmd.h @@ -22,6 +22,11 @@ * Issues related to API usage may receive lower priority support. * * For the usage, see an example in mtmd-cli.cpp + * + * For contributors: + * - Make sure the C API is aligned with the libllama C API (as in llama.h) + * - Do not include model name (e.g., qwen, gemma) in the API, use generic terms instead + * - Keep the API minimal, do not expose internal details unless necessary */ #ifdef LLAMA_SHARED From 380b4c984e06f8d8381392d15814b3392e39560e Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Fri, 12 Dec 2025 23:58:53 +0100 Subject: [PATCH 05/40] common: support negated args (#17919) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * args: support negated args * update docs * fix typo * add more neg options * Apply suggestions from code review Co-authored-by: Sigbjørn Skjæret * rm duplicated arg * fix LLAMA_ARG_NO_HOST * add test --------- Co-authored-by: Sigbjørn Skjæret --- common/arg.cpp | 290 +++++++++++++++++++-------------- common/arg.h | 13 ++ common/preset.cpp | 16 +- examples/gen-docs/gen-docs.cpp | 9 +- tests/test-arg-parser.cpp | 20 ++- tools/server/README.md | 48 +++--- 6 files changed, 235 insertions(+), 161 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 5528eeb169..16cb2e03a6 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -105,6 +105,16 @@ bool common_arg::is_exclude(enum llama_example ex) { bool common_arg::get_value_from_env(std::string & output) const { if (env == nullptr) return false; + if (!args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + char * neg_value = std::getenv(neg_env.c_str()); + if (neg_value) { + output = "0"; // falsey + return true; + } + } char * value = std::getenv(env); if (value) { output = value; @@ -114,6 +124,14 @@ bool common_arg::get_value_from_env(std::string & output) const { } bool common_arg::has_value_from_env() const { + if (env != nullptr && !args_neg.empty()) { + // for compatibility, we need to check LLAMA_ARG_NO_ env as well + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + if (std::getenv(neg_env.c_str())) { + return true; + } + } return env != nullptr && std::getenv(env); } @@ -151,9 +169,10 @@ std::string common_arg::to_string() const { std::string leading_spaces(n_leading_spaces, ' '); std::ostringstream ss; - for (const auto arg : args) { - if (arg == args.front()) { - if (args.size() == 1) { + auto all_args = get_args(); // also contains args_neg + for (const auto & arg : all_args) { + if (arg == all_args.front()) { + if (all_args.size() == 1) { ss << arg; } else { // first arg is usually abbreviation, we need padding to make it more beautiful @@ -162,7 +181,7 @@ std::string common_arg::to_string() const { ss << tmp << spaces; } } else { - ss << arg << (arg != args.back() ? ", " : ""); + ss << arg << (arg != all_args.back() ? ", " : ""); } } if (value_hint) ss << " " << value_hint; @@ -181,6 +200,31 @@ std::string common_arg::to_string() const { return ss.str(); } +std::vector common_arg::get_args() const { + std::vector result; + for (const auto & arg : args) { + result.push_back(std::string(arg)); + } + for (const auto & arg : args_neg) { + result.push_back(std::string(arg)); + } + return result; +} + +std::vector common_arg::get_env() const { + std::vector result; + if (env) { + result.push_back(std::string(env)); + } + if (!args_neg.empty() && env) { + // for compatibility, we need to add LLAMA_ARG_NO_ variant + std::string neg_env = env; + string_replace_all(neg_env, "LLAMA_ARG_", "LLAMA_ARG_NO_"); + result.push_back(neg_env); + } + return result; +} + // // utils // @@ -316,6 +360,16 @@ static std::string get_all_kv_cache_types() { return msg.str(); } +static bool parse_bool_value(const std::string & value) { + if (is_truthy(value)) { + return true; + } else if (is_falsey(value)) { + return false; + } else { + throw std::invalid_argument("invalid boolean value"); + } +} + // // CLI argument parsing functions // @@ -323,10 +377,13 @@ static std::string get_all_kv_cache_types() { static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) { common_params & params = ctx_arg.params; - std::unordered_map arg_to_options; + std::unordered_map> arg_to_options; for (auto & opt : ctx_arg.options) { for (const auto & arg : opt.args) { - arg_to_options[arg] = &opt; + arg_to_options[arg] = {&opt, /* is_positive */ true}; + } + for (const auto & arg : opt.args_neg) { + arg_to_options[arg] = {&opt, /* is_positive */ false}; } } @@ -335,12 +392,15 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context std::string value; if (opt.get_value_from_env(value)) { try { - if (opt.handler_void && (value == "1" || value == "true")) { + if (opt.handler_void && is_truthy(value)) { opt.handler_void(params); } if (opt.handler_int) { opt.handler_int(params, std::stoi(value)); } + if (opt.handler_bool) { + opt.handler_bool(params, parse_bool_value(value)); + } if (opt.handler_string) { opt.handler_string(params, value); continue; @@ -369,7 +429,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context if (arg_to_options.find(arg) == arg_to_options.end()) { throw std::invalid_argument(string_format("error: invalid argument: %s", arg.c_str())); } - auto opt = *arg_to_options[arg]; + auto & tmp = arg_to_options[arg]; + auto opt = *tmp.first; + bool is_positive = tmp.second; if (opt.has_value_from_env()) { fprintf(stderr, "warn: %s environment variable is set, but will be overwritten by command line argument %s\n", opt.env, arg.c_str()); } @@ -378,6 +440,10 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context opt.handler_void(params); continue; } + if (opt.handler_bool) { + opt.handler_bool(params, is_positive); + continue; + } // arg with single value check_arg(i); @@ -402,7 +468,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context throw std::invalid_argument(string_format( "error while handling argument \"%s\": %s\n\n" "usage:\n%s\n\nto show complete usage, run with -h", - arg.c_str(), e.what(), arg_to_options[arg]->to_string().c_str())); + arg.c_str(), e.what(), opt.to_string().c_str())); } } @@ -750,11 +816,11 @@ static std::string list_builtin_chat_templates() { } bool common_arg_utils::is_truthy(const std::string & value) { - return value == "on" || value == "enabled" || value == "1"; + return value == "on" || value == "enabled" || value == "true" || value == "1"; } bool common_arg_utils::is_falsey(const std::string & value) { - return value == "off" || value == "disabled" || value == "0"; + return value == "off" || value == "disabled" || value == "false" || value == "0"; } bool common_arg_utils::is_autoy(const std::string & value) { @@ -839,10 +905,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } )); add_opt(common_arg( + {"--display-prompt"}, {"--no-display-prompt"}, - string_format("don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false"), - [](common_params & params) { - params.display_prompt = false; + string_format("whether to print prompt at generation (default: %s)", params.display_prompt ? "true" : "false"), + [](common_params & params, bool value) { + params.display_prompt = value; } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( @@ -1055,18 +1122,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.kv_unified = true; } ).set_env("LLAMA_ARG_KV_UNIFIED")); - add_opt(common_arg( - {"--no-context-shift"}, - string_format("disables context shift on infinite text generation (default: %s)", params.ctx_shift ? "disabled" : "enabled"), - [](common_params & params) { - params.ctx_shift = false; - } - ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_NO_CONTEXT_SHIFT")); add_opt(common_arg( {"--context-shift"}, - string_format("enables context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"), - [](common_params & params) { - params.ctx_shift = true; + {"--no-context-shift"}, + string_format("whether to use context shift on infinite text generation (default: %s)", params.ctx_shift ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.ctx_shift = value; } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_IMATRIX, LLAMA_EXAMPLE_PERPLEXITY}).set_env("LLAMA_ARG_CONTEXT_SHIFT")); add_opt(common_arg( @@ -1106,20 +1167,22 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_DIFFUSION})); add_opt(common_arg( + {"--perf"}, {"--no-perf"}, - string_format("disable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), - [](common_params & params) { - params.no_perf = true; - params.sampling.no_perf = true; + string_format("whether to enable internal libllama performance timings (default: %s)", params.no_perf ? "true" : "false"), + [](common_params & params, bool value) { + params.no_perf = !value; + params.sampling.no_perf = !value; } - ).set_env("LLAMA_ARG_NO_PERF")); + ).set_env("LLAMA_ARG_PERF")); add_opt(common_arg( + {"--show-timings"}, {"--no-show-timings"}, - string_format("disable timing information after each response (default: %s)", params.show_timings ? "true" : "false"), - [](common_params & params) { - params.show_timings = false; + string_format("whether to show timing information after each response (default: %s)", params.show_timings ? "true" : "false"), + [](common_params & params, bool value) { + params.show_timings = value; } - ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_NO_SHOW_TIMINGS")); + ).set_examples({LLAMA_EXAMPLE_CLI}).set_env("LLAMA_ARG_SHOW_TIMINGS")); add_opt(common_arg( {"-f", "--file"}, "FNAME", "a file containing the prompt (default: none)", @@ -1171,16 +1234,10 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_excludes({LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-e", "--escape"}, - string_format("process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), - [](common_params & params) { - params.escape = true; - } - )); - add_opt(common_arg( {"--no-escape"}, - "do not process escape sequences", - [](common_params & params) { - params.escape = false; + string_format("whether to process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false"), + [](common_params & params, bool value) { + params.escape = value; } )); add_opt(common_arg( @@ -1227,19 +1284,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER})); add_opt(common_arg( {"-cnv", "--conversation"}, - "run in conversation mode:\n" + {"-no-cnv", "--no-conversation"}, + "whether to run in conversation mode:\n" "- does not print special tokens and suffix/prefix\n" "- interactive mode is also enabled\n" "(default: auto enabled if chat template is available)", - [](common_params & params) { - params.conversation_mode = COMMON_CONVERSATION_MODE_ENABLED; - } - ).set_examples({LLAMA_EXAMPLE_COMPLETION})); - add_opt(common_arg( - {"-no-cnv", "--no-conversation"}, - "force disable conversation mode (default: false)", - [](common_params & params) { - params.conversation_mode = COMMON_CONVERSATION_MODE_DISABLED; + [](common_params & params, bool value) { + params.conversation_mode = value ? COMMON_CONVERSATION_MODE_ENABLED : COMMON_CONVERSATION_MODE_DISABLED; } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI})); add_opt(common_arg( @@ -1297,10 +1348,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( + {"--warmup"}, {"--no-warmup"}, - "skip warming up the model with an empty run", - [](common_params & params) { - params.warmup = false; + string_format("whether to perform warmup with an empty run (default: %s)", params.warmup ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.warmup = value; } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_MTMD, LLAMA_EXAMPLE_EMBEDDING, LLAMA_EXAMPLE_RETRIEVAL, LLAMA_EXAMPLE_PERPLEXITY})); add_opt(common_arg( @@ -1702,19 +1754,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_ARG_GRP_ATTN_W").set_examples({LLAMA_EXAMPLE_COMPLETION})); add_opt(common_arg( + {"-kvo", "--kv-offload"}, {"-nkvo", "--no-kv-offload"}, - "disable KV offload", - [](common_params & params) { - params.no_kv_offload = true; + string_format("whether to enable KV cache offloading (default: %s)", params.no_kv_offload ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_kv_offload = !value; } - ).set_env("LLAMA_ARG_NO_KV_OFFLOAD")); + ).set_env("LLAMA_ARG_KV_OFFLOAD")); add_opt(common_arg( + {"--repack"}, {"-nr", "--no-repack"}, - "disable weight repacking", - [](common_params & params) { - params.no_extra_bufts = true; + string_format("whether to enable weight repacking (default: %s)", params.no_extra_bufts ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_extra_bufts = !value; } - ).set_env("LLAMA_ARG_NO_REPACK")); + ).set_env("LLAMA_ARG_REPACK")); add_opt(common_arg( {"--no-host"}, "bypass host buffer allowing extra buffers to be used", @@ -1843,18 +1897,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_PARALLEL})); add_opt(common_arg( {"-cb", "--cont-batching"}, - string_format("enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), - [](common_params & params) { - params.cont_batching = true; + {"-nocb", "--no-cont-batching"}, + string_format("whether to enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.cont_batching = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CONT_BATCHING")); - add_opt(common_arg( - {"-nocb", "--no-cont-batching"}, - "disable continuous batching", - [](common_params & params) { - params.cont_batching = false; - } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_CONT_BATCHING")); add_opt(common_arg( {"-mm", "--mmproj"}, "FILE", "path to a multimodal projector file. see tools/mtmd/README.md\n" @@ -1871,19 +1919,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_URL")); add_opt(common_arg( - {"--no-mmproj"}, - "explicitly disable multimodal projector, useful when using -hf", - [](common_params & params) { - params.no_mmproj = true; + {"--mmproj-auto"}, + {"--no-mmproj", "--no-mmproj-auto"}, + string_format("whether to use multimodal projector file (if available), useful when using -hf (default: %s)", params.no_mmproj ? "disabled" : "enabled"), + [](common_params & params, bool value) { + params.no_mmproj = !value; } - ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ")); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_AUTO")); add_opt(common_arg( + {"--mmproj-offload"}, {"--no-mmproj-offload"}, - "do not offload multimodal projector to GPU", - [](common_params & params) { - params.mmproj_use_gpu = false; + string_format("whether to enable GPU offloading for multimodal projector (default: %s)", params.mmproj_use_gpu ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.mmproj_use_gpu = value; } - ).set_examples(mmproj_examples).set_env("LLAMA_ARG_NO_MMPROJ_OFFLOAD")); + ).set_examples(mmproj_examples).set_env("LLAMA_ARG_MMPROJ_OFFLOAD")); add_opt(common_arg( {"--image", "--audio"}, "FILE", "path to an image or audio file. use with multimodal models, can be repeated if you have multiple files\n", @@ -1923,12 +1973,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_env("LLAMA_ARG_MLOCK")); add_opt(common_arg( + {"--mmap"}, {"--no-mmap"}, - "do not memory-map model (slower load but may reduce pageouts if not using mlock)", - [](common_params & params) { - params.use_mmap = false; + string_format("whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: %s)", params.use_mmap ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_mmap = value; } - ).set_env("LLAMA_ARG_NO_MMAP")); + ).set_env("LLAMA_ARG_MMAP")); add_opt(common_arg( {"--numa"}, "TYPE", "attempt optimizations that help on some NUMA systems\n" @@ -2116,10 +2167,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } )); add_opt(common_arg( + {"--op-offload"}, {"--no-op-offload"}, - string_format("disable offloading host tensor operations to device (default: %s)", params.no_op_offload ? "true" : "false"), - [](common_params & params) { - params.no_op_offload = true; + string_format("whether to offload host tensor operations to device (default: %s)", params.no_op_offload ? "false" : "true"), + [](common_params & params, bool value) { + params.no_op_offload = !value; } )); add_opt(common_arg( @@ -2315,10 +2367,11 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_IMATRIX})); add_opt(common_arg( + {"--ppl"}, {"--no-ppl"}, - string_format("do not compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), - [](common_params & params) { - params.compute_ppl = false; + string_format("whether to compute perplexity (default: %s)", params.compute_ppl ? "true" : "false"), + [](common_params & params, bool value) { + params.compute_ppl = value; } ).set_examples({LLAMA_EXAMPLE_IMATRIX})); add_opt(common_arg( @@ -2437,12 +2490,13 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_API_PREFIX")); add_opt(common_arg( + {"--webui"}, {"--no-webui"}, - string_format("Disable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), - [](common_params & params) { - params.webui = false; + string_format("whether to enable the Web UI (default: %s)", params.webui ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.webui = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_WEBUI")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_WEBUI")); add_opt(common_arg( {"--embedding", "--embeddings"}, string_format("restrict to only support embedding use case; use only with dedicated embedding models (default: %s)", params.embedding ? "enabled" : "disabled"), @@ -2547,18 +2601,12 @@ common_params_context common_params_parser_init(common_params & params, llama_ex ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_PROPS")); add_opt(common_arg( {"--slots"}, - string_format("enable slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), - [](common_params & params) { - params.endpoint_slots = true; + {"--no-slots"}, + string_format("expose slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.endpoint_slots = value; } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_ENDPOINT_SLOTS")); - add_opt(common_arg( - {"--no-slots"}, - "disables slots monitoring endpoint", - [](common_params & params) { - params.endpoint_slots = false; - } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_ENDPOINT_SLOTS")); add_opt(common_arg( {"--slot-save-path"}, "PATH", "path to save slot kv cache (default: disabled)", @@ -2609,26 +2657,21 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_MAX")); add_opt(common_arg( + {"--models-autoload"}, {"--no-models-autoload"}, - "disables automatic loading of models (default: enabled)", - [](common_params & params) { - params.models_autoload = false; + string_format("for router server, whether to automatically load models (default: %s)", params.models_autoload ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.models_autoload = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_MODELS_AUTOLOAD")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_MODELS_AUTOLOAD")); add_opt(common_arg( {"--jinja"}, - string_format("use jinja template for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"), - [](common_params & params) { - params.use_jinja = true; + {"--no-jinja"}, + string_format("whether to use jinja template engine for chat (default: %s)", params.use_jinja ? "enabled" : "disabled"), + [](common_params & params, bool value) { + params.use_jinja = value; } ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_JINJA")); - add_opt(common_arg( - {"--no-jinja"}, - string_format("disable jinja template for chat (default: %s)", params.use_jinja ? "disabled" : "enabled"), - [](common_params & params) { - params.use_jinja = false; - } - ).set_examples({LLAMA_EXAMPLE_SERVER, LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_MTMD}).set_env("LLAMA_ARG_NO_JINJA")); add_opt(common_arg( {"--reasoning-format"}, "FORMAT", "controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:\n" @@ -2673,15 +2716,16 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } ).set_examples({LLAMA_EXAMPLE_COMPLETION, LLAMA_EXAMPLE_CLI, LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_CHAT_TEMPLATE_FILE")); add_opt(common_arg( + {"--prefill-assistant"}, {"--no-prefill-assistant"}, string_format( "whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)\n" "when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled\n" ), - [](common_params & params) { - params.prefill_assistant = false; + [](common_params & params, bool value) { + params.prefill_assistant = value; } - ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_NO_PREFILL_ASSISTANT")); + ).set_examples({LLAMA_EXAMPLE_SERVER}).set_env("LLAMA_ARG_PREFILL_ASSISTANT")); add_opt(common_arg( {"-sps", "--slot-prompt-similarity"}, "SIMILARITY", string_format("how much the prompt of a request must match the prompt of a slot in order to use that slot (default: %.2f, 0.0 = disabled)\n", params.slot_prompt_similarity), diff --git a/common/arg.h b/common/arg.h index 219c115e63..6db38da488 100644 --- a/common/arg.h +++ b/common/arg.h @@ -16,6 +16,7 @@ struct common_arg { std::set examples = {LLAMA_EXAMPLE_COMMON}; std::set excludes = {}; std::vector args; + std::vector args_neg; // for negated args like --no-xxx const char * value_hint = nullptr; // help text or example for arg value const char * value_hint_2 = nullptr; // for second arg value const char * env = nullptr; @@ -25,6 +26,7 @@ struct common_arg { void (*handler_string) (common_params & params, const std::string &) = nullptr; void (*handler_str_str)(common_params & params, const std::string &, const std::string &) = nullptr; void (*handler_int) (common_params & params, int) = nullptr; + void (*handler_bool) (common_params & params, bool) = nullptr; common_arg() = default; @@ -48,6 +50,13 @@ struct common_arg { void (*handler)(common_params & params) ) : args(args), help(help), handler_void(handler) {} + common_arg( + const std::initializer_list & args, + const std::initializer_list & args_neg, + const std::string & help, + void (*handler)(common_params & params, bool) + ) : args(args), args_neg(args_neg), help(help), handler_bool(handler) {} + // support 2 values for arg common_arg( const std::initializer_list & args, @@ -80,6 +89,10 @@ struct common_arg { } return strcmp(args[0], other.args[0]) == 0; } + + // get all args and env vars (including negated args/env) + std::vector get_args() const; + std::vector get_env() const; }; namespace common_arg_utils { diff --git a/common/preset.cpp b/common/preset.cpp index 09ac171b72..729c27f2cf 100644 --- a/common/preset.cpp +++ b/common/preset.cpp @@ -23,8 +23,14 @@ std::vector common_preset::to_args() const { if (opt.value_hint == nullptr && opt.value_hint_2 == nullptr) { // flag option, no value if (common_arg_utils::is_falsey(value)) { - // skip the flag - args.pop_back(); + // use negative arg if available + if (!opt.args_neg.empty()) { + args.back() = opt.args_neg.back(); + } else { + // otherwise, skip the flag + // TODO: maybe throw an error instead? + args.pop_back(); + } } } if (opt.value_hint != nullptr) { @@ -141,10 +147,10 @@ static std::map> parse_ini_from_ static std::map get_map_key_opt(common_params_context & ctx_params) { std::map mapping; for (const auto & opt : ctx_params.options) { - if (opt.env != nullptr) { - mapping[opt.env] = opt; + for (const auto & env : opt.get_env()) { + mapping[env] = opt; } - for (const auto & arg : opt.args) { + for (const auto & arg : opt.get_args()) { mapping[rm_leading_dashes(arg)] = opt; } } diff --git a/examples/gen-docs/gen-docs.cpp b/examples/gen-docs/gen-docs.cpp index 420195f198..e9f7bf9313 100644 --- a/examples/gen-docs/gen-docs.cpp +++ b/examples/gen-docs/gen-docs.cpp @@ -14,12 +14,13 @@ static void write_table_header(std::ofstream & file) { static void write_table_entry(std::ofstream & file, const common_arg & opt) { file << "| `"; // args - for (const auto & arg : opt.args) { - if (arg == opt.args.front()) { + auto all_args = opt.get_args(); + for (const auto & arg : all_args) { + if (arg == all_args.front()) { file << arg; - if (opt.args.size() > 1) file << ", "; + if (all_args.size() > 1) file << ", "; } else { - file << arg << (arg != opt.args.back() ? ", " : ""); + file << arg << (arg != all_args.back() ? ", " : ""); } } // value hint diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index a60ca12fe5..90750b20c2 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -20,20 +20,20 @@ int main(void) { std::unordered_set seen_env_vars; for (const auto & opt : ctx_arg.options) { // check for args duplications - for (const auto & arg : opt.args) { + for (const auto & arg : opt.get_args()) { if (seen_args.find(arg) == seen_args.end()) { seen_args.insert(arg); } else { - fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg); + fprintf(stderr, "test-arg-parser: found different handlers for the same argument: %s", arg.c_str()); exit(1); } } // check for env var duplications - if (opt.env) { - if (seen_env_vars.find(opt.env) == seen_env_vars.end()) { - seen_env_vars.insert(opt.env); + for (const auto & env : opt.get_env()) { + if (seen_env_vars.find(env) == seen_env_vars.end()) { + seen_env_vars.insert(env); } else { - fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", opt.env); + fprintf(stderr, "test-arg-parser: found different handlers for the same env var: %s", env.c_str()); exit(1); } } @@ -115,6 +115,14 @@ int main(void) { assert(params.model.path == "blah.gguf"); assert(params.cpuparams.n_threads == 1010); + printf("test-arg-parser: test negated environment variables\n\n"); + + setenv("LLAMA_ARG_MMAP", "0", true); + setenv("LLAMA_ARG_NO_PERF", "1", true); // legacy format + argv = {"binary_name"}; + assert(true == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + assert(params.use_mmap == false); + assert(params.no_perf == true); printf("test-arg-parser: test environment variables being overwritten\n\n"); diff --git a/tools/server/README.md b/tools/server/README.md index 91cafa9425..073bcd2ccd 100644 --- a/tools/server/README.md +++ b/tools/server/README.md @@ -54,9 +54,8 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--swa-full` | use full-size SWA cache (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/13194#issuecomment-2868343055)
(env: LLAMA_ARG_SWA_FULL) | | `--kv-unified, -kvu` | use single unified KV buffer for the KV cache of all sequences (default: false)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/14363)
(env: LLAMA_ARG_KV_UNIFIED) | | `-fa, --flash-attn [on\|off\|auto]` | set Flash Attention use ('on', 'off', or 'auto', default: 'auto')
(env: LLAMA_ARG_FLASH_ATTN) | -| `--no-perf` | disable internal libllama performance timings (default: false)
(env: LLAMA_ARG_NO_PERF) | -| `-e, --escape` | process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | -| `--no-escape` | do not process escape sequences | +| `--perf, --no-perf` | whether to enable internal libllama performance timings (default: false)
(env: LLAMA_ARG_PERF) | +| `-e, --escape, --no-escape` | whether to process escapes sequences (\n, \r, \t, \', \", \\) (default: true) | | `--rope-scaling {none,linear,yarn}` | RoPE frequency scaling method, defaults to linear unless specified by the model
(env: LLAMA_ARG_ROPE_SCALING_TYPE) | | `--rope-scale N` | RoPE context scaling factor, expands context by a factor of N
(env: LLAMA_ARG_ROPE_SCALE) | | `--rope-freq-base N` | RoPE base frequency, used by NTK-aware scaling (default: loaded from model)
(env: LLAMA_ARG_ROPE_FREQ_BASE) | @@ -66,15 +65,15 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--yarn-attn-factor N` | YaRN: scale sqrt(t) or attention magnitude (default: -1.0)
(env: LLAMA_ARG_YARN_ATTN_FACTOR) | | `--yarn-beta-slow N` | YaRN: high correction dim or alpha (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_SLOW) | | `--yarn-beta-fast N` | YaRN: low correction dim or beta (default: -1.0)
(env: LLAMA_ARG_YARN_BETA_FAST) | -| `-nkvo, --no-kv-offload` | disable KV offload
(env: LLAMA_ARG_NO_KV_OFFLOAD) | -| `-nr, --no-repack` | disable weight repacking
(env: LLAMA_ARG_NO_REPACK) | -| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_NO_HOST) | +| `-kvo, --kv-offload, -nkvo, --no-kv-offload` | whether to enable KV cache offloading (default: enabled)
(env: LLAMA_ARG_KV_OFFLOAD) | +| `--repack, -nr, --no-repack` | whether to enable weight repacking (default: enabled)
(env: LLAMA_ARG_REPACK) | +| `--no-host` | bypass host buffer allowing extra buffers to be used
(env: LLAMA_ARG_HOST) | | `-ctk, --cache-type-k TYPE` | KV cache data type for K
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_K) | | `-ctv, --cache-type-v TYPE` | KV cache data type for V
allowed values: f32, f16, bf16, q8_0, q4_0, q4_1, iq4_nl, q5_0, q5_1
(default: f16)
(env: LLAMA_ARG_CACHE_TYPE_V) | | `-dt, --defrag-thold N` | KV cache defragmentation threshold (DEPRECATED)
(env: LLAMA_ARG_DEFRAG_THOLD) | | `-np, --parallel N` | number of parallel sequences to decode (default: 1)
(env: LLAMA_ARG_N_PARALLEL) | | `--mlock` | force system to keep model in RAM rather than swapping or compressing
(env: LLAMA_ARG_MLOCK) | -| `--no-mmap` | do not memory-map model (slower load but may reduce pageouts if not using mlock)
(env: LLAMA_ARG_NO_MMAP) | +| `--mmap, --no-mmap` | whether to memory-map model (if disabled, slower load but may reduce pageouts if not using mlock) (default: enabled)
(env: LLAMA_ARG_MMAP) | | `--numa TYPE` | attempt optimizations that help on some NUMA systems
- distribute: spread execution evenly over all nodes
- isolate: only spawn threads on CPUs on the node that execution started on
- numactl: use the CPU map provided by numactl
if run without this previously, it is recommended to drop the system page cache before using this
see https://github.com/ggml-org/llama.cpp/issues/1437
(env: LLAMA_ARG_NUMA) | | `-dev, --device ` | comma-separated list of devices to use for offloading (none = don't offload)
use --list-devices to see a list of available devices
(env: LLAMA_ARG_DEVICE) | | `--list-devices` | print list of available devices and exit | @@ -87,7 +86,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `-mg, --main-gpu INDEX` | the GPU to use for the model (with split-mode = none), or for intermediate results and KV (with split-mode = row) (default: 0)
(env: LLAMA_ARG_MAIN_GPU) | | `--check-tensors` | check model tensor data for invalid values (default: false) | | `--override-kv KEY=TYPE:VALUE` | advanced option to override model metadata by key. may be specified multiple times.
types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false | -| `--no-op-offload` | disable offloading host tensor operations to device (default: false) | +| `--op-offload, --no-op-offload` | whether to offload host tensor operations to device (default: true) | | `--lora FNAME` | path to LoRA adapter (can be repeated to use multiple adapters) | | `--lora-scaled FNAME SCALE` | path to LoRA adapter with user defined scaling (can be repeated to use multiple adapters) | | `--control-vector FNAME` | add a control vector
note: this argument can be repeated to add multiple control vectors | @@ -157,19 +156,18 @@ For the ful list of features, please refer to [server's changelog](https://githu | -------- | ----------- | | `--ctx-checkpoints, --swa-checkpoints N` | max number of context checkpoints to create per slot (default: 8)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/15293)
(env: LLAMA_ARG_CTX_CHECKPOINTS) | | `--cache-ram, -cram N` | set the maximum cache size in MiB (default: 8192, -1 - no limit, 0 - disable)
[(more info)](https://github.com/ggml-org/llama.cpp/pull/16391)
(env: LLAMA_ARG_CACHE_RAM) | -| `--no-context-shift` | disables context shift on infinite text generation (default: enabled)
(env: LLAMA_ARG_NO_CONTEXT_SHIFT) | -| `--context-shift` | enables context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | +| `--context-shift, --no-context-shift` | whether to use context shift on infinite text generation (default: disabled)
(env: LLAMA_ARG_CONTEXT_SHIFT) | | `-r, --reverse-prompt PROMPT` | halt generation at PROMPT, return control in interactive mode
| | `-sp, --special` | special tokens output enabled (default: false) | -| `--no-warmup` | skip warming up the model with an empty run | +| `--warmup, --no-warmup` | whether to perform warmup with an empty run (default: enabled) | | `--spm-infill` | use Suffix/Prefix/Middle pattern for infill (instead of Prefix/Suffix/Middle) as some models prefer this. (default: disabled) | | `--pooling {none,mean,cls,last,rank}` | pooling type for embeddings, use model default if unspecified
(env: LLAMA_ARG_POOLING) | -| `-cb, --cont-batching` | enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | -| `-nocb, --no-cont-batching` | disable continuous batching
(env: LLAMA_ARG_NO_CONT_BATCHING) | +| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | +| `-cb, --cont-batching, -nocb, --no-cont-batching` | whether to enable continuous batching (a.k.a dynamic batching) (default: enabled)
(env: LLAMA_ARG_CONT_BATCHING) | | `-mm, --mmproj FILE` | path to a multimodal projector file. see tools/mtmd/README.md
note: if -hf is used, this argument can be omitted
(env: LLAMA_ARG_MMPROJ) | | `-mmu, --mmproj-url URL` | URL to a multimodal projector file. see tools/mtmd/README.md
(env: LLAMA_ARG_MMPROJ_URL) | -| `--no-mmproj` | explicitly disable multimodal projector, useful when using -hf
(env: LLAMA_ARG_NO_MMPROJ) | -| `--no-mmproj-offload` | do not offload multimodal projector to GPU
(env: LLAMA_ARG_NO_MMPROJ_OFFLOAD) | +| `--mmproj-auto, --no-mmproj, --no-mmproj-auto` | whether to use multimodal projector file (if available), useful when using -hf (default: enabled)
(env: LLAMA_ARG_MMPROJ_AUTO) | +| `--mmproj-offload, --no-mmproj-offload` | whether to enable GPU offloading for multimodal projector (default: enabled)
(env: LLAMA_ARG_MMPROJ_OFFLOAD) | | `--image-min-tokens N` | minimum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MIN_TOKENS) | | `--image-max-tokens N` | maximum number of tokens each image can take, only used by vision models with dynamic resolution (default: read from model)
(env: LLAMA_ARG_IMAGE_MAX_TOKENS) | | `--override-tensor-draft, -otd =,...` | override tensor buffer type for draft model | @@ -180,7 +178,7 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--port PORT` | port to listen (default: 8080)
(env: LLAMA_ARG_PORT) | | `--path PATH` | path to serve static files from (default: )
(env: LLAMA_ARG_STATIC_PATH) | | `--api-prefix PREFIX` | prefix path the server serves from, without the trailing slash (default: )
(env: LLAMA_ARG_API_PREFIX) | -| `--no-webui` | Disable the Web UI (default: enabled)
(env: LLAMA_ARG_NO_WEBUI) | +| `--webui, --no-webui` | whether to enable the Web UI (default: enabled)
(env: LLAMA_ARG_WEBUI) | | `--embedding, --embeddings` | restrict to only support embedding use case; use only with dedicated embedding models (default: disabled)
(env: LLAMA_ARG_EMBEDDINGS) | | `--reranking, --rerank` | enable reranking endpoint on server (default: disabled)
(env: LLAMA_ARG_RERANKING) | | `--api-key KEY` | API key to use for authentication (default: none)
(env: LLAMA_API_KEY) | @@ -193,20 +191,19 @@ For the ful list of features, please refer to [server's changelog](https://githu | `--cache-reuse N` | min chunk size to attempt reusing from the cache via KV shifting (default: 0)
[(card)](https://ggml.ai/f0.png)
(env: LLAMA_ARG_CACHE_REUSE) | | `--metrics` | enable prometheus compatible metrics endpoint (default: disabled)
(env: LLAMA_ARG_ENDPOINT_METRICS) | | `--props` | enable changing global properties via POST /props (default: disabled)
(env: LLAMA_ARG_ENDPOINT_PROPS) | -| `--slots` | enable slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | -| `--no-slots` | disables slots monitoring endpoint
(env: LLAMA_ARG_NO_ENDPOINT_SLOTS) | +| `--slots, --no-slots` | expose slots monitoring endpoint (default: enabled)
(env: LLAMA_ARG_ENDPOINT_SLOTS) | | `--slot-save-path PATH` | path to save slot kv cache (default: disabled) | +| `--media-path PATH` | directory for loading local media files; files can be accessed via file:// URLs using relative paths (default: disabled) | | `--models-dir PATH` | directory containing models for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_DIR) | +| `--models-preset PATH` | path to INI file containing model presets for the router server (default: disabled)
(env: LLAMA_ARG_MODELS_PRESET) | | `--models-max N` | for router server, maximum number of models to load simultaneously (default: 4, 0 = unlimited)
(env: LLAMA_ARG_MODELS_MAX) | -| `--models-allow-extra-args` | for router server, allow extra arguments for models; important: some arguments can allow users to access local file system, use with caution (default: disabled)
(env: LLAMA_ARG_MODELS_ALLOW_EXTRA_ARGS) | -| `--no-models-autoload` | disables automatic loading of models (default: enabled)
(env: LLAMA_ARG_NO_MODELS_AUTOLOAD) | -| `--jinja` | use jinja template for chat (default: enabled)

(env: LLAMA_ARG_JINJA) | -| `--no-jinja` | disable jinja template for chat (default: enabled)

(env: LLAMA_ARG_NO_JINJA) | +| `--models-autoload, --no-models-autoload` | for router server, whether to automatically load models (default: enabled)
(env: LLAMA_ARG_MODELS_AUTOLOAD) | +| `--jinja, --no-jinja` | whether to use jinja template engine for chat (default: enabled)
(env: LLAMA_ARG_JINJA) | | `--reasoning-format FORMAT` | controls whether thought tags are allowed and/or extracted from the response, and in which format they're returned; one of:
- none: leaves thoughts unparsed in `message.content`
- deepseek: puts thoughts in `message.reasoning_content`
- deepseek-legacy: keeps `` tags in `message.content` while also populating `message.reasoning_content`
(default: auto)
(env: LLAMA_ARG_THINK) | | `--reasoning-budget N` | controls the amount of thinking allowed; currently only one of: -1 for unrestricted thinking budget, or 0 to disable thinking (default: -1)
(env: LLAMA_ARG_THINK_BUDGET) | | `--chat-template JINJA_TEMPLATE` | set custom jinja chat template (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE) | | `--chat-template-file JINJA_TEMPLATE_FILE` | set custom jinja chat template file (default: template taken from model's metadata)
if suffix/prefix are specified, template will be disabled
only commonly used templates are accepted (unless --jinja is set before this flag):
list of built-in templates:
bailing, bailing-think, bailing2, chatglm3, chatglm4, chatml, command-r, deepseek, deepseek2, deepseek3, exaone3, exaone4, falcon3, gemma, gigachat, glmedge, gpt-oss, granite, grok-2, hunyuan-dense, hunyuan-moe, kimi-k2, llama2, llama2-sys, llama2-sys-bos, llama2-sys-strip, llama3, llama4, megrez, minicpm, mistral-v1, mistral-v3, mistral-v3-tekken, mistral-v7, mistral-v7-tekken, monarch, openchat, orion, pangu-embedded, phi3, phi4, rwkv-world, seed_oss, smolvlm, vicuna, vicuna-orca, yandex, zephyr
(env: LLAMA_ARG_CHAT_TEMPLATE_FILE) | -| `--no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled

(env: LLAMA_ARG_NO_PREFILL_ASSISTANT) | +| `--prefill-assistant, --no-prefill-assistant` | whether to prefill the assistant's response if the last message is an assistant message (default: prefill enabled)
when this flag is set, if the last message is an assistant message then it will be treated as a full message and not prefilled

(env: LLAMA_ARG_PREFILL_ASSISTANT) | | `-sps, --slot-prompt-similarity SIMILARITY` | how much the prompt of a request must match the prompt of a slot in order to use that slot (default: 0.10, 0.0 = disabled)
| | `--lora-init-without-apply` | load LoRA adapters without applying them (apply later via POST /lora-adapters) (default: disabled) | | `-td, --threads-draft N` | number of threads to use during generation (default: same as --threads) | @@ -236,6 +233,11 @@ For the ful list of features, please refer to [server's changelog](https://githu Note: If both command line argument and environment variable are both set for the same param, the argument will take precedence over env var. +For boolean options like `--mmap` or `--kv-offload`, the environment variable is handled as shown in this example: +- `LLAMA_ARG_MMAP=true` means enabled, other accepted values are: `1`, `on`, `enabled` +- `LLAMA_ARG_MMAP=false` means disabled, other accepted values are: `0`, `off`, `disabled` +- If `LLAMA_ARG_NO_MMAP` is present (no matter the value), it means disabling mmap + Example usage of docker compose with environment variables: ```yml From fd1085ffb71dabf4490b428512d0fd6fdc637536 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Sat, 13 Dec 2025 08:34:26 +0100 Subject: [PATCH 06/40] model-conversion : use CONVERTED_MODEL value for converted model [no ci] (#17984) * model-conversion : use CONVERTED_MODEL value for converted model [no ci] This commit updates the model verification scripts to use the CONVERTED_MODEL environment variable instead of using the MODEL_PATH (the original model path) as the basis for the converted model file name. The motivation for this that currently if the converted model file name differs from the original model directory/name the verification scripts will look for the wrong .bin files that were generating when running the models. For example, the following steps were not possible: ```console (venv) $ huggingface-cli download google/gemma-3-270m-it --local-dir ggml-org/gemma-3-270m (venv) $ python3 convert_hf_to_gguf.py ggml-org/gemma-3-270m --outfile test-bf16.gguf --outtype bf16 (venv) $ cd examples/model-conversion/ (venv) $ export MODEL_PATH=../../ggml-org/gemma-3-270m (venv) $ export CONVERTED_MODEL=../../test-bf16.gguf (venv) $ make causal-verify-logits ... Data saved to data/llamacpp-test-bf16.bin Data saved to data/llamacpp-test-bf16.txt Error: llama.cpp logits file not found: data/llamacpp-gemma-3-270m.bin Please run scripts/run-converted-model.sh first to generate this file. make: *** [Makefile:62: causal-verify-logits] Error 1 ``` With the changes in this commit, the above steps will now work as expected. --- .../scripts/causal/compare-logits.py | 24 ++++++++----------- .../scripts/utils/__init__.py | 0 .../scripts/utils/check-nmse.py | 7 ++++-- .../model-conversion/scripts/utils/common.py | 20 ++++++++++++++++ pyrightconfig.json | 2 +- 5 files changed, 36 insertions(+), 17 deletions(-) create mode 100644 examples/model-conversion/scripts/utils/__init__.py create mode 100644 examples/model-conversion/scripts/utils/common.py diff --git a/examples/model-conversion/scripts/causal/compare-logits.py b/examples/model-conversion/scripts/causal/compare-logits.py index 2744789099..894302c69e 100755 --- a/examples/model-conversion/scripts/causal/compare-logits.py +++ b/examples/model-conversion/scripts/causal/compare-logits.py @@ -1,10 +1,13 @@ #!/usr/bin/env python3 -import numpy as np import sys -import os +import numpy as np from pathlib import Path +# Add utils directory to path for direct script execution +sys.path.insert(0, str(Path(__file__).parent.parent / "utils")) +from common import get_model_name_from_env_path # type: ignore[import-not-found] + def quick_logits_check(pytorch_file, llamacpp_file): """Lightweight sanity check before NMSE""" @@ -35,20 +38,13 @@ def quick_logits_check(pytorch_file, llamacpp_file): return True def main(): - model_path = os.getenv('MODEL_PATH') - if not model_path: - print("Error: MODEL_PATH environment variable not set") - sys.exit(1) - - if not os.path.exists(model_path): - print(f"Error: Model file not found: {model_path}") - sys.exit(1) - - model_name = os.path.basename(model_path) + model_name = get_model_name_from_env_path('MODEL_PATH') data_dir = Path("data") - pytorch_file = data_dir / f"pytorch-{model_name}.bin" - llamacpp_file = data_dir / f"llamacpp-{model_name}.bin" + + llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL') + print(f"Using converted model: {llamacpp_model_name}") + llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin" if not pytorch_file.exists(): print(f"Error: PyTorch logits file not found: {pytorch_file}") diff --git a/examples/model-conversion/scripts/utils/__init__.py b/examples/model-conversion/scripts/utils/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/examples/model-conversion/scripts/utils/check-nmse.py b/examples/model-conversion/scripts/utils/check-nmse.py index 939e3153cc..83f63f9ff3 100755 --- a/examples/model-conversion/scripts/utils/check-nmse.py +++ b/examples/model-conversion/scripts/utils/check-nmse.py @@ -5,6 +5,7 @@ import sys import os import argparse from pathlib import Path +from common import get_model_name_from_env_path # type: ignore[import-not-found] def calculate_nmse(reference, test): mse = np.mean((test - reference) ** 2) @@ -67,11 +68,13 @@ def main(): parser.add_argument('-m', '--model-path', required=True, help='Path to the model directory') args = parser.parse_args() - model_name = os.path.basename(args.model_path) + model_name = get_model_name_from_env_path('MODEL_PATH') data_dir = Path("data") pytorch_file = data_dir / f"pytorch-{model_name}.bin" - llamacpp_file = data_dir / f"llamacpp-{model_name}.bin" + + llamacpp_model_name = get_model_name_from_env_path('CONVERTED_MODEL') + llamacpp_file = data_dir / f"llamacpp-{llamacpp_model_name}.bin" print(f"Model name: {model_name}") print(f"PyTorch logits file: {pytorch_file}") diff --git a/examples/model-conversion/scripts/utils/common.py b/examples/model-conversion/scripts/utils/common.py new file mode 100644 index 0000000000..945f9a1a1d --- /dev/null +++ b/examples/model-conversion/scripts/utils/common.py @@ -0,0 +1,20 @@ +#!/usr/bin/env python3 + +import os +import sys + +def get_model_name_from_env_path(env_path_name): + model_path = os.getenv(env_path_name) + if not model_path: + print(f"Error: {env_path_name} environment variable not set") + sys.exit(1) + + if not os.path.exists(model_path): + print(f"Error: Model file not found: {model_path}") + sys.exit(1) + + name = os.path.basename(os.path.normpath(model_path)) + if name.endswith(".gguf"): + name = name[:-5] + + return name diff --git a/pyrightconfig.json b/pyrightconfig.json index 5320fe5864..a7bc007bdc 100644 --- a/pyrightconfig.json +++ b/pyrightconfig.json @@ -1,5 +1,5 @@ { - "extraPaths": ["gguf-py"], + "extraPaths": ["gguf-py", "examples/model-conversion/scripts"], "pythonVersion": "3.9", "pythonPlatform": "All", "reportUnusedImport": "warning", From 2bc94e792867497e2f0088b17d3fd3fcebd1f44b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sat, 13 Dec 2025 08:35:50 +0100 Subject: [PATCH 07/40] add llama-completion to completion-bash executables (#17976) --- common/arg.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/common/arg.cpp b/common/arg.cpp index 16cb2e03a6..788a9ab4e6 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -639,6 +639,7 @@ static void common_params_print_completion(common_params_context & ctx_arg) { "llama-batched-bench", "llama-bench", "llama-cli", + "llama-completion", "llama-convert-llama2c-to-ggml", "llama-cvector-generator", "llama-embedding", From 07a10c1090502163ab48cc14a5dfc7059949a2c9 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 13 Dec 2025 01:40:04 -0600 Subject: [PATCH 08/40] vulkan: Allow non-pow2 n_experts in topk_moe (#17872) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 22 ++++++++++++------- .../ggml-vulkan/vulkan-shaders/topk_moe.comp | 12 +++++++--- tests/test-backend-ops.cpp | 4 ++++ 3 files changed, 27 insertions(+), 11 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index c6f5809ccd..52e7e1e7fe 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -757,7 +757,8 @@ struct vk_device_struct { vk_pipeline pipeline_flash_attn_split_k_reduce; - vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT]; + // [2] is for whether to take n_experts from spec constant (0) or push constant (1) + vk_pipeline pipeline_topk_moe[num_topk_moe_pipelines][TOPK_MOE_COUNT][2]; std::vector all_pipelines; @@ -1149,6 +1150,7 @@ static_assert(sizeof(vk_op_multi_add_push_constants) <= 256); struct vk_op_topk_moe_push_constants { uint32_t n_rows; + uint32_t n_experts_push; uint32_t n_expert_used; float clamp_min; float clamp_max; @@ -4204,10 +4206,12 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f16_f32, "conv2d_dw_whcn_f16_f32", conv2d_dw_whcn_f16_f32_len, conv2d_dw_whcn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_cwhn_f16_f32, "conv2d_dw_cwhn_f16_f32", conv2d_dw_cwhn_f16_f32_len, conv2d_dw_cwhn_f16_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1); - for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); - ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + for (uint32_t use_push = 0; use_push < 2; ++use_push) { + for (uint32_t i = 0; i < num_topk_moe_pipelines; ++i) { + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX][use_push], "topk_moe_f32_early_softmax_"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_EARLY_SOFTMAX_NORM][use_push], "topk_moe_f32_early_softmax_norm"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + ggml_vk_create_pipeline2(device, device->pipeline_topk_moe[i][TOPK_MOE_LATE_SOFTMAX][use_push], "topk_moe_f32_late_softmax"+std::to_string(i), topk_moe_f32_len, topk_moe_f32_data, "main", 3, sizeof(vk_op_topk_moe_push_constants), {1, 1, 1}, {device->subgroup_size, 1u<subgroup_size); + } } for (auto &c : compiles) { @@ -8554,7 +8558,9 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const uint32_t idx = (uint32_t)ceilf(log2f(float(dst->ne[0]))); GGML_ASSERT(idx < num_topk_moe_pipelines); topk_moe_mode mode = ggml_vk_num_additional_ops_to_topk_moe_mode(ctx->num_additional_fused_ops); - return ctx->device->pipeline_topk_moe[idx][mode]; + // use n_experts from push constant if it's not equal to the power of two spec constant + bool use_push = dst->ne[0] != (1u << idx); + return ctx->device->pipeline_topk_moe[idx][mode][use_push]; } if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { @@ -10158,6 +10164,7 @@ static void ggml_vk_topk_moe(ggml_backend_vk_context * ctx, vk_context& subctx, vk_op_topk_moe_push_constants pc {}; pc.n_rows = n_rows; + pc.n_experts_push = n_experts; pc.n_expert_used = n_expert_used; if (mode == TOPK_MOE_EARLY_SOFTMAX_NORM) { ggml_tensor * clamp = cgraph->nodes[node_idx + 7]; @@ -12832,8 +12839,7 @@ static bool ggml_vk_can_fuse_topk_moe(ggml_backend_vk_context * ctx, const struc } const int n_expert = softmax->ne[0]; - // n_expert must be a power of 2 - if (!is_pow2(n_expert) || n_expert > (1 << (num_topk_moe_pipelines-1))) { + if (n_expert > (1 << (num_topk_moe_pipelines-1))) { return false; } diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp index 5cd0785d20..b83a2b9d2d 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/topk_moe.comp @@ -10,6 +10,7 @@ layout (push_constant) uniform parameter { uint n_rows; + uint n_experts_push; uint n_expert_used; float clamp_min; float clamp_max; @@ -18,11 +19,16 @@ layout (push_constant) uniform parameter layout(local_size_x_id = 0, local_size_y = 4, local_size_z = 1) in; layout(constant_id = 0) const uint WARP_SIZE = 32; -layout(constant_id = 1) const uint n_experts = 512; +layout(constant_id = 1) const uint n_experts_spec = 512; layout(constant_id = 2) const bool with_norm = true; layout(constant_id = 3) const bool late_softmax = false; +layout(constant_id = 4) const bool nexperts_use_push = false; -const uint experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1; +uint n_experts = nexperts_use_push ? n_experts_push : n_experts_spec; + +#define CEIL_DIV(a, b) (((a) + (b) - 1) / (b)) + +const uint experts_per_thread = CEIL_DIV(n_experts_spec, WARP_SIZE); layout (binding = 0, std430) readonly buffer Logits {float logits[];}; layout (binding = 1, std430) writeonly buffer Weights {float weights[];}; @@ -94,7 +100,7 @@ void main() { } if (!late_softmax) { - softmax_warp_inplace(wt, n_experts, lane, false); + softmax_warp_inplace(wt, n_experts, lane, nexperts_use_push); } // at this point, each thread holds a portion of softmax, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 308e752b1d..f1bed864e3 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -7971,8 +7971,12 @@ static std::vector> make_test_cases_eval() { for (bool with_norm : {false, true}) { test_cases.emplace_back(new test_topk_moe({8, 22, 1, 1}, 4, with_norm)); + test_cases.emplace_back(new test_topk_moe({31, 22, 1, 1}, 8, with_norm)); test_cases.emplace_back(new test_topk_moe({32, 22, 1, 1}, 8, with_norm)); + test_cases.emplace_back(new test_topk_moe({40, 22, 1, 1}, 8, with_norm)); + test_cases.emplace_back(new test_topk_moe({71, 22, 1, 1}, 8, with_norm)); test_cases.emplace_back(new test_topk_moe({128, 1, 1, 1}, 128, with_norm)); + test_cases.emplace_back(new test_topk_moe({129, 1, 1, 1}, 128, with_norm)); } test_cases.emplace_back(new test_topk_moe({ 8, 22, 1, 1 }, 4, /*with_norm*/ false, /*delayed_softmax*/ true)); From 8e4d678528b2582eb8a9f773309e88b6a167e4cd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sat, 13 Dec 2025 08:40:50 +0100 Subject: [PATCH 09/40] common : skip model validation when --completion-bash is requested (#17975) --- common/arg.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/arg.cpp b/common/arg.cpp index 788a9ab4e6..19f22f883f 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -504,7 +504,7 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context // model is required (except for server) // TODO @ngxson : maybe show a list of available models in CLI in this case - if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage) { + if (params.model.path.empty() && ctx_arg.ex != LLAMA_EXAMPLE_SERVER && !params.usage && !params.completion) { throw std::invalid_argument("error: --model is required\n"); } From 3c6391e748d8c00c45fba033811508288580cdc7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Dec 2025 09:48:34 +0200 Subject: [PATCH 10/40] speculative-simple : free batch on exit (#17985) --- examples/speculative-simple/speculative-simple.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp index a8e53f28eb..0d11d0f803 100644 --- a/examples/speculative-simple/speculative-simple.cpp +++ b/examples/speculative-simple/speculative-simple.cpp @@ -255,6 +255,8 @@ int main(int argc, char ** argv) { LOG_INF("target:\n\n"); common_perf_print(ctx_tgt, smpl); + llama_batch_free(batch_tgt); + common_sampler_free(smpl); common_speculative_free(spec); From 303f8615e94d74f140eb4c3947758c1eca933c3a Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 13 Dec 2025 03:04:29 -0600 Subject: [PATCH 11/40] vulkan: Multi-pass softmax for large number of cols (#17892) When the number of cols is large, split each row across multiple workgroups. There are three phases that communicate partial results through temp buffers: (1) compute max partials (2) take max of partials, compute sum(exp(x-max)) partials (3) sum partials, compute scaled result --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 64 ++++++++++++++- .../vulkan-shaders/soft_max_large1.comp | 62 +++++++++++++++ .../vulkan-shaders/soft_max_large2.comp | 79 +++++++++++++++++++ .../vulkan-shaders/soft_max_large3.comp | 65 +++++++++++++++ .../vulkan-shaders/soft_max_large_common.glsl | 53 +++++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 7 ++ tests/test-backend-ops.cpp | 3 + 7 files changed, 331 insertions(+), 2 deletions(-) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 52e7e1e7fe..0b4f8c36ad 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -722,6 +722,11 @@ struct vk_device_struct { vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; vk_pipeline pipeline_soft_max_f32_wg512, pipeline_soft_max_f32_f16_wg512; vk_pipeline pipeline_soft_max_back_f32; + + vk_pipeline pipeline_soft_max_large1_f32, pipeline_soft_max_large1_f32_f16; + vk_pipeline pipeline_soft_max_large2_f32, pipeline_soft_max_large2_f32_f16; + vk_pipeline pipeline_soft_max_large3_f32, pipeline_soft_max_large3_f32_f16; + vk_pipeline pipeline_rope_norm_f32, pipeline_rope_norm_f16, pipeline_rope_norm_f32_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16, pipeline_rope_neox_f32_f16; vk_pipeline pipeline_rope_multi_f32, pipeline_rope_multi_f16; @@ -3998,6 +4003,13 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_soft_max_f32_f16_wg512, "soft_max_f32_f16_wg512", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 512 }, 1); ggml_vk_create_pipeline(device, device->pipeline_soft_max_back_f32, "soft_max_back_f32", soft_max_back_f32_len, soft_max_back_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32, "soft_max_large1_f32", soft_max_large1_f32_len, soft_max_large1_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32, "soft_max_large2_f32", soft_max_large2_f32_len, soft_max_large2_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32, "soft_max_large3_f32", soft_max_large3_f32_len, soft_max_large3_f32_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large1_f32_f16, "soft_max_large1_f32_f16", soft_max_large1_f32_f16_len, soft_max_large1_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large2_f32_f16, "soft_max_large2_f32_f16", soft_max_large2_f32_f16_len, soft_max_large2_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_soft_max_large3_f32_f16, "soft_max_large3_f32_f16", soft_max_large3_f32_f16_len, soft_max_large3_f32_f16_data, "main", 6, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, { 128, 4 }, 1, true); + ggml_vk_create_pipeline(device, device->pipeline_rope_norm_f32, "rope_norm_f32", rope_norm_f32_len, rope_norm_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_rope_multi_f32, "rope_multi_f32", rope_multi_f32_len, rope_multi_f32_data, "main", 5, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -10117,7 +10129,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, { + vk_op_soft_max_push_constants pc { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], @@ -10128,7 +10140,55 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context& subctx, n_head_log2, nrows_x, src2 != nullptr - }); + }; + + if (ncols <= 16384) { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, nullptr, dst, GGML_OP_SOFT_MAX, std::move(pc)); + } else { + + vk_subbuffer buf_a = ggml_vk_tensor_subbuffer(ctx, src0); + vk_subbuffer buf_b = src1 ? ggml_vk_tensor_subbuffer(ctx, src1) : buf_a; + vk_subbuffer buf_c = src2 ? ggml_vk_tensor_subbuffer(ctx, src2) : buf_a; + vk_subbuffer buf_d = ggml_vk_tensor_subbuffer(ctx, dst); + + uint32_t elems_per_wg = 128 * 4; + uint32_t num_wgs = CEIL_DIV(ncols, elems_per_wg); + size_t tmp_size = num_wgs * nrows_x * sizeof(float); + + if (ctx->prealloc_size_x < tmp_size) { + ctx->prealloc_size_x = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_size_y < tmp_size) { + ctx->prealloc_size_y = tmp_size; + ggml_vk_preallocate_buffers(ctx, subctx); + } + if (ctx->prealloc_x_need_sync || ctx->prealloc_y_need_sync) { + ggml_vk_sync_buffers(ctx, subctx); + } + + vk_subbuffer buf_x = { ctx->prealloc_x, 0, tmp_size }; + vk_subbuffer buf_y = { ctx->prealloc_y, 0, tmp_size }; + + std::array elements = { num_wgs, nrows_x, 1 }; + + vk_pipeline pipeline1 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large1_f32_f16 : ctx->device->pipeline_soft_max_large1_f32; + vk_pipeline pipeline2 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large2_f32_f16 : ctx->device->pipeline_soft_max_large2_f32; + vk_pipeline pipeline3 = src1 && src1->type == GGML_TYPE_F16 ? ctx->device->pipeline_soft_max_large3_f32_f16 : ctx->device->pipeline_soft_max_large3_f32; + + ggml_pipeline_request_descriptor_sets(ctx, pipeline1, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline2, 1); + ggml_pipeline_request_descriptor_sets(ctx, pipeline3, 1); + + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline1, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline2, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + ggml_vk_sync_buffers(ctx, subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline3, { buf_a, buf_b, buf_c, buf_d, buf_x, buf_y }, pc, elements); + + ctx->prealloc_x_need_sync = true; + ctx->prealloc_y_need_sync = true; + } } static void ggml_vk_soft_max_back(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp new file mode 100644 index 0000000000..39c4663912 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large1.comp @@ -0,0 +1,62 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + FLOAT_TYPE a = FLOAT_TYPE(0); + if (col < p.KX) { + a = data_a[rowx * p.KX + col]; + } + + FLOAT_TYPE b = FLOAT_TYPE(0); + if (p.KY > 0 && col < p.KX) { + b = data_b[rowy_start + col]; + } + + FLOAT_TYPE v = a * p.scale + slope * b; + + if (col < p.KX) { + max_val = max(max_val, v); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + if (tid == 0) { + max_val = vals[0]; + data_m[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = max_val; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp new file mode 100644 index 0000000000..69524f5f75 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large2.comp @@ -0,0 +1,79 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + float slope = get_slope(rowx); + + // Find max + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + } + } + + // reduce across the workgroup + vals[tid] = max_val; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + } + barrier(); + } + + max_val = vals[0]; + barrier(); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + // Compute sum{exp(x - max)} + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + // compute exp(a*scale+b*slope), add it to sum + const uint i = rowx * p.KX + col; + FLOAT_TYPE val; + val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy_start + col]) : FLOAT_TYPE(0.0f)) - max_val); + sum += val; + data_d[i] = D_TYPE(val); + } + + // reduce across the workgroup + vals[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + + if (tid == 0) { + sum = vals[0]; + data_s[rowx * gl_NumWorkGroups.x + gl_WorkGroupID.x] = sum; + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp new file mode 100644 index 0000000000..06efd7d9fb --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large3.comp @@ -0,0 +1,65 @@ +#version 450 + +#include "soft_max_large_common.glsl" + +shared FLOAT_TYPE sumsh[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.y; + const uint wg_start = gl_WorkGroupID.x * BLOCK_SIZE * num_iters; + + const uint32_t i03 = rowx / (p.ne01 * p.ne02); + const uint32_t i02 = (rowx - i03 * p.ne01 * p.ne02) / p.ne01; + const uint32_t i01 = rowx % p.ne01; + + uint rowy_start = 0; + if (p.KY > 0) { + rowy_start = i01 * p.nb11 + (i02 % p.ne12) * p.nb12 + (i03 % p.ne13) * p.nb13; + } + + if (rowx >= p.nrows_x) { + return; + } + + FLOAT_TYPE max_val = p.has_sinks == 0 ? uintBitsToFloat(0xFF800000) : data_c[i02]; + FLOAT_TYPE sum = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint i = 0; i < gl_NumWorkGroups.x; i += BLOCK_SIZE) { + if (i + tid < gl_NumWorkGroups.x) { + max_val = max(max_val, data_m[rowx * gl_NumWorkGroups.x + i + tid]); + sum += data_s[rowx * gl_NumWorkGroups.x + i + tid]; + } + } + + // reduce across the workgroup + vals[tid] = max_val; + sumsh[tid] = sum; + barrier(); + [[unroll]] for (uint s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(max_val, vals[tid + s]); + sumsh[tid] += sumsh[tid + s]; + } + barrier(); + } + + max_val = vals[0]; + sum = sumsh[0]; + + if (p.has_sinks != 0) { + sum += FLOAT_TYPE(exp(FLOAT_TYPE(data_c[i02]) - max_val)); + } + + FLOAT_TYPE rcpdivisor = 1.0/sum; + + [[unroll]] for (uint col0 = wg_start, idx = 0; idx < num_iters; col0 += BLOCK_SIZE, ++idx) { + const uint col = col0 + tid; + + if (col >= p.KX) { + continue; + } + + data_d[rowx*p.KX + col] *= D_TYPE(rcpdivisor); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl new file mode 100644 index 0000000000..6636d1f8de --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/soft_max_large_common.glsl @@ -0,0 +1,53 @@ +#extension GL_EXT_control_flow_attributes : enable + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + uint ne00; + uint ne01; + uint ne02; + uint ne12; + uint ne13; + uint nb11; + uint nb12; + uint nb13; + float scale; + float max_bias; + float m0; + float m1; + uint n_head_log2; + uint nrows_x; + uint has_sinks; +} p; + +#include "types.glsl" + +layout(constant_id = 0) const uint BLOCK_SIZE = 128; +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; +layout(constant_id = 1) const uint num_iters = 4; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) readonly buffer Z {float data_c[];}; +layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 4) buffer M {float data_m[];}; +layout (binding = 5) buffer S {float data_s[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +float get_slope(uint rowx) { + float slope = 1.0f; + + // ALiBi + if (p.max_bias > 0.0f) { + const uint h = (rowx / p.ne01) % p.ne02; // head index + + const float base = h < p.n_head_log2 ? p.m0 : p.m1; + const uint exp = h < p.n_head_log2 ? h + 1 : 2*(h - p.n_head_log2) + 1; + + slope = pow(base, exp); + } + + return slope; +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 92bae088b2..72c63e8173 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -899,6 +899,13 @@ void process_shaders() { string_to_spv("soft_max_f32_f16", "soft_max.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); string_to_spv("soft_max_back_f32", "soft_max_back.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large1_f32", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large1_f32_f16", "soft_max_large1.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large2_f32_f16", "soft_max_large2.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("soft_max_large3_f32_f16", "soft_max_large3.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float16_t"}, {"D_TYPE", "float"}})); + string_to_spv("rope_norm_f32", "rope_norm.comp", {{"A_TYPE", "float"}, {"ROPE_D_TYPE", "float"}}); string_to_spv("rope_norm_f16", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}}); string_to_spv("rope_norm_f16_rte", "rope_norm.comp", {{"A_TYPE", "float16_t"}, {"ROPE_D_TYPE", "float16_t"}, {"RTE16", "1"}}); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f1bed864e3..416218b5b8 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -7652,6 +7652,9 @@ static std::vector> make_test_cases_eval() { test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, true, GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, true, GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true, true, GGML_TYPE_F32, {1, 1}, 0.1f, 8.0f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {200001, 2, 3, 1}, true, true, GGML_TYPE_F16, {1, 1}, 0.1f, 8.0f)); + for (float max_bias : {0.0f, 8.0f}) { for (float scale : {1.0f, 0.1f}) { for (int64_t ne0 : {16, 1024}) { From 3229a23fa675ab7316fc53d045ded99f90fc6766 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 13 Dec 2025 03:07:49 -0600 Subject: [PATCH 12/40] vulkan: support GGML_OP_DIAG (#17893) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 24 +++++++++++++++ ggml/src/ggml-vulkan/vulkan-shaders/diag.comp | 29 +++++++++++++++++++ .../vulkan-shaders/vulkan-shaders-gen.cpp | 2 ++ 3 files changed, 55 insertions(+) create mode 100644 ggml/src/ggml-vulkan/vulkan-shaders/diag.comp diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index 0b4f8c36ad..ac3841764e 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -659,6 +659,7 @@ struct vk_device_struct { vk_pipeline pipeline_cos_f32; vk_pipeline pipeline_log[2]; vk_pipeline pipeline_tri[2]; + vk_pipeline pipeline_diag[2]; vk_pipeline pipeline_clamp_f32; vk_pipeline pipeline_pad_f32; vk_pipeline pipeline_roll_f32; @@ -3924,6 +3925,9 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_tri[0], "tri_f32", tri_f32_len, tri_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_tri[1], "tri_f16", tri_f16_len, tri_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_diag[0], "diag_f32", diag_f32_len, diag_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_diag[1], "diag_f16", diag_f16_len, diag_f16_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_pad_f32, "pad_f32", pad_f32_len, pad_f32_data, "main", 2, sizeof(vk_op_pad_push_constants), {512, 1, 1}, {}, 1); @@ -8416,6 +8420,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_tri[dst->type == GGML_TYPE_F16]; } return nullptr; + case GGML_OP_DIAG: + if (src0->type == dst->type && + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16)) { + return ctx->device->pipeline_diag[dst->type == GGML_TYPE_F16]; + } + return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_clamp_f32; @@ -9109,6 +9119,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co case GGML_OP_COS: case GGML_OP_LOG: case GGML_OP_TRI: + case GGML_OP_DIAG: case GGML_OP_CLAMP: case GGML_OP_PAD: case GGML_OP_ROLL: @@ -9796,6 +9807,12 @@ static void ggml_vk_tri(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_TRI, std::move(p)); } +static void ggml_vk_diag(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { + vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst, ggml_nelements(dst)); + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, nullptr, dst, GGML_OP_DIAG, std::move(p)); +} + static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) { vk_op_unary_push_constants p = vk_op_unary_push_constants_init(src0, dst); p.param1 = ggml_get_op_params_f32(dst, 0); @@ -11924,6 +11941,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr case GGML_OP_TRI: ggml_vk_tri(ctx, compute_ctx, src0, node); + break; + case GGML_OP_DIAG: + ggml_vk_diag(ctx, compute_ctx, src0, node); + break; case GGML_OP_CLAMP: ggml_vk_clamp(ctx, compute_ctx, src0, node); @@ -14067,6 +14088,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm return ggml_is_contiguous(op->src[0]) && op->src[0]->type == GGML_TYPE_F32; case GGML_OP_LOG: case GGML_OP_TRI: + case GGML_OP_DIAG: return (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) && op->type == op->src[0]->type; case GGML_OP_ARGSORT: @@ -14657,6 +14679,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph * tensor_clone = ggml_log(ggml_ctx, src_clone[0]); } else if (tensor->op == GGML_OP_TRI) { tensor_clone = ggml_tri(ggml_ctx, src_clone[0], ggml_get_op_params_i32(tensor, 0)); + } else if (tensor->op == GGML_OP_DIAG) { + tensor_clone = ggml_diag(ggml_ctx, src_clone[0]); } else if (tensor->op == GGML_OP_CLAMP) { const float * params = (const float *)tensor->op_params; tensor_clone = ggml_clamp(ggml_ctx, src_clone[0], params[0], params[1]); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp new file mode 100644 index 0000000000..cd3f42f491 --- /dev/null +++ b/ggml/src/ggml-vulkan/vulkan-shaders/diag.comp @@ -0,0 +1,29 @@ +#version 450 + +#include "rte.glsl" +#include "types.glsl" +#include "generic_unary_head.glsl" + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +void main() { + const uint idx = get_idx(); + + if (idx >= p.ne) { + return; + } + + const uint i13 = fastdiv(idx, p.ne1_012mp, p.ne1_012L); + const uint i13_offset = i13 * p.ne12*p.ne11*p.ne10; + const uint i12 = fastdiv(idx - i13_offset, p.ne1_01mp, p.ne1_01L); + const uint i12_offset = i12*p.ne11*p.ne10; + const uint i11 = fastdiv(idx - i13_offset - i12_offset, p.ne1_0mp, p.ne1_0L); + const uint i10 = idx - i13_offset - i12_offset - i11*p.ne10; + + if (i10 == i11) { + const float val = float(data_a[get_aoffset() + i13*p.nb03 + i12*p.nb02 + 0*p.nb01 + i10*p.nb00]); + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(val); + } else { + data_d[get_doffset() + dst_idx(idx)] = D_TYPE(0); + } +} diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index 72c63e8173..f606ea1085 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -854,6 +854,8 @@ void process_shaders() { string_to_spv("tri_f16", "tri.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("tri_f32", "tri.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); + string_to_spv("diag_f16", "diag.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); + string_to_spv("diag_f32", "diag.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); string_to_spv("softplus_f16", "softplus.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}}); string_to_spv("softplus_f32", "softplus.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}}); From 36255a22681583c3787bce778132c89d74da4e4c Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 13 Dec 2025 03:12:53 -0600 Subject: [PATCH 13/40] vulkan: support get_rows for i32 (#17941) --- ggml/src/ggml-vulkan/ggml-vulkan.cpp | 7 +++++++ ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp | 4 ++-- .../src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp | 8 +++++--- 3 files changed, 14 insertions(+), 5 deletions(-) diff --git a/ggml/src/ggml-vulkan/ggml-vulkan.cpp b/ggml/src/ggml-vulkan/ggml-vulkan.cpp index ac3841764e..34ec09d403 100644 --- a/ggml/src/ggml-vulkan/ggml-vulkan.cpp +++ b/ggml/src/ggml-vulkan/ggml-vulkan.cpp @@ -3738,6 +3738,7 @@ static void ggml_vk_load_shaders(vk_device& device) { ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_XS], "get_rows_iq4_xs", get_rows_iq4_xs_len, get_rows_iq4_xs_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_IQ4_NL], "get_rows_iq4_nl", get_rows_iq4_nl_len, get_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_MXFP4], "get_rows_mxfp4", get_rows_mxfp4_len, get_rows_mxfp4_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); + ggml_vk_create_pipeline(device, device->pipeline_get_rows[GGML_TYPE_I32], "get_rows_i32", get_rows_i32_len, get_rows_i32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1024, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f32_f32", get_rows_f32_f32_len, get_rows_f32_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); ggml_vk_create_pipeline(device, device->pipeline_get_rows_f32[GGML_TYPE_F16 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), { 512, 1, 1}, {}, 1); @@ -8294,6 +8295,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const switch (op) { case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); + if (src0->type == GGML_TYPE_I32) { + // i32 src only supports i32 result + GGML_ASSERT(dst->type == GGML_TYPE_I32); + return ctx->device->pipeline_get_rows[src0->type]; + } if (dst->type == GGML_TYPE_F16) { return ctx->device->pipeline_get_rows[src0->type]; } @@ -13964,6 +13970,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm case GGML_TYPE_IQ4_XS: case GGML_TYPE_IQ4_NL: case GGML_TYPE_MXFP4: + case GGML_TYPE_I32: return true; default: return false; diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp index 76d83041ce..e88bdd057e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/get_rows.comp @@ -26,9 +26,9 @@ void main() { const uint d_offset = get_doffset() + i10*p.nb21 + i11*p.nb22 + i12*p.nb23; #if defined(DATA_A_BF16) - FLOAT_TYPE v = FLOAT_TYPE(bf16_to_fp32(data_a[a_offset + i00])); + TEMP_TYPE v = TEMP_TYPE(bf16_to_fp32(data_a[a_offset + i00])); #else - FLOAT_TYPE v = FLOAT_TYPE(data_a[a_offset + i00]); + TEMP_TYPE v = TEMP_TYPE(data_a[a_offset + i00]); #endif #ifndef OPTIMIZATION_ERROR_WORKAROUND data_d[d_offset + i00] = D_TYPE(v); diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp index f606ea1085..b0ade078c7 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp @@ -704,13 +704,15 @@ void process_shaders() { shader = (tname == "f32" || tname == "f16" || tname == "bf16") ? "get_rows.comp" : "get_rows_quant.comp"; if (tname == "f16") { - string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}, {"OPTIMIZATION_ERROR_WORKAROUND", "1"}})); } else { - string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); + string_to_spv("get_rows_" + tname, shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float16_t"}})); } - string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); + string_to_spv("get_rows_" + tname + "_f32", shader, merge_maps(base_dict, {{"TEMP_TYPE", "FLOAT_TYPE"}, {data_a_key, "1"}, {"B_TYPE", "int"}, {"D_TYPE", "float"}})); } + string_to_spv("get_rows_i32", "get_rows.comp", {{"TEMP_TYPE", "uint"}, {"A_TYPE", "uint"}, {"B_TYPE", "int"}, {"D_TYPE", "uint"}}); + string_to_spv("mul_mat_vec_p021_f16_f32_subgroup_add", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}, {"USE_SUBGROUP_ADD", "1"}}); string_to_spv("mul_mat_vec_p021_f16_f32", "mul_mat_vec_p021.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}); string_to_spv("mul_mat_vec_nc_f16_f32", "mul_mat_vec_nc.comp", {{"A_TYPE", "float16_t"}, {"A_TYPE_VEC4", "f16vec4"}, {"B_TYPE", "float"}, {"B_TYPE_VEC4", "vec4"}, {"D_TYPE", "float"}}); From 66ba51252ed1cf08739fbed8a538d3cb3541655d Mon Sep 17 00:00:00 2001 From: Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com> Date: Sat, 13 Dec 2025 08:46:36 -0300 Subject: [PATCH 14/40] cmake: correct scope - link ws2_32 for MinGW/w64devkit builds in cpp-httplib (#17972) * fix - w64devkit build * fix - w64devkit build private scope --- vendor/cpp-httplib/CMakeLists.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/vendor/cpp-httplib/CMakeLists.txt b/vendor/cpp-httplib/CMakeLists.txt index e90e8e2d1b..8f0d15d1fd 100644 --- a/vendor/cpp-httplib/CMakeLists.txt +++ b/vendor/cpp-httplib/CMakeLists.txt @@ -11,8 +11,9 @@ endif() target_link_libraries (${TARGET} PRIVATE Threads::Threads) if (WIN32 AND NOT MSVC) - target_link_libraries(${TARGET} PUBLIC ws2_32) + target_link_libraries(${TARGET} PRIVATE ws2_32) endif() + target_compile_features(${TARGET} PRIVATE cxx_std_17) target_compile_definitions(${TARGET} PRIVATE From 4d5ae24c0ac79c4e360773bac58dd2c2a46b7f67 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 13 Dec 2025 12:53:37 +0100 Subject: [PATCH 15/40] arg: fix common_params_parse not accepting negated arg (#17991) --- common/arg.cpp | 5 ++++- common/arg.h | 2 +- tests/test-arg-parser.cpp | 4 ++++ tools/server/server-models.cpp | 2 +- 4 files changed, 10 insertions(+), 3 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index 19f22f883f..bb2a6840ba 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -724,7 +724,7 @@ static void add_rpc_devices(const std::string & servers) { } } -bool common_params_parse(int argc, char ** argv, llama_example ex, std::map & out_map) { +bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map) { common_params dummy_params; common_params_context ctx_arg = common_params_parser_init(dummy_params, ex, nullptr); @@ -733,6 +733,9 @@ bool common_params_parse(int argc, char ** argv, llama_example ex, std::map & out_map); +bool common_params_to_map(int argc, char ** argv, llama_example ex, std::map & out_map); // initialize argument parser context - used by test-arg-parser and preset common_params_context common_params_parser_init(common_params & params, llama_example ex, void(*print_usage)(int, char **) = nullptr); diff --git a/tests/test-arg-parser.cpp b/tests/test-arg-parser.cpp index 90750b20c2..468d325e22 100644 --- a/tests/test-arg-parser.cpp +++ b/tests/test-arg-parser.cpp @@ -72,6 +72,10 @@ int main(void) { argv = {"binary_name", "--draft", "123"}; assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_EMBEDDING)); + // negated arg + argv = {"binary_name", "--no-mmap"}; + assert(false == common_params_parse(argv.size(), list_str_to_char(argv).data(), params, LLAMA_EXAMPLE_COMMON)); + printf("test-arg-parser: test valid usage\n\n"); diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 6c618a673c..6be5ffbdf0 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -171,7 +171,7 @@ server_presets::server_presets(int argc, char ** argv, common_params & base_para } // read base args from router's argv - common_params_parse(argc, argv, LLAMA_EXAMPLE_SERVER, base_args); + common_params_to_map(argc, argv, LLAMA_EXAMPLE_SERVER, base_args); // remove any router-controlled args from base_args for (const auto & cargs : control_args) { From 5266379bcae74214af397f36aa81b2a08b15d545 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sat, 13 Dec 2025 09:19:51 -0600 Subject: [PATCH 16/40] llama_context: synchronize before reallocating output buffer (#17974) --- src/llama-context.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 2692297dca..9914b3276b 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -1318,6 +1318,7 @@ uint32_t llama_context::output_reserve(int32_t n_outputs) { // This doesn't happen often, but may be annoying in some cases (like the HellaSwag benchmark) LLAMA_LOG_INFO("%s: reallocating output buffer from size %.02f MiB to %.02f MiB\n", __func__, prev_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); #endif + synchronize(); buf_output = nullptr; logits = nullptr; embd = nullptr; From 4ed2bae50d64dcff7f99cb2b28f737fda314abf5 Mon Sep 17 00:00:00 2001 From: Sergey Fedorov Date: Sun, 14 Dec 2025 05:02:43 +0800 Subject: [PATCH 17/40] server-models.cpp: add missing (#18000) Fixes: https://github.com/ggml-org/llama.cpp/issues/17999 --- tools/server/server-models.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/tools/server/server-models.cpp b/tools/server/server-models.cpp index 6be5ffbdf0..3690c0bb82 100644 --- a/tools/server/server-models.cpp +++ b/tools/server/server-models.cpp @@ -16,6 +16,7 @@ #include #include #include +#include #ifdef _WIN32 #include From c00ff929dcfd150234e62f30e863bca4f1337aee Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sat, 13 Dec 2025 22:33:29 +0100 Subject: [PATCH 18/40] scripts: add script to compare logprobs of llama.cpp against other frameworks (#17947) * scripts: add script to compare logits of llama.cpp against other frameworks * accept custom prompt file * fix code style * clarify endpoint * fix displaying * use abs for diff * fix vllm case * rm output file * rename to compare-logprobs * add "pattern" --- scripts/compare-logprobs.py | 281 ++++++++++++++++++++++++++++++++++++ 1 file changed, 281 insertions(+) create mode 100644 scripts/compare-logprobs.py diff --git a/scripts/compare-logprobs.py b/scripts/compare-logprobs.py new file mode 100644 index 0000000000..63861dd9a4 --- /dev/null +++ b/scripts/compare-logprobs.py @@ -0,0 +1,281 @@ +import argparse +import requests +import json +from pathlib import Path +import logging + +logger = logging.getLogger("compare-logprobs") +logging.basicConfig(level=logging.INFO) + + +DESCRIPTION = """ +Compare logits between llama.cpp and another inference engine using OpenAI-compatible server endpoints. + +Unlike compare-logits.py, it allows dumping logits from a hosted API endpoint. Useful when it's not possible to run both models locally. + +Example usage: + Step 1: Dump logits from two different servers + python scripts/compare-logprobs.py dump logits_llama.log http://localhost:8080/v1/completions + python scripts/compare-logprobs.py dump logits_other.log http://other-engine:8000/v1/completions + + (optionally, you can add --api-key if the endpoint requires authentication) + + Step 2: Compare the dumped logits + python scripts/compare-logprobs.py compare logits_llama.log logits_other.log report.md +""" + + +def generate_input_prompt(length: int) -> list[str]: + CORPUS = """ + You are an advanced AI assistant capable of using tools to gather information, perform calculations, or execute tasks. Always think step by step before responding. If a user's query requires external data, computation, or actions beyond your internal knowledge, use the appropriate tools via function calls. + + ### Tool Call Format: + When you need to use a tool, output the call in this exact XML format. Include the opening and closing tags. Do not escape arguments; they will be parsed as plain text. + + You can make multiple calls in one go by placing them one after another. + """ + words = [w.strip() for w in CORPUS.strip().split(" ")] + words = [w for w in words if len(w) > 0] # filter out empty strings + while len(words) < length: + words += words + return words[:length] + + +def dump_logits( + endpoint: str, + output_path: Path, + input_words: list[str], + pattern: list[tuple[bool, int]], + api_key=None, +): + logger.info(f"Dumping logits to {output_path} from endpoint {endpoint}...") + words = input_words + curr_text = "" + n_total = sum(n for get, n in pattern if get) + n_done = 0 + i_cur = 0 + i_total = len(words) + with output_path.open("w") as f: + for get, n in pattern: + if not get: + # skip n words + for i in range(n): + curr_text += words.pop(0) + " " + i_cur += 1 + continue + # get n words + for i in range(n): + curr_text += words.pop(0) + " " + payload = { + "prompt": curr_text.strip(), + "temperature": 0.0, + "top_k": 1, + "max_tokens": 1, + "logprobs": 1, + "stream": False, + } + response = requests.post( + endpoint, + json=payload, + headers={"Authorization": f"Bearer {api_key}"} if api_key else {}, + ) + response.raise_for_status() + data = response.json() + data["__index"] = i_cur # add index for easier debugging later + data = json.dumps(data) + f.write(f"{data}\n") + n_done += 1 + i_cur += 1 + logger.info( + f"\n\n{data}\n\n[Step: {n_done}/{n_total} | Word: {i_cur}/{i_total}]" + ) + logger.info(f"Logits dumped to {output_path}") + + +def get_token_logprobs(data: dict): + logprobs = data["choices"][0]["logprobs"] + if "content" in logprobs: + # llama.cpp case + top = logprobs["content"][0]["top_logprobs"][0] + return top["token"], top["logprob"] + else: + # vllm case + tokens = logprobs["tokens"] + token_logprobs = logprobs["token_logprobs"] + return tokens[0], token_logprobs[0] + + +def clean_text(text: str) -> str: + return ( + "'" + + text.replace("\n", "\\n") + .replace("\t", "\\t") + .replace("\r", "\\r") + .replace("|", "\\|") + + "'" + ) + + +def compare_logits(input1: Path, input2: Path, output_path: Path): + with input1.open("r") as f1, input2.open("r") as f2, output_path.open("w") as fout: + lines1 = f1.readlines() + lines2 = f2.readlines() + + tab_header = [ + "idx", + input1.name, + "logprob_1", + input2.name, + "logprob_2", + "diff (abs)", + ] + tab_entries = [] + tab_max_widths = [len(h) for h in tab_header] + + assert len(lines1) == len( + lines2 + ), "Input files must have the same number of lines." + + fout.write("# Logits Comparison Report\n\n") + for i, (line1, line2) in enumerate(zip(lines1, lines2)): + if not line1.strip() or not line2.strip(): + continue # skip empty lines + + data1 = json.loads(line1) + data2 = json.loads(line2) + + idx1 = data1.get("__index", -1) + idx2 = data2.get("__index", -1) + if idx1 != idx2: + logger.warning( + f"Warning: Mismatched indices at line {i}: {idx1} vs {idx2}" + ) + + token1, logprob1 = get_token_logprobs(data1) + token2, logprob2 = get_token_logprobs(data2) + + token1 = clean_text(token1) + token2 = clean_text(token2) + abs_diff = abs(logprob1 - logprob2) + + tab_entries.append( + ( + str(idx1 + 1), + token1, + f"{logprob1:.4f}", + token2, + f"{logprob2:.4f}", + f"{(abs_diff):.4f}", + ) + ) + + for i in range(len(tab_entries)): + for j in range(len(tab_header)): + tab_max_widths[j] = max(tab_max_widths[j], len(tab_entries[i][j])) + + output = "" + for j in range(len(tab_header)): + output += f"| {tab_header[j]:<{tab_max_widths[j]}} " + output += "|\n" + for j in range(len(tab_header)): + output += f"|{'-' * (tab_max_widths[j] + 2)}" + output += "|\n" + for entry in tab_entries: + for j in range(len(tab_header)): + output += f"| {entry[j]:<{tab_max_widths[j]}} " + output += "|\n" + + logger.info("\n" + output) + fout.write(output) + logger.info(f"Report written to {output_path}") + + +def parse_pattern(pattern: str) -> list[tuple[bool, int]]: + parts = pattern.split(",") + result = [] + for i, part in enumerate(parts): + n = int(part) + if i % 2 == 0: + result.append((True, n)) # get n words + else: + result.append((False, n)) # skip n words + return result + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description=DESCRIPTION, formatter_class=argparse.RawTextHelpFormatter + ) + subparsers = parser.add_subparsers( + dest="verb", required=True, help="action to perform" + ) + + # dump subcommand + parser_dump = subparsers.add_parser("dump", help="dump logits from an endpoint") + parser_dump.add_argument( + "output", type=Path, help="output path for dumped logits (.log)" + ) + parser_dump.add_argument( + "endpoint", type=str, help="OAI-compat /completions endpoint" + ) + parser_dump.add_argument( + "--api-key", + type=str, + default=None, + help="API key for authentication (if required)", + ) + parser_dump.add_argument( + "--file", + type=Path, + default=None, + help="File containing prompt to use instead of the default", + ) + parser_dump.add_argument( + "--pattern", + type=str, + default="10,1000,10,4000,10", + help="Pattern n_get,n_skip,... where n_get is number of words to get and n_skip is number of words to skip (num of words, NOT num of tokens)", + ) + + # compare subcommand + parser_compare = subparsers.add_parser( + "compare", help="compare two dumped logits files" + ) + parser_compare.add_argument("input1", type=Path, help="first input file (.log)") + parser_compare.add_argument("input2", type=Path, help="second input file (.log)") + parser_compare.add_argument( + "output", type=Path, help="output path for comparison report (.md)" + ) + + try: + return parser.parse_args() + except Exception as e: + parser.print_help() + raise e + + +def main(): + args = parse_args() + + if args.verb == "dump": + pattern = parse_pattern(args.pattern) + input_length = sum(n for _, n in pattern) + input_words = generate_input_prompt(input_length) + if args.file is not None: + with args.file.open("r") as f: + input_words = f.read().strip().split(" ") + if input_length < sum(n for _, n in pattern): + raise ValueError( + f"Input file has only {input_length} words, but pattern requires at least {input_length} words." + ) + input_length = len(input_words) + logger.info(f"Using {input_length} words") + dump_logits(args.endpoint, args.output, input_words, pattern, args.api_key) + elif args.verb == "compare": + compare_logits(args.input1, args.input2, args.output) + else: + raise ValueError(f"Unknown verb: {args.verb}") + + +if __name__ == "__main__": + main() From 615655aafe46339f04e2879d14d7d0d68fc2cfcd Mon Sep 17 00:00:00 2001 From: Congcong Cai Date: Fri, 12 Dec 2025 22:37:38 +0800 Subject: [PATCH 19/40] cmake : set `CMAKE_RUNTIME_OUTPUT_DIRECTORY` for non standalone build (ggml/1394) Some backend depends on CMAKE_RUNTIME_OUTPUT_DIRECTORY to create temporary file like metal backened. Missing CMAKE_RUNTIME_OUTPUT_DIRECTORY will cause some cmake error like permission denied (try to copy file to root). This PR wants to setup a default path for CMAKE_RUNTIME_OUTPUT_DIRECTORY when it does not exist. --- ggml/CMakeLists.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ggml/CMakeLists.txt b/ggml/CMakeLists.txt index ab5b4760e2..a65dcfbe1e 100644 --- a/ggml/CMakeLists.txt +++ b/ggml/CMakeLists.txt @@ -54,6 +54,10 @@ if (CMAKE_SOURCE_DIR STREQUAL CMAKE_CURRENT_SOURCE_DIR) # TODO else() set(GGML_STANDALONE OFF) + + if (NOT CMAKE_RUNTIME_OUTPUT_DIRECTORY) + set(CMAKE_RUNTIME_OUTPUT_DIRECTORY ${CMAKE_BINARY_DIR}/bin) + endif() endif() if (EMSCRIPTEN) From 71fdcf0616fa0f93dab37e4c0d0d77e708e398a9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Dec 2025 08:04:09 +0200 Subject: [PATCH 20/40] ggml : arm repack fix build (whisper/0) --- ggml/src/ggml-cpu/arch/arm/repack.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index 683ed8d2df..fc62f1b86e 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -24,6 +24,7 @@ #define UNUSED GGML_UNUSED +#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, int16x8_t * out_mins, int8_t * out_scales) { @@ -46,6 +47,7 @@ static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, scales_u32[1] = (sm[2] & kmask2) | (((sm[0] >> 6) & kmask3) << 4); memcpy(out_scales, scales_u32, 8); } +#endif void ggml_quantize_mat_q8_0_4x4(const float * GGML_RESTRICT x, void * GGML_RESTRICT vy, int64_t k) { assert(QK8_0 == 32); From 0e59224990e6b535e87fbf10851dcd35aed04f3a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Dec 2025 10:07:07 +0200 Subject: [PATCH 21/40] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index a879940eae..5823efac2d 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -55bc9320a4aae82af18e23eefd5de319a755d7b9 +130bc125a88bb57664b88932c48c38a1cb316fac From a63cbafbbc5a7afe80b75486cffa651003aaede4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 13 Dec 2025 22:54:14 +0200 Subject: [PATCH 22/40] ggml : arm repack fix build --- ggml/src/ggml-cpu/arch/arm/repack.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml/src/ggml-cpu/arch/arm/repack.cpp b/ggml/src/ggml-cpu/arch/arm/repack.cpp index fc62f1b86e..fb7f074a85 100644 --- a/ggml/src/ggml-cpu/arch/arm/repack.cpp +++ b/ggml/src/ggml-cpu/arch/arm/repack.cpp @@ -24,7 +24,7 @@ #define UNUSED GGML_UNUSED -#if defined(__aarch64__) && defined(__ARM_NEON) && defined(__ARM_FEATURE_MATMUL_INT8) +#if defined(__aarch64__) && defined(__ARM_NEON) && (defined(__ARM_FEATURE_MATMUL_INT8) || defined(__ARM_FEATURE_DOTPROD)) static inline void decode_q4_Kx8_scales_mins(const uint8_t * scales_in, int16x8_t * out_mins, int8_t * out_scales) { From 609a2d026897829730ca231285469a9c80967fb7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Dec 2025 08:34:56 +0200 Subject: [PATCH 23/40] models : fix YaRN regression + consolidate logic (#18006) * models : fix YaRN regression + consolidate logic * cont : fix the fix * cont : remove header * cont : add header --- src/llama-context.cpp | 38 ++++++++++++++++++++++++++++++++++++++ src/llama-graph.cpp | 2 +- src/llama-hparams.cpp | 11 ----------- src/llama-hparams.h | 7 ------- src/llama-kv-cache.cpp | 2 +- src/llama-model.cpp | 26 -------------------------- 6 files changed, 40 insertions(+), 46 deletions(-) diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 9914b3276b..2a17e44ecd 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -9,6 +9,7 @@ #include "llama-model.h" #include +#include #include #include #include @@ -72,6 +73,43 @@ llama_context::llama_context( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + if (cparams.yarn_ext_factor != 0) { + static auto get_mscale = [](float scale, float mscale) { + return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f); + }; + + const float factor = 1.0f / cparams.rope_freq_scale; + + // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348 + if (hparams.rope_yarn_log_mul != 0.0f) { + // note: here we assume `mscale == 1.0f` + // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f + float mscale = 1.0f; + const float mscale_all_dims = hparams.rope_yarn_log_mul; + + // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] + // special-case DEEPSEEK v2: + // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43 + if (model.arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) { + mscale = mscale_all_dims; + } + + cparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims); + + LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n", + __func__, cparams.yarn_attn_factor, mscale, mscale_all_dims); + } else { + cparams.yarn_attn_factor = get_mscale(factor, 1.0f); + } + + // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor: + // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544 + // + // ref: https://github.com/ggml-org/llama.cpp/discussions/7416 + // https://github.com/ggml-org/llama.cpp/pull/17945 + cparams.yarn_attn_factor *= 1.0f / (1.0f + 0.1f * logf(factor)); + } + cparams.yarn_attn_factor *= hparams.rope_attn_factor; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index a1a32494b7..6cf9a883a6 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -574,7 +574,7 @@ llm_graph_context::llm_graph_context(const llm_graph_params & params) : freq_base (cparams.rope_freq_base), freq_scale (cparams.rope_freq_scale), ext_factor (cparams.yarn_ext_factor), - attn_factor (llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor)), + attn_factor (cparams.yarn_attn_factor), beta_fast (cparams.yarn_beta_fast), beta_slow (cparams.yarn_beta_slow), norm_eps (hparams.f_norm_eps), diff --git a/src/llama-hparams.cpp b/src/llama-hparams.cpp index 277d0bcfd3..96c9598c24 100644 --- a/src/llama-hparams.cpp +++ b/src/llama-hparams.cpp @@ -3,7 +3,6 @@ #include "ggml.h" #include -#include void llama_hparams::set_swa_pattern(uint32_t n_pattern, bool dense_first) { if (dense_first) { @@ -231,13 +230,3 @@ bool llama_hparams::is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama return false; } - -float llama_hparams::yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor) { - GGML_ASSERT(ext_factor >= 0.0f); - - if (ext_factor != 0.0f) { - attn_factor *= 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)); - } - - return attn_factor; -} diff --git a/src/llama-hparams.h b/src/llama-hparams.h index c9960e9169..aab319754e 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -268,13 +268,6 @@ struct llama_hparams { // TODO: think of a better place for this function // TODO: pack the SWA params in a struct? static bool is_masked_swa(uint32_t n_swa, llama_swa_type swa_type, llama_pos p0, llama_pos p1); - - // when YARN is applied with yarn_ext_factor != 0.0f, we need to cancel this factor: - // https://github.com/ggml-org/llama.cpp/blob/a81a569577cc38b32558958b048228150be63eae/ggml/src/ggml-cpu/ops.cpp#L5541-L5544 - // - // ref: https://github.com/ggml-org/llama.cpp/discussions/7416 - // https://github.com/ggml-org/llama.cpp/pull/17945 - static float yarn_attn_factor_adjust(float attn_factor, float freq_scale, float ext_factor); }; static_assert(std::is_trivially_copyable::value, "llama_hparams must be trivially copyable"); diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index 8f94c8820c..bf3de2f2ef 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -1372,7 +1372,7 @@ ggml_tensor * llama_kv_cache::build_rope_shift( const auto & yarn_ext_factor = cparams.yarn_ext_factor; const auto & yarn_beta_fast = cparams.yarn_beta_fast; const auto & yarn_beta_slow = cparams.yarn_beta_slow; - const auto & yarn_attn_factor = llama_hparams::yarn_attn_factor_adjust(cparams.yarn_attn_factor, cparams.rope_freq_scale, cparams.yarn_ext_factor); + const auto & yarn_attn_factor = cparams.yarn_attn_factor; const auto & n_rot = hparams.n_rot; const auto & rope_type = hparams.rope_type == LLAMA_ROPE_TYPE_MROPE || hparams.rope_type == LLAMA_ROPE_TYPE_IMROPE diff --git a/src/llama-model.cpp b/src/llama-model.cpp index e4808b1e1e..5da1dd6dbb 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -2294,32 +2294,6 @@ void llama_model::load_hparams(llama_model_loader & ml) { default: throw std::runtime_error("unsupported model architecture"); } - // ref: https://github.com/huggingface/transformers/blob/6d00f6b0a5679c36510f203e4226e36f517c3032/src/transformers/modeling_rope_utils.py#L336-L348 - if (hparams.rope_yarn_log_mul != 0.0f) { - const float factor = 1.0f / hparams.rope_freq_scale_train; - - // note: here we assume `mscale == 1.0f` - // TODO: start reading the actual value of mscale and handle the case where it is not 1.0f - float mscale = 1.0f; - const float mscale_all_dims = hparams.rope_yarn_log_mul; - - // [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] - // special-case DEEPSEEK v2: - // https://huggingface.co/deepseek-ai/DeepSeek-V2-Lite-Chat/blob/main/config.json#L42-L43 - if (arch == LLM_ARCH_DEEPSEEK2 && mscale_all_dims != 1.0f) { - mscale = mscale_all_dims; - } - - static auto get_mscale = [](float scale, float mscale) { - return scale <= 1.0f ? 1.0f : (0.1f * mscale * logf(scale) + 1.0f); - }; - - hparams.yarn_attn_factor = get_mscale(factor, mscale) / get_mscale(factor, mscale_all_dims); - - LLAMA_LOG_WARN("%s: setting new yarn_attn_factor = %.4f (mscale == %.1f, mscale_all_dim = %.1f)\n", - __func__, hparams.yarn_attn_factor, mscale, mscale_all_dims); - } - pimpl->n_bytes = ml.n_bytes; pimpl->desc_str = arch_name() + " " + type_name() + " " + ml.ftype_name(); From 77ad8542bd09c374d65be8abffd88ae4aa1e704d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Dec 2025 08:58:13 +0200 Subject: [PATCH 24/40] model-conversion : cast logits to float32 (#18009) --- examples/model-conversion/scripts/causal/run-org-model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/model-conversion/scripts/causal/run-org-model.py b/examples/model-conversion/scripts/causal/run-org-model.py index 7d2b80057c..da1132c003 100755 --- a/examples/model-conversion/scripts/causal/run-org-model.py +++ b/examples/model-conversion/scripts/causal/run-org-model.py @@ -200,7 +200,7 @@ with torch.no_grad(): logits = outputs.logits # Extract logits for the last token (next token prediction) - last_logits = logits[0, -1, :].cpu().numpy() + last_logits = logits[0, -1, :].float().cpu().numpy() print(f"Logits shape: {logits.shape}") print(f"Last token logits shape: {last_logits.shape}") From d15d177f43b7dd7f73a965716bd76a3327abb2d6 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Sun, 14 Dec 2025 07:29:37 +0000 Subject: [PATCH 25/40] vulkan: faster q6_k matmul (#17813) * q6_k faster mul mat * 8 values * fix comment * switch to two at a time * start ci for .glsl files --- .github/workflows/build.yml | 6 ++++-- .gitignore | 1 + .../ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl | 13 ++++++++----- 3 files changed, 13 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9fe1401df4..af4c60be64 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -20,7 +20,8 @@ on: '**/*.swift', '**/*.m', '**/*.metal', - '**/*.comp' + '**/*.comp', + '**/*.glsl' ] pull_request: @@ -40,7 +41,8 @@ on: '**/*.swift', '**/*.m', '**/*.metal', - '**/*.comp' + '**/*.comp', + '**/*.glsl' ] concurrency: diff --git a/.gitignore b/.gitignore index 428f084110..05eb578a82 100644 --- a/.gitignore +++ b/.gitignore @@ -54,6 +54,7 @@ /out/ /tmp/ /autogen-*.md +/common/build-info.cpp # Deprecated diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl index ee5ded2e8d..58ede04400 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.glsl @@ -244,17 +244,20 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin const uint iqs = idx % 128; // 0..127 const uint n = iqs / 64; // 0,1 - const uint b = (iqs % 64) / 32; // 0,1 + const uint b = ((iqs % 64) / 32) * 4; // 0,4 const uint is_b = (iqs % 16) / 8; // 0,1 const uint qhshift = ((iqs % 64) / 16) * 2; // 0,2,4,6 const uint is = 8 * n + qhshift + is_b; // 0..15 - const uint qsi = n * 64 + (iqs % 32) * 2; // 0,2,4..126 - const uint qhi = n * 32 + (iqs % 16) * 2; // 0,2,4..62 + const uint qsi = n * 32 + (iqs % 32); // 0..63 + const uint qhi = n * 16 + (iqs % 16); // 0..31 const float dscale = float(data_a[ib].d) * float(data_a[ib].scales[is]); - buf_a[buf_idx] = FLOAT_TYPE_VEC2(dscale * float(int8_t(((data_a[ib].ql[qsi ] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi ] >> qhshift) & 3) << 4)) - 32), - dscale * float(int8_t(((data_a[ib].ql[qsi + 1] >> (b * 4)) & 0xF) | (((data_a[ib].qh[qhi + 1] >> qhshift) & 3) << 4)) - 32)); + const uint ql = (uint(data_a_packed16[ib].ql[qsi]) >> b) & 0x0F0F; + const uint qh = (uint(data_a_packed16[ib].qh[qhi]) >> qhshift) & 0x0303; + const vec2 q = (vec2(unpack8(ql | (qh << 4)).xy) - 32) * dscale; + + buf_a[buf_idx] = FLOAT_TYPE_VEC2(q.x, q.y); #elif defined(DATA_A_IQ1_S) const uint idx = pos_a + col * p.stride_a / LOAD_VEC_A + row; const uint buf_idx = col * SHMEM_STRIDE + row * LOAD_VEC_A / 2; From 4722671641fdc29b371d517db05d4b395c6e068c Mon Sep 17 00:00:00 2001 From: lovedheart <6277001+lovedheart@users.noreply.github.com> Date: Sun, 14 Dec 2025 08:47:49 +0100 Subject: [PATCH 26/40] vulkan: improve mul_mat_vec_iq1_s speed (#17874) --- .../vulkan-shaders/mul_mat_vec_iq1_s.comp | 60 ++++++++++++------- 1 file changed, 38 insertions(+), 22 deletions(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp index 0b74b33212..e6b1f20215 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp @@ -7,34 +7,50 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; -void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { - const uint y_idx = i * QUANT_K + 32 * ib32; +void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, + const uint num_blocks_per_row, const uint first_row, const uint num_rows) { + const uint y_idx_base = i * QUANT_K + 32 * ib32; + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4; + [[unroll]] for (uint l = 0; l < 4; ++l) { + const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]); + const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]); - uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { - const float d = float(data_a[ibi].d); - const uint qh = data_a[ibi].qh[ib32]; - const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); - const float delta = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + // index for data_a + uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i; - [[unroll]] for (uint l = 0; l < 4; ++l) { - const uint qs = data_a[ibi].qs[4 * ib32 + l]; - const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); - const int16_t grid = int16_t(iq1s_grid[qs | (idxhi << 8)]); + [[unroll]] for (uint n = 0; n < num_rows; ++n) { + const float d = float(data_a[ibi].d); + const uint qh = data_a[ibi].qh[ib32]; - [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { - vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]); - vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]); + const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); + const uint qs = data_a[ibi].qs[4 * ib32 + l]; + const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); + const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]); - FLOAT_TYPE sum = FLOAT_TYPE(0.0); - [[unroll]] for (int k = 0; k < 4; ++k) { - sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta, - fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum)); - } - temp[j][n] = fma(dl, sum, temp[j][n]); + const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; + const vec4 delta_v = vec4(delta_val); + const vec4 fbits0 = vec4( + float(bitfieldExtract(grid, 0, 2)), + float(bitfieldExtract(grid, 2, 2)), + float(bitfieldExtract(grid, 4, 2)), + float(bitfieldExtract(grid, 6, 2)) + ); + const vec4 fbits1 = vec4( + float(bitfieldExtract(grid, 8, 2)), + float(bitfieldExtract(grid, 10, 2)), + float(bitfieldExtract(grid, 12, 2)), + float(bitfieldExtract(grid, 14, 2)) + ); + + vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0)); + sum_v = fma(b_val_1, fbits1 + delta_v, sum_v); + FLOAT_TYPE sum = dot(sum_v, vec4(1.0)); + + temp[j][n] = fma(dl, sum, temp[j][n]); + ibi += num_blocks_per_row; } } - ibi += num_blocks_per_row; } } From 3238b1400cf4f0f174c39579d92ce1a8ddb68110 Mon Sep 17 00:00:00 2001 From: Jeff Bolz Date: Sun, 14 Dec 2025 02:00:00 -0600 Subject: [PATCH 27/40] vulkan: Fix data race/hang in scalar/cm1 flash attention (#17887) --- ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp | 3 +++ ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp | 3 +++ 2 files changed, 6 insertions(+) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp index 4bef48b006..0379e5d502 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn.comp @@ -256,6 +256,9 @@ void main() { barrier(); } + // prevent race on tmpsh + barrier(); + // reduce across threads [[unroll]] for (uint32_t r = 0; r < Br; ++r) { diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp index cd82e4abfa..c995ab140e 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/flash_attn_cm1.comp @@ -302,6 +302,9 @@ void main() { barrier(); } + // prevent race on tmpsh + barrier(); + // reduce across threads float rowmaxf[rows_per_thread], eMf[rows_per_thread], Moldf[rows_per_thread]; From 254098a279691615438f1a1bbe87f372199e13ea Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 14 Dec 2025 10:11:13 +0200 Subject: [PATCH 28/40] common : refactor common_sampler + grammar logic changes (#17937) * common : refactor common_sampler + grammar logic changes * tests : increase max_tokens to get needed response * batched : fix uninitialized samplers --- common/arg.cpp | 2 +- common/common.cpp | 192 ++++++++++++------ common/common.h | 31 ++- common/sampling.cpp | 183 +++++++++-------- common/sampling.h | 17 +- common/speculative.cpp | 2 +- examples/batched/batched.cpp | 30 ++- examples/embedding/embedding.cpp | 6 +- examples/eval-callback/eval-callback.cpp | 6 +- examples/lookahead/lookahead.cpp | 6 +- examples/lookup/lookup-create.cpp | 8 +- examples/lookup/lookup-stats.cpp | 8 +- examples/lookup/lookup.cpp | 6 +- examples/parallel/parallel.cpp | 6 +- examples/retrieval/retrieval.cpp | 6 +- examples/save-load-state/save-load-state.cpp | 6 +- .../speculative-simple/speculative-simple.cpp | 12 +- examples/speculative/speculative.cpp | 16 +- examples/training/finetune.cpp | 17 +- tools/completion/completion.cpp | 20 +- tools/cvector-generator/cvector-generator.cpp | 6 +- tools/imatrix/imatrix.cpp | 6 +- tools/mtmd/mtmd-cli.cpp | 6 +- tools/perplexity/perplexity.cpp | 6 +- tools/server/server-context.cpp | 47 ++--- .../tests/unit/test_compat_anthropic.py | 2 +- tools/tts/tts.cpp | 12 +- 27 files changed, 372 insertions(+), 293 deletions(-) diff --git a/common/arg.cpp b/common/arg.cpp index bb2a6840ba..aaa7b92a2e 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -1415,7 +1415,7 @@ common_params_context common_params_parser_init(common_params & params, llama_ex params.sampling.top_k = value; params.sampling.user_sampling_config |= common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_TOP_K; } - ).set_sparam()); + ).set_sparam().set_env("LLAMA_ARG_TOP_K")); add_opt(common_arg( {"--top-p"}, "N", string_format("top-p sampling (default: %.1f, 1.0 = disabled)", (double)params.sampling.top_p), diff --git a/common/common.cpp b/common/common.cpp index 0497f90a28..b76dfa10ea 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1013,31 +1013,40 @@ bool tty_can_use_colors() { // Model utils // -static inline void common_init_sampler_from_model( +// TODO: move to common/sampling +static void common_init_sampler_from_model( const llama_model * model, common_params_sampling & sparams) { const uint64_t config = sparams.user_sampling_config; auto get_int32 = [&](const char * key, int32_t & dst, uint64_t user_config) { - if (config & user_config) return; + if (config & user_config) { + return; + } char buf[64] = {0}; if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { char * end = nullptr; int32_t v = strtol(buf, &end, 10); - if (end && end != buf) dst = v; + if (end && end != buf) { + dst = v; + } } }; auto get_float = [&](const char * key, float & dst, uint64_t user_config) { - if (config & user_config) return; + if (config & user_config) { + return; + } char buf[128] = {0}; if (llama_model_meta_val_str(model, key, buf, sizeof(buf)) > 0) { char * end = nullptr; float v = strtof(buf, &end); - if (end && end != buf) dst = v; + if (end && end != buf) { + dst = v; + } } }; @@ -1065,31 +1074,122 @@ static inline void common_init_sampler_from_model( get_float(llama_model_meta_key_str(LLAMA_MODEL_META_KEY_SAMPLING_MIROSTAT_ETA), sparams.mirostat_eta, common_params_sampling_config::COMMON_PARAMS_SAMPLING_CONFIG_MIROSTAT_ETA); } -struct common_init_result common_init_from_params(common_params & params) { - common_init_result iparams; - auto mparams = common_model_params_to_llama(params); +struct common_init_result::impl { + impl() = default; + ~impl() = default; + + llama_model_ptr model; + llama_context_ptr context; + + std::vector lora; + + std::vector samplers; +}; + +common_init_result::common_init_result(common_params & params) : + pimpl(new impl{}) { + const auto mparams = common_model_params_to_llama(params); llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); if (model == NULL) { - LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); - return iparams; + return; } - common_init_sampler_from_model(model, params.sampling); + pimpl->model.reset(model); const llama_vocab * vocab = llama_model_get_vocab(model); + // updates params.sampling + // TODO: fix naming + common_init_sampler_from_model(model, params.sampling); + auto cparams = common_context_params_to_llama(params); + if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { + LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); + params.sampling.ignore_eos = false; + } + + // initialize once + for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { + if (llama_vocab_is_eog(vocab, i)) { + LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(vocab, i).c_str(), -INFINITY); + params.sampling.logit_bias_eog.push_back({i, -INFINITY}); + } + } + + if (params.sampling.ignore_eos) { + // add EOG biases to the active set of logit biases + params.sampling.logit_bias.insert( + params.sampling.logit_bias.end(), + params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); + } + + //if (params.sampling.penalty_last_n == -1) { + // LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + // params.sampling.penalty_last_n = llama_n_ctx(lctx); + //} + + //if (params.sampling.dry_penalty_last_n == -1) { + // LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); + // params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); + //} + + pimpl->samplers.resize(cparams.n_seq_max); + + for (int i = 0; i < (int) cparams.n_seq_max; ++i) { + pimpl->samplers[i].reset(common_sampler_init(model, params.sampling)); + } + llama_context * lctx = llama_init_from_model(model, cparams); if (lctx == NULL) { LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); - llama_model_free(model); - return iparams; + __func__, params.model.path.c_str()); + return; } + pimpl->context.reset(lctx); +} + +llama_model * common_init_result::model() { + return pimpl->model.get(); +} + +llama_context * common_init_result::context() { + return pimpl->context.get(); +} + +common_sampler * common_init_result::sampler(llama_seq_id seq_id) { + return pimpl->samplers[seq_id].get(); +} + +std::vector & common_init_result::lora() { + return pimpl->lora; +} + +void common_init_result::free_context() { + pimpl->context.reset(); +} + +common_init_result_ptr common_init_from_params(common_params & params) { + common_init_result_ptr res(new common_init_result(params)); + + llama_model * model = res->model(); + if (model == NULL) { + LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", + __func__, params.model.path.c_str()); + return res; + } + + llama_context * lctx = res->context(); + if (lctx == NULL) { + LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", + __func__, params.model.path.c_str()); + return res; + } + + const llama_vocab * vocab = llama_model_get_vocab(model); + if (params.ctx_shift && !llama_memory_can_shift(llama_get_memory(lctx))) { LOG_WRN("%s: KV cache shifting is not supported for this context, disabling KV cache shifting\n", __func__); params.ctx_shift = false; @@ -1101,10 +1201,7 @@ struct common_init_result common_init_from_params(common_params & params) { const auto cvec = common_control_vector_load(params.control_vectors); if (cvec.n_embd == -1) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } int err = llama_apply_adapter_cvec( @@ -1115,10 +1212,7 @@ struct common_init_result common_init_from_params(common_params & params) { params.control_vector_layer_start, params.control_vector_layer_end); if (err) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } } @@ -1142,10 +1236,7 @@ struct common_init_result common_init_from_params(common_params & params) { } if (!ok) { - llama_free(lctx); - llama_model_free(model); - - return iparams; + return res; } } @@ -1155,9 +1246,7 @@ struct common_init_result common_init_from_params(common_params & params) { lora.reset(llama_adapter_lora_init(model, la.path.c_str())); if (lora == nullptr) { LOG_ERR("%s: failed to apply lora adapter '%s'\n", __func__, la.path.c_str()); - llama_free(lctx); - llama_model_free(model); - return iparams; + return res; } char buf[1024]; @@ -1166,43 +1255,13 @@ struct common_init_result common_init_from_params(common_params & params) { la.task_name = buf; llama_adapter_meta_val_str(la.ptr, "adapter.lora.prompt_prefix", buf, sizeof(buf)); la.prompt_prefix = buf; - iparams.lora.emplace_back(std::move(lora)); // copy to list of loaded adapters + res->lora().emplace_back(std::move(lora)); // copy to list of loaded adapters } if (!params.lora_init_without_apply) { common_set_adapter_lora(lctx, params.lora_adapters); } - if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { - LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); - params.sampling.ignore_eos = false; - } - - // initialize once - for (llama_token i = 0; i < llama_vocab_n_tokens(vocab); i++) { - if (llama_vocab_is_eog(vocab, i)) { - LOG_INF("%s: added %s logit bias = %f\n", __func__, common_token_to_piece(lctx, i).c_str(), -INFINITY); - params.sampling.logit_bias_eog.push_back({i, -INFINITY}); - } - } - - if (params.sampling.ignore_eos) { - // add EOG biases to the active set of logit biases - params.sampling.logit_bias.insert( - params.sampling.logit_bias.end(), - params.sampling.logit_bias_eog.begin(), params.sampling.logit_bias_eog.end()); - } - - if (params.sampling.penalty_last_n == -1) { - LOG_INF("%s: setting penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); - params.sampling.penalty_last_n = llama_n_ctx(lctx); - } - - if (params.sampling.dry_penalty_last_n == -1) { - LOG_INF("%s: setting dry_penalty_last_n to ctx_size = %d\n", __func__, llama_n_ctx(lctx)); - params.sampling.dry_penalty_last_n = llama_n_ctx(lctx); - } - if (params.warmup) { LOG_WRN("%s: warming up the model with an empty run - please wait ... (--no-warmup to disable)\n", __func__); @@ -1241,12 +1300,11 @@ struct common_init_result common_init_from_params(common_params & params) { llama_set_warmup(lctx, false); } - iparams.model.reset(model); - iparams.context.reset(lctx); - - return iparams; + return res; } +common_init_result::~common_init_result() = default; + std::string get_model_endpoint() { const char * model_endpoint_env = getenv("MODEL_ENDPOINT"); // We still respect the use of environment-variable "HF_ENDPOINT" for backward-compatibility. @@ -1255,7 +1313,9 @@ std::string get_model_endpoint() { std::string model_endpoint = "https://huggingface.co/"; if (endpoint_env) { model_endpoint = endpoint_env; - if (model_endpoint.back() != '/') model_endpoint += '/'; + if (model_endpoint.back() != '/') { + model_endpoint += '/'; + } } return model_endpoint; } diff --git a/common/common.h b/common/common.h index 2fd83f0cf9..4edb74b706 100644 --- a/common/common.h +++ b/common/common.h @@ -195,7 +195,6 @@ struct common_params_sampling { std::vector dry_sequence_breakers = {"\n", ":", "\"", "*"}; // default sequence breakers for DRY - std::vector samplers = { COMMON_SAMPLER_TYPE_PENALTIES, COMMON_SAMPLER_TYPE_DRY, @@ -216,6 +215,10 @@ struct common_params_sampling { std::vector logit_bias; // logit biases to apply std::vector logit_bias_eog; // pre-calculated logit biases for EOG tokens + bool has_logit_bias() const { + return !logit_bias.empty(); + } + // print the parameters into a string std::string print() const; }; @@ -669,15 +672,29 @@ bool tty_can_use_colors(); // Model utils // -// note: defines object's lifetime -struct common_init_result { - llama_model_ptr model; - llama_context_ptr context; +struct common_sampler; - std::vector lora; +// note: defines the model, context, samplers, ets. lifetimes +struct common_init_result { + common_init_result(common_params & params); + ~common_init_result(); + + llama_model * model(); + llama_context * context(); + common_sampler * sampler(llama_seq_id seq_id); + + std::vector & lora(); + + void free_context(); + +private: + struct impl; + std::unique_ptr pimpl; }; -struct common_init_result common_init_from_params(common_params & params); +using common_init_result_ptr = std::unique_ptr; + +common_init_result_ptr common_init_from_params(common_params & params); struct llama_model_params common_model_params_to_llama ( common_params & params); struct llama_context_params common_context_params_to_llama(const common_params & params); diff --git a/common/sampling.cpp b/common/sampling.cpp index 7a6b7be1e0..6935d84e22 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -104,9 +104,10 @@ struct ring_buffer { struct common_sampler { common_params_sampling params; - struct llama_sampler * grmr; struct llama_sampler * chain; + bool grammar; + ring_buffer prev; std::vector cur; @@ -116,7 +117,6 @@ struct common_sampler { void reset() { prev.clear(); - llama_sampler_reset(grmr); llama_sampler_reset(chain); } @@ -167,10 +167,15 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co lparams.no_perf = params.no_perf; - struct llama_sampler * grmr; + llama_sampler * chain = llama_sampler_chain_init(lparams); + + bool grammar = false; + std::vector samplers; + if (params.grammar.compare(0, 11, "%llguidance") == 0) { #ifdef LLAMA_USE_LLGUIDANCE - grmr = llama_sampler_init_llg(vocab, "lark", params.grammar.c_str()); + samplers.push_back(llama_sampler_init_llg(vocab, "lark", params.grammar.c_str())); + grammar = true; #else GGML_ABORT("llguidance (cmake -DLLAMA_LLGUIDANCE=ON) is not enabled"); #endif // LLAMA_USE_LLGUIDANCE @@ -217,30 +222,23 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co trigger_patterns_c.push_back(regex.c_str()); } - grmr = params.grammar_lazy - ? llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root", - trigger_patterns_c.data(), trigger_patterns_c.size(), - trigger_tokens.data(), trigger_tokens.size()) - : llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root"); - if (!grmr) { - return nullptr; + if (!params.grammar.empty()) { + if (params.grammar_lazy) { + samplers.push_back( + llama_sampler_init_grammar_lazy_patterns(vocab, params.grammar.c_str(), "root", + trigger_patterns_c.data(), trigger_patterns_c.size(), + trigger_tokens.data(), trigger_tokens.size())); + } else { + samplers.push_back(llama_sampler_init_grammar(vocab, params.grammar.c_str(), "root")); + } + + grammar = true; } } - auto * result = new common_sampler { - /* .params = */ params, - /* .grmr = */ grmr, - /* .chain = */ llama_sampler_chain_init(lparams), - /* .prev = */ ring_buffer(std::max(32, params.n_prev)), - /* .cur = */ {}, - /* .cur_p = */ {}, - }; - - llama_sampler_chain_add(result->chain, - llama_sampler_init_logit_bias( - llama_vocab_n_tokens(vocab), - params.logit_bias.size(), - params.logit_bias.data())); + if (params.has_logit_bias()) { + samplers.push_back(llama_sampler_init_logit_bias(llama_vocab_n_tokens(vocab), params.logit_bias.size(), params.logit_bias.data())); + } if (params.mirostat == 0) { for (const auto & cnstr : params.samplers) { @@ -253,58 +251,70 @@ struct common_sampler * common_sampler_init(const struct llama_model * model, co c_breakers.push_back(str.c_str()); } - llama_sampler_chain_add(result->chain, llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); + samplers.push_back(llama_sampler_init_dry (vocab, llama_model_n_ctx_train(model), params.dry_multiplier, params.dry_base, params.dry_allowed_length, params.dry_penalty_last_n, c_breakers.data(), c_breakers.size())); } break; case COMMON_SAMPLER_TYPE_TOP_K: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_k (params.top_k)); + samplers.push_back(llama_sampler_init_top_k (params.top_k)); break; case COMMON_SAMPLER_TYPE_TOP_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_p (params.top_p, params.min_keep)); + samplers.push_back(llama_sampler_init_top_p (params.top_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TOP_N_SIGMA: - llama_sampler_chain_add(result->chain, llama_sampler_init_top_n_sigma (params.top_n_sigma)); + samplers.push_back(llama_sampler_init_top_n_sigma(params.top_n_sigma)); break; case COMMON_SAMPLER_TYPE_MIN_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_min_p (params.min_p, params.min_keep)); + samplers.push_back(llama_sampler_init_min_p (params.min_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_XTC: - llama_sampler_chain_add(result->chain, llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); + samplers.push_back(llama_sampler_init_xtc (params.xtc_probability, params.xtc_threshold, params.min_keep, params.seed)); break; case COMMON_SAMPLER_TYPE_TYPICAL_P: - llama_sampler_chain_add(result->chain, llama_sampler_init_typical (params.typ_p, params.min_keep)); + samplers.push_back(llama_sampler_init_typical (params.typ_p, params.min_keep)); break; case COMMON_SAMPLER_TYPE_TEMPERATURE: - llama_sampler_chain_add(result->chain, llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); + samplers.push_back(llama_sampler_init_temp_ext (params.temp, params.dynatemp_range, params.dynatemp_exponent)); break; case COMMON_SAMPLER_TYPE_INFILL: - llama_sampler_chain_add(result->chain, llama_sampler_init_infill (vocab)); + samplers.push_back(llama_sampler_init_infill (vocab)); break; case COMMON_SAMPLER_TYPE_PENALTIES: - llama_sampler_chain_add(result->chain, llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); + samplers.push_back(llama_sampler_init_penalties (params.penalty_last_n, params.penalty_repeat, params.penalty_freq, params.penalty_present)); break; default: GGML_ASSERT(false && "unknown sampler type"); } } - llama_sampler_chain_add(result->chain, llama_sampler_init_dist(params.seed)); + + samplers.push_back(llama_sampler_init_dist(params.seed)); } else if (params.mirostat == 1) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); + samplers.push_back(llama_sampler_init_temp(params.temp)); + samplers.push_back(llama_sampler_init_mirostat(llama_vocab_n_tokens(vocab), params.seed, params.mirostat_tau, params.mirostat_eta, 100)); } else if (params.mirostat == 2) { - llama_sampler_chain_add(result->chain, llama_sampler_init_temp(params.temp)); - llama_sampler_chain_add(result->chain, llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); + samplers.push_back(llama_sampler_init_temp(params.temp)); + samplers.push_back(llama_sampler_init_mirostat_v2(params.seed, params.mirostat_tau, params.mirostat_eta)); } else { GGML_ASSERT(false && "unknown mirostat version"); } + for (auto * smpl : samplers) { + llama_sampler_chain_add(chain, smpl); + } + + auto * result = new common_sampler { + /* .params = */ params, + /* .chain = */ chain, + /* .grammar = */ grammar, + /* .prev = */ ring_buffer(std::max(32, params.n_prev)), + /* .cur = */ {}, + /* .cur_p = */ {}, + }; + return result; } void common_sampler_free(struct common_sampler * gsmpl) { if (gsmpl) { - llama_sampler_free(gsmpl->grmr); - llama_sampler_free(gsmpl->chain); delete gsmpl; @@ -314,11 +324,24 @@ void common_sampler_free(struct common_sampler * gsmpl) { void common_sampler_accept(struct common_sampler * gsmpl, llama_token token, bool accept_grammar) { const auto tm = gsmpl->tm(); - if (accept_grammar) { - llama_sampler_accept(gsmpl->grmr, token); - } + if (gsmpl->grammar) { + const int n_smpl = llama_sampler_chain_n(gsmpl->chain); - llama_sampler_accept(gsmpl->chain, token); + for (int i = 0; i < n_smpl; i++) { + auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); + + // the grammar sampler is always the first one + if (i == 0) { + if (accept_grammar) { + llama_sampler_accept(smpl, token); + } + } else { + llama_sampler_accept(smpl, token); + } + } + } else { + llama_sampler_accept(gsmpl->chain, token); + } gsmpl->prev.push_back(token); } @@ -329,12 +352,12 @@ void common_sampler_reset(struct common_sampler * gsmpl) { struct common_sampler * common_sampler_clone(common_sampler * gsmpl) { return new common_sampler { - /* .params = */ gsmpl->params, - /* .grmr = */ llama_sampler_clone(gsmpl->grmr), - /* .chain = */ llama_sampler_clone(gsmpl->chain), - /* .prev = */ gsmpl->prev, - /* .cur = */ gsmpl->cur, - /* .cur_p = */ gsmpl->cur_p, + /* .params = */ gsmpl->params, + /* .chain = */ llama_sampler_clone(gsmpl->chain), + /* .grammar = */ gsmpl->grammar, + /* .prev = */ gsmpl->prev, + /* .cur = */ gsmpl->cur, + /* .cur_p = */ gsmpl->cur_p, }; } @@ -383,58 +406,33 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam } } -llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first) { +struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl) { + return gsmpl->chain; +} + +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx) { llama_synchronize(ctx); // start measuring sampling time after the llama_context synchronization in order to not measure any ongoing async operations const auto tm = gsmpl->tm(); - gsmpl->set_logits(ctx, idx); + llama_token id = LLAMA_TOKEN_NULL; - auto & grmr = gsmpl->grmr; auto & chain = gsmpl->chain; auto & cur_p = gsmpl->cur_p; // initialized by set_logits - if (grammar_first) { - llama_sampler_apply(grmr, &cur_p); - } + gsmpl->set_logits(ctx, idx); llama_sampler_apply(chain, &cur_p); GGML_ASSERT(cur_p.selected != -1 && "no selected token during sampling - check your sampling configuration"); - const llama_token id = cur_p.data[cur_p.selected].id; + id = cur_p.data[cur_p.selected].id; - if (grammar_first) { - return id; - } - - // check if it the sampled token fits the grammar - { - llama_token_data single_token_data = { id, 1.0f, 0.0f }; - llama_token_data_array single_token_data_array = { &single_token_data, 1, -1, false }; - - llama_sampler_apply(grmr, &single_token_data_array); - - const bool is_valid = single_token_data_array.data[0].logit != -INFINITY; - if (is_valid) { - return id; - } - } - - // resampling: - // if the token is not valid, sample again, but first apply the grammar sampler and then the sampling chain - gsmpl->set_logits(ctx, idx); - - llama_sampler_apply(grmr, &cur_p); - llama_sampler_apply(chain, &cur_p); - - GGML_ASSERT(cur_p.selected != -1 && "no selected token during re-sampling - check your sampling configuration"); - - return cur_p.data[cur_p.selected].id; + return id; } -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first) { +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft) { GGML_ASSERT(idxs.size() == draft.size() + 1 && "idxs.size() must be draft.size() + 1"); std::vector result; @@ -442,7 +440,7 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample size_t i = 0; for (; i < draft.size(); i++) { - const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]); common_sampler_accept(gsmpl, id, true); @@ -454,7 +452,7 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample } if (i == draft.size()) { - const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i], grammar_first); + const llama_token id = common_sampler_sample(gsmpl, ctx, idxs[i]); common_sampler_accept(gsmpl, id, true); @@ -464,13 +462,13 @@ std::vector common_sampler_sample_and_accept_n(struct common_sample return result; } -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first) { +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft) { std::vector idxs(draft.size() + 1); for (size_t i = 0; i < idxs.size(); ++i) { idxs[i] = i; } - return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft, grammar_first); + return common_sampler_sample_and_accept_n(gsmpl, ctx, idxs, draft); } uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl) { @@ -515,7 +513,8 @@ std::string common_sampler_print(const struct common_sampler * gsmpl) { for (int i = 0; i < llama_sampler_chain_n(gsmpl->chain); i++) { const auto * smpl = llama_sampler_chain_get(gsmpl->chain, i); - result += std::string("-> ") + llama_sampler_name(smpl) + " "; + result += std::string("-> "); + result += std::string(llama_sampler_name(smpl)) + " "; } return result; diff --git a/common/sampling.h b/common/sampling.h index e198eecda3..ace5d3d020 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -48,6 +48,8 @@ struct common_sampler * common_sampler_clone (struct common_sampler * gsmpl); // arguments can be nullptr to skip printing void common_perf_print(const struct llama_context * ctx, const struct common_sampler * gsmpl); +struct llama_sampler * common_sampler_get(const struct common_sampler * gsmpl); + // extended sampling implementation: // // - set logits @@ -55,10 +57,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam // - check if the token fits the grammar (if any) // - if not: resample by first applying the grammar constraints and then sampling again (slower path) // -// if grammar_first is true, the grammar is applied before the samplers (slower) -// useful in cases where all the resulting candidates (not just the sampled one) must fit the grammar -// -llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx, bool grammar_first = false); +llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_context * ctx, int idx); // generalized version of common_sampler_sample // @@ -76,10 +75,10 @@ llama_token common_sampler_sample(struct common_sampler * gsmpl, struct llama_co // // returns at least 1 token, up to idxs.size() // -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft, bool grammar_first = false); +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const std::vector & idxs, const llama_tokens & draft); // assume idxs == [ 0, 1, 2, ..., draft.size() ] -std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft, bool grammar_first = false); +std::vector common_sampler_sample_and_accept_n(struct common_sampler * gsmpl, struct llama_context * ctx, const llama_tokens & draft); uint32_t common_sampler_get_seed(const struct common_sampler * gsmpl); @@ -107,3 +106,9 @@ std::vector common_sampler_types_from_chars(const std: llama_sampler * llama_sampler_init_llg(const llama_vocab * vocab, const char * grammar_kind, const char * grammar_data); + +struct common_sampler_deleter { + void operator()(common_sampler * s) { common_sampler_free(s); } +}; + +typedef std::unique_ptr common_sampler_ptr; diff --git a/common/speculative.cpp b/common/speculative.cpp index 3e83b0964c..1e12383ae6 100644 --- a/common/speculative.cpp +++ b/common/speculative.cpp @@ -315,7 +315,7 @@ llama_tokens common_speculative_gen_draft( for (int i = 0; i < params.n_draft; ++i) { common_batch_clear(batch); - common_sampler_sample(smpl, ctx_dft, 0, true); + common_sampler_sample(smpl, ctx_dft, 0); const auto * cur_p = common_sampler_get_candidates(smpl, true); diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index 1a5de5928a..36a12d299f 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -2,6 +2,7 @@ #include "common.h" #include "log.h" #include "llama.h" +#include "sampling.h" #include #include @@ -64,17 +65,23 @@ int main(int argc, char ** argv) { ctx_params.n_ctx = n_kv_req; ctx_params.n_batch = std::max(n_predict, n_parallel); - llama_context * ctx = llama_init_from_model(model, ctx_params); - auto sparams = llama_sampler_chain_default_params(); sparams.no_perf = false; - llama_sampler * smpl = llama_sampler_chain_init(sparams); + std::vector samplers; - llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); - llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); - llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); - llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); + for (int32_t i = 0; i < n_parallel; ++i) { + llama_sampler * smpl = llama_sampler_chain_init(sparams); + + llama_sampler_chain_add(smpl, llama_sampler_init_top_k(params.sampling.top_k)); + llama_sampler_chain_add(smpl, llama_sampler_init_top_p(params.sampling.top_p, params.sampling.min_keep)); + llama_sampler_chain_add(smpl, llama_sampler_init_temp (params.sampling.temp)); + llama_sampler_chain_add(smpl, llama_sampler_init_dist (params.sampling.seed)); + + samplers.push_back(smpl); + } + + llama_context * ctx = llama_init_from_model(model, ctx_params); if (ctx == NULL) { LOG_ERR("%s: error: failed to create the llama_context\n" , __func__); @@ -173,7 +180,7 @@ int main(int argc, char ** argv) { continue; } - const llama_token new_token_id = llama_sampler_sample(smpl, ctx, i_batch[i]); + const llama_token new_token_id = llama_sampler_sample(samplers[i], ctx, i_batch[i]); // is it an end of generation? -> mark the stream as finished if (llama_vocab_is_eog(vocab, new_token_id) || n_cur == n_predict) { @@ -229,14 +236,17 @@ int main(int argc, char ** argv) { __func__, n_decode, (t_main_end - t_main_start) / 1000000.0f, n_decode / ((t_main_end - t_main_start) / 1000000.0f)); LOG("\n"); - llama_perf_sampler_print(smpl); + llama_perf_sampler_print(samplers[0]); llama_perf_context_print(ctx); fprintf(stderr, "\n"); llama_batch_free(batch); - llama_sampler_free(smpl); + for (auto & sampler_config : samplers) { + llama_sampler_free(sampler_config); + } + llama_free(ctx); llama_model_free(model); diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index fe91b308cd..81111e81b2 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -131,10 +131,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 80c693ce61..408338f1af 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -202,10 +202,10 @@ int main(int argc, char ** argv) { params.warmup = false; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 1e26d8221b..f54cfdd77f 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -55,10 +55,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the target model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); auto * mem = llama_get_memory(ctx); diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 3da45ed9e0..bb94a8fe06 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -18,16 +18,16 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model_ptr & model = llama_init.model; - llama_context_ptr & ctx = llama_init.context; + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); GGML_ASSERT(model != nullptr); // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx.get(), params.prompt, true, true); + inp = common_tokenize(ctx, params.prompt, true, true); fprintf(stderr, "%s: tokenization done\n", __func__); common_ngram_cache ngram_cache; diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index fcb289abe0..135f6fcab9 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -28,13 +28,13 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_context_ptr & ctx = llama_init.context; + llama_context * ctx = llama_init->context(); // tokenize the prompt std::vector inp; - inp = common_tokenize(ctx.get(), params.prompt, true, true); + inp = common_tokenize(ctx, params.prompt, true, true); common_ngram_cache ngram_cache_context; common_ngram_cache ngram_cache_dynamic; @@ -65,7 +65,7 @@ int main(int argc, char ** argv){ } const int n_input = inp.size(); - const int n_ctx = llama_n_ctx(ctx.get()); + const int n_ctx = llama_n_ctx(ctx); int n_drafted = 0; int n_accept = 0; diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index 2bfa26b55f..27f159940a 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -29,10 +29,10 @@ int main(int argc, char ** argv){ llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); const llama_vocab * vocab = llama_model_get_vocab(model); diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index e48f48fc32..c92173ae29 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -192,10 +192,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the target model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); auto * mem = llama_get_memory(ctx); diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 042e12c2bf..2c2143ad10 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -149,10 +149,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 4cd3071f76..39d4464663 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -34,10 +34,10 @@ int main(int argc, char ** argv) { std::string result2; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { fprintf(stderr, "%s : failed to init\n", __func__); diff --git a/examples/speculative-simple/speculative-simple.cpp b/examples/speculative-simple/speculative-simple.cpp index 0d11d0f803..8141052a22 100644 --- a/examples/speculative-simple/speculative-simple.cpp +++ b/examples/speculative-simple/speculative-simple.cpp @@ -40,10 +40,10 @@ int main(int argc, char ** argv) { llama_context * ctx_dft = NULL; // load the target model - common_init_result llama_init_tgt = common_init_from_params(params); + auto llama_init_tgt = common_init_from_params(params); - model_tgt = llama_init_tgt.model.get(); - ctx_tgt = llama_init_tgt.context.get(); + model_tgt = llama_init_tgt->model(); + ctx_tgt = llama_init_tgt->context(); const llama_vocab * vocab = llama_model_get_vocab(model_tgt); @@ -61,10 +61,10 @@ int main(int argc, char ** argv) { params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; params.tensor_buft_overrides = params.speculative.tensor_buft_overrides; - common_init_result llama_init_dft = common_init_from_params(params); + auto llama_init_dft = common_init_from_params(params); - //model_dft = llama_init_dft.model.get(); - ctx_dft = llama_init_dft.context.get(); + //model_dft = llama_init_dft->model(); + ctx_dft = llama_init_dft->context(); if (!common_speculative_are_compatible(ctx_tgt, ctx_dft)) { LOG_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params.speculative.model.path.c_str(), params.model.path.c_str()); diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 5f5ac5eb64..2fb7f6374e 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -71,10 +71,10 @@ int main(int argc, char ** argv) { llama_context * ctx_dft = NULL; // load the target model - common_init_result llama_init_tgt = common_init_from_params(params); + auto llama_init_tgt = common_init_from_params(params); - model_tgt = llama_init_tgt.model.get(); - ctx_tgt = llama_init_tgt.context.get(); + model_tgt = llama_init_tgt->model(); + ctx_tgt = llama_init_tgt->context(); // load the draft model params.devices = params.speculative.devices; @@ -87,10 +87,10 @@ int main(int argc, char ** argv) { params.cpuparams_batch.n_threads = params.speculative.cpuparams_batch.n_threads; params.tensor_buft_overrides = params.speculative.tensor_buft_overrides; - common_init_result llama_init_dft = common_init_from_params(params); + auto llama_init_dft = common_init_from_params(params); - model_dft = llama_init_dft.model.get(); - ctx_dft = llama_init_dft.context.get(); + model_dft = llama_init_dft->model(); + ctx_dft = llama_init_dft->context(); const llama_vocab * vocab_tgt = llama_model_get_vocab(model_tgt); const llama_vocab * vocab_dft = llama_model_get_vocab(model_dft); @@ -242,7 +242,7 @@ int main(int argc, char ** argv) { bool accept = false; if (params.sampling.temp > 0) { // stochastic verification - common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft], true); + common_sampler_sample(smpl, ctx_tgt, drafts[s_keep].i_batch_tgt[i_dft]); auto & dist_tgt = *common_sampler_get_candidates(smpl, true); @@ -491,7 +491,7 @@ int main(int argc, char ** argv) { continue; } - common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft, true); + common_sampler_sample(drafts[s].smpl, ctx_dft, drafts[s].i_batch_dft); const auto * cur_p = common_sampler_get_candidates(drafts[s].smpl, true); diff --git a/examples/training/finetune.cpp b/examples/training/finetune.cpp index 416d8d8f6c..c82de8d35d 100644 --- a/examples/training/finetune.cpp +++ b/examples/training/finetune.cpp @@ -39,9 +39,10 @@ int main(int argc, char ** argv) { llama_backend_init(); llama_numa_init(params.numa); // load the model and apply lora adapter, if any - common_init_result llama_init = common_init_from_params(params); - llama_model_ptr & model = llama_init.model; - llama_context_ptr & ctx = llama_init.context; + auto llama_init = common_init_from_params(params); + + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); @@ -54,8 +55,8 @@ int main(int argc, char ** argv) { LOG_INF("%s\n", common_params_get_system_info(params).c_str()); } - std::vector tokens = common_tokenize(ctx.get(), params.prompt, true); - ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx.get(), tokens, llama_n_ctx(ctx.get()) / 2); + std::vector tokens = common_tokenize(ctx, params.prompt, true); + ggml_opt_dataset_t dataset = common_opt_dataset_init(ctx, tokens, llama_n_ctx(ctx) / 2); struct lr_opt & lr = params.lr; LOG_INF("-optimizer %s -lr0 %.2g -wd %.2g -lr-min %.2g -min-epochs %.2g -epochs %d -period %.2g -val %.2g\n", @@ -70,7 +71,7 @@ int main(int argc, char ** argv) { /*get_opt_pars_ud =*/¶ms.lr, /*optimizer_type =*/params.optimizer, }; - llama_opt_init(ctx.get(), model.get(), lopt_params); + llama_opt_init(ctx, model, lopt_params); const int64_t idata_split = ggml_opt_dataset_ndata(dataset) * (1.0f - params.val_split); @@ -78,7 +79,7 @@ int main(int argc, char ** argv) { ggml_opt_result_t result_eval = ggml_opt_result_init(); for (lr.epoch = 0; lr.epoch < lr.epochs; ++lr.epoch) { - llama_opt_epoch(ctx.get(), dataset, result_train, result_eval, idata_split, + llama_opt_epoch(ctx, dataset, result_train, result_eval, idata_split, ggml_opt_epoch_callback_progress_bar, ggml_opt_epoch_callback_progress_bar); fprintf(stderr, "\n"); @@ -88,7 +89,7 @@ int main(int argc, char ** argv) { ggml_opt_result_free(result_train); ggml_opt_result_free(result_eval); - llama_model_save_to_file(model.get(), params.out_file.c_str()); + llama_model_save_to_file(model, params.out_file.c_str()); llama_backend_free(); diff --git a/tools/completion/completion.cpp b/tools/completion/completion.cpp index cb2641ae0a..85480f3369 100644 --- a/tools/completion/completion.cpp +++ b/tools/completion/completion.cpp @@ -141,13 +141,15 @@ int main(int argc, char ** argv) { // load the model and apply lora adapter, if any LOG_INF("%s: load the model and apply lora adapter, if any\n", __func__); - common_init_result llama_init = common_init_from_params(params); - model = llama_init.model.get(); - ctx = llama_init.context.get(); + auto llama_init = common_init_from_params(params); - if (model == NULL) { - LOG_ERR("%s: error: unable to load model\n", __func__); + ctx = llama_init->context(); + model = llama_init->model(); + smpl = llama_init->sampler(0); + + if (ctx == NULL) { + LOG_ERR("%s: error: unable to create context\n", __func__); return 1; } @@ -474,12 +476,6 @@ int main(int argc, char ** argv) { } } - smpl = common_sampler_init(model, sparams); - if (!smpl) { - LOG_ERR("%s: failed to initialize sampling subsystem\n", __func__); - return 1; - } - LOG_INF("sampler seed: %u\n", common_sampler_get_seed(smpl)); LOG_INF("sampler params: \n%s\n", sparams.print().c_str()); LOG_INF("sampler chain: %s\n", common_sampler_print(smpl).c_str()); @@ -993,8 +989,6 @@ int main(int argc, char ** argv) { LOG("\n\n"); common_perf_print(ctx, smpl); - common_sampler_free(smpl); - llama_backend_free(); ggml_threadpool_free_fn(threadpool); diff --git a/tools/cvector-generator/cvector-generator.cpp b/tools/cvector-generator/cvector-generator.cpp index d2d97e05ce..3ba7c52950 100644 --- a/tools/cvector-generator/cvector-generator.cpp +++ b/tools/cvector-generator/cvector-generator.cpp @@ -419,10 +419,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model to get hparams - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); // int n_ctx = llama_n_ctx(ctx); int n_layers = llama_model_n_layer(model); diff --git a/tools/imatrix/imatrix.cpp b/tools/imatrix/imatrix.cpp index f28a036dee..669de55ddb 100644 --- a/tools/imatrix/imatrix.cpp +++ b/tools/imatrix/imatrix.cpp @@ -1265,10 +1265,10 @@ int main(int argc, char ** argv) { params.warmup = false; // init - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == nullptr || ctx == nullptr) { LOG_ERR("%s : failed to init\n", __func__); diff --git a/tools/mtmd/mtmd-cli.cpp b/tools/mtmd/mtmd-cli.cpp index 25d24603db..332d2049e5 100644 --- a/tools/mtmd/mtmd-cli.cpp +++ b/tools/mtmd/mtmd-cli.cpp @@ -65,7 +65,7 @@ static void sigint_handler(int signo) { struct mtmd_cli_context { mtmd::context_ptr ctx_vision; - common_init_result llama_init; + common_init_result_ptr llama_init; llama_model * model; llama_context * lctx; @@ -89,8 +89,8 @@ struct mtmd_cli_context { llama_pos n_past = 0; mtmd_cli_context(common_params & params) : llama_init(common_init_from_params(params)) { - model = llama_init.model.get(); - lctx = llama_init.context.get(); + model = llama_init->model(); + lctx = llama_init->context(); vocab = llama_model_get_vocab(model); smpl = common_sampler_init(model, params.sampling); n_threads = params.cpuparams.n_threads; diff --git a/tools/perplexity/perplexity.cpp b/tools/perplexity/perplexity.cpp index caf080e8d1..1ead9c871e 100644 --- a/tools/perplexity/perplexity.cpp +++ b/tools/perplexity/perplexity.cpp @@ -2024,10 +2024,10 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // load the model and apply lora adapter, if any - common_init_result llama_init = common_init_from_params(params); + auto llama_init = common_init_from_params(params); - llama_model * model = llama_init.model.get(); - llama_context * ctx = llama_init.context.get(); + auto * model = llama_init->model(); + auto * ctx = llama_init->context(); if (model == NULL) { LOG_ERR("%s: unable to load model\n", __func__); diff --git a/tools/server/server-context.cpp b/tools/server/server-context.cpp index 5a67f508df..90898b5ec4 100644 --- a/tools/server/server-context.cpp +++ b/tools/server/server-context.cpp @@ -153,7 +153,7 @@ struct server_slot { // sampling json json_schema; - struct common_sampler * smpl = nullptr; + common_sampler_ptr smpl; llama_token sampled; // in speculative mode, this is the last accepted token llama_tokens drafted; @@ -510,8 +510,8 @@ struct server_context_impl { common_params params_base; // note: keep these alive - they determine the lifetime of the model, context, etc. - common_init_result llama_init; - common_init_result llama_init_dft; + common_init_result_ptr llama_init; + common_init_result_ptr llama_init_dft; llama_model * model = nullptr; llama_context * ctx = nullptr; @@ -557,9 +557,6 @@ struct server_context_impl { // Clear any sampling context for (server_slot & slot : slots) { - common_sampler_free(slot.smpl); - slot.smpl = nullptr; - llama_free(slot.ctx_dft); slot.ctx_dft = nullptr; @@ -580,8 +577,8 @@ struct server_context_impl { llama_init = common_init_from_params(params_base); - model = llama_init.model.get(); - ctx = llama_init.context.get(); + model = llama_init->model(); + ctx = llama_init->context(); if (model == nullptr) { SRV_ERR("failed to load model, '%s'\n", params_base.model.path.c_str()); @@ -613,25 +610,25 @@ struct server_context_impl { llama_init_dft = common_init_from_params(params_dft); - model_dft = llama_init_dft.model.get(); + model_dft = llama_init_dft->model(); if (model_dft == nullptr) { SRV_ERR("failed to load draft model, '%s'\n", params_base.speculative.model.path.c_str()); return false; } - vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft.context.get()); + vocab_dft_compatible = common_speculative_are_compatible(ctx, llama_init_dft->context()); if (!vocab_dft_compatible) { SRV_INF("the draft model '%s' is not compatible with the target model '%s'. tokens will be translated between the draft and target models.\n", params_base.speculative.model.path.c_str(), params_base.model.path.c_str()); } - const int n_ctx_dft = llama_n_ctx(llama_init_dft.context.get()); + const int n_ctx_dft = llama_n_ctx(llama_init_dft->context()); cparams_dft = common_context_params_to_llama(params_dft); cparams_dft.n_batch = n_ctx_dft; // the context is not needed - we will create one for each slot - llama_init_dft.context.reset(); + llama_init_dft->free_context(); } chat_templates = common_chat_templates_init(model, params_base.chat_template); @@ -1051,18 +1048,15 @@ struct server_context_impl { // initialize samplers { - if (slot.smpl != nullptr) { - common_sampler_free(slot.smpl); - } + slot.smpl.reset(common_sampler_init(model, task.params.sampling)); - slot.smpl = common_sampler_init(model, task.params.sampling); if (slot.smpl == nullptr) { // for now, the only error that may happen here is invalid grammar send_error(task, "Failed to parse grammar", ERROR_TYPE_INVALID_REQUEST); return false; } - SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl).c_str()); + SLT_INF(slot, "sampler chain: %s\n", common_sampler_print(slot.smpl.get()).c_str()); } // initialize draft batch @@ -1216,11 +1210,10 @@ struct server_context_impl { } void populate_token_probs(const server_slot & slot, completion_token_output & result, bool post_sampling, bool special, int idx) const { - size_t n_probs = slot.task->params.sampling.n_probs; - size_t n_vocab = llama_vocab_n_tokens(vocab); + const size_t n_probs = slot.task->params.sampling.n_probs; if (post_sampling) { - const auto * cur_p = common_sampler_get_candidates(slot.smpl, true); + const auto * cur_p = common_sampler_get_candidates(slot.smpl.get(), true); const size_t max_probs = cur_p->size; // set probability for sampled token @@ -1245,7 +1238,7 @@ struct server_context_impl { std::vector cur = get_token_probabilities(ctx, idx); // set probability for sampled token - for (size_t i = 0; i < n_vocab; i++) { + for (size_t i = 0; i < cur.size(); i++) { // set probability for sampled token if (cur[i].id == result.tok) { result.prob = cur[i].p; @@ -1255,7 +1248,7 @@ struct server_context_impl { // set probability for top n_probs tokens result.probs.reserve(n_probs); - for (size_t i = 0; i < std::min(n_vocab, n_probs); i++) { + for (size_t i = 0; i < std::min(cur.size(), n_probs); i++) { result.probs.push_back({ cur[i].id, common_token_to_piece(ctx, cur[i].id, special), @@ -2301,13 +2294,13 @@ struct server_context_impl { GGML_ASSERT(batch.n_tokens > 0); - common_sampler_reset(slot.smpl); + common_sampler_reset(slot.smpl.get()); // Process all prompt tokens through sampler system for (int i = 0; i < slot.task->n_tokens(); ++i) { llama_token id = input_tokens[i]; if (id != LLAMA_TOKEN_NULL) { - common_sampler_accept(slot.smpl, id, false); + common_sampler_accept(slot.smpl.get(), id, false); } } @@ -2525,11 +2518,11 @@ struct server_context_impl { const int tok_idx = slot.i_batch - i; - llama_token id = common_sampler_sample(slot.smpl, ctx, tok_idx); + llama_token id = common_sampler_sample(slot.smpl.get(), ctx, tok_idx); slot.i_batch = -1; - common_sampler_accept(slot.smpl, id, true); + common_sampler_accept(slot.smpl.get(), id, true); slot.n_decoded += 1; @@ -2570,7 +2563,7 @@ struct server_context_impl { size_t n_draft = slot.drafted.size(); // the accepted tokens from the speculation - const auto ids = common_sampler_sample_and_accept_n(slot.smpl, ctx, slot.i_batch_dft, slot.drafted); + const auto ids = common_sampler_sample_and_accept_n(slot.smpl.get(), ctx, slot.i_batch_dft, slot.drafted); slot.i_batch_dft.clear(); slot.drafted.clear(); diff --git a/tools/server/tests/unit/test_compat_anthropic.py b/tools/server/tests/unit/test_compat_anthropic.py index d55dd1d945..e0a003557e 100644 --- a/tools/server/tests/unit/test_compat_anthropic.py +++ b/tools/server/tests/unit/test_compat_anthropic.py @@ -684,7 +684,7 @@ def test_anthropic_streaming_content_block_indices(): # Request that might produce both text and tool use res = server.make_stream_request("POST", "/v1/messages", data={ "model": "test", - "max_tokens": 200, + "max_tokens": 400, "stream": True, "tools": [{ "name": "test_tool", diff --git a/tools/tts/tts.cpp b/tools/tts/tts.cpp index eaf56591d9..8c39fce8ba 100644 --- a/tools/tts/tts.cpp +++ b/tools/tts/tts.cpp @@ -568,10 +568,10 @@ int main(int argc, char ** argv) { llama_context * ctx_ttc = NULL; llama_context * ctx_cts = NULL; - common_init_result llama_init_ttc = common_init_from_params(params); + auto llama_init_ttc = common_init_from_params(params); - model_ttc = llama_init_ttc.model.get(); - ctx_ttc = llama_init_ttc.context.get(); + model_ttc = llama_init_ttc->model(); + ctx_ttc = llama_init_ttc->context(); if (model_ttc == nullptr || ctx_ttc == nullptr) { return ENOENT; @@ -583,10 +583,10 @@ int main(int argc, char ** argv) { params.embedding = true; params.n_ubatch = params.n_batch; - common_init_result llama_init_cts = common_init_from_params(params); + auto llama_init_cts = common_init_from_params(params); - model_cts = llama_init_cts.model.get(); - ctx_cts = llama_init_cts.context.get(); + model_cts = llama_init_cts->model(); + ctx_cts = llama_init_cts->context(); if (model_cts == nullptr || ctx_cts == nullptr) { return ENOENT; From 0759b09c90f9e1bb8beebe74882b9f094b91f7bb Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sun, 14 Dec 2025 13:05:59 +0100 Subject: [PATCH 29/40] graph: add f_attn_temp_offset (#18025) --- src/llama-graph.cpp | 4 ++-- src/llama-graph.h | 5 +++-- src/llama-hparams.h | 1 + src/llama-model.cpp | 5 +++++ 4 files changed, 11 insertions(+), 4 deletions(-) diff --git a/src/llama-graph.cpp b/src/llama-graph.cpp index 6cf9a883a6..8909bbfb95 100644 --- a/src/llama-graph.cpp +++ b/src/llama-graph.cpp @@ -78,7 +78,7 @@ void llm_graph_input_attn_temp::set_input(const llama_ubatch * ubatch) { for (int i = 0; i < n_tokens; ++i) { const float pos = ubatch->pos[i]; attn_scale_data[i] = std::log( - std::floor((pos + 1.0f) / n_attn_temp_floor_scale) + 1.0 + std::floor((pos + f_attn_temp_offset) / n_attn_temp_floor_scale) + 1.0 ) * f_attn_temp_scale + 1.0; } @@ -1203,7 +1203,7 @@ ggml_tensor * llm_graph_context::build_inp_pos() const { } ggml_tensor * llm_graph_context::build_inp_attn_scale() const { - auto inp = std::make_unique(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale); + auto inp = std::make_unique(hparams.n_attn_temp_floor_scale, hparams.f_attn_temp_scale, hparams.f_attn_temp_offset); auto & cur = inp->attn_scale; diff --git a/src/llama-graph.h b/src/llama-graph.h index d0c3934f67..e9d387bd7c 100644 --- a/src/llama-graph.h +++ b/src/llama-graph.h @@ -132,8 +132,8 @@ public: // temperature tuning, used by llama4 class llm_graph_input_attn_temp : public llm_graph_input_i { public: - llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale) - : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale) {} + llm_graph_input_attn_temp(uint32_t n_attn_temp_floor_scale, float f_attn_temp_scale, float f_attn_temp_offset) + : n_attn_temp_floor_scale(n_attn_temp_floor_scale), f_attn_temp_scale(f_attn_temp_scale), f_attn_temp_offset(f_attn_temp_offset) {} virtual ~llm_graph_input_attn_temp() = default; void set_input(const llama_ubatch * ubatch) override; @@ -142,6 +142,7 @@ public: const uint32_t n_attn_temp_floor_scale; const float f_attn_temp_scale; + const float f_attn_temp_offset; }; class llm_graph_input_pos_bucket : public llm_graph_input_i { diff --git a/src/llama-hparams.h b/src/llama-hparams.h index aab319754e..a467c64a14 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -165,6 +165,7 @@ struct llama_hparams { uint32_t n_no_rope_layer_step = 4; uint32_t n_attn_temp_floor_scale = 0; float f_attn_temp_scale = 0.0f; + float f_attn_temp_offset = 0.0f; // offset position index // gemma3n altup uint32_t n_altup = 4; // altup_num_inputs diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 5da1dd6dbb..28f06b4e61 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -668,6 +668,7 @@ void llama_model::load_hparams(llama_model_loader & ml) { hparams.n_swa = 8192; hparams.n_attn_temp_floor_scale = 8192; hparams.f_attn_temp_scale = 0.1f; + hparams.f_attn_temp_offset = 1.0f; hparams.set_swa_pattern(4); // pattern: 3 chunked - 1 full } @@ -1646,6 +1647,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_SCALE, hparams.f_attn_temp_scale, false); ml.get_key(LLM_KV_ATTENTION_TEMPERATURE_LENGTH, hparams.n_attn_temp_floor_scale, false); + hparams.f_attn_temp_offset = 0.0f; + switch (hparams.n_layer) { case 27: type = LLM_TYPE_16B; break; case 60: type = LLM_TYPE_236B; break; @@ -2276,6 +2279,8 @@ void llama_model::load_hparams(llama_model_loader & ml) { ml.get_key(LLM_KV_ROPE_SCALING_YARN_BETA_SLOW, hparams.yarn_beta_slow, false); ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul, 0.0f); + hparams.f_attn_temp_offset = 0.0f; + // TODO: maybe add n_attn_temp_floor_scale as a separate KV? if (hparams.f_attn_temp_scale != 0.0f) { hparams.n_attn_temp_floor_scale = hparams.n_ctx_orig_yarn; From 9e6649ecf244a99749dacc28fc4f49f7d6ad6f60 Mon Sep 17 00:00:00 2001 From: Ruben Ortlam Date: Sun, 14 Dec 2025 14:52:46 +0100 Subject: [PATCH 30/40] vulkan: fix mul_mat_vec_iq1_s formatting (#18026) --- .../vulkan-shaders/mul_mat_vec_iq1_s.comp | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp index e6b1f20215..c5f5e9cbb2 100644 --- a/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp +++ b/ggml/src/ggml-vulkan/vulkan-shaders/mul_mat_vec_iq1_s.comp @@ -10,44 +10,44 @@ FLOAT_TYPE temp[NUM_COLS][NUM_ROWS]; void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) { const uint y_idx_base = i * QUANT_K + 32 * ib32; - [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { + [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) { const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx_base) / 4; - [[unroll]] for (uint l = 0; l < 4; ++l) { + [[unroll]] for (uint l = 0; l < 4; ++l) { const vec4 b_val_0 = vec4(data_b_v4[base_b_idx + 2 * l]); const vec4 b_val_1 = vec4(data_b_v4[base_b_idx + 2 * l + 1]); // index for data_a uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i; - [[unroll]] for (uint n = 0; n < num_rows; ++n) { + [[unroll]] for (uint n = 0; n < num_rows; ++n) { const float d = float(data_a[ibi].d); const uint qh = data_a[ibi].qh[ib32]; const float dl = d * float(2 * bitfieldExtract(qh, 12, 3) + 1); const uint qs = data_a[ibi].qs[4 * ib32 + l]; - const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); + const uint idxhi = bitfieldExtract(qh, 3 * int(l), 3); const uint16_t grid = uint16_t(iq1s_grid[qs | (idxhi << 8)]); const float delta_val = ((qh & 0x8000) != 0) ? -IQ1S_DELTA : IQ1S_DELTA; - const vec4 delta_v = vec4(delta_val); + const vec4 delta_v = vec4(delta_val); const vec4 fbits0 = vec4( float(bitfieldExtract(grid, 0, 2)), float(bitfieldExtract(grid, 2, 2)), float(bitfieldExtract(grid, 4, 2)), float(bitfieldExtract(grid, 6, 2)) - ); + ); const vec4 fbits1 = vec4( float(bitfieldExtract(grid, 8, 2)), float(bitfieldExtract(grid, 10, 2)), float(bitfieldExtract(grid, 12, 2)), float(bitfieldExtract(grid, 14, 2)) ); - + vec4 sum_v = fma(b_val_0, fbits0 + delta_v, vec4(0.0)); sum_v = fma(b_val_1, fbits1 + delta_v, sum_v); - FLOAT_TYPE sum = dot(sum_v, vec4(1.0)); - - temp[j][n] = fma(dl, sum, temp[j][n]); + FLOAT_TYPE sum = dot(sum_v, vec4(1.0)); + + temp[j][n] = fma(dl, sum, temp[j][n]); ibi += num_blocks_per_row; } } From 37f5a1093bf7f6c58630d916871c54e05a8364d7 Mon Sep 17 00:00:00 2001 From: Haowei Wu Date: Sun, 14 Dec 2025 22:57:52 +0800 Subject: [PATCH 31/40] mtmd: enhance image resizing in llava_uhd (#18014) --- tools/mtmd/clip.cpp | 30 +++++++++++++++++------------- 1 file changed, 17 insertions(+), 13 deletions(-) diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index bb922e30b4..5cc4e4b1fa 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -2230,7 +2230,14 @@ struct llava_uhd { clip_image_size refined_size; // size of image right before slicing (must be multiple of slice size) clip_image_size grid_size; // grid_size.width * grid_size.height = number of slices std::vector slices; + + img_tool::resize_algo interpolation_overview = img_tool::RESIZE_ALGO_BILINEAR; + bool padding_overview = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6) + std::array pad_color_overview = {0, 0, 0}; + + img_tool::resize_algo interpolation_refined = img_tool::RESIZE_ALGO_BICUBIC; bool padding_refined = false; // if true, refine image will be padded to the grid size (e.g. llava-1.6) + std::array pad_color_refined = {0, 0, 0}; }; static slice_instructions get_slice_instructions(struct clip_ctx * ctx, const clip_image_size & original_size) { @@ -2257,10 +2264,11 @@ struct llava_uhd { auto refine_size = llava_uhd::select_best_resolution( original_size, ctx->model.hparams.image_res_candidates); - res.overview_size = clip_image_size{slice_size, slice_size}; - res.refined_size = refine_size; - res.grid_size = clip_image_size{0, 0}; - res.padding_refined = true; + res.overview_size = clip_image_size{slice_size, slice_size}; + res.refined_size = refine_size; + res.grid_size = clip_image_size{0, 0}; + res.padding_refined = true; + res.interpolation_refined = img_tool::RESIZE_ALGO_BILINEAR; // preserve old behavior when padding LOG_DBG("%s: using pinpoints for slicing\n", __func__); LOG_DBG("%s: original size: %d x %d, overview size: %d x %d, refined size: %d x %d\n", @@ -2339,12 +2347,13 @@ struct llava_uhd { static std::vector slice_image(const clip_image_u8 * img, const slice_instructions & inst) { std::vector output; - img_tool::resize_algo interpolation = img_tool::RESIZE_ALGO_BILINEAR; // TODO: make it configurable // resize to overview size clip_image_u8_ptr resized_img(clip_image_u8_init()); - img_tool::resize(*img, *resized_img, inst.overview_size, interpolation); + img_tool::resize(*img, *resized_img, inst.overview_size, inst.interpolation_overview, + inst.padding_overview, inst.pad_color_overview); output.push_back(std::move(resized_img)); + if (inst.slices.empty()) { // no slices, just return the resized image return output; @@ -2352,13 +2361,8 @@ struct llava_uhd { // resize to refined size clip_image_u8_ptr refined_img(clip_image_u8_init()); - if (inst.padding_refined) { - img_tool::resize(*img, *refined_img, inst.refined_size, interpolation); - } else { - // only algo bicubic preserves the ratio; old models rely on this behavior - // TODO: do we need to support other algos here? - img_tool::resize(*img, *refined_img, inst.refined_size, img_tool::RESIZE_ALGO_BICUBIC, false); - } + img_tool::resize(*img, *refined_img, inst.refined_size, inst.interpolation_refined, + inst.padding_refined, inst.pad_color_refined); // create slices for (const auto & slice : inst.slices) { From 5c8a717128cc98aa9e5b1c44652f5cf458fd426e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= Date: Sun, 14 Dec 2025 16:04:37 +0100 Subject: [PATCH 32/40] convert : refactor rope scaling handling (#18013) * refactor rope scaling handling * ws-- * missed a couple * use find_hparam --- convert_hf_to_gguf.py | 357 +++++++++++------------------------------- 1 file changed, 88 insertions(+), 269 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 151608d56b..3f861f2a6a 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -136,11 +136,19 @@ class ModelBase: self.remote_hf_model_id = remote_hf_model_id self.sentence_transformers_dense_modules = sentence_transformers_dense_modules self.hparams = ModelBase.load_hparams(self.dir_model, self.is_mistral_format) if hparams is None else hparams + self.rope_parameters = self.hparams.get("rope_parameters", self.hparams.get("rope_scaling")) or {} self.model_tensors = self.index_tensors(remote_hf_model_id=remote_hf_model_id) self.metadata_override = metadata_override self.model_name = model_name self.dir_model_card = dir_model # overridden in convert_lora_to_gguf.py + # Ensure "rope_theta" and "rope_type" is mirrored in rope_parameters + if "full_attention" not in self.rope_parameters and "sliding_attention" not in self.rope_parameters: + if "rope_theta" not in self.rope_parameters and (rope_theta := self.find_hparam(["rope_theta", "global_rope_theta", "rotary_emb_base"], optional=True)) is not None: + self.rope_parameters["rope_theta"] = rope_theta + if "rope_type" not in self.rope_parameters and (rope_type := self.rope_parameters.get("type")) is not None: + self.rope_parameters["rope_type"] = rope_type + # Apply heuristics to figure out typical tensor encoding based on first layer tensor encoding type if self.ftype == gguf.LlamaFileType.GUESSED: # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. @@ -795,7 +803,7 @@ class TextModel(ModelBase): def set_gguf_parameters(self): self.gguf_writer.add_block_count(self.block_count) - if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length"], optional=True)) is not None: + if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx", "n_positions", "max_length", "max_sequence_length", "model_max_length"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) logger.info(f"gguf: context length = {n_ctx}") @@ -815,7 +823,42 @@ class TextModel(ModelBase): self.gguf_writer.add_head_count_kv(n_head_kv) logger.info(f"gguf: key-value head count = {n_head_kv}") - if (rope_theta := self.hparams.get("rope_theta")) is not None: + rope_params = self.rope_parameters.get("full_attention", self.rope_parameters) + if (rope_type := rope_params.get("rope_type")) is not None: + rope_factor = rope_params.get("factor") + rope_gguf_type = gguf.RopeScalingType.NONE + if rope_type == "linear" and rope_factor is not None: + rope_gguf_type = gguf.RopeScalingType.LINEAR + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + self.gguf_writer.add_rope_scaling_factor(rope_factor) + elif rope_type == "yarn" and rope_factor is not None: + rope_gguf_type = gguf.RopeScalingType.YARN + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + self.gguf_writer.add_rope_scaling_factor(rope_factor) + self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"]) + if (yarn_ext_factor := rope_params.get("extrapolation_factor")) is not None: + self.gguf_writer.add_rope_scaling_yarn_ext_factor(yarn_ext_factor) + if (yarn_attn_factor := rope_params.get("attention_factor", rope_params.get("attn_factor"))) is not None: + self.gguf_writer.add_rope_scaling_yarn_attn_factor(yarn_attn_factor) + if (yarn_beta_fast := rope_params.get("beta_fast")) is not None: + self.gguf_writer.add_rope_scaling_yarn_beta_fast(yarn_beta_fast) + if (yarn_beta_slow := rope_params.get("beta_slow")) is not None: + self.gguf_writer.add_rope_scaling_yarn_beta_slow(yarn_beta_slow) + # self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"]) + elif rope_type == "su" or rope_type == "longrope": + rope_gguf_type = gguf.RopeScalingType.LONGROPE + self.gguf_writer.add_rope_scaling_type(rope_gguf_type) + elif rope_type == "dynamic": + # HunYuan, handled in model class + pass + elif rope_type.lower() == "llama3": + # Handled in generate_extra_tensors + pass + else: + logger.warning(f"Unknown RoPE type: {rope_type}") + logger.info(f"gguf: rope scaling type = {rope_gguf_type.name}") + + if (rope_theta := rope_params.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) logger.info(f"gguf: rope theta = {rope_theta}") if (f_rms_eps := self.find_hparam(["rms_norm_eps", "norm_eps"], optional=True)) is not None: @@ -1966,34 +2009,10 @@ class BaichuanModel(TextModel): self._set_vocab_sentencepiece() def set_gguf_parameters(self): - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - raise ValueError("gguf: can not find ctx length parameter.") + super().set_gguf_parameters() self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) - - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: head_count = self.hparams["num_attention_heads"] @@ -2089,34 +2108,10 @@ class XverseModel(TextModel): special_vocab.add_to_gguf(self.gguf_writer) def set_gguf_parameters(self): - head_count = self.hparams["num_attention_heads"] - head_count_kv = self.hparams.get("num_key_value_heads", head_count) - - ctx_length = 0 - if "max_sequence_length" in self.hparams: - ctx_length = self.hparams["max_sequence_length"] - elif "max_position_embeddings" in self.hparams: - ctx_length = self.hparams["max_position_embeddings"] - elif "model_max_length" in self.hparams: - ctx_length = self.hparams["model_max_length"] - else: - raise ValueError("gguf: can not find ctx length parameter.") + super().set_gguf_parameters() self.gguf_writer.add_tensor_data_layout("Meta AI original pth") - self.gguf_writer.add_context_length(ctx_length) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_file_type(self.ftype) - - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -2430,11 +2425,6 @@ class LlamaModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -2518,16 +2508,16 @@ class LlamaModel(TextModel): return [(self.map_tensor_name(name), data_torch)] def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -2564,11 +2554,6 @@ class ArceeModel(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() self._try_set_pooling_type() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) @ModelBase.register("AfmoeForCausalLM") @@ -2851,17 +2836,11 @@ class Mistral3Model(LlamaModel): def set_gguf_parameters(self): super().set_gguf_parameters() - rope_params = self.hparams.get("rope_parameters") + rope_params = self.rope_parameters if self.hparams.get("model_type") == "ministral3": - assert rope_params is not None, "ministral3 must have 'rope_parameters' config" + assert rope_params, "ministral3 must have 'rope_parameters' config" assert rope_params["rope_type"] == "yarn", "ministral3 rope_type must be 'yarn'" - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_params["factor"]) - self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_params["beta_fast"]) - self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_params["beta_slow"]) self.gguf_writer.add_rope_scaling_yarn_log_mul(rope_params["mscale_all_dim"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_params["original_max_position_embeddings"]) - self.gguf_writer.add_rope_freq_base(rope_params["rope_theta"]) self.gguf_writer.add_attn_temperature_scale(rope_params["llama_4_scaling_beta"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None): @@ -2958,7 +2937,7 @@ class DeciModel(TextModel): assert self.block_count == len(self._num_kv_heads) assert self.block_count == len(self._num_heads) assert self.block_count == len(self._ffn_dims) - if (rope_theta := self.hparams.get("rope_theta")) is not None: + if (rope_theta := self.rope_parameters.get("rope_theta")) is not None: self.gguf_writer.add_rope_freq_base(rope_theta) self.gguf_writer.add_head_count_kv(self._num_kv_heads) self.gguf_writer.add_head_count(self._num_heads) @@ -2983,11 +2962,6 @@ class DeciModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -3016,16 +2990,16 @@ class DeciModel(TextModel): return [(self.map_tensor_name(name), data_torch)] def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -3279,10 +3253,6 @@ class MiniCPMModel(TextModel): logit_scale = self.hparams["hidden_size"] / self.hparams["dim_model_base"] self.gguf_writer.add_logit_scale(logit_scale) logger.info(f"gguf: (minicpm) logit_scale = {logit_scale}") - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "longrope": - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LONGROPE) - logger.info(f"gguf: (minicpm) rope_scaling_type = {gguf.RopeScalingType.LONGROPE}") def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: rope_dims = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] @@ -3402,17 +3372,6 @@ class QwenModel(TextModel): def set_vocab(self): self._set_vocab_qwen() - def set_gguf_parameters(self): - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) - self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) - self.gguf_writer.add_file_type(self.ftype) - @ModelBase.register("Qwen2Model", "Qwen2ForCausalLM", "Qwen2AudioForConditionalGeneration") class Qwen2Model(TextModel): @@ -3427,11 +3386,6 @@ class Qwen2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() self._try_set_pooling_type() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if self.hf_arch == "Qwen2Model": @@ -3499,12 +3453,6 @@ class DreamModel(TextModel): # Dream models use non-causal attention for diffusion self.gguf_writer.add_causal_attention(False) - # Handle RoPE scaling similar to Qwen2 - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) # Add Dream-specific parameters mask_token_id = self.hparams.get("mask_token_id") @@ -4048,13 +3996,6 @@ class Qwen2MoeModel(TextModel): if (shared_expert_intermediate_size := self.hparams.get('shared_expert_intermediate_size')) is not None: self.gguf_writer.add_expert_shared_feed_forward_length(shared_expert_intermediate_size) logger.info(f"gguf: expert shared feed forward length = {shared_expert_intermediate_size}") - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) _experts: list[dict[str, Tensor]] | None = None @@ -4656,7 +4597,7 @@ class Phi3MiniModel(TextModel): self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) self.gguf_writer.add_rope_dimension_count(rope_dims) - self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters)["rope_theta"]) self.gguf_writer.add_file_type(self.ftype) sliding_window = self.hparams.get("sliding_window") # use zero value of sliding_window to distinguish Phi-4 from other PHI3 models @@ -4932,7 +4873,7 @@ class Plamo2Model(TextModel): self.gguf_writer.add_value_length(hparams.get("hidden_size_per_head", 128)) self.gguf_writer.add_block_count(self.block_count) self.gguf_writer.add_layer_norm_rms_eps(hparams.get("rms_norm_eps", 1e-06)) - self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 10000)) + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("rope_theta", 10000)) # Mamba parameters self.gguf_writer.add_ssm_state_size(hparams.get("mamba_d_state", 64)) @@ -5130,21 +5071,6 @@ class InternLM2Model(TextModel): special_vocab.add_to_gguf(self.gguf_writer) - def set_gguf_parameters(self): - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) - self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) - self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) - self.gguf_writer.add_file_type(self.ftype) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: num_heads = self.hparams["num_attention_heads"] num_kv_heads = self.hparams["num_key_value_heads"] @@ -5221,11 +5147,6 @@ class InternLM3Model(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") @@ -5588,7 +5509,6 @@ class NomicBertModel(BertModel): def set_gguf_parameters(self): super().set_gguf_parameters() - self.gguf_writer.add_rope_freq_base(self.hparams["rotary_emb_base"]) if self.is_moe: self.gguf_writer.add_moe_every_n_layers(self.hparams["moe_every_n_layers"]) self.gguf_writer.add_expert_count(self.hparams["num_experts"]) @@ -5711,8 +5631,6 @@ class XLMRobertaModel(BertModel): super().set_gguf_parameters() # jina-embeddings-v3 - if rotary_emb_base := self.hparams.get("rotary_emb_base"): - self.gguf_writer.add_rope_freq_base(rotary_emb_base) lora_alpha = self.hparams.get("lora_alpha") if lora_prompt_prefixes := self.hparams.get("task_instructions"): assert self._lora_files and all(lora_name in lora_prompt_prefixes for lora_name in self._lora_files.keys()) @@ -5840,19 +5758,16 @@ class Gemma3Model(TextModel): self._set_vocab_gpt2() def set_gguf_parameters(self): + super().set_gguf_parameters() hparams = self.hparams # some default values are not specified in the hparams self.gguf_writer.add_context_length(hparams.get("max_position_embeddings", 131072)) - self.gguf_writer.add_embedding_length(hparams["hidden_size"]) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) self.gguf_writer.add_head_count(hparams.get("num_attention_heads", 8)) self.gguf_writer.add_layer_norm_rms_eps(self.hparams.get("rms_norm_eps", 1e-6)) self.gguf_writer.add_key_length(hparams.get("head_dim", 256)) self.gguf_writer.add_value_length(hparams.get("head_dim", 256)) - self.gguf_writer.add_file_type(self.ftype) - self.gguf_writer.add_rope_freq_base(hparams.get("rope_theta", 1_000_000.0)) # for global layers + self.gguf_writer.add_rope_freq_base(self.rope_parameters.get("full_attention", self.rope_parameters).get("rope_theta", 1_000_000.0)) # for global layers # attn_logit_softcapping is removed in Gemma3 assert hparams.get("attn_logit_softcapping") is None if (final_logit_softcap := hparams.get("final_logit_softcapping")): @@ -5860,19 +5775,6 @@ class Gemma3Model(TextModel): if hparams.get("sliding_window_pattern") != 1: self.gguf_writer.add_sliding_window(hparams["sliding_window"]) self.gguf_writer.add_head_count_kv(hparams.get("num_key_value_heads", 4)) - if hparams.get("rope_scaling") is not None: - rope_scaling = hparams["rope_scaling"] - if rope_scaling["rope_type"] == "linear": - # important: this rope_scaling is only applied for global layers, and not used by 1B model - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - elif rope_scaling["rope_type"] == "yarn": - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - self.gguf_writer.add_rope_scaling_yarn_ext_factor(rope_scaling["extrapolation_factor"]) - self.gguf_writer.add_rope_scaling_yarn_beta_fast(rope_scaling["beta_fast"]) - self.gguf_writer.add_rope_scaling_yarn_beta_slow(rope_scaling["beta_slow"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: del bid # unused @@ -6776,13 +6678,6 @@ class Olmo2Model(TextModel): def set_gguf_parameters(self): super().set_gguf_parameters() - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_attn_factors(rope_scaling["attention_factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - if "sliding_window" in self.hparams: self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) @@ -7281,16 +7176,11 @@ class DeepseekV2Model(TextModel): self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - + if (rope_mscale_all := self.rope_parameters.get("mscale_all_dim")) is not None: # [TAG_DEEPSEEK2_YARN_LOG_MUL_FIX] # note: for legacy reasons, this is not consistent with the other usages of self.gguf_writer.add_rope_scaling_yarn_log_mul # ref https://github.com/ggml-org/llama.cpp/pull/17945 - self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_scaling["mscale_all_dim"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * rope_mscale_all) _experts: list[dict[str, Tensor]] | None = None @@ -7898,11 +7788,6 @@ class Glm4Model(TextModel): if (rope_dim := self.hparams.get("head_dim")) is None: rope_dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: if name.startswith("model.visual."): # ignore visual part of Glm4v @@ -8240,50 +8125,26 @@ class ExaoneModel(TextModel): model_arch = gguf.MODEL_ARCH.EXAONE def set_gguf_parameters(self): + super().set_gguf_parameters() hparams = self.hparams assert (hparams["activation_function"] == "silu") - max_position_embeddings = hparams["max_position_embeddings"] - embed_dim = hparams["hidden_size"] - num_heads = hparams["num_attention_heads"] - num_kv_heads = hparams.get("num_key_value_heads", num_heads) - layer_norm_eps = hparams["layer_norm_epsilon"] - intermediate_size = hparams["intermediate_size"] if "intermediate_size" in hparams else 4 * embed_dim - # ignore for now as EXAONE-3.0-7.8B-Instruct attentino_dropout is 0.0 - # attention_dropout_rate = hparams["attention_dropout"] - # ignore for now as EXAONE-3.0-7.8B-Instruct embed_dropout is 0.0 - # embed_dropout_rate = hparams["embed_dropout"] - self.gguf_writer.add_embedding_length(embed_dim) - self.gguf_writer.add_head_count(num_heads) - self.gguf_writer.add_head_count_kv(num_kv_heads) - self.gguf_writer.add_context_length(max_position_embeddings) - self.gguf_writer.add_layer_norm_rms_eps(layer_norm_eps) - self.gguf_writer.add_feed_forward_length(intermediate_size) - self.gguf_writer.add_block_count(self.block_count) - self.gguf_writer.add_file_type(self.ftype) - - if (rope_theta := self.hparams.get("rope_theta")) is not None: - self.gguf_writer.add_rope_freq_base(rope_theta) rotary_factor = self.find_hparam(["partial_rotary_factor", "rope_pct"], optional=True) rotary_factor = rotary_factor if rotary_factor is not None else 1.0 self.gguf_writer.add_rope_dimension_count(int(rotary_factor * (hparams["hidden_size"] // hparams["num_attention_heads"]))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = self.rope_parameters.get("rope_theta", 10000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 8.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 8.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -8338,22 +8199,17 @@ class Exaone4Model(TextModel): if len(sliding_window_pattern) == hparams["num_hidden_layers"]: self.gguf_writer.add_sliding_window_pattern(sliding_window_pattern) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "linear" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - def generate_extra_tensors(self) -> Iterable[tuple[str, Tensor]]: - if rope_scaling := self.find_hparam(["rope_scaling"], optional=True): - if rope_scaling.get("rope_type", '').lower() == "llama3": - base = self.hparams.get("rope_theta", 10_000.0) + if rope_params := self.rope_parameters.get("full_attention", self.rope_parameters): + if rope_params.get("rope_type", '').lower() == "llama3": + base = rope_params.get("rope_theta", 10_000.0) if (dim := self.hparams.get("head_dim")) is None: dim = self.hparams["hidden_size"] // self.hparams["num_attention_heads"] freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim)) - factor = rope_scaling.get("factor", 16.0) - low_freq_factor = rope_scaling.get("low_freq_factor", 1.0) - high_freq_factor = rope_scaling.get("high_freq_factor", 4.0) + factor = rope_params.get("factor", 16.0) + low_freq_factor = rope_params.get("low_freq_factor", 1.0) + high_freq_factor = rope_params.get("high_freq_factor", 4.0) old_context_len = self.hparams.get("original_max_position_embeddings", 8192) low_freq_wavelen = old_context_len / low_freq_factor @@ -8664,13 +8520,6 @@ class BailingMoeModel(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(rope_dim) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - else: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) @@ -8777,13 +8626,6 @@ class BailingMoeV2Model(TextModel): rope_dim = hparams["hidden_size"] // hparams["num_attention_heads"] self.gguf_writer.add_rope_dimension_count(int(rope_dim * self.hparams.get("partial_rotary_factor", 0.5))) - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) - else: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.NONE) self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) self.gguf_writer.add_vocab_size(hparams["vocab_size"]) self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) @@ -8862,13 +8704,6 @@ class GroveMoeModel(TextModel): self.gguf_writer.add_experts_per_group(2) # FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376 self.gguf_writer.add_expert_group_scale(0.05) - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) _experts: list[dict[str, Tensor]] | None = None _chunk_experts: list[dict[str, Tensor]] | None = None @@ -9178,7 +9013,7 @@ class FalconH1Model(Mamba2Model): assert self.d_inner % self.d_head == 0, f"SSM inner size {self.d_inner} not a multiple of head dim {self.d_head}" # Add any other Falcon Mamba2 specific configuration - self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) + self.gguf_writer.add_rope_freq_base(self.rope_parameters["rope_theta"]) @ModelBase.register("HunYuanMoEV1ForCausalLM") @@ -9256,12 +9091,11 @@ class HunYuanMoEModel(TextModel): self.gguf_writer.add_expert_shared_count(moe_shared_expert[0]) # Rope - rope_scaling = hparams.get("rope_scaling", {}) - if rope_scaling.get("type") == "dynamic": + if self.rope_parameters.get("rope_type") == "dynamic": # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) - alpha = rope_scaling.get("alpha", 1000) - base = hparams.get("rope_theta", 10000.0) + alpha = self.rope_parameters.get("alpha", 1000) + base = self.rope_parameters.get("rope_theta", 10000.0) dim = (hparams["hidden_size"] // hparams["num_attention_heads"]) # 128 scaled_base = base * (alpha ** (dim / (dim - 2))) # 10000 * (1000 ** (128 / 126)) = 11158839.9251 self.gguf_writer.add_rope_freq_base(scaled_base) @@ -9456,12 +9290,11 @@ class HunYuanModel(TextModel): hparams = self.hparams # Rope - rope_scaling = hparams.get("rope_scaling", {}) - if rope_scaling.get("type") == "dynamic": + if self.rope_parameters.get("rope_type") == "dynamic": # HunYuan uses NTK Aware Alpha based scaling. Original implementation: https://www.reddit.com/r/LocalLLaMA/comments/14lz7j5/ntkaware_scaled_rope_allows_llama_models_to_have/ # 1000 corresponds to a usable context length of 256k (https://github.com/Tencent-Hunyuan/Hunyuan-A13B/blob/main/report/Hunyuan_A13B_Technical_Report.pdf) - alpha = rope_scaling.get("alpha", 50) - base = hparams.get("rope_theta", 10000.0) + alpha = self.rope_parameters.get("alpha", 50) + base = self.rope_parameters.get("rope_theta", 10000.0) dim = hparams["head_dim"] scaled_base = base * (alpha ** (dim / (dim - 2))) self.gguf_writer.add_rope_freq_base(scaled_base) @@ -9612,13 +9445,6 @@ class GptOssModel(TextModel): self.gguf_writer.add_sliding_window(self.hparams["sliding_window"]) self.gguf_writer.add_expert_feed_forward_length(self.hparams["intermediate_size"]) - rope_scaling = self.hparams.get("rope_scaling") or {} - rope_type = rope_scaling.get("rope_type", rope_scaling.get("type")) - assert rope_type == "yarn", f"GPT-OSS only supports yarn rope scaling, got {rope_type}" - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling.get("original_max_position_embeddings", 4096)) - @ModelBase.register("Lfm2ForCausalLM", "LFM2ForCausalLM") class LFM2Model(TextModel): @@ -9791,13 +9617,6 @@ class SmallThinkerModel(TextModel): self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SOFTMAX) else: self.gguf_writer.add_expert_gating_func(gguf.ExpertGatingFuncType.SIGMOID) - # YaRN is not enabled by default - # To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts - rope_scaling = self.hparams.get("rope_scaling") or {} - if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling: - self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) - self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"]) - self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"]) sliding_window_layout = self.hparams.get("sliding_window_layout") if sliding_window_layout: From 52392291b2f3a24b5d3fef4fc0b56f10db358dc1 Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Sun, 14 Dec 2025 22:08:10 +0100 Subject: [PATCH 33/40] preset: handle negated arg, reverse the meaning if needed (#18041) --- common/preset.cpp | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/common/preset.cpp b/common/preset.cpp index 729c27f2cf..60746aad58 100644 --- a/common/preset.cpp +++ b/common/preset.cpp @@ -157,6 +157,21 @@ static std::map get_map_key_opt(common_params_context & return mapping; } +static bool is_bool_arg(const common_arg & arg) { + return !arg.args_neg.empty(); +} + +static std::string parse_bool_arg(const common_arg & arg, const std::string & key, const std::string & value) { + // if this is a negated arg, we need to reverse the value + for (const auto & neg_arg : arg.args_neg) { + if (rm_leading_dashes(neg_arg) == key) { + return common_arg_utils::is_truthy(value) ? "false" : "true"; + } + } + // otherwise, not negated + return value; +} + common_presets common_presets_load(const std::string & path, common_params_context & ctx_params) { common_presets out; auto key_to_opt = get_map_key_opt(ctx_params); @@ -173,8 +188,13 @@ common_presets common_presets_load(const std::string & path, common_params_conte for (const auto & [key, value] : section.second) { LOG_DBG("option: %s = %s\n", key.c_str(), value.c_str()); if (key_to_opt.find(key) != key_to_opt.end()) { - preset.options[key_to_opt[key]] = value; - LOG_DBG("accepted option: %s = %s\n", key.c_str(), value.c_str()); + auto & opt = key_to_opt[key]; + if (is_bool_arg(opt)) { + preset.options[opt] = parse_bool_arg(opt, key, value); + } else { + preset.options[opt] = value; + } + LOG_DBG("accepted option: %s = %s\n", key.c_str(), preset.options[opt].c_str()); } else { // TODO: maybe warn about unknown key? } From 745fa0e78b3d3fd3fd12a260c47300dc3869d07e Mon Sep 17 00:00:00 2001 From: piDack <104877312+piDack@users.noreply.github.com> Date: Mon, 15 Dec 2025 10:18:46 +0800 Subject: [PATCH 34/40] model : add glm-asr support (#17901) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [model] add glm-asr support * fix format for ci * fix convert format for ci * update glm_asr convert script & use build_ffn for glm_asr clip & use build_stack for padding and review * check root architecture for convert hf script * fix conficlt with upstream * fix convert script for glm asr & format clip-impl * format * restore hparams text * improved conversion --------- Co-authored-by: Sigbjørn Skjæret --- convert_hf_to_gguf.py | 86 ++++++++++++++++++++++++++++++- gguf-py/gguf/constants.py | 1 + tools/mtmd/clip-graph.h | 4 ++ tools/mtmd/clip-impl.h | 2 + tools/mtmd/clip-model.h | 1 + tools/mtmd/clip.cpp | 60 ++++++++++++++++++++- tools/mtmd/models/whisper-enc.cpp | 19 ++++--- 7 files changed, 160 insertions(+), 13 deletions(-) diff --git a/convert_hf_to_gguf.py b/convert_hf_to_gguf.py index 3f861f2a6a..ee02cdd91c 100755 --- a/convert_hf_to_gguf.py +++ b/convert_hf_to_gguf.py @@ -713,6 +713,9 @@ class ModelBase: if "llm_config" in config: # rename for InternVL config["text_config"] = config["llm_config"] + if "lm_config" in config: + # rename for GlmASR + config["text_config"] = config["lm_config"] if "thinker_config" in config: # rename for Qwen2.5-Omni config["text_config"] = config["thinker_config"]["text_config"] @@ -1529,6 +1532,21 @@ class TextModel(ModelBase): raise NotImplementedError("Only MEAN, CLS, and LAST pooling types supported") self.gguf_writer.add_pooling_type(pooling_type) + def _set_vocab_glmedge(self): + from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained(self.dir_model) + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=True) + tokens, toktypes, tokpre = self.get_vocab_base() + self.gguf_writer.add_tokenizer_model("gpt2") + self.gguf_writer.add_tokenizer_pre(tokpre) + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_types(toktypes) + special_vocab._set_special_token("eos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("eot", tokenizer.get_added_vocab()["<|user|>"]) + special_vocab._set_special_token("unk", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab._set_special_token("bos", tokenizer.get_added_vocab()["<|endoftext|>"]) + special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_interns1(self): tokens: list[str] = [] toktypes: list[int] = [] @@ -1658,7 +1676,7 @@ class MmprojModel(ModelBase): preprocessor_config: dict[str, Any] global_config: dict[str, Any] - n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth"] + n_block_keys = ["n_layers", "num_hidden_layers", "n_layer", "num_layers", "depth", "encoder_layers"] has_vision_encoder: bool = True # by default has_audio_encoder: bool = False @@ -1734,7 +1752,8 @@ class MmprojModel(ModelBase): return self.global_config.get(config_name) def get_audio_config(self) -> dict[str, Any] | None: - return self.global_config.get("audio_config") + mm_config_key = "whisper_config" if "whisper_config" in self.hparams else "audio_config" + return self.global_config.get(mm_config_key) def set_type(self): self.gguf_writer.add_type(gguf.GGUFType.MMPROJ) @@ -2372,8 +2391,13 @@ class LlamaModel(TextModel): # fix for SmolVLM2, missing `num_attention_heads` in config.json if self.hf_arch == "VLlama3ForCausalLM": self.hparams["num_attention_heads"] = self.hparams.get("num_attention_heads", 32) + hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False) + self.origin_hf_arch = hparams.get('architectures', [None])[0] def set_vocab(self): + if self.origin_hf_arch == "GlmasrModel": + return self._set_vocab_glmedge() + if self.is_mistral_format: return self._set_vocab_mistral() @@ -2444,6 +2468,7 @@ class LlamaModel(TextModel): "vision_language_adapter.", "patch_merger.", "pre_mm_projector_norm", + "audio_encoder.", ] is_multimodal_tensor = "vision_tower" in name \ @@ -8846,6 +8871,63 @@ class UltravoxModel(TextModel): raise NotImplementedError("Ultravox does not have text decoder. Instead, it uses Llama or other models for text. If you want to get the audio encoder, please use --mmproj argument") +@ModelBase.register("GlmasrModel") +class GlmASRWhisperEncoderModel(MmprojModel): + has_vision_encoder = False + has_audio_encoder = True + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + if "hidden_size" not in self.hparams and "intermediate_size" not in self.hparams: + self.hparams["hidden_size"] = self.hparams["d_model"] + self.hparams["intermediate_size"] = self.hparams["encoder_ffn_dim"] + self.hparams["num_attention_heads"] = self.hparams["encoder_attention_heads"] + + def set_gguf_parameters(self): + super().set_gguf_parameters() + self.gguf_writer.add_clip_projector_type(gguf.VisionProjectorType.GLMA) + self.gguf_writer.add_audio_num_mel_bins(self.hparams["num_mel_bins"]) + self.gguf_writer.add_audio_attention_layernorm_eps(self.hparams.get("layer_norm_eps", 1e-5)) + self.gguf_writer.add_audio_stack_factor(self.global_config["merge_factor"]) + + def tensor_force_quant(self, name, new_name, bid, n_dims): + if ".conv" in name and ".weight" in name: + return gguf.GGMLQuantizationType.F16 + return super().tensor_force_quant(name, new_name, bid, n_dims) + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + if name.startswith("model.") or name.startswith("lm_head."): + # skip language model tensors + return [] + + if name.startswith("audio_encoder.whisper."): + name = name.replace("audio_encoder.whisper.","audio_tower.") + if "audio_encoder.layer_norm." in name or "audio_encoder.proj." in name: + name = name.replace("audio_encoder.", "audio_encoder.adapting.") + + if name.startswith("audio_encoder.audio_bos_eos_token."): + return [(self.map_tensor_name("model.vision.boi"), data_torch[0]), (self.map_tensor_name("model.vision.eoi"), data_torch[1])] + + if name.startswith("audio_encoder.adapting."): + name = name.replace("audio_encoder.adapting.","audio.multi_modal_projector.") + if ".layer_norm." in name: + name = name.replace(".layer_norm.", ".ln_pre.") + if ".0." in name: + name = name.replace(".0.", ".linear_1.") + if ".2." in name: + name = name.replace(".2.", ".linear_2.") + if ".proj." in name: + return [] + + if "conv1.bias" in name or "conv2.bias" in name: + # transpose conv1 and conv2 bias + data_torch = data_torch.unsqueeze(-1) + + return [(self.map_tensor_name(name), data_torch)] + + @ModelBase.register("Qwen2AudioForConditionalGeneration") class WhisperEncoderModel(MmprojModel): has_vision_encoder = False # no vision encoder diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2b8489c591..8ef4a23a10 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -3320,6 +3320,7 @@ class VisionProjectorType: ULTRAVOX = "ultravox" INTERNVL = "internvl" QWEN2A = "qwen2a" # audio + GLMA = "glma" # audio QWEN25O = "qwen2.5o" # omni VOXTRAL = "voxtral" LFM2 = "lfm2" diff --git a/tools/mtmd/clip-graph.h b/tools/mtmd/clip-graph.h index 6d303b4e48..17f90e8aa8 100644 --- a/tools/mtmd/clip-graph.h +++ b/tools/mtmd/clip-graph.h @@ -112,4 +112,8 @@ struct clip_graph { // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) // support dynamic resolution ggml_tensor * build_patch_merge_permute(ggml_tensor * cur, int scale_factor); + + // Generic function to stack frames for audio processing + // Abstracts out the StackAudioFrames logic used by ultravox + ggml_tensor * build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed); }; diff --git a/tools/mtmd/clip-impl.h b/tools/mtmd/clip-impl.h index 1726823ec6..9ef0d301e4 100644 --- a/tools/mtmd/clip-impl.h +++ b/tools/mtmd/clip-impl.h @@ -157,6 +157,7 @@ enum projector_type { PROJECTOR_TYPE_INTERNVL, PROJECTOR_TYPE_LLAMA4, PROJECTOR_TYPE_QWEN2A, + PROJECTOR_TYPE_GLMA, PROJECTOR_TYPE_QWEN25O, // will be replaced by QWEN2A or QWEN25VL depending on clip_ctx PROJECTOR_TYPE_VOXTRAL, PROJECTOR_TYPE_LFM2, @@ -183,6 +184,7 @@ static std::map PROJECTOR_TYPE_NAMES = { { PROJECTOR_TYPE_INTERNVL, "internvl"}, { PROJECTOR_TYPE_LLAMA4, "llama4"}, { PROJECTOR_TYPE_QWEN2A, "qwen2a"}, + { PROJECTOR_TYPE_GLMA, "glma"}, { PROJECTOR_TYPE_QWEN25O, "qwen2.5o"}, { PROJECTOR_TYPE_VOXTRAL, "voxtral"}, { PROJECTOR_TYPE_LFM2, "lfm2"}, diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index 51bcce1ebb..bdd4f63e0f 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -256,6 +256,7 @@ struct clip_model { ggml_tensor * conv1d_2_w = nullptr; ggml_tensor * conv1d_2_b = nullptr; ggml_tensor * mm_norm_pre_w = nullptr; + ggml_tensor * mm_norm_pre_b = nullptr; ggml_tensor * mm_norm_mid_w = nullptr; // cogvlm diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index 5cc4e4b1fa..c72c6a6381 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -720,6 +720,32 @@ ggml_tensor * clip_graph::build_rope_2d( return cur; } +// Generic function to stack frames for audio processing +// Abstracts out the StackAudioFrames logic used by ultravox +ggml_tensor * clip_graph::build_stack(ggml_tensor * cur, int32_t stack_factor, int32_t n_embed) { + if (stack_factor <= 1) { + return cur; + } + + int64_t total_elements = ggml_nelements(cur); + int64_t stride = n_embed * stack_factor; + + // Calculate padded length + int64_t padded_len = GGML_PAD(total_elements, stride); + int64_t pad = padded_len - total_elements; + + if (pad > 0) { + // Pad the tensor to make it divisible by stride + cur = ggml_view_1d(ctx0, cur, total_elements, 0); + cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); + } + + // Reshape to [stride, padded_len / stride] + cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, + ggml_row_size(cur->type, stride), 0); + return cur; +} + // aka pixel_shuffle / pixel_unshuffle / patch_merger (Kimi-VL) // support dynamic resolution ggml_tensor * clip_graph::build_patch_merge_permute(ggml_tensor * cur, int scale_factor) { @@ -796,6 +822,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_VOXTRAL: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: { builder = std::make_unique(ctx, img); } break; @@ -1136,10 +1163,12 @@ struct clip_model_loader { } break; case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: case PROJECTOR_TYPE_VOXTRAL: { bool require_stack = model.proj_type == PROJECTOR_TYPE_ULTRAVOX || - model.proj_type == PROJECTOR_TYPE_VOXTRAL; + model.proj_type == PROJECTOR_TYPE_VOXTRAL || + model.proj_type == PROJECTOR_TYPE_GLMA; get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack); if (hparams.n_mel_bins != 128) { throw std::runtime_error(string_format("%s: only 128 mel bins are supported for ultravox\n", __func__)); @@ -1510,6 +1539,21 @@ struct clip_model_loader { model.mm_3_w = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "weight")); model.mm_3_b = get_tensor(string_format(TN_MVLM_PROJ_MLP, 3, "bias")); } break; + case PROJECTOR_TYPE_GLMA: + { + model.conv1d_1_w = get_tensor(string_format(TN_CONV1D, 1, "weight")); + model.conv1d_1_b = get_tensor(string_format(TN_CONV1D, 1, "bias")); + model.conv1d_2_w = get_tensor(string_format(TN_CONV1D, 2, "weight")); + model.conv1d_2_b = get_tensor(string_format(TN_CONV1D, 2, "bias")); + model.mm_1_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "weight")); + model.mm_1_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 1, "bias")); + model.mm_2_w = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "weight")); + model.mm_2_b = get_tensor(string_format(TN_MM_AUDIO_MLP, 2, "bias")); + model.mm_norm_pre_w = get_tensor(string_format(TN_MM_NORM_PRE, "weight")); + model.mm_norm_pre_b = get_tensor(string_format(TN_MM_NORM_PRE, "bias")); + model.mm_boi = get_tensor(string_format(TN_TOK_BOI, "weight")); + model.mm_eoi = get_tensor(string_format(TN_TOK_EOI, "weight")); + } break; case PROJECTOR_TYPE_LLAMA4: { model.mm_model_proj = get_tensor(TN_MM_PROJECTOR); @@ -2895,6 +2939,16 @@ int clip_n_output_tokens(const struct clip_ctx * ctx, struct clip_image_f32 * im n_patches /= 2; } } break; + case PROJECTOR_TYPE_GLMA: + { + n_patches = img->nx; + // whisper downscales input token by half after conv1d + n_patches /= 2; + // reshape by merge_factor + n_patches /= ctx->model.hparams.proj_stack_factor; + // for BOI and EOI token embeddings + n_patches += 2; + } break; case PROJECTOR_TYPE_COGVLM: { n_patches += 2; // for BOI and EOI token embeddings @@ -3230,6 +3284,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima case PROJECTOR_TYPE_IDEFICS3: case PROJECTOR_TYPE_INTERNVL: case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_GLMA: case PROJECTOR_TYPE_ULTRAVOX: case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_VOXTRAL: @@ -3340,6 +3395,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { return ctx->model.mm_model_proj->ne[1]; case PROJECTOR_TYPE_QWEN2A: return ctx->model.mm_fc_w->ne[1]; + case PROJECTOR_TYPE_GLMA: + return ctx->model.mm_2_w->ne[1]; case PROJECTOR_TYPE_LFM2: case PROJECTOR_TYPE_KIMIVL: return ctx->model.mm_2_w->ne[1]; @@ -3386,6 +3443,7 @@ bool clip_has_audio_encoder(const struct clip_ctx * ctx) { bool clip_has_whisper_encoder(const struct clip_ctx * ctx) { return ctx->proj_type() == PROJECTOR_TYPE_ULTRAVOX || ctx->proj_type() == PROJECTOR_TYPE_QWEN2A + || ctx->proj_type() == PROJECTOR_TYPE_GLMA || ctx->proj_type() == PROJECTOR_TYPE_VOXTRAL; } diff --git a/tools/mtmd/models/whisper-enc.cpp b/tools/mtmd/models/whisper-enc.cpp index 07d378b095..2870d854ab 100644 --- a/tools/mtmd/models/whisper-enc.cpp +++ b/tools/mtmd/models/whisper-enc.cpp @@ -30,7 +30,6 @@ ggml_cgraph * clip_graph_whisper_enc::build() { GGML_ASSERT(model.layers[0].q_b); GGML_ASSERT(model.layers[0].v_b); GGML_ASSERT(!model.layers[0].k_b); // no bias for k - GGML_ASSERT(model.post_ln_w && model.post_ln_b); ggml_tensor * pos_embd_selected = ggml_view_2d( ctx0, model.position_embeddings, @@ -49,15 +48,7 @@ ggml_cgraph * clip_graph_whisper_enc::build() { if (model.audio_has_stack_frames()) { // StackAudioFrames // https://huggingface.co/fixie-ai/ultravox-v0_5-llama-3_2-1b/blob/main/ultravox_model.py - int64_t stride = n_embd * hparams.proj_stack_factor; - int64_t padded_len = GGML_PAD(ggml_nelements(cur), stride); - int64_t pad = padded_len - ggml_nelements(cur); - if (pad > 0) { - cur = ggml_view_1d(ctx0, cur, ggml_nelements(cur), 0); - cur = ggml_pad(ctx0, cur, pad, 0, 0, 0); - } - cur = ggml_view_2d(ctx0, cur, stride, padded_len / stride, - ggml_row_size(cur->type, stride), 0); + cur = build_stack(cur, hparams.proj_stack_factor, n_embd); cb(cur, "after_stacked", -1); } @@ -95,6 +86,14 @@ ggml_cgraph * clip_graph_whisper_enc::build() { FFN_GELU_ERF, -1); + } else if (proj_type == PROJECTOR_TYPE_GLMA) { + cur = ggml_norm(ctx0, cur, hparams.eps); + cur = ggml_mul(ctx0, cur, model.mm_norm_pre_w); + cur = ggml_add(ctx0, cur, model.mm_norm_pre_b); + cur = build_stack(cur, hparams.proj_stack_factor, n_embd); + cur = build_ffn(cur, model.mm_1_w, model.mm_1_b, nullptr, nullptr, model.mm_2_w, model.mm_2_b, hparams.ffn_op, 0); + cur = ggml_concat(ctx0, model.mm_boi, cur, 1); + cur = ggml_concat(ctx0, cur, model.mm_eoi, 1); } else { GGML_ABORT("%s: unknown projector type", __func__); } From 4aced7a63156555911157d3002f9d3ddef4a1e55 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Mon, 15 Dec 2025 10:35:15 +0800 Subject: [PATCH 35/40] [SYCL] Support gpt-oss by OPs add-id, mul_mat for mxfp4, swiglu_oai (#17826) * support gpt-oss GPU by OP add-id, mul_mat for mxfp4, swiglu_oai, fix warning * fix fault ut case, update ops.md * rebase, fix format issue --- docs/ops.md | 18 +- docs/ops/SYCL.csv | 1158 ++++++++++++++++++--------- ggml/src/ggml-sycl/add-id.cpp | 77 ++ ggml/src/ggml-sycl/add-id.hpp | 8 + ggml/src/ggml-sycl/common.hpp | 17 + ggml/src/ggml-sycl/convert.cpp | 15 + ggml/src/ggml-sycl/dequantize.hpp | 18 + ggml/src/ggml-sycl/dpct/helper.hpp | 59 +- ggml/src/ggml-sycl/element_wise.cpp | 97 +++ ggml/src/ggml-sycl/element_wise.hpp | 4 + ggml/src/ggml-sycl/ggml-sycl.cpp | 23 +- ggml/src/ggml-sycl/mmvq.cpp | 22 + ggml/src/ggml-sycl/pad.cpp | 10 +- ggml/src/ggml-sycl/ssm_conv.cpp | 2 +- ggml/src/ggml-sycl/vecdotq.hpp | 58 ++ 15 files changed, 1201 insertions(+), 385 deletions(-) create mode 100644 ggml/src/ggml-sycl/add-id.cpp create mode 100644 ggml/src/ggml-sycl/add-id.hpp diff --git a/docs/ops.md b/docs/ops.md index 43163b39ac..b395d2315c 100644 --- a/docs/ops.md +++ b/docs/ops.md @@ -18,12 +18,12 @@ Legend: | ACC | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ADD | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | ADD1 | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | +| ADD_ID | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARANGE | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | ARGMAX | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | -| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | +| ARGSORT | ❌ | ✅ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | CEIL | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| CLAMP | ❌ | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | ❌ | | CONCAT | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | ✅ | ✅ | ❌ | ❌ | ❌ | | CONT | ❌ | 🟡 | ✅ | ✅ | ✅ | 🟡 | 🟡 | ✅ | 🟡 | ❌ | ❌ | | CONV_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ❌ | ❌ | ❌ | @@ -31,7 +31,7 @@ Legend: | CONV_3D | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_1D | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CONV_TRANSPOSE_2D | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| COS | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | | COUNT_EQUAL | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | CPY | ❌ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | ❌ | ❌ | | CROSS_ENTROPY_LOSS | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | @@ -64,7 +64,7 @@ Legend: | IM2COL_3D | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | | L2_NORM | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | LEAKY_RELU | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | -| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | +| LOG | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | | MEAN | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | MUL | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | MUL_MAT | 🟡 | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | 🟡 | @@ -98,14 +98,14 @@ Legend: | SIGMOID | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU | ❌ | ✅ | ✅ | 🟡 | 🟡 | 🟡 | ✅ | 🟡 | ✅ | ❌ | ❌ | | SILU_BACK | ❌ | ❌ | ✅ | ✅ | ❌ | ❌ | ❌ | ✅ | ❌ | ❌ | ❌ | -| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| SIN | ❌ | ✅ | ✅ | ✅ | 🟡 | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | | SOFTCAP | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | ❌ | | SOFTPLUS | ❌ | ❌ | ✅ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | SOFT_MAX | ❌ | 🟡 | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | | SOFT_MAX_BACK | ❌ | ❌ | 🟡 | 🟡 | ❌ | ❌ | 🟡 | ✅ | ❌ | ❌ | ❌ | | SOLVE_TRI | ❌ | ❌ | ✅ | 🟡 | ❌ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | -| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | -| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ❌ | ❌ | ❌ | +| SQR | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | +| SQRT | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ❌ | ❌ | ❌ | | SSM_CONV | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | SSM_SCAN | ❌ | ❌ | ✅ | ✅ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | | STEP | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | ✅ | 🟡 | ✅ | ❌ | ❌ | @@ -113,7 +113,7 @@ Legend: | SUM | ❌ | ✅ | ✅ | 🟡 | 🟡 | ❌ | 🟡 | 🟡 | ❌ | ❌ | ❌ | | SUM_ROWS | ❌ | ✅ | ✅ | 🟡 | ✅ | 🟡 | 🟡 | ✅ | ❌ | ❌ | ❌ | | SWIGLU | ❌ | ✅ | ✅ | ✅ | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | -| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ❌ | 🟡 | ✅ | ❌ | ❌ | +| SWIGLU_OAI | ❌ | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TANH | ❌ | ✅ | ✅ | 🟡 | 🟡 | ✅ | ✅ | 🟡 | ✅ | ❌ | ❌ | | TIMESTEP_EMBEDDING | ❌ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ | ❌ | ❌ | | TOP_K | ❌ | ❌ | ✅ | ❌ | ✅ | ❌ | ❌ | 🟡 | ❌ | ❌ | ❌ | diff --git a/docs/ops/SYCL.csv b/docs/ops/SYCL.csv index 85a45d6ae0..91b442bde8 100644 --- a/docs/ops/SYCL.csv +++ b/docs/ops/SYCL.csv @@ -33,14 +33,14 @@ "SYCL0","SOFTPLUS","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" "SYCL0","GELU_ERF","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","0","no","SYCL" -"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","0","no","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","CEIL","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","ROUND","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[128,2,2,2],v=0","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne_a=[5,7,11,13],v=0","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" "SYCL0","ABS","type=f16,ne_a=[5,7,11,13],v=1","support","1","yes","SYCL" "SYCL0","SGN","type=f16,ne_a=[128,2,2,2],v=1","support","1","yes","SYCL" @@ -287,14 +287,14 @@ "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,swapped=1","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[128,2,2,2],v=1,split","support","1","yes","SYCL" "SYCL0","GEGLU_QUICK","type=f32,ne_a=[5,7,11,13],v=1,split","support","1","yes","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","0","no","SYCL" -"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","0","no","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=0.500000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=0,alpha=1.702000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=0.500000,limit=7.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=2.000000","support","1","yes","SYCL" +"SYCL0","SWIGLU_OAI","type=f32,ne_a=[128,2,2,2],v=1,alpha=1.702000,limit=7.000000","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=76800,m=5,r=4,be1=1,be2=2,v=0","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=256,m=80000,r=70000,be1=2,be2=1,v=0","support","1","yes","SYCL" "SYCL0","GET_ROWS","type=f32,n=256,m=5,r=4,be1=700,be2=100,v=0","support","1","yes","SYCL" @@ -4964,6 +4964,7 @@ "SYCL0","CONV_TRANSPOSE_1D","ne_input=[2,1,1,1],ne_kernel=[3,1,1,1],s0=1,p0=0,d0=1","support","1","yes","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[3,2,3,1],ne_kernel=[2,2,1,3],stride=1","support","0","no","SYCL" "SYCL0","CONV_TRANSPOSE_2D","ne_input=[10,10,9,1],ne_kernel=[3,3,1,9],stride=2","support","0","no","SYCL" +"SYCL0","CONV_TRANSPOSE_2D","ne_input=[129,63,35,1],ne_kernel=[3,3,48,35],stride=1","support","0","no","SYCL" "SYCL0","COUNT_EQUAL","type=f32,ne=[4,500,1,1]","support","1","yes","SYCL" "SYCL0","COUNT_EQUAL","type=f32,ne=[4,5000,1,1]","support","1","yes","SYCL" "SYCL0","ARGMAX","type=f32,ne=[32,1,1,1]","support","1","yes","SYCL" @@ -5419,17 +5420,45 @@ "SYCL0","CPY","type_src=f16,type_dst=f16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","1","yes","SYCL" "SYCL0","CPY","type_src=bf16,type_dst=bf16,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,4,1,1],permute_src=[0,0,0,0],permute_dst=[0,0,0,0],_src_transpose=1","support","0","no","SYCL" +"SYCL0","CPY","type_src=i32,type_dst=i32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","0","no","SYCL" "SYCL0","CPY","type_src=f32,type_dst=f32,ne=[256,1,4,1],permute_src=[1,2,0,3],permute_dst=[0,0,0,0],_src_transpose=0","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[10,10,10,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,1,1,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,1,3,5]","support","1","yes","SYCL" -"SYCL0","CONT","type=f32,ne=[2,3,5,7]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,1,1,1]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,1,3,5]","support","1","yes","SYCL" -"SYCL0","CONT","type=f16,ne=[2,3,5,7]","support","1","yes","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,1,1,1]","support","0","no","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,1,3,5]","support","0","no","SYCL" -"SYCL0","CONT","type=bf16,ne=[2,3,5,7]","support","0","no","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=1","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=i32,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,1,1,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,1,3,5],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[2,3,5,7],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[1,4,4,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[1,8,17,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=f16,ne=[10,10,10,1],use_view_slice=0","support","1","yes","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,1,1,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,1,3,5],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[2,3,5,7],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[1,4,4,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[1,8,17,1],use_view_slice=0","support","0","no","SYCL" +"SYCL0","CONT","type=bf16,ne=[10,10,10,1],use_view_slice=0","support","0","no","SYCL" "SYCL0","ADD","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","SUB","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","MUL","type=f16,ne=[1,1,8,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" @@ -5655,6 +5684,7 @@ "SYCL0","MUL","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","DIV","type=f32,ne=[64,262144,1,1],nr=[1,1,1,1],nf=1","support","1","yes","SYCL" "SYCL0","ADD1","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" +"SYCL0","ADD1","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=0.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=0","support","1","yes","SYCL" "SYCL0","SCALE","type=f32,ne=[10,10,10,10],scale=2.000000,bias=1.000000,inplace=1","support","1","yes","SYCL" @@ -5791,15 +5821,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=4,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=5,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=6,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=7,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=8,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f32,m=16,n=9,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=2,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q2_K,type_b=f32,m=16,n=3,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -5944,15 +5974,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -5971,15 +6001,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6002,15 +6032,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -6029,15 +6059,15 @@ "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=4,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6176,15 +6206,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6207,15 +6237,15 @@ "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" 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"SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q8_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6238,15 +6268,15 @@ "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6269,15 +6299,15 @@ "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_0,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6300,15 +6330,15 @@ "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6331,15 +6361,15 @@ "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" 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+"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_1,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6362,15 +6392,15 @@ "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6393,81 +6423,81 @@ "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=q4_K,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" 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"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[3,2],nr=[2,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=mxfp4,type_b=f16,m=16,n=8,k=256,bs=[1536,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[2,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[1,1],nr=[1,2],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" @@ -6489,12 +6519,12 @@ "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f32,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6520,12 +6550,12 @@ "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,1,3,2],k_v=0,o=1","support","1","yes","SYCL" +"SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=256,bs=[2,3],nr=[1,1],per=[0,3,2,1],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=1,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=8,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=iq2_xxs,type_b=f16,m=16,n=16,k=1024,bs=[3,2],nr=[1,1],per=[0,1,2,3],k_v=0,o=1","support","1","yes","SYCL" @@ -6807,145 +6837,145 @@ "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[2,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[2,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" 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-"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[1,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[1,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1056,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=128,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=128,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1056,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2112,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=f16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" "SYCL0","MUL_MAT","type_a=bf16,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","0","no","SYCL" -"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","0","no","SYCL" +"SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=1057,n=1,k=129,bs=[8,3],nr=[4,1],per=[0,2,1,3],k_v=0,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT","type_a=f32,type_b=f32,m=129,n=1,k=1057,bs=[8,3],nr=[4,1],per=[0,1,2,3],k_v=2113,o=1","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=0,m=32,n=1024,k=16","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=0,m=32,n=8192,k=64","support","1","yes","SYCL" @@ -6954,7 +6984,7 @@ "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=2,n_used=2,b=1,m=32,n=8192,k=64","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=16,n_used=16,b=1,m=50,n=200,k=64","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f16,type_b=f32,n_mats=1,n_used=1,b=0,m=8,n=16,k=1","support","1","yes","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","0","no","SYCL" +"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=32,n_used=2,b=0,m=2880,n=32,k=2880","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=f32,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","1","yes","SYCL" @@ -7387,78 +7417,78 @@ "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=17,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=32,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=q4_K,type_b=f32,n_mats=8,n_used=4,b=1,m=512,n=129,k=256","support","1","yes","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=17,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=32,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=129,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=1,k=256","support","0","no","SYCL" -"SYCL0","MUL_MAT_ID","type_a=mxfp4,type_b=f32,n_mats=4,n_used=1,b=1,m=512,n=4,k=256","support","0","no","SYCL" 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"SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=1,k=256","support","1","yes","SYCL" "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=4,k=256","support","0","no","SYCL" "SYCL0","MUL_MAT_ID","type_a=iq2_xxs,type_b=f32,n_mats=4,n_used=1,b=0,m=512,n=5,k=256","support","0","no","SYCL" @@ -8589,64 +8619,68 @@ "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[1,2],trans_b=0","support","0","no","SYCL" "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,1],trans_b=0","support","0","no","SYCL" "SYCL0","OUT_PROD","type_a=iq2_xxs,type_b=f16,m=256,n=16,k=16,bs=[3,3],nr=[2,2],trans_b=0","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=1","support","0","no","SYCL" -"SYCL0","ADD_ID","type_a=f32,type_b=f32,n_embd=32,n_experts=4,n_experts_used=1,n_token=32","support","0","no","SYCL" 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+"SYCL0","ROUND","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","TRUNC","type=f16,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","SQR","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" "SYCL0","SQRT","type=f32,ne=[10,3,3,2]","support","1","yes","SYCL" "SYCL0","LOG","type=f32,ne=[10,5,4,3]","support","1","yes","SYCL" @@ -8666,9 +8700,13 @@ "SYCL0","CLAMP","type=f32,ne=[7,1,5,3],min=-0.500000,max=0.500000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[7,1,5,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","FLOOR","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","FLOOR","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","CEIL","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" +"SYCL0","CEIL","type=f32,ne=[1024,1024,1,1]","support","1","yes","SYCL" "SYCL0","ROUND","type=f32,ne=[7,1,5,3]","support","1","yes","SYCL" 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+"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=7,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16,10,10,10],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[60,10,10,10],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1023,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1024,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[1025,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[16384,1,1,1],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2047,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2048,2,1,3],k=15,ties=0","support","0","no","SYCL" +"SYCL0","TOP_K","type=f32,ne=[2049,2,1,3],k=15,ties=0","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=0","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=nearest,transpose=1","support","1","yes","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=nearest,flags=none","support","1","yes","SYCL" @@ -9445,6 +9860,10 @@ "SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=bicubic,transpose=1","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bicubic,flags=none","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bicubic,flags=none","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=0","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[512,512,3,2],scale_factor=2,mode=513,transpose=1","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=none","support","0","no","SYCL" +"SYCL0","UPSCALE","type=f32,ne=[5,7,11,13],ne_tgt=[2,5,7,11],mode=bilinear,flags=none","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[2,5,7,11],ne_tgt=[5,7,11,13],mode=bilinear,flags=align_corners","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[1,4,3,2],ne_tgt=[2,8,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" "SYCL0","UPSCALE","type=f32,ne=[4,1,3,2],ne_tgt=[1,1,3,2],mode=bilinear,flags=align_corners","support","0","no","SYCL" @@ -9479,9 +9898,23 @@ "SYCL0","PAD_REFLECT_1D","type=f32,ne_a=[3000,384,4,1],pad_0=10,pad_1=9","support","1","yes","SYCL" "SYCL0","ROLL","shift0=3,shift1=-2,shift3=1,shift4=-1","support","1","yes","SYCL" "SYCL0","ARANGE","type=f32,start=0.000000,stop=10.000000,step=1.000000","support","1","yes","SYCL" +"SYCL0","ARANGE","type=f32,start=0.000000,stop=1048576.000000,step=1.000000","support","1","yes","SYCL" "SYCL0","TIMESTEP_EMBEDDING","type=f32,ne_a=[2,1,1,1],dim=320,max_period=10000","support","1","yes","SYCL" "SYCL0","LEAKY_RELU","type=f32,ne_a=[10,5,4,3],negative_slope=0.100000","support","1","yes","SYCL" "SYCL0","CUMSUM","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[127,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[128,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[128,128,4,4]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[255,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[256,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[511,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[512,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[1023,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[1024,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[2047,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[2048,5,4,3]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[242004,1,1,1]","support","0","no","SYCL" +"SYCL0","CUMSUM","type=f32,ne=[375960,1,1,1]","support","0","no","SYCL" "SYCL0","XIELU","type=f32,ne=[10,5,4,3]","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=3","support","0","no","SYCL" "SYCL0","TRI","type=f32,ne=[10,10,4,3],tri_type=2","support","0","no","SYCL" @@ -9490,6 +9923,7 @@ "SYCL0","FILL","type=f32,ne=[10,10,4,3],c=0.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[303,207,11,3],c=2.000000","support","0","no","SYCL" "SYCL0","FILL","type=f32,ne=[800,600,4,4],c=-152.000000","support","0","no","SYCL" +"SYCL0","FILL","type=f32,ne=[2048,512,2,2],c=3.500000","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[10,10,4,3],ne_rhs=[3,10,4,3]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[11,11,1,1],ne_rhs=[5,11,1,1]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[17,17,2,4],ne_rhs=[9,17,2,4]","support","0","no","SYCL" @@ -9497,6 +9931,8 @@ "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[42,42,5,2],ne_rhs=[10,42,5,2]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,2,2],ne_rhs=[10,64,2,2]","support","0","no","SYCL" "SYCL0","SOLVE_TRI","type=f32,ne_lhs=[100,100,4,4],ne_rhs=[41,100,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[128,128,4,4],ne_rhs=[31,128,4,4]","support","0","no","SYCL" +"SYCL0","SOLVE_TRI","type=f32,ne_lhs=[64,64,4,4],ne_rhs=[300,64,4,4]","support","0","no","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[11,22,33,44],lp0=1,rp0=2,lp1=3,rp1=4,lp2=5,rp2=6,lp3=7,rp3=8,v=0","support","1","yes","SYCL" "SYCL0","PAD","type=f32,ne_a=[512,512,1,1],lp0=0,rp0=1,lp1=0,rp1=1,lp2=0,rp2=0,lp3=0,rp3=0,v=1","support","0","no","SYCL" diff --git a/ggml/src/ggml-sycl/add-id.cpp b/ggml/src/ggml-sycl/add-id.cpp new file mode 100644 index 0000000000..00c073cf93 --- /dev/null +++ b/ggml/src/ggml-sycl/add-id.cpp @@ -0,0 +1,77 @@ +#include +#include "common.hpp" +#include "add-id.hpp" + +static void add_id_kernel( + const float* src0, + const float* src1, + const int32_t* src2, + float* dst, + int64_t ne0, + int64_t ne1, + size_t nb01, + size_t nb02, + size_t nb11, + size_t nb21, + sycl::nd_item<3> item_ct1) { + const int64_t i1 = item_ct1.get_group(2); + const int64_t i2 = item_ct1.get_group(1); + + const int i11 = + *(const int32_t*)((const char*)src2 + i1 * sizeof(int32_t) + i2 * nb21); + + const size_t nb1 = ne0 * sizeof(float); + const size_t nb2 = ne1 * nb1; + + float* dst_row = (float*)((char*)dst + i1 * nb1 + i2 * nb2); + const float* src0_row = + (const float*)((const char*)src0 + i1 * nb01 + i2 * nb02); + const float* src1_row = (const float*)((const char*)src1 + i11 * nb11); + + for (int64_t i0 = item_ct1.get_local_id(2); i0 < ne0; + i0 += item_ct1.get_local_range(2)) { + dst_row[i0] = src0_row[i0] + src1_row[i0]; + } +} + +void ggml_sycl_add_id(ggml_backend_sycl_context& ctx, ggml_tensor* dst) { + const ggml_tensor* src0 = dst->src[0]; + const ggml_tensor* src1 = dst->src[1]; + const ggml_tensor* src2 = dst->src[2]; + + GGML_TENSOR_TERNARY_OP_LOCALS + + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(src2->type == GGML_TYPE_I32); + + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(nb10 == sizeof(float)); + GGML_ASSERT(nb20 == sizeof(int32_t)); + + const float* src0_d = (const float*)src0->data; + const float* src1_d = (const float*)src1->data; + const int32_t* src2_d = (const int32_t*)src2->data; + float* dst_d = (float*)dst->data; + + int threads = std::min((int)ne00, 768); // cols + ctx.stream()->parallel_for( + sycl::nd_range<3>( + sycl::range<3>(1, ne02, ne01) * sycl::range<3>(1, 1, threads), + sycl::range<3>(1, 1, threads)), + [=](sycl::nd_item<3> item_ct1) { + add_id_kernel( + src0_d, + src1_d, + src2_d, + dst_d, + ne0, + ne1, + nb01, + nb02, + nb11, + nb21, + item_ct1); + }); +} diff --git a/ggml/src/ggml-sycl/add-id.hpp b/ggml/src/ggml-sycl/add-id.hpp new file mode 100644 index 0000000000..e1b09ee8c7 --- /dev/null +++ b/ggml/src/ggml-sycl/add-id.hpp @@ -0,0 +1,8 @@ +#ifndef GGML_SYCL_ADD_ID_HPP +#define GGML_SYCL_ADD_ID_HPP + +#include "common.hpp" + +void ggml_sycl_add_id(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + +#endif // GGML_SYCL_ADD_ID_HPP diff --git a/ggml/src/ggml-sycl/common.hpp b/ggml/src/ggml-sycl/common.hpp index 637630c1d2..519638fd41 100644 --- a/ggml/src/ggml-sycl/common.hpp +++ b/ggml/src/ggml-sycl/common.hpp @@ -642,5 +642,22 @@ static __dpct_inline__ sycl::uint2 fast_div_modulo(uint32_t n, const sycl::uint3 return sycl::uint2(div_val, mod_val); } +static __dpct_inline__ int ggml_sycl_dp4a(const int a, const int b, int c) { + return dpct::dp4a(a, b, c); +} + +static __dpct_inline__ float ggml_sycl_e8m0_to_fp32(uint8_t x) { + uint32_t bits; + if (x == 0) { + bits = 0x00400000; + } else { + bits = (uint32_t) x << 23; + } + + float result; + memcpy(&result, &bits, sizeof(float)); + return result; +} + #endif // GGML_SYCL_COMMON_HPP diff --git a/ggml/src/ggml-sycl/convert.cpp b/ggml/src/ggml-sycl/convert.cpp index 7c6ea8a57a..8bdae36458 100644 --- a/ggml/src/ggml-sycl/convert.cpp +++ b/ggml/src/ggml-sycl/convert.cpp @@ -472,6 +472,16 @@ static void dequantize_row_iq4_nl_sycl(const void *vx, dst_t *y, const int64_t k } } +template +static void dequantize_row_mxfp4_sycl(const void * vx, dst_t * y, const int64_t k, dpct::queue_ptr stream) { + const int nb = (k + QK_K - 1) / QK_K; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * sycl::range<3>(1, 1, 32), sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_mxfp4(vx, y, item_ct1); + }); +} + template static void convert_unary_nc(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t s01, const int64_t s02, const int64_t s03, @@ -518,6 +528,7 @@ static void convert_unary_sycl(const void * vx, dst_t * y, const int64_t k, dpct convert_unary_nc_sycl(vx, y, k, 1, 1, 1, k, k, k, queue); } + to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { switch (type) { case GGML_TYPE_Q4_0: @@ -571,6 +582,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) { return dequantize_row_iq4_xs_sycl; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_sycl; + case GGML_TYPE_MXFP4: + return dequantize_row_mxfp4_sycl; case GGML_TYPE_F32: return convert_unary_sycl; #ifdef GGML_SYCL_HAS_BF16 @@ -636,6 +649,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) { return dequantize_row_iq4_xs_sycl; case GGML_TYPE_IQ4_NL: return dequantize_row_iq4_nl_sycl; + case GGML_TYPE_MXFP4: + return dequantize_row_mxfp4_sycl; case GGML_TYPE_F16: return convert_unary_sycl; #ifdef GGML_SYCL_HAS_BF16 diff --git a/ggml/src/ggml-sycl/dequantize.hpp b/ggml/src/ggml-sycl/dequantize.hpp index 540539bb22..da2a605daa 100644 --- a/ggml/src/ggml-sycl/dequantize.hpp +++ b/ggml/src/ggml-sycl/dequantize.hpp @@ -819,5 +819,23 @@ dequantize_block_iq4_xs(const void *__restrict__ vx, dst_t *__restrict__ yy, } } +template +static void dequantize_block_mxfp4(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + // auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int64_t i = item_ct1.get_group(2); + const block_mxfp4 * x = (const block_mxfp4 *) vx + i*(QK_K/QK_MXFP4); + + const int64_t tid = item_ct1.get_local_id(2); + const int64_t il = tid/8; // 0...3 + const int64_t ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 4*il; + const uint8_t * q4 = x[ib].qs + 4*il; + const float d = ggml_sycl_e8m0_to_fp32(x[ib].e); + for (int j = 0; j < 4; ++j) { + y[j+ 0] = d * kvalues_mxfp4[q4[j] & 0xf]*0.5f; + y[j+16] = d * kvalues_mxfp4[q4[j] >> 4]*0.5f; + } +} #endif // GGML_SYCL_DEQUANTIZE_HPP diff --git a/ggml/src/ggml-sycl/dpct/helper.hpp b/ggml/src/ggml-sycl/dpct/helper.hpp index f93cfa701f..30ec1e8daf 100644 --- a/ggml/src/ggml-sycl/dpct/helper.hpp +++ b/ggml/src/ggml-sycl/dpct/helper.hpp @@ -1860,10 +1860,31 @@ namespace dpct : id); } + template + using dot_product_acc_t = std::conditional_t< + std::is_unsigned_v && std::is_unsigned_v, + uint32_t, + int32_t>; + + template + sycl::vec extract_and_sign_or_zero_extend4(T val) { + return sycl::vec(val) + .template as, int8_t, uint8_t>, + 4>>() + .template convert(); + } + template - inline auto dp4a(T1 a, T2 b, T3 c) - { - return syclcompat::dp4a(a, b, c); + inline auto dp4a(T1 a, T2 b, T3 c) { + dot_product_acc_t res = c; + auto va = extract_and_sign_or_zero_extend4(a); + auto vb = extract_and_sign_or_zero_extend4(b); + res += va[0] * vb[0]; + res += va[1] * vb[1]; + res += va[2] * vb[2]; + res += va[3] * vb[3]; + return res; } struct sub_sat @@ -2972,6 +2993,38 @@ namespace dpct atomic_fetch_add(addr, operand, memoryOrder); } + inline unsigned int byte_level_permute( + unsigned int a, unsigned int b, unsigned int s) { + unsigned int ret; + ret = ((((std::uint64_t)b << 32 | a) >> (s & 0x7) * 8) & 0xff) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 4) & 0x7) * 8) & 0xff) + << 8) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 8) & 0x7) * 8) & 0xff) + << 16) | + (((((std::uint64_t)b << 32 | a) >> ((s >> 12) & 0x7) * 8) & 0xff) + << 24); + return ret; + } + + inline uint32_t byte_level_permute_custom( + uint32_t low32, uint32_t high32, uint32_t sel, int mode = 0) { + constexpr uint16_t lookup[6][4] = { + {0x3210, 0x4321, 0x5432, 0x6543}, // Forward 4-byte extract + {0x5670, 0x6701, 0x7012, 0x0123}, // Backward 4-byte extract + {0x0000, 0x1111, 0x2222, 0x3333}, // Replicate 8-bit values + {0x3210, 0x3211, 0x3222, 0x3333}, // Edge clamp left + {0x0000, 0x1110, 0x2210, 0x3210}, // Edge clamp right + {0x1010, 0x3232, 0x1010, 0x3232} // Replicate 16-bit values + }; + + if (mode >= 1 && mode <= 6) { + return byte_level_permute(low32, high32, lookup[mode - 1][sel & 0x3]); + } else if (!mode) { + return byte_level_permute(low32, high32, sel); + } + return 0; + } + } // COPY from DPCT head files #endif // GGML_SYCL_DPCT_HELPER_HPP diff --git a/ggml/src/ggml-sycl/element_wise.cpp b/ggml/src/ggml-sycl/element_wise.cpp index 7d54ce600e..8d83b2446b 100644 --- a/ggml/src/ggml-sycl/element_wise.cpp +++ b/ggml/src/ggml-sycl/element_wise.cpp @@ -911,6 +911,98 @@ static inline void ggml_sycl_op_swiglu(ggml_backend_sycl_context & ctx, ggml_ten }); } +__dpct_inline__ float ggml_sycl_op_swiglu_oai_single(float x, float g, float alpha = 1.702f, float limit = 7.0f) { + x = sycl::fmin(x, limit); + g = sycl::fmax(sycl::fmin(g, limit), -limit); + + float out_glu = x / (1.0f + sycl::native::exp(-x * alpha)); + out_glu = out_glu * (1.0f + g); + return out_glu; +} + + +template +static void swiglu_oai_kernel(const T * x, const T * g, T * dst, const int64_t k, + const int64_t n, const int64_t o0, const int64_t o1, + float alpha, float limit, sycl::nd_item<3> item_ct1) { + const int64_t i = int64_t(item_ct1.get_local_range(2)) * item_ct1.get_group(2) + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + const int64_t j0 = (i / n) * o0 + (i % n); + const int64_t j1 = o0 == o1 ? j0 : (i / n) * o1 + (i % n); + + float xi = x[j0]; + float gi = g[j1]; + + dst[i] = ggml_sycl_op_swiglu_oai_single(xi, gi, alpha, limit); +} + +template +static void swiglu_oai_sycl(const T * x, + const T * g, + T * dst, + const int64_t k, + const int64_t n, + const int64_t o0, + const int64_t o1, + const float alpha, + const float limit, + dpct::queue_ptr stream) { + const int64_t num_blocks = (k + SYCL_GLU_BLOCK_SIZE - 1) / SYCL_GLU_BLOCK_SIZE; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GLU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + swiglu_oai_kernel(x, g, dst, k, n, o0, o1, alpha, limit, item_ct1); + }); +} + +void ggml_sycl_op_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const ggml_tensor * src1 = dst->src[1]; + void * src0_d = src0->data; + void * src1_d = src1 ? src1->data : src0->data; + const int64_t src0_o = src0->nb[1]; + const int64_t src1_o = src1 ? src1->nb[1] : src0->nb[1]; + void * dst_d = dst->data; + const int64_t nc = src1 ? src0->ne[0] : src0->ne[0] / 2; + dpct::queue_ptr stream = ctx.stream(); + + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(src0->nb[0] == ggml_element_size(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->type == dst->type); + GGML_ASSERT(dst->ne[0] == nc); + GGML_ASSERT(ggml_nrows(dst) == ggml_nrows(src0)); + + if (src1) { + GGML_ASSERT(ggml_is_contiguous_1(src1)); + GGML_ASSERT(src1->nb[0] == ggml_element_size(src1)); + GGML_ASSERT(src1->ne[0] == nc); + GGML_ASSERT(src0->type == src1->type); + } + + //const int32_t swapped = ((const int32_t *) dst->op_params)[1]; + const int32_t swapped = ggml_get_op_params_i32(dst, 1); + const float alpha = ggml_get_op_params_f32(dst, 2); + const float limit = ggml_get_op_params_f32(dst, 3); + + float * src0_p = (float *) src0_d; + float * src1_p = (float *) src1_d; + + if (!src1) { + src0_p += swapped ? nc : 0; + src1_p += swapped ? 0 : nc; + } + + swiglu_oai_sycl(src0_p, src1_p, (float *)dst_d, ggml_nelements(dst), nc, src0_o / sizeof(float), src1_o / sizeof(float), alpha, limit, stream); +} + static inline void ggml_sycl_op_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_detail::dispatch_ggml_sycl_op_fused_glu(ctx, dst, [](const auto* x_ptr, const auto* g_ptr, auto* dst_ptr, uint64_t k, uint64_t n, uint64_t o0, uint64_t o1, queue_ptr main_stream) { @@ -1070,6 +1162,11 @@ void ggml_sycl_swiglu(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { ggml_sycl_op_swiglu(ctx, dst); } +void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { + scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); + ggml_sycl_op_swiglu_oai(ctx, dst); +} + void ggml_sycl_geglu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/1); ggml_sycl_op_geglu_erf(ctx, dst); diff --git a/ggml/src/ggml-sycl/element_wise.hpp b/ggml/src/ggml-sycl/element_wise.hpp index fcf93295cb..0913a2e529 100644 --- a/ggml/src/ggml-sycl/element_wise.hpp +++ b/ggml/src/ggml-sycl/element_wise.hpp @@ -5,6 +5,8 @@ #include "ggml.h" #include // For std::numeric_limits +#define SYCL_GLU_BLOCK_SIZE 256 + template T neg_infinity() { return -std::numeric_limits::infinity(); @@ -41,6 +43,8 @@ void ggml_sycl_silu(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_gelu_quick(ggml_backend_sycl_context & ctx, ggml_tensor * dst); +void ggml_sycl_swiglu_oai(ggml_backend_sycl_context & ctx, ggml_tensor * dst); + void ggml_sycl_gelu_erf(ggml_backend_sycl_context & ctx, ggml_tensor * dst); void ggml_sycl_tanh(ggml_backend_sycl_context & ctx, ggml_tensor * dst); diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 7449a91609..e996d98be8 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -39,6 +39,7 @@ #include "ggml-impl.h" #include "ggml-backend-impl.h" +#include "ggml-sycl/add-id.hpp" #include "ggml-sycl/backend.hpp" #include "ggml-sycl/common.hpp" #include "ggml-sycl/element_wise.hpp" @@ -3313,6 +3314,7 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + // mmvq and mmq need the __dp4a instruction which is available for gen12+ // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); @@ -3320,7 +3322,6 @@ static void ggml_sycl_mul_mat(ggml_backend_sycl_context & ctx, const ggml_tensor use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); #endif // SYCL_USE_XMX - // mmvq path is faster in the CUDA backend. if (!g_ggml_sycl_prioritize_dmmv && (ctx.stream()->get_backend() == sycl::backend::ext_oneapi_cuda // Dispatch becomes obscure with the reorder, MMVQ when the reorder optimization @@ -3711,6 +3712,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_OP_ADD1: // TODO: more efficient implementation ggml_sycl_add(ctx, dst); break; + case GGML_OP_ADD_ID: + ggml_sycl_add_id(ctx, dst); + break; case GGML_OP_SUB: ggml_sycl_sub(ctx, dst); break; @@ -3803,6 +3807,9 @@ static bool ggml_sycl_compute_forward(ggml_backend_sycl_context & ctx, struct gg case GGML_GLU_OP_SWIGLU: ggml_sycl_swiglu(ctx, dst); break; + case GGML_GLU_OP_SWIGLU_OAI: + ggml_sycl_swiglu_oai(ctx, dst); + break; case GGML_GLU_OP_GEGLU_ERF: ggml_sycl_geglu_erf(ctx, dst); break; @@ -4397,6 +4404,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_GLU_OP_REGLU: case GGML_GLU_OP_GEGLU: case GGML_GLU_OP_SWIGLU: + case GGML_GLU_OP_SWIGLU_OAI: case GGML_GLU_OP_GEGLU_ERF: case GGML_GLU_OP_GEGLU_QUICK: return ggml_is_contiguous_1(op->src[0]); @@ -4424,15 +4432,18 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g } } ggml_type src0_type = op->src[0]->type; - if (src0_type == GGML_TYPE_BF16 || src0_type == GGML_TYPE_MXFP4) { - // TODO: support MXFP4 + if (src0_type == GGML_TYPE_BF16 ) { + // TODO: support GGML_TYPE_BF16 // FIXME: keep a list of supported types to avoid breaking the backend when a new type is added return false; } + // TODO: The configuration below needs more work to be supported with oneDNN - if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && a->ne[2] > 1 && a->ne[3] > 1) { - return false; + if (ggml_is_permuted(a) && !ggml_is_contiguous(a) && + a->ne[2] > 1 && a->ne[3] > 1 && src0_type == GGML_TYPE_F16) { + return false; } + // TODO: This specific configuration can fail with oneDNN and needs more debugging if (!ggml_is_permuted(a) && ggml_is_permuted(b) && b->ne[2] > 1 && b->ne[3] > 1 && a->ne[0] > 128 && a->ne[2] == 1 && src0_type == GGML_TYPE_F16) { @@ -4553,9 +4564,9 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g case GGML_OP_VIEW: case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: - return true; case GGML_OP_ADD: case GGML_OP_ADD1: + case GGML_OP_ADD_ID: case GGML_OP_SUB: case GGML_OP_COUNT_EQUAL: case GGML_OP_MUL: diff --git a/ggml/src/ggml-sycl/mmvq.cpp b/ggml/src/ggml-sycl/mmvq.cpp index 5b7f064074..316aa0d0fb 100644 --- a/ggml/src/ggml-sycl/mmvq.cpp +++ b/ggml/src/ggml-sycl/mmvq.cpp @@ -595,6 +595,25 @@ static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, } } +static void mul_mat_vec_mxfp4_q8_1_sycl(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_MXFP4 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + + { + stream->submit([&](sycl::handler & cgh) { + cgh.parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] { + mul_mat_vec_q( + vx, vy, dst, ncols, nrows, item_ct1); + }); + }); + } +} + + static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, float *dst, const int ncols, const int nrows, @@ -1123,6 +1142,9 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens case GGML_TYPE_IQ4_XS: mul_mat_vec_iq4_xs_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); break; + case GGML_TYPE_MXFP4: + mul_mat_vec_mxfp4_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream); + break; default: GGML_ABORT("fatal error"); } diff --git a/ggml/src/ggml-sycl/pad.cpp b/ggml/src/ggml-sycl/pad.cpp index 413712c584..f989c5e4b8 100644 --- a/ggml/src/ggml-sycl/pad.cpp +++ b/ggml/src/ggml-sycl/pad.cpp @@ -14,10 +14,10 @@ #include "pad.hpp" static void pad_f32(const float * src, float * dst, - const int lp0, const int rp0, const int lp1, const int rp1, - const int lp2, const int rp2, const int lp3, const int rp3, - const int ne0, const int ne1, const int ne2, const int ne3) { - auto item_ct1 = sycl::ext::oneapi::this_work_item::get_nd_item<3>(); + const int lp0, const int rp0, const int lp1, const int rp1, + const int lp2, const int rp2, const int lp3, const int rp3, + const int ne0, const int ne1, const int ne2, const int ne3, + sycl::nd_item<3> item_ct1) { int i0 = item_ct1.get_local_id(2) + item_ct1.get_group(2) * item_ct1.get_local_range(2); int i1 = item_ct1.get_group(1); @@ -63,7 +63,7 @@ static void pad_f32_sycl(const float *src, float *dst, const int lp0, sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { pad_f32(src, dst, lp0, rp0, lp1, rp1, lp2, rp2, lp3, rp3, ne0, ne1, - ne2, ne3); + ne2, ne3, item_ct1); }); } diff --git a/ggml/src/ggml-sycl/ssm_conv.cpp b/ggml/src/ggml-sycl/ssm_conv.cpp index 0dc0f71c9a..eea9a73d67 100644 --- a/ggml/src/ggml-sycl/ssm_conv.cpp +++ b/ggml/src/ggml-sycl/ssm_conv.cpp @@ -88,7 +88,7 @@ void ggml_sycl_ssm_conv(ggml_backend_sycl_context & ctx, ggml_tensor * dst) { GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(src1->nb[0] == sizeof(float)); - GGML_ASSERT(src0->nb[1] == src0->ne[0] * static_cast(sizeof(float))); + GGML_ASSERT(src0->nb[1] == src0->ne[0] * sizeof(float)); const int src_stride_inner = ncs; const int src_stride_seq = ncs * d_inner; diff --git a/ggml/src/ggml-sycl/vecdotq.hpp b/ggml/src/ggml-sycl/vecdotq.hpp index 4088ddb54f..43482b3672 100644 --- a/ggml/src/ggml-sycl/vecdotq.hpp +++ b/ggml/src/ggml-sycl/vecdotq.hpp @@ -20,6 +20,18 @@ typedef float (*vec_dot_q_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); +static __dpct_inline__ int get_int_b1(const void * x, const int & i32) { + const uint8_t * x8 = (const uint8_t *) x; + + int x32 = x8[4*i32 + 0] << 0; + x32 |= x8[4*i32 + 1] << 8; + x32 |= x8[4*i32 + 2] << 16; + x32 |= x8[4*i32 + 3] << 24; + + return x32; +} + + static __dpct_inline__ int get_int_from_int8(const int8_t* x8, const int& i32) { const uint16_t* x16 = (const uint16_t*)(x8 + sizeof(int) * i32); // assume at least 2 byte @@ -75,6 +87,28 @@ static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4, val2 = v1 | (v2 << 16); } +static __dpct_inline__ sycl::int2 get_int_from_table_16( + const int& q4, const int8_t* table) { + const uint32_t* table32 = (const uint32_t*)table; + uint32_t tmp[2]; + const uint32_t low_high_selection_indices = + (0x32103210 | ((q4 & 0x88888888) >> 1)); +#pragma unroll + for (uint32_t i = 0; i < 2; ++i) { + const uint32_t shift = 16 * i; + + const uint32_t low = + dpct::byte_level_permute(table32[0], table32[1], q4 >> shift); + const uint32_t high = + dpct::byte_level_permute(table32[2], table32[3], q4 >> shift); + tmp[i] = dpct::byte_level_permute( + low, high, low_high_selection_indices >> shift); + } + return sycl::int2( + dpct::byte_level_permute(tmp[0], tmp[1], 0x6420), + dpct::byte_level_permute(tmp[0], tmp[1], 0x7531)); +} + #define VDR_Q2_K_Q8_1_MMVQ 1 // contiguous v/x values @@ -685,6 +719,30 @@ vec_dot_q4_1_q8_1(const void *__restrict__ vbq, return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); } +#define VDR_MXFP4_Q8_1_MMVQ 2 +#define VDR_MXFP4_Q8_1_MMQ 4 + +static __dpct_inline__ float vec_dot_mxfp4_q8_1(const void * __restrict__ vbq, + const block_q8_1 * __restrict__ bq8_1, + const int & iqs) { + const block_mxfp4 * bq4 = (const block_mxfp4 *) vbq; + + const int * q8 = (const int *) bq8_1->qs + iqs; + + int sumi = 0; +#pragma unroll + for (int l = 0; l < VDR_MXFP4_Q8_1_MMVQ; ++l) { + const int aux_q4 = get_int_b1(bq4->qs, iqs + l); + const sycl::int2 v = get_int_from_table_16(aux_q4, kvalues_mxfp4); + sumi = ggml_sycl_dp4a(v.x(), q8[l + 0], sumi); + sumi = ggml_sycl_dp4a(v.y(), q8[l + 4], sumi); + } + + const float d = ggml_sycl_e8m0_to_fp32(bq4->e) * 0.5f * (bq8_1->ds)[0]; + return d * sumi; +} + + static __dpct_inline__ float vec_dot_q5_0_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { From b1f3a6e5db7b782ef077bd0e8253ce03283b1f37 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 15 Dec 2025 09:24:59 +0100 Subject: [PATCH 36/40] llama: automatically set parameters not set by the user in such a way that maximizes GPU utilization (#16653) * llama: automatically fit args to free memory llama-fit-params tool * fix CI * hints for bug reports, ensure no reallocation * fix segfault with Vulkan * add llama-fit-params to CI * fix CI * fix CI * fix CI * minor adjustments * fix assignment of 1 dense layer * fix logger not being reset on model load failure * remove --n-gpu-layer hint on model load failure * fix llama-fit-params verbosity * fix edge case * fix typo [no ci] --- .github/ISSUE_TEMPLATE/011-bug-results.yml | 9 +- ci/run.sh | 6 + common/arg.cpp | 33 +- common/common.cpp | 21 +- common/common.h | 14 +- ggml/include/ggml-alloc.h | 9 + ggml/include/ggml-backend.h | 1 + ggml/include/ggml.h | 3 +- ggml/src/ggml-alloc.c | 68 ++- ggml/src/ggml-backend.cpp | 23 +- ggml/src/ggml.c | 5 + include/llama.h | 19 +- src/llama-context.cpp | 69 ++- src/llama-context.h | 10 +- src/llama-hparams.h | 1 + src/llama-impl.cpp | 4 + src/llama-kv-cache.cpp | 23 +- src/llama-model-loader.cpp | 2 + src/llama-model-loader.h | 2 + src/llama-model.cpp | 38 +- src/llama-quant.cpp | 2 +- src/llama.cpp | 650 ++++++++++++++++++++- tools/CMakeLists.txt | 1 + tools/fit-params/CMakeLists.txt | 8 + tools/fit-params/README.md | 55 ++ tools/fit-params/fit-params.cpp | 62 ++ 26 files changed, 1075 insertions(+), 63 deletions(-) create mode 100644 tools/fit-params/CMakeLists.txt create mode 100644 tools/fit-params/README.md create mode 100644 tools/fit-params/fit-params.cpp diff --git a/.github/ISSUE_TEMPLATE/011-bug-results.yml b/.github/ISSUE_TEMPLATE/011-bug-results.yml index c42a14ff83..b815e70a8d 100644 --- a/.github/ISSUE_TEMPLATE/011-bug-results.yml +++ b/.github/ISSUE_TEMPLATE/011-bug-results.yml @@ -11,7 +11,7 @@ body: (i.e. the generated text) are incorrect or llama.cpp crashes during model evaluation. If you encountered the issue while using an external UI (e.g. ollama), please reproduce your issue using one of the examples/binaries in this repository. - The `llama-cli` binary can be used for simple and reproducible model inference. + The `llama-completion` binary can be used for simple and reproducible model inference. - type: textarea id: version attributes: @@ -74,9 +74,12 @@ body: Please give us a summary of the problem and tell us how to reproduce it. If you can narrow down the bug to specific hardware, compile flags, or command line arguments, that information would be very much appreciated by us. + + If possible, please try to reproduce the issue using `llama-completion` with `-fit off`. + If you can only reproduce the issue with `-fit on`, please provide logs both with and without `--verbose`. placeholder: > - e.g. when I run llama-cli with -ngl 99 I get garbled outputs. - When I use -ngl 0 it works correctly. + e.g. when I run llama-completion with `-fa on` I get garbled outputs for very long prompts. + With short prompts or `-fa off` it works correctly. Here are the exact commands that I used: ... validations: required: true diff --git a/ci/run.sh b/ci/run.sh index 0676504b3e..0a4a0e41eb 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -398,6 +398,8 @@ function gg_run_qwen3_0_6b { ./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc) ./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc) + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + (time ./bin/llama-completion -no-cnv --model ${model_f16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-completion -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log (time ./bin/llama-completion -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -523,6 +525,8 @@ function gg_run_embd_bge_small { ./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0 + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + (time ./bin/llama-embedding --model ${model_f16} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/llama-embedding --model ${model_q8_0} -p "I believe the meaning of life is" -ngl 99 -c 0 --no-op-offload) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -563,6 +567,8 @@ function gg_run_rerank_tiny { model_f16="${path_models}/ggml-model-f16.gguf" + (time ./bin/llama-fit-params --model ${model_f16} 2>&1 | tee -a $OUT/${ci}-fp-f16.log) + # for this model, the SEP token is "" (time ./bin/llama-embedding --model ${model_f16} -p "what is panda?\thi\nwhat is panda?\tit's a bear\nwhat is panda?\tThe giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China." -ngl 99 -c 0 --pooling rank --embd-normalize -1 --no-op-offload --verbose-prompt) 2>&1 | tee -a $OUT/${ci}-rk-f16.log diff --git a/common/arg.cpp b/common/arg.cpp index aaa7b92a2e..acf4c8f8a8 100644 --- a/common/arg.cpp +++ b/common/arg.cpp @@ -20,6 +20,7 @@ #include #include +#include #include #include #include @@ -529,7 +530,9 @@ static bool common_params_parse_ex(int argc, char ** argv, common_params_context params.kv_overrides.back().key[0] = 0; } - if (!params.tensor_buft_overrides.empty()) { + // pad tensor_buft_overrides for llama_params_fit: + const size_t ntbo = llama_max_tensor_buft_overrides(); + while (params.tensor_buft_overrides.size() < ntbo) { params.tensor_buft_overrides.push_back({nullptr, nullptr}); } @@ -2153,6 +2156,34 @@ common_params_context common_params_parser_init(common_params & params, llama_ex } } ).set_env("LLAMA_ARG_MAIN_GPU")); + add_opt(common_arg( + { "-fit", "--fit" }, "[on|off]", + string_format("whether to adjust unset arguments to fit in device memory ('on' or 'off', default: '%s')", params.fit_params ? "on" : "off"), + [](common_params & params, const std::string & value) { + if (is_truthy(value)) { + params.fit_params = true; + } else if (is_falsey(value)) { + params.fit_params = false; + } else { + throw std::runtime_error( + string_format("error: unkown value for --fit: '%s'\n", value.c_str())); + } + } + ).set_env("LLAMA_ARG_FIT")); + add_opt(common_arg( + { "-fitt", "--fit-target" }, "MiB", + string_format("target margin per device for --fit option, default: %zu", params.fit_params_target/(1024*1024)), + [](common_params & params, int value) { + params.fit_params_target = value * size_t(1024*1024); + } + ).set_env("LLAMA_ARG_FIT_TARGET")); + add_opt(common_arg( + { "-fitc", "--fit-ctx" }, "N", + string_format("minimum ctx size that can be set by --fit option, default: %" PRIu32, params.fit_params_min_ctx), + [](common_params & params, int value) { + params.fit_params_min_ctx = value; + } + ).set_env("LLAMA_ARG_FIT_CTX")); add_opt(common_arg( {"--check-tensors"}, string_format("check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false"), diff --git a/common/common.cpp b/common/common.cpp index b76dfa10ea..5a8cf52485 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1088,7 +1088,15 @@ struct common_init_result::impl { common_init_result::common_init_result(common_params & params) : pimpl(new impl{}) { - const auto mparams = common_model_params_to_llama(params); + auto mparams = common_model_params_to_llama(params); + auto cparams = common_context_params_to_llama(params); + + if (params.fit_params) { + LOG_INF("%s: fitting params to device memory, to report bugs during this step use -fit off (or --verbose if you can't)\n", __func__); + llama_params_fit(params.model.path.c_str(), &mparams, &cparams, + params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx, + params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); + } llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams); if (model == NULL) { @@ -1103,8 +1111,6 @@ common_init_result::common_init_result(common_params & params) : // TODO: fix naming common_init_sampler_from_model(model, params.sampling); - auto cparams = common_context_params_to_llama(params); - if (params.sampling.ignore_eos && llama_vocab_eos(vocab) == LLAMA_TOKEN_NULL) { LOG_WRN("%s: warning: vocab does not have an EOS token, ignoring --ignore-eos\n", __func__); params.sampling.ignore_eos = false; @@ -1143,8 +1149,7 @@ common_init_result::common_init_result(common_params & params) : llama_context * lctx = llama_init_from_model(model, cparams); if (lctx == NULL) { - LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str()); return; } @@ -1176,15 +1181,13 @@ common_init_result_ptr common_init_from_params(common_params & params) { llama_model * model = res->model(); if (model == NULL) { - LOG_ERR("%s: failed to load model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); + LOG_ERR("%s: failed to load model '%s'\n", __func__, params.model.path.c_str()); return res; } llama_context * lctx = res->context(); if (lctx == NULL) { - LOG_ERR("%s: failed to create context with model '%s', try reducing --n-gpu-layers if you're running out of VRAM\n", - __func__, params.model.path.c_str()); + LOG_ERR("%s: failed to create context with model '%s'\n", __func__, params.model.path.c_str()); return res; } diff --git a/common/common.h b/common/common.h index 4edb74b706..d70744840f 100644 --- a/common/common.h +++ b/common/common.h @@ -99,6 +99,7 @@ enum llama_example { LLAMA_EXAMPLE_TTS, LLAMA_EXAMPLE_DIFFUSION, LLAMA_EXAMPLE_FINETUNE, + LLAMA_EXAMPLE_FIT_PARAMS, LLAMA_EXAMPLE_COUNT, }; @@ -306,8 +307,8 @@ struct lr_opt { struct ggml_opt_optimizer_params common_opt_lr_pars(void * userdata); struct common_params { - int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 4096; // context size + int32_t n_predict = -1; // max. number of new tokens to predict, -1 == no limit + int32_t n_ctx = 0; // context size, 0 == context the model was trained with int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt @@ -328,9 +329,12 @@ struct common_params { // offload params std::vector devices; // devices to use for offloading - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors - float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + bool fit_params = true; // whether to fit unset model/context parameters to free device memory + size_t fit_params_target = 1024 * 1024*1024; // margin per device in bytes for fitting parameters to free memory + int32_t fit_params_min_ctx = 4096; // minimum context size to set when trying to reduce memory use enum llama_split_mode split_mode = LLAMA_SPLIT_MODE_LAYER; // how to split the model across GPUs diff --git a/ggml/include/ggml-alloc.h b/ggml/include/ggml-alloc.h index 2cb150fd2a..78aa059dde 100644 --- a/ggml/include/ggml-alloc.h +++ b/ggml/include/ggml-alloc.h @@ -53,7 +53,14 @@ GGML_API void ggml_gallocr_free(ggml_gallocr_t galloc); // call with a worst-case graph to avoid buffer reallocations // not strictly required for single buffer usage: ggml_gallocr_alloc_graph will reallocate the buffers automatically if needed // returns false if the buffer allocation failed +// ggml_gallocr_resrve_n_size writes the buffer sizes per galloc buffer that would be allocated by ggml_gallocr_reserve_n to sizes GGML_API bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph * graph); +GGML_API void ggml_gallocr_reserve_n_size( + ggml_gallocr_t galloc, + struct ggml_cgraph * graph, + const int * node_buffer_ids, + const int * leaf_buffer_ids, + size_t * sizes); GGML_API bool ggml_gallocr_reserve_n( ggml_gallocr_t galloc, struct ggml_cgraph * graph, @@ -68,6 +75,8 @@ GGML_API size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_i // Utils // Create a buffer and allocate all the tensors in a ggml_context +// ggml_backend_alloc_ctx_tensors_from_buft_size returns the size of the buffer that would be allocated by ggml_backend_alloc_ctx_tensors_from_buft +GGML_API size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft); GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft); GGML_API struct ggml_backend_buffer * ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend); diff --git a/ggml/include/ggml-backend.h b/ggml/include/ggml-backend.h index f1b7407859..4ed5f35774 100644 --- a/ggml/include/ggml-backend.h +++ b/ggml/include/ggml-backend.h @@ -307,6 +307,7 @@ extern "C" { GGML_API void ggml_backend_sched_free(ggml_backend_sched_t sched); // Initialize backend buffers from a measure graph + GGML_API void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes); GGML_API bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph); // returns success GGML_API int ggml_backend_sched_get_n_backends(ggml_backend_sched_t sched); diff --git a/ggml/include/ggml.h b/ggml/include/ggml.h index 686da3dbd1..20c912d0e9 100644 --- a/ggml/include/ggml.h +++ b/ggml/include/ggml.h @@ -2615,7 +2615,8 @@ extern "C" { // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. - GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); + GGML_API void ggml_log_get(ggml_log_callback * log_callback, void ** user_data); + GGML_API void ggml_log_set(ggml_log_callback log_callback, void * user_data); GGML_API struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor); diff --git a/ggml/src/ggml-alloc.c b/ggml/src/ggml-alloc.c index ec16cbda9f..41419b617b 100644 --- a/ggml/src/ggml-alloc.c +++ b/ggml/src/ggml-alloc.c @@ -594,7 +594,9 @@ static bool ggml_gallocr_is_own(ggml_gallocr_t galloc, struct ggml_tensor * t) { } static bool ggml_gallocr_is_allocated(ggml_gallocr_t galloc, struct ggml_tensor * t) { - return t->data != NULL || ggml_gallocr_hash_get(galloc, t)->allocated; + return t->data != NULL // tensor data already set externally + || t->buffer // tensor on external buffer (but not yet allocated) + || ggml_gallocr_is_own(galloc, t); // tensor will be allocated by galloc } // free the extra space at the end if the new tensor is smaller @@ -823,7 +825,8 @@ static void ggml_gallocr_alloc_graph_impl(ggml_gallocr_t galloc, struct ggml_cgr } } -bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { +static bool ggml_gallocr_reserve_n_impl( + ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, bool no_alloc) { size_t min_hash_size = graph->n_nodes + graph->n_leafs; // add 25% margin to avoid hash collisions min_hash_size += min_hash_size / 4; @@ -928,16 +931,19 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0; if (cur_size > 0) { GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", - __func__, ggml_backend_buft_name(galloc->bufts[i]), - cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); + __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0); } } #endif ggml_vbuffer_free(galloc->buffers[i]); - galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE); - if (galloc->buffers[i] == NULL) { - GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); - return false; + if (no_alloc) { + galloc->buffers[i] = NULL; + } else { + galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE); + if (galloc->buffers[i] == NULL) { + GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size); + return false; + } } } } @@ -945,6 +951,21 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c return true; } +void ggml_gallocr_reserve_n_size( + ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids, size_t * sizes) { + GGML_ASSERT(ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ true)); + for (int i = 0; i < galloc->n_buffers; i++) { + sizes[i] = 0; + for (int c = 0; c < galloc->buf_tallocs[i]->n_chunks; c++) { + sizes[i] += galloc->buf_tallocs[i]->chunks[c]->max_size; + } + } +} + +bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, const int * node_buffer_ids, const int * leaf_buffer_ids) { + return ggml_gallocr_reserve_n_impl(galloc, graph, node_buffer_ids, leaf_buffer_ids, /*no_alloc =*/ false); +} + bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) { return ggml_gallocr_reserve_n(galloc, graph, NULL, NULL); } @@ -1147,7 +1168,8 @@ static bool alloc_tensor_range(struct ggml_context * ctx, return true; } -ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { +static ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft_impl( + struct ggml_context * ctx, ggml_backend_buffer_type_t buft, size_t * nbytes_total, bool no_alloc) { GGML_ASSERT(ggml_get_no_alloc(ctx) == true); size_t alignment = ggml_backend_buft_get_alignment(buft); @@ -1155,6 +1177,7 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte ggml_backend_buffer_t * buffers = NULL; size_t n_buffers = 0; + *nbytes_total = 0; size_t cur_buf_size = 0; struct ggml_tensor * first = ggml_get_first_tensor(ctx); @@ -1166,10 +1189,11 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte if (cur_buf_size > 0 && (cur_buf_size + this_size) > max_size) { // allocate tensors in the current buffer - if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { + if (!no_alloc && !alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { return NULL; } first = t; + *nbytes_total += cur_buf_size; cur_buf_size = this_size; } else { cur_buf_size += this_size; @@ -1178,15 +1202,21 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte // allocate remaining tensors if (cur_buf_size > 0) { - if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { + *nbytes_total += cur_buf_size; + if (!no_alloc && !alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { return NULL; } } + if (no_alloc) { + return NULL; + } + if (n_buffers == 0) { #ifndef NDEBUG GGML_LOG_DEBUG("%s: all tensors in the context are already allocated\n", __func__); #endif + GGML_ASSERT(!buffers); return NULL; } @@ -1196,10 +1226,24 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte } else { buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers); } - free(buffers); + if (buffers) { + free(buffers); // can be NULL if context is empty or no_alloc + } return buffer; } +size_t ggml_backend_alloc_ctx_tensors_from_buft_size(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { + size_t nbytes_total = 0; + ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc=*/ true); + GGML_ASSERT(!buf); + return nbytes_total; +} + +ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { + size_t nbytes_total = 0; + return ggml_backend_alloc_ctx_tensors_from_buft_impl(ctx, buft, &nbytes_total, /*no_alloc =*/ false); +} + ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors(struct ggml_context * ctx, ggml_backend_t backend) { return ggml_backend_alloc_ctx_tensors_from_buft(ctx, ggml_backend_get_default_buffer_type(backend)); } diff --git a/ggml/src/ggml-backend.cpp b/ggml/src/ggml-backend.cpp index 08681f35e3..8547ecc849 100644 --- a/ggml/src/ggml-backend.cpp +++ b/ggml/src/ggml-backend.cpp @@ -36,12 +36,11 @@ const char * ggml_backend_buft_name(ggml_backend_buffer_type_t buft) { } ggml_backend_buffer_t ggml_backend_buft_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + GGML_ASSERT(buft); if (size == 0) { // return a dummy buffer for zero-sized allocations return ggml_backend_buffer_init(buft, {}, NULL, 0); } - - GGML_ASSERT(buft); return buft->iface.alloc_buffer(buft, size); } @@ -128,6 +127,12 @@ void * ggml_backend_buffer_get_base(ggml_backend_buffer_t buffer) { return NULL; } + // FIXME JG: a multi_buffer has a non-zero size, according to the above comment get_base is not optional, + // I don't know whether the above comment is correct + if (!buffer->iface.get_base) { + return NULL; + } + void * base = buffer->iface.get_base(buffer); GGML_ASSERT(base != NULL && "backend buffer base cannot be NULL"); @@ -1727,6 +1732,20 @@ void ggml_backend_sched_reset(ggml_backend_sched_t sched) { sched->is_alloc = false; } +void ggml_backend_sched_reserve_size(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph, size_t * sizes) { + GGML_ASSERT(sched); + GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs); + GGML_ASSERT(sizes); + + ggml_backend_sched_reset(sched); + + ggml_backend_sched_synchronize(sched); + + ggml_backend_sched_split_graph(sched, measure_graph); + + ggml_gallocr_reserve_n_size(sched->galloc, &sched->graph, sched->node_backend_ids, sched->leaf_backend_ids, sizes); +} + bool ggml_backend_sched_reserve(ggml_backend_sched_t sched, struct ggml_cgraph * measure_graph) { GGML_ASSERT(sched); GGML_ASSERT((int)sched->hash_set.size >= measure_graph->n_nodes + measure_graph->n_leafs); diff --git a/ggml/src/ggml.c b/ggml/src/ggml.c index f0913cd359..eb3ae72eaa 100644 --- a/ggml/src/ggml.c +++ b/ggml/src/ggml.c @@ -7566,6 +7566,11 @@ size_t ggml_quantize_chunk( //////////////////////////////////////////////////////////////////////////////// +void ggml_log_get(ggml_log_callback * log_callback, void ** user_data) { + *log_callback = g_logger_state.log_callback; + *user_data = g_logger_state.log_callback_user_data; +} + void ggml_log_set(ggml_log_callback log_callback, void * user_data) { g_logger_state.log_callback = log_callback ? log_callback : ggml_log_callback_default; g_logger_state.log_callback_user_data = user_data; diff --git a/include/llama.h b/include/llama.h index b52eaacfa7..f862930099 100644 --- a/include/llama.h +++ b/include/llama.h @@ -313,6 +313,7 @@ extern "C" { bool check_tensors; // validate model tensor data bool use_extra_bufts; // use extra buffer types (used for weight repacking) bool no_host; // bypass host buffer allowing extra buffers to be used + bool no_alloc; // only load metadata and simulate memory allocations }; // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations @@ -466,10 +467,24 @@ extern "C" { // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); + // fits mparams and cparams to free device memory (assumes system memory is unlimited) + // returns true if the parameters could be successfully modified to fit device memory + // this function is NOT thread safe because it modifies the global llama logger state + LLAMA_API bool llama_params_fit( + const char * path_model, + struct llama_model_params * mparams, + struct llama_context_params * cparams, + float * tensor_split, // writable buffer for tensor split, needs at least llama_max_devices elements + struct llama_model_tensor_buft_override * tensor_buft_overrides, // writable buffer for overrides, needs at least llama_max_tensor_buft_overrides elements + size_t margin, // margin of memory to leave per device in bytes + uint32_t n_ctx_min, // minimum context size to set when trying to reduce memory use + enum ggml_log_level log_level); // minimum log level to print during fitting, lower levels go to debug log + LLAMA_API int64_t llama_time_us(void); LLAMA_API size_t llama_max_devices(void); LLAMA_API size_t llama_max_parallel_sequences(void); + LLAMA_API size_t llama_max_tensor_buft_overrides(void); LLAMA_API bool llama_supports_mmap (void); LLAMA_API bool llama_supports_mlock (void); @@ -1354,7 +1369,9 @@ extern "C" { // Set callback for all future logging events. // If this is not called, or NULL is supplied, everything is output on stderr. - LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); + // The logger state is global so these functions are NOT thread safe. + LLAMA_API void llama_log_get(ggml_log_callback * log_callback, void ** user_data); + LLAMA_API void llama_log_set(ggml_log_callback log_callback, void * user_data); // // Performance utils diff --git a/src/llama-context.cpp b/src/llama-context.cpp index 2a17e44ecd..8786d4ee3e 100644 --- a/src/llama-context.cpp +++ b/src/llama-context.cpp @@ -258,6 +258,7 @@ llama_context::llama_context( backend_buft.clear(); backend_ptrs.clear(); + backend_buf_exp_size.clear(); for (auto & backend : backends) { auto * buft = ggml_backend_get_default_buffer_type(backend.get()); @@ -274,6 +275,7 @@ llama_context::llama_context( backend_buft.push_back(buft); backend_ptrs.push_back(backend.get()); + backend_buf_exp_size.push_back(0); } LLAMA_LOG_DEBUG("%s: backend_ptrs.size() = %zu\n", __func__, backend_ptrs.size()); @@ -389,7 +391,8 @@ llama_context::llama_context( // reserve pp (prompt processing) graph first so that buffers are only allocated once { - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), + model.hparams.no_alloc, model.hparams.no_alloc ? backend_buf_exp_size.data() : nullptr); if (!gf) { if (pipeline_parallel) { LLAMA_LOG_WARN("%s: compute buffer allocation failed, retrying without pipeline parallelism\n", __func__); @@ -407,7 +410,7 @@ llama_context::llama_context( // reserve with tg (token generation) graph to get the number of splits and nodes { - auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get()); + auto * gf = graph_reserve(n_seqs, n_seqs, n_seqs, mctx.get(), model.hparams.no_alloc); if (!gf) { throw std::runtime_error("failed to allocate compute tg buffers"); } @@ -422,7 +425,7 @@ llama_context::llama_context( // // auto * gf = graph_reserve(n_tokens, 1, n_tokens, mctx.get()); // - auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get()); + auto * gf = graph_reserve(n_tokens, n_seqs, n_tokens, mctx.get(), model.hparams.no_alloc); if (!gf) { throw std::runtime_error("failed to allocate compute pp buffers"); } @@ -431,11 +434,13 @@ llama_context::llama_context( for (size_t i = 0; i < backend_ptrs.size(); ++i) { ggml_backend_t backend = backend_ptrs[i]; ggml_backend_buffer_type_t buft = backend_buft[i]; - size_t size = ggml_backend_sched_get_buffer_size(sched.get(), backend); - if (size > 1) { + if (!model.hparams.no_alloc) { + backend_buf_exp_size[i] = ggml_backend_sched_get_buffer_size(sched.get(), backend); + } + if (backend_buf_exp_size[i] > 1) { LLAMA_LOG_INFO("%s: %10s compute buffer size = %8.2f MiB\n", __func__, ggml_backend_buft_name(buft), - size / 1024.0 / 1024.0); + backend_buf_exp_size[i] / 1024.0 / 1024.0); } } @@ -454,6 +459,23 @@ llama_context::llama_context( } llama_context::~llama_context() { + // FIXME this currently results in a use-after-free bug if the model is freed before the context + // if (!model.hparams.no_alloc) { + // for (size_t i = 0; i < backend_ptrs.size(); ++i) { + // ggml_backend_t backend = backend_ptrs[i]; + // ggml_backend_buffer_type_t buft = backend_buft[i]; + + // const size_t size_exp = backend_buf_exp_size[i]; + // const size_t size_act = ggml_backend_sched_get_buffer_size(sched.get(), backend); + // if (size_exp == size_act) { + // LLAMA_LOG_DEBUG("%s: %10s compute buffer size is %8.4f MiB, matches expectation of %8.4f MiB\n", + // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + // } else { + // LLAMA_LOG_WARN("%s: %10s compute buffer size of %8.4f MiB, does not match expectation of %8.4f MiB\n", + // __func__, ggml_backend_buft_name(buft), size_act / (1024.0*1024.0), size_exp / (1024.0*1024.0)); + // } + // } + // } ggml_opt_free(opt_ctx); } @@ -1428,7 +1450,8 @@ llm_graph_result * llama_context::get_gf_res_reserve() const { return static_cast(gf_res_reserve.get()); } -ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only) { +ggml_cgraph * llama_context::graph_reserve( + uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only, size_t * sizes) { LLAMA_LOG_DEBUG("%s: reserving a graph for ubatch with n_tokens = %4u, n_seqs = %2u, n_outputs = %4u\n", __func__, n_tokens, n_seqs, n_outputs); GGML_ASSERT(n_outputs >= 1); @@ -1465,8 +1488,13 @@ ggml_cgraph * llama_context::graph_reserve(uint32_t n_tokens, uint32_t n_seqs, u // initialize scheduler with the specified graph if (split_only) { - ggml_backend_sched_split_graph(sched.get(), gf); + if (sizes) { + ggml_backend_sched_reserve_size(sched.get(), gf, sizes); + } else { + ggml_backend_sched_split_graph(sched.get(), gf); + } } else if (!ggml_backend_sched_reserve(sched.get(), gf)) { + GGML_ASSERT(!sizes); LLAMA_LOG_ERROR("%s: failed to allocate compute buffers\n", __func__); return nullptr; } @@ -2088,15 +2116,26 @@ void llama_context::perf_reset() { std::map llama_context::memory_breakdown() const { std::map ret; - for (const auto & buft_size : model.memory_breakdown()) { - ret[buft_size.first].model += buft_size.second; + for (const auto & [buft, size] : model.memory_breakdown()) { + ret[buft].model += size; } - for (const auto & buft_size : memory->memory_breakdown()) { - ret[buft_size.first].context += buft_size.second; + if (memory) { + for (const auto & [buft, size] : memory->memory_breakdown()) { + ret[buft].context += size; + } } - for (const auto & backend_ptr : backends) { - ggml_backend_t backend = backend_ptr.get(); - ret[ggml_backend_sched_get_buffer_type(sched.get(), backend)].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend); + if (model.hparams.no_alloc) { + for (size_t i = 0; i < backends.size(); ++i) { + ggml_backend_t backend = backends[i].get(); + ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend); + ret[buft].compute += backend_buf_exp_size[i]; + } + } else { + for (const auto & backend_ptr : backends) { + ggml_backend_t backend = backend_ptr.get(); + ggml_backend_buffer_type_t buft = ggml_backend_sched_get_buffer_type(sched.get(), backend); + ret[buft].compute += ggml_backend_sched_get_buffer_size(sched.get(), backend); + } } return ret; } diff --git a/src/llama-context.h b/src/llama-context.h index cd26eafe18..c31101330e 100644 --- a/src/llama-context.h +++ b/src/llama-context.h @@ -26,6 +26,10 @@ struct llama_memory_breakdown_data { size_t model = 0; // memory allocated for the model size_t context = 0; // memory allocated for the context size_t compute = 0; // memory allocated for temporary compute buffers + + size_t total() const { + return model + context + compute; + } }; struct llama_context { @@ -206,7 +210,8 @@ public: ggml_status graph_compute(ggml_cgraph * gf, bool batched); // reserve a graph with a dummy ubatch of the specified size - ggml_cgraph * graph_reserve(uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false); + ggml_cgraph * graph_reserve( + uint32_t n_tokens, uint32_t n_seqs, uint32_t n_outputs, const llama_memory_context_i * mctx, bool split_only = false, size_t * sizes = nullptr); private: llm_graph_params graph_params( @@ -281,9 +286,10 @@ private: std::vector> set_n_threads_fns; - // buffer types used for the compute buffer of each backend + // pointers and buffer types used for the compute buffer of each backend std::vector backend_ptrs; std::vector backend_buft; + std::vector backend_buf_exp_size; // expected buffer sizes llm_graph_result_ptr gf_res_prev; llm_graph_result_ptr gf_res_reserve; diff --git a/src/llama-hparams.h b/src/llama-hparams.h index a467c64a14..cecb476e91 100644 --- a/src/llama-hparams.h +++ b/src/llama-hparams.h @@ -34,6 +34,7 @@ struct llama_hparams_convnext { struct llama_hparams { bool vocab_only; + bool no_alloc; bool rope_finetuned; bool use_par_res; bool swin_norm; diff --git a/src/llama-impl.cpp b/src/llama-impl.cpp index c7a1880aad..8e3e7b223a 100644 --- a/src/llama-impl.cpp +++ b/src/llama-impl.cpp @@ -25,6 +25,10 @@ time_meas::~time_meas() { } } +void llama_log_get(ggml_log_callback * log_callback, void ** user_data) { + ggml_log_get(log_callback, user_data); +} + void llama_log_set(ggml_log_callback log_callback, void * user_data) { ggml_log_set(log_callback, user_data); g_logger_state.log_callback = log_callback ? log_callback : llama_log_callback_default; diff --git a/src/llama-kv-cache.cpp b/src/llama-kv-cache.cpp index bf3de2f2ef..4cbbca0925 100644 --- a/src/llama-kv-cache.cpp +++ b/src/llama-kv-cache.cpp @@ -175,7 +175,15 @@ llama_kv_cache::llama_kv_cache( // allocate tensors and initialize the buffers to avoid NaNs in the padding for (auto & [buft, ctx] : ctx_map) { - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); + ggml_backend_buffer_t buf; + if (model.hparams.no_alloc) { + buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer + for (ggml_tensor * t = ggml_get_first_tensor(ctx.get()); t != nullptr; t = ggml_get_next_tensor(ctx.get(), t)) { + t->buffer = buf; // set dummy buffer for KV cache so that the backend scheduler won't try to allocate it + } + } else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx.get(), buft); // real buffer + } if (!buf) { throw std::runtime_error("failed to allocate buffer for kv cache"); } @@ -482,9 +490,18 @@ llama_pos llama_kv_cache::seq_pos_max(llama_seq_id seq_id) const { std::map llama_kv_cache::memory_breakdown() const { std::map ret; - for (const auto & [_, buf] : ctxs_bufs) { - ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + for (const auto & [ctx, buf] : ctxs_bufs) { + ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf.get()); + + if (hparams.no_alloc) { + GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) == nullptr); + ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft); + } else { + // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base + ret[buft] += ggml_backend_buffer_get_size(buf.get()); + } } + return ret; } diff --git a/src/llama-model-loader.cpp b/src/llama-model-loader.cpp index aa3a65f87a..ca2ea2461d 100644 --- a/src/llama-model-loader.cpp +++ b/src/llama-model-loader.cpp @@ -473,6 +473,7 @@ llama_model_loader::llama_model_loader( std::vector & splits, bool use_mmap, bool check_tensors, + bool no_alloc, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p) { int trace = 0; @@ -716,6 +717,7 @@ llama_model_loader::llama_model_loader( this->use_mmap = use_mmap; this->check_tensors = check_tensors; + this->no_alloc = no_alloc; } std::string llama_model_loader::get_arch_name() const { diff --git a/src/llama-model-loader.h b/src/llama-model-loader.h index c9189f6cb4..0380c92fde 100644 --- a/src/llama-model-loader.h +++ b/src/llama-model-loader.h @@ -71,6 +71,7 @@ struct llama_model_loader { bool use_mmap = false; bool check_tensors; + bool no_alloc; llama_files files; llama_ftype ftype; @@ -97,6 +98,7 @@ struct llama_model_loader { std::vector & splits, // optional, only need if the split does not follow naming scheme bool use_mmap, bool check_tensors, + bool no_alloc, const llama_model_kv_override * param_overrides_p, const llama_model_tensor_buft_override * param_tensor_buft_overrides_p); diff --git a/src/llama-model.cpp b/src/llama-model.cpp index 28f06b4e61..e4d2138056 100644 --- a/src/llama-model.cpp +++ b/src/llama-model.cpp @@ -6606,9 +6606,11 @@ bool llama_model::load_tensors(llama_model_loader & ml) { std::vector bufs; if (ml.use_mmap && use_mmap_buffer && buffer_from_host_ptr_supported && is_default_buft) { + GGML_ASSERT(!ml.no_alloc); for (uint32_t idx = 0; idx < ml.files.size(); idx++) { // only the mmap region containing the tensors in the model is mapped to the backend buffer - // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, then we could just use metal for all layers + // this is important for metal with apple silicon: if the entire model could be mapped to a metal buffer, + // then we could just use metal for all layers // this allows using partial offloading when the model size exceeds the metal buffer size, but not the RAM size void * addr = nullptr; size_t first, last; // NOLINT @@ -6624,9 +6626,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) { bufs.emplace_back(buf); buf_map.emplace(idx, buf); } - } - else { - ggml_backend_buffer_t buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); + } else { + ggml_backend_buffer_t buf; + if (ml.no_alloc) { + buf = ggml_backend_buft_alloc_buffer(buft, /*size =*/ 0); // dummy buffer + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != nullptr; t = ggml_get_next_tensor(ctx, t)) { + t->buffer = buf; // set dummy buffer for weights so that the backend scheduler won't try to allocate them + } + } else { + buf = ggml_backend_alloc_ctx_tensors_from_buft(ctx, buft); // real buffer + } if (buf == nullptr) { throw std::runtime_error(format("unable to allocate %s buffer", ggml_backend_buft_name(buft))); } @@ -6681,6 +6690,10 @@ bool llama_model::load_tensors(llama_model_loader & ml) { } } + if (ml.no_alloc) { + return true; + } + // load tensor data for (auto & [ctx, buf_map] : ctx_buf_maps) { if (!ml.load_all_data(ctx, buf_map, use_mlock ? &pimpl->mlock_mmaps : NULL, params.progress_callback, params.progress_callback_user_data)) { @@ -6723,9 +6736,18 @@ size_t llama_model::n_devices() const { std::map llama_model::memory_breakdown() const { std::map ret; - for (const auto & [_, bufs] : pimpl->ctxs_bufs) { - for (const auto & buf : bufs) { - ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + for (const auto & [ctx, bufs] : pimpl->ctxs_bufs) { + if (hparams.no_alloc) { + GGML_ASSERT(bufs.size() == 1); + ggml_backend_buffer_t buf = bufs[0].get(); + GGML_ASSERT(ggml_backend_buffer_get_base(buf) == nullptr); + ggml_backend_buffer_type_t buft = ggml_backend_buffer_get_type(buf); + ret[buft] += ggml_backend_alloc_ctx_tensors_from_buft_size(ctx.get(), buft); + } else { + for (const auto & buf : bufs) { + // GGML_ASSERT(ggml_backend_buffer_get_base(buf.get()) != nullptr); // multi_buffer does not have a defined base + ret[ggml_backend_buffer_get_type(buf.get())] += ggml_backend_buffer_get_size(buf.get()); + } } } return ret; @@ -6770,6 +6792,7 @@ void llama_model::print_info() const { // hparams LLAMA_LOG_INFO("%s: arch = %s\n", __func__, arch_name().c_str()); LLAMA_LOG_INFO("%s: vocab_only = %d\n", __func__, hparams.vocab_only); + LLAMA_LOG_INFO("%s: no_alloc = %d\n", __func__, hparams.no_alloc); if (!hparams.vocab_only) { LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); @@ -7618,6 +7641,7 @@ llama_model_params llama_model_default_params() { /*.check_tensors =*/ false, /*.use_extra_bufts =*/ true, /*.no_host =*/ false, + /*.no_alloc =*/ false, }; return result; diff --git a/src/llama-quant.cpp b/src/llama-quant.cpp index 351dcb7baa..bc4b05c3b5 100644 --- a/src/llama-quant.cpp +++ b/src/llama-quant.cpp @@ -596,7 +596,7 @@ static void llama_model_quantize_impl(const std::string & fname_inp, const std:: } std::vector splits = {}; - llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, kv_overrides, nullptr); + llama_model_loader ml(fname_inp, splits, use_mmap, /*check_tensors*/ true, /*no_alloc*/ false, kv_overrides, nullptr); ml.init_mappings(false); // no prefetching llama_model model(llama_model_default_params()); diff --git a/src/llama.cpp b/src/llama.cpp index ab2e9868af..7ed34b80ae 100644 --- a/src/llama.cpp +++ b/src/llama.cpp @@ -1,6 +1,9 @@ +#include "llama.h" + #include "llama-impl.h" #include "llama-chat.h" +#include "llama-context.h" #include "llama-mmap.h" #include "llama-vocab.h" #include "llama-model-loader.h" @@ -11,11 +14,14 @@ #include "ggml-backend.h" #include +#include +#include #include #include #include #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -37,6 +43,643 @@ const char * llama_flash_attn_type_name(enum llama_flash_attn_type flash_attn_ty GGML_ABORT("fatal error"); } +struct llama_device_memory_data { + int64_t total; + int64_t free; + llama_memory_breakdown_data mb; +}; + +static std::vector llama_get_device_memory_data( + const char * path_model, const llama_model_params * mparams, const llama_context_params * cparams, + std::vector & devs, uint32_t & hp_ngl, uint32_t & hp_n_ctx_train, uint32_t & hp_n_expert, + const ggml_log_level log_level) { + struct user_data_t { + struct { + ggml_log_callback callback; + void * user_data; + } original_logger; + ggml_log_level min_level; // prints below this log level go to debug log + }; + user_data_t ud; + llama_log_get(&ud.original_logger.callback, &ud.original_logger.user_data); + ud.min_level = log_level; + + llama_log_set([](ggml_log_level level, const char * text, void * user_data) { + const user_data_t * ud = (const user_data_t *) user_data; + const ggml_log_level level_eff = level >= ud->min_level ? level : GGML_LOG_LEVEL_DEBUG; + ud->original_logger.callback(level_eff, text, ud->original_logger.user_data); + }, &ud); + + llama_model_params mparams_copy = *mparams; + mparams_copy.no_alloc = true; + mparams_copy.use_mmap = false; + + llama_model * model = llama_model_load_from_file(path_model, mparams_copy); + if (model == nullptr) { + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + throw std::runtime_error("failed to load model"); + } + + llama_context * ctx = llama_init_from_model(model, *cparams); + if (ctx == nullptr) { + llama_model_free(model); + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + throw std::runtime_error("failed to create llama_context from model"); + } + + std::vector ret(model->devices.size()); + + std::map memory_breakdown = ctx->memory_breakdown(); + + for (const auto & [buft, mb] : memory_breakdown) { + if (ggml_backend_buft_is_host(buft)) { + continue; + } + + ggml_backend_dev_t dev = ggml_backend_buft_get_device(buft); + if (!dev) { + continue; + } + for (size_t i = 0; i < ret.size(); i++) { + if (model->devices[i] == dev) { + ret[i].mb.model += mb.model; + ret[i].mb.context += mb.context; + ret[i].mb.compute += mb.compute; + break; + } + } + } + for (size_t i = 0; i < ret.size(); i++) { + size_t free, total; + ggml_backend_dev_memory(model->devices[i], &free, &total); + ret[i].free = free; + ret[i].total = total; + } + + devs = model->devices; + hp_ngl = model->hparams.n_layer; + hp_n_ctx_train = model->hparams.n_ctx_train; + hp_n_expert = model->hparams.n_expert; + + llama_memory_breakdown_print(ctx); // goes to debug log + + llama_free(ctx); + llama_model_free(model); + llama_log_set(ud.original_logger.callback, ud.original_logger.user_data); + return ret; +} + +// enum to identify part of a layer for distributing its tensors: +enum layer_fraction_t { + LAYER_FRACTION_NONE = 0, // nothing + LAYER_FRACTION_ATTN = 1, // attention + LAYER_FRACTION_UP = 2, // attention + up + LAYER_FRACTION_GATE = 3, // attention + up + gate + LAYER_FRACTION_MOE = 4, // everything but sparse MoE weights +}; +// this enum is only used in llama_params_fit_impl but needs to be defined outside of it to fix a Windows compilation issue + +static void llama_params_fit_impl( + const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams, + float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides, + size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) { + constexpr int64_t MiB = 1024*1024; + const int64_t margin = margin_s; // this function uses int64_t rather than size_t for memory sizes to more conveniently handle deficits + typedef std::vector dmds_t; + const llama_model_params default_mparams = llama_model_default_params(); + + std::vector devs; + uint32_t hp_ngl = 0; // hparams.n_gpu_layers + uint32_t hp_nct = 0; // hparams.n_ctx_train + uint32_t hp_nex = 0; // hparams.n_expert + + // step 1: get data for default parameters and check whether any changes are necessary in the first place + + LLAMA_LOG_DEBUG("%s: getting device memory data for initial parameters:\n", __func__); + const dmds_t dmds_full = llama_get_device_memory_data(path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + const size_t nd = devs.size(); // number of devices + if (nd == 0) { + LLAMA_LOG_INFO("%s: no devices with dedicated memory found\n", __func__); + return; + } + + std::vector dev_names; + { + dev_names.reserve(nd); + size_t max_length = 0; + for (ggml_backend_dev_t dev : devs) { + std::string name = ggml_backend_dev_name(dev); + name += " ("; + name += ggml_backend_dev_description(dev); + name += ")"; + dev_names.push_back(name); + max_length = std::max(max_length, name.length()); + } + for (std::string & dn : dev_names) { + dn.insert(dn.end(), max_length - dn.length(), ' '); + } + } + + int64_t sum_total = 0; + int64_t sum_projected_free = 0; + int64_t min_projected_free = INT64_MAX; + int64_t sum_projected_used = 0; + int64_t sum_projected_ctx = 0; + + if (nd > 1) { + LLAMA_LOG_INFO("%s: projected memory use with initial parameters [MiB]:\n", __func__); + } + for (size_t id = 0; id < nd; id++) { + const llama_device_memory_data & dmd = dmds_full[id]; + + const int64_t projected_used = dmd.mb.total(); + const int64_t projected_free = dmd.free - projected_used; + + sum_total += dmd.total; + sum_projected_used += projected_used; + sum_projected_free += projected_free; + min_projected_free = std::min(min_projected_free, projected_free); + sum_projected_ctx += dmd.mb.context; + + if (nd > 1) { + LLAMA_LOG_INFO("%s: - %s: %6" PRId64 " total, %6" PRId64 " used, %6" PRId64 " %s\n", + __func__, dev_names[id].c_str(), dmd.total/MiB, projected_used/MiB, std::abs(projected_free)/MiB, + projected_free >= 0 ? "surplus" : "deficit"); + } + } + assert(sum_total >= 0 && sum_projected_used >= 0 && sum_projected_ctx >= 0); + assert(sum_projected_used >= sum_projected_ctx); + LLAMA_LOG_INFO("%s: projected to use %" PRId64 " MiB of device memory vs. %" PRId64 " MiB of free device memory\n", + __func__, sum_projected_used/MiB, sum_total/MiB); + if (min_projected_free >= margin) { + if (nd == 1) { + LLAMA_LOG_INFO("%s: will leave %" PRId64 " >= %" PRId64 " MiB of free device memory, no changes needed\n", + __func__, min_projected_free/MiB, margin/MiB); + return; + } + LLAMA_LOG_INFO("%s: will leave at least %" PRId64 " >= %" PRId64 " MiB of free memory on all devices, no changes needed\n", + __func__, min_projected_free/MiB, margin/MiB); + return; + } + + // step 2: try reducing memory use by reducing the context size + + { + int64_t global_surplus = sum_projected_free - int64_t(nd)*margin; + if (global_surplus < 0) { + LLAMA_LOG_INFO(nd == 1 ? + "%s: cannot fulfill margin of %" PRId64 " MiB, need to reduce device memory by %" PRId64 " MiB\n" : + "%s: cannot fulfill margin of %" PRId64 " MiB on all devices, need to use %" PRId64 " MiB less in total\n", + __func__, margin/MiB, -global_surplus/MiB); + if (cparams->n_ctx == 0) { + if (hp_nct > n_ctx_min) { + const int64_t bytes_per_ctx = sum_projected_ctx / hp_nct; + const uint32_t ctx_reduction = std::min( + uint32_t((-global_surplus + bytes_per_ctx - 1) / bytes_per_ctx), hp_nct - n_ctx_min); + cparams->n_ctx = hp_nct - ctx_reduction; + const int64_t memory_reduction = ctx_reduction * bytes_per_ctx; + global_surplus += memory_reduction; + LLAMA_LOG_INFO("%s: context size reduced from %" PRIu32 " to %" PRIu32 " -> need %" PRId64 " MiB less memory in total\n", + __func__, hp_nct, cparams->n_ctx, memory_reduction/MiB); + } else { + LLAMA_LOG_INFO("%s: default model context size is %" PRIu32 " which is <= the min. context size of %" PRIu32 " -> no change\n", + __func__, hp_nct, n_ctx_min); + } + } else { + LLAMA_LOG_INFO("%s: context size set by user to %" PRIu32 " -> no change\n", __func__, cparams->n_ctx); + } + } + if (global_surplus >= 0) { + LLAMA_LOG_INFO("%s: entire model can be fit across devices by reducing context\n", __func__); + return; + } + } + + if (mparams->n_gpu_layers != default_mparams.n_gpu_layers) { + throw std::runtime_error("n_gpu_layers already set by user to " + std::to_string(mparams->n_gpu_layers) + ", abort"); + } + if (nd > 1) { + if (!tensor_split) { + throw std::runtime_error("did not provide a buffer to write the tensor_split to, abort"); + } + if (mparams->tensor_split) { + for (size_t id = 0; id < nd; id++) { + if (mparams->tensor_split[id] != 0.0f) { + throw std::runtime_error("model_params::tensor_split already set by user, abort"); + } + } + } + if (mparams->split_mode == LLAMA_SPLIT_MODE_ROW) { + throw std::runtime_error("changing weight allocation for LLAMA_SPLIT_MODE_ROW not implemented, abort"); + } + if (hp_ngl < 2*nd) { + throw std::runtime_error("model has only " + std::to_string(hp_ngl) + " layers but need at least " + + std::to_string(2*nd) + " to fit memory for " + std::to_string(nd) + " devices, abort"); + } + } + if (!tensor_buft_overrides) { + throw std::runtime_error("did not provide buffer to set tensor_buft_overrides, abort"); + } + if (mparams->tensor_buft_overrides && (mparams->tensor_buft_overrides->pattern || mparams->tensor_buft_overrides->buft)) { + throw std::runtime_error("model_params::tensor_buft_overrides already set by user, abort"); + } + + // step 3: iteratively fill the back to front with "dense" layers + // - for a dense model simply fill full layers, giving each device a contiguous slice of the model + // - for a MoE model, same as dense model but with all MoE tensors in system memory + + // utility function that returns a static C string matching the tensors for a specific layer index and layer fraction: + auto get_overflow_pattern = [&](const size_t il, const layer_fraction_t lf) -> const char * { + constexpr size_t n_strings = 1000; + if (il >= n_strings) { + throw std::runtime_error("at most " + std::to_string(n_strings) + " model layers are supported"); + } + switch (lf) { + case LAYER_FRACTION_ATTN: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|gate|down).*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_UP: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(gate|down).*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_GATE: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_down.*"; + } + return patterns[il].c_str(); + } + case LAYER_FRACTION_MOE: { + static std::array patterns; + if (patterns[il].empty()) { + patterns[il] = "blk\\." + std::to_string(il) + "\\.ffn_(up|down|gate)_(ch|)exps"; + } + return patterns[il].c_str(); + } + default: + GGML_ABORT("fatal error"); + } + }; + + struct ngl_t { + uint32_t n_layer = 0; // number of total layers + uint32_t n_part = 0; // number of partial layers, <= n_layer + + // for the first partial layer varying parts can overflow, all further layers use LAYER_FRACTION_MOE: + layer_fraction_t overflow_type = LAYER_FRACTION_MOE; + }; + + const size_t ntbo = llama_max_tensor_buft_overrides(); + + // utility function to set n_gpu_layers and tensor_split + auto set_ngl_tensor_split_tbo = [&]( + const std::vector & ngl_per_device, + const std::vector & overflow_bufts, + llama_model_params & mparams, + const bool add_nonrepeating) { + mparams.n_gpu_layers = 0; + for (size_t id = 0; id < nd; id++) { + mparams.n_gpu_layers += ngl_per_device[id].n_layer; + if (nd > 1) { + tensor_split[id] = ngl_per_device[id].n_layer; + } + } + assert(uint32_t(mparams.n_gpu_layers) <= hp_ngl); + uint32_t il0 = hp_ngl - mparams.n_gpu_layers; // start index for tensor buft overrides + + if (add_nonrepeating) { + mparams.n_gpu_layers += 1; + tensor_split[nd - 1] += 1; + } + mparams.tensor_split = tensor_split; + + size_t itbo = 0; + for (size_t id = 0; id < nd; id++) { + il0 += ngl_per_device[id].n_layer - ngl_per_device[id].n_part; + for (uint32_t il = il0; il < il0 + ngl_per_device[id].n_part; il++) { + if (itbo + 1 >= ntbo) { + tensor_buft_overrides[itbo].pattern = nullptr; + tensor_buft_overrides[itbo].buft = nullptr; + itbo++; + mparams.tensor_buft_overrides = tensor_buft_overrides; + throw std::runtime_error("llama_params_fit_n_tensor_buft_overrides() == " + + std::to_string(ntbo) + " is insufficient for model\n"); + } + tensor_buft_overrides[itbo].pattern = get_overflow_pattern(il, il == il0 ? ngl_per_device[id].overflow_type : LAYER_FRACTION_MOE); + tensor_buft_overrides[itbo].buft = overflow_bufts[id]; + itbo++; + } + il0 += ngl_per_device[id].n_part; + } + tensor_buft_overrides[itbo].pattern = nullptr; + tensor_buft_overrides[itbo].buft = nullptr; + itbo++; + mparams.tensor_buft_overrides = tensor_buft_overrides; + }; + + // utility function that returns the memory use per device for given numbers of layers per device + auto get_memory_for_layers = [&]( + const char * func_name, + const std::vector & ngl_per_device, + const std::vector & overflow_bufts, + const bool add_nonrepeating) -> std::vector { + llama_model_params mparams_copy = *mparams; + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, mparams_copy, add_nonrepeating); + + const dmds_t dmd_nl = llama_get_device_memory_data( + path_model, &mparams_copy, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + + LLAMA_LOG_DEBUG("%s: memory for test allocation by device:\n", func_name); + for (size_t id = 0; id < nd; id++) { + const ngl_t & n = ngl_per_device[id]; + LLAMA_LOG_DEBUG( + "%s: id=%zu, n_layer=%2" PRIu32 ", n_part=%2" PRIu32 ", overflow_type=%d, mem=%6" PRId64 " MiB\n", + func_name, id, n.n_layer, n.n_part, int(n.overflow_type), dmd_nl[id].mb.total()/MiB); + } + + std::vector ret; + ret.reserve(nd); + for (const llama_device_memory_data & dmd : dmd_nl) { + ret.push_back(dmd.mb.total()); + } + return ret; + }; + + int64_t global_surplus_cpu_moe = 0; + if (hp_nex > 0) { + const static std::string pattern_moe_all = "blk\\.\\d+\\.ffn_(up|down|gate)_(ch|)exps"; // matches all MoE tensors + ggml_backend_buffer_type_t cpu_buft = ggml_backend_cpu_buffer_type(); + tensor_buft_overrides[0] = {pattern_moe_all.c_str(), cpu_buft}; + tensor_buft_overrides[1] = {nullptr, nullptr}; + mparams->tensor_buft_overrides = tensor_buft_overrides; + + LLAMA_LOG_DEBUG("%s: getting device memory data with all MoE tensors moved to system memory:\n", __func__); + const dmds_t dmds_cpu_moe = llama_get_device_memory_data( + path_model, mparams, cparams, devs, hp_ngl, hp_nct, hp_nex, log_level); + + for (const llama_device_memory_data & dmd : dmds_cpu_moe) { + global_surplus_cpu_moe += dmd.free; + global_surplus_cpu_moe -= int64_t(dmd.mb.total()) + margin; + } + + if (global_surplus_cpu_moe > 0) { + LLAMA_LOG_INFO("%s: with only dense weights in device memory there is a total surplus of %" PRId64 " MiB\n", + __func__, global_surplus_cpu_moe/MiB); + } else { + LLAMA_LOG_INFO("%s: with only dense weights in device memory there is still a total deficit of %" PRId64 " MiB\n", + __func__, -global_surplus_cpu_moe/MiB); + } + + // reset + tensor_buft_overrides[0] = {nullptr, nullptr}; + mparams->tensor_buft_overrides = tensor_buft_overrides; + } + + std::vector targets; // maximum acceptable memory use per device + targets.reserve(nd); + for (size_t id = 0; id < nd; id++) { + targets.push_back(dmds_full[id].free - margin); + LLAMA_LOG_DEBUG("%s: id=%zu, target=%" PRId64 " MiB\n", __func__, id, targets[id]/MiB); + } + + // whether for the optimal memory use we expect to load at least some MoE tensors: + const bool partial_moe = hp_nex > 0 && global_surplus_cpu_moe > 0; + + std::vector overflow_bufts; // which bufts the partial layers of a device overflow to: + overflow_bufts.reserve(nd); + for (size_t id = 0; id < nd - 1; ++id) { + overflow_bufts.push_back(ggml_backend_dev_buffer_type(devs[id + 1])); + } + overflow_bufts.push_back(ggml_backend_cpu_buffer_type()); + + std::vector ngl_per_device(nd); + std::vector mem = get_memory_for_layers(__func__, ngl_per_device, overflow_bufts, partial_moe); + if (hp_nex > 0) { + for (size_t id = 0; id < nd; id++) { + ngl_per_device[id].overflow_type = LAYER_FRACTION_MOE; + } + } + + // optimize the number of layers per device using the method of false position: + // - ngl_per_device has 0 layers for each device, lower bound + // - try a "high" configuration where a device is given all unassigned layers + // - interpolate the memory use / layer between low and high linearly to get a guess where it meets our target + // - check memory use of our guess, replace either the low or high bound + // - once we only have a difference of a single layer, stop and return the lower bound that just barely still fits + if (hp_nex == 0) { + LLAMA_LOG_INFO("%s: filling dense layers back-to-front:\n", __func__); + } else { + LLAMA_LOG_INFO("%s: filling dense-only layers back-to-front:\n", __func__); + } + uint32_t n_unassigned = hp_ngl; + for (int id = nd - 1; id >= 0; id--) { + std::vector ngl_per_device_high = ngl_per_device; + ngl_per_device_high[id].n_layer = n_unassigned; + if (hp_nex > 0) { + ngl_per_device_high[id].n_part = ngl_per_device_high[id].n_layer; + } + if (ngl_per_device_high[id].n_layer > 0) { + std::vector mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe); + if (mem_high[id] > targets[id]) { + uint32_t delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer; + while (delta > 1) { + uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]); + step_size = std::max(step_size, uint32_t(1)); + step_size = std::min(step_size, delta - 1); + + std::vector ngl_per_device_test = ngl_per_device; + ngl_per_device_test[id].n_layer += step_size; + if (hp_nex) { + ngl_per_device_test[id].n_part += step_size; + } + const std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + + if (mem_test[id] <= targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + n_unassigned -= ngl_per_device[id].n_layer; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } else { + ngl_per_device_high = ngl_per_device_test; + mem_high = mem_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } + delta = ngl_per_device_high[id].n_layer - ngl_per_device[id].n_layer; + } + } else { + ngl_per_device = ngl_per_device_high; + n_unassigned -= ngl_per_device[id].n_layer; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%d].n_layer=%" PRIu32 "\n", __func__, id, ngl_per_device[id].n_layer); + } + } + + const int64_t projected_margin = dmds_full[id].free - mem[id]; + LLAMA_LOG_INFO( + "%s: - %s: %2" PRIu32 " layers, %6" PRId64 " MiB used, %6" PRId64 " MiB free\n", + __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, mem[id]/MiB, projected_margin/MiB); + } + if (hp_nex == 0 || global_surplus_cpu_moe <= 0) { + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe); + return; + } + + // step 4: for a MoE model where all dense tensors fit, + // convert the dense-only layers in the back to full layers in the front until all devices are full + // essentially the same procedure as for the dense-only layers except front-to-back + // also, try fitting at least part of one more layer to reduce waste for "small" GPUs with e.g. 24 GiB VRAM + + size_t id_dense_start = nd; + for (int id = nd - 1; id >= 0; id--) { + if (ngl_per_device[id].n_layer > 0) { + id_dense_start = id; + continue; + } + break; + } + assert(id_dense_start < nd); + + LLAMA_LOG_INFO("%s: converting dense-only layers to full layers and filling them front-to-back with overflow to next device/system memory:\n", __func__); + for (size_t id = 0; id <= id_dense_start; id++) { + std::vector ngl_per_device_high = ngl_per_device; + for (size_t jd = id_dense_start; jd < nd; jd++) { + const uint32_t n_layer_move = ngl_per_device_high[jd].n_layer; + ngl_per_device_high[id].n_layer += n_layer_move; + ngl_per_device_high[jd].n_layer -= n_layer_move; + ngl_per_device_high[jd].n_part = 0; + } + size_t id_dense_start_high = nd - 1; + std::vector mem_high = get_memory_for_layers(__func__, ngl_per_device_high, overflow_bufts, partial_moe); + + if (mem_high[id] > targets[id]) { + assert(ngl_per_device_high[id].n_layer >= ngl_per_device_high[id].n_part); + assert(ngl_per_device[id].n_layer >= ngl_per_device[id].n_part); + assert((ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + >= ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + uint32_t delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + while (delta > 1) { + uint32_t step_size = int64_t(delta) * (targets[id] - mem[id]) / (mem_high[id] - mem[id]); + step_size = std::max(step_size, uint32_t(1)); + step_size = std::min(step_size, delta - 1); + + std::vector ngl_per_device_test = ngl_per_device; + size_t id_dense_start_test = id_dense_start; + uint32_t n_converted_test = 0; + for (;id_dense_start_test < nd; id_dense_start_test++) { + const uint32_t n_convert_jd = std::min(step_size - n_converted_test, ngl_per_device_test[id_dense_start_test].n_part); + ngl_per_device_test[id_dense_start_test].n_layer -= n_convert_jd; + ngl_per_device_test[id_dense_start_test].n_part -= n_convert_jd; + ngl_per_device_test[id].n_layer += n_convert_jd; + n_converted_test += n_convert_jd; + + if (ngl_per_device_test[id_dense_start_test].n_layer > 0) { + break; + } + } + const std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + + if (mem_test[id] <= targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } else { + ngl_per_device_high = ngl_per_device_test; + mem_high = mem_test; + id_dense_start_high = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device_high[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start_high=%zu\n", + __func__, id, ngl_per_device_high[id].n_layer, ngl_per_device_high[id].n_part, id_dense_start_high); + } + delta = (ngl_per_device_high[id].n_layer - ngl_per_device_high[id].n_part) + - (ngl_per_device[id].n_layer - ngl_per_device[id].n_part); + } + } else { + ngl_per_device = ngl_per_device_high; + id_dense_start = id_dense_start_high; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part)=(%" PRIu32 ", %" PRIu32 "), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + + // try to fit at least part of one more layer + if (ngl_per_device[id_dense_start].n_layer > 0) { + std::vector ngl_per_device_test = ngl_per_device; + size_t id_dense_start_test = id_dense_start; + ngl_per_device_test[id_dense_start_test].n_layer--; + ngl_per_device_test[id_dense_start_test].n_part--; + ngl_per_device_test[id].n_layer++; + ngl_per_device_test[id].n_part++; + if (ngl_per_device_test[id_dense_start_test].n_layer == 0) { + id_dense_start_test++; + } + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_UP; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_UP\n", __func__); + std::vector mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", UP), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_GATE; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_GATE\n", __func__); + mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", GATE), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + } else { + ngl_per_device_test[id].overflow_type = LAYER_FRACTION_ATTN; + LLAMA_LOG_DEBUG("%s: trying to fit one extra layer with overflow_type=LAYER_FRACTION_ATTN\n", __func__); + mem_test = get_memory_for_layers(__func__, ngl_per_device_test, overflow_bufts, partial_moe); + if (mem_test[id] < targets[id]) { + ngl_per_device = ngl_per_device_test; + mem = mem_test; + id_dense_start = id_dense_start_test; + LLAMA_LOG_DEBUG("%s: set ngl_per_device[%zu].(n_layer, n_part, overflow_type)=(%" PRIu32 ", %" PRIu32 ", ATTN), id_dense_start=%zu\n", + __func__, id, ngl_per_device[id].n_layer, ngl_per_device[id].n_part, id_dense_start); + } + } + } + + const int64_t projected_margin = dmds_full[id].free - mem[id]; + LLAMA_LOG_INFO( + "%s: - %s: %2" PRIu32 " layers (%2" PRIu32 " overflowing), %6" PRId64 " MiB used, %6" PRId64 " MiB free\n", + __func__, dev_names[id].c_str(), ngl_per_device[id].n_layer, ngl_per_device[id].n_part, mem[id]/MiB, projected_margin/MiB); + } + + set_ngl_tensor_split_tbo(ngl_per_device, overflow_bufts, *mparams, partial_moe); +} + +bool llama_params_fit( + const char * path_model, struct llama_model_params * mparams, struct llama_context_params * cparams, + float * tensor_split, struct llama_model_tensor_buft_override * tensor_buft_overrides, + size_t margin_s, uint32_t n_ctx_min, enum ggml_log_level log_level) { + const int64_t t0_us = llama_time_us(); + bool ok = true; + try { + llama_params_fit_impl(path_model, mparams, cparams, tensor_split, tensor_buft_overrides, margin_s, n_ctx_min, log_level); + LLAMA_LOG_INFO("%s: successfully fit params to free device memory\n", __func__); + } catch (const std::runtime_error & e) { + LLAMA_LOG_WARN("%s: failed to fit params to free device memory: %s\n", __func__, e.what()); + ok = false; + } + const int64_t t1_us = llama_time_us(); + LLAMA_LOG_INFO("%s: fitting params to free memory took %.2f seconds\n", __func__, (t1_us - t0_us) * 1e-6); + return ok; +} + struct llama_sampler_chain_params llama_sampler_chain_default_params() { struct llama_sampler_chain_params result = { /*.no_perf =*/ true, @@ -49,6 +692,10 @@ size_t llama_max_devices(void) { return 16; } +size_t llama_max_tensor_buft_overrides() { + return 4096; +} + bool llama_supports_mmap(void) { return llama_mmap::SUPPORTED; } @@ -108,11 +755,12 @@ static int llama_model_load(const std::string & fname, std::vector model.t_start_us = tm.t_start_us; try { - llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.kv_overrides, params.tensor_buft_overrides); + llama_model_loader ml(fname, splits, params.use_mmap, params.check_tensors, params.no_alloc, params.kv_overrides, params.tensor_buft_overrides); ml.print_info(); model.hparams.vocab_only = params.vocab_only; + model.hparams.no_alloc = params.no_alloc; try { model.load_arch(ml); diff --git a/tools/CMakeLists.txt b/tools/CMakeLists.txt index 43a0e81949..8df3f41003 100644 --- a/tools/CMakeLists.txt +++ b/tools/CMakeLists.txt @@ -37,4 +37,5 @@ else() add_subdirectory(cvector-generator) add_subdirectory(export-lora) endif() + add_subdirectory(fit-params) endif() diff --git a/tools/fit-params/CMakeLists.txt b/tools/fit-params/CMakeLists.txt new file mode 100644 index 0000000000..34c3373f83 --- /dev/null +++ b/tools/fit-params/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET llama-fit-params) +add_executable(${TARGET} fit-params.cpp) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) + +if(LLAMA_TOOLS_INSTALL) + install(TARGETS ${TARGET} RUNTIME) +endif() diff --git a/tools/fit-params/README.md b/tools/fit-params/README.md new file mode 100644 index 0000000000..8f0c958a2f --- /dev/null +++ b/tools/fit-params/README.md @@ -0,0 +1,55 @@ +# fit-params + +llama.cpp binaries can automatically fit the projected memory use of a model to the free device memory available at runtime, +this is controlled using the CLI arguments starting with `-fit`/`--fit`. +Internally the code is calling `llama_params_fit` to adjust the `llama_model_params` and `llama_context_params` structs. +`llama-fit-params` is a simple utility that prints the CLI arguments corresponding to these adjustments to stdout. +Example usage: + +``` bash +# First, run llama-fit-params and store the results in a file: +> ./build/bin/llama-fit-params --model /opt/models/qwen_3-30b3a-f16.gguf | tee args.txt +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes +build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu +llama_params_fit_impl: projected to use 61807 MiB of device memory vs. 24077 MiB of free device memory +llama_params_fit_impl: cannot fulfill margin of 1024 MiB, need to reduce device memory by 42444 MiB +llama_params_fit_impl: context size reduced from 40960 to 4096 -> need 3456 MiB less memory in total +llama_params_fit_impl: with only dense weights in device memory there is a total surplus of 16164 MiB +llama_params_fit_impl: distributing layers across devices with overflow to next device/system memory: +llama_params_fit_impl: - CUDA0 (NVIDIA GeForce RTX 4090): 48 layers (34 overflowing), 19187 MiB used, 1199 MiB free +llama_params_fit: successfully fit params to free device memory +llama_params_fit: fitting params to free memory took 1.15 seconds +Printing fitted CLI arguments to stdout... +-c 4096 -ngl 48 -ot blk\.14\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.15\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.16\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.17\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.18\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.19\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.20\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.21\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.22\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.23\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.24\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.25\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.26\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.27\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.28\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.29\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.30\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.31\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.32\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.33\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.34\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.35\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.36\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.37\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.38\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.39\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.40\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.41\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.42\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.43\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.44\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.45\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.46\.ffn_(up|down|gate)_(ch|)exps=CPU,blk\.47\.ffn_(up|down|gate)_(ch|)exps=CPU + +# Next, use those results for a llama.cpp binary: +> cat args.txt | xargs ./build/bin/llama-server --model /opt/models/qwen_3-30b3a-f16.gguf +ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no +ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no +ggml_cuda_init: found 1 CUDA devices: + Device 0: NVIDIA GeForce RTX 4090, compute capability 8.9, VMM: yes +build: 6895 (4341dc8bc) with cc (GCC) 15.2.1 20250813 for x86_64-pc-linux-gnu +system info: n_threads = 16, n_threads_batch = 16, total_threads = 32 + +system_info: n_threads = 16 (n_threads_batch = 16) / 32 | CUDA : ARCHS = 890 | USE_GRAPHS = 1 | PEER_MAX_BATCH_SIZE = 128 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | AVX512_BF16 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 | + +main: binding port with default address family +main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 31 +main: loading model +srv load_model: loading model '/opt/models/qwen_3-30b3a-f16.gguf' +llama_params_fit_impl: projected to use 19187 MiB of device memory vs. 24077 MiB of free device memory +llama_params_fit_impl: will leave 1199 >= 1024 MiB of free device memory, no changes needed +llama_params_fit: successfully fit params to free device memory +llama_params_fit: fitting params to free memory took 0.28 seconds +[...] +main: server is listening on http://127.0.0.1:8080 - starting the main loop +srv update_slots: all slots are idle +^Csrv operator(): operator(): cleaning up before exit... + +llama_memory_breakdown_print: | memory breakdown [MiB] | total free self model context compute unaccounted | +llama_memory_breakdown_print: | - CUDA0 (RTX 4090) | 24077 = 945 + (19187 = 17904 + 384 + 898) + 3945 | +llama_memory_breakdown_print: | - Host | 58271 = 58259 + 0 + 12 | +``` diff --git a/tools/fit-params/fit-params.cpp b/tools/fit-params/fit-params.cpp new file mode 100644 index 0000000000..fbf7a2eb37 --- /dev/null +++ b/tools/fit-params/fit-params.cpp @@ -0,0 +1,62 @@ +#include "llama.h" + +#include "arg.h" +#include "common.h" +#include "log.h" + +#include + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +int main(int argc, char ** argv) { + common_params params; + + if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) { + return 1; + } + + common_init(); + llama_backend_init(); + llama_numa_init(params.numa); + auto mparams = common_model_params_to_llama(params); + auto cparams = common_context_params_to_llama(params); + llama_params_fit(params.model.path.c_str(), &mparams, &cparams, + params.tensor_split, params.tensor_buft_overrides.data(), params.fit_params_target, params.fit_params_min_ctx, + params.verbosity >= 4 ? GGML_LOG_LEVEL_DEBUG : GGML_LOG_LEVEL_ERROR); + + LOG_INF("Printing fitted CLI arguments to stdout...\n"); + std::cout << "-c " << cparams.n_ctx; + std::cout << " -ngl " << mparams.n_gpu_layers; + + size_t nd = llama_max_devices(); + while (nd > 1 && mparams.tensor_split[nd - 1] == 0.0f) { + nd--; + } + if (nd > 1) { + for (size_t id = 0; id < nd; id++) { + if (id == 0) { + std::cout << " -ts "; + } + if (id > 0) { + std::cout << ","; + } + std::cout << mparams.tensor_split[id]; + } + } + + const size_t ntbo = llama_max_tensor_buft_overrides(); + for (size_t itbo = 0; itbo < ntbo && mparams.tensor_buft_overrides[itbo].pattern != nullptr; itbo++) { + if (itbo == 0) { + std::cout << " -ot "; + } + if (itbo > 0) { + std::cout << ","; + } + std::cout << mparams.tensor_buft_overrides[itbo].pattern << "=" << ggml_backend_buft_name(mparams.tensor_buft_overrides[itbo].buft); + } + std::cout << "\n"; + + return 0; +} From e73d548659db9297c23a77359776481690b82741 Mon Sep 17 00:00:00 2001 From: Thomas Jarosch Date: Mon, 15 Dec 2025 11:29:29 +0100 Subject: [PATCH 37/40] webui: add "delete all conversations" button to import/export tab (#17444) * webui: add "delete all conversations" button to import/export tab - Add 'Delete all conversations' functionality with confirmation dialog - Add Trash icon and destructive styling for clear visual indication - Redirects to "?new_chat=true#/" by using conversationsStore.deleteAll() * chore: update webui build output --- tools/server/public/index.html.gz | Bin 1445111 -> 1444280 bytes .../ChatSettingsImportExportTab.svelte | 70 +++++++++++++++++- .../src/lib/stores/conversations.svelte.ts | 26 ++++++- 3 files changed, 92 insertions(+), 4 deletions(-) diff --git a/tools/server/public/index.html.gz b/tools/server/public/index.html.gz index 3fd631b77a85820f6fcec9cc3f5e9f60d5ef9fb3..1834d1d91f46fcdaa39f2ccff19e82ee2a2479f3 100644 GIT binary patch delta 1157796 zcmV)hK%>9+nOL}!Sb&59gaU*Ev;?|2f81^y+13Pq-+l_EPIa|3WQw3CrBZ3=z4xqD 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@@ -229,6 +264,25 @@
{/if} + +
+

Delete All Conversations

+ +

+ Permanently delete all conversations and their messages. This action cannot be undone. + Consider exporting your conversations first if you want to keep a backup. +

+ + +
@@ -249,3 +303,15 @@ onCancel={() => (showImportDialog = false)} onConfirm={handleImportConfirm} /> + + diff --git a/tools/server/webui/src/lib/stores/conversations.svelte.ts b/tools/server/webui/src/lib/stores/conversations.svelte.ts index f766561971..3300eb3113 100644 --- a/tools/server/webui/src/lib/stores/conversations.svelte.ts +++ b/tools/server/webui/src/lib/stores/conversations.svelte.ts @@ -385,8 +385,7 @@ class ConversationsStore { this.conversations = this.conversations.filter((c) => c.id !== convId); if (this.activeConversation?.id === convId) { - this.activeConversation = null; - this.activeMessages = []; + this.clearActiveConversation(); await goto(`?new_chat=true#/`); } } catch (error) { @@ -394,6 +393,29 @@ class ConversationsStore { } } + /** + * Deletes all conversations and their messages + */ + async deleteAll(): Promise { + try { + const allConversations = await DatabaseService.getAllConversations(); + + for (const conv of allConversations) { + await DatabaseService.deleteConversation(conv.id); + } + + this.clearActiveConversation(); + this.conversations = []; + + toast.success('All conversations deleted'); + + await goto(`?new_chat=true#/`); + } catch (error) { + console.error('Failed to delete all conversations:', error); + toast.error('Failed to delete conversations'); + } + } + // ───────────────────────────────────────────────────────────────────────────── // Import/Export // ───────────────────────────────────────────────────────────────────────────── From 4a4f7e6550cf5b327ea0fb241ce7417ab46e1ace Mon Sep 17 00:00:00 2001 From: Andrew Aladjev Date: Mon, 15 Dec 2025 13:47:04 +0300 Subject: [PATCH 38/40] cli: fixed dead links to tools/main for cli and completion, fixed code owners (#17993) Co-authored-by: Andrew Aladjev --- CODEOWNERS | 3 ++- README.md | 5 +++-- docs/development/HOWTO-add-model.md | 3 ++- grammars/README.md | 6 +++--- tools/cli/README.md | 1 + tools/completion/README.md | 32 ++++++++++++++--------------- tools/llama-bench/README.md | 2 +- 7 files changed, 28 insertions(+), 24 deletions(-) create mode 100644 tools/cli/README.md diff --git a/CODEOWNERS b/CODEOWNERS index 8e62a36e81..8a0c98c968 100644 --- a/CODEOWNERS +++ b/CODEOWNERS @@ -87,7 +87,8 @@ /tests/ @ggerganov /tests/test-chat-.* @pwilkin /tools/batched-bench/ @ggerganov -/tools/main/ @ggerganov +/tools/cli/ @ngxson +/tools/completion/ @ggerganov /tools/mtmd/ @ngxson /tools/perplexity/ @ggerganov /tools/quantize/ @ggerganov diff --git a/README.md b/README.md index b7d24c9dd7..5f2076d0a3 100644 --- a/README.md +++ b/README.md @@ -313,7 +313,7 @@ The Hugging Face platform provides a variety of online tools for converting, qua To learn more about model quantization, [read this documentation](tools/quantize/README.md) -## [`llama-cli`](tools/main) +## [`llama-cli`](tools/cli) #### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality. @@ -525,7 +525,8 @@ To learn more about model quantization, [read this documentation](tools/quantize ## Other documentation -- [main (cli)](tools/main/README.md) +- [cli](tools/cli/README.md) +- [completion](tools/completion/README.md) - [server](tools/server/README.md) - [GBNF grammars](grammars/README.md) diff --git a/docs/development/HOWTO-add-model.md b/docs/development/HOWTO-add-model.md index 5989b873a6..9d1452e3f0 100644 --- a/docs/development/HOWTO-add-model.md +++ b/docs/development/HOWTO-add-model.md @@ -9,7 +9,8 @@ Adding a model requires few steps: After following these steps, you can open PR. Also, it is important to check that the examples and main ggml backends (CUDA, METAL, CPU) are working with the new architecture, especially: -- [main](/tools/main/) +- [cli](/tools/cli/) +- [completion](/tools/completion/) - [imatrix](/tools/imatrix/) - [quantize](/tools/quantize/) - [server](/tools/server/) diff --git a/grammars/README.md b/grammars/README.md index 11e3b6dd90..daac7f4d8d 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -1,6 +1,6 @@ # GBNF Guide -GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/main` and `tools/server`. +GBNF (GGML BNF) is a format for defining [formal grammars](https://en.wikipedia.org/wiki/Formal_grammar) to constrain model outputs in `llama.cpp`. For example, you can use it to force the model to generate valid JSON, or speak only in emojis. GBNF grammars are supported in various ways in `tools/cli`, `tools/completion` and `tools/server`. ## Background @@ -135,7 +135,7 @@ While semantically correct, the syntax `x? x? x?.... x?` (with N repetitions) ma You can use GBNF grammars: - In [llama-server](../tools/server)'s completion endpoints, passed as the `grammar` body field -- In [llama-cli](../tools/main), passed as the `--grammar` & `--grammar-file` flags +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--grammar` & `--grammar-file` flags - With [test-gbnf-validator](../tests/test-gbnf-validator.cpp), to test them against strings. ## JSON Schemas → GBNF @@ -145,7 +145,7 @@ You can use GBNF grammars: - In [llama-server](../tools/server): - For any completion endpoints, passed as the `json_schema` body field - For the `/chat/completions` endpoint, passed inside the `response_format` body field (e.g. `{"type", "json_object", "schema": {"items": {}}}` or `{ type: "json_schema", json_schema: {"schema": ...} }`) -- In [llama-cli](../tools/main), passed as the `--json` / `-j` flag +- In [llama-cli](../tools/cli) and [llama-completion](../tools/completion), passed as the `--json` / `-j` flag - To convert to a grammar ahead of time: - in CLI, with [examples/json_schema_to_grammar.py](../examples/json_schema_to_grammar.py) - in JavaScript with [json-schema-to-grammar.mjs](../tools/server/public_legacy/json-schema-to-grammar.mjs) (this is used by the [server](../tools/server)'s Web UI) diff --git a/tools/cli/README.md b/tools/cli/README.md new file mode 100644 index 0000000000..1333ed77b7 --- /dev/null +++ b/tools/cli/README.md @@ -0,0 +1 @@ +TODO diff --git a/tools/completion/README.md b/tools/completion/README.md index 54e582de07..57ef394213 100644 --- a/tools/completion/README.md +++ b/tools/completion/README.md @@ -1,4 +1,4 @@ -# llama.cpp/tools/main +# llama.cpp/tools/completion This example program allows you to use various LLaMA language models easily and efficiently. It is specifically designed to work with the [llama.cpp](https://github.com/ggml-org/llama.cpp) project, which provides a plain C/C++ implementation with optional 4-bit quantization support for faster, lower memory inference, and is optimized for desktop CPUs. This program can be used to perform various inference tasks with LLaMA models, including generating text based on user-provided prompts and chat-like interactions with reverse prompts. @@ -27,64 +27,64 @@ Once downloaded, place your model in the models folder in llama.cpp. ##### Input prompt (One-and-done) ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" ``` ##### Conversation mode (Allow for continuous interaction with the model) ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma ``` ##### Conversation mode using built-in jinja chat template ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja ``` ##### One-and-done query using jinja with custom system prompt and a starting prompt ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" ``` ##### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```bash -./llama-cli -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 +./llama-completion -m models/gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ### Windows: ##### Input prompt (One-and-done) ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf -no-cnv --prompt "Once upon a time" ``` ##### Conversation mode (Allow for continuous interaction with the model) ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --chat-template gemma ``` ##### Conversation mode using built-in jinja chat template ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja ``` ##### One-and-done query using jinja with custom system prompt and a starting prompt ```powershell -./llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" +./llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --jinja --single-turn -sys "You are a helpful assistant" -p "Hello" ``` #### Infinite text from a starting prompt (you can use `Ctrl-C` to stop it): ```powershell -llama-cli.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 +llama-completion.exe -m models\gemma-1.1-7b-it.Q4_K_M.gguf --ignore-eos -n -1 ``` ## Common Options -In this section, we cover the most commonly used options for running the `llama-cli` program with the LLaMA models: +In this section, we cover the most commonly used options for running the `llama-completion` program with the LLaMA models: - `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/gemma-1.1-7b-it.Q4_K_M.gguf`; inferred from `--model-url` if set). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file (e.g [https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true](https://huggingface.co/ggml-org/gemma-1.1-7b-it-Q4_K_M-GGUF/resolve/main/gemma-1.1-7b-it.Q4_K_M.gguf?download=true)). @@ -97,7 +97,7 @@ In this section, we cover the most commonly used options for running the `llama- ## Input Prompts -The `llama-cli` program provides several ways to interact with the LLaMA models using input prompts: +The `llama-completion` program provides several ways to interact with the LLaMA models using input prompts: - `--prompt PROMPT`: Provide a prompt directly as a command-line option. - `--file FNAME`: Provide a file containing a prompt or multiple prompts. @@ -107,7 +107,7 @@ The `llama-cli` program provides several ways to interact with the LLaMA models ## Interaction -The `llama-cli` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. +The `llama-completion` program offers a seamless way to interact with LLaMA models, allowing users to engage in real-time conversations or provide instructions for specific tasks. The interactive mode can be triggered using various options, including `--interactive` and `--interactive-first`. In interactive mode, users can participate in text generation by injecting their input during the process. Users can press `Ctrl+C` at any time to interject and type their input, followed by pressing `Return` to submit it to the LLaMA model. To submit additional lines without finalizing input, users can end the current line with a backslash (`\`) and continue typing. @@ -136,7 +136,7 @@ To overcome this limitation, you can use the `--in-prefix` flag to add a space o The `--in-prefix` flag is used to add a prefix to your input, primarily, this is used to insert a space after the reverse prompt. Here's an example of how to use the `--in-prefix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./llama-cli -r "User:" --in-prefix " " +./llama-completion -r "User:" --in-prefix " " ``` ### In-Suffix @@ -144,7 +144,7 @@ The `--in-prefix` flag is used to add a prefix to your input, primarily, this is The `--in-suffix` flag is used to add a suffix after your input. This is useful for adding an "Assistant:" prompt after the user's input. It's added after the new-line character (`\n`) that's automatically added to the end of the user's input. Here's an example of how to use the `--in-suffix` flag in conjunction with the `--reverse-prompt` flag: ```sh -./llama-cli -r "User:" --in-prefix " " --in-suffix "Assistant:" +./llama-completion -r "User:" --in-prefix " " --in-suffix "Assistant:" ``` When --in-prefix or --in-suffix options are enabled the chat template ( --chat-template ) is disabled diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md index 87d9c0a219..c837bb6d26 100644 --- a/tools/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -80,7 +80,7 @@ Each test is repeated the number of times given by `-r`, and the results are ave Using the `-d ` option, each test can be run at a specified context depth, prefilling the KV cache with `` tokens. -For a description of the other options, see the [main example](../main/README.md). +For a description of the other options, see the [completion example](../completion/README.md). > [!NOTE] > The measurements with `llama-bench` do not include the times for tokenization and for sampling. From 96a181a933a34e92c316b0603f0f30cb7f30eefa Mon Sep 17 00:00:00 2001 From: Xuan-Son Nguyen Date: Mon, 15 Dec 2025 14:16:52 +0100 Subject: [PATCH 39/40] mtmd: refactor audio preprocessing (#17978) * mtmd: refactor audio preprocessing * refactor Co-authored-by: Tarek * wip * wip (2) * improve constructor * fix use_natural_log * fix padding for short input * clean up * remove need_chunking --------- Co-authored-by: Tarek --- tools/mtmd/clip-model.h | 9 + tools/mtmd/clip.cpp | 19 +- tools/mtmd/mtmd-audio.cpp | 872 ++++++++++++++------------------------ tools/mtmd/mtmd-audio.h | 43 +- tools/mtmd/mtmd.cpp | 32 +- 5 files changed, 379 insertions(+), 596 deletions(-) diff --git a/tools/mtmd/clip-model.h b/tools/mtmd/clip-model.h index bdd4f63e0f..2f7dbb458e 100644 --- a/tools/mtmd/clip-model.h +++ b/tools/mtmd/clip-model.h @@ -65,6 +65,13 @@ struct clip_hparams { int32_t n_mel_bins = 0; // whisper preprocessor int32_t proj_stack_factor = 0; // ultravox + // audio-to-mel preprocessor params + int32_t audio_chunk_len = -1; // in seconds + int32_t audio_sample_rate = -1; + int32_t audio_n_fft = -1; + int32_t audio_window_len = -1; + int32_t audio_hop_len = -1; + // legacy bool has_llava_projector = false; int minicpmv_version = 0; @@ -278,3 +285,5 @@ struct clip_model { || proj_type == PROJECTOR_TYPE_VOXTRAL; } }; + +const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx); diff --git a/tools/mtmd/clip.cpp b/tools/mtmd/clip.cpp index c72c6a6381..fee49e465c 100644 --- a/tools/mtmd/clip.cpp +++ b/tools/mtmd/clip.cpp @@ -1170,11 +1170,15 @@ struct clip_model_loader { model.proj_type == PROJECTOR_TYPE_VOXTRAL || model.proj_type == PROJECTOR_TYPE_GLMA; get_u32(KEY_A_PROJ_STACK_FACTOR, hparams.proj_stack_factor, require_stack); - if (hparams.n_mel_bins != 128) { - throw std::runtime_error(string_format("%s: only 128 mel bins are supported for ultravox\n", __func__)); - } hparams.ffn_op = FFN_GELU_ERF; log_ffn_op = "gelu_erf"; // temporary solution for logging + + // audio preprocessing params + hparams.audio_chunk_len = 30; // in seconds + hparams.audio_sample_rate = 16000; + hparams.audio_n_fft = 400; + hparams.audio_window_len = 400; + hparams.audio_hop_len = 160; } break; default: break; @@ -1212,6 +1216,11 @@ struct clip_model_loader { LOG_INF("\n--- audio hparams ---\n"); LOG_INF("%s: n_mel_bins: %d\n", __func__, hparams.n_mel_bins); LOG_INF("%s: proj_stack_factor: %d\n", __func__, hparams.proj_stack_factor); + LOG_INF("%s: audio_chunk_len: %d\n", __func__, hparams.audio_chunk_len); + LOG_INF("%s: audio_sample_rate: %d\n", __func__, hparams.audio_sample_rate); + LOG_INF("%s: audio_n_fft: %d\n", __func__, hparams.audio_n_fft); + LOG_INF("%s: audio_window_len: %d\n", __func__, hparams.audio_window_len); + LOG_INF("%s: audio_hop_len: %d\n", __func__, hparams.audio_hop_len); } LOG_INF("\n"); LOG_INF("%s: model size: %.2f MiB\n", __func__, model_size / 1024.0 / 1024.0); @@ -3478,3 +3487,7 @@ void clip_image_f32_batch_add_mel(struct clip_image_f32_batch * batch, int n_mel batch->entries.push_back(clip_image_f32_ptr(audio)); batch->is_audio = true; } + +const clip_hparams * clip_get_hparams(const struct clip_ctx * ctx) { + return &ctx->model.hparams; +} diff --git a/tools/mtmd/mtmd-audio.cpp b/tools/mtmd/mtmd-audio.cpp index 4d053895cd..f68829a61a 100644 --- a/tools/mtmd/mtmd-audio.cpp +++ b/tools/mtmd/mtmd-audio.cpp @@ -11,63 +11,149 @@ // most of the code here is copied from whisper.cpp -// align x to upper multiple of n -#define _ALIGN(x, n) ((((x) + (n) - 1) / (n)) * (n)) +constexpr bool DEBUG = false; -namespace whisper_preprocessor { +struct mtmd_audio_mel_filters { + int32_t n_mel; + int32_t n_fft; -#define SIN_COS_N_COUNT WHISPER_N_FFT -namespace { -struct whisper_global_cache { - // In FFT, we frequently use sine and cosine operations with the same values. - // We can use precalculated values to speed up the process. - float sin_vals[SIN_COS_N_COUNT]; - float cos_vals[SIN_COS_N_COUNT]; + std::vector data; +}; - // Hann window (Use cosf to eliminate difference) - // ref: https://pytorch.org/docs/stable/generated/torch.hann_window.html - // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L147 - float hann_window[WHISPER_N_FFT]; +// note: this global cache is shared among all preprocessors +// if we want to use multiple preprocessors at the same time, +// we will need to enclose it in the preprocessor class in the future +static struct mtmd_audio_global_cache { + // precomputed sin/cos table for FFT + std::vector sin_vals; + std::vector cos_vals; - whisper_global_cache() { - fill_sin_cos_table(); - fill_hann_window(sizeof(hann_window)/sizeof(hann_window[0]), true, hann_window); - } + // hann window + std::vector hann_window; - void fill_sin_cos_table() { - for (int i = 0; i < SIN_COS_N_COUNT; i++) { - double theta = (2 * M_PI * i) / SIN_COS_N_COUNT; + // mel filter bank + mtmd_audio_mel_filters filters; + + void fill_sin_cos_table(int n) { + sin_vals.resize(n); + cos_vals.resize(n); + for (int i = 0; i < n; i++) { + double theta = (2 * M_PI * i) / n; sin_vals[i] = sinf(theta); cos_vals[i] = cosf(theta); } } - void fill_hann_window(int length, bool periodic, float * output) { + void fill_hann_window(int length, bool periodic) { + hann_window.resize(length); int offset = -1; if (periodic) { offset = 0; } for (int i = 0; i < length; i++) { - output[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset))); + hann_window[i] = 0.5 * (1.0 - cosf((2.0 * M_PI * i) / (length + offset))); } } -} global_cache; -} + + // Build mel filterbank matrix [n_mel × n_fft_bins] at runtime. + // n_fft_bins must be (N_fft / 2 + 1). Example: if N_fft=512 -> n_fft_bins=257. + void fill_mel_filterbank_matrix( + int n_mel, + int n_fft, + int sample_rate, // e.g. 16000 + float fmin = 0.0f, // e.g. 0.0 + float fmax = -1.0f, // e.g. sr/2; pass -1 for auto + bool slaney_area_norm = true, + float scale = 1.0f // optional extra scaling; use 1.0f/1000.0f to mimic your code + ) { + GGML_ASSERT(n_mel > 0 && n_fft > 1); + if (fmax <= 0.0f) { + fmax = 0.5f * sample_rate; + } + + // Slaney scale (matches librosa default) + const double min_log_hz = 1000.0; + const double lin_slope = 3 / 200.; + const double min_log_mel = min_log_hz * lin_slope; + const double log_step = log(6.4) / 27.0; + auto hz_to_mel = [min_log_hz, lin_slope, log_step, min_log_mel](const double f_hz) -> double { + return (f_hz < min_log_hz) ? f_hz * lin_slope : min_log_mel + log(f_hz / min_log_hz) / log_step; + }; + auto mel_to_hz = [min_log_hz, lin_slope, log_step, min_log_mel](const double m) -> double { + return (m < min_log_mel) ? m / lin_slope : min_log_hz * exp((m - min_log_mel) * log_step); + }; + + // infer N_fft from n_fft_bins + const double bin_hz_step = double(sample_rate) / double(n_fft); + + // mel grid: n_mel + 2 edges + const double m_lo = hz_to_mel(fmin); + const double m_hi = hz_to_mel(fmax); + std::vector mel_pts(n_mel + 2); + for (int i = 0; i < n_mel + 2; ++i) { + mel_pts[i] = m_lo + (m_hi - m_lo) * (double(i) / (n_mel + 1)); + } + + // convert to Hz + std::vector hz_pts(n_mel + 2); + for (int i = 0; i < n_mel + 2; ++i) { + hz_pts[i] = mel_to_hz(mel_pts[i]); + } + + const int n_fft_bins = n_fft / 2 + 1; + + // filterbank + std::vector out(n_mel * n_fft_bins, 0); + for (int m = 0; m < n_mel; ++m) { + const double f_left = hz_pts[m]; + const double f_center = hz_pts[m + 1]; + const double f_right = hz_pts[m + 2]; + + const double denom_l = std::max(1e-30, f_center - f_left); + const double denom_r = std::max(1e-30, f_right - f_center); + const double enorm = slaney_area_norm ? (2.0 / std::max(1e-30, f_right - f_left)) : 1.0; + + for (int k = 0; k < n_fft_bins; ++k) { + const double f = k * bin_hz_step; + double w = 0.0; + if (f >= f_left && f <= f_center) { + w = (f - f_left) / denom_l; + } else if (f > f_center && f <= f_right) { + w = (f_right - f) / denom_r; + } + out[size_t(m) * size_t(n_fft_bins) + size_t(k)] = float(w * enorm * scale); + } + } + + filters.n_mel = n_mel; + filters.n_fft = n_fft; + filters.data = std::move(out); + + if (DEBUG) { // debug + for (size_t i = 0; i < filters.data.size(); ++i) { + if (filters.data[i] != 0.0f) { + printf("filters[%zu] = %f\n", i, filters.data[i] * 1000.0f); + } + } + } + } +} g_cache; // naive Discrete Fourier Transform // input is real-valued // output is complex-valued -static void dft(const float* in, int N, float* out) { - const int sin_cos_step = SIN_COS_N_COUNT / N; +static void dft(const float * in, int N, float * out) { + const int n_sin_cos_vals = g_cache.sin_vals.size(); + const int sin_cos_step = n_sin_cos_vals / N; for (int k = 0; k < N; k++) { float re = 0; float im = 0; for (int n = 0; n < N; n++) { - int idx = (k * n * sin_cos_step) % (SIN_COS_N_COUNT); // t = 2*M_PI*k*n/N - re += in[n]*global_cache.cos_vals[idx]; // cos(t) - im -= in[n]*global_cache.sin_vals[idx]; // sin(t) + int idx = (k * n * sin_cos_step) % (n_sin_cos_vals); // t = 2*M_PI*k*n/N + re += in[n] * g_cache.cos_vals[idx]; // cos(t) + im -= in[n] * g_cache.sin_vals[idx]; // sin(t) } out[k*2 + 0] = re; @@ -79,7 +165,8 @@ static void dft(const float* in, int N, float* out) { // poor man's implementation - use something better // input is real-valued // output is complex-valued -static void fft(float* in, int N, float* out) { +static void fft(float * in, int N, float * out) { + const int n_sin_cos_vals = g_cache.sin_vals.size(); if (N == 1) { out[0] = in[0]; out[1] = 0; @@ -106,11 +193,11 @@ static void fft(float* in, int N, float* out) { float* odd_fft = even_fft + N; fft(odd, half_N, odd_fft); - const int sin_cos_step = SIN_COS_N_COUNT / N; + const int sin_cos_step = n_sin_cos_vals / N; for (int k = 0; k < half_N; k++) { int idx = k * sin_cos_step; // t = 2*M_PI*k/N - float re = global_cache.cos_vals[idx]; // cos(t) - float im = -global_cache.sin_vals[idx]; // sin(t) + float re = g_cache.cos_vals[idx]; // cos(t) + float im = -g_cache.sin_vals[idx]; // sin(t) float re_odd = odd_fft[2*k + 0]; float im_odd = odd_fft[2*k + 1]; @@ -123,20 +210,34 @@ static void fft(float* in, int N, float* out) { } } +struct filter_params { + int32_t n_mel; + int32_t n_fft_bins; + int32_t hann_window_size; + int32_t hop_length; + int32_t sample_rate; + bool center_padding = false; + float preemph = 0.f; + bool use_natural_log = false; + bool norm_per_feature = false; +}; + static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const std::vector & samples, int n_samples, int frame_size, int frame_step, int n_threads, - const whisper_filters & filters, whisper_mel & mel) { + const filter_params & params, mtmd_audio_mel & out) { std::vector fft_in(frame_size * 2, 0.0); std::vector fft_out(frame_size * 2 * 2 * 2); - int n_fft = filters.n_fft; + int n_fft_bins = params.n_fft_bins; int i = ith; - // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist - WHISPER_ASSERT(n_fft == 1 + (frame_size / 2)); + const auto & filters = g_cache.filters; + // make sure n_fft == 1 + (WHISPER_N_FFT / 2), bin_0 to bin_nyquist + GGML_ASSERT(n_fft_bins == 1 + (frame_size / 2)); + GGML_ASSERT(g_cache.sin_vals.size() == g_cache.cos_vals.size()); // calculate FFT only when fft_in are not all zero - for (; i < std::min(n_samples / frame_step + 1, mel.n_len); i += n_threads) { + for (; i < std::min(n_samples / frame_step + 1, out.n_len); i += n_threads) { const int offset = i * frame_step; // apply Hann window (~10% faster) @@ -154,36 +255,39 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const // Calculate modulus^2 of complex numbers // Use pow(fft_out[2 * j + 0], 2) + pow(fft_out[2 * j + 1], 2) causes inference quality problem? Interesting. - for (int j = 0; j < n_fft; j++) { + for (int j = 0; j < n_fft_bins; j++) { fft_out[j] = (fft_out[2 * j + 0] * fft_out[2 * j + 0] + fft_out[2 * j + 1] * fft_out[2 * j + 1]); } // mel spectrogram - for (int j = 0; j < mel.n_mel; j++) { + for (int j = 0; j < out.n_mel; j++) { double sum = 0.0; // unroll loop (suggested by GH user @lunixbochs) int k = 0; - for (k = 0; k < n_fft - 3; k += 4) { + for (k = 0; k < n_fft_bins - 3; k += 4) { + size_t idx = size_t(j) * size_t(n_fft_bins) + size_t(k); sum += - fft_out[k + 0] * filters.data[j * n_fft + k + 0] + - fft_out[k + 1] * filters.data[j * n_fft + k + 1] + - fft_out[k + 2] * filters.data[j * n_fft + k + 2] + - fft_out[k + 3] * filters.data[j * n_fft + k + 3]; + fft_out[k + 0] * filters.data[idx + 0] + + fft_out[k + 1] * filters.data[idx + 1] + + fft_out[k + 2] * filters.data[idx + 2] + + fft_out[k + 3] * filters.data[idx + 3]; } // handle n_fft remainder - for (; k < n_fft; k++) { - sum += fft_out[k] * filters.data[j * n_fft + k]; + for (; k < n_fft_bins; k++) { + sum += fft_out[k] * filters.data[j * n_fft_bins + k]; } - sum = log10(std::max(sum, 1e-10)); - mel.data[j * mel.n_len + i] = sum; + sum = params.use_natural_log + ? log(sum + 5.960464477539063e-08) + : log10(std::max(sum, 1e-10)); + out.data[j * out.n_len + i] = sum; } } // Otherwise fft_out are all zero - double sum = log10(1e-10); - for (; i < mel.n_len; i += n_threads) { - for (int j = 0; j < mel.n_mel; j++) { - mel.data[j * mel.n_len + i] = sum; + double sum = params.use_natural_log ? log(1e-10) : log10(1e-10); + for (; i < out.n_len; i += n_threads) { + for (int j = 0; j < out.n_mel; j++) { + out.data[j * out.n_len + i] = sum; } } } @@ -191,115 +295,212 @@ static void log_mel_spectrogram_worker_thread(int ith, const float * hann, const // ref: https://github.com/openai/whisper/blob/main/whisper/audio.py#L110-L157 static bool log_mel_spectrogram( const float * samples, - const int n_samples, - const int /*sample_rate*/, - const int frame_size, - const int frame_step, - const int n_mel, - const int n_threads, - const whisper_filters & filters, - const bool debug, - whisper_mel & mel) { + const int n_samples_in, + const int n_threads, + const filter_params & params, + mtmd_audio_mel & out) { //const int64_t t_start_us = ggml_time_us(); + out.n_len_org = n_samples_in; + int n_samples = n_samples_in; + // Hann window - WHISPER_ASSERT(frame_size == WHISPER_N_FFT && "Unsupported frame_size"); - const float * hann = global_cache.hann_window; + const float * hann = g_cache.hann_window.data(); + const int frame_size = (params.n_fft_bins - 1) * 2; + const int frame_step = params.hop_length; - // Calculate the length of padding - int64_t stage_1_pad = WHISPER_SAMPLE_RATE * 30; - int64_t stage_2_pad = frame_size / 2; - - // Initialize a vector and copy data from C array to it. + // Padding std::vector samples_padded; - samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2); - std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad); + if (params.center_padding) { + const auto pad_amount = frame_size / 2; + samples_padded = std::vector(n_samples + 2 * pad_amount, 0); + std::copy(samples, samples + n_samples, samples_padded.data() + pad_amount); + samples = samples_padded.data(); + n_samples = samples_padded.size(); + } else { + // existing padding logic + int64_t stage_1_pad = params.sample_rate * 30; + int64_t stage_2_pad = frame_size / 2; + samples_padded.resize(n_samples + stage_1_pad + stage_2_pad * 2); + std::copy(samples, samples + n_samples, samples_padded.begin() + stage_2_pad); + // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio + std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0); + // reflective pad 200 samples at the beginning of audio + if (n_samples < stage_2_pad + 1) { + // TODO: Handle short audio differently or return error + return false; + } + std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin()); + } - // pad 30 seconds of zeros at the end of audio (480,000 samples) + reflective pad 200 samples at the end of audio - std::fill(samples_padded.begin() + n_samples + stage_2_pad, samples_padded.begin() + n_samples + stage_1_pad + 2 * stage_2_pad, 0); + // preemphasis + if (params.preemph) { + const int pad_amount = frame_size / 2; + const float preemph = 0.97f; + float prev = samples_padded[pad_amount]; + for (int i = pad_amount + 1; i + pad_amount < n_samples; ++i) { + float cur = samples_padded[i]; + samples_padded[i] = cur - preemph * prev; + prev = cur; + } + } - // reflective pad 200 samples at the beginning of audio - std::reverse_copy(samples + 1, samples + 1 + stage_2_pad, samples_padded.begin()); + // pad hann window if it's smaller than frame_size + // TODO: probably unnecessary here? (or better doing it in g_cache?) + std::vector hann_window_padded; + if (params.hann_window_size < frame_size) { + hann_window_padded.resize(frame_size); + const int padding = (frame_size - params.hann_window_size) / 2; + std::copy(hann, hann + params.hann_window_size, &hann_window_padded[padding]); + hann = hann_window_padded.data(); + } - mel.n_mel = n_mel; - // https://github.com/pytorch/pytorch/blob/main/aten/src/ATen/native/SpectralOps.cpp#L936 - // Calculate number of frames + remove the last frame - mel.n_len = (samples_padded.size() - frame_size) / frame_step; - // Calculate semi-padded sample length to ensure compatibility - mel.n_len_org = 1 + (n_samples + stage_2_pad - frame_size) / frame_step; - mel.data.resize(mel.n_mel * mel.n_len); + + out.n_mel = params.n_mel; + out.n_len = (n_samples - frame_size) / frame_step + 1; + // TODO: handle these checks better + if (out.n_mel > 0 && (unsigned long)out.n_len > SIZE_MAX / out.n_mel) { + LOG_ERR("%s: size overflow\n", __func__); + return false; + } + if (n_samples < frame_size) { + LOG_ERR("%s: not enough samples after padding\n", __func__); + return false; + } + out.data.resize(out.n_mel * out.n_len); { std::vector workers(n_threads - 1); for (int iw = 0; iw < n_threads - 1; ++iw) { workers[iw] = std::thread( log_mel_spectrogram_worker_thread, iw + 1, hann, std::cref(samples_padded), - n_samples + stage_2_pad, frame_size, frame_step, n_threads, - std::cref(filters), std::ref(mel)); + n_samples, frame_size, frame_step, n_threads, + std::cref(params), std::ref(out)); } // main thread - log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples + stage_2_pad, frame_size, frame_step, n_threads, filters, mel); - + log_mel_spectrogram_worker_thread(0, hann, samples_padded, n_samples, frame_size, frame_step, n_threads, params, out); for (int iw = 0; iw < n_threads - 1; ++iw) { workers[iw].join(); } } - // clamping and normalization - double mmax = -1e20; - for (int i = 0; i < mel.n_mel*mel.n_len; i++) { - if (mel.data[i] > mmax) { - mmax = mel.data[i]; + const int effective_n_len = n_samples_in / frame_step; + if (params.norm_per_feature) { + for (int i = 0; i < out.n_mel; i++) { + double mean = 0; + for (int j = 0; j < effective_n_len; ++j) { + mean += out.data[i * out.n_len + j]; + } + mean /= effective_n_len; + + double var = 0.0; + for (int j = 0; j < effective_n_len; ++j) { + const double value = out.data[i * out.n_len + j] - mean; + var += value * value; + } + var /= effective_n_len - 1; // unbiased + const double mstd = std::sqrt(var + 1e-5); + + for (int j = 0; j < effective_n_len; ++j) { + auto &value = out.data[i * out.n_len + j]; + value = (value - mean) / mstd; + } + + // pad the rest with zeros + for (int j = effective_n_len; j < out.n_len; ++j) { + out.data[i * out.n_len + j] = 0.0; + } } - } - - mmax -= 8.0; - - for (int i = 0; i < mel.n_mel*mel.n_len; i++) { - if (mel.data[i] < mmax) { - mel.data[i] = mmax; + } else { + // clamping and normalization + double mmax = -1e20; + for (int i = 0; i < out.n_mel*out.n_len; i++) { + if (out.data[i] > mmax) { + mmax = out.data[i]; + } } - mel.data[i] = (mel.data[i] + 4.0)/4.0; + mmax -= 8.0; + + for (int i = 0; i < out.n_mel*out.n_len; i++) { + if (out.data[i] < mmax) { + out.data[i] = mmax; + } + out.data[i] = (out.data[i] + 4.0)/4.0; + } } // Dump log_mel_spectrogram - if (debug) { + if (DEBUG) { std::ofstream outFile("log_mel_spectrogram.json"); outFile << "["; - for (uint64_t i = 0; i < mel.data.size() - 1; i++) { - outFile << mel.data[i] << ", "; + for (uint64_t i = 0; i < out.data.size() - 1; i++) { + outFile << out.data[i] << ", "; } - outFile << mel.data[mel.data.size() - 1] << "]"; + outFile << out.data[out.data.size() - 1] << "]"; outFile.close(); } return true; } -bool preprocess_audio( +// +// mtmd_audio_preprocessor_whisper +// + +void mtmd_audio_preprocessor_whisper::initialize() { + g_cache.fill_sin_cos_table(hparams.audio_n_fft); + g_cache.fill_hann_window(hparams.audio_window_len, true); + g_cache.fill_mel_filterbank_matrix( + hparams.n_mel_bins, + hparams.audio_n_fft, + hparams.audio_sample_rate); +} + +bool mtmd_audio_preprocessor_whisper::preprocess( const float * samples, size_t n_samples, - const whisper_filters & filters, - std::vector & output) { - + std::vector & output) { if (n_samples == 0) { // empty audio return false; } - whisper_mel out_full; + std::vector smpl; + // if input is too short, pad with zeros + // this is to avoid potential issues with stage1/2 padding in log_mel_spectrogram + // TODO: maybe handle this better + size_t min_samples = (size_t)hparams.audio_sample_rate * (hparams.audio_chunk_len + 1); // +1 second margin + if (n_samples < min_samples) { + smpl.resize(min_samples, 0.0f); + std::memcpy(smpl.data(), samples, n_samples * sizeof(float)); + samples = smpl.data(); + n_samples = smpl.size(); + } + + filter_params params; + params.n_mel = hparams.n_mel_bins; + params.n_fft_bins = 1 + (hparams.audio_n_fft / 2); + params.hann_window_size = hparams.audio_window_len; + params.hop_length = hparams.audio_hop_len; + params.sample_rate = hparams.audio_sample_rate; + params.center_padding = false; + params.preemph = 0.0f; // disabled + params.use_natural_log = false; + params.norm_per_feature = false; + + // make sure the global cache is initialized + GGML_ASSERT(!g_cache.sin_vals.empty()); + GGML_ASSERT(!g_cache.cos_vals.empty()); + GGML_ASSERT(!g_cache.filters.data.empty()); + + mtmd_audio_mel out_full; bool ok = log_mel_spectrogram( samples, n_samples, - COMMON_SAMPLE_RATE, - WHISPER_N_FFT, - WHISPER_HOP_LENGTH, - filters.n_mel, 4, // n_threads - filters, - false, // debug + params, out_full); if (!ok) { return false; @@ -307,7 +508,9 @@ bool preprocess_audio( // because the cgraph in clip.cpp only accepts 3000 frames each, we need to split the mel // we always expect the mel to have 3000 silent frames at the end - // printf("n_len %d\n", out_full.n_len); + if (DEBUG) { + printf("output: n_mel = %d, n_len = %d\n", out_full.n_mel, out_full.n_len); + } const size_t frames_per_chunk = 3000; GGML_ASSERT((size_t)out_full.n_len > frames_per_chunk); for (size_t off = 0; off < (size_t)out_full.n_len; off += frames_per_chunk) { @@ -316,7 +519,7 @@ bool preprocess_audio( break; // last uncomplete chunk will always be a padded chunk, safe to ignore } - whisper_mel out_chunk; + mtmd_audio_mel out_chunk; out_chunk.n_len = n_len; out_chunk.n_mel = out_full.n_mel; out_chunk.n_len_org = out_full.n_mel; // unused @@ -332,438 +535,3 @@ bool preprocess_audio( return true; } - -} // namespace whisper_preprocessor - - -// precalculated mel filter banks -// values are multiplied by 1000.0 to save space, and will be divided by 1000.0 in the end of the function -// -// generated from python code: -// -// from numpy import load -// data = load('mel_filters.npz') -// lst = data.files -// for item in lst: -// print(item) -// print(data[item].shape) -// n_mel = data[item].shape[0] -// n_fft = data[item].shape[1] -// for i, row in enumerate(data[item]): -// for j, val in enumerate(row): -// val = val * 1000.0 -// if val != 0: -// print(f"data[{i*n_fft + j}] = {val:.6f};") - -namespace whisper_precalc_filters { - -whisper_preprocessor::whisper_filters get_128_bins() { - whisper_preprocessor::whisper_filters filters; - filters.n_mel = 128; - filters.n_fft = 201; - std::vector data(filters.n_mel * filters.n_fft, 0.0f); - - data[1] = 12.37398665; - data[202] = 30.39256483; - data[404] = 24.74797331; - data[605] = 18.01857911; - data[807] = 37.12195903; - data[1008] = 5.64459199; - data[1009] = 6.72939420; - data[1210] = 36.03715822; - data[1412] = 19.10337992; - data[1613] = 23.66316877; - data[1815] = 31.47736564; - data[2016] = 11.28918398; - data[2017] = 1.08480197; - data[2218] = 41.68175161; - data[2420] = 13.45878839; - data[2621] = 29.30776216; - data[2823] = 25.83277412; - data[3024] = 16.93377644; - data[3226] = 38.20675984; - data[3427] = 4.55979025; - data[3428] = 7.81419594; - data[3629] = 34.95235741; - data[3831] = 20.18818259; - data[4032] = 22.57836796; - data[4234] = 32.56217018; - data[4435] = 10.20438317; - data[4436] = 2.16960395; - data[4637] = 40.59694707; - data[4839] = 14.54358920; - data[5040] = 28.22295949; - data[5242] = 26.91757679; - data[5443] = 15.84897563; - data[5645] = 39.29156065; - data[5846] = 3.47498828; - data[5847] = 8.89899861; - data[6048] = 33.86755288; - data[6250] = 21.27298526; - data[6451] = 21.49356715; - data[6653] = 33.64697099; - data[6854] = 9.11958050; - data[6855] = 3.25440569; - data[7056] = 39.51214626; - data[7258] = 15.62839188; - data[7459] = 27.13815868; - data[7661] = 28.00237760; - data[7862] = 14.76417296; - data[8064] = 40.37636518; - data[8265] = 2.38068704; - data[8266] = 10.20263787; - data[8467] = 31.61146119; - data[8669] = 24.54700135; - data[8870] = 15.32919332; - data[8871] = 1.66583748; - data[9072] = 36.72905266; - data[9274] = 20.09709924; - data[9475] = 16.93102531; - data[9476] = 2.90265540; - data[9677] = 32.84499049; - data[9879] = 23.52004871; - data[10080] = 11.03894413; - data[10081] = 10.72582975; - data[10282] = 22.71829173; - data[10484] = 32.27872774; - data[10685] = 0.11626833; - data[10686] = 22.85348251; - data[10887] = 8.56344029; - data[10888] = 14.97978810; - data[11089] = 15.51398356; - data[11090] = 8.51490628; - data[11291] = 21.10680379; - data[11292] = 3.32652032; - data[11493] = 25.47064796; - data[11695] = 27.35907957; - data[11896] = 0.65853616; - data[11897] = 23.83812517; - data[12098] = 3.44359246; - data[12099] = 21.22455277; - data[12300] = 5.35842171; - data[12301] = 19.42555793; - data[12502] = 6.49324711; - data[12503] = 18.35542172; - data[12704] = 6.93138083; - data[12705] = 17.93504693; - data[12906] = 6.74968259; - data[12907] = 18.09151843; - data[13108] = 6.01899112; - data[13109] = 18.75767298; - data[13310] = 4.80452832; - data[13311] = 19.87172849; - data[13512] = 3.16627859; - data[13513] = 21.37690969; - data[13514] = 1.25317345; - data[13714] = 1.15934468; - data[13715] = 20.80361731; - data[13716] = 4.04486805; - data[13917] = 17.55363122; - data[13918] = 7.08320038; - data[14119] = 14.07538634; - data[14120] = 10.32655034; - data[14321] = 10.40921453; - data[14322] = 13.73696327; - data[14523] = 6.59187697; - data[14524] = 17.27988198; - data[14525] = 1.46804214; - data[14725] = 2.65681883; - data[14726] = 18.09193194; - data[14727] = 5.85655728; - data[14928] = 13.34277913; - data[14929] = 10.28267574; - data[15130] = 8.56800377; - data[15131] = 14.72230814; - data[15132] = 1.04039861; - data[15332] = 3.79085587; - data[15333] = 17.14678481; - data[15334] = 6.11609267; - data[15535] = 11.75929047; - data[15536] = 11.13393717; - data[15737] = 6.43857848; - data[15738] = 16.07806236; - data[15739] = 4.23917221; - data[15939] = 1.19989377; - data[15940] = 12.75671553; - data[15941] = 9.65298992; - data[16142] = 7.06935255; - data[16143] = 14.94054683; - data[16144] = 4.19024844; - data[16344] = 1.51483389; - data[16345] = 12.00899947; - data[16346] = 9.84823331; - data[16547] = 6.10224018; - data[16548] = 15.33857174; - data[16549] = 5.57676842; - data[16749] = 0.36827257; - data[16750] = 9.89749376; - data[16751] = 11.35340426; - data[16752] = 2.05122307; - data[16952] = 3.89297144; - data[16953] = 12.97352277; - data[16954] = 8.06631614; - data[17155] = 6.74493238; - data[17156] = 13.85874674; - data[17157] = 5.41190524; - data[17357] = 0.74220158; - data[17358] = 8.98779090; - data[17359] = 11.37871388; - data[17360] = 3.32958088; - data[17560] = 2.82313535; - data[17561] = 10.68049297; - data[17562] = 9.43340641; - data[17563] = 1.76325557; - data[17763] = 4.39018616; - data[17764] = 11.87758986; - data[17765] = 7.97005836; - data[17766] = 0.66104700; - data[17966] = 5.49466675; - data[17967] = 12.62953598; - data[17968] = 6.93987962; - data[18169] = 6.18401915; - data[18170] = 12.93473132; - data[18171] = 6.29778765; - data[18371] = 0.02325210; - data[18372] = 6.50206627; - data[18373] = 12.32661773; - data[18374] = 6.00216538; - data[18574] = 0.31548753; - data[18575] = 6.48925547; - data[18576] = 12.04130240; - data[18577] = 6.01462880; - data[18777] = 0.29979556; - data[18778] = 6.18288014; - data[18779] = 12.04272825; - data[18780] = 6.29981188; - data[18781] = 0.55689598; - data[18980] = 0.01120471; - data[18981] = 5.61729167; - data[18982] = 11.22337859; - data[18983] = 6.82516303; - data[18984] = 1.35264499; - data[19184] = 4.82410006; - data[19185] = 10.16623247; - data[19186] = 7.56075513; - data[19187] = 2.34590308; - data[19387] = 3.83235747; - data[19388] = 8.92296247; - data[19389] = 8.47910438; - data[19390] = 3.50978645; - data[19590] = 2.66873185; - data[19591] = 7.51965167; - data[19592] = 9.55500547; - data[19593] = 4.81966138; - data[19594] = 0.08431751; - data[19793] = 1.35767367; - data[19794] = 5.98019501; - data[19795] = 10.60271543; - data[19796] = 6.25298498; - data[19797] = 1.74059917; - data[19997] = 4.32644226; - data[19998] = 8.73131864; - data[19999] = 7.78916525; - data[20000] = 3.48923868; - data[20200] = 2.57835095; - data[20201] = 6.77582854; - data[20202] = 9.40941647; - data[20203] = 5.31194592; - data[20204] = 1.21447595; - data[20403] = 0.75411191; - data[20404] = 4.75395704; - data[20405] = 8.75380263; - data[20406] = 7.19209015; - data[20407] = 3.28754401; - data[20607] = 2.68179690; - data[20608] = 6.49331464; - data[20609] = 9.11457930; - data[20610] = 5.39387390; - data[20611] = 1.67316827; - data[20810] = 0.57394296; - data[20811] = 4.20600036; - data[20812] = 7.83805829; - data[20813] = 7.52023002; - data[20814] = 3.97470826; - data[20815] = 0.42918732; - data[21014] = 1.90464477; - data[21015] = 5.36569161; - data[21016] = 8.82673822; - data[21017] = 6.27609482; - data[21018] = 2.89750961; - data[21218] = 2.89885257; - data[21219] = 6.19694078; - data[21220] = 8.56699049; - data[21221] = 5.34748193; - data[21222] = 2.12797290; - data[21421] = 0.44750227; - data[21422] = 3.59030394; - data[21423] = 6.73310598; - data[21424] = 7.77023612; - data[21425] = 4.70231380; - data[21426] = 1.63439126; - data[21625] = 1.01536023; - data[21626] = 4.01018746; - data[21627] = 7.00501446; - data[21628] = 7.23442994; - data[21629] = 4.31095669; - data[21630] = 1.38748321; - data[21829] = 1.33348850; - data[21830] = 4.18730825; - data[21831] = 7.04112789; - data[21832] = 6.93188375; - data[21833] = 4.14605811; - data[21834] = 1.36023236; - data[22033] = 1.42879714; - data[22034] = 4.14824858; - data[22035] = 6.86769979; - data[22036] = 6.83705276; - data[22037] = 4.18239459; - data[22038] = 1.52773573; - data[22237] = 1.32610439; - data[22238] = 3.91751388; - data[22239] = 6.50892360; - data[22240] = 6.92639686; - data[22241] = 4.39672917; - data[22242] = 1.86706171; - data[22441] = 1.04827771; - data[22442] = 3.51767405; - data[22443] = 5.98707050; - data[22444] = 7.17824046; - data[22445] = 4.76767914; - data[22446] = 2.35711760; - data[22645] = 0.61636406; - data[22646] = 2.96949223; - data[22647] = 5.32262027; - data[22648] = 7.57265091; - data[22649] = 5.27558755; - data[22650] = 2.97852419; - data[22651] = 0.68146095; - data[22849] = 0.04971400; - data[22850] = 2.29204819; - data[22851] = 4.53438237; - data[22852] = 6.77671656; - data[22853] = 5.90240723; - data[22854] = 3.71349836; - data[22855] = 1.52458926; - data[23054] = 1.50285335; - data[23055] = 3.63961048; - data[23056] = 5.77636715; - data[23057] = 6.63159089; - data[23058] = 4.54574358; - data[23059] = 2.45989650; - data[23060] = 0.37404924; - data[23258] = 0.61795861; - data[23259] = 2.65410915; - data[23260] = 4.69025923; - data[23261] = 6.72641024; - data[23262] = 5.46034705; - data[23263] = 3.47270933; - data[23264] = 1.48507138; - data[23463] = 1.59233576; - data[23464] = 3.53261665; - data[23465] = 5.47289755; - data[23466] = 6.44368259; - data[23467] = 4.54962999; - data[23468] = 2.65557761; - data[23469] = 0.76152512; - data[23667] = 0.46749352; - data[23668] = 2.31641904; - data[23669] = 4.16534441; - data[23670] = 6.01426978; - data[23671] = 5.67844696; - data[23672] = 3.87357362; - data[23673] = 2.06870004; - data[23674] = 0.26382666; - data[23872] = 1.05349103; - data[23873] = 2.81536230; - data[23874] = 4.57723346; - data[23875] = 6.33910485; - data[23876] = 5.12815686; - data[23877] = 3.40826320; - data[23878] = 1.68837002; - data[24077] = 1.43350090; - data[24078] = 3.11241671; - data[24079] = 4.79133241; - data[24080] = 6.40943693; - data[24081] = 4.77052201; - data[24082] = 3.13160778; - data[24083] = 1.49269309; - data[24281] = 0.02932359; - data[24282] = 1.62918994; - data[24283] = 3.22905602; - data[24284] = 4.82892245; - data[24285] = 6.14671456; - data[24286] = 4.58496623; - data[24287] = 3.02321767; - data[24288] = 1.46146910; - data[24486] = 0.13601698; - data[24487] = 1.66055572; - data[24488] = 3.18509457; - data[24489] = 4.70963307; - data[24490] = 6.04072399; - data[24491] = 4.55250870; - data[24492] = 3.06429295; - data[24493] = 1.57607743; - data[24494] = 0.08786193; - data[24691] = 0.09328097; - data[24692] = 1.54603878; - data[24693] = 2.99879676; - data[24694] = 4.45155473; - data[24695] = 5.90431225; - data[24696] = 4.65566106; - data[24697] = 3.23751615; - data[24698] = 1.81937125; - data[24699] = 0.40122634; - data[24897] = 1.30262633; - data[24898] = 2.68698297; - data[24899] = 4.07133950; - data[24900] = 5.45569602; - data[24901] = 4.87832492; - data[24902] = 3.52695142; - data[24903] = 2.17557792; - data[24904] = 0.82420459; - data[25102] = 0.94595028; - data[25103] = 2.26512621; - data[25104] = 3.58430226; - data[25105] = 4.90347855; - data[25106] = 5.20569785; - data[25107] = 3.91795207; - data[25108] = 2.63020652; - data[25109] = 1.34246063; - data[25110] = 0.05471494; - data[25307] = 0.49037894; - data[25308] = 1.74744334; - data[25309] = 3.00450763; - data[25310] = 4.26157191; - data[25311] = 5.51863620; - data[25312] = 4.39707236; - data[25313] = 3.16995848; - data[25314] = 1.94284460; - data[25315] = 0.71573065; - data[25513] = 1.14698056; - data[25514] = 2.34485767; - data[25515] = 3.54273478; - data[25516] = 4.74061165; - data[25517] = 4.95198462; - data[25518] = 3.78264743; - data[25519] = 2.61331047; - data[25520] = 1.44397374; - data[25521] = 0.27463681; - data[25718] = 0.47569509; - data[25719] = 1.61717169; - data[25720] = 2.75864848; - data[25721] = 3.90012516; - data[25722] = 5.04160160; - data[25723] = 4.45712078; - data[25724] = 3.34284059; - data[25725] = 2.22856039; - data[25726] = 1.11428020; - - for (auto & val : data) { - val /= 1000.0f; - } - - filters.data = std::move(data); - return filters; -} - -} // namespace whisper_precalc_filters diff --git a/tools/mtmd/mtmd-audio.h b/tools/mtmd/mtmd-audio.h index 0e552347a0..1b454337cb 100644 --- a/tools/mtmd/mtmd-audio.h +++ b/tools/mtmd/mtmd-audio.h @@ -1,6 +1,7 @@ #pragma once #include "ggml.h" +#include "clip-model.h" #include #include @@ -8,18 +9,7 @@ #define MTMD_INTERNAL_HEADER -#define WHISPER_ASSERT GGML_ASSERT - -#define WHISPER_SAMPLE_RATE 16000 -#define WHISPER_N_FFT 400 -#define WHISPER_HOP_LENGTH 160 -#define WHISPER_CHUNK_SIZE 30 - -#define COMMON_SAMPLE_RATE 16000 - -namespace whisper_preprocessor { - -struct whisper_mel { +struct mtmd_audio_mel { int n_len; int n_len_org; int n_mel; @@ -27,23 +17,18 @@ struct whisper_mel { std::vector data; }; -struct whisper_filters { - int32_t n_mel; - int32_t n_fft; +struct mtmd_audio_preprocessor { + const clip_hparams & hparams; - std::vector data; + mtmd_audio_preprocessor(const clip_ctx * ctx): hparams(*clip_get_hparams(ctx)) {} + + virtual ~mtmd_audio_preprocessor() = default; + virtual void initialize() = 0; // NOT thread-safe + virtual bool preprocess(const float * samples, size_t n_samples, std::vector & output) = 0; }; -bool preprocess_audio( - const float * samples, - size_t n_samples, - const whisper_filters & filters, - std::vector & output); - -} // namespace whisper_preprocessor - -namespace whisper_precalc_filters { - -whisper_preprocessor::whisper_filters get_128_bins(); - -} // namespace whisper_precalc_filters +struct mtmd_audio_preprocessor_whisper : mtmd_audio_preprocessor { + mtmd_audio_preprocessor_whisper(const clip_ctx * ctx) : mtmd_audio_preprocessor(ctx) {} + void initialize() override; + bool preprocess(const float * samples, size_t n_samples, std::vector & output) override; +}; diff --git a/tools/mtmd/mtmd.cpp b/tools/mtmd/mtmd.cpp index d06fa42e61..c63f299cd9 100644 --- a/tools/mtmd/mtmd.cpp +++ b/tools/mtmd/mtmd.cpp @@ -151,8 +151,7 @@ struct mtmd_context { // string template for slice image delimiters with row/col (idefics3) std::string sli_img_start_tmpl; - // for whisper, we pre-calculate the mel filter bank - whisper_preprocessor::whisper_filters w_filters; + std::unique_ptr audio_preproc; // TODO @ngxson : add timings @@ -317,14 +316,25 @@ struct mtmd_context { GGML_ASSERT(ctx_a != nullptr); projector_type proj = clip_get_projector_type(ctx_a); - if (clip_has_whisper_encoder(ctx_a)) { - // TODO @ngxson : check if model n_mel is 128 or 80 - w_filters = whisper_precalc_filters::get_128_bins(); - } - LOG_WRN("%s: audio input is in experimental stage and may have reduced quality:\n" " https://github.com/ggml-org/llama.cpp/discussions/13759\n", __func__); + // set preprocessor + switch (proj) { + case PROJECTOR_TYPE_QWEN2A: + case PROJECTOR_TYPE_QWEN25O: + case PROJECTOR_TYPE_ULTRAVOX: + case PROJECTOR_TYPE_VOXTRAL: + audio_preproc = std::make_unique(ctx_a); + break; + default: + GGML_ABORT("unsupported audio projector type"); + } + + // initialize audio preprocessor + audio_preproc->initialize(); + + // set special tokens if (proj == PROJECTOR_TYPE_QWEN2A) { // <|audio_bos|> ... (embeddings) ... <|audio_eos|> aud_beg = "<|audio_bos|>"; @@ -653,11 +663,10 @@ struct mtmd_tokenizer { } // preprocess audio - GGML_ASSERT(ctx->w_filters.n_mel); // make sure we have filter preloaded - std::vector mel_spec_chunks; + std::vector mel_spec_chunks; const float * samples = (const float *)bitmap->data.data(); size_t n_samples = bitmap->data.size() / sizeof(float); - bool ok = whisper_preprocessor::preprocess_audio(samples, n_samples, ctx->w_filters, mel_spec_chunks); + bool ok = ctx->audio_preproc->preprocess(samples, n_samples, mel_spec_chunks); if (!ok) { LOG_ERR("Unable to preprocess audio\n"); return 2; @@ -863,8 +872,7 @@ int mtmd_get_audio_bitrate(mtmd_context * ctx) { if (!ctx->ctx_a) { return -1; } - // for now, we assume that all audio models have the same bitrate - return 16000; // 16kHz + return clip_get_hparams(ctx->ctx_a)->audio_sample_rate; } // From 165caaf5fbd359822b176b076f3ef4248309e56d Mon Sep 17 00:00:00 2001 From: Jeremy Demeule Date: Mon, 15 Dec 2025 15:14:49 +0100 Subject: [PATCH 40/40] metal: use shared buffers on eGPU (#17866) * metal: use shared buffers on eGPU With #15906, I noticed on important regression when using metal backend on eGPU. This commit restore the previous behavior and add an option to force its activation. * metal: use shared buffers on eGPU * metal: use shared buffers on eGPU --- ggml/src/ggml-metal/ggml-metal-device.m | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ggml/src/ggml-metal/ggml-metal-device.m b/ggml/src/ggml-metal/ggml-metal-device.m index 7b7d1c1233..f24270bb1c 100644 --- a/ggml/src/ggml-metal/ggml-metal-device.m +++ b/ggml/src/ggml-metal/ggml-metal-device.m @@ -769,9 +769,16 @@ ggml_metal_device_t ggml_metal_device_init(void) { #endif dev->props.use_shared_buffers = dev->props.has_unified_memory; +#if TARGET_OS_OSX + // In case of eGPU, shared memory may be preferable. + dev->props.use_shared_buffers |= [dev->mtl_device location] == MTLDeviceLocationExternal; +#endif if (getenv("GGML_METAL_SHARED_BUFFERS_DISABLE") != NULL) { dev->props.use_shared_buffers = false; } + if (getenv("GGML_METAL_SHARED_BUFFERS_ENABLE") != NULL) { + dev->props.use_shared_buffers = true; + } dev->props.supports_gpu_family_apple7 = [dev->mtl_device supportsFamily:MTLGPUFamilyApple7];

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a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte index 7edce48b9b..1c8b41102a 100644 --- a/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte +++ b/tools/server/webui/src/lib/components/app/chat/ChatSettings/ChatSettingsImportExportTab.svelte @@ -1,9 +1,11 @@