From ffe666572f98a686b17a2cd1dbf4c0a982e5ac0a Mon Sep 17 00:00:00 2001 From: cpumaxx <163466046+cpumaxx@users.noreply.github.com> Date: Mon, 29 Apr 2024 07:34:24 -0700 Subject: [PATCH 001/100] llava-cli : multiple images (#6969) Co-authored-by: root --- common/common.cpp | 4 +-- common/common.h | 4 +-- examples/llava/llava-cli.cpp | 67 ++++++++++++++++++++---------------- 3 files changed, 42 insertions(+), 33 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index aa494291d..fe84039f7 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -893,7 +893,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - params.image = argv[i]; + params.image.emplace_back(argv[i]); return true; } if (arg == "-i" || arg == "--interactive") { @@ -1495,7 +1495,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); - printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); + printf(" --image IMAGE_FILE path to an image file. use with multimodal models. Specify multiple times for batching\n"); if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } diff --git a/common/common.h b/common/common.h index eea63a114..3233d90e6 100644 --- a/common/common.h +++ b/common/common.h @@ -167,8 +167,8 @@ struct gpt_params { std::string cache_type_v = "f16"; // KV cache data type for the V // multimodal models (see examples/llava) - std::string mmproj = ""; // path to multimodal projector - std::string image = ""; // path to an image file + std::string mmproj = ""; // path to multimodal projector + std::vector image; // path to image file(s) }; bool parse_kv_override(const char * data, std::vector & overrides); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index a44c6cd76..157a680b5 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -113,11 +113,11 @@ struct llava_context { }; static void show_additional_info(int /*argc*/, char ** argv) { - LOG_TEE("\n example usage: %s -m --mmproj --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG_TEE("\n example usage: %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); LOG_TEE(" note: a lower temperature value like 0.1 is recommended for better quality.\n"); } -static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params) { +static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_params * params, const std::string & fname) { // load and preprocess the image llava_image_embed * embed = NULL; @@ -133,9 +133,9 @@ static struct llava_image_embed * load_image(llava_context * ctx_llava, gpt_para } params->prompt = remove_image_from_prompt(prompt); } else { - embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, params->image.c_str()); + embed = llava_image_embed_make_with_filename(ctx_llava->ctx_clip, params->n_threads, fname.c_str()); if (!embed) { - LOG_TEE("%s: is %s really an image file?\n", __func__, params->image.c_str()); + fprintf(stderr, "%s: is %s really an image file?\n", __func__, fname.c_str()); return NULL; } } @@ -207,17 +207,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ printf("\n"); } - -static struct llava_context * llava_init(gpt_params * params) { - const char * clip_path = params->mmproj.c_str(); - - auto prompt = params->prompt; - if (prompt.empty()) { - prompt = "describe the image in detail."; - } - - auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); - +static struct llama_model * llava_init(gpt_params * params) { llama_backend_init(); llama_numa_init(params->numa); @@ -228,6 +218,19 @@ static struct llava_context * llava_init(gpt_params * params) { LOG_TEE("%s: error: unable to load model\n" , __func__); return NULL; } + return model; +} + +static struct llava_context * llava_init_context(gpt_params * params, llama_model * model) { + const char * clip_path = params->mmproj.c_str(); + + auto prompt = params->prompt; + if (prompt.empty()) { + prompt = "describe the image in detail."; + } + + auto ctx_clip = clip_model_load(clip_path, /*verbosity=*/ 1); + llama_context_params ctx_params = llama_context_params_from_gpt_params(*params); ctx_params.n_ctx = params->n_ctx < 2048 ? 2048 : params->n_ctx; // we need a longer context size to process image embeddings @@ -286,24 +289,30 @@ int main(int argc, char ** argv) { show_additional_info(argc, argv); return 1; } - - auto ctx_llava = llava_init(¶ms); - if (ctx_llava == NULL) { - LOG_TEE("%s: error: failed to init llava\n", __func__); + auto model = llava_init(¶ms); + if (model == NULL) { + fprintf(stderr, "%s: error: failed to init llava model\n", __func__); return 1; } - auto image_embed = load_image(ctx_llava, ¶ms); - if (!image_embed) { - return 1; + for (auto & image : params.image) { + auto ctx_llava = llava_init_context(¶ms, model); + + auto image_embed = load_image(ctx_llava, ¶ms, image); + if (!image_embed) { + std::cerr << "error: failed to load image " << image << ". Terminating\n\n"; + return 1; + } + + // process the prompt + process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); + + llama_print_timings(ctx_llava->ctx_llama); + llava_image_embed_free(image_embed); + ctx_llava->model = NULL; + llava_free(ctx_llava); } + llama_free_model(model); - // process the prompt - process_prompt(ctx_llava, image_embed, ¶ms, params.prompt); - - llama_print_timings(ctx_llava->ctx_llama); - - llava_image_embed_free(image_embed); - llava_free(ctx_llava); return 0; } From 544f1f10adeec3d5ed91c4d3bd3f903904ecea63 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Apr 2024 17:55:02 +0300 Subject: [PATCH 002/100] ggml : fix __MSC_VER -> _MSC_VER (#6977) ggml-ci --- ggml-impl.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-impl.h b/ggml-impl.h index 2087f7ded..94a1cc668 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -313,7 +313,7 @@ inline static int32x4_t ggml_vdotq_s32(int32x4_t acc, int8x16_t a, int8x16_t b) #endif // defined(__ARM_NEON) -#if defined(__ARM_NEON) && !defined(__MSC_VER) +#if defined(__ARM_NEON) && !defined(_MSC_VER) #define GGML_COMPUTE_FP16_TO_FP32(x) ggml_compute_fp16_to_fp32(x) #define GGML_COMPUTE_FP32_TO_FP16(x) ggml_compute_fp32_to_fp16(x) From d2c898f746a527f09effb061829e68b2e1812a28 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Apr 2024 18:36:39 +0300 Subject: [PATCH 003/100] ci : tmp disable gguf-split (#6983) ggml-ci --- ci/run.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ci/run.sh b/ci/run.sh index da05f0d48..a1cd9908f 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -161,7 +161,7 @@ function gg_run_test_scripts_debug { set -e # TODO: too slow, run on dedicated node - (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + #(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log #(cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log set +e From b8a7a5a90fd3187175d84227dad705ade395ba46 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Mon, 29 Apr 2024 17:02:45 +0100 Subject: [PATCH 004/100] build(cmake): simplify instructions (`cmake -B build && cmake --build build ...`) (#6964) * readme: cmake . -B build && cmake --build build * build: fix typo Co-authored-by: Jared Van Bortel * build: drop implicit . from cmake config command * build: remove another superfluous . * build: update MinGW cmake commands * Update README-sycl.md Co-authored-by: Neo Zhang Jianyu * build: reinstate --config Release as not the default w/ some generators + document how to build Debug * build: revert more --config Release * build: nit / remove -H from cmake example * build: reword debug instructions around single/multi config split --------- Co-authored-by: Jared Van Bortel Co-authored-by: Neo Zhang Jianyu --- .devops/main-intel.Dockerfile | 8 +-- .devops/main-vulkan.Dockerfile | 6 +- .devops/server-intel.Dockerfile | 8 +-- .devops/server-vulkan.Dockerfile | 6 +- .github/workflows/bench.yml | 6 +- .github/workflows/server.yml | 12 ++-- README-sycl.md | 31 +++++----- README.md | 95 ++++++++++++++++--------------- examples/main-cmake-pkg/README.md | 16 ++---- examples/server/README.md | 13 +++-- 10 files changed, 91 insertions(+), 110 deletions(-) diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index 572e5d8ea..274b91b71 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -10,14 +10,12 @@ WORKDIR /app COPY . . -RUN mkdir build && \ - cd build && \ - if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ +RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ echo "LLAMA_SYCL_F16 is set" && \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ fi && \ - cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ - cmake --build . --config Release --target main + cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build build --config Release --target main FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile index bca460365..6c2b2ed5b 100644 --- a/.devops/main-vulkan.Dockerfile +++ b/.devops/main-vulkan.Dockerfile @@ -14,10 +14,8 @@ RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key # Build it WORKDIR /app COPY . . -RUN mkdir build && \ - cd build && \ - cmake .. -DLLAMA_VULKAN=1 && \ - cmake --build . --config Release --target main +RUN cmake -B build -DLLAMA_VULKAN=1 && \ + cmake --build build --config Release --target main # Clean up WORKDIR / diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index 304487335..a8e451fa9 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -10,14 +10,12 @@ WORKDIR /app COPY . . -RUN mkdir build && \ - cd build && \ - if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ +RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ echo "LLAMA_SYCL_F16 is set" && \ export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ fi && \ - cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ - cmake --build . --config Release --target server + cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_CURL=ON ${OPT_SYCL_F16} && \ + cmake --build build --config Release --target server FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile index 7e5a5283b..6e757e171 100644 --- a/.devops/server-vulkan.Dockerfile +++ b/.devops/server-vulkan.Dockerfile @@ -18,10 +18,8 @@ RUN apt-get update && \ # Build it WORKDIR /app COPY . . -RUN mkdir build && \ - cd build && \ - cmake .. -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \ - cmake --build . --config Release --target server +RUN cmake -B build -DLLAMA_VULKAN=1 -DLLAMA_CURL=1 && \ + cmake --build build --config Release --target server # Clean up WORKDIR / diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml index 6dc91d27b..3e968d179 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml @@ -96,9 +96,7 @@ jobs: id: cmake_build run: | set -eux - mkdir build - cd build - cmake .. \ + cmake -B build \ -DLLAMA_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ -DLLAMA_CURL=ON \ @@ -109,7 +107,7 @@ jobs: -DLLAMA_FATAL_WARNINGS=OFF \ -DLLAMA_ALL_WARNINGS=OFF \ -DCMAKE_BUILD_TYPE=Release; - cmake --build . --config Release -j $(nproc) --target server + cmake --build build --config Release -j $(nproc) --target server - name: Download the dataset id: download_dataset diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 79cd7d643..afac89c5b 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -94,15 +94,13 @@ jobs: - name: Build id: cmake_build run: | - mkdir build - cd build - cmake .. \ + cmake -B build \ -DLLAMA_NATIVE=OFF \ -DLLAMA_BUILD_SERVER=ON \ -DLLAMA_CURL=ON \ -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} \ -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; - cmake --build . --config ${{ matrix.build_type }} -j $(nproc) --target server + cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server - name: Tests @@ -143,10 +141,8 @@ jobs: - name: Build id: cmake_build run: | - mkdir build - cd build - cmake .. -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server + cmake -B build -DLLAMA_CURL=ON -DCURL_LIBRARY="$env:RUNNER_TEMP/libcurl/lib/libcurl.dll.a" -DCURL_INCLUDE_DIR="$env:RUNNER_TEMP/libcurl/include" + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} --target server - name: Python setup id: setup_python diff --git a/README-sycl.md b/README-sycl.md index dc98c7b3e..cfa248a95 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -185,9 +185,8 @@ Upon a successful installation, SYCL is enabled for the available intel devices, ```sh git clone https://github.com/oneapi-src/oneMKL cd oneMKL -mkdir -p buildWithCublas && cd buildWithCublas -cmake ../ -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas -make +cmake -B buildWithCublas -DCMAKE_CXX_COMPILER=icpx -DCMAKE_C_COMPILER=icx -DENABLE_MKLGPU_BACKEND=OFF -DENABLE_MKLCPU_BACKEND=OFF -DENABLE_CUBLAS_BACKEND=ON -DTARGET_DOMAINS=blas +cmake --build buildWithCublas --config Release ``` @@ -227,16 +226,15 @@ Similarly, user targeting Nvidia GPUs should expect at least one SYCL-CUDA devic source /opt/intel/oneapi/setvars.sh # Build LLAMA with MKL BLAS acceleration for intel GPU -mkdir -p build && cd build # Option 1: Use FP32 (recommended for better performance in most cases) -cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # Option 2: Use FP16 -cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON +cmake -B build -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -#build all binary -cmake --build . --config Release -j -v +# build all binary +cmake --build build --config Release -j -v ``` #### Nvidia GPU @@ -248,16 +246,15 @@ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/buildWithCublas/include:$CPLUS_INCLUDE_ export CPLUS_INCLUDE_DIR=/path/to/oneMKL/include:$CPLUS_INCLUDE_DIR # Build LLAMA with Nvidia BLAS acceleration through SYCL -mkdir -p build && cd build # Option 1: Use FP32 (recommended for better performance in most cases) -cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx +cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx # Option 2: Use FP16 -cmake .. -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON +cmake -B build -DLLAMA_SYCL=ON -DLLAMA_SYCL_TARGET=NVIDIA -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -#build all binary -cmake --build . --config Release -j -v +# build all binary +cmake --build build --config Release -j -v ``` @@ -412,17 +409,15 @@ b. Download & install mingw-w64 make for Windows provided by w64devkit On the oneAPI command line window, step into the llama.cpp main directory and run the following: ``` -mkdir -p build -cd build @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force # Option 1: Use FP32 (recommended for better performance in most cases) -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release +cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release # Option 2: Or FP16 -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON +cmake -B build -G "MinGW Makefiles" -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON -make -j +cmake --build build --config Release -j ``` Otherwise, run the `win-build-sycl.bat` wrapper which encapsulates the former instructions: diff --git a/README.md b/README.md index a2aa9214f..514ef3af1 100644 --- a/README.md +++ b/README.md @@ -308,6 +308,8 @@ In order to build llama.cpp you have three different options. make ``` + **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1` + - On Windows: 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). @@ -322,12 +324,26 @@ In order to build llama.cpp you have three different options. - Using `CMake`: ```bash - mkdir build - cd build - cmake .. - cmake --build . --config Release + cmake -B build + cmake --build build --config Release ``` + **Note**: for `Debug` builds, there are two cases: + + - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + + ```bash + cmake -B build -DCMAKE_BUILD_TYPE=Debug + cmake --build build + ``` + + - Multi-config generators (`-G` param set to Visual Studio, XCode...): + + ```bash + cmake -B build -G "Xcode" + cmake --build build --config Debug + ``` + - Using `Zig` (version 0.11 or later): Building for optimization levels and CPU features can be accomplished using standard build arguments, for example AVX2, FMA, F16C, @@ -439,10 +455,8 @@ Building the program with BLAS support may lead to some performance improvements - Using `CMake` on Linux: ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS - cmake --build . --config Release + cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS + cmake --build build --config Release ``` - #### BLIS @@ -462,11 +476,9 @@ Building the program with BLAS support may lead to some performance improvements - Using manual oneAPI installation: By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: ```bash - mkdir build - cd build source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON - cmake --build . --config Release + cmake -B build -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON + cmake --build build --config Release ``` - Using oneAPI docker image: @@ -487,10 +499,8 @@ Building the program with BLAS support may lead to some performance improvements - Using `CMake`: ```bash - mkdir build - cd build - cmake .. -DLLAMA_CUDA=ON - cmake --build . --config Release + cmake -B build -DLLAMA_CUDA=ON + cmake --build build --config Release ``` The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: @@ -517,8 +527,8 @@ Building the program with BLAS support may lead to some performance improvements - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ - cmake -H. -Bbuild -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ - && cmake --build build -- -j 16 + cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build --config Release -- -j 16 ``` On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). @@ -564,15 +574,14 @@ Building the program with BLAS support may lead to some performance improvements ```sh git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git - mkdir OpenCL-SDK/build - cd OpenCL-SDK/build - cmake .. -DBUILD_DOCS=OFF \ + cd OpenCL-SDK + cmake -B build -DBUILD_DOCS=OFF \ -DBUILD_EXAMPLES=OFF \ -DBUILD_TESTING=OFF \ -DOPENCL_SDK_BUILD_SAMPLES=OFF \ -DOPENCL_SDK_TEST_SAMPLES=OFF - cmake --build . --config Release - cmake --install . --prefix /some/path + cmake --build build + cmake --install build --prefix /some/path ``` @@ -594,23 +603,23 @@ Building the program with BLAS support may lead to some performance improvements ```cmd set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" git clone https://github.com/CNugteren/CLBlast.git - mkdir CLBlast\build - cd CLBlast\build - cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 - cmake --build . --config Release - cmake --install . --prefix C:/CLBlast + cd CLBlast + cmake -B build -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 + cmake --build build --config Release + cmake --install build --prefix C:/CLBlast ``` + (note: `--config Release` at build time is the default and only relevant for Visual Studio builds - or multi-config Ninja builds) + -
Unix: ```sh git clone https://github.com/CNugteren/CLBlast.git - mkdir CLBlast/build - cd CLBlast/build - cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF - cmake --build . --config Release - cmake --install . --prefix /some/path + cd CLBlast + cmake -B build -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF + cmake --build build --config Release + cmake --install build --prefix /some/path ``` Where `/some/path` is where the built library will be installed (default is `/usr/local`). @@ -624,21 +633,17 @@ Building the program with BLAS support may lead to some performance improvements ``` - CMake (Unix): ```sh - mkdir build - cd build - cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path - cmake --build . --config Release + cmake -B build -DLLAMA_CLBLAST=ON -DCLBlast_DIR=/some/path + cmake --build build --config Release ``` - CMake (Windows): ```cmd set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" git clone https://github.com/ggerganov/llama.cpp cd llama.cpp - mkdir build - cd build - cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 - cmake --build . --config Release - cmake --install . --prefix C:/LlamaCPP + cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 + cmake --build build --config Release + cmake --install build --prefix C:/LlamaCPP ``` ##### Running Llama with CLBlast @@ -694,10 +699,8 @@ Building the program with BLAS support may lead to some performance improvements Then, build llama.cpp using the cmake command below: ```bash - mkdir -p build - cd build - cmake .. -DLLAMA_VULKAN=1 - cmake --build . --config Release + cmake -B build -DLLAMA_VULKAN=1 + cmake --build build --config Release # Test the output binary (with "-ngl 33" to offload all layers to GPU) ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md index f599fbaec..edf20d8db 100644 --- a/examples/main-cmake-pkg/README.md +++ b/examples/main-cmake-pkg/README.md @@ -17,11 +17,9 @@ In this case, CLBlast was already installed so the CMake package is referenced i ```cmd git clone https://github.com/ggerganov/llama.cpp cd llama.cpp -mkdir build -cd build -cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 -cmake --build . --config Release -cmake --install . --prefix C:/LlamaCPP +cmake -B build -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=C:/CLBlast/lib/cmake/CLBlast -G "Visual Studio 17 2022" -A x64 +cmake --build build --config Release +cmake --install build --prefix C:/LlamaCPP ``` ### Build main-cmake-pkg @@ -29,9 +27,7 @@ cmake --install . --prefix C:/LlamaCPP ```cmd cd ..\examples\main-cmake-pkg -mkdir build -cd build -cmake .. -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 -cmake --build . --config Release -cmake --install . --prefix C:/MyLlamaApp +cmake -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="C:/CLBlast/lib/cmake/CLBlast;C:/LlamaCPP/lib/cmake/Llama" -G "Visual Studio 17 2022" -A x64 +cmake --build build --config Release +cmake --install build --prefix C:/MyLlamaApp ``` diff --git a/examples/server/README.md b/examples/server/README.md index 918ac1295..b96a4444a 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -74,15 +74,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - Using `make`: ```bash - make + make server ``` - Using `CMake`: ```bash - cmake --build . --config Release + cmake -B build + cmake --build build --config Release -t server ``` + Binary is at `./build/bin/server` + ## Build with SSL `server` can also be built with SSL support using OpenSSL 3 @@ -99,10 +102,8 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - Using `CMake`: ```bash - mkdir build - cd build - cmake .. -DLLAMA_SERVER_SSL=ON - make server + cmake -B build -DLLAMA_SERVER_SSL=ON + cmake --build build --config Release -t server ``` ## Quick Start From 5539e6fdd1b97e0c37c54d34df67f334fc344a26 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Mon, 29 Apr 2024 19:56:59 +0200 Subject: [PATCH 005/100] main : fix typo in comment in main.cpp (#6985) Signed-off-by: Daniel Bevenius --- examples/main/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index a74d4d9c7..5c693657c 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -324,7 +324,7 @@ int main(int argc, char ** argv) { log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size()); // if we will use the cache for the full prompt without reaching the end of the cache, force - // reevaluation of the last token token to recalculate the cached logits + // reevaluation of the last token to recalculate the cached logits if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1); From b8c1476e44cc1f3a1811613f65251cf779067636 Mon Sep 17 00:00:00 2001 From: Clint Herron Date: Mon, 29 Apr 2024 14:40:14 -0400 Subject: [PATCH 006/100] Extending grammar integration tests (#6644) * Cleaning up integration tests to share code between tests and make it simpler to add new tests. * Add tests around quantifiers to ensure both matching and non-matching compliance. * Add slightly more complex grammar with quantifiers to test references with quantifiers. * Fixing build when C++17 is not present. * Separating test calls to give more helpful stack traces on failure. Adding verbose messages to give visibility for what is being tested. * Adding quotes around strings to explicitly show whitespace * Removing trailing whitespace. * Implementing suggestions from @ochafik -- grammars and test strings now print and flush before tests to aid in debugging segfaults and whatnot. * Cleaning up forgotten symbols. Modifying simple test to use test harness. Added comments for more verbose descriptions of what each test is accomplishing. * Unicode symbol modifications to hopefully make log easier to parse visually. --- tests/test-grammar-integration.cpp | 367 ++++++++++++++++++----------- 1 file changed, 228 insertions(+), 139 deletions(-) diff --git a/tests/test-grammar-integration.cpp b/tests/test-grammar-integration.cpp index 2d8f228e3..1a4004e2a 100644 --- a/tests/test-grammar-integration.cpp +++ b/tests/test-grammar-integration.cpp @@ -10,15 +10,10 @@ #include "unicode.h" #include #include +#include -static void test_simple_grammar() { - // Test case for a simple grammar - const std::string grammar_str = R"""(root ::= expr -expr ::= term ("+" term)* -term ::= number -number ::= [0-9]+)"""; - - grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str()); +static llama_grammar* build_grammar(const std::string & grammar_str) { + auto parsed_grammar = grammar_parser::parse(grammar_str.c_str()); // Ensure we parsed correctly assert(!parsed_grammar.rules.empty()); @@ -30,8 +25,10 @@ number ::= [0-9]+)"""; llama_grammar* grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); - std::string input = "123+456"; + return grammar; +} +static bool match_string(const std::string & input, llama_grammar* grammar) { auto decoded = decode_utf8(input, {}); const auto & code_points = decoded.first; @@ -39,159 +36,67 @@ number ::= [0-9]+)"""; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { auto prev_stacks = grammar->stacks; llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); - assert(!grammar->stacks.empty()); - } - - bool completed_grammar = false; - - for (const auto & stack : grammar->stacks) { - if (stack.empty()) { - completed_grammar = true; - break; + if (grammar->stacks.empty()) { + // no stacks means that the grammar failed to match at this point + return false; } } - assert(completed_grammar); + for (const auto & stack : grammar->stacks) { + if (stack.empty()) { + // An empty stack means that the grammar has been completed + return true; + } + } - // Clean up allocated memory - llama_grammar_free(grammar); + return false; } -static void test_complex_grammar() { - // Test case for a more complex grammar, with both failure strings and success strings - const std::string grammar_str = R"""(root ::= expression -expression ::= term ws (("+"|"-") ws term)* -term ::= factor ws (("*"|"/") ws factor)* -factor ::= number | variable | "(" expression ")" | function-call -number ::= [0-9]+ -variable ::= [a-zA-Z_][a-zA-Z0-9_]* -function-call ::= variable ws "(" (expression ("," ws expression)*)? ")" -ws ::= [ \t\n\r]?)"""; +static void test_grammar(const std::string & test_desc, const std::string & grammar_str, const std::vector & passing_strings, const std::vector & failing_strings) { + fprintf(stderr, "⚫ Testing %s. Grammar: %s\n", test_desc.c_str(), grammar_str.c_str()); + fflush(stderr); - grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str()); - - // Ensure we parsed correctly - assert(!parsed_grammar.rules.empty()); - - // Ensure we have a root node - assert(!(parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end())); - - std::vector grammar_rules(parsed_grammar.c_rules()); - llama_grammar* grammar = llama_grammar_init( - grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); + auto grammar = build_grammar(grammar_str); // Save the original grammar stacks so that we can reset after every new string we want to test auto original_stacks = grammar->stacks; - // Test a few strings - std::vector test_strings_pass = { - "42", - "1*2*3*4*5", - "x", - "x+10", - "x1+y2", - "(a+b)*(c-d)", - "func()", - "func(x,y+2)", - "a*(b+c)-d/e", - "f(g(x),h(y,z))", - "x + 10", - "x1 + y2", - "(a + b) * (c - d)", - "func()", - "func(x, y + 2)", - "a * (b + c) - d / e", - "f(g(x), h(y, z))", - "123+456", - "123*456*789-123/456+789*123", - "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456" - }; - - std::vector test_strings_fail = { - "+", - "/ 3x", - "x + + y", - "a * / b", - "func(,)", - "func(x y)", - "(a + b", - "x + y)", - "a + b * (c - d", - "42 +", - "x +", - "x + 10 +", - "(a + b) * (c - d", - "func(", - "func(x, y + 2", - "a * (b + c) - d /", - "f(g(x), h(y, z)", - "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/", - }; + fprintf(stderr, " 🔵 Valid strings:\n"); // Passing strings - for (const auto & test_string : test_strings_pass) { - auto decoded = decode_utf8(test_string, {}); + for (const auto & test_string : passing_strings) { + fprintf(stderr, " \"%s\" ", test_string.c_str()); + fflush(stderr); - const auto & code_points = decoded.first; + bool matched = match_string(test_string, grammar); - int pos = 0; - for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - ++pos; - auto prev_stacks = grammar->stacks; - llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); - - // Expect that each code point will not cause the grammar to fail - if (grammar->stacks.empty()) { - fprintf(stdout, "Error at position %d\n", pos); - fprintf(stderr, "Unexpected character '%s'\n", unicode_cpt_to_utf8(*it).c_str()); - fprintf(stderr, "Input string is %s:\n", test_string.c_str()); - } - assert(!grammar->stacks.empty()); + if (!matched) { + fprintf(stderr, "❌ (failed to match)\n"); + } else { + fprintf(stdout, "✅︎\n"); } - bool completed_grammar = false; - - for (const auto & stack : grammar->stacks) { - if (stack.empty()) { - completed_grammar = true; - break; - } - } - - assert(completed_grammar); + assert(matched); // Reset the grammar stacks grammar->stacks = original_stacks; } + fprintf(stderr, " 🟠 Invalid strings:\n"); + // Failing strings - for (const auto & test_string : test_strings_fail) { - auto decoded = decode_utf8(test_string, {}); + for (const auto & test_string : failing_strings) { + fprintf(stderr, " \"%s\" ", test_string.c_str()); + fflush(stderr); - const auto & code_points = decoded.first; - bool parse_failed = false; + bool matched = match_string(test_string, grammar); - for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { - auto prev_stacks = grammar->stacks; - llama_grammar_accept(grammar->rules, prev_stacks, *it, grammar->stacks); - if (grammar->stacks.empty()) { - parse_failed = true; - break; - } - assert(!grammar->stacks.empty()); + if (matched) { + fprintf(stderr, "❌ (incorrectly matched)\n"); + } else { + fprintf(stdout, "✅︎\n"); } - - bool completed_grammar = false; - - for (const auto & stack : grammar->stacks) { - if (stack.empty()) { - completed_grammar = true; - break; - } - } - - // Ensure that the grammar is not completed, or that each string failed to match as-expected - assert((!completed_grammar) || parse_failed); + assert(!matched); // Reset the grammar stacks grammar->stacks = original_stacks; @@ -201,7 +106,183 @@ ws ::= [ \t\n\r]?)"""; llama_grammar_free(grammar); } +static void test_simple_grammar() { + // Test case for a simple grammar + test_grammar( + "simple grammar", + R"""( + root ::= expr + expr ::= term ("+" term)* + term ::= number + number ::= [0-9]+)""", + // Passing strings + { + "42", + "1+2+3+4+5", + "123+456", + }, + // Failing strings + { + "+", + "/ 3", + "1+2+3+4+5+", + "12a45", + } + ); +} + +static void test_complex_grammar() { + // Test case for a more complex grammar, with both failure strings and success strings + test_grammar( + "medium complexity grammar", + // Grammar + R"""( + root ::= expression + expression ::= term ws (("+"|"-") ws term)* + term ::= factor ws (("*"|"/") ws factor)* + factor ::= number | variable | "(" expression ")" | function-call + number ::= [0-9]+ + variable ::= [a-zA-Z_][a-zA-Z0-9_]* + function-call ::= variable ws "(" (expression ("," ws expression)*)? ")" + ws ::= [ \t\n\r]?)""", + // Passing strings + { + "42", + "1*2*3*4*5", + "x", + "x+10", + "x1+y2", + "(a+b)*(c-d)", + "func()", + "func(x,y+2)", + "a*(b+c)-d/e", + "f(g(x),h(y,z))", + "x + 10", + "x1 + y2", + "(a + b) * (c - d)", + "func()", + "func(x, y + 2)", + "a * (b + c) - d / e", + "f(g(x), h(y, z))", + "123+456", + "123*456*789-123/456+789*123", + "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456" + }, + // Failing strings + { + "+", + "/ 3x", + "x + + y", + "a * / b", + "func(,)", + "func(x y)", + "(a + b", + "x + y)", + "a + b * (c - d", + "42 +", + "x +", + "x + 10 +", + "(a + b) * (c - d", + "func(", + "func(x, y + 2", + "a * (b + c) - d /", + "f(g(x), h(y, z)", + "123+456*789-123/456+789*123-456/789+123*456-789/123+456*789-123/456+789*123-456/", + } + ); +} + +static void test_quantifiers() { + // A collection of tests to exercise * + and ? quantifiers + + test_grammar( + "* quantifier", + // Grammar + R"""(root ::= "a"*)""", + // Passing strings + { + "", + "a", + "aaaaa", + "aaaaaaaaaaaaaaaaaa", + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + }, + // Failing strings + { + "b", + "ab", + "aab", + "ba", + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" + } + ); + test_grammar( + "+ quantifier", + // Grammar + R"""(root ::= "a"+)""", + // Passing strings + { + "a", + "aaaaa", + "aaaaaaaaaaaaaaaaaa", + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa" + }, + // Failing strings + { + "", + "b", + "ab", + "aab", + "ba", + "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaab" + } + ); + test_grammar( + "? quantifier", + // Grammar + R"""(root ::= "a"?)""", + // Passing strings + { + "", + "a" + }, + // Failing strings + { + "b", + "ab", + "aa", + "ba", + } + ); + test_grammar( + "mixed quantifiers", + // Grammar + R"""( + root ::= cons+ vowel* cons? (vowel cons)* + vowel ::= [aeiouy] + cons ::= [bcdfghjklmnpqrstvwxyz] + )""", + // Passing strings + { + "yes", + "no", + "noyes", + "crwth", + "four", + "bryyyy", + }, + // Failing strings + { + "yess", + "yesno", + "forty", + "catyyy", + } + ); +} + static void test_failure_missing_root() { + fprintf(stderr, "⚫ Testing missing root node:\n"); // Test case for a grammar that is missing a root rule const std::string grammar_str = R"""(rot ::= expr expr ::= term ("+" term)* @@ -215,29 +296,37 @@ number ::= [0-9]+)"""; // Ensure we do NOT have a root node assert(parsed_grammar.symbol_ids.find("root") == parsed_grammar.symbol_ids.end()); + fprintf(stderr, " ✅︎ Passed\n"); } static void test_failure_missing_reference() { + fprintf(stderr, "⚫ Testing missing reference node:\n"); + // Test case for a grammar that is missing a referenced rule - const std::string grammar_str = R"""(root ::= expr + const std::string grammar_str = +R"""(root ::= expr expr ::= term ("+" term)* term ::= numero number ::= [0-9]+)"""; - fprintf(stderr, "Expected error: "); + fprintf(stderr, " Expected error: "); grammar_parser::parse_state parsed_grammar = grammar_parser::parse(grammar_str.c_str()); // Ensure we did NOT parsed correctly assert(parsed_grammar.rules.empty()); - fprintf(stderr, "End of expected error. Test successful.\n"); + fprintf(stderr, " End of expected error.\n"); + fprintf(stderr, " ✅︎ Passed\n"); } int main() { + fprintf(stdout, "Running grammar integration tests...\n"); test_simple_grammar(); test_complex_grammar(); + test_quantifiers(); test_failure_missing_root(); test_failure_missing_reference(); + fprintf(stdout, "All tests passed.\n"); return 0; } From 8843a98c2ba97a25e93319a104f9ddfaf83ce4c4 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Tue, 30 Apr 2024 00:52:50 +0100 Subject: [PATCH 007/100] Improve usability of --model-url & related flags (#6930) * args: default --model to models/ + filename from --model-url or --hf-file (or else legacy models/7B/ggml-model-f16.gguf) * args: main & server now call gpt_params_handle_model_default * args: define DEFAULT_MODEL_PATH + update cli docs * curl: check url of previous download (.json metadata w/ url, etag & lastModified) * args: fix update to quantize-stats.cpp * curl: support legacy .etag / .lastModified companion files * curl: rm legacy .etag file support * curl: reuse regex across headers callback calls * curl: unique_ptr to manage lifecycle of curl & outfile * curl: nit: no need for multiline regex flag * curl: update failed test (model file collision) + gitignore *.gguf.json --- .gitignore | 1 + common/common.cpp | 258 +++++++++--------- common/common.h | 6 +- examples/main/README.md | 2 +- examples/quantize-stats/quantize-stats.cpp | 2 +- examples/server/server.cpp | 4 +- .../server/tests/features/embeddings.feature | 2 +- 7 files changed, 143 insertions(+), 132 deletions(-) diff --git a/.gitignore b/.gitignore index 60f9d1f8d..50ae0973a 100644 --- a/.gitignore +++ b/.gitignore @@ -2,6 +2,7 @@ *.a *.so *.gguf +*.gguf.json *.bin *.exe *.dll diff --git a/common/common.cpp b/common/common.cpp index fe84039f7..099d0356f 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -67,7 +67,6 @@ #include #endif #define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083 -#define LLAMA_CURL_MAX_HEADER_LENGTH 256 #endif // LLAMA_USE_CURL using json = nlohmann::ordered_json; @@ -1324,6 +1323,29 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return false; } +void gpt_params_handle_model_default(gpt_params & params) { + if (!params.hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (params.hf_file.empty()) { + if (params.model.empty()) { + throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); + } + params.hf_file = params.model; + } else if (params.model.empty()) { + params.model = "models/" + string_split(params.hf_file, '/').back(); + } + } else if (!params.model_url.empty()) { + if (params.model.empty()) { + auto f = string_split(params.model_url, '#').front(); + f = string_split(f, '?').front(); + f = string_split(f, '/').back(); + params.model = "models/" + f; + } + } else if (params.model.empty()) { + params.model = DEFAULT_MODEL_PATH; + } +} + bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { bool invalid_param = false; std::string arg; @@ -1352,10 +1374,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); } - // short-hand to avoid specifying --hf-file -> default it to --model - if (!params.hf_repo.empty() && params.hf_file.empty()) { - params.hf_file = params.model; - } + gpt_params_handle_model_default(params); if (params.escape) { process_escapes(params.prompt); @@ -1548,7 +1567,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --control-vector-layer-range START END\n"); printf(" layer range to apply the control vector(s) to, start and end inclusive\n"); printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); + printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH); printf(" -md FNAME, --model-draft FNAME\n"); printf(" draft model for speculative decoding (default: unused)\n"); printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); @@ -1896,59 +1915,75 @@ void llama_batch_add( #ifdef LLAMA_USE_CURL -static bool llama_download_file(CURL * curl, const char * url, const char * path) { +static bool starts_with(const std::string & str, const std::string & prefix) { + // While we wait for C++20's std::string::starts_with... + return str.rfind(prefix, 0) == 0; +} + +static bool llama_download_file(const std::string & url, const std::string & path) { + + // Initialize libcurl + std::unique_ptr curl(curl_easy_init(), &curl_easy_cleanup); + if (!curl) { + fprintf(stderr, "%s: error initializing libcurl\n", __func__); + return false; + } + bool force_download = false; // Set the URL, allow to follow http redirection - curl_easy_setopt(curl, CURLOPT_URL, url); - curl_easy_setopt(curl, CURLOPT_FOLLOWLOCATION, 1L); + curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str()); + curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L); #if defined(_WIN32) // CURLSSLOPT_NATIVE_CA tells libcurl to use standard certificate store of // operating system. Currently implemented under MS-Windows. - curl_easy_setopt(curl, CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); + curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA); #endif // Check if the file already exists locally struct stat model_file_info; - auto file_exists = (stat(path, &model_file_info) == 0); + auto file_exists = (stat(path.c_str(), &model_file_info) == 0); - // If the file exists, check for ${path_model}.etag or ${path_model}.lastModified files - char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; - char etag_path[PATH_MAX] = {0}; - snprintf(etag_path, sizeof(etag_path), "%s.etag", path); - - char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; - char last_modified_path[PATH_MAX] = {0}; - snprintf(last_modified_path, sizeof(last_modified_path), "%s.lastModified", path); + // If the file exists, check its JSON metadata companion file. + std::string metadata_path = path + ".json"; + nlohmann::json metadata; + std::string etag; + std::string last_modified; if (file_exists) { - auto * f_etag = fopen(etag_path, "r"); - if (f_etag) { - if (!fgets(etag, sizeof(etag), f_etag)) { - fprintf(stderr, "%s: unable to read file %s\n", __func__, etag_path); - } else { - fprintf(stderr, "%s: previous file found %s: %s\n", __func__, etag_path, etag); + // Try and read the JSON metadata file (note: stream autoclosed upon exiting this block). + std::ifstream metadata_in(metadata_path); + if (metadata_in.good()) { + try { + metadata_in >> metadata; + fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); + if (metadata.contains("url") && metadata["url"].is_string()) { + auto previous_url = metadata["url"].get(); + if (previous_url != url) { + fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str()); + return false; + } + } + if (metadata.contains("etag") && metadata["etag"].is_string()) { + etag = metadata["etag"]; + } + if (metadata.contains("lastModified") && metadata["lastModified"].is_string()) { + last_modified = metadata["lastModified"]; + } + } catch (const nlohmann::json::exception & e) { + fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); + return false; } - fclose(f_etag); - } - - auto * f_last_modified = fopen(last_modified_path, "r"); - if (f_last_modified) { - if (!fgets(last_modified, sizeof(last_modified), f_last_modified)) { - fprintf(stderr, "%s: unable to read file %s\n", __func__, last_modified_path); - } else { - fprintf(stderr, "%s: previous file found %s: %s\n", __func__, last_modified_path, - last_modified); - } - fclose(f_last_modified); } + } else { + fprintf(stderr, "%s: no previous model file found %s\n", __func__, path.c_str()); } // Send a HEAD request to retrieve the etag and last-modified headers struct llama_load_model_from_url_headers { - char etag[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; - char last_modified[LLAMA_CURL_MAX_HEADER_LENGTH] = {0}; + std::string etag; + std::string last_modified; }; llama_load_model_from_url_headers headers; { @@ -1956,38 +1991,37 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path auto header_callback = [](char * buffer, size_t /*size*/, size_t n_items, void * userdata) -> size_t { llama_load_model_from_url_headers *headers = (llama_load_model_from_url_headers *) userdata; - // Convert header field name to lowercase - for (size_t i = 0; i < n_items && buffer[i] != ':'; ++i) { - buffer[i] = tolower(buffer[i]); - } + static std::regex header_regex("([^:]+): (.*)\r\n"); + static std::regex etag_regex("ETag", std::regex_constants::icase); + static std::regex last_modified_regex("Last-Modified", std::regex_constants::icase); - const char * etag_prefix = "etag: "; - if (strncmp(buffer, etag_prefix, strlen(etag_prefix)) == 0) { - strncpy(headers->etag, buffer + strlen(etag_prefix), n_items - strlen(etag_prefix) - 2); // Remove CRLF - } - - const char * last_modified_prefix = "last-modified: "; - if (strncmp(buffer, last_modified_prefix, strlen(last_modified_prefix)) == 0) { - strncpy(headers->last_modified, buffer + strlen(last_modified_prefix), - n_items - strlen(last_modified_prefix) - 2); // Remove CRLF + std::string header(buffer, n_items); + std::smatch match; + if (std::regex_match(header, match, header_regex)) { + const std::string & key = match[1]; + const std::string & value = match[2]; + if (std::regex_match(key, match, etag_regex)) { + headers->etag = value; + } else if (std::regex_match(key, match, last_modified_regex)) { + headers->last_modified = value; + } } return n_items; }; - curl_easy_setopt(curl, CURLOPT_NOBODY, 1L); // will trigger the HEAD verb - curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 1L); // hide head request progress - curl_easy_setopt(curl, CURLOPT_HEADERFUNCTION, static_cast(header_callback)); - curl_easy_setopt(curl, CURLOPT_HEADERDATA, &headers); + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 1L); // will trigger the HEAD verb + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L); // hide head request progress + curl_easy_setopt(curl.get(), CURLOPT_HEADERFUNCTION, static_cast(header_callback)); + curl_easy_setopt(curl.get(), CURLOPT_HEADERDATA, &headers); - CURLcode res = curl_easy_perform(curl); + CURLcode res = curl_easy_perform(curl.get()); if (res != CURLE_OK) { - curl_easy_cleanup(curl); fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); return false; } long http_code = 0; - curl_easy_getinfo(curl, CURLINFO_RESPONSE_CODE, &http_code); + curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &http_code); if (http_code != 200) { // HEAD not supported, we don't know if the file has changed // force trigger downloading @@ -1996,28 +2030,30 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path } } - // If the ETag or the Last-Modified headers are different: trigger a new download - bool should_download = !file_exists - || force_download - || (strlen(headers.etag) > 0 && strcmp(etag, headers.etag) != 0) - || (strlen(headers.last_modified) > 0 && strcmp(last_modified, headers.last_modified) != 0); + bool should_download = !file_exists || force_download; + if (!should_download) { + if (!etag.empty() && etag != headers.etag) { + fprintf(stderr, "%s: ETag header is different (%s != %s): triggering a new download\n", __func__, etag.c_str(), headers.etag.c_str()); + should_download = true; + } else if (!last_modified.empty() && last_modified != headers.last_modified) { + fprintf(stderr, "%s: Last-Modified header is different (%s != %s): triggering a new download\n", __func__, last_modified.c_str(), headers.last_modified.c_str()); + should_download = true; + } + } if (should_download) { - char path_temporary[PATH_MAX] = {0}; - snprintf(path_temporary, sizeof(path_temporary), "%s.downloadInProgress", path); + std::string path_temporary = path + ".downloadInProgress"; if (file_exists) { - fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path); - if (remove(path) != 0) { - curl_easy_cleanup(curl); - fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path); + fprintf(stderr, "%s: deleting previous downloaded file: %s\n", __func__, path.c_str()); + if (remove(path.c_str()) != 0) { + fprintf(stderr, "%s: unable to delete file: %s\n", __func__, path.c_str()); return false; } } // Set the output file - auto * outfile = fopen(path_temporary, "wb"); + std::unique_ptr outfile(fopen(path_temporary.c_str(), "wb"), fclose); if (!outfile) { - curl_easy_cleanup(curl); - fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path); + fprintf(stderr, "%s: error opening local file for writing: %s\n", __func__, path.c_str()); return false; } @@ -2025,12 +2061,12 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path auto write_callback = [](void * data, size_t size, size_t nmemb, void * fd) -> size_t { return fwrite(data, size, nmemb, (FILE *)fd); }; - curl_easy_setopt(curl, CURLOPT_NOBODY, 0L); - curl_easy_setopt(curl, CURLOPT_WRITEFUNCTION, static_cast(write_callback)); - curl_easy_setopt(curl, CURLOPT_WRITEDATA, outfile); + curl_easy_setopt(curl.get(), CURLOPT_NOBODY, 0L); + curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast(write_callback)); + curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, outfile.get()); // display download progress - curl_easy_setopt(curl, CURLOPT_NOPROGRESS, 0L); + curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 0L); // helper function to hide password in URL auto llama_download_hide_password_in_url = [](const std::string & url) -> std::string { @@ -2049,51 +2085,34 @@ static bool llama_download_file(CURL * curl, const char * url, const char * path // start the download fprintf(stderr, "%s: downloading from %s to %s (server_etag:%s, server_last_modified:%s)...\n", __func__, - llama_download_hide_password_in_url(url).c_str(), path, headers.etag, headers.last_modified); - auto res = curl_easy_perform(curl); + llama_download_hide_password_in_url(url).c_str(), path.c_str(), headers.etag.c_str(), headers.last_modified.c_str()); + auto res = curl_easy_perform(curl.get()); if (res != CURLE_OK) { - fclose(outfile); - curl_easy_cleanup(curl); fprintf(stderr, "%s: curl_easy_perform() failed: %s\n", __func__, curl_easy_strerror(res)); return false; } long http_code = 0; - curl_easy_getinfo (curl, CURLINFO_RESPONSE_CODE, &http_code); + curl_easy_getinfo (curl.get(), CURLINFO_RESPONSE_CODE, &http_code); if (http_code < 200 || http_code >= 400) { - fclose(outfile); - curl_easy_cleanup(curl); fprintf(stderr, "%s: invalid http status code received: %ld\n", __func__, http_code); return false; } - // Clean up - fclose(outfile); + // Causes file to be closed explicitly here before we rename it. + outfile.reset(); - // Write the new ETag to the .etag file - if (strlen(headers.etag) > 0) { - auto * etag_file = fopen(etag_path, "w"); - if (etag_file) { - fputs(headers.etag, etag_file); - fclose(etag_file); - fprintf(stderr, "%s: file etag saved %s: %s\n", __func__, etag_path, headers.etag); - } - } + // Write the updated JSON metadata file. + metadata.update({ + {"url", url}, + {"etag", headers.etag}, + {"lastModified", headers.last_modified} + }); + std::ofstream(metadata_path) << metadata.dump(4); + fprintf(stderr, "%s: file metadata saved: %s\n", __func__, metadata_path.c_str()); - // Write the new lastModified to the .etag file - if (strlen(headers.last_modified) > 0) { - auto * last_modified_file = fopen(last_modified_path, "w"); - if (last_modified_file) { - fputs(headers.last_modified, last_modified_file); - fclose(last_modified_file); - fprintf(stderr, "%s: file last modified saved %s: %s\n", __func__, last_modified_path, - headers.last_modified); - } - } - - if (rename(path_temporary, path) != 0) { - curl_easy_cleanup(curl); - fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary, path); + if (rename(path_temporary.c_str(), path.c_str()) != 0) { + fprintf(stderr, "%s: unable to rename file: %s to %s\n", __func__, path_temporary.c_str(), path.c_str()); return false; } } @@ -2111,15 +2130,7 @@ struct llama_model * llama_load_model_from_url( return NULL; } - // Initialize libcurl - auto * curl = curl_easy_init(); - - if (!curl) { - fprintf(stderr, "%s: error initializing libcurl\n", __func__); - return NULL; - } - - if (!llama_download_file(curl, model_url, path_model)) { + if (!llama_download_file(model_url, path_model)) { return NULL; } @@ -2133,7 +2144,6 @@ struct llama_model * llama_load_model_from_url( auto * ctx_gguf = gguf_init_from_file(path_model, gguf_params); if (!ctx_gguf) { fprintf(stderr, "\n%s: failed to load input GGUF from %s\n", __func__, path_model); - curl_easy_cleanup(curl); return NULL; } @@ -2145,8 +2155,6 @@ struct llama_model * llama_load_model_from_url( gguf_free(ctx_gguf); } - curl_easy_cleanup(curl); - if (n_split > 1) { char split_prefix[PATH_MAX] = {0}; char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0}; @@ -2177,11 +2185,7 @@ struct llama_model * llama_load_model_from_url( char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0}; llama_split_path(split_url, sizeof(split_url), split_url_prefix, download_idx, n_split); - auto * curl = curl_easy_init(); - bool res = llama_download_file(curl, split_url, split_path); - curl_easy_cleanup(curl); - - return res; + return llama_download_file(split_url, split_path); }, idx)); } @@ -2668,7 +2672,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); - fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); diff --git a/common/common.h b/common/common.h index 3233d90e6..8afdf2bdf 100644 --- a/common/common.h +++ b/common/common.h @@ -31,6 +31,8 @@ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) +#define DEFAULT_MODEL_PATH "models/7B/ggml-model-f16.gguf" + // build info extern int LLAMA_BUILD_NUMBER; extern char const *LLAMA_COMMIT; @@ -92,7 +94,7 @@ struct gpt_params { // // sampling parameters struct llama_sampling_params sparams; - std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model = ""; // model path std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string model_url = ""; // model url to download @@ -171,6 +173,8 @@ struct gpt_params { std::vector image; // path to image file(s) }; +void gpt_params_handle_model_default(gpt_params & params); + bool parse_kv_override(const char * data, std::vector & overrides); bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); diff --git a/examples/main/README.md b/examples/main/README.md index 649f4e0f3..e7a38743c 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -66,7 +66,7 @@ main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt In this section, we cover the most commonly used options for running the `main` program with the LLaMA models: -- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`). +- `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.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/models/resolve/main/phi-2/ggml-model-q4_0.gguf). - `-i, --interactive`: Run the program in interactive mode, allowing you to provide input directly and receive real-time responses. - `-ins, --instruct`: Run the program in instruction mode, which is particularly useful when working with Alpaca models. diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 1d05f1391..746df8446 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -23,7 +23,7 @@ #endif struct quantize_stats_params { - std::string model = "models/7B/ggml-model-f16.gguf"; + std::string model = DEFAULT_MODEL_PATH; bool verbose = false; bool per_layer_stats = false; bool print_histogram = false; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2760aea8f..01453af2c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2353,7 +2353,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" disable KV offload\n"); } printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); + printf(" model path (default: models/$filename with filename from --hf-file or --model-url if set, otherwise %s)\n", DEFAULT_MODEL_PATH); printf(" -mu MODEL_URL, --model-url MODEL_URL\n"); printf(" model download url (default: unused)\n"); printf(" -hfr REPO, --hf-repo REPO\n"); @@ -2835,6 +2835,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, } } + gpt_params_handle_model_default(params); + if (!params.kv_overrides.empty()) { params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; diff --git a/examples/server/tests/features/embeddings.feature b/examples/server/tests/features/embeddings.feature index dcf1434f9..6f163ce04 100644 --- a/examples/server/tests/features/embeddings.feature +++ b/examples/server/tests/features/embeddings.feature @@ -5,7 +5,7 @@ Feature: llama.cpp server Background: Server startup Given a server listening on localhost:8080 And a model url https://huggingface.co/ggml-org/models/resolve/main/bert-bge-small/ggml-model-f16.gguf - And a model file ggml-model-f16.gguf + And a model file bert-bge-small.gguf And a model alias bert-bge-small And 42 as server seed And 2 slots From 952d03dbead16e4dbdd1d3458486340673cc2465 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Apr 2024 11:05:25 +0300 Subject: [PATCH 008/100] convert : use utf8 encoding (#7000) * convert : use utf8 encoding * convert : update instructions and warning message --- convert-hf-to-gguf-update.py | 16 ++++++++++------ convert-hf-to-gguf.py | 12 ++++++++---- 2 files changed, 18 insertions(+), 10 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 1c559c3f6..b019c1e3d 100644 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -128,7 +128,7 @@ for model in models: print(f"chkhsh: {chkhsh}") # print the "pre_tokenizer" content from the tokenizer.json - with open(f"models/tokenizers/{name}/tokenizer.json", "r") as f: + with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f: cfg = json.load(f) pre_tokenizer = cfg["pre_tokenizer"] print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) @@ -156,15 +156,19 @@ src_func += " print(f\"chkhsh: {chkhsh}\")\n" src_func += "\n" src_func += " res = None\n" src_func += "\n" -src_func += " # NOTE: if you get an error here, you need to add the model to the if-elif chain below\n" -src_func += " # don't do this manually - use the convert-hf-to-gguf-update.py script!\n" +src_func += " # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script\n" +src_func += " # or pull the latest version of the model from Huggingface\n" +src_func += " # don't edit the hashes manually!\n" src_func += f"{src_ifs}\n" src_func += " if res is None:\n" src_func += " print(\"\\n\")\n" src_func += " print(\"**************************************************************************************\")\n" src_func += " print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n" -src_func += " print(\"** This means that it was not added yet or you are using an older version.\")\n" -src_func += " print(\"** Check convert-hf-to-gguf-update.py and update it accordingly.\")\n" +src_func += " print(\"** There are 2 possible reasons for this:\")\n" +src_func += " print(\"** - the model has not been added to convert-hf-to-gguf-update.py yet\")\n" +src_func += " print(\"** - the pre-tokenization config has changed upstream\")\n" +src_func += " print(\"** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.\")\n" +src_func += " print(\"** ref: https://github.com/ggerganov/llama.cpp/pull/6920\")\n" src_func += " print(\"**\")\n" src_func += " print(f\"** chkhsh: {chkhsh}\")\n" src_func += " print(\"**************************************************************************************\")\n" @@ -249,7 +253,7 @@ for model in models: from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") - with open(f"models/ggml-vocab-{name}.gguf.inp", "w") as f: + with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: for text in tests: f.write(f"{text}") f.write("\n__ggml_vocab_test__\n") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index d1b8cef11..2f146d730 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -279,8 +279,9 @@ class Model(ABC): res = None - # NOTE: if you get an error here, you need to add the model to the if-elif chain below - # don't do this manually - use the convert-hf-to-gguf-update.py script! + # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! if chkhsh == "0ef9807a4087ebef797fc749390439009c3b9eda9ad1a097abbe738f486c01e5": # ref: https://huggingface.co/meta-llama/Meta-Llama-3-8B res = "llama-bpe" @@ -310,8 +311,11 @@ class Model(ABC): print("\n") print("**************************************************************************************") print("** WARNING: The BPE pre-tokenizer was not recognized!") - print("** This means that it was not added yet or you are using an older version.") - print("** Check convert-hf-to-gguf-update.py and update it accordingly.") + print("** There are 2 possible reasons for this:") + print("** - the model has not been added to convert-hf-to-gguf-update.py yet") + print("** - the pre-tokenization config has changed upstream") + print("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + print("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") print("**") print(f"** chkhsh: {chkhsh}") print("**************************************************************************************") From 9c67c2773d4b706cf71d70ecf4aa180b62501960 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Apr 2024 12:16:08 +0300 Subject: [PATCH 009/100] ggml : add Flash Attention (#5021) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * ggml : add ggml_flash_attn_ext API * ggml : fix GQA support in ggml_flash_attn_ext * ggml : online attention (CPU) * metal : initial implementation * metal : f16 precision * metal : reduce branches * metal : specialize for head size * wip : 8 rows per simd group * wip : 4 rows per simd group * wip : template for rows per warp * metal : parallelize across KV size * metal : parallel reduce across heads * metal : efficient flash_attn_f16 implementation * metal : avoid redundant loads of the attention * metal : scale and mask in matrix form * metal : fix comment * llama : avoid ggml_cast, use F32 query * metal : add parallel reduce version (disabled) * metal : move output into local memory + optimize - the result from each simdgroup now stays in the registers - significantly reduced SRAM usage - more efficient skipping of -INF blocks - avoid simdgroup barrier in hot loop - add comments * metal : add tests, fix scaling, support C > 32 * metal : improve precision * ggml : fix f16 mad * metal : minor * metal : support Q > 8 * tests : add ATTN tests * metal : disable buffer allocation logs * tests : more * metal : faster inner loop for C == 32 * metal : fix array initialization * tests : ifdef * ggml : switch to padded F16 mask for ggml_soft_max, ggml_flash_attn_ext * ggml : fix ggml_soft_max mask requirement * cuda : fix soft_max to use correct mask size * cuda : add flash_attn kernel (wip) * metal : optimize softmax for C > 32 * metal : optimize softmax * tests : minor fix * cuda : avoid zeroing fragments * tests : update dims * cuda : fix __hisinf() result check * cuda : avoid warp_reduce for smax * cuda : use int instead of int64_t Noticeably improves performance (thanks to Johannes) * cuda : make loops use the same loop values Thanks Johannes again for the tip * cuda : unroll some of the loops * cuda : avoid __hisinf branches * cuda : use half2 in softmax * cuda : switch to 1 warp for bs > 16 * cuda : speed-up reduce part of the kernel * cuda : unroll Q*K^T loop * cuda : fix -INF block check * cuda : simplify softmax * cuda : fix matrix names * cuda : minor * llama : adapt to F16 KQ_pos * llama : adapt new models to F16 KQ_mask * ggml : fix F16 store (ARM NEON) * llama : fix type of KQ_mask and KQ_pos * ggml : fix CPU soft_max * tests : add hs=256 * cuda : fix build * metal : improve perf via smaller int registers * cuda : adapt soft_max to F16 mask and pos * CUDA: faster FlashAttention, kernel for bs == 1 * 16 cols for Phi-2 * no vec for hs, no hs==256 ncols==32 for Volta * adjust kernel selection logic * 4 warps, 256 stride for all D * no ncols == 64 * Multiple parallel blocks for batch size 1 * fix compile warnings * fix excessive KQ_b loads * fix cmake build * fix KV cache padding, NaN from INFINITY (#6438) * llama : flash_attn cparam + fix defrag * server: support flash_attn param * server: bench: enable flash_attn param * CUDA: refactor host code, dyn. par. blocks * fix flash_attn_vec_f16 race condition * flush softmax exp below threshold to 0 * store temp KQ in registers * Calculate KQ as FP32 if KQV has GGML_PREC_F32 * Add __hgt2_mask implementation for CUDA 11 * fix KQ FP32 precision fpr parallel_blocks > 1 * llama-bench : add -fa,--flash-attn arg * metal : add BS=1 kernel for flash attention (#6508) * metal : add BS=1 kernel for flash attention (wip) * metal : support more than 1 warps * metal : opts * metal : opt * metal : switch to parallel reduce * metal : reduce registers * metal : simplify * metal : initial FA vec kernel * metal : use F32 attention accumulators * batched-bench : add fattn arg * llama : simplify llama_build_kv_store ggml-ci * llama : adapt build_olmo to changes * ggml : fix arm fp16 store on windows * metal : clean-up * metal : clean-up kernel code * metal : minor * tests : remove benchmarks ggml-ci * ggml : fix avx512 const correctness ggml-ci * ggml : fix soft_max with bias on CPU ggml-ci * common : print --flash-attn in help * ggml : fix num dimensions in ggml_flash_attn_ext * llama : force disable flash attention for incompatible models * ggml : ggml_soft_max support F16/F32 mask/pos ggml-ci * cuda : uint -> uint32_t * cuda : "constexpr dim3" -> "const dim3" ggml-ci * cuda : try to fix __hgt2_mask ggml-ci * ggml : add TODO's for F16/F32 mask/pos support in other backends * llama : replace bool need_kq_pos with use_alibi * llama : prep ALiBi support for BERT models ggml-ci * llama : fix n_batch requirements ggml-ci * cont * server : add help for --flash-attn arg * llama : disable FA for AMD * tests : remove TMP_ATTN_BENCH ggml-ci * llama : support save/load state with FA enabled ggml-ci * ci : add CUDA save-load-state tests ggml-ci * llama : llama_kv_cache_clear zeroes data + fix save-load seq ggml-ci * llama : fix copy-paste errors, add TODO * llama : disallow incompatible states * llama : update llama_state_get_size after v_trans field * metal : remove tmp log * llama : add static reminder for llama_state_get_size * metal : fix max nsg ggml-ci * ci : fix arg order ggml-ci --------- Co-authored-by: Johannes Gäßler Co-authored-by: Pierrick HYMBERT --- ci/run.sh | 8 +- common/common.cpp | 7 + common/common.h | 1 + examples/batched-bench/batched-bench.cpp | 28 +- examples/llama-bench/llama-bench.cpp | 30 +- examples/server/bench/bench.py | 1 + examples/server/server.cpp | 3 + ggml-cuda.cu | 6 + ggml-cuda/common.cuh | 40 +- ggml-cuda/fattn.cu | 944 +++++++++++++++++++++++ ggml-cuda/fattn.cuh | 3 + ggml-cuda/softmax.cu | 46 +- ggml-kompute.cpp | 7 + ggml-metal.m | 547 ++++++++----- ggml-metal.metal | 672 +++++++++++++++- ggml-sycl.cpp | 6 +- ggml-vulkan.cpp | 5 + ggml.c | 375 ++++++++- ggml.h | 20 + llama.cpp | 572 +++++++++----- llama.h | 5 +- tests/test-backend-ops.cpp | 52 +- 22 files changed, 2921 insertions(+), 457 deletions(-) create mode 100644 ggml-cuda/fattn.cu create mode 100644 ggml-cuda/fattn.cuh diff --git a/ci/run.sh b/ci/run.sh index a1cd9908f..bf21b6b31 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -336,7 +336,8 @@ function gg_run_open_llama_3b_v2 { (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" @@ -517,7 +518,10 @@ function gg_run_open_llama_7b_v2 { (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log function check_ppl { qnt="$1" diff --git a/common/common.cpp b/common/common.cpp index 099d0356f..243b88abf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -947,6 +947,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.cont_batching = true; return true; } + if (arg == "-fa" || arg == "--flash-attn") { + params.flash_attn = true; + return true; + } if (arg == "--color") { params.use_color = true; return true; @@ -1513,6 +1517,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -ns N, --sequences N number of sequences to decode (default: %d)\n", params.n_sequences); printf(" -ps N, --p-split N speculative decoding split probability (default: %.1f)\n", (double)params.p_split); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models. Specify multiple times for batching\n"); if (llama_supports_mlock()) { @@ -1885,6 +1890,7 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param cparams.cb_eval = params.cb_eval; cparams.cb_eval_user_data = params.cb_eval_user_data; cparams.offload_kqv = !params.no_kv_offload; + cparams.flash_attn = params.flash_attn; cparams.type_k = kv_cache_type_from_str(params.cache_type_k); cparams.type_v = kv_cache_type_from_str(params.cache_type_v); @@ -2707,6 +2713,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); + fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "true" : "false"); fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + llama_max_devices()); diff --git a/common/common.h b/common/common.h index 8afdf2bdf..0618eea74 100644 --- a/common/common.h +++ b/common/common.h @@ -150,6 +150,7 @@ struct gpt_params { bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles bool cont_batching = true; // insert new sequences for decoding on-the-fly + bool flash_attn = false; // flash attention bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 1e34de620..2924d8116 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -32,7 +32,7 @@ int main(int argc, char ** argv) { gpt_params params; if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] \n" , argv[0]); + printf("usage: %s MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [FATTN] [IS_PP_SHARED] [NGL] \n" , argv[0]); printf(" , and PL are comma-separated lists of numbers without spaces\n\n"); printf(" example: %s ggml-model-f16.gguf 2048 2048 512 0 999 128,256,512 128,256 1,2,4,8,16,32\n\n", argv[0]); return 1 ; @@ -41,6 +41,7 @@ int main(int argc, char ** argv) { int n_kv_max = 2048; int n_batch = 2048; int n_ubatch = 512; + bool flash_attn = false; int is_pp_shared = 0; int n_gpu_layers = 0; @@ -66,23 +67,27 @@ int main(int argc, char ** argv) { } if (argc >= 6) { - is_pp_shared = std::atoi(argv[5]); + flash_attn = std::atoi(argv[5]); } if (argc >= 7) { - n_gpu_layers = std::atoi(argv[6]); + is_pp_shared = std::atoi(argv[6]); } if (argc >= 8) { - n_pp = parse_list(argv[7]); + n_gpu_layers = std::atoi(argv[7]); } if (argc >= 9) { - n_tg = parse_list(argv[8]); + n_pp = parse_list(argv[8]); } if (argc >= 10) { - n_pl = parse_list(argv[9]); + n_tg = parse_list(argv[9]); + } + + if (argc >= 11) { + n_pl = parse_list(argv[10]); } // init LLM @@ -108,10 +113,11 @@ int main(int argc, char ** argv) { llama_context_params ctx_params = llama_context_default_params(); - ctx_params.seed = 1234; - ctx_params.n_ctx = n_kv_max; - ctx_params.n_batch = n_batch; - ctx_params.n_ubatch = n_ubatch; + ctx_params.seed = 1234; + ctx_params.n_ctx = n_kv_max; + ctx_params.n_batch = n_batch; + ctx_params.n_ubatch = n_ubatch; + ctx_params.flash_attn = flash_attn; ctx_params.n_threads = params.n_threads; ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; @@ -169,7 +175,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); + LOG_TEE("%s: n_kv_max = %d, n_batch = %d, n_ubatch = %d, flash_attn = %d, is_pp_shared = %d, n_gpu_layers = %d, n_threads = %u, n_threads_batch = %u\n", __func__, n_kv_max, n_batch, n_ubatch, flash_attn, is_pp_shared, n_gpu_layers, ctx_params.n_threads, ctx_params.n_threads_batch); LOG_TEE("\n"); LOG_TEE("|%6s | %6s | %4s | %6s | %8s | %8s | %8s | %8s | %8s | %8s |\n", "PP", "TG", "B", "N_KV", "T_PP s", "S_PP t/s", "T_TG s", "S_TG t/s", "T s", "S t/s"); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8b532c8b6..95c3095dd 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -174,6 +174,7 @@ struct cmd_params { std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; + std::vector flash_attn; std::vector> tensor_split; std::vector use_mmap; std::vector embeddings; @@ -195,6 +196,7 @@ static const cmd_params cmd_params_defaults = { /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, + /* flash_attn */ {false}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* embeddings */ {false}, @@ -220,6 +222,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split (default: 0)\n"); @@ -393,6 +396,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); + } else if (arg == "-fa" || arg == "--flash-attn") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.flash_attn.insert(params.flash_attn.end(), p.begin(), p.end()); } else if (arg == "-mmp" || arg == "--mmap") { if (++i >= argc) { invalid_param = true; @@ -477,6 +487,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } + if (params.flash_attn.empty()) { params.flash_attn = cmd_params_defaults.flash_attn; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.embeddings.empty()) { params.embeddings = cmd_params_defaults.embeddings; } @@ -498,6 +509,7 @@ struct cmd_params_instance { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; + bool flash_attn; std::vector tensor_split; bool use_mmap; bool embeddings; @@ -532,6 +544,7 @@ struct cmd_params_instance { cparams.type_k = type_k; cparams.type_v = type_v; cparams.offload_kqv = !no_kv_offload; + cparams.flash_attn = flash_attn; cparams.embeddings = embeddings; return cparams; @@ -554,6 +567,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) for (const auto & nkvo : params.no_kv_offload) + for (const auto & fa : params.flash_attn) for (const auto & nt : params.n_threads) { for (const auto & n_prompt : params.n_prompt) { if (n_prompt == 0) { @@ -572,6 +586,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, /* .embeddings = */ embd, @@ -596,6 +611,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, /* .tensor_split = */ ts, /* .use_mmap = */ mmp, /* .embeddings = */ embd, @@ -633,6 +649,7 @@ struct test { llama_split_mode split_mode; int main_gpu; bool no_kv_offload; + bool flash_attn; std::vector tensor_split; bool use_mmap; bool embeddings; @@ -657,6 +674,7 @@ struct test { split_mode = inst.split_mode; main_gpu = inst.main_gpu; no_kv_offload = inst.no_kv_offload; + flash_attn = inst.flash_attn; tensor_split = inst.tensor_split; use_mmap = inst.use_mmap; embeddings = inst.embeddings; @@ -731,7 +749,7 @@ struct test { "n_batch", "n_ubatch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", - "main_gpu", "no_kv_offload", + "main_gpu", "no_kv_offload", "flash_attn", "tensor_split", "use_mmap", "embeddings", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", @@ -753,7 +771,7 @@ struct test { } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || - field == "use_mmap" || field == "embeddings") { + field == "flash_attn" || field == "use_mmap" || field == "embeddings") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -787,7 +805,7 @@ struct test { std::to_string(n_batch), std::to_string(n_ubatch), std::to_string(n_threads), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), - std::to_string(main_gpu), std::to_string(no_kv_offload), + std::to_string(main_gpu), std::to_string(no_kv_offload), std::to_string(flash_attn), tensor_split_str, std::to_string(use_mmap), std::to_string(embeddings), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), @@ -955,6 +973,9 @@ struct markdown_printer : public printer { if (field == "no_kv_offload") { return "nkvo"; } + if (field == "flash_attn") { + return "fa"; + } if (field == "use_mmap") { return "mmap"; } @@ -1001,6 +1022,9 @@ struct markdown_printer : public printer { if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { fields.emplace_back("no_kv_offload"); } + if (params.flash_attn.size() > 1 || params.flash_attn != cmd_params_defaults.flash_attn) { + fields.emplace_back("flash_attn"); + } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.emplace_back("tensor_split"); } diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py index 6ca637bdd..86c5de101 100644 --- a/examples/server/bench/bench.py +++ b/examples/server/bench/bench.py @@ -268,6 +268,7 @@ def start_server_background(args): server_args.extend(['--defrag-thold', "0.1"]) server_args.append('--cont-batching') server_args.append('--metrics') + server_args.append('--flash-attn') server_args.extend(['--log-format', "text"]) args = [str(arg) for arg in [server_path, *server_args]] print(f"bench: starting server with: {' '.join(args)}") diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 01453af2c..f60530cf3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2377,6 +2377,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co printf(" --embeddings enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: enabled)\n"); + printf(" -fa, --flash-attn enable Flash Attention (default: %s)\n", params.flash_attn ? "enabled" : "disabled"); printf(" -spf FNAME, --system-prompt-file FNAME\n"); printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); printf(" -ctk TYPE, --cache-type-k TYPE\n"); @@ -2742,6 +2743,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, params.embedding = true; } else if (arg == "-cb" || arg == "--cont-batching") { params.cont_batching = true; + } else if (arg == "-fa" || arg == "--flash-attn") { + params.flash_attn = true; } else if (arg == "-np" || arg == "--parallel") { if (++i >= argc) { invalid_param = true; diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d277104d1..c30554f0c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -14,6 +14,7 @@ #include "ggml-cuda/cpy.cuh" #include "ggml-cuda/diagmask.cuh" #include "ggml-cuda/dmmv.cuh" +#include "ggml-cuda/fattn.cuh" #include "ggml-cuda/getrows.cuh" #include "ggml-cuda/im2col.cuh" #include "ggml-cuda/mmq.cuh" @@ -140,6 +141,7 @@ static ggml_cuda_device_info ggml_cuda_init() { info.devices[id].cc = 100*prop.major + 10*prop.minor; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) info.devices[id].smpb = prop.sharedMemPerBlock; + info.devices[id].nsm = prop.multiProcessorCount; } for (int id = 0; id < info.device_count; ++id) { @@ -2290,6 +2292,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_ARGSORT: ggml_cuda_op_argsort(ctx, dst); break; + case GGML_OP_FLASH_ATTN_EXT: + ggml_cuda_flash_attn_ext(ctx, dst); + break; default: return false; } @@ -2564,6 +2569,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: + case GGML_OP_FLASH_ATTN_EXT: return true; default: return false; diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 481065b2a..156eba6d1 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -142,6 +142,7 @@ #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products #define CC_VOLTA 700 +#define CC_AMPERE 800 #define CC_OFFSET_AMD 1000000 #define CC_RDNA1 (CC_OFFSET_AMD + 1010) #define CC_RDNA2 (CC_OFFSET_AMD + 1030) @@ -271,7 +272,6 @@ static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { return a; } -#ifdef GGML_CUDA_F16 static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll @@ -284,7 +284,6 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { NO_DEVICE_CODE; #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } -#endif // GGML_CUDA_F16 static __device__ __forceinline__ float warp_reduce_max(float x) { #pragma unroll @@ -294,19 +293,26 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { return x; } -//static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -//#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -//#pragma unroll -// for (int mask = 16; mask > 0; mask >>= 1) { -// x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); -// } -// return x; -//#else -// GGML_UNUSED(x); -// NO_DEVICE_CODE; -//#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX -//} +static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + } + return x; +#else + GGML_UNUSED(x); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +} +#if CUDART_VERSION < 12000 +static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) { + const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b))); + const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b))); + return mask_low | mask_high; +} +#endif // CUDART_VERSION < 12000 #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -391,6 +397,11 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) +#define FP16_AVAILABLE defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) ? \ + defined(RDNA1) || defined(RDNA2) || defined(RDNA3) : __CUDA_ARCH__ >= CC_PASCAL + +#define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA + // TODO: move to ggml-common.h static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; @@ -404,6 +415,7 @@ struct ggml_cuda_device_info { struct cuda_device_info { int cc; // compute capability + int nsm; // number of streaming multiprocessors size_t smpb; // max. shared memory per block bool vmm; // virtual memory support size_t vmm_granularity; // granularity of virtual memory diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu new file mode 100644 index 000000000..df1e80068 --- /dev/null +++ b/ggml-cuda/fattn.cu @@ -0,0 +1,944 @@ +#include "common.cuh" +#include "fattn.cuh" + +#include + +#if FP16_MMA_AVAILABLE +#include +#endif + +#define FATTN_KQ_STRIDE 256 +#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. +#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. + +template // D == head size +__launch_bounds__(((D + WARP_SIZE - 1) / WARP_SIZE)*WARP_SIZE, 1) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic = blockIdx.x / parallel_blocks; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < nwarps*WARP_SIZE); + + __shared__ half KQ[nwarps*WARP_SIZE]; + KQ[tid] = -INFINITY; + half2 * KQ2 = (half2 *) KQ; + + half kqmax = -HALF_MAX_HALF; + half kqsum = 0.0f; + + __shared__ half kqmax_shared[WARP_SIZE]; + __shared__ half kqsum_shared[WARP_SIZE]; + if (threadIdx.y == 0) { + kqmax_shared[threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[threadIdx.x] = 0.0f; + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + half2 Q_h2[(D/2 + WARP_SIZE - 1) / WARP_SIZE]; +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + + Q_h2[i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(Q_f2[i].x, Q_f2[i].y); + } + + half2 VKQ = make_half2(0.0f, 0.0f); // Each thread calculates a single VKQ value. + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + half kqmax_new = kqmax; +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + half2 sum2 = make_half2(0.0f, 0.0f); +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + if (k_KQ_0 + WARP_SIZE > D/2 && k_KQ >= D/2) { + break; + } + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; + sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE]; + } + + sum2 = warp_reduce_sum(sum2); + half sum = __low2half(sum2) + __high2half(sum2); + sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f); + kqmax_new = __hmax(kqmax_new, sum); + if (threadIdx.x == 0) { + KQ[i_KQ] = sum; + } + } + + kqmax_new = warp_reduce_max(kqmax_new); + if (threadIdx.x == 0) { + kqmax_shared[threadIdx.y] = kqmax_new; + } + __syncthreads(); + kqmax_new = kqmax_shared[threadIdx.x]; + kqmax_new = warp_reduce_max(kqmax_new); + + const half KQ_max_scale = hexp(kqmax - kqmax_new); + kqmax = kqmax_new; + + const half val = hexp(KQ[tid] - kqmax); + kqsum = kqsum*KQ_max_scale + val; + KQ[tid] = val; + + VKQ *= __half2half2(KQ_max_scale); + + __syncthreads(); + + if (tid < D) { +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; + reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; + VKQ += V_k*KQ2[k0/2]; + } + } + + __syncthreads(); + } + + if (tid >= D) { + kqsum = 0.0f; + } + + kqsum = warp_reduce_sum(kqsum); + if (threadIdx.x == 0) { + kqsum_shared[threadIdx.y] = kqsum; + } + __syncthreads(); + kqsum = kqsum_shared[threadIdx.x]; + kqsum = warp_reduce_sum(kqsum); + + if (tid >= D) { + return; + } + + half dst_val = (__low2half(VKQ) + __high2half(VKQ)); + if (parallel_blocks == 1) { + dst_val /= kqsum; + } + dst[D*gridDim.y*blockIdx.x + D*blockIdx.y + tid] = dst_val; + + if (parallel_blocks == 1 || tid != 0) { + return; + } + dst_meta[ic*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax, kqsum); +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +// D == head size, VKQ_stride == num VKQ rows calculated in parallel: +template +__launch_bounds__(nwarps*WARP_SIZE, 1) +static __global__ void flash_attn_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_MMA_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); + static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); + constexpr int frag_m = ncols == 8 ? 32 : 16; + constexpr int frag_n = ncols == 8 ? 8 : 16; + static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); + typedef nvcuda::wmma::fragment frag_a_K; + typedef nvcuda::wmma::fragment frag_a_V; + typedef nvcuda::wmma::fragment frag_b; + typedef nvcuda::wmma::fragment frag_c_KQ; + typedef nvcuda::wmma::fragment frag_c_VKQ; + + constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. + constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. + static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); + + // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: + constexpr int D_padded = D + 8; + constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; + constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0); + const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0; + const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2); + + const int stride_Q = nb01 / sizeof(float); + const int stride_KV = nb11 / sizeof(half); + + frag_b Q_b[D/16][ncols/frag_n]; + + // A single buffer for temporarily holding tiles of KQ and VKQ parts: + constexpr int mem_KQ = ncols*kqs_padded*kqar; + constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; + __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; + float * KQ_f = (float *) KQ; + half2 * KQ2 = (half2 *) KQ; + + float KQ_rowsum_f[ncols/nwarps] = {0.0f}; + float KQ_max_f[ncols/nwarps]; + float KQ_max_scale_f[ncols/nwarps] = {0.0f}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_f[j] = -FLT_MAX/2.0f; + } + + half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + half2 KQ_max_h2[ncols/nwarps]; + half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); + } + + __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. + half2 * VKQ2 = (half2 *) VKQ; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); + } + } + + // Convert Q to half and apply scale, temporarily store in KQ: +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f; + } + } + + __syncthreads(); + + // Load Q into tensor core fragments/registers since it will be used frequently: +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 16) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded); + } + } + + __syncthreads(); + + // Iterate over ne11 == previous tokens: + for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) { + // Calculate tile of KQ: +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { + frag_c_KQ KQ_c[ncols/frag_n]; +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f); + } +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { + frag_a_K K_a; + nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); + } + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + + // Calculate softmax for each KQ column using the current max. value. + // The divisor is stored in KQ_rowsum and will be applied at the end. +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (std::is_same::value) { + float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k]; + } + + float KQ_max_new = KQ_max_f[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; + KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); + } + KQ_max_new = warp_reduce_max(KQ_max_new); + + const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; + KQ_max_scale_f[j0/nwarps] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_max_scale_f[j0/nwarps] = 0.0f; + } + KQ_max_f[j0/nwarps] = KQ_max_new; + + float KQ_rowsum_add = 0.0f; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps]; + KQ_f_tmp[k0/WARP_SIZE] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_f_tmp[k0/WARP_SIZE] = 0.0f; + } + KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE]; + KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; + } else { + half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k]; + } + + half2 KQ_max_new = KQ_max_h2[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); + KQ_max_new = __hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); + } + KQ_max_new = __half2half2(warp_reduce_max(__hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); + const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; + KQ_max_scale_h2[j0/nwarps] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; + KQ_max_h2[j0/nwarps] = KQ_max_new; + + half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps]; + KQ2_tmp[k0/WARP_SIZE] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask; + KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE]; + KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; + } + } + + __syncthreads(); + + frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + nvcuda::wmma::load_matrix_sync( + KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], + KQ + j0*(kqar*kqs_padded) + k, + kqar*kqs_padded); + } + } + + frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f); + } + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + + frag_a_V v_a; + nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); + } + } + } + + __syncthreads(); + + const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync( + KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), + VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], + D_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + half2 VKQ_scale; + if (std::is_same::value) { + VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); + } else { + VKQ_scale = KQ_max_scale_h2[j0/nwarps]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + + half2 VKQ_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int l = 0; l < VKQ_ratio; ++l) { + VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; + } + VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j_VKQ = j0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + + float KQ_rowsum_j; + if (std::is_same::value) { + KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; + } else { + KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); + } + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + float dst_val = VKQ[j_VKQ*D_padded + i]; + if (parallel_blocks == 1) { + dst_val /= KQ_rowsum_j; + } + dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val; + } + + if (parallel_blocks == 1 || threadIdx.x != 0) { + continue; + } + + float2 dst_meta_val; + if (std::is_same::value) { + dst_meta_val.x = KQ_max_f[j0/nwarps]; + } else { + dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); + } + dst_meta_val.y = KQ_rowsum_j; + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val; + } +#else + NO_DEVICE_CODE; +#endif // FP16_MMA_AVAILABLE +} + +template // D == head size +__launch_bounds__(D, 1) +static __global__ void flash_attn_combine_results( + const float * __restrict__ VKQ_parts, + const float2 * __restrict__ VKQ_meta, + float * __restrict__ dst) { +#if FP16_AVAILABLE + VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; + VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; + dst += D * gridDim.y*blockIdx.x; + + const int tid = threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float2 meta[parallel_blocks]; + if (tid < 2*parallel_blocks) { + ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; + } + + __syncthreads(); + + float kqmax = meta[0].x; +#pragma unroll + for (int l = 1; l < parallel_blocks; ++l) { + kqmax = max(kqmax, meta[l].x); + } + + float VKQ_numerator = 0.0f; + float VKQ_denominator = 0.0f; +#pragma unroll + for (int l = 0; l < parallel_blocks; ++l) { + const float diff = meta[l].x - kqmax; + const float KQ_max_scale = expf(diff); + const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); + *((uint32_t *) &KQ_max_scale) &= ftz_mask; + + VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; + VKQ_denominator += KQ_max_scale * meta[l].y; + } + + dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +constexpr int get_max_power_of_2(int x) { + return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; +} + +static_assert(get_max_power_of_2(1) == 1, "Test failed."); +static_assert(get_max_power_of_2(2) == 2, "Test failed."); +static_assert(get_max_power_of_2(4) == 4, "Test failed."); +static_assert(get_max_power_of_2(6) == 2, "Test failed."); + +// Number of VKQ rows calculated in parallel: +constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { + return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; +} + +static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); +static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); + +template void launch_fattn_vec_f16( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*Q->ne[1], Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale; + memcpy(&scale, KQV->op_params, sizeof(float)); + + flash_attn_vec_ext_f16 + <<>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if (parallel_blocks == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +template void launch_fattn_f16_impl( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale; + memcpy(&scale, KQV->op_params, sizeof(float)); + + flash_attn_ext_f16 + <<>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if ((parallel_blocks) == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +template void launch_fattn_f16( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream +) { + const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + + if (4*blocks_num_pb1 < 2*nsm) { + launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + return; + } + if (2*blocks_num_pb1 < 2*nsm) { + launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + return; + } + launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); +} + +void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(K->type == GGML_TYPE_F16); + GGML_ASSERT(V->type == GGML_TYPE_F16); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); + GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && + "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); + + GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + + ggml_cuda_set_device(ctx.device); + + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + + const int32_t precision = KQV->op_params[1]; + + if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { + constexpr int cols_per_block = 16; + constexpr int nwarps = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 80: + launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 96: + launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 112: + launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + } else { + constexpr int cols_per_block = 32; + constexpr int nwarps = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 80: + launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 96: + launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 112: + launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + // case 256: + // launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + // break; + default: + GGML_ASSERT(false); + break; + } + } + return; + } + + if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_vec_f16<256, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) { + constexpr int cols_per_block = 8; + constexpr int nwarps = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 96: + launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 16; + constexpr int nwarps = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 80: + launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 96: + launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 112: + launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 32; + constexpr int nwarps = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 80: + launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 96: + launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 112: + launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; +} diff --git a/ggml-cuda/fattn.cuh b/ggml-cuda/fattn.cuh new file mode 100644 index 000000000..ad3ca7a8d --- /dev/null +++ b/ggml-cuda/fattn.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index fa8f987cf..6ed225999 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -1,7 +1,17 @@ #include "softmax.cuh" -template -static __global__ void soft_max_f32(const float * x, const float * mask, const float * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { +template +static __device__ __forceinline__ float t2f32(T val) { + return (float) val; +} + +template <> +__device__ float __forceinline__ t2f32(half val) { + return __half2float(val); +} + +template +static __global__ void soft_max_f32(const float * x, const T * mask, const T * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { const int ncols = ncols_template == 0 ? ncols_par : ncols_template; const int tid = threadIdx.x; @@ -43,7 +53,7 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f const int64_t ix = (int64_t)rowx*ncols + col; const int64_t iy = (int64_t)rowy*ncols + col; - const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + const float val = x[ix]*scale + (mask ? t2f32(mask[iy]) : 0.0f) + (pos ? slope*t2f32(pos[col]) : 0.0f); vals[col] = val; max_val = max(max_val, val); @@ -114,7 +124,8 @@ static __global__ void soft_max_f32(const float * x, const float * mask, const f } } -static void soft_max_f32_cuda(const float * x, const float * mask, const float * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { +template +static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { int nth = WARP_SIZE; while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; const dim3 block_dims(nth, 1, 1); @@ -167,15 +178,19 @@ static void soft_max_f32_cuda(const float * x, const float * mask, const float * void ggml_cuda_op_soft_max(ggml_backend_cuda_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]; + const float * src0_d = (const float *)src0->data; - const float * src1_d = src1 ? (const float *)src1->data : nullptr; + const void * src1_d = src1 ? (const void *)src1->data : nullptr; + float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -188,14 +203,25 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); // positions tensor - float * src2_dd = nullptr; + void * src2_d = nullptr; - ggml_tensor * src2 = dst->src[2]; const bool use_src2 = src2 != nullptr; if (use_src2) { - src2_dd = (float *)src2->data; + src2_d = (void *)src2->data; } - soft_max_f32_cuda(src0_d, src1_d, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + + if (use_f16) { + const half * src1_dd = (const half *)src1_d; + const half * src2_dd = (const half *)src2_d; + + soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + } else { + const float * src1_dd = (const float *)src1_d; + const float * src2_dd = (const float *)src2_d; + + soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + } } diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 407062e6f..9a469821d 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1427,6 +1427,7 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml for (int i = node_start; i < node_end; ++i) { struct ggml_tensor * src0 = gf->nodes[i]->src[0]; struct ggml_tensor * src1 = gf->nodes[i]->src[1]; + struct ggml_tensor * src2 = gf->nodes[i]->src[2]; GGML_UNUSED(src2); struct ggml_tensor * dst = gf->nodes[i]; GGML_ASSERT(dst->data != nullptr); @@ -1559,6 +1560,12 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { float scale; memcpy(&scale, dst->op_params, sizeof(float)); + +#pragma message("TODO: add ggml_vk_soft_max() F16/F32 src1 and src2 support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") + GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); + GGML_ASSERT(src2 == nullptr); + ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); } break; case GGML_OP_DIAG_MASK_INF: diff --git a/ggml-metal.m b/ggml-metal.m index 9cb421988..c6d580b84 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -46,8 +46,10 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, GGML_METAL_KERNEL_TYPE_SILU, GGML_METAL_KERNEL_TYPE_SILU_4, - GGML_METAL_KERNEL_TYPE_SOFT_MAX, - GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, + GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, @@ -177,6 +179,14 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, + GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, GGML_METAL_KERNEL_TYPE_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, @@ -443,7 +453,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { } /* - GGML_METAL_LOG_INFO("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ + GGML_METAL_LOG_INFO("%s: loaded %-40s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) kernel->pipeline, \ (int) kernel->pipeline.maxTotalThreadsPerThreadgroup, \ (int) kernel->pipeline.threadExecutionWidth); \ */ @@ -459,172 +469,182 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { return NULL; \ } \ } else { \ - GGML_METAL_LOG_WARN("%s: skipping %-32s (not supported)\n", __func__, "kernel_"#name); \ + GGML_METAL_LOG_WARN("%s: skipping %-40s (not supported)\n", __func__, "kernel_"#name); \ } // simd_sum and simd_max requires MTLGPUFamilyApple7 - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX, soft_max, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_4, soft_max_4, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction); - //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD, add, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ADD_ROW, add_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL, mul, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK_4, gelu_quick_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU, silu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SILU_4, silu_4, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16, soft_max_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4, soft_max_f16_4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32, soft_max_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4, soft_max_f32_4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF, diag_mask_inf, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIAG_MASK_INF_8, diag_mask_inf_8, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F32, get_rows_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_F16, get_rows_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_0, get_rows_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_1, get_rows_q4_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_0, get_rows_q5_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_1, get_rows_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q8_0, get_rows_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q2_K, get_rows_q2_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q3_K, get_rows_q3_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q4_K, get_rows_q4_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q5_K, get_rows_q5_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_S, get_rows_iq3_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_S, get_rows_iq2_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_S, get_rows_iq1_s, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ1_M, get_rows_iq1_m, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_NL, get_rows_iq4_nl, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ4_XS, get_rows_iq4_xs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_NORM, norm, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F32_F32, mul_mv_f32_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F16, mul_mv_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32, mul_mv_f16_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_1ROW, mul_mv_f16_f32_1row, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_F16_F32_L4, mul_mv_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_0_F32, mul_mv_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_1_F32, mul_mv_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_0_F32, mul_mv_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_1_F32, mul_mv_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q8_0_F32, mul_mv_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q2_K_F32, mul_mv_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q3_K_F32, mul_mv_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q4_K_F32, mul_mv_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q5_K_F32, mul_mv_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_S_F32, mul_mv_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_S_F32, mul_mv_iq2_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_S_F32, mul_mv_iq1_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ1_M_F32, mul_mv_iq1_m_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_NL_F32, mul_mv_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ4_XS_F32, mul_mv_iq4_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_1ROW, mul_mv_id_f16_f32_1row, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32_L4, mul_mv_id_f16_f32_l4, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_0_F32, mul_mv_id_q4_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_1_F32, mul_mv_id_q4_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_0_F32, mul_mv_id_q5_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_1_F32, mul_mv_id_q5_1_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q8_0_F32, mul_mv_id_q8_0_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q2_K_F32, mul_mv_id_q2_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q3_K_F32, mul_mv_id_q3_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q4_K_F32, mul_mv_id_q4_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q5_K_F32, mul_mv_id_q5_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_S_F32, mul_mv_id_iq3_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_S_F32, mul_mv_id_iq2_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_S_F32, mul_mv_id_iq1_s_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ1_M_F32, mul_mv_id_iq1_m_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32, mul_mv_id_iq4_nl_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32, mul_mv_id_iq4_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_1_F32, mul_mm_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_0_F32, mul_mm_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_1_F32, mul_mm_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q8_0_F32, mul_mm_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q2_K_F32, mul_mm_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q3_K_F32, mul_mm_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_K_F32, mul_mm_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q5_K_F32, mul_mm_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_S_F32, mul_mm_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_S_F32, mul_mm_iq2_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_S_F32, mul_mm_iq1_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ1_M_F32, mul_mm_iq1_m_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_NL_F32, mul_mm_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ4_XS_F32, mul_mm_iq4_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_1_F32, mul_mm_id_q4_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_0_F32, mul_mm_id_q5_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_1_F32, mul_mm_id_q5_1_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q8_0_F32, mul_mm_id_q8_0_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q2_K_F32, mul_mm_id_q2_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q3_K_F32, mul_mm_id_q3_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_K_F32, mul_mm_id_q4_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q5_K_F32, mul_mm_id_q5_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_S_F32, mul_mm_id_iq3_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_S_F32, mul_mm_id_iq2_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_S_F32, mul_mm_id_iq1_s_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ1_M_F32, mul_mm_id_iq1_m_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_NL_F32, mul_mm_id_iq4_nl_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TIMESTEP_EMBEDDING_F32, timestep_embedding_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARANGE_F32, arange_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_0, cpy_f32_q4_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q4_1, cpy_f32_q4_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_0, cpy_f32_q5_0, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q5_1, cpy_f32_q5_1, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_IQ4_NL, cpy_f32_iq4_nl, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F16, cpy_f16_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F16_F32, cpy_f16_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CONCAT, concat, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SQR, sqr, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); } [metal_library release]; @@ -743,6 +763,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: + case GGML_OP_FLASH_ATTN_EXT: return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: @@ -1326,20 +1347,33 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_SOFT_MAX: { + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); + int nth = 32; // SIMD width id pipeline = nil; + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + if (ne00%4 == 0) { while (nth < ne00/4 && nth < 256) { nth *= 2; } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_4].pipeline; + if (use_f16) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16_4].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32_4].pipeline; + } } else { while (nth < ne00 && nth < 1024) { nth *= 2; } - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX].pipeline; + if (use_f16) { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F16].pipeline; + } else { + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SOFT_MAX_F32].pipeline; + } } float scale; @@ -2503,6 +2537,161 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_OP_FLASH_ATTN_EXT: + { + GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(src0->type == GGML_TYPE_F32); + + struct ggml_tensor * src3 = gf->nodes[i]->src[3]; + + GGML_ASSERT(ggml_are_same_shape(src1, src2)); + GGML_ASSERT(src3); + + size_t offs_src3 = 0; + + id id_src3 = src3 ? ggml_metal_get_buffer(src3, &offs_src3) : nil; + + GGML_ASSERT(!src3 || src3->type == GGML_TYPE_F16); + GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && + "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); + + const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); + const int64_t ne31 = src3 ? src3->ne[1] : 0; + const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); + const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); + + const uint64_t nb30 = src3 ? src3->nb[0] : 0; GGML_UNUSED(nb30); + const uint64_t nb31 = src3 ? src3->nb[1] : 0; + const uint64_t nb32 = src3 ? src3->nb[2] : 0; GGML_UNUSED(nb32); + const uint64_t nb33 = src3 ? src3->nb[3] : 0; GGML_UNUSED(nb33); + + const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); + + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); + + id pipeline = nil; + + bool use_vec_kernel = false; + + if (ne01 >= 4 || (ne00%128 != 0)) { + switch (ne00) { + case 64: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64 ].pipeline; break; + case 80: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80 ].pipeline; break; + case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; + case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + default: + { + GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_METAL_LOG_ERROR("add template specialization for this size\n"); + GGML_ASSERT(false && "add template specialization for this size"); + } + } + } else { + use_vec_kernel = true; + + switch (ne00) { + case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; + case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + default: + { + GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); + GGML_METAL_LOG_ERROR("add template specialization for this size\n"); + GGML_ASSERT(false && "add template specialization for this size"); + } + } + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12]; + [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16]; + [encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20]; + [encoder setBytes:&ne31 length:sizeof( int64_t) atIndex:21]; + [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:22]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:23]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:24]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:25]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:26]; + [encoder setBytes:&scale length:sizeof( float) atIndex:27]; + + if (!use_vec_kernel) { + // half8x8 kernel + const int64_t nqptg = 8; // queries per threadgroup !! sync with kernel template arguments !! + const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! + + GGML_ASSERT(nqptg <= 32); + GGML_ASSERT(nqptg % 8 == 0); + GGML_ASSERT(ncpsg % 32 == 0); + + int64_t nsgmax = 2; + + while (true) { + const size_t smem = nqptg*(ne00 + 2*nsgmax*(ncpsg + nqptg))*(sizeof(float)/2); + if (smem > ctx->device.maxThreadgroupMemoryLength) { + break; + } + nsgmax *= 2; + } + nsgmax /= 2; + + // simdgroups per threadgroup (a.k.a. warps) + const int64_t nsg = ne01 <= nqptg ? MAX(4, MIN(nsgmax, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32))) : 4; + + const size_t smem = nqptg*(ne00 + 2*nsg*(ncpsg + nqptg))*(sizeof(float)/2); + + //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); + GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); + + [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; + } else { + // half1x4 kernel + const int64_t nqptg = 1; // queries per threadgroup !! sync with kernel template arguments !! + const int64_t ncpsg = 32; // cache values per simdgroup !! sync with kernel template arguments !! + + GGML_ASSERT(nqptg <= 32); + GGML_ASSERT(nqptg % 1 == 0); + GGML_ASSERT(ncpsg % 32 == 0); + + // simdgroups per threadgroup (a.k.a. warps) + const int64_t nsgt = MAX(2, MIN(ne11/ncpsg, (int64_t) pipeline.maxTotalThreadsPerThreadgroup/32)); + + int64_t nsg = 1; + while (nsg <= nsgt) { + nsg *= 2; + } + nsg /= 2; + + const size_t smem = (nqptg*(ne00 + 2*nsg*(ncpsg + nqptg)) + nsg*ne00)*(sizeof(float)/2); + + //printf("smem: %zu, max: %zu\n", smem, ctx->device.maxThreadgroupMemoryLength); + GGML_ASSERT(smem <= ctx->device.maxThreadgroupMemoryLength); + [encoder setThreadgroupMemoryLength:GGML_PAD(smem, 16) atIndex:0]; + + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + nqptg - 1)/nqptg, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(32, nsg, 1)]; + } + } break; case GGML_OP_DUP: case GGML_OP_CPY: case GGML_OP_CONT: @@ -2706,10 +2895,13 @@ GGML_CALL static const char * ggml_backend_metal_buffer_type_get_name(ggml_backe UNUSED(buft); } -static void ggml_backend_metal_log_allocated_size(id device) { +static void ggml_backend_metal_log_allocated_size(id device, size_t size_aligned) { +#ifndef GGML_METAL_NDEBUG #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) if (@available(macOS 10.12, iOS 16.0, *)) { - GGML_METAL_LOG_INFO(", (%8.2f / %8.2f)", + GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f / %8.2f)", + __func__, + size_aligned / 1024.0 / 1024.0, device.currentAllocatedSize / 1024.0 / 1024.0, device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); @@ -2719,10 +2911,15 @@ static void ggml_backend_metal_log_allocated_size(id device) { GGML_METAL_LOG_INFO("\n"); } } else { - GGML_METAL_LOG_INFO(", (%8.2f)\n", device.currentAllocatedSize / 1024.0 / 1024.0); + GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, (%8.2f)\n", + __func__, + size_aligned / 1024.0 / 1024.0, + device.currentAllocatedSize / 1024.0 / 1024.0); } +#endif #endif UNUSED(device); + UNUSED(size_aligned); } GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { @@ -2756,8 +2953,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff return NULL; } - GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); - ggml_backend_metal_log_allocated_size(device); + //ggml_backend_metal_log_allocated_size(device, size_aligned); return ggml_backend_buffer_init(buft, ggml_backend_metal_buffer_i, ctx, size); } @@ -2844,7 +3040,7 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, return false; } - GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB", __func__, size_aligned / 1024.0 / 1024.0); + ggml_backend_metal_log_allocated_size(device, size_aligned); ++ctx->n_buffers; } else { @@ -2867,7 +3063,8 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, return false; } - GGML_METAL_LOG_INFO("%s: allocated buffer, size = %8.2f MiB, offs = %12ld", __func__, size_step_aligned / 1024.0 / 1024.0, i); + ggml_backend_metal_log_allocated_size(device, size_step_aligned); + if (i + size_step < size) { GGML_METAL_LOG_INFO("\n"); } @@ -2876,8 +3073,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_metal_buffer_from_ptr(void * data, } } - ggml_backend_metal_log_allocated_size(device); - return ggml_backend_buffer_init(ggml_backend_metal_buffer_type(), ggml_backend_metal_buffer_i, ctx, size); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 191880af1..3d4276ae0 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -352,11 +352,12 @@ kernel void kernel_sum_rows( dst_row[0] = row_sum; } +template kernel void kernel_soft_max( - device const float * src0, - device const float * src1, - device const float * src2, - device float * dst, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -375,10 +376,10 @@ kernel void kernel_soft_max( const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); - device const float * psrc0 = src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - device const float * pmask = src1 != src0 ? src1 + i01*ne00 : nullptr; - device const float * ppos = src2 != src0 ? src2 : nullptr; - device float * pdst = dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; + device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); + device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr; + device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; + device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); float slope = 0.0f; @@ -456,11 +457,12 @@ kernel void kernel_soft_max( } } +template kernel void kernel_soft_max_4( - device const float * src0, - device const float * src1, - device const float * src2, - device float * dst, + device const char * src0, + device const char * src1, + device const char * src2, + device char * dst, constant int64_t & ne00, constant int64_t & ne01, constant int64_t & ne02, @@ -479,10 +481,10 @@ kernel void kernel_soft_max_4( const int64_t i02 = (tgpig - i03*ne02*ne01) / ne01; const int64_t i01 = (tgpig - i03*ne02*ne01 - i02*ne01); - device const float4 * psrc4 = (device const float4 *)(src0 + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); - device const float4 * pmask = src1 != src0 ? (device const float4 *)(src1 + i01*ne00) : nullptr; - device const float4 * ppos = src2 != src0 ? (device const float4 *)(src2) : nullptr; - device float4 * pdst4 = (device float4 *)(dst + i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); + device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; + device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr; + device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; + device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; float slope = 0.0f; @@ -499,7 +501,7 @@ kernel void kernel_soft_max_4( float4 lmax4 = -INFINITY; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)); + lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))); } const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); @@ -525,7 +527,7 @@ kernel void kernel_soft_max_4( // parallel sum float4 lsum4 = 0.0f; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val); + const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))) - max_val); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } @@ -562,6 +564,14 @@ kernel void kernel_soft_max_4( } } +typedef decltype(kernel_soft_max) kernel_soft_max_t; +typedef decltype(kernel_soft_max_4) kernel_soft_max_4_t; + +template [[host_name("kernel_soft_max_f16")]] kernel kernel_soft_max_t kernel_soft_max; +template [[host_name("kernel_soft_max_f32")]] kernel kernel_soft_max_t kernel_soft_max; +template [[host_name("kernel_soft_max_f16_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4; +template [[host_name("kernel_soft_max_f32_4")]] kernel kernel_soft_max_4_t kernel_soft_max_4; + kernel void kernel_diag_mask_inf( device const float * src0, device float * dst, @@ -2084,6 +2094,632 @@ kernel void kernel_leaky_relu_f32( dst[tpig] = src0[tpig] > 0.0f ? src0[tpig] : src0[tpig] * slope; } +typedef void (flash_attn_ext_f16_t)( + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, + constant int64_t & ne31, + constant uint64_t & nb31, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant float & scale, + threadgroup half * shared, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]); + +// ref: https://arxiv.org/pdf/2307.08691.pdf +template // head size, queries per threadgroup, cache items per threadgroup +kernel void kernel_flash_attn_ext_f16( + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, + constant int64_t & ne31, + constant uint64_t & nb31, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant float & scale, + threadgroup half * shared [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const short nsg = ntg.y; // number of simdgroups + + const short iq3 = tgpig[2]; + const short iq2 = tgpig[1]; + const short iq1 = tgpig[0]*Q; + + const short D4 = D/4; + const short D8 = D/8; + const short Q8 = Q/8; + const short NW = N_SIMDWIDTH; + const short SH = (C + Q); // shared memory per simdgroup in (half) + + const short T = D + 2*nsg*SH; // shared memory size per query in (half) + const short TF = T/2; // shared memory size per query in (float) + const short T4 = T/4; // shared memory size per query in (half4) + + threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data + threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 + threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix + + // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) + simdgroup_half8x8 lo[D8]; + + // load heads from Q to shared memory + for (short j = sgitg; j < Q; j += nsg) { + device const float4 * q4 = (device const float4 *) ((device const char *) q + ((iq1 + j)*nb01 + iq2*nb02 + iq3*nb03)); + + for (short i = tiisg; i < D4; i += NW) { + if (iq1 + j < ne01) { + sq4[j*T4 + i] = (half4) q4[i]; + } else { + sq4[j*T4 + i] = 0.0h; + } + } + } + + // zero out lo + for (short i = 0; i < D8; ++i) { + lo[i] = make_filled_simdgroup_matrix(0.0h); + } + + // zero out shared memory SH + for (short j = 0; j < Q; ++j) { + for (short i = tiisg; i < SH; i += NW) { + ss[j*TF + i] = 0.0f; + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + { + float S[Q] = { [0 ... Q-1] = 0.0h }; + float M[Q] = { [0 ... Q-1] = -FLT_MAX/2 }; + + // assume K and V are same shape + const short ne22 = ne12; + const short ne23 = ne13; + + const uint nb21 = nb11; + const uint nb22 = nb12; + const uint nb23 = nb13; + + // broadcast + const short rk2 = ne02/ne12; + const short rk3 = ne03/ne13; + + const short rv2 = ne02/ne22; + const short rv3 = ne03/ne23; + + // k indices + const short ik2 = iq2/rk2; + const short ik3 = iq3/rk3; + + // v indices + const short iv2 = iq2/rv2; + const short iv3 = iq3/rv3; + + // load the queries from shared memory into local memory + simdgroup_half8x8 mq[D8]; + + for (short i = 0; i < D8; ++i) { + simdgroup_load(mq[i], sq + i*8, T); + } + + // pointer to the mask + device const half * mp = (device const half *) (mask + iq1*nb31); + + // prepare diagonal scale matrix + simdgroup_float8x8 mscale(scale); + + // loop over the KV cache + // each simdgroup handles blocks of Q rows and C columns + for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + const int ic = ic0 + C*sgitg; + if (ic >= ne11) { + break; + } + + // Q*K^T + { + for (short cc = 0; cc < C/8; ++cc) { + simdgroup_float8x8 mqk = make_filled_simdgroup_matrix(0.h); + + device const half * pk = (device const half *) ((device const char *) k + ((ic + 8*cc)*nb11 + ik2*nb12 + ik3*nb13)); + + for (short i = 0; i < D8; ++i) { + simdgroup_half8x8 mk; + simdgroup_load(mk, pk + i*8, nb11/sizeof(half), 0, true); // transpose + + simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); + } + + // mqk = mqk*scale + mask + simdgroup_half8x8 mm; + simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); + simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); + + simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + } + } + + // used to detect blocks full of -INF + float smax = -INFINITY; + + // online softmax + { + float ms[Q]; + + for (short j = 0; j < Q; ++j) { + const short p = tiisg; + + const float m = M[j]; + const float s = ss[j*TF + p]; + + smax = simd_max(max(smax, s)); + M[j] = simd_max(max(M[j], s)); + + ms[j] = exp(m - M[j]); + const float vs = exp(s - M[j]); + + S[j] = S[j]*ms[j] + simd_sum(vs); + + // the P matrix from the paper (Q rows, C columns) + ss[j*TF + p] = vs; + } + + // create a QxQ diagonal matrix for rescaling the output + if (tiisg < Q) { + ss[tiisg*TF + C + tiisg] = ms[tiisg]; + } + } + + // skip -INF blocks + if (smax == -INFINITY) { + continue; + } + + // O = diag(ms)*O + { + simdgroup_float8x8 mm; + simdgroup_load(mm, ss + C, TF, 0, false); + + for (short i = 0; i < D8; ++i) { + simdgroup_multiply(lo[i], mm, lo[i]); + } + } + + // O = O + (Q*K^T)*V + { + for (short cc = 0; cc < C/8; ++cc) { + device const half * pv = (device const half *) ((device const char *) v + ((ic + 8*cc)*nb21 + iv2*nb22 + iv3*nb23)); + + for (short i = 0; i < D8; ++i) { + simdgroup_half8x8 mk; + simdgroup_load(mk, pv + i*8, nb21/sizeof(half), 0, false); + + simdgroup_float8x8 mv; + simdgroup_load(mv, ss + 8*cc, TF, 0, false); + + simdgroup_multiply_accumulate(lo[i], mv, mk, lo[i]); + } + } + } + } + + // these are needed for reducing the results from the simdgroups (reuse the ss buffer) + for (short j = 0; j < Q; ++j) { + if (tiisg == 0) { + ss[j*TF + 0] = S[j]; + ss[j*TF + 1] = M[j]; + } + } + } + + // reduce the warps sequentially + for (short sg = 1; sg < nsg; ++sg) { + float S = { 0.0h }; + float M = { -FLT_MAX/2 }; + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // each simdgroup stores its output to shared memory, reusing sq + if (sgitg == sg) { + for (short i = 0; i < D8; ++i) { + simdgroup_store(lo[i], sq + i*8, T, 0, false); + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // the first simdgroup accumulates the results from the other simdgroups + if (sgitg == 0) { + for (short j = 0; j < Q; ++j) { + const float S0 = ss[j*TF + 0]; + const float S1 = ss[j*TF + sg*SH + 0]; + + const float M0 = ss[j*TF + 1]; + const float M1 = ss[j*TF + sg*SH + 1]; + + M = max(M0, M1); + + const float ms0 = exp(M0 - M); + const float ms1 = exp(M1 - M); + + S = S0*ms0 + S1*ms1; + + if (tiisg == 0) { + ss[j*TF + 0] = S; + ss[j*TF + 1] = M; + + ss[j*TF + C + j ] = ms0; + ss[j*TF + C + j + sg*SH] = ms1; + } + } + + // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 + { + simdgroup_half8x8 t; + simdgroup_float8x8 ms0; + simdgroup_float8x8 ms1; + + simdgroup_load(ms0, ss + C, TF, 0, false); + simdgroup_load(ms1, ss + C + sg*SH, TF, 0, false); + + for (short i = 0; i < D8; ++i) { + simdgroup_load (t, sq + i*8, T, 0, false); + simdgroup_multiply(t, ms1, t); + + simdgroup_multiply_accumulate(lo[i], ms0, lo[i], t); + } + } + } + } + + // store result to shared memory (reuse sq) + if (sgitg == 0) { + for (short i = 0; i < D8; ++i) { + simdgroup_store(lo[i], sq + i*8, T, 0, false); + } + } + + device float4 * dst4 = (device float4 *) dst; + + // final rescale with 1/S and store to global memory + if (sgitg == 0) { + for (short j = 0; j < Q && iq1 + j < ne01; ++j) { + const float S = ss[j*TF + 0]; + + for (short i = tiisg; i < D4; i += NW) { + dst4[(iq3*ne2*ne1 + iq2 + (iq1 + j)*ne1)*D4 + i] = (float4) sq4[j*T4 + i]/S; + } + } + } +} + +template [[host_name("kernel_flash_attn_ext_f16_h64" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<64>; +template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<80>; +template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; +template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; +template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; +template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; + +template // head size, queries per threadgroup, cache items per threadgroup +kernel void kernel_flash_attn_ext_vec_f16( + device const char * q, + device const char * k, + device const char * v, + device const char * mask, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant uint64_t & nb13, + constant int64_t & ne31, + constant uint64_t & nb31, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant float & scale, + threadgroup half * shared [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]], + ushort tiisg[[thread_index_in_simdgroup]], + ushort sgitg[[simdgroup_index_in_threadgroup]]) { + const short nsg = ntg.y; // number of simdgroups + + const short iq3 = tgpig[2]; + const short iq2 = tgpig[1]; + const short iq1 = tgpig[0]; + + const short D4 = D/4; + const short NW = N_SIMDWIDTH; + const short SH = (C + Q); // shared memory per simdgroup in (half) + + const short T = D + 2*nsg*SH; // shared memory size per query in (half) + + //threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data + threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 + threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix + threadgroup float4 * ss4 = (threadgroup float4 *) (shared + 2*sgitg*SH + 1*D); // same as above but in half4 + threadgroup half4 * sr4 = (threadgroup half4 *) (shared + sgitg*D + 1*T); // scratch buffer for the results + + // store the result for all queries in local memory in 8x8 matrices (the O matrix from the paper) + half4 lo[D4/NW]; + + // load heads from Q to shared memory + device const float4 * q4 = (device const float4 *) ((device const char *) q + (iq1*nb01 + iq2*nb02 + iq3*nb03)); + + for (short i = tiisg; i < D4; i += NW) { + if (iq1 < ne01) { + sq4[i] = (half4) q4[i]; + } else { + sq4[i] = 0.0h; + } + } + + // zero out lo + for (short i = tiisg; i < D4; i += NW) { + lo[i/NW] = 0.0h; + } + + // zero out shared memory SH + for (short i = tiisg; i < SH/4; i += NW) { + ss4[i] = 0.0h; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + { + float S = { 0.0h }; + float M = { -FLT_MAX/2 }; + + // assume K and V are same shape + const short ne22 = ne12; + const short ne23 = ne13; + + const uint nb21 = nb11; + const uint nb22 = nb12; + const uint nb23 = nb13; + + // broadcast + const short rk2 = ne02/ne12; + const short rk3 = ne03/ne13; + + const short rv2 = ne02/ne22; + const short rv3 = ne03/ne23; + + // k indices + const short ik2 = iq2 / rk2; + const short ik3 = iq3 / rk3; + + // v indices + const short iv2 = iq2 / rv2; + const short iv3 = iq3 / rv3; + + // load the queries from shared memory into local memory + half4 mq[D4]; + + for (short ii = 0; ii < D4; ii += NW) { + short i = ii + tiisg; + mq[i] = sq4[i]; + } + + // pointer to the mask + device const half4 * mp4 = (device const half4 *) (mask + iq1*nb31); + + // loop over the KV cache + // each simdgroup handles blocks of Q rows and C columns + for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { + const int ic = ic0 + C*sgitg; + if (ic >= ne11) { + break; + } + + // Q*K^T + { +#pragma unroll + for (short cc = 0; cc < C/4; ++cc) { + float4 mqk = { 0.0h }; + + device const half4 * pk4 = (device const half4 *) ((device const char *) k + ((ic + 4*cc)*nb11 + ik2*nb12 + ik3*nb13)); + +#pragma unroll + for (short ii = 0; ii < D4; ii += NW) { + const short i = ii + tiisg; + + half4x4 mk; + mk[0] = pk4[i + 0*(nb11/8)]; + mk[1] = pk4[i + 1*(nb11/8)]; + mk[2] = pk4[i + 2*(nb11/8)]; + mk[3] = pk4[i + 3*(nb11/8)]; + + mqk += (float4) (mq[i] * mk); + } + + // reduce the results from the threads in the simdgroup + mqk += simd_shuffle_down(mqk, 16); + mqk += simd_shuffle_down(mqk, 8); + mqk += simd_shuffle_down(mqk, 4); + mqk += simd_shuffle_down(mqk, 2); + mqk += simd_shuffle_down(mqk, 1); + + // mqk = mqk*scale + mask + if (tiisg == 0) { + float4 mm = (float4) mp4[ic/4 + cc]; + mqk = mqk*scale + mm; + + ss4[cc] = mqk; + } + } + } + + // online softmax + { + const short p = tiisg; + + const float m = M; + const float s = ss[p]; + + M = simd_max(max(M, s)); + + const float ms = exp(m - M); + const float vs = exp(s - M); + + S = S*ms + simd_sum(vs); + + // the P matrix from the paper (Q rows, C columns) + ss[p] = vs; + + // O = diag(ms)*O +#pragma unroll + for (short ii = 0; ii < D4; ii += NW) { + const short i = ii + tiisg; + lo[i/NW] *= ms; + } + } + + // O = O + (Q*K^T)*V + { +#pragma unroll + for (short cc = 0; cc < C/4; ++cc) { + device const half4 * pv4 = (device const half4 *) ((device const char *) v + ((ic + 4*cc)*nb21 + iv2*nb22 + iv3*nb23)); + +#pragma unroll + for (short ii = 0; ii < D4; ii += NW) { + const short i = ii + tiisg; + + lo[i/NW] += pv4[i + 0*(nb21/8)] * ss[4*cc + 0]; + lo[i/NW] += pv4[i + 1*(nb21/8)] * ss[4*cc + 1]; + lo[i/NW] += pv4[i + 2*(nb21/8)] * ss[4*cc + 2]; + lo[i/NW] += pv4[i + 3*(nb21/8)] * ss[4*cc + 3]; + } + } + } + + } + + // these are needed for reducing the results from the simdgroups (reuse the ss buffer) + if (tiisg == 0) { + ss[0] = S; + ss[1] = M; + } + } + + // store results to shared memory + for (short ii = 0; ii < D4; ii += NW) { + short i = ii + tiisg; + sr4[i] = lo[ii/NW]; + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + + // parallel reduce + for (short r = nsg/2; r > 0; r >>= 1) { + if (sgitg < r) { + const float S0 = ss[ 0]; + const float S1 = ss[r*SH + 0]; + + const float M0 = ss[ 1]; + const float M1 = ss[r*SH + 1]; + + const float M = max(M0, M1); + + const float ms0 = exp(M0 - M); + const float ms1 = exp(M1 - M); + + const float S = S0*ms0 + S1*ms1; + + if (tiisg == 0) { + ss[0] = S; + ss[1] = M; + } + + // O_0 = diag(ms0)*O_0 + diag(ms1)*O_1 + for (short ii = 0; ii < D4; ii += NW) { + short i = ii + tiisg; + sr4[i] = sr4[i]*ms0 + sr4[i + r*D4]*ms1; + } + } + + threadgroup_barrier(mem_flags::mem_threadgroup); + } + + device float4 * dst4 = (device float4 *) dst; + + // final rescale with 1/S and store to global memory + if (sgitg == 0) { + const float S = ss[0]; + + for (short ii = 0; ii < D4; ii += NW) { + short i = ii + tiisg; + dst4[(iq3*ne2*ne1 + iq2 + (iq1)*ne1)*D4 + i] = (float4) sr4[i]/S; + } + } +} + +template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; +template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; + kernel void kernel_cpy_f16_f16( device const half * src0, device half * dst, diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 2b76b3ebd..57fe4ea3d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -14744,7 +14744,12 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); + const ggml_tensor * src2 = dst->src[2]; + +#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 and src2 support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -14760,7 +14765,6 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, float * src2_dd = nullptr; sycl_pool_alloc src2_f; - ggml_tensor * src2 = dst->src[2]; const bool use_src2 = src2 != nullptr; if (use_src2) { diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1736ab736..f712cdd5a 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -3178,6 +3178,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const } return nullptr; case GGML_OP_SOFT_MAX: +#pragma message("TODO: add ggml_vk_soft_max() F16 src1 and src2 support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } diff --git a/ggml.c b/ggml.c index cb273061c..74ecd5927 100644 --- a/ggml.c +++ b/ggml.c @@ -951,7 +951,7 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { #define GGML_F16_VEC_ZERO GGML_F16x8_ZERO #define GGML_F16_VEC_SET1 GGML_F16x8_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F16x8_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE(p, r[i]) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F16x8_STORE((ggml_fp16_internal_t *)(p), r[i]) #define GGML_F16_VEC_FMA GGML_F16x8_FMA #define GGML_F16_VEC_ADD GGML_F16x8_ADD #define GGML_F16_VEC_MUL GGML_F16x8_MUL @@ -977,7 +977,7 @@ ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type type) { #define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO #define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 #define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) - #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) + #define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE((ggml_fp16_internal_t *)(p), r[i]) #define GGML_F16_VEC_FMA GGML_F32Cx4_FMA #define GGML_F16_VEC_ADD GGML_F32Cx4_ADD #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL @@ -1046,7 +1046,7 @@ do { \ // unlike _mm256_cvt intrinsics that require F16C, _mm512_cvt is defined in AVX512F // so F16C guard isn't required -#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((__m256i *)(x))) +#define GGML_F32Cx16_LOAD(x) _mm512_cvtph_ps(_mm256_loadu_si256((const __m256i *)(x))) #define GGML_F32Cx16_STORE(x, y) _mm256_storeu_si256((__m256i *)(x), _mm512_cvtps_ph(y, 0)) #define GGML_F32Cx16_FMA(a, b, c) _mm512_fmadd_ps(b, c, a) @@ -1144,7 +1144,7 @@ do { \ #if defined(__F16C__) // the _mm256_cvt intrinsics require F16C -#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((__m128i *)(x))) +#define GGML_F32Cx8_LOAD(x) _mm256_cvtph_ps(_mm_loadu_si128((const __m128i *)(x))) #define GGML_F32Cx8_STORE(x, y) _mm_storeu_si128((__m128i *)(x), _mm256_cvtps_ph(y, 0)) #else static inline __m256 __avx_f32cx8_load(ggml_fp16_t *x) { @@ -1662,6 +1662,37 @@ inline static void ggml_vec_mad_f32(const int n, float * restrict y, const float #endif } +inline static void ggml_vec_mad_f16(const int n, ggml_fp16_t * restrict y, const ggml_fp16_t * restrict x, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ax[GGML_F16_ARR]; + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ax[j] = GGML_F16_VEC_LOAD(x + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_FMA(ay[j], ax[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i]) + GGML_FP16_TO_FP32(x[i])*v); + } +#endif +} + // xs and vs are byte strides of x and v inline static void ggml_vec_mad_f32_unroll(const int n, const int xs, const int vs, float * restrict y, const float * restrict xv, const float * restrict vv) { @@ -1746,6 +1777,35 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) { #endif } +inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) { +#if defined(GGML_SIMD) + const int np = (n & ~(GGML_F16_STEP - 1)); + + GGML_F16_VEC vx = GGML_F16_VEC_SET1(v); + + GGML_F16_VEC ay[GGML_F16_ARR]; + + for (int i = 0; i < np; i += GGML_F16_STEP) { + for (int j = 0; j < GGML_F16_ARR; j++) { + ay[j] = GGML_F16_VEC_LOAD(y + i + j*GGML_F16_EPR, j); + ay[j] = GGML_F16_VEC_MUL(ay[j], vx); + + GGML_F16_VEC_STORE(y + i + j*GGML_F16_EPR, ay, j); + } + } + + // leftovers + for (int i = np; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#else + // scalar + for (int i = 0; i < n; ++i) { + y[i] = GGML_FP32_TO_FP16(GGML_FP16_TO_FP32(y[i])*v); + } +#endif +} + inline static void ggml_vec_norm_f32 (const int n, float * s, const float * x) { ggml_vec_dot_f32(n, s, 0, x, 0, x, 0, 1); *s = sqrtf(*s); } inline static void ggml_vec_sqr_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i]*x[i]; } inline static void ggml_vec_sqrt_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = sqrtf(x[i]); } @@ -2000,6 +2060,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "LEAKY_RELU", "FLASH_ATTN", + "FLASH_ATTN_EXT", "FLASH_FF", "FLASH_ATTN_BACK", "SSM_CONV", @@ -2026,7 +2087,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); +static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2090,6 +2151,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "leaky_relu(x)", "flash_attn(x)", + "flash_attn_ext(x)", "flash_ff(x)", "flash_attn_back(x)", "ssm_conv(x)", @@ -2116,7 +2178,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); +static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -4559,6 +4621,8 @@ struct ggml_tensor * ggml_mul_mat( void ggml_mul_mat_set_prec( struct ggml_tensor * a, enum ggml_prec prec) { + GGML_ASSERT(a->op == GGML_OP_MUL_MAT); + const int32_t prec_i32 = (int32_t) prec; ggml_set_op_params_i32(a, 0, prec_i32); @@ -5397,17 +5461,23 @@ static struct ggml_tensor * ggml_soft_max_impl( GGML_ASSERT(ggml_is_contiguous(a)); if (mask) { + GGML_ASSERT(mask->type == GGML_TYPE_F16 || mask->type == GGML_TYPE_F32); GGML_ASSERT(ggml_is_contiguous(mask)); GGML_ASSERT(ggml_is_matrix(mask)); - GGML_ASSERT(ggml_can_repeat_rows(mask, a)); + GGML_ASSERT(mask->ne[0] == a->ne[0]); + GGML_ASSERT(mask->ne[1] >= a->ne[1]); } if (pos) { GGML_ASSERT(ggml_is_vector(pos)); - GGML_ASSERT(pos->type == GGML_TYPE_F32); + GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32); GGML_ASSERT(pos->ne[0] == a->ne[0]); } + if (pos && mask) { + GGML_ASSERT(pos->type == mask->type); + } + if (max_bias > 0.0f) { GGML_ASSERT(pos); } @@ -6216,6 +6286,59 @@ struct ggml_tensor * ggml_flash_attn( return result; } +// ggml_flash_attn_ext + +struct ggml_tensor * ggml_flash_attn_ext( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * mask, + float scale) { + GGML_ASSERT(ggml_can_mul_mat(k, q)); + // TODO: check if vT can be multiplied by (k*qT) + if (mask) { + GGML_ASSERT(ggml_is_contiguous(mask)); + GGML_ASSERT(mask->ne[2] == 1); + GGML_ASSERT(mask->ne[3] == 1); + GGML_ASSERT(mask->ne[1] >= GGML_PAD(q->ne[1], GGML_KQ_MASK_PAD) && + "the Flash-Attention kernel requires the mask to be padded to GGML_KQ_MASK_PAD and at least n_queries big"); + //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); + } + + bool is_node = false; + + if (q->grad || k->grad || v->grad) { + is_node = true; + } + + // permute(0, 2, 1, 3) + int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + float params[] = { scale }; + ggml_set_op_params(result, params, sizeof(params)); + + result->op = GGML_OP_FLASH_ATTN_EXT; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src[0] = q; + result->src[1] = k; + result->src[2] = v; + result->src[3] = mask; + + return result; +} + +void ggml_flash_attn_ext_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec) { + GGML_ASSERT(a->op == GGML_OP_FLASH_ATTN_EXT); + + const int32_t prec_i32 = (int32_t) prec; + + ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos +} + // ggml_flash_ff struct ggml_tensor * ggml_flash_ff( @@ -12255,7 +12378,7 @@ static void ggml_compute_forward_soft_max_f32( GGML_TENSOR_UNARY_OP_LOCALS - const int64_t ne11 = src1 ? src1->ne[1] : 1; + //const int64_t ne11 = src1 ? src1->ne[1] : 1; // TODO: is this supposed to be ceil instead of floor? // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 @@ -12278,19 +12401,31 @@ static void ggml_compute_forward_soft_max_f32( float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching - float * pos = src2 ? (float *) src2->data : src0->data; + ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data; + float * pos_f32 = src2 ? (float *) src2->data : src0->data; + + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); for (int i1 = ir0; i1 < ir1; i1++) { float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); // broadcast the mask across rows - float * mp = src1 ? (float *)((char *) src1->data + (i1%ne11)*src1->nb[1]) : NULL; + ggml_fp16_t * mp_f16 = src1 ? (ggml_fp16_t *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; + float * mp_f32 = src1 ? (float *)((char *) src1->data) + (i1%ne01)*ne00 : NULL; ggml_vec_cpy_f32 (nc, wp, sp); ggml_vec_scale_f32(nc, wp, scale); - if (mp) { - ggml_vec_acc_f32(nc, wp, mp); + if (mp_f32) { + if (use_f16) { + for (int i = 0; i < nc; ++i) { + wp[i] += GGML_FP16_TO_FP32(mp_f16[i]); + } + } else { + for (int i = 0; i < nc; ++i) { + wp[i] += mp_f32[i]; + } + } } // ALiBi bias @@ -12298,8 +12433,14 @@ static void ggml_compute_forward_soft_max_f32( const uint32_t h = (i1/ne01)%ne02; // head const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1); - for (int i = 0; i < nc; i++) { - wp[i] = wp[i] + slope*pos[i]; + if (use_f16) { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]); + } + } else { + for (int i = 0; i < nc; ++i) { + wp[i] += slope*pos_f32[i]; + } } } @@ -14569,6 +14710,198 @@ static void ggml_compute_forward_flash_attn( } } +// ggml_compute_forward_flash_attn_ext + +static void ggml_compute_forward_flash_attn_ext_f16( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + int64_t t0 = ggml_perf_time_us(); + UNUSED(t0); + + GGML_TENSOR_LOCALS(int64_t, neq, q, ne) + GGML_TENSOR_LOCALS(size_t, nbq, q, nb) + GGML_TENSOR_LOCALS(int64_t, nek, k, ne) + GGML_TENSOR_LOCALS(size_t, nbk, k, nb) + GGML_TENSOR_LOCALS(int64_t, nev, v, ne) + GGML_TENSOR_LOCALS(size_t, nbv, v, nb) + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) + GGML_TENSOR_LOCALS(size_t, nb, dst, nb) + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t D = neq0; + const int64_t N = neq1; + + GGML_ASSERT(ne0 == D); + GGML_ASSERT(ne2 == N); + + GGML_ASSERT(nbq0 == sizeof(float)); + GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); + + GGML_ASSERT(neq0 == D); + GGML_ASSERT(nek0 == D); + GGML_ASSERT(nev0 == D); + + GGML_ASSERT(neq1 == N); + GGML_ASSERT(nev0 == D); + + // dst cannot be transposed or permuted + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb0 <= nb1); + GGML_ASSERT(nb1 <= nb2); + GGML_ASSERT(nb2 <= nb3); + + // broadcast factors + const int64_t rk2 = neq2/nek2; + const int64_t rk3 = neq3/nek3; + + const int64_t rv2 = neq2/nev2; + const int64_t rv3 = neq3/nev3; + + if (params->type == GGML_TASK_TYPE_INIT) { + return; + } + + if (params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + // parallelize by q rows using ggml_vec_dot_f32 + + // total rows in q + const int nr = neq1*neq2*neq3; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + float scale = 1.0f; + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + + // loop over n_batch and n_head + for (int ir = ir0; ir < ir1; ++ir) { + // q indices + const int iq3 = ir/(neq2*neq1); + const int iq2 = (ir - iq3*neq2*neq1)/neq1; + const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + + float S = 0.0f; + float M = -INFINITY; + + float * V32 = (float *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32); + ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory + ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D); + + memset(V16, 0, D*sizeof(ggml_fp16_t)); + + const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; + + // k indices + const int ik3 = iq3 / rk3; + const int ik2 = iq2 / rk2; + + // v indices + const int iv3 = iq3 / rv3; + const int iv2 = iq2 / rv2; + + // online softmax / attention + // loop over n_kv and n_head_kv + // ref: https://arxiv.org/pdf/2112.05682.pdf + for (int64_t ic = 0; ic < nek1; ++ic) { + const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + if (mv == -INFINITY) { + continue; + } + + float s; + + // convert Q to F16 in V32 + { + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + + for (int64_t d = 0; d < D; ++d) { + Q16[d] = GGML_FP32_TO_FP16(pq[d]); + } + } + + ggml_vec_dot_f16(D, + &s, 0, + (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, + Q16, 0, 1); + + s = s*scale + mv; + + const float Mold = M; + + float ms = 1.0f; + float vs = 1.0f; + + if (s > M) { + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, V16, ms); + } else { + vs = expf(s - M); + } + + const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + + // V += v*expf(s - M) + ggml_vec_mad_f16(D, V16, v16, vs); + + S = S*ms + vs; + } + + // V /= S + for (int64_t d = 0; d < D; ++d) { + V32[d] = GGML_FP16_TO_FP32(V16[d])/S; + } + + // dst indices + const int i1 = iq1; + const int i2 = iq2; + const int i3 = iq3; + + // original + //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); + + // permute(0, 2, 1, 3) + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1); + } +} + +static void ggml_compute_forward_flash_attn_ext( + const struct ggml_compute_params * params, + const struct ggml_tensor * q, + const struct ggml_tensor * k, + const struct ggml_tensor * v, + const struct ggml_tensor * mask, + struct ggml_tensor * dst) { + switch (dst->op_params[1]) { + case GGML_PREC_DEFAULT: + case GGML_PREC_F32: + { + // uses F32 accumulators + ggml_compute_forward_flash_attn_ext_f16(params, q, k, v, mask, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_flash_ff static void ggml_compute_forward_flash_ff_f16( @@ -16376,6 +16709,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm const bool masked = t != 0; ggml_compute_forward_flash_attn(params, masked, tensor); } break; + case GGML_OP_FLASH_ATTN_EXT: + { + ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); + } break; case GGML_OP_FLASH_FF: { ggml_compute_forward_flash_ff(params, tensor); @@ -17388,6 +17725,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor GGML_ASSERT(false); // TODO: not implemented } break; case GGML_OP_FLASH_ATTN: + case GGML_OP_FLASH_ATTN_EXT: { struct ggml_tensor * flash_grad = NULL; if (src0->grad || src1->grad || tensor->src[2]->grad) { @@ -18160,6 +18498,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ n_tasks = n_threads; } break; case GGML_OP_FLASH_ATTN: + case GGML_OP_FLASH_ATTN_EXT: { n_tasks = n_threads; } break; @@ -18563,6 +18902,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } } break; + case GGML_OP_FLASH_ATTN_EXT: + { + const int64_t ne00 = node->src[0]->ne[0]; // D + + cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size + } break; case GGML_OP_FLASH_FF: { if (node->src[1]->type == GGML_TYPE_F32) { diff --git a/ggml.h b/ggml.h index 86e5a8dc5..a11795973 100644 --- a/ggml.h +++ b/ggml.h @@ -475,6 +475,7 @@ extern "C" { GGML_OP_LEAKY_RELU, GGML_OP_FLASH_ATTN, + GGML_OP_FLASH_ATTN_EXT, GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, GGML_OP_SSM_CONV, @@ -1722,6 +1723,25 @@ extern "C" { struct ggml_tensor * v, bool masked); +#define GGML_KQ_MASK_PAD 32 + + // q: [n_embd, n_batch, n_head, 1] + // k: [n_embd, n_kv, n_head_kv, 1] + // v: [n_embd, n_kv, n_head_kv, 1] !! not transposed !! + // mask: [n_kv, n_batch_pad, 1, 1] !! n_batch_pad = GGML_PAD(n_batch, GGML_KQ_MASK_PAD) !! + // res: [n_embd, n_head, n_batch, 1] !! permuted !! + GGML_API struct ggml_tensor * ggml_flash_attn_ext( + struct ggml_context * ctx, + struct ggml_tensor * q, + struct ggml_tensor * k, + struct ggml_tensor * v, + struct ggml_tensor * mask, + float scale); + + GGML_API void ggml_flash_attn_ext_set_prec( + struct ggml_tensor * a, + enum ggml_prec prec); + GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, diff --git a/llama.cpp b/llama.cpp index 72c10ffc2..18d6297ce 100644 --- a/llama.cpp +++ b/llama.cpp @@ -108,7 +108,6 @@ #define LLAMA_MAX_NODES 8192 #define LLAMA_MAX_EXPERTS 60 - // // logging // @@ -1846,7 +1845,7 @@ struct llama_hparams { float f_logit_scale = 0.0f; bool causal_attn = true; - bool need_kq_pos = false; + bool use_alibi = false; // currently, we need KQ_pos data for ALiBi-based models enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; @@ -1936,6 +1935,7 @@ struct llama_cparams { bool embeddings; bool causal_attn; bool offload_kqv; + bool flash_attn; enum llama_pooling_type pooling_type; @@ -2039,8 +2039,8 @@ struct llama_kv_cache { bool has_shift = false; bool do_defrag = false; bool do_copy = false; - // with recurrent state models, a cell can hold the state for more than one past token - bool recurrent = false; + bool recurrent = false; // with recurrent state models, a cell can hold the state for more than one past token + bool v_trans = true; // the value tensor is transposed // Note: The value of head isn't only used to optimize searching // for a free KV slot. llama_decode_internal also uses it, so it @@ -2339,11 +2339,14 @@ struct llama_context { static bool llama_kv_cache_init( struct llama_kv_cache & cache, - const llama_model & model, + const llama_context * ctx, ggml_type type_k, ggml_type type_v, uint32_t kv_size, bool offload) { + const llama_model & model = ctx->model; + const llama_cparams & cparams = ctx->cparams; + const struct llama_hparams & hparams = model.hparams; const uint32_t n_embd_k_gqa = hparams.n_embd_k_gqa() + hparams.n_embd_k_s(); @@ -2354,6 +2357,7 @@ static bool llama_kv_cache_init( // TODO: find a nicer way to add other recurrent model architectures cache.recurrent = model.arch == LLM_ARCH_MAMBA; + cache.v_trans = !cparams.flash_attn; // TODO: support mixed reccurent Transformer architectues // NOTE: (!a || b) is a logical implication (a -> b) @@ -2566,6 +2570,10 @@ static void llama_kv_cache_clear(struct llama_kv_cache & cache) { } cache.head = 0; cache.used = 0; + + for (auto & buf : cache.bufs) { + ggml_backend_buffer_clear(buf, 0); + } } static bool llama_kv_cache_seq_rm( @@ -4194,7 +4202,7 @@ static void llm_load_hparams( model.ftype = ml.ftype; if (hparams.f_max_alibi_bias > 0.0f) { - hparams.need_kq_pos = true; + hparams.use_alibi = true; } hparams.rope_type = llama_rope_type(&model); @@ -6203,37 +6211,47 @@ static struct ggml_tensor * llm_build_inp_embd( static void llm_build_kv_store( struct ggml_context * ctx, const llama_hparams & hparams, + const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, struct ggml_tensor * k_cur, struct ggml_tensor * v_cur, - int64_t n_ctx, int32_t n_tokens, int32_t kv_head, const llm_build_cb & cb, int64_t il) { + const int64_t n_ctx = cparams.n_ctx; + const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); GGML_ASSERT(kv.size == n_ctx); - // compute the transposed [n_tokens, n_embd] V matrix - assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens); - struct ggml_tensor * v_cur_t = ggml_transpose(ctx, v_cur); - cb(v_cur_t, "v_cur_t", il); - struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, kv.k_l[il], n_tokens*n_embd_k_gqa, (ggml_row_size(kv.k_l[il]->type, n_embd_k_gqa))*kv_head); cb(k_cache_view, "k_cache_view", il); - struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa, - ( n_ctx)*ggml_element_size(kv.v_l[il]), - (kv_head)*ggml_element_size(kv.v_l[il])); + // note: storing RoPE-ed version of K in the KV cache + ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view)); + + assert(v_cur->ne[0] == n_embd_v_gqa && v_cur->ne[1] == n_tokens); + + struct ggml_tensor * v_cache_view = nullptr; + + if (cparams.flash_attn) { + v_cache_view = ggml_view_1d(ctx, kv.v_l[il], n_tokens*n_embd_v_gqa, + (kv_head)*ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa)); + } else { + // note: the V cache is transposed when not using flash attention + v_cache_view = ggml_view_2d(ctx, kv.v_l[il], n_tokens, n_embd_v_gqa, + ( n_ctx)*ggml_element_size(kv.v_l[il]), + (kv_head)*ggml_element_size(kv.v_l[il])); + + v_cur = ggml_transpose(ctx, v_cur); + } cb(v_cache_view, "v_cache_view", il); - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(graph, ggml_cpy(ctx, k_cur, k_cache_view)); - ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur_t, v_cache_view)); + ggml_build_forward_expand(graph, ggml_cpy(ctx, v_cur, v_cache_view)); } static struct ggml_tensor * llm_build_norm( @@ -6453,11 +6471,11 @@ static struct ggml_tensor * llm_build_moe_ffn( return moe_out; } -// if max_alibi_bias > 0 then apply ALiBi static struct ggml_tensor * llm_build_kqv( struct ggml_context * ctx, const llama_model & model, const llama_hparams & hparams, + const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, struct ggml_tensor * wo, @@ -6465,12 +6483,12 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, struct ggml_tensor * kq_pos, - int64_t n_ctx, int32_t n_tokens, int32_t n_kv, float kq_scale, const llm_build_cb & cb, int il) { + const int64_t n_ctx = cparams.n_ctx; const int64_t n_head = hparams.n_head; const int64_t n_head_kv = hparams.n_head_kv; const int64_t n_embd_head_k = hparams.n_embd_head_k; @@ -6488,72 +6506,100 @@ static struct ggml_tensor * llm_build_kqv( 0); cb(k, "k", il); - struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); - cb(kq, "kq", il); + struct ggml_tensor * cur; - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { - // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs - // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 - ggml_mul_mat_set_prec(kq, GGML_PREC_F32); - } + if (cparams.flash_attn) { + GGML_UNUSED(model); + GGML_UNUSED(n_ctx); - if (model.arch == LLM_ARCH_GROK) { - // need to do the following: - // multiply by attn_output_multiplyer of 0.08838834764831845 - // and then : - // kq = 30 * tanh(kq / 30) - // before the softmax below + // note: if this assert triggers, then some check has failed earlier + // the idea is to detect during context creation that ALiBi would be used and disable Flash Attention + GGML_ASSERT(kq_pos == nullptr && "ALiBi is not yet supported with Flash Attention"); - //try from phi2 - //ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + // split cached v into n_head heads (not transposed) + struct ggml_tensor * v = + ggml_view_3d(ctx, kv.v_l[il], + n_embd_head_v, n_kv, n_head_kv, + ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa), + ggml_row_size(kv.v_l[il]->type, n_embd_head_k), + 0); + cb(v, "v", il); - kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f)); - kq = ggml_scale(ctx, kq, 30); - } + cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale); + + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); + } + + cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens); + } else { + struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); + cb(kq, "kq", il); + + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs + // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 + ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + } + + if (model.arch == LLM_ARCH_GROK) { + // need to do the following: + // multiply by attn_output_multiplyer of 0.08838834764831845 + // and then : + // kq = 30 * tanh(kq / 30) + // before the softmax below + + //try from phi2 + //ggml_mul_mat_set_prec(kq, GGML_PREC_F32); + + kq = ggml_tanh(ctx, ggml_scale(ctx, kq, 0.08838834764831845f/30.0f)); + kq = ggml_scale(ctx, kq, 30); + } #if defined(GGML_USE_KOMPUTE) #pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") #pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") - if (hparams.f_max_alibi_bias > 0.0f) { - kq = ggml_scale(ctx, kq, kq_scale); - cb(kq, "kq_scaled", il); + if (hparams.use_alibi) { + kq = ggml_scale(ctx, kq, kq_scale); + cb(kq, "kq_scaled", il); - kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias); - cb(kq, "kq_scaled_alibi", il); + kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias); + cb(kq, "kq_scaled_alibi", il); - kq = ggml_add(ctx, kq, kq_mask); - cb(kq, "kq_masked", il); + kq = ggml_add(ctx, kq, kq_mask); + cb(kq, "kq_masked", il); - kq = ggml_soft_max(ctx, kq); - cb(kq, "kq_soft_max", il); - } else + kq = ggml_soft_max(ctx, kq); + cb(kq, "kq_soft_max", il); + } else #endif - { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); + { + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); + } + + GGML_ASSERT(kv.size == n_ctx); + + // split cached v into n_head heads + struct ggml_tensor * v = + ggml_view_3d(ctx, kv.v_l[il], + n_kv, n_embd_head_v, n_head_kv, + ggml_element_size(kv.v_l[il])*n_ctx, + ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v, + 0); + cb(v, "v", il); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq); + cb(kqv, "kqv", il); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); + cb(kqv_merged, "kqv_merged", il); + + cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); + cb(cur, "kqv_merged_cont", il); } - GGML_ASSERT(kv.size == n_ctx); - - // split cached v into n_head heads - struct ggml_tensor * v = - ggml_view_3d(ctx, kv.v_l[il], - n_kv, n_embd_head_v, n_head_kv, - ggml_element_size(kv.v_l[il])*n_ctx, - ggml_element_size(kv.v_l[il])*n_ctx*n_embd_head_v, - 0); - cb(v, "v", il); - - struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq); - cb(kqv, "kqv", il); - - struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); - cb(kqv_merged, "kqv_merged", il); - - struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); - cb(cur, "kqv_merged_cont", il); - ggml_build_forward_expand(graph, cur); cur = ggml_mul_mat(ctx, wo, cur); @@ -6572,6 +6618,7 @@ static struct ggml_tensor * llm_build_kv( struct ggml_context * ctx, const llama_model & model, const llama_hparams & hparams, + const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, struct ggml_tensor * wo, @@ -6581,7 +6628,6 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, struct ggml_tensor * kq_pos, - int64_t n_ctx, int32_t n_tokens, int32_t kv_head, int32_t n_kv, @@ -6595,12 +6641,12 @@ static struct ggml_tensor * llm_build_kv( ggml_build_forward_expand(graph, k_cur); ggml_build_forward_expand(graph, v_cur); - llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il); + llm_build_kv_store(ctx, hparams, cparams, kv, graph, k_cur, v_cur, n_tokens, kv_head, cb, il); struct ggml_tensor * cur; - cur = llm_build_kqv(ctx, model, hparams, kv, graph, wo, wo_b, - q_cur, kq_mask, kq_pos, n_ctx, n_tokens, n_kv, kq_scale, cb, il); + cur = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b, + q_cur, kq_mask, kq_pos, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); return cur; @@ -6642,6 +6688,8 @@ struct llm_build_context { const int32_t kv_head; // index of where we store new KV data in the cache const int32_t n_orig_ctx; + const bool flash_attn; + const enum llama_pooling_type pooling_type; const enum llama_rope_type rope_type; @@ -6688,6 +6736,7 @@ struct llm_build_context { n_outputs (worst_case ? n_tokens : lctx.n_outputs), kv_head (worst_case ? (kv_self.recurrent ? 0 : kv_self.size - n_tokens) : kv_self.head), n_orig_ctx (cparams.n_yarn_orig_ctx), + flash_attn (cparams.flash_attn), pooling_type (cparams.pooling_type), rope_type (hparams.rope_type), cb (cb), @@ -6802,15 +6851,31 @@ struct llm_build_context { ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa*id)); - ggml_tensor * view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self.v_l[il]->type, kv_self.size), - ggml_row_size(kv_self.v_l[il]->type, i)); + ggml_tensor * view_v_src; + ggml_tensor * view_v_dst; - ggml_tensor * view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], - nm, n_embd_v_gqa, - ggml_row_size(kv_self.v_l[il]->type, kv_self.size), - ggml_row_size(kv_self.v_l[il]->type, id)); + if (flash_attn) { + // NOTE: the V cache is not transposed when using flash attention + view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa), + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*i)); + + view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], + n_embd_v_gqa, nm, + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa), + ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*id)); + } else { + view_v_src = ggml_view_2d(ctx0, kv_self.v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(kv_self.v_l[il]->type, kv_self.size), + ggml_row_size(kv_self.v_l[il]->type, i)); + + view_v_dst = ggml_view_2d(ctx0, kv_self.v_l[il], + nm, n_embd_v_gqa, + ggml_row_size(kv_self.v_l[il]->type, kv_self.size), + ggml_row_size(kv_self.v_l[il]->type, id)); + } ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_k_src, view_k_dst)); ggml_build_forward_expand(gf, ggml_cpy(ctx0, view_v_src, view_v_dst)); @@ -6840,20 +6905,26 @@ struct llm_build_context { struct ggml_tensor * build_inp_KQ_mask(bool causal = true) { if (causal) { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, n_tokens); + lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_kv, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); } else { - lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); + lctx.inp_KQ_mask = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, GGML_PAD(n_tokens, GGML_KQ_MASK_PAD)); } cb(lctx.inp_KQ_mask, "KQ_mask", -1); ggml_set_input(lctx.inp_KQ_mask); - return lctx.inp_KQ_mask; + return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask; } - struct ggml_tensor * build_inp_KQ_pos() { - lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); + struct ggml_tensor * build_inp_KQ_pos(bool causal = true) { + if (causal) { + lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); + } else { + // TODO: this will be needed for ALiBi-based BERT models + // https://github.com/ggerganov/llama.cpp/pull/6826 + lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens); + } cb(lctx.inp_KQ_pos, "KQ_pos", -1); ggml_set_input(lctx.inp_KQ_pos); - return lctx.inp_KQ_pos; + return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_pos, GGML_TYPE_F16) : lctx.inp_KQ_pos; } struct ggml_tensor * build_inp_mean() { @@ -6959,9 +7030,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7099,9 +7170,9 @@ struct llm_build_context { cb(Qcur, "Qcur", il); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7206,9 +7277,9 @@ struct llm_build_context { ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7326,9 +7397,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7451,9 +7522,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -7603,9 +7674,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7715,9 +7786,9 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7919,9 +7990,9 @@ struct llm_build_context { ); cb(Vcur, "Vcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Q, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8015,9 +8086,9 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); cb(Qcur, "Qcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8308,9 +8379,9 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8439,14 +8510,15 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } else { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } } @@ -8588,9 +8660,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8706,9 +8778,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8819,9 +8891,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8933,9 +9005,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9088,9 +9160,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9205,9 +9277,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9318,9 +9390,9 @@ struct llm_build_context { ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } struct ggml_tensor * sa_out = cur; @@ -9421,9 +9493,9 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9528,9 +9600,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9644,9 +9716,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9761,9 +9833,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9891,9 +9963,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10012,9 +10084,9 @@ struct llm_build_context { ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -10131,9 +10203,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10421,9 +10493,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10552,9 +10624,9 @@ struct llm_build_context { ); cb(Kcur, "Kcur", il); - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, nullptr, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10981,7 +11053,9 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - if (hparams.need_kq_pos) { + // ALiBi requires the KQ_pos tensor to provide the sequence position of each token in the batch + // this allows to process multiple sequences in parallel with ALiBi-based models + if (hparams.use_alibi) { const int64_t n_kv = kv_self.n; GGML_ASSERT(lctx.inp_KQ_pos); @@ -11363,7 +11437,7 @@ static int llama_decode_internal( // a heuristic, to avoid attending the full cache if it is not yet utilized // after enough generations, the benefit from this heuristic disappears // if we start defragmenting the cache, the benefit from this will be more important - kv_self.n = std::min(kv_self.size, std::max(32u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 32))); + kv_self.n = std::min(kv_self.size, std::max(256u, GGML_PAD(llama_kv_cache_cell_max(kv_self), 256))); //kv_self.n = llama_kv_cache_cell_max(kv_self); } } @@ -11560,7 +11634,9 @@ static void llama_kv_cache_defrag_internal(struct llama_context & lctx) { // each move requires 6*n_layer tensors (see build_defrag) // - source view, destination view, copy operation // - x2 for keys and values - const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer); + //const uint32_t max_moves = LLAMA_MAX_NODES/(6*n_layer); + // TODO: tmp fix https://github.com/ggerganov/llama.cpp/issues/6685#issuecomment-2057579516 + const uint32_t max_moves = (LLAMA_MAX_NODES - 2*n_layer)/(6*n_layer); // determine which KV cells to move where // @@ -15167,6 +15243,7 @@ struct llama_context_params llama_context_default_params() { /*.logits_all =*/ false, /*.embeddings =*/ false, /*.offload_kqv =*/ true, + /*.flash_attn =*/ false, /*.abort_callback =*/ nullptr, /*.abort_callback_data =*/ nullptr, }; @@ -15333,6 +15410,7 @@ struct llama_context * llama_new_context_with_model( cparams.defrag_thold = params.defrag_thold; cparams.embeddings = params.embeddings; cparams.offload_kqv = params.offload_kqv; + cparams.flash_attn = params.flash_attn; cparams.pooling_type = params.pooling_type; cparams.n_ctx = params.n_ctx == 0 ? hparams.n_ctx_train : params.n_ctx; @@ -15340,12 +15418,20 @@ struct llama_context * llama_new_context_with_model( cparams.rope_freq_scale = params.rope_freq_scale == 0.0f ? hparams.rope_freq_scale_train : params.rope_freq_scale; // this is necessary due to kv_self.n being padded later during inference - cparams.n_ctx = GGML_PAD(cparams.n_ctx, 32); + cparams.n_ctx = GGML_PAD(cparams.n_ctx, 256); // with causal attention, the batch size is limited by the context size cparams.n_batch = hparams.causal_attn ? std::min(cparams.n_ctx, params.n_batch) : params.n_batch; - cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); + // the batch has to be at least GGML_KQ_MASK_PAD because we will be padding the KQ_mask + // this is required by GPU kernels in order to avoid out-of-bounds accesses (e.g. ggml_flash_attn_ext) + // ref: https://github.com/ggerganov/llama.cpp/pull/5021 + if (cparams.n_batch < GGML_KQ_MASK_PAD) { + LLAMA_LOG_WARN("%s: n_batch is less than GGML_KQ_MASK_PAD - increasing to %d\n", __func__, GGML_KQ_MASK_PAD); + cparams.n_batch = GGML_KQ_MASK_PAD; + } + + cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch); cparams.n_yarn_orig_ctx = params.yarn_orig_ctx != 0 ? params.yarn_orig_ctx : hparams.n_yarn_orig_ctx != 0 ? hparams.n_yarn_orig_ctx : @@ -15377,6 +15463,23 @@ struct llama_context * llama_new_context_with_model( } } + if (cparams.flash_attn && hparams.use_alibi) { + LLAMA_LOG_WARN("%s: flash_attn is not yet compatible with ALiBi - forcing off\n", __func__); + cparams.flash_attn = false; + } + + if (cparams.flash_attn && model->arch == LLM_ARCH_GROK) { + LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); + cparams.flash_attn = false; + } + +#ifdef GGML_USE_HIPBLAS + if (cparams.flash_attn) { + LLAMA_LOG_WARN("%s: flash_attn is not yet compatible with HIPBLAS builds - forcing off\n", __func__); + cparams.flash_attn = false; + } +#endif + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } @@ -15384,6 +15487,7 @@ struct llama_context * llama_new_context_with_model( LLAMA_LOG_INFO("%s: n_ctx = %u\n", __func__, cparams.n_ctx); LLAMA_LOG_INFO("%s: n_batch = %u\n", __func__, cparams.n_batch); LLAMA_LOG_INFO("%s: n_ubatch = %u\n", __func__, cparams.n_ubatch); + LLAMA_LOG_INFO("%s: flash_attn = %d\n", __func__, cparams.flash_attn); LLAMA_LOG_INFO("%s: freq_base = %.1f\n", __func__, cparams.rope_freq_base); LLAMA_LOG_INFO("%s: freq_scale = %g\n", __func__, cparams.rope_freq_scale); @@ -15512,7 +15616,7 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(ctx->backend_cpu); - if (!llama_kv_cache_init(ctx->kv_self, ctx->model, type_k, type_v, kv_size, cparams.offload_kqv)) { + if (!llama_kv_cache_init(ctx->kv_self, ctx, type_k, type_v, kv_size, cparams.offload_kqv)) { LLAMA_LOG_ERROR("%s: llama_kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; @@ -16111,6 +16215,7 @@ size_t llama_state_get_size(const struct llama_context * ctx) { const size_t s_kv_head = sizeof(uint32_t); const size_t s_kv_size = sizeof(uint32_t); const size_t s_kv_used = sizeof(uint32_t); + const size_t s_v_trans = sizeof(uint32_t); const size_t s_kv = ctx->kv_self.total_size(); const size_t s_kv_cell = sizeof(llama_pos) + sizeof(size_t) + cparams.n_seq_max*sizeof(llama_seq_id); const size_t s_kv_cells = ctx->kv_self.size * s_kv_cell; @@ -16128,10 +16233,14 @@ size_t llama_state_get_size(const struct llama_context * ctx) { + s_kv_head + s_kv_size + s_kv_used + + s_v_trans + s_kv + s_kv_cells ); + // on session change it is very likely that the state size has changed - so we need to update this function + static_assert(LLAMA_SESSION_VERSION == 6, "So you just bumped the session version - good. But did you remember to update llama_state_get_size?"); + return s_total; } @@ -16277,11 +16386,13 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data const uint32_t kv_size = kv_self.size; const size_t kv_buf_size = kv_self.total_size() / (kv_size ? kv_size : 1) * kv_head; const uint32_t kv_used = kv_self.used; + const uint32_t v_trans = kv_self.v_trans ? 1 : 0; data_ctx->write(&kv_buf_size, sizeof(kv_buf_size)); data_ctx->write(&kv_head, sizeof(kv_head)); data_ctx->write(&kv_size, sizeof(kv_size)); data_ctx->write(&kv_used, sizeof(kv_used)); + data_ctx->write(&v_trans, sizeof(v_trans)); if (kv_buf_size) { const size_t pre_kv_buf_size = data_ctx->get_size_written(); @@ -16294,7 +16405,7 @@ static void llama_state_get_data_internal(struct llama_context * ctx, llama_data ggml_backend_tensor_get(kv_self.k_l[il], tmp_buf.data(), 0, tmp_buf.size()); data_ctx->write(tmp_buf.data(), tmp_buf.size()); - if (kv_self.recurrent) { + if (kv_self.recurrent || !kv_self.v_trans) { // v is contiguous for recurrent models // TODO: use other tensors for state models than k and v const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); @@ -16427,11 +16538,15 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { uint32_t kv_head; uint32_t kv_size; uint32_t kv_used; + uint32_t v_trans; memcpy(&kv_buf_size, inp, sizeof(kv_buf_size)); inp += sizeof(kv_buf_size); memcpy(&kv_head, inp, sizeof(kv_head)); inp += sizeof(kv_head); memcpy(&kv_size, inp, sizeof(kv_size)); inp += sizeof(kv_size); memcpy(&kv_used, inp, sizeof(kv_used)); inp += sizeof(kv_used); + memcpy(&v_trans, inp, sizeof(v_trans)); inp += sizeof(v_trans); + + GGML_ASSERT(kv_self.v_trans == (bool) v_trans); // incompatible V transposition if (kv_self.size != kv_size) { // the KV cache needs to be big enough to load all the KV cells from the saved state @@ -16441,6 +16556,8 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { __func__, kv_head, kv_size, kv_self.size); } + llama_kv_cache_clear(ctx); + if (kv_buf_size) { const size_t pre_kv_buf_size = inp - src; @@ -16452,7 +16569,7 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { ggml_backend_tensor_set(kv_self.k_l[il], inp, 0, k_size); inp += k_size; - if (kv_self.recurrent) { + if (kv_self.recurrent || !kv_self.v_trans) { // v is contiguous for recurrent models // TODO: use other tensors for state models than k and v const size_t v_size = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa*kv_head); @@ -16474,8 +16591,6 @@ size_t llama_state_set_data(struct llama_context * ctx, const uint8_t * src) { GGML_ASSERT(kv_buf_size == inp - src - pre_kv_buf_size); } - llama_kv_cache_clear(ctx); - ctx->kv_self.head = kv_head; ctx->kv_self.used = kv_used; @@ -16735,28 +16850,49 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam } } - // For the values, they are transposed, so we also need the element size and get the element ranges from each row - const uint32_t kv_size = kv_self.size; - for (int il = 0; il < (int)n_layer; ++il) { - // Write value type - const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; - data_ctx.write(&v_type_i, sizeof(v_type_i)); + // TODO: simplify, reduce copy-paste + if (!kv_self.v_trans) { + for (int il = 0; il < (int)n_layer; ++il) { + // Write value type + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + data_ctx.write(&v_type_i, sizeof(v_type_i)); - // Write element size - const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); - data_ctx.write(&v_size_el, sizeof(v_size_el)); + // Write row size of value + const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa); + data_ctx.write(&v_size_row, sizeof(v_size_row)); - // For each row, we get the element values of each cell - for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - // Read each range of cells of v_size_el length each into tmp_buf and write out + // Read each range of cells of v_size length each into tmp_buf and write out for (const auto & range : cell_ranges) { const size_t range_size = range.second - range.first; - const size_t src_offset = (range.first + j * kv_size) * v_size_el; - tmp_buf.resize(range_size * v_size_el); - ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size()); + tmp_buf.resize(range_size * v_size_row); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), range.first * v_size_row, range_size * v_size_row); data_ctx.write(tmp_buf.data(), tmp_buf.size()); } } + } else { + // For the values, they are transposed, so we also need the element size and get the element ranges from each row + const uint32_t kv_size = kv_self.size; + for (int il = 0; il < (int)n_layer; ++il) { + // Write value type + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + data_ctx.write(&v_type_i, sizeof(v_type_i)); + + // Write element size + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + data_ctx.write(&v_size_el, sizeof(v_size_el)); + + // For each row, we get the element values of each cell + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + // Read each range of cells of v_size_el length each into tmp_buf and write out + for (const auto & range : cell_ranges) { + const size_t range_size = range.second - range.first; + const size_t src_offset = (range.first + j * kv_size) * v_size_el; + tmp_buf.resize(range_size * v_size_el); + ggml_backend_tensor_get(kv_self.v_l[il], tmp_buf.data(), src_offset, tmp_buf.size()); + data_ctx.write(tmp_buf.data(), tmp_buf.size()); + } + } + } } return data_ctx.get_size_written(); @@ -16881,41 +17017,75 @@ size_t llama_state_seq_set_data(struct llama_context * ctx, const uint8_t * src, } } - // For each layer, read the values for each cell (transposed) - for (int il = 0; il < (int)n_layer; ++il) { - // Read type of value - int32_t v_type_i_ref; - memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); - inp += sizeof(v_type_i_ref); - const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; - if (v_type_i != v_type_i_ref) { - llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); - LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); - return 0; - } + // TODO: simplify, reduce copy-paste + if (!kv_self.v_trans) { + for (int il = 0; il < (int)n_layer; ++il) { + // Read type of value + int32_t v_type_i_ref; + memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); + inp += sizeof(v_type_i_ref); + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + if (v_type_i != v_type_i_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return 0; + } - // Read element size of value - size_t v_size_el_ref; - memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref)); - inp += sizeof(v_size_el_ref); - const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); - if (v_size_el != v_size_el_ref) { - llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); - LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il); - return 0; - } + // Read row size of value + size_t v_size_row_ref; + memcpy(&v_size_row_ref, inp, sizeof(v_size_row_ref)); + inp += sizeof(v_size_row_ref); + const size_t v_size_row = ggml_row_size(kv_self.v_l[il]->type, n_embd_v_gqa); + if (v_size_row != v_size_row_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value row size (%zu != %zu, layer %d)\n", __func__, v_size_row, v_size_row_ref, il); + return 0; + } - if (cell_count) { - // For each row in the transposed matrix, read the values for the whole cell range - for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { - const size_t dst_offset = (kv_head + j * kv_size) * v_size_el; - ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el); - inp += cell_count * v_size_el; + if (cell_count) { + // Read and set the values for the whole cell range + ggml_backend_tensor_set(kv_self.v_l[il], inp, kv_head * v_size_row, cell_count * v_size_row); + inp += cell_count * v_size_row; + } + } + } else { + // For each layer, read the values for each cell (transposed) + for (int il = 0; il < (int)n_layer; ++il) { + // Read type of value + int32_t v_type_i_ref; + memcpy(&v_type_i_ref, inp, sizeof(v_type_i_ref)); + inp += sizeof(v_type_i_ref); + const int32_t v_type_i = (int32_t)kv_self.v_l[il]->type; + if (v_type_i != v_type_i_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value type (%d != %d, layer %d)\n", __func__, v_type_i, v_type_i_ref, il); + return 0; + } + + // Read element size of value + size_t v_size_el_ref; + memcpy(&v_size_el_ref, inp, sizeof(v_size_el_ref)); + inp += sizeof(v_size_el_ref); + const size_t v_size_el = ggml_type_size(kv_self.v_l[il]->type); + if (v_size_el != v_size_el_ref) { + llama_kv_cache_seq_rm(kv_self, dest_seq_id, -1, -1); + LLAMA_LOG_ERROR("%s: mismatched value element size (%zu != %zu, layer %d)\n", __func__, v_size_el, v_size_el_ref, il); + return 0; + } + + if (cell_count) { + // For each row in the transposed matrix, read the values for the whole cell range + for (uint32_t j = 0; j < n_embd_v_gqa; ++j) { + const size_t dst_offset = (kv_head + j * kv_size) * v_size_el; + ggml_backend_tensor_set(kv_self.v_l[il], inp, dst_offset, cell_count * v_size_el); + inp += cell_count * v_size_el; + } } } } const size_t nread = inp - src; + return nread; } diff --git a/llama.h b/llama.h index 30835de5f..059d78f11 100644 --- a/llama.h +++ b/llama.h @@ -40,7 +40,7 @@ #define LLAMA_FILE_MAGIC_GGSQ 0x67677371u // 'ggsq' #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN -#define LLAMA_SESSION_VERSION 5 +#define LLAMA_SESSION_VERSION 6 #define LLAMA_STATE_SEQ_MAGIC LLAMA_FILE_MAGIC_GGSQ #define LLAMA_STATE_SEQ_VERSION 1 @@ -287,6 +287,7 @@ extern "C" { bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embeddings; // if true, extract embeddings (together with logits) bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU + bool flash_attn; // whether to use flash attention // Abort callback // if it returns true, execution of llama_decode() will be aborted @@ -542,7 +543,7 @@ extern "C" { // Returns the number of used KV cells (i.e. have at least one sequence assigned to them) LLAMA_API int32_t llama_get_kv_cache_used_cells(const struct llama_context * ctx); - // Clear the KV cache + // Clear the KV cache - both cell info is erased and KV data is zeroed LLAMA_API void llama_kv_cache_clear( struct llama_context * ctx); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 02daad24b..b27c1291e 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1090,6 +1090,12 @@ struct test_soft_max : public test_case { return VARS_TO_STR5(type, ne, mask, scale, max_bias); } + // the 1024 test with bias occasionally fails: + // SOFT_MAX(type=f32,ne=[1024,16,1,1],mask=1,scale=1.000000,max_bias=8.000000): [SOFT_MAX] NMSE = 0.000000103 > 0.000000100 FAIL + virtual double max_nmse_err() override { + return 1e-6; + } + test_soft_max(ggml_type type = GGML_TYPE_F32, std::array ne = {10, 10, 10, 10}, bool mask = false, @@ -1101,7 +1107,7 @@ struct test_soft_max : public test_case { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_tensor * mask = nullptr; if (this->mask) { - mask = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]); + mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]); } ggml_tensor * pos = nullptr; if (max_bias > 0.0f) { @@ -1475,6 +1481,34 @@ struct test_leaky_relu : public test_case { } }; +// GGML_OP_FLASH_ATTN_EXT +struct test_flash_attn_ext : public test_case { + const int64_t hs; // head size + const int64_t nh; // num heads + const int64_t kv; // kv size + const int64_t nb; // batch size + + std::string vars() override { + return VARS_TO_STR4(hs, nh, kv, nb); + } + + double max_nmse_err() override { + return 5e-4; + } + + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8) + : hs(hs), nh(nh), kv(kv), nb(nb) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1); + ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); + ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); + ggml_tensor * mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1); + ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs)); + return out; + } +}; + enum llm_norm_type { LLM_NORM, LLM_NORM_RMS, @@ -1661,7 +1695,7 @@ struct test_llama : public test_llm { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1); ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); @@ -1783,7 +1817,7 @@ struct test_falcon : public test_llm { struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F16, hp.n_kv, hp.n_tokens, 1); ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); @@ -2095,7 +2129,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op for (float scale : {1.0f, 0.1f}) { for (int64_t ne0 : {16, 1024}) { for (int64_t ne1 : {16, 1024}) { - test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0, ne1, 1, 1}, mask, scale, max_bias)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {ne0-1, ne1-1, 1, 1}, mask, scale, max_bias)); } } @@ -2139,6 +2173,16 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); + for (int hs : { 64, 80, 128, 256, }) { + for (int nh : { 32, }) { + for (int kv : { 512, 1024, }) { + for (int nb : { 1, 2, 4, 8, }) { + test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb)); + } + } + } + } + // these tests are disabled to save execution time, but they can be handy for debugging #if 0 test_cases.emplace_back(new test_llama(1)); From a68a1e7ed060ee5af2d638585d19e3510ddbf16c Mon Sep 17 00:00:00 2001 From: Kevin Gibbons Date: Tue, 30 Apr 2024 02:34:50 -0700 Subject: [PATCH 010/100] metal : log more info on error (#6987) --- ggml-metal.m | 39 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index c6d580b84..cf3aed488 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2779,6 +2779,45 @@ static enum ggml_status ggml_metal_graph_compute( MTLCommandBufferStatus status = [command_buffer status]; if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); + if (status == MTLCommandBufferStatusError) { + MTLCommandBufferError error_code = [command_buffer error].code; + switch (error_code) { + case MTLCommandBufferErrorNone: + GGML_METAL_LOG_INFO("no error code reported\n"); + break; + case MTLCommandBufferErrorTimeout: + GGML_METAL_LOG_INFO("timeout\n"); + break; + case MTLCommandBufferErrorPageFault: + GGML_METAL_LOG_INFO("unserviceable page fault\n"); + break; + case MTLCommandBufferErrorOutOfMemory: + GGML_METAL_LOG_INFO("out of memory\n"); + break; + case MTLCommandBufferErrorInvalidResource: + GGML_METAL_LOG_INFO("invalid reference to resource\n"); + break; + case MTLCommandBufferErrorMemoryless: + GGML_METAL_LOG_INFO("GPU ran out of one or more of its internal resources that support memoryless render pass attachments\n"); + break; + case MTLCommandBufferErrorDeviceRemoved: + GGML_METAL_LOG_INFO("device removed\n"); + break; + case MTLCommandBufferErrorStackOverflow: + GGML_METAL_LOG_INFO("kernel function of tile shader used too many stack frames\n"); + break; + case MTLCommandBufferErrorAccessRevoked: + GGML_METAL_LOG_INFO("access to device revoked by system\n"); + break; + case MTLCommandBufferErrorInternal: + GGML_METAL_LOG_INFO("internal error\n"); + break; + default: + GGML_METAL_LOG_INFO("unknown error %lu\n", error_code); + break; + } + } + return GGML_STATUS_FAILED; } } From 77e15bec6217a39be59b9cc83d6b9afb6b0d8167 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Apr 2024 15:52:21 +0300 Subject: [PATCH 011/100] metal : remove deprecated error code (#7008) --- ggml-metal.m | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index cf3aed488..0ebe7163e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2800,9 +2800,9 @@ static enum ggml_status ggml_metal_graph_compute( case MTLCommandBufferErrorMemoryless: GGML_METAL_LOG_INFO("GPU ran out of one or more of its internal resources that support memoryless render pass attachments\n"); break; - case MTLCommandBufferErrorDeviceRemoved: - GGML_METAL_LOG_INFO("device removed\n"); - break; + //case MTLCommandBufferErrorDeviceRemoved: + // GGML_METAL_LOG_INFO("device removed\n"); + // break; case MTLCommandBufferErrorStackOverflow: GGML_METAL_LOG_INFO("kernel function of tile shader used too many stack frames\n"); break; From f364eb6fb5d46118a76fa045f487318de4c24961 Mon Sep 17 00:00:00 2001 From: Kevin Gibbons Date: Tue, 30 Apr 2024 08:14:02 -0700 Subject: [PATCH 012/100] switch to using localizedDescription (#7010) --- ggml-metal.m | 38 ++------------------------------------ 1 file changed, 2 insertions(+), 36 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 0ebe7163e..017b72ce9 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2780,42 +2780,8 @@ static enum ggml_status ggml_metal_graph_compute( if (status != MTLCommandBufferStatusCompleted) { GGML_METAL_LOG_INFO("%s: command buffer %d failed with status %lu\n", __func__, i, status); if (status == MTLCommandBufferStatusError) { - MTLCommandBufferError error_code = [command_buffer error].code; - switch (error_code) { - case MTLCommandBufferErrorNone: - GGML_METAL_LOG_INFO("no error code reported\n"); - break; - case MTLCommandBufferErrorTimeout: - GGML_METAL_LOG_INFO("timeout\n"); - break; - case MTLCommandBufferErrorPageFault: - GGML_METAL_LOG_INFO("unserviceable page fault\n"); - break; - case MTLCommandBufferErrorOutOfMemory: - GGML_METAL_LOG_INFO("out of memory\n"); - break; - case MTLCommandBufferErrorInvalidResource: - GGML_METAL_LOG_INFO("invalid reference to resource\n"); - break; - case MTLCommandBufferErrorMemoryless: - GGML_METAL_LOG_INFO("GPU ran out of one or more of its internal resources that support memoryless render pass attachments\n"); - break; - //case MTLCommandBufferErrorDeviceRemoved: - // GGML_METAL_LOG_INFO("device removed\n"); - // break; - case MTLCommandBufferErrorStackOverflow: - GGML_METAL_LOG_INFO("kernel function of tile shader used too many stack frames\n"); - break; - case MTLCommandBufferErrorAccessRevoked: - GGML_METAL_LOG_INFO("access to device revoked by system\n"); - break; - case MTLCommandBufferErrorInternal: - GGML_METAL_LOG_INFO("internal error\n"); - break; - default: - GGML_METAL_LOG_INFO("unknown error %lu\n", error_code); - break; - } + NSString * error_code = [command_buffer error].localizedDescription; + GGML_METAL_LOG_INFO("error: %s\n", [error_code UTF8String]); } return GGML_STATUS_FAILED; From a8f9b076316e16aadd0791015b3bfd446fe1e904 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 30 Apr 2024 23:36:27 +0200 Subject: [PATCH 013/100] perplexity: more statistics, added documentation (#6936) * perplexity: more statistics, added documentation * add LLaMA 3 8b scoreboard --- common/common.h | 2 +- examples/perplexity/README.md | 118 ++++++++++++++- examples/perplexity/perplexity.cpp | 230 ++++++++++++++++++++++------- 3 files changed, 291 insertions(+), 59 deletions(-) diff --git a/common/common.h b/common/common.h index 0618eea74..9252a4b63 100644 --- a/common/common.h +++ b/common/common.h @@ -135,7 +135,7 @@ struct gpt_params { bool multiple_choice = false; // compute TruthfulQA score over random tasks from datafile supplied in prompt size_t multiple_choice_tasks = 0; // number of tasks to use when computing the TruthfulQA score. If 0, all tasks will be computed - bool kl_divergence = false; // compute KL-divergence + bool kl_divergence = false; // compute KL divergence bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index 1a8c0dd64..c5e2bc5de 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -1,8 +1,118 @@ -# perplexity +# Perplexity -TODO +The `perplexity` example can be used to calculate the so-called perplexity value of a language model over a given text corpus. +Perplexity measures how well the model can predict the next token with lower values being better. +Note that perplexity is **not** directly comparable between models, especially if they use different tokenizers. +Also note that finetunes typically result in a higher perplexity value even though the human-rated quality of outputs increases. + +Within llama.cpp the perplexity of base models is used primarily to judge the quality loss from e.g. quantized models vs. FP16. +The convention among contributors is to use the Wikitext-2 test set for testing unless noted otherwise (can be obtained with `scripts/get-wikitext-2.sh`). + +By default only the mean perplexity value and the corresponding uncertainty is calculated. +The uncertainty is determined empirically by assuming a Gaussian distribution of the "correct" logits per and then applying error propagation. + +More statistics can be obtained by recording the logits from the FP16 version of a model. +To do this, supply `perplexity` with `--kl-divergence-base path/to/logit/binary/file.kld`. +The program will then record all logits and save them to the provided path in binary format. +**The logit file will be very large, 11 GiB for LLaMA 2 or 37 GiB for LLaMA 3 when using the Wikitext-2 test set.** +Once you have the file, supply `perplexity` with the quantized model, the logits file via `--kl-divergence-base`, +and finally the `--kl-divergence` argument to indicate that the program should calculate the so-called Kullback-Leibler divergence. +This is a measure of how similar the FP16 and the quantized logit distributions are with a value of 0 indicating that the distribution are the same. +The uncertainty on the mean KL divergence is calculated by assuming the KL divergence per token follows a Gaussian distribution. + +In addition to the KL divergence the following statistics are calculated with `--kl-divergence`: + +* Ratio of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. The logarithm of this metric is also calculated and printed, it is 0 if the logit distributions are the same. +* Difference of mean FP16 PPL and quantized PPL. Uncertainty is estimated on logits, then propagated. +* Mean change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. +* Pearson correlation coefficient of the "correct" token probabilites between models. +* Percentiles of change in "correct" token probability. Positive values mean the model gets better at prediction, negative values mean it gets worse. Can be used to judge noise vs. quality loss from quantization. If the percentiles are symmetric then the quantization is essentially just adding noise. If the negative values are significantly larger than the positive values then this indicates that the model is actually becoming worse from the quantization. +* The root mean square of the change in token probabilities. If you were to assume that the quantization simply causes Gaussian noise on the token probabilities then this would be the standard deviation of said noise. The uncertainty on the value is calculated that the change in token probabilities follows a Gaussian distribution. Related discussion: https://github.com/ggerganov/llama.cpp/discussions/2875 . +* Same top p: Percentage of how often the token was assigned the highest probabilites by both models. The uncertainty is calculated from the Gaussian approximation of the binomial distribution. + +## LLaMA 3 8b Scoreboard + +Results are sorted by Kullback-Leibler divergence relative to FP16. +The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat). + +| Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp | +|--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------| +| f16 | None | 14.97 | 6.233160 ± 0.037828 | - | - | - | - | +| q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % | +| q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % | +| q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % | +| q5_K_S | None | 5.21 | 6.336598 ± 0.038755 | 0.104964 ± 0.003331 | 0.016595 ± 0.000122 | -0.223 ± 0.010 % | 3.918 ± 0.036 % | +| q5_1 | None | 5.65 | 6.337857 ± 0.038677 | 0.106223 ± 0.003476 | 0.018045 ± 0.000139 | -0.287 ± 0.011 % | 4.123 ± 0.039 % | +| q5_0 | None | 5.21 | 6.363224 ± 0.038861 | 0.131591 ± 0.003894 | 0.022239 ± 0.000166 | -0.416 ± 0.012 % | 4.634 ± 0.043 % | +| q4_K_M | WT 10m | 4.58 | 6.382937 ± 0.039055 | 0.151303 ± 0.004429 | 0.028152 ± 0.000240 | -0.389 ± 0.014 % | 5.251 ± 0.049 % | +| q4_K_M | None | 4.58 | 6.407115 ± 0.039119 | 0.175482 ± 0.004620 | 0.031273 ± 0.000238 | -0.596 ± 0.014 % | 5.519 ± 0.050 % | +| q4_K_S | WT 10m | 4.37 | 6.409697 ± 0.039189 | 0.178064 ± 0.004744 | 0.031951 ± 0.000259 | -0.531 ± 0.015 % | 5.645 ± 0.051 % | +| iq4_NL | WT 10m | 4.35 | 6.455593 ± 0.039630 | 0.223959 ± 0.005201 | 0.035742 ± 0.000288 | -0.590 ± 0.016 % | 5.998 ± 0.054 % | +| iq4_XS | WT 10m | 4.14 | 6.459705 ± 0.039595 | 0.228071 ± 0.005207 | 0.036334 ± 0.000284 | -0.668 ± 0.016 % | 6.044 ± 0.054 % | +| q4_K_S | None | 4.37 | 6.500529 ± 0.039778 | 0.268895 ± 0.005638 | 0.043136 ± 0.000314 | -0.927 ± 0.017 % | 6.562 ± 0.055 % | +| q4_1 | None | 4.78 | 6.682737 ± 0.041285 | 0.451103 ± 0.008030 | 0.071683 ± 0.000505 | -0.927 ± 0.017 % | 8.512 ± 0.063 % | +| q4_0 | None | 4.34 | 6.700147 ± 0.041226 | 0.468514 ± 0.007951 | 0.071940 ± 0.000491 | -1.588 ± 0.022 % | 8.434 ± 0.061 % | +| q3_K_L | WT 10m | 4.03 | 6.671223 ± 0.041427 | 0.439590 ± 0.008154 | 0.073077 ± 0.000529 | -0.940 ± 0.023 % | 8.662 ± 0.064 % | +| q3_K_M | WT 10m | 3.74 | 6.734255 ± 0.041838 | 0.502622 ± 0.008901 | 0.084358 ± 0.000588 | -1.198 ± 0.024 % | 9.292 ± 0.065 % | +| q3_K_L | None | 4.03 | 6.787876 ± 0.042104 | 0.556242 ± 0.009171 | 0.087176 ± 0.000614 | -1.532 ± 0.025 % | 9.432 ± 0.067 % | +| q3_K_M | None | 3.74 | 6.888498 ± 0.042669 | 0.656864 ± 0.010071 | 0.101913 ± 0.000677 | -1.990 ± 0.026 % | 10.203 ± 0.068 % | +| iq3_M | WT 10m | 3.53 | 6.898327 ± 0.041643 | 0.666694 ± 0.009449 | 0.102534 ± 0.000663 | -3.178 ± 0.026 % | 10.513 ± 0.066 % | +| iq3_S | WT 10m | 3.42 | 6.965501 ± 0.042406 | 0.733867 ± 0.010245 | 0.111278 ± 0.000710 | -3.066 ± 0.027 % | 10.845 ± 0.068 % | +| iq3_XS | WT 10m | 3.28 | 7.163043 ± 0.043772 | 0.931409 ± 0.012084 | 0.138693 ± 0.000857 | -3.667 ± 0.031 % | 12.148 ± 0.070 % | +| iq3_XXS | WT 10m | 3.05 | 7.458436 ± 0.046404 | 1.226803 ± 0.015234 | 0.183625 ± 0.001042 | -3.918 ± 0.035 % | 13.836 ± 0.074 % | +| q3_K_S | WT 10m | 3.41 | 7.602878 ± 0.046848 | 1.371244 ± 0.015688 | 0.199821 ± 0.001008 | -5.046 ± 0.037 % | 14.980 ± 0.070 % | +| q3_K_S | None | 3.41 | 7.863786 ± 0.048885 | 1.632152 ± 0.017733 | 0.228217 ± 0.001079 | -5.604 ± 0.038 % | 15.541 ± 0.070 % | +| iq2_M | WT 10m | 2.74 | 8.600799 ± 0.055124 | 2.369166 ± 0.025244 | 0.325989 ± 0.00160 | -6.463 ± 0.046 % | 18.519 ± 0.080 % | +| q2_K | WT 10k | 2.96 | 8.652290 ± 0.055572 | 2.420657 ± 0.025587 | 0.331393 ± 0.001562 | -6.606 ± 0.046 % | 18.790 ± 0.078 % | +| q2_K | WT 100k | 2.96 | 8.641993 ± 0.055406 | 2.410359 ± 0.025495 | 0.331672 ± 0.001569 | -6.628 ± 0.047 % | 18.856 ± 0.078 % | +| q2_K | WT 10m | 2.96 | 8.647825 ± 0.055610 | 2.416191 ± 0.025683 | 0.332223 ± 0.001572 | -6.500 ± 0.047 % | 18.881 ± 0.078 % | +| q2_K | WT 1m | 2.96 | 8.674365 ± 0.055743 | 2.442732 ± 0.025843 | 0.335308 ± 0.001576 | -6.634 ± 0.047 % | 19.009 ± 0.079 % | +| q2_K | WT 1k | 2.96 | 8.682605 ± 0.055916 | 2.450972 ± 0.026069 | 0.337093 ± 0.001596 | -6.596 ± 0.047 % | 18.977 ± 0.079 % | +| q2_K_S | WT 10m | 2.96 | 9.323778 ± 0.061551 | 3.092145 ± 0.031914 | 0.403360 ± 0.001787 | -7.131 ± 0.049 % | 20.050 ± 0.081 % | +| q2_K_S | WT 1m | 2.96 | 9.329321 ± 0.061378 | 3.097688 ± 0.031816 | 0.403590 ± 0.001797 | -7.289 ± 0.049 % | 20.123 ± 0.081 % | +| q2_K_S | WT 100k | 2.96 | 9.362973 ± 0.061740 | 3.131339 ± 0.032169 | 0.408367 ± 0.001802 | -7.198 ± 0.050 % | 20.132 ± 0.081 % | +| q2_K_S | WT 10k | 2.96 | 9.376479 ± 0.062045 | 3.144846 ± 0.032464 | 0.408662 ± 0.001819 | -7.141 ± 0.050 % | 20.120 ± 0.081 % | +| q2_K_S | WT 1k | 2.96 | 9.415200 ± 0.062475 | 3.183567 ± 0.032993 | 0.415865 ± 0.001846 | -7.153 ± 0.050 % | 20.311 ± 0.082 % | +| iq2_S | WT 10m | 2.56 | 9.650781 ± 0.063209 | 3.419148 ± 0.034017 | 0.439197 ± 0.001976 | -8.319 ± 0.052 % | 21.491 ± 0.083 % | +| q2_K | None | 2.96 | 9.751568 ± 0.063312 | 3.519934 ± 0.033863 | 0.445132 ± 0.001835 | -9.123 ± 0.051 % | 21.421 ± 0.079 % | +| iq2_XS | WT 10m | 2.43 | 10.761424 ± 0.071056 | 4.529791 ± 0.042229 | 0.546290 ± 0.002133 | -10.576 ± 0.056 % | 23.872 ± 0.082 % | +| iq2_XXS | WT 10m | 2.24 | 14.091782 ± 0.098396 | 7.860148 ± 0.070752 | 0.812022 ± 0.002741 | -14.363 ± 0.065 % | 28.576 ± 0.084 % | +| iq1_M | WT 10m | 2.01 | 25.493722 ± 0.177903 | 19.262089 ± 0.152396 | 1.393084 ± 0.003529 | -24.672 ± 0.077 % | 38.287 ± 0.084 % | +| iq1_S | WT 1m | 1.88 | 58.097760 ± 0.438604 | 51.866126 ± 0.416604 | 2.211278 ± 0.004688 | -32.471 ± 0.087 % | 46.418 ± 0.085 % | +| iq1_S | WT 1k | 1.88 | 58.267851 ± 0.446208 | 52.036218 ± 0.424373 | 2.214858 ± 0.004778 | -31.880 ± 0.089 % | 46.330 ± 0.086 % | +| iq1_S | WT 100k | 1.88 | 58.581498 ± 0.453145 | 52.349864 ± 0.431360 | 2.220834 ± 0.004818 | -32.261 ± 0.089 % | 46.002 ± 0.086 % | +| iq1_S | WT 10m | 1.88 | 60.694593 ± 0.471290 | 54.462959 ± 0.449644 | 2.254554 ± 0.004868 | -31.973 ± 0.088 % | 46.271 ± 0.086 % | +| iq1_S | WT 10k | 1.88 | 63.221324 ± 0.493077 | 56.989691 ± 0.471423 | 2.293527 ± 0.004885 | -32.261 ± 0.089 % | 46.562 ± 0.086 % | + +There seems to be no consistent improvement from using more Wikitext tokens for the importance matrix. +K-quants score better on mean Δp than the legacy quants than e.g. KL divergence would suggest. + +## LLaMA 2 vs. LLaMA 3 Quantization comparison + +| Metric | L2 7b q2_K | L3 8b q2_K | L2 7b q4_K_M | L3 8b q4_K_M | L2 7b q6_K | L3 8b q6_K | L2 7b q8_0 | L3 8b q8_0 | +|-----------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------|---------------------| +| Mean PPL | 5.794552 ± 0.032298 | 9.751568 ± 0.063312 | 5.877078 ± 0.032781 | 6.407115 ± 0.039119 | 5.808494 ± 0.032425 | 6.253382 ± 0.038078 | 5.798542 ± 0.032366 | 6.234284 ± 0.037878 | +| Mean PPL ratio | 1.107955 ± 0.001427 | 1.564849 ± 0.004525 | 1.014242 ± 0.000432 | 1.028160 ± 0.000723 | 1.002406 ± 0.000191 | 1.003490 ± 0.000296 | 1.000689 ± 0.000107 | 1.000425 ± 0.000161 | +| Mean ΔPPL | 0.625552 ± 0.008725 | 3.519934 ± 0.033863 | 0.082526 ± 0.002530 | 0.175482 ± 0.004620 | 0.013941 ± 0.001110 | 0.021748 ± 0.001852 | 0.003990 ± 0.000624 | 0.002650 ± 0.001006 | +| PPL correlation | 97.36% | 89.62% | 99.71% | 99.34% | 99.94% | 99.88% | 99.98% | 99.96% | +| Mean KLD | 0.108903 ± 0.000645 | 0.445132 ± 0.001835 | 0.012686 ± 0.000079 | 0.031273 ± 0.000238 | 0.002098 ± 0.000014 | 0.005452 ± 0.000035 | 0.000369 ± 0.000007 | 0.001355 ± 0.000006 | +| Mean Δp | -2.710 ± 0.023 % | -9.123 ± 0.051 % | -0.416 ± 0.008 % | -0.596 ± 0.014 % | -0.035 ± 0.003 % | -0.007 ± 0.006 % | -0.005 ± 0.002 % | -0.019 ± 0.003 % | +| Maximum Δp | 85.136% | 94.268% | 45.209% | 95.054% | 23.593% | 53.601% | 43.925% | 28.734% | +| 99.9% Δp | 37.184% | 50.003% | 17.461% | 27.084% | 7.798% | 13.613% | 3.387% | 6.402% | +| 99.0% Δp | 18.131% | 25.875% | 7.798% | 12.084% | 3.838% | 6.407% | 1.867% | 3.544% | +| Median Δp | -0.391% | -2.476% | -0.026% | -0.024% | -0.001% | 0.000% | -0.000% | -0.000% | +| 1.0% Δp | -39.762% | -87.173% | -11.433% | -19.567% | -4.222% | -6.767% | -1.862% | -3.698% | +| 0.1% Δp | -79.002% | -98.897% | -26.433% | -56.054% | -9.091% | -16.584% | -3.252% | -6.579% | +| Minimum Δp | -99.915% | -99.965% | -83.383% | -98.699% | -43.142% | -68.487% | -9.343% | -24.301% | +| RMS Δp | 9.762 ± 0.053 % | 21.421 ± 0.079 % | 3.252 ± 0.024 % | 5.519 ± 0.050 % | 1.339 ± 0.010 % | 2.295 ± 0.019 % | 0.618 ± 0.011 % | 1.198 ± 0.007 % | +| Same top p | 85.584 ± 0.086 % | 71.138 ± 0.119 % | 94.665 ± 0.055 % | 91.901 ± 0.072 % | 97.520 ± 0.038 % | 96.031 ± 0.051 % | 98.846 ± 0.026 % | 97.674 ± 0.040 % | + + +## Old Numbers + +
+Llama 2 70B Scoreboard -## Llama 2 70B Scorechart | Quantization | Model size (GiB) | Perplexity | Delta to fp16 | |--------------|------------------|------------|---------------| | Q4_0 | 36.20 | 3.5550 | 3.61% | @@ -18,3 +128,5 @@ TODO | Q5_K_M | 45.41 | 3.4451 | 0.40% | | Q6_K | 52.70 | 3.4367 | 0.16% | | fp16 | 128.5 | 3.4313 | - | + +
diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 9a3374fdc..db6e0949d 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -216,17 +216,22 @@ static void process_logits(std::ostream& out, int n_vocab, const float * logits, } struct kl_divergence_result { - double sum_nll = 0; - double sum_nll2 = 0; - double sum_kld = 0; - double sum_kld2 = 0; - double sum_nll_diff = 0; - double sum_nll_diff2 = 0; - size_t n_same_top = 0; - size_t count = 0; + double sum_nll = 0.0; + double sum_nll2 = 0.0; + double sum_nll_base = 0.0; + double sum_nll_base2 = 0.0; + double sum_nll_nll_base = 0.0; + double sum_kld = 0.0; + double sum_kld2 = 0.0; + double sum_p_diff = 0.0; + double sum_p_diff2 = 0.0; + double sum_p_diff4 = 0.0; + float max_p_diff = 0.0f; + size_t n_same_top = 0.0; + size_t count = 0.0; }; -static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { +static std::pair log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { float max_logit = logits[0]; int imax = 0; for (int i = 1; i < n_vocab; ++i) { @@ -244,12 +249,17 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba const float scale = d[0]; const float min_log_prob = d[1]; base_log_prob += 4; - float nll = max_logit + log_sum_exp - logits[tok]; + + const float nll = max_logit + log_sum_exp - logits[tok]; kld.sum_nll += nll; kld.sum_nll2 += nll*nll; - nll += (scale*base_log_prob[tok] + min_log_prob); - kld.sum_nll_diff += nll; - kld.sum_nll_diff2 += nll*nll; + + const float nll_base = -(scale*base_log_prob[tok] + min_log_prob); + kld.sum_nll_base += nll_base; + kld.sum_nll_base2 += nll_base*nll_base; + + kld.sum_nll_nll_base += nll*nll_base; + max_logit += log_sum_exp; double sum = 0; int imax_base = -1; @@ -269,34 +279,50 @@ static double log_softmax(int n_vocab, const float * logits, const uint16_t * ba kld.sum_kld2 += sum*sum; ++kld.count; if (imax == imax_base) ++kld.n_same_top; - return sum; + + const float p_base = expf(-nll_base); + const float p = expf(-nll); + const float p_diff = p - p_base; + kld.sum_p_diff += p_diff; + const double p_diff2 = p_diff*p_diff; + kld.sum_p_diff2 += p_diff2; + kld.sum_p_diff4 += p_diff2*p_diff2; + kld.max_p_diff = std::max(kld.max_p_diff, std::fabs(p_diff)); + + return std::make_pair(sum, p_diff); } static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, const std::vector & base_log_probs, kl_divergence_result & kld, - float * kld_values) { + float * kld_values, float * p_diff_values) { std::mutex mutex; const int nv = 2*((n_vocab + 1)/2) + 4; int counter = 0; - auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () { + auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values, p_diff_values] () { kl_divergence_result local_kld; while (true) { std::unique_lock lock(mutex); int i = counter++; if (i >= n_token) { - kld.sum_nll += local_kld.sum_nll; - kld.sum_nll2 += local_kld.sum_nll2; - kld.sum_kld += local_kld.sum_kld; - kld.sum_kld2 += local_kld.sum_kld2; - kld.sum_nll_diff += local_kld.sum_nll_diff; - kld.sum_nll_diff2 += local_kld.sum_nll_diff2; - kld.n_same_top += local_kld.n_same_top; - kld.count += local_kld.count; + kld.sum_nll += local_kld.sum_nll; + kld.sum_nll2 += local_kld.sum_nll2; + kld.sum_nll_base += local_kld.sum_nll_base; + kld.sum_nll_base2 += local_kld.sum_nll_base2; + kld.sum_nll_nll_base += local_kld.sum_nll_nll_base; + kld.sum_kld += local_kld.sum_kld; + kld.sum_kld2 += local_kld.sum_kld2; + kld.sum_p_diff += local_kld.sum_p_diff; + kld.sum_p_diff2 += local_kld.sum_p_diff2; + kld.sum_p_diff4 += local_kld.sum_p_diff4; + kld.n_same_top += local_kld.n_same_top; + kld.max_p_diff = std::max(kld.max_p_diff, local_kld.max_p_diff); + kld.count += local_kld.count; break; } lock.unlock(); - double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); - kld_values[i] = (float)v; + std::pair v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); + kld_values[i] = (float)v.first; + p_diff_values[i] = v.second; } }; for (auto & w : workers) { @@ -1711,7 +1737,8 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); - std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); + std::vector p_diff_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); std::vector logits; if (num_batches > 1) { logits.reserve(n_ctx * n_vocab); @@ -1728,9 +1755,18 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { df = df > 0 && count > 10 ? sqrt(df/(count-1)) : 0.; return std::make_pair(f, df); }; + auto covariance = [] (double suma, double sumb, double sumab, size_t count) { + if (count < 10) { + return 0.0; + } + double var = sumab/count - (suma/count)*(sumb/count); + var /= count - 1; + return var; + }; kl_divergence_result kld; - auto kld_ptr = kld_values.data(); + auto kld_ptr = kld_values.data(); + auto p_diff_ptr = p_diff_values.data(); for (int i = 0; i < n_chunk; ++i) { const int start = i * n_ctx; @@ -1785,24 +1821,42 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { } fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); - printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence Same top\n"); + printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL Divergence Δp RMS Same top p\n"); } const int first = n_ctx/2; const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs_uint16, kld, kld_ptr); - kld_ptr += n_ctx - 1 - first; + workers, log_probs_uint16, kld, kld_ptr, p_diff_ptr); + p_diff_ptr += n_ctx - 1 - first; + kld_ptr += n_ctx - 1 - first; - auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); - auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count); - auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); - auto p_top = 1.*kld.n_same_top/kld.count; - auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1)); + printf("%4d", i+1); - printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf %.5f ± %.5f\n", i+1, exp(ppl.first), - log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second, - p_top, d_p_top); + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + printf(" %9.4lf ± %9.4lf", ppl_val, ppl_unc); + + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + printf(" %10.5lf ± %10.5lf", log_ppl_ratio_val, log_ppl_ratio_unc); + + auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + printf(" %10.5lf ± %10.5lf", kl_div.first, kl_div.second); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + printf(" %6.3lf ± %6.3lf %%", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + double p_top_val = 1.*kld.n_same_top/kld.count; + double p_top_unc = sqrt(p_top_val*(1 - p_top_val)/(kld.count - 1)); + printf(" %6.3lf ± %6.3lf %%", 100.0*p_top_val, 100.0*p_top_unc); + + printf("\n"); fflush(stdout); @@ -1813,31 +1867,97 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { if (kld.count < 100) return; // we do not wish to do statistics on so few values std::sort(kld_values.begin(), kld_values.end()); + std::sort(p_diff_values.begin(), p_diff_values.end()); - printf("===== KL-divergence statistics\n"); + printf("====== Perplexity statistics ======\n"); + + auto log_ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); + const double ppl_val = exp(log_ppl.first); + const double ppl_unc = ppl_val * log_ppl.second; // ppl_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl.second ** 2 ) + printf("Mean PPL(Q) : %10.6lf ± %10.6lf\n", ppl_val, ppl_unc); + + auto log_ppl_base = mean_and_uncertainty(kld.sum_nll_base, kld.sum_nll_base2, kld.count); + const double ppl_base_val = exp(log_ppl_base.first); + const double ppl_base_unc = ppl_base_val * log_ppl_base.second; // ppl_base_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_base.second ** 2 ) + printf("Mean PPL(base) : %10.6lf ± %10.6lf\n", ppl_base_val, ppl_base_unc); + + const double log_ppl_cov = covariance(kld.sum_nll, kld.sum_nll_base, kld.sum_nll_nll_base, kld.count); + // printf("Cov(ln(PPL(Q)), ln(PPL(base))): %10.6lf\n", log_ppl_cov); + const double log_ppl_cor = log_ppl_cov / (log_ppl.second*log_ppl_base.second); + printf("Cor(ln(PPL(Q)), ln(PPL(base))): %6.2lf%%\n", 100.0*log_ppl_cor); + + const double log_ppl_ratio_val = log_ppl.first - log_ppl_base.first; + const double log_ppl_ratio_unc = sqrt(log_ppl.second*log_ppl.second + log_ppl_base.second*log_ppl_base.second - 2.0*log_ppl_cov); + printf("Mean ln(PPL(Q)/PPL(base)) : %10.6lf ± %10.6lf\n", log_ppl_ratio_val, log_ppl_ratio_unc); + + const double ppl_ratio_val = exp(log_ppl_ratio_val); + const double ppl_ratio_unc = ppl_ratio_val * log_ppl_ratio_unc; // ppl_ratio_unc = sqrt( (dexp(x) / dx) ** 2 * log_ppl_ratio.second ** 2 ) + printf("Mean PPL(Q)/PPL(base) : %10.6lf ± %10.6lf\n", ppl_ratio_val, ppl_ratio_unc); + + const double ppl_cov = ppl_val * ppl_base_val * log_ppl_cov; + const double ppl_diff_val = ppl_val - ppl_base_val; + const double ppl_diff_unc = sqrt(ppl_unc*ppl_unc + ppl_base_unc*ppl_base_unc - 2.0*ppl_cov); + printf("Mean PPL(Q)-PPL(base) : %10.6lf ± %10.6lf\n", ppl_diff_val, ppl_diff_unc); + + printf("\n"); + + printf("====== KL divergence statistics ======\n"); auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); - printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second); + printf("Mean KLD: %10.6lf ± %10.6lf\n", kl_div.first, kl_div.second); auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1]) : kld_values[kld_values.size()/2]; - printf("Median : %10.6f\n", kld_median); - auto percentile = [&kld_values] (float fraction) { - if (fraction <= 0) return kld_values.front(); - if (fraction >= 1) return kld_values.back(); - float p = fraction*(kld_values.size() - 1); + auto percentile = [] (std::vector values, float fraction) { + if (fraction <= 0) return values.front(); + if (fraction >= 1) return values.back(); + float p = fraction*(values.size() - 1); size_t ip = size_t(p); p -= ip; - return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)]; + return (1 - p)*values[ip] + p*values[std::min(ip+1, values.size()-1)]; }; - printf("Maximum: %10.6f\n", kld_values.back()); - printf("KLD_99 : %10.6f\n", percentile(0.99f)); - printf("KLD_95 : %10.6f\n", percentile(0.95f)); - printf("KLD_90 : %10.6f\n", percentile(0.90f)); + printf("Maximum KLD: %10.6f\n", kld_values.back()); + printf("99.9%% KLD: %10.6f\n", percentile(kld_values, 0.999f)); + printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f)); + printf("99.0%% KLD: %10.6f\n", percentile(kld_values, 0.990f)); + printf("Median KLD: %10.6f\n", kld_median); + printf("10.0%% KLD: %10.6f\n", percentile(kld_values, 0.100f)); + printf(" 5.0%% KLD: %10.6f\n", percentile(kld_values, 0.050f)); + printf(" 1.0%% KLD: %10.6f\n", percentile(kld_values, 0.010f)); + printf("Minimum KLD: %10.6f\n", kld_values.front()); - printf("Minimum: %10.6f\n", kld_values.front()); - printf("KLD_01 : %10.6f\n", percentile(0.01f)); - printf("KLD_05 : %10.6f\n", percentile(0.05f)); - printf("KLD_10 : %10.6f\n", percentile(0.10f)); + printf("\n"); + + printf("====== Token probability statistics ======\n"); + + auto p_diff = mean_and_uncertainty(kld.sum_p_diff, kld.sum_p_diff2, kld.count); + printf("Mean Δp: %6.3lf ± %5.3lf %%\n", 100.0*p_diff.first, 100.0*p_diff.second); + + auto p_diff_median = p_diff_values.size()%2 == 0 ? 0.5f*(p_diff_values[p_diff_values.size()/2] + p_diff_values[p_diff_values.size()/2-1]) + : p_diff_values[p_diff_values.size()/2]; + + printf("Maximum Δp: %6.3lf%%\n", 100.0*p_diff_values.back()); + printf("99.9%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.999f)); + printf("99.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.990f)); + printf("95.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.950f)); + printf("90.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.900f)); + printf("75.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.750f)); + printf("Median Δp: %6.3lf%%\n", 100.0*p_diff_median); + printf("25.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.250f)); + printf("10.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.100f)); + printf(" 5.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.050f)); + printf(" 1.0%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.010f)); + printf(" 0.1%% Δp: %6.3lf%%\n", 100.0*percentile(p_diff_values, 0.001f)); + printf("Minimum Δp: %6.3lf%%\n", 100.0*p_diff_values.front()); + + auto p_diff_mse = mean_and_uncertainty(kld.sum_p_diff2, kld.sum_p_diff4, kld.count); + // printf("MSE Δp : %10.6lf ± %10.6lf\n", p_diff_mse.first, p_diff_mse.second); + + const double p_diff_rms_val = sqrt(p_diff_mse.first); + const double p_diff_rms_unc = 0.5/p_diff_rms_val * p_diff_mse.second; + printf("RMS Δp : %6.3lf ± %5.3lf %%\n", 100.0*p_diff_rms_val, 100.0*p_diff_rms_unc); + + const double same_top_p = 1.0*kld.n_same_top/kld.count; + printf("Same top p: %6.3lf ± %5.3lf %%\n", 100.0*same_top_p, 100.0*sqrt(same_top_p*(1.0 - same_top_p)/(kld.count - 1))); } From c4ec9c0d3d67e6b33638e6dad86419e6fd5ffe01 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 1 May 2024 07:13:59 +0200 Subject: [PATCH 014/100] ci : exempt confirmed bugs from being tagged as stale (#7014) --- .github/workflows/close-issue.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/close-issue.yml b/.github/workflows/close-issue.yml index 7f21daec0..69c9f4f69 100644 --- a/.github/workflows/close-issue.yml +++ b/.github/workflows/close-issue.yml @@ -12,7 +12,7 @@ jobs: steps: - uses: actions/stale@v5 with: - exempt-issue-labels: "refactor,help wanted,good first issue,research" + exempt-issue-labels: "refactor,help wanted,good first issue,research,bug" days-before-issue-stale: 30 days-before-issue-close: 14 stale-issue-label: "stale" From 1613ef8d8eb2479ba55c4d598e08c8f3f18a0fed Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 1 May 2024 14:46:37 +0200 Subject: [PATCH 015/100] CUDA: CUDART < 11.7 workaround for __hmax, __hmax2 (#7019) --- ggml-cuda/common.cuh | 45 +++++++++++++++++++++++++++++++++++++++----- ggml-cuda/fattn.cu | 6 +++--- 2 files changed, 43 insertions(+), 8 deletions(-) diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 156eba6d1..b2627b7b4 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -137,7 +137,8 @@ #define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__) #define WARP_SIZE 32 -#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) +#define CUDART_HMAX 11070 // CUDA 11.7, min. ver. for which __hmax and __hmax2 are known to work (may be higher than needed) +#define CUDART_HMASK 12000 // CUDA 12.0, min. ver. for half2 -> uint mask comparisons #define CC_PASCAL 600 #define MIN_CC_DP4A 610 // minimum compute capability for __dp4a, an intrinsic for byte-wise dot products @@ -293,20 +294,54 @@ static __device__ __forceinline__ float warp_reduce_max(float x) { return x; } +static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + +#if CUDART_VERSION >= CUDART_HMAX + return __hmax(a, b); +#else + return __half2float(a) > __half2float(b) ? a : b; +#endif // CUDART_VERSION >= CUDART_HMAX + +#else + GGML_UNUSED(a); + GGML_UNUSED(b); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +} +static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + +#if CUDART_VERSION >= CUDART_HMAX + return __hmax2(a, b); +#else + half2 ret; + reinterpret_cast(ret.x) = __low2float(a) > __low2float(b) ? __low2half(a) : __low2half(b); + reinterpret_cast(ret.y) = __high2float(a) > __high2float(b) ? __high2half(a) : __high2half(b); + return ret; +#endif // CUDART_VERSION >= CUDART_HMAX + +#else + GGML_UNUSED(a); + GGML_UNUSED(b); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX +} + static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { - x = __hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); } return x; #else GGML_UNUSED(x); NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } -#if CUDART_VERSION < 12000 +#if CUDART_VERSION < CUDART_HMASK static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) { const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b))); const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b))); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index df1e80068..c8a11d173 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -116,7 +116,7 @@ static __global__ void flash_attn_vec_ext_f16( sum2 = warp_reduce_sum(sum2); half sum = __low2half(sum2) + __high2half(sum2); sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f); - kqmax_new = __hmax(kqmax_new, sum); + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); if (threadIdx.x == 0) { KQ[i_KQ] = sum; } @@ -416,9 +416,9 @@ static __global__ void flash_attn_ext_f16( const int k = k0 + threadIdx.x; KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); - KQ_max_new = __hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); + KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); } - KQ_max_new = __half2half2(warp_reduce_max(__hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); + KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; KQ_max_scale_h2[j0/nwarps] = h2exp(diff); const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); From 3ea0d36000e2314baba7e9fc6a97f08670a6f7e4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 1 May 2024 17:52:55 +0200 Subject: [PATCH 016/100] Server: add tests for batch size, different seeds (#6950) --- .../server/tests/features/results.feature | 90 +++++++---- examples/server/tests/features/steps/steps.py | 144 ++++++++++++------ 2 files changed, 155 insertions(+), 79 deletions(-) diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index f17120f7b..aa0b8d0c6 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -7,44 +7,16 @@ Feature: Results And a model file tinyllamas/split/stories15M-00001-of-00003.gguf from HF repo ggml-org/models And a model file test-model-00001-of-00003.gguf And 128 as batch size - And 256 KV cache size + And 1024 KV cache size And 128 max tokens to predict - - Scenario Outline: Multi users completion - Given slots And continuous batching + + Scenario Outline: consistent results with same seed + Given slots Then the server is starting Then the server is healthy - Given 42 as seed - And a prompt: - """ - Write a very long story about AI. - """ - - Given 42 as seed - And a prompt: - """ - Write a very long story about AI. - """ - - Given 42 as seed - And a prompt: - """ - Write a very long story about AI. - """ - - Given 42 as seed - And a prompt: - """ - Write a very long story about AI. - """ - - Given 42 as seed - And a prompt: - """ - Write a very long story about AI. - """ + Given 4 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 Given concurrent completion requests Then the server is busy @@ -55,3 +27,55 @@ Feature: Results | n_slots | | 1 | | 2 | + + Scenario Outline: different results with different seed + Given slots + Then the server is starting + Then the server is healthy + + Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 42 + Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 43 + Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 44 + Given 1 prompts "Title: Little Red Riding Hood But In Space\n\nSummary:" with seed 45 + + Given concurrent completion requests + Then the server is busy + Then the server is idle + And all slots are idle + Then all predictions are different + Examples: + | n_slots | + | 1 | + | 2 | + + Scenario Outline: consistent results with same seed and varying batch size + Given 4 slots + And temperature + # And 0 as draft + Then the server is starting + Then the server is healthy + + Given 1 prompts "Write a very long story about AI." with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Given prompts "Write a very long story about AI." with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Then all predictions are equal + Examples: + | n_parallel | temp | + | 1 | 0.0 | + | 2 | 0.0 | + | 4 | 0.0 | + | 1 | 1.0 | + # FIXME: These tests fail on master. The problem seems to be the unified KV cache. + # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 . + # | 2 | 1.0 | + # | 4 | 1.0 | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index f71e0d706..b8dbef21d 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -65,6 +65,7 @@ def step_server_config(context, server_fqdn, server_port): context.server_seed = None context.user_api_key = None context.response_format = None + context.temperature = None context.tasks_result = [] context.concurrent_tasks = [] @@ -232,15 +233,17 @@ async def step_all_slots_status(context, expected_slot_status_string): @async_run_until_complete async def step_request_completion(context, api_error): expect_api_error = api_error == 'raised' + seeds = await completions_seed(context, num_seeds=1) completion = await request_completion(context.prompts.pop(), + seeds[0] if seeds is not None else seeds, context.base_url, debug=context.debug, n_predict=context.n_predict, cache_prompt=context.cache_prompt, id_slot=context.id_slot, - seed=await completions_seed(context), expect_api_error=expect_api_error, - user_api_key=context.user_api_key) + user_api_key=context.user_api_key, + temperature=context.temperature) context.tasks_result.append(completion) if context.debug: print(f"Completion response: {completion}") @@ -269,6 +272,15 @@ async def step_predictions_equal(context): context.tasks_result = [] +@step('all predictions are different') +@async_run_until_complete +async def step_predictions_equal(context): + n_completions = await gather_tasks_results(context) + assert n_completions >= 2, "need at least 2 completions" + assert_all_predictions_different(context.tasks_result) + context.tasks_result = [] + + @step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') @@ -311,6 +323,11 @@ def step_response_format(context, response_format): context.response_format = json.loads(response_format) +@step('{temperature:f} temperature') +def step_temperature(context, temperature): + context.temperature = temperature + + @step('streaming is {enable_streaming}') def step_streaming(context, enable_streaming): context.enable_streaming = enable_streaming == 'enabled' @@ -353,7 +370,10 @@ def step_n_ubatch(context, n_ubatch): @step('{seed:d} as seed') def step_seed(context, seed): - context.seed = seed + if context.seed is None: + context.seed = [seed] + else: + context.seed.append(seed) @step('a prefix prompt') @@ -413,7 +433,9 @@ async def step_oai_chat_completions(context, api_error): if context.debug: print(f"Submitting OAI compatible completions request...") expect_api_error = api_error == 'raised' + seeds = await completions_seed(context, num_seeds=1), completion = await oai_chat_completions(context.prompts.pop(), + seeds[0] if seeds is not None else seeds, context.system_prompt, context.base_url, '/v1/chat', @@ -429,8 +451,6 @@ async def step_oai_chat_completions(context, api_error): response_format=context.response_format if hasattr(context, 'response_format') else None, - seed=await completions_seed(context), - user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, @@ -457,20 +477,31 @@ def step_a_prompt_prompt(context, prompt): context.n_prompts = len(context.prompts) +@step('{num_prompts:d} prompts {prompt} with seed {seed:d}') +def step_many_prompts(context, num_prompts, prompt, seed): + if context.seed is None: + context.seed = [] + for _ in range(num_prompts): + context.seed.append(seed) + context.prompts.append(prompt) + context.n_prompts = len(context.prompts) + + @step('concurrent completion requests') @async_run_until_complete() async def step_concurrent_completion_requests(context): - await concurrent_requests(context, - request_completion, - # prompt is inserted automatically - context.base_url, - debug=context.debug, - prompt_prefix=context.prompt_prefix, - prompt_suffix=context.prompt_suffix, - n_predict=context.n_predict if hasattr(context, 'n_predict') else None, - seed=await completions_seed(context), - user_api_key=context.user_api_key if hasattr(context, - 'user_api_key') else None) + await concurrent_requests( + context, + request_completion, + # prompt is inserted automatically + context.base_url, + debug=context.debug, + prompt_prefix=context.prompt_prefix, + prompt_suffix=context.prompt_suffix, + n_predict=context.n_predict if hasattr(context, 'n_predict') else None, + user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None, + temperature=context.temperature, + ) @step('concurrent OAI completions requests') @@ -490,7 +521,6 @@ async def step_oai_chat_completions(context): if hasattr(context, 'enable_streaming') else None, response_format=context.response_format if hasattr(context, 'response_format') else None, - seed=await completions_seed(context), user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -512,10 +542,6 @@ async def step_oai_chat_completions(context): if hasattr(context, 'enable_streaming') else None, response_format=context.response_format if hasattr(context, 'response_format') else None, - seed=context.seed - if hasattr(context, 'seed') else - context.server_seed - if hasattr(context, 'server_seed') else None, user_api_key=context.user_api_key if hasattr(context, 'user_api_key') else None) @@ -544,7 +570,7 @@ async def all_prompts_are_predicted(context, expected_predicted_n=None): @async_run_until_complete async def step_compute_embedding(context): context.n_prompts = 1 - context.embeddings = await request_embedding(context_text(context), base_url=context.base_url) + context.embeddings = await request_embedding(context_text(context), None, base_url=context.base_url) @step('all embeddings are the same') @@ -585,7 +611,7 @@ def step_assert_embeddings(context): @async_run_until_complete async def step_oai_compute_embeddings(context): context.n_prompts = 1 - context.embeddings = await request_oai_embeddings(context_text(context), + context.embeddings = await request_oai_embeddings(context_text(context), None, base_url=context.base_url, user_api_key=context.user_api_key, model=context.model) @@ -594,7 +620,7 @@ async def step_oai_compute_embeddings(context): @step('an OAI compatible embeddings computation request for multiple inputs') @async_run_until_complete async def step_oai_compute_embeddings_multiple_inputs(context): - context.embeddings = await request_oai_embeddings(context.prompts, + context.embeddings = await request_oai_embeddings(context.prompts, None, base_url=context.base_url, user_api_key=context.user_api_key, model=context.model) @@ -740,8 +766,9 @@ async def concurrent_requests(context, f_completion, *args, **kwargs): if context.debug: print(f"starting {context.n_prompts} concurrent completion requests...") assert context.n_prompts > 0 + seeds = await completions_seed(context) for prompt_no in range(context.n_prompts): - shifted_args = [context.prompts.pop(), *args] + shifted_args = [context.prompts.pop(), seeds[prompt_no], *args] context.concurrent_tasks.append(asyncio.create_task(f_completion(*shifted_args, **kwargs))) await asyncio.sleep(0.1) @@ -781,6 +808,7 @@ def step_server_responds_with_status_code(context, status_code): async def request_completion(prompt, + seed, base_url, debug=False, prompt_prefix=None, @@ -788,9 +816,9 @@ async def request_completion(prompt, n_predict=None, cache_prompt=False, id_slot=None, - seed=None, expect_api_error=None, - user_api_key=None): + user_api_key=None, + temperature=None): if debug: print(f"Sending completion request: {prompt}") origin = "my.super.domain" @@ -811,7 +839,8 @@ async def request_completion(prompt, "n_predict": n_predict if n_predict is not None else -1, "cache_prompt": cache_prompt, "id_slot": id_slot, - "seed": seed if seed is not None else 42 + "seed": seed if seed is not None else 42, + "temperature": temperature if temperature is not None else "0.8f", }, headers=headers, timeout=3600) as response: @@ -824,6 +853,7 @@ async def request_completion(prompt, async def oai_chat_completions(user_prompt, + seed, system_prompt, base_url, base_path, @@ -833,7 +863,6 @@ async def oai_chat_completions(user_prompt, n_predict=None, enable_streaming=None, response_format=None, - seed=None, user_api_key=None, expect_api_error=None): if debug: @@ -952,7 +981,7 @@ async def oai_chat_completions(user_prompt, return completion_response -async def request_embedding(content, base_url=None): +async def request_embedding(content, seed, base_url=None): async with aiohttp.ClientSession() as session: async with session.post(f'{base_url}/embedding', json={ @@ -963,7 +992,7 @@ async def request_embedding(content, base_url=None): return [response_json['embedding']] -async def request_oai_embeddings(input, +async def request_oai_embeddings(input, seed, base_url=None, user_api_key=None, model=None, async_client=False): # openai client always expects an api_key @@ -1036,21 +1065,31 @@ def assert_n_tokens_predicted(completion_response, expected_predicted_n=None, re f' {n_predicted} <> {expected_predicted_n}') def assert_all_predictions_equal(completion_responses): - content_0 = completion_responses[0]['content'] - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - print(f"content 0: {content_0}") + for i, response_i in enumerate(completion_responses): + content_i = response_i['content'] + print(f"content {i}: {content_i}") + for i, response_i in enumerate(completion_responses): + content_i = response_i['content'] + for j, response_j in enumerate(completion_responses): + if i == j: + continue + content_j = response_j['content'] + assert content_i == content_j, "contents not equal" - i = 1 - for response in completion_responses[1:]: - content = response['content'] - if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': - print(f"content {i}: {content}") - - assert content == content_0, "contents not equal" - - i += 1 +def assert_all_predictions_different(completion_responses): + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + for i, response_i in enumerate(completion_responses): + content_i = response_i['content'] + print(f"content {i}: {content_i}") + for i, response_i in enumerate(completion_responses): + content_i = response_i['content'] + for j, response_j in enumerate(completion_responses): + if i == j: + continue + content_j = response_j['content'] + assert content_i != content_j, "contents not different" async def gather_tasks_results(context): @@ -1145,9 +1184,22 @@ def assert_slots_status(slots, expected_slots): f" = {expected[key]} != {slot[key]}") -async def completions_seed(context): - return context.seed if hasattr(context, 'seed') and context.seed is not None \ - else context.server_seed if hasattr(context, 'server_seed') else None +async def completions_seed(context, num_seeds=None): + if hasattr(context, "seed") and context.seed is not None: + assert len(context.seed) == context.n_prompts + if num_seeds is None: + num_seeds = context.n_prompts + assert num_seeds <= context.n_prompts + seeds = context.seed[:num_seeds] + context.seed = context.seed[num_seeds:] if num_seeds < context.n_prompts else None + return seeds + + if hasattr(context, "server_seed") and context.server_seed is not None: + if num_seeds is None: + return [context.server_seed] * context.n_prompts + else: + return [context.server_seed] * num_seeds + return None def context_text(context): From 8d608a81b7bd170f700648f8214e6f3279d4d715 Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Thu, 2 May 2024 04:27:41 +0900 Subject: [PATCH 017/100] main : fix off by one error for context shift (#6921) --- examples/main/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 5c693657c..eabbc2db3 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -544,7 +544,7 @@ int main(int argc, char ** argv) { // if we run out of context: // - take the n_keep first tokens from the original prompt (via n_past) // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches - if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { + if (n_past + (int) embd.size() + std::max(0, guidance_offset) >= n_ctx) { if (params.n_predict == -2) { LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); break; From b0d943de179ad5dbd83d51f327fb566066f4ccda Mon Sep 17 00:00:00 2001 From: Andrew Downing Date: Wed, 1 May 2024 17:31:30 -0400 Subject: [PATCH 018/100] Update LOG_IMPL and LOG_TEE_IMPL (#7029) ROCm clang defines _MSC_VER which results in the wrong implementation of LOG_IMPL and LOG_TEE_IMPL being compiled. This fixes https://github.com/ggerganov/llama.cpp/issues/6972 --- common/log.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/common/log.h b/common/log.h index 2b2f0e455..6934c57b2 100644 --- a/common/log.h +++ b/common/log.h @@ -234,7 +234,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // INTERNAL, DO NOT USE // USE LOG() INSTEAD // -#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) +#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__) #define LOG_IMPL(str, ...) \ do { \ if (LOG_TARGET != nullptr) \ @@ -257,7 +257,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // INTERNAL, DO NOT USE // USE LOG_TEE() INSTEAD // -#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) +#if !defined(_MSC_VER) || defined(__INTEL_LLVM_COMPILER) || defined(__clang__) #define LOG_TEE_IMPL(str, ...) \ do { \ if (LOG_TARGET != nullptr) \ From 6ecf3189e00a1e8e737a78b6d10e1d7006e050a2 Mon Sep 17 00:00:00 2001 From: alwqx Date: Thu, 2 May 2024 23:56:41 +0800 Subject: [PATCH 019/100] chore: fix typo in llama.cpp (#7032) Co-authored-by: Jared Van Bortel --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 18d6297ce..18b49ec20 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2359,7 +2359,7 @@ static bool llama_kv_cache_init( cache.recurrent = model.arch == LLM_ARCH_MAMBA; cache.v_trans = !cparams.flash_attn; - // TODO: support mixed reccurent Transformer architectues + // TODO: support mixed recurrent Transformer architectures // NOTE: (!a || b) is a logical implication (a -> b) GGML_ASSERT(!cache.recurrent || n_embd_k_gqa == hparams.n_embd_k_s()); GGML_ASSERT(!cache.recurrent || n_embd_v_gqa == hparams.n_embd_v_s()); From 60325fa56f61c228464c9f065db3aa6a61f2156e Mon Sep 17 00:00:00 2001 From: Bartowski Date: Thu, 2 May 2024 19:49:09 -0400 Subject: [PATCH 020/100] Remove .attention from skipped tensors to match more accurately (#7051) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 2f146d730..612aea173 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1427,7 +1427,7 @@ class LlamaModel(Model): experts = dict() for name, data_torch in self.get_tensors(): # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): continue old_dtype = data_torch.dtype From 433def286e98751bf17db75dce53847d075c0be5 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Fri, 3 May 2024 15:24:30 +0200 Subject: [PATCH 021/100] llama : rename ctx to user_data in progress_callback (#7045) * llama : rename ctx to user_data in progress_callback This commit renames the `ctx` parameter to `user_data` in the `llama_progress_callback` typedef. The motivation for this is that other callbacks use `user_data` or `data`, and using `ctx` in this case might be confusing as it could be confused with `llama_context`. --------- Signed-off-by: Daniel Bevenius --- llama.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.h b/llama.h index 059d78f11..790339af9 100644 --- a/llama.h +++ b/llama.h @@ -171,7 +171,7 @@ extern "C" { bool sorted; } llama_token_data_array; - typedef bool (*llama_progress_callback)(float progress, void *ctx); + typedef bool (*llama_progress_callback)(float progress, void * user_data); // Input data for llama_decode // A llama_batch object can contain input about one or many sequences From a2ac89d6efb41b535778bfeaecaae8fe295b6ed3 Mon Sep 17 00:00:00 2001 From: Brian Date: Sat, 4 May 2024 05:36:41 +1000 Subject: [PATCH 022/100] convert.py : add python logging instead of print() (#6511) * convert.py: add python logging instead of print() * convert.py: verbose flag takes priority over dump flag log suppression * convert.py: named instance logging * convert.py: use explicit logger id string * convert.py: convert extra print() to named logger * convert.py: sys.stderr.write --> logger.error * *.py: Convert all python scripts to use logging module * requirements.txt: remove extra line * flake8: update flake8 ignore and exclude to match ci settings * gh-actions: add flake8-no-print to flake8 lint step * pre-commit: add flake8-no-print to flake8 and also update pre-commit version * convert-hf-to-gguf.py: print() to logger conversion * *.py: logging basiconfig refactor to use conditional expression * *.py: removed commented out logging * fixup! *.py: logging basiconfig refactor to use conditional expression * constant.py: logger.error then exit should be a raise exception instead * *.py: Convert logger error and sys.exit() into a raise exception (for atypical error) * gguf-convert-endian.py: refactor convert_byteorder() to use tqdm progressbar * verify-checksum-model.py: This is the result of the program, it should be printed to stdout. * compare-llama-bench.py: add blank line for readability during missing repo response * reader.py: read_gguf_file() use print() over logging * convert.py: warning goes to stderr and won't hurt the dump output * gguf-dump.py: dump_metadata() should print to stdout * convert-hf-to-gguf.py: print --> logger.debug or ValueError() * verify-checksum-models.py: use print() for printing table * *.py: refactor logging.basicConfig() * gguf-py/gguf/*.py: use __name__ as logger name Since they will be imported and not run directly. * python-lint.yml: use .flake8 file instead * constants.py: logger no longer required * convert-hf-to-gguf.py: add additional logging * convert-hf-to-gguf.py: print() --> logger * *.py: fix flake8 warnings * revert changes to convert-hf-to-gguf.py for get_name() * convert-hf-to-gguf-update.py: use triple quoted f-string instead * *.py: accidentally corrected the wrong line * *.py: add compilade warning suggestions and style fixes --- .flake8 | 3 +- .github/workflows/python-lint.yml | 3 +- .pre-commit-config.yaml | 5 +- convert-hf-to-gguf-update.py | 153 +++++++------- convert-hf-to-gguf.py | 271 +++++++++++-------------- convert-llama-ggml-to-gguf.py | 51 ++--- convert-lora-to-ggml.py | 31 +-- convert-persimmon-to-gguf.py | 28 +-- convert.py | 60 +++--- ggml_vk_generate_shaders.py | 12 +- gguf-py/examples/reader.py | 18 +- gguf-py/gguf/constants.py | 4 +- gguf-py/gguf/gguf_reader.py | 4 +- gguf-py/gguf/gguf_writer.py | 5 +- gguf-py/gguf/vocab.py | 42 ++-- gguf-py/scripts/gguf-convert-endian.py | 94 +++++---- gguf-py/scripts/gguf-dump.py | 29 ++- gguf-py/scripts/gguf-set-metadata.py | 31 +-- scripts/compare-llama-bench.py | 40 ++-- scripts/run-with-preset.py | 14 +- scripts/verify-checksum-models.py | 13 +- tests/test-tokenizer-0-bpe.py | 43 ++-- tests/test-tokenizer-0-spm.py | 64 +++--- 23 files changed, 536 insertions(+), 482 deletions(-) diff --git a/.flake8 b/.flake8 index 18fba2c15..608eb8eed 100644 --- a/.flake8 +++ b/.flake8 @@ -1,3 +1,4 @@ [flake8] max-line-length = 125 -ignore = W503 +ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 +exclude = examples/*,examples/*/**,*/**/__init__.py diff --git a/.github/workflows/python-lint.yml b/.github/workflows/python-lint.yml index 5be17f157..a8d46f31d 100644 --- a/.github/workflows/python-lint.yml +++ b/.github/workflows/python-lint.yml @@ -20,5 +20,4 @@ jobs: - name: flake8 Lint uses: py-actions/flake8@v2 with: - ignore: "E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503" - exclude: "examples/*,examples/*/**,*/**/__init__.py,convert-hf-to-gguf-update.py" + plugins: "flake8-no-print" diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 65796fe2e..91d791628 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -3,13 +3,14 @@ exclude: prompts/.*.txt repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v3.2.0 + rev: v4.6.0 hooks: - id: trailing-whitespace - id: end-of-file-fixer - id: check-yaml - id: check-added-large-files - repo: https://github.com/PyCQA/flake8 - rev: 6.0.0 + rev: 7.0.0 hooks: - id: flake8 + additional_dependencies: [flake8-no-print] diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index b019c1e3d..09772f668 100644 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -21,6 +21,7 @@ # TODO: automate the update of convert-hf-to-gguf.py # +import logging import os import requests import sys @@ -28,12 +29,17 @@ import json from hashlib import sha256 from enum import IntEnum, auto +from transformers import AutoTokenizer + +logger = logging.getLogger("convert-hf-to-gguf-update") + class TOKENIZER_TYPE(IntEnum): SPM = auto() BPE = auto() WPM = auto() + # TODO: this string has to exercise as much pre-tokenizer functionality as possible # will be updated with time - contributions welcome chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български \'\'\'\'\'\'```````\"\"\"\"......!!!!!!?????? I\'ve been \'told he\'s there, \'RE you sure? \'M not sure I\'ll make it, \'D you like some tea? We\'Ve a\'lL' @@ -41,36 +47,38 @@ chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍ if len(sys.argv) == 2: token = sys.argv[1] else: - print("Usage: python convert-hf-to-gguf-update.py ") + logger.info("Usage: python convert-hf-to-gguf-update.py ") sys.exit(1) # TODO: add models here, base models preferred models = [ - { "name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, - { "name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, - { "name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, - { "name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, - { "name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, - { "name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, - { "name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, - { "name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, - { "name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, - { "name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, - ] + {"name": "llama-spm", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/meta-llama/Llama-2-7b-hf", }, + {"name": "llama-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/meta-llama/Meta-Llama-3-8B", }, + {"name": "phi-3", "tokt": TOKENIZER_TYPE.SPM, "repo": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct", }, + {"name": "deepseek-llm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-llm-7b-base", }, + {"name": "deepseek-coder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-base", }, + {"name": "falcon", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/tiiuae/falcon-7b", }, + {"name": "bert-bge", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/BAAI/bge-small-en-v1.5", }, + {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, + {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, + {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, +] # make directory "models/tokenizers" if it doesn't exist if not os.path.exists("models/tokenizers"): os.makedirs("models/tokenizers") + def download_file_with_auth(url, token, save_path): headers = {"Authorization": f"Bearer {token}"} response = requests.get(url, headers=headers) if response.status_code == 200: with open(save_path, 'wb') as f: f.write(response.content) - print(f"File {save_path} downloaded successfully") + logger.info(f"File {save_path} downloaded successfully") else: - print(f"Failed to download file. Status code: {response.status_code}") + logger.info(f"Failed to download file. Status code: {response.status_code}") + # download the tokenizer models for model in models: @@ -81,10 +89,10 @@ for model in models: if not os.path.exists(f"models/tokenizers/{name}"): os.makedirs(f"models/tokenizers/{name}") else: - print(f"Directory models/tokenizers/{name} already exists - skipping") + logger.info(f"Directory models/tokenizers/{name} already exists - skipping") continue - print(f"Downloading {name} to models/tokenizers/{name}") + logger.info(f"Downloading {name} to models/tokenizers/{name}") url = f"{repo}/raw/main/config.json" save_path = f"models/tokenizers/{name}/config.json" @@ -115,76 +123,76 @@ for model in models: continue # create the tokenizer - from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") chktok = tokenizer.encode(chktxt) chkhsh = sha256(str(chktok).encode()).hexdigest() - print(f"model: {name}") - print(f"tokt: {tokt}") - print(f"repo: {model['repo']}") - print(f"chktok: {chktok}") - print(f"chkhsh: {chkhsh}") + logger.info(f"model: {name}") + logger.info(f"tokt: {tokt}") + logger.info(f"repo: {model['repo']}") + logger.info(f"chktok: {chktok}") + logger.info(f"chkhsh: {chkhsh}") # print the "pre_tokenizer" content from the tokenizer.json with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f: cfg = json.load(f) pre_tokenizer = cfg["pre_tokenizer"] - print("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) - print(f"\n") + logger.info("") src_ifs += f" if chkhsh == \"{chkhsh}\":\n" src_ifs += f" # ref: {model['repo']}\n" src_ifs += f" res = \"{name}\"\n" -src_func = "" -src_func += " def get_vocab_base_pre(self, tokenizer) -> str:\n" -src_func += " # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that\n" -src_func += " # is specific for the BPE pre-tokenizer used by the model\n" -src_func += " # we will use this unique identifier to write a \"tokenizer.ggml.pre\" entry in the GGUF file which we can\n" -src_func += " # use in llama.cpp to implement the same pre-tokenizer\n" -src_func += "\n" -src_func += f" chktxt = {repr(chktxt)}\n" -src_func += "\n" -src_func += " chktok = tokenizer.encode(chktxt)\n" -src_func += " chkhsh = sha256(str(chktok).encode()).hexdigest()\n" -src_func += "\n" -src_func += " print(f\"chktok: {chktok}\")\n" -src_func += " print(f\"chkhsh: {chkhsh}\")\n" -src_func += "\n" -src_func += " res = None\n" -src_func += "\n" -src_func += " # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script\n" -src_func += " # or pull the latest version of the model from Huggingface\n" -src_func += " # don't edit the hashes manually!\n" -src_func += f"{src_ifs}\n" -src_func += " if res is None:\n" -src_func += " print(\"\\n\")\n" -src_func += " print(\"**************************************************************************************\")\n" -src_func += " print(\"** WARNING: The BPE pre-tokenizer was not recognized!\")\n" -src_func += " print(\"** There are 2 possible reasons for this:\")\n" -src_func += " print(\"** - the model has not been added to convert-hf-to-gguf-update.py yet\")\n" -src_func += " print(\"** - the pre-tokenization config has changed upstream\")\n" -src_func += " print(\"** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.\")\n" -src_func += " print(\"** ref: https://github.com/ggerganov/llama.cpp/pull/6920\")\n" -src_func += " print(\"**\")\n" -src_func += " print(f\"** chkhsh: {chkhsh}\")\n" -src_func += " print(\"**************************************************************************************\")\n" -src_func += " print(\"\\n\")\n" -src_func += " raise NotImplementedError(\"BPE pre-tokenizer was not recognized - update get_vocab_base_pre()\")\n" -src_func += "\n" -src_func += " print(f\"tokenizer.ggml.pre: {res}\")\n" -src_func += " print(f\"chkhsh: {chkhsh}\")\n" -src_func += "\n" -src_func += " return res\n" +src_func = f""" + def get_vocab_base_pre(self, tokenizer) -> str: + # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that + # is specific for the BPE pre-tokenizer used by the model + # we will use this unique identifier to write a "tokenizer.ggml.pre" entry in the GGUF file which we can + # use in llama.cpp to implement the same pre-tokenizer -print(src_func) + chktxt = {repr(chktxt)} -print("\n") -print("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") -print("\n") + chktok = tokenizer.encode(chktxt) + chkhsh = sha256(str(chktok).encode()).hexdigest() + + print(f"chktok: {{chktok}}") + print(f"chkhsh: {{chkhsh}}") + + res = None + + # NOTE: if you get an error here, you need to update the convert-hf-to-gguf-update.py script + # or pull the latest version of the model from Huggingface + # don't edit the hashes manually! +{src_ifs} + if res is None: + print("\\n") + print("**************************************************************************************") + print("** WARNING: The BPE pre-tokenizer was not recognized!") + print("** There are 2 possible reasons for this:") + print("** - the model has not been added to convert-hf-to-gguf-update.py yet") + print("** - the pre-tokenization config has changed upstream") + print("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + print("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + print("**") + print(f"** chkhsh: {{chkhsh}}") + print("**************************************************************************************") + print("\\n") + raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") + + print(f"tokenizer.ggml.pre: {{repr(res)}}") + print(f"chkhsh: {{chkhsh}}") + + return res +""" + +print(src_func) # noqa: NP100 + +logger.info("\n") +logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") +logger.info("\n") # generate tests for each tokenizer model @@ -250,7 +258,6 @@ for model in models: tokt = model["tokt"] # create the tokenizer - from transformers import AutoTokenizer tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: @@ -265,15 +272,15 @@ for model in models: f.write(f" {r}") f.write("\n") - print(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*") + logger.info(f"Tests for {name} written in ./models/ggml-vocab-{name}.gguf.*") # generate commands for creating vocab files -print("\nRun the following commands to generate the vocab files for testing:\n") +logger.info("\nRun the following commands to generate the vocab files for testing:\n") for model in models: name = model["name"] - print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") + logger.info(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") -print("\n") +logger.info("\n") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 612aea173..529326288 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2,6 +2,7 @@ from __future__ import annotations +import logging import argparse import contextlib import json @@ -26,6 +27,8 @@ import gguf from convert import LlamaHfVocab, permute +logger = logging.getLogger("hf-to-gguf") + ###### MODEL DEFINITIONS ###### @@ -76,7 +79,7 @@ class Model(ABC): def get_tensors(self) -> Iterator[tuple[str, Tensor]]: for part_name in self.part_names: - print(f"gguf: loading model part '{part_name}'") + logger.info(f"gguf: loading model part '{part_name}'") ctx: ContextManager[Any] if self.is_safetensors: from safetensors import safe_open @@ -95,42 +98,42 @@ class Model(ABC): if (n_ctx := self.find_hparam(["max_position_embeddings", "n_ctx"], optional=True)) is not None: self.gguf_writer.add_context_length(n_ctx) - print(f"gguf: context length = {n_ctx}") + logger.info(f"gguf: context length = {n_ctx}") n_embd = self.find_hparam(["hidden_size", "n_embd"]) self.gguf_writer.add_embedding_length(n_embd) - print(f"gguf: embedding length = {n_embd}") + logger.info(f"gguf: embedding length = {n_embd}") if (n_ff := self.find_hparam(["intermediate_size", "n_inner"], optional=True)) is not None: self.gguf_writer.add_feed_forward_length(n_ff) - print(f"gguf: feed forward length = {n_ff}") + logger.info(f"gguf: feed forward length = {n_ff}") n_head = self.find_hparam(["num_attention_heads", "n_head"]) self.gguf_writer.add_head_count(n_head) - print(f"gguf: head count = {n_head}") + logger.info(f"gguf: head count = {n_head}") if (n_head_kv := self.hparams.get("num_key_value_heads")) is not None: self.gguf_writer.add_head_count_kv(n_head_kv) - print(f"gguf: key-value head count = {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: self.gguf_writer.add_rope_freq_base(rope_theta) - print(f"gguf: rope theta = {rope_theta}") + logger.info(f"gguf: rope theta = {rope_theta}") if (f_rms_eps := self.hparams.get("rms_norm_eps")) is not None: self.gguf_writer.add_layer_norm_rms_eps(f_rms_eps) - print(f"gguf: rms norm epsilon = {f_rms_eps}") + logger.info(f"gguf: rms norm epsilon = {f_rms_eps}") if (f_norm_eps := self.find_hparam(["layer_norm_eps", "layer_norm_epsilon", "norm_epsilon"], optional=True)) is not None: self.gguf_writer.add_layer_norm_eps(f_norm_eps) - print(f"gguf: layer norm epsilon = {f_norm_eps}") + logger.info(f"gguf: layer norm epsilon = {f_norm_eps}") if (n_experts := self.hparams.get("num_local_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) - print(f"gguf: expert count = {n_experts}") + logger.info(f"gguf: expert count = {n_experts}") if (n_experts_used := self.hparams.get("num_experts_per_tok")) is not None: self.gguf_writer.add_expert_used_count(n_experts_used) - print(f"gguf: experts used count = {n_experts_used}") + logger.info(f"gguf: experts used count = {n_experts_used}") self.gguf_writer.add_file_type(self.ftype) - print(f"gguf: file type = {self.ftype}") + logger.info(f"gguf: file type = {self.ftype}") def write_tensors(self): block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) @@ -151,8 +154,7 @@ class Model(ABC): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -169,7 +171,7 @@ class Model(ABC): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -274,8 +276,8 @@ class Model(ABC): chktok = tokenizer.encode(chktxt) chkhsh = sha256(str(chktok).encode()).hexdigest() - print(f"chktok: {chktok}") - print(f"chkhsh: {chkhsh}") + logger.debug(f"chktok: {chktok}") + logger.debug(f"chkhsh: {chkhsh}") res = None @@ -308,22 +310,22 @@ class Model(ABC): res = "gpt-2" if res is None: - print("\n") - print("**************************************************************************************") - print("** WARNING: The BPE pre-tokenizer was not recognized!") - print("** There are 2 possible reasons for this:") - print("** - the model has not been added to convert-hf-to-gguf-update.py yet") - print("** - the pre-tokenization config has changed upstream") - print("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") - print("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") - print("**") - print(f"** chkhsh: {chkhsh}") - print("**************************************************************************************") - print("\n") + logger.warning("\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {chkhsh}") + logger.warning("**************************************************************************************") + logger.warning("\n") raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") - print(f"tokenizer.ggml.pre: {res}") - print(f"chkhsh: {chkhsh}") + logger.debug(f"tokenizer.ggml.pre: {res}") + logger.debug(f"chkhsh: {chkhsh}") return res @@ -439,9 +441,7 @@ class Model(ABC): if vocab_size > len(tokens): pad_count = vocab_size - len(tokens) - print( - f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]" - ) + logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") for i in range(1, pad_count + 1): tokens.append(f"[PAD{i}]") scores.append(-1000.0) @@ -553,7 +553,7 @@ class BloomModel(Model): ), axis=0, ) - print("re-format attention.linear_qkv.weight") + logger.info("re-format attention.linear_qkv.weight") elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) data = np.concatenate( @@ -564,13 +564,12 @@ class BloomModel(Model): ), axis=0, ) - print("re-format attention.linear_qkv.bias") + logger.info("re-format attention.linear_qkv.bias") # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -587,13 +586,13 @@ class BloomModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + logger.info(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) if not has_lm_head and name == "word_embeddings.weight": self.gguf_writer.add_tensor("output.weight", data) - print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + logger.info(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") @Model.register("MPTForCausalLM") @@ -653,8 +652,7 @@ class MPTModel(Model): else: new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -671,7 +669,7 @@ class MPTModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -697,8 +695,7 @@ class OrionModel(Model): elif "model_max_length" in self.hparams: ctx_length = self.hparams["model_max_length"] else: - print("gguf: can not find ctx length parameter.") - sys.exit() + raise ValueError("gguf: can not find ctx length parameter.") self.gguf_writer.add_file_type(self.ftype) self.gguf_writer.add_name(self.dir_model.name) @@ -736,8 +733,7 @@ class OrionModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -754,7 +750,7 @@ class OrionModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -779,8 +775,7 @@ class BaichuanModel(Model): elif "model_max_length" in self.hparams: ctx_length = self.hparams["model_max_length"] else: - print("gguf: can not find ctx length parameter.") - sys.exit() + raise ValueError("gguf: can not find ctx length parameter.") self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_source_hf_repo(hf_repo) @@ -809,7 +804,7 @@ class BaichuanModel(Model): for i in range(block_count): if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: - print(f"Unpacking and permuting layer {i}") + logger.info(f"Unpacking and permuting layer {i}") model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ self._reverse_hf_permute_part(w, 0, head_count, head_count) model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ @@ -834,8 +829,7 @@ class BaichuanModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -852,7 +846,7 @@ class BaichuanModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: @@ -937,8 +931,7 @@ class XverseModel(Model): elif "model_max_length" in self.hparams: ctx_length = self.hparams["model_max_length"] else: - print("gguf: can not find ctx length parameter.") - sys.exit() + raise ValueError("gguf: can not find ctx length parameter.") self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_source_hf_repo(hf_repo) @@ -987,8 +980,7 @@ class XverseModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1005,7 +997,7 @@ class XverseModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: @@ -1092,8 +1084,7 @@ class FalconModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1110,7 +1101,7 @@ class FalconModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1197,8 +1188,7 @@ class RefactModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1215,7 +1205,7 @@ class RefactModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1264,10 +1254,9 @@ class PersimmonModel(Model): data = data_torch.to(torch.float32).squeeze().numpy() new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1332,8 +1321,7 @@ class StableLMModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1350,7 +1338,7 @@ class StableLMModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.debug(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1366,8 +1354,7 @@ class StableLMModel(Model): merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): data = data.astype(np.float32) @@ -1375,7 +1362,7 @@ class StableLMModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1480,10 +1467,9 @@ class LlamaModel(Model): new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") - print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) continue @@ -1491,8 +1477,7 @@ class LlamaModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1509,7 +1494,7 @@ class LlamaModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1584,10 +1569,9 @@ class GrokModel(Model): new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") - print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) continue @@ -1595,8 +1579,7 @@ class GrokModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1613,7 +1596,7 @@ class GrokModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1646,7 +1629,7 @@ class DbrxModel(Model): self.gguf_writer.add_layer_norm_eps(1e-5) self.gguf_writer.add_file_type(self.ftype) - print(f"gguf: file type = {self.ftype}") + logger.info(f"gguf: file type = {self.ftype}") def write_tensors(self): block_count = self.hparams.get("n_layers") @@ -1689,8 +1672,7 @@ class DbrxModel(Model): # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1698,8 +1680,7 @@ class DbrxModel(Model): # Most of the codebase that takes in 1D tensors only handles F32 tensors # and most of the outputs tensors are F32. if data_dtype != np.float32 and n_dims == 1: - print(f"Can not map tensor {name!r}: all 1D tensors must be F32") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}: all 1D tensors must be F32") # if f32 desired, convert any float16 to float32 if self.ftype == 0 and data_dtype == np.float16: @@ -1709,7 +1690,7 @@ class DbrxModel(Model): if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + logger.debug(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1771,8 +1752,7 @@ class MiniCPMModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1789,7 +1769,7 @@ class MiniCPMModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1855,8 +1835,7 @@ class QwenModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1873,7 +1852,7 @@ class QwenModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -1950,10 +1929,9 @@ class Qwen2MoeModel(Model): new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") - print(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) continue @@ -1961,8 +1939,7 @@ class Qwen2MoeModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -1979,7 +1956,7 @@ class Qwen2MoeModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") + logger.debug(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -2024,8 +2001,7 @@ class GPT2Model(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -2042,13 +2018,13 @@ class GPT2Model(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) # note: GPT2 output is tied to (same as) wte in original model if new_name == "token_embd.weight": - print(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor("output.weight", data) @@ -2087,8 +2063,7 @@ class Phi3MiniModel(Model): tokenizer_path = self.dir_model / 'tokenizer.model' if not tokenizer_path.is_file(): - print(f'Error: Missing {tokenizer_path}', file=sys.stderr) - sys.exit(1) + raise ValueError(f'Error: Missing {tokenizer_path}') tokenizer = SentencePieceProcessor(str(tokenizer_path)) @@ -2126,7 +2101,7 @@ class Phi3MiniModel(Model): for key in added_tokens_json: token_id = added_tokens_json[key] if (token_id >= vocab_size): - print(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') continue tokens[token_id] = key.encode("utf-8") @@ -2208,8 +2183,7 @@ class PlamoModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") # shuffle for broadcasting of gqa in ggml_mul_mat if new_name.endswith("attn_q.weight"): @@ -2240,7 +2214,7 @@ class PlamoModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -2286,8 +2260,7 @@ class CodeShellModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -2304,13 +2277,13 @@ class CodeShellModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) if not has_lm_head and name == "transformer.wte.weight": self.gguf_writer.add_tensor("output.weight", data) - print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + logger.info(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") @Model.register("InternLM2ForCausalLM") @@ -2332,7 +2305,7 @@ class InternLM2Model(Model): toktypes: list[int] = [] if not tokenizer_path.is_file(): - print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + logger.error(f'Error: Missing {tokenizer_path}') sys.exit(1) sentencepiece_model = model.ModelProto() @@ -2349,7 +2322,7 @@ class InternLM2Model(Model): if text == b"\x00": # (TODO): fixme # Hack here and replace the \x00 characters. - print(f"InternLM2 convert token '{text}' to '🐉'!") + logger.debug(f"InternLM2 convert token '{text}' to '🐉'!") text = "🐉" toktype = SentencePieceTokenTypes.NORMAL @@ -2390,7 +2363,7 @@ class InternLM2Model(Model): # TODO: this is a hack, should be fixed # https://github.com/ggerganov/llama.cpp/pull/6745#issuecomment-2067687048 special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) - print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ + logger.warning(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ in chat mode so that the conversation can end normally.") special_vocab.add_to_gguf(self.gguf_writer) @@ -2435,8 +2408,7 @@ in chat mode so that the conversation can end normally.") # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -2453,7 +2425,7 @@ in chat mode so that the conversation can end normally.") if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) def write_tensors(self): @@ -2564,8 +2536,7 @@ class BertModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") # convert any unsupported data types to float32 if data_torch.dtype not in (torch.float16, torch.float32): @@ -2585,7 +2556,7 @@ class BertModel(Model): # if f32 desired, convert any float16 to float32 new_dtype = np.float32 - print(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}") if data.dtype != new_dtype: data = data.astype(new_dtype) @@ -2664,7 +2635,7 @@ class GemmaModel(Model): # lm_head is not used in llama.cpp, while autoawq will include this tensor in model # To prevent errors, skip loading lm_head.weight. if name == "lm_head.weight": - print(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") continue old_dtype = data_torch.dtype @@ -2681,8 +2652,7 @@ class GemmaModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -2693,7 +2663,7 @@ class GemmaModel(Model): if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -2721,7 +2691,7 @@ class MambaModel(Model): else: # Use the GPT-NeoX tokenizer when no tokenizer files are present tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf" - print(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") + logger.warning(f"Using tokenizer from '{os.path.relpath(tokenizer_path, os.getcwd())}'") neox_reader = gguf.GGUFReader(tokenizer_path, "r") field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) @@ -2793,17 +2763,16 @@ class MambaModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") if name.endswith(".A_log"): - print("A_log --> A ==> " + new_name) + logger.debug("A_log --> A ==> " + new_name) data_torch = -torch.exp(data_torch) # assuming token_embd.weight is seen before output.weight if tok_embd is not None and new_name == output_name: if torch.equal(tok_embd, data_torch): - print(f"{output_name} is equivalent to {tok_embd_name}, omitting") + logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") continue if new_name == tok_embd_name: tok_embd = data_torch @@ -2826,7 +2795,7 @@ class MambaModel(Model): if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -2885,8 +2854,7 @@ class OlmoModel(Model): # map tensor names new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) if new_name is None: - print(f"Can not map tensor {name!r}") - sys.exit() + raise ValueError(f"Can not map tensor {name!r}") n_dims = len(data.shape) data_dtype = data.dtype @@ -2903,7 +2871,7 @@ class OlmoModel(Model): if self.ftype == 1 and data_dtype == np.float32 and n_dims == 2: data = data.astype(np.float16) - print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") self.gguf_writer.add_tensor(new_name, data) @@ -2936,6 +2904,7 @@ def parse_args() -> argparse.Namespace: ) parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)") parser.add_argument("--model-name", type=str, default=None, help="name of the model") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") return parser.parse_args() @@ -2943,6 +2912,8 @@ def parse_args() -> argparse.Namespace: def main() -> None: args = parse_args() + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + dir_model = args.model if args.awq_path: @@ -2951,15 +2922,15 @@ def main() -> None: tmp_model_path = args.model / "weighted_model" dir_model = tmp_model_path if tmp_model_path.is_dir(): - print(f"{tmp_model_path} exists as a weighted model.") + logger.info(f"{tmp_model_path} exists as a weighted model.") else: tmp_model_path.mkdir(parents=True, exist_ok=True) - print("Saving new weighted model ...") + logger.info("Saving new weighted model ...") add_scale_weights(str(args.model), str(args.awq_path), str(tmp_model_path)) - print(f"Saved weighted model at {tmp_model_path}.") + logger.info(f"Saved weighted model at {tmp_model_path}.") if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file=sys.stderr) + logger.error(f'Error: {args.model} is not a directory') sys.exit(1) ftype_map = { @@ -2973,7 +2944,7 @@ def main() -> None: # output in the same directory as the model by default fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' - print(f"Loading model: {dir_model.name}") + logger.info(f"Loading model: {dir_model.name}") hparams = Model.load_hparams(dir_model) @@ -2981,20 +2952,20 @@ def main() -> None: model_class = Model.from_model_architecture(hparams["architectures"][0]) model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file) - print("Set model parameters") + logger.info("Set model parameters") model_instance.set_gguf_parameters() - print("Set model tokenizer") + logger.info("Set model tokenizer") model_instance.set_vocab() if args.vocab_only: - print(f"Exporting model vocab to '{fname_out}'") + logger.info(f"Exporting model vocab to '{fname_out}'") model_instance.write_vocab() else: - print(f"Exporting model to '{fname_out}'") + logger.info(f"Exporting model to '{fname_out}'") model_instance.write() - print(f"Model successfully exported to '{fname_out}'") + logger.info(f"Model successfully exported to '{fname_out}'") if __name__ == '__main__': diff --git a/convert-llama-ggml-to-gguf.py b/convert-llama-ggml-to-gguf.py index 5354b748b..9349de3b3 100755 --- a/convert-llama-ggml-to-gguf.py +++ b/convert-llama-ggml-to-gguf.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import os import struct @@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +logger = logging.getLogger("ggml-to-gguf") + class GGMLFormat(IntEnum): GGML = 0 @@ -125,7 +128,6 @@ class Tensor: self.start_offset = offset self.len_bytes = n_bytes offset += n_bytes - # print(n_dims, name_len, dtype, self.dims, self.name, pad) return offset - orig_offset @@ -175,7 +177,7 @@ class GGMLModel: offset += self.validate_header(data, offset) hp = Hyperparameters() offset += hp.load(data, offset) - print(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') + logger.info(f'* File format: {self.file_format.name}v{self.format_version} with ftype {hp.ftype.name}') self.validate_conversion(hp.ftype) vocab = Vocab(load_scores = self.file_format > GGMLFormat.GGML) offset += vocab.load(data, offset, hp.n_vocab) @@ -215,12 +217,12 @@ class GGMLToGGUF: if float(hp.n_head) / float(x) == gqa: n_kv_head = x assert n_kv_head is not None, "Couldn't determine n_kv_head from GQA param" - print(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}') + logger.info(f'- Guessed n_kv_head = {n_kv_head} based on GQA {cfg.gqa}') self.n_kv_head = n_kv_head self.name_map = gguf.get_tensor_name_map(gguf.MODEL_ARCH.LLAMA, ggml_model.hyperparameters.n_layer) def save(self): - print('* Preparing to save GGUF file') + logger.info('* Preparing to save GGUF file') gguf_writer = gguf.GGUFWriter( self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], @@ -230,11 +232,11 @@ class GGMLToGGUF: if self.special_vocab is not None: self.special_vocab.add_to_gguf(gguf_writer) self.add_tensors(gguf_writer) - print(" gguf: write header") + logger.info(" gguf: write header") gguf_writer.write_header_to_file() - print(" gguf: write metadata") + logger.info(" gguf: write metadata") gguf_writer.write_kv_data_to_file() - print(" gguf: write tensors") + logger.info(" gguf: write tensors") gguf_writer.write_tensors_to_file() gguf_writer.close() @@ -250,7 +252,7 @@ class GGMLToGGUF: name = cfg.name if cfg.name is not None else cfg.input.name except UnicodeDecodeError: name = None - print('* Adding model parameters and KV items') + logger.info('* Adding model parameters and KV items') if name is not None: gguf_writer.add_name(name) gguf_writer.add_description(desc) @@ -287,7 +289,7 @@ class GGMLToGGUF: toktypes = [] if self.vocab_override is not None: vo = self.vocab_override - print('* Adding vocab item(s)') + logger.info('* Adding vocab item(s)') for (idx, (vbytes, score, ttype)) in enumerate(vo.all_tokens()): tokens.append(vbytes) scores.append(score) @@ -299,7 +301,7 @@ class GGMLToGGUF: if len(toktypes) > 0: gguf_writer.add_token_types(toktypes) return - print(f'* Adding {hp.n_vocab} vocab item(s)') + logger.info(f'* Adding {hp.n_vocab} vocab item(s)') assert len(self.model.vocab.items) >= 3, 'Cannot handle unexpectedly short model vocab' for (tokid, (vbytes, vscore)) in enumerate(self.model.vocab.items): tt = 1 # Normal @@ -334,7 +336,7 @@ class GGMLToGGUF: def add_tensors(self, gguf_writer): tensor_map = self.name_map data = self.data - print(f'* Adding {len(self.model.tensors)} tensor(s)') + logger.info(f'* Adding {len(self.model.tensors)} tensor(s)') for tensor in self.model.tensors: name = str(tensor.name, 'UTF-8') mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) @@ -344,7 +346,6 @@ class GGMLToGGUF: temp = tempdims[1] tempdims[1] = tempdims[0] tempdims[0] = temp - # print(f'+ {tensor.name} | {mapped_name} {tensor.dims} :: {tempdims}') gguf_writer.add_tensor( mapped_name, data[tensor.start_offset:tensor.start_offset + tensor.len_bytes], @@ -401,33 +402,35 @@ def handle_args(): help="directory containing tokenizer.model, if separate from model file - only meaningful with --model-metadata-dir") parser.add_argument("--vocabtype", default="spm,hfft", help="vocab format - only meaningful with --model-metadata-dir and/or --vocab-dir (default: spm,hfft)") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") return parser.parse_args() def main(): cfg = handle_args() - print(f'* Using config: {cfg}') - print('\n=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===\n') + logging.basicConfig(level=logging.DEBUG if cfg.verbose else logging.INFO) + logger.info(f'* Using config: {cfg}') + logger.warning('=== WARNING === Be aware that this conversion script is best-effort. Use a native GGUF model if possible. === WARNING ===') if cfg.model_metadata_dir is None and (cfg.gqa == 1 or cfg.eps == '5.0e-06'): - print('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') + logger.info('- Note: If converting LLaMA2, specifying "--eps 1e-5" is required. 70B models also need "--gqa 8".') data = np.memmap(cfg.input, mode = 'r') model = GGMLModel() - print('* Scanning GGML input file') + logger.info('* Scanning GGML input file') offset = model.load(data, 0) # noqa - print(f'* GGML model hyperparameters: {model.hyperparameters}') + logger.info(f'* GGML model hyperparameters: {model.hyperparameters}') vocab_override = None params_override = None special_vocab = None if cfg.model_metadata_dir is not None: (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters) - print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') - print(f'* Overriding params: {params_override}') - print(f'* Overriding vocab: {vocab_override}') - print(f'* Special vocab: {special_vocab}') + logger.info('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') + logger.info(f'* Overriding params: {params_override}') + logger.info(f'* Overriding vocab: {vocab_override}') + logger.info(f'* Special vocab: {special_vocab}') else: - print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') + logger.warning('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') if model.file_format == GGMLFormat.GGML: - print('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') + logger.info('! This is a very old GGML file that does not contain vocab scores. Strongly recommend using model metadata!') converter = GGMLToGGUF( model, data, cfg, params_override = params_override, @@ -435,7 +438,7 @@ def main(): special_vocab = special_vocab ) converter.save() - print(f'* Successful completion. Output saved to: {cfg.output}') + logger.info(f'* Successful completion. Output saved to: {cfg.output}') if __name__ == '__main__': diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 9a9936dec..39536feb9 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import json import os import struct @@ -15,6 +16,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) import gguf +logger = logging.getLogger("lora-to-gguf") + NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} @@ -48,11 +51,9 @@ def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_ty if __name__ == '__main__': if len(sys.argv) < 2: - print(f"Usage: python {sys.argv[0]} [arch]") - print( - "Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'" - ) - print(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") + logger.info(f"Usage: python {sys.argv[0]} [arch]") + logger.info("Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'") + logger.info(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") sys.exit(1) input_json = os.path.join(sys.argv[1], "adapter_config.json") @@ -70,7 +71,7 @@ if __name__ == '__main__': arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - print(f"Error: unsupported architecture {arch_name}") + logger.error(f"Error: unsupported architecture {arch_name}") sys.exit(1) arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] @@ -80,21 +81,21 @@ if __name__ == '__main__': params = json.load(f) if params["peft_type"] != "LORA": - print(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") + logger.error(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") sys.exit(1) if params["fan_in_fan_out"] is True: - print("Error: param fan_in_fan_out is not supported") + logger.error("Error: param fan_in_fan_out is not supported") sys.exit(1) if params["bias"] is not None and params["bias"] != "none": - print("Error: param bias is not supported") + logger.error("Error: param bias is not supported") sys.exit(1) # TODO: these seem to be layers that have been trained but without lora. # doesn't seem widely used but eventually should be supported if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - print("Error: param modules_to_save is not supported") + logger.error("Error: param modules_to_save is not supported") sys.exit(1) with open(output_path, "wb") as fout: @@ -125,13 +126,13 @@ if __name__ == '__main__': suffix = k[-len(lora_suffixes[0]):] k = k[: -len(lora_suffixes[0])] else: - print(f"Error: unrecognized tensor name {orig_k}") + logger.error(f"Error: unrecognized tensor name {orig_k}") sys.exit(1) tname = name_map.get_name(k) if tname is None: - print(f"Error: could not map tensor name {orig_k}") - print(" Note: the arch parameter must be specified if the model is not llama") + logger.error(f"Error: could not map tensor name {orig_k}") + logger.error(" Note: the arch parameter must be specified if the model is not llama") sys.exit(1) if suffix == ".lora_A.weight": @@ -141,8 +142,8 @@ if __name__ == '__main__': else: assert False - print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") + logger.info(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") write_tensor_header(fout, tname, t.shape, t.dtype) t.tofile(fout) - print(f"Converted {input_json} and {input_model} to {output_path}") + logger.info(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py index aba575426..07dcade74 100755 --- a/convert-persimmon-to-gguf.py +++ b/convert-persimmon-to-gguf.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import os import sys @@ -14,6 +15,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +logger = logging.getLogger("persimmon-to-gguf") + def _flatten_dict(dct, tensors, prefix=None): assert isinstance(dct, dict) @@ -30,9 +33,9 @@ def _flatten_dict(dct, tensors, prefix=None): def _get_sentencepiece_tokenizer_info(dir_model: Path): tokenizer_path = dir_model / 'adept_vocab.model' - print('gguf: getting sentencepiece tokenizer from', tokenizer_path) + logger.info('getting sentencepiece tokenizer from', tokenizer_path) tokenizer = SentencePieceProcessor(str(tokenizer_path)) - print('gguf: adding tokens') + logger.info('adding tokens') tokens: list[bytes] = [] scores: list[float] = [] toktypes: list[int] = [] @@ -67,8 +70,10 @@ def main(): parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") - parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") + parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") args = parser.parse_args() + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) sys.path.append(str(args.adept_inference_dir)) persimmon_model = torch.load(args.ckpt_path) hparams = persimmon_model['args'] @@ -107,7 +112,7 @@ def main(): gguf_writer.add_eos_token_id(71013) tensor_map = gguf.get_tensor_name_map(arch, block_count) - print(tensor_map) + logger.info(tensor_map) for name in tensors.keys(): data_torch = tensors[name] if name.endswith(".self_attention.rotary_emb.inv_freq"): @@ -117,22 +122,21 @@ def main(): data = data_torch.to(torch.float32).squeeze().numpy() new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() + raise ValueError(f"Can not map tensor '{name}'") + n_dims = len(data.shape) - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}") gguf_writer.add_tensor(new_name, data) - print("gguf: write header") + logger.info("gguf: write header") gguf_writer.write_header_to_file() - print("gguf: write metadata") + logger.info("gguf: write metadata") gguf_writer.write_kv_data_to_file() - print("gguf: write tensors") + logger.info("gguf: write tensors") gguf_writer.write_tensors_to_file() gguf_writer.close() - print(f"gguf: model successfully exported to '{args.outfile}'") - print("") + logger.info(f"gguf: model successfully exported to '{args.outfile}'") if __name__ == '__main__': diff --git a/convert.py b/convert.py index 1c700cf6a..7f0b6b749 100755 --- a/convert.py +++ b/convert.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import concurrent.futures import enum @@ -35,6 +36,8 @@ import gguf if TYPE_CHECKING: from typing_extensions import Self, TypeAlias +logger = logging.getLogger("convert") + if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) @@ -643,7 +646,6 @@ class LlamaHfVocab(Vocab): def permute(weights: NDArray, n_head: int, n_head_kv: int) -> NDArray: - # print( "permute debug " + str(weights.shape[0]) + " x " + str(weights.shape[1]) + " nhead " + str(n_head) + " nheadkv " + str(n_kv_head) ) if n_head_kv is not None and n_head != n_head_kv: n_head = n_head_kv return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) @@ -1033,12 +1035,12 @@ def check_vocab_size(params: Params, vocab: BaseVocab, pad_vocab: bool = False) # Check for a vocab size mismatch if params.n_vocab == vocab.vocab_size: - print("Ignoring added_tokens.json since model matches vocab size without it.") + logger.warning("Ignoring added_tokens.json since model matches vocab size without it.") return if pad_vocab and params.n_vocab > vocab.vocab_size: pad_count = params.n_vocab - vocab.vocab_size - print( + logger.debug( f"Padding vocab with {pad_count} token(s) - through " ) for i in range(1, pad_count + 1): @@ -1166,7 +1168,7 @@ class OutputFile: elapsed = time.time() - start size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) padi = len(str(len(model))) - print( + logger.info( f"[{i + 1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}" ) self.gguf.write_tensor_data(ndarray) @@ -1281,12 +1283,12 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> # HF models permut or pack some of the tensors, so we need to undo that for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" in model: - print(f"Permuting layer {i}") + logger.debug(f"Permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head, params.n_head_kv) # tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: - print(f"Unpacking and permuting layer {i}") + logger.debug(f"Unpacking and permuting layer {i}") tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) @@ -1299,15 +1301,15 @@ def convert_model_names(model: LazyModel, params: Params, skip_unknown: bool) -> tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) if name_new is None: if skip_unknown: - print(f"Unexpected tensor name: {name} - skipping") + logger.warning(f"Unexpected tensor name: {name} - skipping") continue raise ValueError(f"Unexpected tensor name: {name}. Use --skip-unknown to ignore it (e.g. LLaVA)") if tensor_type in should_skip: - print(f"skipping tensor {name_new}") + logger.debug(f"skipping tensor {name_new}") continue - print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") + logger.debug(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") out[name_new] = lazy_tensor return out @@ -1372,7 +1374,7 @@ def load_some_model(path: Path) -> ModelPlus: paths = find_multifile_paths(path) models_plus: list[ModelPlus] = [] for path in paths: - print(f"Loading model file {path}") + logger.info(f"Loading model file {path}") models_plus.append(lazy_load_file(path)) model_plus = merge_multifile_models(models_plus) @@ -1413,7 +1415,7 @@ class VocabFactory: else: raise FileNotFoundError(f"Could not find a tokenizer matching any of {vocab_types}") - print(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}") + logger.info(f"Loaded vocab file {vocab.fname_tokenizer!r}, type {vocab.name!r}") return vocab def load_vocab(self, vocab_types: list[str] | None, model_parent_path: Path) -> tuple[BaseVocab, gguf.SpecialVocab]: @@ -1438,19 +1440,19 @@ def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: }[file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" if ret in model_paths: - sys.stderr.write( + logger.error( f"Error: Default output path ({ret}) would overwrite the input. " - "Please explicitly specify a path using --outfile.\n") + "Please explicitly specify a path using --outfile.") sys.exit(1) return ret def do_dump_model(model_plus: ModelPlus) -> None: - print(f"model_plus.paths = {model_plus.paths!r}") - print(f"model_plus.format = {model_plus.format!r}") - print(f"model_plus.vocab = {model_plus.vocab!r}") + print(f"model_plus.paths = {model_plus.paths!r}") # noqa: NP100 + print(f"model_plus.format = {model_plus.format!r}") # noqa: NP100 + print(f"model_plus.vocab = {model_plus.vocab!r}") # noqa: NP100 for name, lazy_tensor in model_plus.model.items(): - print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") + print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") # noqa: NP100 def main(args_in: list[str] | None = None) -> None: @@ -1473,8 +1475,18 @@ def main(args_in: list[str] | None = None) -> None: parser.add_argument("--big-endian", action="store_true", help="model is executed on big endian machine") parser.add_argument("--pad-vocab", action="store_true", help="add pad tokens when model vocab expects more than tokenizer metadata provides") parser.add_argument("--skip-unknown", action="store_true", help="skip unknown tensor names instead of failing") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") args = parser.parse_args(args_in) + + if args.verbose: + logging.basicConfig(level=logging.DEBUG) + elif args.dump_single or args.dump: + # Avoid printing anything besides the dump output + logging.basicConfig(level=logging.WARNING) + else: + logging.basicConfig(level=logging.INFO) + if args.no_vocab and args.vocab_only: raise ValueError("--vocab-only does not make sense with --no-vocab") @@ -1491,6 +1503,7 @@ def main(args_in: list[str] | None = None) -> None: if args.dump: do_dump_model(model_plus) return + endianess = gguf.GGUFEndian.LITTLE if args.big_endian: endianess = gguf.GGUFEndian.BIG @@ -1513,7 +1526,7 @@ def main(args_in: list[str] | None = None) -> None: "q8_0": GGMLFileType.MostlyQ8_0, }[args.outtype] - print(f"params = {params}") + logger.info(f"params = {params}") model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) @@ -1528,15 +1541,14 @@ def main(args_in: list[str] | None = None) -> None: outfile = args.outfile OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, endianess=endianess, pad_vocab=args.pad_vocab) - print(f"Wrote {outfile}") + logger.info(f"Wrote {outfile}") return if model_plus.vocab is not None and args.vocab_dir is None and not args.no_vocab: vocab = model_plus.vocab - print(f"Vocab info: {vocab}") - print(f"Special vocab info: {special_vocab}") - + logger.info(f"Vocab info: {vocab}") + logger.info(f"Special vocab info: {special_vocab}") model = model_plus.model model = convert_model_names(model, params, args.skip_unknown) ftype = pick_output_type(model, args.outtype) @@ -1544,11 +1556,11 @@ def main(args_in: list[str] | None = None) -> None: outfile = args.outfile or default_outfile(model_plus.paths, ftype) params.ftype = ftype - print(f"Writing {outfile}, format {ftype}") + logger.info(f"Writing {outfile}, format {ftype}") OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency=args.concurrency, endianess=endianess, pad_vocab=args.pad_vocab) - print(f"Wrote {outfile}") + logger.info(f"Wrote {outfile}") if __name__ == '__main__': diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 5dd700963..1d9a0cc82 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -1,11 +1,14 @@ #!/usr/bin/env python +import logging import argparse import asyncio import os import sys from tempfile import gettempdir, NamedTemporaryFile +logger = logging.getLogger("ggml-vk-generate-shaders") + shader_f32 = """ #define FLOAT_TYPE float """ @@ -2498,7 +2501,7 @@ async def string_to_spv(name, code, defines, fp16=True): stdout, stderr = await proc.communicate() - print(" ".join(cmd)) + logger.info(" ".join(cmd)) if proc.returncode: raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}") @@ -2507,7 +2510,7 @@ async def string_to_spv(name, code, defines, fp16=True): cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())]) - print(f"ERROR compiling {name}\n\n{code_with_lines}\n\n{error}") + logger.error(f"cannot compile {name}\n\n{code_with_lines}\n\n{error}") f.close() os.remove(f.name) sys.exit(proc.returncode) @@ -2520,7 +2523,7 @@ async def string_to_spv(name, code, defines, fp16=True): async def main(): - print("ggml_vulkan: Generating and compiling shaders to SPIR-V") + logger.info("ggml_vulkan: Generating and compiling shaders to SPIR-V") tasks = [] @@ -2768,9 +2771,12 @@ if __name__ == "__main__": parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator") parser.add_argument("--glslc", help="Path to glslc") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") args = parser.parse_args() + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + if args.glslc: GLSLC = args.glslc diff --git a/gguf-py/examples/reader.py b/gguf-py/examples/reader.py index 62e0769da..d841048c6 100644 --- a/gguf-py/examples/reader.py +++ b/gguf-py/examples/reader.py @@ -1,8 +1,10 @@ #!/usr/bin/env python3 +import logging import sys from pathlib import Path from gguf.gguf_reader import GGUFReader +logger = logging.getLogger("reader") sys.path.insert(0, str(Path(__file__).parent.parent)) @@ -18,28 +20,28 @@ def read_gguf_file(gguf_file_path): reader = GGUFReader(gguf_file_path) # List all key-value pairs in a columnized format - print("Key-Value Pairs:") + print("Key-Value Pairs:") # noqa: NP100 max_key_length = max(len(key) for key in reader.fields.keys()) for key, field in reader.fields.items(): value = field.parts[field.data[0]] - print(f"{key:{max_key_length}} : {value}") - print("----") + print(f"{key:{max_key_length}} : {value}") # noqa: NP100 + print("----") # noqa: NP100 # List all tensors - print("Tensors:") + print("Tensors:") # noqa: NP100 tensor_info_format = "{:<30} | Shape: {:<15} | Size: {:<12} | Quantization: {}" - print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) - print("-" * 80) + print(tensor_info_format.format("Tensor Name", "Shape", "Size", "Quantization")) # noqa: NP100 + print("-" * 80) # noqa: NP100 for tensor in reader.tensors: shape_str = "x".join(map(str, tensor.shape)) size_str = str(tensor.n_elements) quantization_str = tensor.tensor_type.name - print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) + print(tensor_info_format.format(tensor.name, shape_str, size_str, quantization_str)) # noqa: NP100 if __name__ == '__main__': if len(sys.argv) < 2: - print("Usage: reader.py ") + logger.info("Usage: reader.py ") sys.exit(1) gguf_file_path = sys.argv[1] read_gguf_file(gguf_file_path) diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 6d597bfd9..4f232e18d 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -1,6 +1,5 @@ from __future__ import annotations -import sys from enum import Enum, IntEnum, auto from typing import Any @@ -854,8 +853,7 @@ class GGUFValueType(IntEnum): return GGUFValueType.INT32 # TODO: need help with 64-bit types in Python else: - print("Unknown type:", type(val)) - sys.exit() + raise ValueError(f"Unknown type: {type(val)}") # Note: Does not support GGML_QKK_64 diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 2bdb15525..db8525d85 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -4,6 +4,7 @@ # from __future__ import annotations +import logging import os from collections import OrderedDict from typing import Any, Literal, NamedTuple, TypeVar, Union @@ -27,6 +28,7 @@ from gguf.constants import ( GGUFValueType, ) +logger = logging.getLogger(__name__) READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] @@ -142,7 +144,7 @@ class GGUFReader: # TODO: add option to generate error on duplicate keys # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') - print(f'Warning: Duplicate key {field.name} at offset {field.offset}') + logger.warning(f'Duplicate key {field.name} at offset {field.offset}') self.fields[field.name + '_{}'.format(field.offset)] = field else: self.fields[field.name] = field diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 089aece87..d9cfbf711 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -1,5 +1,6 @@ from __future__ import annotations +import logging import os import shutil import struct @@ -24,6 +25,8 @@ from .constants import ( TokenType, ) +logger = logging.getLogger(__name__) + class WriterState(Enum): EMPTY = auto() @@ -67,7 +70,7 @@ class GGUFWriter: self.use_temp_file = use_temp_file self.temp_file = None self.tensors = [] - print("gguf: This GGUF file is for {0} Endian only".format( + logger.info("gguf: This GGUF file is for {0} Endian only".format( "Big" if self.endianess == GGUFEndian.BIG else "Little", )) self.state = WriterState.EMPTY diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 378eaecad..c97a78f39 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -1,13 +1,15 @@ from __future__ import annotations +import logging import json import os -import sys from pathlib import Path from typing import Any, Callable from .gguf_writer import GGUFWriter +logger = logging.getLogger(__name__) + class SpecialVocab: merges: list[str] @@ -40,38 +42,29 @@ class SpecialVocab: def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: if self.merges: if not quiet: - print(f'gguf: Adding {len(self.merges)} merge(s).') + logger.info(f'Adding {len(self.merges)} merge(s).') gw.add_token_merges(self.merges) elif self.load_merges: - print( - 'gguf: WARNING: Adding merges requested but no merges found, output may be non-functional.', - file = sys.stderr, - ) + logger.warning('Adding merges requested but no merges found, output may be non-functional.') for typ, tokid in self.special_token_ids.items(): id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) if id_handler is None: - print( - f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping', - file = sys.stderr, - ) + logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping') continue if not quiet: - print(f'gguf: Setting special token type {typ} to {tokid}') + logger.info(f'Setting special token type {typ} to {tokid}') id_handler(tokid) for typ, value in self.add_special_token.items(): add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) if add_handler is None: - print( - f'gguf: WARNING: No handler for add_{typ}_token with value {value} - skipping', - file = sys.stderr, - ) + logger.warning(f'No handler for add_{typ}_token with value {value} - skipping') continue if not quiet: - print(f'gguf: Setting add_{typ}_token to {value}') + logger.info(f'Setting add_{typ}_token to {value}') add_handler(value) if self.chat_template is not None: if not quiet: - print(f'gguf: Setting chat_template to {self.chat_template}') + logger.info(f'Setting chat_template to {self.chat_template}') gw.add_chat_template(self.chat_template) def _load(self, path: Path) -> None: @@ -99,10 +92,7 @@ class SpecialVocab: continue parts = line.split(None, 3) if len(parts) != 2: - print( - f'gguf: WARNING: {merges_file.name}: Line {line_num}: Entry malformed, ignoring', - file = sys.stderr, - ) + logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring') continue merges.append(f'{parts[0]} {parts[1]}') self.merges = merges @@ -118,10 +108,7 @@ class SpecialVocab: return self.special_token_ids[typ] = tid return - print( - f'gguf: WARNING: Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping', - file = sys.stderr, - ) + logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping') def _try_load_from_tokenizer_json(self, path: Path) -> bool: tokenizer_file = path / 'tokenizer.json' @@ -144,10 +131,7 @@ class SpecialVocab: if chat_template is None or isinstance(chat_template, (str, list)): self.chat_template = chat_template else: - print( - f'gguf: WARNING: Bad type for chat_template field in {tokenizer_config_file!r} - ignoring', - file = sys.stderr - ) + logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring') for typ in self.special_token_types: add_entry = tokenizer_config.get(f'add_{typ}_token') if isinstance(add_entry, bool): diff --git a/gguf-py/scripts/gguf-convert-endian.py b/gguf-py/scripts/gguf-convert-endian.py index 10a16ad06..b698af0fe 100755 --- a/gguf-py/scripts/gguf-convert-endian.py +++ b/gguf-py/scripts/gguf-convert-endian.py @@ -1,9 +1,11 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import os import sys +from tqdm import tqdm from pathlib import Path import numpy as np @@ -14,6 +16,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / import gguf +logger = logging.getLogger("gguf-convert-endian") + def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None: if np.uint32(1) == np.uint32(1).newbyteorder("<"): @@ -29,11 +33,11 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None else: file_endian = host_endian order = host_endian if args.order == "native" else args.order - print(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") + logger.info(f"* Host is {host_endian.upper()} endian, GGUF file seems to be {file_endian.upper()} endian") if file_endian == order: - print(f"* File is already {order.upper()} endian. Nothing to do.") + logger.info(f"* File is already {order.upper()} endian. Nothing to do.") sys.exit(0) - print("* Checking tensors for conversion compatibility") + logger.info("* Checking tensors for conversion compatibility") for tensor in reader.tensors: if tensor.tensor_type not in ( gguf.GGMLQuantizationType.F32, @@ -41,51 +45,64 @@ def convert_byteorder(reader: gguf.GGUFReader, args: argparse.Namespace) -> None gguf.GGMLQuantizationType.Q8_0, ): raise ValueError(f"Cannot handle type {tensor.tensor_type.name} for tensor {repr(tensor.name)}") - print(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}") + logger.info(f"* Preparing to convert from {file_endian.upper()} to {order.upper()}") if args.dry_run: return - print("\n*** Warning *** Warning *** Warning **") - print("* This conversion process may damage the file. Ensure you have a backup.") + logger.warning("*** Warning *** Warning *** Warning **") + logger.warning("* This conversion process may damage the file. Ensure you have a backup.") if order != host_endian: - print("* Requested endian differs from host, you will not be able to load the model on this machine.") - print("* The file will be modified immediately, so if conversion fails or is interrupted") - print("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") + logger.warning("* Requested endian differs from host, you will not be able to load the model on this machine.") + logger.warning("* The file will be modified immediately, so if conversion fails or is interrupted") + logger.warning("* the file will be corrupted. Enter exactly YES if you are positive you want to proceed:") response = input("YES, I am sure> ") if response != "YES": - print("You didn't enter YES. Okay then, see ya!") + logger.warning("You didn't enter YES. Okay then, see ya!") sys.exit(0) - print(f"\n* Converting fields ({len(reader.fields)})") + logger.info(f"* Converting fields ({len(reader.fields)})") for idx, field in enumerate(reader.fields.values()): - print(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") + logger.info(f"- {idx:4}: Converting field {repr(field.name)}, part count: {len(field.parts)}") for part in field.parts: part.byteswap(inplace=True) - print(f"\n* Converting tensors ({len(reader.tensors)})") - for idx, tensor in enumerate(reader.tensors): - print( - f" - {idx:4}: Converting tensor {repr(tensor.name)}, type={tensor.tensor_type.name}, " - f"elements={tensor.n_elements}... ", - end="", + logger.info(f"* Converting tensors ({len(reader.tensors)})") + + for idx, tensor in enumerate(pbar := tqdm(reader.tensors, desc="Converting tensor")): + log_message = ( + f"Converting tensor {repr(tensor.name)}, " + f"type={tensor.tensor_type.name}, " + f"elements={tensor.n_elements} " ) - tensor_type = tensor.tensor_type + + # Byte-swap each part of the tensor's field for part in tensor.field.parts: part.byteswap(inplace=True) - if tensor_type != gguf.GGMLQuantizationType.Q8_0: + + # Byte-swap tensor data if necessary + if tensor.tensor_type == gguf.GGMLQuantizationType.Q8_0: + # Handle Q8_0 tensor blocks (block_q8_0) + # Specific handling of block_q8_0 is required. + # Each block_q8_0 consists of an f16 delta (scaling factor) followed by 32 int8 quantizations. + + block_size = 34 # 34 bytes = + 32 * + + n_blocks = len(tensor.data) // block_size + for block_num in (inner_pbar := tqdm(range(n_blocks), desc="Byte-swapping Blocks", leave=False)): + block_offs = block_num * block_size + + # Byte-Swap f16 sized delta field + delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) + delta.byteswap(inplace=True) + + # Byte-Swap Q8 weights + if block_num % 100000 == 0: + inner_pbar.set_description(f"Byte-swapping Blocks [{(n_blocks - block_num) // n_blocks}]") + + else: + # Handle other tensor types tensor.data.byteswap(inplace=True) - print() - continue - # A Q8_0 block consists of a f16 delta followed by 32 int8 quants, so 34 bytes - block_size = 34 - n_blocks = len(tensor.data) // block_size - for block_num in range(n_blocks): - block_offs = block_num * block_size - # I know I said f16, but it doesn't matter here - any simple 16 bit type works. - delta = tensor.data[block_offs:block_offs + 2].view(dtype=np.uint16) - delta.byteswap(inplace=True) - if block_num % 100000 == 0: - print(f"[{(n_blocks - block_num) // 1000}K]", end="") - sys.stdout.flush() - print() - print("* Completion") + + pbar.set_description(log_message) + + logger.info("* Completion") def main() -> None: @@ -102,8 +119,13 @@ def main() -> None: "--dry-run", action="store_true", help="Don't actually change anything", ) + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) - print(f'* Loading: {args.model}') + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + logger.info(f'* Loading: {args.model}') reader = gguf.GGUFReader(args.model, 'r' if args.dry_run else 'r+') convert_byteorder(reader, args) diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py index dbf891508..2d3c3943f 100755 --- a/gguf-py/scripts/gguf-dump.py +++ b/gguf-py/scripts/gguf-dump.py @@ -1,6 +1,7 @@ #!/usr/bin/env python3 from __future__ import annotations +import logging import argparse import os import sys @@ -15,6 +16,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / from gguf import GGUFReader, GGUFValueType # noqa: E402 +logger = logging.getLogger("gguf-dump") + def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: host_endian = 'LITTLE' if np.uint32(1) == np.uint32(1).newbyteorder("<") else 'BIG' @@ -29,8 +32,8 @@ def get_file_host_endian(reader: GGUFReader) -> tuple[str, str]: # please see the comments in the modify_gguf.py example. def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: host_endian, file_endian = get_file_host_endian(reader) - print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') - print(f'\n* Dumping {len(reader.fields)} key/value pair(s)') + print(f'* File is {file_endian} endian, script is running on a {host_endian} endian host.') # noqa: NP100 + print(f'* Dumping {len(reader.fields)} key/value pair(s)') # noqa: NP100 for n, field in enumerate(reader.fields.values(), 1): if not field.types: pretty_type = 'N/A' @@ -39,20 +42,21 @@ def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: pretty_type = '[' * nest_count + str(field.types[-1].name) + ']' * nest_count else: pretty_type = str(field.types[-1].name) - print(f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}', end = '') + + log_message = f' {n:5}: {pretty_type:10} | {len(field.data):8} | {field.name}' if len(field.types) == 1: curr_type = field.types[0] if curr_type == GGUFValueType.STRING: - print(' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])), end = '') + log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])) elif field.types[0] in reader.gguf_scalar_to_np: - print(' = {0}'.format(field.parts[-1][0]), end = '') - print() + log_message += ' = {0}'.format(field.parts[-1][0]) + print(log_message) # noqa: NP100 if args.no_tensors: return - print(f'\n* Dumping {len(reader.tensors)} tensor(s)') + print(f'* Dumping {len(reader.tensors)} tensor(s)') # noqa: NP100 for n, tensor in enumerate(reader.tensors, 1): prettydims = ', '.join('{0:5}'.format(d) for d in list(tensor.shape) + [1] * (4 - len(tensor.shape))) - print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') + print(f' {n:5}: {tensor.n_elements:10} | {prettydims} | {tensor.tensor_type.name:7} | {tensor.name}') # noqa: NP100 def dump_metadata_json(reader: GGUFReader, args: argparse.Namespace) -> None: @@ -103,10 +107,17 @@ def main() -> None: parser.add_argument("--no-tensors", action="store_true", help="Don't dump tensor metadata") parser.add_argument("--json", action="store_true", help="Produce JSON output") parser.add_argument("--json-array", action="store_true", help="Include full array values in JSON output (long)") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + if not args.json: - print(f'* Loading: {args.model}') + logger.info(f'* Loading: {args.model}') + reader = GGUFReader(args.model, 'r') + if args.json: dump_metadata_json(reader, args) else: diff --git a/gguf-py/scripts/gguf-set-metadata.py b/gguf-py/scripts/gguf-set-metadata.py index 3ebdfa898..e35b651b8 100755 --- a/gguf-py/scripts/gguf-set-metadata.py +++ b/gguf-py/scripts/gguf-set-metadata.py @@ -1,4 +1,5 @@ #!/usr/bin/env python3 +import logging import argparse import os import sys @@ -10,6 +11,8 @@ if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / from gguf import GGUFReader # noqa: E402 +logger = logging.getLogger("gguf-set-metadata") + def minimal_example(filename: str) -> None: reader = GGUFReader(filename, 'r+') @@ -41,36 +44,33 @@ def minimal_example(filename: str) -> None: def set_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: field = reader.get_field(args.key) if field is None: - print(f'! Field {repr(args.key)} not found', file = sys.stderr) + logger.error(f'! Field {repr(args.key)} not found') sys.exit(1) # Note that field.types is a list of types. This is because the GGUF # format supports arrays. For example, an array of UINT32 would # look like [GGUFValueType.ARRAY, GGUFValueType.UINT32] handler = reader.gguf_scalar_to_np.get(field.types[0]) if field.types else None if handler is None: - print( - f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}', - file = sys.stderr, - ) + logger.error(f'! This tool only supports changing simple values, {repr(args.key)} has unsupported type {field.types}') sys.exit(1) current_value = field.parts[field.data[0]][0] new_value = handler(args.value) - print(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') + logger.info(f'* Preparing to change field {repr(args.key)} from {current_value} to {new_value}') if current_value == new_value: - print(f'- Key {repr(args.key)} already set to requested value {current_value}') + logger.info(f'- Key {repr(args.key)} already set to requested value {current_value}') sys.exit(0) if args.dry_run: sys.exit(0) if not args.force: - print('*** Warning *** Warning *** Warning **') - print('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') - print('* Enter exactly YES if you are positive you want to proceed:') + logger.warning('*** Warning *** Warning *** Warning **') + logger.warning('* Changing fields in a GGUF file can make it unusable. Proceed at your own risk.') + logger.warning('* Enter exactly YES if you are positive you want to proceed:') response = input('YES, I am sure> ') if response != 'YES': - print("You didn't enter YES. Okay then, see ya!") + logger.info("You didn't enter YES. Okay then, see ya!") sys.exit(0) field.parts[field.data[0]][0] = new_value - print('* Field changed. Successful completion.') + logger.info('* Field changed. Successful completion.') def main() -> None: @@ -80,8 +80,13 @@ def main() -> None: parser.add_argument("value", type=str, help="Metadata value to set") parser.add_argument("--dry-run", action="store_true", help="Don't actually change anything") parser.add_argument("--force", action="store_true", help="Change the field without confirmation") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + args = parser.parse_args(None if len(sys.argv) > 1 else ["--help"]) - print(f'* Loading: {args.model}') + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + logger.info(f'* Loading: {args.model}') reader = GGUFReader(args.model, 'r' if args.dry_run else 'r+') set_metadata(reader, args) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index ef7f19ecb..3892fd25c 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 +import logging import argparse import heapq import sys @@ -11,9 +12,11 @@ try: import git from tabulate import tabulate except ImportError as e: - print("ERROR: the following Python libraries are required: GitPython, tabulate.") + print("the following Python libraries are required: GitPython, tabulate.") # noqa: NP100 raise e +logger = logging.getLogger("compare-llama-bench") + # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", @@ -94,8 +97,7 @@ parser.add_argument("-s", "--show", help=help_s) known_args, unknown_args = parser.parse_known_args() if unknown_args: - print(f"ERROR: Received unknown args: {unknown_args}.") - print() + logger.error(f"Received unknown args: {unknown_args}.") parser.print_help() sys.exit(1) @@ -108,8 +110,7 @@ if input_file is None: input_file = sqlite_files[0] if input_file is None: - print("ERROR: Cannot find a suitable input file, please provide one.") - print() + logger.error("Cannot find a suitable input file, please provide one.") parser.print_help() sys.exit(1) @@ -194,23 +195,19 @@ if known_args.baseline is not None: hexsha8_baseline = get_commit_hexsha8(known_args.baseline) name_baseline = known_args.baseline if hexsha8_baseline is None: - print(f"ERROR: cannot find data for baseline={known_args.baseline}.") + logger.error(f"cannot find data for baseline={known_args.baseline}.") sys.exit(1) # Otherwise, search for the most recent parent of master for which there is data: elif repo is not None: hexsha8_baseline = find_parent_in_data(repo.heads.master.commit) if hexsha8_baseline is None: - print("ERROR: No baseline was provided and did not find data for any master branch commits.") - print() + logger.error("No baseline was provided and did not find data for any master branch commits.") parser.print_help() sys.exit(1) else: - print( - "ERROR: No baseline was provided and the current working directory " - "is not part of a git repository from which a baseline could be inferred." - ) - print() + logger.error("No baseline was provided and the current working directory " + "is not part of a git repository from which a baseline could be inferred.") parser.print_help() sys.exit(1) @@ -227,7 +224,7 @@ if known_args.compare is not None: hexsha8_compare = get_commit_hexsha8(known_args.compare) name_compare = known_args.compare if hexsha8_compare is None: - print(f"ERROR: cannot find data for compare={known_args.compare}.") + logger.error(f"cannot find data for compare={known_args.compare}.") sys.exit(1) # Otherwise, search for the commit for llama-bench was most recently run # and that is not a parent of master: @@ -241,16 +238,12 @@ elif repo is not None: break if hexsha8_compare is None: - print("ERROR: No compare target was provided and did not find data for any non-master commits.") - print() + logger.error("No compare target was provided and did not find data for any non-master commits.") parser.print_help() sys.exit(1) else: - print( - "ERROR: No compare target was provided and the current working directory " - "is not part of a git repository from which a compare target could be inferred." - ) - print() + logger.error("No compare target was provided and the current working directory " + "is not part of a git repository from which a compare target could be inferred.\n") parser.print_help() sys.exit(1) @@ -284,8 +277,7 @@ if known_args.show is not None: if prop not in KEY_PROPERTIES[:-2]: # Last two values are n_prompt, n_gen. unknown_cols.append(prop) if unknown_cols: - print(f"ERROR: Unknown values for --show: {', '.join(unknown_cols)}") - print() + logger.error(f"Unknown values for --show: {', '.join(unknown_cols)}") parser.print_usage() sys.exit(1) rows_show = get_rows(show) @@ -369,7 +361,7 @@ if "gpu_info" in show: headers = [PRETTY_NAMES[p] for p in show] headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"] -print(tabulate( +logger.info(tabulate( table, headers=headers, floatfmt=".2f", diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py index a18252730..e986a3604 100755 --- a/scripts/run-with-preset.py +++ b/scripts/run-with-preset.py @@ -1,5 +1,6 @@ #!/usr/bin/env python3 +import logging import argparse import os import subprocess @@ -7,6 +8,8 @@ import sys import yaml +logger = logging.getLogger("run-with-preset") + CLI_ARGS_MAIN_PERPLEXITY = [ "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape", "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag", @@ -56,6 +59,7 @@ parser.add_argument("-bin", "--binary", help="The binary to run.") parser.add_argument("yaml_files", nargs="*", help="Arbitrary number of YAML files from which to read preset values. " "If two files specify the same values the later one will be used.") +parser.add_argument("--verbose", action="store_true", help="increase output verbosity") known_args, unknown_args = parser.parse_known_args() @@ -63,6 +67,8 @@ if not known_args.yaml_files and not unknown_args: parser.print_help() sys.exit(0) +logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO) + props = dict() for yaml_file in known_args.yaml_files: @@ -85,7 +91,7 @@ elif binary.lower().endswith("llama-bench"): elif binary.lower().endswith("server"): cli_args = CLI_ARGS_SERVER else: - print(f"Unknown binary: {binary}") + logger.error(f"Unknown binary: {binary}") sys.exit(1) command_list = [binary] @@ -121,11 +127,11 @@ for cli_arg in cli_args: num_unused = len(props) if num_unused > 10: - print(f"The preset file contained a total of {num_unused} unused properties.") + logger.info(f"The preset file contained a total of {num_unused} unused properties.") elif num_unused > 0: - print("The preset file contained the following unused properties:") + logger.info("The preset file contained the following unused properties:") for prop, value in props.items(): - print(f" {prop}: {value}") + logger.info(f" {prop}: {value}") command_list += unknown_args diff --git a/scripts/verify-checksum-models.py b/scripts/verify-checksum-models.py index dff4b4734..0b5b9aafa 100755 --- a/scripts/verify-checksum-models.py +++ b/scripts/verify-checksum-models.py @@ -1,8 +1,11 @@ #!/usr/bin/env python3 +import logging import os import hashlib +logger = logging.getLogger("verify-checksum-models") + def sha256sum(file): block_size = 16 * 1024 * 1024 # 16 MB block size @@ -27,7 +30,7 @@ hash_list_file = os.path.join(llama_path, "SHA256SUMS") # Check if the hash list file exists if not os.path.exists(hash_list_file): - print(f"Hash list file not found: {hash_list_file}") + logger.error(f"Hash list file not found: {hash_list_file}") exit(1) # Read the hash file content and split it into an array of lines @@ -46,7 +49,7 @@ for line in hash_list: file_path = os.path.join(llama_path, filename) # Informing user of the progress of the integrity check - print(f"Verifying the checksum of {file_path}") + logger.info(f"Verifying the checksum of {file_path}") # Check if the file exists if os.path.exists(file_path): @@ -73,9 +76,9 @@ for line in hash_list: # Print column headers for results table -print("\n" + "filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20)) -print("-" * 80) +print("filename".ljust(40) + "valid checksum".center(20) + "file missing".center(20)) # noqa: NP100 +print("-" * 80) # noqa: NP100 # Output the results as a table for r in results: - print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}") + print(f"{r['filename']:40} {r['valid checksum']:^20} {r['file missing']:^20}") # noqa: NP100 diff --git a/tests/test-tokenizer-0-bpe.py b/tests/test-tokenizer-0-bpe.py index 33a272441..6b70ad031 100644 --- a/tests/test-tokenizer-0-bpe.py +++ b/tests/test-tokenizer-0-bpe.py @@ -7,15 +7,20 @@ # python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/deepseek-coder-6.7b-instruct/ # +import logging import argparse from transformers import AutoTokenizer +logger = logging.getLogger("test-tokenizer-0-bpe") + parser = argparse.ArgumentParser() parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") parser.add_argument("--fname-tok", help="path to a text file to tokenize") -args = parser.parse_args() +parser.add_argument("--verbose", action="store_true", help="increase output verbosity") +args = parser.parse_args() +logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) dir_tokenizer = args.dir_tokenizer tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) @@ -64,30 +69,34 @@ tests = [ ] for text in tests: - print('text: ', text) - print(tokenizer.encode(text)) - print(tokenizer.decode(tokenizer.encode(text))) + logger.info(f"text: {text}") + logger.info(tokenizer.encode(text)) + logger.info(tokenizer.decode(tokenizer.encode(text))) -print("\n\ntests for C++:\n") +logger.info("tests for C++:") for text in tests: res = tokenizer.encode(text) + # Modify text representation for logging k = text.replace('\n', '\\n') k = k.replace('\t', '\\t') k = '"' + k + '"' - print("{ %-24s, { " % k, end='') - for x in res: - print("%7d," % x, end='') - print(" }, },") -print(tokenizer.encode('hello')) -print(tokenizer.encode('world')) -print(tokenizer.encode(' world')) -print(tokenizer.encode('hello world')) + # Log the modified text and its encoding + log_message = "{ %-24s, { " % k + for x in res: + log_message += "%7d," % x + log_message += " }, }," + logger.info(log_message) + +logger.info(tokenizer.encode('hello')) +logger.info(tokenizer.encode('world')) +logger.info(tokenizer.encode(' world')) +logger.info(tokenizer.encode('hello world')) fname_tok = args.fname_tok if fname_tok: - print('tokenizing file: ', fname_tok) + logger.info(f"tokenizing file: {fname_tok}") fname_out = fname_tok + '.tok' with open(fname_tok, 'r', encoding='utf-8') as f: lines = f.readlines() @@ -112,6 +121,6 @@ if fname_tok: # else: # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') - print('len(res): ', len(res)) - print('len(lines): ', len(lines)) - print('results written to: ', fname_out) + logger.info(f"len(res): {len(res)}") + logger.info(f"len(lines): {len(lines)}") + logger.info(f"results written to: {fname_out}") diff --git a/tests/test-tokenizer-0-spm.py b/tests/test-tokenizer-0-spm.py index be12a6b93..4b80a4380 100644 --- a/tests/test-tokenizer-0-spm.py +++ b/tests/test-tokenizer-0-spm.py @@ -7,15 +7,22 @@ # +import logging import argparse from sentencepiece import SentencePieceProcessor +logger = logging.getLogger("test-tokenizer-0-spm") + parser = argparse.ArgumentParser() parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") parser.add_argument("--fname-tok", help="path to a text file to tokenize") +parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + args = parser.parse_args() +logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + dir_tokenizer = args.dir_tokenizer tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model') @@ -65,41 +72,46 @@ tests = [ for text in tests: - print('text: ', text) - print('\nwith bos:') - print(tokenizer.encode(text, add_bos=True)) - print(tokenizer.decode(tokenizer.encode(text, add_bos=True))) - print('\nwithout bos:') - print(tokenizer.encode(text, add_bos=False)) - print(tokenizer.decode(tokenizer.encode(text, add_bos=False))) + message_log = (f"text: {text}\n" + "with bos:\n" + f"{tokenizer.encode(text, add_bos=True)}\n" + f"{tokenizer.decode(tokenizer.encode(text, add_bos=True))}\n" + "without bos:\n" + f"{tokenizer.encode(text, add_bos=False)}\n" + f"{tokenizer.decode(tokenizer.encode(text, add_bos=False))}\n") + logger.info(message_log) -print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello' -print("'" + tokenizer.id_to_piece(29871) + "'") # '_' -print("'" + tokenizer.decode([15043]) + "'") # 'Hello' -print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello' -print("'" + tokenizer.decode([29871, 15043]) + "'") # ' Hello' -print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello Hello' +logger.info(f"'{tokenizer.id_to_piece(15043)}'") # '_Hello' +logger.info(f"'{tokenizer.id_to_piece(29871)}'") # '_' +logger.info(f"'{tokenizer.decode([15043])}'") # 'Hello' +logger.info(f"'{tokenizer.decode([15043, 15043])}'") # 'Hello Hello' +logger.info(f"'{tokenizer.decode([29871, 15043])}'") # ' Hello' +logger.info(f"'{tokenizer.decode([29871, 15043, 29871, 15043])}'") # ' Hello Hello' -print("\n\ntests for C++:\n") +logger.info("\n\ntests for C++:\n") for text in tests: res = tokenizer.encode(text, add_bos=False) + # Modify text representation for logging k = text.replace('\n', '\\n') k = k.replace('\t', '\\t') k = '"' + k + '"' - print("{ %-24s, { " % k, end='') - for x in res: - print("%7d," % x, end='') - print(" }, },") -print(tokenizer.encode('hello')) -print(tokenizer.encode('world')) -print(tokenizer.encode(' world')) -print(tokenizer.encode('hello world')) + # Log the modified text and its encoding + log_message = "{ %-24s, { " % k + for x in res: + log_message += "%7d," % x + log_message += " }, }," + logger.info(log_message) + +logger.info(tokenizer.encode('hello')) +logger.info(tokenizer.encode('world')) +logger.info(tokenizer.encode(' world')) +logger.info(tokenizer.encode('hello world')) fname_tok = args.fname_tok if fname_tok: - print('tokenizing file: ', fname_tok) + logger.info(f"tokenizing file: {fname_tok}") fname_out = fname_tok + '.tok' with open(fname_tok, 'r', encoding='utf-8') as f: lines = f.readlines() @@ -109,6 +121,6 @@ if fname_tok: with open(fname_out, 'w', encoding='utf-8') as f: for x in res: f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') - print('len(res): ', len(res)) - print('len(lines): ', len(lines)) - print('results written to: ', fname_out) + logger.info(f"len(res): {len(res)}") + logger.info(f"len(lines): {len(lines)}") + logger.info(f"results written to: {fname_out}") From 92139b90af4841d7fd060b526bdd443b621770ff Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 4 May 2024 08:32:32 +0300 Subject: [PATCH 023/100] tests : add test-tokenizer-0.sh + fix some tokenizers (#7036) * tests : add test-tokenizer-0.sh * unicode : add all unicode number ranges * starcoder : fix pre-tokenizer * tests : add test that fails with DeepSeek tokenizers * falcon : fix regex * unicode : regenerate unicode tables * refact : add tokenizer model * lint : fix * tests : disable failing tests ggml-ci * refact : add tests files ggml-ci * convert : print -> logging ggml-ci * lint : fix * unicode : digit -> number * phi-3 : update --- .flake8 | 2 +- Makefile | 3 +- convert-hf-to-gguf-update.py | 38 +- convert-hf-to-gguf.py | 5 +- llama.cpp | 21 +- llama.h | 1 + models/ggml-vocab-bert-bge.gguf.inp | 4 + models/ggml-vocab-bert-bge.gguf.out | 2 + models/ggml-vocab-deepseek-coder.gguf.inp | 4 + models/ggml-vocab-deepseek-coder.gguf.out | 2 + models/ggml-vocab-deepseek-llm.gguf.inp | 4 + models/ggml-vocab-deepseek-llm.gguf.out | 2 + models/ggml-vocab-falcon.gguf.inp | 4 + models/ggml-vocab-falcon.gguf.out | 2 + models/ggml-vocab-gpt-2.gguf.inp | 4 + models/ggml-vocab-gpt-2.gguf.out | 2 + models/ggml-vocab-llama-bpe.gguf.inp | 4 + models/ggml-vocab-llama-bpe.gguf.out | 2 + models/ggml-vocab-llama-spm.gguf.inp | 4 + models/ggml-vocab-llama-spm.gguf.out | 2 + models/ggml-vocab-mpt.gguf.inp | 4 + models/ggml-vocab-mpt.gguf.out | 2 + models/ggml-vocab-phi-3.gguf | Bin 725945 -> 725846 bytes models/ggml-vocab-phi-3.gguf.inp | 4 + models/ggml-vocab-phi-3.gguf.out | 2 + models/ggml-vocab-refact.gguf | Bin 1720666 -> 1720710 bytes models/ggml-vocab-refact.gguf.inp | 106 +++ models/ggml-vocab-refact.gguf.out | 43 ++ models/ggml-vocab-starcoder.gguf.inp | 4 + models/ggml-vocab-starcoder.gguf.out | 2 + scripts/gen-unicode-data.py | 66 ++ tests/CMakeLists.txt | 6 +- tests/test-tokenizer-0-bpe.py | 126 ---- tests/test-tokenizer-0-spm.py | 126 ---- tests/test-tokenizer-0.cpp | 39 +- tests/test-tokenizer-0.py | 46 ++ tests/test-tokenizer-0.sh | 34 + unicode-data.cpp | 874 ++++++++++++---------- unicode-data.h | 2 +- unicode.cpp | 22 +- unicode.h | 2 +- 41 files changed, 903 insertions(+), 719 deletions(-) create mode 100644 models/ggml-vocab-refact.gguf.inp create mode 100644 models/ggml-vocab-refact.gguf.out create mode 100644 scripts/gen-unicode-data.py delete mode 100644 tests/test-tokenizer-0-bpe.py delete mode 100644 tests/test-tokenizer-0-spm.py create mode 100644 tests/test-tokenizer-0.py create mode 100755 tests/test-tokenizer-0.sh diff --git a/.flake8 b/.flake8 index 608eb8eed..bc41c2290 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,4 @@ [flake8] max-line-length = 125 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 -exclude = examples/*,examples/*/**,*/**/__init__.py +exclude = examples/*,examples/*/**,*/**/__init__.py,scripts/gen-unicode-data.py,tests/test-tokenizer-0.py diff --git a/Makefile b/Makefile index 0a73f2a58..c568dd008 100644 --- a/Makefile +++ b/Makefile @@ -77,11 +77,10 @@ test: $(TEST_TARGETS) ./$$test_target $(CURDIR)/models/ggml-vocab-llama-bpe.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-phi-3.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-falcon.gguf; \ - ./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-coder.gguf; \ - ./$$test_target $(CURDIR)/models/ggml-vocab-deepseek-llm.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-bert-bge.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-starcoder.gguf; \ ./$$test_target $(CURDIR)/models/ggml-vocab-gpt-2.gguf; \ + ./$$test_target $(CURDIR)/models/ggml-vocab-refact.gguf; \ elif [ "$$test_target" = "tests/test-tokenizer-1-spm" ]; then \ continue; \ elif [ "$$test_target" = "tests/test-tokenizer-1-bpe" ]; then \ diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 09772f668..917a4469d 100644 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -31,6 +31,7 @@ from hashlib import sha256 from enum import IntEnum, auto from transformers import AutoTokenizer +logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("convert-hf-to-gguf-update") @@ -62,6 +63,7 @@ models = [ {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, ] # make directory "models/tokenizers" if it doesn't exist @@ -158,8 +160,8 @@ src_func = f""" chktok = tokenizer.encode(chktxt) chkhsh = sha256(str(chktok).encode()).hexdigest() - print(f"chktok: {{chktok}}") - print(f"chkhsh: {{chkhsh}}") + logger.debug(f"chktok: {{chktok}}") + logger.debug(f"chkhsh: {{chkhsh}}") res = None @@ -168,22 +170,22 @@ src_func = f""" # don't edit the hashes manually! {src_ifs} if res is None: - print("\\n") - print("**************************************************************************************") - print("** WARNING: The BPE pre-tokenizer was not recognized!") - print("** There are 2 possible reasons for this:") - print("** - the model has not been added to convert-hf-to-gguf-update.py yet") - print("** - the pre-tokenization config has changed upstream") - print("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") - print("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") - print("**") - print(f"** chkhsh: {{chkhsh}}") - print("**************************************************************************************") - print("\\n") + logger.warning("\\n") + logger.warning("**************************************************************************************") + logger.warning("** WARNING: The BPE pre-tokenizer was not recognized!") + logger.warning("** There are 2 possible reasons for this:") + logger.warning("** - the model has not been added to convert-hf-to-gguf-update.py yet") + logger.warning("** - the pre-tokenization config has changed upstream") + logger.warning("** Check your model files and convert-hf-to-gguf-update.py and update them accordingly.") + logger.warning("** ref: https://github.com/ggerganov/llama.cpp/pull/6920") + logger.warning("**") + logger.warning(f"** chkhsh: {{chkhsh}}") + logger.warning("**************************************************************************************") + logger.warning("\\n") raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") - print(f"tokenizer.ggml.pre: {{repr(res)}}") - print(f"chkhsh: {{chkhsh}}") + logger.debug(f"tokenizer.ggml.pre: {{repr(res)}}") + logger.debug(f"chkhsh: {{chkhsh}}") return res """ @@ -197,6 +199,8 @@ logger.info("\n") # generate tests for each tokenizer model tests = [ + "ied 4 ½ months", + "Führer", "", " ", " ", @@ -281,6 +285,6 @@ logger.info("\nRun the following commands to generate the vocab files for testin for model in models: name = model["name"] - logger.info(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") + print(f"python3 convert-hf-to-gguf.py models/tokenizers/{name}/ --outfile models/ggml-vocab-{name}.gguf --vocab-only") # noqa: NP100 logger.info("\n") diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 529326288..88c16676b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -308,6 +308,9 @@ class Model(ABC): if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": # ref: https://huggingface.co/openai-community/gpt2 res = "gpt-2" + if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": + # ref: https://huggingface.co/smallcloudai/Refact-1_6-base + res = "refact" if res is None: logger.warning("\n") @@ -324,7 +327,7 @@ class Model(ABC): logger.warning("\n") raise NotImplementedError("BPE pre-tokenizer was not recognized - update get_vocab_base_pre()") - logger.debug(f"tokenizer.ggml.pre: {res}") + logger.debug(f"tokenizer.ggml.pre: {repr(res)}") logger.debug(f"chkhsh: {chkhsh}") return res diff --git a/llama.cpp b/llama.cpp index 18b49ec20..7c7a06df3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4383,6 +4383,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "gpt-2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; + } else if ( + tokenizer_pre == "refact") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -11952,7 +11955,7 @@ static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { GGML_ASSERT(llama_vocab_get_type(vocab) != LLAMA_VOCAB_TYPE_NONE); GGML_ASSERT(llama_is_byte_token(vocab, id)); - const auto& token_data = vocab.id_to_token.at(id); + const auto & token_data = vocab.id_to_token.at(id); switch (llama_vocab_get_type(vocab)) { case LLAMA_VOCAB_TYPE_SPM: { auto buf = token_data.text.substr(3, 2); @@ -12212,14 +12215,13 @@ struct llm_tokenizer_bpe { "\\s?\\p{L}+", "\\s?\\p{P}+", "[一-龥ࠀ-一가-퟿]+", - "\\p{N}+", + "\\p{N}", }); break; case LLAMA_VOCAB_PRE_TYPE_FALCON: word_collection = unicode_regex_split(text, { "[\\p{P}\\$\\+<=>\\^~\\|]+", "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", - "\\p{N}+", "[0-9][0-9][0-9]", }); break; @@ -12235,6 +12237,12 @@ struct llm_tokenizer_bpe { }); break; case LLAMA_VOCAB_PRE_TYPE_STARCODER: + case LLAMA_VOCAB_PRE_TYPE_REFACT: + word_collection = unicode_regex_split(text, { + "\\p{N}", + "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", + }); + break; case LLAMA_VOCAB_PRE_TYPE_GPT2: word_collection = unicode_regex_split(text, { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", @@ -17466,9 +17474,10 @@ int32_t llama_tokenize( static std::string llama_decode_text(const std::string & text) { std::string decoded_text; - auto unicode_sequences = unicode_cpts_from_utf8(text); - for (auto & unicode_sequence : unicode_sequences) { - decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(unicode_sequence)); + + const auto cpts = unicode_cpts_from_utf8(text); + for (const auto cpt : cpts) { + decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt)); } return decoded_text; diff --git a/llama.h b/llama.h index 790339af9..5ff04c1d4 100644 --- a/llama.h +++ b/llama.h @@ -79,6 +79,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_MPT = 5, LLAMA_VOCAB_PRE_TYPE_STARCODER = 6, LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, + LLAMA_VOCAB_PRE_TYPE_REFACT = 8, }; // note: these values should be synchronized with ggml_rope diff --git a/models/ggml-vocab-bert-bge.gguf.inp b/models/ggml-vocab-bert-bge.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-bert-bge.gguf.inp +++ b/models/ggml-vocab-bert-bge.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-bert-bge.gguf.out b/models/ggml-vocab-bert-bge.gguf.out index 969552e1c..e4a76cdb0 100644 --- a/models/ggml-vocab-bert-bge.gguf.out +++ b/models/ggml-vocab-bert-bge.gguf.out @@ -1,3 +1,5 @@ + 29464 2094 1018 1092 2706 + 11865 17875 diff --git a/models/ggml-vocab-deepseek-coder.gguf.inp b/models/ggml-vocab-deepseek-coder.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-deepseek-coder.gguf.inp +++ b/models/ggml-vocab-deepseek-coder.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-coder.gguf.out b/models/ggml-vocab-deepseek-coder.gguf.out index 8ef585c78..9ccc560d6 100644 --- a/models/ggml-vocab-deepseek-coder.gguf.out +++ b/models/ggml-vocab-deepseek-coder.gguf.out @@ -1,3 +1,5 @@ + 1050 207 19 207 19192 4217 + 37 32009 71 6247 207 243 diff --git a/models/ggml-vocab-deepseek-llm.gguf.inp b/models/ggml-vocab-deepseek-llm.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-deepseek-llm.gguf.inp +++ b/models/ggml-vocab-deepseek-llm.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-deepseek-llm.gguf.out b/models/ggml-vocab-deepseek-llm.gguf.out index 0ea9d66e3..fd94b896d 100644 --- a/models/ggml-vocab-deepseek-llm.gguf.out +++ b/models/ggml-vocab-deepseek-llm.gguf.out @@ -1,3 +1,5 @@ + 1052 207 19 207 19109 4223 + 37 100014 71 6245 207 243 diff --git a/models/ggml-vocab-falcon.gguf.inp b/models/ggml-vocab-falcon.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-falcon.gguf.inp +++ b/models/ggml-vocab-falcon.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-falcon.gguf.out b/models/ggml-vocab-falcon.gguf.out index cb8da7b1a..209b04cda 100644 --- a/models/ggml-vocab-falcon.gguf.out +++ b/models/ggml-vocab-falcon.gguf.out @@ -1,3 +1,5 @@ + 878 204 31 3068 133 2137 + 28611 132 30042 204 258 diff --git a/models/ggml-vocab-gpt-2.gguf.inp b/models/ggml-vocab-gpt-2.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-gpt-2.gguf.inp +++ b/models/ggml-vocab-gpt-2.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-gpt-2.gguf.out b/models/ggml-vocab-gpt-2.gguf.out index 9986f38e4..78430f0d3 100644 --- a/models/ggml-vocab-gpt-2.gguf.out +++ b/models/ggml-vocab-gpt-2.gguf.out @@ -1,3 +1,5 @@ + 798 604 25208 1933 + 37 9116 71 11751 220 220 220 diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-llama-bpe.gguf.inp +++ b/models/ggml-vocab-llama-bpe.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out index 4d903e1cd..1f00e3812 100644 --- a/models/ggml-vocab-llama-bpe.gguf.out +++ b/models/ggml-vocab-llama-bpe.gguf.out @@ -1,3 +1,5 @@ + 1142 220 19 220 27154 4038 + 37 51853 261 220 256 diff --git a/models/ggml-vocab-llama-spm.gguf.inp b/models/ggml-vocab-llama-spm.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-llama-spm.gguf.inp +++ b/models/ggml-vocab-llama-spm.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-spm.gguf.out b/models/ggml-vocab-llama-spm.gguf.out index 15d00b106..9c3327cb5 100644 --- a/models/ggml-vocab-llama-spm.gguf.out +++ b/models/ggml-vocab-llama-spm.gguf.out @@ -1,3 +1,5 @@ + 474 287 29871 29946 29871 30226 7378 + 383 4000 261 259 1678 diff --git a/models/ggml-vocab-mpt.gguf.inp b/models/ggml-vocab-mpt.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-mpt.gguf.inp +++ b/models/ggml-vocab-mpt.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-mpt.gguf.out b/models/ggml-vocab-mpt.gguf.out index 1f4b0eb3a..d8d0fe909 100644 --- a/models/ggml-vocab-mpt.gguf.out +++ b/models/ggml-vocab-mpt.gguf.out @@ -1,3 +1,5 @@ + 728 577 24142 2607 + 39 26288 6554 209 50276 diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf index 72fdb409c1b3b62b5aa4bc1628d4ca45080c4ebd..f8022a385e4aa48ca10d40d0f079a25365a7be78 100644 GIT binary patch delta 60 zcmdnFT<6*{orV_17N!>F7M2#)7Pc1l7LFFq7OpMajzWyQ)18F4{ic5v;!c_VK!{s( Qdaf`x7o+C%a$#;K09`i`@&Et; delta 122 zcmcb%OlRkEorV_17N!>F7M2#)7Pc1l7LFFq7OpMajzWwD)18F4{ltnZi%U{-)io7V zYpbi(ZE7I=>5GK84W_>m;+CI2L5Mp=IXAVqI59mnTD>SgCsjRG!PZtmJ+Zhrv$!NN TuS6ZJcltUZZk6e_!rYPo#9Sx% diff --git a/models/ggml-vocab-phi-3.gguf.inp b/models/ggml-vocab-phi-3.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-phi-3.gguf.inp +++ b/models/ggml-vocab-phi-3.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-phi-3.gguf.out b/models/ggml-vocab-phi-3.gguf.out index 15d00b106..9c3327cb5 100644 --- a/models/ggml-vocab-phi-3.gguf.out +++ b/models/ggml-vocab-phi-3.gguf.out @@ -1,3 +1,5 @@ + 474 287 29871 29946 29871 30226 7378 + 383 4000 261 259 1678 diff --git a/models/ggml-vocab-refact.gguf b/models/ggml-vocab-refact.gguf index 8f26cfb76c9f287f5ff06bf0445ba9ac363f11e8..52afcf01aeb7323e0153b2817a1cbfc5a07d787b 100644 GIT binary patch delta 166 zcmYMpu?>Pi7(iivQ9x1Q4nbrYVv5b0)A-Y;zQv~G mp`^ff6BcYZaN)s6fDjQBVkAhRks(Kc5*2DRXfGwZrGEj<_!uq# diff --git a/models/ggml-vocab-refact.gguf.inp b/models/ggml-vocab-refact.gguf.inp new file mode 100644 index 000000000..0a89107c6 --- /dev/null +++ b/models/ggml-vocab-refact.gguf.inp @@ -0,0 +1,106 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-refact.gguf.out b/models/ggml-vocab-refact.gguf.out new file mode 100644 index 000000000..06b15c090 --- /dev/null +++ b/models/ggml-vocab-refact.gguf.out @@ -0,0 +1,43 @@ + 4833 225 38 225 143 140 17723 + 56 2006 3935 265 + + 225 + 261 + 264 + 202 + 203 + 478 + 2831 + 15773 + 8279 5788 + 12000 5788 + 8279 10896 + 12000 10896 + 12000 10896 19 + 8279 30 5788 19 + 12000 30 5788 19 + 458 438 5945 118 252 32 3766 + 105 34 38 42 225 41 102 1707 12530 10180 1479 8278 + 39862 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 + 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 14574 228 14574 254 14574 232 30457 228 14574 236 + 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 308 2585 22680 688 1401 2819 4369 2404 27 + 8279 + 12000 + 225 12000 + 261 12000 + 264 12000 + 264 12000 284 12000 + 308 + 203 280 + 25 34666 + 8279 30 533 25 464 19 4971 884 844 18458 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 + 37 + 37 37 + 37 37 37 + 37 37 37 37 + 37 37 37 37 37 + 37 37 37 37 37 37 + 37 37 37 37 37 37 37 + 37 37 37 37 37 37 37 37 + 37 37 37 37 37 37 37 37 37 + 334 719 8878 202 10885 4222 16104 28570 203 3807 253 227 308 4382 27 18458 133 46113 44967 123 13868 308 12565 19775 33071 40824 733 27 41889 5945 118 252 3807 118 252 225 37 225 37 37 225 37 37 37 225 37 37 37 37 225 37 37 37 37 37 225 37 37 37 37 37 37 225 37 37 37 37 37 37 37 225 37 37 37 37 37 37 37 37 225 37 32 37 225 37 497 37 225 37 1179 37 225 14574 227 14574 133 14574 246 30457 238 14574 242 30457 229 14574 249 14574 134 14574 258 30457 228 14574 258 14574 114 14574 133 14574 232 36628 228 1018 4982 13368 2909 9513 17827 35 37 35 38 35 39 35 11873 47838 20921 16623 13028 8372 1039 9446 40242 13852 2053 8949 12531 1520 10700 5881 9592 13299 914 31753 31359 9163 3202 35472 10397 439 4763 2583 330 102 1455 938 1182 2017 30 330 613 844 3654 49 330 63 646 3654 439 4621 1930 561 30 330 54 844 2124 1629 35993 49 2688 25 7709 312 25 94 62 diff --git a/models/ggml-vocab-starcoder.gguf.inp b/models/ggml-vocab-starcoder.gguf.inp index 0389f00c7..0a89107c6 100644 --- a/models/ggml-vocab-starcoder.gguf.inp +++ b/models/ggml-vocab-starcoder.gguf.inp @@ -1,3 +1,7 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ __ggml_vocab_test__ diff --git a/models/ggml-vocab-starcoder.gguf.out b/models/ggml-vocab-starcoder.gguf.out index cd04254af..ccb55c7fe 100644 --- a/models/ggml-vocab-starcoder.gguf.out +++ b/models/ggml-vocab-starcoder.gguf.out @@ -1,3 +1,5 @@ + 4850 244 57 244 162 159 17722 + 75 2022 3943 284 244 280 diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py new file mode 100644 index 000000000..d49cbf2a0 --- /dev/null +++ b/scripts/gen-unicode-data.py @@ -0,0 +1,66 @@ +import regex + + +def cpt_to_utf8_str(cpt): + if cpt <= 0xFF: + return bytes([cpt, 0, 0, 0]) + elif cpt <= 0xFFFF: + return bytes([cpt & 0xFF, cpt >> 8, 0, 0]) + elif cpt <= 0xFFFFFF: + return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, 0]) + else: + return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, cpt >> 24]) + + +def is_match(codepoint, regex_expr): + try: + res = regex.match(regex_expr, cpt_to_utf8_str(codepoint).decode('utf-32')) + return res is not None + except Exception: + return False + + +def get_matches(regex_expr): + unicode_ranges = [] + current_range = None + + for codepoint in range(0x110000): + if is_match(codepoint, regex_expr): + if current_range is None: + current_range = [codepoint, codepoint] + else: + current_range[1] = codepoint + elif current_range is not None: + unicode_ranges.append(tuple(current_range)) + current_range = None + + if current_range is not None: + unicode_ranges.append(tuple(current_range)) + + return unicode_ranges + + +def print_cat(cat, ranges): + print("const std::vector> unicode_ranges_{} = {{".format(cat)) + cnt = 0 + for start, end in ranges: + if cnt % 4 != 0: + print(" ", end="") + print("{{0x{:08X}, 0x{:08X}}},".format(start, end), end="") + if cnt % 4 == 3: + print("") + cnt += 1 + + if cnt % 4 != 0: + print("") + print("};") + print("") + + +print_cat("number", get_matches(r'\p{N}')) +print_cat("letter", get_matches(r'\p{L}')) +print_cat("whitespace", get_matches(r'\p{Z}')) +print_cat("accent_mark", get_matches(r'\p{M}')) +print_cat("punctuation", get_matches(r'\p{P}')) +print_cat("symbol", get_matches(r'\p{S}')) +print_cat("control", get_matches(r'\p{C}')) diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index d23e7f771..cad703fce 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -74,13 +74,15 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-spm ARGS ${CMAKE llama_test(test-tokenizer-0 NAME test-tokenizer-0-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-phi-3 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-phi-3.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) -llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf) -llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-bert-bge.gguf) # TODO: enable when fixed +# https://github.com/ggerganov/llama.cpp/pull/7036 #llama_test(test-tokenizer-0 NAME test-tokenizer-0-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) +#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-llm ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-llm.gguf) +#llama_test(test-tokenizer-0 NAME test-tokenizer-0-deepseek-coder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-deepseek-coder.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) # build test-tokenizer-1-bpe target once and add many tests add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp) diff --git a/tests/test-tokenizer-0-bpe.py b/tests/test-tokenizer-0-bpe.py deleted file mode 100644 index 6b70ad031..000000000 --- a/tests/test-tokenizer-0-bpe.py +++ /dev/null @@ -1,126 +0,0 @@ -# tests with BPE tokenizer -# -# sample usage: -# -# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/Meta-Llama-3-8B-Instruct/ -# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/falcon-7b/ -# python3 tests/test-tokenizer-0-bpe.py ~/Data/huggingface/deepseek-coder-6.7b-instruct/ -# - -import logging -import argparse - -from transformers import AutoTokenizer - -logger = logging.getLogger("test-tokenizer-0-bpe") - -parser = argparse.ArgumentParser() -parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") -parser.add_argument("--fname-tok", help="path to a text file to tokenize") -parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - -args = parser.parse_args() -logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) -dir_tokenizer = args.dir_tokenizer - -tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) - -tests = [ - "", - " ", - " ", - " ", - "\t", - "\n", - "\n\n", - "\n\n\n", - "\t\n", - "Hello world", - " Hello world", - "Hello World", - " Hello World", - " Hello World!", - "Hello, world!", - " Hello, world!", - " this is 🦙.cpp", - "w048 7tuijk dsdfhu", - "нещо на Български", - "កាន់តែពិសេសអាចខលចេញ", - "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", - "Hello", - " Hello", - " Hello", - " Hello", - " Hello", - " Hello\n Hello", - " (", - "\n =", - "' era", - "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", - "3", - "33", - "333", - "3333", - "33333", - "333333", - "3333333", - "33333333", - "333333333", -] - -for text in tests: - logger.info(f"text: {text}") - logger.info(tokenizer.encode(text)) - logger.info(tokenizer.decode(tokenizer.encode(text))) - -logger.info("tests for C++:") -for text in tests: - res = tokenizer.encode(text) - - # Modify text representation for logging - k = text.replace('\n', '\\n') - k = k.replace('\t', '\\t') - k = '"' + k + '"' - - # Log the modified text and its encoding - log_message = "{ %-24s, { " % k - for x in res: - log_message += "%7d," % x - log_message += " }, }," - logger.info(log_message) - -logger.info(tokenizer.encode('hello')) -logger.info(tokenizer.encode('world')) -logger.info(tokenizer.encode(' world')) -logger.info(tokenizer.encode('hello world')) - -fname_tok = args.fname_tok -if fname_tok: - logger.info(f"tokenizing file: {fname_tok}") - fname_out = fname_tok + '.tok' - with open(fname_tok, 'r', encoding='utf-8') as f: - lines = f.readlines() - s = ''.join(lines) - res = tokenizer.encode(s) - # write to file - with open(fname_out, 'w', encoding='utf-8') as f: - for x in res: - # LLaMA v3 for some reason strips the space for these tokens (and others) - # if x == 662: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # elif x == 1174: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # elif x == 2564: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # elif x == 758: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # elif x == 949: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # elif x == 5354: - # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') - # else: - # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') - f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') - logger.info(f"len(res): {len(res)}") - logger.info(f"len(lines): {len(lines)}") - logger.info(f"results written to: {fname_out}") diff --git a/tests/test-tokenizer-0-spm.py b/tests/test-tokenizer-0-spm.py deleted file mode 100644 index 4b80a4380..000000000 --- a/tests/test-tokenizer-0-spm.py +++ /dev/null @@ -1,126 +0,0 @@ -# tests with SPM tokenizer -# -# sample usage: -# -# python3 tests/test-tokenizer-0-spm.py ~/Data/huggingface/Llama-2-7b-hf/ -# python3 tests/test-tokenizer-0-spm.py ~/Data/huggingface/CodeLlama-34b-Instruct-hf/ -# - - -import logging -import argparse - -from sentencepiece import SentencePieceProcessor - -logger = logging.getLogger("test-tokenizer-0-spm") - -parser = argparse.ArgumentParser() -parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") -parser.add_argument("--fname-tok", help="path to a text file to tokenize") -parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - -args = parser.parse_args() - -logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - -dir_tokenizer = args.dir_tokenizer - -tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model') - -tests = [ - "", - " ", - " ", - " ", - "\t", - "\n", - "\n\n", - "\n\n\n", - "\t\n", - "Hello world", - " Hello world", - "Hello World", - " Hello World", - " Hello World!", - "Hello, world!", - " Hello, world!", - " this is 🦙.cpp", - "w048 7tuijk dsdfhu", - "нещо на Български", - "កាន់តែពិសេសអាចខលចេញ", - "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", - "Hello", - " Hello", - " Hello", - " Hello", - " Hello", - " Hello\n Hello", - " (", - "\n =", - "' era", - "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", - "3", - "33", - "333", - "3333", - "33333", - "333333", - "3333333", - "33333333", - "333333333", -] - - -for text in tests: - message_log = (f"text: {text}\n" - "with bos:\n" - f"{tokenizer.encode(text, add_bos=True)}\n" - f"{tokenizer.decode(tokenizer.encode(text, add_bos=True))}\n" - "without bos:\n" - f"{tokenizer.encode(text, add_bos=False)}\n" - f"{tokenizer.decode(tokenizer.encode(text, add_bos=False))}\n") - logger.info(message_log) - -logger.info(f"'{tokenizer.id_to_piece(15043)}'") # '_Hello' -logger.info(f"'{tokenizer.id_to_piece(29871)}'") # '_' -logger.info(f"'{tokenizer.decode([15043])}'") # 'Hello' -logger.info(f"'{tokenizer.decode([15043, 15043])}'") # 'Hello Hello' -logger.info(f"'{tokenizer.decode([29871, 15043])}'") # ' Hello' -logger.info(f"'{tokenizer.decode([29871, 15043, 29871, 15043])}'") # ' Hello Hello' - -logger.info("\n\ntests for C++:\n") -for text in tests: - res = tokenizer.encode(text, add_bos=False) - - # Modify text representation for logging - k = text.replace('\n', '\\n') - k = k.replace('\t', '\\t') - k = '"' + k + '"' - - # Log the modified text and its encoding - log_message = "{ %-24s, { " % k - for x in res: - log_message += "%7d," % x - log_message += " }, }," - logger.info(log_message) - -logger.info(tokenizer.encode('hello')) -logger.info(tokenizer.encode('world')) -logger.info(tokenizer.encode(' world')) -logger.info(tokenizer.encode('hello world')) - -fname_tok = args.fname_tok -if fname_tok: - logger.info(f"tokenizing file: {fname_tok}") - fname_out = fname_tok + '.tok' - with open(fname_tok, 'r', encoding='utf-8') as f: - lines = f.readlines() - s = ''.join(lines) - res = tokenizer.encode(s, add_bos=True) - # write to file - with open(fname_out, 'w', encoding='utf-8') as f: - for x in res: - f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') - logger.info(f"len(res): {len(res)}") - logger.info(f"len(lines): {len(lines)}") - logger.info(f"results written to: {fname_out}") diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index 5122757cf..d478f1041 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -55,8 +55,10 @@ // return _k_tests; //} -static std::map> read_tests(const std::string & fname_inp, const std::string & fname_out) { - std::map> tests; +using llama_tests = std::map>; + +static llama_tests read_tests(const std::string & fname_inp, const std::string & fname_out) { + llama_tests tests; std::ifstream ifs_inp(fname_inp); if (!ifs_inp) { @@ -175,12 +177,20 @@ int main(int argc, char **argv) { bool success = true; - const auto k_tests = read_tests(fname_inp, fname_out); + const auto k_tests = [&]() -> llama_tests { + if (!fname_text.empty()) { + return {}; + } - if (k_tests.empty()) { - fprintf(stderr, "%s : error: no tests found\n", __func__); - return 1; - } + const auto res = read_tests(fname_inp, fname_out); + + if (res.empty()) { + fprintf(stderr, "%s : error: no tests found\n", __func__); + exit(1); + } + + return res; + }(); const bool add_special = false; @@ -238,7 +248,17 @@ int main(int argc, char **argv) { fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); - const std::vector res = llama_tokenize(ctx, text, add_special); + std::vector res; + + { + const auto t_start = ggml_time_us(); + + res = llama_tokenize(ctx, text, add_special); + + const auto t_end = ggml_time_us(); + + fprintf(stderr, "%s : tokenized in %.3f ms (cpp)\n", __func__, (t_end - t_start) / 1000.0); + } fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); @@ -252,7 +272,8 @@ int main(int argc, char **argv) { } for (const auto & tok : res) { - ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector{tok})) << "'" << std::endl; + //ofs << tok << " '" << string_strip(llama_detokenize_bpe(ctx, std::vector{tok})) << "'" << std::endl; + ofs << tok << "\n"; } } diff --git a/tests/test-tokenizer-0.py b/tests/test-tokenizer-0.py new file mode 100644 index 000000000..8e7638e42 --- /dev/null +++ b/tests/test-tokenizer-0.py @@ -0,0 +1,46 @@ +import time +import argparse + +from transformers import AutoTokenizer + +parser = argparse.ArgumentParser() +parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") +parser.add_argument("--fname-tok", help="path to a text file to tokenize", required=True) +args = parser.parse_args() + +dir_tokenizer = args.dir_tokenizer +fname_tok = args.fname_tok + +tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) + +print('tokenizing file: ', fname_tok) +fname_out = fname_tok + '.tok' +with open(fname_tok, 'r', encoding='utf-8') as f: + lines = f.readlines() + s = ''.join(lines) + t_start = time.time() + res = tokenizer.encode(s, add_special_tokens=False) + t_end = time.time() + print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') + with open(fname_out, 'w', encoding='utf-8') as f: + for x in res: + # LLaMA v3 for some reason strips the space for these tokens (and others) + # if x == 662: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 1174: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 2564: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 758: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 949: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # elif x == 5354: + # f.write(str(x) + ' \' ' + tokenizer.decode(x) + '\'\n') + # else: + # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') + # f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') + f.write(str(x) + '\n') + print('len(res): ', len(res)) + print('len(lines): ', len(lines)) +print('results written to: ', fname_out) diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh new file mode 100755 index 000000000..2fb8632d8 --- /dev/null +++ b/tests/test-tokenizer-0.sh @@ -0,0 +1,34 @@ +#!/bin/bash +# +# Usage: +# +# test-tokenizer-0.sh +# + +if [ $# -ne 2 ]; then + printf "Usage: $0 \n" + exit 1 +fi + +name=$1 +input=$2 + +make -j tests/test-tokenizer-0 + +printf "Testing %s on %s ...\n" $name $input + +python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1 +cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" + +./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1 +cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" + +diff $input.tok $input.tokcpp > /dev/null 2>&1 + +if [ $? -eq 0 ]; then + printf "Tokenization is correct!\n" +else + diff $input.tok $input.tokcpp | head -n 32 + + printf "Tokenization differs!\n" +fi diff --git a/unicode-data.cpp b/unicode-data.cpp index e6bafb3a9..07bf02c45 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -5,27 +5,47 @@ #include #include -const std::vector> unicode_ranges_digit = { -{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x00000660, 0x00000669}, -{0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, {0x000009E6, 0x000009EF}, -{0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, {0x00000B66, 0x00000B6F}, {0x00000BE6, 0x00000BEF}, -{0x00000C66, 0x00000C6F}, {0x00000CE6, 0x00000CEF}, {0x00000D66, 0x00000D6F}, {0x00000DE6, 0x00000DEF}, -{0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F29}, {0x00001040, 0x00001049}, -{0x00001090, 0x00001099}, {0x00001369, 0x00001371}, {0x000017E0, 0x000017E9}, {0x00001810, 0x00001819}, -{0x00001946, 0x0000194F}, {0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, -{0x00001B50, 0x00001B59}, {0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, -{0x00002070, 0x00002070}, {0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002460, 0x00002468}, -{0x00002474, 0x0000247C}, {0x00002488, 0x00002490}, {0x000024EA, 0x000024EA}, {0x000024F5, 0x000024FD}, -{0x000024FF, 0x000024FF}, {0x00002776, 0x0000277E}, {0x00002780, 0x00002788}, {0x0000278A, 0x00002792}, -{0x0000A620, 0x0000A629}, {0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, -{0x0000A9F0, 0x0000A9F9}, {0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, -{0x000104A0, 0x000104A9}, {0x00010A40, 0x00010A43}, {0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E68}, -{0x00011052, 0x0001105A}, {0x00011066, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, -{0x000111D0, 0x000111D9}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, {0x000114D0, 0x000114D9}, -{0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x00011739}, {0x000118E0, 0x000118E9}, -{0x00011950, 0x00011959}, {0x00011C50, 0x00011C59}, {0x00011D50, 0x00011D59}, {0x00011DA0, 0x00011DA9}, -{0x00016A60, 0x00016A69}, {0x00016B50, 0x00016B59}, {0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, -{0x0001E2F0, 0x0001E2F9}, {0x0001E950, 0x0001E959}, {0x0001F100, 0x0001F10A}, {0x0001FBF0, 0x0001FBF9}, +// generated with scripts/gen-unicode-data.py +// +// TODO: generate unicode_map_lowercase +// TODO: generate unicode_map_nfd + +const std::vector> unicode_ranges_number = { +{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE}, +{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, +{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, +{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F}, +{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78}, +{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33}, +{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0}, +{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F}, +{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59}, +{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070}, +{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189}, +{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD}, +{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195}, +{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289}, +{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835}, +{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9}, +{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133}, +{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323}, +{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9}, +{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF}, +{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF}, +{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF}, +{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF}, +{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54}, +{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, +{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, +{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B}, +{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59}, +{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E}, +{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61}, +{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378}, +{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9}, +{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF}, +{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C}, +{0x0001FBF0, 0x0001FBF9}, }; const std::vector> unicode_ranges_letter = { @@ -41,73 +61,73 @@ const std::vector> unicode_ranges_letter = { {0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1}, {0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815}, {0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858}, -{0x00000860, 0x0000086A}, {0x000008A0, 0x000008B4}, {0x000008B6, 0x000008C7}, {0x00000904, 0x00000939}, -{0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, {0x00000971, 0x00000980}, -{0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, {0x000009AA, 0x000009B0}, -{0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, {0x000009CE, 0x000009CE}, -{0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, {0x000009FC, 0x000009FC}, -{0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, {0x00000A2A, 0x00000A30}, -{0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, {0x00000A59, 0x00000A5C}, -{0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, {0x00000A8F, 0x00000A91}, -{0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, {0x00000AB5, 0x00000AB9}, -{0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, {0x00000AF9, 0x00000AF9}, -{0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, {0x00000B2A, 0x00000B30}, -{0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, {0x00000B5C, 0x00000B5D}, -{0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, {0x00000B85, 0x00000B8A}, -{0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, {0x00000B9C, 0x00000B9C}, -{0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, {0x00000BAE, 0x00000BB9}, -{0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, {0x00000C12, 0x00000C28}, -{0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, {0x00000C60, 0x00000C61}, -{0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, {0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, -{0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, {0x00000CBD, 0x00000CBD}, {0x00000CDE, 0x00000CDE}, -{0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, {0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, -{0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, {0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, -{0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, {0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, -{0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, {0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, -{0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, {0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, -{0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, {0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, -{0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, {0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, -{0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, {0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, -{0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, {0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, -{0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, {0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, -{0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, {0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, -{0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, {0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, -{0x00001250, 0x00001256}, {0x00001258, 0x00001258}, {0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, -{0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, {0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, -{0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, {0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, -{0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, {0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, -{0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, {0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, -{0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, {0x00001700, 0x0000170C}, {0x0000170E, 0x00001711}, -{0x00001720, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, {0x0000176E, 0x00001770}, -{0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, {0x00001820, 0x00001878}, -{0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, {0x000018B0, 0x000018F5}, -{0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, {0x00001980, 0x000019AB}, -{0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, {0x00001AA7, 0x00001AA7}, -{0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4B}, {0x00001B83, 0x00001BA0}, {0x00001BAE, 0x00001BAF}, -{0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, {0x00001C5A, 0x00001C7D}, -{0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, {0x00001CE9, 0x00001CEC}, -{0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, {0x00001D00, 0x00001DBF}, -{0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, {0x00001F48, 0x00001F4D}, -{0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, {0x00001F5D, 0x00001F5D}, -{0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, {0x00001FBE, 0x00001FBE}, -{0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, {0x00001FD6, 0x00001FDB}, -{0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, {0x00002071, 0x00002071}, -{0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, {0x00002107, 0x00002107}, -{0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, {0x00002124, 0x00002124}, -{0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, {0x0000212F, 0x00002139}, -{0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, {0x00002183, 0x00002184}, -{0x00002C00, 0x00002C2E}, {0x00002C30, 0x00002C5E}, {0x00002C60, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, -{0x00002CF2, 0x00002CF3}, {0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, -{0x00002D30, 0x00002D67}, {0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, -{0x00002DA8, 0x00002DAE}, {0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, -{0x00002DC8, 0x00002DCE}, {0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, -{0x00003005, 0x00003006}, {0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, -{0x0000309D, 0x0000309F}, {0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, -{0x00003131, 0x0000318E}, {0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, -{0x00004E00, 0x00009FFC}, {0x0000A000, 0x0000A48C}, {0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, -{0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, {0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, -{0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, {0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7BF}, -{0x0000A7C2, 0x0000A7CA}, {0x0000A7F5, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, +{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9}, +{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, +{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, +{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, +{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, +{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, +{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, +{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, +{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, +{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, +{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, +{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, +{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, +{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, +{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, +{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, +{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, +{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, +{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, +{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, +{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, +{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, +{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, +{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, +{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, +{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, +{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, +{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, +{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, +{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, +{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, +{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258}, +{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, +{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, +{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, +{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, +{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, +{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, +{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, +{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, +{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, +{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, +{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0}, +{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, +{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, +{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, +{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, +{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, +{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, +{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, +{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, +{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, +{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, +{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, +{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, +{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3}, +{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67}, +{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE}, +{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE}, +{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006}, +{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F}, +{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E}, +{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C}, +{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, +{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, +{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3}, +{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, {0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7}, {0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946}, {0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4}, @@ -129,51 +149,60 @@ const std::vector> unicode_ranges_letter = { {0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349}, {0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF}, {0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527}, -{0x00010530, 0x00010563}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, -{0x00010800, 0x00010805}, {0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, -{0x0001083C, 0x0001083C}, {0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, -{0x000108E0, 0x000108F2}, {0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, -{0x00010980, 0x000109B7}, {0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, -{0x00010A15, 0x00010A17}, {0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, -{0x00010AC0, 0x00010AC7}, {0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, -{0x00010B60, 0x00010B72}, {0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, -{0x00010CC0, 0x00010CF2}, {0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, -{0x00010F00, 0x00010F1C}, {0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010FB0, 0x00010FC4}, -{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, -{0x00011103, 0x00011126}, {0x00011144, 0x00011144}, {0x00011147, 0x00011147}, {0x00011150, 0x00011172}, -{0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, {0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, -{0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, {0x00011213, 0x0001122B}, {0x00011280, 0x00011286}, -{0x00011288, 0x00011288}, {0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, -{0x000112B0, 0x000112DE}, {0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, -{0x0001132A, 0x00011330}, {0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, -{0x00011350, 0x00011350}, {0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, -{0x0001145F, 0x00011461}, {0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, -{0x00011580, 0x000115AE}, {0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, -{0x00011680, 0x000116AA}, {0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011800, 0x0001182B}, +{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592}, +{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9}, +{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, +{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805}, +{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C}, +{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2}, +{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7}, +{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17}, +{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7}, +{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72}, +{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2}, +{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C}, +{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4}, +{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075}, +{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144}, +{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, +{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, +{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288}, +{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE}, +{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330}, +{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350}, +{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461}, +{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE}, +{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA}, +{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B}, {0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913}, {0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941}, {0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3}, {0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50}, -{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AC0, 0x00011AF8}, {0x00011C00, 0x00011C08}, +{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08}, {0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06}, {0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65}, {0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2}, -{0x00011FB0, 0x00011FB0}, {0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00013000, 0x0001342E}, -{0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, {0x00016AD0, 0x00016AED}, -{0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, {0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, -{0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, {0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, -{0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, {0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, -{0x00018D00, 0x00018D08}, {0x0001B000, 0x0001B11E}, {0x0001B150, 0x0001B152}, {0x0001B164, 0x0001B167}, -{0x0001B170, 0x0001B2FB}, {0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, -{0x0001BC90, 0x0001BC99}, {0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, -{0x0001D4A2, 0x0001D4A2}, {0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, -{0x0001D4BB, 0x0001D4BB}, {0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, -{0x0001D50D, 0x0001D514}, {0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, -{0x0001D540, 0x0001D544}, {0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, -{0x0001D6A8, 0x0001D6C0}, {0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, -{0x0001D716, 0x0001D734}, {0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, -{0x0001D78A, 0x0001D7A8}, {0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001E100, 0x0001E12C}, -{0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, {0x0001E2C0, 0x0001E2EB}, {0x0001E800, 0x0001E8C4}, +{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0}, +{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F}, +{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, +{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, +{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, +{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, +{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3}, +{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132}, +{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB}, +{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99}, +{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2}, +{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB}, +{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514}, +{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544}, +{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0}, +{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734}, +{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8}, +{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A}, +{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, +{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6}, +{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4}, {0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F}, {0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32}, {0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42}, @@ -182,15 +211,14 @@ const std::vector> unicode_ranges_letter = { {0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62}, {0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77}, {0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, -{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DD}, -{0x0002A700, 0x0002B734}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, -{0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, +{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF}, +{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, +{0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, }; const std::vector> unicode_ranges_whitespace = { -{0x00000009, 0x0000000D}, {0x0000001C, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, -{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, -{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, +{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, }; const std::vector> unicode_ranges_accent_mark = { @@ -200,72 +228,77 @@ const std::vector> unicode_ranges_accent_mark = { {0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A}, {0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819}, {0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B}, -{0x000008D3, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, {0x0000093E, 0x0000094F}, -{0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, {0x000009BC, 0x000009BC}, -{0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, {0x000009D7, 0x000009D7}, -{0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, {0x00000A3C, 0x00000A3C}, -{0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, {0x00000A51, 0x00000A51}, -{0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, {0x00000ABC, 0x00000ABC}, -{0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, {0x00000AE2, 0x00000AE3}, -{0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, {0x00000B3E, 0x00000B44}, -{0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, {0x00000B62, 0x00000B63}, -{0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, {0x00000BCA, 0x00000BCD}, -{0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, -{0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, {0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, -{0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, {0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, -{0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, -{0x00000D3E, 0x00000D44}, {0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, -{0x00000D62, 0x00000D63}, {0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, -{0x00000DD6, 0x00000DD6}, {0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, -{0x00000E34, 0x00000E3A}, {0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, -{0x00000EC8, 0x00000ECD}, {0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, -{0x00000F39, 0x00000F39}, {0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, -{0x00000F8D, 0x00000F97}, {0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, -{0x00001056, 0x00001059}, {0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, -{0x00001071, 0x00001074}, {0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, -{0x0000135D, 0x0000135F}, {0x00001712, 0x00001714}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, -{0x00001772, 0x00001773}, {0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, +{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, +{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, +{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, +{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, +{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, +{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, +{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, +{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, +{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, +{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, +{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C}, +{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, +{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, +{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, +{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44}, +{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63}, +{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6}, +{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A}, +{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE}, +{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39}, +{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97}, +{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059}, +{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074}, +{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F}, +{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773}, +{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F}, {0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B}, {0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F}, -{0x00001AB0, 0x00001AC0}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, +{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, {0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37}, {0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4}, -{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DF9}, {0x00001DFB, 0x00001DFF}, {0x000020D0, 0x000020F0}, -{0x00002CEF, 0x00002CF1}, {0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, -{0x00003099, 0x0000309A}, {0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, -{0x0000A6F0, 0x0000A6F1}, {0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, -{0x0000A823, 0x0000A827}, {0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, -{0x0000A8E0, 0x0000A8F1}, {0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, -{0x0000A980, 0x0000A983}, {0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, -{0x0000AA43, 0x0000AA43}, {0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, -{0x0000AAB2, 0x0000AAB4}, {0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, -{0x0000AAEB, 0x0000AAEF}, {0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, -{0x0000FB1E, 0x0000FB1E}, {0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, -{0x000102E0, 0x000102E0}, {0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, -{0x00010A0C, 0x00010A0F}, {0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, -{0x00010D24, 0x00010D27}, {0x00010EAB, 0x00010EAC}, {0x00010F46, 0x00010F50}, {0x00011000, 0x00011002}, -{0x00011038, 0x00011046}, {0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x00011100, 0x00011102}, +{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1}, +{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A}, +{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1}, +{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827}, +{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1}, +{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983}, +{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43}, +{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4}, +{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF}, +{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E}, +{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0}, +{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F}, +{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27}, +{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85}, +{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074}, +{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102}, {0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182}, {0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237}, -{0x0001123E, 0x0001123E}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, {0x0001133B, 0x0001133C}, -{0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, {0x00011357, 0x00011357}, -{0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, {0x00011435, 0x00011446}, -{0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, {0x000115B8, 0x000115C0}, -{0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, {0x0001171D, 0x0001172B}, -{0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, {0x0001193B, 0x0001193E}, -{0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, {0x000119DA, 0x000119E0}, -{0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, {0x00011A3B, 0x00011A3E}, -{0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, {0x00011C2F, 0x00011C36}, -{0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, {0x00011D31, 0x00011D36}, -{0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, {0x00011D47, 0x00011D47}, -{0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, {0x00011EF3, 0x00011EF6}, -{0x00016AF0, 0x00016AF4}, {0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, -{0x00016F8F, 0x00016F92}, {0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, -{0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, {0x0001D185, 0x0001D18B}, -{0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, {0x0001DA3B, 0x0001DA6C}, -{0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, {0x0001DAA1, 0x0001DAAF}, -{0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, {0x0001E023, 0x0001E024}, -{0x0001E026, 0x0001E02A}, {0x0001E130, 0x0001E136}, {0x0001E2EC, 0x0001E2EF}, {0x0001E8D0, 0x0001E8D6}, +{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, +{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, +{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, +{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, +{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, +{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, +{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, +{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, +{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, +{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, +{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, +{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, +{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A}, +{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4}, +{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92}, +{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D}, +{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, +{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, +{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, +{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, +{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136}, +{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6}, {0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF}, }; @@ -276,7 +309,7 @@ const std::vector> unicode_ranges_punctuation = { {0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E}, {0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE}, {0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4}, -{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061E, 0x0000061F}, +{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F}, {0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9}, {0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970}, {0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77}, @@ -286,37 +319,38 @@ const std::vector> unicode_ranges_punctuation = { {0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C}, {0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA}, {0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6}, -{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001BFC, 0x00001BFF}, {0x00001C3B, 0x00001C3F}, -{0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, {0x00002010, 0x00002027}, -{0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, {0x0000207D, 0x0000207E}, -{0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, {0x00002768, 0x00002775}, -{0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, {0x000029D8, 0x000029DB}, -{0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, {0x00002D70, 0x00002D70}, -{0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E52}, {0x00003001, 0x00003003}, -{0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, {0x0000303D, 0x0000303D}, -{0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, {0x0000A60D, 0x0000A60F}, -{0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, {0x0000A874, 0x0000A877}, -{0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, {0x0000A92E, 0x0000A92F}, -{0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, {0x0000AA5C, 0x0000AA5F}, -{0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, {0x0000FD3E, 0x0000FD3F}, -{0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, {0x0000FE63, 0x0000FE63}, -{0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, {0x0000FF05, 0x0000FF0A}, -{0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, {0x0000FF3B, 0x0000FF3D}, -{0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, {0x0000FF5F, 0x0000FF65}, -{0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, {0x0001056F, 0x0001056F}, -{0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, {0x00010A50, 0x00010A58}, -{0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, {0x00010B99, 0x00010B9C}, -{0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, -{0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, {0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, -{0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, {0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, -{0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, {0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, -{0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, {0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, -{0x0001173C, 0x0001173E}, {0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, -{0x00011A3F, 0x00011A46}, {0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011C41, 0x00011C45}, -{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011FFF, 0x00011FFF}, {0x00012470, 0x00012474}, -{0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, {0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, -{0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, {0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, -{0x0001E95E, 0x0001E95F}, +{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF}, +{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, +{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, +{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, +{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, +{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, +{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D}, +{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, +{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, +{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, +{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, +{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, +{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, +{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, +{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, +{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, +{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, +{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, +{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, +{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, +{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89}, +{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, +{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, +{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, +{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, +{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E}, +{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46}, +{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45}, +{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF}, +{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, +{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, +{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F}, }; const std::vector> unicode_ranges_symbol = { @@ -328,40 +362,41 @@ const std::vector> unicode_ranges_symbol = { {0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482}, {0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F}, {0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6}, -{0x000007FE, 0x000007FF}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, {0x00000AF1, 0x00000AF1}, -{0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, {0x00000D4F, 0x00000D4F}, -{0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, {0x00000F13, 0x00000F13}, -{0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, {0x00000F36, 0x00000F36}, -{0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, {0x00000FCE, 0x00000FCF}, -{0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, {0x0000166D, 0x0000166D}, -{0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, {0x00001B61, 0x00001B6A}, -{0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, {0x00001FCD, 0x00001FCF}, -{0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, {0x00002044, 0x00002044}, -{0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, {0x000020A0, 0x000020BF}, -{0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, {0x00002114, 0x00002114}, -{0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, {0x00002127, 0x00002127}, -{0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, {0x00002140, 0x00002144}, -{0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, {0x00002190, 0x00002307}, -{0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, {0x0000249C, 0x000024E9}, -{0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, {0x000027F0, 0x00002982}, -{0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, {0x00002B76, 0x00002B95}, -{0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, {0x00002E80, 0x00002E99}, -{0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFB}, {0x00003004, 0x00003004}, -{0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, {0x0000303E, 0x0000303F}, -{0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, {0x000031C0, 0x000031E3}, -{0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, {0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, -{0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, {0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, -{0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, {0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, -{0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, {0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, -{0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC1}, {0x0000FDFC, 0x0000FDFD}, {0x0000FE62, 0x0000FE62}, -{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, -{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, -{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, -{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, -{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, -{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, -{0x0001BC9C, 0x0001BC9C}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, {0x0001D129, 0x0001D164}, -{0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, {0x0001D1AE, 0x0001D1E8}, +{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, +{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, +{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, +{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, +{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, +{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, +{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, +{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, +{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, +{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, +{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, +{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, +{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, +{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, +{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, +{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, +{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, +{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, +{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFB}, +{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, +{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, +{0x000031C0, 0x000031E3}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, {0x00003250, 0x00003250}, +{0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, {0x00004DC0, 0x00004DFF}, +{0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, {0x0000A789, 0x0000A78A}, +{0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, {0x0000AB5B, 0x0000AB5B}, +{0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, {0x0000FD40, 0x0000FD4F}, +{0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, {0x0000FE64, 0x0000FE66}, +{0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, {0x0000FF1C, 0x0000FF1E}, +{0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, {0x0000FF5E, 0x0000FF5E}, +{0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, {0x00010137, 0x0001013F}, +{0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, {0x000101A0, 0x000101A0}, +{0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, {0x0001173F, 0x0001173F}, +{0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, {0x0001BC9C, 0x0001BC9C}, +{0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, {0x0001D129, 0x0001D164}, +{0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, {0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, {0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, {0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, {0x0001D7A9, 0x0001D7A9}, @@ -371,102 +406,100 @@ const std::vector> unicode_ranges_symbol = { {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, {0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, {0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, {0x0001F260, 0x0001F265}, -{0x0001F300, 0x0001F6D7}, {0x0001F6E0, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, {0x0001F700, 0x0001F773}, -{0x0001F780, 0x0001F7D8}, {0x0001F7E0, 0x0001F7EB}, {0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, -{0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, {0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, -{0x0001F900, 0x0001F978}, {0x0001F97A, 0x0001F9CB}, {0x0001F9CD, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, -{0x0001FA70, 0x0001FA74}, {0x0001FA78, 0x0001FA7A}, {0x0001FA80, 0x0001FA86}, {0x0001FA90, 0x0001FAA8}, -{0x0001FAB0, 0x0001FAB6}, {0x0001FAC0, 0x0001FAC2}, {0x0001FAD0, 0x0001FAD6}, {0x0001FB00, 0x0001FB92}, -{0x0001FB94, 0x0001FBCA}, +{0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, {0x0001F700, 0x0001F776}, +{0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, {0x0001F800, 0x0001F80B}, +{0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, {0x0001F890, 0x0001F8AD}, +{0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, {0x0001FA70, 0x0001FA7C}, +{0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, {0x0001FACE, 0x0001FADB}, +{0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, {0x0001FB94, 0x0001FBCA}, }; const std::vector> unicode_ranges_control = { -{0x00000000, 0x00000008}, {0x0000000E, 0x0000001B}, {0x0000007F, 0x00000084}, {0x00000086, 0x0000009F}, -{0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, {0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, -{0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, {0x00000530, 0x00000530}, {0x00000557, 0x00000558}, -{0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, {0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, -{0x000005F5, 0x00000605}, {0x0000061C, 0x0000061D}, {0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, -{0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, {0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, -{0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, {0x0000085F, 0x0000085F}, {0x0000086B, 0x0000089F}, -{0x000008B5, 0x000008B5}, {0x000008C8, 0x000008D2}, {0x000008E2, 0x000008E2}, {0x00000984, 0x00000984}, -{0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, {0x000009B1, 0x000009B1}, -{0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, {0x000009C9, 0x000009CA}, -{0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, {0x000009E4, 0x000009E5}, -{0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, {0x00000A11, 0x00000A12}, -{0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, {0x00000A37, 0x00000A37}, -{0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, {0x00000A49, 0x00000A4A}, -{0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, {0x00000A5F, 0x00000A65}, -{0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, {0x00000A92, 0x00000A92}, -{0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, {0x00000ABA, 0x00000ABB}, -{0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, {0x00000AD1, 0x00000ADF}, -{0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, {0x00000B04, 0x00000B04}, -{0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, {0x00000B31, 0x00000B31}, -{0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, {0x00000B49, 0x00000B4A}, -{0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E}, {0x00000B64, 0x00000B65}, -{0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D}, {0x00000B91, 0x00000B91}, -{0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D}, {0x00000BA0, 0x00000BA2}, -{0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD}, {0x00000BC3, 0x00000BC5}, -{0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6}, {0x00000BD8, 0x00000BE5}, -{0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11}, {0x00000C29, 0x00000C29}, -{0x00000C3A, 0x00000C3C}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49}, {0x00000C4E, 0x00000C54}, -{0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5F}, {0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, -{0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91}, {0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, -{0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5}, {0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, -{0x00000CD7, 0x00000CDD}, {0x00000CDF, 0x00000CDF}, {0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, -{0x00000CF3, 0x00000CFF}, {0x00000D0D, 0x00000D0D}, {0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, -{0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53}, {0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, -{0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99}, {0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, -{0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9}, {0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, -{0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5}, {0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, -{0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80}, {0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, -{0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4}, {0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, -{0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7}, {0x00000ECE, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, -{0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48}, {0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, -{0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD}, {0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, -{0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF}, {0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, -{0x00001257, 0x00001257}, {0x00001259, 0x00001259}, {0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, -{0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1}, {0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, -{0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7}, {0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, -{0x00001316, 0x00001317}, {0x0000135B, 0x0000135C}, {0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, -{0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF}, {0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, -{0x0000170D, 0x0000170D}, {0x00001715, 0x0000171F}, {0x00001737, 0x0000173F}, {0x00001754, 0x0000175F}, -{0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F}, {0x000017DE, 0x000017DF}, -{0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180F}, {0x0000181A, 0x0000181F}, -{0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF}, {0x0000191F, 0x0000191F}, -{0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943}, {0x0000196E, 0x0000196F}, -{0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF}, {0x000019DB, 0x000019DD}, -{0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E}, {0x00001A8A, 0x00001A8F}, -{0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001AC1, 0x00001AFF}, {0x00001B4C, 0x00001B4F}, -{0x00001B7D, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A}, {0x00001C4A, 0x00001C4C}, -{0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF}, {0x00001CFB, 0x00001CFF}, -{0x00001DFA, 0x00001DFA}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47}, +{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, +{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, +{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, +{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C}, +{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, +{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, +{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2}, +{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, +{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, +{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, +{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, +{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, +{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, +{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, +{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, +{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, +{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, +{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, +{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, +{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, +{0x00000B49, 0x00000B4A}, {0x00000B4E, 0x00000B54}, {0x00000B58, 0x00000B5B}, {0x00000B5E, 0x00000B5E}, +{0x00000B64, 0x00000B65}, {0x00000B78, 0x00000B81}, {0x00000B84, 0x00000B84}, {0x00000B8B, 0x00000B8D}, +{0x00000B91, 0x00000B91}, {0x00000B96, 0x00000B98}, {0x00000B9B, 0x00000B9B}, {0x00000B9D, 0x00000B9D}, +{0x00000BA0, 0x00000BA2}, {0x00000BA5, 0x00000BA7}, {0x00000BAB, 0x00000BAD}, {0x00000BBA, 0x00000BBD}, +{0x00000BC3, 0x00000BC5}, {0x00000BC9, 0x00000BC9}, {0x00000BCE, 0x00000BCF}, {0x00000BD1, 0x00000BD6}, +{0x00000BD8, 0x00000BE5}, {0x00000BFB, 0x00000BFF}, {0x00000C0D, 0x00000C0D}, {0x00000C11, 0x00000C11}, +{0x00000C29, 0x00000C29}, {0x00000C3A, 0x00000C3B}, {0x00000C45, 0x00000C45}, {0x00000C49, 0x00000C49}, +{0x00000C4E, 0x00000C54}, {0x00000C57, 0x00000C57}, {0x00000C5B, 0x00000C5C}, {0x00000C5E, 0x00000C5F}, +{0x00000C64, 0x00000C65}, {0x00000C70, 0x00000C76}, {0x00000C8D, 0x00000C8D}, {0x00000C91, 0x00000C91}, +{0x00000CA9, 0x00000CA9}, {0x00000CB4, 0x00000CB4}, {0x00000CBA, 0x00000CBB}, {0x00000CC5, 0x00000CC5}, +{0x00000CC9, 0x00000CC9}, {0x00000CCE, 0x00000CD4}, {0x00000CD7, 0x00000CDC}, {0x00000CDF, 0x00000CDF}, +{0x00000CE4, 0x00000CE5}, {0x00000CF0, 0x00000CF0}, {0x00000CF4, 0x00000CFF}, {0x00000D0D, 0x00000D0D}, +{0x00000D11, 0x00000D11}, {0x00000D45, 0x00000D45}, {0x00000D49, 0x00000D49}, {0x00000D50, 0x00000D53}, +{0x00000D64, 0x00000D65}, {0x00000D80, 0x00000D80}, {0x00000D84, 0x00000D84}, {0x00000D97, 0x00000D99}, +{0x00000DB2, 0x00000DB2}, {0x00000DBC, 0x00000DBC}, {0x00000DBE, 0x00000DBF}, {0x00000DC7, 0x00000DC9}, +{0x00000DCB, 0x00000DCE}, {0x00000DD5, 0x00000DD5}, {0x00000DD7, 0x00000DD7}, {0x00000DE0, 0x00000DE5}, +{0x00000DF0, 0x00000DF1}, {0x00000DF5, 0x00000E00}, {0x00000E3B, 0x00000E3E}, {0x00000E5C, 0x00000E80}, +{0x00000E83, 0x00000E83}, {0x00000E85, 0x00000E85}, {0x00000E8B, 0x00000E8B}, {0x00000EA4, 0x00000EA4}, +{0x00000EA6, 0x00000EA6}, {0x00000EBE, 0x00000EBF}, {0x00000EC5, 0x00000EC5}, {0x00000EC7, 0x00000EC7}, +{0x00000ECF, 0x00000ECF}, {0x00000EDA, 0x00000EDB}, {0x00000EE0, 0x00000EFF}, {0x00000F48, 0x00000F48}, +{0x00000F6D, 0x00000F70}, {0x00000F98, 0x00000F98}, {0x00000FBD, 0x00000FBD}, {0x00000FCD, 0x00000FCD}, +{0x00000FDB, 0x00000FFF}, {0x000010C6, 0x000010C6}, {0x000010C8, 0x000010CC}, {0x000010CE, 0x000010CF}, +{0x00001249, 0x00001249}, {0x0000124E, 0x0000124F}, {0x00001257, 0x00001257}, {0x00001259, 0x00001259}, +{0x0000125E, 0x0000125F}, {0x00001289, 0x00001289}, {0x0000128E, 0x0000128F}, {0x000012B1, 0x000012B1}, +{0x000012B6, 0x000012B7}, {0x000012BF, 0x000012BF}, {0x000012C1, 0x000012C1}, {0x000012C6, 0x000012C7}, +{0x000012D7, 0x000012D7}, {0x00001311, 0x00001311}, {0x00001316, 0x00001317}, {0x0000135B, 0x0000135C}, +{0x0000137D, 0x0000137F}, {0x0000139A, 0x0000139F}, {0x000013F6, 0x000013F7}, {0x000013FE, 0x000013FF}, +{0x0000169D, 0x0000169F}, {0x000016F9, 0x000016FF}, {0x00001716, 0x0000171E}, {0x00001737, 0x0000173F}, +{0x00001754, 0x0000175F}, {0x0000176D, 0x0000176D}, {0x00001771, 0x00001771}, {0x00001774, 0x0000177F}, +{0x000017DE, 0x000017DF}, {0x000017EA, 0x000017EF}, {0x000017FA, 0x000017FF}, {0x0000180E, 0x0000180E}, +{0x0000181A, 0x0000181F}, {0x00001879, 0x0000187F}, {0x000018AB, 0x000018AF}, {0x000018F6, 0x000018FF}, +{0x0000191F, 0x0000191F}, {0x0000192C, 0x0000192F}, {0x0000193C, 0x0000193F}, {0x00001941, 0x00001943}, +{0x0000196E, 0x0000196F}, {0x00001975, 0x0000197F}, {0x000019AC, 0x000019AF}, {0x000019CA, 0x000019CF}, +{0x000019DB, 0x000019DD}, {0x00001A1C, 0x00001A1D}, {0x00001A5F, 0x00001A5F}, {0x00001A7D, 0x00001A7E}, +{0x00001A8A, 0x00001A8F}, {0x00001A9A, 0x00001A9F}, {0x00001AAE, 0x00001AAF}, {0x00001ACF, 0x00001AFF}, +{0x00001B4D, 0x00001B4F}, {0x00001B7F, 0x00001B7F}, {0x00001BF4, 0x00001BFB}, {0x00001C38, 0x00001C3A}, +{0x00001C4A, 0x00001C4C}, {0x00001C89, 0x00001C8F}, {0x00001CBB, 0x00001CBC}, {0x00001CC8, 0x00001CCF}, +{0x00001CFB, 0x00001CFF}, {0x00001F16, 0x00001F17}, {0x00001F1E, 0x00001F1F}, {0x00001F46, 0x00001F47}, {0x00001F4E, 0x00001F4F}, {0x00001F58, 0x00001F58}, {0x00001F5A, 0x00001F5A}, {0x00001F5C, 0x00001F5C}, {0x00001F5E, 0x00001F5E}, {0x00001F7E, 0x00001F7F}, {0x00001FB5, 0x00001FB5}, {0x00001FC5, 0x00001FC5}, {0x00001FD4, 0x00001FD5}, {0x00001FDC, 0x00001FDC}, {0x00001FF0, 0x00001FF1}, {0x00001FF5, 0x00001FF5}, {0x00001FFF, 0x00001FFF}, {0x0000200B, 0x0000200F}, {0x0000202A, 0x0000202E}, {0x00002060, 0x0000206F}, -{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C0, 0x000020CF}, +{0x00002072, 0x00002073}, {0x0000208F, 0x0000208F}, {0x0000209D, 0x0000209F}, {0x000020C1, 0x000020CF}, {0x000020F1, 0x000020FF}, {0x0000218C, 0x0000218F}, {0x00002427, 0x0000243F}, {0x0000244B, 0x0000245F}, -{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002C2F, 0x00002C2F}, {0x00002C5F, 0x00002C5F}, -{0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26}, {0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, -{0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E}, {0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, -{0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, {0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, -{0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, {0x00002DDF, 0x00002DDF}, {0x00002E53, 0x00002E7F}, -{0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, {0x00002FD6, 0x00002FEF}, {0x00002FFC, 0x00002FFF}, -{0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104}, {0x00003130, 0x00003130}, -{0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EF}, {0x0000321F, 0x0000321F}, {0x00009FFD, 0x00009FFF}, -{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF}, -{0x0000A7C0, 0x0000A7C1}, {0x0000A7CB, 0x0000A7F4}, {0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, -{0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD}, {0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, -{0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE}, {0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, -{0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F}, {0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, -{0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08}, {0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, -{0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F}, {0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, -{0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF}, {0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, +{0x00002B74, 0x00002B75}, {0x00002B96, 0x00002B96}, {0x00002CF4, 0x00002CF8}, {0x00002D26, 0x00002D26}, +{0x00002D28, 0x00002D2C}, {0x00002D2E, 0x00002D2F}, {0x00002D68, 0x00002D6E}, {0x00002D71, 0x00002D7E}, +{0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, {0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, +{0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, {0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, +{0x00002DDF, 0x00002DDF}, {0x00002E5E, 0x00002E7F}, {0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, +{0x00002FD6, 0x00002FEF}, {0x00002FFC, 0x00002FFF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, +{0x00003100, 0x00003104}, {0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EF}, +{0x0000321F, 0x0000321F}, {0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, +{0x0000A6F8, 0x0000A6FF}, {0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, +{0x0000A7DA, 0x0000A7F1}, {0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, +{0x0000A8C6, 0x0000A8CD}, {0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, +{0x0000A9CE, 0x0000A9CE}, {0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, +{0x0000AA4E, 0x0000AA4F}, {0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, +{0x0000AB07, 0x0000AB08}, {0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, +{0x0000AB2F, 0x0000AB2F}, {0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, +{0x0000D7A4, 0x0000D7AF}, {0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000D7FF}, {0x0000E000, 0x0000F8FF}, {0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, {0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, {0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, {0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, -{0x0000FB45, 0x0000FB45}, {0x0000FBC2, 0x0000FBD2}, {0x0000FD40, 0x0000FD4F}, {0x0000FD90, 0x0000FD91}, -{0x0000FDC8, 0x0000FDEF}, {0x0000FDFE, 0x0000FDFF}, {0x0000FE1A, 0x0000FE1F}, {0x0000FE53, 0x0000FE53}, -{0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, {0x0000FEFD, 0x0000FF00}, +{0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2}, {0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, +{0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F}, {0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, +{0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, {0x0000FEFD, 0x0000FEFE}, {0x0000FF00, 0x0000FF00}, {0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, {0x0000FFD8, 0x0000FFD9}, {0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, {0x0000FFFE, 0x0000FFFF}, {0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, {0x0001003E, 0x0001003E}, @@ -476,82 +509,91 @@ const std::vector> unicode_ranges_control = { {0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, {0x0001039E, 0x0001039E}, {0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, {0x000104AA, 0x000104AF}, {0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, {0x00010564, 0x0001056E}, -{0x00010570, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x000107FF}, -{0x00010806, 0x00010807}, {0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, -{0x0001083D, 0x0001083E}, {0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, -{0x000108F3, 0x000108F3}, {0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, -{0x00010940, 0x0001097F}, {0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, -{0x00010A07, 0x00010A0B}, {0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, -{0x00010A3B, 0x00010A3E}, {0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, -{0x00010AE7, 0x00010AEA}, {0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, -{0x00010B73, 0x00010B77}, {0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, -{0x00010C49, 0x00010C7F}, {0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, -{0x00010D3A, 0x00010E5F}, {0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, -{0x00010EB2, 0x00010EFF}, {0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, -{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011070, 0x0001107E}, {0x000110BD, 0x000110BD}, -{0x000110C2, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, -{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, -{0x00011212, 0x00011212}, {0x0001123F, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, -{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, -{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, -{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, -{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, -{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, -{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, -{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, -{0x0001166D, 0x0001167F}, {0x000116B9, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, -{0x0001172C, 0x0001172F}, {0x00011740, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, -{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, -{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, -{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, -{0x00011AA3, 0x00011ABF}, {0x00011AF9, 0x00011BFF}, {0x00011C09, 0x00011C09}, {0x00011C37, 0x00011C37}, +{0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593}, {0x00010596, 0x00010596}, +{0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA}, {0x000105BD, 0x000105FF}, +{0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F}, {0x00010786, 0x00010786}, +{0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807}, {0x00010809, 0x00010809}, +{0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E}, {0x00010856, 0x00010856}, +{0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3}, {0x000108F6, 0x000108FA}, +{0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F}, {0x000109B8, 0x000109BB}, +{0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B}, {0x00010A14, 0x00010A14}, +{0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E}, {0x00010A49, 0x00010A4F}, +{0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA}, {0x00010AF7, 0x00010AFF}, +{0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77}, {0x00010B92, 0x00010B98}, +{0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F}, {0x00010CB3, 0x00010CBF}, +{0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F}, {0x00010E7F, 0x00010E7F}, +{0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC}, {0x00010F28, 0x00010F2F}, +{0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, {0x00010FF7, 0x00010FFF}, +{0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD}, {0x000110C3, 0x000110CF}, +{0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, {0x00011148, 0x0001114F}, +{0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, {0x00011212, 0x00011212}, +{0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, {0x0001128E, 0x0001128E}, +{0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, {0x000112FA, 0x000112FF}, +{0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, {0x00011329, 0x00011329}, +{0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, {0x00011345, 0x00011346}, +{0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, {0x00011358, 0x0001135C}, +{0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, {0x0001145C, 0x0001145C}, +{0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, {0x000115B6, 0x000115B7}, +{0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, {0x0001166D, 0x0001167F}, +{0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, {0x0001172C, 0x0001172F}, +{0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, {0x00011907, 0x00011908}, +{0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, {0x00011936, 0x00011936}, +{0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, {0x000119A8, 0x000119A9}, +{0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, {0x00011AA3, 0x00011AAF}, +{0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09}, {0x00011C37, 0x00011C37}, {0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, {0x00011CA8, 0x00011CA8}, {0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, {0x00011D37, 0x00011D39}, {0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, {0x00011D5A, 0x00011D5F}, {0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, {0x00011D92, 0x00011D92}, -{0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, -{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, -{0x00012544, 0x00012FFF}, {0x0001342F, 0x000143FF}, {0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, -{0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, {0x00016A70, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, -{0x00016AF6, 0x00016AFF}, {0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, -{0x00016B78, 0x00016B7C}, {0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, -{0x00016F88, 0x00016F8E}, {0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, -{0x000187F8, 0x000187FF}, {0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFFF}, {0x0001B11F, 0x0001B14F}, -{0x0001B153, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, -{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CFFF}, -{0x0001D0F6, 0x0001D0FF}, {0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1E9, 0x0001D1FF}, -{0x0001D246, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, -{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, -{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, -{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, -{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, -{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, -{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DFFF}, {0x0001E007, 0x0001E007}, -{0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, {0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E0FF}, -{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E2BF}, -{0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E7FF}, {0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, -{0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, {0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, -{0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, {0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, -{0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, {0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, -{0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, {0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, -{0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, {0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, -{0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, {0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, -{0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, {0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, -{0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, {0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, -{0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, {0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, -{0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, {0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, -{0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, {0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, -{0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, {0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, -{0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, {0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DF}, -{0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, {0x0001F774, 0x0001F77F}, {0x0001F7D9, 0x0001F7DF}, -{0x0001F7EC, 0x0001F7FF}, {0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, -{0x0001F888, 0x0001F88F}, {0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001F979, 0x0001F979}, -{0x0001F9CC, 0x0001F9CC}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, {0x0001FA75, 0x0001FA77}, -{0x0001FA7B, 0x0001FA7F}, {0x0001FA87, 0x0001FA8F}, {0x0001FAA9, 0x0001FAAF}, {0x0001FAB7, 0x0001FABF}, -{0x0001FAC3, 0x0001FACF}, {0x0001FAD7, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, {0x0001FBCB, 0x0001FBEF}, -{0x0001FBFA, 0x0001FFFF}, {0x0002A6DE, 0x0002A6FF}, {0x0002B735, 0x0002B73F}, {0x0002B81E, 0x0002B81F}, -{0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002F7FF}, {0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x000E00FF}, -{0x000E01F0, 0x0010FFFF}, +{0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF}, {0x00011F11, 0x00011F11}, +{0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, {0x00011FF2, 0x00011FFE}, +{0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, {0x00012544, 0x00012F8F}, +{0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF}, {0x00014647, 0x000167FF}, +{0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, {0x00016ABF, 0x00016ABF}, +{0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF}, {0x00016B46, 0x00016B4F}, +{0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C}, {0x00016B90, 0x00016E3F}, +{0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E}, {0x00016FA0, 0x00016FDF}, +{0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF}, {0x00018CD6, 0x00018CFF}, +{0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC}, {0x0001AFFF, 0x0001AFFF}, +{0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154}, {0x0001B156, 0x0001B163}, +{0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, {0x0001BC7D, 0x0001BC7F}, +{0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF}, {0x0001CF2E, 0x0001CF2F}, +{0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF}, {0x0001D127, 0x0001D128}, +{0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF}, {0x0001D2D4, 0x0001D2DF}, +{0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, {0x0001D455, 0x0001D455}, +{0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, {0x0001D4A7, 0x0001D4A8}, +{0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, {0x0001D4C4, 0x0001D4C4}, +{0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, {0x0001D51D, 0x0001D51D}, +{0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, {0x0001D547, 0x0001D549}, +{0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, {0x0001DA8C, 0x0001DA9A}, +{0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, {0x0001DF2B, 0x0001DFFF}, +{0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, {0x0001E025, 0x0001E025}, +{0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, {0x0001E12D, 0x0001E12F}, +{0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, {0x0001E2AF, 0x0001E2BF}, +{0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, {0x0001E7E7, 0x0001E7E7}, +{0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, {0x0001E8C5, 0x0001E8C6}, +{0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, {0x0001E960, 0x0001EC70}, +{0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, {0x0001EE20, 0x0001EE20}, +{0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, {0x0001EE33, 0x0001EE33}, +{0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, {0x0001EE43, 0x0001EE46}, +{0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, {0x0001EE50, 0x0001EE50}, +{0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, {0x0001EE5A, 0x0001EE5A}, +{0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, {0x0001EE63, 0x0001EE63}, +{0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, {0x0001EE78, 0x0001EE78}, +{0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, {0x0001EE9C, 0x0001EEA0}, +{0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, {0x0001EEF2, 0x0001EFFF}, +{0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, {0x0001F0C0, 0x0001F0C0}, +{0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, {0x0001F203, 0x0001F20F}, +{0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, {0x0001F266, 0x0001F2FF}, +{0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, {0x0001F777, 0x0001F77A}, +{0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, {0x0001F80C, 0x0001F80F}, +{0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, {0x0001F8AE, 0x0001F8AF}, +{0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, {0x0001FA7D, 0x0001FA7F}, +{0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, {0x0001FADC, 0x0001FADF}, +{0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, {0x0001FBCB, 0x0001FBEF}, +{0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, {0x0002B81E, 0x0002B81F}, +{0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002F7FF}, {0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, +{0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, }; const std::multimap unicode_map_nfd = { diff --git a/unicode-data.h b/unicode-data.h index cb9dd8aa5..3cf84117c 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -5,7 +5,7 @@ #include #include -extern const std::vector> unicode_ranges_digit; +extern const std::vector> unicode_ranges_number; extern const std::vector> unicode_ranges_letter; extern const std::vector> unicode_ranges_whitespace; extern const std::vector> unicode_ranges_accent_mark; diff --git a/unicode.cpp b/unicode.cpp index f2ccda05f..955c56965 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -110,9 +110,9 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) static std::unordered_map unicode_cpt_type_map() { std::unordered_map cpt_types; - for (auto p : unicode_ranges_digit) { + for (auto p : unicode_ranges_number) { for (auto i = p.first; i <= p.second; ++ i) { - cpt_types[i] = CODEPOINT_TYPE_DIGIT; + cpt_types[i] = CODEPOINT_TYPE_NUMBER; } } for (auto p : unicode_ranges_letter) { @@ -300,13 +300,13 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t collecting_letter = true; collecting = true; } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) { collecting_numeric = true; collecting = true; } else if ( - ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || - (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_DIGIT && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) + ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || + (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_NUMBER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) ) { collecting_special = true; collecting = true; @@ -323,13 +323,13 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) { split_condition = true; } - else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_DIGIT) { + else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) { split_condition = true; } - else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_DIGIT || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { + else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { split_condition = true; } - else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_DIGIT)) { + else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) { split_condition = true; } } @@ -524,19 +524,19 @@ char32_t unicode_tolower(char32_t cp) { std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs) { // unicode categories static const std::map k_ucat_enum = { - { "\\p{N}", CODEPOINT_TYPE_DIGIT }, + { "\\p{N}", CODEPOINT_TYPE_NUMBER }, { "\\p{L}", CODEPOINT_TYPE_LETTER }, { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION }, }; static const std::map k_ucat_cpt = { - { CODEPOINT_TYPE_DIGIT, 0xD1 }, + { CODEPOINT_TYPE_NUMBER, 0xD1 }, { CODEPOINT_TYPE_LETTER, 0xD2 }, { CODEPOINT_TYPE_PUNCTUATION, 0xD3 }, }; static const std::map k_ucat_map = { - { CODEPOINT_TYPE_DIGIT, "\x30-\x39" }, // 0-9 + { CODEPOINT_TYPE_NUMBER, "\x30-\x39" }, // 0-9 { CODEPOINT_TYPE_LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z { CODEPOINT_TYPE_PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} }; diff --git a/unicode.h b/unicode.h index ce2bcef5a..e9026dc81 100644 --- a/unicode.h +++ b/unicode.h @@ -5,7 +5,7 @@ #include #define CODEPOINT_TYPE_UNIDENTIFIED 0 -#define CODEPOINT_TYPE_DIGIT 1 +#define CODEPOINT_TYPE_NUMBER 1 #define CODEPOINT_TYPE_LETTER 2 #define CODEPOINT_TYPE_WHITESPACE 3 #define CODEPOINT_TYPE_ACCENT_MARK 4 From 03fb8a002df2e96104f9e06de9c78d2a8ed91e92 Mon Sep 17 00:00:00 2001 From: maor-ps <154728172+maor-ps@users.noreply.github.com> Date: Sat, 4 May 2024 12:06:40 +0300 Subject: [PATCH 024/100] If first token generated from the server is the stop word the server will crash (#7038) This will reproduce the issue in llama13b { 'prompt': 'Q: hello world \nA: ', 'stop': ['\n'], 'temperature': 0.0, 'n_predict': 10, 'cache_prompt': True, 'n_probs': 10 } --- examples/server/server.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f60530cf3..ff0814b2f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1383,9 +1383,10 @@ struct server_context { if (!slot.params.stream && slot.stopped_word) { const std::vector stop_word_toks = llama_tokenize(ctx, slot.stopping_word, false); + size_t safe_offset = std::min(slot.generated_token_probs.size(), stop_word_toks.size()); probs = std::vector( slot.generated_token_probs.begin(), - slot.generated_token_probs.end() - stop_word_toks.size()); + slot.generated_token_probs.end() - safe_offset); } else { probs = std::vector( slot.generated_token_probs.begin(), From fcd84a0f5a584ab5271745d7ffef21c8a6bc7b0c Mon Sep 17 00:00:00 2001 From: viric Date: Sat, 4 May 2024 15:26:53 +0200 Subject: [PATCH 025/100] Fix Linux /sys cpu path to guess number of cores (#7064) --- common/common.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 243b88abf..467fb014e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -76,7 +76,7 @@ int32_t get_num_physical_cores() { // enumerate the set of thread siblings, num entries is num cores std::unordered_set siblings; for (uint32_t cpu=0; cpu < UINT32_MAX; ++cpu) { - std::ifstream thread_siblings("/sys/devices/system/cpu" + std::ifstream thread_siblings("/sys/devices/system/cpu/cpu" + std::to_string(cpu) + "/topology/thread_siblings"); if (!thread_siblings.is_open()) { break; // no more cpus From cf768b7e71cbcc9886c753ae963c2b68893d02e4 Mon Sep 17 00:00:00 2001 From: Jeximo Date: Sat, 4 May 2024 13:10:15 -0300 Subject: [PATCH 026/100] Tidy Android Instructions README.md (#7016) * Tidy Android Instructions README.md Remove CLBlast instructions(outdated), added OpenBlas. * don't assume git is installed Added apt install git, so that git clone works * removed OpenBlas Linked to Linux build instructions * fix typo Remove word "run" * correct style Co-authored-by: slaren * correct grammar Co-authored-by: slaren * delete reference to Android API * remove Fdroid reference, link directly to Termux Fdroid is not required Co-authored-by: slaren * Update README.md Co-authored-by: slaren --------- Co-authored-by: slaren --- README.md | 44 ++++++++------------------------------------ 1 file changed, 8 insertions(+), 36 deletions(-) diff --git a/README.md b/README.md index 514ef3af1..2f1317662 100644 --- a/README.md +++ b/README.md @@ -977,48 +977,20 @@ Here is a demo of an interactive session running on Pixel 5 phone: https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 -#### Building the Project using Termux (F-Droid) -Termux from F-Droid offers an alternative route to execute the project on an Android device. This method empowers you to construct the project right from within the terminal, negating the requirement for a rooted device or SD Card. - -Outlined below are the directives for installing the project using OpenBLAS and CLBlast. This combination is specifically designed to deliver peak performance on recent devices that feature a GPU. - -If you opt to utilize OpenBLAS, you'll need to install the corresponding package. +#### Build on Android using Termux +[Termux](https://github.com/termux/termux-app#installation) is an alternative to execute `llama.cpp` on an Android device (no root required). ``` -apt install libopenblas +apt update && apt upgrade -y +apt install git ``` -Subsequently, if you decide to incorporate CLBlast, you'll first need to install the requisite OpenCL packages: +It's recommended to move your model inside the `~/` directory for best performance: ``` -apt install ocl-icd opencl-headers opencl-clhpp clinfo +cd storage/downloads +mv model.gguf ~/ ``` -In order to compile CLBlast, you'll need to first clone the respective Git repository, which can be found at this URL: https://github.com/CNugteren/CLBlast. Alongside this, clone this repository into your home directory. Once this is done, navigate to the CLBlast folder and execute the commands detailed below: -``` -cmake . -make -cp libclblast.so* $PREFIX/lib -cp ./include/clblast.h ../llama.cpp -``` - -Following the previous steps, navigate to the LlamaCpp directory. To compile it with OpenBLAS and CLBlast, execute the command provided below: -``` -cp /data/data/com.termux/files/usr/include/openblas/cblas.h . -cp /data/data/com.termux/files/usr/include/openblas/openblas_config.h . -make LLAMA_CLBLAST=1 //(sometimes you need to run this command twice) -``` - -Upon completion of the aforementioned steps, you will have successfully compiled the project. To run it using CLBlast, a slight adjustment is required: a command must be issued to direct the operations towards your device's physical GPU, rather than the virtual one. The necessary command is detailed below: -``` -GGML_OPENCL_PLATFORM=0 -GGML_OPENCL_DEVICE=0 -export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH -``` - -(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ ) - -For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle. - -Place your desired model into the `~/llama.cpp/models/` directory and execute the `./main (...)` script. +[Follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`. ### Docker From 842500144ee02c8b0a46d2cc43880f8d80998fa5 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 4 May 2024 18:56:22 +0200 Subject: [PATCH 027/100] gguf-split: add --no-tensor-first-split (#7072) --- examples/gguf-split/gguf-split.cpp | 19 +++++++++++++++---- examples/gguf-split/tests.sh | 14 +++++++------- ggml.c | 2 +- 3 files changed, 23 insertions(+), 12 deletions(-) diff --git a/examples/gguf-split/gguf-split.cpp b/examples/gguf-split/gguf-split.cpp index 39c75e0a7..e04feeae3 100644 --- a/examples/gguf-split/gguf-split.cpp +++ b/examples/gguf-split/gguf-split.cpp @@ -32,6 +32,7 @@ struct split_params { int n_split_tensors = 128; std::string input; std::string output; + bool no_tensor_first_split = false; bool dry_run = false; }; @@ -49,6 +50,7 @@ static void split_print_usage(const char * executable) { printf(" --merge merge multiple GGUF to a single GGUF\n"); printf(" --split-max-tensors max tensors in each split (default: %d)\n", default_params.n_split_tensors); printf(" --split-max-size N(M|G) max size per split\n"); + printf(" --no-tensor-first-split do not add tensors to the first split (disabled by default)\n"); printf(" --dry-run only print out a split plan and exit, without writing any new files\n"); printf("\n"); } @@ -100,6 +102,10 @@ static void split_params_parse_ex(int argc, const char ** argv, split_params & p arg_found = true; params.dry_run = true; } + if (arg == "--no-tensor-first-split") { + arg_found = true; + params.no_tensor_first_split = true; + } if (is_op_set) { throw std::invalid_argument("error: either --split or --merge can be specified, but not both"); @@ -200,10 +206,10 @@ struct split_strategy { // because we need to know list of tensors for each file in advance, we will build all the ctx_out for all output splits int i_split = -1; struct gguf_context * ctx_out = NULL; - auto new_ctx_out = [&]() { + auto new_ctx_out = [&](bool allow_no_tensors) { i_split++; if (ctx_out != NULL) { - if (gguf_get_n_tensors(ctx_out) == 0) { + if (gguf_get_n_tensors(ctx_out) == 0 && !allow_no_tensors) { fprintf(stderr, "error: one of splits have 0 tensors. Maybe size or tensors limit is too small\n"); exit(EXIT_FAILURE); } @@ -220,7 +226,12 @@ struct split_strategy { }; // initialize ctx_out for the first split - new_ctx_out(); + new_ctx_out(false); + + // skip first split if no_tensor_first_split is set + if (params.no_tensor_first_split) { + new_ctx_out(true); + } // process tensors one by one size_t curr_tensors_size = 0; // current size by counting only tensors size (without metadata) @@ -230,7 +241,7 @@ struct split_strategy { size_t n_bytes = GGML_PAD(ggml_nbytes(t), GGUF_DEFAULT_ALIGNMENT); size_t next_tensors_size = curr_tensors_size + n_bytes; if (should_split(i, next_tensors_size)) { - new_ctx_out(); + new_ctx_out(false); curr_tensors_size = n_bytes; } else { curr_tensors_size = next_tensors_size; diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh index 57588204d..7ca6fa7f2 100755 --- a/examples/gguf-split/tests.sh +++ b/examples/gguf-split/tests.sh @@ -55,15 +55,15 @@ $MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32 echo PASS echo -# 4. Split with no tensor in metadata -#$SPLIT --split-max-tensors 32 --no-tensor-in-metadata $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors -#echo PASS -#echo +# 4. Split with no tensors in the first split +$SPLIT --split-max-tensors 32 --no-tensor-first-split $WORK_PATH/ggml-model-merge.gguf $WORK_PATH/ggml-model-split-32-tensors +echo PASS +echo # 4b. Test the sharded model is loading properly -#$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf --random-prompt --n-predict 32 -#echo PASS -#echo +$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32 +echo PASS +echo # 5. Merge #$SPLIT --merge $WORK_PATH/ggml-model-split-32-tensors-00001-of-00006.gguf $WORK_PATH/ggml-model-merge-2.gguf diff --git a/ggml.c b/ggml.c index 74ecd5927..82179a125 100644 --- a/ggml.c +++ b/ggml.c @@ -21139,7 +21139,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p } // read the tensor infos - { + if (ctx->header.n_tensors > 0) { ctx->infos = GGML_CALLOC(ctx->header.n_tensors, sizeof(struct gguf_tensor_info)); for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { From 6fbd43221167bf96112f899daf22c127b282cbcf Mon Sep 17 00:00:00 2001 From: Brian Date: Sun, 5 May 2024 15:07:48 +1000 Subject: [PATCH 028/100] py : logging and flake8 suppression refactoring (#7081) Set one as executable and add basicConfig() to another. Also added noqa tag to test scripts. --- .flake8 | 15 ++++++++++++++- convert-hf-to-gguf-update.py | 2 ++ convert-lora-to-ggml.py | 1 + scripts/gen-unicode-data.py | 14 +++++++------- tests/test-tokenizer-0.py | 10 +++++----- 5 files changed, 29 insertions(+), 13 deletions(-) mode change 100644 => 100755 convert-hf-to-gguf-update.py diff --git a/.flake8 b/.flake8 index bc41c2290..d64c2564a 100644 --- a/.flake8 +++ b/.flake8 @@ -1,4 +1,17 @@ [flake8] max-line-length = 125 ignore = E203,E211,E221,E225,E231,E241,E251,E261,E266,E501,E701,E704,W503 -exclude = examples/*,examples/*/**,*/**/__init__.py,scripts/gen-unicode-data.py,tests/test-tokenizer-0.py +exclude = + # Do not traverse examples + examples, + # Do not include package initializers + __init__.py, + # No need to traverse our git directory + .git, + # There's no value in checking cache directories + __pycache__, + # No need to include the build path + build, + # This contains builds that we don't want to check + dist # This is generated with `python build .` for package releases +# max-complexity = 10 diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py old mode 100644 new mode 100755 index 917a4469d..b41a9290e --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -1,3 +1,5 @@ +#!/usr/bin/env python3 + # This script downloads the tokenizer models of the specified models from Huggingface and # generates the get_vocab_base_pre() function for convert-hf-to-gguf.py # diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 39536feb9..f09fa85fe 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -16,6 +16,7 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) import gguf +logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("lora-to-gguf") NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py index d49cbf2a0..977e65613 100644 --- a/scripts/gen-unicode-data.py +++ b/scripts/gen-unicode-data.py @@ -41,20 +41,20 @@ def get_matches(regex_expr): def print_cat(cat, ranges): - print("const std::vector> unicode_ranges_{} = {{".format(cat)) + print("const std::vector> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 cnt = 0 for start, end in ranges: if cnt % 4 != 0: - print(" ", end="") - print("{{0x{:08X}, 0x{:08X}}},".format(start, end), end="") + print(" ", end="") # noqa: NP100 + print("{{0x{:08X}, 0x{:08X}}},".format(start, end), end="") # noqa: NP100 if cnt % 4 == 3: - print("") + print("") # noqa: NP100 cnt += 1 if cnt % 4 != 0: - print("") - print("};") - print("") + print("") # noqa: NP100 + print("};") # noqa: NP100 + print("") # noqa: NP100 print_cat("number", get_matches(r'\p{N}')) diff --git a/tests/test-tokenizer-0.py b/tests/test-tokenizer-0.py index 8e7638e42..cd760d1ce 100644 --- a/tests/test-tokenizer-0.py +++ b/tests/test-tokenizer-0.py @@ -13,7 +13,7 @@ fname_tok = args.fname_tok tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) -print('tokenizing file: ', fname_tok) +print('tokenizing file: ', fname_tok) # noqa: NP100 fname_out = fname_tok + '.tok' with open(fname_tok, 'r', encoding='utf-8') as f: lines = f.readlines() @@ -21,7 +21,7 @@ with open(fname_tok, 'r', encoding='utf-8') as f: t_start = time.time() res = tokenizer.encode(s, add_special_tokens=False) t_end = time.time() - print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') + print('\nmain : tokenized in', "{:.3f}".format(1000.0 * (t_end - t_start)), 'ms (py)') # noqa: NP100 with open(fname_out, 'w', encoding='utf-8') as f: for x in res: # LLaMA v3 for some reason strips the space for these tokens (and others) @@ -41,6 +41,6 @@ with open(fname_tok, 'r', encoding='utf-8') as f: # f.write(str(x) + ' \'' + tokenizer.decode(x) + '\'\n') # f.write(str(x) + ' \'' + tokenizer.decode(x).strip() + '\'\n') f.write(str(x) + '\n') - print('len(res): ', len(res)) - print('len(lines): ', len(lines)) -print('results written to: ', fname_out) + print('len(res): ', len(res)) # noqa: NP100 + print('len(lines): ', len(lines)) # noqa: NP100 +print('results written to: ', fname_out) # noqa: NP100 From 889bdd76866ea31a7625ec2dcea63ff469f3e981 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= Date: Sun, 5 May 2024 01:19:30 -0400 Subject: [PATCH 029/100] command-r : add BPE pre-tokenization (#7063) * Add BPE pre-tokenization for Command-R/R+. * Bump transformers convert requirement. * command-r : add individual digits regex --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf-update.py | 9 +++ convert-hf-to-gguf.py | 3 + llama.cpp | 4 + llama.h | 1 + models/ggml-vocab-command-r.gguf | Bin 0 -> 10874545 bytes models/ggml-vocab-command-r.gguf.inp | 106 ++++++++++++++++++++++++++ models/ggml-vocab-command-r.gguf.out | 43 +++++++++++ requirements/requirements-convert.txt | 2 +- tests/CMakeLists.txt | 1 + 9 files changed, 168 insertions(+), 1 deletion(-) create mode 100644 models/ggml-vocab-command-r.gguf create mode 100644 models/ggml-vocab-command-r.gguf.inp create mode 100644 models/ggml-vocab-command-r.gguf.out diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index b41a9290e..46a225462 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -66,6 +66,7 @@ models = [ {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, + {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, ] # make directory "models/tokenizers" if it doesn't exist @@ -106,6 +107,14 @@ for model in models: save_path = f"models/tokenizers/{name}/tokenizer.json" download_file_with_auth(url, token, save_path) + # if downloaded file is less than 1KB, we likely need to download an LFS instead + if os.path.getsize(save_path) < 1024: + # remove the file + os.remove(save_path) + url = f"{repo}/resolve/main/tokenizer.json" + save_path = f"models/tokenizers/{name}/tokenizer.json" + download_file_with_auth(url, token, save_path) + if tokt == TOKENIZER_TYPE.SPM: url = f"{repo}/resolve/main/tokenizer.model" save_path = f"models/tokenizers/{name}/tokenizer.model" diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 88c16676b..f7441e6b8 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -311,6 +311,9 @@ class Model(ABC): if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": # ref: https://huggingface.co/smallcloudai/Refact-1_6-base res = "refact" + if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8": + # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01 + res = "command-r" if res is None: logger.warning("\n") diff --git a/llama.cpp b/llama.cpp index 7c7a06df3..aeb5c08df 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4386,6 +4386,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "refact") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_REFACT; + } else if ( + tokenizer_pre == "command-r") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -12238,6 +12241,7 @@ struct llm_tokenizer_bpe { break; case LLAMA_VOCAB_PRE_TYPE_STARCODER: case LLAMA_VOCAB_PRE_TYPE_REFACT: + case LLAMA_VOCAB_PRE_TYPE_COMMAND_R: word_collection = unicode_regex_split(text, { "\\p{N}", "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", diff --git a/llama.h b/llama.h index 5ff04c1d4..e2fd53ab7 100644 --- a/llama.h +++ b/llama.h @@ -80,6 +80,7 @@ extern "C" { 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z!1Jo|5i66hX4(C$6;Zc*&WLk*JX&|$zCCSQ=4B<@nMi@^<%JlE{x#u9RiUNm8kR(! rYCYHPdg;$vt(L8ZuymTn|2Kr5Pc(Y7xu|R^YW9^P*tQa1z*7DTjIr=@ literal 0 HcmV?d00001 diff --git a/models/ggml-vocab-command-r.gguf.inp b/models/ggml-vocab-command-r.gguf.inp new file mode 100644 index 000000000..0a89107c6 --- /dev/null +++ b/models/ggml-vocab-command-r.gguf.inp @@ -0,0 +1,106 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-command-r.gguf.out b/models/ggml-vocab-command-r.gguf.out new file mode 100644 index 000000000..cc4277daa --- /dev/null +++ b/models/ggml-vocab-command-r.gguf.out @@ -0,0 +1,43 @@ + 2536 228 27 228 22957 6983 + 45 193433 + + 228 + 1667 + 1742 + 205 + 206 + 2126 + 11516 + 34777 + 28339 3845 + 46609 3845 + 28339 3930 + 46609 3930 + 46609 3930 8 + 28339 19 3845 8 + 46609 19 3845 8 + 2075 1801 11254 107 255 21 19317 + 94 23 27 31 228 30 21213 20752 39267 6405 9980 + 4929 40071 2196 3236 8750 1764 37097 41168 + 38111 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 38111 231 38111 257 38111 235 165 24629 38111 239 + 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 1737 10203 109160 1875 2222 2517 3342 12523 16 + 28339 + 46609 + 228 46609 + 1667 46609 + 1742 46609 + 1742 46609 1856 46609 + 1737 + 206 1857 + 14 4515 + 28339 19 1770 14 1954 8 4070 1955 1933 80503 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 + 26 + 26 26 + 26 26 26 + 26 26 26 26 + 26 26 26 26 26 + 26 26 26 26 26 26 + 26 26 26 26 26 26 26 + 26 26 26 26 26 26 26 26 + 26 26 26 26 26 26 26 26 26 + 127731 51628 205 57788 18494 97469 126134 206 2226 256 230 1737 18258 16 80503 122 35927 2226 242 112 57462 1737 54457 223165 106230 2096 16 48389 11254 107 255 2226 107 255 228 26 228 26 26 228 26 26 26 228 26 26 26 26 228 26 26 26 26 26 228 26 26 26 26 26 26 228 26 26 26 26 26 26 26 228 26 26 26 26 26 26 26 26 228 26 21 26 228 26 2271 26 228 26 3834 26 182018 230 174833 38111 249 86325 241 38111 245 86325 232 38111 252 38111 123 38111 261 165 24629 38111 261 38111 103 174833 38111 235 188568 231 5691 12081 13336 2648 29325 14315 24 26 24 27 24 28 24 5123 18372 8391 158343 3512 40071 2196 3236 8750 1764 37097 41168 29721 32797 25646 3802 4975 4975 116167 57178 10251 154048 27292 1767 5125 2632 2155 91 2378 1919 1914 2782 19 2155 3354 1933 5470 38 2155 52 2068 5470 1767 4961 3059 1894 19 2155 43 1933 3026 2725 23186 38 2930 14 20676 1671 14 83 51 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt index a3d6ecec0..5520ba732 100644 --- a/requirements/requirements-convert.txt +++ b/requirements/requirements-convert.txt @@ -1,5 +1,5 @@ numpy~=1.24.4 sentencepiece~=0.1.98 -transformers>=4.35.2,<5.0.0 +transformers>=4.40.1,<5.0.0 gguf>=0.1.0 protobuf>=4.21.0,<5.0.0 diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index cad703fce..208f0cf7b 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -83,6 +83,7 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-bert-bge ARGS ${CMAKE llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-starcoder.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf) # build test-tokenizer-1-bpe target once and add many tests add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp) From ca3632602091e959ed2ad4c09c67a7c790b10d31 Mon Sep 17 00:00:00 2001 From: Lyle Dean <dean@lyle.dev> Date: Sun, 5 May 2024 06:21:46 +0100 Subject: [PATCH 030/100] readme : add note that LLaMA 3 is not supported with convert.py (#7065) --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 2f1317662..6951966f6 100644 --- a/README.md +++ b/README.md @@ -712,6 +712,8 @@ Building the program with BLAS support may lead to some performance improvements To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. +Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. + ```bash # obtain the official LLaMA model weights and place them in ./models ls ./models From 8f8acc8683a00ce40fa5a81161a079d2167126e6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com> Date: Sun, 5 May 2024 13:38:55 +0200 Subject: [PATCH 031/100] Disable benchmark on forked repo (#7034) * Disable benchmark on forked repo * only check owner on schedule event * check owner on push also * more readable as multi-line * ternary won't work * style++ * test++ * enable actions debug * test-- * remove debug * test++ * do debug where we can get logs * test-- * this is driving me crazy * correct github.event usage * remove test condition * correct github.event usage * test++ * test-- * event_name is pull_request_target * test++ * test-- * update ref checks --- .github/workflows/bench.yml | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/.github/workflows/bench.yml b/.github/workflows/bench.yml index 3e968d179..de0d994c8 100644 --- a/.github/workflows/bench.yml +++ b/.github/workflows/bench.yml @@ -52,7 +52,19 @@ jobs: ftype: q4_0 pr_comment_enabled: "true" - if: ${{ github.event.inputs.gpu-series == 'Standard_NC4as_T4_v3' || github.event.schedule || github.event.pull_request || github.head_ref == 'master' || github.ref_name == 'master' || github.event.push.ref == 'refs/heads/master' }} + if: | + inputs.gpu-series == 'Standard_NC4as_T4_v3' + || ( + github.event_name == 'schedule' + && github.ref_name == 'master' + && github.repository_owner == 'ggerganov' + ) + || github.event_name == 'pull_request_target' + || ( + github.event_name == 'push' + && github.event.ref == 'refs/heads/master' + && github.repository_owner == 'ggerganov' + ) steps: - name: Clone id: checkout From 628b299106d1e9476fdecb3cbe546bf5c60f1b89 Mon Sep 17 00:00:00 2001 From: kunnis <kunnis@users.noreply.github.com> Date: Sun, 5 May 2024 07:17:47 -0500 Subject: [PATCH 032/100] Adding support for the --numa argument for llama-bench. (#7080) --- examples/llama-bench/llama-bench.cpp | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 95c3095dd..40128ec44 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -178,6 +178,7 @@ struct cmd_params { std::vector<std::vector<float>> tensor_split; std::vector<bool> use_mmap; std::vector<bool> embeddings; + ggml_numa_strategy numa; int reps; bool verbose; output_formats output_format; @@ -200,6 +201,7 @@ static const cmd_params cmd_params_defaults = { /* tensor_split */ {std::vector<float>(llama_max_devices(), 0.0f)}, /* use_mmap */ {true}, /* embeddings */ {false}, + /* numa */ GGML_NUMA_STRATEGY_DISABLED, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -224,6 +226,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); printf(" -fa, --flash-attn <0|1> (default: %s)\n", join(cmd_params_defaults.flash_attn, ",").c_str()); printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); + printf(" --numa <distribute|isolate|numactl> (default: disabled)\n"); printf(" -embd, --embeddings <0|1> (default: %s)\n", join(cmd_params_defaults.embeddings, ",").c_str()); printf(" -ts, --tensor-split <ts0/ts1/..> (default: 0)\n"); printf(" -r, --repetitions <n> (default: %d)\n", cmd_params_defaults.reps); @@ -396,6 +399,17 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split<bool>(argv[i], split_delim); params.no_kv_offload.insert(params.no_kv_offload.end(), p.begin(), p.end()); + } else if (arg == "--numa") { + if (++i >= argc) { + invalid_param = true; + break; + } else { + std::string value(argv[i]); + /**/ if (value == "distribute" || value == "" ) { params.numa = GGML_NUMA_STRATEGY_DISTRIBUTE; } + else if (value == "isolate") { params.numa = GGML_NUMA_STRATEGY_ISOLATE; } + else if (value == "numactl") { params.numa = GGML_NUMA_STRATEGY_NUMACTL; } + else { invalid_param = true; break; } + } } else if (arg == "-fa" || arg == "--flash-attn") { if (++i >= argc) { invalid_param = true; @@ -1215,6 +1229,7 @@ int main(int argc, char ** argv) { llama_log_set(llama_null_log_callback, NULL); } llama_backend_init(); + llama_numa_init(params.numa); // initialize printer std::unique_ptr<printer> p; From bcdee0daa7c5e8e086b719e5eb4073b00df70e01 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Mon, 6 May 2024 09:31:30 +0300 Subject: [PATCH 033/100] minor : fix trailing whitespace --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6951966f6..885322e68 100644 --- a/README.md +++ b/README.md @@ -712,7 +712,7 @@ Building the program with BLAS support may lead to some performance improvements To obtain the official LLaMA 2 weights please see the <a href="#obtaining-and-using-the-facebook-llama-2-model">Obtaining and using the Facebook LLaMA 2 model</a> section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. -Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. +Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. ```bash # obtain the official LLaMA model weights and place them in ./models From b3a995b416e13ae3123a117a743e11d0ede0ca4c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Mon, 6 May 2024 18:36:06 +0300 Subject: [PATCH 034/100] flake.lock: Update (#7079) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/9126214d0a59633752a136528f5f3b9aa8565b7d?narHash=sha256-sB4SWl2lX95bExY2gMFG5HIzvva5AVMJd4Igm%2BGpZNw%3D' (2024-04-01) → 'github:hercules-ci/flake-parts/e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e?narHash=sha256-yzcRNDoyVP7%2BSCNX0wmuDju1NUCt8Dz9%2BlyUXEI0dbI%3D' (2024-05-02) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/d8fe5e6c92d0d190646fb9f1056741a229980089?dir=lib&narHash=sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk%3D' (2024-03-29) → 'https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz?narHash=sha256-QBx10%2Bk6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94%3D' (2024-05-02) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856?narHash=sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY%3D' (2024-04-25) → 'github:NixOS/nixpkgs/63c3a29ca82437c87573e4c6919b09a24ea61b0f?narHash=sha256-4cPymbty65RvF1DWQfc%2BBc8B233A1BWxJnNULJKQ1EY%3D' (2024-05-02) Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> --- flake.lock | 30 ++++++++++++------------------ 1 file changed, 12 insertions(+), 18 deletions(-) diff --git a/flake.lock b/flake.lock index b738da7c6..c9ead0bf7 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1712014858, - "narHash": "sha256-sB4SWl2lX95bExY2gMFG5HIzvva5AVMJd4Igm+GpZNw=", + "lastModified": 1714641030, + "narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "9126214d0a59633752a136528f5f3b9aa8565b7d", + "rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1714076141, - "narHash": "sha256-Drmja/f5MRHZCskS6mvzFqxEaZMeciScCTFxWVLqWEY=", + "lastModified": 1714635257, + "narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "7bb2ccd8cdc44c91edba16c48d2c8f331fb3d856", + "rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f", "type": "github" }, "original": { @@ -36,20 +36,14 @@ }, "nixpkgs-lib": { "locked": { - "dir": "lib", - "lastModified": 1711703276, - "narHash": "sha256-iMUFArF0WCatKK6RzfUJknjem0H9m4KgorO/p3Dopkk=", - "owner": "NixOS", - "repo": "nixpkgs", - "rev": "d8fe5e6c92d0d190646fb9f1056741a229980089", - "type": "github" + "lastModified": 1714640452, + "narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=", + "type": "tarball", + "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" }, "original": { - "dir": "lib", - "owner": "NixOS", - "ref": "nixos-unstable", - "repo": "nixpkgs", - "type": "github" + "type": "tarball", + "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" } }, "root": { From 858f6b73f6e57a62523d16a955d565254be889b4 Mon Sep 17 00:00:00 2001 From: William Tambellini <william.tambellini@gmail.com> Date: Mon, 6 May 2024 11:12:14 -0700 Subject: [PATCH 035/100] Add an option to build without CUDA VMM (#7067) Add an option to build ggml cuda without CUDA VMM resolves https://github.com/ggerganov/llama.cpp/issues/6889 https://forums.developer.nvidia.com/t/potential-nvshmem-allocated-memory-performance-issue/275416/4 --- CMakeLists.txt | 11 ++++++++++- ggml-cuda.cu | 6 +++--- 2 files changed, 13 insertions(+), 4 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 477c5b57c..0e22ee230 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -103,6 +103,8 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF) +option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF) + option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) @@ -409,6 +411,9 @@ if (LLAMA_CUDA) if (LLAMA_CUDA_FORCE_MMQ) add_compile_definitions(GGML_CUDA_FORCE_MMQ) endif() + if (LLAMA_CUDA_NO_VMM) + add_compile_definitions(GGML_CUDA_NO_VMM) + endif() add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) if (DEFINED LLAMA_CUDA_DMMV_Y) @@ -434,7 +439,11 @@ if (LLAMA_CUDA) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt) endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) + if (LLAMA_CUDA_NO_VMM) + # No VMM requested, no need to link directly with the cuda driver lib (libcuda.so) + else() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cuda_driver) # required by cuDeviceGetAttribute(), cuMemGetAllocationGranularity(...), ... + endif() if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) # 52 == lowest CUDA 12 standard diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c30554f0c..2d1742c82 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -113,7 +113,7 @@ static ggml_cuda_device_info ggml_cuda_init() { for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) CUdevice device; CU_CHECK(cuDeviceGet(&device, id)); CU_CHECK(cuDeviceGetAttribute(&device_vmm, CU_DEVICE_ATTRIBUTE_VIRTUAL_MEMORY_MANAGEMENT_SUPPORTED, device)); @@ -259,7 +259,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { }; // pool with virtual memory -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) struct ggml_cuda_pool_vmm : public ggml_cuda_pool { static const size_t CUDA_POOL_VMM_MAX_SIZE = 1ull << 35; // 32 GB @@ -356,7 +356,7 @@ struct ggml_cuda_pool_vmm : public ggml_cuda_pool { #endif // !defined(GGML_USE_HIPBLAS) std::unique_ptr<ggml_cuda_pool> ggml_backend_cuda_context::new_pool_for_device(int device) { -#if !defined(GGML_USE_HIPBLAS) +#if !defined(GGML_USE_HIPBLAS) && !defined(GGML_CUDA_NO_VMM) if (ggml_cuda_info().devices[device].vmm) { return std::unique_ptr<ggml_cuda_pool>(new ggml_cuda_pool_vmm(device)); } From 947d3ad27d94f1addef76b5d64c314618f063933 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Tue, 7 May 2024 11:08:49 +0300 Subject: [PATCH 036/100] ci : add GG_BUILD_EXTRA_TESTS_0 env (#7098) * ci : add GG_BUILD_EXTRA_TESTS_0 env ggml-ci * Update run.sh ggml-ci --- ci/run.sh | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index bf21b6b31..e67c1a5ff 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -160,9 +160,8 @@ function gg_run_test_scripts_debug { set -e - # TODO: too slow, run on dedicated node - #(cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log - #(cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./examples/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log + (cd ./examples/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log set +e } @@ -695,8 +694,10 @@ test $ret -eq 0 && gg_run ctest_release if [ -z ${GG_BUILD_LOW_PERF} ]; then test $ret -eq 0 && gg_run embd_bge_small - test $ret -eq 0 && gg_run test_scripts_debug - test $ret -eq 0 && gg_run test_scripts_release + if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then + test $ret -eq 0 && gg_run test_scripts_debug + test $ret -eq 0 && gg_run test_scripts_release + fi if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then From 04976db7a819fcf8bfefbfc09a3344210b79dd27 Mon Sep 17 00:00:00 2001 From: omahs <73983677+omahs@users.noreply.github.com> Date: Tue, 7 May 2024 17:20:33 +0200 Subject: [PATCH 037/100] docs: fix typos (#7124) * fix typo * fix typos * fix typo * fix typos * fix typo * fix typos --- docs/BLIS.md | 2 +- docs/HOWTO-add-model.md | 4 ++-- examples/llava/README.md | 2 +- examples/main/README.md | 4 ++-- examples/sycl/README.md | 2 +- grammars/README.md | 2 +- 6 files changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/BLIS.md b/docs/BLIS.md index 0bcd6eeef..c933766b7 100644 --- a/docs/BLIS.md +++ b/docs/BLIS.md @@ -23,7 +23,7 @@ Install BLIS: sudo make install ``` -We recommend using openmp since it's easier to modify the cores been used. +We recommend using openmp since it's easier to modify the cores being used. ### llama.cpp compilation diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md index a56b78344..48769cdf6 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/HOWTO-add-model.md @@ -96,9 +96,9 @@ NOTE: The dimensions in `ggml` are typically in the reverse order of the `pytorc This is the funniest part, you have to provide the inference graph implementation of the new model architecture in `llama_build_graph`. -Have a look to existing implementation like `build_llama`, `build_dbrx` or `build_bert`. +Have a look at existing implementation like `build_llama`, `build_dbrx` or `build_bert`. -When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support of missing backend operations can be added in another PR. +When implementing a new graph, please note that the underlying `ggml` backends might not support them all, support for missing backend operations can be added in another PR. Note: to debug the inference graph: you can use [eval-callback](../examples/eval-callback). diff --git a/examples/llava/README.md b/examples/llava/README.md index d4810d42e..4fb0cf381 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -56,7 +56,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-pa python ./convert.py ../llava-v1.5-7b --skip-unknown ``` -Now both the LLaMA part and the image encoder is in the `llava-v1.5-7b` directory. +Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. ## LLaVA 1.6 gguf conversion 1) First clone a LLaVA 1.6 model: diff --git a/examples/main/README.md b/examples/main/README.md index e7a38743c..97e2ae4c2 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -143,7 +143,7 @@ The `--ctx-size` option allows you to set the size of the prompt context used by ### Extended Context Size -Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model have a context length (max sequence length) of 4096 (4k) and the fine-tuned model have 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. +Some fine-tuned models have extended the context length by scaling RoPE. For example, if the original pre-trained model has a context length (max sequence length) of 4096 (4k) and the fine-tuned model has 32k. That is a scaling factor of 8, and should work by setting the above `--ctx-size` to 32768 (32k) and `--rope-scale` to 8. - `--rope-scale N`: Where N is the linear scaling factor used by the fine-tuned model. @@ -286,7 +286,7 @@ These options help improve the performance and memory usage of the LLaMA models. - `--numa distribute`: Pin an equal proportion of the threads to the cores on each NUMA node. This will spread the load amongst all cores on the system, utilitizing all memory channels at the expense of potentially requiring memory to travel over the slow links between nodes. - `--numa isolate`: Pin all threads to the NUMA node that the program starts on. This limits the number of cores and amount of memory that can be used, but guarantees all memory access remains local to the NUMA node. -- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitraty core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. +- `--numa numactl`: Pin threads to the CPUMAP that is passed to the program by starting it with the numactl utility. This is the most flexible mode, and allow arbitrary core usage patterns, for example a map that uses all the cores on one NUMA nodes, and just enough cores on a second node to saturate the inter-node memory bus. These flags attempt optimizations that help on some systems with non-uniform memory access. This currently consists of one of the above strategies, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop_caches' as root. diff --git a/examples/sycl/README.md b/examples/sycl/README.md index b46f17f39..c589c2d3a 100644 --- a/examples/sycl/README.md +++ b/examples/sycl/README.md @@ -1,6 +1,6 @@ # llama.cpp/example/sycl -This example program provide the tools for llama.cpp for SYCL on Intel GPU. +This example program provides the tools for llama.cpp for SYCL on Intel GPU. ## Tool diff --git a/grammars/README.md b/grammars/README.md index c924e8d46..2b8384d9d 100644 --- a/grammars/README.md +++ b/grammars/README.md @@ -51,7 +51,7 @@ single-line ::= [^\n]+ "\n"` ## Sequences and Alternatives -The order of symbols in a sequence matter. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc. +The order of symbols in a sequence matters. For example, in `"1. " move " " move "\n"`, the `"1. "` must come before the first `move`, etc. Alternatives, denoted by `|`, give different sequences that are acceptable. For example, in `move ::= pawn | nonpawn | castle`, `move` can be a `pawn` move, a `nonpawn` move, or a `castle`. From 3af34c1d1b0da47f85b95f60922abeded1cb5d33 Mon Sep 17 00:00:00 2001 From: RhinoDevel <RhinoDevel@users.noreply.github.com> Date: Tue, 7 May 2024 19:51:31 +0200 Subject: [PATCH 038/100] main : update log text (EOS to EOG) (#7104) * Update log text (EOS to EOG) The log text "found EOS" is no longer always correct, here, because there is now an is-EOG check that also returns true for EOT. * Improve log msg. further by using "an" instead of "some". As suggested, to avoid misunderstanding (no multiple EOG tokens found, just one). --- examples/main/main.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index eabbc2db3..f676ea1ba 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -796,7 +796,7 @@ int main(int argc, char ** argv) { // deal with end of generation tokens in interactive mode if (llama_token_is_eog(model, llama_sampling_last(ctx_sampling))) { - LOG("found EOS token\n"); + LOG("found an EOG token\n"); if (params.interactive) { if (!params.antiprompt.empty()) { From 53d6c52e227dedef347b21e28febcfb9caeecdad Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Tue, 7 May 2024 21:43:13 +0300 Subject: [PATCH 039/100] readme : update hot topics --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 885322e68..75fc10a15 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920** +- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021** +- BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404 - Fix major bug in Metal batched inference https://github.com/ggerganov/llama.cpp/pull/6225 From 260b7c65296fba0568eeb1ff05244ea0be206b54 Mon Sep 17 00:00:00 2001 From: Kyle Mistele <kyle@mistele.com> Date: Tue, 7 May 2024 13:44:29 -0500 Subject: [PATCH 040/100] server : update readme with undocumented options (#7013) --- examples/server/README.md | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index b96a4444a..bf3713640 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -62,6 +62,18 @@ page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/ - `--chat-template JINJA_TEMPLATE`: Set custom jinja chat template. This parameter accepts a string, not a file name. Default: template taken from model's metadata. We only support [some pre-defined templates](https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template) - `--log-disable`: Output logs to stdout only, not to `llama.log`. Default: enabled - `--log-format FORMAT`: Define the log output to FORMAT: json or text Default: `json` +- `--rope-scaling` : RoPE scaling method. Defaults to linear unless otherwise specified by the model. Options are `none`, `linear`, `yarn` +- `--rope-freq-base N` : RoPE frequency base (default: loaded from model) +- `--rope-freq-scale N`: RoPE frequency scaling factor, expands context by a factor of 1/N (e.g. 0.25) +- `--yarn-ext-factor N` : YaRN: extrapolation mix factor (Default: 1.0, 0.0 = full interpolation) +- `--yarn-attn-factor N` : YaRN: scale sqrt(t) or attention magnitude (default: 1.0) +- `--yarn-beta-slow N`: YaRN: High correction dim or alpha (default: 1.0) +- `--yarn-beta-fast N`: YaRN: low correction dim or beta (default: 32.0) +- `--pooling` : Pooling type for embeddings, use model default if unspecified. Options are `none`, `mean`, `cls` +- `-dt N`, `--defrag-thold N`: KV cache defragmentation threshold (default: -1.0, < 0 = disabled) +- `-fa`, `--flash-attn` : enable flash attention (default: disabled). +- `-ctk TYPE`, `--cache-type-k TYPE` : KV cache data type for K (default: `f16`, options `f32`, `f16`, `q8_0`, `q4_0`, `q4_1`, `iq4_nl`, `q5_0`, or `q5_1`) +- `-ctv TYPE`, `--cache-type-v TYPE` : KV cache type for V (default `f16`, see `-ctk` for options) **If compiled with `LLAMA_SERVER_SSL=ON`** - `--ssl-key-file FNAME`: path to file a PEM-encoded SSL private key From b6aa6702030320a3d5fbc2508307af0d7c947e40 Mon Sep 17 00:00:00 2001 From: nopperl <54780682+nopperl@users.noreply.github.com> Date: Tue, 7 May 2024 19:39:43 +0000 Subject: [PATCH 041/100] Fix OLMo HF to GGUF conversion (#6910) --- convert-hf-to-gguf-update.py | 1 + convert-hf-to-gguf.py | 8 ++++++-- llama.cpp | 4 ++++ llama.h | 1 + 4 files changed, 12 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 46a225462..ae901e24c 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -67,6 +67,7 @@ models = [ {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, + {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, ] # make directory "models/tokenizers" if it doesn't exist diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f7441e6b8..f65d9320e 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -314,6 +314,9 @@ class Model(ABC): if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8": # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01 res = "command-r" + if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": + # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf + res = "olmo" if res is None: logger.warning("\n") @@ -2831,8 +2834,9 @@ class OlmoModel(Model): def set_gguf_parameters(self): super().set_gguf_parameters() self.gguf_writer.add_layer_norm_eps(1e-5) - if "clip_qkv" in self.hparams is not None: - self.gguf_writer.add_clamp_kqv(self.hparams["clip_qkv"]) + clip_qkv = self.hparams.get("clip_qkv") + if clip_qkv is not None: + self.gguf_writer.add_clamp_kqv(clip_qkv) # Same as super class, but permuting q_proj, k_proj # Copied from: LlamaModel diff --git a/llama.cpp b/llama.cpp index aeb5c08df..07fde3619 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4389,6 +4389,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "command-r") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R; + } else if ( + tokenizer_pre == "olmo") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -12248,6 +12251,7 @@ struct llm_tokenizer_bpe { }); break; case LLAMA_VOCAB_PRE_TYPE_GPT2: + case LLAMA_VOCAB_PRE_TYPE_OLMO: word_collection = unicode_regex_split(text, { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }); diff --git a/llama.h b/llama.h index e2fd53ab7..d7f4bf8ea 100644 --- a/llama.h +++ b/llama.h @@ -81,6 +81,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, + LLAMA_VOCAB_PRE_TYPE_OLMO = 10, }; // note: these values should be synchronized with ggml_rope From af0a5b616359809ce886ea433acedebb39b12969 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de> Date: Tue, 7 May 2024 23:07:58 +0200 Subject: [PATCH 042/100] server: fix incorrectly reported token probabilities (#7125) * server: normalize token probabilities * fix temperature == 0.0f --- common/sampling.cpp | 5 +++++ common/sampling.h | 1 + examples/server/README.md | 2 +- examples/server/server.cpp | 34 ++++++++++++++++++++++++---------- 4 files changed, 31 insertions(+), 11 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index cc83600d9..3715a7985 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -35,6 +35,8 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ result->prev.resize(params.n_prev); + result->n_considered = 0; + llama_sampling_set_rng_seed(result, params.seed); return result; @@ -64,6 +66,7 @@ void llama_sampling_reset(llama_sampling_context * ctx) { std::fill(ctx->prev.begin(), ctx->prev.end(), 0); ctx->cur.clear(); + ctx->n_considered = 0; } void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) { @@ -253,6 +256,8 @@ static llama_token llama_sampling_sample_impl( } } + ctx_sampling->n_considered = cur_p.size; + return id; } diff --git a/common/sampling.h b/common/sampling.h index cf7081e36..5b73ecdcd 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -81,6 +81,7 @@ struct llama_sampling_context { // TODO: replace with ring-buffer std::vector<llama_token> prev; std::vector<llama_token_data> cur; + size_t n_considered; std::mt19937 rng; }; diff --git a/examples/server/README.md b/examples/server/README.md index bf3713640..a7c3f0b5f 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -272,7 +272,7 @@ node index.js `logit_bias`: Modify the likelihood of a token appearing in the generated text completion. For example, use `"logit_bias": [[15043,1.0]]` to increase the likelihood of the token 'Hello', or `"logit_bias": [[15043,-1.0]]` to decrease its likelihood. Setting the value to false, `"logit_bias": [[15043,false]]` ensures that the token `Hello` is never produced. The tokens can also be represented as strings, e.g. `[["Hello, World!",-0.5]]` will reduce the likelihood of all the individual tokens that represent the string `Hello, World!`, just like the `presence_penalty` does. Default: `[]` - `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token. Default: `0` + `n_probs`: If greater than 0, the response also contains the probabilities of top N tokens for each generated token given the sampling settings. Note that for temperature < 0 the tokens are sampled greedily but token probabilities are still being calculated via a simple softmax of the logits without considering any other sampler settings. Default: `0` `min_keep`: If greater than 0, force samplers to return N possible tokens at minimum. Default: `0` diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ff0814b2f..85ae1ad96 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2266,17 +2266,31 @@ struct server_context { llama_token_data_array cur_p = { slot.ctx_sampling->cur.data(), slot.ctx_sampling->cur.size(), false }; result.tok = id; - const int32_t n_probs = slot.sparams.n_probs; - if (slot.sparams.temp <= 0 && n_probs > 0) { - // for llama_sample_token_greedy we need to sort candidates - llama_sample_softmax(ctx, &cur_p); - } + const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs); + if (n_probs > 0) { + const size_t n_considered = slot.ctx_sampling->n_considered; - for (size_t i = 0; i < std::min(cur_p.size, (size_t) n_probs); ++i) { - result.probs.push_back({ - cur_p.data[i].id, - cur_p.data[i].p - }); + // Make sure at least n_probs top tokens are at the front of the vector: + if (slot.sparams.temp == 0.0f && n_probs > n_considered) { + llama_sample_top_k(ctx, &cur_p, n_probs, 0); + } + + if (slot.sparams.temp == 0.0f) { + // With greedy sampling the probabilities have possibly not been calculated. + for (size_t i = 0; i < n_probs; ++i) { + result.probs.push_back({ + cur_p.data[i].id, + i == 0 ? 1.0f : 0.0f + }); + } + } else { + for (size_t i = 0; i < n_probs; ++i) { + result.probs.push_back({ + cur_p.data[i].id, + i >= n_considered ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. + }); + } + } } if (!process_token(result, slot)) { From 48b2f9c1fc71ab7df5432be2ed9fa7cdf5e8405e Mon Sep 17 00:00:00 2001 From: jukofyork <69222624+jukofyork@users.noreply.github.com> Date: Wed, 8 May 2024 01:24:16 +0100 Subject: [PATCH 043/100] Fixed save_imatrix to match old behaviour for MoE (#7099) * Fixed save_imatrix to match old behaviour for MoE This fix is simple and clear, but unnecessarily doubles the memory overhead.. * Fixed missing idx variable * Unconditionally increment ncall Co-authored-by: slaren <slarengh@gmail.com> * Fixed 2 bugs in save_imatrix() - Fixed segfault bug because the counts vector needed to be created. - Fixed pre-existing bug didn't actually add to the counts for "--combine" option. * ncall needs summing too * Trailing whitespace --------- Co-authored-by: slaren <slarengh@gmail.com> --- examples/imatrix/imatrix.cpp | 36 +++++++++++++++++++++++++++++------- 1 file changed, 29 insertions(+), 7 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 71e7a727f..82b19fc4f 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -19,6 +19,7 @@ struct Stats { std::vector<float> values; + std::vector<int> counts; int ncall = 0; }; @@ -121,12 +122,10 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * auto & e = m_stats[wname]; ++e.ncall; - // NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger - // using the following line, we can correct for that if needed by replacing the line above with: - //if (idx == t->src[0]->ne[0] - 1) ++e.ncall; if (e.values.empty()) { e.values.resize(src1->ne[0]*n_as, 0); + e.counts.resize(src1->ne[0]*n_as, 0); } else if (e.values.size() != (size_t)src1->ne[0]*n_as) { fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as); @@ -153,6 +152,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[e_start + j] += x[j]*x[j]; + e.counts[e_start + j]++; } } } @@ -170,6 +170,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * auto& e = m_stats[wname]; if (e.values.empty()) { e.values.resize(src1->ne[0], 0); + e.counts.resize(src1->ne[0], 0); } else if (e.values.size() != (size_t)src1->ne[0]) { fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]); @@ -183,6 +184,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * const float * x = data + row * src1->ne[0]; for (int j = 0; j < (int)src1->ne[0]; ++j) { e.values[j] += x[j]*x[j]; + e.counts[j]++; } } if (e.ncall > m_last_call) { @@ -222,7 +224,13 @@ void IMatrixCollector::save_imatrix(const char * fname, const char * dataset) co out.write((const char *) &p.second.ncall, sizeof(p.second.ncall)); int nval = p.second.values.size(); out.write((const char *) &nval, sizeof(nval)); - if (nval > 0) out.write((const char *) p.second.values.data(), nval * sizeof(float)); + if (nval > 0) { + std::vector<float> tmp(nval); + for (int i = 0; i < nval; i++) { + tmp[i] = (p.second.values[i] / static_cast<float>(p.second.counts[i])) * static_cast<float>(p.second.ncall); + } + out.write((const char*)tmp.data(), nval*sizeof(float)); + } } // Write the number of call the matrix was computed with @@ -270,14 +278,28 @@ bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_ma imatrix_data = {}; return false; } - e.values.resize(nval); - in.read((char*)e.values.data(), nval*sizeof(float)); + + // When re-called from load_imatrix() with add set, this will already be created. + if (e.values.empty()) { + e.values.resize(nval, 0); + e.counts.resize(nval, 0); + } + + std::vector<float> tmp(nval); + in.read((char*)tmp.data(), nval*sizeof(float)); if (in.fail()) { printf("%s: failed reading data for entry %d\n",__func__,i); imatrix_data = {}; return false; } - e.ncall = ncall; + + // Recreate the state as expected by save_imatrix(), and corerct for weighted sum. + for (int i = 0; i < nval; i++) { + e.values[i] += tmp[i]; + e.counts[i] += ncall; + } + e.ncall += ncall; + } return true; } From c780e75305dba1f67691a8dc0e8bc8425838a452 Mon Sep 17 00:00:00 2001 From: Jeximo <jeximo@gmail.com> Date: Tue, 7 May 2024 21:26:43 -0300 Subject: [PATCH 044/100] Further tidy on Android instructions README.md (#7077) * Further tidy on Android instructions README.md Fixed some logic when following readme direction * Clean up redundent information A new user arriving will see simple directions on llama.cpp homepage * corrected puncuation Period after cmake, colon after termux * re-word for clarity method seems to be more correct, instead of alternative in this context * Organized required packages per build type building llama.cpp with NDK on a pc doesn't require installing clang, cmake, git, or wget in termux. * README.md corrected title * fix trailing whitespace --- README.md | 47 +++++++++++++++++++++-------------------------- 1 file changed, 21 insertions(+), 26 deletions(-) diff --git a/README.md b/README.md index 75fc10a15..1c960b8c1 100644 --- a/README.md +++ b/README.md @@ -936,17 +936,25 @@ If your issue is with model generation quality, then please at least scan the fo ### Android +#### Build on Android using Termux +[Termux](https://github.com/termux/termux-app#installation) is a method to execute `llama.cpp` on an Android device (no root required). +``` +apt update && apt upgrade -y +apt install git make cmake +``` + +It's recommended to move your model inside the `~/` directory for best performance: +``` +cd storage/downloads +mv model.gguf ~/ +``` + +[Get the code](https://github.com/ggerganov/llama.cpp#get-the-code) & [follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`. + #### Building the Project using Android NDK -You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/). - -First, install the essential packages for termux: -``` -pkg install clang wget git cmake -``` -Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: - -You can execute the following commands on your computer to avoid downloading the NDK to your mobile. Of course, you can also do this in Termux. +Obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake. +Execute the following commands on your computer to avoid downloading the NDK to your mobile. Alternatively, you can also do this in Termux: ``` $ mkdir build-android $ cd build-android @@ -954,7 +962,9 @@ $ export NDK=<your_ndk_directory> $ cmake -DCMAKE_TOOLCHAIN_FILE=$NDK/build/cmake/android.toolchain.cmake -DANDROID_ABI=arm64-v8a -DANDROID_PLATFORM=android-23 -DCMAKE_C_FLAGS=-march=armv8.4a+dotprod .. $ make ``` -Install [termux](https://termux.dev/) on your device and run `termux-setup-storage` to get access to your SD card. + +Install [termux](https://github.com/termux/termux-app#installation) on your device and run `termux-setup-storage` to get access to your SD card (if Android 11+ then run the command twice). + Finally, copy these built `llama` binaries and the model file to your device storage. Because the file permissions in the Android sdcard cannot be changed, you can copy the executable files to the `/data/data/com.termux/files/home/bin` path, and then execute the following commands in Termux to add executable permission: (Assumed that you have pushed the built executable files to the /sdcard/llama.cpp/bin path using `adb push`) @@ -976,25 +986,10 @@ $cd /data/data/com.termux/files/home/bin $./main -m ../model/llama-2-7b-chat.Q4_K_M.gguf -n 128 -cml ``` -Here is a demo of an interactive session running on Pixel 5 phone: +Here's a demo of an interactive session running on Pixel 5 phone: https://user-images.githubusercontent.com/271616/225014776-1d567049-ad71-4ef2-b050-55b0b3b9274c.mp4 -#### Build on Android using Termux -[Termux](https://github.com/termux/termux-app#installation) is an alternative to execute `llama.cpp` on an Android device (no root required). -``` -apt update && apt upgrade -y -apt install git -``` - -It's recommended to move your model inside the `~/` directory for best performance: -``` -cd storage/downloads -mv model.gguf ~/ -``` - -[Follow the Linux build instructions](https://github.com/ggerganov/llama.cpp#build) to build `llama.cpp`. - ### Docker #### Prerequisites From c0e6fbf8c380718102bd25fcb8d2e55f8f9480d1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Wed, 8 May 2024 09:14:50 +0300 Subject: [PATCH 045/100] metal : fix unused warning --- ggml-metal.metal | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 3d4276ae0..46c7d5039 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2175,7 +2175,7 @@ kernel void kernel_flash_attn_ext_f16( const short D4 = D/4; const short D8 = D/8; - const short Q8 = Q/8; + //const short Q8 = Q/8; const short NW = N_SIMDWIDTH; const short SH = (C + Q); // shared memory per simdgroup in (half) From 3855416027cb25d9a708ffa5581cf503a87856a6 Mon Sep 17 00:00:00 2001 From: Justine Tunney <jtunney@mozilla.com> Date: Wed, 8 May 2024 02:30:09 -0400 Subject: [PATCH 046/100] ggml : introduce bfloat16 support (#6412) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Introduce bfloat16 support Many models on Hugging Face (e.g. Mistral, TinyLLaMA) use bfloat16 as their canonical floating point format. ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌──┴───┐┌─┴───┐ 0b0000000000000000 brain16 This encoding has the same number of exponent bits as float32. That makes conversion relatively straightforward, even in the absence of hardware support. For example, converting brain16 to binary32 means simply shifting 16 bits to the left. ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌──┴───┐┌─┴───────────────────┐ 0b00000000000000000000000000000000 IEEE binary32 The issue is that converting bf16 to fp16 can result in information loss. Only 13% of bf16 numbers can be precisely represented in fp16 which in practice ends up being 99.71% of Mistral 7b v0.2's weights however there is currently no way other than fp32 to get the others ┌sign │ │ ┌exponent │ │ │ │ ┌mantissa │ │ │ │┌─┴─┐┌─┴──────┐ 0b0000000000000000 IEEE binary16 This change fixes that, by adding a bf16 data type to GGML. Support for CPU inference has been implemented along with optimizations for the AVX2, AVX512, and AVX512BF16 ISAs. Perplexity on Mistral 7b 0.2 improves somewhere around -0.0024 to -0.0046 compared to using fp16 * Remove GGML code that's not needed * Minimize the GGML API surface area for BF16 * Remove bf16 luts * Make the GGML header look nicer * Fix documentation * Apply ggerganov's fixes for test-backend-ops * Add BF16 code for new ggml_validate_row_data() function --- examples/finetune/finetune.cpp | 2 +- examples/quantize/quantize.cpp | 3 +- ggml-impl.h | 77 +++ ggml-metal.m | 2 +- ggml-quants.c | 18 + ggml.c | 1031 +++++++++++++++++++++++++++++++- ggml.h | 20 +- gguf-py/gguf/constants.py | 2 + llama.cpp | 20 +- llama.h | 1 + tests/test-backend-ops.cpp | 6 +- 11 files changed, 1154 insertions(+), 28 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 3da5317b3..22743b1bf 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -575,7 +575,7 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( GGML_ASSERT(tokens_input->type == GGML_TYPE_I32); auto add_to_f32 = [] (struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { - if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16) { + if (ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16 || a->type == GGML_TYPE_BF16) { return ggml_add_cast(ctx, a, b, GGML_TYPE_F32); } else if (a->type == GGML_TYPE_F32) { return ggml_add(ctx, a, b); diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 432cc2b4f..909eab283 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -46,7 +46,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = { { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", }, { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", }, { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", }, - { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, + { "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, -0.0020 ppl @ Mistral-7B", }, + { "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", }, { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching. { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, diff --git a/ggml-impl.h b/ggml-impl.h index 94a1cc668..d85b152bf 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -17,6 +17,83 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +/** + * Converts brain16 to float32. + * + * The bfloat16 floating point format has the following structure: + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───┐ + * 0b0000000000000000 brain16 + * + * Since bf16 has the same number of exponent bits as a 32bit float, + * encoding and decoding numbers becomes relatively straightforward. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌──┴───┐┌─┴───────────────────┐ + * 0b00000000000000000000000000000000 IEEE binary32 + * + * For comparison, the standard fp16 format has fewer exponent bits. + * + * ┌sign + * │ + * │ ┌exponent + * │ │ + * │ │ ┌mantissa + * │ │ │ + * │┌─┴─┐┌─┴──────┐ + * 0b0000000000000000 IEEE binary16 + * + * @see IEEE 754-2008 + */ +static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) { + union { + float f; + uint32_t i; + } u; + u.i = (uint32_t)h.bits << 16; + return u.f; +} + +/** + * Converts float32 to brain16. + * + * This function is binary identical to AMD Zen4 VCVTNEPS2BF16. + * Subnormals shall be flushed to zero, and NANs will be quiet. + * This code should vectorize nicely if using modern compilers. + */ +static inline ggml_bf16_t ggml_compute_fp32_to_bf16(float s) { + ggml_bf16_t h; + union { + float f; + uint32_t i; + } u; + u.f = s; + if ((u.i & 0x7fffffff) > 0x7f800000) { /* nan */ + h.bits = (u.i >> 16) | 64; /* force to quiet */ + return h; + } + if (!(u.i & 0x7f800000)) { /* subnormal */ + h.bits = (u.i & 0x80000000) >> 16; /* flush to zero */ + return h; + } + h.bits = (u.i + (0x7fff + ((u.i >> 16) & 1))) >> 16; + return h; +} + +#define GGML_FP32_TO_BF16(x) ggml_compute_fp32_to_bf16(x) +#define GGML_BF16_TO_FP32(x) ggml_compute_bf16_to_fp32(x) + #ifdef __cplusplus extern "C" { #endif diff --git a/ggml-metal.m b/ggml-metal.m index 017b72ce9..78cac5041 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -803,7 +803,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_DIAG_MASK_INF: case GGML_OP_GET_ROWS: { - return op->ne[3] == 1; + return op->src[0]->type != GGML_TYPE_BF16 && op->ne[3] == 1; } default: return false; diff --git a/ggml-quants.c b/ggml-quants.c index 444d1e55e..9883b6f8c 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -12450,6 +12450,24 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte const size_t nb = nbytes/ggml_type_size(type); switch (type) { + case GGML_TYPE_BF16: + { + int nans = 0; + int infs = 0; + const unsigned short * f = (const unsigned short *) data; + for (size_t i = 0; i < nb; ++i) { + nans += (f[i] & 0x7fff) > 0x7f80; + infs += (f[i] & 0x7fff) == 0x7f80; + } + if (nans) { + fprintf(stderr, "%s: found %d NaNs in row of %zu BF16 values\n", __func__, nans, nb); + return false; + } + if (infs) { + fprintf(stderr, "%s: found %d infinities in row of %zu BF16 values\n", __func__, infs, nb); + return false; + } + } break; case GGML_TYPE_F16: { const ggml_fp16_t * f = (const ggml_fp16_t *) data; diff --git a/ggml.c b/ggml.c index 82179a125..093d38d00 100644 --- a/ggml.c +++ b/ggml.c @@ -322,7 +322,7 @@ static ggml_fp16_t ggml_table_exp_f16[1 << 16]; // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; -const char * ggml_status_to_string(enum ggml_status status) { +GGML_CALL const char * ggml_status_to_string(enum ggml_status status) { switch (status) { case GGML_STATUS_ALLOC_FAILED: return "GGML status: error (failed to allocate memory)"; case GGML_STATUS_FAILED: return "GGML status: error (operation failed)"; @@ -333,16 +333,26 @@ const char * ggml_status_to_string(enum ggml_status status) { return "GGML status: unknown"; } -// note: do not use these inside ggml.c -// these are meant to be used via the ggml.h API float ggml_fp16_to_fp32(ggml_fp16_t x) { +#define ggml_fp16_to_fp32 do_not_use__ggml_fp16_to_fp32__in_ggml return GGML_FP16_TO_FP32(x); } ggml_fp16_t ggml_fp32_to_fp16(float x) { +#define ggml_fp32_to_fp16 do_not_use__ggml_fp32_to_fp16__in_ggml return GGML_FP32_TO_FP16(x); } +float ggml_bf16_to_fp32(ggml_bf16_t x) { +#define ggml_bf16_to_fp32 do_not_use__ggml_bf16_to_fp32__in_ggml + return GGML_BF16_TO_FP32(x); // it just left shifts +} + +ggml_bf16_t ggml_fp32_to_bf16(float x) { +#define ggml_fp32_to_bf16 do_not_use__ggml_fp32_to_bf16__in_ggml + return GGML_FP32_TO_BF16(x); +} + void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n) { for (int64_t i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); @@ -368,6 +378,49 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n) { } } +void ggml_bf16_to_fp32_row(const ggml_bf16_t * x, float * y, int64_t n) { + int64_t i = 0; +#if defined(__AVX512F__) + for (; i + 16 <= n; i += 16) { + _mm512_storeu_ps(y + i, + _mm512_castsi512_ps( + _mm512_slli_epi32( + _mm512_cvtepu16_epi32( + _mm256_loadu_si256( + (const __m256i *)(x + i))), + 16))); + } +#elif defined(__AVX2__) + for (; i + 8 <= n; i += 8) { + _mm256_storeu_ps(y + i, + _mm256_castsi256_ps( + _mm256_slli_epi32( + _mm256_cvtepu16_epi32( + _mm_loadu_si128( + (const __m128i *)(x + i))), + 16))); + } +#endif + for (; i < n; i++) { + y[i] = GGML_BF16_TO_FP32(x[i]); + } +} + +void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { + int i = 0; +#if defined(__AVX512BF16__) + for (; i + 32 <= n; i += 32) { + _mm512_storeu_ps( + (__m512 *)(y + i), + (__m512)_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), + _mm512_loadu_ps(x + i))); + } +#endif + for (; i < n; i++) { + y[i] = GGML_FP32_TO_BF16(x[i]); + } +} + bool ggml_guid_matches(ggml_guid_t guid_a, ggml_guid_t guid_b) { return memcmp(guid_a, guid_b, sizeof(ggml_guid)) == 0; } @@ -503,6 +556,7 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float); static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * restrict x, size_t bx, const float * restrict y, size_t by, int nrc); static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc); +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc); static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { [GGML_TYPE_I8] = { @@ -845,6 +899,18 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .type_size = sizeof(block_q8_K), .is_quantized = true, .from_float = quantize_row_q8_K, + }, + [GGML_TYPE_BF16] = { + .type_name = "bf16", + .blck_size = 1, + .type_size = sizeof(ggml_bf16_t), + .is_quantized = false, + .to_float = (ggml_to_float_t) ggml_bf16_to_fp32_row, + .from_float = (ggml_from_float_t) ggml_fp32_to_bf16_row, + .from_float_reference = (ggml_from_float_t) ggml_fp32_to_bf16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_bf16, + .vec_dot_type = GGML_TYPE_BF16, + .nrows = 1, } }; @@ -1480,6 +1546,8 @@ inline static void ggml_vec_set_i32(const int n, int32_t * x, const int32_t v) { inline static void ggml_vec_set_f16(const int n, ggml_fp16_t * x, const int32_t v) { for (int i = 0; i < n; ++i) x[i] = v; } +inline static void ggml_vec_set_bf16(const int n, ggml_bf16_t * x, const ggml_bf16_t v) { for (int i = 0; i < n; ++i) x[i] = v; } + inline static void ggml_vec_add_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i] + y[i]; } inline static void ggml_vec_add1_f32(const int n, float * z, const float * x, const float v) { for (int i = 0; i < n; ++i) z[i] = x[i] + v; } inline static void ggml_vec_acc_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] += x[i]; } @@ -1498,7 +1566,7 @@ static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * UNUSED(by); UNUSED(bs); -#ifdef GGML_SIMD +#if defined(GGML_SIMD) float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); @@ -1534,6 +1602,70 @@ static void ggml_vec_dot_f32(int n, float * restrict s, size_t bs, const float * *s = sumf; } +static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t * restrict x, size_t bx, ggml_bf16_t * restrict y, size_t by, int nrc) { + assert(nrc == 1); + UNUSED(nrc); + UNUSED(bx); + UNUSED(by); + UNUSED(bs); + int i = 0; + ggml_float sumf = 0; + +#if defined(__AVX512BF16__) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 64 <= n; i += 64) { + c1 = _mm512_dpbf16_ps(c1, (__m512bh)_mm512_loadu_ps((const float *)(x + i)), + (__m512bh)_mm512_loadu_ps((const float *)(y + i))); + c2 = _mm512_dpbf16_ps(c2, (__m512bh)_mm512_loadu_ps((const float *)(x + i + 32)), + (__m512bh)_mm512_loadu_ps((const float *)(y + i + 32))); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#elif defined(__AVX512F__) +#define LOAD(p) _mm512_castsi512_ps(_mm512_slli_epi32(_mm512_cvtepu16_epi32(_mm256_loadu_si256((const __m256i *)(p))), 16)) + __m512 c1 = _mm512_setzero_ps(); + __m512 c2 = _mm512_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm512_add_ps(_mm512_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c2); + } + sumf += (ggml_float)_mm512_reduce_add_ps(c1); + sumf += (ggml_float)_mm512_reduce_add_ps(c2); + +#undef LOAD +#elif defined(__AVX2__) +#define LOAD(p) _mm256_castsi256_ps(_mm256_slli_epi32(_mm256_cvtepu16_epi32(_mm_loadu_si128((const __m128i *)(p))), 16)) + __m256 c1 = _mm256_setzero_ps(); + __m256 c2 = _mm256_setzero_ps(); + __m256 c3 = _mm256_setzero_ps(); + __m256 c4 = _mm256_setzero_ps(); + for (; i + 32 <= n; i += 32) { + c1 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i), LOAD(y + i)), c1); + c2 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 8), LOAD(y + i + 8)), c2); + c3 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 16), LOAD(y + i + 16)), c3); + c4 = _mm256_add_ps(_mm256_mul_ps(LOAD(x + i + 24), LOAD(y + i + 24)), c4); + } + __m128 g; + c1 = _mm256_add_ps(_mm256_add_ps(c1, c3), + _mm256_add_ps(c2, c4)); + g = _mm_add_ps(_mm256_extractf128_ps(c1, 1), + _mm256_castps256_ps128(c1)); + g = _mm_add_ps(g, _mm_movehl_ps(g, g)); + g = _mm_add_ss(g, _mm_movehdup_ps(g)); + sumf += (ggml_float)_mm_cvtss_f32(g); + +#undef LOAD +#endif + + for (; i < n; ++i) { + sumf += (ggml_float)(GGML_BF16_TO_FP32(x[i]) * + GGML_BF16_TO_FP32(y[i])); + } + *s = sumf; +} + static void ggml_vec_dot_f16(int n, float * restrict s, size_t bs, ggml_fp16_t * restrict x, size_t bx, ggml_fp16_t * restrict y, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); @@ -1967,6 +2099,14 @@ inline static void ggml_vec_sum_f16_ggf(const int n, float * s, const ggml_fp16_ *s = sum; } +inline static void ggml_vec_sum_bf16_ggf(const int n, float * s, const ggml_bf16_t * x) { + float sum = 0.0f; + for (int i = 0; i < n; ++i) { + sum += GGML_BF16_TO_FP32(x[i]); + } + *s = sum; +} + inline static void ggml_vec_max_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE float max = -INFINITY; @@ -2377,7 +2517,7 @@ void ggml_numa_init(enum ggml_numa_strategy numa_flag) { // figure out which node we're on uint current_cpu; int getcpu_ret = 0; -#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) +#if __GLIBC__ > 2 || (__GLIBC__ == 2 && __GLIBC_MINOR__ > 28) || defined(__COSMOPOLITAN__) getcpu_ret = getcpu(&current_cpu, &g_state.numa.current_node); #else // old glibc doesn't have a wrapper for this call. Fall back on direct syscall @@ -2588,6 +2728,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { switch (ftype) { case GGML_FTYPE_ALL_F32: wtype = GGML_TYPE_F32; break; case GGML_FTYPE_MOSTLY_F16: wtype = GGML_TYPE_F16; break; + case GGML_FTYPE_MOSTLY_BF16: wtype = GGML_TYPE_BF16; break; case GGML_FTYPE_MOSTLY_Q4_0: wtype = GGML_TYPE_Q4_0; break; case GGML_FTYPE_MOSTLY_Q4_1: wtype = GGML_TYPE_Q4_1; break; case GGML_FTYPE_MOSTLY_Q5_0: wtype = GGML_TYPE_Q5_0; break; @@ -2729,15 +2870,16 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); - ggml_fp16_t ii; for (int i = 0; i < (1 << 16); ++i) { - uint16_t ui = i; - memcpy(&ii, &ui, sizeof(ii)); - const float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); + union { + uint16_t u16; + ggml_fp16_t fp16; + } u = {i}; + float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); + ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -3201,6 +3343,13 @@ struct ggml_tensor * ggml_set_i32 (struct ggml_tensor * tensor, int32_t value) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_fp16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); @@ -3253,6 +3402,13 @@ struct ggml_tensor * ggml_set_f32(struct ggml_tensor * tensor, float value) { ggml_vec_set_f16(nc, (ggml_fp16_t *)(data + i*n1), GGML_FP32_TO_FP16(value)); } } break; + case GGML_TYPE_BF16: + { + assert(tensor->nb[0] == sizeof(ggml_bf16_t)); + for (int i = 0; i < n; i++) { + ggml_vec_set_bf16(nc, (ggml_bf16_t *)(data + i*n1), GGML_FP32_TO_BF16(value)); + } + } break; case GGML_TYPE_F32: { assert(tensor->nb[0] == sizeof(float)); @@ -3320,6 +3476,11 @@ int32_t ggml_get_i32_1d(const struct ggml_tensor * tensor, int i) { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); @@ -3362,6 +3523,11 @@ void ggml_set_i32_1d(const struct ggml_tensor * tensor, int i, int32_t value) { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); @@ -3385,6 +3551,8 @@ int32_t ggml_get_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i return ((int32_t *) data)[0]; case GGML_TYPE_F16: return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); case GGML_TYPE_F32: return ((float *) data)[0]; default: @@ -3413,6 +3581,10 @@ void ggml_set_i32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, { ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; case GGML_TYPE_F32: { ((float *)(data))[0] = value; @@ -3451,6 +3623,11 @@ float ggml_get_f32_1d(const struct ggml_tensor * tensor, int i) { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); return GGML_FP16_TO_FP32(((ggml_fp16_t *)(tensor->data))[i]); } + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + return GGML_BF16_TO_FP32(((ggml_bf16_t *)(tensor->data))[i]); + } case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); @@ -3493,6 +3670,11 @@ void ggml_set_f32_1d(const struct ggml_tensor * tensor, int i, float value) { GGML_ASSERT(tensor->nb[0] == sizeof(ggml_fp16_t)); ((ggml_fp16_t *)(tensor->data))[i] = GGML_FP32_TO_FP16(value); } break; + case GGML_TYPE_BF16: + { + GGML_ASSERT(tensor->nb[0] == sizeof(ggml_bf16_t)); + ((ggml_bf16_t *)(tensor->data))[i] = GGML_FP32_TO_BF16(value); + } break; case GGML_TYPE_F32: { GGML_ASSERT(tensor->nb[0] == sizeof(float)); @@ -3516,6 +3698,8 @@ float ggml_get_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, return ((int32_t *) data)[0]; case GGML_TYPE_F16: return GGML_FP16_TO_FP32(((ggml_fp16_t *) data)[0]); + case GGML_TYPE_BF16: + return GGML_BF16_TO_FP32(((ggml_bf16_t *) data)[0]); case GGML_TYPE_F32: return ((float *) data)[0]; default: @@ -3544,6 +3728,10 @@ void ggml_set_f32_nd(const struct ggml_tensor * tensor, int i0, int i1, int i2, { ((ggml_fp16_t *)(data))[0] = GGML_FP32_TO_FP16(value); } break; + case GGML_TYPE_BF16: + { + ((ggml_bf16_t *)(data))[0] = GGML_FP32_TO_BF16(value); + } break; case GGML_TYPE_F32: { ((float *)(data))[0] = value; @@ -3738,7 +3926,11 @@ static struct ggml_tensor * ggml_add_cast_impl( // TODO: support less-strict constraint // GGML_ASSERT(ggml_can_repeat(b, a)); GGML_ASSERT(ggml_can_repeat_rows(b, a)); - GGML_ASSERT(ggml_is_quantized(a->type) || a->type == GGML_TYPE_F16); // currently only supported for quantized input and f16 + + // currently only supported for quantized input and f16 + GGML_ASSERT(ggml_is_quantized(a->type) || + a->type == GGML_TYPE_F16 || + a->type == GGML_TYPE_BF16); bool is_node = false; @@ -7215,8 +7407,8 @@ static void ggml_compute_forward_dup_same_cont( ((char *) src0->data + ie0*nb00), (ie1 - ie0) * ggml_type_size(src0->type)); } - } + static void ggml_compute_forward_dup_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -7490,6 +7682,366 @@ static void ggml_compute_forward_dup_f16( } } +static void ggml_compute_forward_dup_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + GGML_ASSERT(ggml_nelements(dst) == ggml_nelements(src0)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + GGML_TENSOR_UNARY_OP_LOCALS + + const int ith = params->ith; // thread index + const int nth = params->nth; // number of threads + + if (ggml_is_contiguous(src0) && ggml_is_contiguous(dst) && src0->type == dst->type) { + ggml_compute_forward_dup_same_cont(params, dst); + return; + } + + // parallelize by rows + const int nr = ne01; + // number of rows per thread + const int dr = (nr + nth - 1) / nth; + // row range for this thread + const int ir0 = dr * ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (src0->type == dst->type && + ne00 == ne0 && + nb00 == ggml_type_size(src0->type) && nb0 == ggml_type_size(dst->type)) { + // copy by rows + const size_t rs = ne00*nb00; + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = ir0; i01 < ir1; i01++) { + memcpy( + ((char *) dst->data + i01*nb1 + i02*nb2 + i03*nb3), + ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03), + rs); + } + } + } + return; + } + + // TODO: add more special-case implementations for tensor shapes/strides that can benefit from memcpy + + if (ggml_is_contiguous(dst)) { + if (nb00 == sizeof(ggml_bf16_t)) { + if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + const size_t rs = ne00 * nb00; + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const char * src0_ptr = (char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03; + memcpy(dst_ptr + id, src0_ptr, rs); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(src0_ptr[i00])); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + for (int i00 = 0; i00 < ne00; i00++) { + dst_ptr[id] = GGML_BF16_TO_FP32(src0_ptr[i00]); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (type_traits[dst->type].from_float) { + ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; + float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; + + size_t id = 0; + size_t rs = nb0 * (ne00 / ggml_blck_size(dst->type)); + char * dst_ptr = (char *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += rs * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03); + + for (int i00 = 0; i00 < ne00; i00++) { + src0_f32[i00] = GGML_BF16_TO_FP32(src0_ptr[i00]); + } + + quantize_row_q(src0_f32, dst_ptr + id, ne00); + id += rs; + } + id += rs * (ne01 - ir1); + } + } + } else { + GGML_ASSERT(false); // TODO: implement + } + } else { + //printf("%s: this is not optimal - fix me\n", __func__); + + if (dst->type == GGML_TYPE_F32) { + size_t id = 0; + float * dst_ptr = (float *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_BF16_TO_FP32(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = *src0_ptr; + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else if (dst->type == GGML_TYPE_F16) { + size_t id = 0; + ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*src0_ptr)); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } + } else { + GGML_ASSERT(false); // TODO: implement + } + } + return; + } + + // dst counters + int64_t i10 = 0; + int64_t i11 = 0; + int64_t i12 = 0; + int64_t i13 = 0; + + if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + memcpy(dst_ptr, src0_ptr, sizeof(ggml_bf16_t)); + + if (++i10 == ne00) { + i10 = 0; + if (++i11 == ne01) { + i11 = 0; + if (++i12 == ne02) { + i12 = 0; + if (++i13 == ne03) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_fp16_t *) dst_ptr = GGML_FP32_TO_FP16(GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr)); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else if (dst->type == GGML_TYPE_F32) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(float *) dst_ptr = GGML_BF16_TO_FP32(*(const ggml_bf16_t *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + } else { + GGML_ASSERT(false); // TODO: implement + } +} + static void ggml_compute_forward_dup_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -7617,6 +8169,24 @@ static void ggml_compute_forward_dup_f32( id += ne00 * (ne01 - ir1); } } + } else if (dst->type == GGML_TYPE_BF16) { + size_t id = 0; + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) dst->data; + + for (int i03 = 0; i03 < ne03; i03++) { + for (int i02 = 0; i02 < ne02; i02++) { + id += ne00 * ir0; + for (int i01 = ir0; i01 < ir1; i01++) { + for (int i00 = 0; i00 < ne00; i00++) { + const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + + dst_ptr[id] = GGML_FP32_TO_BF16(*src0_ptr); + id++; + } + } + id += ne00 * (ne01 - ir1); + } + } } else { GGML_ASSERT(false); // TODO: implement } @@ -7736,6 +8306,58 @@ static void ggml_compute_forward_dup_f32( } } } + } else if (dst->type == GGML_TYPE_BF16) { + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + i10 += ne00 * ir0; + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + for (int64_t i01 = ir0; i01 < ir1; i01++) { + for (int64_t i00 = 0; i00 < ne00; i00++) { + const char * src0_ptr = ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); + char * dst_ptr = ((char *) dst->data + i10*nb0 + i11*nb1 + i12*nb2 + i13*nb3); + + *(ggml_bf16_t *) dst_ptr = GGML_FP32_TO_BF16(*(const float *) src0_ptr); + + if (++i10 == ne0) { + i10 = 0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } + i10 += ne00 * (ne01 - ir1); + while (i10 >= ne0) { + i10 -= ne0; + if (++i11 == ne1) { + i11 = 0; + if (++i12 == ne2) { + i12 = 0; + if (++i13 == ne3) { + i13 = 0; + } + } + } + } + } + } } else { GGML_ASSERT(false); // TODO: implement } @@ -7909,6 +8531,10 @@ static void ggml_compute_forward_dup( { ggml_compute_forward_dup_f16(params, dst); } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_dup_bf16(params, dst); + } break; case GGML_TYPE_F32: { ggml_compute_forward_dup_f32(params, dst); @@ -8091,6 +8717,85 @@ static void ggml_compute_forward_add_f16_f32( } } +static void ggml_compute_forward_add_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + if (dst->type == GGML_TYPE_F32) { + GGML_ASSERT( nb0 == sizeof(float)); + } + else { + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + } + + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(float)) { + if (dst->type == GGML_TYPE_BF16) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]); + } + } + } else { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + float * dst_ptr = (float *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + float * src1_ptr = (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_BF16_TO_FP32(src0_ptr[i]) + src1_ptr[i]; + } + } + } + } + else { + // src1 is not contiguous + GGML_ASSERT(false); + } +} + static void ggml_compute_forward_add_f16_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -8147,6 +8852,62 @@ static void ggml_compute_forward_add_f16_f16( } } +static void ggml_compute_forward_add_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, src1) && ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_BINARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + if (nb10 == sizeof(ggml_bf16_t)) { + for (int ir = ir0; ir < ir1; ++ir) { + // src0, src1 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + ggml_bf16_t * src1_ptr = (ggml_bf16_t *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11); + + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + GGML_BF16_TO_FP32(src1_ptr[i])); + } + } + } + else { + // src1 is not contiguous + GGML_ASSERT(false); + } +} + static void ggml_compute_forward_add_q_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -8256,6 +9017,18 @@ static void ggml_compute_forward_add( GGML_ASSERT(false); } } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_bf16_f32(params, dst); + } + else { + GGML_ASSERT(false); + } + } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -8514,6 +9287,110 @@ static void ggml_compute_forward_add1_q_f32( } } +static void ggml_compute_forward_add1_bf16_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + // scalar to add + const float v = *(float *) src1->data; + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + +static void ggml_compute_forward_add1_bf16_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + GGML_ASSERT(ggml_is_scalar(src1)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + // scalar to add + const float v = GGML_BF16_TO_FP32(*(ggml_bf16_t *) src1->data); + + const int ith = params->ith; + const int nth = params->nth; + + const int nr = ggml_nrows(src0); + + GGML_TENSOR_UNARY_OP_LOCALS + + GGML_ASSERT(src0->type == GGML_TYPE_BF16); + GGML_ASSERT(src1->type == GGML_TYPE_BF16); + GGML_ASSERT(dst->type == GGML_TYPE_BF16); + + GGML_ASSERT( nb0 == sizeof(ggml_bf16_t)); + GGML_ASSERT(nb00 == sizeof(ggml_bf16_t)); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int ir = ir0; ir < ir1; ++ir) { + // src0 and dst are same shape => same indices + const int i3 = ir/(ne2*ne1); + const int i2 = (ir - i3*ne2*ne1)/ne1; + const int i1 = (ir - i3*ne2*ne1 - i2*ne1); + + ggml_bf16_t * dst_ptr = (ggml_bf16_t *) ((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 ); + ggml_bf16_t * src0_ptr = (ggml_bf16_t *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01); + for (int i = 0; i < ne0; i++) { + dst_ptr[i] = GGML_FP32_TO_BF16(GGML_BF16_TO_FP32(src0_ptr[i]) + v); + } + } +} + static void ggml_compute_forward_add1( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -8538,6 +9415,18 @@ static void ggml_compute_forward_add1( GGML_ASSERT(false); } } break; + case GGML_TYPE_BF16: + { + if (src1->type == GGML_TYPE_BF16) { + ggml_compute_forward_add1_bf16_bf16(params, dst); + } + else if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add1_bf16_f32(params, dst); + } + else { + GGML_ASSERT(false); + } + } break; case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -8666,6 +9555,7 @@ static void ggml_compute_forward_acc( ggml_compute_forward_acc_f32(params, dst); } break; case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -9187,6 +10077,40 @@ static void ggml_compute_forward_sum_f16( ((ggml_fp16_t *) dst->data)[0] = GGML_FP32_TO_FP16(sum); } +static void ggml_compute_forward_sum_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(params->ith == 0); + assert(ggml_is_scalar(dst)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + assert(src0->nb[0] == sizeof(ggml_bf16_t)); + + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne) + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb) + + float sum = 0; + float row_sum = 0; + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + for (int64_t i01 = 0; i01 < ne01; i01++) { + ggml_vec_sum_bf16_ggf(ne00, + &row_sum, + (ggml_bf16_t *) ((char *) src0->data + i01 * nb01 + i02 * nb02 + i03 * nb03)); + sum += row_sum; + } + } + } + ((ggml_bf16_t *) dst->data)[0] = GGML_FP32_TO_BF16(sum); +} + static void ggml_compute_forward_sum( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -9202,6 +10126,10 @@ static void ggml_compute_forward_sum( { ggml_compute_forward_sum_f16(params, dst); } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_sum_bf16(params, dst); + } break; default: { GGML_ASSERT(false); @@ -9476,6 +10404,7 @@ static void ggml_compute_forward_repeat( switch (src0->type) { case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_I16: { ggml_compute_forward_repeat_f16(params, dst); @@ -11793,6 +12722,7 @@ static void ggml_compute_forward_set( ggml_compute_forward_set_f32(params, dst); } break; case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -11967,6 +12897,49 @@ static void ggml_compute_forward_get_rows_f16( } } +static void ggml_compute_forward_get_rows_bf16( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + GGML_TENSOR_BINARY_OP_LOCALS + + const int64_t nc = ne00; + const int64_t nr = ggml_nelements(src1); + + assert(ne0 == nc); + assert(ne02 == ne11); + assert(nb00 == sizeof(ggml_bf16_t)); + assert(ggml_nrows(dst) == nr); + + const int ith = params->ith; + const int nth = params->nth; + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int64_t i = ir0; i < ir1; ++i) { + const int64_t i12 = i/(ne11*ne10); + const int64_t i11 = (i - i12*ne11*ne10)/ne10; + const int64_t i10 = (i - i12*ne11*ne10 - i11*ne10); + const int64_t i01 = *(int32_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12); + + ggml_bf16_to_fp32_row( + (const void *) ((char *) src0->data + i01*nb01 + i11*nb02 + i12*nb03), + (float *) ((char *) dst->data + i10*nb1 + i11*nb2 + i12*nb3), nc); + } +} + static void ggml_compute_forward_get_rows_f32( const struct ggml_compute_params * params, struct ggml_tensor * dst) { @@ -12044,6 +13017,10 @@ static void ggml_compute_forward_get_rows( { ggml_compute_forward_get_rows_f16(params, dst); } break; + case GGML_TYPE_BF16: + { + ggml_compute_forward_get_rows_bf16(params, dst); + } break; case GGML_TYPE_F32: case GGML_TYPE_I32: { @@ -12739,6 +13716,7 @@ static void ggml_compute_forward_alibi( { ggml_compute_forward_alibi_f32(params, dst); } break; + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -12828,6 +13806,7 @@ static void ggml_compute_forward_clamp( ggml_compute_forward_clamp_f32(params, dst); } break; case GGML_TYPE_F16: + case GGML_TYPE_BF16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: case GGML_TYPE_Q5_0: @@ -15921,6 +16900,7 @@ static void ggml_compute_forward_get_rel_pos( switch (src0->type) { case GGML_TYPE_F16: + case GGML_TYPE_BF16: { ggml_compute_forward_get_rel_pos_f16(params, dst); } break; @@ -18785,7 +19765,10 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa case GGML_OP_CPY: case GGML_OP_DUP: { - if (ggml_is_quantized(node->type)) { + if (ggml_is_quantized(node->type) || + // F16 -> BF16 and BF16 -> F16 copies go through intermediate F32 + (node->src[0]->type == GGML_TYPE_F16 && node->src[1] && node->src[1]->type == GGML_TYPE_BF16) || + (node->src[0]->type == GGML_TYPE_BF16 && node->src[1] && node->src[1]->type == GGML_TYPE_F16)) { cur = ggml_type_size(GGML_TYPE_F32) * node->ne[0] * n_tasks; } } break; @@ -18864,7 +19847,8 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa const int64_t ne10 = node->src[1]->ne[0]; // L const int64_t ne11 = node->src[1]->ne[1]; // Cin - if (node->src[0]->type == GGML_TYPE_F16 && + if ((node->src[0]->type == GGML_TYPE_F16 || + node->src[0]->type == GGML_TYPE_BF16) && node->src[1]->type == GGML_TYPE_F32) { cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02; cur += sizeof(ggml_fp16_t)*ne10*ne11; @@ -18900,6 +19884,9 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_BF16) { + cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 } } break; case GGML_OP_FLASH_ATTN_EXT: @@ -18916,6 +19903,9 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_BF16) { + cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 } } break; case GGML_OP_FLASH_ATTN_BACK: @@ -18929,6 +19919,9 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa } else if (node->src[1]->type == GGML_TYPE_F16) { cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 + } else if (node->src[1]->type == GGML_TYPE_BF16) { + cur = sizeof(float)*mxDn*n_tasks; // TODO: this can become (n_tasks-1) + cur += sizeof(float)*mxDn*n_tasks; // this is overestimated by x2 } } break; @@ -19705,7 +20698,9 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { fprintf(fp, "%d", ggml_get_i32_1d(node, j)); } - else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) { + else if (node->type == GGML_TYPE_F32 || + node->type == GGML_TYPE_F16 || + node->type == GGML_TYPE_BF16) { fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); } else { @@ -20763,6 +21758,12 @@ size_t ggml_quantize_chunk( ggml_fp32_to_fp16_row(src + start, (ggml_fp16_t *)dst + start, n); result = n * elemsize; } break; + case GGML_TYPE_BF16: + { + size_t elemsize = sizeof(ggml_bf16_t); + ggml_fp32_to_bf16_row(src + start, (ggml_bf16_t *)dst + start, n); + result = n * elemsize; + } break; case GGML_TYPE_F32: { size_t elemsize = sizeof(float); diff --git a/ggml.h b/ggml.h index a11795973..fe6053822 100644 --- a/ggml.h +++ b/ggml.h @@ -326,14 +326,20 @@ extern "C" { // get ggml_status name string GGML_API GGML_CALL const char * ggml_status_to_string(enum ggml_status status); + // ieee 754-2008 half-precision float16 + // todo: make this not an integral type typedef uint16_t ggml_fp16_t; + GGML_API float ggml_fp16_to_fp32(ggml_fp16_t); + GGML_API ggml_fp16_t ggml_fp32_to_fp16(float); + GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t *, float *, int64_t); + GGML_API void ggml_fp32_to_fp16_row(const float *, ggml_fp16_t *, int64_t); - // convert FP16 <-> FP32 - GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); - GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x); - - GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int64_t n); - GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int64_t n); + // google brain half-precision bfloat16 + typedef struct { uint16_t bits; } ggml_bf16_t; + GGML_API ggml_bf16_t ggml_fp32_to_bf16(float); + GGML_API float ggml_bf16_to_fp32(ggml_bf16_t); // consider just doing << 16 + GGML_API void ggml_bf16_to_fp32_row(const ggml_bf16_t *, float *, int64_t); + GGML_API void ggml_fp32_to_bf16_row(const float *, ggml_bf16_t *, int64_t); struct ggml_object; struct ggml_context; @@ -370,6 +376,7 @@ extern "C" { GGML_TYPE_I64 = 27, GGML_TYPE_F64 = 28, GGML_TYPE_IQ1_M = 29, + GGML_TYPE_BF16 = 30, GGML_TYPE_COUNT, }; @@ -410,6 +417,7 @@ extern "C" { GGML_FTYPE_MOSTLY_IQ2_S = 21, // except 1d tensors GGML_FTYPE_MOSTLY_IQ4_XS = 22, // except 1d tensors GGML_FTYPE_MOSTLY_IQ1_M = 23, // except 1d tensors + GGML_FTYPE_MOSTLY_BF16 = 24, // except 1d tensors }; // available tensor operations: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 4f232e18d..6e968fc4e 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -817,6 +817,7 @@ class GGMLQuantizationType(IntEnum): I64 = 27 F64 = 28 IQ1_M = 29 + BF16 = 30 class GGUFEndian(IntEnum): @@ -888,6 +889,7 @@ GGML_QUANT_SIZES = { GGMLQuantizationType.I64: (1, 8), GGMLQuantizationType.F64: (1, 8), GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32), + GGMLQuantizationType.BF16: (1, 2), } diff --git a/llama.cpp b/llama.cpp index 07fde3619..26e7a3391 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3175,6 +3175,7 @@ struct llama_model_loader { switch (type_max) { case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break; case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break; + case GGML_TYPE_BF16: ftype = LLAMA_FTYPE_MOSTLY_BF16; break; case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break; case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break; case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break; @@ -3666,6 +3667,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { switch (ftype) { case LLAMA_FTYPE_ALL_F32: return "all F32"; case LLAMA_FTYPE_MOSTLY_F16: return "F16"; + case LLAMA_FTYPE_MOSTLY_BF16: return "BF16"; case LLAMA_FTYPE_MOSTLY_Q4_0: return "Q4_0"; case LLAMA_FTYPE_MOSTLY_Q4_1: return "Q4_1"; case LLAMA_FTYPE_MOSTLY_Q4_1_SOME_F16: @@ -6129,6 +6131,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam || !( model.ftype == LLAMA_FTYPE_ALL_F32 || model.ftype == LLAMA_FTYPE_MOSTLY_F16 || + model.ftype == LLAMA_FTYPE_MOSTLY_BF16 || model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ) @@ -14158,13 +14161,16 @@ static void llama_tensor_dequantize_internal( if (qtype.to_float == NULL) { throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor->type))); } - } else if (tensor->type != GGML_TYPE_F16) { + } else if (tensor->type != GGML_TYPE_F16 && + tensor->type != GGML_TYPE_BF16) { throw std::runtime_error(format("cannot dequantize/convert tensor type %s", ggml_type_name(tensor->type))); } if (nthread < 2) { if (tensor->type == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor->data, f32_output, nelements); + } else if (tensor->type == GGML_TYPE_BF16) { + ggml_bf16_to_fp32_row((ggml_bf16_t *)tensor->data, f32_output, nelements); } else if (ggml_is_quantized(tensor->type)) { qtype.to_float(tensor->data, f32_output, nelements); } else { @@ -14173,7 +14179,14 @@ static void llama_tensor_dequantize_internal( return; } - size_t block_size = tensor->type == GGML_TYPE_F16 ? 1 : (size_t)ggml_blck_size(tensor->type); + size_t block_size; + if (tensor->type == GGML_TYPE_F16 || + tensor->type == GGML_TYPE_BF16) { + block_size = 1; + } else { + block_size = (size_t)ggml_blck_size(tensor->type); + } + size_t block_size_bytes = ggml_type_size(tensor->type); GGML_ASSERT(nelements % block_size == 0); @@ -14192,6 +14205,8 @@ static void llama_tensor_dequantize_internal( auto compute = [qtype] (ggml_type typ, uint8_t * inbuf, float * outbuf, int nels) { if (typ == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels); + } else if (typ == GGML_TYPE_BF16) { + ggml_bf16_to_fp32_row((ggml_bf16_t *)inbuf, outbuf, nels); } else { qtype.to_float(inbuf, outbuf, nels); } @@ -14552,6 +14567,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q5_1: default_type = GGML_TYPE_Q5_1; break; case LLAMA_FTYPE_MOSTLY_Q8_0: default_type = GGML_TYPE_Q8_0; break; case LLAMA_FTYPE_MOSTLY_F16: default_type = GGML_TYPE_F16; break; + case LLAMA_FTYPE_MOSTLY_BF16: default_type = GGML_TYPE_BF16; break; case LLAMA_FTYPE_ALL_F32: default_type = GGML_TYPE_F32; break; // K-quants diff --git a/llama.h b/llama.h index d7f4bf8ea..5761cacf4 100644 --- a/llama.h +++ b/llama.h @@ -137,6 +137,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ2_M = 29, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ4_XS = 30, // except 1d tensors LLAMA_FTYPE_MOSTLY_IQ1_M = 31, // except 1d tensors + LLAMA_FTYPE_MOSTLY_BF16 = 32, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b27c1291e..41718e001 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -50,7 +50,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float)); - } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16) { + } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) { GGML_ASSERT(size % ggml_blck_size(tensor->type) == 0); std::vector<uint8_t> dataq(ggml_row_size(tensor->type, size)); std::vector<float> imatrix(tensor->ne[0], 1.0f); // dummy importance matrix @@ -92,6 +92,8 @@ static std::vector<float> tensor_to_float(const ggml_tensor * t) { size_t i = i3*t->nb[3] + i2*t->nb[2] + i1*t->nb[1] + i0/bs*t->nb[0]; if (t->type == GGML_TYPE_F16) { tv.push_back(ggml_fp16_to_fp32(*(ggml_fp16_t*)&buf[i])); + } else if (t->type == GGML_TYPE_BF16) { + tv.push_back(ggml_bf16_to_fp32(*(ggml_bf16_t*)&buf[i])); } else if (t->type == GGML_TYPE_F32) { tv.push_back(*(float *) &buf[i]); } else if (t->type == GGML_TYPE_I32) { @@ -1898,7 +1900,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op std::default_random_engine rng(0); const ggml_type all_types[] = { - GGML_TYPE_F32, GGML_TYPE_F16, + GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_BF16, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0, From acdce3cdef6fc2f0b7b5623231fd7762c0884d1c Mon Sep 17 00:00:00 2001 From: Brian <mofosyne@gmail.com> Date: Wed, 8 May 2024 18:54:39 +1000 Subject: [PATCH 047/100] compare-llama-bench.py: add missing basicConfig (#7138) * compare-llama-bench.py: add missing basicConfig * compare-llama-bench.py: Add line break between error message and print_help() * Add regular print() markdown table --- scripts/compare-llama-bench.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 3892fd25c..fed3c1ee3 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -93,11 +93,14 @@ help_s = ( "specified values are averaged WITHOUT weighing by the --repetitions parameter of llama-bench." ) parser.add_argument("-s", "--show", help=help_s) +parser.add_argument("--verbose", action="store_true", help="increase output verbosity") known_args, unknown_args = parser.parse_known_args() +logging.basicConfig(level=logging.DEBUG if known_args.verbose else logging.INFO) + if unknown_args: - logger.error(f"Received unknown args: {unknown_args}.") + logger.error(f"Received unknown args: {unknown_args}.\n") parser.print_help() sys.exit(1) @@ -110,7 +113,7 @@ if input_file is None: input_file = sqlite_files[0] if input_file is None: - logger.error("Cannot find a suitable input file, please provide one.") + logger.error("Cannot find a suitable input file, please provide one.\n") parser.print_help() sys.exit(1) @@ -202,12 +205,12 @@ elif repo is not None: hexsha8_baseline = find_parent_in_data(repo.heads.master.commit) if hexsha8_baseline is None: - logger.error("No baseline was provided and did not find data for any master branch commits.") + logger.error("No baseline was provided and did not find data for any master branch commits.\n") parser.print_help() sys.exit(1) else: logger.error("No baseline was provided and the current working directory " - "is not part of a git repository from which a baseline could be inferred.") + "is not part of a git repository from which a baseline could be inferred.\n") parser.print_help() sys.exit(1) @@ -238,7 +241,7 @@ elif repo is not None: break if hexsha8_compare is None: - logger.error("No compare target was provided and did not find data for any non-master commits.") + logger.error("No compare target was provided and did not find data for any non-master commits.\n") parser.print_help() sys.exit(1) else: @@ -361,7 +364,7 @@ if "gpu_info" in show: headers = [PRETTY_NAMES[p] for p in show] headers += ["Test", f"t/s {name_baseline}", f"t/s {name_compare}", "Speedup"] -logger.info(tabulate( +print(tabulate( # noqa: NP100 table, headers=headers, floatfmt=".2f", From 7e0b6a7b3ba94ff624dc27c1e0e735fded8819b8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Wed, 8 May 2024 12:47:07 +0300 Subject: [PATCH 048/100] py : also print the normalizers --- convert-hf-to-gguf-update.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index ae901e24c..a3fe67ee7 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -151,6 +151,8 @@ for model in models: # print the "pre_tokenizer" content from the tokenizer.json with open(f"models/tokenizers/{name}/tokenizer.json", "r", encoding="utf-8") as f: cfg = json.load(f) + normalizer = cfg["normalizer"] + logger.info("normalizer: " + json.dumps(normalizer, indent=4)) pre_tokenizer = cfg["pre_tokenizer"] logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) From 4cd621c26de2095cd7c4464bdec5fe2e696ef3f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?DAN=E2=84=A2?= <dranger003@gmail.com> Date: Wed, 8 May 2024 06:43:23 -0400 Subject: [PATCH 049/100] convert : add BPE pre-tokenization for DBRX (#7132) * Add BPE pre-tokenization for DBRX. * Add vocab GGUFs. * Remove test. * Remove GGUFs. --- convert-hf-to-gguf-update.py | 1 + convert-hf-to-gguf.py | 3 +++ llama.cpp | 4 ++++ llama.h | 1 + 4 files changed, 9 insertions(+) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index a3fe67ee7..b51010983 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -68,6 +68,7 @@ models = [ {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, + {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, ] # make directory "models/tokenizers" if it doesn't exist diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index f65d9320e..8b89575d5 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -317,6 +317,9 @@ class Model(ABC): if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf res = "olmo" + if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e": + # ref: https://huggingface.co/databricks/dbrx-instruct + res = "dbrx" if res is None: logger.warning("\n") diff --git a/llama.cpp b/llama.cpp index 26e7a3391..331c9d472 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4394,6 +4394,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "olmo") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; + } else if ( + tokenizer_pre == "dbrx") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -12200,6 +12203,7 @@ struct llm_tokenizer_bpe { case LLAMA_VOCAB_TYPE_BPE: switch (vocab.type_pre) { case LLAMA_VOCAB_PRE_TYPE_LLAMA3: + case LLAMA_VOCAB_PRE_TYPE_DBRX: word_collection = unicode_regex_split(text, { // original regex from tokenizer.json //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", diff --git a/llama.h b/llama.h index 5761cacf4..388839f39 100644 --- a/llama.h +++ b/llama.h @@ -82,6 +82,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, LLAMA_VOCAB_PRE_TYPE_OLMO = 10, + LLAMA_VOCAB_PRE_TYPE_DBRX = 11, }; // note: these values should be synchronized with ggml_rope From 1fd9c1741d864d01cd7ec6d67227b92d7bfabf22 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen <thichthat@gmail.com> Date: Wed, 8 May 2024 13:24:14 +0200 Subject: [PATCH 050/100] clean up json_value & server_log (#7142) --- examples/server/utils.hpp | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index 1a2212502..af12f497d 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -49,18 +49,18 @@ extern bool server_log_json; #define LOG_WARNING(MSG, ...) server_log("WARN", __func__, __LINE__, MSG, __VA_ARGS__) #define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra); +static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra); template <typename T> -static T json_value(const json &body, const std::string &key, const T &default_value) { +static T json_value(const json & body, const std::string & key, const T & default_value) { // Fallback null to default value - if (body.contains(key) && !body.at(key).is_null()){ + if (body.contains(key) && !body.at(key).is_null()) { try { - return body.value(key, default_value); - } - catch (nlohmann::json_abi_v3_11_3::detail::type_error const&){ - std::string message = "Wrong type supplied for parameter '" + key + "'. Expected '" + typeid(default_value).name() + "', using default value."; - server_log("WARN", __func__, __LINE__, message.c_str(), body); + return body.at(key); + } catch (NLOHMANN_JSON_NAMESPACE::detail::type_error const &) { + std::stringstream ss; + ss << "Wrong type supplied for parameter '" << key << "'. Expected '" << json(default_value).type_name() << "', using default value."; + LOG_WARNING(ss.str().c_str(), body); return default_value; } } else { @@ -68,16 +68,16 @@ static T json_value(const json &body, const std::string &key, const T &default_v } } -static inline void server_log(const char *level, const char *function, int line, const char *message, const nlohmann::ordered_json &extra) { +static inline void server_log(const char * level, const char * function, int line, const char * message, const json & extra) { std::stringstream ss_tid; ss_tid << std::this_thread::get_id(); - json log = nlohmann::ordered_json{ + json log = json{ {"tid", ss_tid.str()}, {"timestamp", time(nullptr)}, }; if (server_log_json) { - log.merge_patch( { + log.merge_patch({ {"level", level}, {"function", function}, {"line", line}, @@ -98,7 +98,7 @@ static inline void server_log(const char *level, const char *function, int line, } std::stringstream ss; ss << buf << " |"; - for (const auto& el : log.items()) + for (const auto & el : log.items()) { const std::string value = el.value().dump(-1, ' ', false, json::error_handler_t::replace); ss << " " << el.key() << "=" << value; From 229ffff872f8ad0d21c997d18ee7a23692ae60a0 Mon Sep 17 00:00:00 2001 From: Ren Xuancheng <jklj077@users.noreply.github.com> Date: Wed, 8 May 2024 20:06:43 +0800 Subject: [PATCH 051/100] llama : add BPE pre-tokenization for Qwen2 (#7114) * Add BPE pre-tokenization for Qwen2. * minor : fixes --------- Co-authored-by: Ren Xuancheng <17811943+jklj077@users.noreply.github.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --- convert-hf-to-gguf-update.py | 1 + convert-hf-to-gguf.py | 3 + llama.cpp | 10 +++ llama.h | 5 +- models/ggml-vocab-qwen2.gguf | Bin 0 -> 5928681 bytes models/ggml-vocab-qwen2.gguf.inp | 106 +++++++++++++++++++++++++++++++ models/ggml-vocab-qwen2.gguf.out | 43 +++++++++++++ tests/CMakeLists.txt | 1 + 8 files changed, 167 insertions(+), 2 deletions(-) create mode 100644 models/ggml-vocab-qwen2.gguf create mode 100644 models/ggml-vocab-qwen2.gguf.inp create mode 100644 models/ggml-vocab-qwen2.gguf.out diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index b51010983..a26f45a5f 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -67,6 +67,7 @@ models = [ {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, + {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, ] diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 8b89575d5..454e9fcdd 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -314,6 +314,9 @@ class Model(ABC): if chkhsh == "9c2227e4dd922002fb81bde4fc02b0483ca4f12911410dee2255e4987644e3f8": # ref: https://huggingface.co/CohereForAI/c4ai-command-r-v01 res = "command-r" + if chkhsh == "e636dc30a262dcc0d8c323492e32ae2b70728f4df7dfe9737d9f920a282b8aea": + # ref: https://huggingface.co/Qwen/Qwen1.5-7B + res = "qwen2" if chkhsh == "b6dc8df998e1cfbdc4eac8243701a65afe638679230920b50d6f17d81c098166": # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf res = "olmo" diff --git a/llama.cpp b/llama.cpp index 331c9d472..9c72d118f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4391,6 +4391,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "command-r") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_COMMAND_R; + } else if ( + tokenizer_pre == "qwen2") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; } else if ( tokenizer_pre == "olmo") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; @@ -12263,6 +12266,13 @@ struct llm_tokenizer_bpe { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }); break; + case LLAMA_VOCAB_PRE_TYPE_QWEN2: + word_collection = unicode_regex_split(text, { + // original regex from tokenizer.json + // "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }); + break; default: // default regex for BPE tokenization pre-processing word_collection = unicode_regex_split(text, { diff --git a/llama.h b/llama.h index 388839f39..0b2e708d0 100644 --- a/llama.h +++ b/llama.h @@ -81,8 +81,9 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, - LLAMA_VOCAB_PRE_TYPE_OLMO = 10, - 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zbpQDDz1O!nFaPA|oz?#FdbPiLdwuW0op<i7_D@a@ROawBrPqhI-+TS#z190}S3pg> zu2I$A>ec5R*fsv@YEQpH`ddebr|ZL0^-Ec;+GSModG#Ll^C61lx9+YF*T?&(2S<mu T9vmOte{lL0BiVI$+0Fj~?G9pM literal 0 HcmV?d00001 diff --git a/models/ggml-vocab-qwen2.gguf.inp b/models/ggml-vocab-qwen2.gguf.inp new file mode 100644 index 000000000..0a89107c6 --- /dev/null +++ b/models/ggml-vocab-qwen2.gguf.inp @@ -0,0 +1,106 @@ +ied 4 ½ months +__ggml_vocab_test__ +Führer +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + +__ggml_vocab_test__ + + +__ggml_vocab_test__ + + + +__ggml_vocab_test__ + + + + +__ggml_vocab_test__ + + +__ggml_vocab_test__ +Hello world +__ggml_vocab_test__ + Hello world +__ggml_vocab_test__ +Hello World +__ggml_vocab_test__ + Hello World +__ggml_vocab_test__ + Hello World! +__ggml_vocab_test__ +Hello, world! +__ggml_vocab_test__ + Hello, world! +__ggml_vocab_test__ + this is 🦙.cpp +__ggml_vocab_test__ +w048 7tuijk dsdfhu +__ggml_vocab_test__ +нещо на Български +__ggml_vocab_test__ +កាន់តែពិសេសអាចខលចេញ +__ggml_vocab_test__ +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token) +__ggml_vocab_test__ +Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello +__ggml_vocab_test__ + Hello + Hello +__ggml_vocab_test__ + ( +__ggml_vocab_test__ + + = +__ggml_vocab_test__ +' era +__ggml_vocab_test__ +Hello, y'all! How are you 😁 ?我想在apple工作1314151天～ +__ggml_vocab_test__ +3 +__ggml_vocab_test__ +33 +__ggml_vocab_test__ +333 +__ggml_vocab_test__ +3333 +__ggml_vocab_test__ +33333 +__ggml_vocab_test__ +333333 +__ggml_vocab_test__ +3333333 +__ggml_vocab_test__ +33333333 +__ggml_vocab_test__ +333333333 +__ggml_vocab_test__ + + + + + + + + + + + +🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL +__ggml_vocab_test__ diff --git a/models/ggml-vocab-qwen2.gguf.out b/models/ggml-vocab-qwen2.gguf.out new file mode 100644 index 000000000..401a510e8 --- /dev/null +++ b/models/ggml-vocab-qwen2.gguf.out @@ -0,0 +1,43 @@ + 1122 220 19 220 26062 3951 + 37 50753 261 + + 220 + 256 + 262 + 197 + 198 + 271 + 1406 + 1572 + 9707 1879 + 21927 1879 + 9707 4337 + 21927 4337 + 21927 4337 0 + 9707 11 1879 0 + 21927 11 1879 0 + 419 374 11162 99 247 13 10821 + 86 15 19 23 220 22 83 1963 41808 11472 2940 16739 + 78762 14144 1456 13073 63471 33594 3038 133178 79012 + 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 147805 148301 147270 44258 223 146848 + 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 320 3243 42365 429 702 1181 1828 3950 8 + 9707 + 21927 + 220 21927 + 256 21927 + 262 21927 + 262 21927 198 262 21927 + 320 + 198 284 + 6 11385 + 9707 11 379 64848 0 2585 525 498 26525 223 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 + 18 + 18 18 + 18 18 18 + 18 18 18 18 + 18 18 18 18 18 + 18 18 18 18 18 18 + 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 + 18 18 18 18 18 18 18 18 18 + 198 4710 14731 65497 7847 1572 2303 78672 10947 145836 320 8252 8 26525 114 378 235 149921 30543 320 35673 99066 97534 8 25521 227 11162 99 247 149955 220 18 220 18 18 220 18 18 18 220 18 18 18 18 220 18 18 18 18 18 220 18 18 18 18 18 18 220 18 18 18 18 18 18 18 220 18 18 18 18 18 18 18 18 220 18 13 18 220 18 496 18 220 18 1112 18 220 146394 97529 241 44258 233 146568 44258 224 147603 20879 115 146280 44258 223 146280 147272 97529 227 144534 937 104100 18493 22377 99257 16 18 16 19 16 20 16 35727 21216 55460 53237 18658 14144 1456 13073 63471 33594 3038 133178 79012 3355 4605 4605 13874 13874 73594 3014 3014 28149 17085 2928 26610 7646 358 3003 1012 364 83 813 566 594 1052 11 364 787 498 2704 30 364 44 537 2704 358 3278 1281 432 11 364 35 498 1075 1045 15243 30 1205 6 42612 264 63866 43 diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 208f0cf7b..d409a1d6b 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -84,6 +84,7 @@ llama_test(test-tokenizer-0 NAME test-tokenizer-0-starcoder ARGS ${CMAKE llama_test(test-tokenizer-0 NAME test-tokenizer-0-gpt-2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-gpt-2.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-refact ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-refact.gguf) llama_test(test-tokenizer-0 NAME test-tokenizer-0-command-r ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-command-r.gguf) +llama_test(test-tokenizer-0 NAME test-tokenizer-0-qwen2 ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-qwen2.gguf) # build test-tokenizer-1-bpe target once and add many tests add_executable(test-tokenizer-1-bpe test-tokenizer-1-bpe.cpp) From ad211edef5db1f1fb955874b7ca6a67bd0c88708 Mon Sep 17 00:00:00 2001 From: 20kdc <asdd2808@gmail.com> Date: Wed, 8 May 2024 13:22:32 +0100 Subject: [PATCH 052/100] convert.py : --vocab-only generates false but valid params (#7027) An example of how this might be used in the style of baby-llama will be attached with this PR. --- convert.py | 47 ++++++++++++++++++++++++++++++----------------- 1 file changed, 30 insertions(+), 17 deletions(-) diff --git a/convert.py b/convert.py index 7f0b6b749..aebfc50f3 100755 --- a/convert.py +++ b/convert.py @@ -1508,25 +1508,27 @@ def main(args_in: list[str] | None = None) -> None: if args.big_endian: endianess = gguf.GGUFEndian.BIG - params = Params.load(model_plus) - if params.n_ctx == -1: - if args.ctx is None: - msg = """\ - The model doesn't have a context size, and you didn't specify one with --ctx - Please specify one with --ctx: - - LLaMA v1: --ctx 2048 - - LLaMA v2: --ctx 4096""" - parser.error(textwrap.dedent(msg)) - params.n_ctx = args.ctx + params = None + if args.pad_vocab or not args.vocab_only: + params = Params.load(model_plus) + if params.n_ctx == -1: + if args.ctx is None: + msg = """\ + The model doesn't have a context size, and you didn't specify one with --ctx + Please specify one with --ctx: + - LLaMA v1: --ctx 2048 + - LLaMA v2: --ctx 4096""" + parser.error(textwrap.dedent(msg)) + params.n_ctx = args.ctx - if args.outtype: - params.ftype = { - "f32": GGMLFileType.AllF32, - "f16": GGMLFileType.MostlyF16, - "q8_0": GGMLFileType.MostlyQ8_0, - }[args.outtype] + if args.outtype: + params.ftype = { + "f32": GGMLFileType.AllF32, + "f16": GGMLFileType.MostlyF16, + "q8_0": GGMLFileType.MostlyQ8_0, + }[args.outtype] - logger.info(f"params = {params}") + logger.info(f"params = {params}") model_parent_path = model_plus.paths[0].parent vocab_path = Path(args.vocab_dir or args.model or model_parent_path) @@ -1539,6 +1541,17 @@ def main(args_in: list[str] | None = None) -> None: if not args.outfile: raise ValueError("need --outfile if using --vocab-only") outfile = args.outfile + if params is None: + params = Params( + n_vocab = vocab.vocab_size, + n_embd = 1, + n_layer = 1, + n_ctx = 1, + n_ff = 1, + n_head = 1, + n_head_kv = 1, + f_norm_eps = 1e-5, + ) OutputFile.write_vocab_only(outfile, params, vocab, special_vocab, endianess=endianess, pad_vocab=args.pad_vocab) logger.info(f"Wrote {outfile}") From 911b3900dded9a1cfe0f0e41b82c7a29baf3a217 Mon Sep 17 00:00:00 2001 From: Johan <JohanAR@users.noreply.github.com> Date: Wed, 8 May 2024 14:27:58 +0200 Subject: [PATCH 053/100] server : add_special option for tokenize endpoint (#7059) --- examples/server/README.md | 2 +- examples/server/server.cpp | 3 +- examples/server/tests/features/server.feature | 14 +++++++- examples/server/tests/features/steps/steps.py | 36 ++++++++++++++++--- 4 files changed, 48 insertions(+), 7 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index a7c3f0b5f..650317991 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -331,7 +331,7 @@ Notice that each `probs` is an array of length `n_probs`. `content`: Set the text to tokenize. - Note that a special `BOS` token is never inserted. + `add_special`: Boolean indicating if special tokens, i.e. `BOS`, should be inserted. Default: `false` - **POST** `/detokenize`: Convert tokens to text. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 85ae1ad96..06c0be567 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -3647,7 +3647,8 @@ int main(int argc, char ** argv) { std::vector<llama_token> tokens; if (body.count("content") != 0) { - tokens = ctx_server.tokenize(body["content"], false); + const bool add_special = json_value(body, "add_special", false); + tokens = ctx_server.tokenize(body["content"], add_special); } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 646a4e49d..d21c09135 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -7,6 +7,7 @@ Feature: llama.cpp server And a model file tinyllamas/stories260K.gguf from HF repo ggml-org/models And a model file test-model.gguf And a model alias tinyllama-2 + And BOS token is 1 And 42 as server seed # KV Cache corresponds to the total amount of tokens # that can be stored across all independent sequences: #4130 @@ -91,7 +92,18 @@ Feature: llama.cpp server """ What is the capital of France ? """ - Then tokens can be detokenize + Then tokens can be detokenized + And tokens do not begin with BOS + + Scenario: Tokenize w/ BOS + Given adding special tokens + When tokenizing: + """ + What is the capital of Germany? + """ + Then tokens begin with BOS + Given first token is removed + Then tokens can be detokenized Scenario: Models available Given available models diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index b8dbef21d..0882a5d36 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -376,6 +376,11 @@ def step_seed(context, seed): context.seed.append(seed) +@step('BOS token is {bos:d}') +def step_bos_token(context, bos): + context.bos = bos + + @step('a prefix prompt') def step_prompt_prefix(context): context.prompt_prefix = context_text(context) @@ -656,21 +661,29 @@ async def all_embeddings_are_generated(context): assert_embeddings(context.tasks_result.pop().pop()) +@step('adding special tokens') +def step_tokenize_set_add_special(context): + context.tokenize_add_special = True + + @step('tokenizing') @async_run_until_complete async def step_tokenize(context): context.tokenized_text = context_text(context) async with aiohttp.ClientSession() as session: + tokenize_args = { + "content": context.tokenized_text, + } + if getattr(context, 'tokenize_add_special', None) is not None: + tokenize_args['add_special'] = context.tokenize_add_special async with session.post(f'{context.base_url}/tokenize', - json={ - "content": context.tokenized_text, - }) as response: + json=tokenize_args) as response: assert response.status == 200 tokenize_json = await response.json() context.tokens = tokenize_json['tokens'] -@step('tokens can be detokenize') +@step('tokens can be detokenized') @async_run_until_complete async def step_detokenize(context): assert len(context.tokens) > 0 @@ -685,6 +698,21 @@ async def step_detokenize(context): assert context.tokenized_text == detokenize_json['content'].strip() +@step('tokens begin with BOS') +def step_strings_for_tokenization(context): + assert context.tokens[0] == context.bos + + +@step('tokens do not begin with BOS') +def step_strings_for_tokenization(context): + assert context.tokens[0] != context.bos + + +@step('first token is removed') +def step_strings_for_tokenization(context): + context.tokens = context.tokens[1:] + + @step('an OPTIONS request is sent from {origin}') @async_run_until_complete async def step_options_request(context, origin): From 465263d0cf1e8f8bc41948332dbd009d27a68590 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 8 May 2024 14:29:23 +0000 Subject: [PATCH 054/100] sgemm : AVX Q4_0 and Q8_0 (#6891) * basic avx implementation * style * combine denibble with load * reduce 256 to 128 (and back!) conversions * sse load * Update sgemm.cpp * oops oops --- sgemm.cpp | 77 ++++++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 56 insertions(+), 21 deletions(-) diff --git a/sgemm.cpp b/sgemm.cpp index 4e0159804..40ba9d7e9 100644 --- a/sgemm.cpp +++ b/sgemm.cpp @@ -1,6 +1,3 @@ -// -*- mode:c++;indent-tabs-mode:nil;c-basic-offset:4;coding:utf-8 -*- -// vi: set et ft=c++ ts=4 sts=4 sw=4 fenc=utf-8 :vi -// // Copyright 2024 Mozilla Foundation // // Permission is hereby granted, free of charge, to any person obtaining @@ -585,15 +582,15 @@ class tinyBLAS_Q0_ARM { }; #endif // __ARM_FEATURE_DOTPROD -#if defined(__AVX2__) || defined(__AVX512F__) +#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) template <typename TA, typename TB, typename TC> -class tinyBLAS_Q0_AVX2 { +class tinyBLAS_Q0_AVX { public: - tinyBLAS_Q0_AVX2(int64_t k, - const TA *A, int64_t lda, - const TB *B, int64_t ldb, - TC *C, int64_t ldc, - int ith, int nth) + tinyBLAS_Q0_AVX(int64_t k, + const TA *A, int64_t lda, + const TB *B, int64_t ldb, + TC *C, int64_t ldc, + int ith, int nth) : A(A), B(B), C(C), k(k), lda(lda), ldb(ldb), ldc(ldc), ith(ith), nth(nth) { } @@ -728,14 +725,34 @@ class tinyBLAS_Q0_AVX2 { __m256 Cv[RN][RM] = {}; for (int64_t l = 0; l < k; ++l) for (int64_t j = 0; j < RN; ++j) - for (int64_t i = 0; i < RM; ++i) + for (int64_t i = 0; i < RM; ++i) { +#if defined(__AVX2__) + __m256 udTmp = updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l), + load(A + lda * (ii + i) + l)), + _mm256_sign_epi8(load(B + ldb * (jj + j) + l), + load(A + lda * (ii + i) + l))); +#else + __m128i ali0 = load0(A + lda * (ii + i) + l); + __m128i ali1 = load1(A + lda * (ii + i) + l); + __m128i blj0 = load0(B + ldb * (jj + j) + l); + __m128i blj1 = load1(B + ldb * (jj + j) + l); + + __m128i sepAA0 = _mm_sign_epi8(ali0, ali0); + __m128i sepAA1 = _mm_sign_epi8(ali1, ali1); + __m128i sepBA0 = _mm_sign_epi8(blj0, ali0); + __m128i sepBA1 = _mm_sign_epi8(blj1, ali1); + + // updot + const __m128i oneFill = _mm_set1_epi16(1); + __m128i mad0 = _mm_maddubs_epi16(sepAA0, sepBA0); + __m128i mad1 = _mm_maddubs_epi16(sepAA1, sepBA1); + __m256 udTmp = _mm256_cvtepi32_ps(MM256_SET_M128I(_mm_madd_epi16(oneFill, mad1), _mm_madd_epi16(oneFill, mad0))); +#endif Cv[j][i] = madd(_mm256_set1_ps(unhalf(A[lda * (ii + i) + l].d) * unhalf(B[ldb * (jj + j) + l].d)), - updot(_mm256_sign_epi8(load(A + lda * (ii + i) + l), - load(A + lda * (ii + i) + l)), - _mm256_sign_epi8(load(B + ldb * (jj + j) + l), - load(A + lda * (ii + i) + l))), - Cv[j][i]); + udTmp, + Cv[j][i]); + } for (int64_t j = 0; j < RN; ++j) for (int64_t i = 0; i < RM; ++i) C[ldc * (jj + j) + (ii + i)] = hsum(Cv[j][i]); @@ -746,10 +763,28 @@ class tinyBLAS_Q0_AVX2 { return _mm256_loadu_si256((const __m256i *)b->qs); } + inline __m128i load0(const block_q8_0 *b) { + return _mm_loadu_si128((const __m128i *)b->qs); + } + + inline __m128i load1(const block_q8_0 *b) { + return _mm_loadu_si128(((const __m128i *)b->qs) + 1); + } + inline __m256i load(const block_q4_0 *b) { return _mm256_sub_epi8(denibble(b->qs), _mm256_set1_epi8(8)); } + inline __m128i load0(const block_q4_0 *b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), x), _mm_set1_epi8(8)); + } + + inline __m128i load1(const block_q4_0 *b) { + const __m128i x = _mm_loadu_si128((const __m128i *)(b->qs)); + return _mm_sub_epi8(_mm_and_si128(_mm_set1_epi8(15), _mm_srli_epi16(x, 4)), _mm_set1_epi8(8)); + } + inline __m256 updot(__m256i u, __m256i s) { __m256i res; #if defined(__AVXVNNI__) || (defined(__AVX512VNNI__) && defined(__AVX512VL__)) @@ -777,7 +812,7 @@ class tinyBLAS_Q0_AVX2 { const int ith; const int nth; }; -#endif // __AVX2__ +#endif // __AVX__ } // namespace @@ -928,8 +963,8 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda case GGML_TYPE_Q8_0: { if (Btype != GGML_TYPE_Q8_0) return false; -#if defined(__AVX2__) || defined(__AVX512F__) - tinyBLAS_Q0_AVX2<block_q8_0, block_q8_0, float> tb{ +#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) + tinyBLAS_Q0_AVX<block_q8_0, block_q8_0, float> tb{ k, (const block_q8_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, @@ -952,8 +987,8 @@ bool llamafile_sgemm(int64_t m, int64_t n, int64_t k, const void *A, int64_t lda case GGML_TYPE_Q4_0: { if (Btype != GGML_TYPE_Q8_0) return false; -#if defined(__AVX2__) || defined(__AVX512F__) - tinyBLAS_Q0_AVX2<block_q4_0, block_q8_0, float> tb{ +#if defined(__AVX2__) || defined(__AVX512F__) || defined(__AVX__) + tinyBLAS_Q0_AVX<block_q4_0, block_q8_0, float> tb{ k, (const block_q4_0 *)A, lda, (const block_q8_0 *)B, ldb, (float *)C, ldc, From 83330d8cd6491e53e1aca4c5dfc47e039b3c04ff Mon Sep 17 00:00:00 2001 From: Dawid Potocki <github@dawidpotocki.com> Date: Thu, 9 May 2024 02:32:32 +1200 Subject: [PATCH 055/100] main : add --conversation / -cnv flag (#7108) --- common/common.cpp | 5 +++++ common/common.h | 1 + examples/main/main.cpp | 11 +++++++---- 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 467fb014e..4a9da284e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -911,6 +911,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.instruct = true; return true; } + if (arg == "-cnv" || arg == "--conversation") { + params.conversation = true; + return true; + } if (arg == "-cml" || arg == "--chatml") { params.chatml = true; return true; @@ -1417,6 +1421,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); + printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n"); printf(" --multiline-input allows you to write or paste multiple lines without ending each in '\\'\n"); diff --git a/common/common.h b/common/common.h index 9252a4b63..6f00a2cca 100644 --- a/common/common.h +++ b/common/common.h @@ -140,6 +140,7 @@ struct gpt_params { bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode + bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it diff --git a/examples/main/main.cpp b/examples/main/main.cpp index f676ea1ba..49acd6bab 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -362,6 +362,9 @@ int main(int argc, char ** argv) { params.interactive_first = true; params.antiprompt.emplace_back("<|im_start|>user\n"); } + else if (params.conversation) { + params.interactive_first = true; + } // enable interactive mode if interactive start is specified if (params.interactive_first) { @@ -733,7 +736,7 @@ int main(int argc, char ** argv) { // display text if (input_echo && display) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation); printf("%s", token_str.c_str()); if (embd.size() > 1) { @@ -816,7 +819,7 @@ int main(int argc, char ** argv) { if (n_past > 0 && is_interacting) { LOG("waiting for user input\n"); - if (params.instruct || params.chatml) { + if (params.conversation || params.instruct || params.chatml) { printf("\n> "); } @@ -826,7 +829,7 @@ int main(int argc, char ** argv) { } std::string buffer; - if (!params.input_prefix.empty()) { + if (!params.input_prefix.empty() && !params.conversation) { LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); printf("%s", params.input_prefix.c_str()); } @@ -850,7 +853,7 @@ int main(int argc, char ** argv) { // Entering a empty line lets the user pass control back if (buffer.length() > 1) { // append input suffix if any - if (!params.input_suffix.empty()) { + if (!params.input_suffix.empty() && !params.conversation) { LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); printf("%s", params.input_suffix.c_str()); } From 26458af1d63c85195cd96cd1673051e332d06d30 Mon Sep 17 00:00:00 2001 From: Gilad S <giladgd@users.noreply.github.com> Date: Wed, 8 May 2024 22:08:10 +0300 Subject: [PATCH 056/100] metal : use `vm_allocate` instead of `posix_memalign` on macOS (#7078) * fix: use `malloc` instead of `posix_memalign` in `ggml-metal.m` to make it not crash Electron proccesses * fix: typo * fix: use `vm_allocate` instead of `posix_memalign` * fix: don't call `newBufferWithBytesNoCopy` with `NULL` when `ggml_metal_host_malloc` returns `NULL` * fix: use `vm_allocate` only on macOS --- ggml-metal.m | 29 ++++++++++++++++++++++------- 1 file changed, 22 insertions(+), 7 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 78cac5041..c6817f01f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -265,11 +265,20 @@ static void ggml_metal_log(enum ggml_log_level level, const char * format, ...){ static void * ggml_metal_host_malloc(size_t n) { void * data = NULL; + +#if TARGET_OS_OSX + kern_return_t err = vm_allocate((vm_map_t) mach_task_self(), (void *) &data, n, VM_FLAGS_ANYWHERE); + if (err != KERN_SUCCESS) { + GGML_METAL_LOG_ERROR("%s: error: vm_allocate failed\n", __func__); + return NULL; + } +#else const int result = posix_memalign((void **) &data, sysconf(_SC_PAGESIZE), n); if (result != 0) { GGML_METAL_LOG_ERROR("%s: error: posix_memalign failed\n", __func__); return NULL; } +#endif return data; } @@ -2840,7 +2849,11 @@ GGML_CALL static void ggml_backend_metal_buffer_free_buffer(ggml_backend_buffer_ ggml_backend_metal_free_device(); if (ctx->owned) { +#if TARGET_OS_OSX + vm_deallocate((vm_map_t)mach_task_self(), (vm_address_t)ctx->all_data, ctx->all_size); +#else free(ctx->all_data); +#endif } free(ctx); @@ -2944,14 +2957,16 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_metal_buffer_type_alloc_buff ctx->owned = true; ctx->n_buffers = 1; - ctx->buffers[0].data = ctx->all_data; - ctx->buffers[0].size = size; - ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data - length:size_aligned - options:MTLResourceStorageModeShared - deallocator:nil]; + if (ctx->all_data != NULL) { + ctx->buffers[0].data = ctx->all_data; + ctx->buffers[0].size = size; + ctx->buffers[0].metal = [device newBufferWithBytesNoCopy:ctx->all_data + length:size_aligned + options:MTLResourceStorageModeShared + deallocator:nil]; + } - if (ctx->buffers[0].metal == nil) { + if (ctx->all_data == NULL || ctx->buffers[0].metal == nil) { GGML_METAL_LOG_ERROR("%s: error: failed to allocate buffer, size = %8.2f MiB\n", __func__, size_aligned / 1024.0 / 1024.0); free(ctx); ggml_backend_metal_free_device(); From bd1871fa2b1bf8a081b43ba9bc85f8ffd46fac46 Mon Sep 17 00:00:00 2001 From: JohnnyB <jboero@users.noreply.github.com> Date: Wed, 8 May 2024 20:12:06 +0100 Subject: [PATCH 057/100] server : add themes + favicon (#6848) * Added themes support with two sample themes and a favicon. * Newline * Newline * Newline * Trailing whitespace * Increased opacity for contrast * Increase opacity. Check actions cancelled for some other priority job and I can't seem to manually re-run them, so MOAR OPACITY * Opacity action trigger. Trying to re-trigger the cancelled action. * One more opacity adjustment This Actions pipeline is failing for random issues. * Delete examples/server/themes/buttons_top/completion.js This will be served from the static string built-in to server. * Delete examples/server/themes/buttons_top/index.js This will be served from the static string built-in to server. * Delete examples/server/themes/wild/completion.js This will be served from the static string built-in to server. * Delete examples/server/themes/buttons_top/json-schema-to-grammar.mjs This will be served from the static string built-in to server. * Delete examples/server/themes/wild/index.js This will be served from the static string built-in to server. * Delete examples/server/themes/wild/json-schema-to-grammar.mjs This will be served from the static string built-in to server. * Replaced underscore. --- examples/server/public/favicon.ico | Bin 0 -> 4122 bytes examples/server/themes/README.md | 5 + examples/server/themes/buttons-top/README.md | 7 + .../server/themes/buttons-top/buttons_top.png | Bin 0 -> 119747 bytes .../server/themes/buttons-top/favicon.ico | Bin 0 -> 4122 bytes examples/server/themes/buttons-top/index.html | 1057 ++++++++++++++++ examples/server/themes/wild/README.md | 5 + examples/server/themes/wild/favicon.ico | Bin 0 -> 4122 bytes examples/server/themes/wild/index.html | 1061 +++++++++++++++++ examples/server/themes/wild/llama_cpp.png | Bin 0 -> 76484 bytes examples/server/themes/wild/llamapattern.png | Bin 0 -> 259586 bytes examples/server/themes/wild/wild.png | Bin 0 -> 496463 bytes 12 files changed, 2135 insertions(+) create mode 100644 examples/server/public/favicon.ico create mode 100644 examples/server/themes/README.md create mode 100644 examples/server/themes/buttons-top/README.md create mode 100644 examples/server/themes/buttons-top/buttons_top.png create mode 100644 examples/server/themes/buttons-top/favicon.ico create mode 100644 examples/server/themes/buttons-top/index.html create mode 100644 examples/server/themes/wild/README.md create mode 100644 examples/server/themes/wild/favicon.ico create mode 100644 examples/server/themes/wild/index.html create mode 100644 examples/server/themes/wild/llama_cpp.png create mode 100644 examples/server/themes/wild/llamapattern.png create mode 100644 examples/server/themes/wild/wild.png diff --git a/examples/server/public/favicon.ico b/examples/server/public/favicon.ico new file mode 100644 index 0000000000000000000000000000000000000000..89e154a0a7525ecd19379e8e200e36851c325d31 GIT binary patch literal 4122 zcmV+#5asUx0096206;(h0096X00a;K02TlM0EtjeM-2)Z3IG5A4M|8uQUCw|KmY&$ zKnMl^0063Kaozv`56ekJK~#90#hZDQRMnlwKli=by1TlndI5R?*;+s#g0#D%Q4nxX z#3hqsJQ+MDax^A6nK|fWl8Hy>M1v<eAv$h}CK{8O<3z_0ClSRBK~V%3K<H*ip`p90 z`&BR1ua@`j{BdjTuBxu;F3k6w0`BA8?RS6s?>-R-1SmHQ!vN3#58wlufO=pE*b8(6 z@slxksSxE}gkcyOPzziHTnj7!nt>`H2DAac0$u<H{@z4zlxv{={(daWaslT6{|a1! z;($!JfmYyAAOJiDM3euS;G8U+Yyy>y0}R9P0^b7e0wyJ&YeL|6YSdNg^hazuO$#X{ zz<%IzA;exOr3QF`Iuwy45C?)l$mjD(P1DM<&fyq02P96-3;Z)`^A!N6)wo!FQ6sI* zUMBn9_}vZ$V;1*pILt4%_Sl-H9gfH2Q7Pp_;7Z^+U`}Ez8^syBfJgiK`ZfUO$>4@^ zh=3dTCa@Z)0Kt8iG_dOIM(V4whp%?)2mz=Q2KW+iCrVtLImVs}oR1>x(?FyQ>&?Qr zL?EH?E(HDvGzs9TuTJBph4nadu<Y@HD0A2Dpf_THQi#34#Pn(eO%*zoPC<`p)1%%4 zI1a1;wgdtJNf|L_4oI;6B;dzDBftYon)t@TdK`J=_2stS<mN%0_(Xt)3J3QtY2w1^ zekN2oacB^YOJ4Xi$f}o)05qU1$~H2TW;rk(5pV+kh%$HL(y3M4c1}ay%?HCV?tSCf zi08|uRq@DIW-_yGxWNFgOCLU;7{_{ZVoU@Gg+icg`z>k=!H*VC!SBgG+)I0UIoKD= zdbVs@6~DQzg(-gbi1!a05$<09S+W@hHc;l%2j#7m3@N3KI{DdvxOSG8bEkOozTXp$ z^NUR#S<ie<jR&uoo^$gN%jTzVe$MBE@#Lew0GmlG&$6Lp0%VxI6(ltG%GOD^9r=fP zWv{^}-PsEK{-w>F-cXtI{pUUj^2GLFvUvn}5D1+NGdNc7;uPR)0H;Ie+*7Laz8H>L z{Cv~V?9N^XE9TYZeE-uEVSez}BgtkA_$9ET+}b~hu@FE*+3yJe-&z=;t}>SiS2%1$ z_@FaebiaT8BpRw+Ip2SHO9$O)OYs`;2VK|8F8fK0g#ZrVti<0ep5aF;pqu+6R<`5{ z!KE!VIp6IyEuPyMN;adwQ$Tlhb$$*TFUCTE3KR!`5KOBXX}&U1m2JwnWJ(3I>htuG z5<A_pfF8(vRDP61fWE#yP+@HoK&7tHn1_{<nCy2mrzR~TSD#jsS7G`q94wihQjBg? 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To try any of these add --path to your run like `server --path=wild`. + +![image](wild/wild.png) diff --git a/examples/server/themes/buttons-top/README.md b/examples/server/themes/buttons-top/README.md new file mode 100644 index 000000000..808c4cf81 --- /dev/null +++ b/examples/server/themes/buttons-top/README.md @@ -0,0 +1,7 @@ +# LLaMA.cpp Server Buttons Top Theme + +Simple tweaks to the UI. Chat buttons at the top of the page instead of bottom so you can hit Stop instead of chasing it down the page. + +To use simply run server with `--path=themes/buttons_top` + +![image](buttons_top.png) diff --git a/examples/server/themes/buttons-top/buttons_top.png b/examples/server/themes/buttons-top/buttons_top.png new file mode 100644 index 0000000000000000000000000000000000000000..c544545196f995d9c5f0e26de14df6254324e26f GIT binary patch literal 119747 zcmXt9bx>P<v<y<*+S1~#!J%kyDDKkYRy4&WxKpIX9a;jkcqvlcwP?`b?gV$&m+!rK zGm)A6ap&gTBYSrD_f=C}0Uw7P2LJ%zD=EI$1_02o002}BEDYo?C*H*q$S-UcMFV#L zfZ*SMZ<JJa0tx_t7NGQATG!k3pmi>ualnV==3P5IeKjBZep^%1@20!o<u+yq$qv*@ z;we&`esq5@*p&%WIwAuM&CNVL{|0q_y0V~oi);6DDWnqj5R2T7Kbj1mCn;iG6I0+9 zfuNjV^V~sLK=o0}`dPOF%}~%$j*+*Oyp7y@`Hww>JPpl_&5d){0atU^b6bO?mONoD z)z&c~ycmZ2g95?y>eO2S*|3I%aP8m1*J6aqCG>HKYy`eB%yzT+i&&1}o}KJ>!n^{! zBX_BRXjdtC@AV_SIy~uuzgPE$epkJX$WQ59#0D2q#di5M#aWMJYL`l;cV-|z#_JMW zGu7+%&yQXxdO8V2EMcbnY3>`1)SPL9hX;6Xk9>fotJP-Fw2PCQr^Ruo>Q6)3s9hQ6 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auto; + + border: 1px solid #ccc; + border-radius: 5px; + padding: 0.5em; + } + + body { + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + } + + p { + overflow-wrap: break-word; + word-wrap: break-word; + hyphens: auto; + margin-top: 0.5em; + margin-bottom: 0.5em; + } + + #write form { + margin: 1em 0 0 0; + display: flex; + flex-direction: column; + gap: 0.5em; + align-items: stretch; + } + + .right { + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + } + + fieldset { + border: none; + padding: 0; + margin: 0; + } + + fieldset.two { + display: grid; + grid-template: "a a"; + gap: 1em; + } + + fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + } + + details { + border: 1px solid #aaa; + border-radius: 4px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; + } + + summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; + } + + details[open] { + padding: 0.5em; + } + + .prob-set { + padding: 0.3em; + border-bottom: 1px solid #ccc; + } + + .popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + } + + textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + } + + pre code { + display: block; + background-color: #222; + color: #ddd; + } + + code { + font-family: monospace; + padding: 0.1em 0.3em; + border-radius: 3px; + } + + fieldset label { + margin: 0.5em 0; + display: block; + } + + fieldset label.slim { + margin: 0 0.5em; + display: inline; + } + + header, + footer { + text-align: center; + } + + footer { + font-size: 80%; + color: #888; + } + + .mode-chat textarea[name=prompt] { + height: 4.5em; + } + + .mode-completion textarea[name=prompt] { + height: 10em; + } + + [contenteditable] { + display: inline-block; + white-space: pre-wrap; + outline: 0px solid transparent; + } + + @keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + + 100% { + background-position: 100%; + } + } + + .loading { + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; + } + + @media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } + + .popover-content { + background-color: black; + } + } + </style> + + <script type="module"> + import { + html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component + } from './index.js'; + + import { llama } from './completion.js'; + import { SchemaConverter } from './json-schema-to-grammar.mjs'; + let selected_image = false; + var slot_id = -1; + + const session = signal({ + prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", + template: "{{prompt}}\n\n{{history}}\n{{char}}:", + historyTemplate: "{{name}}: {{message}}", + transcript: [], + type: "chat", // "chat" | "completion" + char: "Llama", + user: "User", + image_selected: '' + }) + + const params = signal({ + n_predict: 400, + temperature: 0.7, + repeat_last_n: 256, // 0 = disable penalty, -1 = context size + repeat_penalty: 1.18, // 1.0 = disabled + penalize_nl: false, + top_k: 40, // <= 0 to use vocab size + top_p: 0.95, // 1.0 = disabled + min_p: 0.05, // 0 = disabled + tfs_z: 1.0, // 1.0 = disabled + typical_p: 1.0, // 1.0 = disabled + presence_penalty: 0.0, // 0.0 = disabled + frequency_penalty: 0.0, // 0.0 = disabled + mirostat: 0, // 0/1/2 + mirostat_tau: 5, // target entropy + mirostat_eta: 0.1, // learning rate + grammar: '', + n_probs: 0, // no completion_probabilities, + min_keep: 0, // min probs from each sampler, + image_data: [], + cache_prompt: true, + api_key: '' + }) + + /* START: Support for storing prompt templates and parameters in browsers LocalStorage */ + + const local_storage_storageKey = "llamacpp_server_local_storage"; + + function local_storage_setDataFromObject(tag, content) { + localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content)); + } + + function local_storage_setDataFromRawText(tag, content) { + localStorage.setItem(local_storage_storageKey + '/' + tag, content); + } + + function local_storage_getDataAsObject(tag) { + const item = localStorage.getItem(local_storage_storageKey + '/' + tag); + if (!item) { + return null; + } else { + return JSON.parse(item); + } + } + + function local_storage_getDataAsRawText(tag) { + const item = localStorage.getItem(local_storage_storageKey + '/' + tag); + if (!item) { + return null; + } else { + return item; + } + } + + // create a container for user templates and settings + + const savedUserTemplates = signal({}) + const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } }) + + // let's import locally saved templates and settings if there are any + // user templates and settings are stored in one object + // in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" } + + console.log('Importing saved templates') + + let importedTemplates = local_storage_getDataAsObject('user_templates') + + if (importedTemplates) { + // saved templates were successfully imported. + + console.log('Processing saved templates and updating default template') + params.value = { ...params.value, image_data: [] }; + + //console.log(importedTemplates); + savedUserTemplates.value = importedTemplates; + + //override default template + savedUserTemplates.value.default = { session: session.value, params: params.value } + local_storage_setDataFromObject('user_templates', savedUserTemplates.value) + } else { + // no saved templates detected. + + console.log('Initializing LocalStorage and saving default template') + + savedUserTemplates.value = { "default": { session: session.value, params: params.value } } + local_storage_setDataFromObject('user_templates', savedUserTemplates.value) + } + + function userTemplateResetToDefault() { + console.log('Resetting template to default') + selectedUserTemplate.value.name = 'default'; + selectedUserTemplate.value.data = savedUserTemplates.value['default']; + } + + function userTemplateApply(t) { + session.value = t.data.session; + session.value = { ...session.value, image_selected: '' }; + params.value = t.data.params; + params.value = { ...params.value, image_data: [] }; + } + + function userTemplateResetToDefaultAndApply() { + userTemplateResetToDefault() + userTemplateApply(selectedUserTemplate.value) + } + + function userTemplateLoadAndApplyAutosaved() { + // get autosaved last used template + let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last') + + if (lastUsedTemplate) { + + console.log('Autosaved template found, restoring') + + selectedUserTemplate.value = lastUsedTemplate + } + else { + + console.log('No autosaved template found, using default template') + // no autosaved last used template was found, so load from default. + + userTemplateResetToDefault() + } + + console.log('Applying template') + // and update internal data from templates + + userTemplateApply(selectedUserTemplate.value) + } + + //console.log(savedUserTemplates.value) + //console.log(selectedUserTemplate.value) + + function userTemplateAutosave() { + console.log('Template Autosave...') + if (selectedUserTemplate.value.name == 'default') { + // we don't want to save over default template, so let's create a new one + let newTemplateName = 'UserTemplate-' + Date.now().toString() + let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } } + + console.log('Saving as ' + newTemplateName) + + // save in the autosave slot + local_storage_setDataFromObject('user_templates_last', newTemplate) + + // and load it back and apply + userTemplateLoadAndApplyAutosaved() + } else { + local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } }) + } + } + + console.log('Checking for autosaved last used template') + userTemplateLoadAndApplyAutosaved() + + /* END: Support for storing prompt templates and parameters in browsers LocalStorage */ + + const llamaStats = signal(null) + const controller = signal(null) + + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? + const chatStarted = computed(() => session.value.transcript.length > 0) + + const transcriptUpdate = (transcript) => { + session.value = { + ...session.value, + transcript + } + } + + // simple template replace + const template = (str, extraSettings) => { + let settings = session.value; + if (extraSettings) { + settings = { ...settings, ...extraSettings }; + } + return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key])); + } + + async function runLlama(prompt, llamaParams, char) { + const currentMessages = []; + const history = session.value.transcript; + if (controller.value) { + throw new Error("already running"); + } + controller.value = new AbortController(); + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) { + const data = chunk.data; + + if (data.stop) { + while ( + currentMessages.length > 0 && + currentMessages[currentMessages.length - 1].content.match(/\n$/) != null + ) { + currentMessages.pop(); + } + transcriptUpdate([...history, [char, currentMessages]]) + console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); + } else { + currentMessages.push(data); + slot_id = data.slot_id; + if (selected_image && !data.multimodal) { + alert("The server was not compiled for multimodal or the model projector can't be loaded."); + return; + } + transcriptUpdate([...history, [char, currentMessages]]) + } + + if (data.timings) { + llamaStats.value = data; + } + } + + controller.value = null; + } + + // send message to server + const chat = async (msg) => { + if (controller.value) { + console.log('already running...'); + return; + } + + transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) + + let prompt = template(session.value.template, { + message: msg, + history: session.value.transcript.flatMap( + ([name, data]) => + template( + session.value.historyTemplate, + { + name, + message: Array.isArray(data) ? + data.map(msg => msg.content).join('').replace(/^\s/, '') : + data, + } + ) + ).join("\n"), + }); + if (selected_image) { + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; + } + await runLlama(prompt, { + ...params.value, + slot_id: slot_id, + stop: ["</s>", template("{{char}}:"), template("{{user}}:")], + }, "{{char}}"); + } + + const runCompletion = () => { + if (controller.value) { + console.log('already running...'); + return; + } + const { prompt } = session.value; + transcriptUpdate([...session.value.transcript, ["", prompt]]); + runLlama(prompt, { + ...params.value, + slot_id: slot_id, + stop: [], + }, "").finally(() => { + session.value.prompt = session.value.transcript.map(([_, data]) => + Array.isArray(data) ? data.map(msg => msg.content).join('') : data + ).join(''); + session.value.transcript = []; + }) + } + + const stop = (e) => { + e.preventDefault(); + if (controller.value) { + controller.value.abort(); + controller.value = null; + } + } + + const reset = (e) => { + stop(e); + transcriptUpdate([]); + } + + const uploadImage = (e) => { + e.preventDefault(); + document.getElementById("fileInput").click(); + document.getElementById("fileInput").addEventListener("change", function (event) { + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + session.value = { ...session.value, image_selected: image_data }; + params.value = { + ...params.value, image_data: [ + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] + } + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); + } + + function MessageInput() { + const message = useSignal("") + + const submit = (e) => { + stop(e); + chat(message.value); + message.value = ""; + } + + const enterSubmits = (event) => { + if (event.which === 13 && !event.shiftKey) { + submit(event); + } + } + + return html` + <form onsubmit=${submit}> + <div> + <textarea + className=${generating.value ? "loading" : null} + oninput=${(e) => message.value = e.target.value} + onkeypress=${enterSubmits} + placeholder="Say something..." + rows=2 + type="text" + value="${message}" + /> + </div> + <div class="right"> + <button type="submit" disabled=${generating.value}>Send</button> + <button onclick=${uploadImage}>Upload Image</button> + <button onclick=${stop} disabled=${!generating.value}>Stop</button> + <button onclick=${reset}>Reset</button> + </div> + </form> + ` + } + + function CompletionControls() { + const submit = (e) => { + stop(e); + runCompletion(); + } + return html` + <div> + <button onclick=${submit} type="button" disabled=${generating.value}>Start</button> + <button onclick=${stop} disabled=${!generating.value}>Stop</button> + <button onclick=${reset}>Reset</button> + </div>`; + } + + const ChatLog = (props) => { + const messages = session.value.transcript; + const container = useRef(null) + + useEffect(() => { + // scroll to bottom (if needed) + const parent = container.current.parentElement; + if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) { + parent.scrollTo(0, parent.scrollHeight) + } + }, [messages]) + + const isCompletionMode = session.value.type === 'completion' + const chatLine = ([user, data], index) => { + let message + const isArrayMessage = Array.isArray(data) + if (params.value.n_probs > 0 && isArrayMessage) { + message = html`<${Probabilities} data=${data} />` + } else { + const text = isArrayMessage ? + data.map(msg => msg.content).join('').replace(/^\s+/, '') : + data; + message = isCompletionMode ? + text : + html`<${Markdownish} text=${template(text)} />` + } + if (user) { + return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>` + } else { + return isCompletionMode ? + html`<span key=${index}>${message}</span>` : + html`<p key=${index}>${message}</p>` + } + }; + + const handleCompletionEdit = (e) => { + session.value.prompt = e.target.innerText; + session.value.transcript = []; + } + + return html` + <div id="chat" ref=${container} key=${messages.length}> + <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/> + <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}> + ${messages.flatMap(chatLine)} + </span> + </div>`; + }; + + const ConfigForm = (props) => { + const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value } + const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value } + const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) } + const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) } + const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked } + + const grammarJsonSchemaPropOrder = signal('') + const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value + const convertJSONSchemaGrammar = async () => { + try { + let schema = JSON.parse(params.value.grammar) + const converter = new SchemaConverter({ + prop_order: grammarJsonSchemaPropOrder.value + .split(',') + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}), + allow_fetch: true, + }) + schema = await converter.resolveRefs(schema, 'input') + converter.visit(schema, '') + params.value = { + ...params.value, + grammar: converter.formatGrammar(), + } + } catch (e) { + alert(`Convert failed: ${e.message}`) + } + } + + const FloatField = ({ label, max, min, name, step, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} /> + <span>${value}</span> + </div> + ` + }; + + const IntField = ({ label, max, min, name, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} /> + <span>${value}</span> + </div> + ` + }; + + const BoolField = ({ label, name, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} /> + </div> + ` + }; + + const userTemplateReset = (e) => { + e.preventDefault(); + userTemplateResetToDefaultAndApply() + } + + const UserTemplateResetButton = () => { + if (selectedUserTemplate.value.name == 'default') { + return html` + <button disabled>Using default template</button> + ` + } + + return html` + <button onclick=${userTemplateReset}>Reset all to default</button> + ` + }; + + useEffect(() => { + // autosave template on every change + userTemplateAutosave() + }, [session.value, params.value]) + + const GrammarControl = () => ( + html` + <div> + <label for="template">Grammar</label> + <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/> + <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} /> + <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button> + </div> + ` + ); + + const PromptControlFieldSet = () => ( + html` + <fieldset> + <div> + <label htmlFor="prompt">Prompt</label> + <textarea type="text" name="prompt" value="${session.value.prompt}" oninput=${updateSession}/> + </div> + </fieldset> + ` + ); + + const ChatConfigForm = () => ( + html` + ${PromptControlFieldSet()} + + <fieldset class="two"> + <div> + <label for="user">User name</label> + <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} /> + </div> + + <div> + <label for="bot">Bot name</label> + <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} /> + </div> + </fieldset> + + <fieldset> + <div> + <label for="template">Prompt template</label> + <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/> + </div> + + <div> + <label for="template">Chat history template</label> + <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/> + </div> + ${GrammarControl()} + </fieldset> + ` + ); + + const CompletionConfigForm = () => ( + html` + ${PromptControlFieldSet()} + <fieldset>${GrammarControl()}</fieldset> + ` + ); + + return html` + <form> + <fieldset class="two"> + <${UserTemplateResetButton}/> + <div> + <label class="slim"><input type="radio" name="type" value="chat" checked=${session.value.type === "chat"} oninput=${updateSession} /> Chat</label> + <label class="slim"><input type="radio" name="type" value="completion" checked=${session.value.type === "completion"} oninput=${updateSession} /> Completion</label> + </div> + </fieldset> + + ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()} + + <fieldset class="two"> + ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} + ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} + ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} + ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })} + ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} + ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} + ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} + </fieldset> + <details> + <summary>More options</summary> + <fieldset class="two"> + ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} + ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} + ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + </fieldset> + <hr /> + <fieldset class="three"> + <div> + <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label> + <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label> + <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label> + </div> + ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} + ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })} + </fieldset> + <fieldset> + ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })} + </fieldset> + <fieldset> + ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} + </fieldset> + <fieldset> + <label for="api_key">API Key</label> + <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} /> + </fieldset> + </details> + </form> + ` + } + + const probColor = (p) => { + const r = Math.floor(192 * (1 - p)); + const g = Math.floor(192 * p); + return `rgba(${r},${g},0,0.3)`; + } + + const Probabilities = (params) => { + return params.data.map(msg => { + const { completion_probabilities } = msg; + if ( + !completion_probabilities || + completion_probabilities.length === 0 + ) return msg.content + + if (completion_probabilities.length > 1) { + // Not for byte pair + if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content + + const splitData = completion_probabilities.map(prob => ({ + content: prob.content, + completion_probabilities: [prob] + })) + return html`<${Probabilities} data=${splitData} />` + } + + const { probs, content } = completion_probabilities[0] + const found = probs.find(p => p.tok_str === msg.content) + const pColor = found ? probColor(found.prob) : 'transparent' + + const popoverChildren = html` + <div class="prob-set"> + ${probs.map((p, index) => { + return html` + <div + key=${index} + title=${`prob: ${p.prob}`} + style=${{ + padding: '0.3em', + backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent' + }} + > + <span>${p.tok_str}: </span> + <span>${Math.floor(p.prob * 100)}%</span> + </div> + ` + })} + </div> + ` + + return html` + <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}> + ${msg.content.match(/\n/gim) ? html`<br />` : msg.content} + </> + ` + }); + } + + // poor mans markdown replacement + const Markdownish = (params) => { + const md = params.text + .replace(/&/g, '&amp;') + .replace(/</g, '&lt;') + .replace(/>/g, '&gt;') + .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>') + .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>') + .replace(/__(.*?)__/g, '<strong>$1</strong>') + .replace(/\*(.*?)\*/g, '<em>$1</em>') + .replace(/_(.*?)_/g, '<em>$1</em>') + .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>') + .replace(/`(.*?)`/g, '<code>$1</code>') + .replace(/\n/gim, '<br />'); + return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`; + }; + + const ModelGenerationInfo = (params) => { + if (!llamaStats.value) { + return html`<span/>` + } + return html` + <span> + ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second + </span> + ` + } + + // simple popover impl + const Popover = (props) => { + const isOpen = useSignal(false); + const position = useSignal({ top: '0px', left: '0px' }); + const buttonRef = useRef(null); + const popoverRef = useRef(null); + + const togglePopover = () => { + if (buttonRef.current) { + const rect = buttonRef.current.getBoundingClientRect(); + position.value = { + top: `${rect.bottom + window.scrollY}px`, + left: `${rect.left + window.scrollX}px`, + }; + } + isOpen.value = !isOpen.value; + }; + + const handleClickOutside = (event) => { + if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) { + isOpen.value = false; + } + }; + + useEffect(() => { + document.addEventListener('mousedown', handleClickOutside); + return () => { + document.removeEventListener('mousedown', handleClickOutside); + }; + }, []); + + return html` + <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span> + ${isOpen.value && html` + <${Portal} into="#portal"> + <div + ref=${popoverRef} + class="popover-content" + style=${{ + top: position.value.top, + left: position.value.left, + }} + > + ${props.popoverChildren} + </div> + </${Portal}> + `} + `; + }; + + // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js) + /** Redirect rendering of descendants into the given CSS selector */ + class Portal extends Component { + componentDidUpdate(props) { + for (let i in props) { + if (props[i] !== this.props[i]) { + return setTimeout(this.renderLayer); + } + } + } + + componentDidMount() { + this.isMounted = true; + this.renderLayer = this.renderLayer.bind(this); + this.renderLayer(); + } + + componentWillUnmount() { + this.renderLayer(false); + this.isMounted = false; + if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote); + } + + findNode(node) { + return typeof node === 'string' ? document.querySelector(node) : node; + } + + renderLayer(show = true) { + if (!this.isMounted) return; + + // clean up old node if moving bases: + if (this.props.into !== this.intoPointer) { + this.intoPointer = this.props.into; + if (this.into && this.remote) { + this.remote = render(html`<${PortalProxy} />`, this.into, this.remote); + } + this.into = this.findNode(this.props.into); + } + + this.remote = render(html` + <${PortalProxy} context=${this.context}> + ${show && this.props.children || null} + </${PortalProxy}> + `, this.into, this.remote); + } + + render() { + return null; + } + } + // high-order component that renders its first child if it exists. + // used as a conditional rendering proxy. + class PortalProxy extends Component { + getChildContext() { + return this.props.context; + } + render({ children }) { + return children || null; + } + } + + function App(props) { + useEffect(() => { + const query = new URLSearchParams(location.search).get("q"); + if (query) chat(query); + }, []); + + return html` + <div class="mode-${session.value.type}"> + <header> + <h1>llama.cpp</h1> + </header> + + <section id="write"> + <${session.value.type === 'chat' ? MessageInput : CompletionControls} /> + </section> + + <main id="content"> + <${chatStarted.value ? ChatLog : ConfigForm} /> + </main> + + <footer> + <p><${ModelGenerationInfo} /></p> + <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p> + </footer> + </div> + `; + } + + render(h(App), document.querySelector('#container')); + </script> +</head> + +<body> + <div id="container"> + <input type="file" id="fileInput" accept="image/*" style="display: none;"> + </div> + <div id="portal"></div> +</body> + +</html> + diff --git a/examples/server/themes/wild/README.md b/examples/server/themes/wild/README.md new file mode 100644 index 000000000..560bcc81b --- /dev/null +++ b/examples/server/themes/wild/README.md @@ -0,0 +1,5 @@ +# LLaMA.cpp Server Wild Theme + +Simple tweaks to the UI. 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b/examples/server/themes/wild/index.html new file mode 100644 index 000000000..772e716cd --- /dev/null +++ b/examples/server/themes/wild/index.html @@ -0,0 +1,1061 @@ +<html> + +<head> + <meta charset="UTF-8"> + <meta name="viewport" content="width=device-width, initial-scale=1, maximum-scale=1" /> + <meta name="color-scheme" content="light dark"> + <title>llama.cpp - chat</title> + + <style> + body { + font-family: system-ui; + font-size: 90%; + background-image: url('llamapattern.png'); + } + + #container { + margin: 0em auto; + display: flex; + flex-direction: column; + justify-content: space-between; + height: 100%; + } + + main { + margin: 3px; + display: flex; + flex-direction: column; + justify-content: space-between; + gap: 1em; + + flex-grow: 1; + overflow-y: auto; + + border: 1px solid #ccc; + border-radius: 5px; + padding: 0.5em; + + background-color: rgba(255,255,255,0.9); + } + + body { + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + } + + p { + overflow-wrap: break-word; + word-wrap: break-word; + hyphens: auto; + margin-top: 0.5em; + margin-bottom: 0.5em; + } + + #write form { + margin: 1em 0 0 0; + display: flex; + flex-direction: column; + gap: 0.5em; + align-items: stretch; + } + + .right { + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + } + + fieldset { + border: none; + padding: 0; + margin: 0; + } + + fieldset.two { + display: grid; + grid-template: "a a"; + gap: 1em; + } + + fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + } + + details { + border: 1px solid #aaa; + border-radius: 4px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; + } + + summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; + } + + details[open] { + padding: 0.5em; + } + + .prob-set { + padding: 0.3em; + border-bottom: 1px solid #ccc; + } + + .popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 10px rgba(0, 0, 0, 0.1); + } + + textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + } + + pre code { + display: block; + background-color: #222; + color: #ddd; + } + + code { + font-family: monospace; + padding: 0.1em 0.3em; + border-radius: 3px; + } + + fieldset label { + margin: 0.5em 0; + display: block; + } + + fieldset label.slim { + margin: 0 0.5em; + display: inline; + } + + header, + footer { + text-align: center; + } + + footer { + font-size: 80%; + color: #888; + } + + .mode-chat textarea[name=prompt] { + height: 4.5em; + } + + .mode-completion textarea[name=prompt] { + height: 10em; + } + + [contenteditable] { + display: inline-block; + white-space: pre-wrap; + outline: 0px solid transparent; + } + + @keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + + 100% { + background-position: 100%; + } + } + + .loading { + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; + } + + @media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } + + .popover-content { + background-color: black; + } + } + </style> + + <script type="module"> + import { + html, h, signal, effect, computed, render, useSignal, useEffect, useRef, Component + } from './index.js'; + + import { llama } from './completion.js'; + import { SchemaConverter } from './json-schema-to-grammar.mjs'; + let selected_image = false; + var slot_id = -1; + + const session = signal({ + prompt: "This is a conversation between User and Llama, a friendly chatbot. Llama is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision.", + template: "{{prompt}}\n\n{{history}}\n{{char}}:", + historyTemplate: "{{name}}: {{message}}", + transcript: [], + type: "chat", // "chat" | "completion" + char: "Llama", + user: "User", + image_selected: '' + }) + + const params = signal({ + n_predict: 400, + temperature: 0.7, + repeat_last_n: 256, // 0 = disable penalty, -1 = context size + repeat_penalty: 1.18, // 1.0 = disabled + penalize_nl: false, + top_k: 40, // <= 0 to use vocab size + top_p: 0.95, // 1.0 = disabled + min_p: 0.05, // 0 = disabled + tfs_z: 1.0, // 1.0 = disabled + typical_p: 1.0, // 1.0 = disabled + presence_penalty: 0.0, // 0.0 = disabled + frequency_penalty: 0.0, // 0.0 = disabled + mirostat: 0, // 0/1/2 + mirostat_tau: 5, // target entropy + mirostat_eta: 0.1, // learning rate + grammar: '', + n_probs: 0, // no completion_probabilities, + min_keep: 0, // min probs from each sampler, + image_data: [], + cache_prompt: true, + api_key: '' + }) + + /* START: Support for storing prompt templates and parameters in browsers LocalStorage */ + + const local_storage_storageKey = "llamacpp_server_local_storage"; + + function local_storage_setDataFromObject(tag, content) { + localStorage.setItem(local_storage_storageKey + '/' + tag, JSON.stringify(content)); + } + + function local_storage_setDataFromRawText(tag, content) { + localStorage.setItem(local_storage_storageKey + '/' + tag, content); + } + + function local_storage_getDataAsObject(tag) { + const item = localStorage.getItem(local_storage_storageKey + '/' + tag); + if (!item) { + return null; + } else { + return JSON.parse(item); + } + } + + function local_storage_getDataAsRawText(tag) { + const item = localStorage.getItem(local_storage_storageKey + '/' + tag); + if (!item) { + return null; + } else { + return item; + } + } + + // create a container for user templates and settings + + const savedUserTemplates = signal({}) + const selectedUserTemplate = signal({ name: '', template: { session: {}, params: {} } }) + + // let's import locally saved templates and settings if there are any + // user templates and settings are stored in one object + // in form of { "templatename": "templatedata" } and { "settingstemplatename":"settingsdata" } + + console.log('Importing saved templates') + + let importedTemplates = local_storage_getDataAsObject('user_templates') + + if (importedTemplates) { + // saved templates were successfully imported. + + console.log('Processing saved templates and updating default template') + params.value = { ...params.value, image_data: [] }; + + //console.log(importedTemplates); + savedUserTemplates.value = importedTemplates; + + //override default template + savedUserTemplates.value.default = { session: session.value, params: params.value } + local_storage_setDataFromObject('user_templates', savedUserTemplates.value) + } else { + // no saved templates detected. + + console.log('Initializing LocalStorage and saving default template') + + savedUserTemplates.value = { "default": { session: session.value, params: params.value } } + local_storage_setDataFromObject('user_templates', savedUserTemplates.value) + } + + function userTemplateResetToDefault() { + console.log('Resetting template to default') + selectedUserTemplate.value.name = 'default'; + selectedUserTemplate.value.data = savedUserTemplates.value['default']; + } + + function userTemplateApply(t) { + session.value = t.data.session; + session.value = { ...session.value, image_selected: '' }; + params.value = t.data.params; + params.value = { ...params.value, image_data: [] }; + } + + function userTemplateResetToDefaultAndApply() { + userTemplateResetToDefault() + userTemplateApply(selectedUserTemplate.value) + } + + function userTemplateLoadAndApplyAutosaved() { + // get autosaved last used template + let lastUsedTemplate = local_storage_getDataAsObject('user_templates_last') + + if (lastUsedTemplate) { + + console.log('Autosaved template found, restoring') + + selectedUserTemplate.value = lastUsedTemplate + } + else { + + console.log('No autosaved template found, using default template') + // no autosaved last used template was found, so load from default. + + userTemplateResetToDefault() + } + + console.log('Applying template') + // and update internal data from templates + + userTemplateApply(selectedUserTemplate.value) + } + + //console.log(savedUserTemplates.value) + //console.log(selectedUserTemplate.value) + + function userTemplateAutosave() { + console.log('Template Autosave...') + if (selectedUserTemplate.value.name == 'default') { + // we don't want to save over default template, so let's create a new one + let newTemplateName = 'UserTemplate-' + Date.now().toString() + let newTemplate = { 'name': newTemplateName, 'data': { 'session': session.value, 'params': params.value } } + + console.log('Saving as ' + newTemplateName) + + // save in the autosave slot + local_storage_setDataFromObject('user_templates_last', newTemplate) + + // and load it back and apply + userTemplateLoadAndApplyAutosaved() + } else { + local_storage_setDataFromObject('user_templates_last', { 'name': selectedUserTemplate.value.name, 'data': { 'session': session.value, 'params': params.value } }) + } + } + + console.log('Checking for autosaved last used template') + userTemplateLoadAndApplyAutosaved() + + /* END: Support for storing prompt templates and parameters in browsers LocalStorage */ + + const llamaStats = signal(null) + const controller = signal(null) + + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? + const chatStarted = computed(() => session.value.transcript.length > 0) + + const transcriptUpdate = (transcript) => { + session.value = { + ...session.value, + transcript + } + } + + // simple template replace + const template = (str, extraSettings) => { + let settings = session.value; + if (extraSettings) { + settings = { ...settings, ...extraSettings }; + } + return String(str).replaceAll(/\{\{(.*?)\}\}/g, (_, key) => template(settings[key])); + } + + async function runLlama(prompt, llamaParams, char) { + const currentMessages = []; + const history = session.value.transcript; + if (controller.value) { + throw new Error("already running"); + } + controller.value = new AbortController(); + for await (const chunk of llama(prompt, llamaParams, { controller: controller.value, api_url: location.pathname.replace(/\/+$/, '') })) { + const data = chunk.data; + + if (data.stop) { + while ( + currentMessages.length > 0 && + currentMessages[currentMessages.length - 1].content.match(/\n$/) != null + ) { + currentMessages.pop(); + } + transcriptUpdate([...history, [char, currentMessages]]) + console.log("Completion finished: '", currentMessages.map(msg => msg.content).join(''), "', summary: ", data); + } else { + currentMessages.push(data); + slot_id = data.slot_id; + if (selected_image && !data.multimodal) { + alert("The server was not compiled for multimodal or the model projector can't be loaded."); + return; + } + transcriptUpdate([...history, [char, currentMessages]]) + } + + if (data.timings) { + llamaStats.value = data; + } + } + + controller.value = null; + } + + // send message to server + const chat = async (msg) => { + if (controller.value) { + console.log('already running...'); + return; + } + + transcriptUpdate([...session.value.transcript, ["{{user}}", msg]]) + + let prompt = template(session.value.template, { + message: msg, + history: session.value.transcript.flatMap( + ([name, data]) => + template( + session.value.historyTemplate, + { + name, + message: Array.isArray(data) ? + data.map(msg => msg.content).join('').replace(/^\s/, '') : + data, + } + ) + ).join("\n"), + }); + if (selected_image) { + prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:[img-10]${msg}\nASSISTANT:`; + } + await runLlama(prompt, { + ...params.value, + slot_id: slot_id, + stop: ["</s>", template("{{char}}:"), template("{{user}}:")], + }, "{{char}}"); + } + + const runCompletion = () => { + if (controller.value) { + console.log('already running...'); + return; + } + const { prompt } = session.value; + transcriptUpdate([...session.value.transcript, ["", prompt]]); + runLlama(prompt, { + ...params.value, + slot_id: slot_id, + stop: [], + }, "").finally(() => { + session.value.prompt = session.value.transcript.map(([_, data]) => + Array.isArray(data) ? data.map(msg => msg.content).join('') : data + ).join(''); + session.value.transcript = []; + }) + } + + const stop = (e) => { + e.preventDefault(); + if (controller.value) { + controller.value.abort(); + controller.value = null; + } + } + + const reset = (e) => { + stop(e); + transcriptUpdate([]); + } + + const uploadImage = (e) => { + e.preventDefault(); + document.getElementById("fileInput").click(); + document.getElementById("fileInput").addEventListener("change", function (event) { + const selectedFile = event.target.files[0]; + if (selectedFile) { + const reader = new FileReader(); + reader.onload = function () { + const image_data = reader.result; + session.value = { ...session.value, image_selected: image_data }; + params.value = { + ...params.value, image_data: [ + { data: image_data.replace(/data:image\/[^;]+;base64,/, ''), id: 10 }] + } + }; + selected_image = true; + reader.readAsDataURL(selectedFile); + } + }); + } + + function MessageInput() { + const message = useSignal("") + + const submit = (e) => { + stop(e); + chat(message.value); + message.value = ""; + } + + const enterSubmits = (event) => { + if (event.which === 13 && !event.shiftKey) { + submit(event); + } + } + + return html` + <form onsubmit=${submit}> + <div> + <textarea + className=${generating.value ? "loading" : null} + oninput=${(e) => message.value = e.target.value} + onkeypress=${enterSubmits} + placeholder="Say something..." + rows=2 + type="text" + value="${message}" + /> + </div> + <div class="right"> + <button type="submit" disabled=${generating.value}>Send</button> + <button onclick=${uploadImage}>Upload Image</button> + <button onclick=${stop} disabled=${!generating.value}>Stop</button> + <button onclick=${reset}>Reset</button> + </div> + </form> + ` + } + + function CompletionControls() { + const submit = (e) => { + stop(e); + runCompletion(); + } + return html` + <div> + <button onclick=${submit} type="button" disabled=${generating.value}>Start</button> + <button onclick=${stop} disabled=${!generating.value}>Stop</button> + <button onclick=${reset}>Reset</button> + </div>`; + } + + const ChatLog = (props) => { + const messages = session.value.transcript; + const container = useRef(null) + + useEffect(() => { + // scroll to bottom (if needed) + const parent = container.current.parentElement; + if (parent && parent.scrollHeight <= parent.scrollTop + parent.offsetHeight + 300) { + parent.scrollTo(0, parent.scrollHeight) + } + }, [messages]) + + const isCompletionMode = session.value.type === 'completion' + const chatLine = ([user, data], index) => { + let message + const isArrayMessage = Array.isArray(data) + if (params.value.n_probs > 0 && isArrayMessage) { + message = html`<${Probabilities} data=${data} />` + } else { + const text = isArrayMessage ? + data.map(msg => msg.content).join('').replace(/^\s+/, '') : + data; + message = isCompletionMode ? + text : + html`<${Markdownish} text=${template(text)} />` + } + if (user) { + return html`<p key=${index}><strong>${template(user)}:</strong> ${message}</p>` + } else { + return isCompletionMode ? + html`<span key=${index}>${message}</span>` : + html`<p key=${index}>${message}</p>` + } + }; + + const handleCompletionEdit = (e) => { + session.value.prompt = e.target.innerText; + session.value.transcript = []; + } + + return html` + <div id="chat" ref=${container} key=${messages.length}> + <img style="width: 60%;${!session.value.image_selected ? `display: none;` : ``}" src="${session.value.image_selected}"/> + <span contenteditable=${isCompletionMode} ref=${container} oninput=${handleCompletionEdit}> + ${messages.flatMap(chatLine)} + </span> + </div>`; + }; + + const ConfigForm = (props) => { + const updateSession = (el) => session.value = { ...session.value, [el.target.name]: el.target.value } + const updateParams = (el) => params.value = { ...params.value, [el.target.name]: el.target.value } + const updateParamsFloat = (el) => params.value = { ...params.value, [el.target.name]: parseFloat(el.target.value) } + const updateParamsInt = (el) => params.value = { ...params.value, [el.target.name]: Math.floor(parseFloat(el.target.value)) } + const updateParamsBool = (el) => params.value = { ...params.value, [el.target.name]: el.target.checked } + + const grammarJsonSchemaPropOrder = signal('') + const updateGrammarJsonSchemaPropOrder = (el) => grammarJsonSchemaPropOrder.value = el.target.value + const convertJSONSchemaGrammar = async () => { + try { + let schema = JSON.parse(params.value.grammar) + const converter = new SchemaConverter({ + prop_order: grammarJsonSchemaPropOrder.value + .split(',') + .reduce((acc, cur, i) => ({ ...acc, [cur.trim()]: i }), {}), + allow_fetch: true, + }) + schema = await converter.resolveRefs(schema, 'input') + converter.visit(schema, '') + params.value = { + ...params.value, + grammar: converter.formatGrammar(), + } + } catch (e) { + alert(`Convert failed: ${e.message}`) + } + } + + const FloatField = ({ label, max, min, name, step, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" step="${step}" name="${name}" value="${value}" oninput=${updateParamsFloat} /> + <span>${value}</span> + </div> + ` + }; + + const IntField = ({ label, max, min, name, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="range" id="${name}" min="${min}" max="${max}" name="${name}" value="${value}" oninput=${updateParamsInt} /> + <span>${value}</span> + </div> + ` + }; + + const BoolField = ({ label, name, value }) => { + return html` + <div> + <label for="${name}">${label}</label> + <input type="checkbox" id="${name}" name="${name}" checked="${value}" onclick=${updateParamsBool} /> + </div> + ` + }; + + const userTemplateReset = (e) => { + e.preventDefault(); + userTemplateResetToDefaultAndApply() + } + + const UserTemplateResetButton = () => { + if (selectedUserTemplate.value.name == 'default') { + return html` + <button disabled>Using default template</button> + ` + } + + return html` + <button onclick=${userTemplateReset}>Reset all to default</button> + ` + }; + + useEffect(() => { + // autosave template on every change + userTemplateAutosave() + }, [session.value, params.value]) + + const GrammarControl = () => ( + html` + <div> + <label for="template">Grammar</label> + <textarea id="grammar" name="grammar" placeholder="Use gbnf or JSON Schema+convert" value="${params.value.grammar}" rows=4 oninput=${updateParams}/> + <input type="text" name="prop-order" placeholder="order: prop1,prop2,prop3" oninput=${updateGrammarJsonSchemaPropOrder} /> + <button type="button" onclick=${convertJSONSchemaGrammar}>Convert JSON Schema</button> + </div> + ` + ); + + const PromptControlFieldSet = () => ( + html` + <fieldset> + <div> + <label htmlFor="prompt">Prompt</label> + <textarea type="text" name="prompt" value="${session.value.prompt}" oninput=${updateSession}/> + </div> + </fieldset> + ` + ); + + const ChatConfigForm = () => ( + html` + ${PromptControlFieldSet()} + + <fieldset class="two"> + <div> + <label for="user">User name</label> + <input type="text" name="user" value="${session.value.user}" oninput=${updateSession} /> + </div> + + <div> + <label for="bot">Bot name</label> + <input type="text" name="char" value="${session.value.char}" oninput=${updateSession} /> + </div> + </fieldset> + + <fieldset> + <div> + <label for="template">Prompt template</label> + <textarea id="template" name="template" value="${session.value.template}" rows=4 oninput=${updateSession}/> + </div> + + <div> + <label for="template">Chat history template</label> + <textarea id="template" name="historyTemplate" value="${session.value.historyTemplate}" rows=1 oninput=${updateSession}/> + </div> + ${GrammarControl()} + </fieldset> + ` + ); + + const CompletionConfigForm = () => ( + html` + ${PromptControlFieldSet()} + <fieldset>${GrammarControl()}</fieldset> + ` + ); + + return html` + <form> + <fieldset class="two"> + <${UserTemplateResetButton}/> + <div> + <label class="slim"><input type="radio" name="type" value="chat" checked=${session.value.type === "chat"} oninput=${updateSession} /> Chat</label> + <label class="slim"><input type="radio" name="type" value="completion" checked=${session.value.type === "completion"} oninput=${updateSession} /> Completion</label> + </div> + </fieldset> + + ${session.value.type === 'chat' ? ChatConfigForm() : CompletionConfigForm()} + + <fieldset class="two"> + ${IntField({ label: "Predictions", max: 2048, min: -1, name: "n_predict", value: params.value.n_predict })} + ${FloatField({ label: "Temperature", max: 2.0, min: 0.0, name: "temperature", step: 0.01, value: params.value.temperature })} + ${FloatField({ label: "Penalize repeat sequence", max: 2.0, min: 0.0, name: "repeat_penalty", step: 0.01, value: params.value.repeat_penalty })} + ${IntField({ label: "Consider N tokens for penalize", max: 2048, min: 0, name: "repeat_last_n", value: params.value.repeat_last_n })} + ${BoolField({ label: "Penalize repetition of newlines", name: "penalize_nl", value: params.value.penalize_nl })} + ${IntField({ label: "Top-K sampling", max: 100, min: -1, name: "top_k", value: params.value.top_k })} + ${FloatField({ label: "Top-P sampling", max: 1.0, min: 0.0, name: "top_p", step: 0.01, value: params.value.top_p })} + ${FloatField({ label: "Min-P sampling", max: 1.0, min: 0.0, name: "min_p", step: 0.01, value: params.value.min_p })} + </fieldset> + <details> + <summary>More options</summary> + <fieldset class="two"> + ${FloatField({ label: "TFS-Z", max: 1.0, min: 0.0, name: "tfs_z", step: 0.01, value: params.value.tfs_z })} + ${FloatField({ label: "Typical P", max: 1.0, min: 0.0, name: "typical_p", step: 0.01, value: params.value.typical_p })} + ${FloatField({ label: "Presence penalty", max: 1.0, min: 0.0, name: "presence_penalty", step: 0.01, value: params.value.presence_penalty })} + ${FloatField({ label: "Frequency penalty", max: 1.0, min: 0.0, name: "frequency_penalty", step: 0.01, value: params.value.frequency_penalty })} + </fieldset> + <hr /> + <fieldset class="three"> + <div> + <label><input type="radio" name="mirostat" value="0" checked=${params.value.mirostat == 0} oninput=${updateParamsInt} /> no Mirostat</label> + <label><input type="radio" name="mirostat" value="1" checked=${params.value.mirostat == 1} oninput=${updateParamsInt} /> Mirostat v1</label> + <label><input type="radio" name="mirostat" value="2" checked=${params.value.mirostat == 2} oninput=${updateParamsInt} /> Mirostat v2</label> + </div> + ${FloatField({ label: "Mirostat tau", max: 10.0, min: 0.0, name: "mirostat_tau", step: 0.01, value: params.value.mirostat_tau })} + ${FloatField({ label: "Mirostat eta", max: 1.0, min: 0.0, name: "mirostat_eta", step: 0.01, value: params.value.mirostat_eta })} + </fieldset> + <fieldset> + ${IntField({ label: "Show Probabilities", max: 10, min: 0, name: "n_probs", value: params.value.n_probs })} + </fieldset> + <fieldset> + ${IntField({ label: "Min Probabilities from each Sampler", max: 10, min: 0, name: "min_keep", value: params.value.min_keep })} + </fieldset> + <fieldset> + <label for="api_key">API Key</label> + <input type="text" name="api_key" value="${params.value.api_key}" placeholder="Enter API key" oninput=${updateParams} /> + </fieldset> + </details> + </form> + ` + } + + const probColor = (p) => { + const r = Math.floor(192 * (1 - p)); + const g = Math.floor(192 * p); + return `rgba(${r},${g},0,0.3)`; + } + + const Probabilities = (params) => { + return params.data.map(msg => { + const { completion_probabilities } = msg; + if ( + !completion_probabilities || + completion_probabilities.length === 0 + ) return msg.content + + if (completion_probabilities.length > 1) { + // Not for byte pair + if (completion_probabilities[0].content.startsWith('byte: \\')) return msg.content + + const splitData = completion_probabilities.map(prob => ({ + content: prob.content, + completion_probabilities: [prob] + })) + return html`<${Probabilities} data=${splitData} />` + } + + const { probs, content } = completion_probabilities[0] + const found = probs.find(p => p.tok_str === msg.content) + const pColor = found ? probColor(found.prob) : 'transparent' + + const popoverChildren = html` + <div class="prob-set"> + ${probs.map((p, index) => { + return html` + <div + key=${index} + title=${`prob: ${p.prob}`} + style=${{ + padding: '0.3em', + backgroundColor: p.tok_str === content ? probColor(p.prob) : 'transparent' + }} + > + <span>${p.tok_str}: </span> + <span>${Math.floor(p.prob * 100)}%</span> + </div> + ` + })} + </div> + ` + + return html` + <${Popover} style=${{ backgroundColor: pColor }} popoverChildren=${popoverChildren}> + ${msg.content.match(/\n/gim) ? html`<br />` : msg.content} + </> + ` + }); + } + + // poor mans markdown replacement + const Markdownish = (params) => { + const md = params.text + .replace(/&/g, '&amp;') + .replace(/</g, '&lt;') + .replace(/>/g, '&gt;') + .replace(/^#{1,6} (.*)$/gim, '<h3>$1</h3>') + .replace(/\*\*(.*?)\*\*/g, '<strong>$1</strong>') + .replace(/__(.*?)__/g, '<strong>$1</strong>') + .replace(/\*(.*?)\*/g, '<em>$1</em>') + .replace(/_(.*?)_/g, '<em>$1</em>') + .replace(/```.*?\n([\s\S]*?)```/g, '<pre><code>$1</code></pre>') + .replace(/`(.*?)`/g, '<code>$1</code>') + .replace(/\n/gim, '<br />'); + return html`<span dangerouslySetInnerHTML=${{ __html: md }} />`; + }; + + const ModelGenerationInfo = (params) => { + if (!llamaStats.value) { + return html`<span/>` + } + return html` + <span> + ${llamaStats.value.tokens_predicted} predicted, ${llamaStats.value.tokens_cached} cached, ${llamaStats.value.timings.predicted_per_token_ms.toFixed()}ms per token, ${llamaStats.value.timings.predicted_per_second.toFixed(2)} tokens per second + </span> + ` + } + + // simple popover impl + const Popover = (props) => { + const isOpen = useSignal(false); + const position = useSignal({ top: '0px', left: '0px' }); + const buttonRef = useRef(null); + const popoverRef = useRef(null); + + const togglePopover = () => { + if (buttonRef.current) { + const rect = buttonRef.current.getBoundingClientRect(); + position.value = { + top: `${rect.bottom + window.scrollY}px`, + left: `${rect.left + window.scrollX}px`, + }; + } + isOpen.value = !isOpen.value; + }; + + const handleClickOutside = (event) => { + if (popoverRef.current && !popoverRef.current.contains(event.target) && !buttonRef.current.contains(event.target)) { + isOpen.value = false; + } + }; + + useEffect(() => { + document.addEventListener('mousedown', handleClickOutside); + return () => { + document.removeEventListener('mousedown', handleClickOutside); + }; + }, []); + + return html` + <span style=${props.style} ref=${buttonRef} onClick=${togglePopover}>${props.children}</span> + ${isOpen.value && html` + <${Portal} into="#portal"> + <div + ref=${popoverRef} + class="popover-content" + style=${{ + top: position.value.top, + left: position.value.left, + }} + > + ${props.popoverChildren} + </div> + </${Portal}> + `} + `; + }; + + // Source: preact-portal (https://github.com/developit/preact-portal/blob/master/src/preact-portal.js) + /** Redirect rendering of descendants into the given CSS selector */ + class Portal extends Component { + componentDidUpdate(props) { + for (let i in props) { + if (props[i] !== this.props[i]) { + return setTimeout(this.renderLayer); + } + } + } + + componentDidMount() { + this.isMounted = true; + this.renderLayer = this.renderLayer.bind(this); + this.renderLayer(); + } + + componentWillUnmount() { + this.renderLayer(false); + this.isMounted = false; + if (this.remote && this.remote.parentNode) this.remote.parentNode.removeChild(this.remote); + } + + findNode(node) { + return typeof node === 'string' ? document.querySelector(node) : node; + } + + renderLayer(show = true) { + if (!this.isMounted) return; + + // clean up old node if moving bases: + if (this.props.into !== this.intoPointer) { + this.intoPointer = this.props.into; + if (this.into && this.remote) { + this.remote = render(html`<${PortalProxy} />`, this.into, this.remote); + } + this.into = this.findNode(this.props.into); + } + + this.remote = render(html` + <${PortalProxy} context=${this.context}> + ${show && this.props.children || null} + </${PortalProxy}> + `, this.into, this.remote); + } + + render() { + return null; + } + } + // high-order component that renders its first child if it exists. + // used as a conditional rendering proxy. + class PortalProxy extends Component { + getChildContext() { + return this.props.context; + } + render({ children }) { + return children || null; + } + } + + function App(props) { + useEffect(() => { + const query = new URLSearchParams(location.search).get("q"); + if (query) chat(query); + }, []); + + return html` + <div class="mode-${session.value.type}"> + <header> + <img src="llama_cpp.png" style="width:100%"/> + </header> + + <section id="write"> + <${session.value.type === 'chat' ? MessageInput : CompletionControls} /> + </section> + + <main id="content"> + <${chatStarted.value ? ChatLog : ConfigForm} /> + </main> + + + <footer> + <p><${ModelGenerationInfo} /></p> + <p>Powered by <a href="https://github.com/ggerganov/llama.cpp">llama.cpp</a> and <a href="https://ggml.ai">ggml.ai</a>.</p> + </footer> + </div> + `; + } + + render(h(App), document.querySelector('#container')); + </script> +</head> + +<body> + <div id="container"> + <input type="file" id="fileInput" accept="image/*" style="display: none;"> + </div> + <div id="portal"></div> +</body> + +</html> + diff --git a/examples/server/themes/wild/llama_cpp.png b/examples/server/themes/wild/llama_cpp.png new file mode 100644 index 0000000000000000000000000000000000000000..bad1dc9fcdb200d27058c851dfba649332674906 GIT binary patch literal 76484 zcmXtfWmuE%`}e4kBc!`iIz|Xcj*>=ckggHZ-6aju-QB53N;jjWLAo0OfoI>}bNufY zJ9h8YuIoBKamMvkO+^lbL52YU06+@zG8zB?5*hsaUo;^6(~cnl0szne6l5f|JeN;8 zeUcgGy_o;{-LFUb&NWs2q%vINzxh^Lb%;vS?j=p%?uAB{5XL})j_|9HT(=QAMnAGn zTIW8sWOdRs^saTPdMLN4^}gDQBc3sKa$M1@&K_O&Ex9h32&_kobr6J%DD(cHwsJh? 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HcmV?d00001 From 9da243b36ac0b9d609adfaaa4c8f1cc8c592f737 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Wed, 8 May 2024 22:14:39 +0300 Subject: [PATCH 058/100] Revert "llava : add support for moondream vision language model (#6899)" This reverts commit 46e12c4692a37bdd31a0432fc5153d7d22bc7f72. --- README.md | 1 - examples/llava/clip.cpp | 71 +++++++---------------------------------- 2 files changed, 11 insertions(+), 61 deletions(-) diff --git a/README.md b/README.md index 1c960b8c1..156860470 100644 --- a/README.md +++ b/README.md @@ -140,7 +140,6 @@ Typically finetunes of the base models below are supported as well. - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) -- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) **HTTP server** diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index e3c9bcd43..f568f470c 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -104,7 +104,6 @@ static std::string format(const char * fmt, ...) { #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" #define TN_PATCH_EMBD "v.patch_embd.weight" -#define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" @@ -426,7 +425,6 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; struct ggml_tensor * patch_embeddings; - struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; struct ggml_tensor * pre_ln_w; @@ -503,11 +501,6 @@ struct clip_ctx { bool use_gelu = false; int32_t ftype = 1; - bool has_class_embedding = true; - bool has_pre_norm = true; - bool has_post_norm = false; - bool has_patch_bias = false; - struct gguf_context * ctx_gguf; struct ggml_context * ctx_data; @@ -533,7 +526,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); - const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); + const int num_positions = num_patches + 1; const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; @@ -564,23 +557,16 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); - if (ctx->has_patch_bias) { - // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); - inp = ggml_add(ctx0, inp, model.patch_bias); - } - // concat class_embeddings and patch_embeddings - struct ggml_tensor * embeddings = inp; - if (ctx->has_class_embedding) { - embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); - } + struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); ggml_set_name(embeddings, "embeddings"); ggml_set_input(embeddings); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); ggml_set_name(positions, "positions"); @@ -590,7 +576,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm - if (ctx->has_pre_norm) { + { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -678,14 +664,6 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = cur; } - // post-layernorm - if (ctx->has_post_norm) { - embeddings = ggml_norm(ctx0, embeddings, eps); - ggml_set_name(embeddings, "post_ln"); - - embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); - } - // llava projector { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); @@ -1170,39 +1148,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } - try { - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); - new_clip->has_class_embedding = true; - } catch (const std::exception& e) { - new_clip->has_class_embedding = false; - } - - try { - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); - new_clip->has_pre_norm = true; - } catch (std::exception & e) { - new_clip->has_pre_norm = false; - } - - try { - vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); - vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); - new_clip->has_post_norm = true; - } catch (std::exception & e) { - new_clip->has_post_norm = false; - } - - try { - vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); - new_clip->has_patch_bias = true; - } catch (std::exception & e) { - new_clip->has_patch_bias = false; - } - try { vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); } catch(const std::exception& e) { LOG_TEE("%s: failed to load vision model tensors\n", __func__); } From c12452c7aec8a02264afc00196a13caa591a13ac Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de> Date: Wed, 8 May 2024 21:53:08 +0200 Subject: [PATCH 059/100] JSON: [key] -> .at(key), assert() -> GGML_ASSERT (#7143) --- common/common.cpp | 14 ++--- common/json-schema-to-grammar.h | 4 ++ examples/server/server.cpp | 74 ++++++++++++++------------- examples/server/utils.hpp | 8 +-- tests/test-json-schema-to-grammar.cpp | 1 + 5 files changed, 56 insertions(+), 45 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 4a9da284e..0535508ba 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,4 +1,6 @@ #include "common.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT #include "json.hpp" #include "json-schema-to-grammar.h" #include "llama.h" @@ -1969,18 +1971,18 @@ static bool llama_download_file(const std::string & url, const std::string & pat try { metadata_in >> metadata; fprintf(stderr, "%s: previous metadata file found %s: %s\n", __func__, metadata_path.c_str(), metadata.dump().c_str()); - if (metadata.contains("url") && metadata["url"].is_string()) { - auto previous_url = metadata["url"].get<std::string>(); + if (metadata.contains("url") && metadata.at("url").is_string()) { + auto previous_url = metadata.at("url").get<std::string>(); if (previous_url != url) { fprintf(stderr, "%s: Model URL mismatch: %s != %s\n", __func__, url.c_str(), previous_url.c_str()); return false; } } - if (metadata.contains("etag") && metadata["etag"].is_string()) { - etag = metadata["etag"]; + if (metadata.contains("etag") && metadata.at("etag").is_string()) { + etag = metadata.at("etag"); } - if (metadata.contains("lastModified") && metadata["lastModified"].is_string()) { - last_modified = metadata["lastModified"]; + if (metadata.contains("lastModified") && metadata.at("lastModified").is_string()) { + last_modified = metadata.at("lastModified"); } } catch (const nlohmann::json::exception & e) { fprintf(stderr, "%s: error reading metadata file %s: %s\n", __func__, metadata_path.c_str(), e.what()); diff --git a/common/json-schema-to-grammar.h b/common/json-schema-to-grammar.h index e1abed303..41623b346 100644 --- a/common/json-schema-to-grammar.h +++ b/common/json-schema-to-grammar.h @@ -1,4 +1,8 @@ #pragma once + +#include "ggml.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT #include "json.hpp" std::string json_schema_to_grammar(const nlohmann::ordered_json& schema); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 06c0be567..305f79492 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -12,6 +12,8 @@ // increase max payload length to allow use of larger context size #define CPPHTTPLIB_FORM_URL_ENCODED_PAYLOAD_MAX_LENGTH 1048576 #include "httplib.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT #include "json.hpp" // auto generated files (update with ./deps.sh) @@ -859,7 +861,7 @@ struct server_context { slot.sparams.min_keep = json_value(data, "min_keep", default_sparams.min_keep); // process "json_schema" and "grammar" - if (data.contains("json_schema") && !data["json_schema"].is_null() && data.contains("grammar") && !data["grammar"].is_null()) { + if (data.contains("json_schema") && !data.at("json_schema").is_null() && data.contains("grammar") && !data.at("grammar").is_null()) { send_error(task, "Either \"json_schema\" or \"grammar\" can be specified, but not both", ERROR_TYPE_INVALID_REQUEST); return false; } else if (data.contains("json_schema") && !data.contains("grammar")) { @@ -1512,7 +1514,7 @@ struct server_context { // add subtasks for (int i = 0; i < prompt_count; i++) { json subtask_data = multiprompt_task.data; - subtask_data["prompt"] = subtask_data["prompt"][i]; + subtask_data["prompt"] = subtask_data.at("prompt")[i]; // subtasks inherit everything else (infill mode, embedding mode, etc.) request_completion(subtask_ids[i], id_multi, subtask_data, multiprompt_task.infill, multiprompt_task.embedding); @@ -1532,7 +1534,7 @@ struct server_context { } if (task.data.contains("system_prompt")) { - system_prompt_set(task.data["system_prompt"]); + system_prompt_set(task.data.at("system_prompt")); for (server_slot & slot : slots) { slot.n_past = 0; @@ -1644,7 +1646,7 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_SAVE: { - int id_slot = task.data["id_slot"]; + int id_slot = task.data.at("id_slot"); server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1654,8 +1656,8 @@ struct server_context { const size_t token_count = slot->cache_tokens.size(); const int64_t t_start = ggml_time_us(); - std::string filename = task.data["filename"]; - std::string filepath = task.data["filepath"]; + std::string filename = task.data.at("filename"); + std::string filepath = task.data.at("filepath"); const size_t nwrite = llama_state_seq_save_file(ctx, filepath.c_str(), slot->id + 1, slot->cache_tokens.data(), token_count); @@ -1679,7 +1681,7 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_RESTORE: { - int id_slot = task.data["id_slot"]; + int id_slot = task.data.at("id_slot"); server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -1688,8 +1690,8 @@ struct server_context { const int64_t t_start = ggml_time_us(); - std::string filename = task.data["filename"]; - std::string filepath = task.data["filepath"]; + std::string filename = task.data.at("filename"); + std::string filepath = task.data.at("filepath"); slot->cache_tokens.resize(slot->n_ctx); size_t token_count = 0; @@ -1721,7 +1723,7 @@ struct server_context { } break; case SERVER_TASK_TYPE_SLOT_ERASE: { - int id_slot = task.data["id_slot"]; + int id_slot = task.data.at("id_slot"); server_slot * slot = get_slot(id_slot); if (slot == nullptr) { send_error(task, "Invalid slot ID", ERROR_TYPE_INVALID_REQUEST); @@ -3136,8 +3138,8 @@ int main(int argc, char ** argv) { server_task_result result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); - const int n_idle_slots = result.data["idle"]; - const int n_processing_slots = result.data["processing"]; + const int n_idle_slots = result.data.at("idle"); + const int n_processing_slots = result.data.at("processing"); json health = { {"status", "ok"}, @@ -3147,7 +3149,7 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK if (sparams.slots_endpoint && req.has_param("include_slots")) { - health["slots"] = result.data["slots"]; + health["slots"] = result.data.at("slots"); } if (n_idle_slots == 0) { @@ -3191,7 +3193,7 @@ int main(int argc, char ** argv) { server_task_result result = ctx_server.queue_results.recv(task.id); ctx_server.queue_results.remove_waiting_task_id(task.id); - res.set_content(result.data["slots"].dump(), "application/json"); + res.set_content(result.data.at("slots").dump(), "application/json"); res.status = 200; // HTTP OK }; @@ -3218,32 +3220,32 @@ int main(int argc, char ** argv) { json data = result.data; - const uint64_t n_prompt_tokens_processed = data["n_prompt_tokens_processed"]; - const uint64_t t_prompt_processing = data["t_prompt_processing"]; + const uint64_t n_prompt_tokens_processed = data.at("n_prompt_tokens_processed"); + const uint64_t t_prompt_processing = data.at("t_prompt_processing"); - const uint64_t n_tokens_predicted = data["n_tokens_predicted"]; - const uint64_t t_tokens_generation = data["t_tokens_generation"]; + const uint64_t n_tokens_predicted = data.at("n_tokens_predicted"); + const uint64_t t_tokens_generation = data.at("t_tokens_generation"); - const int32_t kv_cache_used_cells = data["kv_cache_used_cells"]; + const int32_t kv_cache_used_cells = data.at("kv_cache_used_cells"); // metrics definition: https://prometheus.io/docs/practices/naming/#metric-names json all_metrics_def = json { {"counter", {{ {"name", "prompt_tokens_total"}, {"help", "Number of prompt tokens processed."}, - {"value", (uint64_t) data["n_prompt_tokens_processed_total"]} + {"value", (uint64_t) data.at("n_prompt_tokens_processed_total")} }, { {"name", "prompt_seconds_total"}, {"help", "Prompt process time"}, - {"value", (uint64_t) data["t_prompt_processing_total"] / 1.e3} + {"value", (uint64_t) data.at("t_prompt_processing_total") / 1.e3} }, { {"name", "tokens_predicted_total"}, {"help", "Number of generation tokens processed."}, - {"value", (uint64_t) data["n_tokens_predicted_total"]} + {"value", (uint64_t) data.at("n_tokens_predicted_total")} }, { {"name", "tokens_predicted_seconds_total"}, {"help", "Predict process time"}, - {"value", (uint64_t) data["t_tokens_generation_total"] / 1.e3} + {"value", (uint64_t) data.at("t_tokens_generation_total") / 1.e3} }}}, {"gauge", {{ {"name", "prompt_tokens_seconds"}, @@ -3260,15 +3262,15 @@ int main(int argc, char ** argv) { },{ {"name", "kv_cache_tokens"}, {"help", "KV-cache tokens."}, - {"value", (uint64_t) data["kv_cache_tokens_count"]} + {"value", (uint64_t) data.at("kv_cache_tokens_count")} },{ {"name", "requests_processing"}, {"help", "Number of request processing."}, - {"value", (uint64_t) data["processing"]} + {"value", (uint64_t) data.at("processing")} },{ {"name", "requests_deferred"}, {"help", "Number of request deferred."}, - {"value", (uint64_t) data["deferred"]} + {"value", (uint64_t) data.at("deferred")} }}} }; @@ -3279,8 +3281,8 @@ int main(int argc, char ** argv) { const auto & metrics_def = el.value(); for (const auto & metric_def : metrics_def) { - const std::string name = metric_def["name"]; - const std::string help = metric_def["help"]; + const std::string name = metric_def.at("name"); + const std::string help = metric_def.at("help"); auto value = json_value(metric_def, "value", 0.); prometheus << "# HELP llamacpp:" << name << " " << help << "\n" @@ -3289,7 +3291,7 @@ int main(int argc, char ** argv) { } } - const int64_t t_start = data["t_start"]; + const int64_t t_start = data.at("t_start"); res.set_header("Process-Start-Time-Unix", std::to_string(t_start)); res.set_content(prometheus.str(), "text/plain; version=0.0.4"); @@ -3298,7 +3300,7 @@ int main(int argc, char ** argv) { const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); - std::string filename = request_data["filename"]; + std::string filename = request_data.at("filename"); if (!validate_file_name(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; @@ -3328,7 +3330,7 @@ int main(int argc, char ** argv) { const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); - std::string filename = request_data["filename"]; + std::string filename = request_data.at("filename"); if (!validate_file_name(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; @@ -3648,7 +3650,7 @@ int main(int argc, char ** argv) { std::vector<llama_token> tokens; if (body.count("content") != 0) { const bool add_special = json_value(body, "add_special", false); - tokens = ctx_server.tokenize(body["content"], add_special); + tokens = ctx_server.tokenize(body.at("content"), add_special); } const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json; charset=utf-8"); @@ -3660,7 +3662,7 @@ int main(int argc, char ** argv) { std::string content; if (body.count("tokens") != 0) { - const std::vector<llama_token> tokens = body["tokens"]; + const std::vector<llama_token> tokens = body.at("tokens"); content = tokens_to_str(ctx_server.ctx, tokens.cbegin(), tokens.cend()); } @@ -3683,10 +3685,10 @@ int main(int argc, char ** argv) { json prompt; if (body.count("input") != 0) { is_openai = true; - prompt = body["input"]; + prompt = body.at("input"); } else if (body.count("content") != 0) { // with "content", we only support single prompt - prompt = std::vector<std::string>{body["content"]}; + prompt = std::vector<std::string>{body.at("content")}; } else { res_error(res, format_error_response("\"input\" or \"content\" must be provided", ERROR_TYPE_INVALID_REQUEST)); return; @@ -3705,7 +3707,7 @@ int main(int argc, char ** argv) { if (!result.error) { if (result.data.count("results")) { // result for multi-task - responses = result.data["results"]; + responses = result.data.at("results"); } else { // result for single task responses = std::vector<json>{result.data}; diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index af12f497d..d872b63f5 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -3,6 +3,8 @@ #include "llama.h" #include "common.h" +// Change JSON_ASSERT from assert() to GGML_ASSERT: +#define JSON_ASSERT GGML_ASSERT #include "json.hpp" #include <string> @@ -373,11 +375,11 @@ static json oaicompat_completion_params_parse( llama_params["top_p"] = json_value(body, "top_p", 1.0); // Apply chat template to the list of messages - llama_params["prompt"] = format_chat(model, chat_template, body["messages"]); + llama_params["prompt"] = format_chat(model, chat_template, body.at("messages")); // Handle "stop" field - if (body.contains("stop") && body["stop"].is_string()) { - llama_params["stop"] = json::array({body["stop"].get<std::string>()}); + if (body.contains("stop") && body.at("stop").is_string()) { + llama_params["stop"] = json::array({body.at("stop").get<std::string>()}); } else { llama_params["stop"] = json_value(body, "stop", json::array()); } diff --git a/tests/test-json-schema-to-grammar.cpp b/tests/test-json-schema-to-grammar.cpp index b2ce4d260..c5361b5b8 100755 --- a/tests/test-json-schema-to-grammar.cpp +++ b/tests/test-json-schema-to-grammar.cpp @@ -2,6 +2,7 @@ #undef NDEBUG #endif +#include <cassert> #include <fstream> #include <sstream> #include <regex> From bc4bba364fb96d908f2698e908648df5e6f55e02 Mon Sep 17 00:00:00 2001 From: agray3 <agray3@users.noreply.github.com> Date: Wed, 8 May 2024 21:55:49 +0100 Subject: [PATCH 060/100] Introduction of CUDA Graphs to LLama.cpp (#6766) * DRAFT: Introduction of CUDA Graphs to LLama.cpp * FIx issues raised in comments * Tidied to now only use CUDA runtime (not mixed with driver calls) * disable for multi-gpu and batch size > 1 * Disable CUDA graphs for old GPU arch and with env var * added missing CUDA_CHECKs * Addressed comments * further addressed comments * limit to GGML_ALLOW_CUDA_GRAPHS defined in llama.cpp cmake * Added more comprehensive graph node checking * With mechanism to fall back if graph capture fails * Revert "With mechanism to fall back if graph capture fails" This reverts commit eb9f15fb6fcb81384f732c4601a5b25c016a5143. * Fall back if graph capture fails and address other comments * - renamed GGML_ALLOW_CUDA_GRAPHS to GGML_CUDA_USE_GRAPHS - rename env variable to disable CUDA graphs to GGML_CUDA_DISABLE_GRAPHS - updated Makefile build to enable CUDA graphs - removed graph capture failure checking in ggml_cuda_error using a global variable to track this is not thread safe, but I am also not safistied with checking an error by string if this is necessary to workaround some issues with graph capture with eg. cuBLAS, we can pass the ggml_backend_cuda_context to the error checking macro and store the result in the context - fixed several resource leaks - fixed issue with zero node graphs - changed fixed size arrays to vectors - removed the count of number of evaluations before start capturing, and instead changed the capture mode to relaxed - removed the check for multiple devices so that it is still possible to use a single device, instead checks for split buffers to disable cuda graphs with -sm row - changed the op for checking batch size to GGML_OP_ADD, should be more reliable than GGML_OP_SOFT_MAX - code style fixes - things to look into - VRAM usage of the cudaGraphExec_t, if it is significant we may need to make it optional - possibility of using cudaStreamBeginCaptureToGraph to keep track of which ggml graph nodes correspond to which cuda graph nodes * fix build without cuda graphs * remove outdated comment * replace minimum cc value with a constant --------- Co-authored-by: slaren <slarengh@gmail.com> --- CMakeLists.txt | 1 + Makefile | 2 +- ggml-cuda.cu | 300 +++++++++++++++++++++++++++++++++++++++++-- ggml-cuda/clamp.cu | 1 - ggml-cuda/common.cuh | 40 ++++++ ggml-cuda/convert.cu | 4 +- ggml-cuda/cpy.cu | 29 +++++ ggml-cuda/cpy.cuh | 2 + ggml-cuda/mmq.cu | 30 ++--- ggml-cuda/mmvq.cu | 6 +- ggml-cuda/scale.cu | 1 - 11 files changed, 372 insertions(+), 44 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 0e22ee230..07b3a4388 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -405,6 +405,7 @@ if (LLAMA_CUDA) list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") add_compile_definitions(GGML_USE_CUDA) + add_compile_definitions(GGML_CUDA_USE_GRAPHS) if (LLAMA_CUDA_FORCE_DMMV) add_compile_definitions(GGML_CUDA_FORCE_DMMV) endif() diff --git a/Makefile b/Makefile index c568dd008..3fa56d13a 100644 --- a/Makefile +++ b/Makefile @@ -433,7 +433,7 @@ ifdef LLAMA_CUDA else CUDA_PATH ?= /usr/local/cuda endif - MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include + MK_CPPFLAGS += -DGGML_USE_CUDA -I$(CUDA_PATH)/include -I$(CUDA_PATH)/targets/$(UNAME_M)-linux/include -DGGML_CUDA_USE_GRAPHS MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2d1742c82..6f89a7cc3 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1647,7 +1647,7 @@ static void ggml_cuda_op_mul_mat( } } -static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ +static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); GGML_ASSERT(ggml_backend_buffer_is_cuda(src0->buffer)); GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation @@ -1670,7 +1670,7 @@ static void ggml_cuda_mul_mat_vec_p021(ggml_backend_cuda_context & ctx, const gg ggml_mul_mat_p021_f16_f32_cuda(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); } -static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst){ +static void ggml_cuda_mul_mat_vec_nc(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(!ggml_is_transposed(src0)); GGML_ASSERT(!ggml_is_transposed(src1)); GGML_ASSERT(!ggml_is_permuted(src0)); @@ -2410,32 +2410,304 @@ GGML_CALL static void ggml_backend_cuda_synchronize(ggml_backend_t backend) { GGML_UNUSED(backend); } +static void set_ggml_graph_node_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { + graph_node_properties->node_address = node->data; + graph_node_properties->node_op = node->op; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + graph_node_properties->ne[i] = node->ne[i]; + graph_node_properties->nb[i] = node->nb[i]; + } + for (int i = 0; i < GGML_MAX_SRC; i++) { + graph_node_properties->src_address[i] = node->src[i] ? node->src[i]->data : nullptr; + } +} + +static bool ggml_graph_node_has_matching_properties(ggml_tensor * node, ggml_graph_node_properties * graph_node_properties) { + if (node->data != graph_node_properties->node_address && + node->op != GGML_OP_CPY && + node->op != GGML_OP_VIEW) { + return false; + } + + if (node->op != graph_node_properties->node_op) { + return false; + } + + for (int i = 0; i < GGML_MAX_DIMS; i++) { + if (node->ne[i] != graph_node_properties->ne[i]) { + return false; + } + if (node->nb[i] != graph_node_properties->nb[i]) { + return false; + } + } + + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (node->src[i] && + node->src[i]->data != graph_node_properties->src_address[i] && + node->op != GGML_OP_CPY && + node->op != GGML_OP_VIEW + ) { + return false; + } + } + return true; +} + GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *)backend->context; ggml_cuda_set_device(cuda_ctx->device); - for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_tensor * node = cgraph->nodes[i]; +#ifdef USE_CUDA_GRAPH + static const bool disable_cuda_graphs_due_to_env = (getenv("GGML_CUDA_DISABLE_GRAPHS") != nullptr); - if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { - continue; + // Objects required for CUDA Graph + if (cuda_ctx->cuda_graph == nullptr) { + cuda_ctx->cuda_graph.reset(new ggml_cuda_graph()); + } + + bool use_cuda_graph = true; + bool cuda_graph_update_required = false; + // pointer to CUDA cpy kernel, which is required to identify + // kernel parameters which need updated in the graph for each token + void * ggml_cuda_cpy_fn_ptr = nullptr; + + if (cuda_ctx->cuda_graph->graph == nullptr) { + if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { + cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; +#ifndef NDEBUG + fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__); +#endif + } + } + + // Disable CUDA graphs in presence of env var, old GPU, use-case which is changing too rapidly, + // or previous graph capture failure. + // Also disable for multi-gpu for now. TO DO investigate + if (disable_cuda_graphs_due_to_env + || cuda_ctx->cuda_graph->disable_due_to_gpu_arch + || cuda_ctx->cuda_graph->disable_due_to_too_many_updates + || cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture) { + use_cuda_graph = false; + } + + if (use_cuda_graph) { + if (cuda_ctx->cuda_graph->instance == nullptr) { + cuda_graph_update_required = true; } + // Check if the graph size has changed + if (cuda_ctx->cuda_graph->ggml_graph_properties.size() != (size_t)cgraph->n_nodes) { + cuda_graph_update_required = true; + cuda_ctx->cuda_graph->ggml_graph_properties.resize(cgraph->n_nodes); + } + + // Loop over nodes in GGML graph to determine if CUDA graph update is required + // and store properties to allow this comparison for the next token + for (int i = 0; i < cgraph->n_nodes; i++) { + bool has_matching_properties = true; + if (!cuda_graph_update_required) { + has_matching_properties = ggml_graph_node_has_matching_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]); + } + if (!has_matching_properties) { + cuda_graph_update_required = true; + } + set_ggml_graph_node_properties(cgraph->nodes[i], &cuda_ctx->cuda_graph->ggml_graph_properties[i]); + } + + // Loop over nodes in GGML graph to obtain info needed for CUDA graph + cuda_ctx->cuda_graph->updated_kernel_arg.clear(); + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + + if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { + use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG - assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); - for (int j = 0; j < GGML_MAX_SRC; j++) { - if (node->src[j] != nullptr) { - assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); + fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__); +#endif + } + + if (node->op == GGML_OP_MUL_MAT_ID) { + use_cuda_graph = false; // This node type is not supported by CUDA graph capture +#ifndef NDEBUG + fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__); +#endif + } + + if (node->op == GGML_OP_ADD && node->src[1] && node->src[1]->ne[1] > 1) { + // disable CUDA graphs for batch size > 1 for now. + // Changes in batch size or context size can cause changes to the grid size of some kernels. + use_cuda_graph = false; +#ifndef NDEBUG + fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); +#endif + } + + if (node->op == GGML_OP_CPY) { + // store the copy op parameter which changes with each token. + cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); + if (ggml_cuda_cpy_fn_ptr == nullptr) { + // store a pointer to the copy op CUDA kernel to identify it later + ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + } + } + + if (!use_cuda_graph) { + break; } } + + // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. + if (cuda_graph_update_required) { + cuda_ctx->cuda_graph->number_consecutive_updates++; + } else { + cuda_ctx->cuda_graph->number_consecutive_updates = 0; + } + + if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) { + cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true; +#ifndef NDEBUG + fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); +#endif + } + } + + if (use_cuda_graph && cuda_graph_update_required) { // Start CUDA graph capture + CUDA_CHECK(cudaStreamBeginCapture(cuda_ctx->stream(), cudaStreamCaptureModeRelaxed)); + } + +#else + bool use_cuda_graph = false; + bool cuda_graph_update_required = false; +#endif // USE_CUDA_GRAPH + + bool graph_evaluated_or_captured = false; + + while (!graph_evaluated_or_captured) { + // Only perform the graph execution if CUDA graphs are not enabled, or we are capturing the graph. + // With the use of CUDA graphs, the execution will be performed by the graph launch. + if (!use_cuda_graph || cuda_graph_update_required) { + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + + if (ggml_is_empty(node) || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + continue; + } + +#ifndef NDEBUG + assert(node->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device)); + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j] != nullptr) { + assert(node->src[j]->buffer->buft == ggml_backend_cuda_buffer_type(cuda_ctx->device) || ggml_backend_buffer_is_cuda_split(node->src[j]->buffer)); + } + } #endif - bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); - if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); + if (!ok) { + fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + } + GGML_ASSERT(ok); + } } - GGML_ASSERT(ok); + +#ifdef USE_CUDA_GRAPH + if (use_cuda_graph && cuda_graph_update_required) { // End CUDA graph capture + if (cuda_ctx->cuda_graph->graph != nullptr) { + CUDA_CHECK(cudaGraphDestroy(cuda_ctx->cuda_graph->graph)); + cuda_ctx->cuda_graph->graph = nullptr; + } + CUDA_CHECK(cudaStreamEndCapture(cuda_ctx->stream(), &cuda_ctx->cuda_graph->graph)); + +#if 0 + if (disable_cuda_graphs_due_to_failed_capture) { + use_cuda_graph = false; + cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true; +#ifndef NDEBUG + fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__); +#endif + } else { + graph_evaluated_or_captured = true; // CUDA graph has been captured + } +#endif + graph_evaluated_or_captured = true; // CUDA graph has been captured + } else { + graph_evaluated_or_captured = true; // ggml graph has been directly evaluated + } + } + + if (use_cuda_graph) { + if (cuda_ctx->cuda_graph->instance == nullptr) { // Create executable graph from captured graph. + CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); + } + + // Perform update to graph (if required for this token), and change copy parameter (required for every token) + + if (cuda_graph_update_required) { + // Extract nodes from graph + if (cuda_ctx->cuda_graph->num_nodes == 0) { + // First call with null argument gets number of nodes in graph + CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, nullptr, &cuda_ctx->cuda_graph->num_nodes)); + } + // Subsequent call with non-null argument gets nodes + cuda_ctx->cuda_graph->nodes.resize(cuda_ctx->cuda_graph->num_nodes); + cuda_ctx->cuda_graph->params.resize(cuda_ctx->cuda_graph->num_nodes); + if (cuda_ctx->cuda_graph->num_nodes > 0) { + CUDA_CHECK(cudaGraphGetNodes(cuda_ctx->cuda_graph->graph, cuda_ctx->cuda_graph->nodes.data(), &cuda_ctx->cuda_graph->num_nodes)); + + // Loop over nodes, and extract kernel parameters from each node + for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { + cudaGraphNodeType node_type; + CUDA_CHECK(cudaGraphNodeGetType(cuda_ctx->cuda_graph->nodes[i], &node_type)); + if (node_type == cudaGraphNodeTypeKernel) { + cudaError_t stat = cudaGraphKernelNodeGetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i]); // Get params using runtime + if (stat == cudaErrorInvalidDeviceFunction) { + // Fails due to incorrect handling by CUDA runtime of CUDA BLAS node. + // We don't need to update blas nodes, so clear error and move on. + cudaGetLastError(); + } else { + GGML_ASSERT(stat == cudaSuccess); + } + } + } + } + } + + // One of the arguments to the copy kernel is updated for each token, hence we need to + // replace that argument with the updated value in the CUDA graph + if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured + int k = 0; + for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { + if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) { + char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); + cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; + CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); + } + } + } + + // Update graph executable + cudaGraphExecUpdateResultInfo result_info; + cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); + if (stat == cudaErrorGraphExecUpdateFailure) { +#ifndef NDEBUG + fprintf(stderr, "%s: CUDA graph update failed\n", __func__); +#endif + // The pre-existing graph exec cannot be updated due to violated constraints + // so instead clear error and re-instantiate + cudaGetLastError(); + CUDA_CHECK(cudaGraphExecDestroy(cuda_ctx->cuda_graph->instance)); + cuda_ctx->cuda_graph->instance = nullptr; + CUDA_CHECK(cudaGraphInstantiate(&cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, NULL, NULL, 0)); + } else { + GGML_ASSERT(stat == cudaSuccess); + } + // Launch graph + CUDA_CHECK(cudaGraphLaunch(cuda_ctx->cuda_graph->instance, cuda_ctx->stream())); +#else + graph_evaluated_or_captured = true; +#endif // USE_CUDA_GRAPH } return GGML_STATUS_SUCCESS; diff --git a/ggml-cuda/clamp.cu b/ggml-cuda/clamp.cu index 379ded042..8009a3e3d 100644 --- a/ggml-cuda/clamp.cu +++ b/ggml-cuda/clamp.cu @@ -31,5 +31,4 @@ void ggml_cuda_op_clamp(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); clamp_f32_cuda(src0_d, dst_d, min, max, ggml_nelements(src0), stream); - CUDA_CHECK(cudaGetLastError()); } diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index b2627b7b4..a4197f11b 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -19,6 +19,7 @@ #include <cassert> #include <cfloat> #include <string> +#include <vector> #if defined(GGML_USE_HIPBLAS) #include <hip/hip_runtime.h> @@ -526,6 +527,43 @@ struct ggml_tensor_extra_gpu { cudaEvent_t events[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS]; // events for synchronizing multiple GPUs }; + +#if (CUDART_VERSION >= 12000) && defined(GGML_CUDA_USE_GRAPHS) +#define USE_CUDA_GRAPH +#endif + +struct ggml_graph_node_properties { + void * node_address; + ggml_op node_op; + int64_t ne[GGML_MAX_DIMS]; + size_t nb[GGML_MAX_DIMS]; + void * src_address[GGML_MAX_SRC]; +}; + +struct ggml_cuda_graph { +#ifdef USE_CUDA_GRAPH + ~ggml_cuda_graph() { + if (instance != nullptr) { + CUDA_CHECK(cudaGraphExecDestroy(instance)); + } + if (graph != nullptr) { + CUDA_CHECK(cudaGraphDestroy(graph)); + } + } + cudaGraph_t graph = nullptr; + cudaGraphExec_t instance = nullptr; + size_t num_nodes = 0; + std::vector<cudaGraphNode_t> nodes; + std::vector<cudaKernelNodeParams> params; + bool disable_due_to_gpu_arch = false; + bool disable_due_to_too_many_updates = false; + bool disable_due_to_failed_graph_capture = false; + int number_consecutive_updates = 0; + std::vector<ggml_graph_node_properties> ggml_graph_properties; + std::vector<char **> updated_kernel_arg; +#endif +}; + struct ggml_backend_cuda_context { int device; std::string name; @@ -534,6 +572,8 @@ struct ggml_backend_cuda_context { cudaStream_t streams[GGML_CUDA_MAX_DEVICES][GGML_CUDA_MAX_STREAMS] = { { nullptr } }; cublasHandle_t cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; + std::unique_ptr<ggml_cuda_graph> cuda_graph; + explicit ggml_backend_cuda_context(int device) : device(device), name(GGML_CUDA_NAME + std::to_string(device)) { diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index 75e50c985..830e2d756 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -727,7 +727,6 @@ static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict_ } to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { - int id; switch (type) { case GGML_TYPE_Q4_0: return dequantize_row_q4_0_cuda; @@ -738,8 +737,7 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { case GGML_TYPE_Q5_1: return dequantize_block_cuda<QK5_1, QR5_1, dequantize_q5_1>; case GGML_TYPE_Q8_0: - CUDA_CHECK(cudaGetDevice(&id)); - if (ggml_cuda_info().devices[id].cc >= CC_PASCAL) { + if (ggml_cuda_info().devices[ggml_cuda_get_device()].cc >= CC_PASCAL) { return dequantize_block_q8_0_f16_cuda; } return dequantize_block_cuda<QK8_0, QR8_0, dequantize_q8_0>; diff --git a/ggml-cuda/cpy.cu b/ggml-cuda/cpy.cu index 16d9c8fff..12d741f01 100644 --- a/ggml-cuda/cpy.cu +++ b/ggml-cuda/cpy.cu @@ -459,3 +459,32 @@ void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; ggml_cuda_cpy(ctx, src0, dst); } + +void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1) { + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { + return (void*) cpy_f32_f16<cpy_1_f32_f32>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { + return (void*) cpy_f32_f16<cpy_1_f32_f16>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { + return (void*) cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { + return (void*) cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { + return (void*) cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) { + return (void*) cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_IQ4_NL) { + return (void*) cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>; + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_1) { + return (void*) cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>; + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { + return (void*) cpy_f32_f16<cpy_1_f32_f16>; + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + return (void*) cpy_f32_f16<cpy_1_f16_f32>; + } else { + fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, + ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ASSERT(false); + } +} + diff --git a/ggml-cuda/cpy.cuh b/ggml-cuda/cpy.cuh index f0b2c453b..796167426 100644 --- a/ggml-cuda/cpy.cuh +++ b/ggml-cuda/cpy.cuh @@ -5,3 +5,5 @@ void ggml_cuda_cpy(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, ggml_tensor * src1); void ggml_cuda_dup(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void* ggml_cuda_cpy_fn(const ggml_tensor * src0, ggml_tensor * src1); diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 60d6616a8..7948f1b12 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -1735,8 +1735,7 @@ static void ggml_mul_mat_q4_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -1780,8 +1779,7 @@ static void ggml_mul_mat_q4_1_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -1825,8 +1823,7 @@ static void ggml_mul_mat_q5_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -1870,8 +1867,7 @@ static void ggml_mul_mat_q5_1_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -1915,8 +1911,7 @@ static void ggml_mul_mat_q8_0_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -1960,8 +1955,7 @@ static void ggml_mul_mat_q2_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -2007,8 +2001,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda( #if QK_K == 256 - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -2053,8 +2046,7 @@ static void ggml_mul_mat_q4_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -2098,8 +2090,7 @@ static void ggml_mul_mat_q5_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; @@ -2143,8 +2134,7 @@ static void ggml_mul_mat_q6_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); const int compute_capability = ggml_cuda_info().devices[id].cc; int mmq_x, mmq_y, nwarps; diff --git a/ggml-cuda/mmvq.cu b/ggml-cuda/mmvq.cu index 396559001..65cc1bcaa 100644 --- a/ggml-cuda/mmvq.cu +++ b/ggml-cuda/mmvq.cu @@ -89,8 +89,7 @@ static void mul_mat_vec_q_cuda( GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= MMVQ_MAX_BATCH_SIZE); - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); int64_t nwarps = 1; int64_t rows_per_cuda_block = 1; @@ -328,8 +327,7 @@ void ggml_cuda_op_mul_mat_vec_q( const int64_t ne0 = dst->ne[0]; - int id; - CUDA_CHECK(cudaGetDevice(&id)); + int id = ggml_cuda_get_device(); // the main device has a larger memory buffer to hold the results from all GPUs // nrows_dst == nrows of the matrix that the kernel writes into diff --git a/ggml-cuda/scale.cu b/ggml-cuda/scale.cu index 6e3617d1c..1405e066e 100644 --- a/ggml-cuda/scale.cu +++ b/ggml-cuda/scale.cu @@ -28,5 +28,4 @@ void ggml_cuda_op_scale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&scale, dst->op_params, sizeof(float)); scale_f32_cuda(src0_d, dst_d, scale, ggml_nelements(src0), stream); - CUDA_CHECK(cudaGetLastError()); } From f98eb31c517c95960df1d0abc48002787f145f3b Mon Sep 17 00:00:00 2001 From: compilade <git@compilade.net> Date: Wed, 8 May 2024 18:16:38 -0400 Subject: [PATCH 061/100] convert-hf : save memory with lazy evaluation (#7075) * convert-hf : begin refactoring write_tensor * convert : upgrade to sentencepiece v0.2.0 * convert-hf : remove unused n_dims in extra_*_tensors * convert-hf : simplify MoE weights stacking * convert-hf : flake8 linter doesn't like semicolons * convert-hf : allow unusual model part names For example, loading `model-00001-of-00001.safetensors` now works. * convert-hf : fix stacking MoE expert tensors `torch.stack` and `torch.cat` don't do the same thing. * convert-hf : fix Mamba conversion Tested to work even with a SentencePiece-based tokenizer. * convert : use a string for the SentencePiece tokenizer path * convert-hf : display tensor shape * convert-hf : convert norms to f32 by default * convert-hf : sort model part names `os.listdir` is said to list files in arbitrary order. Sorting the file names should let "model-00009-of-00042.safetensors" be loaded before "model-00010-of-00042.safetensors". * convert-hf : use an ABC for Model again It seems Protocol can't be used as a statically type-checked ABC, because its subclasses also can't be instantiated. (why did it seem to work?) At least there's still a way to throw an error when forgetting to define the `model_arch` property of any registered Model subclasses. * convert-hf : use a plain class for Model, and forbid direct instantiation There are no abstract methods used anyway, so using ABC isn't really necessary. * convert-hf : more consistent formatting of cmdline args * convert-hf : align the message logged for converted tensors * convert-hf : fix Refact conversion * convert-hf : save memory with lazy evaluation * convert-hf : flake8 doesn't like lowercase L as a variable name * convert-hf : remove einops requirement for InternLM2 * convert-hf : faster model parts loading Instead of pre-loading them all into a dict, iterate on the tensors in the model parts progressively as needed in Model.write_tensors Conversion for some architectures relies on checking for the presence of specific tensor names, so for multi-part models, the weight map is read from the relevant json file to quickly get these names up-front. * convert-hf : minor changes for consistency * gguf-py : add tqdm as a dependency It's small, and used for a progress bar in GGUFWriter.write_tensors_to_file --- convert-hf-to-gguf.py | 2021 +++++++---------- convert.py | 20 +- examples/server/tests/features/steps/steps.py | 2 +- gguf-py/gguf/constants.py | 2 +- gguf-py/gguf/gguf_reader.py | 8 +- gguf-py/gguf/gguf_writer.py | 77 +- gguf-py/gguf/vocab.py | 6 +- gguf-py/pyproject.toml | 1 + gguf-py/scripts/gguf-dump.py | 2 +- gguf-py/scripts/gguf-new-metadata.py | 12 +- pyrightconfig.json | 3 + ...requirements-convert-hf-to-gguf-update.txt | 1 - .../requirements-convert-hf-to-gguf.txt | 1 - requirements/requirements-convert.txt | 2 +- 14 files changed, 873 insertions(+), 1285 deletions(-) create mode 100644 pyrightconfig.json diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 454e9fcdd..1dc18b2a5 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -9,11 +9,10 @@ import json import os import re import sys -from abc import ABC, abstractmethod from enum import IntEnum from pathlib import Path from hashlib import sha256 -from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterator, Sequence, TypeVar, cast +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast, overload import numpy as np import torch @@ -25,7 +24,7 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -from convert import LlamaHfVocab, permute +from convert import LlamaHfVocab logger = logging.getLogger("hf-to-gguf") @@ -44,29 +43,55 @@ class SentencePieceTokenTypes(IntEnum): AnyModel = TypeVar("AnyModel", bound="type[Model]") -class Model(ABC): +class Model: _model_classes: dict[str, type[Model]] = {} - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool): + dir_model: Path + ftype: int + fname_out: Path + is_big_endian: bool + endianess: gguf.GGUFEndian + use_temp_file: bool + lazy: bool + part_names: list[str] + is_safetensors: bool + hparams: dict[str, Any] + gguf_writer: gguf.GGUFWriter + block_count: int + tensor_map: gguf.TensorNameMap + tensor_names: set[str] | None + + # subclasses should define this! + model_arch: gguf.MODEL_ARCH + + def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): + if self.__class__ == Model: + raise TypeError(f"{self.__class__.__name__!r} should not be directly instantiated") self.dir_model = dir_model self.ftype = ftype self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE self.use_temp_file = use_temp_file - self.is_safetensors = self._is_model_safetensors() - self.num_parts = Model.count_model_parts(self.dir_model, ".safetensors" if self.is_safetensors else ".bin") - self.part_names = self._get_part_names() + self.lazy = not eager + self.part_names = Model.get_model_part_names(self.dir_model, ".safetensors") + self.is_safetensors = len(self.part_names) > 0 + if not self.is_safetensors: + self.part_names = Model.get_model_part_names(self.dir_model, ".bin") self.hparams = Model.load_hparams(self.dir_model) self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) + self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) + self.tensor_names = None - @property - @abstractmethod - def model_arch(self) -> gguf.MODEL_ARCH: - pass + @classmethod + def __init_subclass__(cls): + # can't use an abstract property, because overriding it without type errors + # would require using decorated functions instead of simply defining the property + if "model_arch" not in cls.__dict__: + raise TypeError(f"Missing property 'model_arch' for {cls.__name__!r}") - def find_hparam(self, keys: Sequence[str], optional: bool = False) -> Any: + def find_hparam(self, keys: Iterable[str], optional: bool = False) -> Any: key = next((k for k in keys if k in self.hparams), None) if key is not None: return self.hparams[key] @@ -78,6 +103,22 @@ class Model(ABC): self._set_vocab_gpt2() def get_tensors(self) -> Iterator[tuple[str, Tensor]]: + tensor_names_from_parts: set[str] = set() + + if len(self.part_names) > 1: + self.tensor_names = set() + index_name = "model.safetensors" if self.is_safetensors else "pytorch_model.bin" + index_name += ".index.json" + logger.info(f"gguf: loading model weight map from '{index_name}'") + with open(self.dir_model / index_name, "r", encoding="utf-8") as f: + index: dict[str, Any] = json.load(f) + weight_map = index.get("weight_map") + if weight_map is None or not isinstance(weight_map, dict): + raise ValueError(f"Can't load 'weight_map' from {index_name!r}") + self.tensor_names.update(weight_map.keys()) + else: + self.tensor_names = tensor_names_from_parts + for part_name in self.part_names: logger.info(f"gguf: loading model part '{part_name}'") ctx: ContextManager[Any] @@ -88,10 +129,33 @@ class Model(ABC): ctx = contextlib.nullcontext(torch.load(str(self.dir_model / part_name), map_location="cpu", mmap=True, weights_only=True)) with ctx as model_part: + tensor_names_from_parts.update(model_part.keys()) + for name in model_part.keys(): data = model_part.get_tensor(name) if self.is_safetensors else model_part[name] + if self.lazy: + data = LazyTorchTensor.from_eager(data) yield name, data + # only verify tensor name presence; it doesn't matter if they are not in the right files + if len(sym_diff := tensor_names_from_parts.symmetric_difference(self.tensor_names)) > 0: + raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}") + + def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str: + name: str = gguf.TENSOR_NAMES[key] + if key not in gguf.MODEL_TENSORS[self.model_arch]: + raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}") + if "{bid}" in name: + assert bid is not None + name = name.format(bid=bid) + return name + suffix + + def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str: + new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes) + if new_name is None: + raise ValueError(f"Can not map tensor {name!r}") + return new_name + def set_gguf_parameters(self): self.gguf_writer.add_name(self.dir_model.name) self.gguf_writer.add_block_count(self.block_count) @@ -135,12 +199,27 @@ class Model(ABC): self.gguf_writer.add_file_type(self.ftype) logger.info(f"gguf: file type = {self.ftype}") + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + return [(self.map_tensor_name(name), data_torch)] + + def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del name, new_name, bid, n_dims # unused + + return False + + def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del name, new_name, bid, n_dims # unused + + return False + def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") + for name, data_torch in self.get_tensors(): # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): + if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): continue old_dtype = data_torch.dtype @@ -149,37 +228,52 @@ class Model(ABC): if data_torch.dtype not in (torch.float16, torch.float32): data_torch = data_torch.to(torch.float32) - data = data_torch.squeeze().numpy() + # use the first number-like part of the tensor name as the block id + bid = None + for part in name.split("."): + if part.isdecimal(): + bid = int(part) + break - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + for new_name, data in ((n, d.squeeze().numpy()) for n, d in self.modify_tensors(data_torch, name, bid)): + data: np.ndarray = data # type hint + n_dims = len(data.shape) + data_dtype = data.dtype - n_dims = len(data.shape) - data_dtype = data.dtype + # if f32 desired, convert any float16 to float32 + if self.ftype == 0 and data_dtype == np.float16: + data = data.astype(np.float32) - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + # when both are True, f32 should win + extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims) + extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims) - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): - data = data.astype(np.float32) + # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors + extra_f32 = extra_f32 or n_dims == 1 or new_name.endswith("_norm.weight") - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) + # if f16 desired, convert any float32 2-dim weight tensors to float16 + extra_f16 = extra_f16 or (name.endswith(".weight") and n_dims >= 2) - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + # when both extra_f32 and extra_f16 are False, convert to float32 by default + if self.ftype == 1 and data_dtype == np.float16 and (extra_f32 or not extra_f16): + data = data.astype(np.float32) - self.gguf_writer.add_tensor(new_name, data) + if self.ftype == 1 and data_dtype == np.float32 and extra_f16 and not extra_f32: + data = data.astype(np.float16) + + # reverse shape to make it similar to the internal ggml dimension order + shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}" + + # n_dims is implicit in the shape + logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data.dtype}, shape = {shape_str}") + + self.gguf_writer.add_tensor(new_name, data) def write(self): self.write_tensors() self.gguf_writer.write_header_to_file() self.gguf_writer.write_kv_data_to_file() - self.gguf_writer.write_tensors_to_file() + self.gguf_writer.write_tensors_to_file(progress=True) self.gguf_writer.close() def write_vocab(self): @@ -188,16 +282,18 @@ class Model(ABC): self.gguf_writer.close() @staticmethod - def count_model_parts(dir_model: Path, prefix: str) -> int: - num_parts = 0 + def get_model_part_names(dir_model: Path, suffix: str) -> list[str]: + part_names: list[str] = [] for filename in os.listdir(dir_model): - if filename.endswith(prefix): - num_parts += 1 + if filename.endswith(suffix): + part_names.append(filename) - return num_parts + part_names.sort() + + return part_names @staticmethod - def load_hparams(dir_model): + def load_hparams(dir_model: Path): with open(dir_model / "config.json", "r", encoding="utf-8") as f: return json.load(f) @@ -205,32 +301,19 @@ class Model(ABC): def register(cls, *names: str) -> Callable[[AnyModel], AnyModel]: assert names - def func(modelcls: type[Model]): + def func(modelcls: AnyModel) -> AnyModel: for name in names: cls._model_classes[name] = modelcls return modelcls return func @classmethod - def from_model_architecture(cls, arch): + def from_model_architecture(cls, arch: str) -> type[Model]: try: return cls._model_classes[arch] except KeyError: raise NotImplementedError(f'Architecture {arch!r} not supported!') from None - def _is_model_safetensors(self) -> bool: - return Model.count_model_parts(self.dir_model, ".safetensors") > 0 - - def _get_part_names(self): - if self.is_safetensors: - if self.num_parts == 1: # there's only one .safetensors file - return ("model.safetensors",) - return (f"model-{n:05}-of-{self.num_parts:05}.safetensors" for n in range(1, self.num_parts + 1)) - - if self.num_parts == 1: # there's only one .bin file - return ("pytorch_model.bin",) - return (f"pytorch_model-{n:05}-of-{self.num_parts:05}.bin" for n in range(1, self.num_parts + 1)) - # used for GPT-2 BPE and WordPiece vocabs def get_vocab_base(self) -> tuple[list[str], list[int], str]: tokens: list[str] = [] @@ -420,22 +503,24 @@ class Model(ABC): if not tokenizer_path.is_file(): raise FileNotFoundError(f"File not found: {tokenizer_path}") - tokenizer = SentencePieceProcessor(str(tokenizer_path)) + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) for token_id in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(token_id) + piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") - score = tokenizer.get_score(token_id) + score = tokenizer.GetScore(token_id) toktype = SentencePieceTokenTypes.NORMAL - if tokenizer.is_unknown(token_id): + if tokenizer.IsUnknown(token_id): toktype = SentencePieceTokenTypes.UNKNOWN - elif tokenizer.is_control(token_id): + elif tokenizer.IsControl(token_id): toktype = SentencePieceTokenTypes.CONTROL - elif tokenizer.is_unused(token_id): + elif tokenizer.IsUnused(token_id): toktype = SentencePieceTokenTypes.UNUSED - elif tokenizer.is_byte(token_id): + elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE tokens.append(text) @@ -458,7 +543,7 @@ class Model(ABC): pad_count = vocab_size - len(tokens) logger.debug(f"Padding vocab with {pad_count} token(s) - [PAD1] through [PAD{pad_count}]") for i in range(1, pad_count + 1): - tokens.append(f"[PAD{i}]") + tokens.append(bytes(f"[PAD{i}]", encoding="utf-8")) scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.UNUSED) @@ -533,81 +618,52 @@ class BloomModel(Model): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): - block_count = self.hparams["n_layer"] - tensors = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - has_lm_head = True + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) - for name, data_torch in tensors.items(): - if "lm_head.weight" not in tensors.keys() and "output.weight" not in tensors.keys(): - has_lm_head = False + name = re.sub(r'transformer\.', '', name) - name = re.sub(r'transformer\.', '', name) + tensors: list[tuple[str, Tensor]] = [] - old_dtype = data_torch.dtype + if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + tensors.append((self.map_tensor_name(name), data_torch)) - data = data_torch.squeeze().numpy() + if name == "word_embeddings.weight": + assert self.tensor_names is not None - if re.match(r"h\.\d+\.self_attention\.query_key_value\.weight", name): - # Map bloom-style qkv_linear to gpt-style qkv_linear - # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa - # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa - qkv_weights = data.reshape((n_head, 3, n_embed // n_head, n_embed)) - data = np.concatenate( - ( - qkv_weights[:, 0, :, :].reshape((-1, n_embed)), - qkv_weights[:, 1, :, :].reshape((-1, n_embed)), - qkv_weights[:, 2, :, :].reshape((-1, n_embed)), - ), - axis=0, - ) - logger.info("re-format attention.linear_qkv.weight") - elif re.match(r"h\.\d+\.self_attention\.query_key_value\.bias", name): - qkv_bias = data.reshape((n_head, 3, n_embed // n_head)) - data = np.concatenate( - ( - qkv_bias[:, 0, :].reshape((n_embed,)), - qkv_bias[:, 1, :].reshape((n_embed,)), - qkv_bias[:, 2, :].reshape((n_embed,)), - ), - axis=0, - ) - logger.info("re-format attention.linear_qkv.bias") + # TODO: tie them at runtime, don't duplicate in the model file + if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")): + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"=> {new_name}, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "word_embeddings.weight": - self.gguf_writer.add_tensor("output.weight", data) - logger.info(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + return tensors @Model.register("MPTForCausalLM") @@ -643,50 +699,16 @@ class MPTModel(Model): else: self.gguf_writer.add_max_alibi_bias(0.0) - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - old_dtype = data_torch.dtype + if "scales" in name: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias", ".scales")) + new_name = new_name.replace("scales", "act.scales") + else: + new_name = self.map_tensor_name(name, try_suffixes=(".weight", ".bias")) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - if "scales" in name: - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias", ".scales")) - if new_name is not None: - new_name = new_name.replace("scales", "act.scales") - else: - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(new_name, data_torch)] @Model.register("OrionForCausalLM") @@ -726,48 +748,6 @@ class OrionModel(Model): # ref: https://huggingface.co/OrionStarAI/Orion-14B-Chat/blob/276a17221ce42beb45f66fac657a41540e71f4f5/modeling_orion.py#L570-L571 self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) - def write_tensors(self): - # Collect tensors from generator object - model_kv = dict(self.get_tensors()) - block_count = self.hparams["num_hidden_layers"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - @Model.register("BaichuanForCausalLM", "BaiChuanForCausalLM") class BaichuanModel(Model): @@ -809,60 +789,26 @@ class BaichuanModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - def write_tensors(self): - # Collect tensors from generator object - model_kv = dict(self.get_tensors()) - block_count = self.hparams["num_hidden_layers"] + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: head_count = self.hparams["num_attention_heads"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) head_count_kv = self.hparams.get("num_key_value_heads", head_count) - for i in range(block_count): - if (w := model_kv.get(f"model.layers.{i}.self_attn.W_pack.weight")) is not None: - logger.info(f"Unpacking and permuting layer {i}") - model_kv[f"model.layers.{i}.self_attn.q_proj.weight"] = \ - self._reverse_hf_permute_part(w, 0, head_count, head_count) - model_kv[f"model.layers.{i}.self_attn.k_proj.weight"] = \ - self._reverse_hf_permute_part(w, 1, head_count, head_count_kv) - model_kv[f"model.layers.{i}.self_attn.v_proj.weight"] = \ - self._reverse_hf_part(w, 2) - del model_kv[f"model.layers.{i}.self_attn.W_pack.weight"] + tensors: list[tuple[str, Tensor]] = [] - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue + if bid is not None and name == f"model.layers.{bid}.self_attn.W_pack.weight": + logger.info(f"Unpacking and permuting layer {bid}") + tensors = [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), + self._reverse_hf_permute_part(data_torch, 0, head_count, head_count)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), + self._reverse_hf_permute_part(data_torch, 1, head_count, head_count_kv)), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), + self._reverse_hf_part(data_torch, 2)), + ] + else: + tensors = [(self.map_tensor_name(name), data_torch)] - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) + return tensors def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: if n_kv_head is not None and n_head != n_kv_head: @@ -894,7 +840,7 @@ class XverseModel(Model): dir_model = self.dir_model hparams = self.hparams - tokens: list[bytearray] = [] + tokens: list[bytes] = [] toktypes: list[int] = [] from transformers import AutoTokenizer @@ -902,7 +848,7 @@ class XverseModel(Model): vocab_size = hparams.get("vocab_size", len(tokenizer.vocab)) assert max(tokenizer.vocab.values()) < vocab_size - reverse_vocab = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} + reverse_vocab: dict[int, str] = {id_: encoded_tok for encoded_tok, id_ in tokenizer.vocab.items()} added_vocab = tokenizer.get_added_vocab() for token_id in range(vocab_size): @@ -965,55 +911,19 @@ class XverseModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - def write_tensors(self): - # Collect tensors from generator object - model_kv = dict(self.get_tensors()) - block_count = self.hparams["num_hidden_layers"] + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + head_count = self.hparams["num_attention_heads"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) head_count_kv = self.hparams.get("num_key_value_heads", head_count) - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue + # HF models permute some of the tensors, so we need to undo that + if name.endswith("q_proj.weight"): + data_torch = self._reverse_hf_permute(data_torch, head_count, head_count) + if name.endswith("k_proj.weight"): + data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv) - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # HF models permute some of the tensors, so we need to undo that - if name.endswith(("q_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, head_count, head_count) - if name.endswith(("k_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, head_count, head_count_kv) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] def _reverse_hf_permute(self, weights: Tensor, n_head: int, n_kv_head: int | None = None) -> Tensor: if n_kv_head is not None and n_head != n_kv_head: @@ -1054,71 +964,31 @@ class FalconModel(Model): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): - block_count = self.hparams.get("num_hidden_layers") - if block_count is None: - block_count = self.hparams["n_layer"] # old name + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - n_head = self.hparams.get("num_attention_heads") - if n_head is None: - n_head = self.hparams["n_head"] # old name + # QKV tensor transform + # The original query_key_value tensor contains n_head_kv "kv groups", + # each consisting of n_head/n_head_kv query weights followed by one key + # and one value weight (shared by all query heads in the kv group). + # This layout makes it a big pain to work with in GGML. + # So we rearrange them here,, so that we have n_head query weights + # followed by n_head_kv key weights followed by n_head_kv value weights, + # in contiguous fashion. + # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - n_head_kv = self.hparams.get("num_kv_heads") - if n_head_kv is None: - n_head_kv = self.hparams.get("n_head_kv", 1) # old name + if "query_key_value" in name: + n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_kv_heads", "n_head_kv"], optional=True) or 1 + head_dim = self.hparams["hidden_size"] // n_head - head_dim = self.hparams["hidden_size"] // n_head - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) + q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) + k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) + v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) + data_torch = torch.cat((q, k, v)).reshape_as(data_torch) - for name, data_torch in self.get_tensors(): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # QKV tensor transform - # The original query_key_value tensor contains n_head_kv "kv groups", - # each consisting of n_head/n_head_kv query weights followed by one key - # and one value weight (shared by all query heads in the kv group). - # This layout makes it a big pain to work with in GGML. - # So we rearrange them here,, so that we have n_head query weights - # followed by n_head_kv key weights followed by n_head_kv value weights, - # in contiguous fashion. - # ref: https://github.com/jploski/ggml/blob/falcon40b/examples/falcon/convert-hf-to-ggml.py - - if "query_key_value" in name: - qkv = data_torch.view(n_head_kv, n_head // n_head_kv + 2, head_dim, head_dim * n_head) - q = qkv[:, :-2].reshape(n_head * head_dim, head_dim * n_head) - k = qkv[:, [-2]].reshape(n_head_kv * head_dim, head_dim * n_head) - v = qkv[:, [-1]].reshape(n_head_kv * head_dim, head_dim * n_head) - data_torch = torch.cat((q, k, v)).reshape_as(data_torch) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] @Model.register("GPTBigCodeForCausalLM") @@ -1164,7 +1034,7 @@ class RefactModel(Model): self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim hidden_dim = int(2 * inner_dim / 3) @@ -1173,56 +1043,23 @@ class RefactModel(Model): n_head = self.hparams["n_head"] n_head_kv = 1 head_dim = self.hparams["n_embd"] // n_head - block_count = self.hparams["n_layer"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + tensors: list[tuple[str, Tensor]] = [] - tensors = dict(self.get_tensors()) - for i in range(block_count): - if (w := tensors.get(f"transformer.h.{i}.attn.kv.weight")) is not None: - tensors[f"model.layers.{i}.self_attn.k_proj.weight"] = w[:n_head_kv * head_dim] - tensors[f"model.layers.{i}.self_attn.v_proj.weight"] = w[n_head_kv * head_dim:] - del tensors[f"transformer.h.{i}.attn.kv.weight"] - if (w := tensors.get(f"transformer.h.{i}.attn.q.weight")) is not None: - tensors[f"model.layers.{i}.self_attn.q_proj.weight"] = w - del tensors[f"transformer.h.{i}.attn.q.weight"] - if (w := tensors.get(f"transformer.h.{i}.mlp.gate_up_proj.weight")) is not None: - tensors[f"model.layers.{i}.mlp.gate_proj.weight"] = w[:ff_dim] - tensors[f"model.layers.{i}.mlp.up_proj.weight"] = w[ff_dim:] - del tensors[f"transformer.h.{i}.mlp.gate_up_proj.weight"] + if bid is not None: + if name == f"transformer.h.{bid}.attn.kv.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), data_torch[:n_head_kv * head_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), data_torch[n_head_kv * head_dim:])) + elif name == f"transformer.h.{bid}.attn.q.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), data_torch)) + elif name == f"transformer.h.{bid}.mlp.gate_up_proj.weight": + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_GATE, bid), data_torch[:ff_dim])) + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.FFN_UP, bid), data_torch[ff_dim:])) - for name, data_torch in tensors.items(): - old_dtype = data_torch.dtype + if len(tensors) == 0: + tensors.append((self.map_tensor_name(name), data_torch)) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight",)) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return tensors @Model.register("PersimmonForCausalLM") @@ -1257,22 +1094,11 @@ class PersimmonModel(Model): # self.gguf_writer.add_bos_token_id(71013) # self.gguf_writer.add_eos_token_id(71013) - def write_tensors(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del name, new_name, bid, n_dims # unused - for name, data_torch in self.get_tensors(): - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data_torch.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data_torch.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - n_dims = len(data.shape) - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) + # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) + return True @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") @@ -1302,84 +1128,67 @@ class StableLMModel(Model): self.gguf_writer.add_parallel_residual(hparams["use_parallel_residual"] if "use_parallel_residual" in hparams else True) self.gguf_writer.add_layer_norm_eps(self.find_hparam(["layer_norm_eps", "norm_eps"])) + _q_norms: list[dict[str, Tensor]] | None = None + _k_norms: list[dict[str, Tensor]] | None = None + + 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["num_key_value_heads"] + + if name.find("q_layernorm.norms") != -1: + assert bid is not None + + if self._q_norms is None: + self._q_norms = [{} for _ in range(self.block_count)] + + self._q_norms[bid][name] = data_torch + + if len(self._q_norms[bid]) >= n_head: + return self._stack_qk_norm(bid, n_head, self._q_norms[bid], "q_layernorm") + else: + return [] + + if name.find("k_layernorm.norms") != -1: + assert bid is not None + + if self._k_norms is None: + self._k_norms = [{} for _ in range(self.block_count)] + + self._k_norms[bid][name] = data_torch + + if len(self._k_norms[bid]) >= n_kv_head: + return self._stack_qk_norm(bid, n_kv_head, self._k_norms[bid], "k_layernorm") + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def _stack_qk_norm(self, bid: int, n_head: int, norms: dict[str, Tensor], layer_name: str = "q_layernorm"): + datas: list[Tensor] = [] + # extract the norms in order + for xid in range(n_head): + ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" + datas.append(norms[ename]) + del norms[ename] + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" + new_name = self.map_tensor_name(merged_name) + + return [(new_name, data_torch)] + def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("num_attention_heads") - n_kv_head = self.hparams.get("num_key_value_heads") - q_norms = dict() - k_norms = dict() - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue + super().write_tensors() - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - n_dims = len(data.shape) - if name.find("q_layernorm.norms") != -1: - q_norms[name] = data - if len(q_norms) >= (block_count * n_head): - self._stack_qk_norm(block_count, name, tensor_map, n_head, q_norms, n_dims, layer_name="q_layernorm") - continue - if name.find("k_layernorm.norms") != -1: - k_norms[name] = data - if len(k_norms) >= (block_count * n_kv_head): - self._stack_qk_norm(block_count, name, tensor_map, n_kv_head, k_norms, n_dims, layer_name="k_layernorm") - continue - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.debug(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - def _stack_qk_norm(self, block_count, name, tensor_map, n_head, norms, n_dims, layer_name="q_layernorm"): - for bid in range(block_count): - datas = [] - for xid in range(n_head): - ename = f"model.layers.{bid}.self_attn.{layer_name}.norms.{xid}.weight" - datas.append(norms[ename]) - del norms[ename] - data = np.stack(datas, axis=0) - data_dtype = data.dtype - merged_name = f"model.layers.{bid}.self_attn.{layer_name}.weight" - new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and not new_name.endswith("_norm.weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + if self._q_norms is not None or self._k_norms is not None: + # flatten two `list[dict[str, Tensor]]` into a single `list[str]` + norms = ( + [k for d in self._q_norms for k in d.keys()] if self._q_norms is not None else [] + ) + ( + [k for d in self._k_norms for k in d.keys()] if self._k_norms is not None else [] + ) + if len(norms) > 0: + raise ValueError(f"Unprocessed norms: {norms}") @Model.register("LlamaForCausalLM", "MistralForCausalLM", "MixtralForCausalLM") @@ -1419,102 +1228,69 @@ class LlamaModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) - # Same as super class, but permuting q_proj, k_proj - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("num_attention_heads") + @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: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + + _experts: list[dict[str, Tensor]] | None = None + + 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") - n_experts = self.hparams.get("num_local_experts") - experts = dict() - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".rotary_emb.inv_freq")): - continue - old_dtype = data_torch.dtype + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] - data = data_torch.numpy() + assert bid is not None - if name.endswith("q_proj.weight"): - data = permute(data, n_head, n_head) - if name.endswith("k_proj.weight"): - data = permute(data, n_head, n_kv_head) + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] - data = data.squeeze() + self._experts[bid][name] = data_torch - # process the experts separately - if name.find("block_sparse_moe.experts") != -1: - experts[name] = data - if len(experts) >= n_experts: - # merge the experts into a single 3d tensor - for bid in range(block_count): - for wid in range(1, 4): - full = True - for xid in range(n_experts): - ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight" - if ename not in experts: - full = False - break - if not full: - continue + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] - datas = [] - for xid in range(n_experts): - ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.w{wid}.weight" - datas.append(experts[ename]) - del experts[ename] + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] - data = np.stack(datas, axis=0) - data_dtype = data.dtype + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + data_torch = torch.stack(datas, dim=0) - if self.ftype == 1 and data_dtype == np.float32: - data = data.astype(np.float16) + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" - merged_name = f"layers.{bid}.feed_forward.experts.w{wid}.weight" + new_name = self.map_tensor_name(merged_name) - new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + tensors.append((new_name, data_torch)) + return tensors + else: + return [] - logger.info(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + return [(self.map_tensor_name(name), data_torch)] - self.gguf_writer.add_tensor(new_name, data) - continue + def write_tensors(self): + super().write_tensors() - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # 1d tensors need to be converted to float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if len(experts) > 0: - raise ValueError(f"Unprocessed experts: {experts.keys()}") + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") @Model.register("GrokForCausalLM") @@ -1531,89 +1307,44 @@ class GrokModel(Model): super().set_gguf_parameters() self.gguf_writer.add_name("Grok") - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_experts = self.hparams.get("num_local_experts") - experts = dict() - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue + _experts: list[dict[str, Tensor]] | None = None - old_dtype = data_torch.dtype + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find(".moe.") != -1: + n_experts = self.hparams["num_local_experts"] - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + assert bid is not None - data = data_torch.squeeze().numpy() + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] - # process the experts separately - if name.find(".moe.") != -1: - experts[name] = data - if len(experts) >= n_experts: - # merge the experts into a single 3d tensor - for bid in range(block_count): - for wid in ["linear", "linear_1", "linear_v"]: - full = True - for xid in range(n_experts): - ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" - if ename not in experts: - full = False - break - if not full: - continue + self._experts[bid][name] = data_torch - datas = [] - for xid in range(n_experts): - ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" - datas.append(experts[ename]) - del experts[ename] + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] - data = np.stack(datas, axis=0) - data_dtype = data.dtype + # merge the experts into a single 3d tensor + for wid in ["linear", "linear_1", "linear_v"]: + datas: list[Tensor] = [] - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + for xid in range(n_experts): + ename = f"transformer.decoder_layer.{bid}.moe.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] - if self.ftype == 1 and data_dtype == np.float32: - data = data.astype(np.float16) + data_torch = torch.stack(datas, dim=0) - merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight" + merged_name = f"transformer.decoder_layer.{bid}.moe.{wid}.weight" - new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + new_name = self.map_tensor_name(merged_name) - logger.info(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") + tensors.append((new_name, data_torch)) + return tensors + else: + return [] - self.gguf_writer.add_tensor(new_name, data) - continue - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] @Model.register("DbrxForCausalLM") @@ -1646,68 +1377,45 @@ class DbrxModel(Model): self.gguf_writer.add_file_type(self.ftype) logger.info(f"gguf: file type = {self.ftype}") - def write_tensors(self): - block_count = self.hparams.get("n_layers") - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in self.get_tensors(): - n_expert = self.hparams["ffn_config"]["moe_num_experts"] - n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] - n_embd = self.hparams["d_model"] + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose - # original implementation expects (n_expert, n_ff, n_embd) for all experts weights - # But llama.cpp moe graph works differently - # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions - # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor - exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} - "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} - "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} - experts = False - for exp_tensor_name in exp_tensor_names.keys(): - if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: - experts = True - data_torch = data_torch.view(n_expert, n_ff, n_embd) - if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: - data_torch = data_torch.permute(*permute_tensor) - break + n_expert = self.hparams["ffn_config"]["moe_num_experts"] + n_ff = self.hparams["ffn_config"]["ffn_hidden_size"] + n_embd = self.hparams["d_model"] - old_dtype = data_torch.dtype + # Specific behavior for experts tensors: suffix .weight, view as 3D and transpose + # original implementation expects (n_expert, n_ff, n_embd) for all experts weights + # But llama.cpp moe graph works differently + # AND the dimensions in ggml are typically in the reverse order of the pytorch dimensions + # so (n_expert, n_ff, n_embd) in pytorch is {n_embd, n_ff, n_expert} in ggml_tensor + exp_tensor_names = {"ffn.experts.mlp.w1": None, # LLM_TENSOR_FFN_GATE_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + "ffn.experts.mlp.w2": (0, 2, 1), # LLM_TENSOR_FFN_DOWN_EXPS ggml_tensor->ne{n_ff, n_embd, n_expert} + "ffn.experts.mlp.v1": None} # LLM_TENSOR_FFN_UP_EXPS ggml_tensor->ne{n_embd, n_ff, n_expert} + experts = False - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + for exp_tensor_name in exp_tensor_names.keys(): + if name.find(exp_tensor_name) != -1 and name.find(".weight") == -1: + experts = True + data_torch = data_torch.view(n_expert, n_ff, n_embd) + if (permute_tensor := exp_tensor_names[exp_tensor_name]) is not None: + data_torch = data_torch.permute(*permute_tensor) + break - data = data_torch.squeeze().numpy() + # map tensor names + # In MoE models the ffn tensors are typically most of the model weights, + # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. + # Every other model has the weight names ending in .weight, + # let's assume that is the convention which is not the case for dbrx: + # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 + new_name = self.map_tensor_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) - # map tensor names - # In MoE models the ffn tensors are typically most of the model weights, - # and need to be quantizable. Quantize expects tensor names to be suffixed by .weight. - # Every other model has the weight names ending in .weight, - # let's assume that is the convention which is not the case for dbrx: - # https://huggingface.co/databricks/dbrx-instruct/blob/main/model.safetensors.index.json#L15 - new_name = tensor_map.get_name(name if not experts else name + ".weight", try_suffixes=(".weight",)) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + return [(new_name, data_torch)] - n_dims = len(data.shape) - data_dtype = data.dtype + def extra_f16_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del name, new_name, bid # unused - # Most of the codebase that takes in 1D tensors only handles F32 tensors - # and most of the outputs tensors are F32. - if data_dtype != np.float32 and n_dims == 1: - raise ValueError(f"Can not map tensor {name!r}: all 1D tensors must be F32") - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and n_dims > 1: - data = data.astype(np.float16) - - logger.debug(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return n_dims > 1 @Model.register("MiniCPMForCausalLM") @@ -1740,53 +1448,19 @@ class MiniCPMModel(Model): .reshape(weights.shape) ) - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("num_attention_heads") + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue - old_dtype = data_torch.dtype + # HF models permute some of the tensors, so we need to undo that + if name.endswith(("q_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) + if name.endswith(("k_proj.weight")): + data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # HF models permute some of the tensors, so we need to undo that - if name.endswith(("q_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_head) - if name.endswith(("k_proj.weight")): - data_torch = self._reverse_hf_permute(data_torch, n_head, n_kv_head) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] @Model.register("QWenLMHeadModel") @@ -1830,46 +1504,6 @@ class QwenModel(Model): self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) self.gguf_writer.add_layer_norm_rms_eps(self.hparams["layer_norm_epsilon"]) - def write_tensors(self): - block_count = self.hparams["num_hidden_layers"] - model_kv = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - @Model.register("Qwen2ForCausalLM") class Qwen2Model(Model): @@ -1891,92 +1525,52 @@ class Qwen2MoeModel(Model): if (n_experts := self.hparams.get("num_experts")) is not None: self.gguf_writer.add_expert_count(n_experts) + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("experts") != -1: + n_experts = self.hparams["num_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_experts = self.hparams.get("num_experts") - experts = dict() - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq")): - continue + super().write_tensors() - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # process the experts separately - if name.find("experts") != -1: - experts[name] = data - if len(experts) >= n_experts * 3: - # merge the experts into a single 3d tensor - for bid in range(block_count): - for w_name in ["down_proj", "gate_proj", "up_proj"]: - full = True - for xid in range(n_experts): - ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" - if ename not in experts: - full = False - break - if not full: - continue - - datas = [] - for xid in range(n_experts): - ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" - datas.append(experts[ename]) - del experts[ename] - - data = np.stack(datas, axis=0) - data_dtype = data.dtype - - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - if self.ftype == 1 and data_dtype == np.float32: - data = data.astype(np.float16) - - merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" - - new_name = tensor_map.get_name(merged_name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - logger.debug(f"{new_name}, n_dims = {len(data.shape)}, shape = {data.shape} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - continue - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and (n_dims == 1 or new_name.endswith("_norm.weight")): - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.debug(f"{new_name}, n_dims = {n_dims}, shape = {data.shape}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if len(experts) > 0: - raise ValueError(f"Unprocessed experts: {experts.keys()}") + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") @Model.register("GPT2LMHeadModel") @@ -1993,54 +1587,27 @@ class GPT2Model(Model): self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_epsilon"]) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - for name, data_torch in self.get_tensors(): - # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")): - continue + tensors: list[tuple[str, Tensor]] = [] - if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): - data_torch = data_torch.transpose(1, 0) + # we don't need these + if name.endswith((".attn.bias", ".attn.masked_bias")): + return tensors - old_dtype = data_torch.dtype + if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): + data_torch = data_torch.transpose(1, 0) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + new_name = self.map_tensor_name(name) - data = data_torch.squeeze().numpy() + tensors.append((new_name, data_torch)) - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + # note: GPT2 output is tied to (same as) wte in original model + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - # note: GPT2 output is tied to (same as) wte in original model - if new_name == "token_embd.weight": - logger.info(f"output.weight, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor("output.weight", data) + return tensors @Model.register("PhiForCausalLM") @@ -2080,7 +1647,8 @@ class Phi3MiniModel(Model): if not tokenizer_path.is_file(): raise ValueError(f'Error: Missing {tokenizer_path}') - tokenizer = SentencePieceProcessor(str(tokenizer_path)) + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) @@ -2090,18 +1658,18 @@ class Phi3MiniModel(Model): for token_id in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(token_id) + piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") - score = tokenizer.get_score(token_id) + score = tokenizer.GetScore(token_id) toktype = SentencePieceTokenTypes.NORMAL - if tokenizer.is_unknown(token_id): + if tokenizer.IsUnknown(token_id): toktype = SentencePieceTokenTypes.UNKNOWN - elif tokenizer.is_control(token_id): + elif tokenizer.IsControl(token_id): toktype = SentencePieceTokenTypes.CONTROL - elif tokenizer.is_unused(token_id): + elif tokenizer.IsUnused(token_id): toktype = SentencePieceTokenTypes.UNUSED - elif tokenizer.is_byte(token_id): + elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE tokens[token_id] = text @@ -2187,51 +1755,18 @@ class PlamoModel(Model): data_torch = torch.reshape(data_torch, (5120, 5120)) return data_torch - def write_tensors(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - for name, data_torch in self.get_tensors(): - if "self_attn.rotary_emb.inv_freq" in name: - continue + new_name = self.map_tensor_name(name) - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + # shuffle for broadcasting of gqa in ggml_mul_mat + if new_name.endswith("attn_q.weight"): + data_torch = self.shuffle_attn_q_weight(data_torch) + elif new_name.endswith("attn_output.weight"): + data_torch = self.shuffle_attn_output_weight(data_torch) - # shuffle for broadcasting of gqa in ggml_mul_mat - if new_name.endswith("attn_q.weight"): - data_torch = self.shuffle_attn_q_weight(data_torch) - elif new_name.endswith("attn_output.weight"): - data_torch = self.shuffle_attn_output_weight(data_torch) - - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(new_name, data_torch)] @Model.register("CodeShellForCausalLM") @@ -2254,51 +1789,21 @@ class CodeShellModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(1.0) - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - tensors = dict(self.get_tensors()) - has_lm_head = "lm_head.weight" in tensors.keys() or "output.weight" in tensors.keys() - for name, data_torch in tensors.items(): - # we don't need these - if name.endswith((".attn.rotary_emb.inv_freq")): - continue + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - old_dtype = data_torch.dtype + new_name = self.map_tensor_name(name) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + tensors: list[tuple[str, Tensor]] = [(new_name, data_torch)] - data = data_torch.squeeze().numpy() + if new_name == self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD): + assert self.tensor_names is not None - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + if all(s not in self.tensor_names for s in ("lm_head.weight", "output.weight")): + # copy tok_embd.weight to output.weight + tensors.append((self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT), data_torch)) - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) - - if not has_lm_head and name == "transformer.wte.weight": - self.gguf_writer.add_tensor("output.weight", data) - logger.info(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + return tensors @Model.register("InternLM2ForCausalLM") @@ -2327,27 +1832,29 @@ class InternLM2Model(Model): sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix - tokenizer = SentencePieceProcessor(str(tokenizer_path)) + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) for token_id in range(vocab_size): - piece = tokenizer.id_to_piece(token_id) + piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") - score = tokenizer.get_score(token_id) + score = tokenizer.GetScore(token_id) if text == b"\x00": # (TODO): fixme # Hack here and replace the \x00 characters. - logger.debug(f"InternLM2 convert token '{text}' to '🐉'!") - text = "🐉" + logger.warning(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉".encode("utf-8") toktype = SentencePieceTokenTypes.NORMAL - if tokenizer.is_unknown(token_id): + if tokenizer.IsUnknown(token_id): toktype = SentencePieceTokenTypes.UNKNOWN - elif tokenizer.is_control(token_id): + elif tokenizer.IsControl(token_id): toktype = SentencePieceTokenTypes.CONTROL - elif tokenizer.is_unused(token_id): + elif tokenizer.IsUnused(token_id): toktype = SentencePieceTokenTypes.UNUSED - elif tokenizer.is_byte(token_id): + elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE tokens.append(text) @@ -2384,13 +1891,15 @@ in chat mode so that the conversation can end normally.") special_vocab.add_to_gguf(self.gguf_writer) def _try_get_sft_eos(self, tokenizer): - unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]') - im_end_list = tokenizer.encode('<|im_end|>') + unused_145_list = tokenizer.Encode('[UNUSED_TOKEN_145]') + im_end_list = tokenizer.Encode('<|im_end|>') + eos_token = None assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1) if len(unused_145_list) == 1: eos_token = unused_145_list[0] if len(im_end_list) == 1: eos_token = im_end_list[0] + assert eos_token return eos_token def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int): @@ -2411,71 +1920,36 @@ in chat mode so that the conversation can end normally.") 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"]) - def post_write_tensors(self, tensor_map, name, data_torch): - old_dtype = data_torch.dtype - - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - self.gguf_writer.add_tensor(new_name, data) - - def write_tensors(self): - from einops import rearrange - - num_heads = self.hparams.get("num_attention_heads") - num_kv_heads = self.hparams.get("num_key_value_heads") - hidden_size = self.hparams.get("hidden_size") + 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"] + hidden_size = self.hparams["hidden_size"] q_per_kv = num_heads // num_kv_heads head_dim = hidden_size // num_heads num_groups = num_heads // q_per_kv - block_count = self.hparams["num_hidden_layers"] - model_kv = dict(self.get_tensors()) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv" - for name, data_torch in model_kv.items(): - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - if re.match(qkv_pattern, name): - bid = re.findall(qkv_pattern, name)[0] - qkv = data_torch - qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) - q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] - # The model weights of q and k equire additional reshape. - q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) - k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) - v = rearrange(v, " o g n i -> o (g n i)").T - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) - self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v) - else: - self.post_write_tensors(tensor_map, name, data_torch) + if re.match(qkv_pattern, name): + bid = re.findall(qkv_pattern, name)[0] + qkv = data_torch + # qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) + qkv = qkv.T.reshape((-1, num_groups, q_per_kv + 2, head_dim)) + q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] + # The model weights of q and k equire additional reshape. + # q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) + q = self._hf_permute_qk(q.reshape((q.shape[0], -1)).T, num_heads, num_heads) + # k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) + k = self._hf_permute_qk(k.reshape((k.shape[0], -1)).T, num_heads, num_kv_heads) + # v = rearrange(v, " o g n i -> o (g n i)").T + v = v.reshape((v.shape[0], -1)).T + return [ + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_Q, bid), q), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_K, bid), k), + (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_V, bid), v), + ] + else: + return [(self.map_tensor_name(name), data_torch)] @Model.register("BertModel", "CamembertModel") @@ -2540,43 +2014,20 @@ class BertModel(Model): special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) - def write_tensors(self): - tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) - tensors = dict(self.get_tensors()) - for name, data_torch in tensors.items(): - # we are only using BERT for embeddings so we don't need the pooling layer - if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): - continue # we don't need these + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + # we are only using BERT for embeddings so we don't need the pooling layer + if name in ("embeddings.position_ids", "pooler.dense.weight", "pooler.dense.bias"): + return [] # we don't need these - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + return [(self.map_tensor_name(name), data_torch)] - data = data_torch.squeeze().numpy() - n_dims = len(data.shape) - new_dtype: type[np.floating[Any]] + def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del new_name, bid, n_dims # unused - if ( - self.ftype == 1 and name.endswith(".weight") and n_dims == 2 - and name != "embeddings.token_type_embeddings.weight" # not used with get_rows, must be F32 - ): - # if f16 desired, convert any float32 2-dim weight tensors to float16 - new_dtype = np.float16 - else: - # if f32 desired, convert any float16 to float32 - new_dtype = np.float32 - - logger.info(f"{new_name}, n_dims = {n_dims}, {data_torch.dtype} --> {new_dtype}") - - if data.dtype != new_dtype: - data = data.astype(new_dtype) - - self.gguf_writer.add_tensor(new_name, data) + # not used with get_rows, must be F32 + return name == "embeddings.token_type_embeddings.weight" @Model.register("NomicBertModel") @@ -2642,45 +2093,20 @@ class GemmaModel(Model): self.gguf_writer.add_value_length(hparams["head_dim"]) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - for name, data_torch in self.get_tensors(): - # lm_head is not used in llama.cpp, while autoawq will include this tensor in model - # To prevent errors, skip loading lm_head.weight. - if name == "lm_head.weight": - logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") - continue + # lm_head is not used in llama.cpp, while autoawq will include this tensor in model + # To prevent errors, skip loading lm_head.weight. + if name == "lm_head.weight": + logger.debug(f"Skipping get tensor {name!r} in safetensors so that convert can end normally.") + return [] - old_dtype = data_torch.dtype + # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 + if name.endswith("norm.weight"): + data_torch = data_torch + 1 - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) - - # ref: https://github.com/huggingface/transformers/blob/fc37f38915372c15992b540dfcbbe00a916d4fc6/src/transformers/models/gemma/modeling_gemma.py#L89 - if name.endswith("norm.weight"): - data_torch = data_torch + 1 - data = data_torch.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] @Model.register("Starcoder2ForCausalLM") @@ -2703,6 +2129,8 @@ class MambaModel(Model): if (self.dir_model / "tokenizer.json").is_file(): self._set_vocab_gpt2() + elif (self.dir_model / "tokenizer.model").is_file(): + self._set_vocab_sentencepiece() else: # Use the GPT-NeoX tokenizer when no tokenizer files are present tokenizer_path = Path(sys.path[0]) / "models" / "ggml-vocab-gpt-neox.gguf" @@ -2710,28 +2138,34 @@ class MambaModel(Model): neox_reader = gguf.GGUFReader(tokenizer_path, "r") field = neox_reader.get_field(gguf.Keys.Tokenizer.MODEL) - self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1])) + self.gguf_writer.add_tokenizer_model(bytes(field.parts[-1]).decode("utf-8") if field else "gpt2") field = neox_reader.get_field(gguf.Keys.Tokenizer.PRE) - self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1])) + self.gguf_writer.add_tokenizer_pre(bytes(field.parts[-1]).decode("utf-8") if field else "mpt") field = neox_reader.get_field(gguf.Keys.Tokenizer.LIST) + assert field self.gguf_writer.add_token_list([bytes(field.parts[i]) for i in field.data][:vocab_size]) field = neox_reader.get_field(gguf.Keys.Tokenizer.TOKEN_TYPE) + assert field self.gguf_writer.add_token_types([field.parts[i].tolist()[0] for i in field.data][:vocab_size]) field = neox_reader.get_field(gguf.Keys.Tokenizer.MERGES) + assert field self.gguf_writer.add_token_merges([bytes(field.parts[i]) for i in field.data]) field = neox_reader.get_field(gguf.Keys.Tokenizer.BOS_ID) - self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0]) + self.gguf_writer.add_bos_token_id(field.parts[-1].tolist()[0] if field else 1) field = neox_reader.get_field(gguf.Keys.Tokenizer.EOS_ID) - self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0]) + self.gguf_writer.add_eos_token_id(field.parts[-1].tolist()[0] if field else 0) field = neox_reader.get_field(gguf.Keys.Tokenizer.UNK_ID) - self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0]) + self.gguf_writer.add_unk_token_id(field.parts[-1].tolist()[0] if field else 0) + + field = neox_reader.get_field(gguf.Keys.Tokenizer.PAD_ID) + self.gguf_writer.add_pad_token_id(field.parts[-1].tolist()[0] if field else 0) def set_gguf_parameters(self): d_model = self.find_hparam(["hidden_size", "d_model"]) @@ -2760,59 +2194,42 @@ class MambaModel(Model): self.gguf_writer.add_layer_norm_rms_eps(rms_norm_eps) self.gguf_writer.add_file_type(self.ftype) - def write_tensors(self): - block_count = self.hparams["n_layer"] - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + _tok_embd = None - tok_embd = None - tok_embd_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.TOKEN_EMBD] + ".weight" - output_name = gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.OUTPUT] + ".weight" + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused - for name, data_torch in self.get_tensors(): - old_dtype = data_torch.dtype + output_name = self.format_tensor_name(gguf.MODEL_TENSOR.OUTPUT) + tok_embd_name = self.format_tensor_name(gguf.MODEL_TENSOR.TOKEN_EMBD) - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + new_name = self.map_tensor_name(name) - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") + if name.endswith(".A_log"): + logger.debug("A_log --> A ==> " + new_name) + data_torch = -torch.exp(data_torch) - if name.endswith(".A_log"): - logger.debug("A_log --> A ==> " + new_name) - data_torch = -torch.exp(data_torch) + # assuming token_embd.weight is seen before output.weight + if self._tok_embd is not None and new_name == output_name: + if torch.equal(self._tok_embd, data_torch): + logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") + return [] + elif new_name == tok_embd_name: + self._tok_embd = data_torch - # assuming token_embd.weight is seen before output.weight - if tok_embd is not None and new_name == output_name: - if torch.equal(tok_embd, data_torch): - logger.debug(f"{output_name} is equivalent to {tok_embd_name}, omitting") - continue - if new_name == tok_embd_name: - tok_embd = data_torch + return [(new_name, data_torch)] - data = data_torch.squeeze().numpy() + def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: + del n_dims # unused - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert big float32 2-dim weight tensors to float16 - new_weight_name = new_name[:-len(".weight")] if new_name.endswith(".weight") else "" - if self.ftype == 1 and data_dtype == np.float32 and new_weight_name.endswith((".ssm_in", ".ssm_out", "token_embd", "output")) and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return bid is not None and new_name in ( + self.format_tensor_name(n, bid, ".weight" if name.endswith(".weight") else "") for n in [ + gguf.MODEL_TENSOR.SSM_CONV1D, + gguf.MODEL_TENSOR.SSM_X, + gguf.MODEL_TENSOR.SSM_DT, + gguf.MODEL_TENSOR.SSM_A, + gguf.MODEL_TENSOR.SSM_D, + ] + ) @Model.register("CohereForCausalLM") @@ -2846,55 +2263,144 @@ class OlmoModel(Model): # Same as super class, but permuting q_proj, k_proj # Copied from: LlamaModel - def write_tensors(self): - block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers", self.hparams.get("n_layer"))) - tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) - n_head = self.hparams.get("num_attention_heads") + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - for name, data_torch in self.get_tensors(): - old_dtype = data_torch.dtype - # convert any unsupported data types to float32 - if data_torch.dtype not in (torch.float16, torch.float32): - data_torch = data_torch.to(torch.float32) + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) - data = data_torch.numpy() - - if name.endswith("q_proj.weight"): - data = permute(data, n_head, n_head) - if name.endswith("k_proj.weight"): - data = permute(data, n_head, n_kv_head) - - data = data.squeeze() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor {name!r}") - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # 1d tensors need to be converted to float32 - if self.ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if self.ftype == 1 and data_dtype == np.float32 and n_dims == 2: - data = data.astype(np.float16) - - logger.info(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") - - self.gguf_writer.add_tensor(new_name, data) + return [(self.map_tensor_name(name), data_torch)] ###### CONVERSION LOGIC ###### +# tree of lazy tensors +class LazyTorchTensor: + _meta: Tensor + _data: Tensor | None + _args: tuple + _func: Callable[[tuple], Tensor] | None + + def __init__(self, *, meta: Tensor, data: Tensor | None = None, args: tuple = (), func: Callable[[tuple], Tensor] | None = None): + self._meta = meta + self._data = data + self._args = args + self._func = func + + @staticmethod + def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: + # TODO: dict and set + if isinstance(o, (list, tuple)): + L = [] + for item in o: + L.append(LazyTorchTensor._recurse_apply(item, fn)) + if isinstance(o, tuple): + L = tuple(L) + return L + elif isinstance(o, LazyTorchTensor): + return fn(o) + else: + return o + + def _wrap_fn(self, fn: Callable, use_self: bool = False) -> Callable[[Any], LazyTorchTensor]: + def wrapped_fn(*args, **kwargs): + if kwargs is None: + kwargs = {} + args = ((self,) if use_self else ()) + args + + meta_args = LazyTorchTensor._recurse_apply(args, lambda t: t._meta) + + return LazyTorchTensor(meta=fn(*meta_args, **kwargs), args=args, func=lambda a: fn(*a, **kwargs)) + return wrapped_fn + + def __getattr__(self, __name: str) -> Any: + meta_attr = getattr(self._meta, __name) + if callable(meta_attr): + return self._wrap_fn(getattr(torch.Tensor, __name), use_self=True) + elif isinstance(meta_attr, torch.Tensor): + # for things like self.T + return self._wrap_fn(lambda s: getattr(s, __name))(self) + else: + return meta_attr + + _dtype_map: dict[torch.dtype, type] = { + torch.float16: np.float16, + torch.float32: np.float32, + } + + def numpy(self) -> gguf.LazyTensor: + dtype = self._dtype_map[self.dtype] + return gguf.LazyTensor(lambda: LazyTorchTensor.to_eager(self).numpy(), dtype=dtype, shape=self.shape) + + @overload + @staticmethod + def to_eager(t: Tensor | LazyTorchTensor) -> Tensor: ... + + @overload + @staticmethod + def to_eager(t: tuple) -> tuple: ... + + @staticmethod + def to_eager(t: Any) -> Any: + def simple_to_eager(_t: LazyTorchTensor) -> Tensor: + # wake up the lazy tensor + if _t._data is None and _t._func is not None: + # recurse into its arguments + _t._args = LazyTorchTensor.to_eager(_t._args) + _t._data = _t._func(_t._args) + if _t._data is not None: + return _t._data + else: + raise ValueError(f"Could not compute lazy tensor {_t!r} with args {_t._args!r}") + + # recurse into lists and/or tuples, keeping their structure + return LazyTorchTensor._recurse_apply(t, simple_to_eager) + + @staticmethod + def from_eager(t: Tensor) -> Tensor: + if (t.__class__ == LazyTorchTensor): + return t + return LazyTorchTensor(meta=t.detach().to("meta"), data=t) # type: ignore + + @classmethod + def __torch_function__(cls, func, types, args=(), kwargs=None): + del types # unused + + if kwargs is None: + kwargs = {} + + if func is torch.Tensor.numpy: + return args[0].numpy() + if func is torch.equal: + eager_args = LazyTorchTensor.to_eager(args) + return func(*eager_args, **kwargs) + + return LazyTorchTensor._wrap_fn(args[0], func)(*args, **kwargs) + + # special methods bypass __getattr__, so they need to be added manually + # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup + # NOTE: LazyTorchTensor can't be a subclass of Tensor (and then be used + # as self._meta is currently used), because then the following + # operations would by default not be wrapped, and so not propagated + # when the tensor is made eager. + # It's better to get non-silent errors for not-yet-supported operators. + # TODO: add more when needed to avoid clutter, or find a more concise way + def __neg__(self, *args): # mamba + return self._wrap_fn(torch.Tensor.__neg__)(self, *args) + + def __add__(self, *args): # gemma + return self._wrap_fn(torch.Tensor.__add__)(self, *args) + + def __getitem__(self, *args): # bloom falcon refact internlm2 + return self._wrap_fn(torch.Tensor.__getitem__)(self, *args) + + def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser( description="Convert a huggingface model to a GGML compatible file") @@ -2904,7 +2410,8 @@ def parse_args() -> argparse.Namespace: ) parser.add_argument( "--awq-path", type=Path, default=None, - help="Path to scale awq cache file") + help="Path to scale awq cache file", + ) parser.add_argument( "--outfile", type=Path, help="path to write to; default: based on input", @@ -2913,14 +2420,30 @@ def parse_args() -> argparse.Namespace: "--outtype", type=str, choices=["f32", "f16"], default="f16", help="output format - use f32 for float32, f16 for float16", ) - parser.add_argument("--bigendian", action="store_true", help="model is executed on big endian machine") + parser.add_argument( + "--bigendian", action="store_true", + help="model is executed on big endian machine", + ) parser.add_argument( "model", type=Path, help="directory containing model file", ) - parser.add_argument("--use-temp-file", action="store_true", help="use the tempfile library while processing (helpful when running out of memory, process killed)") - parser.add_argument("--model-name", type=str, default=None, help="name of the model") - parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + parser.add_argument( + "--use-temp-file", action="store_true", + help="use the tempfile library while processing (helpful when running out of memory, process killed)", + ) + parser.add_argument( + "--no-lazy", action="store_true", + help="use more RAM by computing all outputs before writing (use in case lazy evaluation is broken)", + ) + parser.add_argument( + "--model-name", type=str, default=None, + help="name of the model", + ) + parser.add_argument( + "--verbose", action="store_true", + help="increase output verbosity", + ) return parser.parse_args() @@ -2966,7 +2489,7 @@ def main() -> None: with torch.inference_mode(): model_class = Model.from_model_architecture(hparams["architectures"][0]) - model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy) logger.info("Set model parameters") model_instance.set_gguf_parameters() diff --git a/convert.py b/convert.py index aebfc50f3..148bfd66a 100755 --- a/convert.py +++ b/convert.py @@ -284,6 +284,7 @@ class Params: n_experts = None n_experts_used = None f_rope_freq_base = None + n_ff = None # hack to determine LLaMA v1 vs v2 vs CodeLlama if config.get("moe"): @@ -308,6 +309,8 @@ class Params: n_experts_used = config["moe"]["num_experts_per_tok"] f_rope_freq_base = 1e6 + assert n_ff is not None + return Params( n_vocab = model["tok_embeddings.weight"].shape[0], n_embd = config["dim"], @@ -462,7 +465,8 @@ class SentencePieceVocab(Vocab): # not found in alternate location either raise FileNotFoundError('Cannot find tokenizer.model') - self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) + self.sentencepiece_tokenizer = SentencePieceProcessor() + self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) vocab_size = self.sentencepiece_tokenizer.vocab_size() new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} @@ -482,23 +486,23 @@ class SentencePieceVocab(Vocab): def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.sentencepiece_tokenizer for i in range(tokenizer.vocab_size()): - piece = tokenizer.id_to_piece(i) + piece = tokenizer.IdToPiece(i) text = piece.encode("utf-8") - score: float = tokenizer.get_score(i) + score: float = tokenizer.GetScore(i) toktype = gguf.TokenType.NORMAL - if tokenizer.is_unknown(i): + if tokenizer.IsUnknown(i): toktype = gguf.TokenType.UNKNOWN - if tokenizer.is_control(i): + if tokenizer.IsControl(i): toktype = gguf.TokenType.CONTROL # NOTE: I think added_tokens are user defined. # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED - if tokenizer.is_unused(i): + if tokenizer.IsUnused(i): toktype = gguf.TokenType.UNUSED - if tokenizer.is_byte(i): + if tokenizer.IsByte(i): toktype = gguf.TokenType.BYTE yield text, score, toktype @@ -906,7 +910,7 @@ class LazyUnpickler(pickle.Unpickler): def rebuild_from_type_v2(func, new_type, args, state): return func(*args) - CLASSES = { + CLASSES: dict[tuple[str, str], type[LazyTensor] | LazyStorageKind] = { # getattr used here as a workaround for mypy not being smart enough to determine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 0882a5d36..f4b1ac1d7 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -939,7 +939,7 @@ async def oai_chat_completions(user_prompt, while event_received: event_received = False async for line_in_bytes in response.content: - line = line_in_bytes.decode('utf8') + line = line_in_bytes.decode('utf-8') line = line.rstrip('\n').rstrip('\r') if line == '': continue diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 6e968fc4e..5951c0bb0 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -860,7 +860,7 @@ class GGUFValueType(IntEnum): # Note: Does not support GGML_QKK_64 QK_K = 256 # Items here are (block size, type size) -GGML_QUANT_SIZES = { +GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.F32: (1, 4), GGMLQuantizationType.F16: (1, 2), GGMLQuantizationType.Q4_0: (32, 2 + 16), diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index db8525d85..21b089f8a 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -65,7 +65,7 @@ class ReaderTensor(NamedTuple): class GGUFReader: # I - same as host, S - swapped - byte_order: Literal['I' | 'S'] = 'I' + byte_order: Literal['I'] | Literal['S'] = 'I' alignment: int = GGUF_DEFAULT_ALIGNMENT # Note: Internal helper, API may change. @@ -83,7 +83,7 @@ class GGUFReader: GGUFValueType.BOOL: np.bool_, } - def __init__(self, path: os.PathLike[str] | str, mode: Literal['r' | 'r+' | 'c'] = 'r'): + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r'] | Literal['r+'] | Literal['c'] = 'r'): self.data = np.memmap(path, mode = mode) offs = 0 if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: @@ -128,7 +128,7 @@ class GGUFReader: return self.tensors[idx] def _get( - self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I' | 'S' | '<'] = None, + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I'] | Literal['S'] | Literal['<'] = None, ) -> npt.NDArray[Any]: count = int(count) itemsize = int(np.empty([], dtype = dtype).itemsize) @@ -250,7 +250,7 @@ class GGUFReader: raise ValueError(f'Found duplicated tensor with name {tensor_name}') tensor_names.add(tensor_name) ggml_type = GGMLQuantizationType(raw_dtype[0]) - n_elems = np.prod(dims) + n_elems = int(np.prod(dims)) block_size, type_size = GGML_QUANT_SIZES[ggml_type] n_bytes = n_elems * type_size // block_size data_offs = int(start_offs + offset_tensor[0]) diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d9cfbf711..8dcf9330b 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -7,7 +7,7 @@ import struct import tempfile from enum import Enum, auto from io import BufferedWriter -from typing import IO, Any, Sequence, Mapping +from typing import IO, Any, Callable, Sequence, Mapping from string import ascii_letters, digits import numpy as np @@ -28,6 +28,47 @@ from .constants import ( logger = logging.getLogger(__name__) +class LazyTensor: + data: Callable[[], np.ndarray[Any, Any]] + # to avoid too deep recursion + functions: list[Callable[[np.ndarray[Any, Any]], np.ndarray[Any, Any]]] + dtype: np.dtype[Any] + shape: tuple[int, ...] + + def __init__(self, data: Callable[[], np.ndarray[Any, Any]], *, dtype: type, shape: tuple[int, ...]): + self.data = data + self.functions = [] + self.dtype = np.dtype(dtype) + self.shape = shape + + def astype(self, dtype: type, **kwargs) -> LazyTensor: + self.functions.append(lambda n: n.astype(dtype, **kwargs)) + self.dtype = np.dtype(dtype) + return self + + @property + def nbytes(self) -> int: + size = 1 + for n in self.shape: + size *= n + return size * self.dtype.itemsize + + def tofile(self, *args, **kwargs) -> None: + data = self.data() + for f in self.functions: + data = f(data) + assert data.shape == self.shape + assert data.dtype == self.dtype + assert data.nbytes == self.nbytes + self.functions = [] + self.data = lambda: data + data.tofile(*args, **kwargs) + + def byteswap(self, *args, **kwargs) -> LazyTensor: + self.functions.append(lambda n: n.byteswap(*args, **kwargs)) + return self + + class WriterState(Enum): EMPTY = auto() HEADER = auto() @@ -38,7 +79,7 @@ class WriterState(Enum): class GGUFWriter: fout: BufferedWriter temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any]] + tensors: list[np.ndarray[Any, Any] | LazyTensor] _simple_value_packing = { GGUFValueType.UINT8: "B", GGUFValueType.INT8: "b", @@ -176,7 +217,7 @@ class GGUFWriter: if pack_fmt is not None: self.kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) elif vtype == GGUFValueType.STRING: - encoded_val = val.encode("utf8") if isinstance(val, str) else val + encoded_val = val.encode("utf-8") if isinstance(val, str) else val self.kv_data += self._pack("Q", len(encoded_val)) self.kv_data += encoded_val elif vtype == GGUFValueType.ARRAY and isinstance(val, Sequence) and val: @@ -205,7 +246,7 @@ class GGUFWriter: raise ValueError(f'Duplicated tensor name {name}') self.ti_names.add(name) - encoded_name = name.encode("utf8") + encoded_name = name.encode("utf-8") self.ti_data += self._pack("Q", len(encoded_name)) self.ti_data += encoded_name n_dims = len(tensor_shape) @@ -237,7 +278,7 @@ class GGUFWriter: self.ti_data_count += 1 def add_tensor( - self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, + self, name: str, tensor: np.ndarray[Any, Any] | LazyTensor, raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.endianess == GGUFEndian.BIG: @@ -262,7 +303,7 @@ class GGUFWriter: if pad != 0: fp.write(bytes([0] * pad)) - def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: + def write_tensor_data(self, tensor: np.ndarray[Any, Any] | LazyTensor) -> None: if self.state is not WriterState.TI_DATA: raise ValueError(f'Expected output file to contain tensor info, got {self.state}') @@ -272,15 +313,33 @@ class GGUFWriter: tensor.tofile(self.fout) self.write_padding(self.fout, tensor.nbytes) - def write_tensors_to_file(self) -> None: + def write_tensors_to_file(self, *, progress: bool = False) -> None: self.write_ti_data_to_file() self.write_padding(self.fout, self.fout.tell()) if self.temp_file is None: + self.tensors.reverse() # to pop from the "beginning" in constant time + + if progress: + from tqdm import tqdm + + total_bytes = sum(t.nbytes for t in self.tensors) + + bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) + + while True: + try: + tensor = self.tensors.pop() + except IndexError: + break + tensor.tofile(self.fout) + bar.update(tensor.nbytes) + self.write_padding(self.fout, tensor.nbytes) + return while True: try: - tensor = self.tensors.pop(0) + tensor = self.tensors.pop() except IndexError: break tensor.tofile(self.fout) @@ -479,7 +538,7 @@ class GGUFWriter: self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None: - if isinstance(value, list): + if not isinstance(value, str): template_default = None template_names = set() diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index c97a78f39..3ba99be4f 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -4,7 +4,7 @@ import logging import json import os from pathlib import Path -from typing import Any, Callable +from typing import Any, Callable, Sequence, Mapping, Iterable from .gguf_writer import GGUFWriter @@ -15,11 +15,11 @@ class SpecialVocab: merges: list[str] add_special_token: dict[str, bool] special_token_ids: dict[str, int] - chat_template: str | None + chat_template: str | Sequence[Mapping[str, str]] | None def __init__( self, path: str | os.PathLike[str], load_merges: bool = False, - special_token_types: tuple[str, ...] | None = None, + special_token_types: Iterable[str] | None = None, n_vocab: int | None = None, ): self.special_token_ids = {} diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index d1d876d6d..36e63ee3b 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -21,6 +21,7 @@ classifiers = [ [tool.poetry.dependencies] python = ">=3.8" numpy = ">=1.17" +tqdm = ">=4.27" [tool.poetry.dev-dependencies] pytest = "^5.2" diff --git a/gguf-py/scripts/gguf-dump.py b/gguf-py/scripts/gguf-dump.py index 2d3c3943f..1a37a7b91 100755 --- a/gguf-py/scripts/gguf-dump.py +++ b/gguf-py/scripts/gguf-dump.py @@ -47,7 +47,7 @@ def dump_metadata(reader: GGUFReader, args: argparse.Namespace) -> None: if len(field.types) == 1: curr_type = field.types[0] if curr_type == GGUFValueType.STRING: - log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf8')[:60])) + log_message += ' = {0}'.format(repr(str(bytes(field.parts[-1]), encoding='utf-8')[:60])) elif field.types[0] in reader.gguf_scalar_to_np: log_message += ' = {0}'.format(field.parts[-1][0]) print(log_message) # noqa: NP100 diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py index 3444ab418..c8e3a83df 100644 --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -7,7 +7,7 @@ import json from pathlib import Path import numpy as np -from typing import Any, Mapping, Sequence +from typing import Any, Sequence # Necessary to load the local gguf package if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): @@ -34,7 +34,7 @@ def get_byteorder(reader: gguf.GGUFReader) -> gguf.GGUFEndian: return host_endian -def decode_field(field: gguf.ReaderField) -> Any: +def decode_field(field: gguf.ReaderField | None) -> Any: if field and field.types: main_type = field.types[0] @@ -42,11 +42,11 @@ def decode_field(field: gguf.ReaderField) -> Any: sub_type = field.types[-1] if sub_type == gguf.GGUFValueType.STRING: - return [str(bytes(field.parts[idx]), encoding='utf8') for idx in field.data] + return [str(bytes(field.parts[idx]), encoding='utf-8') for idx in field.data] else: return [pv for idx in field.data for pv in field.parts[idx].tolist()] if main_type == gguf.GGUFValueType.STRING: - return str(bytes(field.parts[-1]), encoding='utf8') + return str(bytes(field.parts[-1]), encoding='utf-8') else: return field.parts[-1][0] @@ -59,7 +59,7 @@ def get_field_data(reader: gguf.GGUFReader, key: str) -> Any: return decode_field(field) -def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: Mapping[str, str], remove_metadata: Sequence[str]) -> None: +def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: dict[str, str], remove_metadata: Sequence[str]) -> None: for field in reader.fields.values(): # Suppress virtual fields and fields written by GGUFWriter if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'): @@ -101,7 +101,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new for tensor in reader.tensors: # Dimensions are written in reverse order, so flip them first - shape = np.flipud(tensor.shape) + shape = np.flipud(tensor.shape).tolist() writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) writer.write_header_to_file() diff --git a/pyrightconfig.json b/pyrightconfig.json new file mode 100644 index 000000000..020a71a4e --- /dev/null +++ b/pyrightconfig.json @@ -0,0 +1,3 @@ +{ + "extraPaths": ["gguf-py"], +} diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt index 6ce840d73..6ac402610 100644 --- a/requirements/requirements-convert-hf-to-gguf-update.txt +++ b/requirements/requirements-convert-hf-to-gguf-update.txt @@ -1,3 +1,2 @@ -r ./requirements-convert.txt torch~=2.1.1 -einops~=0.7.0 diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt index 6ce840d73..6ac402610 100644 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -1,3 +1,2 @@ -r ./requirements-convert.txt torch~=2.1.1 -einops~=0.7.0 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert.txt index 5520ba732..7ab1228cb 100644 --- a/requirements/requirements-convert.txt +++ b/requirements/requirements-convert.txt @@ -1,5 +1,5 @@ numpy~=1.24.4 -sentencepiece~=0.1.98 +sentencepiece~=0.2.0 transformers>=4.40.1,<5.0.0 gguf>=0.1.0 protobuf>=4.21.0,<5.0.0 From 4426e2987b566f09c7aa96ada9706cc778637620 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel <jared@nomic.ai> Date: Wed, 8 May 2024 19:55:32 -0400 Subject: [PATCH 062/100] cmake : fix typo (#7151) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 07b3a4388..aa65b0d6c 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -431,7 +431,7 @@ if (LLAMA_CUDA) if (LLAMA_STATIC) if (WIN32) - # As of 12.3.1 CUDA Tookit for Windows does not offer a static cublas library + # As of 12.3.1 CUDA Toolkit for Windows does not offer a static cublas library set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas CUDA::cublasLt) else () set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart_static CUDA::cublas_static CUDA::cublasLt_static) From 07cd41d0965829463eff73eda3348aedbfd3a444 Mon Sep 17 00:00:00 2001 From: Ahmet Zeer <ahmed.zeer@std.yildiz.edu.tr> Date: Thu, 9 May 2024 11:16:45 +0300 Subject: [PATCH 063/100] TypoFix (#7162) --- examples/convert-llama2c-to-ggml/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index 6da2d7e18..742dcf7a3 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -2,7 +2,7 @@ This example reads weights from project [llama2.c](https://github.com/karpathy/llama2.c) and saves them in ggml compatible format. The vocab that is available in `models/ggml-vocab.bin` is used by default. -To convert the model first download the models from the [llma2.c](https://github.com/karpathy/llama2.c) repository: +To convert the model first download the models from the [llama2.c](https://github.com/karpathy/llama2.c) repository: `$ make -j` From 47345248827f426038098d016ed11975021b4919 Mon Sep 17 00:00:00 2001 From: Albert Jin <albert.jin@gmail.com> Date: Thu, 9 May 2024 17:34:37 +0800 Subject: [PATCH 064/100] opencl : alignment size converted from bits to bytes (#7090) * opencl alignment size should be converted from bits to bytes Reference: https://registry.khronos.org/OpenCL/specs/3.0-unified/html/OpenCL_API.html#CL_DEVICE_MEM_BASE_ADDR_ALIGN > Alignment requirement (in bits) for sub-buffer offsets. * Update ggml-opencl.cpp for readability using division instead of shift Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> --------- Co-authored-by: Jared Van Bortel <cebtenzzre@gmail.com> --- ggml-opencl.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index b3f8b7eaf..880a14958 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2119,6 +2119,7 @@ static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_ if (alignment == (cl_uint)-1) { ggml_cl_init(); clGetDeviceInfo(device, CL_DEVICE_MEM_BASE_ADDR_ALIGN, sizeof(cl_uint), &alignment, NULL); + alignment /= 8; // bits to bytes } return alignment; From 22842164bcae3251b81ad9e497a16ef66833cb9e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Sigbj=C3=B8rn=20Skj=C3=A6ret?= <sigbjorn.skjaeret@scala.com> Date: Thu, 9 May 2024 12:56:00 +0200 Subject: [PATCH 065/100] gguf-py : add special token modification capability (#7166) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add special token modification capability To be able to fix/amend special tokens in a GGUF let's add two new arguments: * `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<｜fim▁begin｜>"` * `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006 So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following: ```bash python3 gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<｜fim▁begin｜>" --special-token middle "<｜fim▁hole｜>" --special-token suffix "<｜fim▁end｜>" ``` * improve help text * flake-- * fix multiple tokens warning * make script executable * switch to namedtuple, no need to dataclass * typing++ * add progress bar * Add special token modification capability To be able to fix/amend special tokens in a GGUF let's add two new arguments: * `--special-token <name> <value>` where `<name>` can be bos, eos, prefix, middle, etc. while `<value>` is the token value, f.ex. `"<｜fim▁begin｜>"` * `--special-token-by-id <name> <id>` where `<id>` is the ID of the token, f.ex. 32006 So, in order to f.ex. add fill-in-middle tokens to a GGUF you would do the following: ```bash gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<｜fim▁begin｜>" --special-token middle "<｜fim▁end｜>" --special-token suffix "<｜fim▁hole｜>" ``` (yes, fim_end is the `middle` token, because completion is a `prefix`/`suffix`/`middle` sequence (where `middle` is unfilled)) or ```bash gguf-new-metadata.py input.gguf output.gguf --special-token prefix "<fim_prefix>" --special-token middle "<fim_middle>" --special-token suffix "<fim_suffix>" ``` etc... NB: The tokens have to exist already, trying to add non-existent token name/IDs will be ignored (with a warning), while non-existent values will fail (with an error). * improve help text * flake-- * fix multiple tokens warning * make script executable * switch to namedtuple, no need to dataclass * typing++ * add progress bar * fail on invalid token id --- gguf-py/scripts/gguf-new-metadata.py | 92 ++++++++++++++++++++++------ 1 file changed, 72 insertions(+), 20 deletions(-) mode change 100644 => 100755 gguf-py/scripts/gguf-new-metadata.py diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py old mode 100644 new mode 100755 index c8e3a83df..63d3c5d8f --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -7,7 +7,8 @@ import json from pathlib import Path import numpy as np -from typing import Any, Sequence +from tqdm import tqdm +from typing import Any, Sequence, NamedTuple # Necessary to load the local gguf package if "NO_LOCAL_GGUF" not in os.environ and (Path(__file__).parent.parent.parent / 'gguf-py').exists(): @@ -18,6 +19,12 @@ import gguf logger = logging.getLogger("gguf-new-metadata") +class MetadataDetails(NamedTuple): + type: gguf.GGUFValueType + value: Any + description: str = '' + + def get_byteorder(reader: gguf.GGUFReader) -> gguf.GGUFEndian: if np.uint32(1) == np.uint32(1).newbyteorder("<"): # Host is little endian @@ -59,7 +66,16 @@ def get_field_data(reader: gguf.GGUFReader, key: str) -> Any: return decode_field(field) -def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: dict[str, str], remove_metadata: Sequence[str]) -> None: +def find_token(token_list: Sequence[int], token: str) -> Sequence[int]: + token_ids = [index for index, value in enumerate(token_list) if value == token] + + if len(token_ids) == 0: + raise LookupError(f'Unable to find "{token}" in token list!') + + return token_ids + + +def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new_metadata: dict[str, MetadataDetails], remove_metadata: Sequence[str]) -> None: for field in reader.fields.values(): # Suppress virtual fields and fields written by GGUFWriter if field.name == gguf.Keys.General.ARCHITECTURE or field.name.startswith('GGUF.'): @@ -75,54 +91,64 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new logger.debug(f'Removing {field.name}') continue - old_val = decode_field(field) + old_val = MetadataDetails(field.types[0], decode_field(field)) val = new_metadata.get(field.name, old_val) if field.name in new_metadata: - logger.debug(f'Modifying {field.name}: "{old_val}" -> "{val}"') + logger.debug(f'Modifying {field.name}: "{old_val.value}" -> "{val.value}" {val.description}') del new_metadata[field.name] - elif val is not None: + elif val.value is not None: logger.debug(f'Copying {field.name}') - if val is not None: + if val.value is not None: writer.add_key(field.name) - writer.add_val(val, field.types[0]) + writer.add_val(val.value, val.type) if gguf.Keys.Tokenizer.CHAT_TEMPLATE in new_metadata: logger.debug('Adding chat template(s)') - writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE]) + writer.add_chat_template(new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE].value) del new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] - # TODO: Support other types than string? for key, val in new_metadata.items(): - logger.debug(f'Adding {key}: {val}') + logger.debug(f'Adding {key}: "{val.value}" {val.description}') writer.add_key(key) - writer.add_val(val, gguf.GGUFValueType.STRING) + writer.add_val(val.value, val.type) + + total_bytes = 0 for tensor in reader.tensors: + total_bytes += tensor.n_bytes # Dimensions are written in reverse order, so flip them first shape = np.flipud(tensor.shape).tolist() writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) + bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) + writer.write_header_to_file() writer.write_kv_data_to_file() writer.write_ti_data_to_file() for tensor in reader.tensors: writer.write_tensor_data(tensor.data) + bar.update(tensor.n_bytes) writer.close() def main() -> None: + tokenizer_metadata = (getattr(gguf.Keys.Tokenizer, n) for n in gguf.Keys.Tokenizer.__dict__.keys() if not n.startswith('_')) + token_names = dict((n.split('.')[-1][:-len('_token_id')], n) for n in tokenizer_metadata if n.endswith('_token_id')) + parser = argparse.ArgumentParser(description="Make a copy of a GGUF file with new metadata") parser.add_argument("input", type=Path, help="GGUF format model input filename") parser.add_argument("output", type=Path, help="GGUF format model output filename") - parser.add_argument("--general-name", type=str, help="The models general.name") - parser.add_argument("--general-description", type=str, help="The models general.description") - parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)") - parser.add_argument("--chat-template-config", type=Path, help="Config file (tokenizer_config.json) containing chat template(s)") - parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model") + parser.add_argument("--general-name", type=str, help="The models general.name", metavar='"name"') + parser.add_argument("--general-description", type=str, help="The models general.description", metavar='"Description ..."') + parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."') + parser.add_argument("--chat-template-config", type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json') + parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model", metavar='general.url') + parser.add_argument("--special-token", action="append", type=str, help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '"<token>"')) + parser.add_argument("--special-token-by-id", action="append", type=str, help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0')) parser.add_argument("--force", action="store_true", help="Bypass warnings without confirmation") parser.add_argument("--verbose", action="store_true", help="Increase output verbosity") args = parser.parse_args(None if len(sys.argv) > 2 else ["--help"]) @@ -133,20 +159,20 @@ def main() -> None: remove_metadata = args.remove_metadata or [] if args.general_name: - new_metadata[gguf.Keys.General.NAME] = args.general_name + new_metadata[gguf.Keys.General.NAME] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_name) if args.general_description: - new_metadata[gguf.Keys.General.DESCRIPTION] = args.general_description + new_metadata[gguf.Keys.General.DESCRIPTION] = MetadataDetails(gguf.GGUFValueType.STRING, args.general_description) if args.chat_template: - new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, json.loads(args.chat_template) if args.chat_template.startswith('[') else args.chat_template) if args.chat_template_config: with open(args.chat_template_config, 'r') as fp: config = json.load(fp) template = config.get('chat_template') if template: - new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = template + new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) if remove_metadata: logger.warning('*** Warning *** Warning *** Warning **') @@ -166,6 +192,32 @@ def main() -> None: arch = get_field_data(reader, gguf.Keys.General.ARCHITECTURE) endianess = get_byteorder(reader) + token_list = get_field_data(reader, gguf.Keys.Tokenizer.LIST) or [] + + for name, token in args.special_token or []: + if name not in token_names: + logger.warning(f'Unknown special token "{name}", ignoring...') + else: + ids = find_token(token_list, token) + new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, ids[0], f'= {token}') + + if len(ids) > 1: + logger.warning(f'Multiple "{token}" tokens found, choosing ID {ids[0]}, use --special-token-by-id if you want another:') + logger.warning(', '.join(str(i) for i in ids)) + + for name, id_string in args.special_token_by_id or []: + if name not in token_names: + logger.warning(f'Unknown special token "{name}", ignoring...') + elif not id_string.isdecimal(): + raise LookupError(f'Token ID "{id_string}" is not a valid ID!') + else: + id_int = int(id_string) + + if id_int >= 0 and id_int < len(token_list): + new_metadata[token_names[name]] = MetadataDetails(gguf.GGUFValueType.UINT32, id_int, f'= {token_list[id_int]}') + else: + raise LookupError(f'Token ID {id_int} is not within token list!') + if os.path.isfile(args.output) and not args.force: logger.warning('*** Warning *** Warning *** Warning **') logger.warning(f'* The "{args.output}" GGUF file already exists, it will be overwritten!') From fd9f92b154850014146f61717cd292a59a5cee5a Mon Sep 17 00:00:00 2001 From: Daniel Bevenius <daniel.bevenius@gmail.com> Date: Thu, 9 May 2024 13:03:29 +0200 Subject: [PATCH 066/100] llama : update llama_timings.n_p_eval setting (#7160) This commit changes the value assigned to llama_timings.n_p_eval when ctx->n_p_eval is 0 to be 1 instead of 1 which is the current value. The motivation for this change is that if session caching is enabled, for example using the `--prompt-cache main-session.txt` command line argument for the main example, and if the same prompt is used then on subsequent runs, the prompt tokens will not actually be passed to llama_decode, and n_p_eval will not be updated by llama_synchoronize. But the value of n_p_eval will be set 1 by llama_get_timings because ctx->n_p_eval will be 0. This could be interpreted as 1 token was evaluated for the prompt which could be misleading for applications using this value. Signed-off-by: Daniel Bevenius <daniel.bevenius@gmail.com> --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 9c72d118f..806c20932 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17879,7 +17879,7 @@ struct llama_timings llama_get_timings(struct llama_context * ctx) { /*.t_eval_ms =*/ 1e-3 * ctx->t_eval_us, /*.n_sample =*/ std::max(1, ctx->n_sample), - /*.n_p_eval =*/ std::max(1, ctx->n_p_eval), + /*.n_p_eval =*/ std::max(0, ctx->n_p_eval), /*.n_eval =*/ std::max(1, ctx->n_eval), }; From f31ec120bc36c6270e4948e6a065a7c4cfa0c404 Mon Sep 17 00:00:00 2001 From: Galunid <karolek1231456@gmail.com> Date: Thu, 9 May 2024 14:13:05 +0200 Subject: [PATCH 067/100] Add warning if token is invalid (#7173) --- convert-hf-to-gguf-update.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index a26f45a5f..6fdb502f9 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -49,6 +49,10 @@ chktxt = '\n \n\n \n\n\n \t \t\t \t\n \n \n \n \n🚀 (normal) 😶‍ if len(sys.argv) == 2: token = sys.argv[1] + if not token.startswith("hf_"): + logger.info("Huggingface token seems invalid") + logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>") + sys.exit(1) else: logger.info("Usage: python convert-hf-to-gguf-update.py <huggingface_token>") sys.exit(1) From a743d76a01f23038b2c85af1e9048ee836767b44 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de> Date: Thu, 9 May 2024 14:32:02 +0200 Subject: [PATCH 068/100] CUDA: generalize FP16 fattn vec kernel (#7061) * CUDA: generalize FP16 fattn vec kernel * disable unsupported head sizes for AMD in test * try AMD fix * fix batch size 2-8 * partially revert changes --- ggml-cuda/common.cuh | 252 +++++++++++++++------------- ggml-cuda/fattn.cu | 331 ++++++++++++++++++++++++++----------- llama.cpp | 7 - tests/test-backend-ops.cpp | 4 + 4 files changed, 374 insertions(+), 220 deletions(-) diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index a4197f11b..44e67e040 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -234,122 +234,6 @@ typedef float dfloat; // dequantize float typedef float2 dfloat2; #endif //GGML_CUDA_F16 -[[noreturn]] -static __device__ void no_device_code( - const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { - -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", - file_name, line, function_name, arch); - GGML_UNUSED(arch_list); -#else - printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", - file_name, line, function_name, arch, arch_list); -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - __trap(); - - GGML_UNUSED(no_device_code); // suppress unused function warning -} - -#ifdef __CUDA_ARCH__ -#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__)) -#else -#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.") -#endif // __CUDA_ARCH__ - -static __device__ __forceinline__ float warp_reduce_sum(float x) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x += __shfl_xor_sync(0xffffffff, x, mask, 32); - } - return x; -} - -static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); - a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); - } - return a; -} - -static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); - } - return a; -#else - GGML_UNUSED(a); - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -} - -static __device__ __forceinline__ float warp_reduce_max(float x) { -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); - } - return x; -} - -static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - -#if CUDART_VERSION >= CUDART_HMAX - return __hmax(a, b); -#else - return __half2float(a) > __half2float(b) ? a : b; -#endif // CUDART_VERSION >= CUDART_HMAX - -#else - GGML_UNUSED(a); - GGML_UNUSED(b); - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX -} -static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) - -#if CUDART_VERSION >= CUDART_HMAX - return __hmax2(a, b); -#else - half2 ret; - reinterpret_cast<half&>(ret.x) = __low2float(a) > __low2float(b) ? __low2half(a) : __low2half(b); - reinterpret_cast<half&>(ret.y) = __high2float(a) > __high2float(b) ? __high2half(a) : __high2half(b); - return ret; -#endif // CUDART_VERSION >= CUDART_HMAX - -#else - GGML_UNUSED(a); - GGML_UNUSED(b); - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX -} - -static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); - } - return x; -#else - GGML_UNUSED(x); - NO_DEVICE_CODE; -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL -} - -#if CUDART_VERSION < CUDART_HMASK -static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) { - const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b))); - const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b))); - return mask_low | mask_high; -} -#endif // CUDART_VERSION < 12000 - #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 @@ -433,11 +317,143 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { } #endif // defined(GGML_USE_HIPBLAS) -#define FP16_AVAILABLE defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) ? \ - defined(RDNA1) || defined(RDNA2) || defined(RDNA3) : __CUDA_ARCH__ >= CC_PASCAL +#define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL #define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +static bool fp16_mma_available(const int cc) { + return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; +} + +[[noreturn]] +static __device__ void no_device_code( + const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", + file_name, line, function_name, arch); + GGML_UNUSED(arch_list); +#else + printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", + file_name, line, function_name, arch, arch_list); +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + __trap(); + + GGML_UNUSED(no_device_code); // suppress unused function warning +} + +#ifdef __CUDA_ARCH__ +#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__)) +#else +#define NO_DEVICE_CODE //GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.") +#endif // __CUDA_ARCH__ + +static __device__ __forceinline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, mask, 32); + } + return x; +} + +static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + } + return a; +} + +static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { +#if FP16_AVAILABLE + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + const half2 a_other = __shfl_xor_sync(0xffffffff, a, mask, 32); + reinterpret_cast<half&>(a.x) += __low2half(a_other); + reinterpret_cast<half&>(a.y) += __high2half(a_other); + } + return a; +#else +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a = __hadd2(a, __shfl_xor_sync(0xffffffff, a, mask, 32)); + } + return a; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + +#else + NO_DEVICE_CODE; + return a; +#endif // FP16_AVAILABLE +} + +static __device__ __forceinline__ float warp_reduce_max(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x = fmaxf(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + } + return x; +} + +static __device__ __forceinline__ half ggml_cuda_hmax(const half a, const half b) { +#if FP16_AVAILABLE + +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX + return __float2half(fmaxf(__half2float(a), __half2float(b))); +#else + return __hmax(a, b); +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && CUDART_VERSION < CUDART_HMAX + +#else + NO_DEVICE_CODE; + GGML_UNUSED(b); + return a; +#endif // FP16_AVAILABLE +} + +static __device__ __forceinline__ half2 ggml_cuda_hmax2(const half2 a, const half2 b) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) + +#if CUDART_VERSION >= CUDART_HMAX + return __hmax2(a, b); +#else + half2 ret; + reinterpret_cast<half&>(ret.x) = __float2half(fmaxf( __low2float(a), __low2float(b))); + reinterpret_cast<half&>(ret.y) = __float2half(fmaxf(__high2float(a), __high2float(b))); + return ret; +#endif // CUDART_VERSION >= CUDART_HMAX + +#else + GGML_UNUSED(a); + GGML_UNUSED(b); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +} + +static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x = ggml_cuda_hmax2(x, __shfl_xor_sync(0xffffffff, x, mask, 32)); + } + return x; +#else + GGML_UNUSED(x); + NO_DEVICE_CODE; +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL +} + +#if CUDART_VERSION < CUDART_HMASK +static __device__ __forceinline__ uint32_t __hgt2_mask(const half2 a, const half2 b) { + const uint32_t mask_low = 0x0000FFFF * (float( __low2half(a)) > float( __low2half(b))); + const uint32_t mask_high = 0xFFFF0000 * (float(__high2half(a)) > float(__high2half(b))); + return mask_low | mask_high; +} +#endif // CUDART_VERSION < 12000 + // TODO: move to ggml-common.h static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113}; diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index c8a11d173..7c486f482 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -11,8 +11,10 @@ #define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. #define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. -template<int D, int parallel_blocks> // D == head size -__launch_bounds__(((D + WARP_SIZE - 1) / WARP_SIZE)*WARP_SIZE, 1) +template<int D, int ncols, int parallel_blocks> // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_vec_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, @@ -44,55 +46,77 @@ static __global__ void flash_attn_vec_ext_f16( #if FP16_AVAILABLE //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - const int ic = blockIdx.x / parallel_blocks; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic); + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic; + const half * maskh = (const half *) mask + ne11*ic0; const int stride_KV = nb11 / sizeof(half); const int stride_KV2 = nb11 / sizeof(half2); - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < nwarps*WARP_SIZE); + __builtin_assume(tid < D); - __shared__ half KQ[nwarps*WARP_SIZE]; - KQ[tid] = -INFINITY; + __shared__ half KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } half2 * KQ2 = (half2 *) KQ; - half kqmax = -HALF_MAX_HALF; - half kqsum = 0.0f; + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; - __shared__ half kqmax_shared[WARP_SIZE]; - __shared__ half kqsum_shared[WARP_SIZE]; - if (threadIdx.y == 0) { - kqmax_shared[threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[threadIdx.x] = 0.0f; + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } } __syncthreads(); // Convert Q to half2 and store in registers: - half2 Q_h2[(D/2 + WARP_SIZE - 1) / WARP_SIZE]; + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; #pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D/2 && i >= D/2) { - break; - } + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; - Q_h2[i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(Q_f2[i].x, Q_f2[i].y); + const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } } - half2 VKQ = make_half2(0.0f, 0.0f); // Each thread calculates a single VKQ value. + half2 VKQ[ncols] = {{0.0f, 0.0f}}; - const int k_start = parallel_blocks == 1 ? 0 : ip*D; + const int k_start = parallel_blocks == 1 ? 0 : ip*D; for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { // Calculate KQ tile and keep track of new maximum KQ values: - half kqmax_new = kqmax; + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + #pragma unroll for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { const int i_KQ = i_KQ_0 + threadIdx.y; @@ -101,89 +125,112 @@ static __global__ void flash_attn_vec_ext_f16( break; } - half2 sum2 = make_half2(0.0f, 0.0f); + half2 sum2[ncols] = {{0.0f, 0.0f}}; #pragma unroll for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { const int k_KQ = k_KQ_0 + threadIdx.x; - if (k_KQ_0 + WARP_SIZE > D/2 && k_KQ >= D/2) { - break; - } const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; - sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE]; - } - - sum2 = warp_reduce_sum(sum2); - half sum = __low2half(sum2) + __high2half(sum2); - sum += mask ? maskh[k_VKQ_0 + i_KQ] : __float2half(0.0f); - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - if (threadIdx.x == 0) { - KQ[i_KQ] = sum; - } - } - - kqmax_new = warp_reduce_max(kqmax_new); - if (threadIdx.x == 0) { - kqmax_shared[threadIdx.y] = kqmax_new; - } - __syncthreads(); - kqmax_new = kqmax_shared[threadIdx.x]; - kqmax_new = warp_reduce_max(kqmax_new); - - const half KQ_max_scale = hexp(kqmax - kqmax_new); - kqmax = kqmax_new; - - const half val = hexp(KQ[tid] - kqmax); - kqsum = kqsum*KQ_max_scale + val; - KQ[tid] = val; - - VKQ *= __half2half2(KQ_max_scale); - - __syncthreads(); - - if (tid < D) { #pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; + for (int j = 0; j < ncols; ++j) { + sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] = warp_reduce_sum(sum2[j]); + half sum = __low2half(sum2[j]) + __high2half(sum2[j]); + sum += mask ? maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); } - half2 V_k; - reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; - VKQ += V_k*KQ2[k0/2]; + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; + reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; } } __syncthreads(); } - if (tid >= D) { - kqsum = 0.0f; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } } - kqsum = warp_reduce_sum(kqsum); - if (threadIdx.x == 0) { - kqsum_shared[threadIdx.y] = kqsum; - } __syncthreads(); - kqsum = kqsum_shared[threadIdx.x]; - kqsum = warp_reduce_sum(kqsum); - if (tid >= D) { - return; +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - half dst_val = (__low2half(VKQ) + __high2half(VKQ)); - if (parallel_blocks == 1) { - dst_val /= kqsum; + if (parallel_blocks != 1 && tid != 0) { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); + } } - dst[D*gridDim.y*blockIdx.x + D*blockIdx.y + tid] = dst_val; - - if (parallel_blocks == 1 || tid != 0) { - return; - } - dst_meta[ic*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax, kqsum); #else NO_DEVICE_CODE; #endif // FP16_AVAILABLE @@ -191,7 +238,9 @@ static __global__ void flash_attn_vec_ext_f16( // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t> +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_ext_f16( const char * __restrict__ Q, const char * __restrict__ K, @@ -573,7 +622,9 @@ static __global__ void flash_attn_ext_f16( } template<int D, int parallel_blocks> // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) static __global__ void flash_attn_combine_results( const float * __restrict__ VKQ_parts, const float2 * __restrict__ VKQ_meta, @@ -642,7 +693,7 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); -template <int D, int parallel_blocks> void launch_fattn_vec_f16( +template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16( const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, ggml_cuda_pool & pool, cudaStream_t main_stream ) { @@ -656,13 +707,13 @@ template <int D, int parallel_blocks> void launch_fattn_vec_f16( constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*Q->ne[1], Q->ne[2], Q->ne[3]); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); const int shmem = 0; float scale; memcpy(&scale, KQV->op_params, sizeof(float)); - flash_attn_vec_ext_f16<D, parallel_blocks> + flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks> <<<blocks_num, block_dim, shmem, main_stream>>> ( (const char *) Q->data, (const char *) K->data, @@ -783,10 +834,99 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst ggml_cuda_set_device(ctx.device); + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; const int32_t precision = KQV->op_params[1]; + if (!fp16_mma_available(cc)) { + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + if (precision != GGML_PREC_DEFAULT) { if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; @@ -845,16 +985,17 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { + constexpr int cols_per_block = 1; constexpr int parallel_blocks = 4; switch (Q->ne[0]) { case 64: - launch_fattn_vec_f16< 64, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); break; case 128: - launch_fattn_vec_f16<128, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); break; case 256: - launch_fattn_vec_f16<256, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); break; default: GGML_ASSERT(false); diff --git a/llama.cpp b/llama.cpp index 806c20932..7572f8d56 100644 --- a/llama.cpp +++ b/llama.cpp @@ -15519,13 +15519,6 @@ struct llama_context * llama_new_context_with_model( cparams.flash_attn = false; } -#ifdef GGML_USE_HIPBLAS - if (cparams.flash_attn) { - LLAMA_LOG_WARN("%s: flash_attn is not yet compatible with HIPBLAS builds - forcing off\n", __func__); - cparams.flash_attn = false; - } -#endif - if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 41718e001..0d66de5d9 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2175,7 +2175,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + for (int hs : { 64, 128, }) { // other head sizes not implemented +#else for (int hs : { 64, 80, 128, 256, }) { +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) for (int nh : { 32, }) { for (int kv : { 512, 1024, }) { for (int nb : { 1, 2, 4, 8, }) { From 43248e559472556f368988575d9fba906b3eb139 Mon Sep 17 00:00:00 2001 From: jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> Date: Thu, 9 May 2024 15:30:44 +0200 Subject: [PATCH 069/100] llama3 custom regex split (#6965) * merged the changes from deepseeker models to main branch * Moved regex patterns to unicode.cpp and updated unicode.h * Moved header files * Resolved issues * added and refactored unicode_regex_split and related functions * Updated/merged the deepseek coder pr * Refactored code * Adding unicode regex mappings * Adding unicode regex function * Added needed functionality, testing remains * Fixed issues * Fixed issue with gpt2 regex custom preprocessor * unicode : fix? unicode_wstring_to_utf8 * lint : fix whitespaces * tests : add tokenizer tests for numbers * unicode : remove redundant headers * tests : remove and rename tokenizer test scripts * tests : add sample usage * gguf-py : reader prints warnings on duplicate keys * llama : towards llama3 tokenization support (wip) * unicode : shot in the dark to fix tests on Windows * unicode : first try custom implementations * convert : add "tokenizer.ggml.pre" GGUF KV (wip) * llama : use new pre-tokenizer type * convert : fix pre-tokenizer type writing * lint : fix * make : add test-tokenizer-0-llama-v3 * wip * models : add llama v3 vocab file * llama : adapt punctuation regex + add llama 3 regex * minor * unicode : set bomb * unicode : set bomb * unicode : always use std::wregex * unicode : support \p{N}, \p{L} and \p{P} natively * unicode : try fix windows * unicode : category support via std::regex * unicode : clean-up * unicode : simplify * llama3 custom regex split * convert : add convert-hf-to-gguf-update.py ggml-ci * lint : update * convert : add falcon ggml-ci * unicode : normalize signatures * lint : fix * lint : fix * convert : remove unused functions * convert : add comments * convert : exercise contractions ggml-ci * Using char32_t for codepoints * lint : fix * already exists unicode_tolower() * Typing * Restore BOM * cmake : refactor test targets * tests : refactor vocab tests ggml-ci * tests : add more vocabs and tests ggml-ci * unicode : cleanup * scripts : ignore new update script in check-requirements.sh * Fix merge * models : add phi-3, mpt, gpt-2, starcoder * tests : disable obsolete ggml-ci * tests : use faster bpe test ggml-ci * llama : more prominent warning for old BPE models * tests : disable test-tokenizer-1-bpe due to slowness ggml-ci * Move unused variable value * GPT2 custom regex split * Add alternative regex for custom aplit llama3 Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * Style * Add bruteforce random tests for token encoding * wip: fixing unicode codepoint ranges * Fix merge * Unicode tables: separator, lowercase, uppercase and whitespace * llama3 custom regex split: fix \s * Restore BOM * Style * wip: generate NDF table * Ignore special tokens for testing * Clean gen-unicode-data.py * Refactor random tokenizer test * lint : fix * tests : add fail test for llama-bpe --------- Co-authored-by: Jaggzh <jaggz.h@gmail.com> Co-authored-by: Kazim Abrar Mahi <kazimabrarmahi135@gmail.com> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> Co-authored-by: jaime-m-p <> --- convert-hf-to-gguf-update.py | 1 + llama.cpp | 2 +- scripts/gen-unicode-data.py | 78 +- tests/test-tokenizer-random.py | 295 ++++++++ unicode-data.cpp | 1262 ++++++++++++++++++++++---------- unicode-data.h | 1 + unicode.cpp | 374 +++++++--- unicode.h | 4 +- 8 files changed, 1468 insertions(+), 549 deletions(-) create mode 100644 tests/test-tokenizer-random.py diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 6fdb502f9..e64687722 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -261,6 +261,7 @@ tests = [ "3333333", "33333333", "333333333", + # "Cửa Việt", # llama-bpe fails on this chktxt, ] diff --git a/llama.cpp b/llama.cpp index 7572f8d56..e7b3fd8b4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12488,7 +12488,7 @@ struct llm_tokenizer_wpm { continue; } code = unicode_tolower(code); - if (type == CODEPOINT_TYPE_WHITESPACE) { + if (type == CODEPOINT_TYPE_SEPARATOR) { code = ' '; } std::string s = unicode_cpt_to_utf8(code); diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py index 977e65613..37d1e396c 100644 --- a/scripts/gen-unicode-data.py +++ b/scripts/gen-unicode-data.py @@ -1,31 +1,14 @@ import regex -def cpt_to_utf8_str(cpt): - if cpt <= 0xFF: - return bytes([cpt, 0, 0, 0]) - elif cpt <= 0xFFFF: - return bytes([cpt & 0xFF, cpt >> 8, 0, 0]) - elif cpt <= 0xFFFFFF: - return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, 0]) - else: - return bytes([cpt & 0xFF, (cpt >> 8) & 0xFF, (cpt >> 16) & 0xFF, cpt >> 24]) - - -def is_match(codepoint, regex_expr): - try: - res = regex.match(regex_expr, cpt_to_utf8_str(codepoint).decode('utf-32')) - return res is not None - except Exception: - return False - - def get_matches(regex_expr): + regex_expr_compiled = regex.compile(regex_expr) unicode_ranges = [] current_range = None for codepoint in range(0x110000): - if is_match(codepoint, regex_expr): + char = chr(codepoint) + if regex_expr_compiled.match(char): if current_range is None: current_range = [codepoint, codepoint] else: @@ -40,27 +23,42 @@ def get_matches(regex_expr): return unicode_ranges -def print_cat(cat, ranges): - print("const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 - cnt = 0 - for start, end in ranges: - if cnt % 4 != 0: - print(" ", end="") # noqa: NP100 - print("{{0x{:08X}, 0x{:08X}}},".format(start, end), end="") # noqa: NP100 - if cnt % 4 == 3: - print("") # noqa: NP100 - cnt += 1 - - if cnt % 4 != 0: - print("") # noqa: NP100 +def print_cat(mode, cat, ranges): + if mode == "range": + print("const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 + if mode == "map": + print("const std::map<uint32_t, uint32_t> unicode_map_{} = {{".format(cat)) # noqa: NP100 + for i, values in enumerate(ranges): + end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", " + values = ["0x%08X" % value for value in values] + print("{" + ", ".join(values) + "}", end=end) # noqa: NP100 print("};") # noqa: NP100 print("") # noqa: NP100 -print_cat("number", get_matches(r'\p{N}')) -print_cat("letter", get_matches(r'\p{L}')) -print_cat("whitespace", get_matches(r'\p{Z}')) -print_cat("accent_mark", get_matches(r'\p{M}')) -print_cat("punctuation", get_matches(r'\p{P}')) -print_cat("symbol", get_matches(r'\p{S}')) -print_cat("control", get_matches(r'\p{C}')) +print_cat("range", "number", get_matches(r'\p{N}')) +print_cat("range", "letter", get_matches(r'\p{L}')) +print_cat("range", "separator", get_matches(r'\p{Z}')) +print_cat("range", "accent_mark", get_matches(r'\p{M}')) +print_cat("range", "punctuation", get_matches(r'\p{P}')) +print_cat("range", "symbol", get_matches(r'\p{S}')) +print_cat("range", "control", get_matches(r'\p{C}')) + +print_cat("range", "whitespace", get_matches(r'\s')) + + +map_lowercase = [] +map_uppercase = [] +for codepoint in range(0x110000): + char = chr(codepoint) + lower = ord(char.lower()[0]) + upper = ord(char.upper()[0]) + if codepoint != lower: + map_lowercase.append((codepoint, lower)) + if codepoint != upper: + map_uppercase.append((codepoint, upper)) +print_cat("map", "lowercase", map_lowercase) +print_cat("map", "uppercase", map_uppercase) + + +# TODO: generate unicode_map_nfd diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py new file mode 100644 index 000000000..5b2ab8ef7 --- /dev/null +++ b/tests/test-tokenizer-random.py @@ -0,0 +1,295 @@ +# Test libllama tokenizer == AutoTokenizer. +# Brute force random tokens/text generation. +# +# Sample usage: +# +# python3 tests/test-tokenizer-random.py ./models/ggml-vocab-llama-bpe.gguf ./models/tokenizers/llama-bpe +# + +import time +import logging +import argparse +import subprocess +import random + +from typing import Iterator + +import cffi +from transformers import AutoTokenizer, PreTrainedTokenizerBase + +logger = logging.getLogger("test-tokenizer-random-bpe") + + +class LibLlama: + + DEFAULT_PATH_LLAMA_H = "./llama.h" + DEFAULT_PATH_LIBLLAMA = "./build/libllama.so" # CMakeLists.txt: BUILD_SHARED_LIBS ON + + def __init__(self, path_llama_h: str = None, path_libllama: str = None): + path_llama_h = path_llama_h or self.DEFAULT_PATH_LLAMA_H + path_libllama = path_libllama or self.DEFAULT_PATH_LIBLLAMA + (self.ffi, self.lib) = self._load_libllama_cffi(path_llama_h, path_libllama) + self.lib.llama_backend_init() + + def _load_libllama_cffi(self, path_llama_h: str, path_libllama: str): + cmd = ["gcc", "-E", "-P", "-D__restrict=", "-D__attribute__(x)=", "-D__asm__(x)=", path_llama_h] + res = subprocess.run(cmd, stdout=subprocess.PIPE) + assert (res.returncode == 0) + source = res.stdout.decode() + ffi = cffi.FFI() + if True: # workarounds for pycparser + source = "typedef struct { } __builtin_va_list;" + "\n" + source + source = source.replace("sizeof (int)", str(ffi.sizeof("int"))) + source = source.replace("sizeof (void *)", str(ffi.sizeof("void*"))) + source = source.replace("sizeof (size_t)", str(ffi.sizeof("size_t"))) + source = source.replace("sizeof(int32_t)", str(ffi.sizeof("int32_t"))) + ffi.cdef(source, override=True) + lib = ffi.dlopen(path_libllama) + return (ffi, lib) + + def model_default_params(self, **kwargs): + mparams = self.lib.llama_model_default_params() + for k, v in kwargs.items(): + setattr(mparams, k, v) + return mparams + + def context_default_params(self, **kwargs): + cparams = self.lib.llama_context_default_params() + for k, v in kwargs.items(): + setattr(cparams, k, v) + return cparams + + +class LibLlamaModel: + + def __init__(self, libllama: LibLlama, path_model: str, mparams={}, cparams={}): + self.lib = libllama.lib + self.ffi = libllama.ffi + if isinstance(mparams, dict): + mparams = libllama.model_default_params(**mparams) + self.model = self.lib.llama_load_model_from_file(path_model.encode(), mparams) + if not self.model: + raise RuntimeError("error: failed to load model '%s'" % path_model) + if isinstance(cparams, dict): + cparams = libllama.context_default_params(**cparams) + self.ctx = self.lib.llama_new_context_with_model(self.model, cparams) + if not self.ctx: + raise RuntimeError("error: failed to create context for model '%s'" % path_model) + n_tokens_max = self.lib.llama_n_ctx(self.ctx) + self.token_ids = self.ffi.new("llama_token[]", n_tokens_max) + + def free(self): + if self.ctx: + self.lib.llama_free(self.ctx) + if self.model: + self.lib.llama_free_model(self.model) + self.ctx = None + self.model = None + self.lib = None + + def tokenize(self, text: str, n_tokens_max: int = 0, add_special: bool = False, parse_special: bool = False) -> list[int]: + n_tokens_max = n_tokens_max if n_tokens_max > 0 else len(self.token_ids) + text = text.encode("utf-8") + num = self.lib.llama_tokenize(self.model, text, len(text), self.token_ids, n_tokens_max, add_special, parse_special) + if num < 0: + return [] + return list(self.token_ids[0:num]) + + +def generator_custom_text() -> Iterator[str]: + """General tests""" + yield from [ + "", + " ", + " ", + " ", + "\t", + "\n", + "\n\n", + "\n\n\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + " (", + "\n =", + "' era", + "Hello, y'all! How are you 😁 ?我想在apple工作1314151天～", + "3", + "33", + "333", + "3333", + "33333", + "333333", + "3333333", + "33333333", + "333333333", + ] + + +def generator_custom_text_edge_cases() -> Iterator[str]: + """Edge cases found while debugging""" + yield from [ + '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} + '¼-a', # unicode_ranges_digit, 0x00BC + '½-a', # unicode_ranges_digit, 0x00BD + '¾-a', # unicode_ranges_digit, 0x00BE + 'a 〇b', # unicode_ranges_digit, 0x3007 + 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms + '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) + '<s>a' # TODO: Phi-3 fail + ] + + +def generator_random_chars(iterations = 100) -> Iterator[str]: + """Brute force random text with simple characters""" + + WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) + CHARS = list(set(""" + ABCDEFGHIJKLMNOPQRSTUVWXYZ + abcdefghijklmnopqrstuvwxyz + ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ + áéíóúàèìòùâêîôûäëïöü + .-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_ + """)) + + rand = random.Random() + for m in range(iterations): + rand.seed(m) + text = [] + num_words = rand.randint(300, 400) + for i in range(num_words): + k = rand.randint(1, 7) + word = rand.choices(CHARS, k=k) + space = rand.choice(WHITESPACES) + text.append("".join(word) + space) + yield "".join(text) + + +def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: + """Brute force random text with vocab characters""" + + vocab_ids = list(tokenizer.vocab.values()) + vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True) + vocab_chars = list(set(vocab_text)) + del vocab_ids, vocab_text + + rand = random.Random() + for m in range(iterations): + rand.seed(m) + text = rand.choices(vocab_chars, k=1024) + yield "".join(text) + + +def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: + """Brute force random text from vocab tokens""" + + space_id = tokenizer.encode(" ", add_special_tokens=False)[0] + vocab_ids = list(tokenizer.vocab.values()) + vocab_ids = list(sorted(vocab_ids + vocab_ids)) + for i in range(1, len(vocab_ids), 2): + vocab_ids[i] = space_id + vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True) + vocab_tokens = vocab_tokens.split(" ") + del vocab_ids + + yield from vocab_tokens + + rand = random.Random() + for m in range(iterations): + rand.seed(m) + text = [] + num_words = rand.randint(300, 400) + for i in range(num_words): + k = rand.randint(1, 3) + tokens = rand.choices(vocab_tokens, k=k) + tokens = [t.strip(" \n\r\t") for t in tokens] + sep = rand.choice(" \n\r\t") + text.append("".join(tokens) + sep) + yield "".join(text) + + +def generator_random_bytes(iterations = 100) -> Iterator[str]: + """Brute force random bytes""" + + WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) + + rand = random.Random() + for m in range(iterations): + rand.seed(m) + text = [] + num_words = rand.randint(300, 400) + for i in range(num_words): + k = rand.randint(1, 8) + word = [chr(r) for r in rand.randbytes(k) if r] + word.append(rand.choice(WHITESPACES)) + text.append("".join(word)) + yield "".join(text) + + +def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]): + + def find_first_mismatch(ids1: list[int], ids2: list[int]): + for i, (a,b) in enumerate(zip(ids1, ids2)): + if a != b: + return i + if len(ids1) == len(ids2): + return -1 + return min(len(ids1), len(ids2)) + + t0 = time.perf_counter() + logger.info("%s: %s" % (generator.__name__, "ini")) + for text in generator: + ids1 = model.tokenize(text, add_special=False, parse_special=False) + ids2 = tokenizer.encode(text, add_special_tokens=False) + if ids1 != ids2: + i = find_first_mismatch(ids1, ids2) + ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1] + ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1] + text2 = tokenizer.decode(ids2, skip_special_tokens=True) + assert (text2 in text) + logger.info(" Text: " + repr(text2)) + logger.info(" TokenIDs: " + str(ids1)) + logger.info(" Expected: " + str(ids2)) + raise Exception() + t1 = time.perf_counter() + logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0)) + + +if __name__ == "__main__": + + parser = argparse.ArgumentParser() + parser.add_argument("vocab_file", help="path to vocab 'gguf' file") + parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") + parser.add_argument("--verbose", action="store_true", help="increase output verbosity") + args = parser.parse_args() + + logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) + + model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048)) + + tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) + + test_compare_tokenizer(model, tokenizer, generator_custom_text()) + test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases()) + test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000)) + test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000)) + test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000)) + # test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL + + model.free() diff --git a/unicode-data.cpp b/unicode-data.cpp index 07bf02c45..c54175fc3 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -7,7 +7,6 @@ // generated with scripts/gen-unicode-data.py // -// TODO: generate unicode_map_lowercase // TODO: generate unicode_map_nfd const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number = { @@ -213,12 +212,18 @@ const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter = { {0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, {0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF}, {0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, -{0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, +{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, +}; + +const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator = { +{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, +{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, }; const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace = { -{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, -{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, +{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, +{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, }; const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark = { @@ -380,38 +385,39 @@ const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_symbol = { {0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, {0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, {0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, -{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFB}, +{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF}, {0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, {0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, -{0x000031C0, 0x000031E3}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, {0x00003250, 0x00003250}, -{0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, {0x00004DC0, 0x00004DFF}, -{0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, {0x0000A789, 0x0000A78A}, -{0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, {0x0000AB5B, 0x0000AB5B}, -{0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, {0x0000FD40, 0x0000FD4F}, -{0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, {0x0000FE64, 0x0000FE66}, -{0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, {0x0000FF1C, 0x0000FF1E}, -{0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, {0x0000FF5E, 0x0000FF5E}, -{0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, {0x00010137, 0x0001013F}, -{0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, {0x000101A0, 0x000101A0}, -{0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, {0x0001173F, 0x0001173F}, -{0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, {0x0001BC9C, 0x0001BC9C}, -{0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, {0x0001D129, 0x0001D164}, -{0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, {0x0001D1AE, 0x0001D1EA}, -{0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, {0x0001D6C1, 0x0001D6C1}, -{0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, {0x0001D735, 0x0001D735}, -{0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, {0x0001D7A9, 0x0001D7A9}, -{0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, {0x0001DA6D, 0x0001DA74}, -{0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, {0x0001E2FF, 0x0001E2FF}, -{0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, {0x0001EEF0, 0x0001EEF1}, -{0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, {0x0001F0B1, 0x0001F0BF}, -{0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, {0x0001F1E6, 0x0001F202}, -{0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, {0x0001F260, 0x0001F265}, -{0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, {0x0001F700, 0x0001F776}, -{0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, {0x0001F800, 0x0001F80B}, -{0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, {0x0001F890, 0x0001F8AD}, -{0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, {0x0001FA70, 0x0001FA7C}, -{0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, {0x0001FACE, 0x0001FADB}, -{0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, {0x0001FB94, 0x0001FBCA}, +{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, +{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, +{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, +{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, +{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, +{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, +{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, +{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, +{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, +{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, +{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, +{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, +{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, +{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, +{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, +{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, +{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, +{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, +{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, +{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, +{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, +{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, +{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, +{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, +{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, +{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, +{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, +{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, +{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, +{0x0001FB94, 0x0001FBCA}, }; const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control = { @@ -484,116 +490,842 @@ const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_control = { {0x00002D97, 0x00002D9F}, {0x00002DA7, 0x00002DA7}, {0x00002DAF, 0x00002DAF}, {0x00002DB7, 0x00002DB7}, {0x00002DBF, 0x00002DBF}, {0x00002DC7, 0x00002DC7}, {0x00002DCF, 0x00002DCF}, {0x00002DD7, 0x00002DD7}, {0x00002DDF, 0x00002DDF}, {0x00002E5E, 0x00002E7F}, {0x00002E9A, 0x00002E9A}, {0x00002EF4, 0x00002EFF}, -{0x00002FD6, 0x00002FEF}, {0x00002FFC, 0x00002FFF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, -{0x00003100, 0x00003104}, {0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EF}, -{0x0000321F, 0x0000321F}, {0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, -{0x0000A6F8, 0x0000A6FF}, {0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, -{0x0000A7DA, 0x0000A7F1}, {0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, -{0x0000A8C6, 0x0000A8CD}, {0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, -{0x0000A9CE, 0x0000A9CE}, {0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, -{0x0000AA4E, 0x0000AA4F}, {0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, -{0x0000AB07, 0x0000AB08}, {0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, -{0x0000AB2F, 0x0000AB2F}, {0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, -{0x0000D7A4, 0x0000D7AF}, {0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000D7FF}, {0x0000E000, 0x0000F8FF}, -{0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, {0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, -{0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, {0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, -{0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2}, {0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, -{0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F}, {0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, -{0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, {0x0000FEFD, 0x0000FEFE}, {0x0000FF00, 0x0000FF00}, -{0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, {0x0000FFD8, 0x0000FFD9}, -{0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, {0x0000FFFE, 0x0000FFFF}, -{0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, {0x0001003E, 0x0001003E}, -{0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF}, {0x00010103, 0x00010106}, -{0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F}, {0x000101A1, 0x000101CF}, -{0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF}, {0x000102FC, 0x000102FF}, -{0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, {0x0001039E, 0x0001039E}, -{0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, {0x000104AA, 0x000104AF}, -{0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, {0x00010564, 0x0001056E}, -{0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593}, {0x00010596, 0x00010596}, -{0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA}, {0x000105BD, 0x000105FF}, -{0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F}, {0x00010786, 0x00010786}, -{0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807}, {0x00010809, 0x00010809}, -{0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E}, {0x00010856, 0x00010856}, -{0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3}, {0x000108F6, 0x000108FA}, -{0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F}, {0x000109B8, 0x000109BB}, -{0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B}, {0x00010A14, 0x00010A14}, -{0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E}, {0x00010A49, 0x00010A4F}, -{0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA}, {0x00010AF7, 0x00010AFF}, -{0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77}, {0x00010B92, 0x00010B98}, -{0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F}, {0x00010CB3, 0x00010CBF}, -{0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F}, {0x00010E7F, 0x00010E7F}, -{0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC}, {0x00010F28, 0x00010F2F}, -{0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, {0x00010FF7, 0x00010FFF}, -{0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD}, {0x000110C3, 0x000110CF}, -{0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, {0x00011148, 0x0001114F}, -{0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, {0x00011212, 0x00011212}, -{0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, {0x0001128E, 0x0001128E}, -{0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, {0x000112FA, 0x000112FF}, -{0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, {0x00011329, 0x00011329}, -{0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, {0x00011345, 0x00011346}, -{0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, {0x00011358, 0x0001135C}, -{0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, {0x0001145C, 0x0001145C}, -{0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, {0x000115B6, 0x000115B7}, -{0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, {0x0001166D, 0x0001167F}, -{0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, {0x0001172C, 0x0001172F}, -{0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, {0x00011907, 0x00011908}, -{0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, {0x00011936, 0x00011936}, -{0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, {0x000119A8, 0x000119A9}, -{0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, {0x00011AA3, 0x00011AAF}, -{0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09}, {0x00011C37, 0x00011C37}, -{0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, {0x00011CA8, 0x00011CA8}, -{0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, {0x00011D37, 0x00011D39}, -{0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, {0x00011D5A, 0x00011D5F}, -{0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, {0x00011D92, 0x00011D92}, -{0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF}, {0x00011F11, 0x00011F11}, -{0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, {0x00011FF2, 0x00011FFE}, -{0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, {0x00012544, 0x00012F8F}, -{0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF}, {0x00014647, 0x000167FF}, -{0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, {0x00016ABF, 0x00016ABF}, -{0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF}, {0x00016B46, 0x00016B4F}, -{0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C}, {0x00016B90, 0x00016E3F}, -{0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E}, {0x00016FA0, 0x00016FDF}, -{0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF}, {0x00018CD6, 0x00018CFF}, -{0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC}, {0x0001AFFF, 0x0001AFFF}, -{0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154}, {0x0001B156, 0x0001B163}, -{0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, {0x0001BC7D, 0x0001BC7F}, -{0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF}, {0x0001CF2E, 0x0001CF2F}, -{0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF}, {0x0001D127, 0x0001D128}, -{0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF}, {0x0001D2D4, 0x0001D2DF}, -{0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, {0x0001D455, 0x0001D455}, -{0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, {0x0001D4A7, 0x0001D4A8}, -{0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, {0x0001D4C4, 0x0001D4C4}, -{0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, {0x0001D51D, 0x0001D51D}, -{0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, {0x0001D547, 0x0001D549}, -{0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, {0x0001DA8C, 0x0001DA9A}, -{0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, {0x0001DF2B, 0x0001DFFF}, -{0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, {0x0001E025, 0x0001E025}, -{0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, {0x0001E12D, 0x0001E12F}, -{0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, {0x0001E2AF, 0x0001E2BF}, -{0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, {0x0001E7E7, 0x0001E7E7}, -{0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, {0x0001E8C5, 0x0001E8C6}, -{0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, {0x0001E960, 0x0001EC70}, -{0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, {0x0001EE20, 0x0001EE20}, -{0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, {0x0001EE33, 0x0001EE33}, -{0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, {0x0001EE43, 0x0001EE46}, -{0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, {0x0001EE50, 0x0001EE50}, -{0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, {0x0001EE5A, 0x0001EE5A}, -{0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, {0x0001EE63, 0x0001EE63}, -{0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, {0x0001EE78, 0x0001EE78}, -{0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, {0x0001EE9C, 0x0001EEA0}, -{0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, {0x0001EEF2, 0x0001EFFF}, -{0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, {0x0001F0C0, 0x0001F0C0}, -{0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, {0x0001F203, 0x0001F20F}, -{0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, {0x0001F266, 0x0001F2FF}, -{0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, {0x0001F777, 0x0001F77A}, -{0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, {0x0001F80C, 0x0001F80F}, -{0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, {0x0001F8AE, 0x0001F8AF}, -{0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, {0x0001FA7D, 0x0001FA7F}, -{0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, {0x0001FADC, 0x0001FADF}, -{0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, {0x0001FBCB, 0x0001FBEF}, -{0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, {0x0002B81E, 0x0002B81F}, -{0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002F7FF}, {0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, -{0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, +{0x00002FD6, 0x00002FEF}, {0x00003040, 0x00003040}, {0x00003097, 0x00003098}, {0x00003100, 0x00003104}, +{0x00003130, 0x00003130}, {0x0000318F, 0x0000318F}, {0x000031E4, 0x000031EE}, {0x0000321F, 0x0000321F}, +{0x0000A48D, 0x0000A48F}, {0x0000A4C7, 0x0000A4CF}, {0x0000A62C, 0x0000A63F}, {0x0000A6F8, 0x0000A6FF}, +{0x0000A7CB, 0x0000A7CF}, {0x0000A7D2, 0x0000A7D2}, {0x0000A7D4, 0x0000A7D4}, {0x0000A7DA, 0x0000A7F1}, +{0x0000A82D, 0x0000A82F}, {0x0000A83A, 0x0000A83F}, {0x0000A878, 0x0000A87F}, {0x0000A8C6, 0x0000A8CD}, +{0x0000A8DA, 0x0000A8DF}, {0x0000A954, 0x0000A95E}, {0x0000A97D, 0x0000A97F}, {0x0000A9CE, 0x0000A9CE}, +{0x0000A9DA, 0x0000A9DD}, {0x0000A9FF, 0x0000A9FF}, {0x0000AA37, 0x0000AA3F}, {0x0000AA4E, 0x0000AA4F}, +{0x0000AA5A, 0x0000AA5B}, {0x0000AAC3, 0x0000AADA}, {0x0000AAF7, 0x0000AB00}, {0x0000AB07, 0x0000AB08}, +{0x0000AB0F, 0x0000AB10}, {0x0000AB17, 0x0000AB1F}, {0x0000AB27, 0x0000AB27}, {0x0000AB2F, 0x0000AB2F}, +{0x0000AB6C, 0x0000AB6F}, {0x0000ABEE, 0x0000ABEF}, {0x0000ABFA, 0x0000ABFF}, {0x0000D7A4, 0x0000D7AF}, +{0x0000D7C7, 0x0000D7CA}, {0x0000D7FC, 0x0000F8FF}, {0x0000FA6E, 0x0000FA6F}, {0x0000FADA, 0x0000FAFF}, +{0x0000FB07, 0x0000FB12}, {0x0000FB18, 0x0000FB1C}, {0x0000FB37, 0x0000FB37}, {0x0000FB3D, 0x0000FB3D}, +{0x0000FB3F, 0x0000FB3F}, {0x0000FB42, 0x0000FB42}, {0x0000FB45, 0x0000FB45}, {0x0000FBC3, 0x0000FBD2}, +{0x0000FD90, 0x0000FD91}, {0x0000FDC8, 0x0000FDCE}, {0x0000FDD0, 0x0000FDEF}, {0x0000FE1A, 0x0000FE1F}, +{0x0000FE53, 0x0000FE53}, {0x0000FE67, 0x0000FE67}, {0x0000FE6C, 0x0000FE6F}, {0x0000FE75, 0x0000FE75}, +{0x0000FEFD, 0x0000FF00}, {0x0000FFBF, 0x0000FFC1}, {0x0000FFC8, 0x0000FFC9}, {0x0000FFD0, 0x0000FFD1}, +{0x0000FFD8, 0x0000FFD9}, {0x0000FFDD, 0x0000FFDF}, {0x0000FFE7, 0x0000FFE7}, {0x0000FFEF, 0x0000FFFB}, +{0x0000FFFE, 0x0000FFFF}, {0x0001000C, 0x0001000C}, {0x00010027, 0x00010027}, {0x0001003B, 0x0001003B}, +{0x0001003E, 0x0001003E}, {0x0001004E, 0x0001004F}, {0x0001005E, 0x0001007F}, {0x000100FB, 0x000100FF}, +{0x00010103, 0x00010106}, {0x00010134, 0x00010136}, {0x0001018F, 0x0001018F}, {0x0001019D, 0x0001019F}, +{0x000101A1, 0x000101CF}, {0x000101FE, 0x0001027F}, {0x0001029D, 0x0001029F}, {0x000102D1, 0x000102DF}, +{0x000102FC, 0x000102FF}, {0x00010324, 0x0001032C}, {0x0001034B, 0x0001034F}, {0x0001037B, 0x0001037F}, +{0x0001039E, 0x0001039E}, {0x000103C4, 0x000103C7}, {0x000103D6, 0x000103FF}, {0x0001049E, 0x0001049F}, +{0x000104AA, 0x000104AF}, {0x000104D4, 0x000104D7}, {0x000104FC, 0x000104FF}, {0x00010528, 0x0001052F}, +{0x00010564, 0x0001056E}, {0x0001057B, 0x0001057B}, {0x0001058B, 0x0001058B}, {0x00010593, 0x00010593}, +{0x00010596, 0x00010596}, {0x000105A2, 0x000105A2}, {0x000105B2, 0x000105B2}, {0x000105BA, 0x000105BA}, +{0x000105BD, 0x000105FF}, {0x00010737, 0x0001073F}, {0x00010756, 0x0001075F}, {0x00010768, 0x0001077F}, +{0x00010786, 0x00010786}, {0x000107B1, 0x000107B1}, {0x000107BB, 0x000107FF}, {0x00010806, 0x00010807}, +{0x00010809, 0x00010809}, {0x00010836, 0x00010836}, {0x00010839, 0x0001083B}, {0x0001083D, 0x0001083E}, +{0x00010856, 0x00010856}, {0x0001089F, 0x000108A6}, {0x000108B0, 0x000108DF}, {0x000108F3, 0x000108F3}, +{0x000108F6, 0x000108FA}, {0x0001091C, 0x0001091E}, {0x0001093A, 0x0001093E}, {0x00010940, 0x0001097F}, +{0x000109B8, 0x000109BB}, {0x000109D0, 0x000109D1}, {0x00010A04, 0x00010A04}, {0x00010A07, 0x00010A0B}, +{0x00010A14, 0x00010A14}, {0x00010A18, 0x00010A18}, {0x00010A36, 0x00010A37}, {0x00010A3B, 0x00010A3E}, +{0x00010A49, 0x00010A4F}, {0x00010A59, 0x00010A5F}, {0x00010AA0, 0x00010ABF}, {0x00010AE7, 0x00010AEA}, +{0x00010AF7, 0x00010AFF}, {0x00010B36, 0x00010B38}, {0x00010B56, 0x00010B57}, {0x00010B73, 0x00010B77}, +{0x00010B92, 0x00010B98}, {0x00010B9D, 0x00010BA8}, {0x00010BB0, 0x00010BFF}, {0x00010C49, 0x00010C7F}, +{0x00010CB3, 0x00010CBF}, {0x00010CF3, 0x00010CF9}, {0x00010D28, 0x00010D2F}, {0x00010D3A, 0x00010E5F}, +{0x00010E7F, 0x00010E7F}, {0x00010EAA, 0x00010EAA}, {0x00010EAE, 0x00010EAF}, {0x00010EB2, 0x00010EFC}, +{0x00010F28, 0x00010F2F}, {0x00010F5A, 0x00010F6F}, {0x00010F8A, 0x00010FAF}, {0x00010FCC, 0x00010FDF}, +{0x00010FF7, 0x00010FFF}, {0x0001104E, 0x00011051}, {0x00011076, 0x0001107E}, {0x000110BD, 0x000110BD}, +{0x000110C3, 0x000110CF}, {0x000110E9, 0x000110EF}, {0x000110FA, 0x000110FF}, {0x00011135, 0x00011135}, +{0x00011148, 0x0001114F}, {0x00011177, 0x0001117F}, {0x000111E0, 0x000111E0}, {0x000111F5, 0x000111FF}, +{0x00011212, 0x00011212}, {0x00011242, 0x0001127F}, {0x00011287, 0x00011287}, {0x00011289, 0x00011289}, +{0x0001128E, 0x0001128E}, {0x0001129E, 0x0001129E}, {0x000112AA, 0x000112AF}, {0x000112EB, 0x000112EF}, +{0x000112FA, 0x000112FF}, {0x00011304, 0x00011304}, {0x0001130D, 0x0001130E}, {0x00011311, 0x00011312}, +{0x00011329, 0x00011329}, {0x00011331, 0x00011331}, {0x00011334, 0x00011334}, {0x0001133A, 0x0001133A}, +{0x00011345, 0x00011346}, {0x00011349, 0x0001134A}, {0x0001134E, 0x0001134F}, {0x00011351, 0x00011356}, +{0x00011358, 0x0001135C}, {0x00011364, 0x00011365}, {0x0001136D, 0x0001136F}, {0x00011375, 0x000113FF}, +{0x0001145C, 0x0001145C}, {0x00011462, 0x0001147F}, {0x000114C8, 0x000114CF}, {0x000114DA, 0x0001157F}, +{0x000115B6, 0x000115B7}, {0x000115DE, 0x000115FF}, {0x00011645, 0x0001164F}, {0x0001165A, 0x0001165F}, +{0x0001166D, 0x0001167F}, {0x000116BA, 0x000116BF}, {0x000116CA, 0x000116FF}, {0x0001171B, 0x0001171C}, +{0x0001172C, 0x0001172F}, {0x00011747, 0x000117FF}, {0x0001183C, 0x0001189F}, {0x000118F3, 0x000118FE}, +{0x00011907, 0x00011908}, {0x0001190A, 0x0001190B}, {0x00011914, 0x00011914}, {0x00011917, 0x00011917}, +{0x00011936, 0x00011936}, {0x00011939, 0x0001193A}, {0x00011947, 0x0001194F}, {0x0001195A, 0x0001199F}, +{0x000119A8, 0x000119A9}, {0x000119D8, 0x000119D9}, {0x000119E5, 0x000119FF}, {0x00011A48, 0x00011A4F}, +{0x00011AA3, 0x00011AAF}, {0x00011AF9, 0x00011AFF}, {0x00011B0A, 0x00011BFF}, {0x00011C09, 0x00011C09}, +{0x00011C37, 0x00011C37}, {0x00011C46, 0x00011C4F}, {0x00011C6D, 0x00011C6F}, {0x00011C90, 0x00011C91}, +{0x00011CA8, 0x00011CA8}, {0x00011CB7, 0x00011CFF}, {0x00011D07, 0x00011D07}, {0x00011D0A, 0x00011D0A}, +{0x00011D37, 0x00011D39}, {0x00011D3B, 0x00011D3B}, {0x00011D3E, 0x00011D3E}, {0x00011D48, 0x00011D4F}, +{0x00011D5A, 0x00011D5F}, {0x00011D66, 0x00011D66}, {0x00011D69, 0x00011D69}, {0x00011D8F, 0x00011D8F}, +{0x00011D92, 0x00011D92}, {0x00011D99, 0x00011D9F}, {0x00011DAA, 0x00011EDF}, {0x00011EF9, 0x00011EFF}, +{0x00011F11, 0x00011F11}, {0x00011F3B, 0x00011F3D}, {0x00011F5A, 0x00011FAF}, {0x00011FB1, 0x00011FBF}, +{0x00011FF2, 0x00011FFE}, {0x0001239A, 0x000123FF}, {0x0001246F, 0x0001246F}, {0x00012475, 0x0001247F}, +{0x00012544, 0x00012F8F}, {0x00012FF3, 0x00012FFF}, {0x00013430, 0x0001343F}, {0x00013456, 0x000143FF}, +{0x00014647, 0x000167FF}, {0x00016A39, 0x00016A3F}, {0x00016A5F, 0x00016A5F}, {0x00016A6A, 0x00016A6D}, +{0x00016ABF, 0x00016ABF}, {0x00016ACA, 0x00016ACF}, {0x00016AEE, 0x00016AEF}, {0x00016AF6, 0x00016AFF}, +{0x00016B46, 0x00016B4F}, {0x00016B5A, 0x00016B5A}, {0x00016B62, 0x00016B62}, {0x00016B78, 0x00016B7C}, +{0x00016B90, 0x00016E3F}, {0x00016E9B, 0x00016EFF}, {0x00016F4B, 0x00016F4E}, {0x00016F88, 0x00016F8E}, +{0x00016FA0, 0x00016FDF}, {0x00016FE5, 0x00016FEF}, {0x00016FF2, 0x00016FFF}, {0x000187F8, 0x000187FF}, +{0x00018CD6, 0x00018CFF}, {0x00018D09, 0x0001AFEF}, {0x0001AFF4, 0x0001AFF4}, {0x0001AFFC, 0x0001AFFC}, +{0x0001AFFF, 0x0001AFFF}, {0x0001B123, 0x0001B131}, {0x0001B133, 0x0001B14F}, {0x0001B153, 0x0001B154}, +{0x0001B156, 0x0001B163}, {0x0001B168, 0x0001B16F}, {0x0001B2FC, 0x0001BBFF}, {0x0001BC6B, 0x0001BC6F}, +{0x0001BC7D, 0x0001BC7F}, {0x0001BC89, 0x0001BC8F}, {0x0001BC9A, 0x0001BC9B}, {0x0001BCA0, 0x0001CEFF}, +{0x0001CF2E, 0x0001CF2F}, {0x0001CF47, 0x0001CF4F}, {0x0001CFC4, 0x0001CFFF}, {0x0001D0F6, 0x0001D0FF}, +{0x0001D127, 0x0001D128}, {0x0001D173, 0x0001D17A}, {0x0001D1EB, 0x0001D1FF}, {0x0001D246, 0x0001D2BF}, +{0x0001D2D4, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, +{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, +{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, +{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, +{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, +{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, +{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, +{0x0001DF2B, 0x0001DFFF}, {0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, +{0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, +{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, +{0x0001E2AF, 0x0001E2BF}, {0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, +{0x0001E7E7, 0x0001E7E7}, {0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, +{0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, +{0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, +{0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, +{0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, +{0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, +{0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, +{0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, +{0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, +{0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, +{0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, +{0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, +{0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, +{0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, +{0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, +{0x0001F777, 0x0001F77A}, {0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, +{0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, +{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, +{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, +{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, +{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, +{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF}, +{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, +}; + +const std::map<char32_t, char32_t> unicode_map_lowercase = { +{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064}, +{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068}, +{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C}, +{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070}, +{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074}, +{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078}, +{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1}, +{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5}, +{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9}, +{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED}, +{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1}, +{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 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0x0000A797}, {0x0000A798, 0x0000A799}, +{0x0000A79A, 0x0000A79B}, {0x0000A79C, 0x0000A79D}, {0x0000A79E, 0x0000A79F}, {0x0000A7A0, 0x0000A7A1}, +{0x0000A7A2, 0x0000A7A3}, {0x0000A7A4, 0x0000A7A5}, {0x0000A7A6, 0x0000A7A7}, {0x0000A7A8, 0x0000A7A9}, +{0x0000A7AA, 0x00000266}, {0x0000A7AB, 0x0000025C}, {0x0000A7AC, 0x00000261}, {0x0000A7AD, 0x0000026C}, +{0x0000A7AE, 0x0000026A}, {0x0000A7B0, 0x0000029E}, {0x0000A7B1, 0x00000287}, {0x0000A7B2, 0x0000029D}, +{0x0000A7B3, 0x0000AB53}, {0x0000A7B4, 0x0000A7B5}, {0x0000A7B6, 0x0000A7B7}, {0x0000A7B8, 0x0000A7B9}, +{0x0000A7BA, 0x0000A7BB}, {0x0000A7BC, 0x0000A7BD}, {0x0000A7BE, 0x0000A7BF}, {0x0000A7C2, 0x0000A7C3}, +{0x0000A7C4, 0x0000A794}, {0x0000A7C5, 0x00000282}, {0x0000A7C6, 0x00001D8E}, {0x0000A7C7, 0x0000A7C8}, +{0x0000A7C9, 0x0000A7CA}, {0x0000A7F5, 0x0000A7F6}, {0x0000FF21, 0x0000FF41}, {0x0000FF22, 0x0000FF42}, +{0x0000FF23, 0x0000FF43}, {0x0000FF24, 0x0000FF44}, {0x0000FF25, 0x0000FF45}, {0x0000FF26, 0x0000FF46}, +{0x0000FF27, 0x0000FF47}, {0x0000FF28, 0x0000FF48}, {0x0000FF29, 0x0000FF49}, {0x0000FF2A, 0x0000FF4A}, +{0x0000FF2B, 0x0000FF4B}, {0x0000FF2C, 0x0000FF4C}, {0x0000FF2D, 0x0000FF4D}, {0x0000FF2E, 0x0000FF4E}, +{0x0000FF2F, 0x0000FF4F}, {0x0000FF30, 0x0000FF50}, {0x0000FF31, 0x0000FF51}, {0x0000FF32, 0x0000FF52}, +{0x0000FF33, 0x0000FF53}, {0x0000FF34, 0x0000FF54}, {0x0000FF35, 0x0000FF55}, {0x0000FF36, 0x0000FF56}, +{0x0000FF37, 0x0000FF57}, {0x0000FF38, 0x0000FF58}, {0x0000FF39, 0x0000FF59}, {0x0000FF3A, 0x0000FF5A}, +{0x00010400, 0x00010428}, {0x00010401, 0x00010429}, {0x00010402, 0x0001042A}, {0x00010403, 0x0001042B}, +{0x00010404, 0x0001042C}, {0x00010405, 0x0001042D}, {0x00010406, 0x0001042E}, {0x00010407, 0x0001042F}, +{0x00010408, 0x00010430}, {0x00010409, 0x00010431}, {0x0001040A, 0x00010432}, {0x0001040B, 0x00010433}, +{0x0001040C, 0x00010434}, {0x0001040D, 0x00010435}, {0x0001040E, 0x00010436}, {0x0001040F, 0x00010437}, +{0x00010410, 0x00010438}, {0x00010411, 0x00010439}, {0x00010412, 0x0001043A}, {0x00010413, 0x0001043B}, +{0x00010414, 0x0001043C}, {0x00010415, 0x0001043D}, {0x00010416, 0x0001043E}, {0x00010417, 0x0001043F}, +{0x00010418, 0x00010440}, {0x00010419, 0x00010441}, {0x0001041A, 0x00010442}, {0x0001041B, 0x00010443}, +{0x0001041C, 0x00010444}, {0x0001041D, 0x00010445}, {0x0001041E, 0x00010446}, {0x0001041F, 0x00010447}, +{0x00010420, 0x00010448}, {0x00010421, 0x00010449}, {0x00010422, 0x0001044A}, {0x00010423, 0x0001044B}, +{0x00010424, 0x0001044C}, {0x00010425, 0x0001044D}, {0x00010426, 0x0001044E}, {0x00010427, 0x0001044F}, +{0x000104B0, 0x000104D8}, {0x000104B1, 0x000104D9}, {0x000104B2, 0x000104DA}, {0x000104B3, 0x000104DB}, +{0x000104B4, 0x000104DC}, {0x000104B5, 0x000104DD}, {0x000104B6, 0x000104DE}, {0x000104B7, 0x000104DF}, +{0x000104B8, 0x000104E0}, {0x000104B9, 0x000104E1}, {0x000104BA, 0x000104E2}, {0x000104BB, 0x000104E3}, +{0x000104BC, 0x000104E4}, {0x000104BD, 0x000104E5}, {0x000104BE, 0x000104E6}, {0x000104BF, 0x000104E7}, +{0x000104C0, 0x000104E8}, {0x000104C1, 0x000104E9}, {0x000104C2, 0x000104EA}, {0x000104C3, 0x000104EB}, +{0x000104C4, 0x000104EC}, {0x000104C5, 0x000104ED}, {0x000104C6, 0x000104EE}, {0x000104C7, 0x000104EF}, +{0x000104C8, 0x000104F0}, {0x000104C9, 0x000104F1}, {0x000104CA, 0x000104F2}, {0x000104CB, 0x000104F3}, +{0x000104CC, 0x000104F4}, {0x000104CD, 0x000104F5}, {0x000104CE, 0x000104F6}, {0x000104CF, 0x000104F7}, +{0x000104D0, 0x000104F8}, {0x000104D1, 0x000104F9}, {0x000104D2, 0x000104FA}, {0x000104D3, 0x000104FB}, +{0x00010C80, 0x00010CC0}, {0x00010C81, 0x00010CC1}, {0x00010C82, 0x00010CC2}, {0x00010C83, 0x00010CC3}, +{0x00010C84, 0x00010CC4}, {0x00010C85, 0x00010CC5}, {0x00010C86, 0x00010CC6}, {0x00010C87, 0x00010CC7}, +{0x00010C88, 0x00010CC8}, {0x00010C89, 0x00010CC9}, {0x00010C8A, 0x00010CCA}, {0x00010C8B, 0x00010CCB}, +{0x00010C8C, 0x00010CCC}, {0x00010C8D, 0x00010CCD}, {0x00010C8E, 0x00010CCE}, {0x00010C8F, 0x00010CCF}, +{0x00010C90, 0x00010CD0}, {0x00010C91, 0x00010CD1}, {0x00010C92, 0x00010CD2}, {0x00010C93, 0x00010CD3}, +{0x00010C94, 0x00010CD4}, {0x00010C95, 0x00010CD5}, {0x00010C96, 0x00010CD6}, {0x00010C97, 0x00010CD7}, +{0x00010C98, 0x00010CD8}, {0x00010C99, 0x00010CD9}, {0x00010C9A, 0x00010CDA}, {0x00010C9B, 0x00010CDB}, +{0x00010C9C, 0x00010CDC}, {0x00010C9D, 0x00010CDD}, {0x00010C9E, 0x00010CDE}, {0x00010C9F, 0x00010CDF}, +{0x00010CA0, 0x00010CE0}, {0x00010CA1, 0x00010CE1}, {0x00010CA2, 0x00010CE2}, {0x00010CA3, 0x00010CE3}, +{0x00010CA4, 0x00010CE4}, {0x00010CA5, 0x00010CE5}, {0x00010CA6, 0x00010CE6}, {0x00010CA7, 0x00010CE7}, +{0x00010CA8, 0x00010CE8}, {0x00010CA9, 0x00010CE9}, {0x00010CAA, 0x00010CEA}, {0x00010CAB, 0x00010CEB}, +{0x00010CAC, 0x00010CEC}, {0x00010CAD, 0x00010CED}, {0x00010CAE, 0x00010CEE}, {0x00010CAF, 0x00010CEF}, +{0x00010CB0, 0x00010CF0}, {0x00010CB1, 0x00010CF1}, {0x00010CB2, 0x00010CF2}, {0x000118A0, 0x000118C0}, +{0x000118A1, 0x000118C1}, {0x000118A2, 0x000118C2}, {0x000118A3, 0x000118C3}, {0x000118A4, 0x000118C4}, +{0x000118A5, 0x000118C5}, {0x000118A6, 0x000118C6}, {0x000118A7, 0x000118C7}, {0x000118A8, 0x000118C8}, +{0x000118A9, 0x000118C9}, {0x000118AA, 0x000118CA}, {0x000118AB, 0x000118CB}, {0x000118AC, 0x000118CC}, +{0x000118AD, 0x000118CD}, {0x000118AE, 0x000118CE}, {0x000118AF, 0x000118CF}, {0x000118B0, 0x000118D0}, +{0x000118B1, 0x000118D1}, {0x000118B2, 0x000118D2}, {0x000118B3, 0x000118D3}, {0x000118B4, 0x000118D4}, +{0x000118B5, 0x000118D5}, {0x000118B6, 0x000118D6}, {0x000118B7, 0x000118D7}, {0x000118B8, 0x000118D8}, +{0x000118B9, 0x000118D9}, {0x000118BA, 0x000118DA}, {0x000118BB, 0x000118DB}, {0x000118BC, 0x000118DC}, +{0x000118BD, 0x000118DD}, {0x000118BE, 0x000118DE}, {0x000118BF, 0x000118DF}, {0x00016E40, 0x00016E60}, +{0x00016E41, 0x00016E61}, {0x00016E42, 0x00016E62}, {0x00016E43, 0x00016E63}, {0x00016E44, 0x00016E64}, +{0x00016E45, 0x00016E65}, {0x00016E46, 0x00016E66}, {0x00016E47, 0x00016E67}, {0x00016E48, 0x00016E68}, +{0x00016E49, 0x00016E69}, {0x00016E4A, 0x00016E6A}, {0x00016E4B, 0x00016E6B}, {0x00016E4C, 0x00016E6C}, +{0x00016E4D, 0x00016E6D}, {0x00016E4E, 0x00016E6E}, {0x00016E4F, 0x00016E6F}, {0x00016E50, 0x00016E70}, +{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74}, +{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78}, +{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C}, +{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922}, +{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926}, +{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A}, +{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E}, +{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932}, +{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936}, +{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A}, +{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E}, +{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942}, +{0x0001E921, 0x0001E943}, +}; + +const std::map<char32_t, char32_t> unicode_map_uppercase = { +{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044}, +{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048}, +{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C}, +{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050}, +{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054}, +{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058}, +{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053}, +{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3}, +{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7}, +{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB}, +{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF}, +{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3}, +{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8}, +{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC}, +{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100}, +{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108}, +{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110}, +{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118}, +{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120}, +{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128}, +{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049}, +{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139}, +{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141}, +{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC}, +{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150}, +{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158}, +{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160}, +{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168}, +{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170}, +{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179}, +{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243}, +{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B}, +{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D}, +{0x0000019E, 0x00000220}, 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{0x0000ABA7, 0x000013D7}, {0x0000ABA8, 0x000013D8}, {0x0000ABA9, 0x000013D9}, +{0x0000ABAA, 0x000013DA}, {0x0000ABAB, 0x000013DB}, {0x0000ABAC, 0x000013DC}, {0x0000ABAD, 0x000013DD}, +{0x0000ABAE, 0x000013DE}, {0x0000ABAF, 0x000013DF}, {0x0000ABB0, 0x000013E0}, {0x0000ABB1, 0x000013E1}, +{0x0000ABB2, 0x000013E2}, {0x0000ABB3, 0x000013E3}, {0x0000ABB4, 0x000013E4}, {0x0000ABB5, 0x000013E5}, +{0x0000ABB6, 0x000013E6}, {0x0000ABB7, 0x000013E7}, {0x0000ABB8, 0x000013E8}, {0x0000ABB9, 0x000013E9}, +{0x0000ABBA, 0x000013EA}, {0x0000ABBB, 0x000013EB}, {0x0000ABBC, 0x000013EC}, {0x0000ABBD, 0x000013ED}, +{0x0000ABBE, 0x000013EE}, {0x0000ABBF, 0x000013EF}, {0x0000FB00, 0x00000046}, {0x0000FB01, 0x00000046}, +{0x0000FB02, 0x00000046}, {0x0000FB03, 0x00000046}, {0x0000FB04, 0x00000046}, {0x0000FB05, 0x00000053}, +{0x0000FB06, 0x00000053}, {0x0000FB13, 0x00000544}, {0x0000FB14, 0x00000544}, {0x0000FB15, 0x00000544}, +{0x0000FB16, 0x0000054E}, {0x0000FB17, 0x00000544}, {0x0000FF41, 0x0000FF21}, {0x0000FF42, 0x0000FF22}, +{0x0000FF43, 0x0000FF23}, {0x0000FF44, 0x0000FF24}, {0x0000FF45, 0x0000FF25}, {0x0000FF46, 0x0000FF26}, +{0x0000FF47, 0x0000FF27}, {0x0000FF48, 0x0000FF28}, {0x0000FF49, 0x0000FF29}, {0x0000FF4A, 0x0000FF2A}, +{0x0000FF4B, 0x0000FF2B}, {0x0000FF4C, 0x0000FF2C}, {0x0000FF4D, 0x0000FF2D}, {0x0000FF4E, 0x0000FF2E}, +{0x0000FF4F, 0x0000FF2F}, {0x0000FF50, 0x0000FF30}, {0x0000FF51, 0x0000FF31}, {0x0000FF52, 0x0000FF32}, +{0x0000FF53, 0x0000FF33}, {0x0000FF54, 0x0000FF34}, {0x0000FF55, 0x0000FF35}, {0x0000FF56, 0x0000FF36}, +{0x0000FF57, 0x0000FF37}, {0x0000FF58, 0x0000FF38}, {0x0000FF59, 0x0000FF39}, {0x0000FF5A, 0x0000FF3A}, +{0x00010428, 0x00010400}, {0x00010429, 0x00010401}, {0x0001042A, 0x00010402}, {0x0001042B, 0x00010403}, +{0x0001042C, 0x00010404}, {0x0001042D, 0x00010405}, {0x0001042E, 0x00010406}, {0x0001042F, 0x00010407}, +{0x00010430, 0x00010408}, {0x00010431, 0x00010409}, {0x00010432, 0x0001040A}, {0x00010433, 0x0001040B}, +{0x00010434, 0x0001040C}, {0x00010435, 0x0001040D}, {0x00010436, 0x0001040E}, {0x00010437, 0x0001040F}, +{0x00010438, 0x00010410}, {0x00010439, 0x00010411}, {0x0001043A, 0x00010412}, {0x0001043B, 0x00010413}, +{0x0001043C, 0x00010414}, {0x0001043D, 0x00010415}, {0x0001043E, 0x00010416}, {0x0001043F, 0x00010417}, +{0x00010440, 0x00010418}, {0x00010441, 0x00010419}, {0x00010442, 0x0001041A}, {0x00010443, 0x0001041B}, +{0x00010444, 0x0001041C}, {0x00010445, 0x0001041D}, {0x00010446, 0x0001041E}, {0x00010447, 0x0001041F}, +{0x00010448, 0x00010420}, {0x00010449, 0x00010421}, {0x0001044A, 0x00010422}, {0x0001044B, 0x00010423}, +{0x0001044C, 0x00010424}, {0x0001044D, 0x00010425}, {0x0001044E, 0x00010426}, {0x0001044F, 0x00010427}, +{0x000104D8, 0x000104B0}, {0x000104D9, 0x000104B1}, {0x000104DA, 0x000104B2}, {0x000104DB, 0x000104B3}, +{0x000104DC, 0x000104B4}, {0x000104DD, 0x000104B5}, {0x000104DE, 0x000104B6}, {0x000104DF, 0x000104B7}, +{0x000104E0, 0x000104B8}, {0x000104E1, 0x000104B9}, {0x000104E2, 0x000104BA}, {0x000104E3, 0x000104BB}, +{0x000104E4, 0x000104BC}, {0x000104E5, 0x000104BD}, {0x000104E6, 0x000104BE}, {0x000104E7, 0x000104BF}, +{0x000104E8, 0x000104C0}, {0x000104E9, 0x000104C1}, {0x000104EA, 0x000104C2}, {0x000104EB, 0x000104C3}, +{0x000104EC, 0x000104C4}, {0x000104ED, 0x000104C5}, {0x000104EE, 0x000104C6}, {0x000104EF, 0x000104C7}, +{0x000104F0, 0x000104C8}, {0x000104F1, 0x000104C9}, {0x000104F2, 0x000104CA}, {0x000104F3, 0x000104CB}, +{0x000104F4, 0x000104CC}, {0x000104F5, 0x000104CD}, {0x000104F6, 0x000104CE}, {0x000104F7, 0x000104CF}, +{0x000104F8, 0x000104D0}, {0x000104F9, 0x000104D1}, {0x000104FA, 0x000104D2}, {0x000104FB, 0x000104D3}, +{0x00010CC0, 0x00010C80}, {0x00010CC1, 0x00010C81}, {0x00010CC2, 0x00010C82}, {0x00010CC3, 0x00010C83}, +{0x00010CC4, 0x00010C84}, {0x00010CC5, 0x00010C85}, {0x00010CC6, 0x00010C86}, {0x00010CC7, 0x00010C87}, +{0x00010CC8, 0x00010C88}, {0x00010CC9, 0x00010C89}, {0x00010CCA, 0x00010C8A}, {0x00010CCB, 0x00010C8B}, +{0x00010CCC, 0x00010C8C}, {0x00010CCD, 0x00010C8D}, {0x00010CCE, 0x00010C8E}, {0x00010CCF, 0x00010C8F}, +{0x00010CD0, 0x00010C90}, {0x00010CD1, 0x00010C91}, {0x00010CD2, 0x00010C92}, {0x00010CD3, 0x00010C93}, +{0x00010CD4, 0x00010C94}, {0x00010CD5, 0x00010C95}, {0x00010CD6, 0x00010C96}, {0x00010CD7, 0x00010C97}, +{0x00010CD8, 0x00010C98}, {0x00010CD9, 0x00010C99}, {0x00010CDA, 0x00010C9A}, {0x00010CDB, 0x00010C9B}, +{0x00010CDC, 0x00010C9C}, {0x00010CDD, 0x00010C9D}, {0x00010CDE, 0x00010C9E}, {0x00010CDF, 0x00010C9F}, +{0x00010CE0, 0x00010CA0}, {0x00010CE1, 0x00010CA1}, {0x00010CE2, 0x00010CA2}, {0x00010CE3, 0x00010CA3}, +{0x00010CE4, 0x00010CA4}, {0x00010CE5, 0x00010CA5}, {0x00010CE6, 0x00010CA6}, {0x00010CE7, 0x00010CA7}, +{0x00010CE8, 0x00010CA8}, {0x00010CE9, 0x00010CA9}, {0x00010CEA, 0x00010CAA}, {0x00010CEB, 0x00010CAB}, +{0x00010CEC, 0x00010CAC}, {0x00010CED, 0x00010CAD}, {0x00010CEE, 0x00010CAE}, {0x00010CEF, 0x00010CAF}, +{0x00010CF0, 0x00010CB0}, {0x00010CF1, 0x00010CB1}, {0x00010CF2, 0x00010CB2}, {0x000118C0, 0x000118A0}, +{0x000118C1, 0x000118A1}, {0x000118C2, 0x000118A2}, {0x000118C3, 0x000118A3}, {0x000118C4, 0x000118A4}, +{0x000118C5, 0x000118A5}, {0x000118C6, 0x000118A6}, {0x000118C7, 0x000118A7}, {0x000118C8, 0x000118A8}, +{0x000118C9, 0x000118A9}, {0x000118CA, 0x000118AA}, {0x000118CB, 0x000118AB}, {0x000118CC, 0x000118AC}, +{0x000118CD, 0x000118AD}, {0x000118CE, 0x000118AE}, {0x000118CF, 0x000118AF}, {0x000118D0, 0x000118B0}, +{0x000118D1, 0x000118B1}, {0x000118D2, 0x000118B2}, {0x000118D3, 0x000118B3}, {0x000118D4, 0x000118B4}, +{0x000118D5, 0x000118B5}, {0x000118D6, 0x000118B6}, {0x000118D7, 0x000118B7}, {0x000118D8, 0x000118B8}, +{0x000118D9, 0x000118B9}, {0x000118DA, 0x000118BA}, {0x000118DB, 0x000118BB}, {0x000118DC, 0x000118BC}, +{0x000118DD, 0x000118BD}, {0x000118DE, 0x000118BE}, {0x000118DF, 0x000118BF}, {0x00016E60, 0x00016E40}, +{0x00016E61, 0x00016E41}, {0x00016E62, 0x00016E42}, {0x00016E63, 0x00016E43}, {0x00016E64, 0x00016E44}, +{0x00016E65, 0x00016E45}, {0x00016E66, 0x00016E46}, {0x00016E67, 0x00016E47}, {0x00016E68, 0x00016E48}, +{0x00016E69, 0x00016E49}, {0x00016E6A, 0x00016E4A}, {0x00016E6B, 0x00016E4B}, {0x00016E6C, 0x00016E4C}, +{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50}, +{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54}, +{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58}, +{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C}, +{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900}, +{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904}, +{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908}, +{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C}, +{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910}, +{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914}, +{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918}, +{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C}, +{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920}, +{0x0001E943, 0x0001E921}, }; const std::multimap<uint32_t, uint32_t> unicode_map_nfd = { @@ -1449,245 +2181,3 @@ const std::multimap<uint32_t, uint32_t> unicode_map_nfd = { {0x0002FA16, 0x00004D56}, {0x0002FA17, 0x00009EF9}, {0x0002FA18, 0x00009EFE}, {0x0002FA19, 0x00009F05}, {0x0002FA1A, 0x00009F0F}, {0x0002FA1B, 0x00009F16}, {0x0002FA1D, 0x0002A600}, }; - -const std::map<char32_t, char32_t> unicode_map_lowercase = { -{0x00041, 0x00061}, {0x00042, 0x00062}, {0x00043, 0x00063}, {0x00044, 0x00064}, {0x00045, 0x00065}, {0x00046, 0x00066}, -{0x00047, 0x00067}, {0x00048, 0x00068}, {0x00049, 0x00069}, {0x0004A, 0x0006A}, {0x0004B, 0x0006B}, {0x0004C, 0x0006C}, -{0x0004D, 0x0006D}, {0x0004E, 0x0006E}, {0x0004F, 0x0006F}, {0x00050, 0x00070}, {0x00051, 0x00071}, {0x00052, 0x00072}, -{0x00053, 0x00073}, {0x00054, 0x00074}, {0x00055, 0x00075}, {0x00056, 0x00076}, {0x00057, 0x00077}, {0x00058, 0x00078}, -{0x00059, 0x00079}, {0x0005A, 0x0007A}, {0x000C0, 0x000E0}, {0x000C1, 0x000E1}, {0x000C2, 0x000E2}, {0x000C3, 0x000E3}, -{0x000C4, 0x000E4}, {0x000C5, 0x000E5}, {0x000C6, 0x000E6}, {0x000C7, 0x000E7}, {0x000C8, 0x000E8}, {0x000C9, 0x000E9}, -{0x000CA, 0x000EA}, {0x000CB, 0x000EB}, {0x000CC, 0x000EC}, {0x000CD, 0x000ED}, {0x000CE, 0x000EE}, {0x000CF, 0x000EF}, -{0x000D0, 0x000F0}, {0x000D1, 0x000F1}, {0x000D2, 0x000F2}, {0x000D3, 0x000F3}, {0x000D4, 0x000F4}, {0x000D5, 0x000F5}, -{0x000D6, 0x000F6}, {0x000D8, 0x000F8}, {0x000D9, 0x000F9}, {0x000DA, 0x000FA}, {0x000DB, 0x000FB}, {0x000DC, 0x000FC}, -{0x000DD, 0x000FD}, {0x000DE, 0x000FE}, {0x00100, 0x00101}, {0x00102, 0x00103}, {0x00104, 0x00105}, {0x00106, 0x00107}, -{0x00108, 0x00109}, {0x0010A, 0x0010B}, {0x0010C, 0x0010D}, {0x0010E, 0x0010F}, {0x00110, 0x00111}, {0x00112, 0x00113}, -{0x00114, 0x00115}, {0x00116, 0x00117}, {0x00118, 0x00119}, {0x0011A, 0x0011B}, {0x0011C, 0x0011D}, {0x0011E, 0x0011F}, -{0x00120, 0x00121}, {0x00122, 0x00123}, {0x00124, 0x00125}, {0x00126, 0x00127}, {0x00128, 0x00129}, {0x0012A, 0x0012B}, -{0x0012C, 0x0012D}, {0x0012E, 0x0012F}, {0x00130, 0x00069}, {0x00132, 0x00133}, {0x00134, 0x00135}, {0x00136, 0x00137}, -{0x00139, 0x0013A}, {0x0013B, 0x0013C}, {0x0013D, 0x0013E}, {0x0013F, 0x00140}, {0x00141, 0x00142}, {0x00143, 0x00144}, -{0x00145, 0x00146}, {0x00147, 0x00148}, {0x0014A, 0x0014B}, {0x0014C, 0x0014D}, {0x0014E, 0x0014F}, {0x00150, 0x00151}, -{0x00152, 0x00153}, {0x00154, 0x00155}, {0x00156, 0x00157}, {0x00158, 0x00159}, 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0x104F1}, {0x104CA, 0x104F2}, {0x104CB, 0x104F3}, {0x104CC, 0x104F4}, -{0x104CD, 0x104F5}, {0x104CE, 0x104F6}, {0x104CF, 0x104F7}, {0x104D0, 0x104F8}, {0x104D1, 0x104F9}, {0x104D2, 0x104FA}, -{0x104D3, 0x104FB}, {0x10570, 0x10597}, {0x10571, 0x10598}, {0x10572, 0x10599}, {0x10573, 0x1059A}, {0x10574, 0x1059B}, -{0x10575, 0x1059C}, {0x10576, 0x1059D}, {0x10577, 0x1059E}, {0x10578, 0x1059F}, {0x10579, 0x105A0}, {0x1057A, 0x105A1}, -{0x1057C, 0x105A3}, {0x1057D, 0x105A4}, {0x1057E, 0x105A5}, {0x1057F, 0x105A6}, {0x10580, 0x105A7}, {0x10581, 0x105A8}, -{0x10582, 0x105A9}, {0x10583, 0x105AA}, {0x10584, 0x105AB}, {0x10585, 0x105AC}, {0x10586, 0x105AD}, {0x10587, 0x105AE}, -{0x10588, 0x105AF}, {0x10589, 0x105B0}, {0x1058A, 0x105B1}, {0x1058C, 0x105B3}, {0x1058D, 0x105B4}, {0x1058E, 0x105B5}, -{0x1058F, 0x105B6}, {0x10590, 0x105B7}, {0x10591, 0x105B8}, {0x10592, 0x105B9}, {0x10594, 0x105BB}, {0x10595, 0x105BC}, -{0x10C80, 0x10CC0}, {0x10C81, 0x10CC1}, {0x10C82, 0x10CC2}, {0x10C83, 0x10CC3}, {0x10C84, 0x10CC4}, {0x10C85, 0x10CC5}, -{0x10C86, 0x10CC6}, {0x10C87, 0x10CC7}, {0x10C88, 0x10CC8}, {0x10C89, 0x10CC9}, {0x10C8A, 0x10CCA}, {0x10C8B, 0x10CCB}, -{0x10C8C, 0x10CCC}, {0x10C8D, 0x10CCD}, {0x10C8E, 0x10CCE}, {0x10C8F, 0x10CCF}, {0x10C90, 0x10CD0}, {0x10C91, 0x10CD1}, -{0x10C92, 0x10CD2}, {0x10C93, 0x10CD3}, {0x10C94, 0x10CD4}, {0x10C95, 0x10CD5}, {0x10C96, 0x10CD6}, {0x10C97, 0x10CD7}, -{0x10C98, 0x10CD8}, {0x10C99, 0x10CD9}, {0x10C9A, 0x10CDA}, {0x10C9B, 0x10CDB}, {0x10C9C, 0x10CDC}, {0x10C9D, 0x10CDD}, -{0x10C9E, 0x10CDE}, {0x10C9F, 0x10CDF}, {0x10CA0, 0x10CE0}, {0x10CA1, 0x10CE1}, {0x10CA2, 0x10CE2}, {0x10CA3, 0x10CE3}, -{0x10CA4, 0x10CE4}, {0x10CA5, 0x10CE5}, {0x10CA6, 0x10CE6}, {0x10CA7, 0x10CE7}, {0x10CA8, 0x10CE8}, {0x10CA9, 0x10CE9}, -{0x10CAA, 0x10CEA}, {0x10CAB, 0x10CEB}, {0x10CAC, 0x10CEC}, {0x10CAD, 0x10CED}, {0x10CAE, 0x10CEE}, {0x10CAF, 0x10CEF}, -{0x10CB0, 0x10CF0}, {0x10CB1, 0x10CF1}, {0x10CB2, 0x10CF2}, {0x118A0, 0x118C0}, {0x118A1, 0x118C1}, {0x118A2, 0x118C2}, -{0x118A3, 0x118C3}, {0x118A4, 0x118C4}, {0x118A5, 0x118C5}, {0x118A6, 0x118C6}, {0x118A7, 0x118C7}, {0x118A8, 0x118C8}, -{0x118A9, 0x118C9}, {0x118AA, 0x118CA}, {0x118AB, 0x118CB}, {0x118AC, 0x118CC}, {0x118AD, 0x118CD}, {0x118AE, 0x118CE}, -{0x118AF, 0x118CF}, {0x118B0, 0x118D0}, {0x118B1, 0x118D1}, {0x118B2, 0x118D2}, {0x118B3, 0x118D3}, {0x118B4, 0x118D4}, -{0x118B5, 0x118D5}, {0x118B6, 0x118D6}, {0x118B7, 0x118D7}, {0x118B8, 0x118D8}, {0x118B9, 0x118D9}, {0x118BA, 0x118DA}, -{0x118BB, 0x118DB}, {0x118BC, 0x118DC}, {0x118BD, 0x118DD}, {0x118BE, 0x118DE}, {0x118BF, 0x118DF}, {0x16E40, 0x16E60}, -{0x16E41, 0x16E61}, {0x16E42, 0x16E62}, {0x16E43, 0x16E63}, {0x16E44, 0x16E64}, {0x16E45, 0x16E65}, {0x16E46, 0x16E66}, -{0x16E47, 0x16E67}, {0x16E48, 0x16E68}, {0x16E49, 0x16E69}, {0x16E4A, 0x16E6A}, {0x16E4B, 0x16E6B}, {0x16E4C, 0x16E6C}, -{0x16E4D, 0x16E6D}, {0x16E4E, 0x16E6E}, {0x16E4F, 0x16E6F}, {0x16E50, 0x16E70}, {0x16E51, 0x16E71}, {0x16E52, 0x16E72}, -{0x16E53, 0x16E73}, {0x16E54, 0x16E74}, {0x16E55, 0x16E75}, {0x16E56, 0x16E76}, {0x16E57, 0x16E77}, {0x16E58, 0x16E78}, -{0x16E59, 0x16E79}, {0x16E5A, 0x16E7A}, {0x16E5B, 0x16E7B}, {0x16E5C, 0x16E7C}, {0x16E5D, 0x16E7D}, {0x16E5E, 0x16E7E}, -{0x16E5F, 0x16E7F}, {0x1E900, 0x1E922}, {0x1E901, 0x1E923}, {0x1E902, 0x1E924}, {0x1E903, 0x1E925}, {0x1E904, 0x1E926}, -{0x1E905, 0x1E927}, {0x1E906, 0x1E928}, {0x1E907, 0x1E929}, {0x1E908, 0x1E92A}, {0x1E909, 0x1E92B}, {0x1E90A, 0x1E92C}, -{0x1E90B, 0x1E92D}, {0x1E90C, 0x1E92E}, {0x1E90D, 0x1E92F}, {0x1E90E, 0x1E930}, {0x1E90F, 0x1E931}, {0x1E910, 0x1E932}, -{0x1E911, 0x1E933}, {0x1E912, 0x1E934}, {0x1E913, 0x1E935}, {0x1E914, 0x1E936}, {0x1E915, 0x1E937}, {0x1E916, 0x1E938}, -{0x1E917, 0x1E939}, {0x1E918, 0x1E93A}, {0x1E919, 0x1E93B}, {0x1E91A, 0x1E93C}, {0x1E91B, 0x1E93D}, {0x1E91C, 0x1E93E}, -{0x1E91D, 0x1E93F}, {0x1E91E, 0x1E940}, {0x1E91F, 0x1E941}, {0x1E920, 0x1E942}, {0x1E921, 0x1E943}, -}; diff --git a/unicode-data.h b/unicode-data.h index 3cf84117c..3cccf2068 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -7,6 +7,7 @@ extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_number; extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_letter; +extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_separator; extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_whitespace; extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_accent_mark; extern const std::vector<std::pair<uint32_t, uint32_t>> unicode_ranges_punctuation; diff --git a/unicode.cpp b/unicode.cpp index 955c56965..ca03c49d3 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -9,6 +9,7 @@ #include <stdexcept> #include <string> #include <unordered_map> +#include <unordered_set> #include <utility> #include <vector> #include <locale> @@ -111,27 +112,27 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) static std::unordered_map<uint32_t, int> unicode_cpt_type_map() { std::unordered_map<uint32_t, int> cpt_types; for (auto p : unicode_ranges_number) { - for (auto i = p.first; i <= p.second; ++ i) { + for (auto i = p.first; i <= p.second; ++i) { cpt_types[i] = CODEPOINT_TYPE_NUMBER; } } for (auto p : unicode_ranges_letter) { - for (auto i = p.first; i <= p.second; ++ i) { + for (auto i = p.first; i <= p.second; ++i) { cpt_types[i] = CODEPOINT_TYPE_LETTER; } } - for (auto p : unicode_ranges_whitespace) { - for (auto i = p.first; i <= p.second; ++ i) { - cpt_types[i] = CODEPOINT_TYPE_WHITESPACE; + for (auto p : unicode_ranges_separator) { + for (auto i = p.first; i <= p.second; ++i) { + cpt_types[i] = CODEPOINT_TYPE_SEPARATOR; } } for (auto p : unicode_ranges_accent_mark) { - for (auto i = p.first; i <= p.second; ++ i) { + for (auto i = p.first; i <= p.second; ++i) { cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK; } } for (auto p : unicode_ranges_punctuation) { - for (auto i = p.first; i <= p.second; ++ i) { + for (auto i = p.first; i <= p.second; ++i) { cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION; } } @@ -141,7 +142,7 @@ static std::unordered_map<uint32_t, int> unicode_cpt_type_map() { } } for (auto p : unicode_ranges_control) { - for (auto i = p.first; i <= p.second; ++ i) { + for (auto i = p.first; i <= p.second; ++i) { cpt_types[i] = CODEPOINT_TYPE_CONTROL; } } @@ -224,138 +225,256 @@ static std::vector<size_t> unicode_regex_split_custom_gpt2(const std::string & t std::vector<size_t> bpe_offsets; // store the offset of each word bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size + const auto cpts = unicode_cpts_from_utf8(text); + size_t start = 0; + for (auto offset : offsets) { + const size_t offset_ini = start; + const size_t offset_end = start + offset; + assert(offset_end <= cpts.size()); + start = offset_end; + + auto _get_cpt = [&] (const size_t pos) -> char32_t { + return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; + }; + + auto _get_cpt_type = [&] (const size_t pos) -> int { + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + }; + + size_t _prev_end = offset_ini; + auto _add_token = [&] (const size_t end) -> size_t { + assert(_prev_end <= end && end <= offset_end); + size_t len = end - _prev_end; + if (len > 0) { + bpe_offsets.push_back(len); + } + _prev_end = end; + //if (len > 0) { + // std::string s = ""; + // for(size_t p = end-len; p < end; p++) + // s += unicode_cpt_to_utf8(cpts[p]); + // printf(">>> '%s'\n", s.c_str()); + //} + return len; + }; + + for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { + const char32_t cpt = _get_cpt(pos); + const int cpt_type = _get_cpt_type(pos); + + // regex: 's|'t|'re|'ve|'m|'ll|'d + if (cpt == '\'' && pos+1 < offset_end) { + char32_t cpt_next = _get_cpt(pos+1); + if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') { + pos += _add_token(pos+2); + continue; + } + if (pos+2 < offset_end) { + char32_t cpt_next_next = _get_cpt(pos+2); + if ((cpt_next == 'r' && cpt_next_next == 'e') || + (cpt_next == 'v' && cpt_next_next == 'e') || + (cpt_next == 'l' && cpt_next_next == 'l')) { + pos += _add_token(pos+3); + continue; + } + } + } + + char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); + int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); + // regex: <space>?\p{L}+ + if (cpt2_type == CODEPOINT_TYPE_LETTER) { + pos += (cpt == ' '); + while (cpt2_type == CODEPOINT_TYPE_LETTER) { + cpt2_type = _get_cpt_type(++pos); + } + _add_token(pos); + continue; + } + // regex: <space>?\p{N}+ + if (cpt2_type == CODEPOINT_TYPE_NUMBER) { + pos += (cpt == ' '); + while (cpt2_type == CODEPOINT_TYPE_NUMBER) { + cpt2_type = _get_cpt_type(++pos); + } + _add_token(pos); + continue; + } + // regex: <space>?[^\s\p{L}\p{N}]+ + if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + pos += (cpt == ' '); + while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + cpt2_type = _get_cpt_type(++pos); + cpt2 = _get_cpt(pos); + } + _add_token(pos); + continue; + } + + size_t num_whitespaces = 0; + while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + num_whitespaces++; + } + + // regex: \s+(?!\S) + if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) { + pos += num_whitespaces - 1; + _add_token(pos); + continue; + } + + // regex: \s+ + if (num_whitespaces > 0) { + pos += num_whitespaces; + _add_token(pos); + continue; + } + + // no matches + _add_token(++pos); + } + } + + return bpe_offsets; +} + +// LLAMA3 system regex: "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+" +static std::vector<size_t> unicode_regex_split_custom_llama3(const std::string & text, const std::vector<size_t> & offsets) { + std::vector<size_t> bpe_offsets; // store the offset of each word + bpe_offsets.reserve(offsets.size()); // Reserve memory for the approximate size const auto cpts = unicode_cpts_from_utf8(text); + size_t start = 0; for (auto offset : offsets) { - std::string token; + const size_t offset_ini = start; + const size_t offset_end = start + offset; + assert(offset_end <= cpts.size()); + start = offset_end; - bool collecting_numeric = false; - bool collecting_letter = false; - bool collecting_special = false; - bool collecting_whitespace_lookahead = false; - bool collecting = false; + auto _get_cpt = [&] (const size_t pos) -> char32_t { + return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; + }; - std::vector<std::string> text_utf; - text_utf.reserve(offset); + auto _get_cpt_type = [&] (const size_t pos) -> int { + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + }; - for (size_t i = start; i < start + offset; ++i) { - text_utf.emplace_back(unicode_cpt_to_utf8(cpts[i])); - } + size_t _prev_end = offset_ini; + auto _add_token = [&] (const size_t end) -> size_t { + assert(_prev_end <= end && end <= offset_end); + size_t len = end - _prev_end; + if (len > 0) { + bpe_offsets.push_back(len); + } + _prev_end = end; + //if (len > 0) { + // std::string s = ""; + // for(size_t p = end-len; p < end; p++) + // s += unicode_cpt_to_utf8(cpts[p]); + // printf(">>> '%s'\n", s.c_str()); + //} + return len; + }; - for (int i = 0; i < (int)text_utf.size(); i++) { - const std::string & utf_char = text_utf[i]; - bool split_condition = false; - int bytes_remain = text_utf.size() - i; + for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { + const char32_t cpt = _get_cpt(pos); + const int cpt_type = _get_cpt_type(pos); - // forward backward lookups - const std::string & utf_char_next = (i + 1 < (int)text_utf.size()) ? text_utf[i + 1] : ""; - const std::string & utf_char_next_next = (i + 2 < (int)text_utf.size()) ? text_utf[i + 2] : ""; - - // handling contractions - if (!split_condition && bytes_remain >= 2) { - // 's|'t|'m|'d - if (utf_char == "\'" && (utf_char_next == "s" || utf_char_next == "t" || utf_char_next == "m" || utf_char_next == "d")) { - split_condition = true; - } - if (split_condition) { - if (token.size()) { - bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); - } - token = utf_char + utf_char_next; - bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); - token = ""; - i++; + // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive + if (cpt == '\'' && pos+1 < offset_end) { + char32_t cpt_next = unicode_tolower(_get_cpt(pos+1)); + if (cpt_next == 's' || cpt_next == 't' || cpt_next == 'm' || cpt_next == 'd') { + pos += _add_token(pos+2); continue; } - } - if (!split_condition && bytes_remain >= 3) { - // 're|'ve|'ll - if (utf_char == "\'" && ( - (utf_char_next == "r" && utf_char_next_next == "e") || - (utf_char_next == "v" && utf_char_next_next == "e") || - (utf_char_next == "l" && utf_char_next_next == "l")) - ) { - split_condition = true; - } - if (split_condition) { - // current token + next token can be defined - if (token.size()) { - bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + if (pos+2 < offset_end) { + char32_t cpt_next_next = unicode_tolower(_get_cpt(pos+2)); + if ((cpt_next == 'r' && cpt_next_next == 'e') || + (cpt_next == 'v' && cpt_next_next == 'e') || + (cpt_next == 'l' && cpt_next_next == 'l')) { + pos += _add_token(pos+3); + continue; } - token = utf_char; - token += utf_char_next; - token += utf_char_next_next; + } + } - bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); - token = ""; - i += 2; + // regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct? + if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) { + if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) { // one or more letters + pos++; + while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) { + pos++; + } + _add_token(pos); continue; } } - if (!split_condition && !collecting) { - if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER)) { - collecting_letter = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) { - collecting_numeric = true; - collecting = true; - } - else if ( - ((unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) && (unicode_cpt_type(utf_char) != CODEPOINT_TYPE_WHITESPACE)) || - (token.empty() && utf_char == " " && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_LETTER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_NUMBER && unicode_cpt_type(utf_char_next) != CODEPOINT_TYPE_WHITESPACE) - ) { - collecting_special = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE && unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_WHITESPACE) { - collecting_whitespace_lookahead = true; - collecting = true; - } - else if (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE) { - split_condition = true; - } - } - else if (!split_condition && collecting) { - if (collecting_letter && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_LETTER) { - split_condition = true; - } - else if (collecting_numeric && unicode_cpt_type(utf_char) != CODEPOINT_TYPE_NUMBER) { - split_condition = true; - } - else if (collecting_special && (unicode_cpt_type(utf_char) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_NUMBER || unicode_cpt_type(utf_char) == CODEPOINT_TYPE_WHITESPACE)) { - split_condition = true; - } - else if (collecting_whitespace_lookahead && (unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_LETTER || unicode_cpt_type(utf_char_next) == CODEPOINT_TYPE_NUMBER)) { - split_condition = true; + // regex: \p{N}{1,3} + if (cpt_type == CODEPOINT_TYPE_NUMBER) { + size_t ini = pos; + while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) { + if (++pos - ini >= 3 ) { + _add_token(pos); + ini = pos; + } } + _add_token(pos); + continue; } - if (utf_char_next == "") { - split_condition = true; // final - token += utf_char; + // regex: <space>?[^\s\p{L}\p{N}]+[\r\n]* + char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); + int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); + if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + pos += (cpt == ' '); + while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + cpt2_type = _get_cpt_type(++pos); + cpt2 = _get_cpt(pos); + } + while (cpt2 == '\r' || cpt2 == '\n') { + cpt2 = _get_cpt(++pos); + } + _add_token(pos); + continue; } - if (split_condition) { - if (token.size()) { - bpe_offsets.emplace_back(unicode_cpts_from_utf8(token).size()); + size_t num_whitespaces = 0; + size_t last_end_r_or_n = 0; + while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + char32_t cpt2 = _get_cpt(pos+num_whitespaces); + if (cpt2 == '\r' || cpt2 == '\n') { + last_end_r_or_n = pos + num_whitespaces + 1; } - token = utf_char; - collecting = false; - collecting_letter = false; - collecting_numeric = false; - collecting_special = false; - collecting_whitespace_lookahead = false; + num_whitespaces++; } - else { - token += utf_char; + + // regex: \s*[\r\n]+ + if (last_end_r_or_n > 0) { + pos = last_end_r_or_n; + _add_token(pos); + continue; } + + // regex: \s+(?!\S) + if (num_whitespaces > 1 && _get_cpt(pos+num_whitespaces) != 0) { + pos += num_whitespaces - 1; + _add_token(pos); + continue; + } + + // regex: \s+ + if (num_whitespaces > 0) { + pos += num_whitespaces; + _add_token(pos); + continue; + } + + // no matches + _add_token(++pos); } - - start += offset; } return bpe_offsets; @@ -424,14 +543,14 @@ static std::vector<size_t> unicode_regex_split_stl(const std::string & text, con static std::vector<size_t> unicode_regex_split_custom(const std::string & text, const std::string & regex_expr, const std::vector<size_t> & offsets) { std::vector<size_t> bpe_offsets; - (void)(text); - (void)(regex_expr); - (void)(offsets); - // TODO: this implementation is actually wrong, uncomment and run: - // make -j && ./bin/test-tokenizer-0 ../models/ggml-vocab-gpt-2.gguf - //if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") { - // bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets); - //} + if (regex_expr == "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)") { + bpe_offsets = unicode_regex_split_custom_gpt2(text, offsets); + } else if ( + regex_expr == "(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+" || + regex_expr == "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+") { + + bpe_offsets = unicode_regex_split_custom_llama3(text, offsets); + } return bpe_offsets; } @@ -506,6 +625,19 @@ int unicode_cpt_type(const std::string & utf8) { return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset)); } +bool unicode_cpt_is_whitespace(uint32_t cp) { + static const std::unordered_set<uint32_t> is_whitespace = [] { + std::unordered_set<uint32_t> is_whitespace; + for (auto p : unicode_ranges_whitespace) { + for (auto i = p.first; i <= p.second; ++i) { + is_whitespace.insert(i); + } + } + return is_whitespace; + }(); + return (bool)is_whitespace.count(cp); +} + std::string unicode_byte_to_utf8(uint8_t byte) { static std::unordered_map<uint8_t, std::string> map = unicode_byte_to_utf8_map(); return map.at(byte); diff --git a/unicode.h b/unicode.h index e9026dc81..d6a14d470 100644 --- a/unicode.h +++ b/unicode.h @@ -7,7 +7,7 @@ #define CODEPOINT_TYPE_UNIDENTIFIED 0 #define CODEPOINT_TYPE_NUMBER 1 #define CODEPOINT_TYPE_LETTER 2 -#define CODEPOINT_TYPE_WHITESPACE 3 +#define CODEPOINT_TYPE_SEPARATOR 3 #define CODEPOINT_TYPE_ACCENT_MARK 4 #define CODEPOINT_TYPE_PUNCTUATION 5 #define CODEPOINT_TYPE_SYMBOL 6 @@ -21,6 +21,8 @@ std::vector<uint32_t> unicode_cpts_normalize_nfd(const std::vector<uint32_t> & c int unicode_cpt_type(uint32_t cp); int unicode_cpt_type(const std::string & utf8); +bool unicode_cpt_is_whitespace(uint32_t cp); + std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8); From 0961d866044143eefec5b525e991611f73fd4f6e Mon Sep 17 00:00:00 2001 From: l3utterfly <gc.pthzfoldr@gmail.com> Date: Thu, 9 May 2024 22:32:40 +0900 Subject: [PATCH 070/100] readme : add app (#6371) * added Layla to supported UIs * Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 156860470..3c4b6d692 100644 --- a/README.md +++ b/README.md @@ -175,6 +175,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [nat/openplayground](https://github.com/nat/openplayground) - [Faraday](https://faraday.dev/) (proprietary) - [LMStudio](https://lmstudio.ai/) (proprietary) +- [Layla](https://play.google.com/store/apps/details?id=com.laylalite) (proprietary) - [LocalAI](https://github.com/mudler/LocalAI) (MIT) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) From d46dbc76f8770caec0175f1e57777173c70556a0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Thu, 9 May 2024 16:40:42 +0300 Subject: [PATCH 071/100] readme : add scheduled server workflow status badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 3c4b6d692..ff00f9966 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) -[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) From befddd0f15de6efb15d7e7f5b527dfb671f4196f Mon Sep 17 00:00:00 2001 From: 0cc4m <picard12@live.de> Date: Thu, 9 May 2024 20:39:54 +0200 Subject: [PATCH 072/100] Vulkan Bugfixes and Improvements (#7084) * Modify mat mat mul shader for mul_mat_id, modify mat vec mul shaders for single call batch operation * Further work towards MoE, disabled for now * Disable MoE code (not ready yet), fix a number of bugs in shaders and Vulkan code * Add softmax with f16 mask and pos buffer support * Disable mul_mat_id shaders for now * Fix flake8 * Fix validation errors caused by empty buffers on larger batch sizes --- ggml-vulkan-shaders.hpp | 76726 +++++++++++++++++++--------------- ggml-vulkan.cpp | 1167 +- ggml_vk_generate_shaders.py | 716 +- 3 files changed, 43648 insertions(+), 34961 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index fcb6f384b..d3a2fcf38 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -6563,14 +6563,14 @@ const uint64_t dequant_q8_0_len = 4780; unsigned char diag_mask_inf_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x4c,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, -0x32,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, +0x45,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -6580,23 +6580,23 @@ unsigned char diag_mask_inf_f32_data[] = { 0x48,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00, 0x15,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x2a,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2b,0x00,0x00,0x00, +0x37,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x38,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x38,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3a,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x2f,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x30,0x00,0x00,0x00, +0x42,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x43,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x43,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x30,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x32,0x00,0x00,0x00, +0x47,0x00,0x03,0x00,0x43,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x45,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x49,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x4b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00, @@ -6617,30 +6617,30 @@ unsigned char diag_mask_inf_f32_data[] = { 0x18,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0x1d,0x00,0x00,0x00, -0x16,0x00,0x03,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x2b,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x2c,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 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0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4a,0x00,0x00,0x00, +0x05,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x4c,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfb,0x00,0x03,0x00,0x11,0x00,0x00,0x00, -0x4b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4b,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x4d,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, @@ -6655,39 +6655,41 @@ unsigned char diag_mask_inf_f32_data[] = { 0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x1e,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x1f,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x4a,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x20,0x00,0x00,0x00, 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+0x3e,0x00,0x00,0x00,0x47,0x00,0x00,0x00,0x45,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x36,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x3e,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x28,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x49,0x00,0x00,0x00,0x48,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x35,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4c,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x4c,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t diag_mask_inf_f32_len = 1480; +const uint64_t diag_mask_inf_f32_len = 1504; unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -7840,110 +7842,133 @@ const uint64_t get_rows_f32_f32_len = 2976; unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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+0xfd,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t get_rows_q5_1_len = 4128; unsigned char get_rows_q5_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xe9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, -0x36,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0xd2,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x7b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 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-0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -11298,7 +11300,7 @@ unsigned char matmul_f16_data[] = { 0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -11308,19 +11310,19 @@ unsigned char matmul_f16_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xf6,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0xf7,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xf9,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xfa,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0xf9,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xfa,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x03,0x01,0x00,0x00,0xf6,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x05,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x04,0x01,0x00,0x00, @@ -11329,20 +11331,20 @@ unsigned char matmul_f16_data[] = { 0x0c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x14,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x1a,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0xf6,0x00,0x00,0x00,0x1e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x22,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x22,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x23,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x23,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x44,0x01,0x00,0x00,0xf6,0x00,0x00,0x00,0x43,0x01,0x00,0x00, @@ -11350,32 +11352,32 @@ unsigned char matmul_f16_data[] = { 0x44,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x45,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x4e,0x01,0x00,0x00,0xf6,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x4f,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, 0x3b,0x00,0x04,0x00,0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x63,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x63,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x68,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x21,0x01,0x00,0x00, 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+11570,59 @@ unsigned char matmul_f16_data[] = { 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0xf3,0x00,0x00,0x00, 0xf4,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0xf4,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xfe,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0xb8,0x02,0x00,0x00, +0xfe,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0xb7,0x02,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0xfe,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x00,0x01,0x00,0x00, -0x6e,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x0d,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,0x98,0x00,0x00,0x00, +0x6f,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x0d,0x01,0x00,0x00,0xfe,0x00,0x00,0x00,0x99,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0e,0x01,0x00,0x00, -0xbc,0x02,0x00,0x00,0x0d,0x01,0x00,0x00,0x80,0x00,0x05,0x00, +0xbb,0x02,0x00,0x00,0x0d,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 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+0x06,0x00,0x00,0x00,0xa9,0x02,0x00,0x00,0x3f,0x00,0x00,0x00, 0xd6,0x00,0x00,0x00,0xa6,0x02,0x00,0x00,0x42,0x02,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x45,0x02,0x00,0x00, 0xa9,0x02,0x00,0x00,0xbf,0x00,0x00,0x00,0xf6,0x00,0x04,0x00, @@ -13683,29 +13653,29 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x41,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x47,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x47,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00, -0xaa,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x40,0x02,0x00,0x00, +0xaa,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x40,0x02,0x00,0x00, 0xa4,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00, 0xc2,0x00,0x00,0x00,0x4d,0x02,0x00,0x00,0xaa,0x02,0x00,0x00, -0x60,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x49,0x02,0x00,0x00, +0x61,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x49,0x02,0x00,0x00, 0x4a,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x4d,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x49,0x02,0x00,0x00, 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+0x2e,0x02,0x00,0x00,0x59,0x02,0x00,0x00,0x84,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x69,0x00,0x00,0x00, 0xbc,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x5d,0x02,0x00,0x00,0x5a,0x02,0x00,0x00,0x5c,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x5f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x5f,0x02,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00, -0xac,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0x48,0x02,0x00,0x00, +0xac,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0x48,0x02,0x00,0x00, 0xa2,0x02,0x00,0x00,0x62,0x02,0x00,0x00,0xb0,0x00,0x05,0x00, 0xc2,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0xac,0x02,0x00,0x00, 0xbc,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x61,0x02,0x00,0x00, @@ -13714,22 +13684,22 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x67,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x67,0x02,0x00,0x00, 0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0xae,0x02,0x00,0x00, -0x3e,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xa0,0x02,0x00,0x00, +0x3f,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0xa0,0x02,0x00,0x00, 0x6a,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00, -0x6d,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x62,0x00,0x00,0x00, +0x6d,0x02,0x00,0x00,0xae,0x02,0x00,0x00,0x63,0x00,0x00,0x00, 0xf6,0x00,0x04,0x00,0x69,0x02,0x00,0x00,0x6a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x6d,0x02,0x00,0x00, 0x68,0x02,0x00,0x00,0x69,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x68,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x70,0x02,0x00,0x00,0x55,0x02,0x00,0x00,0xae,0x02,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00,0x73,0x02,0x00,0x00, -0x70,0x02,0x00,0x00,0x36,0x00,0x00,0x00,0xf7,0x00,0x03,0x00, +0x70,0x02,0x00,0x00,0x37,0x00,0x00,0x00,0xf7,0x00,0x03,0x00, 0x75,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x73,0x02,0x00,0x00,0x74,0x02,0x00,0x00,0x75,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x74,0x02,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x78,0x02,0x00,0x00,0x5d,0x02,0x00,0x00, -0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x79,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x79,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00, 0x79,0x02,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00, 0x7b,0x02,0x00,0x00,0x78,0x02,0x00,0x00,0x7a,0x02,0x00,0x00, @@ -13741,8 +13711,8 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x7c,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x7e,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x7d,0x02,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x86,0x02,0x00,0x00,0x5d,0x02,0x00,0x00, -0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x88,0x02,0x00,0x00,0x12,0x00,0x00,0x00,0x87,0x02,0x00,0x00, +0xac,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x88,0x02,0x00,0x00,0x14,0x00,0x00,0x00,0x87,0x02,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x02,0x00,0x00, 0x88,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x8a,0x02,0x00,0x00,0x86,0x02,0x00,0x00,0x89,0x02,0x00,0x00, @@ -13757,7 +13727,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xac,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x95,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x94,0x02,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x97,0x02,0x00,0x00, -0xaa,0x02,0x00,0x00,0x62,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0xaa,0x02,0x00,0x00,0x63,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x98,0x02,0x00,0x00,0x95,0x02,0x00,0x00, 0x97,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9a,0x02,0x00,0x00,0x98,0x02,0x00,0x00,0xae,0x02,0x00,0x00, @@ -13765,7 +13735,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xca,0x00,0x00,0x00,0x9a,0x02,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00, 0x41,0x00,0x06,0x00,0x9d,0x02,0x00,0x00,0x9e,0x02,0x00,0x00, -0x82,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0x8f,0x02,0x00,0x00, +0x82,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0x8f,0x02,0x00,0x00, 0x3e,0x00,0x03,0x00,0x9e,0x02,0x00,0x00,0x9c,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x7e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x6a,0x02,0x00,0x00, @@ -13792,55 +13762,55 @@ const uint64_t matmul_f16_aligned_fp32_len = 10288; unsigned char matmul_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xda,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 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0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -13972,19 +13942,19 @@ unsigned char matmul_f16_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xf6,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 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+13963,20 @@ unsigned char matmul_f16_f32_data[] = { 0x0c,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x14,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x1a,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0xf6,0x00,0x00,0x00,0x1e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x22,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x22,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x23,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x23,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x44,0x01,0x00,0x00,0xf6,0x00,0x00,0x00,0x43,0x01,0x00,0x00, @@ -14014,7 +13984,7 @@ unsigned char matmul_f16_f32_data[] = { 0x44,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x45,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x4e,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x4f,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, @@ -14022,25 +13992,25 @@ unsigned char matmul_f16_f32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x5c,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x6a,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6a,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6c,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6b,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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+0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xa6,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbc,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, @@ -14050,20 +14020,20 @@ unsigned char matmul_f16_f32_data[] = { 0x06,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3b,0x02,0x00,0x00, -0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x81,0x02,0x00,0x00, -0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x82,0x02,0x00,0x00, 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0x09,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, @@ -14493,258 +14463,255 @@ unsigned char matmul_f16_f32_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x0b,0x02,0x00,0x00, 0x12,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x14,0x02,0x00,0x00,0x16,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x19,0x02,0x00,0x00,0xd1,0x02,0x00,0x00, +0x06,0x00,0x00,0x00,0x19,0x02,0x00,0x00,0xd0,0x02,0x00,0x00, 0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xf3,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xf5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xee,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1b,0x02,0x00,0x00, -0xcf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xce,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0xeb,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xe6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0xe6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -16557,77 +16491,77 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 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+0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -16636,8 +16570,8 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -16647,17 +16581,17 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0xf7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xf9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x02,0x01,0x00,0x00,0x10,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00, 0x1e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00, @@ -16667,19 +16601,19 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x15,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x1b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x1b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x22,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x22,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x23,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x23,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x44,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x43,0x01,0x00,0x00, @@ -16687,7 +16621,7 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x44,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x45,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x4e,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x4f,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, @@ -16695,25 +16629,25 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x5c,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x64,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x69,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x69,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6a,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6a,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x6e,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6f,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x72,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0x8e,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x8d,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x8f,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0x8e,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xba,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xbb,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0xba,0x01,0x00,0x00, @@ -16721,20 +16655,20 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0xbb,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xcd,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xfc,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x2f,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x7d,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0x7e,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x7f,0x02,0x00,0x00, -0x80,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x85,0x02,0x00,0x00,0x05,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0xcd,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xfc,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 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matmul_f16_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, 0xfa,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0x46,0x01,0x00,0x00, -0x51,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x02,0x00,0x00, +0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00, +0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, @@ -17398,15 +17332,15 @@ unsigned char matmul_f16_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x51,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x37,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7d,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x36,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x7e,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x7d,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x7e,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x80,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x80,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0x7d,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x7f,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -17417,77 +17351,77 @@ unsigned char matmul_f16_fp32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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+0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -17507,17 +17441,17 @@ unsigned char matmul_f16_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xf8,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0xf7,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xf9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0xf9,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x02,0x01,0x00,0x00,0x10,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x02,0x01,0x00,0x00, 0x1e,0x00,0x03,0x00,0x04,0x01,0x00,0x00,0x03,0x01,0x00,0x00, @@ -17527,19 +17461,19 @@ unsigned char matmul_f16_fp32_data[] = { 0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x15,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x1b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x1b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x20,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x21,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x22,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x22,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x23,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x23,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x44,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x43,0x01,0x00,0x00, @@ -17547,32 +17481,32 @@ unsigned char matmul_f16_fp32_data[] = { 0x44,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x45,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x4e,0x01,0x00,0x00,0x02,0x01,0x00,0x00,0x1e,0x00,0x03,0x00, 0x4f,0x01,0x00,0x00,0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, 0x3b,0x00,0x04,0x00,0x50,0x01,0x00,0x00,0x51,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x64,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, 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-0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x8e,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x8d,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x8f,0x01,0x00,0x00,0x07,0x00,0x00,0x00, 0x8e,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x9f,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x9f,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xba,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xbb,0x01,0x00,0x00, 0xc3,0x00,0x00,0x00,0xba,0x01,0x00,0x00,0x20,0x00,0x04,0x00, @@ -17580,21 +17514,21 @@ unsigned char matmul_f16_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xcd,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfc,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x2f,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0x37,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x7d,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x7e,0x02,0x00,0x00,0x7d,0x02,0x00,0x00,0x20,0x00,0x04,0x00, -0x7f,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x7e,0x02,0x00,0x00, -0x3b,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x80,0x02,0x00,0x00, -0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x85,0x02,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x92,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x9b,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, +0x36,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x7c,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, +0x7d,0x02,0x00,0x00,0x7c,0x02,0x00,0x00,0x20,0x00,0x04,0x00, +0x7e,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x7d,0x02,0x00,0x00, +0x3b,0x00,0x04,0x00,0x7e,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x84,0x02,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x06,0x00,0x00,0x00,0x91,0x02,0x00,0x00,0x84,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x9a,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x39,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00, 0x3f,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x29,0x01,0x00,0x00, @@ -17825,18 +17759,18 @@ unsigned char matmul_f16_fp32_data[] = { 0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x3f,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x60,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x40,0x01,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x7d,0x00,0x00,0x00, -0xb7,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x7e,0x00,0x00,0x00, +0xb6,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x4b,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, -0x4b,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x84,0x00,0x05,0x00, +0x4b,0x01,0x00,0x00,0x79,0x00,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x49,0x01,0x00,0x00, 0xaa,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0xa1,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x44,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x46,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x3f,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0xa6,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_f16_fp32_len = 10276; unsigned char matmul_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xda,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, @@ -18259,15 +18193,15 @@ unsigned char matmul_f32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x52,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x3b,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x3a,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x80,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, 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0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -18357,8 +18291,8 @@ unsigned char matmul_f32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -18368,18 +18302,18 @@ unsigned char matmul_f32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x39,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x43,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x44,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x43,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x45,0x01,0x00,0x00,0xf6,0x00,0x00,0x00, @@ -18409,32 +18343,32 @@ unsigned char matmul_f32_data[] = { 0x04,0x00,0x00,0x00,0x45,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0x46,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x4b,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x50,0x01,0x00,0x00,0x4f,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x50,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x51,0x01,0x00,0x00, 0x52,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x22,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x6a,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 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0x07,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xa6,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0xf6,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbc,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, @@ -18444,20 +18378,20 @@ unsigned char matmul_f32_data[] = { 0x06,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, 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0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x96,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, +0x95,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0xc8,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x07,0x00,0x00,0x00, @@ -18467,158 +18401,158 @@ unsigned char matmul_f32_data[] = { 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 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+18579,32 @@ unsigned char matmul_f32_data[] = { 0x16,0x01,0x00,0x00,0x14,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x17,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1a,0x01,0x00,0x00, -0x73,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x84,0x00,0x05,0x00, +0x74,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x1c,0x01,0x00,0x00,0x1a,0x01,0x00,0x00, 0x1b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x1e,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,0x6e,0x00,0x00,0x00, +0x1e,0x01,0x00,0x00,0x1c,0x01,0x00,0x00,0x6f,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x15,0x01,0x00,0x00,0x20,0x01,0x00,0x00, 0xfb,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00, 0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xf5,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xf5,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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-0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3a,0x01,0x00,0x00, +0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3a,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x3a,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 0xc1,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x38,0x01,0x00,0x00, 0x2a,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x39,0x01,0x00,0x00, @@ -18688,17 +18622,17 @@ unsigned char matmul_f32_data[] = { 0xfa,0x00,0x04,0x00,0x40,0x01,0x00,0x00,0x41,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x41,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4a,0x01,0x00,0x00, -0x7d,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0x84,0x00,0x05,0x00, +0x7e,0x00,0x00,0x00,0xb9,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x4e,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,0x78,0x00,0x00,0x00, +0x4e,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,0x79,0x00,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00, 0x4a,0x01,0x00,0x00,0xaa,0x00,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0xc1,0x02,0x00,0x00, +0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0xc0,0x02,0x00,0x00, 0x59,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x5c,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x78,0x00,0x00,0x00, +0x5c,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x79,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x11,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, -0x52,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x5c,0x01,0x00,0x00, +0x52,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x5c,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0x5e,0x01,0x00,0x00, 0x5d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0xf6,0x00,0x00,0x00, 0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -18706,173 +18640,173 @@ unsigned char matmul_f32_data[] = { 0x4e,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x60,0x01,0x00,0x00, 0x5f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x61,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 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+19161,7 @@ unsigned char matmul_f32_aligned_data[] = { 0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -19240,855 +19171,817 @@ unsigned char matmul_f32_aligned_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, 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0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -20180,7 +20073,7 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00, @@ -20190,721 +20083,725 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 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-0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -21027,7 +20924,7 @@ unsigned char matmul_f32_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -21037,18 +20934,18 @@ unsigned char matmul_f32_fp32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 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-0xfe,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x02,0x01,0x00,0x00, +0xfe,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x02,0x01,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x03,0x01,0x00,0x00, 0x02,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x04,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -21057,18 +20954,18 @@ unsigned char matmul_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x13,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x1f,0x01,0x00,0x00, -0x1e,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, 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0x04,0x00,0x00,0x00,0x42,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0x43,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x4c,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x4d,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x4d,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x4e,0x01,0x00,0x00, 0x4f,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 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0x8a,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x8c,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0x8b,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb7,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xb8,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0xb7,0x01,0x00,0x00, @@ -21109,20 +21006,20 @@ unsigned char matmul_f32_fp32_data[] = { 0xb8,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc2,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xca,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf9,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 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0x05,0x02,0x00,0x00,0x04,0x02,0x00,0x00,0x41,0x00,0x05,0x00, @@ -21549,171 +21446,171 @@ unsigned char matmul_f32_fp32_data[] = { 0x32,0x00,0x00,0x00,0x05,0x02,0x00,0x00,0x0b,0x02,0x00,0x00, 0x0e,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x0d,0x02,0x00,0x00, 0x0f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x12,0x02,0x00,0x00,0xca,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0x12,0x02,0x00,0x00,0xc9,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0xee,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0xf0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0xe9,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xe9,0x01,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,0xc8,0x02,0x00,0x00, +0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,0xc7,0x02,0x00,0x00, 0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xe6,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xe8,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xe1,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xe1,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x16,0x02,0x00,0x00, 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-0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -21891,8 +21788,8 @@ unsigned char matmul_q2_k_f32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -21902,9 +21799,9 @@ unsigned char matmul_q2_k_f32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -21933,9 +21830,9 @@ unsigned char matmul_q2_k_f32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x01,0x00,0x00, 0x03,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x55,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x55,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x54,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x56,0x01,0x00,0x00, 0x19,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x57,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x56,0x01,0x00,0x00, @@ -21944,15 +21841,15 @@ unsigned char matmul_q2_k_f32_data[] = { 0x04,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x66,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x65,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x69,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x87,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x88,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x87,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x19,0x01,0x00,0x00, @@ -21960,7 +21857,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x04,0x00,0x00,0x00,0x89,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0x8a,0x01,0x00,0x00,0x8b,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x8f,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x93,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x95,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -21968,27 +21865,27 @@ unsigned char matmul_q2_k_f32_data[] = { 0x96,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xa1,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xaa,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0xae,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xb0,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x66,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xb1,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xb0,0x01,0x00,0x00, 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0x2b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x22,0x01,0x00,0x00, -0x30,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, +0x30,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfb,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x16,0x01,0x00,0x00,0x31,0x01,0x00,0x00, 0x30,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x41,0x00,0x07,0x00, 0x37,0x01,0x00,0x00,0x38,0x01,0x00,0x00,0x1f,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x85,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,0x39,0x01,0x00,0x00, 0x38,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x33,0x01,0x00,0x00, 0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x51,0x00,0x05,0x00, @@ -22229,7 +22126,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x12,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x13,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x2c,0x01,0x00,0x00, 0x45,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x13,0x01,0x00,0x00, -0x49,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x4e,0x03,0x00,0x00, +0x49,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x4d,0x03,0x00,0x00, 0x70,0x00,0x04,0x00,0x33,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, 0x49,0x01,0x00,0x00,0x8e,0x00,0x05,0x00,0x33,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x42,0x01,0x00,0x00, @@ -22249,29 +22146,29 @@ unsigned char matmul_q2_k_f32_data[] = { 0x5d,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x58,0x01,0x00,0x00, 0xf7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00, 0x5c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x60,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x60,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x62,0x01,0x00,0x00, 0x53,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x62,0x01,0x00,0x00, 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0x7d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x81,0x01,0x00,0x00,0xf3,0x02,0x00,0x00,0x78,0x00,0x00,0x00, +0x81,0x01,0x00,0x00,0xf2,0x02,0x00,0x00,0x79,0x00,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x83,0x01,0x00,0x00, -0x81,0x01,0x00,0x00,0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x81,0x01,0x00,0x00,0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x7e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x7e,0x01,0x00,0x00, 0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x84,0x01,0x00,0x00, 0x7c,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,0x83,0x01,0x00,0x00, @@ -22289,17 +22186,17 @@ unsigned char matmul_q2_k_f32_data[] = { 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x84,0x01,0x00,0x00, 0x85,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x85,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x8e,0x01,0x00,0x00,0x7d,0x00,0x00,0x00,0x04,0x03,0x00,0x00, +0x8e,0x01,0x00,0x00,0x7e,0x00,0x00,0x00,0x03,0x03,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00, 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+22205,172 @@ unsigned char matmul_q2_k_f32_data[] = { 0xa5,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0x86,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00, -0x7d,0x00,0x00,0x00,0x04,0x03,0x00,0x00,0x84,0x00,0x05,0x00, +0x7e,0x00,0x00,0x00,0x03,0x03,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xa9,0x01,0x00,0x00, 0xaa,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xad,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x78,0x00,0x00,0x00, +0xad,0x01,0x00,0x00,0xab,0x01,0x00,0x00,0x79,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x5d,0x01,0x00,0x00,0xaf,0x01,0x00,0x00, 0x8b,0x01,0x00,0x00,0xad,0x01,0x00,0x00,0x3e,0x00,0x03,0x00, 0xaf,0x01,0x00,0x00,0xae,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0x86,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x86,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0x70,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x70,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x84,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x60,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00, @@ -22837,7 +22734,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -22847,10 +22744,10 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x04,0x01,0x00,0x00, @@ -22877,9 +22774,9 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x47,0x01,0x00,0x00, 0x03,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x55,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x55,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x54,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x56,0x01,0x00,0x00, 0x19,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x57,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x56,0x01,0x00,0x00, @@ -22888,17 +22785,17 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x04,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x66,0x01,0x00,0x00, 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0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x89,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x19,0x01,0x00,0x00, @@ -22922,21 +22819,21 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd1,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd2,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd3,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xd2,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xd2,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd6,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xd7,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0xd7,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 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+22843,21 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x9a,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x99,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0xa2,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0xe8,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0xe9,0x02,0x00,0x00,0xe8,0x02,0x00,0x00,0x20,0x00,0x04,0x00, -0xea,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0xe9,0x02,0x00,0x00, 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0x46,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x45,0x01,0x00,0x00, 0xc7,0x00,0x05,0x00,0x13,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x46,0x01,0x00,0x00,0x67,0x03,0x00,0x00,0x70,0x00,0x04,0x00, +0x46,0x01,0x00,0x00,0x66,0x03,0x00,0x00,0x70,0x00,0x04,0x00, 0x33,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x49,0x01,0x00,0x00, 0x8e,0x00,0x05,0x00,0x33,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 0x4a,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x51,0x00,0x05,0x00, @@ -23198,50 +23095,50 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x5e,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0xf8,0x00,0x00,0x00, 0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00,0x5c,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00, -0xf8,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x51,0x00,0x05,0x00, +0xf8,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x51,0x00,0x05,0x00, 0xc4,0x00,0x00,0x00,0x62,0x01,0x00,0x00,0x53,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0x63,0x01,0x00,0x00,0x62,0x01,0x00,0x00,0x41,0x00,0x05,0x00, 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0x41,0x00,0x08,0x00,0x97,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, -0x95,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x95,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, 0xa0,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x5d,0x01,0x00,0x00, @@ -23250,7 +23147,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, 0x88,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x97,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,0x95,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xa7,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,0xa7,0x01,0x00,0x00, @@ -23259,8 +23156,8 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xa9,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0x88,0x01,0x00,0x00, 0x47,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x97,0x01,0x00,0x00, -0xad,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x7f,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x47,0x01,0x00,0x00, +0xad,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x7f,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x47,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xae,0x01,0x00,0x00, 0xad,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0xaf,0x01,0x00,0x00,0xae,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -23269,8 +23166,8 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xaf,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xb3,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0xb2,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x97,0x01,0x00,0x00,0xb5,0x01,0x00,0x00, -0x95,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x95,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xb6,0x01,0x00,0x00,0xb5,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0xb7,0x01,0x00,0x00, 0xb6,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x5d,0x01,0x00,0x00, @@ -23279,8 +23176,8 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, 0x88,0x01,0x00,0x00,0xba,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x97,0x01,0x00,0x00,0xbd,0x01,0x00,0x00,0x95,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x7f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x01,0x00,0x00,0xbd,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0xbf,0x01,0x00,0x00,0xbe,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x5d,0x01,0x00,0x00,0xc0,0x01,0x00,0x00, @@ -23288,7 +23185,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xc0,0x01,0x00,0x00,0xbf,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00,0x88,0x01,0x00,0x00, 0xc2,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x97,0x01,0x00,0x00, -0xc5,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0xc5,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x7f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xc6,0x01,0x00,0x00, 0xc5,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00, @@ -23298,7 +23195,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xc7,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xcb,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0xca,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x97,0x01,0x00,0x00,0xcd,0x01,0x00,0x00, -0x95,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, +0x95,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xce,0x01,0x00,0x00,0xcd,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0xcf,0x01,0x00,0x00, @@ -23306,162 +23203,162 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0xd0,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0xcb,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0xd0,0x01,0x00,0x00,0xcf,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd5,0x01,0x00,0x00, -0x25,0x03,0x00,0x00,0xd3,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0x24,0x03,0x00,0x00,0xd3,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0x6d,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x6f,0x01,0x00,0x00, 0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0xd6,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xd9,0x01,0x00,0x00,0x28,0x03,0x00,0x00,0xd7,0x01,0x00,0x00, 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+0x06,0x00,0x00,0x00,0x0d,0x03,0x00,0x00,0x14,0x03,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa7,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa9,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q2_k_f32_aligned_len = 11800; unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x3e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x20,0x01,0x00,0x00, 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-0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, @@ -23721,15 +23618,15 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x91,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x93,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x93,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x79,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x02,0x00,0x00, 0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xbf,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xc0,0x02,0x00,0x00, +0xbe,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xbf,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0xc0,0x02,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00, +0x47,0x00,0x03,0x00,0xbf,0x02,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xc1,0x02,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xc1,0x02,0x00,0x00, 0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -23740,79 +23637,79 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -23821,7 +23718,7 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00, @@ -23831,10 +23728,10 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -23863,9 +23760,9 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x40,0x01,0x00,0x00,0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x03,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x55,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x55,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x57,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, 0x56,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x58,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -23874,17 +23771,17 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x64,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x66,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x65,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x67,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x66,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x68,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x67,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x67,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x88,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x88,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x8a,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x89,0x01,0x00,0x00, @@ -23901,21 +23798,21 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x65,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xb1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0xb1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xb4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xcf,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xd0,0x01,0x00,0x00, +0xb4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xcf,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xd0,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xd1,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0xd0,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe1,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfc,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xfd,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -23924,21 +23821,21 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0f,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 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+0x0c,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3b,0x00,0x04,0x00, +0xc0,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xc6,0x02,0x00,0x00, 0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xd4,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2c,0x00,0x05,0x00,0x14,0x01,0x00,0x00, -0x3e,0x03,0x00,0x00,0x48,0x01,0x00,0x00,0x48,0x01,0x00,0x00, +0xd3,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x2c,0x00,0x05,0x00,0x14,0x01,0x00,0x00, +0x3d,0x03,0x00,0x00,0x48,0x01,0x00,0x00,0x48,0x01,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc9,0x00,0x00,0x00, @@ -23948,159 +23845,159 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00, 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0x50,0x00,0x05,0x00,0x14,0x01,0x00,0x00,0x2d,0x01,0x00,0x00, 0x26,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x23,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x0e,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x01,0x00,0x00, 0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00, 0x41,0x00,0x07,0x00,0x38,0x01,0x00,0x00,0x39,0x01,0x00,0x00, -0x20,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x1b,0x01,0x00,0x00, +0x20,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, +0x87,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x1b,0x01,0x00,0x00, 0x3a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x34,0x01,0x00,0x00,0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00, 0x51,0x00,0x05,0x00,0xc4,0x00,0x00,0x00,0x3e,0x01,0x00,0x00, @@ -24156,7 +24053,7 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0xc2,0x00,0x05,0x00,0x14,0x01,0x00,0x00,0x47,0x01,0x00,0x00, 0x2d,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0xc7,0x00,0x05,0x00, 0x14,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x47,0x01,0x00,0x00, -0x3e,0x03,0x00,0x00,0x70,0x00,0x04,0x00,0x34,0x01,0x00,0x00, +0x3d,0x03,0x00,0x00,0x70,0x00,0x04,0x00,0x34,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x8e,0x00,0x05,0x00, 0x34,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 0x43,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0xc4,0x00,0x00,0x00, @@ -24174,54 +24071,54 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x5d,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x59,0x01,0x00,0x00, 0xf8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x5e,0x01,0x00,0x00, 0x5c,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x60,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x60,0x01,0x00,0x00,0xf8,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 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+24126,169 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0xa4,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x87,0x01,0x00,0x00, 0x48,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0x95,0x01,0x00,0x00, -0xa8,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0xa8,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x7e,0x01,0x00,0x00,0x48,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0xa8,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x5d,0x01,0x00,0x00,0xaa,0x01,0x00,0x00, 0x8c,0x01,0x00,0x00,0xa6,0x01,0x00,0x00,0x3e,0x00,0x03,0x00, 0xaa,0x01,0x00,0x00,0xa9,0x01,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,0xfc,0x02,0x00,0x00, +0x06,0x00,0x00,0x00,0xaf,0x01,0x00,0x00,0xfb,0x02,0x00,0x00, 0xad,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x6c,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x6e,0x01,0x00,0x00,0xe0,0x00,0x04,0x00, 0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0xb0,0x01,0x00,0x00, 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+0xe2,0x02,0x00,0x00,0xec,0x02,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x86,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x88,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x81,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xe4,0x02,0x00,0x00,0xeb,0x02,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q2_k_f32_aligned_fp32_len = 11068; unsigned char matmul_q2_k_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x48,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 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+24564,77 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x0b,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x12,0x00,0x00,0x00, +0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 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0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -24747,7 +24644,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -24757,10 +24654,10 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 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-0x37,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x56,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x55,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x57,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x56,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x57,0x01,0x00,0x00, @@ -24799,50 +24696,50 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x5c,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x39,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x65,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x64,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x66,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x65,0x01,0x00,0x00, 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-0x1f,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, +0x1f,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x16,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x30,0x01,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x32,0x01,0x00,0x00, 0x31,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0x37,0x01,0x00,0x00, -0x38,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x85,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x38,0x01,0x00,0x00,0x1f,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfb,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x38,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x33,0x01,0x00,0x00,0x3a,0x01,0x00,0x00, 0x39,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00, @@ -25082,7 +24979,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x12,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x13,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0x2c,0x01,0x00,0x00,0x45,0x01,0x00,0x00, 0xc7,0x00,0x05,0x00,0x13,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x46,0x01,0x00,0x00,0x47,0x03,0x00,0x00,0x70,0x00,0x04,0x00, +0x46,0x01,0x00,0x00,0x46,0x03,0x00,0x00,0x70,0x00,0x04,0x00, 0x33,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x49,0x01,0x00,0x00, 0x8e,0x00,0x05,0x00,0x33,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 0x4a,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x51,0x00,0x05,0x00, @@ -25101,27 +24998,27 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x58,0x01,0x00,0x00,0xf7,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, 0x5d,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0xf7,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00, 0x61,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x5c,0x01,0x00,0x00,0x62,0x01,0x00,0x00, 0x58,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00, 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+0x7f,0x01,0x00,0x00,0xeb,0x02,0x00,0x00,0x79,0x00,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x81,0x01,0x00,0x00, -0x7f,0x01,0x00,0x00,0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x7f,0x01,0x00,0x00,0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x7c,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x01,0x00,0x00, 0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00,0x82,0x01,0x00,0x00, 0x7a,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,0x81,0x01,0x00,0x00, @@ -25139,190 +25036,190 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x82,0x01,0x00,0x00, 0x83,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x83,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x8c,0x01,0x00,0x00,0x7d,0x00,0x00,0x00,0xfd,0x02,0x00,0x00, +0x8c,0x01,0x00,0x00,0x7e,0x00,0x00,0x00,0xfc,0x02,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x8e,0x01,0x00,0x00, 0x8c,0x01,0x00,0x00,0x8d,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x90,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, 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+0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0xed,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x87,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x82,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x82,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00, +0xec,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x7f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x81,0x02,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q2_k_f32_fp32_len = 11120; unsigned char matmul_q3_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -25509,47 +25406,47 @@ unsigned char matmul_q3_k_f32_data[] = 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0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -25692,10 +25589,10 @@ unsigned char matmul_q3_k_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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0xe0,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0xc3,0x00,0x00,0x00, @@ -26202,22 +26099,22 @@ unsigned char matmul_q3_k_f32_data[] = { 0x3e,0x00,0x03,0x00,0xe4,0x01,0x00,0x00,0xe3,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdf,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xdf,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xeb,0x01,0x00,0x00,0x7f,0x03,0x00,0x00,0xe9,0x01,0x00,0x00, +0xeb,0x01,0x00,0x00,0x7e,0x03,0x00,0x00,0xe9,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xde,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xed,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xed,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, -0x06,0x00,0x00,0x00,0x80,0x03,0x00,0x00,0x3e,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x7f,0x03,0x00,0x00,0x3f,0x00,0x00,0x00, 0xde,0x00,0x00,0x00,0x34,0x02,0x00,0x00,0xf0,0x01,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0xf3,0x01,0x00,0x00, -0x80,0x03,0x00,0x00,0xa6,0x00,0x00,0x00,0xf6,0x00,0x04,0x00, +0x7f,0x03,0x00,0x00,0xa6,0x00,0x00,0x00,0xf6,0x00,0x04,0x00, 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0x24,0x02,0x00,0x00,0x23,0x02,0x00,0x00,0x41,0x00,0x05,0x00, @@ -26253,173 +26150,173 @@ unsigned char matmul_q3_k_f32_data[] = { 0x12,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x25,0x02,0x00,0x00, 0x24,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x29,0x02,0x00,0x00,0x7d,0x00,0x00,0x00, -0x80,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x29,0x02,0x00,0x00,0x7e,0x00,0x00,0x00, +0x7f,0x03,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x2b,0x02,0x00,0x00,0x29,0x02,0x00,0x00,0x2a,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00, -0x2b,0x02,0x00,0x00,0x78,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x2b,0x02,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0xc5,0x01,0x00,0x00,0x2f,0x02,0x00,0x00,0x0b,0x02,0x00,0x00, 0x2d,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x2f,0x02,0x00,0x00, 0x2e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x06,0x02,0x00,0x00, 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0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbd,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -26786,8 +26683,8 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0xc0,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -26797,9 +26694,9 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x02,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -26824,14 +26721,14 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x3b,0x00,0x04,0x00,0x39,0x01,0x00,0x00,0x3a,0x01,0x00,0x00, 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0xa8,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0xa9,0x01,0x00,0x00, @@ -26839,18 +26736,18 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0xc5,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x35,0x01,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0xe6,0x01,0x00,0x00,0xe5,0x01,0x00,0x00,0x39,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xe6,0x01,0x00,0x00,0xe5,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe7,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0xe6,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe8,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xe7,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe9,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xe8,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x04,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x09,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x04,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x09,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0a,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x09,0x02,0x00,0x00,0x1c,0x00,0x04,0x00,0x0b,0x02,0x00,0x00, 0x35,0x01,0x00,0x00,0x0a,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -26874,21 +26771,21 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x51,0x02,0x00,0x00,0x51,0x00,0x00,0x00,0xe6,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x52,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x51,0x02,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 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-0x82,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, +0xde,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0x3f,0x00,0x00,0x00, +0xa9,0x00,0x06,0x00,0x15,0x00,0x00,0x00,0xdf,0x01,0x00,0x00, +0xde,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, +0x82,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, 0xd5,0x01,0x00,0x00,0xdf,0x01,0x00,0x00,0x6f,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xe1,0x01,0x00,0x00,0xe0,0x01,0x00,0x00, 0x85,0x00,0x05,0x00,0xc4,0x00,0x00,0x00,0xe2,0x01,0x00,0x00, @@ -27310,43 +27207,43 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0xe4,0x01,0x00,0x00,0xe3,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xeb,0x01,0x00,0x00, -0xa0,0x03,0x00,0x00,0xe9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0x9f,0x03,0x00,0x00,0xe9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0xed,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 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-0x34,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x15,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x19,0x02,0x00,0x00,0x18,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x35,0x01,0x00,0x00,0x1a,0x02,0x00,0x00, 0x19,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc5,0x01,0x00,0x00, 0x1b,0x02,0x00,0x00,0x0d,0x02,0x00,0x00,0x08,0x02,0x00,0x00, 0x3e,0x00,0x03,0x00,0x1b,0x02,0x00,0x00,0x1a,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1d,0x02,0x00,0x00, -0x08,0x02,0x00,0x00,0x39,0x00,0x00,0x00,0x41,0x00,0x08,0x00, +0x08,0x02,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x17,0x02,0x00,0x00,0x1f,0x02,0x00,0x00,0x15,0x02,0x00,0x00, -0x34,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x20,0x02,0x00,0x00,0x1f,0x02,0x00,0x00,0x73,0x00,0x04,0x00, 0x35,0x01,0x00,0x00,0x21,0x02,0x00,0x00,0x20,0x02,0x00,0x00, 0x41,0x00,0x05,0x00,0xc5,0x01,0x00,0x00,0x22,0x02,0x00,0x00, @@ -27354,8 +27251,8 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x22,0x02,0x00,0x00,0x21,0x02,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x24,0x02,0x00,0x00,0x08,0x02,0x00,0x00, 0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x17,0x02,0x00,0x00, -0x26,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x34,0x00,0x00,0x00, -0xff,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x26,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x35,0x00,0x00,0x00, +0xff,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x27,0x02,0x00,0x00, 0x26,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0x35,0x01,0x00,0x00, 0x28,0x02,0x00,0x00,0x27,0x02,0x00,0x00,0x41,0x00,0x05,0x00, @@ -27364,8 +27261,8 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x28,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x2c,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x2b,0x02,0x00,0x00, 0x41,0x00,0x08,0x00,0x17,0x02,0x00,0x00,0x2e,0x02,0x00,0x00, -0x15,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xff,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x2b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00, +0x15,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x2b,0x02,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x2f,0x02,0x00,0x00,0x2e,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x35,0x01,0x00,0x00,0x30,0x02,0x00,0x00, 0x2f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xc5,0x01,0x00,0x00, @@ -27374,8 +27271,8 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x33,0x02,0x00,0x00, 0x08,0x02,0x00,0x00,0x14,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x17,0x02,0x00,0x00,0x35,0x02,0x00,0x00,0x15,0x02,0x00,0x00, -0x34,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x36,0x02,0x00,0x00,0x35,0x02,0x00,0x00,0x73,0x00,0x04,0x00, 0x35,0x01,0x00,0x00,0x37,0x02,0x00,0x00,0x36,0x02,0x00,0x00, 0x41,0x00,0x05,0x00,0xc5,0x01,0x00,0x00,0x38,0x02,0x00,0x00, @@ -27383,8 +27280,8 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x38,0x02,0x00,0x00,0x37,0x02,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x08,0x02,0x00,0x00, 0x3a,0x02,0x00,0x00,0x41,0x00,0x08,0x00,0x17,0x02,0x00,0x00, -0x3d,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x34,0x00,0x00,0x00, -0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x3d,0x02,0x00,0x00,0x15,0x02,0x00,0x00,0x35,0x00,0x00,0x00, +0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x3e,0x02,0x00,0x00, 0x3d,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0x35,0x01,0x00,0x00, 0x3f,0x02,0x00,0x00,0x3e,0x02,0x00,0x00,0x41,0x00,0x05,0x00, @@ -27393,7 +27290,7 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x3f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x43,0x02,0x00,0x00,0x08,0x02,0x00,0x00,0x42,0x02,0x00,0x00, 0x41,0x00,0x08,0x00,0x17,0x02,0x00,0x00,0x45,0x02,0x00,0x00, -0x15,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xff,0x01,0x00,0x00, +0x15,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x46,0x02,0x00,0x00,0x45,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x35,0x01,0x00,0x00,0x47,0x02,0x00,0x00, @@ -27403,170 +27300,170 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00, 0x08,0x02,0x00,0x00,0x4a,0x02,0x00,0x00,0x41,0x00,0x08,0x00, 0x17,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,0x15,0x02,0x00,0x00, -0x34,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xff,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0x2a,0x03,0x00,0x00,0xf8,0x00,0x02,0x00, +0x2a,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x8d,0x03,0x00,0x00,0x90,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x27,0x03,0x00,0x00,0xf8,0x00,0x02,0x00, +0x29,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q3_k_f32_aligned_len = 13704; unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x9d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x9c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -27755,47 +27652,47 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 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-0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -27830,15 +27727,15 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x12,0x02,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x12,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x03,0x00,0x00, +0x47,0x00,0x04,0x00,0xf8,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3e,0x03,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x40,0x03,0x00,0x00,0x00,0x00,0x00,0x00, +0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x3f,0x03,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x40,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x42,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x42,0x03,0x00,0x00,0x21,0x00,0x00,0x00, +0x3f,0x03,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x41,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x41,0x03,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -27849,78 +27746,78 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x14,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 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-0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x60,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00, @@ -27930,7 +27827,7 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -27940,10 +27837,10 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x06,0x01,0x00,0x00, @@ -27967,14 +27864,14 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x38,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x39,0x01,0x00,0x00, 0x3a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x3e,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x30,0x01,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x43,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x43,0x01,0x00,0x00, 0x0f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x9c,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x20,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xa8,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, 0xa7,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xa9,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -27983,17 +27880,17 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xe3,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe3,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xe4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe6,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xe5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xe6,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xe6,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x08,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x07,0x02,0x00,0x00,0x1c,0x00,0x04,0x00, 0x09,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x08,0x02,0x00,0x00, @@ -28012,21 +27909,21 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x2b,0x02,0x00,0x00,0x51,0x00,0x00,0x00,0xe4,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x2c,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x2b,0x02,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x2d,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x2c,0x02,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x30,0x02,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x34,0x02,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4f,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x50,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x4f,0x02,0x00,0x00, 0x20,0x00,0x04,0x00,0x51,0x02,0x00,0x00,0x07,0x00,0x00,0x00, 0x50,0x02,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x61,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x61,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7c,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0xbc,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x7d,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x7c,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -28034,20 +27931,20 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x87,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x8f,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xf1,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xf0,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0xf9,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x3f,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x40,0x03,0x00,0x00,0x3f,0x03,0x00,0x00,0x20,0x00,0x04,0x00, -0x41,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,0x40,0x03,0x00,0x00, -0x3b,0x00,0x04,0x00,0x41,0x03,0x00,0x00,0x42,0x03,0x00,0x00, -0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x47,0x03,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x54,0x03,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x36,0x00,0x05,0x00, +0xf8,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x3e,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, +0x3f,0x03,0x00,0x00,0x3e,0x03,0x00,0x00,0x20,0x00,0x04,0x00, +0x40,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,0x3f,0x03,0x00,0x00, +0x3b,0x00,0x04,0x00,0x40,0x03,0x00,0x00,0x41,0x03,0x00,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x46,0x03,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x06,0x00,0x00,0x00,0x53,0x03,0x00,0x00,0x84,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00, 0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00, @@ -28057,158 +27954,158 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 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0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00, 0xda,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0xab,0x00,0x05,0x00, 0xc2,0x00,0x00,0x00,0xdd,0x01,0x00,0x00,0xdc,0x01,0x00,0x00, -0x3e,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,0x13,0x00,0x00,0x00, -0xde,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xa9,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x13,0x00,0x00,0x00, +0x3f,0x00,0x00,0x00,0xa9,0x00,0x06,0x00,0x15,0x00,0x00,0x00, +0xde,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xa9,0x00,0x00,0x00,0x82,0x00,0x05,0x00,0x15,0x00,0x00,0x00, 0xdf,0x01,0x00,0x00,0xd4,0x01,0x00,0x00,0xde,0x01,0x00,0x00, 0x6f,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, 0xdf,0x01,0x00,0x00,0x85,0x00,0x05,0x00,0xc4,0x00,0x00,0x00, @@ -28444,48 +28341,48 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0xe2,0x01,0x00,0x00,0xe1,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xe0,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xe0,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe9,0x01,0x00,0x00, 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+0x12,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xfd,0x01,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x1c,0x02,0x00,0x00,0x1b,0x02,0x00,0x00,0x41,0x00,0x05,0x00, 0xc4,0x01,0x00,0x00,0x1d,0x02,0x00,0x00,0x0b,0x02,0x00,0x00, 0x19,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0x1d,0x02,0x00,0x00, 0x1c,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x1f,0x02,0x00,0x00,0x06,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, 0x41,0x00,0x07,0x00,0x14,0x02,0x00,0x00,0x21,0x02,0x00,0x00, -0x12,0x02,0x00,0x00,0x34,0x00,0x00,0x00,0xfd,0x01,0x00,0x00, +0x12,0x02,0x00,0x00,0x35,0x00,0x00,0x00,0xfd,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x22,0x02,0x00,0x00,0x21,0x02,0x00,0x00,0x41,0x00,0x05,0x00, 0xc4,0x01,0x00,0x00,0x23,0x02,0x00,0x00,0x0b,0x02,0x00,0x00, @@ -28493,168 +28390,168 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x22,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x26,0x02,0x00,0x00,0x06,0x02,0x00,0x00,0x25,0x02,0x00,0x00, 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+0x01,0x03,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x64,0x03,0x00,0x00,0x67,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xfe,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x00,0x03,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q3_k_f32_aligned_fp32_len = 12972; unsigned char matmul_q3_k_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa1,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xa0,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -28841,47 +28738,47 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 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-0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -28916,15 +28813,15 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x14,0x02,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x14,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xfa,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x40,0x03,0x00,0x00, +0x47,0x00,0x04,0x00,0xf9,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3f,0x03,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, 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0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -29014,8 +28911,8 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -29025,9 +28922,9 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -29053,14 +28950,14 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x38,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x39,0x01,0x00,0x00, 0x3a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x3e,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x30,0x01,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x43,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x43,0x01,0x00,0x00, 0x0f,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x9c,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x20,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0xa6,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xa8,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0xa7,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xa9,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -29069,15 +28966,15 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xe3,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0xe4,0x01,0x00,0x00,0xe3,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xe4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe6,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xe5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xe6,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xe6,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x05,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x06,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x05,0x02,0x00,0x00,0x1c,0x00,0x04,0x00, 0x07,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x06,0x02,0x00,0x00, @@ -29085,7 +28982,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x07,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x08,0x02,0x00,0x00, 0x09,0x02,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0d,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x11,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x12,0x02,0x00,0x00,0x11,0x02,0x00,0x00,0x20,0x00,0x04,0x00, 0x13,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x12,0x02,0x00,0x00, @@ -29093,25 +28990,25 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x1f,0x02,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x27,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,0x51,0x00,0x00,0x00, 0xe4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2d,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x2c,0x02,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x2c,0x02,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,0x86,0x00,0x00,0x00, -0x2d,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x2d,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x31,0x02,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x35,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0x51,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x50,0x02,0x00,0x00,0x20,0x00,0x04,0x00,0x52,0x02,0x00,0x00, 0x07,0x00,0x00,0x00,0x51,0x02,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x62,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x7e,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x7d,0x02,0x00,0x00, @@ -29119,20 +29016,20 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x7e,0x02,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x88,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x90,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xbf,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 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+0xf8,0x00,0x02,0x00,0x02,0x03,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x65,0x03,0x00,0x00,0x68,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xff,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x01,0x03,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q3_k_f32_fp32_len = 13024; unsigned char matmul_q4_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x1c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x1b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -29931,47 +29828,47 @@ unsigned char matmul_q4_0_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -30098,8 +29995,8 @@ unsigned char matmul_q4_0_f32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -30109,9 +30006,9 @@ unsigned char matmul_q4_0_f32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -30131,9 +30028,9 @@ unsigned char matmul_q4_0_f32_data[] = { 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x01,0x00,0x00,0x00,0x00,0x00,0x41,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x23,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 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0xc3,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x0b,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x10,0x01,0x00,0x00,0x11,0x01,0x00,0x00, -0x07,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, +0x07,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x12,0x01,0x00,0x00,0x11,0x01,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00, @@ -30394,7 +30291,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x1b,0x01,0x00,0x00,0x50,0x00,0x05,0x00,0x14,0x01,0x00,0x00, 0x1d,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x1c,0x01,0x00,0x00, 0x83,0x00,0x05,0x00,0x14,0x01,0x00,0x00,0x20,0x01,0x00,0x00, -0x1d,0x01,0x00,0x00,0x1b,0x03,0x00,0x00,0x8e,0x00,0x05,0x00, +0x1d,0x01,0x00,0x00,0x1a,0x03,0x00,0x00,0x8e,0x00,0x05,0x00, 0x14,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x20,0x01,0x00,0x00, 0x0c,0x01,0x00,0x00,0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00, 0x2a,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x00,0x00,0x00,0x00, @@ -30410,22 +30307,22 @@ unsigned char matmul_q4_0_f32_data[] = { 0x2c,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0x27,0x01,0x00,0x00, 0x2f,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x33,0x01,0x00,0x00, 0x32,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x3a,0x01,0x00,0x00,0xd0,0x02,0x00,0x00,0x38,0x01,0x00,0x00, +0x3a,0x01,0x00,0x00,0xcf,0x02,0x00,0x00,0x38,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xde,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x3c,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x3c,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, -0x06,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x3e,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x3f,0x00,0x00,0x00, 0xde,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x3f,0x01,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x42,0x01,0x00,0x00, -0xd1,0x02,0x00,0x00,0xa6,0x00,0x00,0x00,0xf6,0x00,0x04,0x00, +0xd0,0x02,0x00,0x00,0xa6,0x00,0x00,0x00,0xf6,0x00,0x04,0x00, 0x3e,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 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0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x50,0x01,0x00,0x00, -0xc0,0x02,0x00,0x00,0x78,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, +0xbf,0x02,0x00,0x00,0x79,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, 0xc1,0x00,0x00,0x00,0x52,0x01,0x00,0x00,0x50,0x01,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4d,0x01,0x00,0x00, +0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4d,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x4d,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 0xc1,0x00,0x00,0x00,0x53,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 0x3d,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, @@ -30443,17 +30340,17 @@ unsigned char matmul_q4_0_f32_data[] = { 0xfa,0x00,0x04,0x00,0x53,0x01,0x00,0x00,0x54,0x01,0x00,0x00, 0x75,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x54,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00, -0x7d,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x84,0x00,0x05,0x00, +0x7e,0x00,0x00,0x00,0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, 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+30358,173 @@ unsigned char matmul_q4_0_f32_data[] = { 0x61,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x74,0x01,0x00,0x00, 0x73,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x75,0x01,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0x7d,0x00,0x00,0x00, -0xd1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0x7e,0x00,0x00,0x00, +0xd0,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x7a,0x01,0x00,0x00,0x78,0x01,0x00,0x00,0x79,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00, -0x7a,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x7a,0x01,0x00,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x2c,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x5a,0x01,0x00,0x00, 0x7c,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x7e,0x01,0x00,0x00, 0x7d,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x55,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x55,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 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0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -31020,9 +30917,9 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x1f,0x01,0x00,0x00,0x00,0x00,0x00,0x41, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x24,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x24,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x26,0x01,0x00,0x00,0x01,0x01,0x00,0x00, 0x25,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x27,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -31031,17 +30928,17 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x01,0x01,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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+0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x5b,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x5a,0x01,0x00,0x00, @@ -31066,21 +30963,21 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xa8,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, 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+0x3a,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x67,0x01,0x00,0x00, +0x6f,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x4f,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x70,0x01,0x00,0x00, 0x6f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, 0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -31350,8 +31247,8 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x71,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x74,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x41,0x00,0x08,0x00,0x67,0x01,0x00,0x00,0x76,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x65,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x77,0x01,0x00,0x00,0x76,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x78,0x01,0x00,0x00, 0x77,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x2d,0x01,0x00,0x00, @@ -31360,7 +31257,7 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00, 0x58,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x67,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x65,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x7b,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x7f,0x01,0x00,0x00,0x7e,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x7f,0x01,0x00,0x00, @@ -31369,8 +31266,8 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x81,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x58,0x01,0x00,0x00, 0x83,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x67,0x01,0x00,0x00, -0x86,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x4f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x86,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x4f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x87,0x01,0x00,0x00, 0x86,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, 0x88,0x01,0x00,0x00,0x87,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -31379,8 +31276,8 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x88,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x8c,0x01,0x00,0x00,0x58,0x01,0x00,0x00,0x8b,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x67,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x65,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x90,0x01,0x00,0x00, 0x8f,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x2d,0x01,0x00,0x00, @@ -31389,7 +31286,7 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x94,0x01,0x00,0x00, 0x58,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x67,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x65,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x97,0x01,0x00,0x00,0x96,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0x97,0x01,0x00,0x00, @@ -31398,7 +31295,7 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x99,0x01,0x00,0x00,0x98,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x9c,0x01,0x00,0x00,0x58,0x01,0x00,0x00, 0x9b,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x67,0x01,0x00,0x00, -0x9e,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x34,0x00,0x00,0x00, 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matmul_q4_0_f32_aligned_len = 11156; unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x0d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x0c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x08,0x01,0x00,0x00, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x08,0x01,0x00,0x00, 0x28,0x01,0x00,0x00,0x5b,0x01,0x00,0x00,0x62,0x01,0x00,0x00, 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0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00, @@ -31928,10 +31825,10 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00, @@ -31950,9 +31847,9 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x15,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x1f,0x01,0x00,0x00, 0x00,0x00,0x00,0x41,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x24,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x25,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x24,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x25,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x24,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x26,0x01,0x00,0x00, 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-0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x57,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x59,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -31990,21 +31887,21 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x34,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x7b,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x7b,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 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-0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xcd,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xcc,0x01,0x00,0x00, @@ -32012,21 +31909,21 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0xcd,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd7,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xdf,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x0e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x41,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 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0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -32789,7 +32686,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -32799,10 +32696,10 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xf9,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x00,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -32821,9 +32718,9 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x01,0x00,0x00,0x00,0x00,0x00,0x41, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x23,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x23,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x23,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x25,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x24,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -32832,15 +32729,15 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x32,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x33,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x34,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x35,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x35,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x56,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x55,0x01,0x00,0x00, @@ -32848,7 +32745,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x56,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x57,0x01,0x00,0x00, 0x58,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x60,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x62,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x61,0x01,0x00,0x00, @@ -32856,25 +32753,25 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x6e,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x33,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x7b,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x7b,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x7c,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x7c,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x58,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x51,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x51,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xb4,0x02,0x00,0x00,0xb9,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x4e,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x50,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q4_0_f32_fp32_len = 10444; unsigned char matmul_q4_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf6,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -33497,47 +33394,47 @@ unsigned char matmul_q4_1_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 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-0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -33568,15 +33465,15 @@ unsigned char matmul_q4_1_f32_data[] = { 0x47,0x00,0x04,0x00,0x6a,0x01,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6a,0x01,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x55,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9b,0x02,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9c,0x02,0x00,0x00, +0x54,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x9a,0x02,0x00,0x00,0x06,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9b,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x9c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9c,0x02,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x02,0x00,0x00, +0x9b,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9b,0x02,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9d,0x02,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x9e,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x9d,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00, @@ -33587,77 +33484,77 @@ unsigned char matmul_q4_1_f32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00, -0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x11,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 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0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -33676,10 +33573,10 @@ unsigned char matmul_q4_1_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x00,0x01,0x00,0x00, @@ -33697,9 +33594,9 @@ unsigned char matmul_q4_1_f32_data[] = { 0x15,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x17,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x28,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x29,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x28,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x29,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x28,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x2a,0x01,0x00,0x00, 0x00,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x2b,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x2a,0x01,0x00,0x00, @@ -33708,15 +33605,15 @@ unsigned char matmul_q4_1_f32_data[] = { 0x04,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x3a,0x01,0x00,0x00, -0x39,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x39,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3d,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x5b,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x5d,0x01,0x00,0x00,0x00,0x01,0x00,0x00, @@ -33724,7 +33621,7 @@ unsigned char matmul_q4_1_f32_data[] = { 0x04,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0x5e,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x67,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x68,0x01,0x00,0x00,0x67,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x69,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -33732,27 +33629,27 @@ unsigned char matmul_q4_1_f32_data[] = { 0x6a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x75,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x00,0x01,0x00,0x00,0x82,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x84,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x3a,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x84,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x86,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x85,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x89,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8a,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xa9,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0xa8,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0xaa,0x01,0x00,0x00,0x07,0x00,0x00,0x00, 0xa9,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xba,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xc0,0x01,0x00,0x00, +0xba,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xc0,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xd6,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, @@ -33761,20 +33658,20 @@ unsigned char matmul_q4_1_f32_data[] = { 0xd7,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xe9,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x18,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x4d,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x55,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x9b,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x9c,0x02,0x00,0x00,0x9b,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0x9c,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x9d,0x02,0x00,0x00, -0x9e,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xa3,0x02,0x00,0x00,0x05,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb0,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0xe9,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x18,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x4c,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x0a,0x00,0x00,0x00,0x54,0x02,0x00,0x00,0x01,0x00,0x00,0x00, +0x1d,0x00,0x03,0x00,0x9a,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, +0x1e,0x00,0x03,0x00,0x9b,0x02,0x00,0x00,0x9a,0x02,0x00,0x00, +0x20,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, +0x9b,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x9c,0x02,0x00,0x00, +0x9d,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0xa2,0x02,0x00,0x00,0x05,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xaf,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc8,0x00,0x00,0x00, @@ -33784,175 +33681,175 @@ unsigned char matmul_q4_1_f32_data[] = { 0x07,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00, 0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, -0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 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0x23,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, @@ -34215,172 +34112,172 @@ unsigned char matmul_q4_1_f32_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x25,0x02,0x00,0x00, 0x2c,0x02,0x00,0x00,0x2f,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x2e,0x02,0x00,0x00,0x30,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0xed,0x02,0x00,0x00, +0x06,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0xec,0x02,0x00,0x00, 0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x0d,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x0f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x08,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x08,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x35,0x02,0x00,0x00, -0xeb,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xea,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x05,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x07,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x00,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x00,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xbb,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbd,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -34561,8 +34458,8 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0xc0,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -34572,9 +34469,9 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -34593,9 +34490,9 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x02,0x01,0x00,0x00,0x17,0x00,0x04,0x00,0x1a,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x2b,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x2a,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x2b,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x2c,0x01,0x00,0x00, @@ -34603,18 +34500,18 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x32,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0x3b,0x01,0x00,0x00,0x3a,0x01,0x00,0x00,0x39,0x00,0x00,0x00, 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+0x5e,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5f,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x5e,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x60,0x01,0x00,0x00, 0x01,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x20,0x00,0x04,0x00, @@ -34639,21 +34536,21 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x3b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xa7,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xa7,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xa8,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0xa8,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00, 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0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, @@ -34663,20 +34560,20 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x06,0x00,0x00,0x00,0x04,0x02,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0c,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x78,0x02,0x00,0x00, -0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, -0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbf,0x02,0x00,0x00, -0xbe,0x02,0x00,0x00,0x20,0x00,0x04,0x00,0xc0,0x02,0x00,0x00, -0x0c,0x00,0x00,0x00,0xbf,0x02,0x00,0x00,0x3b,0x00,0x04,0x00, -0xc0,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xc6,0x02,0x00,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x77,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbd,0x02,0x00,0x00, +0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, +0xbd,0x02,0x00,0x00,0x20,0x00,0x04,0x00,0xbf,0x02,0x00,0x00, +0x0c,0x00,0x00,0x00,0xbe,0x02,0x00,0x00,0x3b,0x00,0x04,0x00, +0xbf,0x02,0x00,0x00,0xc0,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xc5,0x02,0x00,0x00, 0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xd3,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, +0xd2,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 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0x11,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x16,0x01,0x00,0x00, -0x17,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xff,0x00,0x00,0x00, +0x17,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfb,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0xff,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x18,0x01,0x00,0x00, 0x17,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x19,0x01,0x00,0x00,0x18,0x01,0x00,0x00,0xc7,0x00,0x05,0x00, @@ -34883,43 +34780,43 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x32,0x01,0x00,0x00,0x39,0x01,0x00,0x00,0x2d,0x01,0x00,0x00, 0x35,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x39,0x01,0x00,0x00, 0x38,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x40,0x01,0x00,0x00,0xf8,0x02,0x00,0x00,0x3e,0x01,0x00,0x00, +0x40,0x01,0x00,0x00,0xf7,0x02,0x00,0x00,0x3e,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xdf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x42,0x01,0x00,0x00, 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0x75,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x32,0x01,0x00,0x00, @@ -34928,7 +34825,7 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00, 0x5d,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x6c,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x7c,0x01,0x00,0x00,0x7b,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x7c,0x01,0x00,0x00, @@ -34937,8 +34834,8 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x7e,0x01,0x00,0x00,0x7d,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, 0x80,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6c,0x01,0x00,0x00, -0x83,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x54,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x80,0x01,0x00,0x00, +0x83,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x54,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x80,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x84,0x01,0x00,0x00, 0x83,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, 0x85,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -34947,8 +34844,8 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x85,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x89,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x88,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x6c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00, -0x6a,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x54,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x6a,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x54,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x8c,0x01,0x00,0x00,0x8b,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x8d,0x01,0x00,0x00, 0x8c,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x32,0x01,0x00,0x00, @@ -34957,8 +34854,8 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00, 0x5d,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x6c,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x6a,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x94,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x32,0x01,0x00,0x00,0x96,0x01,0x00,0x00, @@ -34966,7 +34863,7 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x96,0x01,0x00,0x00,0x95,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, 0x98,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x6c,0x01,0x00,0x00, -0x9b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x9b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x54,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x9c,0x01,0x00,0x00, 0x9b,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, @@ -34976,7 +34873,7 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x9d,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xa1,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0xa0,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x6c,0x01,0x00,0x00,0xa3,0x01,0x00,0x00, -0x6a,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x54,0x01,0x00,0x00, +0x6a,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x54,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x80,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0xa3,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0xa5,0x01,0x00,0x00, @@ -34984,162 +34881,162 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0xa6,0x01,0x00,0x00,0x62,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0xa6,0x01,0x00,0x00,0xa5,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xab,0x01,0x00,0x00, -0xf9,0x02,0x00,0x00,0xa9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xf8,0x02,0x00,0x00,0xa9,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0x42,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x44,0x01,0x00,0x00, 0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0xac,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xaf,0x01,0x00,0x00,0xfc,0x02,0x00,0x00,0xad,0x01,0x00,0x00, +0xaf,0x01,0x00,0x00,0xfb,0x02,0x00,0x00,0xad,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00, -0x00,0x03,0x00,0x00,0xb0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xff,0x02,0x00,0x00,0xb0,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xb4,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xb4,0x01,0x00,0x00, -0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00,0x02,0x03,0x00,0x00, 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= { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x67,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x67,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x4e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x4d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x95,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x97,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x97,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -35508,10 +35405,10 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfa,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -35529,9 +35426,9 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x17,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x29,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x29,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2a,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x2c,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x2b,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -35540,17 +35437,17 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x38,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x39,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x3a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x3b,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3b,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x5e,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x5d,0x01,0x00,0x00, @@ -35569,21 +35466,21 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x80,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x39,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x81,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x80,0x01,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x82,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x81,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xa5,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0xa6,0x01,0x00,0x00,0x07,0x00,0x00,0x00, 0xa5,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xb6,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xb6,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd1,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0xbc,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xd2,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0xd1,0x01,0x00,0x00,0x20,0x00,0x04,0x00, @@ -35591,20 +35488,20 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x46,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x45,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 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+0x06,0x00,0x00,0x00,0xa8,0x02,0x00,0x00,0x84,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00, 0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00, @@ -35614,174 +35511,174 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 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+36173,15 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x68,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x68,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x4e,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x4d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x95,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x94,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x95,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x97,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -36406,9 +36303,9 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x01,0x01,0x00,0x00,0x17,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x2a,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x29,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x2b,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x2a,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x2b,0x01,0x00,0x00, @@ -36416,50 +36313,50 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x30,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00, 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+36364,20 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xdc,0x01,0x00,0x00, 0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe4,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x46,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x45,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0x4e,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x94,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x95,0x02,0x00,0x00,0x94,0x02,0x00,0x00,0x20,0x00,0x04,0x00, -0x96,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x95,0x02,0x00,0x00, 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+0xf9,0x00,0x02,0x00,0x7b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x7d,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xb5,0x02,0x00,0x00,0xc1,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x73,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x75,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5e,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb7,0x02,0x00,0x00, +0xbf,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x5b,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5d,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x56,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xb9,0x02,0x00,0x00,0xbe,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x53,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x55,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q4_1_f32_fp32_len = 10512; unsigned char matmul_q4_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x8e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x8d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -37088,47 +36985,47 @@ unsigned char matmul_q4_k_f32_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, 0x21,0x01,0x00,0x00,0x78,0x01,0x00,0x00,0xc6,0x01,0x00,0x00, -0xd1,0x01,0x00,0x00,0xbc,0x02,0x00,0x00,0x05,0x03,0x00,0x00, +0xd1,0x01,0x00,0x00,0xbb,0x02,0x00,0x00,0x04,0x03,0x00,0x00, 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+0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x60,0x00,0x00,0x00, +0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, @@ -37160,15 +37057,15 @@ unsigned char matmul_q4_k_f32_data[] = { 0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xbc,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x02,0x03,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x03,0x00,0x00, +0xbb,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x01,0x03,0x00,0x00,0x06,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x02,0x03,0x00,0x00, 0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x03,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x03,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x03,0x00,0x00, +0x02,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x02,0x03,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x03,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x05,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x04,0x03,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00, @@ -37179,77 +37076,77 @@ unsigned char matmul_q4_k_f32_data[] = { 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x10,0x00, -0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -37258,7 +37155,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -37268,10 +37165,10 @@ unsigned char matmul_q4_k_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x80,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -37295,14 +37192,14 @@ unsigned char matmul_q4_k_f32_data[] = { 0x18,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x28,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x30,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x01,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x35,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x01,0x00,0x00, 0x3f,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x37,0x01,0x00,0x00, 0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x74,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x75,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x74,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x74,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x76,0x01,0x00,0x00,0x17,0x01,0x00,0x00, 0x75,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x77,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x76,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -37311,15 +37208,15 @@ unsigned char matmul_q4_k_f32_data[] = { 0x17,0x01,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa0,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0xa0,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0xa1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa2,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xa3,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xa3,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0xc4,0x01,0x00,0x00,0x17,0x01,0x00,0x00,0xc3,0x01,0x00,0x00, @@ -37327,7 +37224,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0xc4,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0xc5,0x01,0x00,0x00, 0xc6,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xca,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xce,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xcf,0x01,0x00,0x00,0xce,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xd0,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xcf,0x01,0x00,0x00, @@ -37335,26 +37232,26 @@ unsigned char matmul_q4_k_f32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xdc,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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-0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x10,0x02,0x00,0x00, +0xf4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x10,0x02,0x00,0x00, 0x17,0x01,0x00,0x00,0x0f,0x02,0x00,0x00,0x20,0x00,0x04,0x00, 0x11,0x02,0x00,0x00,0x07,0x00,0x00,0x00,0x10,0x02,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x21,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x27,0x02,0x00,0x00,0x07,0x00,0x00,0x00, 0x17,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x3d,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, @@ -37364,20 +37261,20 @@ unsigned char matmul_q4_k_f32_data[] = { 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0x06,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x0f,0x01,0x00,0x00, 0x12,0x01,0x00,0x00,0x41,0x00,0x07,0x00,0x23,0x01,0x00,0x00, -0x24,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x24,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfb,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x14,0x01,0x00,0x00,0x26,0x01,0x00,0x00, 0x25,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00, @@ -37570,25 +37467,25 @@ unsigned char matmul_q4_k_f32_data[] = { 0xfa,0x00,0x04,0x00,0x29,0x01,0x00,0x00,0x2a,0x01,0x00,0x00, 0x45,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x2a,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00, -0x21,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, +0x21,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 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+0x27,0x03,0x00,0x00,0x2c,0x03,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xc3,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q4_k_f32_len = 12072; unsigned char matmul_q4_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa8,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xa7,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -38099,47 +37996,47 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, 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+0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, @@ -38174,15 +38071,15 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xd1,0x01,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xde,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x24,0x03,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x25,0x03,0x00,0x00, +0xdd,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x23,0x03,0x00,0x00,0x06,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x24,0x03,0x00,0x00, 0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x25,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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-0xbb,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xbb,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x50,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xbc,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xbb,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbd,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -38273,8 +38170,8 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xc0,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -38284,9 +38181,9 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x02,0x01,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -38310,14 +38207,14 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x0c,0x00,0x00,0x00,0x19,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x31,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x1a,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x1a,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x36,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x38,0x01,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x4e,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x4e,0x01,0x00,0x00, 0x0f,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x75,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x76,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x75,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x76,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x75,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x77,0x01,0x00,0x00, 0x18,0x01,0x00,0x00,0x76,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x78,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x77,0x01,0x00,0x00, @@ -38326,17 +38223,17 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x04,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0xa2,0x01,0x00,0x00, -0xa1,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0xa1,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xc5,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xc7,0x01,0x00,0x00,0x18,0x01,0x00,0x00, @@ -38360,21 +38257,21 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x0d,0x02,0x00,0x00, 0x51,0x00,0x00,0x00,0xa2,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x0e,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x0d,0x02,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x0d,0x02,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x0f,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x0e,0x02,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x0e,0x02,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x12,0x02,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x13,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0x13,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x16,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x31,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x32,0x02,0x00,0x00, +0x16,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x31,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x32,0x02,0x00,0x00, 0x18,0x01,0x00,0x00,0x31,0x02,0x00,0x00,0x20,0x00,0x04,0x00, 0x33,0x02,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x02,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x49,0x02,0x00,0x00,0x07,0x00,0x00,0x00, 0x18,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -38384,20 +38281,20 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6a,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x72,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd6,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd5,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0xde,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x24,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x25,0x03,0x00,0x00,0x24,0x03,0x00,0x00,0x20,0x00,0x04,0x00, -0x26,0x03,0x00,0x00,0x0c,0x00,0x00,0x00,0x25,0x03,0x00,0x00, -0x3b,0x00,0x04,0x00,0x26,0x03,0x00,0x00,0x27,0x03,0x00,0x00, -0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x2c,0x03,0x00,0x00,0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0x84,0x00,0x00,0x00, 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-0x25,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x25,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfc,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x15,0x01,0x00,0x00,0x27,0x01,0x00,0x00, 0x26,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc2,0x00,0x00,0x00, @@ -38590,25 +38487,25 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xfa,0x00,0x04,0x00,0x2a,0x01,0x00,0x00,0x2b,0x01,0x00,0x00, 0x46,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x2b,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x31,0x01,0x00,0x00,0x32,0x01,0x00,0x00, -0x22,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, +0x22,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, 0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00, 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-0x7c,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x42,0x01,0x00,0x00, -0x41,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x13,0x00,0x00,0x00, +0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x42,0x01,0x00,0x00, +0x41,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00, 0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00,0x36,0x01,0x00,0x00, 0x72,0x00,0x04,0x00,0x38,0x01,0x00,0x00,0x44,0x01,0x00,0x00, 0x43,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x1a,0x01,0x00,0x00, @@ -38617,26 +38514,26 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00, 0x0d,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x31,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x22,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x49,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x4a,0x01,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 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-0xc4,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x41,0x00,0x08,0x00, +0xc4,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0xd3,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,0xd1,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xdc,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0xdc,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x8b,0x01,0x00,0x00,0xde,0x01,0x00,0x00, @@ -38756,8 +38653,8 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xde,0x01,0x00,0x00,0xdd,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,0xc4,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0xd3,0x01,0x00,0x00, -0xe2,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xbb,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xe2,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xbb,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xe3,0x01,0x00,0x00, 0xe2,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0xe4,0x01,0x00,0x00,0xe3,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -38766,8 +38663,8 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xe4,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xe8,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,0xe7,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0xd3,0x01,0x00,0x00,0xea,0x01,0x00,0x00, -0xd1,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, +0xd1,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xeb,0x01,0x00,0x00,0xea,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0xec,0x01,0x00,0x00, 0xeb,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x8b,0x01,0x00,0x00, @@ -38776,8 +38673,8 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x01,0x00,0x00, 0xc4,0x01,0x00,0x00,0x29,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0xd3,0x01,0x00,0x00,0xf1,0x01,0x00,0x00,0xd1,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xf2,0x01,0x00,0x00,0xf1,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0xf3,0x01,0x00,0x00,0xf2,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x8b,0x01,0x00,0x00,0xf4,0x01,0x00,0x00, @@ -38785,8 +38682,8 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xf4,0x01,0x00,0x00,0xf3,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf7,0x01,0x00,0x00,0xc4,0x01,0x00,0x00, 0xf6,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0xd3,0x01,0x00,0x00, -0xf9,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0xf9,0x01,0x00,0x00,0xd1,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xfa,0x01,0x00,0x00, 0xf9,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0xfb,0x01,0x00,0x00,0xfa,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -38795,7 +38692,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xfb,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xff,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,0xfe,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0xd3,0x01,0x00,0x00,0x01,0x02,0x00,0x00, -0xd1,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, +0xd1,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x02,0x02,0x00,0x00,0x01,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x03,0x02,0x00,0x00, @@ -38805,170 +38702,170 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x07,0x02,0x00,0x00, 0xc4,0x01,0x00,0x00,0x06,0x02,0x00,0x00,0x41,0x00,0x08,0x00, 0xd3,0x01,0x00,0x00,0x09,0x02,0x00,0x00,0xd1,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xbb,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, 0xe7,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x0a,0x02,0x00,0x00,0x09,0x02,0x00,0x00,0x73,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0x0b,0x02,0x00,0x00,0x0a,0x02,0x00,0x00, 0x41,0x00,0x05,0x00,0x8b,0x01,0x00,0x00,0x0c,0x02,0x00,0x00, 0xc9,0x01,0x00,0x00,0x07,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x0c,0x02,0x00,0x00,0x0b,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x11,0x02,0x00,0x00,0x5f,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0x11,0x02,0x00,0x00,0x5e,0x03,0x00,0x00, 0x0f,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xa9,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xab,0x01,0x00,0x00,0xe0,0x00,0x04,0x00, 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+38881,172 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0xae,0x02,0x00,0x00, 0xb5,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0xb7,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0x76,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00,0x75,0x03,0x00,0x00, 0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x96,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x98,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x91,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x91,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbe,0x02,0x00,0x00, -0x74,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x73,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x89,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x89,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xc0,0x02,0x00,0x00,0x72,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, 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+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x03,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x05,0x03,0x00,0x00,0xf9,0x00,0x02,0x00, +0xee,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xee,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x03,0x00,0x00, +0x4f,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xeb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xed,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0xe6,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xe6,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x49,0x03,0x00,0x00,0x4e,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xe3,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xe5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q4_k_f32_aligned_len = 12648; unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x7d,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -39157,47 +39054,47 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x78,0x01,0x00,0x00, -0xc6,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xb4,0x02,0x00,0x00, -0xfd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0x22,0x01,0x00,0x00,0x78,0x01,0x00,0x00, +0xc6,0x01,0x00,0x00,0xcd,0x01,0x00,0x00,0xb3,0x02,0x00,0x00, +0xfc,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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-0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x53,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -39229,15 +39126,15 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xcd,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xcd,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xb4,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xfa,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0xb3,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf9,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0xfb,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0xfa,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0xfa,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0xfb,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xfd,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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-0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -39339,10 +39236,10 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x20,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x01,0x00,0x00, @@ -39364,14 +39261,14 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x22,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x24,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x19,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x1a,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x1a,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x37,0x01,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, 0x0f,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x74,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x75,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x74,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x75,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x74,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x76,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x77,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x76,0x01,0x00,0x00, @@ -39380,17 +39277,17 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, -0x9e,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x9e,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbd,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc2,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc3,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xc2,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xc4,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -39409,21 +39306,21 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0xe6,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x9f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xe6,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xe6,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xe8,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xe7,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0xe7,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xeb,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xef,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x0a,0x02,0x00,0x00, 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+39588,106 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x5f,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x30,0x01,0x00,0x00, -0x63,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x63,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00,0x64,0x01,0x00,0x00, 0x63,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x81,0x00,0x00,0x00, +0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x82,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x66,0x01,0x00,0x00, 0x65,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x67,0x01,0x00,0x00,0x60,0x01,0x00,0x00, 0x66,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2b,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x2b,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 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+0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xc1,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xc3,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0xbc,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbc,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1f,0x03,0x00,0x00, +0x24,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xb9,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xbb,0x02,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q4_k_f32_aligned_fp32_len = 11900; unsigned char matmul_q4_k_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x87,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x86,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -40153,47 +40050,47 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 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0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -40244,77 +40141,77 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x11,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x15,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 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-0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xb9,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x53,0x00,0x00,0x00, +0xb9,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x54,0x00,0x00,0x00, 0xb8,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xba,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x4f,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xba,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x50,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -40323,8 +40220,8 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -40334,9 +40231,9 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -40360,14 +40257,14 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x18,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x19,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x19,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x37,0x01,0x00,0x00,0x08,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, 0x0f,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x74,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x75,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x74,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x75,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x74,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x76,0x01,0x00,0x00, 0xc3,0x00,0x00,0x00,0x75,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x77,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x76,0x01,0x00,0x00, @@ -40376,15 +40273,15 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x9e,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, -0x9e,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x9e,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa2,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc1,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xc0,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xc2,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, @@ -40392,7 +40289,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0xc3,0x01,0x00,0x00,0xc4,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xcc,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xcd,0x01,0x00,0x00,0xcc,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0xce,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -40400,25 +40297,25 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0xcf,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xda,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe2,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe8,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x39,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe9,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xe8,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xe8,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xec,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xed,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0xed,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf0,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x0b,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x0c,0x02,0x00,0x00, +0xf0,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x0b,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x0c,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x0b,0x02,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x02,0x00,0x00,0x07,0x00,0x00,0x00,0x0c,0x02,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x1d,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x38,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0x39,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, @@ -40427,20 +40324,20 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x86,0x00,0x00,0x00, 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+0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xb4,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xfa,0x02,0x00,0x00, +0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xfb,0x02,0x00,0x00, +0xfa,0x02,0x00,0x00,0x20,0x00,0x04,0x00,0xfc,0x02,0x00,0x00, +0x0c,0x00,0x00,0x00,0xfb,0x02,0x00,0x00,0x3b,0x00,0x04,0x00, +0xfc,0x02,0x00,0x00,0xfd,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x02,0x03,0x00,0x00, 0x05,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x10,0x03,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, +0x0f,0x03,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0xc8,0x00,0x00,0x00,0xc9,0x00,0x00,0x00,0x07,0x00,0x00,0x00, @@ -40450,158 +40347,158 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 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+0xe7,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0xea,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xec,0x00,0x00,0x00,0x39,0x03,0x00,0x00,0xeb,0x00,0x00,0x00, +0xec,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0xeb,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xee,0x00,0x00,0x00, -0xec,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x84,0x00,0x05,0x00, +0xec,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0xe7,0x00,0x00,0x00, 0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xf6,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xf6,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, 0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xee,0x00,0x00,0x00, @@ -40623,8 +40520,8 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x13,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x12,0x01,0x00,0x00, 0x41,0x00,0x07,0x00,0x23,0x01,0x00,0x00,0x24,0x01,0x00,0x00, -0x21,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00, +0x21,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x14,0x01,0x00,0x00,0x26,0x01,0x00,0x00,0x25,0x01,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x29,0x01,0x00,0x00, @@ -40633,25 +40530,25 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x29,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x45,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x2a,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x21,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 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0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x3f,0x01,0x00,0x00, 0x3e,0x01,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x40,0x01,0x00,0x00,0x3f,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00, -0xc7,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x42,0x01,0x00,0x00, +0x15,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x40,0x01,0x00,0x00, +0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x42,0x01,0x00,0x00, 0x41,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x72,0x00,0x04,0x00, 0x37,0x01,0x00,0x00,0x43,0x01,0x00,0x00,0x42,0x01,0x00,0x00, 0x7c,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x44,0x01,0x00,0x00, @@ -40659,26 +40556,26 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x45,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x0c,0x01,0x00,0x00, 0x28,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x30,0x01,0x00,0x00, -0x49,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x49,0x01,0x00,0x00,0x21,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 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-0x47,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x46,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -41154,47 +41051,47 @@ unsigned char matmul_q5_0_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x50,0x01,0x00,0x00, -0x83,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x79,0x02,0x00,0x00, -0xc2,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0x09,0x01,0x00,0x00,0x50,0x01,0x00,0x00, +0x83,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x78,0x02,0x00,0x00, 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+41123,15 @@ unsigned char matmul_q5_0_f32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x8e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x79,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbf,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x78,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xbe,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0xc0,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0xbf,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0xc0,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xc2,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xc2,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0xbf,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0xc1,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xc1,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -41245,77 +41142,77 @@ unsigned char matmul_q5_0_f32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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+0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xbb,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -41324,8 +41221,8 @@ unsigned char matmul_q5_0_f32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -41335,9 +41232,9 @@ unsigned char matmul_q5_0_f32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -41356,7 +41253,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x0b,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x01,0x01,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x15,0x01,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x15,0x01,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x27,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x30,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x03,0x01,0x00,0x00, @@ -41364,9 +41261,9 @@ unsigned char matmul_q5_0_f32_data[] = { 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0x47,0x01,0x00,0x00,0x00,0x00,0x80,0x41,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x4c,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x4e,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x4d,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x4e,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x4f,0x01,0x00,0x00, @@ -41374,51 +41271,51 @@ unsigned char matmul_q5_0_f32_data[] = { 0x55,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x39,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5f,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x5e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 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0x1c,0x00,0x04,0x00,0xcd,0x01,0x00,0x00,0x00,0x01,0x00,0x00, 0xcc,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xce,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0xcd,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xde,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xe4,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfa,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, @@ -41428,21 +41325,21 @@ unsigned char matmul_q5_0_f32_data[] = { 0x06,0x00,0x00,0x00,0x05,0x02,0x00,0x00,0x86,0x00,0x00,0x00, 0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0d,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 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0x2b,0x01,0x00,0x00,0x2a,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x09,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x85,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xfe,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x03,0x01,0x00,0x00, 0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00, @@ -41660,7 +41557,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x50,0x00,0x05,0x00,0x34,0x01,0x00,0x00,0x46,0x01,0x00,0x00, 0x3e,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x83,0x00,0x05,0x00, 0x34,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x46,0x01,0x00,0x00, -0x46,0x03,0x00,0x00,0x8e,0x00,0x05,0x00,0x34,0x01,0x00,0x00, +0x45,0x03,0x00,0x00,0x8e,0x00,0x05,0x00,0x34,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x0e,0x01,0x00,0x00, 0x51,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x53,0x01,0x00,0x00, 0x4b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x73,0x00,0x04,0x00, @@ -41676,31 +41573,31 @@ unsigned char matmul_q5_0_f32_data[] = { 0x5c,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x58,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x63,0x01,0x00,0x00, -0xfb,0x02,0x00,0x00,0x61,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xfa,0x02,0x00,0x00,0x61,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xde,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0x65,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x65,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00, -0xfc,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xde,0x00,0x00,0x00, +0xfb,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xde,0x00,0x00,0x00, 0xac,0x01,0x00,0x00,0x68,0x01,0x00,0x00,0xb0,0x00,0x05,0x00, -0xc1,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,0xfc,0x02,0x00,0x00, +0xc1,0x00,0x00,0x00,0x6b,0x01,0x00,0x00,0xfb,0x02,0x00,0x00, 0xa6,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x67,0x01,0x00,0x00, 0x68,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 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+0xf9,0x00,0x02,0x00,0x7e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x80,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q5_0_f32_len = 11076; unsigned char matmul_q5_0_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x62,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x61,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -42082,47 +41979,47 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x51,0x01,0x00,0x00, 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+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -42157,15 +42054,15 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x8e,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x8e,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe2,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x9b,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe1,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0xe3,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0xe3,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00, @@ -42257,7 +42154,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -42267,10 +42164,10 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfa,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -42288,7 +42185,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x0a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x12,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x02,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x02,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x16,0x01,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x31,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -42296,9 +42193,9 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x00,0x00,0x80,0x41, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x4e,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x01,0x01,0x00,0x00, 0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x50,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x4f,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -42307,17 +42204,17 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x01,0x01,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x5e,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x60,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x62,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x61,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x83,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x84,0x01,0x00,0x00,0x01,0x01,0x00,0x00,0x83,0x01,0x00,0x00, @@ -42342,21 +42239,21 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xcb,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xcc,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xcb,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xcb,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xcd,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xd0,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xd1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0xd1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xd4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xef,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xf0,0x01,0x00,0x00, +0xd4,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xef,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xf0,0x01,0x00,0x00, 0x01,0x01,0x00,0x00,0xef,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xf1,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0xf0,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x01,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x07,0x02,0x00,0x00,0x07,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x1d,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -42366,21 +42263,21 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x28,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x30,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5f,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x94,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x93,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, -0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 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-0x34,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x8e,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x78,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x99,0x01,0x00,0x00,0x98,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x9a,0x01,0x00,0x00, 0x99,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x56,0x01,0x00,0x00, @@ -42659,7 +42556,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x9d,0x01,0x00,0x00, 0x81,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x90,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,0xa0,0x01,0x00,0x00, @@ -42668,8 +42565,8 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0x81,0x01,0x00,0x00, 0xa4,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x90,0x01,0x00,0x00, -0xa7,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x78,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, +0xa7,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x78,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xa8,0x01,0x00,0x00, 0xa7,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, 0xa9,0x01,0x00,0x00,0xa8,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -42678,8 +42575,8 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xa9,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xad,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0xac,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x90,0x01,0x00,0x00,0xaf,0x01,0x00,0x00, -0x8e,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x78,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x8e,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x78,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xb0,0x01,0x00,0x00,0xaf,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0xb1,0x01,0x00,0x00, 0xb0,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x56,0x01,0x00,0x00, @@ -42688,8 +42585,8 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb5,0x01,0x00,0x00, 0x81,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x90,0x01,0x00,0x00,0xb7,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x78,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xb8,0x01,0x00,0x00,0xb7,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0xb8,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x56,0x01,0x00,0x00,0xba,0x01,0x00,0x00, @@ -42697,7 +42594,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xba,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xbd,0x01,0x00,0x00,0x81,0x01,0x00,0x00, 0xbc,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x90,0x01,0x00,0x00, -0xbf,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0xbf,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x78,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xc0,0x01,0x00,0x00, 0xbf,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, @@ -42707,7 +42604,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0xc1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xc5,0x01,0x00,0x00,0x81,0x01,0x00,0x00,0xc4,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x90,0x01,0x00,0x00,0xc7,0x01,0x00,0x00, -0x8e,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x78,0x01,0x00,0x00, +0x8e,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x78,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 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11668; unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x38,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x37,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0x0a,0x01,0x00,0x00, 0x51,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x8b,0x01,0x00,0x00, -0x72,0x02,0x00,0x00,0xbb,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 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0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, @@ -43130,15 +43027,15 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x89,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x8b,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x8b,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x72,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x71,0x02,0x00,0x00, 0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xb8,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0xb9,0x02,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb9,0x02,0x00,0x00, +0xb7,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0xb8,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xb8,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00, @@ -43240,10 +43137,10 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00, @@ -43262,16 +43159,16 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x20,0x00,0x04,0x00,0x0c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x12,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x10,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x01,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x28,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x31,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x17,0x00,0x04,0x00, 0x35,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x48,0x01,0x00,0x00, 0x00,0x00,0x80,0x41,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x4d,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x4e,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x4d,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x4e,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x4d,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x4f,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x4f,0x01,0x00,0x00, @@ -43280,17 +43177,17 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x5d,0x01,0x00,0x00, -0x5c,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x5c,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x60,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x80,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x82,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -43309,21 +43206,21 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x5d,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xa4,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xa4,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xa5,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0xa5,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xaa,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 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0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, @@ -43356,177 +43253,177 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, -0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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-0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x02,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x02,0x01,0x00,0x00, 0x1a,0x01,0x00,0x00,0x19,0x01,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x1b,0x01,0x00,0x00,0x1a,0x01,0x00,0x00, 0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x01,0x00,0x00, @@ -43536,16 +43433,16 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00,0xa9,0x00,0x00,0x00, 0xc7,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00, 0x23,0x01,0x00,0x00,0xfa,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00, +0x15,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x24,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x29,0x01,0x00,0x00, 0xff,0x00,0x00,0x00,0x28,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x2a,0x01,0x00,0x00,0x1c,0x01,0x00,0x00, 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-0x4a,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x37,0x03,0x00,0x00, +0x4a,0x01,0x00,0x00,0x47,0x01,0x00,0x00,0x36,0x03,0x00,0x00, 0x8e,0x00,0x05,0x00,0x35,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, 0x4a,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x51,0x00,0x05,0x00, 0xc4,0x00,0x00,0x00,0x54,0x01,0x00,0x00,0x4c,0x01,0x00,0x00, @@ -43577,48 +43474,48 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x5b,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x58,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0x5b,0x01,0x00,0x00,0x5a,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x01,0x00,0x00, -0xf4,0x02,0x00,0x00,0x60,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xf3,0x02,0x00,0x00,0x60,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xdd,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xdf,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0x64,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x64,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0x06,0x00,0x00,0x00, -0xf5,0x02,0x00,0x00,0x3e,0x00,0x00,0x00,0xdf,0x00,0x00,0x00, +0xf4,0x02,0x00,0x00,0x3f,0x00,0x00,0x00,0xdf,0x00,0x00,0x00, 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0xc4,0x00,0x00,0x00,0x43,0x02,0x00,0x00,0x42,0x02,0x00,0x00, @@ -43801,220 +43698,220 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x43,0x02,0x00,0x00, 0x49,0x02,0x00,0x00,0x4c,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x4b,0x02,0x00,0x00,0x4d,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00,0x0c,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0x50,0x02,0x00,0x00,0x0b,0x03,0x00,0x00, 0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x2c,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x2e,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x27,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x52,0x02,0x00,0x00, -0x0a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x09,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x24,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x26,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x1f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x1f,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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0x84,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -44144,7 +44041,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -44154,10 +44051,10 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xf9,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x00,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -44175,7 +44072,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x09,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x11,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x01,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x01,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x15,0x01,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -44183,9 +44080,9 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0x47,0x01,0x00,0x00,0x00,0x00,0x80,0x41, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x4c,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x4c,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x4c,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x4d,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x4f,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x4e,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -44194,15 +44091,15 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x5b,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x5b,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x5d,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5f,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x5e,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x5e,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x7f,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x7e,0x01,0x00,0x00, @@ -44210,7 +44107,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x7f,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x80,0x01,0x00,0x00, 0x81,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x89,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x8b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x8a,0x01,0x00,0x00, @@ -44218,25 +44115,25 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x97,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0xa4,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xa4,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0xa5,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0xa5,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xaa,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xad,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xc9,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0xc8,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xca,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0xc9,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xda,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf5,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xf6,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0xf5,0x01,0x00,0x00, @@ -44244,21 +44141,21 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0xf6,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x08,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x6a,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0xba,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0xb9,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0xba,0x02,0x00,0x00, -0xbb,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xc0,0x02,0x00,0x00,0x05,0x00,0x00,0x00, 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0x45,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0x50,0x00,0x05,0x00, 0x34,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x3e,0x01,0x00,0x00, 0x45,0x01,0x00,0x00,0x83,0x00,0x05,0x00,0x34,0x01,0x00,0x00, -0x49,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x3f,0x03,0x00,0x00, +0x49,0x01,0x00,0x00,0x46,0x01,0x00,0x00,0x3e,0x03,0x00,0x00, 0x8e,0x00,0x05,0x00,0x34,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, 0x49,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x51,0x00,0x05,0x00, 0xc3,0x00,0x00,0x00,0x53,0x01,0x00,0x00,0x4b,0x01,0x00,0x00, @@ -44490,31 +44387,31 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x5a,0x01,0x00,0x00,0x50,0x01,0x00,0x00,0x57,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0x5a,0x01,0x00,0x00,0x59,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00, -0xf4,0x02,0x00,0x00,0x5f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xf3,0x02,0x00,0x00,0x5f,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xde,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0x63,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -45063,7 +44960,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -45073,10 +44970,10 @@ unsigned char matmul_q5_1_f32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xf9,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x00,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -45097,9 +44994,9 @@ unsigned char matmul_q5_1_f32_data[] = { 0x0c,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x17,0x00,0x04,0x00, 0x2f,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x47,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x47,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x47,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x49,0x01,0x00,0x00,0x00,0x01,0x00,0x00, 0x48,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x4a,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -45108,15 +45005,15 @@ unsigned char matmul_q5_1_f32_data[] = { 0x00,0x01,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x58,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x58,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x59,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x5a,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x5b,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x5b,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x7c,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x7b,0x01,0x00,0x00, @@ -45124,7 +45021,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x7c,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x7d,0x01,0x00,0x00, 0x7e,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x86,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x87,0x01,0x00,0x00,0x86,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x88,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x87,0x01,0x00,0x00, @@ -45132,26 +45029,26 @@ unsigned char matmul_q5_1_f32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x94,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9d,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x00,0x01,0x00,0x00,0xa1,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x39,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00, 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+0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xdf,0x01,0x00,0x00,0x07,0x00,0x00,0x00, 0x00,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf5,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, @@ -45161,20 +45058,20 @@ unsigned char matmul_q5_1_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x00,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x08,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 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+45782,15 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x87,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x89,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x89,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x96,0x02,0x00,0x00, 0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xdd,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0xde,0x02,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xde,0x02,0x00,0x00, +0xdc,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0xdd,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0xdd,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0xde,0x02,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xe0,0x02,0x00,0x00,0x22,0x00,0x00,0x00, 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0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x01,0x01,0x00,0x00, @@ -46019,9 +45916,9 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x2c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x02,0x01,0x00,0x00, 0x17,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x48,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x49,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x48,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x49,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x48,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x4a,0x01,0x00,0x00, 0x01,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x4b,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x4a,0x01,0x00,0x00, @@ -46030,17 +45927,17 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x04,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x5a,0x01,0x00,0x00, -0x59,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x59,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5c,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x7d,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x7f,0x01,0x00,0x00,0x01,0x01,0x00,0x00, @@ -46066,21 +45963,21 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0xc6,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x5a,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x01,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc8,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xc7,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xcb,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcc,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 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-0x89,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x73,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x89,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x94,0x01,0x00,0x00,0x93,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0x95,0x01,0x00,0x00, 0x94,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x51,0x01,0x00,0x00, @@ -46377,7 +46274,7 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x98,0x01,0x00,0x00, 0x7c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x8b,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x89,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x9b,0x01,0x00,0x00,0x9a,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0x9c,0x01,0x00,0x00,0x9b,0x01,0x00,0x00, @@ -46386,8 +46283,8 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x9d,0x01,0x00,0x00,0x9c,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x7c,0x01,0x00,0x00, 0x9f,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x8b,0x01,0x00,0x00, -0xa2,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x73,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, +0xa2,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x73,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, 0xa2,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, 0xa4,0x01,0x00,0x00,0xa3,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -46396,8 +46293,8 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0xa4,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xa8,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0xa7,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x8b,0x01,0x00,0x00,0xaa,0x01,0x00,0x00, -0x89,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x73,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x89,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x73,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0xaa,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00,0xac,0x01,0x00,0x00, 0xab,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x51,0x01,0x00,0x00, @@ -46406,8 +46303,8 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00, 0x7c,0x01,0x00,0x00,0xaf,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x8b,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,0x89,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x73,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xb3,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x01,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0xb3,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x51,0x01,0x00,0x00,0xb5,0x01,0x00,0x00, @@ -46415,7 +46312,7 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0xb5,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xb8,0x01,0x00,0x00,0x7c,0x01,0x00,0x00, 0xb7,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x8b,0x01,0x00,0x00, -0xba,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0xba,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x73,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xbb,0x01,0x00,0x00, 0xba,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x01,0x01,0x00,0x00, @@ -46425,7 +46322,7 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0xbc,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xc0,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0xbf,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x8b,0x01,0x00,0x00,0xc2,0x01,0x00,0x00, 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0xc0,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -46959,9 +46856,9 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -46983,9 +46880,9 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x02,0x01,0x00,0x00,0x17,0x00,0x04,0x00,0x30,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x4a,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x49,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x4b,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x4a,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x4b,0x01,0x00,0x00, @@ -46993,18 +46890,18 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x50,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0x58,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x39,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x58,0x01,0x00,0x00,0x57,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x59,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x58,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5a,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x59,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5b,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x5a,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x76,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x7b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x76,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x7b,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7c,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x7b,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x7d,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x20,0x00,0x04,0x00, @@ -47022,21 +46919,21 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 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0xc4,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,0x3d,0x02,0x00,0x00, @@ -47509,220 +47406,220 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x3e,0x02,0x00,0x00, 0x44,0x02,0x00,0x00,0x47,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x46,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x07,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x06,0x03,0x00,0x00, 0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x29,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x22,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00, -0x05,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x04,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x1f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x21,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x1a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x1a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -47852,7 +47749,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -47862,10 +47759,10 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xf9,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfd,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x00,0x01,0x00,0x00, @@ -47886,9 +47783,9 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x2b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x17,0x00,0x04,0x00,0x2f,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x47,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x48,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x47,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x48,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x47,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x49,0x01,0x00,0x00, 0xc3,0x00,0x00,0x00,0x48,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x4a,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x49,0x01,0x00,0x00, @@ -47897,15 +47794,15 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x57,0x01,0x00,0x00, -0x56,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x56,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x59,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x78,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x78,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x7a,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, @@ -47913,7 +47810,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x04,0x00,0x00,0x00,0x7a,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0x7b,0x01,0x00,0x00,0x7c,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x84,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x85,0x01,0x00,0x00,0x84,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x86,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -47921,25 +47818,25 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x87,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x92,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x9a,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x57,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x39,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x9f,0x01,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd5,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf0,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xf1,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, @@ -47948,20 +47845,20 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x06,0x00,0x00,0x00,0xfb,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 0xb8,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x03,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x65,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x6d,0x02,0x00,0x00, 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0xc3,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,0x3d,0x02,0x00,0x00, @@ -48417,172 +48314,172 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x3e,0x02,0x00,0x00, 0x44,0x02,0x00,0x00,0x47,0x02,0x00,0x00,0x3e,0x00,0x03,0x00, 0x46,0x02,0x00,0x00,0x48,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x07,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0x4b,0x02,0x00,0x00,0x06,0x03,0x00,0x00, 0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x27,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0x29,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0x22,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x22,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4d,0x02,0x00,0x00, -0x05,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x04,0x03,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, 0x1f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x21,0x02,0x00,0x00, 0xf9,0x00,0x02,0x00,0x1a,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0x1a,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0xb2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -48591,47 +48488,47 @@ unsigned char matmul_q5_k_f32_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, 0x2e,0x01,0x00,0x00,0x83,0x01,0x00,0x00,0xeb,0x01,0x00,0x00, -0xf6,0x01,0x00,0x00,0xe1,0x02,0x00,0x00,0x2a,0x03,0x00,0x00, +0xf6,0x01,0x00,0x00,0xe0,0x02,0x00,0x00,0x29,0x03,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -48764,7 +48661,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -48774,10 +48671,10 @@ unsigned char matmul_q5_k_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x80,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -48804,31 +48701,31 @@ unsigned char matmul_q5_k_f32_data[] = { 0x25,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x3c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x18,0x01,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x41,0x01,0x00,0x00, -0x3f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x41,0x01,0x00,0x00, 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0xe9,0x01,0x00,0x00,0x24,0x01,0x00,0x00,0xe8,0x01,0x00,0x00, @@ -48836,7 +48733,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0xe9,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0xea,0x01,0x00,0x00, 0xeb,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xef,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xf3,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xf4,0x01,0x00,0x00,0xf3,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xf5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xf4,0x01,0x00,0x00, @@ -48844,26 +48741,26 @@ unsigned char matmul_q5_k_f32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x01,0x02,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0a,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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-0x34,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x35,0x02,0x00,0x00, +0x19,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x34,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0x35,0x02,0x00,0x00, 0x24,0x01,0x00,0x00,0x34,0x02,0x00,0x00,0x20,0x00,0x04,0x00, 0x36,0x02,0x00,0x00,0x07,0x00,0x00,0x00,0x35,0x02,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x46,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x4c,0x02,0x00,0x00,0x07,0x00,0x00,0x00, 0x24,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x62,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, @@ -48873,20 +48770,20 @@ unsigned char matmul_q5_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6d,0x02,0x00,0x00, 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@@ -49645,47 +49542,47 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, 0x2f,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0xee,0x01,0x00,0x00, -0xf6,0x01,0x00,0x00,0x03,0x03,0x00,0x00,0x4c,0x03,0x00,0x00, +0xf6,0x01,0x00,0x00,0x02,0x03,0x00,0x00,0x4b,0x03,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xeb,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xea,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xec,0x01,0x00,0x00,0x25,0x01,0x00,0x00, @@ -49912,21 +49809,21 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00, 0x51,0x00,0x00,0x00,0xc7,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x33,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x34,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 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0x02,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x05,0x02,0x00,0x00,0xe9,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x41,0x00,0x08,0x00,0xf8,0x01,0x00,0x00,0x07,0x02,0x00,0x00, -0xf6,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0xf6,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x08,0x02,0x00,0x00,0x07,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x25,0x01,0x00,0x00,0x09,0x02,0x00,0x00, 0x08,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xa3,0x01,0x00,0x00, @@ -50356,7 +50253,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x0d,0x02,0x00,0x00, 0xe9,0x01,0x00,0x00,0x0c,0x02,0x00,0x00,0x41,0x00,0x08,0x00, 0xf8,0x01,0x00,0x00,0x0f,0x02,0x00,0x00,0xf6,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x02,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x10,0x02,0x00,0x00,0x0f,0x02,0x00,0x00,0x73,0x00,0x04,0x00, 0x25,0x01,0x00,0x00,0x11,0x02,0x00,0x00,0x10,0x02,0x00,0x00, @@ -50365,8 +50262,8 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x12,0x02,0x00,0x00,0x11,0x02,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,0xe9,0x01,0x00,0x00, 0x36,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0xf8,0x01,0x00,0x00, -0x16,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xe0,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x16,0x02,0x00,0x00,0xf6,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xe0,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x17,0x02,0x00,0x00, 0x16,0x02,0x00,0x00,0x73,0x00,0x04,0x00,0x25,0x01,0x00,0x00, 0x18,0x02,0x00,0x00,0x17,0x02,0x00,0x00,0x41,0x00,0x05,0x00, @@ -50375,8 +50272,8 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x18,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x1c,0x02,0x00,0x00,0xe9,0x01,0x00,0x00,0x1b,0x02,0x00,0x00, 0x41,0x00,0x08,0x00,0xf8,0x01,0x00,0x00,0x1e,0x02,0x00,0x00, -0xf6,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0xf6,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xe0,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0x1f,0x02,0x00,0x00,0x1e,0x02,0x00,0x00, 0x73,0x00,0x04,0x00,0x25,0x01,0x00,0x00,0x20,0x02,0x00,0x00, 0x1f,0x02,0x00,0x00,0x41,0x00,0x05,0x00,0xa3,0x01,0x00,0x00, @@ -50385,7 +50282,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x24,0x02,0x00,0x00, 0xe9,0x01,0x00,0x00,0x23,0x02,0x00,0x00,0x41,0x00,0x08,0x00, 0xf8,0x01,0x00,0x00,0x26,0x02,0x00,0x00,0xf6,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xe0,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, 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0x25,0x01,0x00,0x00,0xd2,0x02,0x00,0x00,0xd1,0x02,0x00,0x00, @@ -50574,171 +50471,171 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x32,0x00,0x00,0x00,0xd3,0x02,0x00,0x00,0xda,0x02,0x00,0x00, 0xdd,0x02,0x00,0x00,0x3e,0x00,0x03,0x00,0xdc,0x02,0x00,0x00, 0xde,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xe1,0x02,0x00,0x00,0x9b,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, +0xe1,0x02,0x00,0x00,0x9a,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0xbb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, 0xbd,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xb6,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0xb6,0x02,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0x99,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0xe3,0x02,0x00,0x00,0x98,0x03,0x00,0x00, 0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x02,0x00,0x00, 0xf8,0x00,0x02,0x00,0xb5,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, 0xae,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0xae,0x02,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xe5,0x02,0x00,0x00, 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+0xf8,0x00,0x02,0x00,0x13,0x03,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x6c,0x03,0x00,0x00,0x74,0x03,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x10,0x03,0x00,0x00, +0xf8,0x00,0x02,0x00,0x12,0x03,0x00,0x00,0xf9,0x00,0x02,0x00, +0x0b,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0b,0x03,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x6e,0x03,0x00,0x00, +0x73,0x03,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x08,0x03,0x00,0x00,0xf8,0x00,0x02,0x00,0x0a,0x03,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q5_k_f32_aligned_len = 13172; unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xa3,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xa2,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -50746,47 +50643,47 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x83,0x01,0x00,0x00, -0xeb,0x01,0x00,0x00,0xf2,0x01,0x00,0x00,0xd9,0x02,0x00,0x00, -0x22,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x83,0x01,0x00,0x00, +0xeb,0x01,0x00,0x00,0xf2,0x01,0x00,0x00,0xd8,0x02,0x00,0x00, +0x21,0x03,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 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+0x4d,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x60,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -50821,15 +50718,15 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xf2,0x01,0x00,0x00, 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+0x21,0x03,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x21,0x03,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -50840,78 +50737,78 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x11,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -50931,10 +50828,10 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x20,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x01,0x00,0x00, @@ -50959,33 +50856,33 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x2f,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x31,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x26,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x3c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, -0x19,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x19,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x41,0x01,0x00,0x00,0x3f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x58,0x01,0x00,0x00,0x0f,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x80,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x7f,0x01,0x00,0x00, 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0x84,0x00,0x00,0x00,0xc5,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xc6,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe2,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe7,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe8,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0xe7,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xe9,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -51004,21 +50901,21 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0x0b,0x02,0x00,0x00,0x51,0x00,0x00,0x00, 0xc4,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, -0x0b,0x02,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x0b,0x02,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x0d,0x02,0x00,0x00,0x86,0x00,0x00,0x00, -0x0c,0x02,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x02,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x10,0x02,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x11,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x2f,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0x30,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2f,0x02,0x00,0x00,0x20,0x00,0x04,0x00,0x31,0x02,0x00,0x00, 0x07,0x00,0x00,0x00,0x30,0x02,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x41,0x02,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x5d,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x5c,0x02,0x00,0x00, @@ -51026,20 +50923,20 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x5d,0x02,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x67,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x6f,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x9e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, 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+0x15,0x00,0x00,0x00,0x62,0x01,0x00,0x00,0x61,0x01,0x00,0x00, +0xc5,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x63,0x01,0x00,0x00, 0x59,0x01,0x00,0x00,0x62,0x01,0x00,0x00,0x72,0x00,0x04,0x00, 0x1f,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00, 0x7c,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x65,0x01,0x00,0x00, @@ -51292,145 +51189,145 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x6a,0x01,0x00,0x00,0x54,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x6a,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x3c,0x01,0x00,0x00, -0x6e,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x6e,0x01,0x00,0x00,0x2f,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0xfc,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x6f,0x01,0x00,0x00, 0x6e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00, -0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x81,0x00,0x00,0x00, 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0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -51959,8 +51856,8 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -51970,9 +51867,9 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -51999,31 +51896,31 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe5,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xe6,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0xe5,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0xe7,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, @@ -52031,7 +51928,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x04,0x00,0x00,0x00,0xe7,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, 0xe8,0x01,0x00,0x00,0xe9,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xed,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xf1,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xf2,0x01,0x00,0x00,0xf1,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0xf3,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, @@ -52039,25 +51936,25 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 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0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, 0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xee,0x00,0x00,0x00, @@ -52263,13 +52160,13 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x13,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x12,0x01,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1c,0x01,0x00,0x00, 0xfe,0x00,0x00,0x00,0x06,0x01,0x00,0x00,0xc4,0x00,0x05,0x00, -0x13,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0xcf,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x1d,0x01,0x00,0x00,0xcf,0x00,0x00,0x00, 0x1c,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x1e,0x01,0x00,0x00, 0x1f,0x01,0x00,0x00,0x1d,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0x20,0x01,0x00,0x00,0x1f,0x01,0x00,0x00, 0x41,0x00,0x07,0x00,0x30,0x01,0x00,0x00,0x31,0x01,0x00,0x00, -0x2e,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, -0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x25,0x01,0x00,0x00, +0x2e,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x25,0x01,0x00,0x00, 0x32,0x01,0x00,0x00,0x31,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x21,0x01,0x00,0x00,0x33,0x01,0x00,0x00,0x32,0x01,0x00,0x00, 0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00,0x36,0x01,0x00,0x00, @@ -52278,25 +52175,25 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x36,0x01,0x00,0x00,0x37,0x01,0x00,0x00,0x50,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x37,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x3c,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,0x2e,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0x3e,0x01,0x00,0x00,0x3d,0x01,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x3f,0x01,0x00,0x00,0x3e,0x01,0x00,0x00, -0x7c,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x40,0x01,0x00,0x00, -0x3f,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x13,0x00,0x00,0x00, +0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x40,0x01,0x00,0x00, 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0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q5_k_f32_fp32_len = 12476; unsigned char matmul_q6_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x44,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x43,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -52831,47 +52728,47 @@ unsigned char matmul_q6_k_f32_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, 0x33,0x01,0x00,0x00,0x44,0x01,0x00,0x00,0xaf,0x01,0x00,0x00, -0xba,0x01,0x00,0x00,0xa5,0x02,0x00,0x00,0xee,0x02,0x00,0x00, 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+52911,10 @@ unsigned char matmul_q6_k_f32_data[] = { 0x07,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x80,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -53042,9 +52939,9 @@ unsigned char matmul_q6_k_f32_data[] = { 0x35,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x2e,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x3b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 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0x81,0x01,0x00,0x00,0x75,0x01,0x00,0x00,0x80,0x01,0x00,0x00, 0x72,0x00,0x04,0x00,0x2c,0x01,0x00,0x00,0x82,0x01,0x00,0x00, -0x81,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x81,0x01,0x00,0x00,0x72,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x83,0x01,0x00,0x00,0x82,0x01,0x00,0x00,0x82,0x00,0x05,0x00, -0x13,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00, +0x15,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0x6f,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x84,0x01,0x00,0x00,0x85,0x00,0x05,0x00, 0xc3,0x00,0x00,0x00,0x86,0x01,0x00,0x00,0x3f,0x01,0x00,0x00, @@ -53409,22 +53306,22 @@ unsigned char matmul_q6_k_f32_data[] = { 0x66,0x01,0x00,0x00,0x88,0x01,0x00,0x00,0x44,0x01,0x00,0x00, 0x69,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x88,0x01,0x00,0x00, 0x87,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x8f,0x01,0x00,0x00,0x23,0x03,0x00,0x00,0x8d,0x01,0x00,0x00, +0x8f,0x01,0x00,0x00,0x22,0x03,0x00,0x00,0x8d,0x01,0x00,0x00, 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-0x24,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x9e,0x01,0x00,0x00,0x12,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x23,0x03,0x00,0x00,0x41,0x00,0x05,0x00,0x17,0x00,0x00,0x00, +0x9e,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9f,0x01,0x00,0x00, 0x9e,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00, 0xa0,0x01,0x00,0x00,0x9d,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, @@ -53432,9 +53329,9 @@ unsigned char matmul_q6_k_f32_data[] = { 0xfa,0x00,0x04,0x00,0xa0,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, 0xa2,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa1,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00, -0x13,0x03,0x00,0x00,0x78,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, +0x12,0x03,0x00,0x00,0x79,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, 0xc1,0x00,0x00,0x00,0xa7,0x01,0x00,0x00,0xa5,0x01,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x01,0x00,0x00, +0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa2,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xa2,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 0xc1,0x00,0x00,0x00,0xa8,0x01,0x00,0x00,0xa0,0x01,0x00,0x00, 0x92,0x01,0x00,0x00,0xa7,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, @@ -53442,17 +53339,17 @@ unsigned char matmul_q6_k_f32_data[] = { 0xfa,0x00,0x04,0x00,0xa8,0x01,0x00,0x00,0xa9,0x01,0x00,0x00, 0xca,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa9,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00, -0x7d,0x00,0x00,0x00,0x24,0x03,0x00,0x00,0x84,0x00,0x05,0x00, +0x7e,0x00,0x00,0x00,0x23,0x03,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xb4,0x01,0x00,0x00,0xb2,0x01,0x00,0x00, 0xb3,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xb6,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0x78,0x00,0x00,0x00, +0xb6,0x01,0x00,0x00,0xb4,0x01,0x00,0x00,0x79,0x00,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xc1,0x01,0x00,0x00, 0xb2,0x01,0x00,0x00,0xaa,0x00,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xc2,0x01,0x00,0x00,0x2b,0x03,0x00,0x00, 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+0xb4,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xad,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xad,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x10,0x03,0x00,0x00,0x13,0x03,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xaa,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xac,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q6_k_f32_len = 11764; unsigned char matmul_q6_k_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x65,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x64,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, @@ -53816,47 +53713,47 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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-0x47,0x00,0x04,0x00,0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xa7,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, @@ -53894,15 +53791,15 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x47,0x00,0x04,0x00,0xb9,0x01,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x01,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xc6,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x0c,0x03,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0d,0x03,0x00,0x00, +0xc5,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x03,0x00,0x00,0x06,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x0c,0x03,0x00,0x00, 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0xc0,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, @@ -54004,9 +53901,9 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x02,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -54030,9 +53927,9 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x35,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x2e,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x3b,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2c,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x40,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x41,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x40,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x41,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x42,0x01,0x00,0x00, 0x2e,0x01,0x00,0x00,0x41,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x43,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x42,0x01,0x00,0x00, @@ -54040,24 +53937,24 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x49,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x29,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00,0x04,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x52,0x01,0x00,0x00, -0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x52,0x01,0x00,0x00, +0x0f,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x61,0x01,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x66,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x2e,0x01,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x89,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00, -0x8a,0x01,0x00,0x00,0x89,0x01,0x00,0x00,0x39,0x00,0x00,0x00, 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+0xad,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xae,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0xad,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0xaf,0x01,0x00,0x00, 0x2e,0x01,0x00,0x00,0xae,0x01,0x00,0x00,0x20,0x00,0x04,0x00, @@ -54081,21 +53978,21 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0xf5,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x8a,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf6,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xf5,0x01,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf7,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xf6,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfa,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfb,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 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-0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf5,0x00,0x00,0x00, 0xf7,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xfc,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, @@ -54292,12 +54189,12 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x06,0x01,0x00,0x00,0x07,0x01,0x00,0x00,0x89,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0xff,0x00,0x00,0x00, 0x0b,0x01,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x0d,0x01,0x00,0x00,0x0c,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x11,0x01,0x00,0x00, 0x06,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x12,0x01,0x00,0x00,0x11,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x15,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x03,0x01,0x00,0x00, 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0x41,0x00,0x05,0x00,0x66,0x01,0x00,0x00,0xc6,0x01,0x00,0x00, @@ -54446,8 +54343,8 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0xc6,0x01,0x00,0x00,0xc5,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xc8,0x01,0x00,0x00,0xac,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0xbb,0x01,0x00,0x00, -0xca,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xa3,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xca,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xa3,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xcb,0x01,0x00,0x00, 0xca,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x2e,0x01,0x00,0x00, 0xcc,0x01,0x00,0x00,0xcb,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -54456,8 +54353,8 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0xcc,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xd0,0x01,0x00,0x00,0xac,0x01,0x00,0x00,0xcf,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0xbb,0x01,0x00,0x00,0xd2,0x01,0x00,0x00, -0xb9,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, +0xb9,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, +0x35,0x00,0x00,0x00,0xcf,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xd3,0x01,0x00,0x00,0xd2,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,0xd4,0x01,0x00,0x00, 0xd3,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x66,0x01,0x00,0x00, @@ -54466,8 +54363,8 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xd7,0x01,0x00,0x00, 0xac,0x01,0x00,0x00,0x4d,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0xbb,0x01,0x00,0x00,0xd9,0x01,0x00,0x00,0xb9,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xda,0x01,0x00,0x00,0xd9,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x2e,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,0xda,0x01,0x00,0x00, 0x41,0x00,0x05,0x00,0x66,0x01,0x00,0x00,0xdc,0x01,0x00,0x00, @@ -54475,8 +54372,8 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0xdc,0x01,0x00,0x00,0xdb,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xdf,0x01,0x00,0x00,0xac,0x01,0x00,0x00, 0xde,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0xbb,0x01,0x00,0x00, -0xe1,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0xe1,0x01,0x00,0x00,0xb9,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0xe2,0x01,0x00,0x00, 0xe1,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x2e,0x01,0x00,0x00, 0xe3,0x01,0x00,0x00,0xe2,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -54485,7 +54382,7 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0xe3,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xe7,0x01,0x00,0x00,0xac,0x01,0x00,0x00,0xe6,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0xbb,0x01,0x00,0x00,0xe9,0x01,0x00,0x00, -0xb9,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, +0xb9,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xea,0x01,0x00,0x00,0xe9,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x2e,0x01,0x00,0x00,0xeb,0x01,0x00,0x00, @@ -54495,170 +54392,170 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xef,0x01,0x00,0x00, 0xac,0x01,0x00,0x00,0xee,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0xbb,0x01,0x00,0x00,0xf1,0x01,0x00,0x00,0xb9,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0xd0,0x00,0x00,0x00, 0xcf,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xf2,0x01,0x00,0x00,0xf1,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 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+0x31,0x03,0x00,0x00,0x34,0x03,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xcb,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0xcd,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q6_k_f32_aligned_len = 12324; unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3c,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x3b,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -54847,47 +54744,47 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa7,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -54922,15 +54819,15 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb6,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb6,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9d,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe3,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x9c,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xe2,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, 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+0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0xba,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc0,0x00,0x00,0x00, @@ -55022,7 +54919,7 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -55032,10 +54929,10 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x02,0x01,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x07,0x01,0x00,0x00, @@ -55060,32 +54957,32 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x2f,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x3c,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x2d,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00, 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-0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xac,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0xab,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0xad,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xac,0x01,0x00,0x00, @@ -55104,21 +55001,21 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0xcf,0x01,0x00,0x00,0x51,0x00,0x00,0x00,0x88,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd0,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xcf,0x01,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd1,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xd0,0x01,0x00,0x00, -0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xd4,0x01,0x00,0x00,0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xd5,0x01,0x00,0x00,0x86,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xba,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0xbc,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0xbc,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0xb9,0x00,0x00,0x00,0xbd,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -55991,8 +55888,8 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -56002,9 +55899,9 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x80,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x01,0x01,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -56030,31 +55927,31 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x2e,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x3b,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x2c,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x41,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x42,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x41,0x01,0x00,0x00, 0x20,0x00,0x04,0x00,0x43,0x01,0x00,0x00,0x04,0x00,0x00,0x00, 0x42,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x43,0x01,0x00,0x00, 0x44,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x49,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x29,0x01,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x4d,0x01,0x00,0x00, -0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xaa,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0xa9,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0xab,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0xaa,0x01,0x00,0x00, @@ -56062,7 +55959,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0xab,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0xac,0x01,0x00,0x00, 0xad,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb1,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb5,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xb6,0x01,0x00,0x00,0xb5,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xb7,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0xb6,0x01,0x00,0x00, @@ -56070,25 +55967,25 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 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+55993,20 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x22,0x02,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x2c,0x02,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x34,0x02,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x63,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x96,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x9e,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0xe4,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0xe5,0x02,0x00,0x00,0xe4,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0xe6,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0xe5,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0xe6,0x02,0x00,0x00, -0xe7,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xec,0x02,0x00,0x00,0x05,0x00,0x00,0x00, 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-0x13,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x81,0x01,0x00,0x00, -0x82,0x00,0x05,0x00,0x13,0x00,0x00,0x00,0x83,0x01,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x01,0x00,0x00,0x81,0x01,0x00,0x00, +0x82,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x83,0x01,0x00,0x00, 0x82,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x6f,0x00,0x04,0x00, 0xc3,0x00,0x00,0x00,0x84,0x01,0x00,0x00,0x83,0x01,0x00,0x00, 0x85,0x00,0x05,0x00,0xc3,0x00,0x00,0x00,0x85,0x01,0x00,0x00, @@ -56391,22 +56288,22 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x65,0x01,0x00,0x00,0x86,0x01,0x00,0x00,0x44,0x01,0x00,0x00, 0x68,0x01,0x00,0x00,0x3e,0x00,0x03,0x00,0x86,0x01,0x00,0x00, 0x85,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x8d,0x01,0x00,0x00,0x1c,0x03,0x00,0x00,0x8b,0x01,0x00,0x00, +0x8d,0x01,0x00,0x00,0x1b,0x03,0x00,0x00,0x8b,0x01,0x00,0x00, 0xf9,0x00,0x02,0x00,0xdc,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0xde,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8f,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x8f,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 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+0x9c,0x01,0x00,0x00,0x14,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x9d,0x01,0x00,0x00, 0x9c,0x01,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00, 0x9e,0x01,0x00,0x00,0x9b,0x01,0x00,0x00,0x9d,0x01,0x00,0x00, @@ -56414,9 +56311,9 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0xfa,0x00,0x04,0x00,0x9e,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, 0xa0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x9f,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, -0x0c,0x03,0x00,0x00,0x78,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, +0x0b,0x03,0x00,0x00,0x79,0x00,0x00,0x00,0xb0,0x00,0x05,0x00, 0xc1,0x00,0x00,0x00,0xa5,0x01,0x00,0x00,0xa3,0x01,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa0,0x01,0x00,0x00, +0x8e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa0,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0xa0,0x01,0x00,0x00,0xf5,0x00,0x07,0x00, 0xc1,0x00,0x00,0x00,0xa6,0x01,0x00,0x00,0x9e,0x01,0x00,0x00, 0x90,0x01,0x00,0x00,0xa5,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, @@ -56424,190 +56321,190 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0xfa,0x00,0x04,0x00,0xa6,0x01,0x00,0x00,0xa7,0x01,0x00,0x00, 0xc7,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xa7,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xb0,0x01,0x00,0x00, -0x7d,0x00,0x00,0x00,0x1d,0x03,0x00,0x00,0x84,0x00,0x05,0x00, +0x7e,0x00,0x00,0x00,0x1c,0x03,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0xb0,0x01,0x00,0x00, 0xb1,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xb4,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,0x78,0x00,0x00,0x00, +0xb4,0x01,0x00,0x00,0xb2,0x01,0x00,0x00,0x79,0x00,0x00,0x00, 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbf,0x01,0x00,0x00, 0xb0,0x01,0x00,0x00,0xaa,0x00,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0xc0,0x01,0x00,0x00,0x24,0x03,0x00,0x00, +0x06,0x00,0x00,0x00,0xc0,0x01,0x00,0x00,0x23,0x03,0x00,0x00, 0xbf,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xc2,0x01,0x00,0x00,0xc0,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 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+0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xa3,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xa5,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; const uint64_t matmul_q6_k_f32_fp32_len = 11644; unsigned char matmul_q8_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xf1,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -56793,47 +56690,47 @@ unsigned char matmul_q8_0_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x0a,0x01,0x00,0x00,0x28,0x01,0x00,0x00, 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+0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x6d,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -56862,15 +56759,15 @@ unsigned char matmul_q8_0_f32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x66,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x51,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x50,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x96,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x98,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x98,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 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0xbf,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0xc3,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, @@ -56971,9 +56868,9 @@ unsigned char matmul_q8_0_f32_data[] = { 0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00,0xcb,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0xcc,0x00,0x00,0x00, 0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xf3,0x00,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xfa,0x00,0x00,0x00, 0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00, @@ -56993,9 +56890,9 @@ unsigned char matmul_q8_0_f32_data[] = { 0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x15,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x03,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x37,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x24,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x26,0x01,0x00,0x00,0x02,0x01,0x00,0x00,0x25,0x01,0x00,0x00, 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0xf3,0x00,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xf6,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0xf6,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, 0xf4,0x00,0x00,0x00,0xf6,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0xee,0x00,0x00,0x00, @@ -57240,23 +57137,23 @@ unsigned char matmul_q8_0_f32_data[] = { 0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0xff,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x07,0x00, 0x0c,0x01,0x00,0x00,0x0d,0x01,0x00,0x00,0x0a,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x0e,0x01,0x00,0x00, 0x0d,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00,0x41,0x00,0x08,0x00, 0x15,0x01,0x00,0x00,0x16,0x01,0x00,0x00,0x0a,0x01,0x00,0x00, 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+0x06,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0xc0,0x02,0x00,0x00, +0x79,0x00,0x00,0x00,0xb0,0x00,0x05,0x00,0xc1,0x00,0x00,0x00, +0x53,0x01,0x00,0x00,0x51,0x01,0x00,0x00,0x8e,0x00,0x00,0x00, 0xf9,0x00,0x02,0x00,0x4e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, 0x4e,0x01,0x00,0x00,0xf5,0x00,0x07,0x00,0xc1,0x00,0x00,0x00, 0x54,0x01,0x00,0x00,0x4c,0x01,0x00,0x00,0x3e,0x01,0x00,0x00, @@ -57308,18 +57205,18 @@ unsigned char matmul_q8_0_f32_data[] = { 0x56,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x54,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x76,0x01,0x00,0x00, 0xf8,0x00,0x02,0x00,0x55,0x01,0x00,0x00,0x80,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x7d,0x00,0x00,0x00, -0xd2,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x5e,0x01,0x00,0x00,0x7e,0x00,0x00,0x00, +0xd1,0x02,0x00,0x00,0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x60,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x62,0x01,0x00,0x00, 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+0x80,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xb8,0x02,0x00,0x00,0xc4,0x02,0x00,0x00, +0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x61,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x61,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00, +0xc2,0x02,0x00,0x00,0xcf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x5e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x59,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x59,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xbc,0x02,0x00,0x00,0xc1,0x02,0x00,0x00,0xcf,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x56,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x58,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q8_0_f32_len = 10608; unsigned char matmul_q8_0_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x15,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x14,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, 0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -57682,47 +57579,47 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x4c,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x29,0x01,0x00,0x00, -0x5e,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x74,0x02,0x00,0x00, -0xbd,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00, +0x4d,0x00,0x00,0x00,0x0b,0x01,0x00,0x00,0x29,0x01,0x00,0x00, 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0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -57754,15 +57651,15 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x66,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x66,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x74,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xba,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x73,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0xbb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0xbb,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0xba,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0xba,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0xbb,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0xbd,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0xbd,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0xba,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0xbc,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xbc,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -57773,78 +57670,78 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x0d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x1e,0x00,0x10,0x00,0x10,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x1e,0x00,0x10,0x00,0x12,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -57864,10 +57761,10 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfb,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x03,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -57886,9 +57783,9 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x16,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x04,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x26,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x25,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x25,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x27,0x01,0x00,0x00,0x03,0x01,0x00,0x00, 0x26,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x28,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x27,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -57897,17 +57794,17 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x03,0x01,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x36,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x36,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x37,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x39,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x38,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3a,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x39,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x39,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5b,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x5a,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x5c,0x01,0x00,0x00,0x03,0x01,0x00,0x00,0x5b,0x01,0x00,0x00, @@ -57932,21 +57829,21 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa3,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x77,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0xa3,0x01,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa5,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0xa4,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa8,0x01,0x00,0x00, 0x08,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xa9,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0xa9,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xac,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xc7,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xc8,0x01,0x00,0x00, +0xac,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0xc7,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x1c,0x00,0x04,0x00,0xc8,0x01,0x00,0x00, 0x03,0x01,0x00,0x00,0xc7,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0xc9,0x01,0x00,0x00,0x07,0x00,0x00,0x00,0xc8,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xd9,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xdf,0x01,0x00,0x00,0x07,0x00,0x00,0x00, 0x03,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf5,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -57956,20 +57853,20 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x00,0x02,0x00,0x00, 0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x08,0x02,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00, 0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 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+0x06,0x00,0x00,0x00,0xce,0x02,0x00,0x00,0x84,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x36,0x00,0x05,0x00, 0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0xc9,0x00,0x00,0x00,0xca,0x00,0x00,0x00, @@ -57979,158 +57876,158 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, -0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, -0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, 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0xc4,0x00,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0x72,0x01,0x00,0x00, 0x71,0x01,0x00,0x00,0x41,0x00,0x05,0x00,0x2e,0x01,0x00,0x00, @@ -58220,7 +58117,7 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x75,0x01,0x00,0x00, 0x59,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x41,0x00,0x08,0x00, 0x68,0x01,0x00,0x00,0x77,0x01,0x00,0x00,0x66,0x01,0x00,0x00, -0x34,0x00,0x00,0x00,0x50,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x35,0x00,0x00,0x00,0x50,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00, 0x78,0x01,0x00,0x00,0x77,0x01,0x00,0x00,0x73,0x00,0x04,0x00, 0x03,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x78,0x01,0x00,0x00, @@ -58229,8 +58126,8 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x7a,0x01,0x00,0x00,0x79,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x59,0x01,0x00,0x00, 0x7c,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x68,0x01,0x00,0x00, -0x7f,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0x50,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x7c,0x01,0x00,0x00, +0x7f,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0x50,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x7c,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x80,0x01,0x00,0x00, 0x7f,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x03,0x01,0x00,0x00, 0x81,0x01,0x00,0x00,0x80,0x01,0x00,0x00,0x41,0x00,0x05,0x00, @@ -58239,8 +58136,8 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x81,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x85,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x84,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x68,0x01,0x00,0x00,0x87,0x01,0x00,0x00, -0x66,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x50,0x01,0x00,0x00, -0xd0,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x66,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x50,0x01,0x00,0x00, +0xd0,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 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0x92,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x95,0x01,0x00,0x00,0x59,0x01,0x00,0x00, 0x94,0x01,0x00,0x00,0x41,0x00,0x08,0x00,0x68,0x01,0x00,0x00, -0x97,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x34,0x00,0x00,0x00, +0x97,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x35,0x00,0x00,0x00, 0x50,0x01,0x00,0x00,0xd0,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x98,0x01,0x00,0x00, 0x97,0x01,0x00,0x00,0x73,0x00,0x04,0x00,0x03,0x01,0x00,0x00, @@ -58268,7 +58165,7 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x99,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x9d,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x9c,0x01,0x00,0x00, 0x41,0x00,0x08,0x00,0x68,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, -0x66,0x01,0x00,0x00,0x34,0x00,0x00,0x00,0x50,0x01,0x00,0x00, +0x66,0x01,0x00,0x00,0x35,0x00,0x00,0x00,0x50,0x01,0x00,0x00, 0xd0,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xa0,0x01,0x00,0x00,0x9f,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0x03,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, @@ -58276,162 +58173,162 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0xa2,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x9d,0x01,0x00,0x00, 0x3e,0x00,0x03,0x00,0xa2,0x01,0x00,0x00,0xa1,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xa7,0x01,0x00,0x00, -0xf5,0x02,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xf4,0x02,0x00,0x00,0xa5,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0x3e,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x40,0x01,0x00,0x00, 0xe0,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0xa8,0x01,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0xab,0x01,0x00,0x00,0xf8,0x02,0x00,0x00,0xa9,0x01,0x00,0x00, +0xab,0x01,0x00,0x00,0xf7,0x02,0x00,0x00,0xa9,0x01,0x00,0x00, 0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x01,0x00,0x00, -0xfc,0x02,0x00,0x00,0xac,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0xfb,0x02,0x00,0x00,0xac,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, 0xb0,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0xb0,0x01,0x00,0x00, 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+58585,15 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x61,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x63,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x63,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x02,0x00,0x00, 0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x90,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x91,0x02,0x00,0x00,0x00,0x00,0x00,0x00, -0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x91,0x02,0x00,0x00, +0x8f,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x90,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x90,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x93,0x02,0x00,0x00,0x22,0x00,0x00,0x00, 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+0x84,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xbb,0x00,0x00,0x00, 0xbc,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xbd,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xba,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xc0,0x00,0x00,0x00,0x84,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -58788,7 +58685,7 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0xbc,0x00,0x00,0x00,0x14,0x00,0x02,0x00,0xc2,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0xc4,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00, 0x84,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00, @@ -58798,10 +58695,10 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x07,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0xc4,0x00,0x00,0x00,0xcc,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, +0xc4,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xd0,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfb,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xff,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x03,0x01,0x00,0x00, @@ -58820,9 +58717,9 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x11,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x16,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x04,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x25,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x26,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00, +0x25,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, +0x26,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x25,0x01,0x00,0x00,0x1c,0x00,0x04,0x00,0x27,0x01,0x00,0x00, 0xc4,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x28,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x27,0x01,0x00,0x00, @@ -58831,17 +58728,17 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x04,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x01,0x00,0x00,0x00, 0x33,0x00,0x06,0x00,0x09,0x00,0x00,0x00,0x35,0x01,0x00,0x00, -0x34,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x34,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00, 0x51,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x38,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x6c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x6d,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x53,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x58,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x59,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x58,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x5a,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, @@ -58860,21 +58757,21 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x7c,0x01,0x00,0x00,0x77,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x7c,0x01,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x7d,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x7d,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x77,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xa1,0x01,0x00,0x00,0xc4,0x00,0x00,0x00, 0xa0,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xa2,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcd,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xce,0x01,0x00,0x00,0xc4,0x00,0x00,0x00,0xcd,0x01,0x00,0x00, @@ -58882,20 +58779,20 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0xce,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd8,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb9,0x00,0x00,0x00, 0xbf,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xe0,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x42,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0x92,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0x91,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x92,0x02,0x00,0x00, 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0x84,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xf7,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x6f,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0xf8,0x00,0x00,0x00,0xf5,0x00,0x00,0x00, 0xf7,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0xfc,0x00,0x00,0x00,0xef,0x00,0x00,0x00,0xfb,0x00,0x00,0x00, @@ -59065,24 +58962,24 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0xef,0x00,0x00,0x00,0xff,0x00,0x00,0x00,0x84,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x01,0x01,0x00,0x00,0x00,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x0d,0x01,0x00,0x00, -0x0e,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x34,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x0e,0x01,0x00,0x00,0x0b,0x01,0x00,0x00,0x35,0x00,0x00,0x00, +0xfc,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x03,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x0e,0x01,0x00,0x00, 0x73,0x00,0x04,0x00,0xc4,0x00,0x00,0x00,0x10,0x01,0x00,0x00, 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+0x57,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x59,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x52,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x52,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xb5,0x02,0x00,0x00,0xba,0x02,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x4f,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x51,0x02,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t matmul_q8_0_f32_aligned_fp32_len = 10452; unsigned char matmul_q8_0_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xeb,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xea,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, 0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, 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+0x47,0x00,0x04,0x00,0x61,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x6c,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xa6,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb8,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, @@ -59564,15 +59461,15 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x62,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x64,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x64,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x4a,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x49,0x02,0x00,0x00, 0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x90,0x02,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbf,0x00,0x00,0x00, @@ -59663,7 +59560,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x14,0x00,0x02,0x00,0xc1,0x00,0x00,0x00,0x16,0x00,0x03,0x00, 0xc3,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x84,0x00,0x00,0x00, -0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc5,0x00,0x00,0x00,0x84,0x00,0x00,0x00, 0xc4,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc6,0x00,0x00,0x00,0x84,0x00,0x00,0x00, @@ -59673,10 +59570,10 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0xc7,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0xc3,0x00,0x00,0x00, 0xcb,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0xcc,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc3,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, +0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xcf,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0xf3,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xfa,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xfe,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, 0x16,0x00,0x03,0x00,0x02,0x01,0x00,0x00,0x10,0x00,0x00,0x00, @@ -59695,9 +59592,9 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x15,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x03,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x24,0x01,0x00,0x00, -0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x25,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x24,0x01,0x00,0x00, +0x84,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x24,0x01,0x00,0x00, 0x1c,0x00,0x04,0x00,0x26,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0x25,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0x27,0x01,0x00,0x00, 0x04,0x00,0x00,0x00,0x26,0x01,0x00,0x00,0x3b,0x00,0x04,0x00, @@ -59706,15 +59603,15 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0xc3,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x33,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x33,0x00,0x06,0x00, 0x09,0x00,0x00,0x00,0x34,0x01,0x00,0x00,0x33,0x01,0x00,0x00, -0x39,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x3a,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x35,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x34,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x36,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x35,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x37,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x36,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x36,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x55,0x01,0x00,0x00,0x1c,0x00,0x04,0x00, 0x57,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x56,0x01,0x00,0x00, @@ -59722,7 +59619,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x57,0x01,0x00,0x00,0x3b,0x00,0x04,0x00,0x58,0x01,0x00,0x00, 0x59,0x01,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x5d,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x61,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x62,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x20,0x00,0x04,0x00, 0x63,0x01,0x00,0x00,0x0c,0x00,0x00,0x00,0x62,0x01,0x00,0x00, @@ -59730,25 +59627,25 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x6f,0x01,0x00,0x00, 0x0c,0x00,0x00,0x00,0xc3,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x77,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7c,0x01,0x00,0x00,0x51,0x00,0x00,0x00, 0x34,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x01,0x00,0x00,0x84,0x00,0x00,0x00, -0x7c,0x01,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x7c,0x01,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x01,0x00,0x00,0x86,0x00,0x00,0x00, -0x7d,0x01,0x00,0x00,0x6c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x7d,0x01,0x00,0x00,0x6d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x81,0x01,0x00,0x00,0x08,0x01,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x82,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x85,0x01,0x00,0x00, -0x86,0x00,0x00,0x00,0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa0,0x01,0x00,0x00, -0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x1c,0x00,0x04,0x00,0xa1,0x01,0x00,0x00,0xc3,0x00,0x00,0x00, 0xa0,0x01,0x00,0x00,0x20,0x00,0x04,0x00,0xa2,0x01,0x00,0x00, 0x07,0x00,0x00,0x00,0xa1,0x01,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xb2,0x01,0x00,0x00,0x80,0x00,0x00,0x00, -0x6c,0x00,0x00,0x00,0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xcd,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0xbb,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0xce,0x01,0x00,0x00,0xc3,0x00,0x00,0x00,0xcd,0x01,0x00,0x00, @@ -59756,20 +59653,20 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0xce,0x01,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xd8,0x01,0x00,0x00,0x86,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, 0xbe,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xe0,0x01,0x00,0x00,0x80,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, -0x39,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x13,0x00,0x00,0x00, -0x42,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x0a,0x00,0x00,0x00,0x4a,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x1d,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, -0x1e,0x00,0x03,0x00,0x91,0x02,0x00,0x00,0x90,0x02,0x00,0x00, -0x20,0x00,0x04,0x00,0x92,0x02,0x00,0x00,0x0c,0x00,0x00,0x00, -0x91,0x02,0x00,0x00,0x3b,0x00,0x04,0x00,0x92,0x02,0x00,0x00, -0x93,0x02,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 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+0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x36,0x00,0x05,0x00,0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, 0x05,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0xc8,0x00,0x00,0x00, @@ -59779,159 +59676,159 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x07,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00, 0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, -0x0e,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x1e,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x41,0x00,0x05,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x12,0x00,0x00,0x00, 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mul_mat_vec_q3_K_f32_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x45,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x59,0x00,0x00,0x00,0x88,0x00,0x00,0x00,0xb0,0x00,0x00,0x00, +0xc7,0x00,0x00,0x00,0x38,0x03,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 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3124; +const uint64_t mul_mat_vec_q4_0_f32_f32_len = 3928; + +unsigned char mul_mat_vec_q4_1_f16_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x39,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x4f,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x5b,0x00,0x00,0x00, +0x8e,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x07,0x01,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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= 4456; + +unsigned char mul_mat_vec_q5_1_f16_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x79,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x76,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, +0xb0,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0x29,0x01,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x16,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t mul_mat_vec_q5_K_f16_f32_len = 13288; + +unsigned char mul_mat_vec_q5_K_f32_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xa0,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, +0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x59,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0xbc,0x00,0x00,0x00, +0x2e,0x02,0x00,0x00,0x93,0x04,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t mul_mat_vec_q8_0_f32_f32_len = 3884; unsigned char norm_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb8,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x97,0x00,0x00,0x00, +0x29,0x00,0x00,0x00,0x36,0x00,0x00,0x00,0x9a,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -66970,15 +74183,15 @@ unsigned char norm_f32_data[] = { 0x34,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x36,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x36,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x95,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x97,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x97,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xad,0x00,0x00,0x00, +0x98,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x9a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x9a,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb0,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -67026,138 +74239,142 @@ unsigned char norm_f32_data[] = { 0x08,0x01,0x00,0x00,0x2b,0x00,0x04,0x00,0x2a,0x00,0x00,0x00, 0x56,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x2b,0x00,0x04,0x00, 0x2a,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x14,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0xac,0xc5,0x27,0x37,0x1d,0x00,0x03,0x00,0x94,0x00,0x00,0x00, -0x14,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x95,0x00,0x00,0x00, -0x94,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x96,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 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+0x8e,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t norm_f32_len = 2572; +const uint64_t norm_f32_len = 2624; unsigned char relu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -68914,7 +76131,7 @@ unsigned char scale_f32_data[] = { 0x41,0x00,0x05,0x00,0xec,0x00,0x00,0x00,0xed,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0xeb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x11,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0xed,0x00,0x00,0x00, -0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0xef,0x00,0x00,0x00, 0xea,0x00,0x00,0x00,0xee,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0xe8,0x00,0x00,0x00,0xf0,0x00,0x00,0x00,0xd7,0x00,0x00,0x00, 0xcc,0x00,0x00,0x00,0xdf,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, @@ -69448,6 +76665,427 @@ unsigned char soft_max_f32_data[] = { }; const uint64_t soft_max_f32_len = 4916; +unsigned char soft_max_f32_f16_data[] = { 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+0x6c,0x00,0x00,0x00,0xf6,0x00,0x04,0x00,0x36,0x01,0x00,0x00, +0x35,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, +0x3c,0x01,0x00,0x00,0x35,0x01,0x00,0x00,0x36,0x01,0x00,0x00, +0xf8,0x00,0x02,0x00,0x35,0x01,0x00,0x00,0x84,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x40,0x01,0x00,0x00,0x13,0x00,0x00,0x00, +0x6c,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x42,0x01,0x00,0x00,0x40,0x01,0x00,0x00,0x51,0x01,0x00,0x00, +0x41,0x00,0x06,0x00,0x7c,0x00,0x00,0x00,0x44,0x01,0x00,0x00, +0x0e,0x01,0x00,0x00,0x6a,0x00,0x00,0x00,0x42,0x01,0x00,0x00, +0x3d,0x00,0x04,0x00,0x16,0x00,0x00,0x00,0x45,0x01,0x00,0x00, +0x44,0x01,0x00,0x00,0x88,0x00,0x05,0x00,0x16,0x00,0x00,0x00, +0x46,0x01,0x00,0x00,0x45,0x01,0x00,0x00,0x31,0x01,0x00,0x00, +0x3e,0x00,0x03,0x00,0x44,0x01,0x00,0x00,0x46,0x01,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x49,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x59,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x34,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x36,0x01,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t soft_max_f32_f16_len = 5000; + unsigned char split_k_reduce_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index f712cdd5a..95f718974 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -120,8 +120,16 @@ struct vk_device { vk_matmul_pipeline pipeline_dequant_mul_mat_mat[VK_NUM_TYPES]; + vk_matmul_pipeline pipeline_matmul_id_f32; + vk_matmul_pipeline pipeline_matmul_id_f16; + vk_matmul_pipeline pipeline_matmul_id_f16_f32; + + vk_matmul_pipeline pipeline_dequant_mul_mat_mat_id[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; - vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f16_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_id_f32[VK_NUM_TYPES]; vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; @@ -139,7 +147,7 @@ struct vk_device { vk_pipeline pipeline_silu_f32; vk_pipeline pipeline_relu_f32; vk_pipeline pipeline_diag_mask_inf_f32; - vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_soft_max_f32, pipeline_soft_max_f32_f16; vk_pipeline pipeline_rope_f32, pipeline_rope_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; vk_pipeline pipeline_argsort_f32; @@ -215,6 +223,21 @@ struct vk_submission { typedef std::vector<vk_submission> vk_sequence; +struct vk_mat_mat_push_constants { + uint32_t M; uint32_t N; uint32_t K; + uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; uint32_t k_split; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t expert_stride_b; uint32_t expert_stride_d; + uint32_t idx; uint32_t nbi1; uint32_t n_as; +}; + +struct vk_mat_vec_push_constants { + uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; +}; + struct vk_op_push_constants { uint32_t KX; uint32_t KY; @@ -1003,201 +1026,422 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>(); + /*ctx->device->pipeline_matmul_id_f32 = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_matmul_id_f16_f32 = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_matmul_id_f16 = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared<vk_matmul_pipeline_struct>(); + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared<vk_matmul_pipeline_struct>();*/ + if (device->fp16) { - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_len, matmul_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_len, matmul_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_len, matmul_f16_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_len, matmul_f16_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_len, matmul_q4_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_len, matmul_q4_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_0_f32_l", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_0_f32_m", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_0_f32_s", matmul_q4_1_f32_len, matmul_q4_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_1_f32_aligned_len, matmul_q4_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_len, matmul_q5_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_len, matmul_q5_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_len, matmul_q5_1_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_len, matmul_q5_1_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_len, matmul_q8_0_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_len, matmul_q8_0_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_len, matmul_q2_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_len, matmul_q2_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_len, matmul_q3_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_len, matmul_q3_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_len, matmul_q4_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_len, matmul_q4_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_len, matmul_q5_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_len, matmul_q5_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_len, matmul_q6_k_f32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_len, matmul_q6_k_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_len, matmul_id_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_len, matmul_id_f16_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_len, matmul_id_f16_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_len, matmul_id_f16_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_len, matmul_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_len, matmul_id_q4_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_len, matmul_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_len, matmul_id_q5_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_len, matmul_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_len, matmul_id_q5_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_len, matmul_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_len, matmul_id_q8_0_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_len, matmul_id_q2_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_len, matmul_id_q2_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_len, matmul_id_q3_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_len, matmul_id_q3_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_len, matmul_id_q4_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_len, matmul_id_q4_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_len, matmul_id_q5_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_len, matmul_id_q5_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_len, matmul_id_q6_k_f32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_len, matmul_id_q6_k_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/ } else { - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->l, "matmul_f32_l", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->m, "matmul_f32_m", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->s, "matmul_f32_s", matmul_f32_fp32_len, matmul_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_l, "matmul_f32_aligned_l", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_l, "matmul_f16_aligned_l", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_m, "matmul_f16_aligned_m", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->a_s, "matmul_f16_aligned_s", matmul_f16_aligned_fp32_len, matmul_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->l, "matmul_f16_f32_l", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->m, "matmul_f16_f32_m", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->s, "matmul_f16_f32_s", matmul_f16_f32_fp32_len, matmul_f16_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16_f32->a_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_fp32_len, matmul_f16_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->l, "matmul_q4_0_f32_l", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->m, "matmul_q4_0_f32_m", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->s, "matmul_q4_0_f32_s", matmul_q4_0_f32_fp32_len, matmul_q4_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_l, "matmul_q4_0_f32_aligned_l", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_m, "matmul_q4_0_f32_aligned_m", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0]->a_s, "matmul_q4_0_f32_aligned_s", matmul_q4_0_f32_aligned_fp32_len, matmul_q4_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->l, "matmul_q4_1_f32_l", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->m, "matmul_q4_1_f32_m", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->s, "matmul_q4_1_f32_s", matmul_q4_1_f32_fp32_len, matmul_q4_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_l, "matmul_q4_1_f32_aligned_l", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_m, "matmul_q4_1_f32_aligned_m", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_1]->a_s, "matmul_q4_1_f32_aligned_s", matmul_q4_1_f32_aligned_fp32_len, matmul_q4_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->l, "matmul_q5_0_f32_l", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->m, "matmul_q5_0_f32_m", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->s, "matmul_q5_0_f32_s", matmul_q5_0_f32_fp32_len, matmul_q5_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_l, "matmul_q5_0_f32_aligned_l", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_m, "matmul_q5_0_f32_aligned_m", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_0]->a_s, "matmul_q5_0_f32_aligned_s", matmul_q5_0_f32_aligned_fp32_len, matmul_q5_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->l, "matmul_q5_1_f32_l", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->m, "matmul_q5_1_f32_m", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->s, "matmul_q5_1_f32_s", matmul_q5_1_f32_fp32_len, matmul_q5_1_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_l, "matmul_q5_1_f32_aligned_l", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_m, "matmul_q5_1_f32_aligned_m", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_1]->a_s, "matmul_q5_1_f32_aligned_s", matmul_q5_1_f32_aligned_fp32_len, matmul_q5_1_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->l, "matmul_q8_0_f32_l", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->m, "matmul_q8_0_f32_m", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->s, "matmul_q8_0_f32_s", matmul_q8_0_f32_fp32_len, matmul_q8_0_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_l, "matmul_q8_0_f32_aligned_l", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_m, "matmul_q8_0_f32_aligned_m", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q8_0]->a_s, "matmul_q8_0_f32_aligned_s", matmul_q8_0_f32_aligned_fp32_len, matmul_q8_0_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->l, "matmul_q2_k_f32_l", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->m, "matmul_q2_k_f32_m", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->s, "matmul_q2_k_f32_s", matmul_q2_k_f32_fp32_len, matmul_q2_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_l, "matmul_q2_k_f32_aligned_l", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_m, "matmul_q2_k_f32_aligned_m", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q2_K]->a_s, "matmul_q2_k_f32_aligned_s", matmul_q2_k_f32_aligned_fp32_len, matmul_q2_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->l, "matmul_q3_k_f32_l", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->m, "matmul_q3_k_f32_m", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->s, "matmul_q3_k_f32_s", matmul_q3_k_f32_fp32_len, matmul_q3_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_l, "matmul_q3_k_f32_aligned_l", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_m, "matmul_q3_k_f32_aligned_m", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q3_K]->a_s, "matmul_q3_k_f32_aligned_s", matmul_q3_k_f32_aligned_fp32_len, matmul_q3_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->l, "matmul_q4_k_f32_l", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->m, "matmul_q4_k_f32_m", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->s, "matmul_q4_k_f32_s", matmul_q4_k_f32_fp32_len, matmul_q4_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_l, "matmul_q4_k_f32_aligned_l", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_m, "matmul_q4_k_f32_aligned_m", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_K]->a_s, "matmul_q4_k_f32_aligned_s", matmul_q4_k_f32_aligned_fp32_len, matmul_q4_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->l, "matmul_q5_k_f32_l", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->m, "matmul_q5_k_f32_m", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->s, "matmul_q5_k_f32_s", matmul_q5_k_f32_fp32_len, matmul_q5_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_l, "matmul_q5_k_f32_aligned_l", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_m, "matmul_q5_k_f32_aligned_m", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q5_K]->a_s, "matmul_q5_k_f32_aligned_s", matmul_q5_k_f32_aligned_fp32_len, matmul_q5_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_mmq_l, l_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_mmq_m, m_align); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->l, "matmul_q6_k_f32_l", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->m, "matmul_q6_k_f32_m", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->s, "matmul_q6_k_f32_s", matmul_q6_k_f32_fp32_len, matmul_q6_k_f32_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_l, "matmul_q6_k_f32_aligned_l", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_m, "matmul_q6_k_f32_aligned_m", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K]->a_s, "matmul_q6_k_f32_aligned_s", matmul_q6_k_f32_aligned_fp32_len, matmul_q6_k_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->l, "matmul_id_f32_l", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->m, "matmul_id_f32_m", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->s, "matmul_id_f32_s", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_l, "matmul_id_f32_aligned_l", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_m, "matmul_id_f32_aligned_m", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f32->a_s, "matmul_id_f32_aligned_s", matmul_id_f32_aligned_fp32_len, matmul_id_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->m, "matmul_id_f16_m", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->s, "matmul_id_f16_s", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_l, "matmul_id_f16_aligned_l", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_m, "matmul_id_f16_aligned_m", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16->a_s, "matmul_id_f16_aligned_s", matmul_id_f16_aligned_fp32_len, matmul_id_f16_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->l, "matmul_id_f16_f32_l", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->m, "matmul_id_f16_f32_m", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->s, "matmul_id_f16_f32_s", matmul_id_f16_f32_fp32_len, matmul_id_f16_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_l, "matmul_id_f16_f32_aligned_l", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_m, "matmul_id_f16_f32_aligned_m", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_id_f16_f32->a_s, "matmul_id_f16_f32_aligned_s", matmul_id_f16_f32_aligned_fp32_len, matmul_id_f16_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_0_f32_fp32_len, matmul_id_q4_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_0_f32_aligned_fp32_len, matmul_id_q4_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->l, "matmul_id_q4_0_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->m, "matmul_id_q4_0_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->s, "matmul_id_q4_0_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_l, "matmul_id_q4_0_f32_aligned_l", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_m, "matmul_id_q4_0_f32_aligned_m", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_1]->a_s, "matmul_id_q4_0_f32_aligned_s", matmul_id_q4_1_f32_aligned_fp32_len, matmul_id_q4_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->l, "matmul_id_q5_0_f32_l", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->m, "matmul_id_q5_0_f32_m", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->s, "matmul_id_q5_0_f32_s", matmul_id_q5_0_f32_fp32_len, matmul_id_q5_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_l, "matmul_id_q5_0_f32_aligned_l", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_m, "matmul_id_q5_0_f32_aligned_m", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_0]->a_s, "matmul_id_q5_0_f32_aligned_s", matmul_id_q5_0_f32_aligned_fp32_len, matmul_id_q5_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->l, "matmul_id_q5_1_f32_l", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->m, "matmul_id_q5_1_f32_m", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->s, "matmul_id_q5_1_f32_s", matmul_id_q5_1_f32_fp32_len, matmul_id_q5_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_l, "matmul_id_q5_1_f32_aligned_l", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_m, "matmul_id_q5_1_f32_aligned_m", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_1]->a_s, "matmul_id_q5_1_f32_aligned_s", matmul_id_q5_1_f32_aligned_fp32_len, matmul_id_q5_1_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->l, "matmul_id_q8_0_f32_l", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->m, "matmul_id_q8_0_f32_m", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->s, "matmul_id_q8_0_f32_s", matmul_id_q8_0_f32_fp32_len, matmul_id_q8_0_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_l, "matmul_id_q8_0_f32_aligned_l", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_m, "matmul_id_q8_0_f32_aligned_m", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q8_0]->a_s, "matmul_id_q8_0_f32_aligned_s", matmul_id_q8_0_f32_aligned_fp32_len, matmul_id_q8_0_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->l, "matmul_id_q2_k_f32_l", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->m, "matmul_id_q2_k_f32_m", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->s, "matmul_id_q2_k_f32_s", matmul_id_q2_k_f32_fp32_len, matmul_id_q2_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_l, "matmul_id_q2_k_f32_aligned_l", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_m, "matmul_id_q2_k_f32_aligned_m", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q2_K]->a_s, "matmul_id_q2_k_f32_aligned_s", matmul_id_q2_k_f32_aligned_fp32_len, matmul_id_q2_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->l, "matmul_id_q3_k_f32_l", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->m, "matmul_id_q3_k_f32_m", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->s, "matmul_id_q3_k_f32_s", matmul_id_q3_k_f32_fp32_len, matmul_id_q3_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_l, "matmul_id_q3_k_f32_aligned_l", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_m, "matmul_id_q3_k_f32_aligned_m", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K]->a_s, "matmul_id_q3_k_f32_aligned_s", matmul_id_q3_k_f32_aligned_fp32_len, matmul_id_q3_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->l, "matmul_id_q4_k_f32_l", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->m, "matmul_id_q4_k_f32_m", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->s, "matmul_id_q4_k_f32_s", matmul_id_q4_k_f32_fp32_len, matmul_id_q4_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_l, "matmul_id_q4_k_f32_aligned_l", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_m, "matmul_id_q4_k_f32_aligned_m", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K]->a_s, "matmul_id_q4_k_f32_aligned_s", matmul_id_q4_k_f32_aligned_fp32_len, matmul_id_q4_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->l, "matmul_id_q5_k_f32_l", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->m, "matmul_id_q5_k_f32_m", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->s, "matmul_id_q5_k_f32_s", matmul_id_q5_k_f32_fp32_len, matmul_id_q5_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_l, "matmul_id_q5_k_f32_aligned_l", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_m, "matmul_id_q5_k_f32_aligned_m", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K]->a_s, "matmul_id_q5_k_f32_aligned_s", matmul_id_q5_k_f32_aligned_fp32_len, matmul_id_q5_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->l, "matmul_id_q6_k_f32_l", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->m, "matmul_id_q6_k_f32_m", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->s, "matmul_id_q6_k_f32_s", matmul_id_q6_k_f32_fp32_len, matmul_id_q6_k_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_l, "matmul_id_q6_k_f32_aligned_l", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_mmq_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_m, "matmul_id_q6_k_f32_aligned_m", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_mmq_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K]->a_s, "matmul_id_q6_k_f32_aligned_s", matmul_id_q6_k_f32_aligned_fp32_len, matmul_id_q6_k_f32_aligned_fp32_data, "main", 4, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_mmq_s, s_align);*/ } - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(uint32_t), {1, 1, 1}, { device->subgroup_size }, 1); + // mul mat vec + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32_f32", mul_mat_vec_f16_f32_f32_len, mul_mat_vec_f16_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32_f32", mul_mat_vec_q4_0_f32_f32_len, mul_mat_vec_q4_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32_f32", mul_mat_vec_q4_1_f32_f32_len, mul_mat_vec_q4_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32_f32", mul_mat_vec_q5_0_f32_f32_len, mul_mat_vec_q5_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32_f32", mul_mat_vec_q5_1_f32_f32_len, mul_mat_vec_q5_1_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32_f32", mul_mat_vec_q8_0_f32_f32_len, mul_mat_vec_q8_0_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32_f32", mul_mat_vec_q2_K_f32_f32_len, mul_mat_vec_q2_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32_f32", mul_mat_vec_q3_K_f32_f32_len, mul_mat_vec_q3_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32_f32", mul_mat_vec_q4_K_f32_f32_len, mul_mat_vec_q4_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32_f32", mul_mat_vec_q5_K_f32_f32_len, mul_mat_vec_q5_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32_f32", mul_mat_vec_q6_K_f32_f32_len, mul_mat_vec_q6_K_f32_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f16_f32", mul_mat_vec_f16_f16_f32_len, mul_mat_vec_f16_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f16_f32", mul_mat_vec_q4_0_f16_f32_len, mul_mat_vec_q4_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f16_f32", mul_mat_vec_q4_1_f16_f32_len, mul_mat_vec_q4_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f16_f32", mul_mat_vec_q5_0_f16_f32_len, mul_mat_vec_q5_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f16_f32", mul_mat_vec_q5_1_f16_f32_len, mul_mat_vec_q5_1_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f16_f32", mul_mat_vec_q8_0_f16_f32_len, mul_mat_vec_q8_0_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f16_f32", mul_mat_vec_q2_K_f16_f32_len, mul_mat_vec_q2_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f16_f32", mul_mat_vec_q3_K_f16_f32_len, mul_mat_vec_q3_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f16_f32", mul_mat_vec_q4_K_f16_f32_len, mul_mat_vec_q4_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f16_f32", mul_mat_vec_q5_K_f16_f32_len, mul_mat_vec_q5_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f16_f32", mul_mat_vec_q6_K_f16_f32_len, mul_mat_vec_q6_K_f16_f32_data, "main", 3, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + + /*ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F16 ], "mul_mat_vec_id_f16_f32", mul_mat_vec_id_f16_f32_len, mul_mat_vec_id_f16_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_0], "mul_mat_vec_id_q4_0_f32", mul_mat_vec_id_q4_0_f32_len, mul_mat_vec_id_q4_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_1], "mul_mat_vec_id_q4_1_f32", mul_mat_vec_id_q4_1_f32_len, mul_mat_vec_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_0], "mul_mat_vec_id_q5_0_f32", mul_mat_vec_id_q5_0_f32_len, mul_mat_vec_id_q5_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_1], "mul_mat_vec_id_q5_1_f32", mul_mat_vec_id_q5_1_f32_len, mul_mat_vec_id_q5_1_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q8_0], "mul_mat_vec_id_q8_0_f32", mul_mat_vec_id_q8_0_f32_len, mul_mat_vec_id_q8_0_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q2_K], "mul_mat_vec_id_q2_K_f32", mul_mat_vec_id_q2_K_f32_len, mul_mat_vec_id_q2_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q3_K], "mul_mat_vec_id_q3_K_f32", mul_mat_vec_id_q3_K_f32_len, mul_mat_vec_id_q3_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q4_K], "mul_mat_vec_id_q4_K_f32", mul_mat_vec_id_q4_K_f32_len, mul_mat_vec_id_q4_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q5_K], "mul_mat_vec_id_q5_K_f32", mul_mat_vec_id_q5_K_f32_len, mul_mat_vec_id_q5_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_Q6_K], "mul_mat_vec_id_q6_K_f32", mul_mat_vec_id_q6_K_f32_len, mul_mat_vec_id_q6_K_f32_data, "main", 4, sizeof(vk_mat_vec_push_constants), {1, 1, 1}, { device->subgroup_size }, 1);*/ // dequant shaders ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", dequant_f32_len, dequant_f32_data, "main", 2, 5 * sizeof(uint32_t), {256 * 16, 1, 1}, {}, 1); @@ -1258,6 +1502,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -1686,11 +1931,48 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte return ctx->device->pipeline_dequant_mul_mat_mat[src0_type]; } -static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { +static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_context * ctx, ggml_type src0_type, ggml_type src1_type) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_mul_mat_mat_id_pipeline()" << std::endl; +#endif + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_id_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return ctx->device->pipeline_matmul_id_f16_f32; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_id_f16; + } + + GGML_ASSERT(src1_type == GGML_TYPE_F32); + + switch (src0_type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + break; + default: + return nullptr; + } + + return ctx->device->pipeline_dequant_mul_mat_mat_id[src0_type]; +} + +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type a_type, ggml_type b_type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif - switch (type) { + GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); + + switch (a_type) { case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -1707,7 +1989,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - return ctx->device->pipeline_dequant_mul_mat_vec_f32[type]; + return b_type == GGML_TYPE_F32 ? ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[a_type] : ctx->device->pipeline_dequant_mul_mat_vec_f16_f32[a_type]; } static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { @@ -1913,6 +2195,9 @@ static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx } static size_t ggml_vk_align_size(size_t width, size_t align) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_align_size(" << width << ", " << align << ")" << std::endl; +#endif return CEIL_DIV(width, align) * align; } @@ -2368,11 +2653,13 @@ static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")" << std::endl; #endif - if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { - return 4; - } + // if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { + // return 4; + // } return 1; + + GGML_UNUSED(m); GGML_UNUSED(n); GGML_UNUSED(k); } static vk_pipeline ggml_vk_guess_matmul_pipeline_amd(ggml_backend_vk_context * ctx, vk_matmul_pipeline& mmp, int m, int n, bool aligned) { @@ -2424,25 +2711,58 @@ static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ct #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif - return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, false)->align; + return ggml_vk_guess_matmul_pipeline(ctx, mmp, m, n, true)->align; } -static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { +static void ggml_vk_matmul( + ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, + vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, + uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, + uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, + uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, + uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif ggml_vk_sync_buffers(subctx); if (split_k == 1) { - const std::array<uint32_t, 14> pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); + const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch }); return; } GGML_ASSERT(batch_stride_d == m * n); - const std::array<uint32_t, 14> pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; + const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); + ggml_vk_sync_buffers(subctx); + const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k }; + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); +} + +static void ggml_vk_matmul_id( + ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, + vk_subbuffer&& ids, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& a, vk_subbuffer&& split_k_buffer, + uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, + uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, + uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, + uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; +#endif + ggml_vk_sync_buffers(subctx); + if (split_k == 1) { + const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, d, a }, sizeof(vk_mat_mat_push_constants), &pc, { m, n, batch }); + return; + } + + GGML_ASSERT(batch_stride_d == m * n); + + const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d, expert_stride_b, expert_stride_d, idx, nbi1, n_as }; + // Make sure enough workgroups get assigned for split k to work + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { ids, b, split_k_buffer, a }, sizeof(vk_mat_mat_push_constants), &pc1, { (CEIL_DIV(m, pipeline->wg_denoms[0]) * pipeline->wg_denoms[0]) * split_k, n, batch }); ggml_vk_sync_buffers(subctx); const std::array<uint32_t, 2> pc2 = { (uint32_t)(m * n * batch), split_k }; ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); @@ -2557,7 +2877,7 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su const int d_ne = ne11 * ne01; const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); - const bool aligned = ne10 == kpad; + const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); @@ -2655,7 +2975,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su } // compute - ggml_vk_matmul(ctx, subctx, pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul( + ctx, subctx, pipeline, + { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, + { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, + ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, + 0, 0, 0, 0, 1 + ); // NOLINT if (dst->backend == GGML_BACKEND_TYPE_CPU) { // copy dst to host @@ -2685,8 +3011,10 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context GGML_ASSERT(ne11 == 1); - const uint64_t nb2 = dst->nb[2]; - const uint64_t nb3 = dst->nb[3]; + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + const uint64_t ne22 = dst->ne[2]; + const uint64_t ne23 = dst->ne[3]; const uint64_t r2 = ne12 / ne02; const uint64_t r3 = ne13 / ne03; @@ -2718,6 +3046,9 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context const bool qx_needs_dequant = x_non_contig; const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + const uint64_t x_ne = ne01 * ne00; const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; @@ -2770,7 +3101,7 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context } else { to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); @@ -2793,43 +3124,25 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); } - for (uint64_t i13 = 0; i13 < ne13; i13++) { - const uint64_t i03 = i13 / r3; - for (uint64_t i12 = 0; i12 < ne12; i12++) { - const uint64_t i02 = i12 / r2; + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_batch_y = ne10*ne11; - const uint64_t it_idx0 = (i03 * ne02 + i02); - const uint64_t it_idx1 = (i13 * ne12 + i12); - const uint64_t x_offset = x_buf_offset + x_sz * it_idx0; - const uint64_t qy_offset = qy_buf_offset + qy_sz * it_idx1; - const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; - const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - - const uint64_t y_buffer_offset = (y_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t y_shader_offset = y_offset - y_buffer_offset; - - const uint64_t d_buffer_offset = (d_offset / ctx->device->properties.limits.minStorageBufferOffsetAlignment) * ctx->device->properties.limits.minStorageBufferOffsetAlignment; - const uint64_t d_shader_offset = d_offset - d_buffer_offset; - - if (!y_non_contig && qy_needs_dequant) { - const std::vector<uint32_t> pc = { (uint32_t)ne11, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(y_ne / 32) }; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)y_ne, 1, 1}); - } - - // compute - const std::array<uint32_t, 3> pc = { (uint32_t)ne00, (uint32_t)(y_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type))}; - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne); - } - } + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + const vk_mat_vec_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, + (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, + stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), + }; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1}); } static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3011,7 +3324,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU); } -static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif @@ -3026,9 +3339,347 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, } } -// static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { -// -// } +/*static void ggml_vk_mul_mat_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * ids, ggml_tensor * dst) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << ids << ", name=" << ids->name << ", type=" << ids->type << ", backend=" << ids->backend << ", ne0=" << ids->ne[0] << ", ne1=" << ids->ne[1] << ", ne2=" << ids->ne[2] << ", ne3=" << ids->ne[3] << ", nb0=" << ids->nb[0] << ", nb1=" << ids->nb[1] << ", nb2=" << ids->nb[2] << ", nb3=" << ids->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(src0->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + const uint32_t nb11 = src1->nb[1]; + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + const uint32_t nbi1 = src0->nb[1]; + const uint32_t idx = ((uint32_t *) dst->op_params)[0]; + const uint64_t n_as = ne02; + + GGML_ASSERT(n_as <= 8); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + ggml_tensor_extra_gpu * extra_ids = (ggml_tensor_extra_gpu *) ids->extra; + + vk_buffer d_Qx; + size_t qx_buf_offset = 0; + vk_buffer d_Qy; + size_t qy_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + } + + const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); + + const bool y_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + + vk_matmul_pipeline mmp = ggml_vk_get_mul_mat_mat_id_pipeline(ctx, src0->type, y_non_contig ? GGML_TYPE_F16 : src1->type); + + const bool qx_needs_dequant = mmp == nullptr || x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !y_f32_kernel) || y_non_contig; + + if (mmp == nullptr) { + GGML_ASSERT(false); + } + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + const int x_ne = ne01 * ne00; + const int y_ne = ne11 * ne10; + const int d_ne = ne11 * ne01; + + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, ne11)); + const bool aligned = ne10 == kpad && ne01 > 8 && ne11 > 8; + + const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); + + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, ne11, aligned); + + const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t x_sz = !qx_needs_dequant ? qx_sz : sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = y_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t d_sz = sizeof(float) * d_ne; + + vk_buffer d_D = extra->buffer_gpu.lock(); + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); + vk_buffer d_X; + uint64_t x_buf_offset = 0; + vk_buffer d_Y; + uint64_t y_buf_offset = 0; + if (!src0_uma) { + d_Qx = extra_src0->buffer_gpu.lock(); + qx_buf_offset = extra_src0->offset; + GGML_ASSERT(d_Qx != nullptr); + } + if (!src1_uma) { + d_Qy = extra_src1->buffer_gpu.lock(); + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qy != nullptr); + } + if (qx_needs_dequant) { + d_X = ctx->prealloc_x; + GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant) { + d_Y = ctx->prealloc_y; + GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + // Allocate descriptor sets + ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); + if (qx_needs_dequant) { + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, 1); + } + if (split_k > 1) { + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->device->pipeline_matmul_split_k_reduce, 1); + } + + if (x_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); + } else if (qx_needs_dequant) { + const std::vector<uint32_t> pc = { (uint32_t)ne01, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)(ggml_nelements(src0)) }; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(uint32_t), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + } + if (y_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); + } + + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_batch_y = ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + ggml_vk_matmul( + ctx, subctx, pipeline, + { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, + { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, + ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, + nb11 / ggml_type_size(src1->type), ne20, idx, nbi1, n_as + ); // NOLINT +} + +static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + GGML_ASSERT(ne11 == 1); + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + const uint64_t ne22 = dst->ne[2]; + const uint64_t ne23 = dst->ne[3]; + + const uint64_t nb22 = dst->nb[2]; + const uint64_t nb23 = dst->nb[3]; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer d_Qx; + size_t qx_buf_offset = 0; + vk_buffer d_Qy; + size_t qy_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (ctx->device->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + } + + const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !ggml_vk_dim01_contiguous(src1); + + const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; + + const bool qx_needs_dequant = x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + const uint64_t x_ne = ne01 * ne00; + const uint64_t y_ne = ne11 * ne10; + const uint64_t d_ne = ne11 * ne01; + + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device->properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t d_sz = sizeof(float) * d_ne; + + vk_buffer d_D = extra->buffer_gpu.lock(); + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + vk_buffer d_X; + uint64_t x_buf_offset = 0; + vk_buffer d_Y; + uint64_t y_buf_offset = 0; + if(!src0_uma) { + d_Qx = extra_src0->buffer_gpu.lock(); + qx_buf_offset = extra_src0->offset; + GGML_ASSERT(d_Qx != nullptr); + } + if(!src1_uma) { + d_Qy = extra_src1->buffer_gpu.lock(); + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qy != nullptr); + } + if (qx_needs_dequant) { + d_X = ctx->prealloc_x; + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant) { + d_Y = ctx->prealloc_y; + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + vk_pipeline to_fp16_vk_0 = nullptr; + vk_pipeline to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); + } + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type, src1->type); + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + // Allocate descriptor sets + if (qx_needs_dequant) { + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_pipeline_allocate_descriptor_sets(ctx, to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + } + ggml_pipeline_allocate_descriptor_sets(ctx, dmmv, ne12 * ne13); + + if (x_non_contig) { + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }); + } + if (y_non_contig) { + GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }); + } + + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_batch_y = ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src0) && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !qy_needs_dequant) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + const vk_mat_vec_push_constants pc = { + (uint32_t)ne00, (uint32_t)ne10, (uint32_t)ne10, (uint32_t)ne01, + (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, + stride_batch_x, stride_batch_y, (uint32_t)(ne20*ne21), + // 0, 0, 0, 0, 1 + }; + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, dmmv, { { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne22 * ne23} }, sizeof(vk_mat_vec_push_constants), &pc, { (uint32_t)ne01, (uint32_t)(ne12 * ne13), 1}); +}*/ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat @@ -3178,14 +3829,15 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const } return nullptr; case GGML_OP_SOFT_MAX: -#pragma message("TODO: add ggml_vk_soft_max() F16 src1 and src2 support") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") - GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); + GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32 || src2->type == GGML_TYPE_F16); if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_soft_max_f32_f16; + } return nullptr; case GGML_OP_ROPE: { @@ -3233,6 +3885,21 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } } +static bool ggml_vk_op_supports_incontiguous(ggml_op op) { + switch (op) { + case GGML_OP_CPY: + case GGML_OP_GET_ROWS: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + return true; + default: + return false; + } +} + template<typename PC> static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG @@ -3284,6 +3951,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c return; } + const bool op_supports_incontiguous = ggml_vk_op_supports_incontiguous(op); + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; @@ -3345,7 +4014,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c GGML_ASSERT(d_Z != nullptr); } - if (op == GGML_OP_CPY || op == GGML_OP_GET_ROWS) { + if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; d_sz = ggml_nbytes(dst); @@ -3364,7 +4033,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c std::array<uint32_t, 3> elements; // Single call if dimension 2 is contiguous - if (op == GGML_OP_CPY || op == GGML_OP_GET_ROWS || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { + if (op_supports_incontiguous || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, 1); switch (dst->op) { @@ -3385,7 +4054,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c break; } - if (op != GGML_OP_CPY && op != GGML_OP_GET_ROWS) { + if (!op_supports_incontiguous) { if (x_sz != VK_WHOLE_SIZE) { x_sz *= ne02 * ne03; } @@ -3403,14 +4072,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; } else { - subbuf_y = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + subbuf_y = { d_X, 0, d_X->size }; } vk_subbuffer subbuf_z; if (use_src2) { subbuf_z = { d_Z, z_buf_offset, z_sz }; } else { - subbuf_z = { ctx->prealloc_y, 0, ctx->prealloc_y->size }; + subbuf_z = { d_X, 0, d_X->size }; } ggml_vk_sync_buffers(subctx); @@ -3582,7 +4251,9 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons } static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); + float * op_params = (float *)dst->op_params; + + ggml_vk_op_f32<vk_op_push_constants>(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { @@ -3617,7 +4288,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { ncols, - nrows_y, + src1 != nullptr ? nrows_y : (uint32_t)0, src2 != nullptr ? (uint32_t)1 : (uint32_t)0, scale, max_bias, m0, m1, @@ -3834,7 +4505,10 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); for (size_t i = 0; i < num_it; i++) { ggml_vk_ctx_begin(ctx, subctx); - ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_matmul( + ctx, subctx, p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), + m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1 + ); ggml_vk_ctx_end(subctx); } @@ -4339,7 +5013,10 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, vk_context * subctx = ggml_vk_create_context(ctx, ctx->device->compute_queue); for (size_t i = 0; i < num_it; i++) { ggml_vk_ctx_begin(ctx, subctx); - ggml_vk_matmul(ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_matmul( + ctx, subctx, p, ggml_vk_subbuffer(qx_buf), ggml_vk_subbuffer(y_buf), ggml_vk_subbuffer(d_buf), ggml_vk_subbuffer(ctx->prealloc_split_k), + m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n, 0, 0, 0, 0, 1 + ); ggml_vk_ctx_end(subctx); } @@ -4590,6 +5267,8 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q5_K); ggml_vk_test_dequant(ctx, 7680, GGML_TYPE_Q6_K); + ggml_vk_test_matmul<ggml_fp16_t, ggml_fp16_t>(ctx, 512, 512, 100, 32, 100, 1, 2); + ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 0); ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 1); ggml_vk_test_matmul<float, float>(ctx, 128, 512, 512, 2, 100, 1, 2); @@ -4739,7 +5418,7 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) { + if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(node)) { return; } @@ -5539,7 +6218,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen int last_node = cgraph->n_nodes - 1; // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly - while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU) { + while (last_node > 0 && (cgraph->nodes[last_node]->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(cgraph->nodes[last_node]))) { last_node -= 1; } @@ -5577,6 +6256,8 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen } GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + // ggml_backend_vk_context * ctx = (ggml_backend_vk_context *) backend->context; + switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -6199,7 +6880,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { - tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(int *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; @@ -6247,6 +6928,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ tensor_clone = ggml_transpose(ggml_ctx, src0_clone); } else if (tensor->op == GGML_OP_GET_ROWS) { tensor_clone = ggml_get_rows(ggml_ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_ARGSORT) { + tensor_clone = ggml_argsort(ggml_ctx, src0_clone, (ggml_sort_order) *(int *)tensor->op_params); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); @@ -6280,7 +6963,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ free(src1_buffer); } if (src2 != nullptr) { - free(src1_buffer); + free(src2_buffer); } ggml_free(ggml_ctx); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 1d9a0cc82..162cf5c6e 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -167,47 +167,54 @@ struct block_q6_K # Dequant functions shader_float_dequant_func = """ -#define DEQUANT_FUNC vec2 v = vec2(ib, ib); // data_a[ib], data_a[ib + 1]); +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + return vec2(data_a[a_offset + ib], data_a[a_offset + ib + 1]); +} """ shader_q4_0_dequant_func = """ -#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ -const uint vui = uint(data_a[ib].qs[iqs]); \ -vec2 v = vec2(vui & 0xF, vui >> 4); \ -v = (v - 8.0f)*d; +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2(vui & 0xF, vui >> 4) - 8.0f) * d; +} """ shader_q4_1_dequant_func = """ -#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ -const float m = float(data_a[ib].m); \ -const uint vui = uint(data_a[ib].qs[iqs]); \ -vec2 v = vec2(vui & 0xF, vui >> 4); \ -v = v*d + m; +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2(vui & 0xF, vui >> 4) * d + m; +} """ shader_q5_0_dequant_func = """ -#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ -const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ -const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ -const uint vui = uint(data_a[ib].qs[iqs]); \ -vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = (v - 16.0f) * d; +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const uint uint_qh = uint(data_a[a_offset + ib].qh[1]) << 16 | data_a[a_offset + ib].qh[0]; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return (vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) - 16.0f) * d; +} """ shader_q5_1_dequant_func = """ -#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ -const float m = float(data_a[ib].m); \ -const uint uint_qh = data_a[ib].qh; \ -const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ -const uint vui = uint(data_a[ib].qs[iqs]); \ -vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = v*d + m; +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + const float m = float(data_a[a_offset + ib].m); + const uint uint_qh = data_a[a_offset + ib].qh; + const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); + const uint vui = uint(data_a[a_offset + ib].qs[iqs]); + return vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y) * d + m; +} """ shader_q8_0_dequant_func = """ -#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ -vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \ -v = v * d; +vec2 dequantize(uint ib, uint iqs, uint a_offset) { + const float d = float(data_a[a_offset + ib].d); + return vec2(int(data_a[a_offset + ib].qs[iqs]), int(data_a[a_offset + ib].qs[iqs + 1])) * d; +} """ # MULMAT @@ -217,6 +224,15 @@ mulmat_head = """#version 450 #extension GL_EXT_control_flow_attributes : enable #extension GL_EXT_shader_16bit_storage : require +#ifdef MUL_MAT_ID +#extension GL_EXT_buffer_reference2 : require +#extension GL_EXT_nonuniform_qualifier : require +#extension GL_EXT_scalar_block_layout : require +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require + +#define EXPERT_COUNT 8 +#endif + #ifndef LOAD_VEC_A #define LOAD_VEC_A 1 #endif @@ -232,6 +248,10 @@ layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; +#endif + layout (push_constant) uniform parameter { uint M; @@ -250,6 +270,21 @@ layout (push_constant) uniform parameter uint batch_stride_a; uint batch_stride_b; uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint expert_stride_a; + uint expert_stride_b0; + uint expert_stride_b1; + uint expert_stride_d; + + uint ids_stride; + + uint n_as; + uint nei0; + uint nei1; + uint nbi1; + uint ne11; +#endif } p; layout (constant_id = 1) const uint BM = 64; @@ -265,9 +300,20 @@ layout (constant_id = 9) const uint WARP = 32; shared FLOAT_TYPE buf_a[BM * (BK+1)]; shared FLOAT_TYPE buf_b[BN * (BK+1)]; +#ifdef MUL_MAT_ID +shared u8vec2 rowids[2048]; +#endif + void main() { - const uint i13 = gl_GlobalInvocationID.z / p.ne12; - const uint i12 = gl_GlobalInvocationID.z % p.ne12; +#ifdef MUL_MAT_ID + const uint batch_idx = gl_GlobalInvocationID.z / p.n_as; + const uint expert_idx = gl_GlobalInvocationID.z % p.n_as; +#else + const uint batch_idx = gl_GlobalInvocationID.z; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; const uint i03 = i13 / p.broadcast3; const uint i02 = i12 / p.broadcast2; @@ -299,11 +345,35 @@ void main() { const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK; const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK; +#ifdef MUL_MAT_ID + uint _ne1 = 0; + for (uint ii1 = 0; ii1 < p.nei1; ii1++) { + for (uint ii0 = 0; ii0 < p.nei0; ii0++) { + if (data_ids[ii1*p.nbi1 + ii0] == expert_idx) { + rowids[_ne1] = u8vec2(ii0, ii1); + _ne1++; + } + } + } + + const u8vec2 id = rowids[ir * BN + ic]; +#endif + const uint start_k = ik * p.k_split; const uint end_k = min(p.K, (ik + 1) * p.k_split); - uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; - uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; + uint pos_a = ( +#ifdef MUL_MAT_ID + expert_idx * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; + uint pos_b = ( +#ifdef MUL_MAT_ID + id.y * p.expert_stride_b1 + + (id.x % p.ne11) * p.expert_stride_b0 + +#endif + batch_idx * p.batch_stride_b + + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; float sums[WMITER * TM * WNITER * TN]; FLOAT_TYPE cache_a[WMITER * TM]; @@ -611,7 +681,11 @@ mulmat_body2 = """ const uint dr = ir * BM + warp_r * WM; const uint dc = ic * BN + warp_c * WN; - const uint offsets = gl_GlobalInvocationID.z * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; + const uint offsets = +#ifdef MUL_MAT_ID + expert_idx * p.expert_stride_d + +#endif + batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { @@ -1077,21 +1151,54 @@ mul_mat_vec_head = """#version 450 #extension GL_EXT_shader_16bit_storage : require #extension GL_EXT_shader_8bit_storage : require +#ifdef MUL_MAT_ID +#define EXPERT_COUNT 8 +#endif +""" + + +mul_mat_vec_layout = """ +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +#ifdef MUL_MAT_ID +layout (binding = 3) readonly buffer IDS {int data_ids[];}; +#endif + layout (push_constant) uniform parameter { uint ncols; - uint b_offset; - uint d_offset; + uint stride_a; + uint stride_b; + uint stride_d; + + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; + +#ifdef MUL_MAT_ID + uint expert_stride_a; + uint expert_stride_b0; + uint expert_stride_b1; + uint expert_stride_d0; + uint expert_stride_d1; + + uint ne11; + uint nei0; + uint nbi1; + uint n_as; +#endif } p; """ mul_mat_vec_body = """ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - layout (constant_id = 0) const uint BLOCK_SIZE = 32; shared FLOAT_TYPE tmp[BLOCK_SIZE]; @@ -1099,6 +1206,41 @@ shared FLOAT_TYPE tmp[BLOCK_SIZE]; void main() { const uint row = gl_WorkGroupID.x; const uint tid = gl_LocalInvocationID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; @@ -1110,11 +1252,11 @@ void main() { const uint iqs = (col%QUANT_K)/QUANT_R; // quant index const uint iybs = col - col%QUANT_K; // y block start index - DEQUANT_FUNC + vec2 v = dequantize(ib, iqs, a_offset / QUANT_K); // matrix multiplication - tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]) + - FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); + tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[b_offset + iybs + iqs]) + + FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[b_offset + iybs + iqs + y_offset]); } // sum up partial sums and write back result @@ -1126,7 +1268,7 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ @@ -1135,17 +1277,48 @@ void main() { mul_mat_vec_q2_K_body = """ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = row*num_blocks_per_row; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -1171,22 +1344,22 @@ void main() { FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3); - sum2 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF); + sum1 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3) + + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3); + sum2 += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF) + + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF); } tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; } @@ -1200,24 +1373,55 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ mul_mat_vec_q3_K_body = """ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = row*num_blocks_per_row; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -1244,14 +1448,14 @@ void main() { FLOAT_TYPE sum = FLOAT_TYPE(0.0); for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)); + sum += FLOAT_TYPE(data_b[b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)); } tmp[16 * ix + tid] += d * sum; } @@ -1265,24 +1469,55 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ mul_mat_vec_q4_K_body = """ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = row*num_blocks_per_row; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -1336,15 +1571,15 @@ void main() { const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); - const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1 + data_b[p.b_offset + y1_idx + 2] * q4_2 + data_b[p.b_offset + y1_idx + 3] * q4_3); - const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_4 + data_b[p.b_offset + y1_idx + 33] * q4_5 + data_b[p.b_offset + y1_idx + 34] * q4_6 + data_b[p.b_offset + y1_idx + 35] * q4_7); - const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx] * q4_8 + data_b[p.b_offset + y2_idx + 1] * q4_9 + data_b[p.b_offset + y2_idx + 2] * q4_10 + data_b[p.b_offset + y2_idx + 3] * q4_11); - const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_12 + data_b[p.b_offset + y2_idx + 33] * q4_13 + data_b[p.b_offset + y2_idx + 34] * q4_14 + data_b[p.b_offset + y2_idx + 35] * q4_15); + const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1 + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * q4_3); + const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_5 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * q4_7); + const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx]) * q4_8 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_9 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * q4_10 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * q4_11); + const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_12 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_13 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * q4_14 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * q4_15); const FLOAT_TYPE smin = FLOAT_TYPE( - data_b[p.b_offset + y1_idx ] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx ] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7 - + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7 - + data_b[p.b_offset + y1_idx + 2] * sc2 + data_b[p.b_offset + y1_idx + 34] * sc3 + data_b[p.b_offset + y2_idx + 2] * sc6 + data_b[p.b_offset + y2_idx + 34] * sc7 - + data_b[p.b_offset + y1_idx + 3] * sc2 + data_b[p.b_offset + y1_idx + 35] * sc3 + data_b[p.b_offset + y2_idx + 3] * sc6 + data_b[p.b_offset + y2_idx + 35] * sc7 + FLOAT_TYPE(data_b[b_offset + y1_idx ]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx ]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 + + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 + + FLOAT_TYPE(data_b[b_offset + y1_idx + 2]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 34]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 2]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 34]) * sc7 + + FLOAT_TYPE(data_b[b_offset + y1_idx + 3]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 35]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 3]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 35]) * sc7 ); tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); #else @@ -1357,13 +1592,13 @@ void main() { const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); - const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx ] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1); - const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_2 + data_b[p.b_offset + y1_idx + 33] * q4_3); - const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx ] * q4_4 + data_b[p.b_offset + y2_idx + 1] * q4_5); - const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_6 + data_b[p.b_offset + y2_idx + 33] * q4_7); + const FLOAT_TYPE sx = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx ]) * q4_0 + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * q4_1); + const FLOAT_TYPE sy = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * q4_2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * q4_3); + const FLOAT_TYPE sz = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx ]) * q4_4 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * q4_5); + const FLOAT_TYPE sw = FLOAT_TYPE(FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * q4_6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * q4_7); const FLOAT_TYPE smin = FLOAT_TYPE( - data_b[p.b_offset + y1_idx] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7 - + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7 + FLOAT_TYPE(data_b[b_offset + y1_idx]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * sc7 + + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * sc2 + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * sc3 + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * sc6 + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * sc7 ); tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); @@ -1379,24 +1614,55 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ mul_mat_vec_q5_K_body = """ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - shared FLOAT_TYPE tmp[32]; void main() { const uint row = gl_WorkGroupID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = row*num_blocks_per_row; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; const uint tid = gl_LocalInvocationID.x/2; // 0...31 or 0...16 const uint ix = gl_LocalInvocationID.x%2; // 0 or 0, 1 @@ -1450,32 +1716,32 @@ void main() { const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); const FLOAT_TYPE sx = FLOAT_TYPE( - data_b[p.b_offset + y1_idx ] * (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 1] * (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 16] * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 17] * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)) + FLOAT_TYPE(data_b[b_offset + y1_idx ]) * (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) * (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 17]) * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)) ); const FLOAT_TYPE sy = FLOAT_TYPE( - data_b[p.b_offset + y1_idx + 32] * (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 33] * (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 48] * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y1_idx + 49] * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)) + FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) * (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) * (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y1_idx + 49]) * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)) ); const FLOAT_TYPE sz = FLOAT_TYPE( - data_b[p.b_offset + y2_idx ] * (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 1] * (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 16] * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 17] * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)) + FLOAT_TYPE(data_b[b_offset + y2_idx ]) * (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) * (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 17]) * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)) ); const FLOAT_TYPE sw = FLOAT_TYPE( - data_b[p.b_offset + y2_idx + 32] * (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 33] * (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 48] * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)) - + data_b[p.b_offset + y2_idx + 49] * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)) + FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) * (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) * (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)) + + FLOAT_TYPE(data_b[b_offset + y2_idx + 49]) * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)) ); const FLOAT_TYPE smin = FLOAT_TYPE( - (data_b[p.b_offset + y1_idx] + data_b[p.b_offset + y1_idx + 1] + data_b[p.b_offset + y1_idx + 16] + data_b[p.b_offset + y1_idx + 17]) * sc2 + (data_b[p.b_offset + y1_idx + 32] + data_b[p.b_offset + y1_idx + 33] + data_b[p.b_offset + y1_idx + 48] + data_b[p.b_offset + y1_idx + 49]) * sc3 - + (data_b[p.b_offset + y2_idx] + data_b[p.b_offset + y2_idx + 1] + data_b[p.b_offset + y2_idx + 16] + data_b[p.b_offset + y2_idx + 17]) * sc6 + (data_b[p.b_offset + y2_idx + 32] + data_b[p.b_offset + y2_idx + 33] + data_b[p.b_offset + y2_idx + 48] + data_b[p.b_offset + y2_idx + 49]) * sc7 + (FLOAT_TYPE(data_b[b_offset + y1_idx]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 17])) * sc2 + (FLOAT_TYPE(data_b[b_offset + y1_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y1_idx + 49])) * sc3 + + (FLOAT_TYPE(data_b[b_offset + y2_idx]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 1]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 16]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 17])) * sc6 + (FLOAT_TYPE(data_b[b_offset + y2_idx + 32]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 33]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 48]) + FLOAT_TYPE(data_b[b_offset + y2_idx + 49])) * sc7 ); tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); } @@ -1489,25 +1755,55 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ mul_mat_vec_q6_K_body = """ layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; -layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; - shared FLOAT_TYPE tmp[32]; void main() { - const uint block_size = gl_WorkGroupSize.x; const uint row = gl_WorkGroupID.x; + const uint batch_idx = gl_GlobalInvocationID.y; +#ifdef MUL_MAT_ID + const uint expert_idx1 = gl_GlobalInvocationID.z / p.nei0; + const uint expert_idx0 = gl_GlobalInvocationID.z % p.nei0; +#endif + + const uint i13 = batch_idx / p.ne12; + const uint i12 = batch_idx % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + +#ifdef MUL_MAT_ID + const uint expert_id = data_ids[expert_idx1 * p.nbi1 + expert_idx0]; +#endif + + const uint a_offset = +#ifdef MUL_MAT_ID + expert_id * p.expert_stride_a + +#endif + batch_idx_a * p.batch_stride_a; + const uint b_offset = +#ifdef MUL_MAT_ID + (expert_idx0 % p.ne11) * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_b; + const uint d_offset = +#ifdef MUL_MAT_ID + expert_idx0 * p.expert_stride_b0 + + expert_idx1 * p.expert_stride_b1 + +#endif + batch_idx * p.batch_stride_d; const uint num_blocks_per_row = p.ncols / QUANT_K; - const uint ib0 = row*num_blocks_per_row; + const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; const uint tid = gl_LocalInvocationID.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const uint ix = gl_LocalInvocationID.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -1538,22 +1834,22 @@ void main() { const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); #if K_QUANTS_PER_ITERATION == 1 - FLOAT_TYPE sum = FLOAT_TYPE(data_b[p.b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32); + FLOAT_TYPE sum = FLOAT_TYPE(data_b[b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32); tmp[16 * ix + tid] += sum; #else FLOAT_TYPE sum = FLOAT_TYPE(0.0); [[unroll]] for (int l = 0; l < 4; ++l) { - sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32) - + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32); + sum += FLOAT_TYPE(data_b[b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32); } tmp[16 * ix + tid] += sum; #endif @@ -1568,7 +1864,7 @@ void main() { barrier(); } if (tid == 0) { - dst[p.d_offset + row] = D_TYPE(tmp[0]); + data_d[d_offset + row] = D_TYPE(tmp[0]); } } """ @@ -1749,12 +2045,13 @@ layout (push_constant) uniform parameter float param1; float param2; } p;""" -generic_unary_op_funcs = """ +generic_unary_op_layout = """ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; -layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];};""" +generic_unary_op_funcs = """ uint src0_idx(uint idx) { const uint i03 = idx / (p.ne02*p.ne01*p.ne00); const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; @@ -1782,7 +2079,7 @@ void main() { } """ -generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_funcs}\n{generic_unary_op_main}" +generic_unary_op_combined = f"{generic_unary_op_head}\n{generic_unary_op_layout}\n{generic_unary_op_funcs}\n{generic_unary_op_main}" generic_binary_op_head = """#version 450 @@ -1798,13 +2095,14 @@ layout (push_constant) uniform parameter float param1; float param2; } p;""" -generic_binary_op_funcs = """ +generic_binary_op_layout = """ layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];};""" +generic_binary_op_funcs = """ uint src0_idx(uint idx) { const uint i03 = idx / (p.ne02*p.ne01*p.ne00); const uint i03_offset = i03 * p.ne02*p.ne01*p.ne00; @@ -1843,7 +2141,7 @@ void main() { } """ -generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_funcs}\n{generic_binary_op_main}" +generic_binary_op_combined = f"{generic_binary_op_head}\n{generic_binary_op_layout}\n{generic_binary_op_funcs}\n{generic_binary_op_main}" # MUL F32 mul_body = """ @@ -1859,7 +2157,7 @@ add_body = """ # SCALE scale_body = """ - data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(p.param1)); + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(p.param1)); } """ @@ -1934,7 +2232,7 @@ void main() { const uint iybs = i00 - i00%QUANT_K; // dst block start index const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; - DEQUANT_FUNC + vec2 v = dequantize(ib, iqs, 0); data_d[d_offset + iybs + iqs ] = D_TYPE(v.x); data_d[d_offset + iybs + iqs + y_offset] = D_TYPE(v.y); @@ -2033,7 +2331,11 @@ void main() { } const uint i = row*p.ncols + col; - data_d[i] = D_TYPE(data_a[i] - float(uint(col > p.n_past + row % p.rows_per_channel) * 0xFFFFFFFF)); + if (col > p.n_past + row % p.rows_per_channel) { + data_d[i] = D_TYPE(uintBitsToFloat(0xFF800000)); + } else { + data_d[i] = D_TYPE(data_a[i]); + } } """ @@ -2053,8 +2355,6 @@ void main() { const uint row = gl_WorkGroupID.x; const uint tid = gl_LocalInvocationID.x; - const float eps = 1e-5f; - sum[tid] = vec2(0.0f, 0.0f); [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { @@ -2074,7 +2374,7 @@ void main() { const float mean = sum[0].x / p.KX; const float var = sum[0].y / p.KX - mean * mean; - const float inv_std = inversesqrt(var + 1e-5f); + const float inv_std = inversesqrt(var + p.param1); [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std); @@ -2175,7 +2475,7 @@ void main() { vals[tid] = uintBitsToFloat(0xFF800000); [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { - vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * data_c[col] : 0.0f)); + vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * FLOAT_TYPE(data_c[col]) : 0.0f)); } barrier(); @@ -2545,13 +2845,13 @@ async def main(): stream.clear() stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) stream.clear() stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) @@ -2603,6 +2903,68 @@ async def main(): tasks.append(string_to_spv("matmul_q6_k_f32", "".join(stream), {"LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_q6_k_f32_aligned", "".join(stream), {"LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + # MUL_MAT_ID + # stream.clear() + # stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # tasks.append(string_to_spv("matmul_id_f16", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_f16_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + + # tasks.append(string_to_spv("matmul_id_f16_f32", "".join(stream), {"MUL_MAT_ID": "1", "A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_f16_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q4_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q4_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_1_defines, mulmat_body1, mulmat_load_q4_1, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q4_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q4_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_0_defines, mulmat_body1, mulmat_load_q5_0, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q5_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q5_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_1_defines, mulmat_body1, mulmat_load_q5_1, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q5_1_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_1", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q5_1_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_1", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q8_0_defines, mulmat_body1, mulmat_load_q8_0, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q8_0_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q8_0", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q8_0_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q8_0", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q2_K_defines, mulmat_body1, mulmat_load_q2_K, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q2_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q2_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q2_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q2_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q3_K_defines, mulmat_body1, mulmat_load_q3_K, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q3_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q3_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q3_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q3_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_K_defines, mulmat_body1, mulmat_load_q4_K, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q4_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q4_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q4_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q4_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q5_K_defines, mulmat_body1, mulmat_load_q5_K, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q5_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q5_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q5_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q5_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # stream.clear() + # stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q6_K_defines, mulmat_body1, mulmat_load_q6_K, mulmat_body2)) + # tasks.append(string_to_spv("matmul_id_q6_k_f32", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "A_TYPE": "block_q6_K", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + # tasks.append(string_to_spv("matmul_id_q6_k_f32_aligned", "".join(stream), {"MUL_MAT_ID": "1", "LOAD_VEC_A": 2, "LOAD_VEC_B": load_vec, "A_TYPE": "block_q6_K", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + # Shaders where precision is needed, so no fp16 version # mul mat vec @@ -2611,31 +2973,34 @@ async def main(): stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_float_dequant_func, mul_mat_vec_body)) + stream.extend((shader_f16_defines, mul_mat_vec_layout, shader_float_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body)) + stream.extend((shader_q4_0_defines, mul_mat_vec_layout, shader_q4_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body)) + stream.extend((shader_q4_1_defines, mul_mat_vec_layout, shader_q4_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body)) + stream.extend((shader_q5_0_defines, mul_mat_vec_layout, shader_q5_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body)) + stream.extend((shader_q5_1_defines, mul_mat_vec_layout, shader_q5_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body)) + stream.extend((shader_q8_0_defines, mul_mat_vec_layout, shader_q8_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body)) + stream.extend((shader_q2_K_defines, mul_mat_vec_layout, mul_mat_vec_q2_K_body)) elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, mul_mat_vec_q3_K_body)) + stream.extend((shader_q3_K_defines, mul_mat_vec_layout, mul_mat_vec_q3_K_body)) elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, mul_mat_vec_q4_K_body)) + stream.extend((shader_q4_K_defines, mul_mat_vec_layout, mul_mat_vec_q4_K_body)) elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, mul_mat_vec_q5_K_body)) + stream.extend((shader_q5_K_defines, mul_mat_vec_layout, mul_mat_vec_q5_K_body)) elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, mul_mat_vec_q6_K_body)) + stream.extend((shader_q6_K_defines, mul_mat_vec_layout, mul_mat_vec_q6_K_body)) else: continue - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f16_f32", "".join(stream), {"B_TYPE": "float16_t", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) + + # tasks.append(string_to_spv(f"mul_mat_vec_id_{type_names[i]}_f32", "".join(stream), {"MUL_MAT_ID": "1", "B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) # Dequant shaders for i in range(0, VK_NUM_TYPES): @@ -2677,20 +3042,20 @@ async def main(): optimization_workaround = False if i == GGML_TYPE_F32: - stream.extend((shader_f32_defines, generic_binary_op_funcs, get_rows_float_body)) + stream.extend((shader_f32_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body)) elif i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, generic_binary_op_funcs, get_rows_float_body)) + stream.extend((shader_f16_defines, generic_binary_op_layout, generic_binary_op_funcs, get_rows_float_body)) optimization_workaround = True elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body)) + stream.extend((shader_q4_0_defines, generic_binary_op_layout, shader_q4_0_dequant_func, generic_binary_op_funcs, get_rows_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body)) + stream.extend((shader_q4_1_defines, generic_binary_op_layout, shader_q4_1_dequant_func, generic_binary_op_funcs, get_rows_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body)) + stream.extend((shader_q5_0_defines, generic_binary_op_layout, shader_q5_0_dequant_func, generic_binary_op_funcs, get_rows_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body)) + stream.extend((shader_q5_1_defines, generic_binary_op_layout, shader_q5_1_dequant_func, generic_binary_op_funcs, get_rows_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body)) + stream.extend((shader_q8_0_defines, generic_binary_op_layout, shader_q8_0_dequant_func, generic_binary_op_funcs, get_rows_body)) else: continue @@ -2729,6 +3094,7 @@ async def main(): tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("soft_max_f32", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "C_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("soft_max_f32_f16", f"{soft_max_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float16_t", "C_TYPE": "float16_t", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) From eaf4bd8b399a6ed4b834f91d22409d2a05c4d266 Mon Sep 17 00:00:00 2001 From: slaren <slarengh@gmail.com> Date: Fri, 10 May 2024 01:04:12 +0200 Subject: [PATCH 073/100] eval-callback : fix conversion to float (#7184) --- examples/eval-callback/eval-callback.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index 29b5f3b3c..e670d3769 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -52,15 +52,15 @@ static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0]; float v; if (type == GGML_TYPE_F16) { - v = ggml_fp16_to_fp32(*(ggml_fp16_t *) data + i); + v = ggml_fp16_to_fp32(*(ggml_fp16_t *) &data[i]); } else if (type == GGML_TYPE_F32) { - v = *(float *) data + i; + v = *(float *) &data[i]; } else if (type == GGML_TYPE_I32) { - v = (float) *(int32_t *) data + i; + v = (float) *(int32_t *) &data[i]; } else if (type == GGML_TYPE_I16) { - v = (float) *(int16_t *) data + i; + v = (float) *(int16_t *) &data[i]; } else if (type == GGML_TYPE_I8) { - v = (float) *(int8_t *) data + i; + v = (float) *(int8_t *) &data[i]; } else { GGML_ASSERT(false); } From 8c570c9496212073079476651c7517c02581101f Mon Sep 17 00:00:00 2001 From: Ouadie EL FAROUKI <ouadie.elfarouki@codeplay.com> Date: Fri, 10 May 2024 01:32:15 +0100 Subject: [PATCH 074/100] Minor arithmetic improvement to mmvq wrapper kernel (#7172) --- ggml-sycl.cpp | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 57fe4ea3d..79aec4d9f 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8330,24 +8330,26 @@ static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict_ const int blocks_per_row = ncols / qk; const int blocks_per_warp = vdr * WARP_SIZE / qi; -// partial sum for each thread + const int qi_vdr = (qi / vdr); // N_threads processing 1 qk block + + // partial sum for each thread float tmp = 0.0f; const block_q_t * x = (const block_q_t *) vx; const block_q8_1 * y = (const block_q8_1 *) vy; - for (int i = item_ct1.get_local_id(2) / (qi / vdr); i < blocks_per_row; + for (int i = item_ct1.get_local_id(2) / qi_vdr; i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row*blocks_per_row + i; // x block index + const int ibx = row * blocks_per_row + i; // x block index - const int iby = i * (qk/QK8_1); // y block index that aligns with ibx + const int iby = i * (qk / QK8_1); // y block index that aligns with ibx - const int iqs = - vdr * - (item_ct1.get_local_id(2) % - (qi / vdr)); // x block quant index when casting the quants to int + const int iqs = + vdr * + (item_ct1.get_local_id(2) - + i * qi_vdr); // x block quant index when casting the quants to int - tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs); + tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs); } // sum up partial sums and write back result From d11afd665241c1b3910ab5f040d0216403019d87 Mon Sep 17 00:00:00 2001 From: Andrei <abetlen@gmail.com> Date: Fri, 10 May 2024 02:41:10 -0400 Subject: [PATCH 075/100] llava : fix moondream support (#7163) * Revert "Revert "llava : add support for moondream vision language model (#6899)"" This reverts commit 9da243b36ac0b9d609adfaaa4c8f1cc8c592f737. * Fix num_positions and embeddings initialization --- README.md | 1 + examples/llava/clip.cpp | 89 ++++++++++++++++++++++++++++++++--------- 2 files changed, 71 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index ff00f9966..9f2f8df64 100644 --- a/README.md +++ b/README.md @@ -140,6 +140,7 @@ Typically finetunes of the base models below are supported as well. - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) +- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) **HTTP server** diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f568f470c..95fbe3d02 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -104,6 +104,7 @@ static std::string format(const char * fmt, ...) { #define TN_POS_EMBD "%s.position_embd.weight" #define TN_CLASS_EMBD "v.class_embd" #define TN_PATCH_EMBD "v.patch_embd.weight" +#define TN_PATCH_BIAS "v.patch_embd.bias" #define TN_ATTN_K "%s.blk.%d.attn_k.%s" #define TN_ATTN_Q "%s.blk.%d.attn_q.%s" #define TN_ATTN_V "%s.blk.%d.attn_v.%s" @@ -425,6 +426,7 @@ struct clip_vision_model { // embeddings struct ggml_tensor * class_embedding; struct ggml_tensor * patch_embeddings; + struct ggml_tensor * patch_bias; struct ggml_tensor * position_embeddings; struct ggml_tensor * pre_ln_w; @@ -501,6 +503,11 @@ struct clip_ctx { bool use_gelu = false; int32_t ftype = 1; + bool has_class_embedding = true; + bool has_pre_norm = true; + bool has_post_norm = false; + bool has_patch_bias = false; + struct gguf_context * ctx_gguf; struct ggml_context * ctx_data; @@ -526,7 +533,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); const int num_patches_per_side = image_size / patch_size; GGML_UNUSED(num_patches_per_side); - const int num_positions = num_patches + 1; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); const int hidden_size = hparams.hidden_size; const int n_head = hparams.n_head; const int d_head = hidden_size / n_head; @@ -557,16 +564,23 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 inp = ggml_reshape_3d(ctx0, inp, num_patches, hidden_size, batch_size); inp = ggml_cont(ctx0, ggml_permute(ctx0, inp, 1, 0, 2, 3)); + if (ctx->has_patch_bias) { + // inp = ggml_add(ctx0, inp, ggml_repeat(ctx0, model.patch_bias, inp)); + inp = ggml_add(ctx0, inp, model.patch_bias); + } + // concat class_embeddings and patch_embeddings - struct ggml_tensor * embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); - ggml_set_name(embeddings, "embeddings"); - ggml_set_input(embeddings); + struct ggml_tensor * embeddings = inp; + if (ctx->has_class_embedding) { + embeddings = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, hidden_size, num_positions, batch_size); + ggml_set_name(embeddings, "embeddings"); + ggml_set_input(embeddings); + embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); + embeddings = ggml_acc(ctx0, embeddings, inp, + embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); + } - embeddings = ggml_acc(ctx0, embeddings, model.class_embedding, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], 0); - - embeddings = ggml_acc(ctx0, embeddings, inp, - embeddings->nb[1], embeddings->nb[2], embeddings->nb[3], model.class_embedding->nb[1]); struct ggml_tensor * positions = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, num_positions); ggml_set_name(positions, "positions"); @@ -576,7 +590,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 ggml_add(ctx0, embeddings, ggml_get_rows(ctx0, model.position_embeddings, positions)); // pre-layernorm - { + if (ctx->has_pre_norm) { embeddings = ggml_norm(ctx0, embeddings, eps); ggml_set_name(embeddings, "pre_ln"); @@ -664,6 +678,14 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = cur; } + // post-layernorm + if (ctx->has_post_norm) { + embeddings = ggml_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "post_ln"); + + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.post_ln_w), model.post_ln_b); + } + // llava projector { embeddings = ggml_reshape_2d(ctx0, embeddings, embeddings->ne[0], embeddings->ne[1]); @@ -1148,12 +1170,39 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { } + try { + vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); + new_clip->has_class_embedding = true; + } catch (const std::exception& e) { + new_clip->has_class_embedding = false; + } + + try { + vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); + vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); + new_clip->has_pre_norm = true; + } catch (std::exception & e) { + new_clip->has_pre_norm = false; + } + + try { + vision_model.post_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "weight")); + vision_model.post_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_POST, "v", "bias")); + new_clip->has_post_norm = true; + } catch (std::exception & e) { + new_clip->has_post_norm = false; + } + + try { + vision_model.patch_bias = get_tensor(new_clip->ctx_data, TN_PATCH_BIAS); + new_clip->has_patch_bias = true; + } catch (std::exception & e) { + new_clip->has_patch_bias = false; + } + try { vision_model.patch_embeddings = get_tensor(new_clip->ctx_data, TN_PATCH_EMBD); - vision_model.class_embedding = get_tensor(new_clip->ctx_data, TN_CLASS_EMBD); vision_model.position_embeddings = get_tensor(new_clip->ctx_data, format(TN_POS_EMBD, "v")); - vision_model.pre_ln_w = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "weight")); - vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); } catch(const std::exception& e) { LOG_TEE("%s: failed to load vision model tensors\n", __func__); } @@ -1797,7 +1846,7 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima const int image_size = hparams.image_size; const int patch_size = hparams.patch_size; const int num_patches = ((image_size / patch_size) * (image_size / patch_size)); - const int num_positions = num_patches + 1; + const int num_positions = num_patches + (ctx->has_class_embedding ? 1 : 0); { struct ggml_tensor * inp_raw = ggml_graph_get_tensor(gf, "inp_raw"); @@ -1825,12 +1874,14 @@ bool clip_image_batch_encode(clip_ctx * ctx, const int n_threads, const clip_ima } { - struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); + if (ctx->has_class_embedding) { + struct ggml_tensor * embeddings = ggml_graph_get_tensor(gf, "embeddings"); - void* zero_mem = malloc(ggml_nbytes(embeddings)); - memset(zero_mem, 0, ggml_nbytes(embeddings)); - ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); - free(zero_mem); + void* zero_mem = malloc(ggml_nbytes(embeddings)); + memset(zero_mem, 0, ggml_nbytes(embeddings)); + ggml_backend_tensor_set(embeddings, zero_mem, 0, ggml_nbytes(embeddings)); + free(zero_mem); + } } { From f89fe2732c5709f6e86d5f4aee2e6d2a561f2eb2 Mon Sep 17 00:00:00 2001 From: HanishKVC <hanishkvc@gmail.com> Date: Fri, 10 May 2024 15:51:58 +0530 Subject: [PATCH 076/100] Main+: optionally allow special tokens from user in interactive mode (#7097) @hanishkvc added a new `--interactive-specials` flag which would allow for inserting special tokens from user side into the embedding stream. --- common/common.cpp | 6 ++++++ common/common.h | 1 + examples/main/main.cpp | 2 +- 3 files changed, 8 insertions(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index 0535508ba..484e67334 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -901,6 +901,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.interactive = true; return true; } + if (arg == "--interactive-specials") { + params.interactive_specials = true; + return true; + } if (arg == "--embedding") { params.embedding = true; return true; @@ -1422,6 +1426,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -h, --help show this help message and exit\n"); printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); + printf(" --interactive-specials allow special tokens in user text, in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); @@ -2652,6 +2657,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str()); fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); + fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false"); fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); fprintf(stream, "keep: %d # default: 0\n", params.n_keep); fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); diff --git a/common/common.h b/common/common.h index 6f00a2cca..d80344f2a 100644 --- a/common/common.h +++ b/common/common.h @@ -140,6 +140,7 @@ struct gpt_params { bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode + bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 49acd6bab..f3e445c16 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -879,7 +879,7 @@ int main(int argc, char ** argv) { } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, params.interactive_specials); const auto line_sfx = ::llama_tokenize(ctx, params.input_suffix, false, true); LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp).c_str()); From 4e3880978f8b1bf546dd4e6f3b524d6b8739c49c Mon Sep 17 00:00:00 2001 From: Justine Tunney <jtunney@mozilla.com> Date: Fri, 10 May 2024 07:01:08 -0400 Subject: [PATCH 077/100] Fix memory bug in grammar parser (#7194) The llama.cpp grammar parser had a bug where forgetting to add a closing quotation mark to strings would cause parsing to crash. Anyone running a server on a public endpoint is advised to upgrade. To reproduce this bug ./llamafile -m foo.gguf -p bar --grammar 'root::="' Credit for discovering and reporting this issue goes to Eclypsium Security Researcher Richard Johnson <Richard.johnson@eclypsium.com>. --- common/common.cpp | 8 +++----- common/grammar-parser.cpp | 9 +++++++++ examples/llava/llava-cli.cpp | 5 +++++ examples/main/main.cpp | 4 ++++ 4 files changed, 21 insertions(+), 5 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 484e67334..ba1ecf0e5 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1371,14 +1371,12 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { std::replace(arg.begin(), arg.end(), '_', '-'); } - if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { throw std::invalid_argument("error: unknown argument: " + arg); } - } - - if (invalid_param) { - throw std::invalid_argument("error: invalid parameter for argument: " + arg); + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument: " + arg); + } } if (params.prompt_cache_all && diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index 2a1301569..fecb7cd71 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -142,6 +142,9 @@ namespace grammar_parser { pos++; last_sym_start = out_elements.size(); while (*pos != '"') { + if (!*pos) { + throw std::runtime_error("unexpected end of input"); + } auto char_pair = parse_char(pos); pos = char_pair.second; out_elements.push_back({LLAMA_GRETYPE_CHAR, char_pair.first}); @@ -156,6 +159,9 @@ namespace grammar_parser { } last_sym_start = out_elements.size(); while (*pos != ']') { + if (!*pos) { + throw std::runtime_error("unexpected end of input"); + } auto char_pair = parse_char(pos); pos = char_pair.second; enum llama_gretype type = last_sym_start < out_elements.size() @@ -164,6 +170,9 @@ namespace grammar_parser { out_elements.push_back({type, char_pair.first}); if (pos[0] == '-' && pos[1] != ']') { + if (!pos[1]) { + throw std::runtime_error("unexpected end of input"); + } auto endchar_pair = parse_char(pos + 1); pos = endchar_pair.second; out_elements.push_back({LLAMA_GRETYPE_CHAR_RNG_UPPER, endchar_pair.first}); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 157a680b5..da60ddf2f 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -189,6 +189,11 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ LOG_TEE("\n"); struct llama_sampling_context * ctx_sampling = llama_sampling_init(params->sparams); + if (!ctx_sampling) { + fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } + std::string response = ""; for (int i = 0; i < max_tgt_len; i++) { const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index f3e445c16..9dee41001 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -523,6 +523,10 @@ int main(int argc, char ** argv) { } struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); + if (!ctx_sampling) { + fprintf(stderr, "%s: failed to initialize sampling subsystem\n", __func__); + exit(1); + } while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict From 25c6e82e7a1ad25a42b0894e87d9b5c557409516 Mon Sep 17 00:00:00 2001 From: slaren <slarengh@gmail.com> Date: Fri, 10 May 2024 14:28:01 +0200 Subject: [PATCH 078/100] llama : use n_vocab to differentiate between mistral 7B and llama3 8B (#7200) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index e7b3fd8b4..2f1123d4e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3860,7 +3860,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = hparams.n_head == hparams.n_head_kv ? e_model::MODEL_7B : e_model::MODEL_8B; break; // LLaMa 8B v3 uses GQA + case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; case 40: model.type = e_model::MODEL_13B; break; case 48: model.type = e_model::MODEL_34B; break; case 60: model.type = e_model::MODEL_30B; break; From 8c660242d708d3913a2adc2b6e4a9ee9cf5e4ce7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Fri, 10 May 2024 17:53:04 +0300 Subject: [PATCH 079/100] convert : print "ignore_merges" field --- convert-hf-to-gguf-update.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index e64687722..b5eb41eac 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -161,6 +161,8 @@ for model in models: logger.info("normalizer: " + json.dumps(normalizer, indent=4)) pre_tokenizer = cfg["pre_tokenizer"] logger.info("pre_tokenizer: " + json.dumps(pre_tokenizer, indent=4)) + if "ignore_merges" in cfg["model"]: + logger.info("ignore_merges: " + json.dumps(cfg["model"]["ignore_merges"], indent=4)) logger.info("") From 18e437665ce626dddbd79119aa7498493e7cb13b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Fri, 10 May 2024 18:20:10 +0300 Subject: [PATCH 080/100] metal : fix flash attention kernel requirements (#7169) * metal : fix flash attention kernel requirements ggml-ci * metal : fix ggml_metal_supports_op ggml-ci --- ggml-metal.m | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index c6817f01f..18ce5b88a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -633,14 +633,14 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_DESC, argsort_f32_i32_desc, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_LEAKY_RELU_F32, leaky_relu_f32, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H64, flash_attn_ext_f16_h64, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H80, flash_attn_ext_f16_h80, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); @@ -772,8 +772,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_ARGSORT: case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: return true; + case GGML_OP_FLASH_ATTN_EXT: + return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: return ctx->support_simdgroup_reduction && From e849648888a11de13aaaa4cb2eda3f5a9c7b444d Mon Sep 17 00:00:00 2001 From: slaren <slarengh@gmail.com> Date: Fri, 10 May 2024 18:03:54 +0200 Subject: [PATCH 081/100] llama-bench : add pp+tg test type (#7199) --- examples/llama-bench/README.md | 18 +++++--- examples/llama-bench/llama-bench.cpp | 63 ++++++++++++++++++++++++---- scripts/compare-llama-bench.py | 8 +++- 3 files changed, 74 insertions(+), 15 deletions(-) diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 10f37b441..857840564 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -26,16 +26,21 @@ options: -m, --model <filename> (default: models/7B/ggml-model-q4_0.gguf) -p, --n-prompt <n> (default: 512) -n, --n-gen <n> (default: 128) - -b, --batch-size <n> (default: 512) - -ctk <t>, --cache-type-k <t> (default: f16) - -ctv <t>, --cache-type-v <t> (default: f16) - -t, --threads <n> (default: 112) + -pg <pp,tg> (default: 512,128) + -b, --batch-size <n> (default: 2048) + -ub, --ubatch-size <n> (default: 512) + -ctk, --cache-type-k <t> (default: f16) + -ctv, --cache-type-v <t> (default: f16) + -t, --threads <n> (default: 16) -ngl, --n-gpu-layers <n> (default: 99) -sm, --split-mode <none|layer|row> (default: layer) -mg, --main-gpu <i> (default: 0) -nkvo, --no-kv-offload <0|1> (default: 0) + -fa, --flash-attn <0|1> (default: 0) -mmp, --mmap <0|1> (default: 1) - -ts, --tensor_split <ts0/ts1/..> (default: 0) + --numa <distribute|isolate|numactl> (default: disabled) + -embd, --embeddings <0|1> (default: 0) + -ts, --tensor-split <ts0/ts1/..> (default: 0) -r, --repetitions <n> (default: 5) -o, --output <csv|json|md|sql> (default: md) -v, --verbose (default: 0) @@ -43,10 +48,11 @@ options: Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` -llama-bench can perform two types of tests: +llama-bench can perform three types of tests: - Prompt processing (pp): processing a prompt in batches (`-p`) - Text generation (tg): generating a sequence of tokens (`-n`) +- Prompt processing + text generation (pg): processing a prompt followed by generating a sequence of tokens (`-pg`) With the exception of `-r`, `-o` and `-v`, all options can be specified multiple times to run multiple tests. Each pp and tg test is run with all combinations of the specified options. To specify multiple values for an option, the values can be separated by commas (e.g. `-n 16,32`), or the option can be specified multiple times (e.g. `-n 16 -n 32`). diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 40128ec44..8b965e199 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -161,10 +161,17 @@ static const char * split_mode_str(llama_split_mode mode) { } } +static std::string pair_str(const std::pair<int, int> & p) { + static char buf[32]; + snprintf(buf, sizeof(buf), "%d,%d", p.first, p.second); + return buf; +} + struct cmd_params { std::vector<std::string> model; std::vector<int> n_prompt; std::vector<int> n_gen; + std::vector<std::pair<int, int>> n_pg; std::vector<int> n_batch; std::vector<int> n_ubatch; std::vector<ggml_type> type_k; @@ -188,6 +195,7 @@ static const cmd_params cmd_params_defaults = { /* model */ {"models/7B/ggml-model-q4_0.gguf"}, /* n_prompt */ {512}, /* n_gen */ {128}, + /* n_pg */ {{512, 128}}, /* n_batch */ {2048}, /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, @@ -215,10 +223,11 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -m, --model <filename> (default: %s)\n", join(cmd_params_defaults.model, ",").c_str()); printf(" -p, --n-prompt <n> (default: %s)\n", join(cmd_params_defaults.n_prompt, ",").c_str()); printf(" -n, --n-gen <n> (default: %s)\n", join(cmd_params_defaults.n_gen, ",").c_str()); + printf(" -pg <pp,tg> (default: %s)\n", join(transform_to_str(cmd_params_defaults.n_pg, pair_str), ",").c_str()); printf(" -b, --batch-size <n> (default: %s)\n", join(cmd_params_defaults.n_batch, ",").c_str()); - printf(" -ub N, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); - printf(" -ctk <t>, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); - printf(" -ctv <t>, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); + printf(" -ub, --ubatch-size <n> (default: %s)\n", join(cmd_params_defaults.n_ubatch, ",").c_str()); + printf(" -ctk, --cache-type-k <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_k, ggml_type_name), ",").c_str()); + printf(" -ctv, --cache-type-v <t> (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads <n> (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers <n> (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); printf(" -sm, --split-mode <none|layer|row> (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); @@ -304,6 +313,17 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split<int>(argv[i], split_delim); params.n_gen.insert(params.n_gen.end(), p.begin(), p.end()); + } else if (arg == "-pg") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split<std::string>(argv[i], ','); + if (p.size() != 2) { + invalid_param = true; + break; + } + params.n_pg.push_back({std::stoi(p[0]), std::stoi(p[1])}); } else if (arg == "-b" || arg == "--batch-size") { if (++i >= argc) { invalid_param = true; @@ -493,6 +513,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.model.empty()) { params.model = cmd_params_defaults.model; } if (params.n_prompt.empty()) { params.n_prompt = cmd_params_defaults.n_prompt; } if (params.n_gen.empty()) { params.n_gen = cmd_params_defaults.n_gen; } + if (params.n_pg.empty()) { params.n_pg = cmd_params_defaults.n_pg; } if (params.n_batch.empty()) { params.n_batch = cmd_params_defaults.n_batch; } if (params.n_ubatch.empty()) { params.n_ubatch = cmd_params_defaults.n_ubatch; } if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } @@ -632,6 +653,31 @@ static std::vector<cmd_params_instance> get_cmd_params_instances(const cmd_param }; instances.push_back(instance); } + + for (const auto & n_pg : params.n_pg) { + if (n_pg.first == 0 && n_pg.second == 0) { + continue; + } + cmd_params_instance instance = { + /* .model = */ m, + /* .n_prompt = */ n_pg.first, + /* .n_gen = */ n_pg.second, + /* .n_batch = */ nb, + /* .n_ubatch = */ nub, + /* .type_k = */ tk, + /* .type_v = */ tv, + /* .n_threads = */ nt, + /* .n_gpu_layers = */ nl, + /* .split_mode = */ sm, + /* .main_gpu = */ mg, + /* .no_kv_offload= */ nkvo, + /* .flash_attn = */ fa, + /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, + /* .embeddings = */ embd, + }; + instances.push_back(instance); + } } return instances; @@ -965,6 +1011,9 @@ struct markdown_printer : public printer { if (field == "n_gpu_layers") { return 3; } + if (field == "test") { + return 13; + } int width = std::max((int)field.length(), 10); @@ -1091,12 +1140,11 @@ struct markdown_printer : public printer { value = test::get_backend(); } else if (field == "test") { if (t.n_prompt > 0 && t.n_gen == 0) { - snprintf(buf, sizeof(buf), "pp %d", t.n_prompt); + snprintf(buf, sizeof(buf), "pp%d", t.n_prompt); } else if (t.n_gen > 0 && t.n_prompt == 0) { - snprintf(buf, sizeof(buf), "tg %d", t.n_gen); + snprintf(buf, sizeof(buf), "tg%d", t.n_gen); } else { - assert(false); - exit(1); + snprintf(buf, sizeof(buf), "pp%d+tg%d", t.n_prompt, t.n_gen); } value = buf; } else if (field == "t/s") { @@ -1297,6 +1345,7 @@ int main(int argc, char ** argv) { llama_kv_cache_clear(ctx); uint64_t t_start = get_time_ns(); + if (t.n_prompt > 0) { test_prompt(ctx, t.n_prompt, 0, t.n_batch, t.n_threads); } diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index fed3c1ee3..0ede9e67c 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -325,8 +325,12 @@ table = [] for row in rows_show: n_prompt = int(row[-4]) n_gen = int(row[-3]) - assert n_prompt == 0 or n_gen == 0 - test_name = f"tg{n_gen}" if n_prompt == 0 else f"pp{n_prompt}" + if n_prompt != 0 and n_gen == 0: + test_name = f"pp{n_prompt}" + elif n_prompt == 0 and n_gen != 0: + test_name = f"tg{n_gen}" + else: + test_name = f"pp{n_prompt}+tg{n_gen}" # Regular columns test name avg t/s values Speedup # VVVVVVVVVVVVV VVVVVVVVV VVVVVVVVVVVVVV VVVVVVV table.append(list(row[:-4]) + [test_name] + list(row[-2:]) + [float(row[-1]) / float(row[-2])]) From 9cb317f77e53067f7a138cc89ef7657148eae8e6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 10:32:41 +0300 Subject: [PATCH 082/100] ggml : full ALiBi support (#7192) * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * ggml : fix assert message * vulkan : add dev notes * ggml : require mask when using ALiBi ggml-ci * convert : fix convert for refact models --- convert-hf-to-gguf.py | 12 ++ ggml-cuda.cu | 5 - ggml-cuda/alibi.cu | 63 ------- ggml-cuda/alibi.cuh | 5 - ggml-cuda/fattn.cu | 72 ++++++-- ggml-cuda/softmax.cu | 55 +++--- ggml-kompute.cpp | 12 +- ggml-metal.m | 148 ++++++---------- ggml-metal.metal | 120 ++++++------- ggml-sycl.cpp | 138 ++------------- ggml-vulkan.cpp | 6 +- ggml.c | 309 +++++---------------------------- ggml.h | 18 +- gguf-py/gguf/tensor_mapping.py | 4 + llama.cpp | 178 +++++++------------ tests/test-backend-ops.cpp | 30 ++-- 16 files changed, 350 insertions(+), 825 deletions(-) delete mode 100644 ggml-cuda/alibi.cu delete mode 100644 ggml-cuda/alibi.cuh diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 1dc18b2a5..3315ca74b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1013,6 +1013,18 @@ class StarCoderModel(Model): class RefactModel(Model): model_arch = gguf.MODEL_ARCH.REFACT + def set_vocab(self): + super().set_vocab() + + # TODO: how to determine special FIM tokens automatically? + special_vocab = gguf.SpecialVocab(self.dir_model, load_merges=False, + special_token_types = ['prefix', 'suffix', 'middle', 'fsep', 'eot']) + special_vocab._set_special_token("prefix", 1) + special_vocab._set_special_token("suffix", 3) + special_vocab._set_special_token("middle", 2) + special_vocab._set_special_token("fsep", 4) # is this correct? + special_vocab.add_to_gguf(self.gguf_writer) + def set_gguf_parameters(self): hidden_dim = self.hparams["n_embd"] inner_dim = 4 * hidden_dim diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 6f89a7cc3..c5c778796 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4,7 +4,6 @@ #include "ggml-cuda/common.cuh" #include "ggml-cuda/acc.cuh" -#include "ggml-cuda/alibi.cuh" #include "ggml-cuda/arange.cuh" #include "ggml-cuda/argsort.cuh" #include "ggml-cuda/binbcast.cuh" @@ -2277,9 +2276,6 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_OP_ROPE: ggml_cuda_op_rope(ctx, dst); break; - case GGML_OP_ALIBI: - ggml_cuda_op_alibi(ctx, dst); - break; case GGML_OP_IM2COL: ggml_cuda_op_im2col(ctx, dst); break; @@ -2829,7 +2825,6 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: - case GGML_OP_ALIBI: case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: diff --git a/ggml-cuda/alibi.cu b/ggml-cuda/alibi.cu deleted file mode 100644 index 6c7f1fd95..000000000 --- a/ggml-cuda/alibi.cu +++ /dev/null @@ -1,63 +0,0 @@ -#include "alibi.cuh" - -static __global__ void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, - const int n_heads_log2_floor, const float m0, const float m1) { - const int col = blockDim.x*blockIdx.x + threadIdx.x; - - if (col >= ncols) { - return; - } - - const int row = blockDim.y*blockIdx.y + threadIdx.y; - const int i = row*ncols + col; - - const int k = row/k_rows; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - dst[i] = col * m_k + x[i]; -} - -static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, - const int k_rows, const int n_heads_log2_floor, const float m0, - const float m1, cudaStream_t stream) { - const dim3 block_dims(CUDA_ALIBI_BLOCK_SIZE, 1, 1); - const int num_blocks_x = (ncols + CUDA_ALIBI_BLOCK_SIZE - 1) / (CUDA_ALIBI_BLOCK_SIZE); - const dim3 block_nums(num_blocks_x, nrows, 1); - alibi_f32<<<block_nums, block_dims, 0, stream>>>(x, dst, ncols, k_rows, n_heads_log2_floor, m0, m1); -} - -void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * src0 = dst->src[0]; - const float * src0_d = (const float *)src0->data; - float * dst_d = (float *)dst->data; - cudaStream_t stream = ctx.stream(); - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - //GGML_ASSERT(ne01 + n_past == ne00); - GGML_ASSERT(n_head == ne02); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - alibi_f32_cuda(src0_d, dst_d, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, stream); -} diff --git a/ggml-cuda/alibi.cuh b/ggml-cuda/alibi.cuh deleted file mode 100644 index 630adfc7f..000000000 --- a/ggml-cuda/alibi.cuh +++ /dev/null @@ -1,5 +0,0 @@ -#include "common.cuh" - -#define CUDA_ALIBI_BLOCK_SIZE 32 - -void ggml_cuda_op_alibi(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index 7c486f482..ac5d6672b 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -23,6 +23,10 @@ static __global__ void flash_attn_vec_ext_f16( float * __restrict__ dst, float2 * __restrict__ dst_meta, const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, const int ne00, const int ne01, const int ne02, @@ -58,6 +62,18 @@ static __global__ void flash_attn_vec_ext_f16( const int stride_KV = nb11 / sizeof(half); const int stride_KV2 = nb11 / sizeof(half2); + half slopeh = __float2half(1.0f); + + // ALiBi + if (max_bias > 0.0f) { + const int h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slopeh = __float2half(powf(base, exph)); + } + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); constexpr int nwarps = D / WARP_SIZE; const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; @@ -141,7 +157,7 @@ static __global__ void flash_attn_vec_ext_f16( for (int j = 0; j < ncols; ++j) { sum2[j] = warp_reduce_sum(sum2[j]); half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); if (ncols == 1) { kqmax_new = ggml_cuda_hmax(kqmax_new, sum); @@ -249,6 +265,10 @@ static __global__ void flash_attn_ext_f16( float * __restrict__ dst, float2 * __restrict__ dst_meta, const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, const int ne00, const int ne01, const int ne02, @@ -305,6 +325,20 @@ static __global__ void flash_attn_ext_f16( const int stride_Q = nb01 / sizeof(float); const int stride_KV = nb11 / sizeof(half); + half slopeh = __float2half(1.0f); + half2 slope2 = make_half2(1.0f, 1.0f); + + // ALiBi + if (max_bias > 0.0f) { + const int h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slopeh = __float2half(powf(base, exph)); + slope2 = make_half2(slopeh, slopeh); + } + frag_b Q_b[D/16][ncols/frag_n]; // A single buffer for temporarily holding tiles of KQ and VKQ parts: @@ -421,7 +455,7 @@ static __global__ void flash_attn_ext_f16( for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { const int k = k0 + threadIdx.x; - KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; + KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); } KQ_max_new = warp_reduce_max(KQ_max_new); @@ -464,7 +498,7 @@ static __global__ void flash_attn_ext_f16( for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { const int k = k0 + threadIdx.x; - KQ2_tmp[k0/WARP_SIZE] += mask ? mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); + KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); } KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); @@ -710,8 +744,17 @@ template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_ const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); const int shmem = 0; - float scale; - memcpy(&scale, KQV->op_params, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks> <<<blocks_num, block_dim, shmem, main_stream>>> ( @@ -720,7 +763,7 @@ template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_ (const char *) V->data, mask ? ((const char *) mask->data) : nullptr, parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, + scale, max_bias, m0, m1, n_head_log2, Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], K->ne[0], K->ne[1], K->ne[2], K->ne[3], mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, @@ -761,8 +804,17 @@ template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename K const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]); const int shmem = 0; - float scale; - memcpy(&scale, KQV->op_params, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); flash_attn_ext_f16<D, cols_per_block, nwarps, get_VKQ_stride(D, nwarps, frag_m), parallel_blocks, KQ_acc_t> <<<blocks_num, block_dim, shmem, main_stream>>> ( @@ -771,7 +823,7 @@ template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename K (const char *) V->data, mask ? ((const char *) mask->data) : nullptr, (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, + scale, max_bias, m0, m1, n_head_log2, Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], K->ne[0], K->ne[1], K->ne[2], K->ne[3], mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, @@ -837,7 +889,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; - const int32_t precision = KQV->op_params[1]; + const int32_t precision = KQV->op_params[2]; if (!fp16_mma_available(cc)) { GGML_ASSERT(precision == GGML_PREC_DEFAULT); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index 6ed225999..ca85285a3 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -11,7 +11,7 @@ __device__ float __forceinline__ t2f32<half>(half val) { } template <bool vals_smem, int ncols_template, int block_size_template, typename T> -static __global__ void soft_max_f32(const float * x, const T * mask, const T * pos, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { +static __global__ void soft_max_f32(const float * x, const T * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2) { const int ncols = ncols_template == 0 ? ncols_par : ncols_template; const int tid = threadIdx.x; @@ -23,16 +23,16 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { const int h = rowx/nrows_y; // head index const float base = h < n_head_log2 ? m0 : m1; - const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - slope = powf(base, exp); + slope = powf(base, exph); } extern __shared__ float data_soft_max_f32[]; @@ -53,7 +53,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p const int64_t ix = (int64_t)rowx*ncols + col; const int64_t iy = (int64_t)rowy*ncols + col; - const float val = x[ix]*scale + (mask ? t2f32(mask[iy]) : 0.0f) + (pos ? slope*t2f32(pos[col]) : 0.0f); + const float val = x[ix]*scale + (mask ? slope*t2f32(mask[iy]) : 0.0f); vals[col] = val; max_val = max(max_val, val); @@ -125,7 +125,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, const T * p } template<typename T> -static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { +static void soft_max_f32_cuda(const float * x, const T * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, cudaStream_t stream) { int nth = WARP_SIZE; while (nth < ncols_x && nth < CUDA_SOFT_MAX_BLOCK_SIZE) nth *= 2; const dim3 block_dims(nth, 1, 1); @@ -133,8 +133,8 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, fl const size_t shmem = (GGML_PAD(ncols_x, WARP_SIZE) + WARP_SIZE)*sizeof(float); static_assert(CUDA_SOFT_MAX_BLOCK_SIZE == 1024, "These values need to be adjusted."); - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -142,43 +142,42 @@ static void soft_max_f32_cuda(const float * x, const T * mask, const T * pos, fl if (shmem < ggml_cuda_info().devices[ggml_cuda_get_device()].smpb) { switch (ncols_x) { case 32: - soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 32, 32><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 64: - soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 64, 64><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 128: - soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 128, 128><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 256: - soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 256, 256><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 512: - soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 512, 512><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 1024: - soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 1024, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 2048: - soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 2048, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; case 4096: - soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 4096, 1024><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; default: - soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<true, 0, 0><<<block_nums, block_dims, shmem, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); break; } } else { const size_t shmem_low = WARP_SIZE*sizeof(float); - soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, pos, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); + soft_max_f32<false, 0, 0><<<block_nums, block_dims, shmem_low, stream>>>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2); } } void ggml_cuda_op_soft_max(ggml_backend_cuda_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]; const float * src0_d = (const float *)src0->data; const void * src1_d = src1 ? (const void *)src1->data : nullptr; @@ -190,7 +189,6 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { GGML_ASSERT( dst->type == GGML_TYPE_F32); GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -202,26 +200,15 @@ void ggml_cuda_op_soft_max(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); - // positions tensor - void * src2_d = nullptr; - - const bool use_src2 = src2 != nullptr; - - if (use_src2) { - src2_d = (void *)src2->data; - } - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); if (use_f16) { const half * src1_dd = (const half *)src1_d; - const half * src2_dd = (const half *)src2_d; - soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); } else { const float * src1_dd = (const float *)src1_d; - const float * src2_dd = (const float *)src2_d; - soft_max_f32_cuda(src0_d, src1_dd, src2_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); + soft_max_f32_cuda(src0_d, src1_dd, dst_d, ne00, nrows_x, nrows_y, scale, max_bias, stream); } } diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 9a469821d..3f033d58b 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1559,12 +1559,18 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml case GGML_OP_SOFT_MAX: { float scale; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias; -#pragma message("TODO: add ggml_vk_soft_max() F16/F32 src1 and src2 support") + memcpy(&scale, (float *)dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *)dst->op_params + 1, sizeof(float)); + +#pragma message("TODO: add ggml_vk_soft_max() F16 src1 support") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1t == GGML_TYPE_F32); - GGML_ASSERT(src2 == nullptr); + +#pragma message("TODO: add ALiBi support") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") + GGML_ASSERT(max_bias == 0.0f); ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); } break; diff --git a/ggml-metal.m b/ggml-metal.m index 18ce5b88a..1bbb8fb4f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -169,7 +169,6 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, - GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, @@ -623,7 +622,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ4_XS_F32, mul_mm_id_iq4_xs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); @@ -759,7 +757,6 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_GROUP_NORM: return ctx->support_simdgroup_reduction; case GGML_OP_NORM: - case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: return true; @@ -1358,13 +1355,12 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_SOFT_MAX: { GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F16 || src1->type == GGML_TYPE_F32); - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F16 || src2->type == GGML_TYPE_F32); int nth = 32; // SIMD width id<MTLComputePipelineState> pipeline = nil; - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); if (ne00%4 == 0) { while (nth < ne00/4 && nth < 256) { @@ -1395,8 +1391,8 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t nrows_x = ggml_nrows(src0); const int64_t nrows_y = src0->ne[1]; - const uint32_t n_head_kv = nrows_x/nrows_y; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head_kv)); + const uint32_t n_head = nrows_x/nrows_y; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -1408,20 +1404,15 @@ static enum ggml_status ggml_metal_graph_compute( } else { [encoder setBuffer:id_src0 offset:offs_src0 atIndex:1]; } - if (id_src2) { - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - } else { - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; - } - [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:4]; - [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:5]; - [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:6]; - [encoder setBytes:&scale length:sizeof(scale) atIndex:7]; - [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:8]; - [encoder setBytes:&m0 length:sizeof(m0) atIndex:9]; - [encoder setBytes:&m1 length:sizeof(m1) atIndex:10]; - [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:11]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:4]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:5]; + [encoder setBytes:&scale length:sizeof(scale) atIndex:6]; + [encoder setBytes:&max_bias length:sizeof(max_bias) atIndex:7]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:8]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:9]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:10]; [encoder setThreadgroupMemoryLength:32*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01*ne02*ne03, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; @@ -2226,49 +2217,6 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(nrows, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT((src0t == GGML_TYPE_F32)); - - const int nth = MIN(1024, ne00); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_ALIBI_F32].pipeline; - - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:2]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:6]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:10]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:14]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&m0 length:sizeof( float) atIndex:18]; - [encoder setBytes:&m1 length:sizeof( float) atIndex:19]; - [encoder setBytes:&n_heads_log2_floor length:sizeof(int) atIndex:20]; - - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; - } break; case GGML_OP_ROPE: { GGML_ASSERT(ne10 == ne02); @@ -2566,7 +2514,7 @@ static enum ggml_status ggml_metal_graph_compute( "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); - const int64_t ne31 = src3 ? src3->ne[1] : 0; + //const int64_t ne31 = src3 ? src3->ne[1] : 0; const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); const int64_t ne33 = src3 ? src3->ne[3] : 0; GGML_UNUSED(ne33); @@ -2578,7 +2526,16 @@ static enum ggml_status ggml_metal_graph_compute( const enum ggml_type src2t = src2 ? src2->type : GGML_TYPE_COUNT; GGML_UNUSED(src2t); float scale; - memcpy(&scale, dst->op_params, sizeof(float)); + float max_bias; + + memcpy(&scale, ((int32_t *) dst->op_params) + 0, sizeof(scale)); + memcpy(&max_bias, ((int32_t *) dst->op_params) + 1, sizeof(max_bias)); + + const uint32_t n_head = src0->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); id<MTLComputePipelineState> pipeline = nil; @@ -2615,34 +2572,37 @@ static enum ggml_status ggml_metal_graph_compute( } [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; - [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12]; - [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15]; - [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16]; - [encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19]; - [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20]; - [encoder setBytes:&ne31 length:sizeof( int64_t) atIndex:21]; - [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:22]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:23]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:24]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:25]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:26]; - [encoder setBytes:&scale length:sizeof( float) atIndex:27]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + [encoder setBuffer:id_src3 offset:offs_src3 atIndex:3]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:4]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:8]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:12]; + [encoder setBytes:&ne10 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne11 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne12 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&ne13 length:sizeof( int64_t) atIndex:16]; + [encoder setBytes:&nb10 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb11 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb12 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&nb13 length:sizeof(uint64_t) atIndex:20]; + [encoder setBytes:&nb31 length:sizeof(uint64_t) atIndex:21]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:22]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:23]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:24]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:25]; + [encoder setBytes:&scale length:sizeof( float) atIndex:26]; + [encoder setBytes:&max_bias length:sizeof( float) atIndex:27]; + [encoder setBytes:&m0 length:sizeof(m0) atIndex:28]; + [encoder setBytes:&m1 length:sizeof(m1) atIndex:29]; + [encoder setBytes:&n_head_log2 length:sizeof(n_head_log2) atIndex:30]; if (!use_vec_kernel) { // half8x8 kernel diff --git a/ggml-metal.metal b/ggml-metal.metal index 46c7d5039..ee9de57a3 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -356,7 +356,6 @@ template<typename T> kernel void kernel_soft_max( device const char * src0, device const char * src1, - device const char * src2, device char * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -378,10 +377,9 @@ kernel void kernel_soft_max( device const float * psrc0 = (device const float *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00 : nullptr; - device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; device float * pdst = (device float *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00); - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -397,7 +395,7 @@ kernel void kernel_soft_max( float lmax = -INFINITY; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)); + lmax = MAX(lmax, psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)); } // find the max value in the block @@ -422,7 +420,7 @@ kernel void kernel_soft_max( // parallel sum float lsum = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f)) - max_val); + const float exp_psrc0 = exp((psrc0[i00]*scale + (pmask ? slope*pmask[i00] : 0.0f)) - max_val); lsum += exp_psrc0; pdst[i00] = exp_psrc0; } @@ -461,7 +459,6 @@ template<typename T> kernel void kernel_soft_max_4( device const char * src0, device const char * src1, - device const char * src2, device char * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -483,10 +480,9 @@ kernel void kernel_soft_max_4( device const float4 * psrc4 = (device const float4 *) src0 + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; device const T * pmask = src1 != src0 ? (device const T *) src1 + i01*ne00/4 : nullptr; - device const T * ppos = src2 != src0 ? (device const T *) src2 : nullptr; device float4 * pdst4 = (device float4 *) dst + (i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00)/4; - float slope = 0.0f; + float slope = 1.0f; if (max_bias > 0.0f) { const int64_t h = i02; @@ -501,7 +497,7 @@ kernel void kernel_soft_max_4( float4 lmax4 = -INFINITY; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))); + lmax4 = fmax(lmax4, psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))); } const float lmax = MAX(MAX(lmax4[0], lmax4[1]), MAX(lmax4[2], lmax4[3])); @@ -527,7 +523,7 @@ kernel void kernel_soft_max_4( // parallel sum float4 lsum4 = 0.0f; for (int i00 = tpitg; i00 < ne00/4; i00 += ntg) { - const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? pmask[i00] : 0.0f) + (ppos ? slope*ppos[i00] : 0.0f))) - max_val); + const float4 exp_psrc4 = exp((psrc4[i00]*scale + (float4)((pmask ? slope*pmask[i00] : 0.0f))) - max_val); lsum4 += exp_psrc4; pdst4[i00] = exp_psrc4; } @@ -1595,60 +1591,6 @@ kernel void kernel_mul_mv_f16_f32_l4( } } -kernel void kernel_alibi_f32( - device const float * src0, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne03, - constant uint64_t & nb00, - constant uint64_t & nb01, - constant uint64_t & nb02, - constant uint64_t & nb03, - constant int64_t & ne0, - constant int64_t & ne1, - constant int64_t & ne2, - constant int64_t & ne3, - constant uint64_t & nb0, - constant uint64_t & nb1, - constant uint64_t & nb2, - constant uint64_t & nb3, - constant float & m0, - constant float & m1, - constant int & n_heads_log2_floor, - uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig[2]; - const int64_t i02 = tgpig[1]; - const int64_t i01 = tgpig[0]; - - const int64_t n = i03*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00; - - const int64_t i3 = n / (ne2*ne1*ne0); - const int64_t i2 = (n - i3*ne2*ne1*ne0) / (ne1*ne0); - const int64_t i1 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0) / ne0; - //const int64_t i0 = (n - i3*ne2*ne1*ne0 - i2*ne1*ne0 - i1*ne0); - - const int64_t k = i3*ne3 + i2; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = pow(m0, k + 1); - } else { - m_k = pow(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - device char * dst_row = (device char *) dst + i3*nb3 + i2*nb2 + i1*nb1; - device const char * src_row = (device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01; - for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { - const float src_v = *(device float *)(src_row + i00*nb00); - device float * dst_v = (device float *)(dst_row + i00*nb0); - *dst_v = i00 * m_k + src_v; - } -} - static float rope_yarn_ramp(const float low, const float high, const int i0) { const float y = (i0 / 2 - low) / max(0.001f, high - low); return 1.0f - min(1.0f, max(0.0f, y)); @@ -2116,13 +2058,16 @@ typedef void (flash_attn_ext_f16_t)( constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, constant uint64_t & nb31, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2154,13 +2099,16 @@ kernel void kernel_flash_attn_ext_f16( constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, constant uint64_t & nb31, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2257,6 +2205,19 @@ kernel void kernel_flash_attn_ext_f16( // prepare diagonal scale matrix simdgroup_float8x8 mscale(scale); + // prepare diagonal slope matrix + simdgroup_float8x8 mslope(1.0f); + + // ALiBi + if (max_bias > 0.0f) { + const short h = iq2; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + mslope = simdgroup_float8x8(pow(base, exph)); + } + // loop over the KV cache // each simdgroup handles blocks of Q rows and C columns for (int ic0 = 0; ic0 < ne11; ic0 += C*nsg) { @@ -2279,9 +2240,10 @@ kernel void kernel_flash_attn_ext_f16( simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); } - // mqk = mqk*scale + mask + // mqk = mqk*scale + mask*slope simdgroup_half8x8 mm; simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); + simdgroup_multiply(mm, mslope, mm); simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); simdgroup_store(mqk, ss + 8*cc, TF, 0, false); @@ -2472,13 +2434,16 @@ kernel void kernel_flash_attn_ext_vec_f16( constant uint64_t & nb11, constant uint64_t & nb12, constant uint64_t & nb13, - constant int64_t & ne31, constant uint64_t & nb31, constant int64_t & ne0, constant int64_t & ne1, constant int64_t & ne2, constant int64_t & ne3, constant float & scale, + constant float & max_bias, + constant float & m0, + constant float & m1, + constant uint32_t & n_head_log2, threadgroup half * shared [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], @@ -2497,6 +2462,18 @@ kernel void kernel_flash_attn_ext_vec_f16( const short T = D + 2*nsg*SH; // shared memory size per query in (half) + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const short h = iq2; + + const float base = h < n_head_log2 ? m0 : m1; + const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = pow(base, exp); + } + //threadgroup half * sq = (threadgroup half *) (shared + 0*D); // holds the query data threadgroup half4 * sq4 = (threadgroup half4 *) (shared + 0*D); // same as above but in half4 threadgroup float * ss = (threadgroup float *) (shared + 2*sgitg*SH + 1*D); // scratch buffer for attention and diagonal matrix @@ -2603,10 +2580,10 @@ kernel void kernel_flash_attn_ext_vec_f16( mqk += simd_shuffle_down(mqk, 2); mqk += simd_shuffle_down(mqk, 1); - // mqk = mqk*scale + mask + // mqk = mqk*scale + mask*slope if (tiisg == 0) { float4 mm = (float4) mp4[ic/4 + cc]; - mqk = mqk*scale + mm; + mqk = mqk*scale + mm*slope; ss4[cc] = mqk; } @@ -2840,7 +2817,8 @@ kernel void kernel_cpy_f32_f16( for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - dst_data[i00] = src[0]; + // TODO: is there a better way to handle -INFINITY? + dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0]; } } diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 79aec4d9f..e93d2af63 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3154,7 +3154,6 @@ typedef float (*vec_dot_q_mul_mat_sycl_t)( #define SYCL_SCALE_BLOCK_SIZE 256 #define SYCL_CLAMP_BLOCK_SIZE 256 #define SYCL_ROPE_BLOCK_SIZE 256 -#define SYCL_ALIBI_BLOCK_SIZE 32 #define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 #define SYCL_QUANTIZE_BLOCK_SIZE 256 #define SYCL_DEQUANTIZE_BLOCK_SIZE 256 @@ -9316,32 +9315,6 @@ static void rope_glm_f32( dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; } -static void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, - const int n_heads_log2_floor, const float m0, const float m1, - const sycl::nd_item<3> &item_ct1) { - const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) + - item_ct1.get_local_id(2); - - if (col >= ncols) { - return; - } - - const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) + - item_ct1.get_local_id(1); - const int i = row*ncols + col; - - const int k = row/k_rows; - - float m_k; - if (k < n_heads_log2_floor) { - m_k = dpct::pow(m0, k + 1); - } else { - m_k = dpct::pow(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - dst[i] = col * m_k + x[i]; -} - static void k_sum_rows_f32(const float * x, float * dst, const int ncols, const sycl::nd_item<3> &item_ct1) { const int row = item_ct1.get_group(1); @@ -9443,7 +9416,7 @@ static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, con template <bool vals_smem, int ncols_template, int block_size_template> -static void soft_max_f32(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, +static void soft_max_f32(const float * x, const float * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, const sycl::nd_item<3> &item_ct1, float *buf) { const int ncols = ncols_template == 0 ? ncols_par : ncols_template; @@ -9457,7 +9430,7 @@ static void soft_max_f32(const float * x, const float * mask, const float *pos, const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -9482,7 +9455,7 @@ static void soft_max_f32(const float * x, const float * mask, const float *pos, const int ix = rowx*ncols + col; const int iy = rowy*ncols + col; - const float val = x[ix]*scale + (mask ? mask[iy] : 0.0f) + (pos ? slope*pos[col] : 0.0f); + const float val = x[ix]*scale + (mask ? slope*mask[iy] : 0.0f); vals[col] = val; max_val = sycl::max(max_val, val); @@ -12964,20 +12937,6 @@ static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows, }); } -static void alibi_f32_sycl(const float *x, float *dst, const int ncols, - const int nrows, const int k_rows, - const int n_heads_log2_floor, const float m0, - const float m1, dpct::queue_ptr stream) { - const sycl::range<3> block_dims(1, 1, SYCL_ALIBI_BLOCK_SIZE); - const int num_blocks_x = (ncols + SYCL_ALIBI_BLOCK_SIZE - 1) / (SYCL_ALIBI_BLOCK_SIZE); - const sycl::range<3> block_nums(1, nrows, num_blocks_x); - stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - alibi_f32(x, dst, ncols, k_rows, - n_heads_log2_floor, m0, m1, item_ct1); - }); -} - static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, const int nrows, dpct::queue_ptr stream) { const sycl::range<3> block_dims(1, 1, WARP_SIZE); @@ -13058,7 +13017,7 @@ static void diag_mask_inf_f32_sycl(const float *x, float *dst, } template <bool vals_smem, int ncols_template, int block_size_template> -static void soft_max_f32_submitter(const float * x, const float * mask, const float *pos, float * dst, const int ncols_par, +static void soft_max_f32_submitter(const float * x, const float * mask, float * dst, const int ncols_par, const int nrows_y, const float scale, const float max_bias, const float m0, const float m1, uint32_t n_head_log2, sycl::range<3> block_nums, sycl::range<3> block_dims, const size_t n_local_scratch, dpct::queue_ptr stream) { @@ -13068,7 +13027,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, const fl cgh.parallel_for( sycl::nd_range<3>(block_nums * block_dims, block_dims), [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { - soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, pos, dst, ncols_par, + soft_max_f32<vals_smem, ncols_template, block_size_template>(x, mask, dst, ncols_par, nrows_y, scale, max_bias, m0, m1, n_head_log2, item_ct1, local_buf_acc.get_pointer()); @@ -13076,7 +13035,7 @@ static void soft_max_f32_submitter(const float * x, const float * mask, const fl }); } -static void soft_max_f32_sycl(const float * x, const float * mask, const float * pos, +static void soft_max_f32_sycl(const float * x, const float * mask, float * dst, const int ncols_x, const int nrows_x, const int nrows_y, const float scale, const float max_bias, dpct::queue_ptr stream) { @@ -13098,60 +13057,60 @@ static void soft_max_f32_sycl(const float * x, const float * mask, const float * const size_t local_mem_size = stream->get_device().get_info<sycl::info::device::local_mem_size>(); if (n_local_scratch*sizeof(float) < local_mem_size) { if (ncols_x > max_block_size) { - soft_max_f32_submitter<true, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); return; } switch (ncols_x) { case 32: - soft_max_f32_submitter<true, 32, 32>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 32, 32>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 64: - soft_max_f32_submitter<true, 64, 64>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 64, 64>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 128: - soft_max_f32_submitter<true, 128, 128>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 128, 128>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 256: - soft_max_f32_submitter<true, 256, 256>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 256, 256>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 512: - soft_max_f32_submitter<true, 512, 512>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 512, 512>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 1024: - soft_max_f32_submitter<true, 1024, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 1024, 1024>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 2048: - soft_max_f32_submitter<true, 2048, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 2048, 1024>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; case 4096: - soft_max_f32_submitter<true, 4096, 1024>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 4096, 1024>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; default: - soft_max_f32_submitter<true, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<true, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, n_local_scratch, stream); break; } } else { - soft_max_f32_submitter<false, 0, 0>(x, mask, pos, dst, ncols_x, nrows_y, scale, + soft_max_f32_submitter<false, 0, 0>(x, mask, dst, ncols_x, nrows_y, scale, max_bias, m0, m1, n_head_log2, block_nums, block_dims, WARP_SIZE, stream); } @@ -14562,36 +14521,6 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, (void) src1_dd; } -inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, - ggml_tensor *dst, const float *src0_dd, - const float *src1_dd, float *dst_dd, - const dpct::queue_ptr &main_stream) { - - GGML_ASSERT(src0->type == GGML_TYPE_F32); - GGML_ASSERT( dst->type == GGML_TYPE_F32); - - GGML_TENSOR_LOCALS_3(int64_t, ne0, src0, ne); - const int64_t nrows = ggml_nrows(src0); - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - //GGML_ASSERT(ne01 + n_past == ne00); - GGML_ASSERT(n_head == ne02); - - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - alibi_f32_sycl(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream); - - (void) src1; - (void) src1_dd; -} - static void ggml_sycl_op_pool2d(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, const float *src0_dd, const float *src1_dd, @@ -14746,12 +14675,9 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); - const ggml_tensor * src2 = dst->src[2]; - -#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 and src2 support") +#pragma message("TODO: add ggml_sycl_op_soft_max() F16 src1 support") #pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5021") GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32); // src2 contains positions and it is optional const int64_t ne00 = src0->ne[0]; const int64_t nrows_x = ggml_nrows(src0); @@ -14763,25 +14689,7 @@ inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, memcpy(&scale, dst->op_params + 0, sizeof(float)); memcpy(&max_bias, dst->op_params + 1, sizeof(float)); - // positions tensor - float * src2_dd = nullptr; - sycl_pool_alloc<float> src2_f; - - const bool use_src2 = src2 != nullptr; - - if (use_src2) { - const bool src2_on_device = src2->backend == GGML_BACKEND_TYPE_GPU; - - if (src2_on_device) { - ggml_tensor_extra_gpu * src2_extra = (ggml_tensor_extra_gpu *) src2->extra; - src2_dd = (float *) src2_extra->data_device[g_main_device]; - } else { - src2_dd = src2_f.alloc(ggml_nelements(src2)); - SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src2_dd, src2, 0, 0, 0, 1, main_stream)); - } - } - - soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, src2_dd, dst_dd, ne00, + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, max_bias, main_stream); } @@ -16232,10 +16140,6 @@ static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope); } -static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); -} - static void ggml_sycl_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pool2d); } @@ -16612,9 +16516,6 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ case GGML_OP_ROPE: func = ggml_sycl_rope; break; - case GGML_OP_ALIBI: - func = ggml_sycl_alibi; - break; case GGML_OP_IM2COL: func = ggml_sycl_im2col; break; @@ -17744,7 +17645,6 @@ GGML_CALL static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, cons case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: - case GGML_OP_ALIBI: case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 95f718974..b9449be03 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -3830,9 +3830,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return nullptr; case GGML_OP_SOFT_MAX: GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); - GGML_ASSERT(!src2 || src2->type == GGML_TYPE_F32 || src2->type == GGML_TYPE_F16); - if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && (src2 == nullptr || src2->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { + if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { @@ -4286,6 +4285,9 @@ 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); +#pragma message("TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated") +#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") + ggml_vk_op_f32<vk_op_soft_max_push_constants>(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, diff --git a/ggml.c b/ggml.c index 093d38d00..4ee5d24af 100644 --- a/ggml.c +++ b/ggml.c @@ -2185,7 +2185,6 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "SOFT_MAX_BACK", "ROPE", "ROPE_BACK", - "ALIBI", "CLAMP", "CONV_TRANSPOSE_1D", "IM2COL", @@ -2227,7 +2226,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2276,7 +2275,6 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "soft_max_back(x)", "rope(x)", "rope_back(x)", - "alibi(x)", "clamp(x)", "conv_transpose_1d(x)", "im2col(x)", @@ -2318,7 +2316,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 77, "GGML_OP_COUNT != 77"); +static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -5646,7 +5644,6 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias, bool inplace) { @@ -5660,18 +5657,8 @@ static struct ggml_tensor * ggml_soft_max_impl( GGML_ASSERT(mask->ne[1] >= a->ne[1]); } - if (pos) { - GGML_ASSERT(ggml_is_vector(pos)); - GGML_ASSERT(pos->type == GGML_TYPE_F16 || pos->type == GGML_TYPE_F32); - GGML_ASSERT(pos->ne[0] == a->ne[0]); - } - - if (pos && mask) { - GGML_ASSERT(pos->type == mask->type); - } - if (max_bias > 0.0f) { - GGML_ASSERT(pos); + GGML_ASSERT(mask); } bool is_node = false; @@ -5689,7 +5676,6 @@ static struct ggml_tensor * ggml_soft_max_impl( result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = mask; - result->src[2] = pos; return result; } @@ -5697,23 +5683,22 @@ static struct ggml_tensor * ggml_soft_max_impl( struct ggml_tensor * ggml_soft_max( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, false); + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, false); } struct ggml_tensor * ggml_soft_max_inplace( struct ggml_context * ctx, struct ggml_tensor * a) { - return ggml_soft_max_impl(ctx, a, NULL, NULL, 1.0f, 0.0f, true); + return ggml_soft_max_impl(ctx, a, NULL, 1.0f, 0.0f, true); } struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias) { - return ggml_soft_max_impl(ctx, a, mask, pos, scale, max_bias, false); + return ggml_soft_max_impl(ctx, a, mask, scale, max_bias, false); } // ggml_soft_max_back @@ -5928,37 +5913,6 @@ struct ggml_tensor * ggml_rope_back( return result; } -// ggml_alibi - -struct ggml_tensor * ggml_alibi( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past, - int n_head, - float bias_max) { - GGML_ASSERT(n_past >= 0); - bool is_node = false; - - if (a->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - // TODO: when implement backward, fix this: - //struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - struct ggml_tensor * result = ggml_view_tensor(ctx, a); - - int32_t op_params[3] = { n_past, n_head }; - memcpy(op_params + 2, &bias_max, sizeof(float)); - ggml_set_op_params(result, op_params, sizeof(op_params)); - - result->op = GGML_OP_ALIBI; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - - return result; -} - // ggml_clamp struct ggml_tensor * ggml_clamp( @@ -6486,9 +6440,11 @@ struct ggml_tensor * ggml_flash_attn_ext( struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, - float scale) { + float scale, + float max_bias) { GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) + if (mask) { GGML_ASSERT(ggml_is_contiguous(mask)); GGML_ASSERT(mask->ne[2] == 1); @@ -6498,6 +6454,10 @@ struct ggml_tensor * ggml_flash_attn_ext( //GGML_ASSERT(ggml_can_repeat_rows(mask, qk)); } + if (max_bias > 0.0f) { + GGML_ASSERT(mask); + } + bool is_node = false; if (q->grad || k->grad || v->grad) { @@ -6508,7 +6468,7 @@ struct ggml_tensor * ggml_flash_attn_ext( int64_t ne[4] = { q->ne[0], q->ne[2], q->ne[1], q->ne[3] }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - float params[] = { scale }; + float params[] = { scale, max_bias }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_FLASH_ATTN_EXT; @@ -6528,7 +6488,7 @@ void ggml_flash_attn_ext_set_prec( const int32_t prec_i32 = (int32_t) prec; - ggml_set_op_params_i32(a, 1, prec_i32); // scale is on first pos + ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second } // ggml_flash_ff @@ -13333,7 +13293,6 @@ static void ggml_compute_forward_soft_max_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; - const struct ggml_tensor * src2 = dst->src[2]; assert(ggml_is_contiguous(dst)); assert(ggml_are_same_shape(src0, dst)); @@ -13359,8 +13318,8 @@ static void ggml_compute_forward_soft_max_f32( // TODO: is this supposed to be ceil instead of floor? // https://huggingface.co/mosaicml/mpt-7b/blob/main/attention.py#L370 - const uint32_t n_head_kv = ne02; - const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head_kv)); + const uint32_t n_head = ne02; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); @@ -13377,13 +13336,13 @@ static void ggml_compute_forward_soft_max_f32( float * wp = (float *) params->wdata + (nc + CACHE_LINE_SIZE_F32) * ith; - // when max_bias <= 0.0f, src2 is not used and we default it to src0 to avoid branching - ggml_fp16_t * pos_f16 = src2 ? (ggml_fp16_t *) src2->data : src0->data; - float * pos_f32 = src2 ? (float *) src2->data : src0->data; - - const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16) || (src2 && src2->type == GGML_TYPE_F16); + const bool use_f16 = (src1 && src1->type == GGML_TYPE_F16); for (int i1 = ir0; i1 < ir1; i1++) { + // ALiBi + const uint32_t h = (i1/ne01)%ne02; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + float * sp = (float *)((char *) src0->data + i1*src0->nb[1]); float * dp = (float *)((char *) dst->data + i1*dst->nb[1]); @@ -13396,27 +13355,11 @@ static void ggml_compute_forward_soft_max_f32( if (mp_f32) { if (use_f16) { for (int i = 0; i < nc; ++i) { - wp[i] += GGML_FP16_TO_FP32(mp_f16[i]); + wp[i] += slope*GGML_FP16_TO_FP32(mp_f16[i]); } } else { for (int i = 0; i < nc; ++i) { - wp[i] += mp_f32[i]; - } - } - } - - // ALiBi bias - if (max_bias > 0.0f) { - const uint32_t h = (i1/ne01)%ne02; // head - const float slope = h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1); - - if (use_f16) { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*GGML_FP16_TO_FP32(pos_f16[i]); - } - } else { - for (int i = 0; i < nc; ++i) { - wp[i] += slope*pos_f32[i]; + wp[i] += slope*mp_f32[i]; } } } @@ -13578,178 +13521,6 @@ static void ggml_compute_forward_soft_max_back( } } -// ggml_compute_forward_alibi - -static void ggml_compute_forward_alibi_f32( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(params->ith == 0); - - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int64_t ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 - const int64_t ne1 = src0->ne[1]; // seq_len_without_past - const int64_t ne2 = src0->ne[2]; // n_head -> this is k - //const int64_t ne3 = src0->ne[3]; // 1 -> bsz - - const int64_t n = ggml_nrows(src0); - const int64_t ne2_ne3 = n/ne1; // ne2*ne3 - - const size_t nb0 = src0->nb[0]; - const size_t nb1 = src0->nb[1]; - const size_t nb2 = src0->nb[2]; - //const int nb3 = src0->nb[3]; - - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(n_head == ne2); - - // add alibi to src0 (KQ_scaled) - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - for (int64_t k = 0; k < ne2_ne3; k++) { - // TODO: k*nb2 or k*nb3 - float m_k; - - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - for (int64_t i = 0; i < ne0; i++) { - for (int64_t j = 0; j < ne1; j++) { - float * const src = (float *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); - pdst[0] = i * m_k + src[0]; - } - } - } -} - -static void ggml_compute_forward_alibi_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - assert(params->ith == 0); - - if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - //const int n_past = ((int32_t *) dst->op_params)[0]; - const int n_head = ((int32_t *) dst->op_params)[1]; - float max_bias; - memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); - - const int ne0 = src0->ne[0]; // all_seq_len = n_past + ne1 - const int ne1 = src0->ne[1]; // seq_len_without_past - const int ne2 = src0->ne[2]; // n_head -> this is k - //const int ne3 = src0->ne[3]; // 1 -> bsz - - const int n = ggml_nrows(src0); - const int ne2_ne3 = n/ne1; // ne2*ne3 - - const int nb0 = src0->nb[0]; - const int nb1 = src0->nb[1]; - const int nb2 = src0->nb[2]; - //const int nb3 = src0->nb[3]; - - GGML_ASSERT(nb0 == sizeof(ggml_fp16_t)); - //GGML_ASSERT(ne1 + n_past == ne0); (void) n_past; - GGML_ASSERT(n_head == ne2); - - // add alibi to src0 (KQ_scaled) - const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); - - const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); - - for (int k = 0; k < ne2_ne3; k++) { - // TODO: k*nb2 or k*nb3 - float m_k; - - if (k < n_heads_log2_floor) { - m_k = powf(m0, k + 1); - } else { - m_k = powf(m1, 2 * (k - n_heads_log2_floor) + 1); - } - - for (int i = 0; i < ne0; i++) { - for (int j = 0; j < ne1; j++) { - ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i*nb0 + j*nb1 + k*nb2); - float * pdst = (float *)((char *) dst->data + i*nb0 + j*nb1 + k*nb2); - - // we return F32 - pdst[0] = i * m_k + GGML_FP16_TO_FP32(src[0]); - } - } - } -} - -static void ggml_compute_forward_alibi( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * src0 = dst->src[0]; - - switch (src0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_alibi_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_alibi_f32(params, dst); - } break; - case GGML_TYPE_BF16: - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - case GGML_TYPE_IQ2_XXS: - case GGML_TYPE_IQ2_XS: - case GGML_TYPE_IQ3_XXS: - case GGML_TYPE_IQ1_S: - case GGML_TYPE_IQ1_M: - case GGML_TYPE_IQ4_NL: - case GGML_TYPE_IQ4_XS: - case GGML_TYPE_IQ3_S: - case GGML_TYPE_IQ2_S: - case GGML_TYPE_Q8_K: - case GGML_TYPE_I8: - case GGML_TYPE_I16: - case GGML_TYPE_I32: - case GGML_TYPE_I64: - case GGML_TYPE_F64: - case GGML_TYPE_COUNT: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_clamp static void ggml_compute_forward_clamp_f32( @@ -15763,8 +15534,17 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - float scale = 1.0f; - memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) dst->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) dst->op_params + 1, sizeof(float)); + + const uint32_t n_head = neq2; + const uint32_t n_head_log2 = 1u << (uint32_t) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { @@ -15773,6 +15553,9 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); + const uint32_t h = iq2; // head + const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; + float S = 0.0f; float M = -INFINITY; @@ -15796,7 +15579,7 @@ static void ggml_compute_forward_flash_attn_ext_f16( // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf for (int64_t ic = 0; ic < nek1; ++ic) { - const float mv = mp ? GGML_FP16_TO_FP32(mp[ic]) : 0.0f; + const float mv = mp ? slope*GGML_FP16_TO_FP32(mp[ic]) : 0.0f; if (mv == -INFINITY) { continue; } @@ -15867,7 +15650,7 @@ static void ggml_compute_forward_flash_attn_ext( const struct ggml_tensor * v, const struct ggml_tensor * mask, struct ggml_tensor * dst) { - switch (dst->op_params[1]) { + switch (dst->op_params[2]) { case GGML_PREC_DEFAULT: case GGML_PREC_F32: { @@ -17630,10 +17413,6 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_rope_back(params, tensor); } break; - case GGML_OP_ALIBI: - { - ggml_compute_forward_alibi(params, tensor); - } break; case GGML_OP_CLAMP: { ggml_compute_forward_clamp(params, tensor); @@ -18652,10 +18431,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented @@ -19428,10 +19203,6 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ { n_tasks = n_threads; } break; - case GGML_OP_ALIBI: - { - n_tasks = 1; //TODO - } break; case GGML_OP_CLAMP: { n_tasks = 1; //TODO diff --git a/ggml.h b/ggml.h index fe6053822..76c332831 100644 --- a/ggml.h +++ b/ggml.h @@ -468,7 +468,6 @@ extern "C" { GGML_OP_SOFT_MAX_BACK, GGML_OP_ROPE, GGML_OP_ROPE_BACK, - GGML_OP_ALIBI, GGML_OP_CLAMP, GGML_OP_CONV_TRANSPOSE_1D, GGML_OP_IM2COL, @@ -1428,15 +1427,13 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); - // fused soft_max(a*scale + mask + pos[i]*(ALiBi slope)) + // fused soft_max(a*scale + mask*(ALiBi slope)) // mask is optional - // pos is required when max_bias > 0.0f // max_bias = 0.0f for no ALiBi GGML_API struct ggml_tensor * ggml_soft_max_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * mask, - struct ggml_tensor * pos, float scale, float max_bias); @@ -1538,16 +1535,6 @@ extern "C" { float xpos_base, bool xpos_down); - // alibi position embedding - // in-place, returns view(a) - GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_alibi( - struct ggml_context * ctx, - struct ggml_tensor * a, - int n_past, - int n_head, - float bias_max), - "use ggml_soft_max_ext instead (will be removed in Mar 2024)"); - // clamp // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_clamp( @@ -1744,7 +1731,8 @@ extern "C" { struct ggml_tensor * k, struct ggml_tensor * v, struct ggml_tensor * mask, - float scale); + float scale, + float max_bias); GGML_API void ggml_flash_attn_ext_set_prec( struct ggml_tensor * a, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index e5750d419..990fe63c2 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -137,6 +137,7 @@ class TensorNameMap: "layers.{bid}.attention.wk", # llama-pth "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j + "transformer.h.{bid}.attn.k", # refact "model.layers.layers.{bid}.self_attn.k_proj", # plamo "model.layers.{bid}.attention.wk", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.key" # Grok @@ -148,6 +149,7 @@ class TensorNameMap: "layers.{bid}.attention.wv", # llama-pth "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j + "transformer.h.{bid}.attn.v", # refact "model.layers.layers.{bid}.self_attn.v_proj", # plamo "model.layers.{bid}.attention.wv", # internlm2 "transformer.decoder_layer.{bid}.multi_head_attention.value" # Grok @@ -229,6 +231,7 @@ class TensorNameMap: "layers.{bid}.feed_forward.w3", # llama-pth "encoder.layer.{bid}.intermediate.dense", # bert "transformer.h.{bid}.mlp.fc_in", # gpt-j + "transformer.h.{bid}.mlp.linear_3", # refact "language_model.encoder.layers.{bid}.mlp.dense_h_to_4h", # persimmon "model.layers.{bid}.mlp.dense_h_to_4h", # persimmon "transformer.h.{bid}.mlp.w1", # qwen @@ -266,6 +269,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 "encoder.layers.{bid}.mlp.fc12", # nomic-bert + "transformer.h.{bid}.mlp.linear_1", # refact ), MODEL_TENSOR.FFN_GATE_EXP: ( diff --git a/llama.cpp b/llama.cpp index 2f1123d4e..dede68cb5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1845,7 +1845,7 @@ struct llama_hparams { float f_logit_scale = 0.0f; bool causal_attn = true; - bool use_alibi = false; // currently, we need KQ_pos data for ALiBi-based models + bool use_alibi = false; enum llama_pooling_type pooling_type = LLAMA_POOLING_TYPE_NONE; enum llama_rope_type rope_type = LLAMA_ROPE_TYPE_NONE; @@ -2317,7 +2317,6 @@ struct llama_context { struct ggml_tensor * inp_pos; // I32 [n_batch] struct ggml_tensor * inp_out_ids; // I32 [n_outputs] struct ggml_tensor * inp_KQ_mask; // F32 [kv_size, n_batch] - struct ggml_tensor * inp_KQ_pos; // F32 [n_kv] struct ggml_tensor * inp_K_shift; // I32 [kv_size] struct ggml_tensor * inp_mean; // F32 [n_batch, n_batch] struct ggml_tensor * inp_cls; // I32 [n_batch] @@ -6500,7 +6499,6 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * wo_b, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, - struct ggml_tensor * kq_pos, int32_t n_tokens, int32_t n_kv, float kq_scale, @@ -6530,10 +6528,6 @@ static struct ggml_tensor * llm_build_kqv( GGML_UNUSED(model); GGML_UNUSED(n_ctx); - // note: if this assert triggers, then some check has failed earlier - // the idea is to detect during context creation that ALiBi would be used and disable Flash Attention - GGML_ASSERT(kq_pos == nullptr && "ALiBi is not yet supported with Flash Attention"); - // split cached v into n_head heads (not transposed) struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], @@ -6543,7 +6537,7 @@ static struct ggml_tensor * llm_build_kqv( 0); cb(v, "v", il); - cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale); + cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias); if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); @@ -6574,28 +6568,8 @@ static struct ggml_tensor * llm_build_kqv( kq = ggml_scale(ctx, kq, 30); } -#if defined(GGML_USE_KOMPUTE) -#pragma message("TODO: ALiBi support in ggml_soft_max_ext is not implemented for Kompute") -#pragma message(" Falling back to ggml_alibi(). Will become an error in Mar 2024") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/5488") - if (hparams.use_alibi) { - kq = ggml_scale(ctx, kq, kq_scale); - cb(kq, "kq_scaled", il); - - kq = ggml_alibi(ctx, kq, /*n_past*/ 0, n_head, hparams.f_max_alibi_bias); - cb(kq, "kq_scaled_alibi", il); - - kq = ggml_add(ctx, kq, kq_mask); - cb(kq, "kq_masked", il); - - kq = ggml_soft_max(ctx, kq); - cb(kq, "kq_soft_max", il); - } else -#endif - { - kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_pos, kq_scale, hparams.f_max_alibi_bias); - cb(kq, "kq_soft_max_ext", il); - } + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, hparams.f_max_alibi_bias); + cb(kq, "kq_soft_max_ext", il); GGML_ASSERT(kv.size == n_ctx); @@ -6645,7 +6619,6 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * v_cur, struct ggml_tensor * q_cur, struct ggml_tensor * kq_mask, - struct ggml_tensor * kq_pos, int32_t n_tokens, int32_t kv_head, int32_t n_kv, @@ -6664,7 +6637,7 @@ static struct ggml_tensor * llm_build_kv( struct ggml_tensor * cur; cur = llm_build_kqv(ctx, model, hparams, cparams, kv, graph, wo, wo_b, - q_cur, kq_mask, kq_pos, n_tokens, n_kv, kq_scale, cb, il); + q_cur, kq_mask, n_tokens, n_kv, kq_scale, cb, il); cb(cur, "kqv_out", il); return cur; @@ -6771,18 +6744,17 @@ struct llm_build_context { ctx0 = ggml_init(params); - lctx.inp_tokens = nullptr; - lctx.inp_embd = nullptr; - lctx.inp_pos = nullptr; + lctx.inp_tokens = nullptr; + lctx.inp_embd = nullptr; + lctx.inp_pos = nullptr; lctx.inp_out_ids = nullptr; lctx.inp_KQ_mask = nullptr; - lctx.inp_KQ_pos = nullptr; lctx.inp_K_shift = nullptr; - lctx.inp_mean = nullptr; - lctx.inp_cls = nullptr; - lctx.inp_s_copy = nullptr; - lctx.inp_s_mask = nullptr; - lctx.inp_s_seq = nullptr; + lctx.inp_mean = nullptr; + lctx.inp_cls = nullptr; + lctx.inp_s_copy = nullptr; + lctx.inp_s_mask = nullptr; + lctx.inp_s_seq = nullptr; } void free() { @@ -6932,19 +6904,6 @@ struct llm_build_context { return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_mask, GGML_TYPE_F16) : lctx.inp_KQ_mask; } - struct ggml_tensor * build_inp_KQ_pos(bool causal = true) { - if (causal) { - lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_kv); - } else { - // TODO: this will be needed for ALiBi-based BERT models - // https://github.com/ggerganov/llama.cpp/pull/6826 - lctx.inp_KQ_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_F32, n_tokens); - } - cb(lctx.inp_KQ_pos, "KQ_pos", -1); - ggml_set_input(lctx.inp_KQ_pos); - return flash_attn ? ggml_cast(ctx0, lctx.inp_KQ_pos, GGML_TYPE_F16) : lctx.inp_KQ_pos; - } - struct ggml_tensor * build_inp_mean() { lctx.inp_mean = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_tokens, n_tokens); cb(lctx.inp_mean, "inp_mean", -1); @@ -7050,7 +7009,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7143,9 +7102,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7190,7 +7146,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7260,9 +7216,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -7297,7 +7250,7 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7417,7 +7370,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7542,7 +7495,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -7694,7 +7647,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -7806,7 +7759,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8010,7 +7963,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8076,9 +8029,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * inpSA = inpL; @@ -8106,7 +8056,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8246,7 +8196,7 @@ struct llm_build_context { struct ggml_tensor * kq = ggml_mul_mat(ctx0, k, q); cb(kq, "kq", il); - kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, nullptr, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); + kq = ggml_soft_max_ext(ctx0, kq, KQ_mask, 1.0f/sqrtf(float(n_embd_head)), hparams.f_max_alibi_bias); cb(kq, "kq_soft_max_ext", il); struct ggml_tensor * v = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, Vcur, n_embd_gqa, n_tokens))); @@ -8363,9 +8313,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - inpL = llm_build_norm(ctx0, inpL, hparams, model.tok_norm, model.tok_norm_b, @@ -8399,7 +8346,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8464,9 +8411,6 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // positions of the tokens in the KV cache - struct ggml_tensor * KQ_pos = build_inp_KQ_pos(); - if (model.pos_embd) { // inp_pos - contains the positions struct ggml_tensor * inp_pos = build_inp_pos(); @@ -8530,13 +8474,13 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } else { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } } @@ -8680,7 +8624,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8798,7 +8742,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -8911,7 +8855,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9025,7 +8969,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9180,7 +9124,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9297,7 +9241,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -9410,7 +9354,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } struct ggml_tensor * sa_out = cur; @@ -9513,7 +9457,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9620,7 +9564,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9736,7 +9680,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9853,7 +9797,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -9983,7 +9927,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10104,7 +10048,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f, cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f, cb, il); } if (il == n_layer - 1) { @@ -10223,7 +10167,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10513,7 +10457,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -10644,7 +10588,7 @@ struct llm_build_context { cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, model.layers[il].wo, nullptr, - Kcur, Vcur, Qcur, KQ_mask, nullptr, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); } if (il == n_layer - 1) { @@ -11032,11 +10976,21 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { f = -INFINITY; } else { - f = 0.0f; + if (hparams.use_alibi) { + f = -fabs(lctx.kv_self.cells[i].pos - pos); + } else { + f = 0.0f; + } } data[h*(n_kv*n_tokens) + j*n_kv + i] = f; } } + + for (int i = n_tokens; i < GGML_PAD(n_tokens, GGML_KQ_MASK_PAD); ++i) { + for (int j = 0; j < n_kv; ++j) { + data[h*(n_kv*n_tokens) + i*n_kv + j] = -INFINITY; + } + } } } else { // when using kv cache, the mask needs to match the kv cache size @@ -11055,7 +11009,11 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { float f = -INFINITY; for (int s = 0; s < batch.n_seq_id[i]; ++s) { if (batch.seq_id[i][s] == seq_id) { - f = 0.0f; + if (hparams.use_alibi) { + f = -fabs(batch.pos[i] - batch.pos[j]); + } else { + f = 0.0f; + } break; } } @@ -11071,21 +11029,6 @@ static void llama_set_inputs(llama_context & lctx, const llama_batch & batch) { } } - // ALiBi requires the KQ_pos tensor to provide the sequence position of each token in the batch - // this allows to process multiple sequences in parallel with ALiBi-based models - if (hparams.use_alibi) { - const int64_t n_kv = kv_self.n; - - GGML_ASSERT(lctx.inp_KQ_pos); - GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_pos->buffer)); - - float * data = (float *) lctx.inp_KQ_pos->data; - - for (int i = 0; i < n_kv; ++i) { - data[i] = float(lctx.kv_self.cells[i].pos); - } - } - if (cparams.pooling_type == LLAMA_POOLING_TYPE_MEAN) { const int64_t n_tokens = batch.n_tokens; @@ -15509,11 +15452,6 @@ struct llama_context * llama_new_context_with_model( } } - if (cparams.flash_attn && hparams.use_alibi) { - LLAMA_LOG_WARN("%s: flash_attn is not yet compatible with ALiBi - forcing off\n", __func__); - cparams.flash_attn = false; - } - if (cparams.flash_attn && model->arch == LLM_ARCH_GROK) { LLAMA_LOG_WARN("%s: flash_attn is not compatible with Grok - forcing off\n", __func__); cparams.flash_attn = false; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 0d66de5d9..731788b95 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1111,11 +1111,7 @@ struct test_soft_max : public test_case { if (this->mask) { mask = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, ne[0], ne[1]); } - ggml_tensor * pos = nullptr; - if (max_bias > 0.0f) { - pos = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ne[0]); - } - ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, pos, scale, max_bias); + ggml_tensor * out = ggml_soft_max_ext(ctx, a, mask, scale, max_bias); return out; } }; @@ -1490,23 +1486,25 @@ struct test_flash_attn_ext : public test_case { const int64_t kv; // kv size const int64_t nb; // batch size + const float max_bias; // ALiBi + std::string vars() override { - return VARS_TO_STR4(hs, nh, kv, nb); + return VARS_TO_STR5(hs, nh, kv, nb, max_bias); } double max_nmse_err() override { return 5e-4; } - test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8) - : hs(hs), nh(nh), kv(kv), nb(nb) {} + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, float max_bias = 0.0f) + : hs(hs), nh(nh), kv(kv), nb(nb), max_bias(max_bias) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1); ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); ggml_tensor * mask = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1); - ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs)); + ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, mask, 1.0f/sqrtf(hs), max_bias); return out; } }; @@ -1611,7 +1609,7 @@ public: struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); - kq = ggml_soft_max_ext(ctx, kq, kq_mask, nullptr, kq_scale, 0.0f); + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale, 0.0f); // split cached v into n_head heads struct ggml_tensor * v = @@ -2128,6 +2126,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op #endif for (bool mask : {false, true}) { for (float max_bias : {0.0f, 8.0f}) { + if (!mask && max_bias > 0.0f) continue; for (float scale : {1.0f, 0.1f}) { for (int64_t ne0 : {16, 1024}) { for (int64_t ne1 : {16, 1024}) { @@ -2141,7 +2140,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 0.0f)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 0.0f)); - test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, false, 0.1f, 8.0f)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f)); for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { @@ -2180,10 +2178,12 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op #else for (int hs : { 64, 80, 128, 256, }) { #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - for (int nh : { 32, }) { - for (int kv : { 512, 1024, }) { - for (int nb : { 1, 2, 4, 8, }) { - test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb)); + for (float max_bias : {0.0f, 8.0f}) { + for (int nh : { 32, }) { + for (int kv : { 512, 1024, }) { + for (int nb : { 1, 2, 4, 8, }) { + test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, max_bias)); + } } } } From b83cc3f5b303ff30c52874b2d5864dc6385ebf9f Mon Sep 17 00:00:00 2001 From: Joan Fontanals <jfontanalsmartinez@gmail.com> Date: Sat, 11 May 2024 09:46:09 +0200 Subject: [PATCH 083/100] llama : add Jina Embeddings architecture (#6826) * feat: first things to do * feat: create tensors for Jina architecture * fix: use other tensors * feat: embedding gets results * fix: fix usage of ALIBI * fix: clean prints * fix: do some cleanup unused vars * fix: revert changes to Makefile and CMakeLists * fix: revert some changes * fix: fix small detail * fix: fix convert formatting * fix: fix linting and editor * feat: set proper vocab settings * fix: JinaBertForMaskedLM registration * feat: support q_normalization and k_normalization in Jina arch * feat: handle gpt2 tokenizer with Jina architecture * feat: example comments in embedding * feat: rename Jina Bert to Jina Bert V2 * fix: add some changes as per review * feat: proper KQ_pos for Jina embeddings * feat: add capacity to load models ES and DE for Spanish * llama : fix pre-tokenizers * ggml : full ALiBi support * ggml : update ggml_soft_max_ext() CUDA, SYCL * ggml : ggml_flash_attn_ext() support ALiBi (CPU) * ggml : ggml_flash_attn_ext() support ALiBi (Metal) * ggml : fix warning * ggml : ggml_flash_attn_ext() support ALiBi (CUDA) ggml-ci * minor : clean-up * embedding : add warning about missing SEP --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --- convert-hf-to-gguf-update.py | 3 + convert-hf-to-gguf.py | 48 +++++++- examples/embedding/embedding.cpp | 12 +- gguf-py/gguf/constants.py | 18 +++ gguf-py/gguf/tensor_mapping.py | 6 + llama.cpp | 190 ++++++++++++++++++++++++------- 6 files changed, 236 insertions(+), 41 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index b5eb41eac..e757d5ccb 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -74,6 +74,9 @@ models = [ {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, {"name": "olmo", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/allenai/OLMo-1.7-7B-hf", }, {"name": "dbrx", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/databricks/dbrx-base", }, + {"name": "jina-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! + {"name": "jina-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, + {"name": "jina-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, ] # make directory "models/tokenizers" if it doesn't exist diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 3315ca74b..fbaed64da 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -404,8 +404,17 @@ class Model: # ref: https://huggingface.co/allenai/OLMo-1.7-7B-hf res = "olmo" if chkhsh == "a8594e3edff7c29c003940395316294b2c623e09894deebbc65f33f1515df79e": - # ref: https://huggingface.co/databricks/dbrx-instruct + # ref: https://huggingface.co/databricks/dbrx-base res = "dbrx" + if chkhsh == "0876d13b50744004aa9aeae05e7b0647eac9d801b5ba4668afc01e709c15e19f": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-en + res = "jina-en" + if chkhsh == "171aeeedd6fb548d418a7461d053f11b6f1f1fc9b387bd66640d28a4b9f5c643": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-es + res = "jina-es" + if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": + # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de + res = "jina-de" if res is None: logger.warning("\n") @@ -2289,6 +2298,43 @@ class OlmoModel(Model): return [(self.map_tensor_name(name), data_torch)] +@Model.register("JinaBertModel", "JinaBertForMaskedLM") +class JinaBertV2Model(BertModel): + model_arch = gguf.MODEL_ARCH.JINA_BERT_V2 + + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.intermediate_size = self.hparams["intermediate_size"] + + def get_tensors(self): + for name, data in super().get_tensors(): + if 'gated_layers' in name: + d1 = data[:self.intermediate_size, :] + name1 = name.replace('gated_layers', 'gated_layers_w') + d2 = data[self.intermediate_size:, :] + name2 = name.replace('gated_layers', 'gated_layers_v') + yield name1, d1 + yield name2, d2 + continue + + yield name, data + + def set_vocab(self, *args, **kwargs): + tokenizer_class = 'BertTokenizer' + with open(self.dir_model / "tokenizer_config.json", "r", encoding="utf-8") as f: + tokenizer_class = json.load(f)['tokenizer_class'] + + if tokenizer_class == 'BertTokenizer': + super().set_vocab() + elif tokenizer_class == 'RobertaTokenizer': + self._set_vocab_gpt2() + self.gguf_writer.add_token_type_count(2) + else: + raise NotImplementedError(f'Tokenizer {tokenizer_class} is not supported for JinaBertModel') + self.gguf_writer.add_add_bos_token(True) + self.gguf_writer.add_add_eos_token(True) + + ###### CONVERSION LOGIC ###### diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 6a93147d7..c85a2da53 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -49,6 +49,12 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu } float * out = output + batch.seq_id[i][0] * n_embd; + //TODO: I would also add a parameter here to enable normalization or not. + /*fprintf(stdout, "unnormalized_embedding:"); + for (int hh = 0; hh < n_embd; hh++) { + fprintf(stdout, "%9.6f ", embd[hh]); + } + fprintf(stdout, "\n");*/ llama_embd_normalize(embd, out, n_embd); } } @@ -123,10 +129,12 @@ int main(int argc, char ** argv) { inputs.push_back(inp); } - // add SEP if not present + // check if the last token is SEP + // it should be automatically added by the tokenizer when 'tokenizer.ggml.add_eos_token' is set to 'true' for (auto & inp : inputs) { if (inp.empty() || inp.back() != llama_token_sep(model)) { - inp.push_back(llama_token_sep(model)); + fprintf(stderr, "%s: warning: last token in the prompt is not SEP\n", __func__); + fprintf(stderr, "%s: 'tokenizer.ggml.add_eos_token' should be set to 'true' in the GGUF header\n", __func__); } } diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 5951c0bb0..a4fbfc5e0 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -118,6 +118,7 @@ class MODEL_ARCH(IntEnum): REFACT = auto() BERT = auto() NOMIC_BERT = auto() + JINA_BERT_V2 = auto() BLOOM = auto() STABLELM = auto() QWEN = auto() @@ -195,6 +196,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", MODEL_ARCH.BLOOM: "bloom", MODEL_ARCH.STABLELM: "stablelm", MODEL_ARCH.QWEN: "qwen", @@ -380,6 +382,22 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_UP, MODEL_TENSOR.LAYER_OUT_NORM, ], + MODEL_ARCH.JINA_BERT_V2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.LAYER_OUT_NORM, + ], MODEL_ARCH.MPT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 990fe63c2..8e1cac915 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -243,6 +243,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.w3", # internlm2 "encoder.layers.{bid}.mlp.fc11", # nomic-bert "model.layers.{bid}.mlp.c_fc", # starcoder2 + "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -269,6 +270,7 @@ class TensorNameMap: "model.layers.layers.{bid}.mlp.gate_proj", # plamo "model.layers.{bid}.feed_forward.w1", # internlm2 "encoder.layers.{bid}.mlp.fc12", # nomic-bert + "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 "transformer.h.{bid}.mlp.linear_1", # refact ), @@ -303,6 +305,7 @@ class TensorNameMap: "model.layers.{bid}.feed_forward.w2", # internlm2 "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 + "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -321,6 +324,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.q_layernorm", # persimmon "model.layers.{bid}.self_attn.q_norm", # cohere "transformer.blocks.{bid}.attn.q_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_q" # jina-bert-v2 ), MODEL_TENSOR.ATTN_K_NORM: ( @@ -328,6 +332,7 @@ class TensorNameMap: "model.layers.{bid}.self_attn.k_layernorm", # persimmon "model.layers.{bid}.self_attn.k_norm", # cohere "transformer.blocks.{bid}.attn.k_ln", # sea-lion + "encoder.layer.{bid}.attention.self.layer_norm_k" # jina-bert-v2 ), MODEL_TENSOR.ROPE_FREQS: ( @@ -338,6 +343,7 @@ class TensorNameMap: "encoder.layer.{bid}.output.LayerNorm", # bert "encoder.layers.{bid}.norm2", # nomic-bert "transformer.decoder_layer.{bid}.rms_norm_3", # Grok + "encoder.layer.{bid}.mlp.layernorm", # jina-bert-v2 ), MODEL_TENSOR.SSM_IN: ( diff --git a/llama.cpp b/llama.cpp index dede68cb5..cdff28cda 100644 --- a/llama.cpp +++ b/llama.cpp @@ -205,6 +205,7 @@ enum llm_arch { LLM_ARCH_REFACT, LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, + LLM_ARCH_JINA_BERT_V2, LLM_ARCH_BLOOM, LLM_ARCH_STABLELM, LLM_ARCH_QWEN, @@ -228,39 +229,40 @@ enum llm_arch { }; static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = { - { LLM_ARCH_LLAMA, "llama" }, - { LLM_ARCH_FALCON, "falcon" }, - { LLM_ARCH_GROK, "grok" }, - { LLM_ARCH_GPT2, "gpt2" }, - { LLM_ARCH_GPTJ, "gptj" }, - { LLM_ARCH_GPTNEOX, "gptneox" }, - { LLM_ARCH_MPT, "mpt" }, - { LLM_ARCH_BAICHUAN, "baichuan" }, - { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_PERSIMMON, "persimmon" }, - { LLM_ARCH_REFACT, "refact" }, - { LLM_ARCH_BERT, "bert" }, - { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, - { LLM_ARCH_BLOOM, "bloom" }, - { LLM_ARCH_STABLELM, "stablelm" }, - { LLM_ARCH_QWEN, "qwen" }, - { LLM_ARCH_QWEN2, "qwen2" }, - { LLM_ARCH_QWEN2MOE, "qwen2moe" }, - { LLM_ARCH_PHI2, "phi2" }, - { LLM_ARCH_PHI3, "phi3" }, - { LLM_ARCH_PLAMO, "plamo" }, - { LLM_ARCH_CODESHELL, "codeshell" }, - { LLM_ARCH_ORION, "orion" }, - { LLM_ARCH_INTERNLM2, "internlm2" }, - { LLM_ARCH_MINICPM, "minicpm" }, - { LLM_ARCH_GEMMA, "gemma" }, - { LLM_ARCH_STARCODER2, "starcoder2" }, - { LLM_ARCH_MAMBA, "mamba" }, - { LLM_ARCH_XVERSE, "xverse" }, - { LLM_ARCH_COMMAND_R, "command-r" }, - { LLM_ARCH_DBRX, "dbrx" }, - { LLM_ARCH_OLMO, "olmo" }, - { LLM_ARCH_UNKNOWN, "(unknown)" }, + { LLM_ARCH_LLAMA, "llama" }, + { LLM_ARCH_FALCON, "falcon" }, + { LLM_ARCH_GROK, "grok" }, + { LLM_ARCH_GPT2, "gpt2" }, + { LLM_ARCH_GPTJ, "gptj" }, + { LLM_ARCH_GPTNEOX, "gptneox" }, + { LLM_ARCH_MPT, "mpt" }, + { LLM_ARCH_BAICHUAN, "baichuan" }, + { LLM_ARCH_STARCODER, "starcoder" }, + { LLM_ARCH_PERSIMMON, "persimmon" }, + { LLM_ARCH_REFACT, "refact" }, + { LLM_ARCH_BERT, "bert" }, + { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, + { LLM_ARCH_JINA_BERT_V2, "jina-bert-v2" }, + { LLM_ARCH_BLOOM, "bloom" }, + { LLM_ARCH_STABLELM, "stablelm" }, + { LLM_ARCH_QWEN, "qwen" }, + { LLM_ARCH_QWEN2, "qwen2" }, + { LLM_ARCH_QWEN2MOE, "qwen2moe" }, + { LLM_ARCH_PHI2, "phi2" }, + { LLM_ARCH_PHI3, "phi3" }, + { LLM_ARCH_PLAMO, "plamo" }, + { LLM_ARCH_CODESHELL, "codeshell" }, + { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, + { LLM_ARCH_GEMMA, "gemma" }, + { LLM_ARCH_STARCODER2, "starcoder2" }, + { LLM_ARCH_MAMBA, "mamba" }, + { LLM_ARCH_XVERSE, "xverse" }, + { LLM_ARCH_COMMAND_R, "command-r" }, + { LLM_ARCH_DBRX, "dbrx" }, + { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_UNKNOWN, "(unknown)" }, }; enum llm_kv { @@ -691,6 +693,25 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_JINA_BERT_V2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_TOKEN_EMBD_NORM, "token_embd_norm" }, + { LLM_TENSOR_TOKEN_TYPES, "token_types" }, + { LLM_TENSOR_ATTN_OUT_NORM, "blk.%d.attn_output_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_LAYER_OUT_NORM, "blk.%d.layer_output_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_BLOOM, { @@ -3778,6 +3799,12 @@ static void llm_load_hparams( // get hparams kv ml.get_key(LLM_KV_VOCAB_SIZE, hparams.n_vocab, false) || ml.get_arr_n(LLM_KV_TOKENIZER_LIST, hparams.n_vocab); + + // everything past this point is not vocab-related + if (hparams.vocab_only) { + return; + } + ml.get_key(LLM_KV_CONTEXT_LENGTH, hparams.n_ctx_train); ml.get_key(LLM_KV_EMBEDDING_LENGTH, hparams.n_embd); ml.get_key(LLM_KV_FEED_FORWARD_LENGTH, hparams.n_ff); @@ -3961,6 +3988,19 @@ static void llm_load_hparams( model.type = e_model::MODEL_335M; break; // bge-large } } break; + case LLM_ARCH_JINA_BERT_V2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_ATTENTION_CAUSAL, hparams.causal_attn); + ml.get_key(LLM_KV_TOKENIZER_TOKEN_TYPE_COUNT, hparams.n_vocab_type); + ml.get_key(LLM_KV_POOLING_TYPE, hparams.pooling_type); + hparams.f_max_alibi_bias = 8.0f; + + switch (hparams.n_layer) { + case 4: model.type = e_model::MODEL_33M; break; // jina-embeddings-small + case 12: model.type = e_model::MODEL_137M; break; // jina-embeddings-base + } + } break; case LLM_ARCH_NOMIC_BERT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -4382,7 +4422,9 @@ static void llm_load_vocab( tokenizer_pre == "starcoder") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STARCODER; } else if ( - tokenizer_pre == "gpt-2") { + tokenizer_pre == "gpt-2" || + tokenizer_pre == "jina-es" || + tokenizer_pre == "jina-de") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_GPT2; } else if ( tokenizer_pre == "refact") { @@ -5241,6 +5283,50 @@ static bool llm_load_tensors( layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); } } break; + case LLM_ARCH_JINA_BERT_V2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // word_embeddings + model.type_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_TYPES, "weight"), {n_embd, n_vocab_type}); //token_type_embeddings + model.tok_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "weight"), {n_embd}); // LayerNorm + model.tok_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD_NORM, "bias"), {n_embd}); //LayerNorm bias + + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; // JinaBertLayer + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); + + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false); + + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); + + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false); + + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); //output_dens + layer.bo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); //output_dens + + layer.attn_out_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "weight", i), {n_embd}); //output_norm + layer.attn_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT_NORM, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.layer_out_norm = ml.create_tensor(ctx_split, tn(LLM_TENSOR_LAYER_OUT_NORM, "weight", i), {n_embd}); + layer.layer_out_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_LAYER_OUT_NORM, "bias", i), {n_embd}); + } + } break; case LLM_ARCH_BLOOM: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); @@ -6317,7 +6403,7 @@ static struct ggml_tensor * llm_build_ffn( llm_ffn_gate_type type_gate, const llm_build_cb & cb, int il) { - struct ggml_tensor * tmp = ggml_mul_mat(ctx, up, cur); + struct ggml_tensor * tmp = up ? ggml_mul_mat(ctx, up, cur) : cur; cb(tmp, "ffn_up", il); if (up_b) { @@ -8118,8 +8204,11 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; + struct ggml_tensor * inp_pos = nullptr; - struct ggml_tensor * inp_pos = build_inp_pos(); + if (model.arch != LLM_ARCH_JINA_BERT_V2) { + inp_pos = build_inp_pos(); + } struct ggml_tensor * inp_mean = build_inp_mean(); struct ggml_tensor * inp_cls = build_inp_cls(); @@ -8150,13 +8239,26 @@ struct llm_build_context { struct ggml_tensor * Vcur; // self-attention - if (model.arch == LLM_ARCH_BERT) { + if (model.arch == LLM_ARCH_BERT || model.arch == LLM_ARCH_JINA_BERT_V2) { Qcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wq, cur), model.layers[il].bq); cb(Qcur, "Qcur", il); + if (model.layers[il].attn_q_norm) { + Qcur = llm_build_norm(ctx0, Qcur, hparams, + model.layers[il].attn_q_norm, + model.layers[il].attn_q_norm_b, + LLM_NORM, cb, il); + } + Kcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wk, cur), model.layers[il].bk); cb(Kcur, "Kcur", il); + if (model.layers[il].attn_k_norm) { + Kcur = llm_build_norm(ctx0, Kcur, hparams, + model.layers[il].attn_k_norm, + model.layers[il].attn_k_norm_b, + LLM_NORM, cb, il); + } Vcur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.layers[il].wv, cur), model.layers[il].bv); cb(Vcur, "Vcur", il); @@ -8247,6 +8349,13 @@ struct llm_build_context { model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + } else if (model.arch == LLM_ARCH_JINA_BERT_V2) { + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_PAR, cb, il); } else { cur = llm_build_ffn(ctx0, cur, model.layers[il].ffn_up, NULL, @@ -10769,6 +10878,7 @@ static struct ggml_cgraph * llama_build_graph( result = llm.build_refact(); } break; case LLM_ARCH_BERT: + case LLM_ARCH_JINA_BERT_V2: case LLM_ARCH_NOMIC_BERT: { result = llm.build_bert(); @@ -12695,7 +12805,10 @@ static std::vector<llama_vocab::id> llama_tokenize_internal(const llama_vocab & } } - GGML_ASSERT(vocab.special_add_eos != 1); + if (add_special && vocab.special_add_eos == 1) { + GGML_ASSERT(vocab.special_add_eos != -1); + output.push_back(vocab.special_eos_id); + } } break; case LLAMA_VOCAB_TYPE_WPM: { @@ -15746,6 +15859,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: case LLM_ARCH_MAMBA: + case LLM_ARCH_JINA_BERT_V2: return LLAMA_ROPE_TYPE_NONE; // use what we call a normal RoPE, operating on pairs of consecutive head values From 5ae3426b0b64672991563d4c28b2018b9f961467 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de> Date: Sat, 11 May 2024 10:11:28 +0200 Subject: [PATCH 084/100] server: fix reported top tokens for temperature 0 (#7203) --- common/sampling.cpp | 6 +++--- common/sampling.h | 2 +- examples/server/server.cpp | 6 +++--- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index 3715a7985..f0f1b92d3 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -35,7 +35,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ result->prev.resize(params.n_prev); - result->n_considered = 0; + result->n_valid = 0; llama_sampling_set_rng_seed(result, params.seed); @@ -66,7 +66,7 @@ void llama_sampling_reset(llama_sampling_context * ctx) { std::fill(ctx->prev.begin(), ctx->prev.end(), 0); ctx->cur.clear(); - ctx->n_considered = 0; + ctx->n_valid = 0; } void llama_sampling_set_rng_seed(struct llama_sampling_context * ctx, uint32_t seed) { @@ -256,7 +256,7 @@ static llama_token llama_sampling_sample_impl( } } - ctx_sampling->n_considered = cur_p.size; + ctx_sampling->n_valid = temp == 0.0f ? 0 : cur_p.size; return id; } diff --git a/common/sampling.h b/common/sampling.h index 5b73ecdcd..655732ad1 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -81,7 +81,7 @@ struct llama_sampling_context { // TODO: replace with ring-buffer std::vector<llama_token> prev; std::vector<llama_token_data> cur; - size_t n_considered; + size_t n_valid; // Number of correct top tokens with correct probabilities. std::mt19937 rng; }; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 305f79492..2bf4026d5 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2270,10 +2270,10 @@ struct server_context { const size_t n_probs = std::min(cur_p.size, (size_t) slot.sparams.n_probs); if (n_probs > 0) { - const size_t n_considered = slot.ctx_sampling->n_considered; + const size_t n_valid = slot.ctx_sampling->n_valid; // Make sure at least n_probs top tokens are at the front of the vector: - if (slot.sparams.temp == 0.0f && n_probs > n_considered) { + if (slot.sparams.temp == 0.0f && n_probs > n_valid) { llama_sample_top_k(ctx, &cur_p, n_probs, 0); } @@ -2289,7 +2289,7 @@ struct server_context { for (size_t i = 0; i < n_probs; ++i) { result.probs.push_back({ cur_p.data[i].id, - i >= n_considered ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. + i >= n_valid ? 0.0f : cur_p.data[i].p // Tokens filtered out due to e.g. top_k have 0 probability. }); } } From f99e1e456eaf69cc38c1982a2693ce41c0f897ef Mon Sep 17 00:00:00 2001 From: Haoxiang Fei <tonyfettes@tonyfettes.com> Date: Sat, 11 May 2024 16:12:06 +0800 Subject: [PATCH 085/100] llama : lookup word in vocab before doing BPE merges (#7193) * fix: llama-3 ignore_merges * test: add test for llama-3 bpe ignore_merges * fix: set ignore_merges only for llama-3 * fix: test-tokenizer-1-bpe --ingore-merges detection * fix: copy to fix fallthrough * fix: change ignore_merges to bool * fix: add ignore merges tests to cmake * llama : alternative merge ignore logic --------- Co-authored-by: Haoxiang Fei <feihaoxiang@idea.edu.cn> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> --- llama.cpp | 14 ++++++++++++- models/ggml-vocab-llama-bpe.gguf.inp | 2 ++ models/ggml-vocab-llama-bpe.gguf.out | 1 + tests/CMakeLists.txt | 2 +- tests/test-tokenizer-1-bpe.cpp | 30 +++++++++++++++++++++++++--- 5 files changed, 44 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index cdff28cda..e91ad7285 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12253,13 +12253,14 @@ struct llm_tokenizer_bpe { void tokenize(const std::string & text, std::vector<llama_vocab::id> & output) { int final_prev_index = -1; + bool ignore_merges = false; std::vector<std::string> word_collection; switch (vocab.type) { case LLAMA_VOCAB_TYPE_BPE: switch (vocab.type_pre) { case LLAMA_VOCAB_PRE_TYPE_LLAMA3: - case LLAMA_VOCAB_PRE_TYPE_DBRX: + ignore_merges = true; word_collection = unicode_regex_split(text, { // original regex from tokenizer.json //"(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", @@ -12268,6 +12269,12 @@ struct llm_tokenizer_bpe { "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", }); break; + case LLAMA_VOCAB_PRE_TYPE_DBRX: + word_collection = unicode_regex_split(text, { + // same as llama3 + "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", + }); + break; case LLAMA_VOCAB_PRE_TYPE_DEEPSEEK_LLM: word_collection = unicode_regex_split(text, { "[\r\n]", @@ -12351,6 +12358,11 @@ struct llm_tokenizer_bpe { int index = 0; size_t offset = 0; + if (ignore_merges && vocab.token_to_id.find(word) != vocab.token_to_id.end()) { + symbols.emplace_back(llm_symbol{-1, -1, word.c_str(), word.size()}); + offset = word.size(); + } + while (offset < word.size()) { llm_symbol sym; size_t char_len = std::min(word.size() - offset, (size_t) ::utf8_len(word[offset])); diff --git a/models/ggml-vocab-llama-bpe.gguf.inp b/models/ggml-vocab-llama-bpe.gguf.inp index 0a89107c6..9380bf355 100644 --- a/models/ggml-vocab-llama-bpe.gguf.inp +++ b/models/ggml-vocab-llama-bpe.gguf.inp @@ -104,3 +104,5 @@ __ggml_vocab_test__ 🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ 🦙🦙 3 33 333 3333 33333 333333 3333333 33333333 3.3 3..3 3...3 កាន់តែពិសេសអាច😁 ?我想在apple工作1314151天～ ------======= нещо на Български ''''''```````""""......!!!!!!?????? I've been 'told he's there, 'RE you sure? 'M not sure I'll make it, 'D you like some tea? We'Ve a'lL __ggml_vocab_test__ + Việt +__ggml_vocab_test__ diff --git a/models/ggml-vocab-llama-bpe.gguf.out b/models/ggml-vocab-llama-bpe.gguf.out index 1f00e3812..1f3607fb6 100644 --- a/models/ggml-vocab-llama-bpe.gguf.out +++ b/models/ggml-vocab-llama-bpe.gguf.out @@ -41,3 +41,4 @@ 8765 8765 1644 8765 8765 8765 198 4815 15073 66597 8004 1602 2355 79772 11187 9468 248 222 320 8416 8 27623 114 102470 9468 234 104 31643 320 36773 100166 98634 8 26602 227 11410 99 247 9468 99 247 220 18 220 1644 220 8765 220 8765 18 220 8765 1644 220 8765 8765 220 8765 8765 18 220 8765 8765 1644 220 18 13 18 220 18 497 18 220 18 1131 18 220 21549 222 98629 241 45358 233 21549 237 45358 224 21549 244 21549 115 21549 253 45358 223 21549 253 21549 95 98629 227 76460 223 949 37046 101067 19000 23182 102301 9263 18136 16 36827 21909 56560 54337 19175 102118 13373 64571 34694 3114 112203 80112 3436 106451 14196 14196 74694 3089 3089 29249 17523 3001 27708 7801 358 3077 1027 364 83 820 568 596 1070 11 364 793 499 2771 30 364 44 539 2771 358 3358 1304 433 11 364 35 499 1093 1063 15600 30 1226 6 43712 264 64966 43 + 101798 diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index d409a1d6b..766a01752 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -92,7 +92,7 @@ target_link_libraries(test-tokenizer-1-bpe PRIVATE common) install(TARGETS test-tokenizer-1-bpe RUNTIME) # TODO: disabled due to slowness -#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf) +#llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-llama-bpe ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama-bpe.gguf --ignore-merges) #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-falcon ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-aquila ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-aquila.gguf) #llama_test(test-tokenizer-1-bpe NAME test-tokenizer-1-mpt ARGS ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-mpt.gguf) diff --git a/tests/test-tokenizer-1-bpe.cpp b/tests/test-tokenizer-1-bpe.cpp index a0e2caf94..209a04ad6 100644 --- a/tests/test-tokenizer-1-bpe.cpp +++ b/tests/test-tokenizer-1-bpe.cpp @@ -13,15 +13,27 @@ #include <vector> int main(int argc, char **argv) { - if (argc < 2) { - fprintf(stderr, "Usage: %s <vocab-file>\n", argv[0]); + if (argc < 2 || argc > 3) { + fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]); return 1; } const std::string fname = argv[1]; + bool ignore_merges = false; + if (argc == 3) { + if (std::strcmp(argv[2], "--ignore-merges") != 0) { + fprintf(stderr, "Usage: %s <vocab-file> [--ignore-merges]\n", argv[0]); + return 1; + } + ignore_merges = true; + } fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); + if (ignore_merges) { + fprintf(stderr, "%s : ignoring merges for tokens inside vocab\n", __func__); + } + llama_model * model; llama_context * ctx; @@ -65,7 +77,19 @@ int main(int argc, char **argv) { std::string str = llama_detokenize_bpe(ctx, std::vector<int>(1, i)); try { auto cps = unicode_cpts_from_utf8(str); - std::vector<llama_token> tokens = llama_tokenize(ctx, str, false); + std::vector<llama_token> tokens = llama_tokenize(ctx, str, false, true); + if (ignore_merges && tokens.size() > 1) { + fprintf(stderr, + "%s : error: token %d detokenizes to '%s'(%zu) but " + "tokenization of this to multiple tokens: [", + __func__, i, str.c_str(), str.length()); + fprintf(stderr, "%d", tokens[0]); + for (size_t i = 1; i < tokens.size(); i++) { + fprintf(stderr, ", %d", tokens[i]); + } + fprintf(stderr, "]\n"); + return 2; + } std::string check = llama_detokenize_bpe(ctx, tokens); if (check != str) { fprintf(stderr, "%s : error: token %d detokenizes to '%s'(%zu) but tokenization of this detokenizes to '%s'(%zu)\n", From 988631335a20d06497f58be0b8ba13adb4323a22 Mon Sep 17 00:00:00 2001 From: Steve Grubb <ausearch.1@gmail.com> Date: Sat, 11 May 2024 04:13:02 -0400 Subject: [PATCH 086/100] server : free llama_batch on exit (#7212) * [server] Cleanup a memory leak on exit There are a couple memory leaks on exit of the server. This hides others. After cleaning this up, you can see leaks on slots. But that is another patch to be sent after this. * make tab into spaces --- examples/server/server.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 2bf4026d5..55c1d4129 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -673,6 +673,8 @@ struct server_context { llama_free_model(model); model = nullptr; } + + llama_batch_free(batch); } bool load_model(const gpt_params & params_) { From 3292733f95d4632a956890a438af5192e7031c12 Mon Sep 17 00:00:00 2001 From: CrispStrobe <154636388+CrispStrobe@users.noreply.github.com> Date: Sat, 11 May 2024 10:18:35 +0200 Subject: [PATCH 087/100] convert : skip unaccessible HF repos (#7210) --- convert-hf-to-gguf-update.py | 22 ++++++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index e757d5ccb..cd2674a0e 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -145,8 +145,17 @@ for model in models: if tokt == TOKENIZER_TYPE.SPM: continue + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + # create the tokenizer - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + try: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Error loading tokenizer for model {name}. The model may not exist or is not accessible with the provided token. Error: {e}") + continue # Skip to the next model if the tokenizer can't be loaded chktok = tokenizer.encode(chktxt) chkhsh = sha256(str(chktok).encode()).hexdigest() @@ -287,8 +296,17 @@ for model in models: name = model["name"] tokt = model["tokt"] + # Skip if the tokenizer folder does not exist or there are other download issues previously + if not os.path.exists(f"models/tokenizers/{name}"): + logger.warning(f"Directory for tokenizer {name} not found. Skipping...") + continue + # create the tokenizer - tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + try: + tokenizer = AutoTokenizer.from_pretrained(f"models/tokenizers/{name}") + except OSError as e: + logger.error(f"Failed to load tokenizer for model {name}. Error: {e}") + continue # Skip this model and continue with the next one in the loop with open(f"models/ggml-vocab-{name}.gguf.inp", "w", encoding="utf-8") as f: for text in tests: From ef0d5e3ec9f99003af3ff326384816c02850ea3f Mon Sep 17 00:00:00 2001 From: Borislav Stanimirov <b.stanimirov@abv.bg> Date: Thu, 25 Apr 2024 17:24:07 +0300 Subject: [PATCH 088/100] build: fix and ignore msvc warnings (ggml/805) --- ggml-backend.c | 4 ++-- ggml-quants.c | 6 ++++++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/ggml-backend.c b/ggml-backend.c index f5bdcf078..dd090a583 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1182,9 +1182,9 @@ static int ggml_backend_sched_backend_id_from_cur(ggml_backend_sched_t sched, st static char * fmt_size(size_t size) { static char buffer[128]; if (size >= 1024*1024) { - sprintf(buffer, "%zuM", size/1024/1024); + snprintf(buffer, sizeof(buffer), "%zuM", size/1024/1024); } else { - sprintf(buffer, "%zuK", size/1024); + snprintf(buffer, sizeof(buffer), "%zuK", size/1024); } return buffer; } diff --git a/ggml-quants.c b/ggml-quants.c index 9883b6f8c..00334c5fe 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,6 +14,12 @@ #include <stdlib.h> // for qsort #include <stdio.h> // for GGML_ASSERT +#if defined(_MSC_VER) +// disable "possible loss of data" to avoid warnings for hundreds of casts +// we should just be careful :) +#pragma warning(disable: 4244 4267) +#endif + #define UNUSED GGML_UNUSED // some compilers don't provide _mm256_set_m128i, e.g. gcc 7 From f5ef34e428f3886544590ecb2d532e4d333c114c Mon Sep 17 00:00:00 2001 From: Justina Cho <justcho5@gmail.com> Date: Wed, 1 May 2024 14:44:26 -0700 Subject: [PATCH 089/100] feat: implemented sigmoid function (ggml/806) * added sigmoid function * implemented metal kernel for sigmoid * implemented cuda kernel for sigmoid * added sigmoid unary op and incremented count --- ggml-cuda.cu | 4 +++ ggml-cuda/unary.cu | 26 ++++++++++++++++ ggml-cuda/unary.cuh | 3 ++ ggml-metal.m | 15 ++++++++++ ggml-metal.metal | 7 +++++ ggml.c | 73 ++++++++++++++++++++++++++++++++++++++++++++- ggml.h | 9 ++++++ 7 files changed, 136 insertions(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c5c778796..5b6c90919 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2204,6 +2204,9 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg case GGML_UNARY_OP_RELU: ggml_cuda_op_relu(ctx, dst); break; + case GGML_UNARY_OP_SIGMOID: + ggml_cuda_op_sigmoid(ctx, dst); + break; case GGML_UNARY_OP_HARDSIGMOID: ggml_cuda_op_hardsigmoid(ctx, dst); break; @@ -2716,6 +2719,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSIGMOID: case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: diff --git a/ggml-cuda/unary.cu b/ggml-cuda/unary.cu index 1a7f09469..ac03d5c6f 100644 --- a/ggml-cuda/unary.cu +++ b/ggml-cuda/unary.cu @@ -48,6 +48,15 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } +static __global__ void sigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = 1.0f / (1.0f + expf(-x[i])); +} + static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -108,6 +117,11 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ relu_f32<<<num_blocks, CUDA_RELU_BLOCK_SIZE, 0, stream>>>(x, dst, k); } +static void sigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_SIGMOID_BLOCK_SIZE - 1) / CUDA_SIGMOID_BLOCK_SIZE; + sigmoid_f32<<<num_blocks, CUDA_SIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k); +} + static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; hardsigmoid_f32<<<num_blocks, CUDA_HARDSIGMOID_BLOCK_SIZE, 0, stream>>>(x, dst, k); @@ -188,6 +202,18 @@ void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { relu_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); } +void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * src0 = dst->src[0]; + const float * src0_d = (const float *)src0->data; + float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sigmoid_f32_cuda(src0_d, dst_d, ggml_nelements(src0), stream); +} + void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const float * src0_d = (const float *)src0->data; diff --git a/ggml-cuda/unary.cuh b/ggml-cuda/unary.cuh index 2002ed989..a1d07c04f 100644 --- a/ggml-cuda/unary.cuh +++ b/ggml-cuda/unary.cuh @@ -4,6 +4,7 @@ #define CUDA_SILU_BLOCK_SIZE 256 #define CUDA_TANH_BLOCK_SIZE 256 #define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_SIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSIGMOID_BLOCK_SIZE 256 #define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 @@ -18,6 +19,8 @@ void ggml_cuda_op_tanh(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_relu(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +void ggml_cuda_op_sigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + void ggml_cuda_op_hardsigmoid(ggml_backend_cuda_context & ctx, ggml_tensor * dst); void ggml_cuda_op_hardswish(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-metal.m b/ggml-metal.m index 1bbb8fb4f..66c398d54 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -40,6 +40,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_CLAMP, GGML_METAL_KERNEL_TYPE_TANH, GGML_METAL_KERNEL_TYPE_RELU, + GGML_METAL_KERNEL_TYPE_SIGMOID, GGML_METAL_KERNEL_TYPE_GELU, GGML_METAL_KERNEL_TYPE_GELU_4, GGML_METAL_KERNEL_TYPE_GELU_QUICK, @@ -493,6 +494,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_TANH, tanh, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RELU, relu, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SIGMOID, sigmoid, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU, gelu, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_4, gelu_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GELU_QUICK, gelu_quick, true); @@ -730,6 +732,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const switch (ggml_get_unary_op(op)) { case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_SILU: @@ -1237,6 +1240,18 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; + case GGML_UNARY_OP_SIGMOID: + { + id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_SIGMOID].pipeline; + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_UNARY_OP_GELU: diff --git a/ggml-metal.metal b/ggml-metal.metal index ee9de57a3..0c6d32798 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -229,6 +229,13 @@ kernel void kernel_relu( dst[tpig] = max(0.0f, src0[tpig]); } +kernel void kernel_sigmoid( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + dst[tpig] = 1.0f / (1.0f + exp(-src0[tpig])); +} + kernel void kernel_tanh( device const float * src0, device float * dst, diff --git a/ggml.c b/ggml.c index 4ee5d24af..4f3011583 100644 --- a/ggml.c +++ b/ggml.c @@ -1949,6 +1949,7 @@ inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } inline static void ggml_vec_leaky_relu_f32 (const int n, float * y, const float * x, const float ns) { for (int i = 0; i < n; ++i) y[i] = ((x[i] > 0.f) ? x[i] : 0.f) + ns * ((x[i] < 0.0f) ? x[i] : 0.f); } +inline static void ggml_vec_sigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = 1.f / (1.f + expf(-x[i])); } // TODO: optimize performance inline static void ggml_vec_hardswish_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } inline static void ggml_vec_hardsigmoid_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); } @@ -2329,6 +2330,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "TANH", "ELU", "RELU", + "SIGMOID", "GELU", "GELU_QUICK", "SILU", @@ -2336,7 +2338,7 @@ static const char * GGML_UNARY_OP_NAME[GGML_UNARY_OP_COUNT] = { "HARDSIGMOID", }; -static_assert(GGML_UNARY_OP_COUNT == 12, "GGML_UNARY_OP_COUNT != 12"); +static_assert(GGML_UNARY_OP_COUNT == 13, "GGML_UNARY_OP_COUNT != 13"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); @@ -4561,6 +4563,20 @@ struct ggml_tensor * ggml_leaky_relu( return result; } +// ggml_sigmoid + +struct ggml_tensor * ggml_sigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary(ctx, a, GGML_UNARY_OP_SIGMOID); +} + +struct ggml_tensor * ggml_sigmoid_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_unary_inplace(ctx, a, GGML_UNARY_OP_SIGMOID); +} + // ggml_gelu struct ggml_tensor * ggml_gelu( @@ -10852,6 +10868,52 @@ static void ggml_compute_forward_relu( } } +// ggml_compute_forward_sigmoid + +static void ggml_compute_forward_sigmoid_f32( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_sigmoid_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_sigmoid( + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { + + const struct ggml_tensor * src0 = dst->src[0]; + + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_sigmoid_f32(params, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_gelu static void ggml_compute_forward_gelu_f32( @@ -16617,6 +16679,10 @@ static void ggml_compute_forward_unary( { ggml_compute_forward_relu(params, dst); } break; + case GGML_UNARY_OP_SIGMOID: + { + ggml_compute_forward_sigmoid(params, dst); + } break; case GGML_UNARY_OP_GELU: { ggml_compute_forward_gelu(params, dst); @@ -18601,6 +18667,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; + case GGML_UNARY_OP_SIGMOID: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_UNARY_OP_GELU: { GGML_ASSERT(false); // TODO: not implemented @@ -19130,6 +19200,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ case GGML_UNARY_OP_TANH: case GGML_UNARY_OP_ELU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_SIGMOID: case GGML_UNARY_OP_HARDSWISH: // to opt for multiple threads case GGML_UNARY_OP_HARDSIGMOID: // to opt for multiple threads { diff --git a/ggml.h b/ggml.h index 76c332831..3fe95ed57 100644 --- a/ggml.h +++ b/ggml.h @@ -519,6 +519,7 @@ extern "C" { GGML_UNARY_OP_TANH, GGML_UNARY_OP_ELU, GGML_UNARY_OP_RELU, + GGML_UNARY_OP_SIGMOID, GGML_UNARY_OP_GELU, GGML_UNARY_OP_GELU_QUICK, GGML_UNARY_OP_SILU, @@ -1073,6 +1074,14 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sigmoid( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_sigmoid_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a); From fae9d234b6606693704eca62fe4aefbb6c6abb45 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 12:02:39 +0300 Subject: [PATCH 090/100] sync : ggml ggml-ci --- 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 d4400695b..c5bd3ef55 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -98875cdb7e9ceeb726d1c196d2fecb3cbb59b93a +8cd3975bf21657c6d1e80c7c61830977b962539e From 5a419926b0c4efab0531401aea91522aaea9fd07 Mon Sep 17 00:00:00 2001 From: compilade <git@compilade.net> Date: Sat, 11 May 2024 11:06:26 -0400 Subject: [PATCH 091/100] convert-hf : support bfloat16 conversion (#7158) * convert-hf : support bfloat16 conversion * gguf-py : flake8 fixes * convert-hf : add missing space after comma * convert-hf : get bit-exact same output as ./quantize The quantization version was missing. * convert-hf : don't round bf16 NANs * convert-hf : save some memory with np.int16 intermediate bf16 weights * convert-hf : more closely match llama.cpp with which weights to keep in f32 * convert-hf : add --outtype auto-f16 A reason for this to exist is for model quantizers who want an initial GGUF with the most fidelity to the original model while still using a 16-bit float type instead of 32-bit floats. * convert-hf : remove a semicolon because flake8 doesn't like it It's a reflex from when programming in C/C++, I guess. * convert-hf : support outtype templating in outfile name * convert-hf : rename --outtype auto-f16 to --outtype auto --- convert-hf-to-gguf.py | 265 ++++++++++++++++++------------------ gguf-py/gguf/__init__.py | 1 + gguf-py/gguf/constants.py | 44 ++++++ gguf-py/gguf/gguf_writer.py | 51 +------ gguf-py/gguf/lazy.py | 225 ++++++++++++++++++++++++++++++ 5 files changed, 404 insertions(+), 182 deletions(-) create mode 100644 gguf-py/gguf/lazy.py diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index fbaed64da..ec7f4dd75 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -12,7 +12,7 @@ import sys from enum import IntEnum from pathlib import Path from hashlib import sha256 -from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast, overload +from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast import numpy as np import torch @@ -48,7 +48,6 @@ class Model: dir_model: Path ftype: int - fname_out: Path is_big_endian: bool endianess: gguf.GGUFEndian use_temp_file: bool @@ -56,20 +55,20 @@ class Model: part_names: list[str] is_safetensors: bool hparams: dict[str, Any] - gguf_writer: gguf.GGUFWriter block_count: int tensor_map: gguf.TensorNameMap tensor_names: set[str] | None + fname_out: Path + gguf_writer: gguf.GGUFWriter # subclasses should define this! model_arch: gguf.MODEL_ARCH - def __init__(self, dir_model: Path, ftype: int, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): - if self.__class__ == Model: - raise TypeError(f"{self.__class__.__name__!r} should not be directly instantiated") + def __init__(self, dir_model: Path, ftype: gguf.LlamaFileType, fname_out: Path, is_big_endian: bool, use_temp_file: bool, eager: bool): + if type(self) is Model: + raise TypeError(f"{type(self).__name__!r} should not be directly instantiated") self.dir_model = dir_model self.ftype = ftype - self.fname_out = fname_out self.is_big_endian = is_big_endian self.endianess = gguf.GGUFEndian.BIG if is_big_endian else gguf.GGUFEndian.LITTLE self.use_temp_file = use_temp_file @@ -79,10 +78,23 @@ class Model: if not self.is_safetensors: self.part_names = Model.get_model_part_names(self.dir_model, ".bin") self.hparams = Model.load_hparams(self.dir_model) - self.gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) self.block_count = self.find_hparam(["n_layers", "num_hidden_layers", "n_layer"]) self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count) self.tensor_names = None + if self.ftype == gguf.LlamaFileType.GUESSED: + # NOTE: can't use field "torch_dtype" in config.json, because some finetunes lie. + _, first_tensor = next(self.get_tensors()) + if first_tensor.dtype == torch.float16: + logger.info(f"choosing --outtype f16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_F16 + else: + logger.info(f"choosing --outtype bf16 from first tensor type ({first_tensor.dtype})") + self.ftype = gguf.LlamaFileType.MOSTLY_BF16 + ftype_up: str = self.ftype.name.partition("_")[2].upper() + ftype_lw: str = ftype_up.lower() + # allow templating the file name with the output ftype, useful with the "auto" ftype + self.fname_out = fname_out.parent / fname_out.name.format(ftype_lw, outtype=ftype_lw, ftype=ftype_lw, OUTTYPE=ftype_up, FTYPE=ftype_up) + self.gguf_writer = gguf.GGUFWriter(self.fname_out, gguf.MODEL_ARCH_NAMES[self.model_arch], endianess=self.endianess, use_temp_file=self.use_temp_file) @classmethod def __init_subclass__(cls): @@ -142,14 +154,27 @@ class Model: raise ValueError(f"Mismatch between weight map and model parts for tensor names: {sym_diff}") def format_tensor_name(self, key: gguf.MODEL_TENSOR, bid: int | None = None, suffix: str = ".weight") -> str: - name: str = gguf.TENSOR_NAMES[key] if key not in gguf.MODEL_TENSORS[self.model_arch]: raise ValueError(f"Missing {key!r} for MODEL_TENSORS of {self.model_arch!r}") + name: str = gguf.TENSOR_NAMES[key] if "{bid}" in name: assert bid is not None name = name.format(bid=bid) return name + suffix + def match_model_tensor_name(self, name: str, key: gguf.MODEL_TENSOR, bid: int | None, suffix: str = ".weight") -> bool: + if key not in gguf.MODEL_TENSORS[self.model_arch]: + return False + key_name: str = gguf.TENSOR_NAMES[key] + if "{bid}" in key_name: + if bid is None: + return False + key_name = key_name.format(bid=bid) + else: + if bid is not None: + return False + return name == (key_name + suffix) + def map_tensor_name(self, name: str, try_suffixes: Sequence[str] = (".weight", ".bias")) -> str: new_name = self.tensor_map.get_name(key=name, try_suffixes=try_suffixes) if new_name is None: @@ -215,6 +240,23 @@ class Model: return False def write_tensors(self): + # same as ggml_compute_fp32_to_bf16 in ggml-impl.h + def np_fp32_to_bf16(n: np.ndarray): + # force nan to quiet + n = np.where((n & 0x7fffffff) > 0x7f800000, (n & 0xffff0000) | (64 << 16), n) + # flush subnormals to zero + n = np.where((n & 0x7f800000) == 0, n & 0x80000000, n) + # round to nearest even + n = (n + (0x7fff + ((n >> 16) & 1))) >> 16 + return n.astype(np.int16) + + # Doing this row-wise is much, much faster than element-wise, hence the signature + v_fp32_to_bf16 = np.vectorize(np_fp32_to_bf16, otypes=[np.int16], signature="(n)->(n)") + if self.lazy: + # TODO: find a way to implicitly wrap np.vectorize functions + # NOTE: the type is changed to reflect otypes passed to np.vectorize above + v_fp32_to_bf16 = gguf.LazyNumpyTensor._wrap_fn(v_fp32_to_bf16, meta_noop=np.int16) + max_name_len = max(len(s) for _, s in self.tensor_map.mapping.values()) + len(".weight,") for name, data_torch in self.get_tensors(): @@ -239,35 +281,60 @@ class Model: data: np.ndarray = data # type hint n_dims = len(data.shape) data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if self.ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) + data_qtype: gguf.GGMLQuantizationType | None = None # when both are True, f32 should win extra_f32 = self.extra_f32_tensors(name, new_name, bid, n_dims) extra_f16 = self.extra_f16_tensors(name, new_name, bid, n_dims) # Most of the codebase that takes in 1D tensors or norms only handles F32 tensors - extra_f32 = extra_f32 or n_dims == 1 or new_name.endswith("_norm.weight") + # Conditions should closely match those in llama_model_quantize_internal in llama.cpp + extra_f32 = any(cond for cond in ( + extra_f32, + n_dims == 1, + new_name.endswith("_norm.weight"), + )) + + # Some tensor types are always in float32 + extra_f32 = extra_f32 or any(self.match_model_tensor_name(new_name, key, bid) for key in ( + gguf.MODEL_TENSOR.FFN_GATE_INP, + gguf.MODEL_TENSOR.POS_EMBD, + gguf.MODEL_TENSOR.TOKEN_TYPES, + )) # if f16 desired, convert any float32 2-dim weight tensors to float16 - extra_f16 = extra_f16 or (name.endswith(".weight") and n_dims >= 2) + extra_f16 = any(cond for cond in ( + extra_f16, + (name.endswith(".weight") and n_dims >= 2), + )) - # when both extra_f32 and extra_f16 are False, convert to float32 by default - if self.ftype == 1 and data_dtype == np.float16 and (extra_f32 or not extra_f16): - data = data.astype(np.float32) + if self.ftype != gguf.LlamaFileType.ALL_F32 and extra_f16 and not extra_f32: + if self.ftype == gguf.LlamaFileType.MOSTLY_F16: + if data_dtype != np.float16: + data = data.astype(np.float16) + data_qtype = gguf.GGMLQuantizationType.F16 - if self.ftype == 1 and data_dtype == np.float32 and extra_f16 and not extra_f32: - data = data.astype(np.float16) + elif self.ftype == gguf.LlamaFileType.MOSTLY_BF16: + if data_dtype != np.float32: + data = data.astype(np.float32) + data = v_fp32_to_bf16(data.view(np.int32)) + assert data.dtype == np.int16 + data_qtype = gguf.GGMLQuantizationType.BF16 + + else: # by default, convert to float32 + if data_dtype != np.float32: + data = data.astype(np.float32) + data_qtype = gguf.GGMLQuantizationType.F32 + + assert data_qtype is not None # reverse shape to make it similar to the internal ggml dimension order shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}" # n_dims is implicit in the shape - logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data.dtype}, shape = {shape_str}") + logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") - self.gguf_writer.add_tensor(new_name, data) + self.gguf_writer.add_tensor(new_name, data, raw_dtype=data_qtype) def write(self): self.write_tensors() @@ -2044,12 +2111,6 @@ class BertModel(Model): return [(self.map_tensor_name(name), data_torch)] - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del new_name, bid, n_dims # unused - - # not used with get_rows, must be F32 - return name == "embeddings.token_type_embeddings.weight" - @Model.register("NomicBertModel") class NomicBertModel(BertModel): @@ -2339,92 +2400,40 @@ class JinaBertV2Model(BertModel): # tree of lazy tensors -class LazyTorchTensor: - _meta: Tensor - _data: Tensor | None - _args: tuple - _func: Callable[[tuple], Tensor] | None - - def __init__(self, *, meta: Tensor, data: Tensor | None = None, args: tuple = (), func: Callable[[tuple], Tensor] | None = None): - self._meta = meta - self._data = data - self._args = args - self._func = func - - @staticmethod - def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: - # TODO: dict and set - if isinstance(o, (list, tuple)): - L = [] - for item in o: - L.append(LazyTorchTensor._recurse_apply(item, fn)) - if isinstance(o, tuple): - L = tuple(L) - return L - elif isinstance(o, LazyTorchTensor): - return fn(o) - else: - return o - - def _wrap_fn(self, fn: Callable, use_self: bool = False) -> Callable[[Any], LazyTorchTensor]: - def wrapped_fn(*args, **kwargs): - if kwargs is None: - kwargs = {} - args = ((self,) if use_self else ()) + args - - meta_args = LazyTorchTensor._recurse_apply(args, lambda t: t._meta) - - return LazyTorchTensor(meta=fn(*meta_args, **kwargs), args=args, func=lambda a: fn(*a, **kwargs)) - return wrapped_fn - - def __getattr__(self, __name: str) -> Any: - meta_attr = getattr(self._meta, __name) - if callable(meta_attr): - return self._wrap_fn(getattr(torch.Tensor, __name), use_self=True) - elif isinstance(meta_attr, torch.Tensor): - # for things like self.T - return self._wrap_fn(lambda s: getattr(s, __name))(self) - else: - return meta_attr +class LazyTorchTensor(gguf.LazyBase): + _tensor_type = torch.Tensor + # to keep the type-checker happy + dtype: torch.dtype + shape: torch.Size + # only used when converting a torch.Tensor to a np.ndarray _dtype_map: dict[torch.dtype, type] = { torch.float16: np.float16, torch.float32: np.float32, } - def numpy(self) -> gguf.LazyTensor: + def numpy(self) -> gguf.LazyNumpyTensor: dtype = self._dtype_map[self.dtype] - return gguf.LazyTensor(lambda: LazyTorchTensor.to_eager(self).numpy(), dtype=dtype, shape=self.shape) + return gguf.LazyNumpyTensor( + meta=np.lib.stride_tricks.as_strided(np.zeros(1, dtype), self.shape, (0 for _ in self.shape)), + lazy=self._lazy, + args=(self,), + func=(lambda s: s[0].numpy()) + ) - @overload - @staticmethod - def to_eager(t: Tensor | LazyTorchTensor) -> Tensor: ... - - @overload - @staticmethod - def to_eager(t: tuple) -> tuple: ... - - @staticmethod - def to_eager(t: Any) -> Any: - def simple_to_eager(_t: LazyTorchTensor) -> Tensor: - # wake up the lazy tensor - if _t._data is None and _t._func is not None: - # recurse into its arguments - _t._args = LazyTorchTensor.to_eager(_t._args) - _t._data = _t._func(_t._args) - if _t._data is not None: - return _t._data - else: - raise ValueError(f"Could not compute lazy tensor {_t!r} with args {_t._args!r}") - - # recurse into lists and/or tuples, keeping their structure - return LazyTorchTensor._recurse_apply(t, simple_to_eager) - - @staticmethod - def from_eager(t: Tensor) -> Tensor: - if (t.__class__ == LazyTorchTensor): + @classmethod + def eager_to_meta(cls, t: Tensor) -> Tensor: + if t.is_meta: return t - return LazyTorchTensor(meta=t.detach().to("meta"), data=t) # type: ignore + return t.detach().to("meta") + + @classmethod + def meta_with_dtype(cls, m: Tensor, dtype: torch.dtype) -> Tensor: + m = m.detach() + if not m.is_meta: + m = m.to("meta") + m.dtype = dtype + return m @classmethod def __torch_function__(cls, func, types, args=(), kwargs=None): @@ -2435,28 +2444,8 @@ class LazyTorchTensor: if func is torch.Tensor.numpy: return args[0].numpy() - if func is torch.equal: - eager_args = LazyTorchTensor.to_eager(args) - return func(*eager_args, **kwargs) - return LazyTorchTensor._wrap_fn(args[0], func)(*args, **kwargs) - - # special methods bypass __getattr__, so they need to be added manually - # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup - # NOTE: LazyTorchTensor can't be a subclass of Tensor (and then be used - # as self._meta is currently used), because then the following - # operations would by default not be wrapped, and so not propagated - # when the tensor is made eager. - # It's better to get non-silent errors for not-yet-supported operators. - # TODO: add more when needed to avoid clutter, or find a more concise way - def __neg__(self, *args): # mamba - return self._wrap_fn(torch.Tensor.__neg__)(self, *args) - - def __add__(self, *args): # gemma - return self._wrap_fn(torch.Tensor.__add__)(self, *args) - - def __getitem__(self, *args): # bloom falcon refact internlm2 - return self._wrap_fn(torch.Tensor.__getitem__)(self, *args) + return LazyTorchTensor._wrap_fn(func)(*args, **kwargs) def parse_args() -> argparse.Namespace: @@ -2472,11 +2461,11 @@ def parse_args() -> argparse.Namespace: ) parser.add_argument( "--outfile", type=Path, - help="path to write to; default: based on input", + help="path to write to; default: based on input. {ftype} will be replaced by the outtype.", ) parser.add_argument( - "--outtype", type=str, choices=["f32", "f16"], default="f16", - help="output format - use f32 for float32, f16 for float16", + "--outtype", type=str, choices=["f32", "f16", "bf16", "auto"], default="f16", + help="output format - use f32 for float32, f16 for float16, bf16 for bfloat16, auto for the highest-fidelity 16-bit float type depending on the first loaded tensor type", ) parser.add_argument( "--bigendian", action="store_true", @@ -2530,16 +2519,18 @@ def main() -> None: logger.error(f'Error: {args.model} is not a directory') sys.exit(1) - ftype_map = { - "f32": gguf.GGMLQuantizationType.F32, - "f16": gguf.GGMLQuantizationType.F16, + ftype_map: dict[str, gguf.LlamaFileType] = { + "f32": gguf.LlamaFileType.ALL_F32, + "f16": gguf.LlamaFileType.MOSTLY_F16, + "bf16": gguf.LlamaFileType.MOSTLY_BF16, + "auto": gguf.LlamaFileType.GUESSED, } if args.outfile is not None: fname_out = args.outfile else: # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{args.outtype}.gguf' + fname_out = dir_model / 'ggml-model-{ftype}.gguf' logger.info(f"Loading model: {dir_model.name}") @@ -2555,14 +2546,16 @@ def main() -> None: logger.info("Set model tokenizer") model_instance.set_vocab() + model_instance.gguf_writer.add_quantization_version(gguf.GGML_QUANT_VERSION) + if args.vocab_only: - logger.info(f"Exporting model vocab to '{fname_out}'") + logger.info(f"Exporting model vocab to '{model_instance.fname_out}'") model_instance.write_vocab() else: - logger.info(f"Exporting model to '{fname_out}'") + logger.info(f"Exporting model to '{model_instance.fname_out}'") model_instance.write() - logger.info(f"Model successfully exported to '{fname_out}'") + logger.info(f"Model successfully exported to '{model_instance.fname_out}'") if __name__ == '__main__': diff --git a/gguf-py/gguf/__init__.py b/gguf-py/gguf/__init__.py index 110ab342c..e5d5806c8 100644 --- a/gguf-py/gguf/__init__.py +++ b/gguf-py/gguf/__init__.py @@ -1,4 +1,5 @@ from .constants import * +from .lazy import * from .gguf_reader import * from .gguf_writer import * from .tensor_mapping import * diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index a4fbfc5e0..978fcada3 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -10,6 +10,7 @@ from typing import Any GGUF_MAGIC = 0x46554747 # "GGUF" GGUF_VERSION = 3 GGUF_DEFAULT_ALIGNMENT = 32 +GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h # # metadata keys @@ -838,6 +839,49 @@ class GGMLQuantizationType(IntEnum): BF16 = 30 +# TODO: add GGMLFileType from ggml_ftype in ggml.h + + +# from llama_ftype in llama.h +# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE. +class LlamaFileType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 # except 1d tensors + MOSTLY_Q4_0 = 2 # except 1d tensors + MOSTLY_Q4_1 = 3 # except 1d tensors + MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16 + # MOSTLY_Q4_2 = 5 # support has been removed + # MOSTLY_Q4_3 = 6 # support has been removed + MOSTLY_Q8_0 = 7 # except 1d tensors + MOSTLY_Q5_0 = 8 # except 1d tensors + MOSTLY_Q5_1 = 9 # except 1d tensors + MOSTLY_Q2_K = 10 # except 1d tensors + MOSTLY_Q3_K_S = 11 # except 1d tensors + MOSTLY_Q3_K_M = 12 # except 1d tensors + MOSTLY_Q3_K_L = 13 # except 1d tensors + MOSTLY_Q4_K_S = 14 # except 1d tensors + MOSTLY_Q4_K_M = 15 # except 1d tensors + MOSTLY_Q5_K_S = 16 # except 1d tensors + MOSTLY_Q5_K_M = 17 # except 1d tensors + MOSTLY_Q6_K = 18 # except 1d tensors + MOSTLY_IQ2_XXS = 19 # except 1d tensors + MOSTLY_IQ2_XS = 20 # except 1d tensors + MOSTLY_Q2_K_S = 21 # except 1d tensors + MOSTLY_IQ3_XS = 22 # except 1d tensors + MOSTLY_IQ3_XXS = 23 # except 1d tensors + MOSTLY_IQ1_S = 24 # except 1d tensors + MOSTLY_IQ4_NL = 25 # except 1d tensors + MOSTLY_IQ3_S = 26 # except 1d tensors + MOSTLY_IQ3_M = 27 # except 1d tensors + MOSTLY_IQ2_S = 28 # except 1d tensors + MOSTLY_IQ2_M = 29 # except 1d tensors + MOSTLY_IQ4_XS = 30 # except 1d tensors + MOSTLY_IQ1_M = 31 # except 1d tensors + MOSTLY_BF16 = 32 # except 1d tensors + + GUESSED = 1024 # not specified in the model file + + class GGUFEndian(IntEnum): LITTLE = 0 BIG = 1 diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 8dcf9330b..96574358d 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -7,7 +7,7 @@ import struct import tempfile from enum import Enum, auto from io import BufferedWriter -from typing import IO, Any, Callable, Sequence, Mapping +from typing import IO, Any, Sequence, Mapping from string import ascii_letters, digits import numpy as np @@ -28,47 +28,6 @@ from .constants import ( logger = logging.getLogger(__name__) -class LazyTensor: - data: Callable[[], np.ndarray[Any, Any]] - # to avoid too deep recursion - functions: list[Callable[[np.ndarray[Any, Any]], np.ndarray[Any, Any]]] - dtype: np.dtype[Any] - shape: tuple[int, ...] - - def __init__(self, data: Callable[[], np.ndarray[Any, Any]], *, dtype: type, shape: tuple[int, ...]): - self.data = data - self.functions = [] - self.dtype = np.dtype(dtype) - self.shape = shape - - def astype(self, dtype: type, **kwargs) -> LazyTensor: - self.functions.append(lambda n: n.astype(dtype, **kwargs)) - self.dtype = np.dtype(dtype) - return self - - @property - def nbytes(self) -> int: - size = 1 - for n in self.shape: - size *= n - return size * self.dtype.itemsize - - def tofile(self, *args, **kwargs) -> None: - data = self.data() - for f in self.functions: - data = f(data) - assert data.shape == self.shape - assert data.dtype == self.dtype - assert data.nbytes == self.nbytes - self.functions = [] - self.data = lambda: data - data.tofile(*args, **kwargs) - - def byteswap(self, *args, **kwargs) -> LazyTensor: - self.functions.append(lambda n: n.byteswap(*args, **kwargs)) - return self - - class WriterState(Enum): EMPTY = auto() HEADER = auto() @@ -79,7 +38,7 @@ class WriterState(Enum): class GGUFWriter: fout: BufferedWriter temp_file: tempfile.SpooledTemporaryFile[bytes] | None - tensors: list[np.ndarray[Any, Any] | LazyTensor] + tensors: list[np.ndarray[Any, Any]] _simple_value_packing = { GGUFValueType.UINT8: "B", GGUFValueType.INT8: "b", @@ -278,7 +237,7 @@ class GGUFWriter: self.ti_data_count += 1 def add_tensor( - self, name: str, tensor: np.ndarray[Any, Any] | LazyTensor, raw_shape: Sequence[int] | None = None, + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None, ) -> None: if self.endianess == GGUFEndian.BIG: @@ -303,7 +262,7 @@ class GGUFWriter: if pad != 0: fp.write(bytes([0] * pad)) - def write_tensor_data(self, tensor: np.ndarray[Any, Any] | LazyTensor) -> None: + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: if self.state is not WriterState.TI_DATA: raise ValueError(f'Expected output file to contain tensor info, got {self.state}') @@ -391,7 +350,7 @@ class GGUFWriter: def add_name(self, name: str) -> None: self.add_string(Keys.General.NAME, name) - def add_quantization_version(self, quantization_version: GGMLQuantizationType) -> None: + def add_quantization_version(self, quantization_version: int) -> None: self.add_uint32( Keys.General.QUANTIZATION_VERSION, quantization_version) diff --git a/gguf-py/gguf/lazy.py b/gguf-py/gguf/lazy.py new file mode 100644 index 000000000..650bea11c --- /dev/null +++ b/gguf-py/gguf/lazy.py @@ -0,0 +1,225 @@ +from __future__ import annotations +from abc import ABC, ABCMeta, abstractmethod + +import logging +from typing import Any, Callable +from collections import deque + +import numpy as np +from numpy.typing import DTypeLike + + +logger = logging.getLogger(__name__) + + +class LazyMeta(ABCMeta): + + def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs): + def __getattr__(self, __name: str) -> Any: + meta_attr = getattr(self._meta, __name) + if callable(meta_attr): + return type(self)._wrap_fn( + (lambda s, *args, **kwargs: getattr(s, __name)(*args, **kwargs)), + use_self=self, + ) + elif isinstance(meta_attr, self._tensor_type): + # e.g. self.T with torch.Tensor should still be wrapped + return type(self)._wrap_fn(lambda s: getattr(s, __name))(self) + else: + # no need to wrap non-tensor properties, + # and they likely don't depend on the actual contents of the tensor + return meta_attr + + namespace["__getattr__"] = __getattr__ + + # need to make a builder for the wrapped wrapper to copy the name, + # or else it fails with very cryptic error messages, + # because somehow the same string would end up in every closures + def mk_wrap(op_name: str, *, meta_noop: bool = False): + # need to wrap the wrapper to get self + def wrapped_special_op(self, *args, **kwargs): + return type(self)._wrap_fn( + getattr(type(self)._tensor_type, op_name), + meta_noop=meta_noop, + )(self, *args, **kwargs) + return wrapped_special_op + + # special methods bypass __getattr__, so they need to be added manually + # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup + # NOTE: doing this from a metaclass is very convenient + # TODO: make this even more comprehensive + for binary_op in ( + "lt", "le", "eq", "ne", "ge", "gt", "not" + "abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul", + "neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor", + "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor", + "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor", + ): + attr_name = f"__{binary_op}__" + # the result of these operators usually has the same shape and dtype as the input, + # so evaluation on the meta tensor can be skipped. + namespace[attr_name] = mk_wrap(attr_name, meta_noop=True) + + for special_op in ( + "getitem", "setitem", "len", + ): + attr_name = f"__{special_op}__" + namespace[attr_name] = mk_wrap(attr_name, meta_noop=False) + + return super().__new__(cls, name, bases, namespace, **kwargs) + + +# Tree of lazy tensors +class LazyBase(ABC, metaclass=LazyMeta): + _tensor_type: type + _meta: Any + _data: Any | None + _lazy: deque[LazyBase] # shared within a graph, to avoid deep recursion when making eager + _args: tuple + _func: Callable[[tuple], Any] | None + + def __init__(self, *, meta: Any, data: Any | None = None, lazy: deque[LazyBase] | None = None, args: tuple = (), func: Callable[[tuple], Any] | None = None): + super().__init__() + self._meta = meta + self._data = data + self._lazy = lazy if lazy is not None else deque() + self._args = args + self._func = func + assert self._func is not None or self._data is not None + if self._data is None: + self._lazy.append(self) + + def __init_subclass__(cls) -> None: + if "_tensor_type" not in cls.__dict__: + raise TypeError(f"property '_tensor_type' must be defined for {cls!r}") + return super().__init_subclass__() + + @staticmethod + def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: + # TODO: dict and set + if isinstance(o, (list, tuple)): + L = [] + for item in o: + L.append(LazyBase._recurse_apply(item, fn)) + if isinstance(o, tuple): + L = tuple(L) + return L + elif isinstance(o, LazyBase): + return fn(o) + else: + return o + + @classmethod + def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike = False) -> Callable[[Any], Any]: + def wrapped_fn(*args, **kwargs): + if kwargs is None: + kwargs = {} + args = ((use_self,) if use_self is not None else ()) + args + + meta_args = LazyBase._recurse_apply(args, lambda t: t._meta) + + if isinstance(meta_noop, bool) and not meta_noop: + try: + res = fn(*meta_args, **kwargs) + except NotImplementedError: + # running some operations on PyTorch's Meta tensors can cause this exception + res = None + else: + # some operators don't need to actually run on the meta tensors + assert len(args) > 0 + res = args[0] + assert isinstance(res, cls) + res = res._meta + # allow operations to override the dtype + if meta_noop is not True: + res = cls.meta_with_dtype(res, meta_noop) + + if isinstance(res, cls._tensor_type): + def collect_replace(t: LazyBase): + if collect_replace.shared_lazy is None: + collect_replace.shared_lazy = t._lazy + else: + collect_replace.shared_lazy.extend(t._lazy) + t._lazy = collect_replace.shared_lazy + + # emulating a static variable + collect_replace.shared_lazy = None + + LazyBase._recurse_apply(args, collect_replace) + + shared_lazy = collect_replace.shared_lazy + + return cls(meta=cls.eager_to_meta(res), lazy=shared_lazy, args=args, func=lambda a: fn(*a, **kwargs)) + else: + del res # not needed + # non-tensor return likely relies on the contents of the args + # (e.g. the result of torch.equal) + eager_args = cls.to_eager(args) + return fn(*eager_args, **kwargs) + return wrapped_fn + + @classmethod + def to_eager(cls, t: Any) -> Any: + def simple_to_eager(_t: LazyBase) -> Any: + def already_eager_to_eager(_t: LazyBase) -> Any: + assert _t._data is not None + return _t._data + + while _t._data is None: + lt = _t._lazy.popleft() + if lt._data is not None: + raise ValueError(f"{lt} did not belong in the lazy queue") + assert lt._func is not None + lt._args = cls._recurse_apply(lt._args, already_eager_to_eager) + lt._data = lt._func(lt._args) + # sanity check + assert lt._data.dtype == lt._meta.dtype + assert lt._data.shape == lt._meta.shape + + return _t._data + + # recurse into lists and/or tuples, keeping their structure + return cls._recurse_apply(t, simple_to_eager) + + @classmethod + def eager_to_meta(cls, t: Any) -> Any: + return cls.meta_with_dtype(t, t.dtype) + + # must be overridden, meta tensor init is backend-specific + @classmethod + @abstractmethod + def meta_with_dtype(cls, m: Any, dtype: Any) -> Any: pass + + @classmethod + def from_eager(cls, t: Any) -> Any: + if type(t) is cls: + # already eager + return t + elif isinstance(t, cls._tensor_type): + return cls(meta=cls.eager_to_meta(t), data=t) + else: + return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}") + + +class LazyNumpyTensor(LazyBase): + _tensor_type = np.ndarray + + @classmethod + def meta_with_dtype(cls, m: np.ndarray[Any, Any], dtype: DTypeLike) -> np.ndarray[Any, Any]: + # The initial idea was to use np.nan as the fill value, + # but non-float types like np.int16 can't use that. + # So zero it is. + cheat = np.zeros(1, dtype) + return np.lib.stride_tricks.as_strided(cheat, m.shape, (0 for _ in m.shape)) + + def astype(self, dtype, *args, **kwargs): + meta = type(self).meta_with_dtype(self._meta, dtype) + full_args = (self, dtype,) + args + # very important to pass the shared _lazy deque, or else there's an infinite loop somewhere. + return type(self)(meta=meta, args=full_args, lazy=self._lazy, func=(lambda a: a[0].astype(*a[1:], **kwargs))) + + def tofile(self, *args, **kwargs): + eager = LazyNumpyTensor.to_eager(self) + return eager.tofile(*args, **kwargs) + + # TODO: __array_function__ From 72c177c1f6c16693eee319d4ebd4eaab5e630dd2 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen <thichthat@gmail.com> Date: Sat, 11 May 2024 17:28:10 +0200 Subject: [PATCH 092/100] fix system prompt handling (#7153) --- examples/server/server.cpp | 20 +++++++------------- 1 file changed, 7 insertions(+), 13 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 55c1d4129..ceaeb1f76 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -651,9 +651,6 @@ struct server_context { std::string system_prompt; std::vector<llama_token> system_tokens; - std::string name_user; // this should be the antiprompt - std::string name_assistant; - // slots / clients std::vector<server_slot> slots; json default_generation_settings_for_props; @@ -1100,15 +1097,11 @@ struct server_context { system_need_update = false; } - void system_prompt_set(const json & sys_props) { - system_prompt = sys_props.value("prompt", ""); - name_user = sys_props.value("anti_prompt", ""); - name_assistant = sys_props.value("assistant_name", ""); + bool system_prompt_set(const std::string & sys_prompt) { + system_prompt = sys_prompt; LOG_VERBOSE("system prompt process", { {"system_prompt", system_prompt}, - {"name_user", name_user}, - {"name_assistant", name_assistant}, }); // release all slots @@ -1117,6 +1110,7 @@ struct server_context { } system_need_update = true; + return true; } bool process_token(completion_token_output & result, server_slot & slot) { @@ -1536,7 +1530,8 @@ struct server_context { } if (task.data.contains("system_prompt")) { - system_prompt_set(task.data.at("system_prompt")); + std::string sys_prompt = json_value(task.data, "system_prompt", std::string()); + system_prompt_set(sys_prompt); for (server_slot & slot : slots) { slot.n_past = 0; @@ -2920,7 +2915,7 @@ int main(int argc, char ** argv) { server_params_parse(argc, argv, sparams, params); if (!sparams.system_prompt.empty()) { - ctx_server.system_prompt_set(json::parse(sparams.system_prompt)); + ctx_server.system_prompt_set(sparams.system_prompt); } if (params.model_alias == "unknown") { @@ -3409,8 +3404,7 @@ int main(int argc, char ** argv) { const auto handle_props = [&ctx_server](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { - { "user_name", ctx_server.name_user.c_str() }, - { "assistant_name", ctx_server.name_assistant.c_str() }, + { "system_prompt", ctx_server.system_prompt.c_str() }, { "default_generation_settings", ctx_server.default_generation_settings_for_props }, { "total_slots", ctx_server.params.n_parallel } }; From fed0108491a3a3cbec6c6480dc8667ffff9d7659 Mon Sep 17 00:00:00 2001 From: Josh Ramer <josh.ramer@icloud.com> Date: Sat, 11 May 2024 12:26:35 -0500 Subject: [PATCH 093/100] Scripting & documenting debugging one test without anything else in the loop. (#7096) * A little documentation that shares my quick tips for working in the repository. * Update startup-testing-debugging.md * script that shows a menu of tests to pick from & run the debugger on * debug-test.sh: Refactor CLI help message * debug-test.sh: documentation update * debug-test.sh: CLI Help output corrections * debug-test.sh: minor doc fix --------- authored-by: Josh Ramer <ubuntu@ip-172-31-32-53.ec2.internal> Assisted-by: brian khuu <mofosyne@gmail.com> --- docs/debugging-tests.md | 88 ++++++++++++++++++++++++++++++ scripts/debug-test.sh | 117 ++++++++++++++++++++++++++++++++++++++++ 2 files changed, 205 insertions(+) create mode 100644 docs/debugging-tests.md create mode 100755 scripts/debug-test.sh diff --git a/docs/debugging-tests.md b/docs/debugging-tests.md new file mode 100644 index 000000000..51a125e19 --- /dev/null +++ b/docs/debugging-tests.md @@ -0,0 +1,88 @@ +# Debugging Tests Tips + +## How to run & debug a specific test without anything else to keep the feedback loop short? + +There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. + +For example, running the following command will output an interactive list from which you can select a test. It takes this form: + +`debug-test.sh [OPTION]... <test_regex> <test_number>` + +It will then build & run in the debugger for you. + +```bash +./scripts/debug-test.sh test-tokenizer + +# Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: +>>> b main +``` + +For further reference use `debug-test.sh -h` to print help. + +&nbsp; + +### How does the script work? +If you want to be able to use the concepts contained in the script separately, the important ones are briefly outlined below. + +#### Step 1: Reset and Setup folder context + +From base of this repository, let's create `build-ci-debug` as our build context. + +```bash +rm -rf build-ci-debug && mkdir build-ci-debug && cd build-ci-debug +``` + +#### Step 2: Setup Build Environment and Compile Test Binaries + +Setup and trigger a build under debug mode. You may adapt the arguments as needed, but in this case these are sane defaults. + +```bash +cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. +make -j +``` + +#### Step 3.1: Identify Test Command for Debugging + +The output of this command will give you the command & arguments needed to run GDB. + +* `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +* `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +* `-V` : Verbose Mode + +```bash +ctest -R "test-tokenizer" -V -N +``` + +This may return output similar to below (focusing on key lines to pay attention to): + +```bash +... +1: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +1: Working Directory: . +Labels: main + Test #1: test-tokenizer-0-llama-spm +... +4: Test command: ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-falcon.gguf" +4: Working Directory: . +Labels: main + Test #4: test-tokenizer-0-falcon +... +``` + +So for test #1 we can tell these two pieces of relevant information: +* Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` +* Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` + +#### Step 3.2: Run GDB on test command + +Based on the ctest 'test command' report above we can then run a gdb session via this command below: + +```bash +gdb --args ${Test Binary} ${Test GGUF Model} +``` + +Example: + +```bash +gdb --args ~/llama.cpp/build-ci-debug/bin/test-tokenizer-0 "~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf" +``` diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh new file mode 100755 index 000000000..231a23d69 --- /dev/null +++ b/scripts/debug-test.sh @@ -0,0 +1,117 @@ +#!/bin/bash +test_suite=${1:-} +test_number=${2:-} + +PROG=${0##*/} +build_dir="build-ci-debug" + +if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then + echo "Usage: $PROG [OPTION]... <test_regex> (test_number)" + echo "Debug specific ctest program." + echo + echo "Options:" + echo " -h, --help Display this help and exit" + echo + echo "Arguments:" + echo " <test_regex> (Mandatory) Supply one regex to the script to filter tests" + echo " (test_number) (Optional) Test number to run a specific test" + echo + echo "Example:" + echo " $PROG test-tokenizer" + echo " $PROG test-tokenizer 3" + echo + exit 0 +fi + +# Function to select and debug a test +function select_test() { + test_suite=${1:-test} + test_number=${2:-} + + # Sanity Check If Tests Is Detected + printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" + tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) + if [ ${#tests[@]} -eq 0 ] + then + echo "No tests avaliable... check your compliation process..." + echo "Exiting." + exit 1 + fi + + if [ -z $test_number ] + then + # List out avaliable tests + printf "Which test would you like to debug?\n" + id=0 + for s in "${tests[@]}" + do + echo "Test# ${id}" + echo " $s" + ((id++)) + done + + # Prompt user which test they wanted to run + printf "\nRun test#? " + read test_number + else + printf "\nUser Already Requested #${test_number}" + fi + + # Start GDB with the requested test binary and arguments + printf "Debugging(GDB) test: ${tests[test_number]}\n" + # Change IFS (Internal Field Separator) + sIFS=$IFS + IFS=$'\n' + + # Get test args + gdb_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) + IFS=$sIFS + printf "Debug arguments: ${gdb_args[test_number]}\n\n" + + # Expand paths if needed + args=() + for x in $(echo ${gdb_args[test_number]} | sed -e 's/"\/\<//' -e 's/\>"//') + do + args+=($(echo $x | sed -e 's/.*\/..\//..\//')) + done + + # Execute debugger + echo "gdb args: ${args[@]}" + gdb --args ${args[@]} +} + +# Step 0: Check the args +if [ -z "$test_suite" ] +then + echo "Usage: $PROG [OPTION]... <test_regex> (test_number)" + echo "Supply one regex to the script to filter tests," + echo "and optionally a test number to run a specific test." + echo "Use --help flag for full instructions" + exit 1 +fi + +# Step 1: Reset and Setup folder context +## Sanity check that we are actually in a git repo +repo_root=$(git rev-parse --show-toplevel) +if [ ! -d "$repo_root" ]; then + echo "Error: Not in a Git repository." + exit 1 +fi + +## Reset folder to root context of git repo +pushd "$repo_root" || exit 1 + +## Create and enter build directory +rm -rf "$build_dir" && mkdir "$build_dir" || exit 1 + +# Step 2: Setup Build Environment and Compile Test Binaries +cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON || exit 1 +pushd "$build_dir" && make -j || exit 1 + +# Step 3: Debug the Test +select_test "$test_suite" "$test_number" + +# Step 4: Return to the directory from which the user ran the command. +popd || exit 1 +popd || exit 1 +popd || exit 1 From 325756d28df7d018a7bac424e1b3bc8acb4ecf07 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 16:25:50 +0300 Subject: [PATCH 094/100] ggml : resolve merge (ggml/0) ggml-ci --- ggml.c | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 4f3011583..b96a82a41 100644 --- a/ggml.c +++ b/ggml.c @@ -4,7 +4,6 @@ #include "ggml-impl.h" #include "ggml-quants.h" #include "ggml.h" -#include "sgemm.h" #if defined(_MSC_VER) || defined(__MINGW32__) #include <malloc.h> // using malloc.h with MSC/MINGW @@ -37,6 +36,10 @@ #undef GGML_USE_LLAMAFILE #endif +#ifdef GGML_USE_LLAMAFILE +#include "sgemm.h" +#endif + #if defined(_MSC_VER) // disable "possible loss of data" to avoid hundreds of casts // we should just be careful :) From 6aeff24f8b91e145e92d17ec7ce3adc4ef60b8e9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 16:57:53 +0300 Subject: [PATCH 095/100] metal : fix indent (ggml/0) --- ggml-metal.m | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 66c398d54..28dec762a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1195,24 +1195,24 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_CLAMP: - { - id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; + { + id<MTLComputePipelineState> pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CLAMP].pipeline; - float min; - float max; - memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); - memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); + float min; + float max; + memcpy(&min, ((int32_t *) dst->op_params) + 0, sizeof(float)); + memcpy(&max, ((int32_t *) dst->op_params) + 1, sizeof(float)); - [encoder setComputePipelineState:pipeline]; - [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; - [encoder setBytes:&min length:sizeof(min) atIndex:2]; - [encoder setBytes:&max length:sizeof(max) atIndex:3]; + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&min length:sizeof(min) atIndex:2]; + [encoder setBytes:&max length:sizeof(max) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst); - [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; - } break; + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_UNARY: switch (ggml_get_unary_op(gf->nodes[i])) { // we are not taking into account the strides, so for now require contiguous tensors From 1622ac023f42e5e01c163321cd98c6596aa9402d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 21:35:05 +0300 Subject: [PATCH 096/100] 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 c5bd3ef55..46b874c64 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -8cd3975bf21657c6d1e80c7c61830977b962539e +d2ad1793f45922665c99f5cf4244dcab77e16c00 From 7bd4ffb78062587e4012a1c24186223f09b1bc70 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sat, 11 May 2024 21:36:20 +0300 Subject: [PATCH 097/100] metal : fix warnings (skipme) (#0) --- ggml-metal.metal | 4 ++-- scripts/sync-ggml.last | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 0c6d32798..7af4e8f93 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2217,7 +2217,7 @@ kernel void kernel_flash_attn_ext_f16( // ALiBi if (max_bias > 0.0f) { - const short h = iq2; + const uint32_t h = iq2; const float base = h < n_head_log2 ? m0 : m1; const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; @@ -2473,7 +2473,7 @@ kernel void kernel_flash_attn_ext_vec_f16( // ALiBi if (max_bias > 0.0f) { - const short h = iq2; + const uint32_t h = iq2; const float base = h < n_head_log2 ? m0 : m1; const int exp = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 46b874c64..1ea320429 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -d2ad1793f45922665c99f5cf4244dcab77e16c00 +30f54cbb3ada3e4c5bc6924de3e5918e5be4ff11 From b228aba91ac2cd9eb90e9d423ba1d0d20e0117e2 Mon Sep 17 00:00:00 2001 From: slaren <slarengh@gmail.com> Date: Sun, 12 May 2024 02:29:33 +0200 Subject: [PATCH 098/100] remove convert-lora-to-ggml.py (#7204) --- CMakeLists.txt | 11 -- ci/run.sh | 95 ----------- convert-lora-to-ggml.py | 150 ------------------ requirements.txt | 1 - .../requirements-convert-lora-to-ggml.txt | 2 - 5 files changed, 259 deletions(-) delete mode 100755 convert-lora-to-ggml.py delete mode 100644 requirements/requirements-convert-lora-to-ggml.txt diff --git a/CMakeLists.txt b/CMakeLists.txt index aa65b0d6c..362ab3673 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1281,17 +1281,6 @@ install( WORLD_READ WORLD_EXECUTE DESTINATION ${CMAKE_INSTALL_BINDIR}) -install( - FILES convert-lora-to-ggml.py - PERMISSIONS - OWNER_READ - OWNER_WRITE - OWNER_EXECUTE - GROUP_READ - GROUP_EXECUTE - WORLD_READ - WORLD_EXECUTE - DESTINATION ${CMAKE_INSTALL_BINDIR}) if (LLAMA_METAL) install( FILES ggml-metal.metal diff --git a/ci/run.sh b/ci/run.sh index e67c1a5ff..d5972480b 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -365,47 +365,6 @@ function gg_run_open_llama_3b_v2 { cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - # lora - function compare_ppl { - qnt="$1" - ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" - return 20 - fi - - printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" - return 0 - } - - path_lora="../models-mnt/open-llama/3B-v2/lora" - path_shakespeare="../models-mnt/shakespeare" - - shakespeare="${path_shakespeare}/shakespeare.txt" - lora_shakespeare="${path_lora}/ggml-adapter-model.bin" - - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin - gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt - - python3 ../convert-lora-to-ggml.py ${path_lora} - - # f16 - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log - compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log - compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 + f16 lora-base - (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log - compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - set +e } @@ -416,7 +375,6 @@ function gg_sum_open_llama_3b_v2 { gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" - gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" @@ -429,11 +387,6 @@ function gg_sum_open_llama_3b_v2 { gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" - gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" - gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" - gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" - gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" - gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" } # open_llama_7b_v2 @@ -549,48 +502,6 @@ function gg_run_open_llama_7b_v2 { cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - # lora - function compare_ppl { - qnt="$1" - ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" - return 20 - fi - - printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" - return 0 - } - - path_lora="../models-mnt/open-llama/7B-v2/lora" - path_shakespeare="../models-mnt/shakespeare" - - shakespeare="${path_shakespeare}/shakespeare.txt" - lora_shakespeare="${path_lora}/ggml-adapter-model.bin" - - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json - gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin - gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt - - python3 ../convert-lora-to-ggml.py ${path_lora} - - # f16 - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log - (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log - compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # currently not supported by the CUDA backend - # q8_0 - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log - #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - - # q8_0 + f16 lora-base - #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log - #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log - set +e } @@ -601,7 +512,6 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" - gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" @@ -614,11 +524,6 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" - gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" - gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" - #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" - #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" - #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" } # bge-small diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py deleted file mode 100755 index f09fa85fe..000000000 --- a/convert-lora-to-ggml.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import logging -import json -import os -import struct -import sys -from pathlib import Path -from typing import Any, BinaryIO, Sequence - -import numpy as np -import torch - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) -import gguf - -logging.basicConfig(level=logging.DEBUG) -logger = logging.getLogger("lora-to-gguf") - -NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} - - -def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: - fout.write(b"ggla"[::-1]) # magic (ggml lora) - fout.write(struct.pack("i", 1)) # file version - fout.write(struct.pack("i", params["r"])) - # https://opendelta.readthedocs.io/en/latest/modules/deltas.html says that `lora_alpha` is an int - # but some models ship a float value instead - # let's convert to int, but fail if lossless conversion is not possible - assert ( - int(params["lora_alpha"]) == params["lora_alpha"] - ), "cannot convert float to int losslessly" - fout.write(struct.pack("i", int(params["lora_alpha"]))) - - -def write_tensor_header(fout: BinaryIO, name: str, shape: Sequence[int], data_type: np.dtype[Any]) -> None: - sname = name.encode("utf-8") - fout.write( - struct.pack( - "iii", - len(shape), - len(sname), - NUMPY_TYPE_TO_FTYPE[data_type.name], - ) - ) - fout.write(struct.pack("i" * len(shape), *shape[::-1])) - fout.write(sname) - fout.seek((fout.tell() + 31) & -32) - - -if __name__ == '__main__': - if len(sys.argv) < 2: - logger.info(f"Usage: python {sys.argv[0]} <path> [arch]") - logger.info("Path must contain HuggingFace PEFT LoRA files 'adapter_config.json' and 'adapter_model.bin'") - logger.info(f"Arch must be one of {list(gguf.MODEL_ARCH_NAMES.values())} (default: llama)") - sys.exit(1) - - input_json = os.path.join(sys.argv[1], "adapter_config.json") - input_model = os.path.join(sys.argv[1], "adapter_model.bin") - output_path = os.path.join(sys.argv[1], "ggml-adapter-model.bin") - - if os.path.exists(input_model): - model = torch.load(input_model, map_location="cpu") - else: - input_model = os.path.join(sys.argv[1], "adapter_model.safetensors") - # lazy import load_file only if lora is in safetensors format. - from safetensors.torch import load_file - model = load_file(input_model, device="cpu") - - arch_name = sys.argv[2] if len(sys.argv) == 3 else "llama" - - if arch_name not in gguf.MODEL_ARCH_NAMES.values(): - logger.error(f"Error: unsupported architecture {arch_name}") - sys.exit(1) - - arch = list(gguf.MODEL_ARCH_NAMES.keys())[list(gguf.MODEL_ARCH_NAMES.values()).index(arch_name)] - name_map = gguf.TensorNameMap(arch, 200) # 200 layers ought to be enough for anyone - - with open(input_json, "r") as f: - params = json.load(f) - - if params["peft_type"] != "LORA": - logger.error(f"Error: unsupported adapter type {params['peft_type']}, expected LORA") - sys.exit(1) - - if params["fan_in_fan_out"] is True: - logger.error("Error: param fan_in_fan_out is not supported") - sys.exit(1) - - if params["bias"] is not None and params["bias"] != "none": - logger.error("Error: param bias is not supported") - sys.exit(1) - - # TODO: these seem to be layers that have been trained but without lora. - # doesn't seem widely used but eventually should be supported - if params["modules_to_save"] is not None and len(params["modules_to_save"]) > 0: - logger.error("Error: param modules_to_save is not supported") - sys.exit(1) - - with open(output_path, "wb") as fout: - fout.truncate() - - write_file_header(fout, params) - for k, v in model.items(): - orig_k = k - if k.endswith(".default.weight"): - k = k.replace(".default.weight", ".weight") - if k in ["llama_proj.weight", "llama_proj.bias"]: - continue - if k.endswith("lora_A.weight"): - if v.dtype != torch.float16 and v.dtype != torch.float32: - v = v.float() - v = v.T - else: - v = v.float() - - t = v.detach().numpy() - - prefix = "base_model.model." - if k.startswith(prefix): - k = k[len(prefix) :] - - lora_suffixes = (".lora_A.weight", ".lora_B.weight") - if k.endswith(lora_suffixes): - suffix = k[-len(lora_suffixes[0]):] - k = k[: -len(lora_suffixes[0])] - else: - logger.error(f"Error: unrecognized tensor name {orig_k}") - sys.exit(1) - - tname = name_map.get_name(k) - if tname is None: - logger.error(f"Error: could not map tensor name {orig_k}") - logger.error(" Note: the arch parameter must be specified if the model is not llama") - sys.exit(1) - - if suffix == ".lora_A.weight": - tname += ".weight.loraA" - elif suffix == ".lora_B.weight": - tname += ".weight.loraB" - else: - assert False - - logger.info(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") - write_tensor_header(fout, tname, t.shape, t.dtype) - t.tofile(fout) - - logger.info(f"Converted {input_json} and {input_model} to {output_path}") diff --git a/requirements.txt b/requirements.txt index fc1e28278..e7d14e16a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,5 +9,4 @@ -r ./requirements/requirements-convert-hf-to-gguf.txt -r ./requirements/requirements-convert-hf-to-gguf-update.txt -r ./requirements/requirements-convert-llama-ggml-to-gguf.txt --r ./requirements/requirements-convert-lora-to-ggml.txt -r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-lora-to-ggml.txt b/requirements/requirements-convert-lora-to-ggml.txt deleted file mode 100644 index 6ac402610..000000000 --- a/requirements/requirements-convert-lora-to-ggml.txt +++ /dev/null @@ -1,2 +0,0 @@ --r ./requirements-convert.txt -torch~=2.1.1 From 6f1b63606fc68a09d62d1d74dbd156c35219026d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov <ggerganov@gmail.com> Date: Sun, 12 May 2024 18:30:23 +0300 Subject: [PATCH 099/100] cmake : fix version cmp (#7227) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 362ab3673..1c3b5c8e4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -296,7 +296,7 @@ if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) endif() - if ($(CMAKE_VERSION) VERSION_GREATER_EQUAL 3.22) + if (CMAKE_VERSION VERSION_GREATER_EQUAL 3.22) set(BLA_SIZEOF_INTEGER 8) endif() From dc685be46622a8fabfd57cfa804237c8f15679b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= <johannesg@5d6.de> Date: Sun, 12 May 2024 19:40:45 +0200 Subject: [PATCH 100/100] CUDA: add FP32 FlashAttention vector kernel (#7188) * CUDA: add FP32 FlashAttention vector kernel * fixup! CUDA: add FP32 FlashAttention vector kernel * fixup! fixup! CUDA: add FP32 FlashAttention vector kernel * fixup! fixup! fixup! CUDA: add FP32 FlashAttention vector kernel --- ggml-cuda.cu | 11 +- ggml-cuda/common.cuh | 4 + ggml-cuda/fattn-common.cuh | 47 ++++ ggml-cuda/fattn-vec-f16.cu | 430 +++++++++++++++++++++++++++++++++ ggml-cuda/fattn-vec-f16.cuh | 5 + ggml-cuda/fattn-vec-f32.cu | 384 +++++++++++++++++++++++++++++ ggml-cuda/fattn-vec-f32.cuh | 3 + ggml-cuda/fattn.cu | 468 ++---------------------------------- tests/test-backend-ops.cpp | 5 +- 9 files changed, 899 insertions(+), 458 deletions(-) create mode 100644 ggml-cuda/fattn-common.cuh create mode 100644 ggml-cuda/fattn-vec-f16.cu create mode 100644 ggml-cuda/fattn-vec-f16.cuh create mode 100644 ggml-cuda/fattn-vec-f32.cu create mode 100644 ggml-cuda/fattn-vec-f32.cuh diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5b6c90919..75a2ad480 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2713,6 +2713,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + ggml_backend_cuda_context * cuda_ctx = (ggml_backend_cuda_context *) backend->context; switch (op->op) { case GGML_OP_UNARY: switch (ggml_get_unary_op(op)) { @@ -2840,8 +2841,16 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ARANGE: case GGML_OP_TIMESTEP_EMBEDDING: case GGML_OP_LEAKY_RELU: - case GGML_OP_FLASH_ATTN_EXT: return true; + case GGML_OP_FLASH_ATTN_EXT: +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128; +#else + if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) { + return true; + } + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) default: return false; } diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 44e67e040..b6f0bc36a 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -321,6 +321,10 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #define FP16_MMA_AVAILABLE !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_VOLTA +static bool fast_fp16_available(const int cc) { + return cc >= CC_PASCAL && cc != 610; +} + static bool fp16_mma_available(const int cc) { return cc < CC_OFFSET_AMD && cc >= CC_VOLTA; } diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh new file mode 100644 index 000000000..33f640691 --- /dev/null +++ b/ggml-cuda/fattn-common.cuh @@ -0,0 +1,47 @@ +#define FATTN_KQ_STRIDE 256 +#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. +#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. + +template<int D, int parallel_blocks> // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_combine_results( + const float * __restrict__ VKQ_parts, + const float2 * __restrict__ VKQ_meta, + float * __restrict__ dst) { + VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; + VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; + dst += D * gridDim.y*blockIdx.x; + + const int tid = threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float2 meta[parallel_blocks]; + if (tid < 2*parallel_blocks) { + ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; + } + + __syncthreads(); + + float kqmax = meta[0].x; +#pragma unroll + for (int l = 1; l < parallel_blocks; ++l) { + kqmax = max(kqmax, meta[l].x); + } + + float VKQ_numerator = 0.0f; + float VKQ_denominator = 0.0f; +#pragma unroll + for (int l = 0; l < parallel_blocks; ++l) { + const float diff = meta[l].x - kqmax; + const float KQ_max_scale = expf(diff); + const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); + *((uint32_t *) &KQ_max_scale) &= ftz_mask; + + VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; + VKQ_denominator += KQ_max_scale * meta[l].y; + } + + dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; +} diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu new file mode 100644 index 000000000..cbf5f7835 --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cu @@ -0,0 +1,430 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f16.cuh" + +template<int D, int ncols, int parallel_blocks> // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + half slopeh = __float2half(1.0f); + + // ALiBi + if (max_bias > 0.0f) { + const int h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slopeh = __float2half(powf(base, exph)); + } + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ half KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + half2 * KQ2 = (half2 *) KQ; + + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; + + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + half2 VKQ[ncols] = {{0.0f, 0.0f}}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + half2 sum2[ncols] = {{0.0f, 0.0f}}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum2[j] = warp_reduce_sum(sum2[j]); + half sum = __low2half(sum2[j]) + __high2half(sum2[j]); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); + } + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; + reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid != 0) { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); + } + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc<float> dst_tmp(pool); + ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks> + <<<blocks_num, block_dim, shmem, main_stream>>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if (parallel_blocks == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results<D, parallel_blocks> + <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 256: + launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } +} + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh new file mode 100644 index 000000000..c7023610a --- /dev/null +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -0,0 +1,5 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + +void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu new file mode 100644 index 000000000..40c336ce3 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cu @@ -0,0 +1,384 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f32.cuh" + +template<int D, int ncols, int parallel_blocks> // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV = nb11 / sizeof(half); + const int stride_KV2 = nb11 / sizeof(half2); + + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const int h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = powf(base, exph); + } + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -FLT_MAX/2.0f; + } + + float kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -FLT_MAX/2.0f; + } + float kqsum[ncols] = {0.0f}; + + __shared__ float kqmax_shared[ncols][WARP_SIZE]; + __shared__ float kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 and store in registers: + float2 Q_h2[ncols][D/(2*WARP_SIZE)]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_h2[j][i0/WARP_SIZE] = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE].x *= scale; + Q_h2[j][i0/WARP_SIZE].y *= scale; + } + } + + float VKQ[ncols] = {0.0f}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + + float sum[ncols] = {0.0f}; +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x; + sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y; + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + sum[j] = warp_reduce_sum(sum[j]); + sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum[j]; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const float val = expf(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= KQ_max_scale; + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < D; ++k) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { + break; + } + + const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_ki*KQ[j*D + k]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + float dst_val = VKQ[j_VKQ]; + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid != 0) { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); + } + } +} + +template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f32( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc<float> dst_tmp(pool); + ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + flash_attn_vec_ext_f32<D, cols_per_block, parallel_blocks> + <<<blocks_num, block_dim, shmem, main_stream>>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if (parallel_blocks == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results<D, parallel_blocks> + <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + switch (Q->ne[0]) { + case 64: + launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh new file mode 100644 index 000000000..614d54ae3 --- /dev/null +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index ac5d6672b..419f8e752 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -1,4 +1,7 @@ #include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-vec-f16.cuh" +#include "fattn-vec-f32.cuh" #include "fattn.cuh" #include <cstdint> @@ -7,251 +10,6 @@ #include <mma.h> #endif -#define FATTN_KQ_STRIDE 256 -#define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. -#define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. - -template<int D, int ncols, int parallel_blocks> // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - half slopeh = __float2half(1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const int h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - } - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ half KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -HALF_MAX_HALF; - } - half2 * KQ2 = (half2 *) KQ; - - half kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -HALF_MAX_HALF; - } - half kqsum[ncols] = {0.0f}; - - __shared__ half kqmax_shared[ncols][WARP_SIZE]; - __shared__ half kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - half2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; - Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); - } - } - - half2 VKQ[ncols] = {{0.0f, 0.0f}}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, - // see https://github.com/ggerganov/llama.cpp/pull/7061 . - // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). - half kqmax_new = kqmax[0]; - half kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - half2 sum2[ncols] = {{0.0f, 0.0f}}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] = warp_reduce_sum(sum2[j]); - half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); - - if (ncols == 1) { - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - } else { - kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); - } - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const half val = hexp(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= __half2half2(KQ_max_scale); - } - - __syncthreads(); - -#pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; - } - - half2 V_k; - reinterpret_cast<half&>(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast<half&>(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid != 0) { -#pragma unroll - for (int j = 0; j < ncols; ++j) { - dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); - } - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - // D == head size, VKQ_stride == num VKQ rows calculated in parallel: template<int D, int ncols, int nwarps, int VKQ_stride, int parallel_blocks, typename KQ_acc_t> #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) @@ -655,54 +413,6 @@ static __global__ void flash_attn_ext_f16( #endif // FP16_MMA_AVAILABLE } -template<int D, int parallel_blocks> // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_combine_results( - const float * __restrict__ VKQ_parts, - const float2 * __restrict__ VKQ_meta, - float * __restrict__ dst) { -#if FP16_AVAILABLE - VKQ_parts += parallel_blocks*D * gridDim.y*blockIdx.x; - VKQ_meta += parallel_blocks * gridDim.y*blockIdx.x; - dst += D * gridDim.y*blockIdx.x; - - const int tid = threadIdx.x; - __builtin_assume(tid < D); - - __shared__ float2 meta[parallel_blocks]; - if (tid < 2*parallel_blocks) { - ((float *) meta)[threadIdx.x] = ((const float *)VKQ_meta) [blockIdx.y*(2*parallel_blocks) + tid]; - } - - __syncthreads(); - - float kqmax = meta[0].x; -#pragma unroll - for (int l = 1; l < parallel_blocks; ++l) { - kqmax = max(kqmax, meta[l].x); - } - - float VKQ_numerator = 0.0f; - float VKQ_denominator = 0.0f; -#pragma unroll - for (int l = 0; l < parallel_blocks; ++l) { - const float diff = meta[l].x - kqmax; - const float KQ_max_scale = expf(diff); - const uint32_t ftz_mask = 0xFFFFFFFF * (diff > SOFTMAX_FTZ_THRESHOLD); - *((uint32_t *) &KQ_max_scale) &= ftz_mask; - - VKQ_numerator += KQ_max_scale * VKQ_parts[l*gridDim.y*D + blockIdx.y*D + tid]; - VKQ_denominator += KQ_max_scale * meta[l].y; - } - - dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - constexpr int get_max_power_of_2(int x) { return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; } @@ -727,66 +437,6 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); -template <int D, int cols_per_block, int parallel_blocks> void launch_fattn_vec_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc<float> dst_tmp(pool); - ggml_cuda_pool_alloc<float2> dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_vec_ext_f16<D, cols_per_block, parallel_blocks> - <<<blocks_num, block_dim, shmem, main_stream>>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results<D, parallel_blocks> - <<<blocks_num_combine, block_dim_combine, shmem_combine, main_stream>>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - template <int D, int cols_per_block, int nwarps, int parallel_blocks, typename KQ_acc_t> void launch_fattn_f16_impl( const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, ggml_cuda_pool & pool, cudaStream_t main_stream @@ -891,95 +541,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst const int32_t precision = KQV->op_params[2]; + if (!fast_fp16_available(cc)) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + return; + } + if (!fp16_mma_available(cc)) { - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); return; } if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + return; + } + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { constexpr int cols_per_block = 16; constexpr int nwarps = 4; @@ -1037,22 +614,7 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst } if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 256: - launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 731788b95..45a2cb85a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -2,6 +2,7 @@ #include <ggml-alloc.h> #include <ggml-backend.h> #include <ggml-backend-impl.h> + #include <algorithm> #include <array> #include <cfloat> @@ -2173,11 +2174,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_timestep_embedding()); test_cases.emplace_back(new test_leaky_relu()); -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) - for (int hs : { 64, 128, }) { // other head sizes not implemented -#else for (int hs : { 64, 80, 128, 256, }) { -#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) for (float max_bias : {0.0f, 8.0f}) { for (int nh : { 32, }) { for (int kv : { 512, 1024, }) {