diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 059fd2695..c01006efe 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6ecf3bcc7..0314d469b 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 432fb5dad..6d5943a2f 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -18,7 +18,7 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENV LC_ALL=C.utf8 diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile index b937a4829..23f428944 100644 --- a/.devops/main-cuda.Dockerfile +++ b/.devops/main-cuda.Dockerfile @@ -23,7 +23,7 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} # Enable CUDA ENV LLAMA_CUDA=1 -RUN make +RUN make -j$(nproc) FROM ${BASE_CUDA_RUN_CONTAINER} as runtime diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index 274b91b71..7516c8313 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile index 0a706dc73..37576d68e 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/main-rocm.Dockerfile @@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1 ENV CC=/opt/rocm/llvm/bin/clang ENV CXX=/opt/rocm/llvm/bin/clang++ -RUN make +RUN make -j$(nproc) ENTRYPOINT [ "/app/main" ] diff --git a/.devops/main.Dockerfile b/.devops/main.Dockerfile index 3ab1decd6..763d75fce 100644 --- a/.devops/main.Dockerfile +++ b/.devops/main.Dockerfile @@ -9,7 +9,7 @@ WORKDIR /app COPY . . -RUN make +RUN make -j$(nproc) FROM ubuntu:$UBUNTU_VERSION as runtime diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile index 59a52ba21..7f5228185 100644 --- a/.devops/server-cuda.Dockerfile +++ b/.devops/server-cuda.Dockerfile @@ -25,7 +25,7 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) FROM ${BASE_CUDA_RUN_CONTAINER} as runtime diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index a8e451fa9..13d00b737 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git libcurl4-openssl-dev @@ -19,6 +27,14 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile index c02a31dd8..a6b76dee8 100644 --- a/.devops/server-rocm.Dockerfile +++ b/.devops/server-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) ENTRYPOINT [ "/app/server" ] diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile index be964e0e8..0d09d3627 100644 --- a/.devops/server.Dockerfile +++ b/.devops/server.Dockerfile @@ -11,7 +11,7 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) FROM ubuntu:$UBUNTU_VERSION as runtime diff --git a/.devops/tools.sh b/.devops/tools.sh index 3a7d274e4..97424c3aa 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -8,7 +8,7 @@ arg1="$1" shift if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$@" + python3 ./convert-hf-to-gguf.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then ./quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml new file mode 100644 index 000000000..bfb9d9a06 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/01-bug-low.yml @@ -0,0 +1,50 @@ +name: Low Severity Bugs +description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches) +title: "Bug: " +labels: ["bug-unconfirmed", "low severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml new file mode 100644 index 000000000..e8297eea0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/02-bug-medium.yml @@ -0,0 +1,50 @@ +name: Medium Severity Bug +description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable) +title: "Bug: " +labels: ["bug-unconfirmed", "medium severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml new file mode 100644 index 000000000..3c9d50d16 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/03-bug-high.yml @@ -0,0 +1,50 @@ +name: High Severity Bug +description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow) +title: "Bug: " +labels: ["bug-unconfirmed", "high severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml new file mode 100644 index 000000000..d089d5fa1 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/04-bug-critical.yml @@ -0,0 +1,50 @@ +name: Critical Severity Bug +description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss) +title: "Bug: " +labels: ["bug-unconfirmed", "critical severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/05-enhancement.yml new file mode 100644 index 000000000..58fca7318 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/05-enhancement.yml @@ -0,0 +1,51 @@ +name: Enhancement +description: Used to request enhancements for llama.cpp +title: "Feature Request: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your enhancement request. + options: + - label: I am running the latest code. Mention the version if possible as well. + required: true + - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). + required: true + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share. + required: true + + - type: textarea + id: feature-description + attributes: + label: Feature Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + placeholder: Detailed description of the enhancement + validations: + required: true + + - type: textarea + id: motivation + attributes: + label: Motivation + description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + placeholder: Explanation of why this feature is needed and its benefits + validations: + required: true + + - type: textarea + id: possible-implementation + attributes: + label: Possible Implementation + description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. + placeholder: Detailed description of potential implementation + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/06-research.yml new file mode 100644 index 000000000..3ae4e9f8c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/06-research.yml @@ -0,0 +1,52 @@ +name: Research +description: Track new technical research area +title: "Research: " +labels: ["research 🔬"] +body: + - type: markdown + attributes: + value: | + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + + - type: checkboxes + id: research-stage + attributes: + label: Research Stage + description: Track general state of this research ticket + options: + - label: Background Research (Let's try to avoid reinventing the wheel) + - label: Hypothesis Formed (How do you think this will work and it's effect?) + - label: Strategy / Implementation Forming + - label: Analysis of results + - label: Debrief / Documentation (So people in the future can learn from us) + + - type: textarea + id: background + attributes: + label: Previous existing literature and research + description: Whats the current state of the art and whats the motivation for this research? + + - type: textarea + id: hypothesis + attributes: + label: Hypothesis + description: How do you think this will work and it's effect? + + - type: textarea + id: implementation + attributes: + label: Implementation + description: Got an approach? e.g. a PR ready to go? + + - type: textarea + id: analysis + attributes: + label: Analysis + description: How does the proposed implementation behave? + + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/07-refactor.yml new file mode 100644 index 000000000..3a68d3d53 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/07-refactor.yml @@ -0,0 +1,28 @@ +name: Refactor (Maintainers) +description: Used to track refactoring opportunities +title: "Refactor: " +labels: ["refactor"] +body: + - type: markdown + attributes: + value: | + Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of the pain points you are trying to solve. + placeholder: Detailed description behind your motivation to request refactor + validations: + required: true + + - type: textarea + id: possible-approaches + attributes: + label: Possible Refactor Approaches + description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list. + placeholder: Your idea of possible refactoring opportunity/approaches + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md deleted file mode 100644 index 49812832c..000000000 --- a/.github/ISSUE_TEMPLATE/bug.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -name: Bug template -about: Used to report bugs in llama.cpp -labels: ["bug-unconfirmed"] -assignees: '' - ---- - -Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug. - -If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests). diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..c88134dbb --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,13 @@ +blank_issues_enabled: true +contact_links: + - name: Got an idea? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas + about: Pop it there. It may then become an enhancement ticket. + - name: Got a question? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a + about: Ask a question there! + - name: Want to contribute? + url: https://github.com/ggerganov/llama.cpp/wiki/contribute + about: Head to the contribution guide page of the wiki for areas you can help with + + diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md deleted file mode 100644 index dcffda750..000000000 --- a/.github/ISSUE_TEMPLATE/enhancement.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -name: Enhancement template -about: Used to request enhancements for llama.cpp -labels: ["enhancement"] -assignees: '' - ---- - -# Prerequisites - -Please answer the following questions for yourself before submitting an issue. - -- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now. -- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). -- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed). -- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share. - -# Feature Description - -Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. - -# Motivation - -Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. - -# Possible Implementation - -If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. diff --git a/.github/labeler.yml b/.github/labeler.yml index fca60594f..97d739b58 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -1,5 +1,16 @@ # https://github.com/actions/labeler - +Kompute: + - changed-files: + - any-glob-to-any-file: + - ggml-kompute.h + - ggml-kompute.cpp + - README-kompute.md +Apple Metal: + - changed-files: + - any-glob-to-any-file: + - ggml-metal.h + - ggml-metal.cpp + - README-metal.md SYCL: - changed-files: - any-glob-to-any-file: @@ -9,6 +20,7 @@ SYCL: Nvidia GPU: - changed-files: - any-glob-to-any-file: + - ggml-cuda.h - ggml-cuda/** Vulkan: - changed-files: @@ -62,6 +74,8 @@ server: ggml: - changed-files: - any-glob-to-any-file: + - ggml.c + - ggml.h - ggml-*.c - ggml-*.h - ggml-cuda/** @@ -71,3 +85,6 @@ nix: - "**/*.nix" - .github/workflows/nix-*.yml - .devops/nix/nixpkgs-instances.nix +embedding: + - changed-files: + - any-glob-to-any-file: examples/embedding/ diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 7b616281b..93669d531 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -294,12 +294,22 @@ jobs: - name: Build id: cmake_build + if: ${{ matrix.sanitizer != 'THREAD' }} run: | mkdir build cd build cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + - name: Build (no OpenMP) + id: cmake_build_no_openmp + if: ${{ matrix.sanitizer == 'THREAD' }} + run: | + mkdir build + cd build + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -DLLAMA_OPENMP=OFF + cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + - name: Test id: cmake_test run: | @@ -678,8 +688,6 @@ jobs: env: OPENBLAS_VERSION: 0.3.23 - OPENCL_VERSION: 2023.04.17 - CLBLAST_VERSION: 1.6.0 SDE_VERSION: 9.33.0-2024-01-07 VULKAN_VERSION: 1.3.261.1 @@ -696,8 +704,6 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - build: 'avx512-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON' - - build: 'clblast-x64' - defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - build: 'openblas-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute-x64' @@ -722,27 +728,6 @@ jobs: run: | git submodule update --init kompute - - name: Download OpenCL SDK - id: get_opencl - if: ${{ matrix.build == 'clblast-x64' }} - run: | - curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip" - mkdir $env:RUNNER_TEMP/opencl - tar.exe -xvf $env:RUNNER_TEMP/opencl.zip --strip-components=1 -C $env:RUNNER_TEMP/opencl - - - name: Download CLBlast - id: get_clblast - if: ${{ matrix.build == 'clblast-x64' }} - run: | - curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z" - curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE" - 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/clblast.7z - rename-item $env:RUNNER_TEMP/CLBlast-${env:CLBLAST_VERSION}-windows-x64 clblast - foreach ($f in (gci -Recurse -Path "$env:RUNNER_TEMP/clblast" -Filter '*.cmake')) { - $txt = Get-Content -Path $f -Raw - $txt.Replace('C:/vcpkg/packages/opencl_x64-windows/', "$($env:RUNNER_TEMP.Replace('\','/'))/opencl/") | Set-Content -Path $f -Encoding UTF8 - } - - name: Download OpenBLAS id: get_openblas if: ${{ matrix.build == 'openblas-x64' }} @@ -776,13 +761,6 @@ jobs: cmake -S . -B build ${{ matrix.defines }} cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - - name: Add clblast.dll - id: add_clblast_dll - if: ${{ matrix.build == 'clblast-x64' }} - run: | - cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release - cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt - - name: Add libopenblas.dll id: add_libopenblas_dll if: ${{ matrix.build == 'openblas-x64' }} @@ -806,7 +784,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 @@ -1061,7 +1039,7 @@ jobs: # hypervisor: 'qemu' # run: | # sudo pkg update -# sudo pkg install -y gmake automake autoconf pkgconf llvm15 clinfo clover opencl clblast openblas +# sudo pkg install -y gmake automake autoconf pkgconf llvm15 openblas # gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j `sysctl -n hw.ncpu` release: diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml deleted file mode 100644 index 747c35cc0..000000000 --- a/.github/workflows/zig-build.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Zig CI - -on: - pull_request: - push: - branches: - - master - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -jobs: - build: - strategy: - fail-fast: false - matrix: - runs-on: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.runs-on }} - steps: - - uses: actions/checkout@v4 - with: - submodules: recursive - fetch-depth: 0 - - uses: goto-bus-stop/setup-zig@v2 - with: - version: 0.11.0 - - name: Build Summary - run: zig build --summary all -freference-trace diff --git a/.gitignore b/.gitignore index 50ae0973a..5223c6963 100644 --- a/.gitignore +++ b/.gitignore @@ -34,9 +34,11 @@ ggml-metal-embed.metal lcov-report/ gcovr-report/ +tags build* !build.zig cmake-build-* +android-ndk-* out/ tmp/ @@ -105,6 +107,7 @@ examples/jeopardy/results.txt examples/server/*.html.hpp examples/server/*.js.hpp examples/server/*.mjs.hpp +examples/server/*.css.hpp poetry.lock poetry.toml diff --git a/CMakeLists.txt b/CMakeLists.txt index 9cc60039a..cf37d5bb2 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -72,6 +72,7 @@ else() set(INS_ENB ON) endif() +option(LLAMA_SVE "llama: enable SVE" OFF) option(LLAMA_AVX "llama: enable AVX" ${INS_ENB}) option(LLAMA_AVX2 "llama: enable AVX2" ${INS_ENB}) option(LLAMA_AVX512 "llama: enable AVX512" OFF) @@ -105,11 +106,11 @@ 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_CUDA_FA_ALL_QUANTS "llama: compile all quants for FlashAttention" 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) -option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_VULKAN "llama: use Vulkan" OFF) option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) @@ -124,7 +125,7 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) option(LLAMA_RPC "llama: use RPC" OFF) -option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) +option(LLAMA_OPENMP "llama: use OpenMP" ON) option(LLAMA_SYCL "llama: use SYCL" OFF) option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device") @@ -295,6 +296,17 @@ if (LLAMA_METAL) ) endif() +if (LLAMA_OPENMP) + find_package(OpenMP) + if (OpenMP_FOUND) + message(STATUS "OpenMP found") + add_compile_definitions(GGML_USE_OPENMP) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} OpenMP::OpenMP_C OpenMP::OpenMP_CXX) + else() + message(WARNING "OpenMP not found") + endif() +endif() + if (LLAMA_BLAS) if (LLAMA_STATIC) set(BLA_STATIC ON) @@ -384,10 +396,6 @@ if (LLAMA_LLAMAFILE) set(GGML_SOURCES_LLAMAFILE sgemm.cpp) endif() -if (LLAMA_QKK_64) - add_compile_definitions(GGML_QKK_64) -endif() - if (LLAMA_CUBLAS) message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead") set(LLAMA_CUDA ON) @@ -406,6 +414,8 @@ if (LLAMA_CUDA) file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) add_compile_definitions(GGML_USE_CUDA) add_compile_definitions(GGML_CUDA_USE_GRAPHS) @@ -431,6 +441,18 @@ if (LLAMA_CUDA) if (LLAMA_CUDA_NO_PEER_COPY) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() if (LLAMA_STATIC) if (WIN32) @@ -479,22 +501,6 @@ if (LLAMA_RPC) set(GGML_SOURCES_RPC ggml-rpc.cpp) endif() -if (LLAMA_CLBLAST) - find_package(CLBlast) - if (CLBlast_FOUND) - message(STATUS "CLBlast found") - - set(GGML_HEADERS_OPENCL ggml-opencl.h) - set(GGML_SOURCES_OPENCL ggml-opencl.cpp) - - add_compile_definitions(GGML_USE_CLBLAST) - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} clblast) - else() - message(WARNING "CLBlast not found") - endif() -endif() - if (LLAMA_VULKAN) find_package(Vulkan) if (Vulkan_FOUND) @@ -505,6 +511,12 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_USE_VULKAN) + # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build + # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) + endif() + if (LLAMA_VULKAN_CHECK_RESULTS) add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) endif() @@ -528,12 +540,17 @@ if (LLAMA_VULKAN) endif() if (LLAMA_HIPBLAS) - if ($ENV{ROCM_PATH}) - set(ROCM_PATH $ENV{ROCM_PATH}) + if (NOT EXISTS $ENV{ROCM_PATH}) + if (NOT EXISTS /opt/rocm) + set(ROCM_PATH /usr) + else() + set(ROCM_PATH /opt/rocm) + endif() else() - set(ROCM_PATH /opt/rocm) + set(ROCM_PATH $ENV{ROCM_PATH}) endif() list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) + list(APPEND CMAKE_PREFIX_PATH "${ROCM_PATH}/lib64/cmake") # CMake on Windows doesn't support the HIP language yet if(WIN32) @@ -569,6 +586,8 @@ if (LLAMA_HIPBLAS) file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA) @@ -588,6 +607,19 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + endif() + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) @@ -626,6 +658,10 @@ if (LLAMA_SYCL) add_compile_definitions(GGML_SYCL_F16) endif() + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_SYCL_FORCE_MMQ) + endif() + add_compile_options(-I./) #include DPCT add_compile_options(-I/${SYCL_INCLUDE_DIR}) @@ -741,6 +777,7 @@ if (LLAMA_KOMPUTE) kompute-shaders/op_mul_mat_q4_0.comp kompute-shaders/op_mul_mat_q4_1.comp kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f32.comp kompute-shaders/op_getrows_f16.comp kompute-shaders/op_getrows_q4_0.comp kompute-shaders/op_getrows_q4_1.comp @@ -773,6 +810,7 @@ if (LLAMA_KOMPUTE) shaderop_mul_mat_q4_0.h shaderop_mul_mat_q4_1.h shaderop_mul_mat_q6_k.h + shaderop_getrows_f32.h shaderop_getrows_f16.h shaderop_getrows_q4_0.h shaderop_getrows_q4_1.h @@ -1039,6 +1077,9 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR # Raspberry Pi 3, 4, Zero 2 (32-bit) list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() + if (LLAMA_SVE) + list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + endif() endif() elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND @@ -1207,7 +1248,6 @@ add_library(ggml OBJECT ggml-quants.c ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -1295,8 +1335,9 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}") + "${GGML_HEADERS_CUDA}" + "${GGML_HEADERS_METAL}" + "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") install(TARGETS ggml PUBLIC_HEADER) @@ -1305,7 +1346,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR} install(TARGETS llama LIBRARY PUBLIC_HEADER) install( - FILES convert.py + FILES convert-hf-to-gguf.py PERMISSIONS OWNER_READ OWNER_WRITE @@ -1332,6 +1373,13 @@ if (LLAMA_METAL) endif() endif() +configure_file(cmake/llama.pc.in + "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + @ONLY) + +install(FILES "${CMAKE_CURRENT_BINARY_DIR}/llama.pc" + DESTINATION lib/pkgconfig) + # # programs, examples and tests # diff --git a/CMakePresets.json b/CMakePresets.json index ad1af7ecc..e2b7a79e3 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -1,4 +1,4 @@ -{ +{ "version": 4, "configurePresets": [ { @@ -40,6 +40,10 @@ { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, - { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] }, + + { "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] }, + { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] } ] } diff --git a/Makefile b/Makefile index 6b7c853b3..802ee6a47 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ # Define the default target now so that it is always the first target BUILD_TARGETS = \ main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \ - simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama beam-search \ + simple batched batched-bench save-load-state server gguf gguf-split eval-callback llama-bench libllava.a llava-cli baby-llama \ retrieval speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey gritlm tests/test-c.o # Binaries only useful for tests @@ -57,6 +57,8 @@ ifeq ($(UNAME_S),Darwin) LLAMA_METAL := 1 endif + LLAMA_NO_OPENMP := 1 + ifneq ($(UNAME_P),arm) SYSCTL_M := $(shell sysctl -n hw.optional.arm64 2>/dev/null) ifeq ($(SYSCTL_M),1) @@ -67,6 +69,10 @@ ifeq ($(UNAME_S),Darwin) endif endif +ifdef LLAMA_RPC + BUILD_TARGETS += rpc-server +endif + default: $(BUILD_TARGETS) test: $(TEST_TARGETS) @@ -135,12 +141,16 @@ MK_NVCCFLAGS = -std=c++11 ifdef LLAMA_FAST MK_CFLAGS += -Ofast HOST_CXXFLAGS += -Ofast +ifndef LLAMA_DEBUG MK_NVCCFLAGS += -O3 +endif # LLAMA_DEBUG else MK_CFLAGS += -O3 MK_CXXFLAGS += -O3 +ifndef LLAMA_DEBUG MK_NVCCFLAGS += -O3 -endif +endif # LLAMA_DEBUG +endif # LLAMA_FAST ifndef LLAMA_NO_CCACHE CCACHE := $(shell which ccache) @@ -201,9 +211,10 @@ ifdef LLAMA_SCHED_MAX_COPIES endif ifdef LLAMA_DEBUG - MK_CFLAGS += -O0 -g - MK_CXXFLAGS += -O0 -g - MK_LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g + MK_NVCCFLAGS += -O0 -g ifeq ($(UNAME_S),Linux) MK_CPPFLAGS += -D_GLIBCXX_ASSERTIONS @@ -389,10 +400,6 @@ else MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif -ifdef LLAMA_QKK_64 - MK_CPPFLAGS += -DGGML_QKK_64 -endif - ifndef LLAMA_NO_ACCELERATE # Mac OS - include Accelerate framework. # `-framework Accelerate` works both with Apple Silicon and Mac Intel @@ -404,6 +411,12 @@ ifndef LLAMA_NO_ACCELERATE endif endif # LLAMA_NO_ACCELERATE +ifndef LLAMA_NO_OPENMP + MK_CPPFLAGS += -DGGML_USE_OPENMP + MK_CFLAGS += -fopenmp + MK_CXXFLAGS += -fopenmp +endif # LLAMA_NO_OPENMP + ifdef LLAMA_OPENBLAS MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) @@ -420,11 +433,25 @@ ifdef LLAMA_BLIS MK_LDFLAGS += -lblis -L/usr/local/lib endif # LLAMA_BLIS +ifdef LLAMA_RPC + MK_CPPFLAGS += -DGGML_USE_RPC + OBJS += ggml-rpc.o +endif # LLAMA_RPC + ifdef LLAMA_CUBLAS # LLAMA_CUBLAS is deprecated and will be removed in the future LLAMA_CUDA := 1 endif +OBJS_CUDA_TEMP_INST = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu)) +ifdef LLAMA_CUDA_FA_ALL_QUANTS + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu)) +else + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu)) +endif # LLAMA_CUDA_FA_ALL_QUANTS + ifdef LLAMA_CUDA ifneq ('', '$(wildcard /opt/cuda)') CUDA_PATH ?= /opt/cuda @@ -435,6 +462,7 @@ ifdef LLAMA_CUDA MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(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)) + OBJS += $(OBJS_CUDA_TEMP_INST) MK_NVCCFLAGS += -use_fast_math ifdef LLAMA_FATAL_WARNINGS MK_NVCCFLAGS += -Werror all-warnings @@ -445,6 +473,9 @@ endif # JETSON_EOL_MODULE_DETECT ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG +ifdef LLAMA_CUDA_DEBUG + MK_NVCCFLAGS += --device-debug +endif # LLAMA_CUDA_DEBUG ifdef LLAMA_CUDA_NVCC NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else @@ -494,7 +525,10 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) -endif +endif # LLAMA_CUDA_CCBIN +ifdef LLAMA_CUDA_FA_ALL_QUANTS + MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # LLAMA_CUDA_FA_ALL_QUANTS ifdef JETSON_EOL_MODULE_DETECT define NVCC_COMPILE @@ -506,30 +540,13 @@ define NVCC_COMPILE endef # NVCC_COMPILE endif # JETSON_EOL_MODULE_DETECT -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(NVCC_COMPILE) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(NVCC_COMPILE) endif # LLAMA_CUDA -ifdef LLAMA_CLBLAST - MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) - MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) - MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) - - # Mac provides OpenCL as a framework - ifeq ($(UNAME_S),Darwin) - MK_LDFLAGS += -lclblast -framework OpenCL - else - MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) - endif - OBJS += ggml-opencl.o - -ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h - $(CXX) $(CXXFLAGS) -c $< -o $@ -endif # LLAMA_CLBLAST - ifdef LLAMA_VULKAN MK_CPPFLAGS += -DGGML_USE_VULKAN MK_LDFLAGS += -lvulkan @@ -572,6 +589,7 @@ ifdef LLAMA_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64 MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) @@ -585,11 +603,12 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) + OBJS += $(OBJS_CUDA_TEMP_INST) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< endif # LLAMA_HIPBLAS @@ -627,11 +646,26 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h endif endif # LLAMA_METAL +OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o +COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h +COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o + ifndef LLAMA_NO_LLAMAFILE sgemm.o: sgemm.cpp sgemm.h ggml.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif +ifdef LLAMA_RPC +ggml-rpc.o: ggml-rpc.cpp ggml-rpc.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +rpc-server.o: examples/rpc/rpc-server.cpp ggml-rpc.h + $(CXX) $(CXXFLAGS) -c $< -o $@ + +rpc-server: rpc-server.o ggml.o llama.o $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +endif # LLAMA_RPC + GF_CC := $(CC) include scripts/get-flags.mk @@ -711,14 +745,9 @@ unicode.o: unicode.cpp unicode.h unicode-data.o: unicode-data.cpp unicode-data.h $(CXX) $(CXXFLAGS) -c $< -o $@ -OBJS += ggml-alloc.o ggml-backend.o ggml-quants.o unicode.o unicode-data.o - llama.o: llama.cpp unicode.h ggml.h ggml-alloc.h ggml-backend.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -COMMON_H_DEPS = common/common.h common/sampling.h common/log.h llama.h -COMMON_DEPS = common.o sampling.o grammar-parser.o build-info.o json-schema-to-grammar.o - common.o: common/common.cpp $(COMMON_H_DEPS) $(CXX) $(CXXFLAGS) -c $< -o $@ @@ -749,6 +778,7 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) rm -vrf ggml-cuda/*.o + rm -vrf ggml-cuda/template-instances/*.o find examples pocs -type f -name "*.o" -delete # @@ -817,7 +847,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/colorthemes.css.hpp examples/server/style.css.hpp examples/server/theme-beeninorder.css.hpp examples/server/theme-ketivah.css.hpp examples/server/theme-mangotango.css.hpp examples/server/theme-playground.css.hpp examples/server/theme-polarnight.css.hpp examples/server/theme-snowstorm.css.hpp examples/server/index.html.hpp examples/server/index-new.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/system-prompts.js.hpp examples/server/prompt-formats.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) @@ -867,10 +897,6 @@ baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) tra $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) -beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) - $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) - finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) diff --git a/README-sycl.md b/README-sycl.md index cfa248a95..62b38135c 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -29,7 +29,7 @@ The llama.cpp SYCL backend is designed to support **Intel GPU** firstly. Based o When targeting **Intel CPU**, it is recommended to use llama.cpp for [Intel oneMKL](README.md#intel-onemkl) backend. -It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, CLBlast etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose. +It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, cuBLAS, etc..*. In beginning work, the oneAPI's [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) open-source migration tool (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) was used for this purpose. ## News @@ -54,10 +54,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, ## OS -| OS | Status | Verified | -|---------|---------|------------------------------------| -| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 | -| Windows | Support | Windows 11 | +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux | +| Windows | Support | Windows 11 | ## Hardware @@ -70,7 +70,7 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, |-------------------------------|---------|---------------------------------------| | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | -| Intel Arc Series | Support | Arc 770, 730M | +| Intel Arc Series | Support | Arc 770, 730M, Arc A750 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | | Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | diff --git a/README.md b/README.md index f4088c05e..9d2a59d89 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,9 @@ ![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) [![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) +[![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) +[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) [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) @@ -20,7 +22,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021** +- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430 +- 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 @@ -74,7 +77,7 @@ variety of hardware - locally and in the cloud. - AVX, AVX2 and AVX512 support for x86 architectures - 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use - Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) -- Vulkan, SYCL, and (partial) OpenCL backend support +- Vulkan and SYCL backend support - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has @@ -127,6 +130,7 @@ Typically finetunes of the base models below are supported as well. - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) - [x] [OLMo](https://allenai.org/olmo) +- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia) (instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md)) @@ -140,11 +144,14 @@ Typically finetunes of the base models below are supported as well. - [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) +- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) **HTTP server** [llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. +[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser. + **Bindings:** - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) @@ -198,9 +205,14 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [AIKit](https://github.com/sozercan/aikit) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* +**Tools:** + +- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML + --- Here is a typical run using LLaMA v2 13B on M2 Ultra: @@ -309,8 +321,6 @@ In order to build llama.cpp you have four 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). @@ -322,40 +332,38 @@ In order to build llama.cpp you have four different options. make ``` + - Notes: + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, run `make LLAMA_DEBUG=1` + - Using `CMake`: - ```bash - cmake -B build - cmake --build build --config Release - ``` + ```bash + cmake -B build + cmake --build build --config Release + ``` - **Note**: for `Debug` builds, there are two cases: + **Notes**: - - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, there are two cases: + + 1. 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...): + 2. 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, - it's also possible to cross compile for other operating systems and architectures: - - ```bash - zig build -Doptimize=ReleaseFast -Dtarget=x86_64-windows-gnu -Dcpu=x86_64+avx2+fma+f16c - ``` - - The `zig targets` command will give you valid options to use. - - Using `gmake` (FreeBSD): 1. Install and activate [DRM in FreeBSD](https://wiki.freebsd.org/Graphics) @@ -363,15 +371,18 @@ In order to build llama.cpp you have four different options. 3. Install compilation dependencies. ```bash - sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \ - opencl clblast openblas + sudo pkg install gmake automake autoconf pkgconf llvm15 openblas gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 ``` - **Notes:** With this packages you can build llama.cpp with OPENBLAS and - CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read - the instructions for use and activate this options in this document below. +### Homebrew + +On Mac and Linux, the homebrew package manager can be used via +``` +brew install llama.cpp +``` +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 ### Metal Build @@ -383,7 +394,7 @@ argument. ### BLAS Build -Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use: +Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS. There are currently several different BLAS implementations available for build and use: - #### Accelerate Framework: @@ -471,10 +482,12 @@ Building the program with BLAS support may lead to some performance improvements |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | | | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | + | LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | - #### hipBLAS @@ -535,111 +548,6 @@ Building the program with BLAS support may lead to some performance improvements | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the HIP mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per HIP thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | -- #### CLBlast - - OpenCL acceleration is provided by the matrix multiplication kernels from the [CLBlast](https://github.com/CNugteren/CLBlast) project and custom kernels for ggml that can generate tokens on the GPU. - - You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - - For Ubuntu, Debian, and Fedora the packages `opencl-headers`, `ocl-icd` may be needed. - - - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. - - -
- Installing the OpenCL SDK from source - - ```sh - git clone --recurse-submodules https://github.com/KhronosGroup/OpenCL-SDK.git - 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 build - cmake --install build --prefix /some/path - ``` -
- - ##### Installing CLBlast - - Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. - - Linux packaging: - Fedora Linux: - ```bash - sudo dnf install clblast - ``` - - Alternatively, they may be built from source. - - -
- Windows: - - ```cmd - set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" - git clone https://github.com/CNugteren/CLBlast.git - 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 - 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`). -
- - ##### Building Llama with CLBlast - - - Build with make: - ```sh - make LLAMA_CLBLAST=1 - ``` - - CMake (Unix): - ```sh - 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 - 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 - - The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. - - To select the correct platform (driver) and device (GPU), you can use the environment variables `GGML_OPENCL_PLATFORM` and `GGML_OPENCL_DEVICE`. - The selection can be a number (starting from 0) or a text string to search: - - ```sh - GGML_OPENCL_PLATFORM=1 ./main ... - GGML_OPENCL_DEVICE=2 ./main ... - GGML_OPENCL_PLATFORM=Intel ./main ... - GGML_OPENCL_PLATFORM=AMD GGML_OPENCL_DEVICE=1 ./main ... - ``` - - The default behavior is to find the first GPU device, but when it is an integrated GPU on a laptop, for instance, the selectors are useful. - Using the variables it is possible to select a CPU-based driver as well, if so desired. - - You can get a list of platforms and devices from the `clinfo -l` command, etc. - - #### Vulkan **With docker**: @@ -690,7 +598,8 @@ Building the program with BLAS support may lead to some performance improvements To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model 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` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives. +It 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 @@ -707,10 +616,10 @@ ls ./models python3 -m pip install -r requirements.txt # convert the model to ggml FP16 format -python3 convert.py models/mymodel/ +python3 convert-hf-to-gguf.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocab-type bpe +python convert-hf-to-gguf.py models/mymodel/ --vocab-type bpe # quantize the model to 4-bits (using Q4_K_M method) ./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M diff --git a/build.zig b/build.zig deleted file mode 100644 index 96783574f..000000000 --- a/build.zig +++ /dev/null @@ -1,172 +0,0 @@ -// Compatible with Zig Version 0.11.0 -const std = @import("std"); -const ArrayList = std.ArrayList; -const Compile = std.Build.Step.Compile; -const ConfigHeader = std.Build.Step.ConfigHeader; -const Mode = std.builtin.Mode; -const CrossTarget = std.zig.CrossTarget; - -const Maker = struct { - builder: *std.build.Builder, - target: CrossTarget, - optimize: Mode, - enable_lto: bool, - - include_dirs: ArrayList([]const u8), - cflags: ArrayList([]const u8), - cxxflags: ArrayList([]const u8), - objs: ArrayList(*Compile), - - fn addInclude(m: *Maker, dir: []const u8) !void { - try m.include_dirs.append(dir); - } - fn addProjectInclude(m: *Maker, path: []const []const u8) !void { - try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path)); - } - fn addCFlag(m: *Maker, flag: []const u8) !void { - try m.cflags.append(flag); - } - fn addCxxFlag(m: *Maker, flag: []const u8) !void { - try m.cxxflags.append(flag); - } - fn addFlag(m: *Maker, flag: []const u8) !void { - try m.addCFlag(flag); - try m.addCxxFlag(flag); - } - - fn init(builder: *std.build.Builder) !Maker { - const target = builder.standardTargetOptions(.{}); - const zig_version = @import("builtin").zig_version_string; - const commit_hash = try std.ChildProcess.exec( - .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } }, - ); - try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt( - \\int LLAMA_BUILD_NUMBER = {}; - \\char const *LLAMA_COMMIT = "{s}"; - \\char const *LLAMA_COMPILER = "Zig {s}"; - \\char const *LLAMA_BUILD_TARGET = "{s}"; - \\ - , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) })); - var m = Maker{ - .builder = builder, - .target = target, - .optimize = builder.standardOptimizeOption(.{}), - .enable_lto = false, - .include_dirs = ArrayList([]const u8).init(builder.allocator), - .cflags = ArrayList([]const u8).init(builder.allocator), - .cxxflags = ArrayList([]const u8).init(builder.allocator), - .objs = ArrayList(*Compile).init(builder.allocator), - }; - - try m.addCFlag("-std=c11"); - try m.addCxxFlag("-std=c++11"); - try m.addProjectInclude(&.{}); - try m.addProjectInclude(&.{"common"}); - return m; - } - - fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile { - const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize }); - if (o.target.getAbi() != .msvc) - o.defineCMacro("_GNU_SOURCE", null); - - if (std.mem.endsWith(u8, src, ".c")) { - o.addCSourceFiles(&.{src}, m.cflags.items); - o.linkLibC(); - } else { - o.addCSourceFiles(&.{src}, m.cxxflags.items); - if (o.target.getAbi() == .msvc) { - o.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - o.linkLibCpp(); - } - } - for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i }); - o.want_lto = m.enable_lto; - return o; - } - - fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile { - const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize }); - e.addCSourceFiles(&.{src}, m.cxxflags.items); - for (deps) |d| e.addObject(d); - for (m.objs.items) |o| e.addObject(o); - for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i }); - - // https://github.com/ziglang/zig/issues/15448 - if (e.target.getAbi() == .msvc) { - e.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - e.linkLibCpp(); - } - m.builder.installArtifact(e); - e.want_lto = m.enable_lto; - return e; - } -}; - -pub fn build(b: *std.build.Builder) !void { - var make = try Maker.init(b); - make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; - - const ggml = make.obj("ggml", "ggml.c"); - const sgemm = make.obj("sgemm", "sgemm.cpp"); - const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); - const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); - const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); - const unicode = make.obj("unicode", "unicode.cpp"); - const unicode_data = make.obj("unicode-data", "unicode-data.cpp"); - const llama = make.obj("llama", "llama.cpp"); - const buildinfo = make.obj("common", "common/build-info.cpp"); - const common = make.obj("common", "common/common.cpp"); - const console = make.obj("console", "common/console.cpp"); - const sampling = make.obj("sampling", "common/sampling.cpp"); - const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); - const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp"); - const train = make.obj("train", "common/train.cpp"); - const clip = make.obj("clip", "examples/llava/clip.cpp"); - const llava = make.obj("llava", "examples/llava/llava.cpp"); - - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); - if (server.target.isWindows()) { - server.linkSystemLibrary("ws2_32"); - } - - const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" }; - for (server_assets) |asset| { - const input_path = b.fmt("examples/server/public/{s}", .{asset}); - const output_path = b.fmt("examples/server/{s}.hpp", .{asset}); - - // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`: - - const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize)); - defer b.allocator.free(input); - - var buf = std.ArrayList(u8).init(b.allocator); - defer buf.deinit(); - - for (input) |byte| { - try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte}); - } - - var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_"); - defer b.allocator.free(name); - std.mem.replaceScalar(u8, name, '.', '_'); - - try std.fs.cwd().writeFile(output_path, b.fmt( - "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n", - .{ name, buf.items, name, input.len }, - )); - - std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path }); - } -} diff --git a/ci/run.sh b/ci/run.sh index d5972480b..3fc5f48b2 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -202,12 +202,15 @@ function gg_sum_test_scripts_release { } function gg_get_model { - local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf" - local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" - if [[ -s $gguf_3b ]]; then - echo -n "$gguf_3b" - elif [[ -s $gguf_7b ]]; then - echo -n "$gguf_7b" + local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf" + local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf" + local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" + if [[ -s $gguf_0 ]]; then + echo -n "$gguf_0" + elif [[ -s $gguf_1 ]]; then + echo -n "$gguf_1" + elif [[ -s $gguf_2 ]]; then + echo -n "$gguf_2" else echo >&2 "No model found. Can't run gg_run_ctest_with_model." exit 1 @@ -256,139 +259,6 @@ function gg_sum_ctest_with_model_release { gg_printf '```\n' } -# open_llama_3b_v2 - -function gg_run_open_llama_3b_v2 { - cd ${SRC} - - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json - - gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip - unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ - head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw - - path_models="../models-mnt/open-llama/3B-v2" - path_wiki="../models-mnt/wikitext/wikitext-2-raw" - - rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release - - set -e - - (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - - python3 ../convert.py ${path_models} - - model_f16="${path_models}/ggml-model-f16.gguf" - model_q8_0="${path_models}/ggml-model-q8_0.gguf" - model_q4_0="${path_models}/ggml-model-q4_0.gguf" - model_q4_1="${path_models}/ggml-model-q4_1.gguf" - model_q5_0="${path_models}/ggml-model-q5_0.gguf" - model_q5_1="${path_models}/ggml-model-q5_1.gguf" - model_q2_k="${path_models}/ggml-model-q2_k.gguf" - model_q3_k="${path_models}/ggml-model-q3_k.gguf" - model_q4_k="${path_models}/ggml-model-q4_k.gguf" - model_q5_k="${path_models}/ggml-model-q5_k.gguf" - model_q6_k="${path_models}/ggml-model-q6_k.gguf" - - wiki_test_60="${path_wiki}/wiki.test-60.raw" - - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 - ./bin/quantize ${model_f16} ${model_q4_0} q4_0 - ./bin/quantize ${model_f16} ${model_q4_1} q4_1 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (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 -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - - function check_ppl { - qnt="$1" - ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" - return 20 - fi - - printf ' - %s @ %s OK\n' "$qnt" "$ppl" - return 0 - } - - check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - - cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - - set +e -} - -function gg_sum_open_llama_3b_v2 { - gg_printf '### %s\n\n' "${ci}" - - gg_printf 'OpenLLaMA 3B-v2:\n' - gg_printf '- status: %s\n' "$(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 '- 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)" - gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" - gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" - gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" - gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" - gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" - gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" - 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)" -} - # open_llama_7b_v2 # requires: GG_BUILD_CUDA @@ -417,7 +287,7 @@ function gg_run_open_llama_7b_v2 { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert.py ${path_models} + python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -526,6 +396,272 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" } +# pythia_1.4b + +function gg_run_pythia_1_4b { + cd ${SRC} + + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw + + path_models="../models-mnt/pythia/1.4B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test_60="${path_wiki}/wiki.test-60.raw" + + ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k + ./bin/quantize ${model_f16} ${model_q3_k} q3_k + ./bin/quantize ${model_f16} ${model_q4_k} q4_k + ./bin/quantize ${model_f16} ${model_q5_k} q5_k + ./bin/quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (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 -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_1_4b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 1.4B:\n' + gg_printf '- status: %s\n' "$(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 '- 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)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + 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)" +} + +# pythia_2_8b +# requires: GG_BUILD_CUDA + +function gg_run_pythia_2_8b { + cd ${SRC} + + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/pythia/2.8B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./bin/quantize ${model_f16} ${model_q8_0} q8_0 + ./bin/quantize ${model_f16} ${model_q4_0} q4_0 + ./bin/quantize ${model_f16} ${model_q4_1} q4_1 + ./bin/quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k + ./bin/quantize ${model_f16} ${model_q3_k} q3_k + ./bin/quantize ${model_f16} ${model_q4_k} q4_k + ./bin/quantize ${model_f16} ${model_q5_k} q5_k + ./bin/quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (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 -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="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_2_8b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 2.8B:\n' + gg_printf '- status: %s\n' "$(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 '- 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)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + 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)" +} + # bge-small function gg_run_embd_bge_small { @@ -552,7 +688,7 @@ function gg_run_embd_bge_small { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -606,9 +742,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then - test $ret -eq 0 && gg_run open_llama_3b_v2 + test $ret -eq 0 && gg_run pythia_1_4b else - test $ret -eq 0 && gg_run open_llama_7b_v2 + test $ret -eq 0 && gg_run pythia_2_8b + #test $ret -eq 0 && gg_run open_llama_7b_v2 fi test $ret -eq 0 && gg_run ctest_with_model_debug test $ret -eq 0 && gg_run ctest_with_model_release diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake index 46fba6514..802379680 100644 --- a/cmake/arm64-windows-llvm.cmake +++ b/cmake/arm64-windows-llvm.cmake @@ -9,7 +9,7 @@ set( CMAKE_CXX_COMPILER clang++ ) set( CMAKE_C_COMPILER_TARGET ${target} ) set( CMAKE_CXX_COMPILER_TARGET ${target} ) -set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" ) +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast -fno-finite-math-only" ) set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/llama.pc.in b/cmake/llama.pc.in new file mode 100644 index 000000000..326acbb61 --- /dev/null +++ b/cmake/llama.pc.in @@ -0,0 +1,10 @@ +prefix=@CMAKE_INSTALL_PREFIX@ +exec_prefix=${prefix} +libdir=${exec_prefix}/lib +includedir=${prefix}/include + +Name: llama +Description: Port of Facebook's LLaMA model in C/C++ +Version: @PROJECT_VERSION@ +Libs: -L${libdir} -lllama +Cflags: -I${includedir} diff --git a/common/common.cpp b/common/common.cpp index ae11650b4..c8df9a4ce 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -7,20 +7,21 @@ #include #include +#include #include +#include +#include #include #include #include -#include #include +#include #include #include #include #include #include #include -#include -#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -73,7 +74,11 @@ using json = nlohmann::ordered_json; -int32_t get_num_physical_cores() { +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores std::unordered_set siblings; @@ -142,9 +147,9 @@ static bool is_running_on_efficiency_core(void) { return core_type == intel_atom; } -static int count_math_cpus(int cpu_count) { +static int cpu_count_math_cpus(int n_cpu) { int result = 0; - for (int cpu = 0; cpu < cpu_count; ++cpu) { + for (int cpu = 0; cpu < n_cpu; ++cpu) { if (pin_cpu(cpu)) { return -1; } @@ -162,16 +167,16 @@ static int count_math_cpus(int cpu_count) { /** * Returns number of CPUs on system that are useful for math. */ -int get_math_cpu_count() { +int32_t cpu_get_num_math() { #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) - int cpu_count = sysconf(_SC_NPROCESSORS_ONLN); - if (cpu_count < 1) { - return get_num_physical_cores(); + int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); + if (n_cpu < 1) { + return cpu_get_num_physical_cores(); } if (is_hybrid_cpu()) { cpu_set_t affinity; if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { - int result = count_math_cpus(cpu_count); + int result = cpu_count_math_cpus(n_cpu); pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); if (result > 0) { return result; @@ -179,109 +184,104 @@ int get_math_cpu_count() { } } #endif - return get_num_physical_cores(); + return cpu_get_num_physical_cores(); } -void process_escapes(std::string & input) { - std::size_t input_len = input.length(); - std::size_t output_idx = 0; +// +// CLI argument parsing +// - for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { - if (input[input_idx] == '\\' && input_idx + 1 < input_len) { - switch (input[++input_idx]) { - case 'n': input[output_idx++] = '\n'; break; - case 'r': input[output_idx++] = '\r'; break; - case 't': input[output_idx++] = '\t'; break; - case '\'': input[output_idx++] = '\''; break; - case '\"': input[output_idx++] = '\"'; break; - case '\\': input[output_idx++] = '\\'; break; - case 'x': - // Handle \x12, etc - if (input_idx + 2 < input_len) { - const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; - char *err_p = nullptr; - const long val = std::strtol(x, &err_p, 16); - if (err_p == x + 2) { - input_idx += 2; - input[output_idx++] = char(val); - break; - } - } - // fall through - default: input[output_idx++] = '\\'; - input[output_idx++] = input[input_idx]; break; +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"); } - } else { - input[output_idx++] = input[input_idx]; + params.hf_file = params.model; + } else if (params.model.empty()) { + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + params.model = cache_directory + 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; + const std::string arg_prefix = "--"; + llama_sampling_params & sparams = params.sparams; + + for (int i = 1; i < argc; i++) { + arg = argv[i]; + 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); } } - input.resize(output_idx); + if (params.prompt_cache_all && (params.interactive || params.interactive_first)) { + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } + + gpt_params_handle_model_default(params); + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + string_process_escapes(sparams.cfg_negative_prompt); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + return true; } bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { - bool result = true; - try { - if (!gpt_params_parse_ex(argc, argv, params)) { - gpt_print_usage(argc, argv, gpt_params()); - exit(0); - } - } - catch (const std::invalid_argument & ex) { - fprintf(stderr, "%s\n", ex.what()); - gpt_print_usage(argc, argv, gpt_params()); - exit(1); - } - return result; -} + const auto params_org = params; // the example can modify the default params -bool parse_kv_override(const char * data, std::vector & overrides) { - const char * sep = strchr(data, '='); - if (sep == nullptr || sep - data >= 128) { - fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); - return false; - } - llama_model_kv_override kvo; - std::strncpy(kvo.key, data, sep - data); - kvo.key[sep - data] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.val_i64 = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.val_f64 = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.val_bool = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.val_bool = false; - } else { - fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); + try { + if (!gpt_params_parse_ex(argc, argv, params) || params.usage) { + params = params_org; + params.usage = true; return false; } - } else if (strncmp(sep, "str:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; - if (strlen(sep) > 127) { - fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); - return false; - } - strncpy(kvo.val_str, sep, 127); - kvo.val_str[127] = '\0'; - } else { - fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); + } catch (const std::invalid_argument & ex) { + fprintf(stderr, "%s\n", ex.what()); return false; } - overrides.emplace_back(std::move(kvo)); + return true; } bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { + const char split_delim = ','; + llama_sampling_params & sparams = params.sparams; if (arg == "-s" || arg == "--seed") { @@ -289,7 +289,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - // This is temporary, in the future the samplign state will be moved fully to llama_sampling_context. + // TODO: this is temporary, in the future the sampling state will be moved fully to llama_sampling_context. params.seed = std::stoul(argv[i]); sparams.seed = std::stoul(argv[i]); return true; @@ -350,6 +350,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.escape = true; return true; } + if (arg == "--no-escape") { + params.escape = false; + return true; + } if (arg == "--prompt-cache") { if (++i >= argc) { invalid_param = true; @@ -404,7 +408,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } return true; } - if (arg == "-n" || arg == "--n-predict") { + if (arg == "-n" || arg == "--predict" || arg == "--n-predict") { if (++i >= argc) { invalid_param = true; return true; @@ -546,7 +550,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); + sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true); return true; } if (arg == "--sampling-seq") { @@ -554,7 +558,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - sparams.samplers_sequence = sampler_types_from_chars(argv[i]); + sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]); return true; } if (arg == "--top-p") { @@ -901,30 +905,22 @@ 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; + if (arg == "-sp" || arg == "--special") { + params.special = true; return true; } - if (arg == "--embedding") { + if (arg == "--embedding" || arg == "--embeddings") { params.embedding = true; return true; } - if (arg == "--interactive-first") { + if (arg == "-if" || arg == "--interactive-first") { params.interactive_first = true; return true; } - if (arg == "-ins" || arg == "--instruct") { - 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; - } if (arg == "--infill") { params.infill = true; return true; @@ -961,7 +957,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.flash_attn = true; return true; } - if (arg == "--color") { + if (arg == "-co" || arg == "--color") { params.use_color = true; return true; } @@ -969,26 +965,26 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.use_mlock = true; return true; } - if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { + if (arg == "-ngl" || arg == "--gpu-layers" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; return true; } params.n_gpu_layers = std::stoi(argv[i]); if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } return true; } - if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { + if (arg == "-ngld" || arg == "--gpu-layers-draft" || arg == "--gpu-layers-draft") { if (++i >= argc) { invalid_param = true; return true; } params.n_gpu_layers_draft = std::stoi(argv[i]); if (!llama_supports_gpu_offload()) { - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: not compiled with GPU offload support, --gpu-layers-draft option will be ignored\n"); fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); } return true; @@ -999,9 +995,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } params.main_gpu = std::stoi(argv[i]); -#ifndef GGML_USE_CUDA_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the main GPU has no effect.\n"); -#endif // GGML_USE_CUDA_SYCL +#ifndef GGML_USE_CUDA_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL_VULKAN return true; } if (arg == "--split-mode" || arg == "-sm") { @@ -1027,9 +1023,9 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } -#ifndef GGML_USE_CUDA_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUDA_SYCL +#ifndef GGML_USE_CUDA_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without CUDA/SYCL/Vulkan. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUDA_SYCL_VULKAN return true; } if (arg == "--tensor-split" || arg == "-ts") { @@ -1084,6 +1080,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa else { invalid_param = true; } return true; } + if (arg == "-v" || arg == "--verbose") { + params.verbose = true; + return true; + } if (arg == "--verbose-prompt") { params.verbose_prompt = true; return true; @@ -1148,6 +1148,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.ppl_stride = std::stoi(argv[i]); return true; } + if (arg == "--ppl-output-type") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.ppl_output_type = std::stoi(argv[i]); + return true; + } if (arg == "-ptc" || arg == "--print-token-count") { if (++i >= argc) { invalid_param = true; @@ -1160,14 +1168,6 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.check_tensors = true; return true; } - if (arg == "--ppl-output-type") { - if (++i >= argc) { - invalid_param = true; - return true; - } - params.ppl_output_type = std::stoi(argv[i]); - return true; - } if (arg == "--hellaswag") { params.hellaswag = true; return true; @@ -1239,19 +1239,15 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa } return true; } - if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, gpt_params()); - exit(0); + if (arg == "-h" || arg == "--help" || arg == "--usage" ) { + params.usage = true; + return true; } if (arg == "--version") { fprintf(stderr, "version: %d (%s)\n", LLAMA_BUILD_NUMBER, LLAMA_COMMIT); fprintf(stderr, "built with %s for %s\n", LLAMA_COMPILER, LLAMA_BUILD_TARGET); exit(0); } - if (arg == "--random-prompt") { - params.random_prompt = true; - return true; - } if (arg == "--in-prefix-bos") { params.input_prefix_bos = true; return true; @@ -1311,13 +1307,236 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - if (!parse_kv_override(argv[i], params.kv_overrides)) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; return true; } return true; } + if (arg == "--host") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.hostname = argv[i]; + return true; + } + if (arg == "--port") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.port = std::stoi(argv[i]); + return true; + } + if (arg == "--path") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.public_path = argv[i]; + return true; + } + if (arg == "--api-key") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.api_keys.push_back(argv[i]); + return true; + } + if (arg == "--api-key-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + std::ifstream key_file(argv[i]); + if (!key_file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + std::string key; + while (std::getline(key_file, key)) { + if (!key.empty()) { + params.api_keys.push_back(key); + } + } + key_file.close(); + return true; + } + if (arg == "--ssl-key-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.ssl_file_key = argv[i]; + return true; + } + if (arg == "--ssl-cert-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.ssl_file_cert = argv[i]; + return true; + } + if (arg == "--timeout" || arg == "-to") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.timeout_read = std::stoi(argv[i]); + params.timeout_write = std::stoi(argv[i]); + return true; + } + if (arg == "-spf" || arg == "--system-prompt-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + std::ifstream file(argv[i]); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + std::string system_prompt; + std::copy( + std::istreambuf_iterator(file), + std::istreambuf_iterator(), + std::back_inserter(system_prompt) + ); + params.system_prompt = system_prompt; + return true; + } + if (arg == "--log-format") { + if (++i >= argc) { + invalid_param = true; + return true; + } + if (std::strcmp(argv[i], "json") == 0) { + params.log_json = true; + } else if (std::strcmp(argv[i], "text") == 0) { + params.log_json = false; + } else { + invalid_param = true; + return true; + } + return true; + } + if (arg == "--no-slots") { + params.endpoint_slots = false; + return true; + } + if (arg == "--metrics") { + params.endpoint_metrics = true; + return true; + } + if (arg == "--slot-save-path") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.slot_save_path = argv[i]; + // if doesn't end with DIRECTORY_SEPARATOR, add it + if (!params.slot_save_path.empty() && params.slot_save_path[params.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { + params.slot_save_path += DIRECTORY_SEPARATOR; + } + return true; + } + if (arg == "--chat-template") { + if (++i >= argc) { + invalid_param = true; + return true; + } + if (!llama_chat_verify_template(argv[i])) { + fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); + fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); + invalid_param = true; + return true; + } + params.chat_template = argv[i]; + return true; + } + if (arg == "-pps") { + params.is_pp_shared = true; + return true; + } + if (arg == "-npp") { + if (++i >= argc) { + invalid_param = true; + return true; + } + auto p = string_split(argv[i], split_delim); + params.n_pp.insert(params.n_pp.end(), p.begin(), p.end()); + return true; + } + if (arg == "-ntg") { + if (++i >= argc) { + invalid_param = true; + return true; + } + auto p = string_split(argv[i], split_delim); + params.n_tg.insert(params.n_tg.end(), p.begin(), p.end()); + return true; + } + if (arg == "-npl") { + if (++i >= argc) { + invalid_param = true; + return true; + } + auto p = string_split(argv[i], split_delim); + params.n_pl.insert(params.n_pl.end(), p.begin(), p.end()); + return true; + } + if (arg == "--context-file") { + if (++i >= argc) { + invalid_param = true; + return true; + } + std::ifstream file(argv[i], std::ios::binary); + if (!file) { + fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); + invalid_param = true; + return true; + } + params.context_files.push_back(argv[i]); + return true; + } + if (arg == "--chunk-size") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.chunk_size = std::stoi(argv[i]); + return true; + } + if (arg == "--chunk-separator") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.chunk_separator = argv[i]; + return true; + } + if (arg == "--junk") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.n_junk = std::stoi(argv[i]); + return true; + } + if (arg == "--pos") { + if (++i >= argc) { + invalid_param = true; + return true; + } + params.i_pos = std::stoi(argv[i]); + return true; + } #ifndef LOG_DISABLE_LOGS // Parse args for logging parameters if (log_param_single_parse(argv[i])) { @@ -1345,285 +1564,314 @@ 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()) { - std::string cache_directory = get_cache_directory(); - const bool success = create_directory_with_parents(cache_directory); - if (!success) { - throw std::runtime_error("failed to create cache directory: " + cache_directory); - } - params.model = cache_directory + 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; - } -} +#ifdef __GNUC__ +#ifdef __MINGW32__ +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(gnu_printf, __VA_ARGS__))) +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) __attribute__((format(printf, __VA_ARGS__))) +#endif +#else +#define LLAMA_COMMON_ATTRIBUTE_FORMAT(...) +#endif -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { - bool invalid_param = false; - std::string arg; - const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sparams; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - 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 (params.prompt_cache_all && - (params.interactive || params.interactive_first || - params.instruct)) { - - throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); - } - - gpt_params_handle_model_default(params); - - if (params.escape) { - process_escapes(params.prompt); - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); - process_escapes(sparams.cfg_negative_prompt); - for (auto & antiprompt : params.antiprompt) { - process_escapes(antiprompt); - } - } - - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - return true; -} - -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { +void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; std::string sampler_type_chars; std::string sampler_type_names; for (const auto sampler_type : sparams.samplers_sequence) { sampler_type_chars += static_cast(sampler_type); - sampler_type_names += sampler_type_to_name_string(sampler_type) + ";"; + sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";"; } sampler_type_names.pop_back(); - printf("\n"); - printf("usage: %s [options]\n", argv[0]); - printf("\n"); - printf("options:\n"); - 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"); - 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"); - printf(" -r PROMPT, --reverse-prompt PROMPT\n"); - printf(" halt generation at PROMPT, return control in interactive mode\n"); - printf(" (can be specified more than once for multiple prompts).\n"); - printf(" --color colorise output to distinguish prompt and user input from generations\n"); - printf(" -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); - printf(" -t N, --threads N number of threads to use during generation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" -td N, --threads-draft N"); - printf(" number of threads to use during generation (default: same as --threads)\n"); - printf(" -tbd N, --threads-batch-draft N\n"); - printf(" number of threads to use during batch and prompt processing (default: same as --threads-draft)\n"); - printf(" -p PROMPT, --prompt PROMPT\n"); - printf(" prompt to start generation with (default: empty)\n"); - printf(" -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); - printf(" --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); - printf(" --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); - printf(" not supported with --interactive or other interactive options\n"); - printf(" --prompt-cache-ro if specified, uses the prompt cache but does not update it.\n"); - printf(" --random-prompt start with a randomized prompt.\n"); - printf(" --in-prefix-bos prefix BOS to user inputs, preceding the `--in-prefix` string\n"); - printf(" --in-prefix STRING string to prefix user inputs with (default: empty)\n"); - printf(" --in-suffix STRING string to suffix after user inputs with (default: empty)\n"); - printf(" -f FNAME, --file FNAME\n"); - printf(" prompt file to start generation.\n"); - printf(" -bf FNAME, --binary-file FNAME\n"); - printf(" binary file containing multiple choice tasks.\n"); - printf(" -n N, --n-predict N number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)\n", params.n_predict); - printf(" -c N, --ctx-size N size of the prompt context (default: %d, 0 = loaded from model)\n", params.n_ctx); - printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); - printf(" -ub N, --ubatch-size N\n"); - printf(" physical maximum batch size (default: %d)\n", params.n_ubatch); - printf(" --samplers samplers that will be used for generation in the order, separated by \';\'\n"); - printf(" (default: %s)\n", sampler_type_names.c_str()); - printf(" --sampling-seq simplified sequence for samplers that will be used (default: %s)\n", sampler_type_chars.c_str()); - printf(" --top-k N top-k sampling (default: %d, 0 = disabled)\n", sparams.top_k); - printf(" --top-p N top-p sampling (default: %.1f, 1.0 = disabled)\n", (double)sparams.top_p); - printf(" --min-p N min-p sampling (default: %.1f, 0.0 = disabled)\n", (double)sparams.min_p); - printf(" --tfs N tail free sampling, parameter z (default: %.1f, 1.0 = disabled)\n", (double)sparams.tfs_z); - printf(" --typical N locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)\n", (double)sparams.typical_p); - printf(" --repeat-last-n N last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)\n", sparams.penalty_last_n); - printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); - printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); - printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); - printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); - printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); - printf(" --mirostat N use Mirostat sampling.\n"); - printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); - printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); - printf(" --mirostat-lr N Mirostat learning rate, parameter eta (default: %.1f)\n", (double)sparams.mirostat_eta); - printf(" --mirostat-ent N Mirostat target entropy, parameter tau (default: %.1f)\n", (double)sparams.mirostat_tau); - printf(" -l TOKEN_ID(+/-)BIAS, --logit-bias TOKEN_ID(+/-)BIAS\n"); - printf(" modifies the likelihood of token appearing in the completion,\n"); - printf(" i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n"); - printf(" or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'\n"); - printf(" --grammar GRAMMAR BNF-like grammar to constrain generations (see samples in grammars/ dir)\n"); - printf(" --grammar-file FNAME file to read grammar from\n"); - printf(" -j SCHEMA, --json-schema SCHEMA\n"); - printf(" JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object.\n"); - printf(" For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead\n"); - printf(" --cfg-negative-prompt PROMPT\n"); - printf(" negative prompt to use for guidance. (default: empty)\n"); - printf(" --cfg-negative-prompt-file FNAME\n"); - printf(" negative prompt file to use for guidance. (default: empty)\n"); - printf(" --cfg-scale N strength of guidance (default: %f, 1.0 = disable)\n", sparams.cfg_scale); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-scale N RoPE context scaling factor, expands context by a factor of N\n"); - printf(" --rope-freq-base N RoPE base frequency, used by NTK-aware scaling (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-orig-ctx N YaRN: original context size of model (default: 0 = model training context size)\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls}\n"); - printf(" pooling type for embeddings, use model default if unspecified\n"); - printf(" -dt N, --defrag-thold N\n"); - printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" --ignore-eos ignore end of stream token and continue generating (implies --logit-bias 2-inf)\n"); - printf(" --penalize-nl penalize newline tokens\n"); - printf(" --temp N temperature (default: %.1f)\n", (double)sparams.temp); - printf(" --all-logits return logits for all tokens in the batch (default: disabled)\n"); - printf(" --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); - printf(" --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); - printf(" --winogrande compute Winogrande score over random tasks from datafile supplied with -f\n"); - printf(" --winogrande-tasks N number of tasks to use when computing the Winogrande score (default: %zu)\n", params.winogrande_tasks); - printf(" --multiple-choice compute multiple choice score over random tasks from datafile supplied with -f\n"); - printf(" --multiple-choice-tasks N number of tasks to use when computing the multiple choice score (default: %zu)\n", params.winogrande_tasks); - printf(" --kl-divergence computes KL-divergence to logits provided via --kl-divergence-base\n"); - printf(" --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); - printf(" --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); - printf(" --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); - printf(" -np N, --parallel N number of parallel sequences to decode (default: %d)\n", params.n_parallel); - 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"); + struct option_info { + LLAMA_COMMON_ATTRIBUTE_FORMAT(4, 5) + option_info(const std::string & tags, const char * args, const char * desc, ...) : tags(tags), args(args), desc(desc) { + va_list args_list; + va_start(args_list, desc); + char buffer[1024]; + vsnprintf(buffer, sizeof(buffer), desc, args_list); + va_end(args_list); + this->desc = buffer; + } + + option_info(const std::string & grp) : grp(grp) {} + + std::string tags; + std::string args; + std::string desc; + std::string grp; + }; + + std::vector options; + + // TODO: filter by tags + + options.push_back({ "general" }); + options.push_back({ "*", "-h, --help, --usage", "print usage and exit" }); + options.push_back({ "*", " --version", "show version and build info" }); + options.push_back({ "*", "-v, --verbose", "print verbose information" }); + options.push_back({ "*", " --verbose-prompt", "print a verbose prompt before generation (default: %s)", params.verbose_prompt ? "true" : "false" }); + options.push_back({ "*", " --no-display-prompt", "don't print prompt at generation (default: %s)", !params.display_prompt ? "true" : "false" }); + options.push_back({ "*", "-co, --color", "colorise output to distinguish prompt and user input from generations (default: %s)", params.use_color ? "true" : "false" }); + options.push_back({ "*", "-s, --seed SEED", "RNG seed (default: %d, use random seed for < 0)", params.seed }); + options.push_back({ "*", "-t, --threads N", "number of threads to use during generation (default: %d)", params.n_threads }); + options.push_back({ "*", "-tb, --threads-batch N", "number of threads to use during batch and prompt processing (default: same as --threads)" }); + options.push_back({ "speculative", "-td, --threads-draft N", "number of threads to use during generation (default: same as --threads)" }); + options.push_back({ "speculative", "-tbd, --threads-batch-draft N", + "number of threads to use during batch and prompt processing (default: same as --threads-draft)" }); + options.push_back({ "speculative", " --draft N", "number of tokens to draft for speculative decoding (default: %d)", params.n_draft }); + options.push_back({ "speculative", "-ps, --p-split N", "speculative decoding split probability (default: %.1f)", (double)params.p_split }); + options.push_back({ "*", "-lcs, --lookup-cache-static FNAME", + "path to static lookup cache to use for lookup decoding (not updated by generation)" }); + options.push_back({ "*", "-lcd, --lookup-cache-dynamic FNAME", + "path to dynamic lookup cache to use for lookup decoding (updated by generation)" }); + + options.push_back({ "*", "-c, --ctx-size N", "size of the prompt context (default: %d, 0 = loaded from model)", params.n_ctx }); + options.push_back({ "*", "-n, --predict N", "number of tokens to predict (default: %d, -1 = infinity, -2 = until context filled)", params.n_predict }); + options.push_back({ "*", "-b, --batch-size N", "logical maximum batch size (default: %d)", params.n_batch }); + options.push_back({ "*", "-ub, --ubatch-size N", "physical maximum batch size (default: %d)", params.n_ubatch }); + options.push_back({ "*", " --keep N", "number of tokens to keep from the initial prompt (default: %d, -1 = all)", params.n_keep }); + options.push_back({ "*", " --chunks N", "max number of chunks to process (default: %d, -1 = all)", params.n_chunks }); + options.push_back({ "*", "-fa, --flash-attn", "enable Flash Attention (default: %s)", params.flash_attn ? "enabled" : "disabled" }); + options.push_back({ "*", "-p, --prompt PROMPT", "prompt to start generation with (default: '%s')", params.prompt.c_str() }); + options.push_back({ "*", "-f, --file FNAME", "a file containing the prompt (default: none)" }); + options.push_back({ "*", "-bf, --binary-file FNAME", "binary file containing the prompt (default: none)" }); + options.push_back({ "*", "-e, --escape", "process escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\) (default: %s)", params.escape ? "true" : "false" }); + options.push_back({ "*", " --no-escape", "do not process escape sequences" }); + options.push_back({ "main", "-ptc, --print-token-count N", "print token count every N tokens (default: %d)", params.n_print }); + options.push_back({ "main", " --prompt-cache FNAME", "file to cache prompt state for faster startup (default: none)" }); + options.push_back({ "main", " --prompt-cache-all", "if specified, saves user input and generations to cache as well\n" + "not supported with --interactive or other interactive options" }); + options.push_back({ "main", " --prompt-cache-ro", "if specified, uses the prompt cache but does not update it" }); + options.push_back({ "main", "-r, --reverse-prompt PROMPT", + "halt generation at PROMPT, return control in interactive mode\n" + "can be specified more than once for multiple prompts" }); + options.push_back({ "main", "-sp, --special", "special tokens output enabled (default: %s)", params.special ? "true" : "false" }); + options.push_back({ "main", "-cnv, --conversation", "run in conversation mode (does not print special tokens and suffix/prefix) (default: %s)", params.conversation ? "true" : "false" }); + options.push_back({ "main infill", "-i, --interactive", "run in interactive mode (default: %s)", params.interactive ? "true" : "false" }); + options.push_back({ "main infill", "-if, --interactive-first", "run in interactive mode and wait for input right away (default: %s)", params.interactive_first ? "true" : "false" }); + options.push_back({ "main infill", "-mli, --multiline-input", "allows you to write or paste multiple lines without ending each in '\\'" }); + options.push_back({ "main infill", " --in-prefix-bos", "prefix BOS to user inputs, preceding the `--in-prefix` string" }); + options.push_back({ "main infill", " --in-prefix STRING", "string to prefix user inputs with (default: empty)" }); + options.push_back({ "main infill", " --in-suffix STRING", "string to suffix after user inputs with (default: empty)" }); + + options.push_back({ "sampling" }); + options.push_back({ "*", " --samplers SAMPLERS", "samplers that will be used for generation in the order, separated by \';\'\n" + "(default: %s)", sampler_type_names.c_str() }); + options.push_back({ "*", " --sampling-seq SEQUENCE", + "simplified sequence for samplers that will be used (default: %s)", sampler_type_chars.c_str() }); + options.push_back({ "*", " --ignore-eos", "ignore end of stream token and continue generating (implies --logit-bias EOS-inf)" }); + options.push_back({ "*", " --penalize-nl", "penalize newline tokens (default: %s)", sparams.penalize_nl ? "true" : "false" }); + options.push_back({ "*", " --temp N", "temperature (default: %.1f)", (double)sparams.temp }); + options.push_back({ "*", " --top-k N", "top-k sampling (default: %d, 0 = disabled)", sparams.top_k }); + options.push_back({ "*", " --top-p N", "top-p sampling (default: %.1f, 1.0 = disabled)", (double)sparams.top_p }); + options.push_back({ "*", " --min-p N", "min-p sampling (default: %.1f, 0.0 = disabled)", (double)sparams.min_p }); + options.push_back({ "*", " --tfs N", "tail free sampling, parameter z (default: %.1f, 1.0 = disabled)", (double)sparams.tfs_z }); + options.push_back({ "*", " --typical N", "locally typical sampling, parameter p (default: %.1f, 1.0 = disabled)", (double)sparams.typical_p }); + options.push_back({ "*", " --repeat-last-n N", "last n tokens to consider for penalize (default: %d, 0 = disabled, -1 = ctx_size)", sparams.penalty_last_n }); + options.push_back({ "*", " --repeat-penalty N", "penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)", (double)sparams.penalty_repeat }); + options.push_back({ "*", " --presence-penalty N", "repeat alpha presence penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_present }); + options.push_back({ "*", " --frequency-penalty N", "repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)", (double)sparams.penalty_freq }); + options.push_back({ "*", " --dynatemp-range N", "dynamic temperature range (default: %.1f, 0.0 = disabled)", (double)sparams.dynatemp_range }); + options.push_back({ "*", " --dynatemp-exp N", "dynamic temperature exponent (default: %.1f)", (double)sparams.dynatemp_exponent }); + options.push_back({ "*", " --mirostat N", "use Mirostat sampling.\n" + "Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n" + "(default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)", sparams.mirostat }); + options.push_back({ "*", " --mirostat-lr N", "Mirostat learning rate, parameter eta (default: %.1f)", (double)sparams.mirostat_eta }); + options.push_back({ "*", " --mirostat-ent N", "Mirostat target entropy, parameter tau (default: %.1f)", (double)sparams.mirostat_tau }); + options.push_back({ "*", " -l TOKEN_ID(+/-)BIAS", "modifies the likelihood of token appearing in the completion,\n" + "i.e. `--logit-bias 15043+1` to increase likelihood of token ' Hello',\n" + "or `--logit-bias 15043-1` to decrease likelihood of token ' Hello'" }); + options.push_back({ "main", " --cfg-negative-prompt PROMPT", + "negative prompt to use for guidance (default: '%s')", sparams.cfg_negative_prompt.c_str() }); + options.push_back({ "main", " --cfg-negative-prompt-file FNAME", + "negative prompt file to use for guidance" }); + options.push_back({ "main", " --cfg-scale N", "strength of guidance (default: %.1f, 1.0 = disable)", (double)sparams.cfg_scale }); + + options.push_back({ "grammar" }); + options.push_back({ "*", " --grammar GRAMMAR", "BNF-like grammar to constrain generations (see samples in grammars/ dir) (default: '%s')", sparams.grammar.c_str() }); + options.push_back({ "*", " --grammar-file FNAME", "file to read grammar from" }); + options.push_back({ "*", "-j, --json-schema SCHEMA", + "JSON schema to constrain generations (https://json-schema.org/), e.g. `{}` for any JSON object\n" + "For schemas w/ external $refs, use --grammar + example/json_schema_to_grammar.py instead" }); + + options.push_back({ "embedding" }); + options.push_back({ "embedding", " --pooling {none,mean,cls}", + "pooling type for embeddings, use model default if unspecified" }); + + options.push_back({ "context hacking" }); + options.push_back({ "*", " --rope-scaling {none,linear,yarn}", + "RoPE frequency scaling method, defaults to linear unless specified by the model" }); + options.push_back({ "*", " --rope-scale N", "RoPE context scaling factor, expands context by a factor of N" }); + options.push_back({ "*", " --rope-freq-base N", "RoPE base frequency, used by NTK-aware scaling (default: loaded from model)" }); + options.push_back({ "*", " --rope-freq-scale N", "RoPE frequency scaling factor, expands context by a factor of 1/N" }); + options.push_back({ "*", " --yarn-orig-ctx N", "YaRN: original context size of model (default: %d = model training context size)", params.yarn_orig_ctx }); + options.push_back({ "*", " --yarn-ext-factor N", "YaRN: extrapolation mix factor (default: %.1f, 0.0 = full interpolation)", (double)params.yarn_ext_factor }); + options.push_back({ "*", " --yarn-attn-factor N", "YaRN: scale sqrt(t) or attention magnitude (default: %.1f)", (double)params.yarn_attn_factor }); + options.push_back({ "*", " --yarn-beta-slow N", "YaRN: high correction dim or alpha (default: %.1f)", (double)params.yarn_beta_slow }); + options.push_back({ "*", " --yarn-beta-fast N", "YaRN: low correction dim or beta (default: %.1f)", (double)params.yarn_beta_fast }); + options.push_back({ "*", "-gan, --grp-attn-n N", "group-attention factor (default: %d)", params.grp_attn_n }); + options.push_back({ "*", "-gaw, --grp-attn-w N", "group-attention width (default: %.1f)", (double)params.grp_attn_w }); + options.push_back({ "*", "-dkvc, --dump-kv-cache", "verbose print of the KV cache" }); + options.push_back({ "*", "-nkvo, --no-kv-offload", "disable KV offload" }); + options.push_back({ "*", "-ctk, --cache-type-k TYPE", "KV cache data type for K (default: %s)", params.cache_type_k.c_str() }); + options.push_back({ "*", "-ctv, --cache-type-v TYPE", "KV cache data type for V (default: %s)", params.cache_type_v.c_str() }); + + options.push_back({ "perplexity" }); + options.push_back({ "perplexity", " --all-logits", "return logits for all tokens in the batch (default: %s)", params.logits_all ? "true" : "false" }); + options.push_back({ "perplexity", " --hellaswag", "compute HellaSwag score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --hellaswag-tasks N", "number of tasks to use when computing the HellaSwag score (default: %zu)", params.hellaswag_tasks }); + options.push_back({ "perplexity", " --winogrande", "compute Winogrande score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --winogrande-tasks N", "number of tasks to use when computing the Winogrande score (default: %zu)", params.winogrande_tasks }); + options.push_back({ "perplexity", " --multiple-choice", "compute multiple choice score over random tasks from datafile supplied with -f" }); + options.push_back({ "perplexity", " --multiple-choice-tasks N", + "number of tasks to use when computing the multiple choice score (default: %zu)", params.multiple_choice_tasks }); + options.push_back({ "perplexity", " --kl-divergence", "computes KL-divergence to logits provided via --kl-divergence-base" }); + options.push_back({ "perplexity", " --ppl-stride N", "stride for perplexity calculation (default: %d)", params.ppl_stride }); + options.push_back({ "perplexity", " --ppl-output-type {0,1}", + "output type for perplexity calculation (default: %d)", params.ppl_output_type }); + + options.push_back({ "parallel" }); + options.push_back({ "*", "-dt, --defrag-thold N", "KV cache defragmentation threshold (default: %.1f, < 0 - disabled)", (double)params.defrag_thold }); + options.push_back({ "*", "-np, --parallel N", "number of parallel sequences to decode (default: %d)", params.n_parallel }); + options.push_back({ "*", "-ns, --sequences N", "number of sequences to decode (default: %d)", params.n_sequences }); + options.push_back({ "*", "-cb, --cont-batching", "enable continuous batching (a.k.a dynamic batching) (default: %s)", params.cont_batching ? "enabled" : "disabled" }); + + options.push_back({ "multi-modality" }); + options.push_back({ "*", " --mmproj FILE", "path to a multimodal projector file for LLaVA. see examples/llava/README.md" }); + options.push_back({ "*", " --image FILE", "path to an image file. use with multimodal models. Specify multiple times for batching" }); + + options.push_back({ "backend" }); + options.push_back({ "*", " --rpc SERVERS", "comma separated list of RPC servers" }); if (llama_supports_mlock()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); + options.push_back({ "*", " --mlock", "force system to keep model in RAM rather than swapping or compressing" }); } if (llama_supports_mmap()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + options.push_back({ "*", " --no-mmap", "do not memory-map model (slower load but may reduce pageouts if not using mlock)" }); } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided by numactl\n"); - printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); - printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); + options.push_back({ "*", " --numa TYPE", "attempt optimizations that help on some NUMA systems\n" + " - distribute: spread execution evenly over all nodes\n" + " - isolate: only spawn threads on CPUs on the node that execution started on\n" + " - numactl: use the CPU map provided by numactl\n" + "if run without this previously, it is recommended to drop the system page cache before using this\n" + "see https://github.com/ggerganov/llama.cpp/issues/1437" }); + if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); + options.push_back({ "*", "-ngl, --gpu-layers N", + "number of layers to store in VRAM" }); + options.push_back({ "*", "-ngld, --gpu-layers-draft N", + "number of layers to store in VRAM for the draft model" }); + options.push_back({ "*", "-sm, --split-mode SPLIT_MODE", + "how to split the model across multiple GPUs, one of:\n" + " - none: use one GPU only\n" + " - layer (default): split layers and KV across GPUs\n" + " - row: split rows across GPUs" }); + options.push_back({ "*", "-ts, --tensor-split SPLIT", + "fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1" }); + options.push_back({ "*", "-mg, --main-gpu i", "the GPU to use for the model (with split-mode = none),\n" + "or for intermediate results and KV (with split-mode = row) (default: %d)", params.main_gpu }); } - printf(" --rpc SERVERS comma separated list of RPC servers\n"); - printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); - printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); - printf(" -gan N, --grp-attn-n N\n"); - printf(" group-attention factor (default: %d)\n", params.grp_attn_n); - printf(" -gaw N, --grp-attn-w N\n"); - printf(" group-attention width (default: %.1f)\n", (double)params.grp_attn_w); - printf(" -dkvc, --dump-kv-cache\n"); - printf(" verbose print of the KV cache\n"); - printf(" -nkvo, --no-kv-offload\n"); - printf(" disable KV offload\n"); - printf(" -ctk TYPE, --cache-type-k TYPE\n"); - printf(" KV cache data type for K (default: %s)\n", params.cache_type_k.c_str()); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: %s)\n", params.cache_type_v.c_str()); - printf(" --simple-io use basic IO for better compatibility in subprocesses and limited consoles\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-scaled FNAME S apply LoRA adapter with user defined scaling S (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --control-vector FNAME\n"); - printf(" add a control vector\n"); - printf(" --control-vector-scaled FNAME S\n"); - printf(" add a control vector with user defined scaling S\n"); - 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: 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"); - printf(" model download url (default: unused)\n"); - printf(" -hfr REPO, --hf-repo REPO\n"); - printf(" Hugging Face model repository (default: unused)\n"); - printf(" -hff FILE, --hf-file FILE\n"); - printf(" Hugging Face model file (default: unused)\n"); - printf(" -ld LOGDIR, --logdir LOGDIR\n"); - printf(" path under which to save YAML logs (no logging if unset)\n"); - printf(" -lcs FNAME, --lookup-cache-static FNAME\n"); - printf(" path to static lookup cache to use for lookup decoding (not updated by generation)\n"); - printf(" -lcd FNAME, --lookup-cache-dynamic FNAME\n"); - printf(" path to dynamic lookup cache to use for lookup decoding (updated by generation)\n"); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -ptc N, --print-token-count N\n"); - printf(" print token count every N tokens (default: %d)\n", params.n_print); - printf(" --check-tensors check model tensor data for invalid values\n"); - printf("\n"); + + options.push_back({ "model" }); + options.push_back({ "*", " --check-tensors", "check model tensor data for invalid values (default: %s)", params.check_tensors ? "true" : "false" }); + options.push_back({ "*", " --override-kv KEY=TYPE:VALUE", + "advanced option to override model metadata by key. may be specified multiple times.\n" + "types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false" }); + options.push_back({ "*", " --lora FNAME", "apply LoRA adapter (implies --no-mmap)" }); + options.push_back({ "*", " --lora-scaled FNAME S", "apply LoRA adapter with user defined scaling S (implies --no-mmap)" }); + options.push_back({ "*", " --lora-base FNAME", "optional model to use as a base for the layers modified by the LoRA adapter" }); + options.push_back({ "*", " --control-vector FNAME", "add a control vector" }); + options.push_back({ "*", " --control-vector-scaled FNAME SCALE", + "add a control vector with user defined scaling SCALE" }); + options.push_back({ "*", " --control-vector-layer-range START END", + "layer range to apply the control vector(s) to, start and end inclusive" }); + options.push_back({ "*", "-m, --model FNAME", "model path (default: models/$filename with filename from --hf-file\n" + "or --model-url if set, otherwise %s)", DEFAULT_MODEL_PATH }); + options.push_back({ "*", "-md, --model-draft FNAME", "draft model for speculative decoding (default: unused)" }); + options.push_back({ "*", "-mu, --model-url MODEL_URL", "model download url (default: unused)" }); + options.push_back({ "*", "-hfr, --hf-repo REPO", "Hugging Face model repository (default: unused)" }); + options.push_back({ "*", "-hff, --hf-file FILE", "Hugging Face model file (default: unused)" }); + + options.push_back({ "retrieval" }); + options.push_back({ "retrieval", " --context-file FNAME", "file to load context from (repeat to specify multiple files)" }); + options.push_back({ "retrieval", " --chunk-size N", "minimum length of embedded text chunks (default: %d)", params.chunk_size }); + options.push_back({ "retrieval", " --chunk-separator STRING", + "separator between chunks (default: '%s')", params.chunk_separator.c_str() }); + + options.push_back({ "passkey" }); + options.push_back({ "passkey", " --junk N", "number of times to repeat the junk text (default: %d)", params.n_junk }); + options.push_back({ "passkey", " --pos N", "position of the passkey in the junk text (default: %d)", params.i_pos }); + + options.push_back({ "bench" }); + options.push_back({ "bench", "-pps", "is the prompt shared across parallel sequences (default: %s)", params.is_pp_shared ? "true" : "false" }); + options.push_back({ "bench", "-npp n0,n1,...", "number of prompt tokens" }); + options.push_back({ "bench", "-ntg n0,n1,...", "number of text generation tokens" }); + options.push_back({ "bench", "-npl n0,n1,...", "number of parallel prompts" }); + + options.push_back({ "server" }); + options.push_back({ "server", " --host HOST", "ip address to listen (default: %s)", params.hostname.c_str() }); + options.push_back({ "server", " --port PORT", "port to listen (default: %d)", params.port }); + options.push_back({ "server", " --path PATH", "path to serve static files from (default: %s)", params.public_path.c_str() }); + options.push_back({ "server", " --embedding(s)", "enable embedding endpoint (default: %s)", params.embedding ? "enabled" : "disabled" }); + options.push_back({ "server", " --api-key KEY", "API key to use for authentication (default: none)" }); + options.push_back({ "server", " --api-key-file FNAME", "path to file containing API keys (default: none)" }); + options.push_back({ "server", " --ssl-key-file FNAME", "path to file a PEM-encoded SSL private key" }); + options.push_back({ "server", " --ssl-cert-file FNAME", "path to file a PEM-encoded SSL certificate" }); + options.push_back({ "server", " --timeout N", "server read/write timeout in seconds (default: %d)", params.timeout_read }); + options.push_back({ "server", " --system-prompt-file FNAME", + "set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications" }); + options.push_back({ "server", " --log-format {text,json}", + "log output format: json or text (default: json)" }); + options.push_back({ "server", " --metrics", "enable prometheus compatible metrics endpoint (default: %s)", params.endpoint_metrics ? "enabled" : "disabled" }); + options.push_back({ "server", " --no-slots", "disables slots monitoring endpoint (default: %s)", params.endpoint_slots ? "enabled" : "disabled" }); + options.push_back({ "server", " --slot-save-path PATH", "path to save slot kv cache (default: disabled)" }); + options.push_back({ "server", " --chat-template JINJA_TEMPLATE", + "set custom jinja chat template (default: template taken from model's metadata)\n" + "only commonly used templates are accepted:\n" + "https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template" }); + #ifndef LOG_DISABLE_LOGS - log_print_usage(); + options.push_back({ "logging" }); + options.push_back({ "*", " --simple-io", "use basic IO for better compatibility in subprocesses and limited consoles" }); + options.push_back({ "*", "-ld, --logdir LOGDIR", "path under which to save YAML logs (no logging if unset)" }); + options.push_back({ "logging", " --log-test", "Run simple logging test" }); + options.push_back({ "logging", " --log-disable", "Disable trace logs" }); + options.push_back({ "logging", " --log-enable", "Enable trace logs" }); + options.push_back({ "logging", " --log-file FNAME", "Specify a log filename (without extension)" }); + options.push_back({ "logging", " --log-new", "Create a separate new log file on start. " + "Each log file will have unique name: \"..log\"" }); + options.push_back({ "logging", " --log-append", "Don't truncate the old log file." }); #endif // LOG_DISABLE_LOGS + + printf("usage: %s [options]\n", argv[0]); + + for (const auto & o : options) { + if (!o.grp.empty()) { + printf("\n%s:\n\n", o.grp.c_str()); + continue; + } + printf(" %-32s", o.args.c_str()); + if (o.args.length() > 30) { + printf("\n%34s", ""); + } + + const auto desc = o.desc; + size_t start = 0; + size_t end = desc.find('\n'); + while (end != std::string::npos) { + printf("%s\n%34s", desc.substr(start, end - start).c_str(), ""); + start = end + 1; + end = desc.find('\n', start); + } + + printf("%s\n", desc.substr(start).c_str()); + } + printf("\n"); } -std::string get_system_info(const gpt_params & params) { +std::string gpt_params_get_system_info(const gpt_params & params) { std::ostringstream os; os << "system_info: n_threads = " << params.n_threads; @@ -1635,27 +1883,141 @@ std::string get_system_info(const gpt_params & params) { return os.str(); } -std::string gpt_random_prompt(std::mt19937 & rng) { - const int r = rng() % 10; - switch (r) { - case 0: return "So"; - case 1: return "Once upon a time"; - case 2: return "When"; - case 3: return "The"; - case 4: return "After"; - case 5: return "If"; - case 6: return "import"; - case 7: return "He"; - case 8: return "She"; - case 9: return "They"; +// +// String utils +// + +std::vector string_split(std::string input, char separator) { + std::vector parts; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(0, separator_pos); + parts.emplace_back(part); + input = input.substr(separator_pos + 1); + separator_pos = input.find(separator); + } + parts.emplace_back(input); + return parts; +} + +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + +std::string string_get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +void string_process_escapes(std::string & input) { + std::size_t input_len = input.length(); + std::size_t output_idx = 0; + + for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { + if (input[input_idx] == '\\' && input_idx + 1 < input_len) { + switch (input[++input_idx]) { + case 'n': input[output_idx++] = '\n'; break; + case 'r': input[output_idx++] = '\r'; break; + case 't': input[output_idx++] = '\t'; break; + case '\'': input[output_idx++] = '\''; break; + case '\"': input[output_idx++] = '\"'; break; + case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + } + // fall through + default: input[output_idx++] = '\\'; + input[output_idx++] = input[input_idx]; break; + } + } else { + input[output_idx++] = input[input_idx]; + } } - GGML_UNREACHABLE(); + input.resize(output_idx); } +bool string_parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; + } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); + return false; + } + overrides.emplace_back(std::move(kvo)); + return true; +} + +// +// Filesystem utils +// + // Validate if a filename is safe to use // To validate a full path, split the path by the OS-specific path separator, and validate each part with this function -bool validate_file_name(const std::string & filename) { +bool fs_validate_filename(const std::string & filename) { if (!filename.length()) { // Empty filename invalid return false; @@ -1724,120 +2086,198 @@ bool validate_file_name(const std::string & filename) { return true; } -// -// String utils -// +// returns true if successful, false otherwise +bool fs_create_directory_with_parents(const std::string & path) { +#ifdef _WIN32 + std::wstring_convert> converter; + std::wstring wpath = converter.from_bytes(path); -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); + // if the path already exists, check whether it's a directory + const DWORD attributes = GetFileAttributesW(wpath.c_str()); + if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return true; } - parts.emplace_back(input); - return parts; -} -std::string string_strip(const std::string & str) { - size_t start = 0; - size_t end = str.size(); - while (start < end && std::isspace(str[start])) { - start++; - } - while (end > start && std::isspace(str[end - 1])) { - end--; - } - return str.substr(start, end - start); -} + size_t pos_slash = 0; -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { - std::unordered_map sampler_canonical_name_map { - {"top_k", llama_sampler_type::TOP_K}, - {"top_p", llama_sampler_type::TOP_P}, - {"typical_p", llama_sampler_type::TYPICAL_P}, - {"min_p", llama_sampler_type::MIN_P}, - {"tfs_z", llama_sampler_type::TFS_Z}, - {"temperature", llama_sampler_type::TEMPERATURE} - }; + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { + const std::wstring subpath = wpath.substr(0, pos_slash); + const wchar_t * test = subpath.c_str(); - // since samplers names are written multiple ways - // make it ready for both system names and input names - std::unordered_map sampler_alt_name_map { - {"top-k", llama_sampler_type::TOP_K}, - {"top-p", llama_sampler_type::TOP_P}, - {"nucleus", llama_sampler_type::TOP_P}, - {"typical-p", llama_sampler_type::TYPICAL_P}, - {"typical", llama_sampler_type::TYPICAL_P}, - {"min-p", llama_sampler_type::MIN_P}, - {"tfs-z", llama_sampler_type::TFS_Z}, - {"tfs", llama_sampler_type::TFS_Z}, - {"temp", llama_sampler_type::TEMPERATURE} - }; + const bool success = CreateDirectoryW(test, NULL); + if (!success) { + const DWORD error = GetLastError(); - std::vector sampler_types; - sampler_types.reserve(names.size()); - for (const auto & name : names) - { - auto sampler_item = sampler_canonical_name_map.find(name); - if (sampler_item != sampler_canonical_name_map.end()) - { - sampler_types.push_back(sampler_item->second); - } - else - { - if (allow_alt_names) - { - sampler_item = sampler_alt_name_map.find(name); - if (sampler_item != sampler_alt_name_map.end()) - { - sampler_types.push_back(sampler_item->second); + // if the path already exists, ensure that it's a directory + if (error == ERROR_ALREADY_EXISTS) { + const DWORD attributes = GetFileAttributesW(subpath.c_str()); + if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return false; } + } else { + return false; } } + + pos_slash += 1; } - return sampler_types; -} -std::vector sampler_types_from_chars(const std::string & names_string) { - std::unordered_map sampler_name_map { - {'k', llama_sampler_type::TOP_K}, - {'p', llama_sampler_type::TOP_P}, - {'y', llama_sampler_type::TYPICAL_P}, - {'m', llama_sampler_type::MIN_P}, - {'f', llama_sampler_type::TFS_Z}, - {'t', llama_sampler_type::TEMPERATURE} - }; + return true; +#else + // if the path already exists, check whether it's a directory + struct stat info; + if (stat(path.c_str(), &info) == 0) { + return S_ISDIR(info.st_mode); + } - std::vector sampler_types; - sampler_types.reserve(names_string.size()); - for (const auto & c : names_string) { - const auto sampler_item = sampler_name_map.find(c); - if (sampler_item != sampler_name_map.end()) { - sampler_types.push_back(sampler_item->second); + size_t pos_slash = 1; // skip leading slashes for directory creation + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { + const std::string subpath = path.substr(0, pos_slash); + struct stat info; + + // if the path already exists, ensure that it's a directory + if (stat(subpath.c_str(), &info) == 0) { + if (!S_ISDIR(info.st_mode)) { + return false; + } + } else { + // create parent directories + const int ret = mkdir(subpath.c_str(), 0755); + if (ret != 0) { + return false; + } } + + pos_slash += 1; } - return sampler_types; + + return true; +#endif // _WIN32 } -std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { - switch (sampler_type) { - case llama_sampler_type::TOP_K: return "top_k"; - case llama_sampler_type::TFS_Z: return "tfs_z"; - case llama_sampler_type::TYPICAL_P: return "typical_p"; - case llama_sampler_type::TOP_P: return "top_p"; - case llama_sampler_type::MIN_P: return "min_p"; - case llama_sampler_type::TEMPERATURE: return "temperature"; - default : return ""; +std::string fs_get_cache_directory() { + std::string cache_directory = ""; + auto ensure_trailing_slash = [](std::string p) { + // Make sure to add trailing slash + if (p.back() != DIRECTORY_SEPARATOR) { + p += DIRECTORY_SEPARATOR; + } + return p; + }; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + } else { +#ifdef __linux__ + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("LOCALAPPDATA"); +#endif // __linux__ + cache_directory = ensure_trailing_slash(cache_directory); + cache_directory += "llama.cpp"; } + return ensure_trailing_slash(cache_directory); } + // // Model utils // +std::tuple llama_init_from_gpt_params(gpt_params & params) { + auto mparams = llama_model_params_from_gpt_params(params); + + llama_model * model = nullptr; + + if (!params.hf_repo.empty() && !params.hf_file.empty()) { + model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); + } else if (!params.model_url.empty()) { + model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); + } else { + model = llama_load_model_from_file(params.model.c_str(), mparams); + } + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); + return std::make_tuple(nullptr, nullptr); + } + + auto cparams = llama_context_params_from_gpt_params(params); + + llama_context * lctx = llama_new_context_with_model(model, cparams); + if (lctx == NULL) { + fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + if (!params.control_vectors.empty()) { + if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); + + const auto cvec = llama_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + int err = llama_control_vector_apply(lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { + const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); + float lora_scale = std::get<1>(params.lora_adapter[i]); + int err = llama_model_apply_lora_from_file(model, + lora_adapter.c_str(), + lora_scale, + ((i > 0) || params.lora_base.empty()) + ? NULL + : params.lora_base.c_str(), + params.n_threads); + if (err != 0) { + fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + if (params.ignore_eos) { + params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; + } + + if (params.warmup) { + LOG("warming up the model with an empty run\n"); + + std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_kv_cache_clear(lctx); + llama_synchronize(lctx); + llama_reset_timings(lctx); + } + + return std::make_tuple(model, lctx); +} + struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { auto mparams = llama_model_default_params(); @@ -1923,27 +2363,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param return cparams; } -void llama_batch_clear(struct llama_batch & batch) { - batch.n_tokens = 0; -} - -void llama_batch_add( - struct llama_batch & batch, - llama_token id, - llama_pos pos, - const std::vector & seq_ids, - bool logits) { - batch.token [batch.n_tokens] = id; - batch.pos [batch.n_tokens] = pos; - batch.n_seq_id[batch.n_tokens] = seq_ids.size(); - for (size_t i = 0; i < seq_ids.size(); ++i) { - batch.seq_id[batch.n_tokens][i] = seq_ids[i]; - } - batch.logits [batch.n_tokens] = logits; - - batch.n_tokens++; -} - #ifdef LLAMA_USE_CURL static bool starts_with(const std::string & str, const std::string & prefix) { @@ -2274,90 +2693,29 @@ struct llama_model * llama_load_model_from_hf( #endif // LLAMA_USE_CURL -std::tuple llama_init_from_gpt_params(gpt_params & params) { - auto mparams = llama_model_params_from_gpt_params(params); +// +// Batch utils +// - llama_model * model = nullptr; +void llama_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} - if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); - } else if (!params.model_url.empty()) { - model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); - } else { - model = llama_load_model_from_file(params.model.c_str(), mparams); +void llama_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos; + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; } + batch.logits [batch.n_tokens] = logits; - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); - return std::make_tuple(nullptr, nullptr); - } - - auto cparams = llama_context_params_from_gpt_params(params); - - llama_context * lctx = llama_new_context_with_model(model, cparams); - if (lctx == NULL) { - fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - if (!params.control_vectors.empty()) { - if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; - if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); - - const auto cvec = llama_control_vector_load(params.control_vectors); - if (cvec.n_embd == -1) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - int err = llama_control_vector_apply(lctx, - cvec.data.data(), - cvec.data.size(), - cvec.n_embd, - params.control_vector_layer_start, - params.control_vector_layer_end); - if (err) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { - const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); - float lora_scale = std::get<1>(params.lora_adapter[i]); - int err = llama_model_apply_lora_from_file(model, - lora_adapter.c_str(), - lora_scale, - ((i > 0) || params.lora_base.empty()) - ? NULL - : params.lora_base.c_str(), - params.n_threads); - if (err != 0) { - fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - if (params.ignore_eos) { - params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; - } - - if (params.warmup) { - LOG("warming up the model with an empty run\n"); - - std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); - llama_kv_cache_clear(lctx); - llama_synchronize(lctx); - llama_reset_timings(lctx); - } - - return std::make_tuple(model, lctx); + batch.n_tokens++; } // @@ -2445,346 +2803,17 @@ bool llama_should_add_bos_token(const llama_model * model) { return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); } -// -// YAML utils -// - -// returns true if successful, false otherwise -bool create_directory_with_parents(const std::string & path) { -#ifdef _WIN32 - std::wstring_convert> converter; - std::wstring wpath = converter.from_bytes(path); - - // if the path already exists, check whether it's a directory - const DWORD attributes = GetFileAttributesW(wpath.c_str()); - if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return true; - } - - size_t pos_slash = 0; - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { - const std::wstring subpath = wpath.substr(0, pos_slash); - const wchar_t * test = subpath.c_str(); - - const bool success = CreateDirectoryW(test, NULL); - if (!success) { - const DWORD error = GetLastError(); - - // if the path already exists, ensure that it's a directory - if (error == ERROR_ALREADY_EXISTS) { - const DWORD attributes = GetFileAttributesW(subpath.c_str()); - if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return false; - } - } else { - return false; - } - } - - pos_slash += 1; - } - - return true; -#else - // if the path already exists, check whether it's a directory - struct stat info; - if (stat(path.c_str(), &info) == 0) { - return S_ISDIR(info.st_mode); - } - - size_t pos_slash = 1; // skip leading slashes for directory creation - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { - const std::string subpath = path.substr(0, pos_slash); - struct stat info; - - // if the path already exists, ensure that it's a directory - if (stat(subpath.c_str(), &info) == 0) { - if (!S_ISDIR(info.st_mode)) { - return false; - } - } else { - // create parent directories - const int ret = mkdir(subpath.c_str(), 0755); - if (ret != 0) { - return false; - } - } - - pos_slash += 1; - } - - return true; -#endif // _WIN32 -} - -std::string get_cache_directory() { - std::string cache_directory = ""; - if (getenv("LLAMA_CACHE")) { - cache_directory = std::getenv("LLAMA_CACHE"); - if (cache_directory.back() != DIRECTORY_SEPARATOR) { - cache_directory += DIRECTORY_SEPARATOR; - } - } else { -#ifdef __linux__ - if (std::getenv("XDG_CACHE_HOME")) { - cache_directory = std::getenv("XDG_CACHE_HOME"); - } else { - cache_directory = std::getenv("HOME") + std::string("/.cache/"); - } -#elif defined(__APPLE__) - cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); -#elif defined(_WIN32) - cache_directory = std::getenv("APPDATA"); -#endif // __linux__ - cache_directory += "llama.cpp"; - cache_directory += DIRECTORY_SEPARATOR; - } - return cache_directory; -} - -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%e, ", data[i]); - } - fprintf(stream, "%e]\n", data.back()); -} - -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%d, ", data[i]); - } - fprintf(stream, "%d]\n", data.back()); -} - -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) { - std::string data_str(data == NULL ? "" : data); - - if (data_str.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - size_t pos_start = 0; - size_t pos_found = 0; - - if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { - data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); - data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); - data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); - data_str = "\"" + data_str + "\""; - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - fprintf(stream, "%s: |\n", prop_name); - while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { - fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); - pos_start = pos_found + 1; - } -} - -std::string get_sortable_timestamp() { - using clock = std::chrono::system_clock; - - const clock::time_point current_time = clock::now(); - const time_t as_time_t = clock::to_time_t(current_time); - char timestamp_no_ns[100]; - std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); - - const int64_t ns = std::chrono::duration_cast( - current_time.time_since_epoch() % 1000000000).count(); - char timestamp_ns[11]; - snprintf(timestamp_ns, 11, "%09" PRId64, ns); - - return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); -} - -void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const llama_sampling_params & sparams = params.sparams; - - fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); - fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); - fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); - fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); - fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); - fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); - fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); - fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); - fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); - fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); - -#ifdef NDEBUG - fprintf(stream, "debug: false\n"); -#else - fprintf(stream, "debug: true\n"); -#endif // NDEBUG - - fprintf(stream, "model_desc: %s\n", model_desc); - fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); - -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ - - fprintf(stream, "time: %s\n", timestamp.c_str()); - - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); - - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); - fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); - fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); - fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); - fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); - fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); - fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); - dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str()); - fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); - fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - - const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); - const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; - fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); - - dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - 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()); - - fprintf(stream, "logit_bias:\n"); - for (std::pair lb : sparams.logit_bias) { - if (ignore_eos && lb.first == logit_bias_eos->first) { - continue; - } - fprintf(stream, " %d: %f", lb.first, lb.second); - } - - fprintf(stream, "lora:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) != 1.0f) { - continue; - } - fprintf(stream, " - %s\n", std::get<0>(la).c_str()); - } - fprintf(stream, "lora_scaled:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) == 1.0f) { - continue; - } - fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); - } - fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); - fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); - fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); - fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); - 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: %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); - fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); - fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); - fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); - fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); - dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str()); - fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); - fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); - fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); - dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); - - fprintf(stream, "reverse_prompt:\n"); - for (std::string ap : params.antiprompt) { - size_t pos = 0; - while ((pos = ap.find('\n', pos)) != std::string::npos) { - ap.replace(pos, 1, "\\n"); - pos += 1; - } - - fprintf(stream, " - %s\n", ap.c_str()); - } - - fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); - fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - 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()); - dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); - - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); - fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); - fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); - fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); - fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); - fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); - fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); +bool llama_chat_verify_template(const std::string & tmpl) { + llama_chat_message chat[] = {{"user", "test"}}; + int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); + return res >= 0; } // // KV cache utils // -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", @@ -2807,7 +2836,7 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", @@ -2855,6 +2884,10 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } +// +// Embedding utils +// + void llama_embd_normalize(const float * inp, float * out, int n) { double sum = 0.0; for (int i = 0; i < n; i++) { @@ -3039,3 +3072,222 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%e, ", data[i]); + } + fprintf(stream, "%e]\n", data.back()); +} + +void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%d, ", data[i]); + } + fprintf(stream, "%d]\n", data.back()); +} + +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) { + std::string data_str(data == NULL ? "" : data); + + if (data_str.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + size_t pos_start = 0; + size_t pos_found = 0; + + if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { + data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); + data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); + data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); + data_str = "\"" + data_str + "\""; + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + if (data_str.find('\n') == std::string::npos) { + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + fprintf(stream, "%s: |\n", prop_name); + while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { + fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); + pos_start = pos_found + 1; + } +} + +void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { + const llama_sampling_params & sparams = params.sparams; + + fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); + fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); + fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "true" : "false"); + fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); + fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); + fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); + fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false"); + fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); + fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); + fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); + fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); + +#ifdef NDEBUG + fprintf(stream, "debug: false\n"); +#else + fprintf(stream, "debug: true\n"); +#endif // NDEBUG + + fprintf(stream, "model_desc: %s\n", model_desc); + fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); + +#ifdef __OPTIMIZE__ + fprintf(stream, "optimize: true\n"); +#else + fprintf(stream, "optimize: false\n"); +#endif // __OPTIMIZE__ + + fprintf(stream, "time: %s\n", timestamp.c_str()); + + fprintf(stream, "\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "# User Inputs #\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "\n"); + + fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); + fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); + yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); + fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); + fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); + fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); + fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); + fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); + fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); + fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); + yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str()); + fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); + fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); + fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); + + const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); + const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; + fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); + + yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); + fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); + yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); + fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "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()); + + fprintf(stream, "logit_bias:\n"); + for (std::pair lb : sparams.logit_bias) { + if (ignore_eos && lb.first == logit_bias_eos->first) { + continue; + } + fprintf(stream, " %d: %f", lb.first, lb.second); + } + + fprintf(stream, "lora:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) != 1.0f) { + continue; + } + fprintf(stream, " - %s\n", std::get<0>(la).c_str()); + } + fprintf(stream, "lora_scaled:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) == 1.0f) { + continue; + } + fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); + } + fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); + fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); + fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); + fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); + 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: %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); + fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); + fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); + fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); + fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); + fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); + fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); + fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); + yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str()); + fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); + fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); + fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); + yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); + fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); + + fprintf(stream, "reverse_prompt:\n"); + for (std::string ap : params.antiprompt) { + size_t pos = 0; + while ((pos = ap.find('\n', pos)) != std::string::npos) { + ap.replace(pos, 1, "\\n"); + pos += 1; + } + + fprintf(stream, " - %s\n", ap.c_str()); + } + + fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); + fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); + 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()); + yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); + + fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); + fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); + fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); + fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); + fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); + fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); + fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); + fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); +} diff --git a/common/common.h b/common/common.h index a8e5e50e6..e0a08a61b 100644 --- a/common/common.h +++ b/common/common.h @@ -27,7 +27,7 @@ #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) #define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) @@ -35,14 +35,18 @@ // build info extern int LLAMA_BUILD_NUMBER; -extern char const *LLAMA_COMMIT; -extern char const *LLAMA_COMPILER; -extern char const *LLAMA_BUILD_TARGET; +extern char const * LLAMA_COMMIT; +extern char const * LLAMA_COMPILER; +extern char const * LLAMA_BUILD_TARGET; struct llama_control_vector_load_info; -int get_math_cpu_count(); -int32_t get_num_physical_cores(); +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores(); +int32_t cpu_get_num_math(); // // CLI argument parsing @@ -51,12 +55,12 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed - int32_t n_threads = get_math_cpu_count(); + int32_t n_threads = cpu_get_num_math(); int32_t n_threads_draft = -1; int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) int32_t n_threads_batch_draft = -1; int32_t n_predict = -1; // new tokens to predict - int32_t n_ctx = 512; // context size + int32_t n_ctx = 0; // context size int32_t n_batch = 2048; // logical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_ubatch = 512; // physical batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt @@ -95,23 +99,23 @@ struct gpt_params { // // sampling parameters struct llama_sampling_params sparams; - std::string model = ""; // model path - std::string model_draft = ""; // draft model for speculative decoding + 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 - std::string hf_repo = ""; // HF repo - std::string hf_file = ""; // HF file + std::string model_url = ""; // model url to download + std::string hf_repo = ""; // HF repo + std::string hf_file = ""; // HF file std::string prompt = ""; - std::string prompt_file = ""; // store the external prompt file name - std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state - std::string input_prefix = ""; // string to prefix user inputs with - std::string input_suffix = ""; // string to suffix user inputs with - std::vector antiprompt; // string upon seeing which more user input is prompted - std::string logdir = ""; // directory in which to save YAML log files + std::string prompt_file = ""; // store the external prompt file name + std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state + std::string input_prefix = ""; // string to prefix user inputs with + std::string input_suffix = ""; // string to suffix user inputs with + std::string logdir = ""; // directory in which to save YAML log files std::string lookup_cache_static = ""; // path of static ngram cache file for lookup decoding std::string lookup_cache_dynamic = ""; // path of dynamic ngram cache file for lookup decoding - std::string logits_file = ""; // file for saving *all* logits + std::string logits_file = ""; // file for saving *all* logits + std::vector antiprompt; // strings upon which more user input is prompted (a.k.a. reverse prompts) std::vector kv_overrides; // TODO: avoid tuple, use struct @@ -123,8 +127,8 @@ struct gpt_params { int32_t control_vector_layer_start = -1; // layer range for control vector int32_t control_vector_layer_end = -1; // layer range for control vector - int ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. - int ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line + int32_t ppl_stride = 0; // stride for perplexity calculations. If left at 0, the pre-existing approach will be used. + int32_t ppl_output_type = 0; // = 0 -> ppl output is as usual, = 1 -> ppl output is num_tokens, ppl, one per line // (which is more convenient to use for plotting) // bool hellaswag = false; // compute HellaSwag score over random tasks from datafile supplied in prompt @@ -138,18 +142,17 @@ struct gpt_params { bool kl_divergence = false; // compute KL divergence - bool random_prompt = false; // do not randomize prompt if none provided + bool usage = false; // print usage bool use_color = false; // use color to distinguish generations and inputs + bool special = false; // enable special token output bool interactive = false; // interactive mode - bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode + bool interactive_first = false; // wait for user input immediately 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 bool embedding = false; // get only sentence embedding - bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\" - bool interactive_first = false; // wait for user input immediately + bool escape = true; // escape "\n", "\r", "\t", "\'", "\"", and "\\" 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 @@ -157,10 +160,10 @@ struct gpt_params { bool input_prefix_bos = false; // prefix BOS to user inputs, preceding input_prefix bool ignore_eos = false; // ignore generated EOS tokens - bool instruct = false; // instruction mode (used for Alpaca models) bool logits_all = false; // return logits for all tokens in the batch bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory + bool verbose = false; bool verbose_prompt = false; // print prompt tokens before generation bool display_prompt = true; // print prompt before generation bool infill = false; // use infill mode @@ -175,37 +178,92 @@ struct gpt_params { // multimodal models (see examples/llava) std::string mmproj = ""; // path to multimodal projector std::vector image; // path to image file(s) + + // server params + int32_t port = 8080; + int32_t timeout_read = 600; + int32_t timeout_write = timeout_read; + int32_t n_threads_http = -1; + + std::string hostname = "127.0.0.1"; + std::string public_path = ""; + std::string chat_template = ""; + std::string system_prompt = ""; + + std::vector api_keys; + + std::string ssl_file_key = ""; + std::string ssl_file_cert = ""; + + bool endpoint_slots = true; + bool endpoint_metrics = false; + + bool log_json = false; + + std::string slot_save_path; + + // batched-bench params + bool is_pp_shared = false; + + std::vector n_pp; + std::vector n_tg; + std::vector n_pl; + + // retrieval params + std::vector context_files; // context files to embed + + int32_t chunk_size = 64; // chunk size for context embedding + + std::string chunk_separator = "\n"; // chunk separator for context embedding + + // passkey params + int32_t n_junk = 250; // number of times to repeat the junk text + int32_t i_pos = -1; // position of the passkey in the junk text }; 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); +bool gpt_params_parse (int argc, char ** argv, gpt_params & params); +bool gpt_params_find_arg (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); +void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params); -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); - -bool gpt_params_parse(int argc, char ** argv, gpt_params & params); - -void gpt_print_usage(int argc, char ** argv, const gpt_params & params); - -bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); - -std::string get_system_info(const gpt_params & params); - -std::string gpt_random_prompt(std::mt19937 & rng); - -void process_escapes(std::string& input); - -bool validate_file_name(const std::string & filename); +std::string gpt_params_get_system_info(const gpt_params & params); // // String utils // -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); -std::vector sampler_types_from_chars(const std::string & names_string); std::vector string_split(std::string input, char separator); + std::string string_strip(const std::string & str); -std::string sampler_type_to_name_string(llama_sampler_type sampler_type); +std::string string_get_sortable_timestamp(); + +template +static std::vector string_split(const std::string & str, char delim) { + std::vector values; + std::istringstream str_stream(str); + std::string token; + while (std::getline(str_stream, token, delim)) { + T value; + std::istringstream token_stream(token); + token_stream >> value; + values.push_back(value); + } + return values; +} + +bool string_parse_kv_override(const char * data, std::vector & overrides); +void string_process_escapes(std::string & input); + +// +// Filesystem utils +// + +bool fs_validate_filename(const std::string & filename); +bool fs_create_directory_with_parents(const std::string & path); + +std::string fs_get_cache_directory(); // // Model utils @@ -277,29 +335,21 @@ std::string llama_detokenize_bpe( bool llama_should_add_bos_token(const llama_model * model); // -// YAML utils +// Chat template utils // -bool create_directory_with_parents(const std::string & path); -std::string get_cache_directory(); -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); -std::string get_sortable_timestamp(); - -void dump_non_result_info_yaml( - FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); +// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid +bool llama_chat_verify_template(const std::string & tmpl); // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); // // Embedding utils @@ -333,6 +383,20 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data); +void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); + +void yaml_dump_non_result_info( + FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); + diff --git a/common/sampling.cpp b/common/sampling.cpp index 7fc2e2158..f1f803516 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -125,7 +125,7 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { std::string result = "CFG -> Penalties "; if (params.mirostat == 0) { for (auto sampler_type : params.samplers_sequence) { - const auto sampler_type_name = sampler_type_to_name_string(sampler_type); + const auto sampler_type_name = llama_sampling_type_to_str(sampler_type); if (!sampler_type_name.empty()) { result += "-> " + sampler_type_name + " "; } @@ -137,6 +137,87 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { return result; } +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) { + switch (sampler_type) { + case llama_sampler_type::TOP_K: return "top_k"; + case llama_sampler_type::TFS_Z: return "tfs_z"; + case llama_sampler_type::TYPICAL_P: return "typical_p"; + case llama_sampler_type::TOP_P: return "top_p"; + case llama_sampler_type::MIN_P: return "min_p"; + case llama_sampler_type::TEMPERATURE: return "temperature"; + default : return ""; + } +} + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + {"top_k", llama_sampler_type::TOP_K}, + {"top_p", llama_sampler_type::TOP_P}, + {"typical_p", llama_sampler_type::TYPICAL_P}, + {"min_p", llama_sampler_type::MIN_P}, + {"tfs_z", llama_sampler_type::TFS_Z}, + {"temperature", llama_sampler_type::TEMPERATURE} + }; + + // since samplers names are written multiple ways + // make it ready for both system names and input names + std::unordered_map sampler_alt_name_map { + {"top-k", llama_sampler_type::TOP_K}, + {"top-p", llama_sampler_type::TOP_P}, + {"nucleus", llama_sampler_type::TOP_P}, + {"typical-p", llama_sampler_type::TYPICAL_P}, + {"typical", llama_sampler_type::TYPICAL_P}, + {"min-p", llama_sampler_type::MIN_P}, + {"tfs-z", llama_sampler_type::TFS_Z}, + {"tfs", llama_sampler_type::TFS_Z}, + {"temp", llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names.size()); + for (const auto & name : names) + { + auto sampler_item = sampler_canonical_name_map.find(name); + if (sampler_item != sampler_canonical_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + else + { + if (allow_alt_names) + { + sampler_item = sampler_alt_name_map.find(name); + if (sampler_item != sampler_alt_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + } + } + } + return sampler_types; +} + +std::vector llama_sampling_types_from_chars(const std::string & names_string) { + std::unordered_map sampler_name_map { + {'k', llama_sampler_type::TOP_K}, + {'p', llama_sampler_type::TOP_P}, + {'y', llama_sampler_type::TYPICAL_P}, + {'m', llama_sampler_type::MIN_P}, + {'f', llama_sampler_type::TFS_Z}, + {'t', llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names_string.size()); + for (const auto & c : names_string) { + const auto sampler_item = sampler_name_map.find(c); + if (sampler_item != sampler_name_map.end()) { + sampler_types.push_back(sampler_item->second); + } + } + return sampler_types; +} + // no reasons to expose this function in header static void sampler_queue( struct llama_context * ctx_main, diff --git a/common/sampling.h b/common/sampling.h index 655732ad1..eeaa53b8b 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -116,6 +116,11 @@ std::string llama_sampling_print(const llama_sampling_params & params); // Print sampling order into a string std::string llama_sampling_order_print(const llama_sampling_params & params); +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type); + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names); +std::vector llama_sampling_types_from_chars(const std::string & names_string); + // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call diff --git a/common/train.cpp b/common/train.cpp index 0dbfd24df..fef1e57c9 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() { params.custom_n_ctx = false; - params.use_flash = true; + params.use_flash = false; params.use_checkpointing = true; params.sample_start = ""; @@ -1380,7 +1380,7 @@ bool consume_common_train_arg( void finish_processing_train_args(struct train_params_common * params) { if (params->escape) { - process_escapes(params->sample_start); + string_process_escapes(params->sample_start); } } diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 1923b88ba..84b72348d 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -81,6 +81,7 @@ models = [ {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, ] diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6357d4034..ad071b974 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -14,6 +14,7 @@ from pathlib import Path from hashlib import sha256 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast +import math import numpy as np import torch @@ -24,8 +25,6 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -from convert import LlamaHfVocab - logger = logging.getLogger("hf-to-gguf") @@ -312,11 +311,10 @@ class Model: data = data.astype(np.float32) data_qtype = gguf.GGMLQuantizationType.F32 - block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype] + shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape + # reverse shape to make it similar to the internal ggml dimension order - shape_str = f"""{{{', '.join(str(n) for n in reversed( - (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size)) - )}}}""" + shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}" # n_dims is implicit in the shape logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") @@ -473,6 +471,9 @@ class Model: if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de res = "jina-v2-de" + if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": + # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct + res = "smaug-bpe" if res is None: logger.warning("\n") @@ -631,7 +632,7 @@ class Model: special_vocab.add_to_gguf(self.gguf_writer) def _set_vocab_llama_hf(self): - vocab = LlamaHfVocab(self.dir_model) + vocab = gguf.LlamaHfVocab(self.dir_model) tokens = [] scores = [] toktypes = [] @@ -672,6 +673,44 @@ class GPTNeoXModel(Model): self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + 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")) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"gpt_neox\.layers\.\d+\.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"gpt_neox\.layers\.\d+\.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") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + @Model.register("BloomForCausalLM") class BloomModel(Model): @@ -1276,6 +1315,17 @@ 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"]) + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + @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: @@ -1290,9 +1340,9 @@ class LlamaModel(Model): n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - if name.endswith("q_proj.weight"): + if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) - if name.endswith("k_proj.weight"): + if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately @@ -1784,23 +1834,59 @@ class Phi3MiniModel(Model): def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) - rot_pct = 1.0 n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) rms_eps = self.find_hparam(["rms_norm_eps"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head self.gguf_writer.add_name("Phi3") - self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) - + self.gguf_writer.add_context_length(max_pos_embds) + self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) self.gguf_writer.add_embedding_length(n_embd) - self.gguf_writer.add_feed_forward_length(8192) + self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) - self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_rope_dimension_count(rope_dims) + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) + # write rope scaling for long context (128k) model + rope_scaling = self.find_hparam(['rope_scaling'], True) + if (rope_scaling is None): + return + + scale = max_pos_embds / orig_max_pos_embds + + rope_scaling_type = rope_scaling.get('type', '').lower() + if len(rope_scaling_type) == 0: + raise KeyError('Missing the required key rope_scaling.type') + + if rope_scaling_type == 'su': + attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 + elif rope_scaling_type == 'yarn': + attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 + else: + raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') + + self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) + + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32)) + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32)) + @Model.register("PlamoForCausalLM") class PlamoModel(Model): @@ -2318,7 +2404,8 @@ class CommandR2Model(Model): # max_position_embeddings = 8192 in config.json but model was actually # trained on 128k context length - self.hparams["max_position_embeddings"] = self.hparams["model_max_length"] + # aya-23 models don't have model_max_length specified + self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) def set_gguf_parameters(self): super().set_gguf_parameters() @@ -2391,6 +2478,236 @@ class JinaBertV2Model(BertModel): self.gguf_writer.add_add_eos_token(True) +@Model.register("ArcticForCausalLM") +class ArcticModel(Model): + model_arch = gguf.MODEL_ARCH.ARCTIC + + def set_vocab(self): + # The reason for using a custom implementation here is that the + # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from + # tokenizer.model and used them as BOS and EOS instead of adding new tokens. + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + # Read the whole vocabulary from the tokenizer.model file + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + # Use the added_tokens_decoder field from tokeniser_config.json as the source + # of information about added/redefined tokens and modify them accordingly. + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + + if "added_tokens_decoder" in tokenizer_config_json: + added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"] + for token_id, token_json in added_tokens_decoder.items(): + token_id = int(token_id) + if (token_id >= vocab_size): + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + token_content = token_json["content"] + token_type = SentencePieceTokenTypes.USER_DEFINED + token_score = -10000.0 + + # Map unk_token to UNKNOWN, other special tokens to CONTROL + # Set the score to 0.0 as in the original tokenizer.model + if ("special" in token_json) and token_json["special"]: + if token_content == tokenizer_config_json["unk_token"]: + token_type = SentencePieceTokenTypes.UNKNOWN + else: + token_type = SentencePieceTokenTypes.CONTROL + token_score = 0.0 + + logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})") + tokens[token_id] = token_content.encode("utf-8") + toktypes[token_id] = token_type + scores[token_id] = token_score + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + + _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") + + 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) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_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 wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + 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] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.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): + super().write_tensors() + + 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("DeepseekV2ForCausalLM") +class DeepseekV2Model(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["v_head_dim"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"]) + + _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("mlp.experts") != -1: + n_experts = self.hparams["n_routed_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): + super().write_tensors() + + 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}") + + ###### CONVERSION LOGIC ###### @@ -2523,7 +2840,12 @@ def main() -> None: hparams = Model.load_hparams(dir_model) with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) + try: + model_class = Model.from_model_architecture(hparams["architectures"][0]) + except NotImplementedError: + logger.error(f"Model {hparams['architectures'][0]} is not supported") + sys.exit(1) + 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") diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md index 48769cdf6..138124248 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/HOWTO-add-model.md @@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M ### 1. Convert the model to GGUF This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. -Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). +Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format). The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index b40ee4ccb..53002f8e1 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -15,7 +15,6 @@ else() add_subdirectory(baby-llama) add_subdirectory(batched) add_subdirectory(batched-bench) - add_subdirectory(beam-search) add_subdirectory(benchmark) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(embedding) diff --git a/examples/batched-bench/README.md b/examples/batched-bench/README.md index bf951baf7..fa4baf640 100644 --- a/examples/batched-bench/README.md +++ b/examples/batched-bench/README.md @@ -10,16 +10,16 @@ There are 2 modes of operation: - `prompt is shared` - there is a common prompt of size `PP` used by all batches (i.e. `N_KV = PP + B*TG`) ```bash -./batched-bench MODEL_PATH [N_KV_MAX] [N_BATCH] [N_UBATCH] [IS_PP_SHARED] [NGL] [MMQ] +./batched-bench -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps] # LLaMA 7B, F16, N_KV_MAX = 16384 (8GB), prompt not shared -./batched-bench ./models/llama-7b/ggml-model-f16.gguf 16384 2048 512 0 99 +./batched-bench -m ./models/llama-7b/ggml-model-f16.gguf -c 16384 -b 2048 -ub 512 -ngl 99 # LLaMA 7B, Q8_0, N_KV_MAX = 16384 (8GB), prompt is shared -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 16384 2048 512 1 99 +./batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 16384 -b 2048 -ub 512 -ngl 99 -pps # custom set of batches -./batched-bench ./models/llama-7b/ggml-model-q8_0.gguf 2048 512 512 0 999 0 128,256,512 128,256 1,2,4,8,16,32 +./batched-bench -m ./models/llama-7b/ggml-model-q8_0.gguf -c 2048 -b 512 -ub 512 -ngl 999 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 ``` ## Sample results diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 2924d8116..718f0a61a 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -28,67 +28,27 @@ static std::vector parse_list(char * p) { return ret; } +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -c 2048 -b 2048 -ub 512 -npp 128,256,512 -ntg 128,256 -npl 1,2,4,8,16,32 [-pps]\n", argv[0]); + LOG_TEE("\n"); +} + 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] [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 ; + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - 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; + int is_pp_shared = params.is_pp_shared; - std::vector n_pp = { 128, 256, 512, 1024, 2048, 3584, 7680, }; - std::vector n_tg = { 128, 256, }; - std::vector n_pl = { 1, 2, 4, 8, 16, 32, }; - //std::vector n_pl = { 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 32, }; - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_kv_max = std::atoi(argv[2]); - } - - if (argc >= 4) { - n_batch = std::atoi(argv[3]); - } - - if (argc >= 5) { - n_ubatch = std::atoi(argv[4]); - } - - if (argc >= 6) { - flash_attn = std::atoi(argv[5]); - } - - if (argc >= 7) { - is_pp_shared = std::atoi(argv[6]); - } - - if (argc >= 8) { - n_gpu_layers = std::atoi(argv[7]); - } - - if (argc >= 9) { - n_pp = parse_list(argv[8]); - } - - if (argc >= 10) { - n_tg = parse_list(argv[9]); - } - - if (argc >= 11) { - n_pl = parse_list(argv[10]); - } + std::vector n_pp = params.n_pp; + std::vector n_tg = params.n_tg; + std::vector n_pl = params.n_pl; // init LLM @@ -97,12 +57,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - const std::vector t_split(llama_max_devices(), 0.0f); - - model_params.n_gpu_layers = n_gpu_layers; - model_params.tensor_split = t_split.data(); + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -111,16 +66,7 @@ int main(int argc, char ** argv) { return 1; } - 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.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; + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); // ensure enough sequences are available ctx_params.n_seq_max = *std::max_element(n_pl.begin(), n_pl.end()); @@ -132,6 +78,8 @@ int main(int argc, char ** argv) { return 1; } + const int32_t n_kv_max = llama_n_ctx(ctx); + llama_batch batch = llama_batch_init(n_kv_max, 0, 1); // decode in batches of ctx_params.n_batch tokens @@ -175,7 +123,7 @@ int main(int argc, char ** argv) { } LOG_TEE("\n"); - 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("%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, params.n_batch, params.n_ubatch, params.flash_attn, params.is_pp_shared, params.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/batched/README.md b/examples/batched/README.md index 5d7303317..ed204c308 100644 --- a/examples/batched/README.md +++ b/examples/batched/README.md @@ -3,7 +3,7 @@ The example demonstrates batched generation from a given prompt ```bash -./batched ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" 4 +./batched -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" -np 4 ... diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index be30d20bf..62d9b144d 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -7,48 +7,31 @@ #include #include +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32 -np 4\n", argv[0]); + LOG_TEE("\n"); +} + int main(int argc, char ** argv) { gpt_params params; - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT] [PARALLEL] [LEN] [NGL]\n" , argv[0]); - return 1 ; + params.prompt = "Hello my name is"; + params.n_predict = 32; + + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } + // number of parallel batches - int n_parallel = 1; + int n_parallel = params.n_parallel; // total length of the sequences including the prompt - int n_len = 32; - - // number of layers to offload to the GPU - int n_gpu_layers = 0; - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - params.prompt = argv[2]; - } - - if (argc >= 4) { - n_parallel = std::atoi(argv[3]); - } - - if (argc >= 5) { - n_len = std::atoi(argv[4]); - } - - if (argc >= 6) { - n_gpu_layers = std::atoi(argv[5]); - } - - if (params.prompt.empty()) { - params.prompt = "Hello my name is"; - } - - process_escapes(params.prompt); + int n_predict = 32; // init LLM @@ -57,9 +40,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - model_params.n_gpu_layers = n_gpu_layers; + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -73,18 +54,14 @@ int main(int argc, char ** argv) { std::vector tokens_list; tokens_list = ::llama_tokenize(model, params.prompt, true); - const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size())*n_parallel; + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size())*n_parallel; // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); - ctx_params.seed = 1234; ctx_params.n_ctx = n_kv_req; - ctx_params.n_batch = std::max(n_len, n_parallel); - ctx_params.n_seq_max = n_parallel; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_batch = std::max(n_predict, n_parallel); llama_context * ctx = llama_new_context_with_model(model, ctx_params); @@ -93,9 +70,9 @@ int main(int argc, char ** argv) { return 1; } - const int n_ctx = llama_n_ctx(ctx); + const int n_ctx = llama_n_ctx(ctx); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); + LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_batch = %u, n_parallel = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, ctx_params.n_batch, n_parallel, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { @@ -156,7 +133,7 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - while (n_cur <= n_len) { + while (n_cur <= n_predict) { // prepare the next batch llama_batch_clear(batch); @@ -192,7 +169,7 @@ int main(int argc, char ** argv) { //const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); // is it an end of generation? -> mark the stream as finished - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { i_batch[i] = -1; LOG_TEE("\n"); if (n_parallel > 1) { diff --git a/examples/beam-search/CMakeLists.txt b/examples/beam-search/CMakeLists.txt deleted file mode 100644 index f0e37468b..000000000 --- a/examples/beam-search/CMakeLists.txt +++ /dev/null @@ -1,5 +0,0 @@ -set(TARGET beam-search) -add_executable(${TARGET} beam-search.cpp) -install(TARGETS ${TARGET} RUNTIME) -target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) -target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp deleted file mode 100644 index 3d34378a5..000000000 --- a/examples/beam-search/beam-search.cpp +++ /dev/null @@ -1,188 +0,0 @@ -#include "common.h" -#include "llama.h" - -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - -#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) -#include -#include -#elif defined (_WIN32) -#define WIN32_LEAN_AND_MEAN -#ifndef NOMINMAX -# define NOMINMAX -#endif -#include -#include -#endif - -// Used for debugging to print out beam tokens. -struct ostream_beam_view { - llama_context * ctx; - llama_beam_view beam_view; -}; - -static std::ostream & operator<<(std::ostream & os, const ostream_beam_view & obv) { - os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens("; - for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) { - os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]); - } - return os << ')'; -} - -// Put here anything you want back in beam_search_callback(). -struct beam_search_callback_data { - llama_context * ctx; - std::vector response; -}; - -// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. -// For example, eob can be flagged due to maximum token length, stop words, etc. -static bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, size_t n_tokens) { - return n_tokens && llama_token_is_eog(llama_get_model(callback_data.ctx), tokens[n_tokens-1]); -} - -// Function matching type llama_beam_search_callback_fn_t. -// Custom callback example is called each time the beams lengths increase: -// * Show progress by printing ',' following by number of convergent beam tokens if any. -// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. -// This is also called when the stop condition is met. -// Collect tokens into std::vector response which is pointed to by callback_data. -static void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { - auto& callback_data = *static_cast(callback_data_ptr); - // Mark beams as EOS as needed. - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - llama_beam_view& beam_view = beams_state.beam_views[i]; - if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) { - beam_view.eob = true; - } - } - printf(","); // Show progress - if (const size_t n = beams_state.common_prefix_length) { - callback_data.response.resize(callback_data.response.size() + n); - assert(0u < beams_state.n_beams); - const llama_token * tokens = beams_state.beam_views[0].tokens; - std::copy(tokens, tokens + n, callback_data.response.end() - n); - printf("%zu", n); - } - fflush(stdout); -#if 1 // DEBUG: print current beams for this iteration - std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n"; - for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { - std::cout << "beams["< 3 ) - { - params.prompt = argv[3]; - } - - if ( params.prompt.empty() ) - { - params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n"; - } - - //--------------------------------- - // Init LLM : - //--------------------------------- - - llama_backend_init(); - llama_numa_init(params.numa); - - llama_model * model; - llama_context * ctx; - - std::tie(model, ctx) = llama_init_from_gpt_params( params ); - - if ( model == NULL ) - { - fprintf( stderr , "%s: error: unable to load model\n" , __func__ ); - return 1; - } - - //--------------------------------- - // Tokenize the prompt : - //--------------------------------- - - std::vector tokens_list = llama_tokenize(ctx, params.prompt, true); - - const size_t max_context_size = llama_n_ctx( ctx ); - const size_t max_tokens_list_size = max_context_size - 4 ; - - if (tokens_list.size() > max_tokens_list_size) - { - fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" , - __func__ , tokens_list.size() , max_tokens_list_size ); - return 1; - } - - fprintf( stderr, "\n\n" ); - - // Print the tokens from the prompt : - - for( auto id : tokens_list ) - { - std::cout << llama_token_to_piece(ctx, id); - } - std::cout << std::flush; - - int n_past = 0; - - if (llama_decode(ctx, llama_batch_get_one(tokens_list.data(), tokens_list.size(), n_past, 0))) - { - fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ ); - return 1; - } - n_past += tokens_list.size(); - - beam_search_callback_data callback_data{ctx, {}}; - size_t const beam_width = static_cast(params.n_beams); - int const n_predict = 256; - llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict); - - std::cout << "\n\n"; - for (llama_token const token_id : callback_data.response) { - std::cout << llama_token_to_piece(ctx,token_id); - } - std::cout << std::endl; - - llama_free( ctx ); - llama_free_model( model ); - - llama_backend_free(); - - return 0; -} diff --git a/convert.py b/examples/convert-legacy-llama.py similarity index 82% rename from convert.py rename to examples/convert-legacy-llama.py index da1247957..fd8401015 100755 --- a/convert.py +++ b/examples/convert-legacy-llama.py @@ -24,14 +24,16 @@ from abc import ABC, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional +from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional import numpy as np -from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) + # use .parent.parent since we are in "examples" directory + sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py')) + import gguf +from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab if TYPE_CHECKING: from typing_extensions import Self, TypeAlias @@ -380,306 +382,6 @@ class Metadata: return metadata -# -# vocab -# - - -@runtime_checkable -class BaseVocab(Protocol): - tokenizer_model: ClassVar[str] - name: ClassVar[str] - - -class NoVocab(BaseVocab): - tokenizer_model = "no_vocab" - name = "no_vocab" - - def __repr__(self) -> str: - return "" - - -@runtime_checkable -class Vocab(BaseVocab, Protocol): - vocab_size: int - added_tokens_dict: dict[str, int] - added_tokens_list: list[str] - fname_tokenizer: Path - - def __init__(self, base_path: Path): ... - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... - - -class BpeVocab(Vocab): - tokenizer_model = "gpt2" - name = "bpe" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - - if (fname_tokenizer := base_path / 'vocab.json').exists(): - # "slow" tokenizer - with open(fname_tokenizer, encoding="utf-8") as f: - self.vocab = json.load(f) - - try: - # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - else: - # "fast" tokenizer - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding="utf-8") as f: - tokenizer_json = json.load(f) - - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - if ( - tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'ByteLevel' - ): - raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') - - self.vocab = tokenizer_model["vocab"] - - if (added := tokenizer_json.get('added_tokens')) is not None: - # Added tokens here can be duplicates of the main vocabulary. - added_tokens = {item['content']: item['id'] - for item in added - if item['content'] not in self.vocab} - - vocab_size = len(self.vocab) - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - expected_end_id = vocab_size + len(actual_ids) - 1 - raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " - f"{vocab_size} - {expected_end_id}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_dict = added_tokens - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} - - for i, _ in enumerate(self.vocab): - yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.CONTROL - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.bpe_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class SentencePieceVocab(Vocab): - tokenizer_model = "llama" - name = "spm" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - if (fname_tokenizer := base_path / 'tokenizer.model').exists(): - # normal location - try: - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): - # not found in alternate location either - raise FileNotFoundError('Cannot find tokenizer.model') - - 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} - expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) - actual_new_ids = sorted(new_tokens.keys()) - - if expected_new_ids != actual_new_ids: - raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") - - # Token pieces that were added to the base vocabulary. - self.added_tokens_dict = added_tokens - self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.IdToPiece(i) - text = piece.encode("utf-8") - score: float = tokenizer.GetScore(i) - - toktype = gguf.TokenType.NORMAL - if tokenizer.IsUnknown(i): - toktype = gguf.TokenType.UNKNOWN - 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.IsUnused(i): - toktype = gguf.TokenType.UNUSED - if tokenizer.IsByte(i): - toktype = gguf.TokenType.BYTE - - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class LlamaHfVocab(Vocab): - tokenizer_model = "llama" - name = "hfft" - - def __init__(self, base_path: Path): - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding='utf-8') as f: - tokenizer_json = json.load(f) - - # pre-check so we know if we need transformers - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - is_llama3 = ( - tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) - and not tokenizer_model.get('byte_fallback', True) - ) - if is_llama3: - raise TypeError('Llama 3 must be converted with BpeVocab') - - if not is_llama3 and ( - tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'Sequence' - ): - raise FileNotFoundError('Cannot find Llama BPE tokenizer') - - try: - from transformers import AutoTokenizer - except ImportError as e: - raise ImportError( - "To use LlamaHfVocab, please install the `transformers` package. " - "You can install it with `pip install transformers`." - ) from e - - # Allow the tokenizer to default to slow or fast versions. - # Explicitly set tokenizer to use local paths. - self.tokenizer = AutoTokenizer.from_pretrained( - base_path, - cache_dir=base_path, - local_files_only=True, - ) - assert self.tokenizer.is_fast # assume tokenizer.json is used - - # Initialize lists and dictionaries for added tokens - self.added_tokens_list = [] - self.added_tokens_dict = dict() - self.added_tokens_ids = set() - - # Process added tokens - for tok, tokidx in sorted( - self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] - ): - # Only consider added tokens that are not in the base vocabulary - if tokidx >= self.tokenizer.vocab_size: - self.added_tokens_list.append(tok) - self.added_tokens_dict[tok] = tokidx - self.added_tokens_ids.add(tokidx) - - # Store special tokens and their IDs - self.specials = { - tok: self.tokenizer.get_vocab()[tok] - for tok in self.tokenizer.all_special_tokens - } - self.special_ids = set(self.tokenizer.all_special_ids) - - # Set vocabulary sizes - self.vocab_size_base = self.tokenizer.vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - - self.fname_tokenizer = fname_tokenizer - - def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = { - id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() - } - - for token_id in range(self.vocab_size_base): - # Skip processing added tokens here - if token_id in self.added_tokens_ids: - continue - - # Convert token text to bytes - token_text = reverse_vocab[token_id].encode("utf-8") - - # Yield token text, score, and type - yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, token_text, self.special_ids # Reuse already stored special IDs - ) - - def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: - # Special case for byte tokens - if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): - return gguf.TokenType.BYTE - - # Determine token type based on whether it's a special token - return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL - - def get_token_score(self, token_id: int) -> float: - # Placeholder for actual logic to determine the token's score - # This needs to be implemented based on specific requirements - return -1000.0 # Default score - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - if text in self.specials: - toktype = self.get_token_type(self.specials[text], b'', self.special_ids) - score = self.get_token_score(self.specials[text]) - else: - toktype = gguf.TokenType.USER_DEFINED - score = -1000.0 - - yield text.encode("utf-8"), score, toktype - - def has_newline_token(self): - return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.hf_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - # # data loading # TODO: reuse (probably move to gguf.py?) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 746c3fbef..8ca9f8915 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -774,7 +774,7 @@ static struct train_params get_default_train_params() { params.samples_start_after_nl = false; params.use_adam = true; - params.use_flash = true; + params.use_flash = false; params.use_scratch = true; // only adam diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 0c921ed69..244751e00 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -63,6 +63,7 @@ int main(int argc, char ** argv) { gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -79,9 +80,6 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); @@ -107,7 +105,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // split the prompt into lines diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index e670d3769..64cd338c2 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -140,20 +140,18 @@ static bool run(llama_context * ctx, const gpt_params & params) { } int main(int argc, char ** argv) { - callback_data cb_data; gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } print_build_info(); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); @@ -176,7 +174,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = run(ctx, params); diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 22743b1bf..22425730f 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -563,8 +563,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, 0, + return ggml_rope_ext(ctx, + t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; @@ -643,7 +643,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/examples/gguf-split/tests.sh b/examples/gguf-split/tests.sh index 7ca6fa7f2..3bc0fa471 100755 --- a/examples/gguf-split/tests.sh +++ b/examples/gguf-split/tests.sh @@ -41,7 +41,7 @@ echo PASS echo # 2b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -51,7 +51,7 @@ echo PASS echo # 3b. Test the merged model is loading properly -$MAIN --model $WORK_PATH/ggml-model-merge.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-merge.gguf --n-predict 32 echo PASS echo @@ -61,7 +61,7 @@ echo PASS echo # 4b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-32-tensors-00001-of-00007.gguf --n-predict 32 echo PASS echo @@ -71,7 +71,7 @@ echo #echo # 5b. Test the merged model is loading properly -#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --random-prompt --n-predict 32 +#$MAIN --model $WORK_PATH/ggml-model-merge-2.gguf --n-predict 32 #echo PASS #echo @@ -81,7 +81,7 @@ echo PASS echo # 6b. Test the sharded model is loading properly -$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-split-2G-00001-of-00002.gguf --n-predict 32 echo PASS echo diff --git a/examples/gritlm/gritlm.cpp b/examples/gritlm/gritlm.cpp index 52fd719b3..213515791 100644 --- a/examples/gritlm/gritlm.cpp +++ b/examples/gritlm/gritlm.cpp @@ -153,7 +153,9 @@ static std::string gritlm_instruction(const std::string & instruction) { int main(int argc, char * argv[]) { gpt_params params; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 82b19fc4f..e050c09d2 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -533,7 +533,6 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool } int main(int argc, char ** argv) { - StatParams sparams; std::string prev_result_file; std::string combine_files; @@ -581,7 +580,9 @@ int main(int argc, char ** argv) { gpt_params params; params.n_batch = 512; - if (!gpt_params_parse(args.size(), args.data(), params)) { + + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -597,9 +598,6 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } sparams.dataset = params.prompt_file; g_collector.set_parameters(std::move(sparams)); @@ -667,7 +665,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index afac145f6..0e4ec79c6 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -50,9 +50,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -70,7 +70,7 @@ static void write_logfile( fprintf(logfile, "binary: infill\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -78,8 +78,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -107,6 +107,7 @@ int main(int argc, char ** argv) { g_params = ¶ms; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -139,27 +140,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } - if (params.instruct) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for instruct mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (params.chatml) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for chatml mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.antiprompt.empty()) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for antiprompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } if (!params.interactive_first && (params.input_prefix.empty() && params.input_suffix.empty())) { printf("\n************\n"); printf("%s: please use '--interactive_first' or specify '--in_prefix' and/or '--in_suffix'\n", __func__); @@ -167,20 +147,6 @@ int main(int argc, char ** argv) { return 0; } - if (params.random_prompt) { - printf("\n************\n"); - printf("%s: please use the 'main' tool for random prompt mode\n", __func__); - printf("************\n\n"); - - return 0; - } - if (!params.path_prompt_cache.empty()) { - printf("\n************\n"); - printf("%s: infill does not support prompt caching\n", __func__); - printf("************\n\n"); - - return 0; - } if (params.rope_freq_base != 0.0) { LOG_TEE("%s: warning: changing RoPE frequency base to %g.\n", __func__, params.rope_freq_base); @@ -207,17 +173,13 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; - llama_context * ctx_guidance = NULL; + g_model = &model; g_ctx = &ctx; // load the model and apply lora adapter, if any LOG("%s: load the model and apply lora adapter, if any\n", __func__); std::tie(model, ctx) = llama_init_from_gpt_params(params); - if (sparams.cfg_scale > 1.f) { - struct llama_context_params lparams = llama_context_params_from_gpt_params(params); - ctx_guidance = llama_new_context_with_model(model, lparams); - } if (model == NULL) { LOG_TEE("%s: error: unable to load model\n", __func__); @@ -236,7 +198,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } const bool add_bos = llama_should_add_bos_token(model); GGML_ASSERT(llama_add_eos_token(model) != 1); @@ -273,25 +235,6 @@ int main(int argc, char ** argv) { LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp).c_str()); } - // Tokenize negative prompt - std::vector guidance_inp; - int guidance_offset = 0; - int original_prompt_len = 0; - if (ctx_guidance) { - LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(sparams.cfg_negative_prompt)); - - guidance_inp = ::llama_tokenize(ctx_guidance, sparams.cfg_negative_prompt, true); - LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp).c_str()); - - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, true); - LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp).c_str()); - - original_prompt_len = original_inp.size(); - guidance_offset = (int)guidance_inp.size() - original_prompt_len; - LOG("original_prompt_len: %s", log_tostr(original_prompt_len)); - LOG("guidance_offset: %s", log_tostr(guidance_offset)); - } - if ((int) embd_inp.size() > n_ctx - 4) { LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; @@ -319,15 +262,6 @@ int main(int argc, char ** argv) { LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); } - if (ctx_guidance) { - LOG_TEE("\n"); - LOG_TEE("%s: negative prompt: '%s'\n", __func__, sparams.cfg_negative_prompt.c_str()); - LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); - for (int i = 0; i < (int) guidance_inp.size(); i++) { - LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); - } - } - if (params.n_keep > 0) { LOG_TEE("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { @@ -395,12 +329,11 @@ int main(int argc, char ** argv) { is_interacting = params.interactive_first; } - bool input_echo = true; + bool input_echo = true; - int n_past = 0; - int n_remain = params.n_predict; - int n_consumed = 0; - int n_past_guidance = 0; + int n_past = 0; + int n_remain = params.n_predict; + int n_consumed = 0; std::vector input_tokens; g_input_tokens = &input_tokens; std::vector output_tokens; g_output_tokens = &output_tokens; @@ -410,7 +343,6 @@ int main(int argc, char ** argv) { console::set_display(console::prompt); std::vector embd; - std::vector embd_guidance; struct llama_sampling_context * ctx_sampling = llama_sampling_init(sparams); @@ -436,7 +368,7 @@ 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() > 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; @@ -453,11 +385,7 @@ int main(int argc, char ** argv) { n_past -= n_discard; - if (ctx_guidance) { - n_past_guidance -= n_discard; - } - - LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + LOG("after swap: n_past = %d\n", n_past); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd).c_str()); @@ -465,45 +393,6 @@ int main(int argc, char ** argv) { // evaluate tokens in batches // embd is typically prepared beforehand to fit within a batch, but not always - - if (ctx_guidance) { - int input_size = 0; - llama_token * input_buf = NULL; - - if (n_past_guidance < (int) guidance_inp.size()) { - // Guidance context should have the same data with these modifications: - // - // * Replace the initial prompt - // * Shift everything by guidance_offset - embd_guidance = guidance_inp; - if (embd.begin() + original_prompt_len < embd.end()) { - embd_guidance.insert( - embd_guidance.end(), - embd.begin() + original_prompt_len, - embd.end() - ); - } - - input_buf = embd_guidance.data(); - input_size = embd_guidance.size(); - - LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance).c_str()); - } else { - input_buf = embd.data(); - input_size = embd.size(); - } - - for (int i = 0; i < input_size; i += params.n_batch) { - int n_eval = std::min(input_size - i, params.n_batch); - if (llama_decode(ctx_guidance, llama_batch_get_one(input_buf + i, n_eval, n_past_guidance, 0))) { - LOG_TEE("%s : failed to eval\n", __func__); - return 1; - } - - n_past_guidance += n_eval; - } - } - for (int i = 0; i < (int) embd.size(); i += params.n_batch) { int n_eval = (int) embd.size() - i; if (n_eval > params.n_batch) { @@ -525,11 +414,9 @@ int main(int argc, char ** argv) { } embd.clear(); - embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - - const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); + const llama_token id = llama_sampling_sample(ctx_sampling, ctx, nullptr); llama_sampling_accept(ctx_sampling, ctx, id, true); @@ -583,7 +470,6 @@ int main(int argc, char ** argv) { // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { - // deal with eot token in infill mode if ((llama_sampling_last(ctx_sampling) == llama_token_eot(model) || is_interacting) && params.interactive){ if (is_interacting && !params.interactive_first) { @@ -621,8 +507,8 @@ int main(int argc, char ** argv) { if (params.escape) { //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); } suff_rm_leading_spc = params.escape; if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { @@ -644,7 +530,6 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); embd_inp.push_back(llama_token_middle(model)); embd.clear(); - embd_guidance.clear(); n_remain = params.n_predict; n_past = 0; n_consumed = 0; @@ -751,7 +636,6 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); - if (ctx_guidance) { llama_free(ctx_guidance); } llama_free(ctx); llama_free_model(model); diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index 857840564..fd95b35f4 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -162,7 +162,7 @@ $ ./llama-bench -o csv ``` ```csv -build_commit,build_number,cuda,opencl,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts +build_commit,build_number,cuda,metal,gpu_blas,blas,cpu_info,gpu_info,model_filename,model_type,model_size,model_n_params,n_batch,n_threads,f16_kv,n_gpu_layers,main_gpu,mul_mat_q,tensor_split,n_prompt,n_gen,test_time,avg_ns,stddev_ns,avg_ts,stddev_ts "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","512","0","2023-09-23T12:09:01Z","212155977","732372","2413.341687","8.305961" "3469684","1275","1","0","0","1","1","13th Gen Intel(R) Core(TM) i9-13900K","NVIDIA GeForce RTX 3090 Ti","models/7B/ggml-model-q4_0.gguf","llama 7B mostly Q4_0","3825065984","6738415616","512","16","1","99","0","1","0.00","0","128","2023-09-23T12:09:02Z","969320879","2728399","132.052051","0.371342" ``` @@ -179,7 +179,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -210,7 +209,6 @@ $ ./llama-bench -o json "build_commit": "3469684", "build_number": 1275, "cuda": true, - "opencl": false, "metal": false, "gpu_blas": true, "blas": true, @@ -253,7 +251,6 @@ CREATE TABLE IF NOT EXISTS test ( build_commit TEXT, build_number INTEGER, cuda INTEGER, - opencl INTEGER, metal INTEGER, gpu_blas INTEGER, blas INTEGER, @@ -279,6 +276,6 @@ CREATE TABLE IF NOT EXISTS test ( stddev_ts REAL ); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); -INSERT INTO test (build_commit, build_number, cuda, opencl, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '512', '0', '2023-09-23T12:10:30Z', '212693772', '743623', '2407.240204', '8.409634'); +INSERT INTO test (build_commit, build_number, cuda, metal, gpu_blas, blas, cpu_info, gpu_info, model_filename, model_type, model_size, model_n_params, n_batch, n_threads, f16_kv, n_gpu_layers, main_gpu, mul_mat_q, tensor_split, n_prompt, n_gen, test_time, avg_ns, stddev_ns, avg_ts, stddev_ts) VALUES ('3469684', '1275', '1', '0', '0', '1', '1', '13th Gen Intel(R) Core(TM) i9-13900K', 'NVIDIA GeForce RTX 3090 Ti', 'models/7B/ggml-model-q4_0.gguf', 'llama 7B mostly Q4_0', '3825065984', '6738415616', '512', '16', '1', '99', '0', '1', '0.00', '0', '128', '2023-09-23T12:10:31Z', '977925003', '4037361', '130.891159', '0.537692'); ``` diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8b965e199..5c31548a6 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -41,20 +41,6 @@ static std::string join(const std::vector & values, const std::string & delim return str.str(); } -template -static std::vector split(const std::string & str, char delim) { - std::vector values; - std::istringstream str_stream(str); - std::string token; - while (std::getline(str_stream, token, delim)) { - T value; - std::istringstream token_stream(token); - token_stream >> value; - values.push_back(value); - } - return values; -} - template static std::vector transform_to_str(const std::vector & values, F f) { std::vector str_values; @@ -140,10 +126,11 @@ static std::string get_gpu_info() { } // command line params -enum output_formats {CSV, JSON, MARKDOWN, SQL}; +enum output_formats {NONE, CSV, JSON, MARKDOWN, SQL}; static const char * output_format_str(output_formats format) { switch (format) { + case NONE: return "none"; case CSV: return "csv"; case JSON: return "json"; case MARKDOWN: return "md"; @@ -152,6 +139,23 @@ static const char * output_format_str(output_formats format) { } } +static bool output_format_from_str(const std::string & s, output_formats & format) { + if (s == "none") { + format = NONE; + } else if (s == "csv") { + format = CSV; + } else if (s == "json") { + format = JSON; + } else if (s == "md") { + format = MARKDOWN; + } else if (s == "sql") { + format = SQL; + } else { + return false; + } + return true; +} + static const char * split_mode_str(llama_split_mode mode) { switch (mode) { case LLAMA_SPLIT_MODE_NONE: return "none"; @@ -178,6 +182,7 @@ struct cmd_params { std::vector type_v; std::vector n_threads; std::vector n_gpu_layers; + std::vector rpc_servers; std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; @@ -189,30 +194,33 @@ struct cmd_params { int reps; bool verbose; output_formats output_format; + output_formats output_format_stderr; }; 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}, - /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_math_cpu_count()}, - /* n_gpu_layers */ {99}, - /* 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}, - /* numa */ GGML_NUMA_STRATEGY_DISABLED, - /* reps */ 5, - /* verbose */ false, - /* output_format */ MARKDOWN + /* model */ {"models/7B/ggml-model-q4_0.gguf"}, + /* n_prompt */ {512}, + /* n_gen */ {128}, + /* n_pg */ {}, + /* n_batch */ {2048}, + /* n_ubatch */ {512}, + /* type_k */ {GGML_TYPE_F16}, + /* type_v */ {GGML_TYPE_F16}, + /* n_threads */ {cpu_get_num_math()}, + /* n_gpu_layers */ {99}, + /* rpc_servers */ {""}, + /* 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}, + /* numa */ GGML_NUMA_STRATEGY_DISABLED, + /* reps */ 5, + /* verbose */ false, + /* output_format */ MARKDOWN, + /* output_format_stderr */ NONE, }; static void print_usage(int /* argc */, char ** argv) { @@ -230,6 +238,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -rpc, --rpc (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str()); 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()); @@ -240,6 +249,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -ts, --tensor-split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); printf(" -o, --output (default: %s)\n", output_format_str(cmd_params_defaults.output_format)); + printf(" -oe, --output-err (default: %s)\n", output_format_str(cmd_params_defaults.output_format_stderr)); printf(" -v, --verbose (default: %s)\n", cmd_params_defaults.verbose ? "1" : "0"); printf("\n"); printf("Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times.\n"); @@ -281,6 +291,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { params.verbose = cmd_params_defaults.verbose; params.output_format = cmd_params_defaults.output_format; + params.output_format_stderr = cmd_params_defaults.output_format_stderr; params.reps = cmd_params_defaults.reps; for (int i = 1; i < argc; i++) { @@ -297,28 +308,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.model.insert(params.model.end(), p.begin(), p.end()); } else if (arg == "-p" || arg == "--n-prompt") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_prompt.insert(params.n_prompt.end(), p.begin(), p.end()); } else if (arg == "-n" || arg == "--n-gen") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(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(argv[i], ','); + auto p = string_split(argv[i], ','); if (p.size() != 2) { invalid_param = true; break; @@ -329,21 +340,21 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_batch.insert(params.n_batch.end(), p.begin(), p.end()); } else if (arg == "-ub" || arg == "--ubatch-size") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_ubatch.insert(params.n_ubatch.end(), p.begin(), p.end()); } else if (arg == "-ctk" || arg == "--cache-type-k") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -359,7 +370,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector types; for (const auto & t : p) { ggml_type gt = ggml_type_from_name(t); @@ -375,21 +386,27 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_threads.insert(params.n_threads.end(), p.begin(), p.end()); } else if (arg == "-ngl" || arg == "--n-gpu-layers") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (arg == "-rpc" || arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers.push_back(argv[i]); } else if (arg == "-sm" || arg == "--split-mode") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); std::vector modes; for (const auto & m : p) { llama_split_mode mode; @@ -411,13 +428,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - params.main_gpu = split(argv[i], split_delim); + params.main_gpu = string_split(argv[i], split_delim); } else if (arg == "-nkvo" || arg == "--no-kv-offload") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(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) { @@ -435,28 +452,28 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_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; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); } else if (arg == "-embd" || arg == "--embeddings") { if (++i >= argc) { invalid_param = true; break; } - auto p = split(argv[i], split_delim); + auto p = string_split(argv[i], split_delim); params.embeddings.insert(params.embeddings.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; break; } - for (auto ts : split(argv[i], split_delim)) { + for (auto ts : string_split(argv[i], split_delim)) { // split string by ; and / const std::regex regex{R"([;/]+)"}; std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; @@ -484,18 +501,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { invalid_param = true; break; } - if (argv[i] == std::string("csv")) { - params.output_format = CSV; - } else if (argv[i] == std::string("json")) { - params.output_format = JSON; - } else if (argv[i] == std::string("md")) { - params.output_format = MARKDOWN; - } else if (argv[i] == std::string("sql")) { - params.output_format = SQL; - } else { + invalid_param = !output_format_from_str(argv[i], params.output_format); + } else if (arg == "-oe" || arg == "--output-err") { + if (++i >= argc) { invalid_param = true; break; } + invalid_param = !output_format_from_str(argv[i], params.output_format_stderr); } else if (arg == "-v" || arg == "--verbose") { params.verbose = true; } else { @@ -519,6 +531,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } + if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; } 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; } @@ -541,6 +554,7 @@ struct cmd_params_instance { ggml_type type_v; int n_threads; int n_gpu_layers; + std::string rpc_servers; llama_split_mode split_mode; int main_gpu; bool no_kv_offload; @@ -553,6 +567,9 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; + if (!rpc_servers.empty()) { + mparams.rpc_servers = rpc_servers.c_str(); + } mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); @@ -564,6 +581,7 @@ struct cmd_params_instance { bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && + rpc_servers == other.rpc_servers && split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && @@ -592,6 +610,7 @@ static std::vector get_cmd_params_instances(const cmd_param // this ordering minimizes the number of times that each model needs to be reloaded for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) + for (const auto & rpc : params.rpc_servers) for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) @@ -618,6 +637,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -643,6 +663,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -668,6 +689,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -687,11 +709,11 @@ struct test { static const std::string build_commit; static const int build_number; static const bool cuda; - static const bool opencl; static const bool vulkan; static const bool kompute; static const bool metal; static const bool sycl; + static const bool rpc; static const bool gpu_blas; static const bool blas; static const std::string cpu_info; @@ -775,9 +797,6 @@ struct test { if (cuda) { return GGML_CUDA_NAME; } - if (opencl) { - return "OpenCL"; - } if (vulkan) { return "Vulkan"; } @@ -790,6 +809,9 @@ struct test { if (sycl) { return GGML_SYCL_NAME; } + if (rpc) { + return "RPC"; + } if (gpu_blas) { return "GPU BLAS"; } @@ -803,7 +825,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", + "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", @@ -829,7 +851,7 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || + if (field == "cuda" || field == "vulkan" || field == "kompute" || field == "metal" || field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || field == "flash_attn" || field == "use_mmap" || field == "embeddings") { return BOOL; @@ -858,8 +880,8 @@ struct test { } std::vector values = { build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(cuda), std::to_string(vulkan), std::to_string(vulkan), + std::to_string(metal), std::to_string(sycl), std::to_string(rpc), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_ubatch), @@ -887,13 +909,13 @@ struct test { const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cuda(); -const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::vulkan = !!ggml_cpu_has_vulkan(); const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); const bool test::sycl = !!ggml_cpu_has_sycl(); +const bool test::rpc = !!ggml_cpu_has_rpc(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -1254,6 +1276,22 @@ static void llama_null_log_callback(enum ggml_log_level level, const char * text (void) user_data; } +static std::unique_ptr create_printer(output_formats format) { + switch (format) { + case NONE: + return nullptr; + case CSV: + return std::unique_ptr(new csv_printer()); + case JSON: + return std::unique_ptr(new json_printer()); + case MARKDOWN: + return std::unique_ptr(new markdown_printer()); + case SQL: + return std::unique_ptr(new sql_printer()); + } + GGML_ASSERT(false); +} + int main(int argc, char ** argv) { // try to set locale for unicode characters in markdown setlocale(LC_CTYPE, ".UTF-8"); @@ -1280,26 +1318,18 @@ int main(int argc, char ** argv) { llama_numa_init(params.numa); // initialize printer - std::unique_ptr p; - switch (params.output_format) { - case CSV: - p.reset(new csv_printer()); - break; - case JSON: - p.reset(new json_printer()); - break; - case MARKDOWN: - p.reset(new markdown_printer()); - break; - case SQL: - p.reset(new sql_printer()); - break; - default: - assert(false); - exit(1); + std::unique_ptr p = create_printer(params.output_format); + std::unique_ptr p_err = create_printer(params.output_format_stderr); + + if (p) { + p->fout = stdout; + p->print_header(params); + } + + if (p_err) { + p_err->fout = stderr; + p_err->print_header(params); } - p->fout = stdout; - p->print_header(params); std::vector params_instances = get_cmd_params_instances(params); @@ -1357,7 +1387,15 @@ int main(int argc, char ** argv) { t.samples_ns.push_back(t_ns); } - p->print_test(t); + if (p) { + p->print_test(t); + fflush(p->fout); + } + + if (p_err) { + p_err->print_test(t); + fflush(p_err->fout); + } llama_print_timings(ctx); @@ -1366,7 +1404,13 @@ int main(int argc, char ** argv) { llama_free_model(lmodel); - p->print_footer(); + if (p) { + p->print_footer(); + } + + if (p_err) { + p_err->print_footer(); + } llama_backend_free(); diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index d42140efe..8d1b37195 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -7,8 +7,6 @@ android { namespace = "com.example.llama" compileSdk = 34 - ndkVersion = "26.1.10909125" - defaultConfig { applicationId = "com.example.llama" minSdk = 33 @@ -20,17 +18,6 @@ android { vectorDrawables { useSupportLibrary = true } - ndk { - // Add NDK properties if wanted, e.g. - // abiFilters += listOf("arm64-v8a") - } - externalNativeBuild { - cmake { - arguments += "-DCMAKE_BUILD_TYPE=Release" - cppFlags += listOf() - arguments += listOf() - } - } } buildTypes { @@ -55,17 +42,6 @@ android { composeOptions { kotlinCompilerExtensionVersion = "1.5.1" } - packaging { - resources { - excludes += "/META-INF/{AL2.0,LGPL2.1}" - } - } - externalNativeBuild { - cmake { - path = file("src/main/cpp/CMakeLists.txt") - version = "3.22.1" - } - } } dependencies { @@ -78,6 +54,7 @@ dependencies { implementation("androidx.compose.ui:ui-graphics") implementation("androidx.compose.ui:ui-tooling-preview") implementation("androidx.compose.material3:material3") + implementation(project(":llama")) testImplementation("junit:junit:4.13.2") androidTestImplementation("androidx.test.ext:junit:1.1.5") androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt index be95e2221..45ac29938 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -1,5 +1,6 @@ package com.example.llama +import android.llama.cpp.LLamaAndroid import android.util.Log import androidx.compose.runtime.getValue import androidx.compose.runtime.mutableStateOf @@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope import kotlinx.coroutines.flow.catch import kotlinx.coroutines.launch -class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { +class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() { companion object { @JvmStatic private val NanosPerSecond = 1_000_000_000.0 @@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { - llm.unload() + llamaAndroid.unload() } catch (exc: IllegalStateException) { messages += exc.message!! } @@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { messages += "" viewModelScope.launch { - llm.send(text) + llamaAndroid.send(text) .catch { Log.e(tag, "send() failed", it) messages += it.message!! @@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { val start = System.nanoTime() - val warmupResult = llm.bench(pp, tg, pl, nr) + val warmupResult = llamaAndroid.bench(pp, tg, pl, nr) val end = System.nanoTime() messages += warmupResult @@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { return@launch } - messages += llm.bench(512, 128, 1, 3) + messages += llamaAndroid.bench(512, 128, 1, 3) } catch (exc: IllegalStateException) { Log.e(tag, "bench() failed", exc) messages += exc.message!! @@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { fun load(pathToModel: String) { viewModelScope.launch { try { - llm.load(pathToModel) + llamaAndroid.load(pathToModel) messages += "Loaded $pathToModel" } catch (exc: IllegalStateException) { Log.e(tag, "load() failed", exc) diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts index 50ebc8211..acd1ada7d 100644 --- a/examples/llama.android/build.gradle.kts +++ b/examples/llama.android/build.gradle.kts @@ -2,4 +2,5 @@ plugins { id("com.android.application") version "8.2.0" apply false id("org.jetbrains.kotlin.android") version "1.9.0" apply false + id("com.android.library") version "8.2.0" apply false } diff --git a/examples/llama.android/llama/.gitignore b/examples/llama.android/llama/.gitignore new file mode 100644 index 000000000..796b96d1c --- /dev/null +++ b/examples/llama.android/llama/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/CMakeLists.txt similarity index 98% rename from examples/llama.android/app/src/main/cpp/CMakeLists.txt rename to examples/llama.android/llama/CMakeLists.txt index 4536974a5..a5618cac0 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/llama/CMakeLists.txt @@ -42,7 +42,7 @@ add_subdirectory(../../../../../../ build-llama) # used in the AndroidManifest.xml file. add_library(${CMAKE_PROJECT_NAME} SHARED # List C/C++ source files with relative paths to this CMakeLists.txt. - llama-android.cpp) + llama-android.cpp) # Specifies libraries CMake should link to your target library. You # can link libraries from various origins, such as libraries defined in this diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts new file mode 100644 index 000000000..0a3806172 --- /dev/null +++ b/examples/llama.android/llama/build.gradle.kts @@ -0,0 +1,68 @@ +plugins { + id("com.android.library") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "android.llama.cpp" + compileSdk = 34 + + defaultConfig { + minSdk = 33 + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + consumerProguardFiles("consumer-rules.pro") + ndk { + // Add NDK properties if wanted, e.g. + // abiFilters += listOf("arm64-v8a") + } + externalNativeBuild { + cmake { + arguments += "-DCMAKE_BUILD_TYPE=Release" + cppFlags += listOf() + arguments += listOf() + + cppFlags("") + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + externalNativeBuild { + cmake { + path("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.appcompat:appcompat:1.6.1") + implementation("com.google.android.material:material:1.11.0") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") +} diff --git a/examples/llama.android/llama/consumer-rules.pro b/examples/llama.android/llama/consumer-rules.pro new file mode 100644 index 000000000..e69de29bb diff --git a/examples/llama.android/llama/proguard-rules.pro b/examples/llama.android/llama/proguard-rules.pro new file mode 100644 index 000000000..f1b424510 --- /dev/null +++ b/examples/llama.android/llama/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt new file mode 100644 index 000000000..05d6ab5d2 --- /dev/null +++ b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt @@ -0,0 +1,24 @@ +package android.llama.cpp + +import androidx.test.platform.app.InstrumentationRegistry +import androidx.test.ext.junit.runners.AndroidJUnit4 + +import org.junit.Test +import org.junit.runner.RunWith + +import org.junit.Assert.* + +/** + * Instrumented test, which will execute on an Android device. + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +@RunWith(AndroidJUnit4::class) +class ExampleInstrumentedTest { + @Test + fun useAppContext() { + // Context of the app under test. + val appContext = InstrumentationRegistry.getInstrumentation().targetContext + assertEquals("android.llama.cpp.test", appContext.packageName) + } +} diff --git a/examples/llama.android/llama/src/main/AndroidManifest.xml b/examples/llama.android/llama/src/main/AndroidManifest.xml new file mode 100644 index 000000000..8bdb7e14b --- /dev/null +++ b/examples/llama.android/llama/src/main/AndroidManifest.xml @@ -0,0 +1,4 @@ + + + + diff --git a/examples/llama.android/llama/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt new file mode 100644 index 000000000..42ebaad49 --- /dev/null +++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt @@ -0,0 +1,49 @@ +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html. +# For more examples on how to use CMake, see https://github.com/android/ndk-samples. + +# Sets the minimum CMake version required for this project. +cmake_minimum_required(VERSION 3.22.1) + +# Declares the project name. The project name can be accessed via ${ PROJECT_NAME}, +# Since this is the top level CMakeLists.txt, the project name is also accessible +# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level +# build script scope). +project("llama-android") + +include(FetchContent) +FetchContent_Declare( + llama + GIT_REPOSITORY https://github.com/ggerganov/llama.cpp + GIT_TAG master +) + +# Also provides "common" +FetchContent_MakeAvailable(llama) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. +# +# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define +# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} +# is preferred for the same purpose. +# +# In order to load a library into your app from Java/Kotlin, you must call +# System.loadLibrary() and pass the name of the library defined here; +# for GameActivity/NativeActivity derived applications, the same library name must be +# used in the AndroidManifest.xml file. +add_library(${CMAKE_PROJECT_NAME} SHARED + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) + +# Specifies libraries CMake should link to your target library. You +# can link libraries from various origins, such as libraries defined in this +# build script, prebuilt third-party libraries, or Android system libraries. +target_link_libraries(${CMAKE_PROJECT_NAME} + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp similarity index 92% rename from examples/llama.android/app/src/main/cpp/llama-android.cpp rename to examples/llama.android/llama/src/main/cpp/llama-android.cpp index 4af9de303..874158ef0 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) { extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { +Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) { llama_model_params model_params = llama_model_default_params(); auto path_to_model = env->GetStringUTFChars(filename, 0); @@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { +Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) { llama_free_model(reinterpret_cast(model)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { +Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) { auto model = reinterpret_cast(jmodel); if (!model) { @@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) { llama_free(reinterpret_cast(context)); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) { llama_backend_free(); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) { llama_log_set(log_callback, NULL); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_bench_1model( +Java_android_llama_cpp_LLamaAndroid_bench_1model( JNIEnv *env, jobject, jlong context_pointer, @@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { +Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { llama_batch_free(*reinterpret_cast(batch_pointer)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { +Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. @@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) { llama_backend_init(); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { +Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) { return env->NewStringUTF(llama_print_system_info()); } extern "C" JNIEXPORT jint JNICALL -Java_com_example_llama_Llm_completion_1init( +Java_android_llama_cpp_LLamaAndroid_completion_1init( JNIEnv *env, jobject, jlong context_pointer, @@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init( extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_completion_1loop( +Java_android_llama_cpp_LLamaAndroid_completion_1loop( JNIEnv * env, jobject, jlong context_pointer, @@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { llama_kv_cache_clear(reinterpret_cast(context)); } diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt similarity index 97% rename from examples/llama.android/app/src/main/java/com/example/llama/Llm.kt rename to examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt index d86afee37..6c63e54e0 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt +++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt @@ -1,4 +1,4 @@ -package com.example.llama +package android.llama.cpp import android.util.Log import kotlinx.coroutines.CoroutineDispatcher @@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext import java.util.concurrent.Executors import kotlin.concurrent.thread -class Llm { +class LLamaAndroid { private val tag: String? = this::class.simpleName private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } @@ -165,8 +165,8 @@ class Llm { } // Enforce only one instance of Llm. - private val _instance: Llm = Llm() + private val _instance: LLamaAndroid = LLamaAndroid() - fun instance(): Llm = _instance + fun instance(): LLamaAndroid = _instance } } diff --git a/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt new file mode 100644 index 000000000..cbbb974d3 --- /dev/null +++ b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt @@ -0,0 +1,17 @@ +package android.llama.cpp + +import org.junit.Test + +import org.junit.Assert.* + +/** + * Example local unit test, which will execute on the development machine (host). + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +class ExampleUnitTest { + @Test + fun addition_isCorrect() { + assertEquals(4, 2 + 2) + } +} diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts index 2ba32c4fa..c7c1a034a 100644 --- a/examples/llama.android/settings.gradle.kts +++ b/examples/llama.android/settings.gradle.kts @@ -15,3 +15,4 @@ dependencyResolutionManagement { rootProject.name = "LlamaAndroid" include(":app") +include(":llama") diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 413e433dd..74f021dec 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -54,10 +54,10 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldpv2 ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py path/to/MobileVLM-1.7B +python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B ``` 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k` diff --git a/examples/llava/README.md b/examples/llava/README.md index 4fb0cf381..8d1ae5270 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py ../llava-v1.5-7b --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown ``` Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. @@ -92,7 +92,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto 6) Then convert the model to gguf format: ```console -python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` 7) And finally we can run the llava-cli using the 1.6 model version: diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 45bdad689..ca3631384 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ +/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */ CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index a6d67e5d7..8c7dd2ae3 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -112,9 +112,12 @@ struct llava_context { struct llama_model * model = NULL; }; -static void show_additional_info(int /*argc*/, char ** argv) { - 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 void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\n example usage:\n"); + LOG_TEE("\n %s -m --mmproj --image --image [--temp 0.1] [-p \"describe the image in detail.\"]\n", argv[0]); + LOG_TEE("\n 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, const std::string & fname) { @@ -278,7 +281,7 @@ int main(int argc, char ** argv) { gpt_params params; if (!gpt_params_parse(argc, argv, params)) { - show_additional_info(argc, argv); + print_usage(argc, argv, params); return 1; } @@ -290,8 +293,7 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - gpt_print_usage(argc, argv, params); - show_additional_info(argc, argv); + print_usage(argc, argv, {}); return 1; } auto model = llava_init(¶ms); diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index f80f727a7..17cb4d5e5 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -1,3 +1,3 @@ --r ../../requirements/requirements-convert.txt +-r ../../requirements/requirements-convert-legacy-llama.txt pillow~=10.2.0 torch~=2.1.1 diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 9c3540b20..fb20ad93f 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -37,7 +37,8 @@ struct ngram_container { int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -174,7 +175,7 @@ int main(int argc, char ** argv) { // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/ diff --git a/examples/lookup/lookup-create.cpp b/examples/lookup/lookup-create.cpp index 1c230c966..d713f6f21 100644 --- a/examples/lookup/lookup-create.cpp +++ b/examples/lookup/lookup-create.cpp @@ -14,8 +14,10 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } + // init llama.cpp llama_backend_init(); llama_numa_init(params.numa); diff --git a/examples/lookup/lookup-stats.cpp b/examples/lookup/lookup-stats.cpp index 87ecc0a4f..0b171c872 100644 --- a/examples/lookup/lookup-stats.cpp +++ b/examples/lookup/lookup-stats.cpp @@ -16,6 +16,7 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index eebbd00a5..80ecd925d 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -15,6 +15,7 @@ int main(int argc, char ** argv){ gpt_params params; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -121,7 +122,7 @@ int main(int argc, char ** argv){ // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // print current draft sequence diff --git a/examples/main-cmake-pkg/README.md b/examples/main-cmake-pkg/README.md index edf20d8db..a88e92f23 100644 --- a/examples/main-cmake-pkg/README.md +++ b/examples/main-cmake-pkg/README.md @@ -8,16 +8,14 @@ Because this example is "outside of the source tree", it is important to first b ### Considerations -When hardware acceleration libraries are used (e.g. CUDA, Metal, CLBlast, etc.), CMake must be able to locate the associated CMake package. In the example below, when building _main-cmake-pkg_ notice the `CMAKE_PREFIX_PATH` includes the Llama CMake package location _in addition to_ the CLBlast package—which was used when compiling _llama.cpp_. +When hardware acceleration libraries are used (e.g. CUDA, Metal, etc.), CMake must be able to locate the associated CMake package. ### Build llama.cpp and install to C:\LlamaCPP directory -In this case, CLBlast was already installed so the CMake package is referenced in `CMAKE_PREFIX_PATH`. - ```cmd git clone https://github.com/ggerganov/llama.cpp cd llama.cpp -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 -B build -DBUILD_SHARED_LIBS=OFF -G "Visual Studio 17 2022" -A x64 cmake --build build --config Release cmake --install build --prefix C:/LlamaCPP ``` @@ -27,7 +25,7 @@ cmake --install build --prefix C:/LlamaCPP ```cmd cd ..\examples\main-cmake-pkg -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 -B build -DBUILD_SHARED_LIBS=OFF -DCMAKE_PREFIX_PATH="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/main/README.md b/examples/main/README.md index ee930f4e7..4eaa68475 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -53,13 +53,13 @@ The following command generates "infinite" text from a starting prompt (you can #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin --ignore-eos -n -1 --random-prompt +./main -m models/7B/ggml-model.bin --ignore-eos -n -1 ``` #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 --random-prompt +main.exe -m models\7B\ggml-model.bin --ignore-eos -n -1 ``` ## Common Options @@ -80,7 +80,6 @@ The `main` program provides several ways to interact with the LLaMA models using - `--prompt PROMPT`: Provide a prompt directly as a command-line option. - `--file FNAME`: Provide a file containing a prompt or multiple prompts. - `--interactive-first`: Run the program in interactive mode and wait for input right away. (More on this below.) -- `--random-prompt`: Start with a randomized prompt. ## Interaction diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 832b51ee0..b97b7b793 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -60,9 +60,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -80,7 +80,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -88,8 +88,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -122,8 +122,10 @@ int main(int argc, char ** argv) { g_params = ¶ms; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } + llama_sampling_params & sparams = params.sparams; #ifndef LOG_DISABLE_LOGS @@ -180,9 +182,6 @@ int main(int argc, char ** argv) { LOG_TEE("%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } LOG("%s: llama backend init\n", __func__); llama_backend_init(); @@ -219,7 +218,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } std::string path_session = params.path_prompt_cache; @@ -250,11 +249,8 @@ int main(int argc, char ** argv) { std::vector embd_inp; - if (params.interactive_first || params.instruct || params.chatml || !params.prompt.empty() || session_tokens.empty()) { + if (params.interactive_first || !params.prompt.empty() || session_tokens.empty()) { LOG("tokenize the prompt\n"); - if (params.chatml) { - params.prompt = "<|im_start|>system\n" + params.prompt + "<|im_end|>"; - } embd_inp = ::llama_tokenize(ctx, params.prompt, true, true); } else { LOG("use session tokens\n"); @@ -332,37 +328,13 @@ int main(int argc, char ** argv) { } // number of tokens to keep when resetting context - if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size() || params.instruct || params.chatml) { + if (params.n_keep < 0 || params.n_keep > (int) embd_inp.size()) { params.n_keep = (int)embd_inp.size(); } else { params.n_keep += add_bos; // always keep the BOS token } - // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true, true); - const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false, true); - - LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx).c_str()); - LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx).c_str()); - - // chatml prefix & suffix - const auto cml_pfx = ::llama_tokenize(ctx, "\n<|im_start|>user\n", true, true); - const auto cml_sfx = ::llama_tokenize(ctx, "<|im_end|>\n<|im_start|>assistant\n", false, true); - - LOG("cml_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_pfx).c_str()); - LOG("cml_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, cml_sfx).c_str()); - - // in instruct mode, we inject a prefix and a suffix to each input by the user - if (params.instruct) { - params.interactive_first = true; - params.antiprompt.emplace_back("### Instruction:\n\n"); - } - // similar for chatml mode - else if (params.chatml) { - params.interactive_first = true; - params.antiprompt.emplace_back("<|im_start|>user\n"); - } - else if (params.conversation) { + if (params.conversation) { params.interactive_first = true; } @@ -474,12 +446,12 @@ int main(int argc, char ** argv) { LOG_TEE("\n\n"); if (params.interactive) { - const char *control_message; + const char * control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to the AI, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to the AI.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } @@ -740,18 +712,26 @@ 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, !params.conversation); - printf("%s", token_str.c_str()); + const std::string token_str = llama_token_to_piece(ctx, id, params.special); + // Console/Stream Output + fprintf(stdout, "%s", token_str.c_str()); + + // Record Displayed Tokens To Log + // Note: Generated tokens are created one by one hence this check if (embd.size() > 1) { + // Incoming Requested Tokens input_tokens.push_back(id); } else { + // Outgoing Generated Tokens output_tokens.push_back(id); output_ss << token_str; } + + fflush(stdout); } - fflush(stdout); } + // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); @@ -815,15 +795,13 @@ int main(int argc, char ** argv) { is_interacting = true; printf("\n"); - } else if (params.instruct || params.chatml) { - is_interacting = true; } } if (n_past > 0 && is_interacting) { LOG("waiting for user input\n"); - if (params.conversation || params.instruct || params.chatml) { + if (params.conversation) { printf("\n> "); } @@ -866,24 +844,12 @@ int main(int argc, char ** argv) { const size_t original_size = embd_inp.size(); - // instruct mode: insert instruction prefix - if (params.instruct && !is_antiprompt) { - LOG("inserting instruction prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); - } - // chatml mode: insert user chat prefix - if (params.chatml && !is_antiprompt) { - LOG("inserting chatml prefix\n"); - n_consumed = embd_inp.size(); - embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end()); - } if (params.escape) { - process_escapes(buffer); + string_process_escapes(buffer); } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); - const auto line_inp = ::llama_tokenize(ctx, buffer, false, params.interactive_specials); + const auto line_inp = ::llama_tokenize(ctx, buffer, false, false); 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()); @@ -892,17 +858,6 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); embd_inp.insert(embd_inp.end(), line_sfx.begin(), line_sfx.end()); - // instruct mode: insert response suffix - if (params.instruct) { - LOG("inserting instruction suffix\n"); - embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); - } - // chatml mode: insert assistant chat suffix - if (params.chatml) { - LOG("inserting chatml suffix\n"); - embd_inp.insert(embd_inp.end(), cml_sfx.begin(), cml_sfx.end()); - } - for (size_t i = original_size; i < embd_inp.size(); ++i) { const llama_token token = embd_inp[i]; output_tokens.push_back(token); @@ -927,7 +882,7 @@ int main(int argc, char ** argv) { } // end of generation - if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.instruct || params.interactive || params.chatml)) { + if (!embd.empty() && llama_token_is_eog(model, embd.back()) && !(params.interactive)) { LOG_TEE(" [end of text]\n"); break; } diff --git a/examples/make-ggml.py b/examples/make-ggml.py deleted file mode 100755 index c73485ebf..000000000 --- a/examples/make-ggml.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -""" -This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them. - -Usage: -python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] - -Arguments: -- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. -- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox. -- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. -- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. -- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. -- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. - -Old quant types (some base model types require these): -- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M -- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L -- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M -- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M - -New quant types (recommended): -- Q2_K: smallest, extreme quality loss - not recommended -- Q3_K: alias for Q3_K_M -- Q3_K_S: very small, very high quality loss -- Q3_K_M: very small, very high quality loss -- Q3_K_L: small, substantial quality loss -- Q4_K: alias for Q4_K_M -- Q4_K_S: small, significant quality loss -- Q4_K_M: medium, balanced quality - recommended -- Q5_K: alias for Q5_K_M -- Q5_K_S: large, low quality loss - recommended -- Q5_K_M: large, very low quality loss - recommended -- Q6_K: very large, extremely low quality loss -- Q8_0: very large, extremely low quality loss - not recommended -- F16: extremely large, virtually no quality loss - not recommended -- F32: absolutely huge, lossless - not recommended -""" -import subprocess -subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) - -import argparse -import os -from huggingface_hub import snapshot_download - -def main(model, model_type, outname, outdir, quants, keep_fp16): - if not os.path.isdir(model): - print(f"Model not found at {model}. Downloading...") - try: - if outname is None: - outname = model.split('/')[-1] - model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') - except Exception as e: - raise Exception(f"Could not download the model: {e}") - - if outdir is None: - outdir = f'../models/{outname}' - - if not os.path.isfile(f"{model}/config.json"): - raise Exception(f"Could not find config.json in {model}") - - os.makedirs(outdir, exist_ok=True) - - print("Building llama.cpp") - subprocess.run(f"cd .. && make quantize", shell=True, check=True) - - fp16 = f"{outdir}/{outname}.gguf.fp16.bin" - - print(f"Making unquantised GGUF at {fp16}") - if not os.path.isfile(fp16): - if model_type != "llama": - subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True) - else: - subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) - else: - print(f"Unquantised GGML already exists at: {fp16}") - - print("Making quants") - for type in quants: - outfile = f"{outdir}/{outname}.gguf.{type}.bin" - print(f"Making {type} : {outfile}") - subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) - - if not keep_fp16: - os.remove(fp16) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') - parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name') - parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.') - parser.add_argument('--outname', default=None, help='Output model(s) name') - parser.add_argument('--outdir', default=None, help='Output directory') - parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') - parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) - - args = parser.parse_args() - - main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 7c5595d6e..7faeaec97 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -100,7 +100,8 @@ int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } @@ -210,7 +211,7 @@ int main(int argc, char ** argv) { while (true) { if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } llama_batch_clear(batch); diff --git a/examples/passkey/README.md b/examples/passkey/README.md index 4a22bb559..9e7a119ba 100644 --- a/examples/passkey/README.md +++ b/examples/passkey/README.md @@ -8,5 +8,5 @@ See the following PRs for more info: ### Usage ```bash -make -j && ./passkey ./models/llama-7b-v2/ggml-model-f16.gguf 250 +make -j && ./passkey -m ./models/llama-7b-v2/ggml-model-f16.gguf --junk 250 ``` diff --git a/examples/passkey/passkey.cpp b/examples/passkey/passkey.cpp index f2ef9ca10..d03215cd1 100644 --- a/examples/passkey/passkey.cpp +++ b/examples/passkey/passkey.cpp @@ -6,46 +6,32 @@ #include #include +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf --junk 250 --pos 90 --keep 32 --grp-attn-n 2 [--seed 1234]\n", argv[0]); + LOG_TEE("\n"); +} + int main(int argc, char ** argv) { gpt_params params; - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH N_JUNK N_GRP I_POS SEED\n" , argv[0]); - return 1 ; + params.n_junk = 250; + params.n_keep = 32; + params.i_pos = -1; + + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } - int seed = -1; + srand(params.seed == LLAMA_DEFAULT_SEED ? time(NULL) : params.seed); - int n_junk = 250; // number of times to repeat the junk text - int n_keep = 32; // number of tokens in the prompt prefix - int n_grp = 1; // if more than 1 - perform LongLM SelfExtend - int i_pos = -1; // position of the passkey in the junk text - - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - n_junk = std::stoi(argv[2]); - } - - if (argc >= 4) { - n_grp = std::stoi(argv[3]); - } - - if (argc >= 5) { - i_pos = std::stoi(argv[4]); - } - - if (argc >= 6) { - seed = std::stoi(argv[5]); - } - - if (seed == -1) { - seed = time(NULL); - } - - srand(seed); + int n_junk = params.n_junk; + int n_keep = params.n_keep; + int n_grp = params.grp_attn_n; + int i_pos = params.i_pos; if (i_pos == -1) { i_pos = rand() % n_junk; @@ -76,9 +62,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - model_params.n_gpu_layers = 99; // offload all layers to the GPU + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -89,13 +73,9 @@ int main(int argc, char ** argv) { // initialize the context - llama_context_params ctx_params = llama_context_default_params(); + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); - ctx_params.seed = seed; - ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; - ctx_params.n_batch = 512; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + ctx_params.n_ctx = llama_n_ctx_train(model)*n_grp + n_keep; GGML_ASSERT(ctx_params.n_batch % n_grp == 0 && "n_batch must be divisible by n_grp"); @@ -135,7 +115,7 @@ int main(int argc, char ** argv) { LOG_TEE("prompt tokens: %d\n", n_tokens_all); //LOG_TEE("prompt: %s\n", params.prompt.c_str()); - llama_batch batch = llama_batch_init(512, 0, 1); + llama_batch batch = llama_batch_init(params.n_batch, 0, 1); int n_past = 0; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bae014e6f..0bd78c21a 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -44,9 +44,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -64,7 +64,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -72,9 +72,9 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_vector_float_yaml(logfile, "logits", results.logits); + yaml_dump_vector_float(logfile, "logits", results.logits); fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - dump_vector_float_yaml(logfile, "probs", results.probs); + yaml_dump_vector_float(logfile, "probs", results.probs); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -1032,7 +1032,7 @@ struct winogrande_entry { std::vector seq_tokens[2]; }; -static std::vector load_winogrande_from_csv(const std::string& prompt) { +static std::vector load_winogrande_from_csv(const std::string & prompt) { std::vector result; std::istringstream in(prompt); std::string line; @@ -1964,12 +1964,14 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { int main(int argc, char ** argv) { gpt_params params; + params.n_ctx = 512; + params.logits_all = true; + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } - params.logits_all = true; - const int32_t n_ctx = params.n_ctx; if (n_ctx <= 0) { @@ -2006,9 +2008,6 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); - if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); - } llama_backend_init(); llama_numa_init(params.numa); @@ -2027,6 +2026,7 @@ int main(int argc, char ** argv) { } const int n_ctx_train = llama_n_ctx_train(model); + if (params.n_ctx > n_ctx_train) { fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n", __func__, n_ctx_train, params.n_ctx); @@ -2035,7 +2035,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } struct results_perplexity results; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index cbb452334..28584e14b 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -259,7 +259,7 @@ int main(int argc, char ** argv) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { - if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) { + if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh index a3ca74c68..38e28ffc3 100644 --- a/examples/quantize/tests.sh +++ b/examples/quantize/tests.sh @@ -47,7 +47,7 @@ echo PASS echo # 3a. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --n-predict 32 echo PASS echo @@ -57,7 +57,7 @@ echo PASS echo # 4b. Test the requanted model is loading properly -$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --random-prompt --n-predict 32 +$MAIN --model $WORK_PATH/ggml-model-requant-merge.gguf --n-predict 32 echo PASS echo diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 5ba71e76a..55b7b2f70 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -4,72 +4,12 @@ #include #include -struct retrieval_params { - std::vector context_files; // context files to embed - int32_t chunk_size = 64; // chunk size for context embedding - std::string chunk_separator = "\n"; // chunk separator for context embedding -}; +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); -static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) { - gpt_print_usage(argc, argv, gpt_params); - printf("retrieval options:\n"); - printf(" --context-file FNAME file containing context to embed.\n"); - printf(" specify multiple files by providing --context-file option multiple times.\n"); - printf(" --chunk-size N minimum length of embedded text chunk (default:%d)\n", params.chunk_size); - printf(" --chunk-separator STRING\n"); - printf(" string to separate chunks (default: \"\\n\")\n"); - printf("\n"); -} - -static void retrieval_params_parse(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & retrieval_params) { - int i = 1; - std::string arg; - while (i < argc) { - arg = argv[i]; - bool invalid_gpt_param = false; - if(gpt_params_find_arg(argc, argv, argv[i], gpt_params, i, invalid_gpt_param)) { - if (invalid_gpt_param) { - fprintf(stderr, "error: invalid argument: %s\n", arg.c_str()); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - // option was parsed by gpt_params_find_arg - } else if (arg == "--context-file") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --context-file\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - // store the external file name in params - retrieval_params.context_files.push_back(argv[i]); - } else if (arg == "--chunk-size") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --chunk-size\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - retrieval_params.chunk_size = std::stoi(argv[i]); - } else if (arg == "--chunk-separator") { - if (++i >= argc) { - fprintf(stderr, "error: missing argument for --chunk-separator\n"); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - retrieval_params.chunk_separator = argv[i]; - } else { - // unknown argument - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - retrieval_params_print_usage(argc, argv, gpt_params, retrieval_params); - exit(1); - } - i++; - } + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s --model ./models/bge-base-en-v1.5-f16.gguf --top-k 3 --context-file README.md --context-file License --chunk-size 100 --chunk-separator .\n", argv[0]); + LOG_TEE("\n"); } struct chunk { @@ -171,33 +111,35 @@ static void batch_decode(llama_context * ctx, llama_batch & batch, float * outpu int main(int argc, char ** argv) { gpt_params params; - retrieval_params retrieval_params; - retrieval_params_parse(argc, argv, params, retrieval_params); + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; + } // For BERT models, batch size must be equal to ubatch size params.n_ubatch = params.n_batch; + params.embedding = true; - if (retrieval_params.chunk_size <= 0) { + if (params.chunk_size <= 0) { fprintf(stderr, "chunk_size must be positive\n"); return 1; } - if (retrieval_params.context_files.empty()) { + if (params.context_files.empty()) { fprintf(stderr, "context_files must be specified\n"); return 1; } - params.embedding = true; print_build_info(); printf("processing files:\n"); - for (auto & context_file : retrieval_params.context_files) { + for (auto & context_file : params.context_files) { printf("%s\n", context_file.c_str()); } std::vector chunks; - for (auto & context_file : retrieval_params.context_files) { - std::vector file_chunk = chunk_file(context_file, retrieval_params.chunk_size, retrieval_params.chunk_separator); + for (auto & context_file : params.context_files) { + std::vector file_chunk = chunk_file(context_file, params.chunk_size, params.chunk_separator); chunks.insert(chunks.end(), file_chunk.begin(), file_chunk.end()); } printf("Number of chunks: %ld\n", chunks.size()); @@ -226,7 +168,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // max batch size @@ -242,7 +184,7 @@ int main(int argc, char ** argv) { return 1; } // add eos if not present - if (inp.empty() || inp.back() != llama_token_eos(model)) { + if (llama_token_eos(model) >= 0 && (inp.empty() || inp.back() != llama_token_eos(model))) { inp.push_back(llama_token_eos(model)); } chunk.tokens = inp; diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 7c15d2aa4..62d828250 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -6,6 +6,10 @@ #include "ggml-metal.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #include "ggml-rpc.h" #ifdef _WIN32 # include @@ -79,6 +83,12 @@ static ggml_backend_t create_backend() { if (!backend) { fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); } +#elif GGML_USE_SYCL + fprintf(stderr, "%s: using SYCL backend\n", __func__); + backend = ggml_backend_sycl_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); + } #endif // if there aren't GPU Backends fallback to CPU backend diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index c3b766882..00c2277ac 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -11,6 +11,7 @@ int main(int argc, char ** argv) { params.prompt = "The quick brown fox"; if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 4b89c5302..dab709619 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -8,9 +8,20 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS + colorthemes.css + style.css + theme-beeninorder.css + theme-ketivah.css + theme-mangotango.css + theme-playground.css + theme-polarnight.css + theme-snowstorm.css index.html + index-new.html index.js completion.js + system-prompts.js + prompt-formats.js json-schema-to-grammar.mjs ) foreach(asset ${PUBLIC_ASSETS}) diff --git a/examples/server/public/colorthemes.css b/examples/server/public/colorthemes.css new file mode 100755 index 000000000..b1e2b8b70 --- /dev/null +++ b/examples/server/public/colorthemes.css @@ -0,0 +1,402 @@ +@import url("theme-snowstorm.css"); +@import url("theme-polarnight.css"); +@import url("theme-ketivah.css"); +@import url("theme-mangotango.css"); +@import url("theme-playground.css"); +@import url("theme-beeninorder.css"); + +:root { +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); +} + +/* + +.theme-template { + + + If light theme: should go from bright to darker + If dark theme: should go from dark to brighter + ideally this should not be anything but steps of + gray or slightly variants from it + + --primary-color-1: #2E3440; + --primary-color-2: #3B4252; + --primary-color-3: #434C5E; + --primary-color-4: #4C566A; + + + + If light theme: should go from dark to brighter + If dark theme: should go from bright to darker + ideally this should not be anything but steps of + gray or slightly variants from it + + --secondary-color-1: #ECEFF4; + --secondary-color-2: #E5E9F0; + --secondary-color-3: #D8DEE9; + --secondary-color-4: #C8CED9; + + + + Choose wisely nuance colors. It is not easy to find + 4 harmonizing nuance colors. But keep in mind, that + only one accent color could work too. + + --theme-nuance-color-1: #8FBCBB; + --theme-nuance-color-2: #88C0D0; + --theme-nuance-color-3: #81A1C1; + --theme-nuance-color-4: #5E81AC; + + + + adapt the color red, orange, yellow, green, + purple to the 'mood' of your overall design + e.g is it low-contrast? vibrant? dynamic? etc + + --theme-red-color: #BF616A; + --theme-orange-color: #D08770; + --theme-yellow-color: #EBCB8B; + --theme-green-color: #A3BE8C; + --theme-purple-color: #B48EAD; + + + +NOTE: comment all those line `--- ...` out +------------------------------------------------ +--background-color-1: +--background-color-2: +--background-color-3: +--background-color-4: + +--border-color-1: +--border-color-2: +--border-color-3: + +--border-focus-color: +--border-focus-shadow: + +--text-color-plain: +--text-color-subtile-1: +--text-color-subtile-2: + +--code-background-color: +--code-text-color: + +--ui-range-thumb-color: +--ui-range-thumb-border: + +--textarea-border-color: + + + +------------------------------------------- +--button-alert-text-hover: +--button-alert-color-hover: +--button-alert-border-hover: + +--button-alert-text-active: +--button-alert-color-active: +--button-alert-border-active: + + + +----------- PRIMARY ----------------------- +--button should immediately catch the eye-- + +--button-primary-text: +--button-primary-color: +--button-primary-border: + + +---------hover---------- +--button-primary-text-hover: +--button-primary-color-hover: +--button-primary-border-hover: + + +---------active--------- +--button-primary-text-active: +--button-primary-color-active: +--button-primary-border-active: + + + +------------ SECONDARY ------------------------ +--button should NOT immediately catch the eye-- + +--button-secondary-text: +--button-secondary-color: +--button-secondary-border: + + +---------hover---------- +--button-secondary-text-hover: +--button-secondary-color-hover: +--button-secondary-border-hover: + + +---------active--------- +--button-secondary-text-active: +--button-secondary-color-active: +--button-secondary-border-active: + + + +---------- TERTIARY ----------------------- +---------- disabled buttons --------------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + + +---------hover---------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + +} + +*/ diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html new file mode 100644 index 000000000..d571c2779 --- /dev/null +++ b/examples/server/public/index-new.html @@ -0,0 +1,1178 @@ + + + + + + + + + llama.cpp - chat + + + + + + + + + +
+ +
+
+ + + diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 2961999f2..2f60a76e8 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -12,6 +12,18 @@ font-size: 90%; } + .grid-container { + display: grid; + grid-template-columns: auto auto auto; + padding: 10px; + } + + .grid-item { + padding: 5px; + /* font-size: 30px; */ + text-align: center; + } + #container { margin: 0em auto; display: flex; @@ -35,6 +47,67 @@ padding: 0.5em; } + h1 { + text-align: center; + } + + .customlink:link { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + border-radius: 4px; + padding: 8px; + } + + .customlink:visited { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:hover { + color: white; + background-color: #0070ee; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:active { + color: #0070ee; + background-color: #80b3ef; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + body { max-width: 600px; min-width: 300px; @@ -594,7 +667,7 @@ message = html`<${Probabilities} data=${data} />` } else { const text = isArrayMessage ? - data.map(msg => msg.content).join('').replace(/^\s+/, '') : + data.map(msg => msg.content).join('') : data; message = isCompletionMode ? text : @@ -877,19 +950,30 @@ // poor mans markdown replacement const Markdownish = (params) => { - const md = params.text - .replace(/&/g, '&') - .replace(//g, '>') - .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

$2

') - .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/```.*?\n([\s\S]*?)```/g, '
$1
') - .replace(/`(.*?)`/g, '$1') - .replace(/\n/gim, '
'); - return html``; + const chunks = params.text.split('```'); + + for (let i = 0; i < chunks.length; i++) { + if (i % 2 === 0) { // outside code block + chunks[i] = chunks[i] + .replace(/&/g, '&') + .replace(//g, '>') + .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

$2

') + .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/```.*?\n([\s\S]*?)```/g, '
$1
') + .replace(/`(.*?)`/g, '$1') + .replace(/\n/gim, '
'); + } else { // inside code block + chunks[i] = `
${chunks[i]}
`; + } + } + + const restoredText = chunks.join(''); + + return html``; }; const ModelGenerationInfo = (params) => { @@ -903,6 +987,7 @@ ` } + // simple popover impl const Popover = (props) => { const isOpen = useSignal(false); @@ -1023,7 +1108,11 @@ return html`
-

llama.cpp

+
+
+

llama.cpp

+
New UI
+
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a/examples/server/public/prompt-formats.js b/examples/server/public/prompt-formats.js new file mode 100644 index 000000000..73ddb7187 --- /dev/null +++ b/examples/server/public/prompt-formats.js @@ -0,0 +1,331 @@ +// extended list +export const promptFormats = { + "alpaca": { + template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "chatml": { + template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`, + + historyTemplate: `<|im_start|>{{name}}\n{{message}}`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "<|im_end|>\n", + + stops: "" + }, + + // ---------------------------- + + "commandr": { + template: `<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`, + + historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`, + + char: "CHATBOT_TOKEN", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER_TOKEN", + userMsgPrefix: "", + userMsgSuffix: "<|END_OF_TURN_TOKEN|>", + + stops: "" + }, + // ref: https://docs.cohere.com/docs/prompting-command-r + + // ---------------------------- + + "llama2": { + template: `[INST] <>\n{{prompt}}\n<>\n\nTest Message [/INST] Test Successfull {{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "[INST] ", + userMsgSuffix: " [/INST]", + + stops: "" + }, + // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + + // ---------------------------- + + "llama3": { + template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`, + + historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|eot_id|>" + }, + // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3 + + // ---------------------------- + + "openchat": { + template: `{{history}}{{char}}`, + + historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "phi3": { + template: `{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|end|>" + }, + // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format + + // ---------------------------- + + "vicuna": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}\n`, + + char: "ASSISTANT", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1 + + // ---------------------------- + + "deepseekCoder": { + template: `{{prompt}}{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|EOT|>" + }, + + // ---------------------------- + + "med42": { + template: `<|system|>: {{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>: {{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "prompter", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "neuralchat": { + template: `### System:\n{{prompt}}\n{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "nousHermes": { + template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Input", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "openchatMath": { + template: `{{history}}{{char}}`, + + historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "orion": { + template: `Human: Test Message\n\nAssistant: Test Successful{{history}}{{char}}:`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant ", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "\n\n", + + stops: "" + }, + + // ---------------------------- + + "sauerkraut": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: ` + {{name}}: {{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "starlingCode": { + template: `{{history}}{{char}}`, + + historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "yi34b": { + template: `{{history}} {{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "zephyr": { + template: `<|system|>\n{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + } + }; diff --git a/examples/server/public/style.css b/examples/server/public/style.css new file mode 100755 index 000000000..087cc62da --- /dev/null +++ b/examples/server/public/style.css @@ -0,0 +1,954 @@ +@import url("colorthemes.css"); + +body { + font-family: 'Arial', sans-serif; + font-size: 90%; + background-color: var(--background-color-1); + color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */ + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + transition: background-color 0.3s; +} + +::selection { + color: var(--button-primary-text) ; + background: var(--button-primary-color); +} + +code, pre code { + font-family: 'Courier New', monospace; +} + +#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 var(--border-color-3); + border-radius: 5px; + padding: 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; + margin-bottom: 30px; +} + +.two-columns { + width: 97%; + max-width: 97%; + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1em; + position: relative; +} + +.json-schema-controls { + margin-top: 10px; + width: 100%; + max-width: 100%; + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.json-schema-controls > * { + flex: 1; +} + +/* titles of the details-summary boxes */ +.summary-title { + font-weight: 600; + font-size: x-small; + color: var(--text-color-subtile-1); + text-transform: uppercase; + /* transition: ; */ +} + +fieldset { + border: none; + padding: 0; + margin: 0; + color: var(--text-color-plain); +} + +fieldset.two { + display: grid; + grid-template: "a a a"; + gap: 1em; + align-items: center; + font-size: x-small; + color: var(--text-color-plain); +} + +fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + font-size: x-small; + color: var(--text-color-plain); +} + +/* titles of name fields*/ +fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* titles of params fields*/ +fieldset.params { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-4); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +fieldset.dropdowns { + -webkit-appearance: none; + display: flex; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: red; + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* input of name fields*/ +.names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.chat-id-color { + color: var(--chat-id-color); +} + +details { + border: 1px solid var(--border-color-2); + border-radius: 5px; + 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; +} + +textarea-sec, input-sec, button-sec { + padding: 10px; + height: 40px; + align-items: center; +} + +textarea-sec::placeholder, input-sec::placeholder { + padding-left: 10px; +} + +.toggleCheckbox { + display: none; +} + +.toggleContainer { + position: relative; + display: grid; + grid-template-columns: repeat(2, 1fr); + width: fit-content; + border: 3px solid var(--border-color-2); + border-radius: 20px; + background: var(--border-color-2); + font-size: small; + cursor: pointer; + overflow: hidden; +} + +/* toggle button current state */ +.toggleContainer::before { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + content: ''; + position: absolute; + width: 50%; + height: 100%; + left: 0%; + border-radius: 20px; + transition: all 0.3s; +} + +.toggleContainer div { + padding: 6px; + text-align: center; + z-index: 1; + transition: color 0.3s; +} + +.toggleCheckbox:checked + .toggleContainer::before { + left: 50%; +} + +.toggleCheckbox:checked + .toggleContainer div:first-child { + color: var(--text-color-subtile-2); +} + +.toggleCheckbox:checked + .toggleContainer div:last-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:first-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:last-child { + color: var(--text-color-subtile-2); +} + +select { + padding: 5px; + margin-right: 5px; + border-radius: 4px; + border: 1px solid var(--secondary-color-4); + background-color: var(--primary-color-3); + color: var(--secondary-color-4); + cursor: pointer; +} + +select:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 1px var(--border-focus-shadow); +} + +.button-container { + display: flex; + justify-content: flex-end; +} + +button { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 30px #ffffff; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +button:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + font-size: x-small; + font-weight: 600; +} + +button:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +button:disabled { + color: var(--button-tertiary-text); + background-color: var(--button-tertiary-color); + border: 1px solid var(--button-tertiary-border); + font-size: x-small; + font-weight: 600; + cursor: not-allowed; +} + +.reset-button { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + width: fit-content; + height: fit-content; + font-size: x-small; + font-weight: 600; + border-radius: 50px; + overflow: hidden; +} + +.reset-button:hover { + color: var(--button-alert-text-hover); + background-color: var(--button-alert-color-hover); + border: 1px solid var(--button-alert-border-hover); + font-size: x-small; + font-weight: 600; +} + +.reset-button:active { + color: var(--button-alert-text-active); + background-color: var(--button-alert-color-active); + border: 1px solid var(--button-alert-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-grammar { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-back { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +.button-back:hover { + color: var(--button-secondary-text-hover); + background-color: var(--button-secondary-color-hover); + border: 1px solid var(--button-secondary-border-hover); + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 4px 2px; + transition: background-color 0.1s; + cursor: pointer; + border-radius: 12px; +} + +.button-back:active { + color: var(--button-secondary-text-active); + background-color: var(--button-secondary-color-active); + border: 1px solid var(--button-secondary-border-active); + font-size: x-small; + font-weight: 600; +} + +.prob-set { + padding: 0.3em; + border-bottom: 1px solid red; /* unknown */ +} + +.popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 13px rgba(0, 0, 0, 0.1); +} + +.grammar { + width: 97%; + max-width: 97%; +} + +textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + max-width: 100%; + border-radius: 8px; + border: 1px solid var(--border-color-1); + resize: none; + height: 6em; +} + +textarea:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* "props" frame */ +input[type="text"], +input[type="range"] { + padding: 5px; + border-radius: 8px; + border: 1px solid var(--border-color-1); +} + +/* "names and props" frame focused*/ +input[type="text"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +input[type="range"]:hover { + opacity: 1; +} + +input[type="range"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); + background-size: var(--slider-track-size-focus); +} + +input[type="range"]::-moz-range-thumb { + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"] { + -webkit-appearance: none; + width: 80%; + height: 1px; + border: 1px solid var(--border-color-1); + border-radius: 8px; + background: var(--border-color-2); + outline: none; + opacity: 0.7; + -webkit-transition: .2s; + transition: opacity .2s; +} + +input[type="range"]::-webkit-slider-thumb { + -webkit-appearance: none; + appearance: none; + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"]::-webkit-slider-runnable-track { + background-size: var(--slider-track-size); +} + +input[type="radio"] { + accent-color: var(--theme-nuance-color-2); +} + +.chat-input-container { + position: relative; + max-width: 97%; + min-width: 97%; +} + +.chat-input-label { + position: absolute; + top: 0; + left: 0; + color: var(--text-color-plain); + pointer-events: none; + margin-left: 5px; + margin-top: 5px; +} + +textarea#chat-input { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#chat-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.input-container { + position: relative; + box-sizing: border-box; + width: 100%; /* Setzt die Breite auf 100% */ + max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */ +} + +.input-container:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} +/* titles of name fields*/ +/* fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} */ + +/* input of name fields*/ +/* .names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} */ + +fieldset.apiKey { + width: 100%; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.apiKey { + font-family: Arial, sans-serif; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.apiKey input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey label { + display: inline-block; + width: auto; + margin-right: 5px; +} + +textarea#api_key { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#api_key:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* embedded title of the system prompt text area */ +.input-label { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + border-radius: 8px 8px 0px 0px; + padding-top: 10px; + padding-left: 13px; + padding-right: 0px; + margin-top: 1px; + margin-left: 1px; + margin-right: 20px; + text-transform: uppercase; + font-weight: 600; + font-size: small; + background: rgba(255, 255, 255, 0.5); + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); /* for safari */ + width: 97%; + /* display: block; + box-sizing: border-box; */ +} + +/* embedded title of the prompt style areas */ +.input-label-sec { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + margin-left: 13px; + margin-top: 16px; + text-transform: uppercase; + font-weight: 600; + font-size: x-small; +} + +/* system prompt input area */ +textarea.persistent-input { + padding-top: 42px; + padding-left: 11px; + width: 97%; + max-width: 97%; + height: 50px; + font-size: medium; + overscroll-behavior: contain; +} + +/* system prompt box */ +.persistent-input { + height: auto; + width: 100%; + max-width: 100%; + min-height: 50px; + padding: 3px; + transition: min-height 0.3s ease; +} + +/* chat history box */ +.persistent-input:focus { + height: auto; + min-height: 150px; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +textarea.persistent-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* prompt style input area */ +textarea.persistent-input-sec { + width: 97%; + max-width: 97%; + padding-top: 42px; + padding-left: 11px; + font-size: small; + border: 1px solid var(--border-color-1); + overscroll-behavior: contain; +} + +textarea.persistent-input-sec:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* chat history box */ +.persistent-input-sec { + height: auto; + min-height: 150px; +} + +img { + border-radius: 8px; + display: block; + margin-left: auto; + margin-right: auto; + width: 50%; +} + +/* code area background */ +pre code { + display: block; + background-color: var(--code-background-color); + color: var(--code-text-color); + padding: 0.2em 0.2em; + border-radius: 5px; +} + +/* code area text */ +code { + font-family: monospace; + font-weight: bold; + padding: 0.1em 0.3em; + border-radius: 5px; +} + +fieldset label { + margin: 0.5em 0; + display: block; +} + +fieldset label.slim { + margin: 0 0.5em; + display: inline; +} + +header { + display: flex; + justify-content: space-between; + align-items: center; + text-align: center; + padding-left: 15px; +} + +.generation-statistics:hover { + color: var(--theme-nuance-color-4); + cursor: default; +} + +footer { + font-size: 80%; + color: var(--background-color-3); + text-align: center; + cursor: default; +} + +footer a { + color: var(--background-color-4); /* Color of the link */ + text-decoration: none; /* No underlining */ + font-weight: bold; /* Bold print */ +} + +footer a:hover { + color: var(--theme-nuance-color-4); /* Color of the link when hovering */ + text-decoration: underline; /* Underlining when hovering */ +} + +.mode-chat textarea[name=prompt] { + height: 8.5em; + border: 1px solid var(--primary-color-3); +} + +.mode-completion textarea[name=prompt] { + height: 30em; + border: 1px solid var(--primary-color-3); +} + +@keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + 100% { + background-position: 100%; + } +} + +.loading { + 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; +} + +.dropbtn { + color: var(--button-primary-color); + background-color: var(--background-color-1); + border: 1px solid var(--background-color-1); + transition: background-color 0.1s; + border-radius: 4px 4px 0px 0px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 2px #99999990; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 5px 20px; + display: inline-block; + cursor: pointer; + top: 0; +} + +.dropbtn svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-color); +} + +.dropbtn:hover svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-text); +} + +.dropbtn:focus { + outline: none; /* Removes the blue border that appears when the button is focused */ +} + +.dropdown { + position: relative; + display: inline-block; +} + +.dropdown-content { + /* display: none; */ + position: absolute; + right: 0; + text-align: end; + color: var(--button-secondary-color); + background-color: var(--text-color-subtile-2); + border-radius: 4px 4px 4px 4px; + min-width: 160px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + z-index: 1; + /* Verstecke den Inhalt sofort */ + opacity: 0; + visibility: hidden; + /* übergangsverzögerung für das Verschwinden */ + transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out; + transition-delay: 0.2s; +} + +#dropdown-content {transition-timing-function: ease;} + +.dropdown-content:hover { + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a { + color: var(--border-color-2); + padding: 12px 16px; + border-radius: 4px 4px 4px 4px; + text-decoration: none; + display: block; + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a:hover { + color: var(--border-color-2); + background-color: var(--text-color-subtile-1); + font-weight: 600; +} + +.dropdown:hover .dropdown-content { + /* display: block; */ + border-radius: 4px 4px 4px 4px; + /* Übergang ohne Verzögerung für das Erscheinen */ + opacity: 1; + visibility: visible; + transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s; +} + +.dropdown:hover .dropbtn { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + font-size: x-small; + font-weight: 600; + stroke: var(--button-primary-text); +} + +.dropdown:hover .dropbtn svg{ + stroke: var(--button-primary-text); +} + +/* .dropdown:active .dropbtn { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; + background-color: var(-background-color-4); +} */ + +/* .omni { + display: flex; + justify-content: space-between; + align-items: center; + padding: 0.5em; + border: 1px solid var(--border-color-3); + border-radius: 5px; + margin: 0.5em 0; +} */ diff --git a/examples/server/public/system-prompts.js b/examples/server/public/system-prompts.js new file mode 100644 index 000000000..f7df7d648 --- /dev/null +++ b/examples/server/public/system-prompts.js @@ -0,0 +1,68 @@ +export const systemPrompts = { + default: { + systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision." + }, + empty: { + systemPrompt: "" + }, + airoboros: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request." + }, + alpaca: { + systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request." + }, + atlas: { + systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)." + }, + atlas_de: { + systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)." + }, + commandrempty: { + systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n" + }, + commandrexample: { + systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available." + }, + cot: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query." + }, + deduce: { + systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer." + }, + deepseekcoder: { + systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer." + }, + jordan: { + systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information." + }, + leomistral: { + systemPrompt: "Du bist ein hilfreicher Assistent." + }, + med42: { + systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE." + }, + mistralopenorca: { + systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!" + }, + migeltot: { + systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers." + }, + orcamini: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can." + }, + samantha: { + systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha." + }, + sauerkraut: { + systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten." + }, + scarlett: { + systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI." + }, + synthia: { + systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." + }, + vicuna: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input." + }, + }; diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public/theme-beeninorder.css new file mode 100755 index 000000000..f6e0e2900 --- /dev/null +++ b/examples/server/public/theme-beeninorder.css @@ -0,0 +1,228 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration was a batman wallpaper that i have on my phone */ + +.theme-beeninorder { + +--primary-color-1: hsl(202, 11%, 19%); +--primary-color-2: hsl(202, 11%, 23%); +--primary-color-3: hsl(201, 11%, 28%); +--primary-color-4: hsl(201, 11%, 40%); + +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-4: hsl(201, 11%, 60%); + + +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(201, 11%, 19%); + --primary-color-1-hue: 201; + --primary-color-1-saturation: 11%; + --primary-color-1-lightness: 19%; + +--primary-color-2: hsl(201, 11%, 23%); + --primary-color-2-hue: 201; + --primary-color-2-saturation: 11%; + --primary-color-2-lightness: 23%; + +--primary-color-3: hsl(201, 11%, 28%); + --primary-color-3-hue: 201; + --primary-color-3-saturation: 11%; + --primary-color-3-lightness: 28%; + +--primary-color-4: hsl(201, 11%, 40%); + --primary-color-4-hue: 201; + --primary-color-4-saturation: 11%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-1-hue: 201; +--secondary-color-1-saturation: 11%; +--secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-2-hue: 201; +--secondary-color-2-saturation: 11%; +--secondary-color-2-lightness: 74%; + +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-3-hue: 201; +--secondary-color-3-saturation: 11%; +--secondary-color-3-lightness: 67%; + +--secondary-color-4: hsl(201, 11%, 60%); +--secondary-color-4-hue: 201; +--secondary-color-4-saturation: 11%; +--secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-1-hue: 44.5; + --theme-nuance-color-1-saturation: 96.7%; + --theme-nuance-color-1-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-2-hue: 44.5; + --theme-nuance-color-2-saturation: 96.7%; + --theme-nuance-color-2-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-3-hue: 44.5; + --theme-nuance-color-3-saturation: 96.7%; + --theme-nuance-color-3-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-4-hue: 44.5; + --theme-nuance-color-4-saturation: 96.7%; + --theme-nuance-color-4-lightness: 52.9%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(232, 40%, 45%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(232, 30%, 40%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(201, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public/theme-ketivah.css new file mode 100755 index 000000000..ee80f3c14 --- /dev/null +++ b/examples/server/public/theme-ketivah.css @@ -0,0 +1,201 @@ +/* Author: Yazan Agha-Schrader */ + +.theme-ketivah { + + /* ---------- PRIMARY COLORS ----------------- */ + --primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + + --primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + + --primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + + --primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + /* ---------- SECONDARY COLORS --------------- */ + --secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + + --secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + + --secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + + --secondary-color-4: hsl(0, 0.0%, 36.1%); + --secondary-color-4-hue: 0.0; + --secondary-color-4-saturation: 0.0%; + --secondary-color-4-lightness: 36.1%; + + /* ----------- NUANCES COLORS ---------------- */ + --theme-nuance-color-1: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + + --theme-nuance-color-2: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + + --theme-nuance-color-3: hsl(165.2, 0%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + + --theme-nuance-color-4: hsl(164.9, 0%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + /* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(0.3, 80.0%, 50.0%); + --theme-orange-color: #e76f51; + --theme-yellow-color: hsl(60, 70.6%, 73.3%); + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(0.3, 70.0%, 45.0%); + + /* ------------------------------------------- */ + --background-color-1: var(--primary-color-1); + --background-color-2: var(--primary-color-2); + --background-color-3: var(--primary-color-3); + --background-color-4: var(--primary-color-4); + + --border-color-1: var(--primary-color-2); + --border-color-2: var(--primary-color-3); + --border-color-3: var(--primary-color-4); + + --border-focus-color: var(--theme-nuance-color-2); + --border-focus-shadow: var(--theme-nuance-color-1); + + --text-color-plain: var(--secondary-color-1); + --text-color-subtile-1: var(--secondary-color-2); + --text-color-subtile-2: var(--secondary-color-3); + + --code-background-color: var(--secondary-color-2); + --code-text-color: var(--primary-color-2); + + --ui-range-thumb-color: var(--primary-color-4); + --ui-range-thumb-border: var(--ui-ranger-thumb-color); + + --textarea-border-color: var(--secondary-color-4); + + --chat-id-color: var(--theme-nuance-color-4); + + /* ------------------------------------------- */ + --button-alert-text-hover: var(--primary-color-1); + --button-alert-color-hover: var(--theme-purple-color); + --button-alert-border-hover: var(--theme-purple-color); + + --button-alert-text-active: var(--primary-color-1); + --button-alert-color-active: var(--theme-red-color); + --button-alert-border-active: var(--theme-red-color); + + /* ----------- PRIMARY BUTTONS --------------- */ + /* - button should immediately catch the eye - */ + --button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color: var(--theme-nuance-color-3); + --button-primary-border: var(--theme-nuance-color-3); + + /* ---------hover---------- */ + --button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + --button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + /* ---------active--------- */ + --button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + + --button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + --button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + /* ---------- SECONDARY BUTTONS -------------- */ + /* these should NOT immediately catch the eye */ + --button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + + --button-secondary-color: var(--primary-color-3); + --button-secondary-border: var(--primary-color-3); + + /* ---------hover---------- */ + --button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-hover: var(--primary-color-4); + --button-secondary-border-hover: var(--primary-color-4); + + /* ---------active--------- */ + --button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + --button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + /* ---------- TERTIARY BUTTONS --------------- */ + /* ---------- disabled buttons --------------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + /* ---------hover---------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + } diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public/theme-mangotango.css new file mode 100755 index 000000000..e43380245 --- /dev/null +++ b/examples/server/public/theme-mangotango.css @@ -0,0 +1,216 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */ + +.theme-mangotango { + +--primary-color-1: hsl(192, 8.5%, 11.6%); +--primary-color-2: hsl(192, 8.5%, 21%); +--primary-color-3: hsl(192, 8.5%, 30%); +--primary-color-4: hsl(192, 8.5%, 40%); + +--secondary-color-1: hsl(192, 8.5%, 80%); +--secondary-color-2: hsl(192, 8.5%, 73%); +--secondary-color-3: hsl(192, 8.5%, 66%); +--secondary-color-4: hsl(192, 8.5%, 60%); + +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(192, 8.5%, 11.6%); + --primary-color-1-saturation: 8.5%; + --primary-color-1-lightness: 11.6%; + +--primary-color-2: hsl(192, 8.5%, 21%); + --primary-color-2-saturation: 8.5%; + --primary-color-2-lightness: 21%; + +--primary-color-3: hsl(192, 8.5%, 30%); + --primary-color-3-saturation: 8.5%; + --primary-color-3-lightness: 30%; + +--primary-color-4: hsl(192, 8.5%, 40%); + --primary-color-4-saturation: 8.5%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(192, 8.5%, 80%); + --secondary-color-1-saturation: 8.5%; + --secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(192, 8.5%, 73%); + --secondary-color-2-saturation: 8.5%; + --secondary-color-2-lightness: 73%; + +--secondary-color-3: hsl(192, 8.5%, 66%); + --secondary-color-3-saturation: 8.5%; + --secondary-color-3-lightness: 66%; + +--secondary-color-4: hsl(192, 8.5%, 60%); + --secondary-color-4-saturation: 8.5%; + --secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-1-saturation: 100%; + --theme-nuance-color-1-lightness: 60.2%; + +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-2-saturation: 100%; + --theme-nuance-color-2-lightness: 60.2%; + +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-3-saturation: 100%; + --theme-nuance-color-3-lightness: 60.2%; + +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-4-saturation: 100%; + --theme-nuance-color-4-lightness: 60.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(325, 60%, 50%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-blue-color: hsl(192, 95%, 40%); + --theme-purple-color: hsl(192, 80%, 35%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-blue-color); +--button-alert-border-active: var(--theme-blue-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(192, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-playground.css b/examples/server/public/theme-playground.css new file mode 100755 index 000000000..9d56a7182 --- /dev/null +++ b/examples/server/public/theme-playground.css @@ -0,0 +1,221 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */ + +.theme-playground { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + +--primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + +--primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + +--primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + +--secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + +--secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + +--secondary-color-4: hsl(0, 0%, 36.1%); + --secondary-color-4-hue: 0; + --secondary-color-4-saturation: 0%; + --secondary-color-4-lightness: 36.1%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + +--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + +--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + +--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(0.3, 80%, 50%); +--theme-orange-color: #e76f51; +--theme-yellow-color: hsl(60, 70.6%, 73.3%); +--theme-green-color: #A3BE8C; +--theme-purple-color: hsl(0.3, 70%, 45%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--primary-color-4); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public/theme-polarnight.css new file mode 100755 index 000000000..2bcfb33d8 --- /dev/null +++ b/examples/server/public/theme-polarnight.css @@ -0,0 +1,253 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-polarnight { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(220.0, 16.4%, 21.6%) ; + --primary-color-1-hue: 220.0; + --primary-color-1-saturation: 16.4%; + --primary-color-1-lightness: 21.6%; + +--primary-color-2: hsl(221.7, 16.3%, 27.6%) ; + -primary-color-2-hue: 221.7; + --primary-color-2-saturation: 16.3%; + --primary-color-2-lightness: 27.6%; + +--primary-color-3: hsl(220.0, 16.8%, 31.6%) ; + --primary-color-3-hue: 220.0; + --primary-color-3-saturation: 16.8%; + --primary-color-3-lightness: 31.6%; + +--primary-color-4: hsl(220.0, 16.5%, 35.7%); + --primary-color-4-hue: 220.0; + --primary-color-4-saturation: 16.5%; + --primary-color-4-lightness: 35.7%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(217.5, 26.7%, 94.1%); + --secondary-color-1-hue: 217.5; + --secondary-color-1-saturation: 26.7%; + --secondary-color-1-lightness: 94.1%; + +--secondary-color-2: hsl(218.2, 26.8%, 92.0%); + --secondary-color-2-hue: 218.2; + --secondary-color-2-saturation: 26.8%; + --secondary-color-2-lightness: 92.0%; + +--secondary-color-3: hsl(218.8, 27.9%, 88.0%); + --secondary-color-3-hue: 218.8; + --secondary-color-3-saturation: 27.9%; + --secondary-color-3-lightness: 88.0%; + +--secondary-color-4: hsl(218.8, 18.3%, 81.8%); + --secondary-color-4-hue: 218.8; + --secondary-color-4-saturation: 18.3%; + --secondary-color-4-lightness: 81.8%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(354.3, 42.3%, 56.5%); +--theme-orange-color: hsl(20, 85%, 50%); +--theme-yellow-color: hsl(20, 75%, 45%); +--theme-green-color: hsl( 92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------------ */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-yellow-color); +--button-alert-border-hover: var(--theme-yellow-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-orange-color); +--button-alert-border-active: var(--theme-orange-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) - 35%), + calc(var(--secondary-color-1-lightness) + 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 25%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public/theme-snowstorm.css new file mode 100755 index 000000000..7bb227594 --- /dev/null +++ b/examples/server/public/theme-snowstorm.css @@ -0,0 +1,251 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-snowstorm { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public_simplechat/datautils.mjs b/examples/server/public_simplechat/datautils.mjs new file mode 100644 index 000000000..75159d6b1 --- /dev/null +++ b/examples/server/public_simplechat/datautils.mjs @@ -0,0 +1,266 @@ +//@ts-check +// Helpers to work with different data types +// by Humans for All +// + +/** + * Given the limited context size of local LLMs and , many a times when context gets filled + * between the prompt and the response, it can lead to repeating text garbage generation. + * And many a times setting penalty wrt repeatation leads to over-intelligent garbage + * repeatation with slight variations. These garbage inturn can lead to overloading of the + * available model context, leading to less valuable response for subsequent prompts/queries, + * if chat history is sent to ai model. + * + * So two simple minded garbage trimming logics are experimented below. + * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and + * * another based on char-histogram-driven garbage trimming. + * * in future characteristic of histogram over varying lengths could be used to allow for + * a more aggressive and adaptive trimming logic. + */ + + +/** + * Simple minded logic to help remove repeating garbage at end of the string. + * The repeatation needs to be perfectly matching. + * + * The logic progressively goes on probing for longer and longer substring based + * repeatation, till there is no longer repeatation. Inturn picks the one with + * the longest chain. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number} maxMatchLenThreshold + */ +export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) { + let rCnt = [0]; + let maxMatchLen = maxSubL; + let iMML = -1; + for(let subL=1; subL < maxSubL; subL++) { + rCnt.push(0); + let i; + let refS = sIn.substring(sIn.length-subL, sIn.length); + for(i=sIn.length; i > 0; i -= subL) { + let curS = sIn.substring(i-subL, i); + if (refS != curS) { + let curMatchLen = rCnt[subL]*subL; + if (maxMatchLen < curMatchLen) { + maxMatchLen = curMatchLen; + iMML = subL; + } + break; + } + rCnt[subL] += 1; + } + } + console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt); + if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) { + return {trimmed: false, data: sIn}; + } + console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen); + let iEnd = sIn.length - maxMatchLen; + return { trimmed: true, data: sIn.substring(0, iEnd) }; +} + + +/** + * Simple minded logic to help remove repeating garbage at end of the string, till it cant. + * If its not able to trim, then it will try to skip a char at end and then trim, a few times. + * This ensures that even if there are multiple runs of garbage with different patterns, the + * logic still tries to munch through them. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number | undefined} [maxMatchLenThreshold] + */ +export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) { + let sCur = sIn; + let sSaved = ""; + let iTry = 0; + while(true) { + let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold); + if (got.trimmed != true) { + if (iTry == 0) { + sSaved = got.data; + } + iTry += 1; + if (iTry >= skipMax) { + return sSaved; + } + got.data = got.data.substring(0,got.data.length-1); + } else { + iTry = 0; + } + sCur = got.data; + } +} + + +/** + * A simple minded try trim garbage at end using histogram driven characteristics. + * There can be variation in the repeatations, as long as no new char props up. + * + * This tracks the chars and their frequency in a specified length of substring at the end + * and inturn checks if moving further into the generated text from the end remains within + * the same char subset or goes beyond it and based on that either trims the string at the + * end or not. This allows to filter garbage at the end, including even if there are certain + * kind of small variations in the repeated text wrt position of seen chars. + * + * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that + * a given type of char ie numerals or alphabets or other types dont cross the specified + * maxType limit. This allows intermixed text garbage to be identified and trimmed. + * + * ALERT: This is not perfect and only provides a rough garbage identification logic. + * Also it currently only differentiates between character classes wrt english. + * + * @param {string} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) { + if (sIn.length < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + let iAlp = 0; + let iNum = 0; + let iOth = 0; + // Learn + let hist = {}; + let iUniq = 0; + for(let i=0; i= maxUniq) { + break; + } + hist[c] = 1; + } + } + console.debug("DBUG:TrimHistGarbage:", hist); + if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) { + return { trimmed: false, data: sIn }; + } + // Catch and Trim + for(let i=0; i < sIn.length; i++) { + let c = sIn[sIn.length-1-i]; + if (!(c in hist)) { + if (i < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i); + return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) }; + } + } + console.debug("DBUG:TrimHistGarbage:Trimmed fully"); + return { trimmed: true, data: "" }; +} + +/** + * Keep trimming repeatedly using hist_garbage logic, till you no longer can. + * This ensures that even if there are multiple runs of garbage with different patterns, + * the logic still tries to munch through them. + * + * @param {any} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) { + let sCur = sIn; + while (true) { + let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold); + if (!got.trimmed) { + return got.data; + } + sCur = got.data; + } +} + +/** + * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as + * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying. + * @param {string} sIn + */ +export function trim_garbage_at_end(sIn) { + let sCur = sIn; + for(let i=0; i<2; i++) { + sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72); + sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12); + } + return sCur; +} + + +/** + * NewLines array helper. + * Allow for maintaining a list of lines. + * Allow for a line to be builtup/appended part by part. + */ +export class NewLines { + + constructor() { + /** @type {string[]} */ + this.lines = []; + } + + /** + * Extracts lines from the passed string and inturn either + * append to a previous partial line or add a new line. + * @param {string} sLines + */ + add_append(sLines) { + let aLines = sLines.split("\n"); + let lCnt = 0; + for(let line of aLines) { + lCnt += 1; + // Add back newline removed if any during split + if (lCnt < aLines.length) { + line += "\n"; + } else { + if (sLines.endsWith("\n")) { + line += "\n"; + } + } + // Append if required + if (lCnt == 1) { + let lastLine = this.lines[this.lines.length-1]; + if (lastLine != undefined) { + if (!lastLine.endsWith("\n")) { + this.lines[this.lines.length-1] += line; + continue; + } + } + } + // Add new line + this.lines.push(line); + } + } + + /** + * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest] + * Optionally control whether only full lines (ie those with newline at end) will be returned + * or will a partial line without a newline at end (can only be the last line) be returned. + * @param {boolean} bFullWithNewLineOnly + */ + shift(bFullWithNewLineOnly=true) { + let line = this.lines[0]; + if (line == undefined) { + return undefined; + } + if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){ + return undefined; + } + return this.lines.shift(); + } + +} diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html new file mode 100644 index 000000000..f6413016f --- /dev/null +++ b/examples/server/public_simplechat/index.html @@ -0,0 +1,51 @@ + + + + SimpleChat LlamaCppEtal + + + + + + + + + + + +
+ +
+

SimpleChat

+ +
+ +
+ +
+
+ + +
+ +
+
+

You need to have javascript enabled.

+
+ +
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+ + +
+ +
+ + diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md new file mode 100644 index 000000000..36a46885d --- /dev/null +++ b/examples/server/public_simplechat/readme.md @@ -0,0 +1,271 @@ + +# SimpleChat + +by Humans for All. + + +## overview + +This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints +in a simple way with minimal code from a common code base. Inturn additionally it tries to allow single or +multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their +own system prompts. + +This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated, +or potentially as it is being generated, in a streamed manner from the server/ai-model. + +Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you +open SimpleChat, option is provided to restore the old chat session, if a matching one exists. + +The UI follows a responsive web design so that the layout can adapt to available display space in a usable +enough manner, in general. + +Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool +console. Parallely some of the directly useful to end-user settings can also be changed using the provided +settings ui. + +NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide +any adaptive culling of old messages nor of replacing them with summary of their content etal. However there +is a optional sliding window based chat logic, which provides a simple minded culling of old messages from +the chat history before sending to the ai model. + +NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now. +However if someone wants they can update the js file or equivalent member in gMe as needed. + +NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very +limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui. + + +## usage + +One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web +frontend to configure the server over http(s) or so, then run this web frontend using something like python's +http module. + +### running using examples/server + +bin/server -m path/model.gguf --path ../examples/server/public_simplechat [--port PORT] + +### running using python3's server module + +first run examples/server +* bin/server -m path/model.gguf + +next run this web front end in examples/server/public_simplechat +* cd ../examples/server/public_simplechat +* python3 -m http.server PORT + +### using the front end + +Open this simple web front end from your local browser + +* http://127.0.0.1:PORT/index.html + +Once inside + +* If you want to, you can change many of the default global settings + * the base url (ie ip addr / domain name, port) + * chat (default) vs completion mode + * try trim garbage in response or not + * amount of chat history in the context sent to server/ai-model + * oneshot or streamed mode. + +* In completion mode + * one normally doesnt use a system prompt in completion mode. + * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message. + If the model requires any prefix wrt user role messages, then the end user has to + explicitly add the needed prefix, when they enter their chat message. + Similarly if the model requires any prefix to trigger assistant/ai-model response, + then the end user needs to enter the same. + This keeps the logic simple, while still giving flexibility to the end user to + manage any templating/tagging requirement wrt their messages to the model. + * the logic doesnt insert newline at the begining and end wrt the prompt message generated. + However if the chat being sent to /completions end point has more than one role's message, + then insert newline when moving from one role's message to the next role's message, so + that it can be clearly identified/distinguished. + * given that /completions endpoint normally doesnt add additional chat-templating of its + own, the above ensures that end user can create a custom single/multi message combo with + any tags/special-tokens related chat templating to test out model handshake. Or enduser + can use it just for normal completion related/based query. + +* If you want to provide a system prompt, then ideally enter it first, before entering any user query. + Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting + responses with a suitable system prompt. + * if chat.add_system_begin is used + * you cant change the system prompt, after it is has been submitted once along with user query. + * you cant set a system prompt, after you have submitted any user query + * if chat.add_system_anytime is used + * one can change the system prompt any time during chat, by changing the contents of system prompt. + * inturn the updated/changed system prompt will be inserted into the chat session. + * this allows for the subsequent user chatting to be driven by the new system prompt set above. + +* Enter your query and either press enter or click on the submit button. + If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. + +* Wait for the logic to communicate with the server and get the response. + * the user is not allowed to enter any fresh query during this time. + * the user input box will be disabled and a working message will be shown in it. + * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent. + +* just refresh the page, to reset wrt the chat history and or system prompt and start afresh. + +* Using NewChat one can start independent chat sessions. + * two independent chat sessions are setup by default. + +* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of + interest, will display the full chat history till then wrt same, if you want full history for printing. + + +## Devel note + +### Reason behind this + +The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable +by developers who may not be from web frontend background (so inturn may not be familiar with template / +end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things. + +And given that the idea is also to help explore/experiment for developers, some flexibility is provided +to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects). +Skeletal logic has been implemented to explore some of the end points and ideas/implications around them. + + +### General + +Me/gMe consolidates the settings which control the behaviour into one object. +One can see the current settings, as well as change/update them using browsers devel-tool/console. +It is attached to the document object. Some of these can also be updated using the Settings UI. + + baseURL - the domain-name/ip-address and inturn the port to send the request. + + bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing + of the generated response. + + the logic assumes that the text sent from the server follows utf-8 encoding. + + in streaming mode - if there is any exception, the logic traps the same and tries to ensure + that text generated till then is not lost. + + if a very long text is being generated, which leads to no user interaction for sometime and + inturn the machine goes into power saving mode or so, the platform may stop network connection, + leading to exception. + + apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model. + + bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when + communicating with the server or only sends the latest user query/message. + + bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the + messages that get inserted into prompt field wrt /Completion endpoint. + + bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be + trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of + subsequent chat history. At the same time the actual trimmed text is shown to the user, once + when it was generated, so user can check if any useful info/data was there in the response. + + One may be able to request the ai-model to continue (wrt the last response) (if chat-history + is enabled as part of the chat-history-in-context setting), and chances are the ai-model will + continue starting from the trimmed part, thus allows long response to be recovered/continued + indirectly, in many cases. + + The histogram/freq based trimming logic is currently tuned for english language wrt its + is-it-a-alpabetic|numeral-char regex match logic. + + chatRequestOptions - maintains the list of options/fields to send along with chat request, + irrespective of whether /chat/completions or /completions endpoint. + + If you want to add additional options/fields to send to the server/ai-model, and or + modify the existing options value or remove them, for now you can update this global var + using browser's development-tools/console. + + For string and numeric fields in chatRequestOptions, including even those added by a user + at runtime by directly modifying gMe.chatRequestOptions, setting ui entries will be auto + created. + + headers - maintains the list of http headers sent when request is made to the server. By default + Content-Type is set to application/json. Additionally Authorization entry is provided, which can + be set if needed using the settings ui. + + iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. + This is disabled by default. However if enabled, then in addition to latest system message, only + the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses + from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled, + only user messages after the latest system message/prompt will be considered. + + This specified sliding window user message count also includes the latest user query. + <0 : Send entire chat history to server + 0 : Send only the system message if any to the server + >0 : Send the latest chat history from the latest system prompt, limited to specified cnt. + + +By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the +implications of loading of the ai-model's context window by chat history, wrt chat response to +some extent in a simple crude way. You may also want to control the context size enabled when +the server loads ai-model, on the server end. + + +Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js +may not be visible. Also remember that just refreshing/reloading page in browser or for that +matter clearing site data, dont directly override site caching in all cases. Worst case you may +have to change port. Or in dev tools of browser, you may be able to disable caching fully. + + +Currently the server to communicate with is maintained globally and not as part of a specific +chat session. So if one changes the server ip/url in setting, then all chat sessions will auto +switch to this new server, when you try using those sessions. + + +By switching between chat.add_system_begin/anytime, one can control whether one can change +the system prompt, anytime during the conversation or only at the beginning. + + +### Default setup + +By default things are setup to try and make the user experience a bit better, if possible. +However a developer when testing the server of ai-model may want to change these value. + +Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be +just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of +full chat history. This way if there is any response with garbage/repeatation, it doesnt +mess with things beyond the next question/request/query, in some ways. The trim garbage +option also tries to help avoid issues with garbage in the context to an extent. + +Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space +available wrt next query-response. However dont forget that the server when started should +also be started with a model context size of 1k or more, to be on safe side. + + The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the + internal n_predict, for now add the same here on the client side, maybe later add max_tokens + to /completions endpoint handling code on server side. + +NOTE: One may want to experiment with frequency/presence penalty fields in chatRequestOptions +wrt the set of fields sent to server along with the user query. To check how the model behaves +wrt repeatations in general in the generated text response. + +A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by +using the providing settings ui. + + +### OpenAi / Equivalent API WebService + +One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint +for a minimal chatting experimentation by setting the below. + +* the baseUrl in settings ui + * https://api.openai.com/v1 or similar + +* Wrt request body - gMe.chatRequestOptions + * model (settings ui) + * any additional fields if required in future + +* Wrt request headers - gMe.headers + * Authorization (available through settings ui) + * Bearer THE_OPENAI_API_KEY + * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so + +NOTE: Not tested, as there is no free tier api testing available. However logically this might +work. + + +## At the end + +Also a thank you to all open source and open model developers, who strive for the common good. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css new file mode 100644 index 000000000..13bfb80b4 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.css @@ -0,0 +1,79 @@ +/** + * the styling of the simplechat web frontend + * by Humans for All + */ + +#fullbody { + height: 98vh; +} + +.heading { + background-color: lightgray; +} + +.session-selected { + background-color: lightblue; +} + +.role-system { + background-color: lightblue; +} +.role-user { + background-color: lightgray; +} +.role-trim { + background-color: lightpink; +} + +.gridx2 { + display: grid; + grid-template-columns: repeat(2, 1fr); + border-bottom-style: dotted; + border-bottom-width: thin; + border-bottom-color: lightblue; +} + +.flex-grow { + flex-grow: 1; +} +.float-right { + float: right; +} + +#chat-div { + overflow: scroll; + flex-grow: 1; + flex-shrink: 1; + min-height: 40vh; +} +button { + min-width: 8vw; +} + +.sameline { + display: flex; + flex-direction: row; +} +.samecolumn { + display: flex; + flex-direction: column; +} + +.ul1 { + padding-inline-start: 2vw; +} +.ul2 { + padding-inline-start: 2vw; +} + +* { + margin: 0.6vmin; +} + +@media print { + + #fullbody { + height: auto; + } + +} diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js new file mode 100644 index 000000000..25afb2564 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.js @@ -0,0 +1,921 @@ +// @ts-check +// A simple completions and chat/completions test related web front end logic +// by Humans for All + +import * as du from "./datautils.mjs"; +import * as ui from "./ui.mjs" + +class Roles { + static System = "system"; + static User = "user"; + static Assistant = "assistant"; +} + +class ApiEP { + static Type = { + Chat: "chat", + Completion: "completion", + } + static UrlSuffix = { + 'chat': `/chat/completions`, + 'completion': `/completions`, + } + + /** + * Build the url from given baseUrl and apiEp id. + * @param {string} baseUrl + * @param {string} apiEP + */ + static Url(baseUrl, apiEP) { + if (baseUrl.endsWith("/")) { + baseUrl = baseUrl.substring(0, baseUrl.length-1); + } + return `${baseUrl}${this.UrlSuffix[apiEP]}`; + } + +} + + +let gUsageMsg = ` +

Usage

+
    +
  • System prompt above, to try control ai response characteristics.
    • +
      +
    • Completion mode - no system prompt normally.
      • +
    +
  • Use shift+enter for inserting enter/newline.
    • +
  • Enter your query to ai assistant below.
    • +
  • Default ContextWindow = [System, Last Query+Resp, Cur Query].
    • +
      +
    • ChatHistInCtxt, MaxTokens, ModelCtxt window to expand
      • +
    +
+`; + + +/** @typedef {{role: string, content: string}[]} ChatMessages */ + +/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */ + +class SimpleChat { + + /** + * @param {string} chatId + */ + constructor(chatId) { + this.chatId = chatId; + /** + * Maintain in a form suitable for common LLM web service chat/completions' messages entry + * @type {ChatMessages} + */ + this.xchat = []; + this.iLastSys = -1; + this.latestResponse = ""; + } + + clear() { + this.xchat = []; + this.iLastSys = -1; + } + + ods_key() { + return `SimpleChat-${this.chatId}` + } + + save() { + /** @type {SimpleChatODS} */ + let ods = {iLastSys: this.iLastSys, xchat: this.xchat}; + localStorage.setItem(this.ods_key(), JSON.stringify(ods)); + } + + load() { + let sods = localStorage.getItem(this.ods_key()); + if (sods == null) { + return; + } + /** @type {SimpleChatODS} */ + let ods = JSON.parse(sods); + this.iLastSys = ods.iLastSys; + this.xchat = ods.xchat; + } + + /** + * Recent chat messages. + * If iRecentUserMsgCnt < 0 + * Then return the full chat history + * Else + * Return chat messages from latest going back till the last/latest system prompt. + * While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt. + * @param {number} iRecentUserMsgCnt + */ + recent_chat(iRecentUserMsgCnt) { + if (iRecentUserMsgCnt < 0) { + return this.xchat; + } + if (iRecentUserMsgCnt == 0) { + console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent"); + } + /** @type{ChatMessages} */ + let rchat = []; + let sysMsg = this.get_system_latest(); + if (sysMsg.length != 0) { + rchat.push({role: Roles.System, content: sysMsg}); + } + let iUserCnt = 0; + let iStart = this.xchat.length; + for(let i=this.xchat.length-1; i > this.iLastSys; i--) { + if (iUserCnt >= iRecentUserMsgCnt) { + break; + } + let msg = this.xchat[i]; + if (msg.role == Roles.User) { + iStart = i; + iUserCnt += 1; + } + } + for(let i = iStart; i < this.xchat.length; i++) { + let msg = this.xchat[i]; + if (msg.role == Roles.System) { + continue; + } + rchat.push({role: msg.role, content: msg.content}); + } + return rchat; + } + + /** + * Collate the latest response from the server/ai-model, as it is becoming available. + * This is mainly useful for the stream mode. + * @param {string} content + */ + append_response(content) { + this.latestResponse += content; + } + + /** + * Add an entry into xchat + * @param {string} role + * @param {string|undefined|null} content + */ + add(role, content) { + if ((content == undefined) || (content == null) || (content == "")) { + return false; + } + this.xchat.push( {role: role, content: content} ); + if (role == Roles.System) { + this.iLastSys = this.xchat.length - 1; + } + this.save(); + return true; + } + + /** + * Show the contents in the specified div + * @param {HTMLDivElement} div + * @param {boolean} bClear + */ + show(div, bClear=true) { + if (bClear) { + div.replaceChildren(); + } + let last = undefined; + for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) { + let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div); + entry.className = `role-${x.role}`; + last = entry; + } + if (last !== undefined) { + last.scrollIntoView(false); + } else { + if (bClear) { + div.innerHTML = gUsageMsg; + gMe.setup_load(div, this); + gMe.show_info(div); + } + } + return last; + } + + /** + * Setup the fetch headers. + * It picks the headers from gMe.headers. + * It inserts Authorization only if its non-empty. + * @param {string} apiEP + */ + fetch_headers(apiEP) { + let headers = new Headers(); + for(let k in gMe.headers) { + let v = gMe.headers[k]; + if ((k == "Authorization") && (v.trim() == "")) { + continue; + } + headers.append(k, v); + } + return headers; + } + + /** + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + * The needed fields/options are picked from a global object. + * Add optional stream flag, if required. + * Convert the json into string. + * @param {Object} obj + */ + request_jsonstr_extend(obj) { + for(let k in gMe.chatRequestOptions) { + obj[k] = gMe.chatRequestOptions[k]; + } + if (gMe.bStream) { + obj["stream"] = true; + } + return JSON.stringify(obj); + } + + /** + * Return a string form of json object suitable for chat/completions + */ + request_messages_jsonstr() { + let req = { + messages: this.recent_chat(gMe.iRecentUserMsgCnt), + } + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for /completions + * @param {boolean} bInsertStandardRolePrefix Insert ": " as prefix wrt each role's message + */ + request_prompt_jsonstr(bInsertStandardRolePrefix) { + let prompt = ""; + let iCnt = 0; + for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) { + iCnt += 1; + if (iCnt > 1) { + prompt += "\n"; + } + if (bInsertStandardRolePrefix) { + prompt += `${chat.role}: `; + } + prompt += `${chat.content}`; + } + let req = { + prompt: prompt, + } + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for specified api endpoint. + * @param {string} apiEP + */ + request_jsonstr(apiEP) { + if (apiEP == ApiEP.Type.Chat) { + return this.request_messages_jsonstr(); + } else { + return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); + } + } + + /** + * Extract the ai-model/assistant's response from the http response got. + * Optionally trim the message wrt any garbage at the end. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + assistant = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Extract the ai-model/assistant's response from the http response got in streaming mode. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract_stream(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + if (respBody["choices"][0]["finish_reason"] !== "stop") { + assistant = respBody["choices"][0]["delta"]["content"]; + } + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Allow setting of system prompt, but only at begining. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_begin(sysPrompt, msgTag) { + if (this.xchat.length == 0) { + if (sysPrompt.length > 0) { + return this.add(Roles.System, sysPrompt); + } + } else { + if (sysPrompt.length > 0) { + if (this.xchat[0].role !== Roles.System) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You need to specify system prompt before any user query, ignoring...`); + } else { + if (this.xchat[0].content !== sysPrompt) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You cant change system prompt, mid way through, ignoring...`); + } + } + } + } + return false; + } + + /** + * Allow setting of system prompt, at any time. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_anytime(sysPrompt, msgTag) { + if (sysPrompt.length <= 0) { + return false; + } + + if (this.iLastSys < 0) { + return this.add(Roles.System, sysPrompt); + } + + let lastSys = this.xchat[this.iLastSys].content; + if (lastSys !== sysPrompt) { + return this.add(Roles.System, sysPrompt); + } + return false; + } + + /** + * Retrieve the latest system prompt. + */ + get_system_latest() { + if (this.iLastSys == -1) { + return ""; + } + let sysPrompt = this.xchat[this.iLastSys].content; + return sysPrompt; + } + + + /** + * Handle the multipart response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response_multipart(resp, apiEP, elDiv) { + let elP = ui.el_create_append_p("", elDiv); + if (!resp.body) { + throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body..."); + } + let tdUtf8 = new TextDecoder("utf-8"); + let rr = resp.body.getReader(); + this.latestResponse = ""; + let xLines = new du.NewLines(); + while(true) { + let { value: cur, done: done } = await rr.read(); + if (cur) { + let curBody = tdUtf8.decode(cur, {stream: true}); + console.debug("DBUG:SC:PART:Str:", curBody); + xLines.add_append(curBody); + } + while(true) { + let curLine = xLines.shift(!done); + if (curLine == undefined) { + break; + } + if (curLine.trim() == "") { + continue; + } + if (curLine.startsWith("data:")) { + curLine = curLine.substring(5); + } + let curJson = JSON.parse(curLine); + console.debug("DBUG:SC:PART:Json:", curJson); + this.append_response(this.response_extract_stream(curJson, apiEP)); + } + elP.innerText = this.latestResponse; + elP.scrollIntoView(false); + if (done) { + break; + } + } + console.debug("DBUG:SC:PART:Full:", this.latestResponse); + return this.latestResponse; + } + + /** + * Handle the oneshot response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + */ + async handle_response_oneshot(resp, apiEP) { + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + return this.response_extract(respBody, apiEP); + } + + /** + * Handle the response from the server be it in oneshot or multipart/stream mode. + * Also take care of the optional garbage trimming. + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response(resp, apiEP, elDiv) { + let theResp = { + assistant: "", + trimmed: "", + } + if (gMe.bStream) { + try { + theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv); + this.latestResponse = ""; + } catch (error) { + theResp.assistant = this.latestResponse; + this.add(Roles.Assistant, theResp.assistant); + this.latestResponse = ""; + throw error; + } + } else { + theResp.assistant = await this.handle_response_oneshot(resp, apiEP); + } + if (gMe.bTrimGarbage) { + let origMsg = theResp.assistant; + theResp.assistant = du.trim_garbage_at_end(origMsg); + theResp.trimmed = origMsg.substring(theResp.assistant.length); + } + this.add(Roles.Assistant, theResp.assistant); + return theResp; + } + +} + + +class MultiChatUI { + + constructor() { + /** @type {Object} */ + this.simpleChats = {}; + /** @type {string} */ + this.curChatId = ""; + + // the ui elements + this.elInSystem = /** @type{HTMLInputElement} */(document.getElementById("system-in")); + this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); + this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); + this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); + this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading")); + this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings")); + + this.validate_element(this.elInSystem, "system-in"); + this.validate_element(this.elDivChat, "chat-div"); + this.validate_element(this.elInUser, "user-in"); + this.validate_element(this.elDivHeading, "heading"); + this.validate_element(this.elDivChat, "sessions-div"); + this.validate_element(this.elBtnSettings, "settings"); + } + + /** + * Check if the element got + * @param {HTMLElement | null} el + * @param {string} msgTag + */ + validate_element(el, msgTag) { + if (el == null) { + throw Error(`ERRR:SimpleChat:MCUI:${msgTag} element missing in html...`); + } else { + console.debug(`INFO:SimpleChat:MCUI:${msgTag} Id[${el.id}] Name[${el["name"]}]`); + } + } + + /** + * Reset user input ui. + * * clear user input + * * enable user input + * * set focus to user input + */ + ui_reset_userinput() { + this.elInUser.value = ""; + this.elInUser.disabled = false; + this.elInUser.focus(); + } + + /** + * Setup the needed callbacks wrt UI, curChatId to defaultChatId and + * optionally switch to specified defaultChatId. + * @param {string} defaultChatId + * @param {boolean} bSwitchSession + */ + setup_ui(defaultChatId, bSwitchSession=false) { + + this.curChatId = defaultChatId; + if (bSwitchSession) { + this.handle_session_switch(this.curChatId); + } + + this.elBtnSettings.addEventListener("click", (ev)=>{ + this.elDivChat.replaceChildren(); + gMe.show_settings(this.elDivChat); + }); + + this.elBtnUser.addEventListener("click", (ev)=>{ + if (this.elInUser.disabled) { + return; + } + this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{ + let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; + console.error(msg.replace("\n", ":")); + alert(msg); + this.ui_reset_userinput(); + }); + }); + + this.elInUser.addEventListener("keyup", (ev)=> { + // allow user to insert enter into their message using shift+enter. + // while just pressing enter key will lead to submitting. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInUser.value; + this.elInUser.value = value.substring(0,value.length-1); + this.elBtnUser.click(); + ev.preventDefault(); + } + }); + + this.elInSystem.addEventListener("keyup", (ev)=> { + // allow user to insert enter into the system prompt using shift+enter. + // while just pressing enter key will lead to setting the system prompt. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInSystem.value; + this.elInSystem.value = value.substring(0,value.length-1); + let chat = this.simpleChats[this.curChatId]; + chat.add_system_anytime(this.elInSystem.value, this.curChatId); + chat.show(this.elDivChat); + ev.preventDefault(); + } + }); + + } + + /** + * Setup a new chat session and optionally switch to it. + * @param {string} chatId + * @param {boolean} bSwitchSession + */ + new_chat_session(chatId, bSwitchSession=false) { + this.simpleChats[chatId] = new SimpleChat(chatId); + if (bSwitchSession) { + this.handle_session_switch(chatId); + } + } + + + /** + * Handle user query submit request, wrt specified chat session. + * @param {string} chatId + * @param {string} apiEP + */ + async handle_user_submit(chatId, apiEP) { + + let chat = this.simpleChats[chatId]; + + // In completion mode, if configured, clear any previous chat history. + // So if user wants to simulate a multi-chat based completion query, + // they will have to enter the full thing, as a suitable multiline + // user input/query. + if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) { + chat.clear(); + } + + chat.add_system_anytime(this.elInSystem.value, chatId); + + let content = this.elInUser.value; + if (!chat.add(Roles.User, content)) { + console.debug(`WARN:SimpleChat:MCUI:${chatId}:HandleUserSubmit:Ignoring empty user input...`); + return; + } + chat.show(this.elDivChat); + + let theUrl = ApiEP.Url(gMe.baseURL, apiEP); + let theBody = chat.request_jsonstr(apiEP); + + this.elInUser.value = "working..."; + this.elInUser.disabled = true; + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let theHeaders = chat.fetch_headers(apiEP); + let resp = await fetch(theUrl, { + method: "POST", + headers: theHeaders, + body: theBody, + }); + + let theResp = await chat.handle_response(resp, apiEP, this.elDivChat); + if (chatId == this.curChatId) { + chat.show(this.elDivChat); + if (theResp.trimmed.length > 0) { + let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat); + p.className="role-trim"; + } + } else { + console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); + } + this.ui_reset_userinput(); + } + + /** + * Show buttons for NewChat and available chat sessions, in the passed elDiv. + * If elDiv is undefined/null, then use this.elDivSessions. + * Take care of highlighting the selected chat-session's btn. + * @param {HTMLDivElement | undefined} elDiv + */ + show_sessions(elDiv=undefined) { + if (!elDiv) { + elDiv = this.elDivSessions; + } + elDiv.replaceChildren(); + // Btn for creating new chat session + let btnNew = ui.el_create_button("New CHAT", (ev)=> { + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); + alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); + return; + } + let chatId = `Chat${Object.keys(this.simpleChats).length}`; + let chatIdGot = prompt("INFO:SimpleChat\nMCUI:NewChat\nEnter id for new chat session", chatId); + if (!chatIdGot) { + console.error("ERRR:SimpleChat:MCUI:NewChat:Skipping based on user request..."); + return; + } + this.new_chat_session(chatIdGot, true); + this.create_session_btn(elDiv, chatIdGot); + ui.el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + }); + elDiv.appendChild(btnNew); + // Btns for existing chat sessions + let chatIds = Object.keys(this.simpleChats); + for(let cid of chatIds) { + let btn = this.create_session_btn(elDiv, cid); + if (cid == this.curChatId) { + btn.className = "session-selected"; + } + } + } + + create_session_btn(elDiv, cid) { + let btn = ui.el_create_button(cid, (ev)=>{ + let target = /** @type{HTMLButtonElement} */(ev.target); + console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:SessionClick:${target.id}:Current session [${this.curChatId}] awaiting response, ignoring switch...`); + alert("ERRR:SimpleChat\nMCUI:SessionClick\nWait for response to pending query, before switching"); + return; + } + this.handle_session_switch(target.id); + ui.el_children_config_class(elDiv, target.id, "session-selected", ""); + }); + elDiv.appendChild(btn); + return btn; + } + + /** + * Switch ui to the specified chatId and set curChatId to same. + * @param {string} chatId + */ + async handle_session_switch(chatId) { + let chat = this.simpleChats[chatId]; + if (chat == undefined) { + console.error(`ERRR:SimpleChat:MCUI:HandleSessionSwitch:${chatId} missing...`); + return; + } + this.elInSystem.value = chat.get_system_latest(); + this.elInUser.value = ""; + chat.show(this.elDivChat); + this.elInUser.focus(); + this.curChatId = chatId; + console.log(`INFO:SimpleChat:MCUI:HandleSessionSwitch:${chatId} entered...`); + } + +} + + +class Me { + + constructor() { + this.baseURL = "http://127.0.0.1:8080"; + this.defaultChatIds = [ "Default", "Other" ]; + this.multiChat = new MultiChatUI(); + this.bStream = true; + this.bCompletionFreshChatAlways = true; + this.bCompletionInsertStandardRolePrefix = false; + this.bTrimGarbage = true; + this.iRecentUserMsgCnt = 2; + this.sRecentUserMsgCnt = { + "Full": -1, + "Last0": 1, + "Last1": 2, + "Last2": 3, + "Last4": 5, + }; + this.apiEP = ApiEP.Type.Chat; + this.headers = { + "Content-Type": "application/json", + "Authorization": "", // Authorization: Bearer OPENAI_API_KEY + } + // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + this.chatRequestOptions = { + "model": "gpt-3.5-turbo", + "temperature": 0.7, + "max_tokens": 1024, + "n_predict": 1024, + //"frequency_penalty": 1.2, + //"presence_penalty": 1.2, + }; + } + + /** + * Disable console.debug by mapping it to a empty function. + */ + debug_disable() { + this.console_debug = console.debug; + console.debug = () => { + + }; + } + + /** + * Setup the load saved chat ui. + * @param {HTMLDivElement} div + * @param {SimpleChat} chat + */ + setup_load(div, chat) { + if (!(chat.ods_key() in localStorage)) { + return; + } + div.innerHTML += `

Restore

+

Load previously saved chat session, if available

`; + let btn = ui.el_create_button(chat.ods_key(), (ev)=>{ + console.log("DBUG:SimpleChat:SC:Load", chat); + chat.load(); + queueMicrotask(()=>{ + chat.show(div); + this.multiChat.elInSystem.value = chat.get_system_latest(); + }); + }); + div.appendChild(btn); + } + + /** + * Show the configurable parameters info in the passed Div element. + * @param {HTMLDivElement} elDiv + * @param {boolean} bAll + */ + show_info(elDiv, bAll=false) { + + let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv); + p.className = "role-system"; + + if (bAll) { + + ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv); + + ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv); + + ui.el_create_append_p(`bStream:${this.bStream}`, elDiv); + + ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv); + + ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv); + + ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv); + + ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv); + + ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv); + + } + + ui.el_create_append_p(`chatRequestOptions:${JSON.stringify(this.chatRequestOptions, null, " - ")}`, elDiv); + ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv); + + } + + /** + * Auto create ui input elements for fields in ChatRequestOptions + * Currently supports text and number field types. + * @param {HTMLDivElement} elDiv + */ + show_settings_chatrequestoptions(elDiv) { + let typeDict = { + "string": "text", + "number": "number", + }; + let fs = document.createElement("fieldset"); + let legend = document.createElement("legend"); + legend.innerText = "ChatRequestOptions"; + fs.appendChild(legend); + elDiv.appendChild(fs); + for(const k in this.chatRequestOptions) { + let val = this.chatRequestOptions[k]; + let type = typeof(val); + if (!((type == "string") || (type == "number"))) { + continue; + } + let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.chatRequestOptions[k], (val)=>{ + if (type == "number") { + val = Number(val); + } + this.chatRequestOptions[k] = val; + }); + fs.appendChild(inp.div); + } + } + + /** + * Show settings ui for configurable parameters, in the passed Div element. + * @param {HTMLDivElement} elDiv + */ + show_settings(elDiv) { + + let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{ + this.baseURL = val; + }); + elDiv.appendChild(inp.div); + + inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{ + this.headers["Authorization"] = val; + }); + inp.el.placeholder = "Bearer OPENAI_API_KEY"; + elDiv.appendChild(inp.div); + + let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{ + this.bStream = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{ + this.bCompletionFreshChatAlways = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{ + this.bCompletionInsertStandardRolePrefix = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{ + this.bTrimGarbage = val; + }); + elDiv.appendChild(bb.div); + + let sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{ + this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val]; + }); + elDiv.appendChild(sel.div); + + sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{ + this.apiEP = ApiEP.Type[val]; + }); + elDiv.appendChild(sel.div); + + this.show_settings_chatrequestoptions(elDiv); + + } + +} + + +/** @type {Me} */ +let gMe; + +function startme() { + console.log("INFO:SimpleChat:StartMe:Starting..."); + gMe = new Me(); + gMe.debug_disable(); + document["gMe"] = gMe; + document["du"] = du; + for (let cid of gMe.defaultChatIds) { + gMe.multiChat.new_chat_session(cid); + } + gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true); + gMe.multiChat.show_sessions(); +} + +document.addEventListener("DOMContentLoaded", startme); diff --git a/examples/server/public_simplechat/ui.mjs b/examples/server/public_simplechat/ui.mjs new file mode 100644 index 000000000..b2d5b9aea --- /dev/null +++ b/examples/server/public_simplechat/ui.mjs @@ -0,0 +1,211 @@ +//@ts-check +// Helpers to work with html elements +// by Humans for All +// + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +export function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + +/** + * Create a para and set it up. Optionaly append it to a passed parent. + * @param {string} text + * @param {HTMLElement | undefined} elParent + * @param {string | undefined} id + */ +export function el_create_append_p(text, elParent=undefined, id=undefined) { + let para = document.createElement("p"); + para.innerText = text; + if (id) { + para.id = id; + } + if (elParent) { + elParent.appendChild(para); + } + return para; +} + +/** + * Create a button which represents bool value using specified text wrt true and false. + * When ever user clicks the button, it will toggle the value and update the shown text. + * + * @param {string} id + * @param {{true: string, false: string}} texts + * @param {boolean} defaultValue + * @param {function(boolean):void} cb + */ +export function el_create_boolbutton(id, texts, defaultValue, cb) { + let el = document.createElement("button"); + el["xbool"] = defaultValue; + el["xtexts"] = structuredClone(texts); + el.innerText = el["xtexts"][String(defaultValue)]; + if (id) { + el.id = id; + } + el.addEventListener('click', (ev)=>{ + el["xbool"] = !el["xbool"]; + el.innerText = el["xtexts"][String(el["xbool"])]; + cb(el["xbool"]); + }) + return el; +} + +/** + * Create a div wrapped button which represents bool value using specified text wrt true and false. + * @param {string} id + * @param {string} label + * @param {{ true: string; false: string; }} texts + * @param {boolean} defaultValue + * @param {(arg0: boolean) => void} cb + * @param {string} className + */ +export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let btn = el_create_boolbutton(id, texts, defaultValue, cb); + div.appendChild(btn); + return { div: div, el: btn }; +} + + +/** + * Create a select ui element, with a set of options to select from. + * * options: an object which contains name-value pairs + * * defaultOption: the value whose name should be choosen, by default. + * * cb : the call back returns the name string of the option selected. + * + * @param {string} id + * @param {Object} options + * @param {*} defaultOption + * @param {function(string):void} cb + */ +export function el_create_select(id, options, defaultOption, cb) { + let el = document.createElement("select"); + el["xselected"] = defaultOption; + el["xoptions"] = structuredClone(options); + for(let cur of Object.keys(options)) { + let op = document.createElement("option"); + op.value = cur; + op.innerText = cur; + if (options[cur] == defaultOption) { + op.selected = true; + } + el.appendChild(op); + } + if (id) { + el.id = id; + el.name = id; + } + el.addEventListener('change', (ev)=>{ + let target = /** @type{HTMLSelectElement} */(ev.target); + console.log("DBUG:UI:Select:", id, ":", target.value); + cb(target.value); + }) + return el; +} + +/** + * Create a div wrapped select ui element, with a set of options to select from. + * + * @param {string} id + * @param {any} label + * @param {{ [x: string]: any; }} options + * @param {any} defaultOption + * @param {(arg0: string) => void} cb + * @param {string} className + */ +export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let sel = el_create_select(id, options,defaultOption, cb); + div.appendChild(sel); + return { div: div, el: sel }; +} + + +/** + * Create a input ui element. + * + * @param {string} id + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + */ +export function el_create_input(id, type, defaultValue, cb) { + let el = document.createElement("input"); + el.type = type; + el.value = defaultValue; + if (id) { + el.id = id; + } + el.addEventListener('change', (ev)=>{ + cb(el.value); + }) + return el; +} + +/** + * Create a div wrapped input. + * + * @param {string} id + * @param {string} label + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + * @param {string} className + */ +export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let el = el_create_input(id, type, defaultValue, cb); + div.appendChild(el); + return { div: div, el: el }; +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6af5cb96e..d581cad95 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -17,9 +17,20 @@ #include "json.hpp" // auto generated files (update with ./deps.sh) +#include "colorthemes.css.hpp" +#include "style.css.hpp" +#include "theme-beeninorder.css.hpp" +#include "theme-ketivah.css.hpp" +#include "theme-mangotango.css.hpp" +#include "theme-playground.css.hpp" +#include "theme-polarnight.css.hpp" +#include "theme-snowstorm.css.hpp" #include "index.html.hpp" +#include "index-new.html.hpp" #include "index.js.hpp" #include "completion.js.hpp" +#include "system-prompts.js.hpp" +#include "prompt-formats.js.hpp" #include "json-schema-to-grammar.mjs.hpp" #include @@ -112,29 +123,6 @@ struct slot_params { json input_suffix; }; -struct server_params { - int32_t port = 8080; - int32_t read_timeout = 600; - int32_t write_timeout = 600; - int32_t n_threads_http = -1; - - std::string hostname = "127.0.0.1"; - std::string public_path = ""; - std::string chat_template = ""; - std::string system_prompt = ""; - - std::vector api_keys; - -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - std::string ssl_key_file = ""; - std::string ssl_cert_file = ""; -#endif - - bool slots_endpoint = true; - bool metrics_endpoint = false; - std::string slot_save_path; -}; - struct server_slot { int id; int id_task = -1; @@ -1019,7 +1007,7 @@ struct server_context { sampler_names.emplace_back(sampler_name); } } - slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + slot.sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false); } else { slot.sparams.samplers_sequence = default_sparams.samplers_sequence; } @@ -1250,13 +1238,13 @@ struct server_context { } json get_formated_generation(const server_slot & slot) const { - const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); + const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); const bool ignore_eos = eos_bias != slot.sparams.logit_bias.end() && eos_bias->second < 0.0f && std::isinf(eos_bias->second); std::vector samplers_sequence; samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); for (const auto & sampler_type : slot.sparams.samplers_sequence) { - samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); + samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type)); } return json { @@ -2323,561 +2311,6 @@ struct server_context { } }; -static void server_print_usage(const char * argv0, const gpt_params & params, const server_params & sparams) { - printf("usage: %s [options]\n", argv0); - printf("\n"); - printf("options:\n"); - printf(" -h, --help show this help message and exit\n"); - printf(" -v, --verbose verbose output (default: %s)\n", server_verbose ? "enabled" : "disabled"); - printf(" -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); - printf(" -tb N, --threads-batch N number of threads to use during batch and prompt processing (default: same as --threads)\n"); - printf(" --threads-http N number of threads in the http server pool to process requests (default: max(hardware concurrency - 1, --parallel N + 2))\n"); - printf(" -c N, --ctx-size N size of the prompt context (default: %d)\n", params.n_ctx); - printf(" --rope-scaling {none,linear,yarn}\n"); - printf(" RoPE frequency scaling method, defaults to linear unless specified by the model\n"); - printf(" --rope-freq-base N RoPE base frequency (default: loaded from model)\n"); - printf(" --rope-freq-scale N RoPE frequency scaling factor, expands context by a factor of 1/N\n"); - printf(" --yarn-ext-factor N YaRN: extrapolation mix factor (default: 1.0, 0.0 = full interpolation)\n"); - printf(" --yarn-attn-factor N YaRN: scale sqrt(t) or attention magnitude (default: 1.0)\n"); - printf(" --yarn-beta-slow N YaRN: high correction dim or alpha (default: %.1f)\n", params.yarn_beta_slow); - printf(" --yarn-beta-fast N YaRN: low correction dim or beta (default: %.1f)\n", params.yarn_beta_fast); - printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n"); - printf(" -dt N, --defrag-thold N\n"); - printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold); - printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch); - printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch); - if (llama_supports_mlock()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); - } - if (llama_supports_mmap()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); - } - printf(" --numa TYPE attempt optimizations that help on some NUMA systems\n"); - printf(" - distribute: spread execution evenly over all nodes\n"); - printf(" - isolate: only spawn threads on CPUs on the node that execution started on\n"); - printf(" - numactl: use the CPU map provided my numactl\n"); - if (llama_supports_gpu_offload()) { - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); - printf(" -nkvo, --no-kv-offload\n"); - printf(" disable KV offload\n"); - } - printf(" -m FNAME, --model FNAME\n"); - 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"); - printf(" Hugging Face model repository (default: unused)\n"); - printf(" -hff FILE, --hf-file FILE\n"); - printf(" Hugging Face model file (default: unused)\n"); - printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --rpc SERVERS comma separated list of RPC servers\n"); - printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - printf(" --ssl-key-file FNAME path to file a PEM-encoded SSL private key\n"); - printf(" --ssl-cert-file FNAME path to file a PEM-encoded SSL certificate\n"); -#endif - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - 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"); - printf(" KV cache data type for K (default: f16)\n"); - printf(" -ctv TYPE, --cache-type-v TYPE\n"); - printf(" KV cache data type for V (default: f16)\n"); - printf(" --log-format log output format: json or text (default: json)\n"); - printf(" --log-disable disables logging to a file.\n"); - printf(" --slots-endpoint-disable disables slots monitoring endpoint.\n"); - printf(" --metrics enable prometheus compatible metrics endpoint (default: %s).\n", sparams.metrics_endpoint ? "enabled" : "disabled"); - printf(" --slot-save-path PATH path to save slot kv cache (default: disabled)\n"); - printf("\n"); - printf(" -n, --n-predict maximum tokens to predict (default: %d)\n", params.n_predict); - printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool, str. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`\n"); - printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`\n"); - printf(" --chat-template JINJA_TEMPLATE\n"); - printf(" set custom jinja chat template (default: template taken from model's metadata)\n"); - printf(" only commonly used templates are accepted:\n"); - printf(" https://github.com/ggerganov/llama.cpp/wiki/Templates-supported-by-llama_chat_apply_template\n"); - printf("\n"); -} - -static void server_params_parse(int argc, char ** argv, server_params & sparams, gpt_params & params) { - gpt_params default_params; - server_params default_sparams; - - std::string arg; - bool invalid_param = false; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg == "--port") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.port = std::stoi(argv[i]); - } else if (arg == "--rpc") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rpc_servers = argv[i]; - } else if (arg == "--host") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.hostname = argv[i]; - } else if (arg == "--path") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.public_path = argv[i]; - } else if (arg == "--api-key") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.api_keys.push_back(argv[i]); - } else if (arg == "--api-key-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream key_file(argv[i]); - if (!key_file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string key; - while (std::getline(key_file, key)) { - if (key.size() > 0) { - sparams.api_keys.push_back(key); - } - } - key_file.close(); - - } -#ifdef CPPHTTPLIB_OPENSSL_SUPPORT - else if (arg == "--ssl-key-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.ssl_key_file = argv[i]; - } else if (arg == "--ssl-cert-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.ssl_cert_file = argv[i]; - } -#endif - else if (arg == "--timeout" || arg == "-to") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.read_timeout = std::stoi(argv[i]); - sparams.write_timeout = std::stoi(argv[i]); - } else if (arg == "-m" || arg == "--model") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model = argv[i]; - } else if (arg == "-mu" || arg == "--model-url") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_url = argv[i]; - } else if (arg == "-hfr" || arg == "--hf-repo") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.hf_repo = argv[i]; - } else if (arg == "-hff" || arg == "--hf-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.hf_file = argv[i]; - } else if (arg == "-a" || arg == "--alias") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.model_alias = argv[i]; - } else if (arg == "-h" || arg == "--help") { - server_print_usage(argv[0], default_params, default_sparams); - exit(0); - } else if (arg == "-c" || arg == "--ctx-size" || arg == "--ctx_size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ctx = std::stoi(argv[i]); - } else if (arg == "--rope-scaling") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_NONE; } - else if (value == "linear") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_LINEAR; } - else if (value == "yarn") { params.rope_scaling_type = LLAMA_ROPE_SCALING_TYPE_YARN; } - else { invalid_param = true; break; } - } else if (arg == "--rope-freq-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_base = std::stof(argv[i]); - } else if (arg == "--rope-freq-scale") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.rope_freq_scale = std::stof(argv[i]); - } else if (arg == "--yarn-ext-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_ext_factor = std::stof(argv[i]); - } - else if (arg == "--yarn-attn-factor") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_attn_factor = std::stof(argv[i]); - } else if (arg == "--yarn-beta-fast") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_fast = std::stof(argv[i]); - } else if (arg == "--yarn-beta-slow") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.yarn_beta_slow = std::stof(argv[i]); - } else if (arg == "--pooling") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string value(argv[i]); - /**/ if (value == "none") { params.pooling_type = LLAMA_POOLING_TYPE_NONE; } - else if (value == "mean") { params.pooling_type = LLAMA_POOLING_TYPE_MEAN; } - else if (value == "cls") { params.pooling_type = LLAMA_POOLING_TYPE_CLS; } - else { invalid_param = true; break; } - } else if (arg == "--defrag-thold" || arg == "-dt") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.defrag_thold = std::stof(argv[i]); - } else if (arg == "--threads" || arg == "-t") { - if (++i >= argc) - { - invalid_param = true; - break; - } - params.n_threads = std::stoi(argv[i]); - } else if (arg == "--grp-attn-n" || arg == "-gan") { - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_n = std::stoi(argv[i]); - } else if (arg == "--grp-attn-w" || arg == "-gaw") { - if (++i >= argc) { - invalid_param = true; - break; - } - - params.grp_attn_w = std::stoi(argv[i]); - } else if (arg == "--threads-batch" || arg == "-tb") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_threads_batch = std::stoi(argv[i]); - } else if (arg == "--threads-http") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.n_threads_http = std::stoi(argv[i]); - } else if (arg == "-b" || arg == "--batch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_batch = std::stoi(argv[i]); - } else if (arg == "-ub" || arg == "--ubatch-size") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_ubatch = std::stoi(argv[i]); - } else if (arg == "--gpu-layers" || arg == "-ngl" || arg == "--n-gpu-layers") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (llama_supports_gpu_offload()) { - params.n_gpu_layers = std::stoi(argv[i]); - } else { - LOG_WARNING( - "Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " - "See main README.md for information on enabling GPU BLAS support", - {{"n_gpu_layers", params.n_gpu_layers}}); - } - } else if (arg == "-nkvo" || arg == "--no-kv-offload") { - params.no_kv_offload = true; - } else if (arg == "--split-mode" || arg == "-sm") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::string arg_next = argv[i]; - if (arg_next == "none") { - params.split_mode = LLAMA_SPLIT_MODE_NONE; - } else if (arg_next == "layer") { - params.split_mode = LLAMA_SPLIT_MODE_LAYER; - } else if (arg_next == "row") { - params.split_mode = LLAMA_SPLIT_MODE_ROW; - } else { - invalid_param = true; - break; - } -#ifndef GGML_USE_CUDA - fprintf(stderr, "warning: llama.cpp was compiled without CUDA. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUDA - } else if (arg == "--tensor-split" || arg == "-ts") { - if (++i >= argc) { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) - std::string arg_next = argv[i]; - - // split string by , and / - const std::regex regex{R"([,/]+)"}; - std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; - std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= llama_max_devices()); - - for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { - if (i_device < split_arg.size()) { - params.tensor_split[i_device] = std::stof(split_arg[i_device]); - } else { - params.tensor_split[i_device] = 0.0f; - } - } -#else - LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a tensor split.\n", {}); -#endif // GGML_USE_CUDA - } else if (arg == "--main-gpu" || arg == "-mg") { - if (++i >= argc) { - invalid_param = true; - break; - } -#if defined(GGML_USE_CUDA) || defined(GGML_USE_SYCL) - params.main_gpu = std::stoi(argv[i]); -#else - LOG_WARNING("llama.cpp was compiled without CUDA. It is not possible to set a main GPU.", {}); -#endif - } else if (arg == "--lora") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(argv[i], 1.0f); - params.use_mmap = false; - } else if (arg == "--lora-scaled") { - if (++i >= argc) { - invalid_param = true; - break; - } - const char * lora_adapter = argv[i]; - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); - params.use_mmap = false; - } else if (arg == "--lora-base") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.lora_base = argv[i]; - } else if (arg == "-v" || arg == "--verbose") { -#if SERVER_VERBOSE != 1 - LOG_WARNING("server.cpp is not built with verbose logging.", {}); -#else - server_verbose = true; -#endif - } else if (arg == "--mlock") { - params.use_mlock = true; - } else if (arg == "--no-mmap") { - params.use_mmap = false; - } 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 == "--embedding" || arg == "--embeddings") { - 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; - break; - } - params.n_parallel = std::stoi(argv[i]); - } else if (arg == "-n" || arg == "--n-predict") { - if (++i >= argc) { - invalid_param = true; - break; - } - params.n_predict = std::stoi(argv[i]); - } else if (arg == "-spf" || arg == "--system-prompt-file") { - if (++i >= argc) { - invalid_param = true; - break; - } - std::ifstream file(argv[i]); - if (!file) { - fprintf(stderr, "error: failed to open file '%s'\n", argv[i]); - invalid_param = true; - break; - } - std::string system_prompt; - std::copy( - std::istreambuf_iterator(file), - std::istreambuf_iterator(), - std::back_inserter(system_prompt) - ); - sparams.system_prompt = system_prompt; - } else if (arg == "-ctk" || arg == "--cache-type-k") { - params.cache_type_k = argv[++i]; - } else if (arg == "-ctv" || arg == "--cache-type-v") { - params.cache_type_v = argv[++i]; - } else if (arg == "--log-format") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (std::strcmp(argv[i], "json") == 0) { - server_log_json = true; - } else if (std::strcmp(argv[i], "text") == 0) { - server_log_json = false; - } else { - invalid_param = true; - break; - } - } else if (arg == "--log-disable") { - log_set_target(stdout); - LOG_INFO("logging to file is disabled.", {}); - } else if (arg == "--slots-endpoint-disable") { - sparams.slots_endpoint = false; - } else if (arg == "--metrics") { - sparams.metrics_endpoint = true; - } else if (arg == "--slot-save-path") { - if (++i >= argc) { - invalid_param = true; - break; - } - sparams.slot_save_path = argv[i]; - // if doesn't end with DIRECTORY_SEPARATOR, add it - if (!sparams.slot_save_path.empty() && sparams.slot_save_path[sparams.slot_save_path.size() - 1] != DIRECTORY_SEPARATOR) { - sparams.slot_save_path += DIRECTORY_SEPARATOR; - } - } else if (arg == "--chat-template") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (!verify_custom_template(argv[i])) { - fprintf(stderr, "error: the supplied chat template is not supported: %s\n", argv[i]); - fprintf(stderr, "note: llama.cpp does not use jinja parser, we only support commonly used templates\n"); - invalid_param = true; - break; - } - sparams.chat_template = argv[i]; - } else if (arg == "--override-kv") { - if (++i >= argc) { - invalid_param = true; - break; - } - if (!parse_kv_override(argv[i], params.kv_overrides)) { - fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); - invalid_param = true; - break; - } - } else { - fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } - } - - gpt_params_handle_model_default(params); - - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - if (invalid_param) { - fprintf(stderr, "error: invalid parameter for argument: %s\n", arg.c_str()); - server_print_usage(argv[0], default_params, default_sparams); - exit(1); - } -} - static void log_server_request(const httplib::Request & req, const httplib::Response & res) { // skip GH copilot requests when using default port if (req.path == "/v1/health" || req.path == "/v1/completions") { @@ -2918,16 +2351,22 @@ int main(int argc, char ** argv) { log_disable(); #endif // own arguments required by this example - gpt_params params; - server_params sparams; + gpt_params params; + + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); + return 1; + } + + // TODO: not great to use extern vars + server_log_json = params.log_json; + server_verbose = params.verbose; // struct that contains llama context and inference server_context ctx_server; - server_params_parse(argc, argv, sparams, params); - - if (!sparams.system_prompt.empty()) { - ctx_server.system_prompt_set(sparams.system_prompt); + if (!params.system_prompt.empty()) { + ctx_server.system_prompt_set(params.system_prompt); } if (params.model_alias == "unknown") { @@ -2951,10 +2390,10 @@ int main(int argc, char ** argv) { std::unique_ptr svr; #ifdef CPPHTTPLIB_OPENSSL_SUPPORT - if (sparams.ssl_key_file != "" && sparams.ssl_cert_file != "") { - LOG_INFO("Running with SSL", {{"key", sparams.ssl_key_file}, {"cert", sparams.ssl_cert_file}}); + if (params.ssl_file_key != "" && params.ssl_file_cert != "") { + LOG_INFO("Running with SSL", {{"key", params.ssl_file_key}, {"cert", params.ssl_file_cert}}); svr.reset( - new httplib::SSLServer(sparams.ssl_cert_file.c_str(), sparams.ssl_key_file.c_str()) + new httplib::SSLServer(params.ssl_file_cert.c_str(), params.ssl_file_key.c_str()) ); } else { LOG_INFO("Running without SSL", {}); @@ -3008,24 +2447,24 @@ int main(int argc, char ** argv) { }); // set timeouts and change hostname and port - svr->set_read_timeout (sparams.read_timeout); - svr->set_write_timeout(sparams.write_timeout); + svr->set_read_timeout (params.timeout_read); + svr->set_write_timeout(params.timeout_write); - if (!svr->bind_to_port(sparams.hostname, sparams.port)) { - fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); + if (!svr->bind_to_port(params.hostname, params.port)) { + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", params.hostname.c_str(), params.port); return 1; } std::unordered_map log_data; - log_data["hostname"] = sparams.hostname; - log_data["port"] = std::to_string(sparams.port); + log_data["hostname"] = params.hostname; + log_data["port"] = std::to_string(params.port); - if (sparams.api_keys.size() == 1) { - auto key = sparams.api_keys[0]; + if (params.api_keys.size() == 1) { + auto key = params.api_keys[0]; log_data["api_key"] = "api_key: ****" + key.substr(std::max((int)(key.length() - 4), 0)); - } else if (sparams.api_keys.size() > 1) { - log_data["api_key"] = "api_key: " + std::to_string(sparams.api_keys.size()) + " keys loaded"; + } else if (params.api_keys.size() > 1) { + log_data["api_key"] = "api_key: " + std::to_string(params.api_keys.size()) + " keys loaded"; } // load the model @@ -3042,10 +2481,10 @@ int main(int argc, char ** argv) { const auto model_meta = ctx_server.model_meta(); // if a custom chat template is not supplied, we will use the one that comes with the model (if any) - if (sparams.chat_template.empty()) { + if (params.chat_template.empty()) { if (!ctx_server.validate_model_chat_template()) { LOG_ERROR("The chat template that comes with this model is not yet supported, falling back to chatml. This may cause the model to output suboptimal responses", {}); - sparams.chat_template = "chatml"; + params.chat_template = "chatml"; } } @@ -3057,11 +2496,11 @@ int main(int argc, char ** argv) { chat.push_back({{"role", "assistant"}, {"content", "Hi there"}}); chat.push_back({{"role", "user"}, {"content", "How are you?"}}); - const std::string chat_example = format_chat(ctx_server.model, sparams.chat_template, chat); + const std::string chat_example = format_chat(ctx_server.model, params.chat_template, chat); LOG_INFO("chat template", { {"chat_example", chat_example}, - {"built_in", sparams.chat_template.empty()}, + {"built_in", params.chat_template.empty()}, }); } @@ -3069,7 +2508,7 @@ int main(int argc, char ** argv) { // Middlewares // - auto middleware_validate_api_key = [&sparams, &res_error](const httplib::Request & req, httplib::Response & res) { + auto middleware_validate_api_key = [¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { // TODO: should we apply API key to all endpoints, including "/health" and "/models"? static const std::set protected_endpoints = { "/props", @@ -3087,7 +2526,7 @@ int main(int argc, char ** argv) { }; // If API key is not set, skip validation - if (sparams.api_keys.empty()) { + if (params.api_keys.empty()) { return true; } @@ -3102,7 +2541,7 @@ int main(int argc, char ** argv) { std::string prefix = "Bearer "; if (auth_header.substr(0, prefix.size()) == prefix) { std::string received_api_key = auth_header.substr(prefix.size()); - if (std::find(sparams.api_keys.begin(), sparams.api_keys.end(), received_api_key) != sparams.api_keys.end()) { + if (std::find(params.api_keys.begin(), params.api_keys.end(), received_api_key) != params.api_keys.end()) { return true; // API key is valid } } @@ -3157,7 +2596,7 @@ int main(int argc, char ** argv) { }; res.status = 200; // HTTP OK - if (sparams.slots_endpoint && req.has_param("include_slots")) { + if (params.endpoint_slots && req.has_param("include_slots")) { health["slots"] = result.data.at("slots"); } @@ -3183,7 +2622,7 @@ int main(int argc, char ** argv) { }; const auto handle_slots = [&](const httplib::Request &, httplib::Response & res) { - if (!sparams.slots_endpoint) { + if (!params.endpoint_slots) { res_error(res, format_error_response("This server does not support slots endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -3207,7 +2646,7 @@ int main(int argc, char ** argv) { }; const auto handle_metrics = [&](const httplib::Request &, httplib::Response & res) { - if (!sparams.metrics_endpoint) { + if (!params.endpoint_metrics) { res_error(res, format_error_response("This server does not support metrics endpoint.", ERROR_TYPE_NOT_SUPPORTED)); return; } @@ -3307,14 +2746,14 @@ int main(int argc, char ** argv) { res.status = 200; // HTTP OK }; - const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { + const auto handle_slots_save = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } - std::string filepath = sparams.slot_save_path + filename; + std::string filepath = params.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_SAVE; @@ -3337,14 +2776,14 @@ 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) { + const auto handle_slots_restore = [&ctx_server, &res_error, ¶ms](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } - std::string filepath = sparams.slot_save_path + filename; + std::string filepath = params.slot_save_path + filename; server_task task; task.type = SERVER_TASK_TYPE_SLOT_RESTORE; @@ -3519,9 +2958,9 @@ int main(int argc, char ** argv) { res.set_content(models.dump(), "application/json; charset=utf-8"); }; - const auto handle_chat_completions = [&ctx_server, &sparams, &res_error](const httplib::Request & req, httplib::Response & res) { + const auto handle_chat_completions = [&ctx_server, ¶ms, &res_error](const httplib::Request & req, httplib::Response & res) { res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); - json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), sparams.chat_template); + json data = oaicompat_completion_params_parse(ctx_server.model, json::parse(req.body), params.chat_template); const int id_task = ctx_server.queue_tasks.get_new_id(); @@ -3746,17 +3185,29 @@ int main(int argc, char ** argv) { // // register static assets routes - if (!sparams.public_path.empty()) { + if (!params.public_path.empty()) { // Set the base directory for serving static files - svr->set_base_dir(sparams.public_path); + svr->set_base_dir(params.public_path); } // using embedded static files - svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); - svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); - svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); - svr->Get("/json-schema-to-grammar.mjs", handle_static_file( - json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); + svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); + svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); + svr->Get("/json-schema-to-grammar.mjs", handle_static_file(json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + + // add new-ui files + svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8")); + svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8")); + svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8")); + svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8")); + svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8")); // register API routes svr->Get ("/health", handle_health); @@ -3775,7 +3226,7 @@ int main(int argc, char ** argv) { svr->Post("/v1/embeddings", handle_embeddings); svr->Post("/tokenize", handle_tokenize); svr->Post("/detokenize", handle_detokenize); - if (!sparams.slot_save_path.empty()) { + if (!params.slot_save_path.empty()) { // only enable slot endpoints if slot_save_path is set svr->Post("/slots/:id_slot", handle_slots_action); } @@ -3783,12 +3234,12 @@ int main(int argc, char ** argv) { // // Start the server // - if (sparams.n_threads_http < 1) { + if (params.n_threads_http < 1) { // +2 threads for monitoring endpoints - sparams.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); + params.n_threads_http = std::max(params.n_parallel + 2, (int32_t) std::thread::hardware_concurrency() - 1); } - log_data["n_threads_http"] = std::to_string(sparams.n_threads_http); - svr->new_task_queue = [&sparams] { return new httplib::ThreadPool(sparams.n_threads_http); }; + log_data["n_threads_http"] = std::to_string(params.n_threads_http); + svr->new_task_queue = [¶ms] { return new httplib::ThreadPool(params.n_threads_http); }; LOG_INFO("HTTP server listening", log_data); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index d8a2286e4..b7bfb41d3 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -116,13 +116,6 @@ static inline void server_log(const char * level, const char * function, int lin // chat template utils // -// Check if the template supplied via "--chat-template" is supported or not. Returns true if it's valid -inline bool verify_custom_template(const std::string & tmpl) { - llama_chat_message chat[] = {{"user", "test"}}; - int res = llama_chat_apply_template(nullptr, tmpl.c_str(), chat, 1, true, nullptr, 0); - return res >= 0; -} - // Format given chat. If tmpl is empty, we take the template from model metadata inline std::string format_chat(const struct llama_model * model, const std::string & tmpl, const std::vector & messages) { size_t alloc_size = 0; diff --git a/examples/simple/README.md b/examples/simple/README.md index 5d24b1046..49e24501c 100644 --- a/examples/simple/README.md +++ b/examples/simple/README.md @@ -3,7 +3,7 @@ The purpose of this example is to demonstrate a minimal usage of llama.cpp for generating text with a given prompt. ```bash -./simple ./models/llama-7b-v2/ggml-model-f16.gguf "Hello my name is" +./simple -m ./models/llama-7b-v2/ggml-model-f16.gguf -p "Hello my name is" ... diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index b0f8e0fdc..69a92cf7d 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -6,28 +6,27 @@ #include #include +static void print_usage(int argc, char ** argv, const gpt_params & params) { + gpt_params_print_usage(argc, argv, params); + + LOG_TEE("\nexample usage:\n"); + LOG_TEE("\n %s -m model.gguf -p \"Hello my name is\" -n 32\n", argv[0]); + LOG_TEE("\n"); +} + int main(int argc, char ** argv) { gpt_params params; - if (argc == 1 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH [PROMPT]\n" , argv[0]); - return 1 ; - } + params.prompt = "Hello my name is"; + params.n_predict = 32; - if (argc >= 2) { - params.model = argv[1]; - } - - if (argc >= 3) { - params.prompt = argv[2]; - } - - if (params.prompt.empty()) { - params.prompt = "Hello my name is"; + if (!gpt_params_parse(argc, argv, params)) { + print_usage(argc, argv, params); + return 1; } // total length of the sequence including the prompt - const int n_len = 32; + const int n_predict = params.n_predict; // init LLM @@ -36,9 +35,7 @@ int main(int argc, char ** argv) { // initialize the model - llama_model_params model_params = llama_model_default_params(); - - // model_params.n_gpu_layers = 99; // offload all layers to the GPU + llama_model_params model_params = llama_model_params_from_gpt_params(params); llama_model * model = llama_load_model_from_file(params.model.c_str(), model_params); @@ -49,12 +46,7 @@ int main(int argc, char ** argv) { // initialize the context - llama_context_params ctx_params = llama_context_default_params(); - - ctx_params.seed = 1234; - ctx_params.n_ctx = 2048; - ctx_params.n_threads = params.n_threads; - ctx_params.n_threads_batch = params.n_threads_batch == -1 ? params.n_threads : params.n_threads_batch; + llama_context_params ctx_params = llama_context_params_from_gpt_params(params); llama_context * ctx = llama_new_context_with_model(model, ctx_params); @@ -69,14 +61,14 @@ int main(int argc, char ** argv) { tokens_list = ::llama_tokenize(ctx, params.prompt, true); const int n_ctx = llama_n_ctx(ctx); - const int n_kv_req = tokens_list.size() + (n_len - tokens_list.size()); + const int n_kv_req = tokens_list.size() + (n_predict - tokens_list.size()); - LOG_TEE("\n%s: n_len = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_len, n_ctx, n_kv_req); + LOG_TEE("\n%s: n_predict = %d, n_ctx = %d, n_kv_req = %d\n", __func__, n_predict, n_ctx, n_kv_req); // make sure the KV cache is big enough to hold all the prompt and generated tokens if (n_kv_req > n_ctx) { LOG_TEE("%s: error: n_kv_req > n_ctx, the required KV cache size is not big enough\n", __func__); - LOG_TEE("%s: either reduce n_len or increase n_ctx\n", __func__); + LOG_TEE("%s: either reduce n_predict or increase n_ctx\n", __func__); return 1; } @@ -115,7 +107,7 @@ int main(int argc, char ** argv) { const auto t_main_start = ggml_time_us(); - while (n_cur <= n_len) { + while (n_cur <= n_predict) { // sample the next token { auto n_vocab = llama_n_vocab(model); @@ -134,7 +126,7 @@ int main(int argc, char ** argv) { const llama_token new_token_id = llama_sample_token_greedy(ctx, &candidates_p); // is it an end of generation? - if (llama_token_is_eog(model, new_token_id) || n_cur == n_len) { + if (llama_token_is_eog(model, new_token_id) || n_cur == n_predict) { LOG_TEE("\n"); break; diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp index 12e46fbc9..0939a1a6a 100644 --- a/examples/speculative/speculative.cpp +++ b/examples/speculative/speculative.cpp @@ -27,7 +27,8 @@ struct seq_draft { int main(int argc, char ** argv) { gpt_params params; - if (gpt_params_parse(argc, argv, params) == false) { + if (!gpt_params_parse(argc, argv, params)) { + gpt_params_print_usage(argc, argv, params); return 1; } diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat index 1b0dc41ba..b8037aae8 100644 --- a/examples/sycl/win-build-sycl.bat +++ b/examples/sycl/win-build-sycl.bat @@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR :: for FP16 :: faster for long-prompt inference -:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON :: for FP32 -cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release if %errorlevel% neq 0 goto ERROR :: build example/main only :: make main diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 8b1baea80..54c9834af 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -3,40 +3,390 @@ #include #include +#include #include #include -int main(int argc, char ** argv) { - if (argc < 3 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]); +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#include +#include // For CommandLineToArgvW +#endif + +static void print_usage_information(const char * argv0, FILE * stream) { + fprintf(stream, "usage: %s [options]\n\n", argv0); + fprintf(stream, "The tokenize program tokenizes a prompt using a given model,\n"); + fprintf(stream, "and prints the resulting tokens to standard output.\n\n"); + fprintf(stream, "It needs a model file, a prompt, and optionally other flags\n"); + fprintf(stream, "to control the behavior of the tokenizer.\n\n"); + fprintf(stream, " The possible options are:\n"); + fprintf(stream, "\n"); + fprintf(stream, " -h, --help print this help and exit\n"); + fprintf(stream, " -m MODEL_PATH, --model MODEL_PATH path to model.\n"); + fprintf(stream, " --ids if given, only print numerical token IDs, and not token strings.\n"); + fprintf(stream, " The output format looks like [1, 2, 3], i.e. parseable by Python.\n"); + fprintf(stream, " -f PROMPT_FNAME, --file PROMPT_FNAME read prompt from a file.\n"); + fprintf(stream, " -p PROMPT, --prompt PROMPT read prompt from the argument.\n"); + fprintf(stream, " --stdin read prompt from standard input.\n"); + fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n"); + fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n"); +} + +static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) text; + (void) user_data; +} + +static std::string read_prompt_from_file(const char * filepath, bool & success) { + success = false; + + std::ifstream in(filepath, std::ios::binary); + if (!in) { + fprintf(stderr, "%s: could not open file '%s' for reading: %s\n", __func__, filepath, strerror(errno)); + return std::string(); + } + // do not assume the file is seekable (e.g. /dev/stdin) + std::stringstream buffer; + buffer << in.rdbuf(); + if (in.fail()) { + fprintf(stderr, "%s: could not read the entire file '%s': %s\n", __func__, filepath, strerror(errno)); + return std::string(); + } + + success = true; + return buffer.str(); +} + +// +// Function: ingest_args(...) -> vector +// +// Takes argc and argv arguments, and converts them to a vector of UTF-8 encoded +// strings, as an STL vector. +// +// In particular, it handles character encoding shenanigans on Windows. +// +// Note: raw_argc and raw_argv are not actually read at all on Windows. +// On Windows we call GetCommandLineW to get the arguments in wchar_t +// format, ignoring the regular argc/argv arguments to main(). +// +// TODO: potential opportunity to roll common stuff into common/console.cpp +// in relation to Windows wchar_t shenanigans. +static std::vector ingest_args(int raw_argc, char ** raw_argv) { + std::vector argv; + + // Handle Windows, if given non-ASCII arguments. + // We convert wchar_t arguments into UTF-8 char* on this platform. + // Lets you invoke 'tokenize' on Windows cmd.exe with non-ASCII characters + // without throwing tantrums. +#if defined(_WIN32) + int argc; + const LPWSTR cmdline_wargv = GetCommandLineW(); + LPWSTR * wargv = CommandLineToArgvW(cmdline_wargv, &argc); + + // silence unused arg warnings + (void) raw_argc; + (void) raw_argv; + + for (int i = 0; i < argc; ++i) { + int length_needed = WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), 0, 0, NULL, NULL); + char * output_buf = (char *) calloc(length_needed+1, sizeof(char)); + GGML_ASSERT(output_buf); + + WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), output_buf, length_needed, NULL, NULL); + output_buf[length_needed] = '\0'; + + argv.push_back(output_buf); + free(output_buf); + } + + LocalFree((HLOCAL) wargv); +#else + int argc = raw_argc; + for (int i = 0; i < argc; ++i) { + argv.push_back(raw_argv[i]); + } +#endif + + GGML_ASSERT((unsigned int) argc == argv.size()); + + return argv; +} + +// +// Function: write_utf8_cstr_to_stdout(const char *) -> +// +// writes a string to standard output; taking into account that on Windows +// to display correctly you have to use special handling. Works even if the +// user has not set a unicode code page on a Windows cmd.exe. +// +// In case of invalid UTF-8, invalid_utf8 is set to true on Windows, and something +// a human-readable is written instead. +// +// On non-Windows systems, simply printfs() the string. +static void write_utf8_cstr_to_stdout(const char * str, bool & invalid_utf8) { + invalid_utf8 = false; + +#if defined(_WIN32) + // Are we in a console? + HANDLE hConsole = GetStdHandle(STD_OUTPUT_HANDLE); + DWORD dwMode = 0; + + // According to Microsoft docs: + // "WriteConsole fails if it is used with a standard handle that is redirected to a file." + // Also according to the docs, you can use GetConsoleMode to check for that. + if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) { + printf("%s", str); + return; + } + + // MultiByteToWideChar reports an error if str is empty, don't report + // them as invalid_utf8. + if (*str == 0) { + return; + } + int length_needed = MultiByteToWideChar(CP_UTF8, MB_ERR_INVALID_CHARS, str, strlen(str), NULL, 0); + if (length_needed == 0) { + DWORD err = GetLastError(); + if (err == ERROR_NO_UNICODE_TRANSLATION) { + invalid_utf8 = true; + int len = strlen(str); + printf("<"); + for (int i = 0; i < len; ++i) { + if (i > 0) { + printf(" "); + } + printf("%02x", (uint8_t) str[i]); + } + printf(">"); + return; + } + GGML_ASSERT(false && "MultiByteToWideChar() failed in an unexpected way."); + } + + LPWSTR wstr = (LPWSTR) calloc(length_needed+1, sizeof(*wstr)); + GGML_ASSERT(wstr); + + MultiByteToWideChar(CP_UTF8, 0, str, strlen(str), wstr, length_needed); + WriteConsoleW(hConsole, wstr, length_needed, NULL, NULL); + + free(wstr); +#else + // TODO: reporting invalid_utf8 would be useful on non-Windows too. + // printf will silently just write bad unicode. + printf("%s", str); +#endif +} + +int main(int raw_argc, char ** raw_argv) { + const std::vector argv = ingest_args(raw_argc, raw_argv); + const int argc = argv.size(); + + if (argc <= 1) { + print_usage_information(argv[0].c_str(), stderr); return 1; } - const char * model_path = argv[1]; - const char * prompt = argv[2]; + ////// + // Read out all the command line arguments. + ////// - const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids"; + // variables where to put any arguments we see. + bool printing_ids = false; + bool no_bos = false; + bool disable_logging = false; + const char * model_path = NULL; + const char * prompt_path = NULL; + const char * prompt_arg = NULL; + + // track which arguments were explicitly given + // used for sanity checking down the line + bool model_path_set = false; + bool prompt_path_set = false; + bool prompt_set = false; + bool stdin_set = false; + + int iarg = 1; + for (; iarg < argc; ++iarg) { + std::string arg{argv[iarg]}; + if (arg == "-h" || arg == "--help") { + print_usage_information(argv[0].c_str(), stdout); + return 0; + } + else if (arg == "--ids") { + printing_ids = true; + } + else if (arg == "-m" || arg == "--model") { + if (model_path_set) { + fprintf(stderr, "Error: -m or --model specified multiple times.\n"); + return 1; + } + model_path = argv[++iarg].c_str(); + model_path_set = true; + } + else if (arg == "--no-bos") { + no_bos = true; + } + else if (arg == "-p" || arg == "--prompt") { + if (prompt_set) { + fprintf(stderr, "Error: -p or --prompt specified multiple times.\n"); + return 1; + } + prompt_arg = argv[++iarg].c_str(); + prompt_set = true; + } + else if (arg == "-f" || arg == "--file") { + if (prompt_path_set) { + fprintf(stderr, "Error: -f or --file specified multiple times.\n"); + return 1; + } + prompt_path = argv[++iarg].c_str(); + prompt_path_set = true; + } + else if (arg == "--stdin") { + stdin_set = true; + } + else if (arg == "--log-disable") { + disable_logging = true; + } + else { + fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str()); + return 1; + } + } + + ////// + // Sanity check the command line arguments. + ////// + + // Check that we have the required stuff set. + if (model_path_set && model_path == NULL) { + fprintf(stderr, "Error: --model requires an argument.\n"); + return 1; + } + if (!model_path_set) { + fprintf(stderr, "Error: must specify --model.\n"); + return 1; + } + if (prompt_path_set && prompt_path == NULL) { + fprintf(stderr, "Error: --file requires an argument.\n"); + return 1; + } + if (prompt_set && prompt_arg == NULL) { + fprintf(stderr, "Error: --prompt requires an argument.\n"); + return 1; + } + const int prompts_set = !!(prompt_path_set) + !!(prompt_set) + !!(stdin_set); + if (prompts_set > 1) { + fprintf(stderr, "Error: --stdin, --file and --prompt are mutually exclusive.\n"); + return 1; + } + // Must have some prompt. + if (prompts_set == 0) { + fprintf(stderr, "Error: must specify one of: --stdin, --file or --prompt.\n"); + return 1; + } + + GGML_ASSERT(model_path); + GGML_ASSERT(prompt_path || prompt_arg || stdin_set); + + ////// + // Figure out where will the prompt come from. + ////// + + std::string prompt; + if (prompt_path_set) { + bool success = false; + prompt = read_prompt_from_file(prompt_path, success); + if (!success) { + return 1; + } + } else if (prompt_set) { + prompt = prompt_arg; + } else { + GGML_ASSERT(stdin_set); + // we read stdin *after* loading model (early exit if model cannot + // be loaded, which can be a nicer user experience) + } + + ////// + // Start actually doing the tokenizing stuff. + ////// + +#ifdef LOG_DISABLE_LOGS + disable_logging = true; +#endif + + if (disable_logging) { + llama_log_set(llama_log_callback_null, NULL); + } llama_backend_init(); llama_model_params model_params = llama_model_default_params(); model_params.vocab_only = true; llama_model * model = llama_load_model_from_file(model_path, model_params); + if (!model) { + fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path); + return 1; + } llama_context_params ctx_params = llama_context_default_params(); llama_context * ctx = llama_new_context_with_model(model, ctx_params); + if (!ctx) { + fprintf(stderr, "Error: could not create context.\n"); + return 1; + } + + // read entire prompt from stdin? + if (stdin_set) { + GGML_ASSERT(!prompt_path_set && !prompt_set); + + std::stringstream stdin_buffer; + stdin_buffer << std::cin.rdbuf(); + if (std::cin.fail()) { + fprintf(stderr, "Error: could not read the entire standard input.\n"); + return 1; + } + + prompt = stdin_buffer.str(); + } + + const bool model_wants_add_bos = llama_should_add_bos_token(model); + const bool add_bos = model_wants_add_bos && !no_bos; std::vector tokens; + tokens = ::llama_tokenize(model, prompt, add_bos, true); - tokens = ::llama_tokenize(model, prompt, true, true); + if (printing_ids) { + printf("["); + } for (int i = 0; i < (int) tokens.size(); i++) { if (printing_ids) { - printf("%d\n", tokens[i]); + if (i > 0) { + printf(", "); + } + printf("%d", tokens[i]); } else { - printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str()); + bool invalid_utf8 = false; + printf("%6d -> '", tokens[i]); + write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8); + if (invalid_utf8) { + printf("' (utf-8 decode failure)\n"); + } else { + printf("'\n"); + } } } + if (printing_ids) { + printf("]\n"); + } + + // silence valgrind + llama_free(ctx); + llama_free_model(model); + return 0; } diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 587418cc7..e2f85c682 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -301,8 +301,8 @@ static struct ggml_tensor * llama_build_train_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom( - ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + return ggml_rope_ext( + ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; @@ -341,7 +341,8 @@ static struct ggml_tensor * llama_build_train_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/flake.lock b/flake.lock index c9ead0bf7..09047ab10 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1714641030, - "narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=", + "lastModified": 1717285511, + "narHash": "sha256-iKzJcpdXih14qYVcZ9QC9XuZYnPc6T8YImb6dX166kw=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e", + "rev": "2a55567fcf15b1b1c7ed712a2c6fadaec7412ea8", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1714635257, - "narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=", + "lastModified": 1716948383, + "narHash": "sha256-SzDKxseEcHR5KzPXLwsemyTR/kaM9whxeiJohbL04rs=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f", + "rev": "ad57eef4ef0659193044870c731987a6df5cf56b", "type": "github" }, "original": { @@ -36,14 +36,14 @@ }, "nixpkgs-lib": { "locked": { - "lastModified": 1714640452, - "narHash": "sha256-QBx10+k6JWz6u7VsohfSw8g8hjdBZEf8CFzXH1/1Z94=", + "lastModified": 1717284937, + "narHash": "sha256-lIbdfCsf8LMFloheeE6N31+BMIeixqyQWbSr2vk79EQ=", "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz" }, "original": { "type": "tarball", - "url": "https://github.com/NixOS/nixpkgs/archive/50eb7ecf4cd0a5756d7275c8ba36790e5bd53e33.tar.gz" + "url": "https://github.com/NixOS/nixpkgs/archive/eb9ceca17df2ea50a250b6b27f7bf6ab0186f198.tar.gz" } }, "root": { diff --git a/flake.nix b/flake.nix index 9cd3756e5..0a52ea52e 100644 --- a/flake.nix +++ b/flake.nix @@ -159,7 +159,6 @@ windows = config.legacyPackages.llamaPackagesWindows.llama-cpp; } // lib.optionalAttrs pkgs.stdenv.isLinux { - opencl = config.packages.default.override { useOpenCL = true; }; cuda = config.legacyPackages.llamaPackagesCuda.llama-cpp; mpi-cpu = config.packages.default.override { useMpi = true; }; diff --git a/ggml-alloc.c b/ggml-alloc.c index 1fbd376ed..73a3c1575 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -377,7 +377,7 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs galloc->bufts = calloc(n_bufs, sizeof(ggml_backend_buffer_type_t)); GGML_ASSERT(galloc->bufts != NULL); - galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t) * n_bufs); + galloc->buffers = calloc(n_bufs, sizeof(ggml_backend_buffer_t)); GGML_ASSERT(galloc->buffers != NULL); galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *)); @@ -750,7 +750,7 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * // this tensor was allocated without ggml-backend return; } - ggml_backend_view_init(galloc->buffers[buffer_id], tensor); + ggml_backend_view_init(tensor); } } else { if (tensor->data == NULL) { @@ -899,12 +899,12 @@ static bool alloc_tensor_range(struct ggml_context * ctx, if (t->view_src == NULL) { ggml_tallocr_alloc(&tallocr, t); } else if (t->buffer == NULL) { - ggml_backend_view_init(buffer, t); + ggml_backend_view_init(t); } } else { if (t->view_src != NULL && t->buffer == NULL) { // view of a pre-allocated tensor - ggml_backend_view_init(buffer, t); + ggml_backend_view_init(t); } } } diff --git a/ggml-backend.c b/ggml-backend.c index 9e35ce98d..05737ed69 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -151,7 +151,7 @@ void ggml_backend_buffer_reset(ggml_backend_buffer_t buffer) { bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst) { ggml_backend_buffer_t dst_buf = dst->view_src ? dst->view_src->buffer : dst->buffer; if (dst_buf->iface.cpy_tensor) { - return src->buffer->iface.cpy_tensor(dst_buf, src, dst); + return dst_buf->iface.cpy_tensor(dst_buf, src, dst); } return false; } @@ -1887,15 +1887,15 @@ ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, // utils -void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { +void ggml_backend_view_init(struct ggml_tensor * tensor) { GGML_ASSERT(tensor->buffer == NULL); GGML_ASSERT(tensor->view_src != NULL); GGML_ASSERT(tensor->view_src->buffer != NULL); GGML_ASSERT(tensor->view_src->data != NULL); - tensor->buffer = buffer; + tensor->buffer = tensor->view_src->buffer; tensor->data = (char *)tensor->view_src->data + tensor->view_offs; - ggml_backend_buffer_init_tensor(buffer, tensor); + ggml_backend_buffer_init_tensor(tensor->buffer, tensor); } void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr) { @@ -1954,7 +1954,7 @@ static void graph_copy_init_tensor(struct ggml_hash_set hash_set, struct ggml_te struct ggml_tensor * dst = node_copies[id]; if (dst->view_src != NULL) { graph_copy_init_tensor(hash_set, node_copies, node_init, src->view_src); - ggml_backend_view_init(dst->view_src->buffer, dst); + ggml_backend_view_init(dst); } else { ggml_backend_tensor_copy(src, dst); diff --git a/ggml-backend.h b/ggml-backend.h index 744b6a774..c582b0685 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -225,7 +225,7 @@ extern "C" { // Tensor initialization GGML_API void ggml_backend_tensor_alloc(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor, void * addr); - GGML_API void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); + GGML_API void ggml_backend_view_init(struct ggml_tensor * tensor); #ifdef __cplusplus diff --git a/ggml-common.h b/ggml-common.h index 43c7978a0..77e6bfba4 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -65,13 +65,8 @@ typedef sycl::half2 ggml_half2; // QK = number of values after dequantization // QK_K = super-block size -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else #define QK_K 256 #define K_SCALE_SIZE 12 -#endif // GGML_QKK_64 #if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL) // QR = QK / number of values before dequantization @@ -131,13 +126,8 @@ typedef sycl::half2 ggml_half2; #define QI4_NL (QK4_NL / (4*QR4_NL)) #define QR4_NL 2 -#if QK_K == 64 -#define QI4_XS QI4_NL -#define QR4_XS QR4_NL -#else #define QI4_XS (QK_K / (4*QR4_XS)) #define QR4_XS 8 -#endif #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP @@ -228,15 +218,6 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wro // weight is represented as x = a * q // 16 blocks of 16 elements each // Effectively 3.4375 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[2]; - ggml_half d; // super-block scale -} block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); -#else typedef struct { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits @@ -244,20 +225,11 @@ typedef struct { ggml_half d; // super-block scale } block_q3_K; static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); -#endif // 4-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 4.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else typedef struct { union { struct { @@ -270,21 +242,11 @@ typedef struct { uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); -#endif // 5-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 5.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d; // super-block scale - int8_t scales[QK_K/16]; // 8-bit block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else typedef struct { union { struct { @@ -298,7 +260,6 @@ typedef struct { uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif // 6-bit quantization // weight is represented as x = a * q @@ -356,11 +317,7 @@ typedef struct { static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); // 3.4375 bpw -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else #define IQ3S_N_SCALE QK_K/64 -#endif typedef struct { ggml_half d; uint8_t qs[QK_K/4]; @@ -381,16 +338,9 @@ static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wron typedef struct { uint8_t qs[QK_K/8]; // grid index, low 8 bits uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8) -#if QK_K == 64 - ggml_half d; -#endif uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64) } block_iq1_m; -#if QK_K == 64 -static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding"); -#else static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding"); -#endif // Used by IQ1_M quants typedef union { @@ -406,9 +356,6 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding"); -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -#else typedef struct { ggml_half d; uint16_t scales_h; @@ -416,7 +363,6 @@ typedef struct { uint8_t qs[QK_K/2]; } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); -#endif #endif // GGML_COMMON_DECL #endif // GGML_COMMON_DECL diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b82167cbf..c81c6a0d7 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -119,6 +119,20 @@ int ggml_cuda_get_device() { return id; } +static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { + ggml_cuda_set_device(device); +#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) + auto res = hipMallocManaged(ptr, size); + if (res == hipSuccess) { + // if error we "need" to know why... + CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device)); + } + return res; +#else + return cudaMalloc(ptr, size); +#endif +} + static ggml_cuda_device_info ggml_cuda_init() { #ifdef __HIP_PLATFORM_AMD__ // Workaround for a rocBLAS bug when using multiple graphics cards: @@ -271,7 +285,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); ggml_cuda_set_device(device); - CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); + CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device)); *actual_size = look_ahead_size; pool_size += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC @@ -537,7 +551,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 void * dev_ptr; - cudaError_t err = cudaMalloc(&dev_ptr, size); + cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); if (err != cudaSuccess) { // clear the error cudaGetLastError(); @@ -798,7 +812,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first ggml_cuda_set_device(id); char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); + CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id)); // set padding to 0 to avoid possible NaN values if (size > original_size) { @@ -1856,7 +1870,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co } } #else - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 // use cublasGemmStridedBatchedEx CUBLAS_CHECK( @@ -2510,9 +2524,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool use_cuda_graph = true; bool cuda_graph_update_required = false; - // pointer to CUDA cpy kernel, which is required to identify + // vector of pointers to CUDA cpy kernels, which are required to identify // kernel parameters which need updated in the graph for each token - void * ggml_cuda_cpy_fn_ptr = nullptr; + std::vector ggml_cuda_cpy_fn_ptrs; if (cuda_ctx->cuda_graph->graph == nullptr) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { @@ -2588,9 +2602,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t 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]); + // store a pointer to each copy op CUDA kernel to identify it later + void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) { + ggml_cuda_cpy_fn_ptrs.push_back(ptr); } } @@ -2687,10 +2702,8 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t 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)); - } + // 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); @@ -2720,7 +2733,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t 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) { + if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) { 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])); @@ -2871,7 +2884,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + return true; case GGML_OP_ROPE: + return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: @@ -2888,10 +2903,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons #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) { + if (op->src[0]->ne[0] == 128) { return true; } - return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; + if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { + return true; + } + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA && + op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16; #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 8f6fd71cf..22872ca5c 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -79,13 +79,8 @@ #define cudaHostRegisterReadOnly hipHostRegisterReadOnly #define cudaHostUnregister hipHostUnregister #define cudaLaunchHostFunc hipLaunchHostFunc -#ifdef GGML_HIP_UMA -#define cudaMalloc hipMallocManaged -#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size) -#else #define cudaMalloc hipMalloc #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) -#endif #define cudaMemcpy hipMemcpy #define cudaMemcpyAsync hipMemcpyAsync #define cudaMemcpyPeerAsync hipMemcpyPeerAsync diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu index 2941d2f17..dac10ec36 100644 --- a/ggml-cuda/concat.cu +++ b/ggml-cuda/concat.cu @@ -1,15 +1,69 @@ #include "concat.cuh" -static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { +// contiguous kernels +static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; } - // operation + int offset_dst = nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; + + if (nidx < ne00) { // src0 + int offset_src = + nidx + + blockIdx.y * ne00 + + blockIdx.z * ne00 * gridDim.y; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + (nidx - ne00) + + blockIdx.y * (ne0 - ne00) + + blockIdx.z * (ne0 - ne00) * gridDim.y; + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + + if (blockIdx.y < ne01) { // src0 + int offset_src = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + nidx + + (blockIdx.y - ne01) * ne0 + + blockIdx.z * ne0 * (gridDim.y - ne01); + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + if (blockIdx.z < ne02) { // src0 int offset_src = nidx + @@ -25,25 +79,118 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, } } -static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { +static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; dim3 gridDim(num_blocks, ne1, ne2); - concat_f32<<>>(x, y, dst, ne0, ne02); + if (dim == 0) { + concat_f32_dim0<<>>(x, y, dst, ne0, ne00); + return; + } + if (dim == 1) { + concat_f32_dim1<<>>(x, y, dst, ne0, ne01); + return; + } + concat_f32_dim2<<>>(x, y, dst, ne0, ne02); } +// non-contiguous kernel (slow) +static __global__ void concat_f32_non_cont( + const char * src0, + const char * src1, + char * dst, + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne03, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, + int64_t /*ne10*/, + int64_t /*ne11*/, + int64_t /*ne12*/, + int64_t /*ne13*/, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, + int64_t ne0, + int64_t /*ne1*/, + int64_t /*ne2*/, + int64_t /*ne3*/, + uint64_t nb0, + uint64_t nb1, + uint64_t nb2, + uint64_t nb3, + int32_t dim) { + const int64_t i3 = blockIdx.z; + const int64_t i2 = blockIdx.y; + const int64_t i1 = blockIdx.x; + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + + const float * x; + + for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); + } else { + x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); + } + + float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; + } +} + + void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const float * src0_d = (const float *)src0->data; - const float * src1_d = (const float *)src1->data; - float * dst_d = (float *)dst->data; + cudaStream_t stream = ctx.stream(); + const int32_t dim = ((int32_t *) dst->op_params)[0]; + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); - for (int i3 = 0; i3 < dst->ne[3]; i3++) { - concat_f32_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream); + if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + const float * src0_d = (const float *)src0->data; + const float * src1_d = (const float *)src1->data; + + float * dst_d = (float *)dst->data; + + if (dim != 3) { + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_cuda( + src0_d + i3 * (src0->nb[3] / 4), + src1_d + i3 * (src1->nb[3] / 4), + dst_d + i3 * ( dst->nb[3] / 4), + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], dim, stream); + } + } else { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream)); + } + } else { + dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]); + concat_f32_non_cont<<>>( + (const char *)src0->data, + (const char *)src1->data, + ( char *)dst->data, + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], + dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim); } } diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index 830e2d756..c0a444707 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -131,7 +131,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t const block_q2_K * x = (const block_q2_K *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t n = tid/32; const int64_t l = tid - 32*n; const int64_t is = 8*n + l/16; @@ -145,17 +144,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); -#else - const int64_t is = tid/16; // 0 or 1 - const int64_t il = tid%16; // 0...15 - const uint8_t q = x[i].qs[il] >> (2*is); - dst_t * y = yy + i*QK_K + 16*is + il; - float dall = __low2half(x[i].dm); - float dmin = __high2half(x[i].dm); - y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); -#endif - } template @@ -164,7 +152,6 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; const block_q3_K * x = (const block_q3_K *) vx; -#if QK_K == 256 const int64_t r = threadIdx.x/4; const int64_t tid = r/2; const int64_t is0 = r%2; @@ -188,31 +175,8 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t const uint8_t * hm = x[i].hmask; for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -#else - const int64_t tid = threadIdx.x; - const int64_t is = tid/16; // 0 or 1 - const int64_t il = tid%16; // 0...15 - const int64_t im = il/8; // 0...1 - const int64_t in = il%8; // 0...7 - - dst_t * y = yy + i*QK_K + 16*is + il; - - const uint8_t q = x[i].qs[il] >> (2*is); - const uint8_t h = x[i].hmask[in] >> (2*is + im); - const float d = (float)x[i].d; - - if (is == 0) { - y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } else { - y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } -#endif - } -#if QK_K == 256 static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -221,7 +185,6 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -229,7 +192,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 32 threads const int64_t tid = threadIdx.x; const int64_t il = tid/8; @@ -253,15 +215,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int64_t tid = threadIdx.x; - const uint8_t * q = x[i].qs; - dst_t * y = yy + i*QK_K; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); - y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); -#endif } template @@ -270,7 +223,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; const int64_t il = tid/16; // il is in 0...3 @@ -297,18 +249,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t hm <<= 1; y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; -#else - const int64_t tid = threadIdx.x; - const uint8_t q = x[i].qs[tid]; - const int64_t im = tid/8; // 0...3 - const int64_t in = tid%8; // 0...7 - const int64_t is = tid/16; // 0 or 1 - const uint8_t h = x[i].qh[in] >> im; - const float d = x[i].d; - dst_t * y = yy + i*QK_K + tid; - y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); - y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); -#endif } template @@ -316,7 +256,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t const block_q6_K * x = (const block_q6_K *) vx; const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; @@ -336,24 +275,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -#else - - // assume 32 threads - const int64_t tid = threadIdx.x; - const int64_t ip = tid/16; // 0 or 1 - const int64_t il = tid - 16*ip; // 0...15 - - dst_t * y = yy + i*QK_K + 16*ip + il; - - const float d = x[i].d; - - const uint8_t ql = x[i].ql[16*ip + il]; - const uint8_t qh = x[i].qh[il] >> (2*ip); - const int8_t * sc = x[i].scales; - - y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); -#endif } template @@ -363,7 +284,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -374,10 +294,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -387,7 +303,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const block_iq2_xs * x = (const block_iq2_xs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -396,10 +311,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -409,7 +320,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const block_iq2_s * x = (const block_iq2_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -417,10 +327,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -430,7 +336,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -445,10 +350,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -458,7 +359,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const block_iq3_s * x = (const block_iq3_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -471,10 +371,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -484,7 +380,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ const block_iq1_s * x = (const block_iq1_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -497,10 +392,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -510,7 +401,6 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ const block_iq1_m * x = (const block_iq1_m *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -527,13 +417,8 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } - template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -550,10 +435,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } - } -#if QK_K != 64 template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { const int64_t i = blockIdx.x; @@ -570,7 +453,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } } -#endif template static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { @@ -592,21 +474,13 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * template static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q2_K<<>>(vx, y); -#else - dequantize_block_q2_K<<>>(vx, y); -#endif } template static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q3_K<<>>(vx, y); -#else - dequantize_block_q3_K<<>>(vx, y); -#endif } template @@ -632,21 +506,13 @@ static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k template static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q5_K<<>>(vx, y); -#else - dequantize_block_q5_K<<>>(vx, y); -#endif } template static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q6_K<<>>(vx, y); -#else - dequantize_block_q6_K<<>>(vx, y); -#endif } template @@ -700,11 +566,7 @@ static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t template static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_block_iq4_nl<<>>(vx, y); -#else dequantize_block_iq4_xs<<>>(vx, y); -#endif } template diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu index 7313e3e17..47d4d5d9e 100644 --- a/ggml-cuda/dmmv.cu +++ b/ggml-cuda/dmmv.cu @@ -22,7 +22,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -71,37 +70,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, tmp += dall * sum1 - dmin * sum2; } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; - - uint32_t uaux[2]; - const uint8_t * d = (const uint8_t *)uaux; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint32_t * s = (const uint32_t *)x[i].scales; - - uaux[0] = s[0] & 0x0f0f0f0f; - uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - - const float2 dall = __half22float2(x[i].dm); - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t ql = q[l]; - sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) - + y[l+16] * d[1] * ((ql >> 2) & 3) - + y[l+32] * d[2] * ((ql >> 4) & 3) - + y[l+48] * d[3] * ((ql >> 6) & 3); - sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; - } - tmp += dall.x * sum1 - dall.y * sum2; - } -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -123,8 +91,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -175,34 +141,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, tmp += d * sum; } -#else - - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 - const int in = offset/8; // 0 or 1 - const int im = offset%8; // 0...7 - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint8_t * s = x[i].scales; - - const float dall = (float)x[i].d; - - float sum = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t hl = x[i].hmask[im+l] >> in; - const uint8_t ql = q[l]; - sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) - + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) - + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) - + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); - } - tmp += sum; - } -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -221,7 +159,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const block_q4_K * x = (const block_q4_K *)vx + ib0; -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -306,36 +243,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, #endif } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - - const int step = tid * K_QUANTS_PER_ITERATION; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - float tmp = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const float * y = yy + i*QK_K + step; - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) - + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) - + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) - + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); - } - tmp += sum; - } - -#endif // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -355,7 +262,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -426,30 +332,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; } -#else - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); - const int step = tid * K_QUANTS_PER_ITERATION; - const int im = step/8; - const int in = step%8; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const int8_t * s = x[i].scales; - const float * y = yy + i*QK_K + step; - const float d = x[i].d; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - const uint8_t h = x[i].qh[in+j] >> im; - sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) - + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) - + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) - + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); - } - tmp += sum; - } -#endif - // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); @@ -470,8 +352,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const block_q6_K * x = (const block_q6_K *)vx + ib0; -#if QK_K == 256 - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -526,37 +406,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, } -#else - - const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 - - const int step = tid * K_QUANTS_PER_ITERATION; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + step; - const uint8_t * ql = x[i].ql + step; - const uint8_t * qh = x[i].qh + step; - const int8_t * s = x[i].scales; - - const float d = x[i+0].d; - - float sum = 0; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) - + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) - + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) - + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); - } - tmp += sum; - - } - -#endif - // sum up partial sums and write back result tmp = warp_reduce_sum(tmp); diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index 1dd519bde..c00f8606a 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -1,4 +1,8 @@ +#pragma once + #include "common.cuh" +#include "convert.cuh" +#include "vecdotq.cuh" #include @@ -34,11 +38,523 @@ typedef void (* fattn_kernel_t)( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, const int ne3); +typedef half (*vec_dot_KQ_f16_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); +typedef float (*vec_dot_KQ_f32_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; + GGML_UNUSED(Q_v); + + half sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_0; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_1; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid4d8 = __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid4d8 + m4s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_0; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */; + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_1; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid5d8 = __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid5d8 + m5s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A + + const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_0; + const int iqs = k_KQ % QI8_0; + + const int v = get_int_from_int8(K_q8_0[ib].qs, iqs); + + T Q_d; + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]); + } else { + const float2 * Q_ds = (const float2 *) Q_ds_v; + Q_d = Q_ds[k_KQ_0/WARP_SIZE].x; + } + + sum += vec_dot_q8_0_q8_1_impl(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d); + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) { + + const half2 * K_h2 = (const half2 *) K_c; + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_h2 = (const half2 *) Q_v; + + 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; + + const half2 K_ik = K_h2[k_KQ]; + sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE]; + } + + return __low2half(sum2) + __high2half(sum2); + } +#endif // FP16_AVAILABLE + + const float2 * Q_f2 = (const float2 *) Q_v; + + float sum = 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_KQ]; + sum += __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x; + sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y; + } + + return sum; +} + +template +static __device__ __forceinline__ void quantize_q8_1_to_shared( + const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) { + + float vals[sizeof(int)] = {0.0f}; +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + vals[l] = scale * x[4*threadIdx.x + l]; + } + + float amax = fabsf(vals[0]); + float sum = vals[0]; +#pragma unroll + for (int l = 1; l < sizeof(int); ++l) { + amax = fmaxf(amax, fabsf(vals[l])); + sum += vals[l]; + } +#pragma unroll + for (int mask = QI8_1/2; mask > 0; mask >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32)); + sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, 32); + } + + const float d = amax / 127; + int q32 = 0; + int8_t * q8 = (int8_t *) &q32; + + if (d != 0.0f) { +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + q8[l] = roundf(vals[l] / d); + } + } + + yq32[threadIdx.x] = q32; + if (threadIdx.x % QI8_1 == 0) { + if (std::is_same::value) { + ((half2 *) yds)[threadIdx.x/QI8_1] = make_half2(d, sum); + } else { + ((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum); + } + } +} + +typedef half (*dequantize_1_f16_t)(const void *, const int64_t); +typedef float (*dequantize_1_f32_t)(const void *, const int64_t); + +template +static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) { + const block_q4_0 * x = (const block_q4_0 *) vx; + + const int64_t ib = i / QK4_0; + const int iqs = i % (QK4_0/2); + const int shift = (i % QK4_0) / (QK4_0/2); + + const T d = x[ib].d; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F) - 8; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) { + const block_q4_1 * x = (const block_q4_1 *) vx; + + const int64_t ib = i / QK4_1; + const int iqs = i % (QK4_1/2); + const int shift = (i % QK4_1) / (QK4_1/2); + + const half2 dm = x[ib].dm; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F); + +#if FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) { + const block_q5_0 * x = (const block_q5_0 *) vx; + + const int64_t ib = i / QK5_0; + const int idq = i % QK5_0; + const int iqs = i % (QK5_0/2); + const int shift = (i % QK5_0) / (QK5_0/2); + + const T d = x[ib].d; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh) - 16; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) { + const block_q5_1 * x = (const block_q5_1 *) vx; + + const int64_t ib = i / QK5_1; + const int idq = i % QK5_1; + const int iqs = i % (QK5_1/2); + const int shift = (i % QK5_1) / (QK5_1/2); + + const half2 dm = x[ib].dm; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh); + +#if FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) { + const block_q8_0 * x = (const block_q8_0 *) vx; + + const int64_t ib = i / QK8_0; + const int iqs = i % QK8_0; + + const T d = x[ib].d; + const int q = x[ib].qs[iqs]; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) { + const half * x = (const half *) vx; + + return x[i]; +} + +template +constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +template +constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + template // D == head size #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) @@ -83,8 +599,32 @@ static __global__ void flash_attn_combine_results( dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; } +static void on_no_fattn_vec_case(const int D) { + if (D == 64) { + fprintf(stderr, "Unsupported KV type combination for head_size 64.\n"); + fprintf(stderr, "By default only f16 KV cache is supported.\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n"); + GGML_ASSERT(false); + } else if (D == 128) { + fprintf(stderr, "Unsupported KV type combination for head_size 128.\n"); + fprintf(stderr, "Supported combinations:\n"); + fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n"); + fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n"); + fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n"); + GGML_ASSERT(false); + } else { + fprintf(stderr, "Unsupported KV type combination for head_size 256.\n"); + fprintf(stderr, "Only f16 is supported.\n"); + GGML_ASSERT(false); + } +} + template -void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { +void launch_fattn( + ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, + const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V +) { const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; @@ -94,8 +634,6 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern 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); @@ -107,9 +645,49 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern ggml_cuda_pool & pool = ctx.pool(); cudaStream_t main_stream = ctx.stream(); + ggml_cuda_pool_alloc K_f16(pool); + ggml_cuda_pool_alloc V_f16(pool); ggml_cuda_pool_alloc dst_tmp(pool); ggml_cuda_pool_alloc dst_tmp_meta(pool); + char * K_data = (char *) K->data; + size_t nb11 = K->nb[1]; + size_t nb12 = K->nb[2]; + size_t nb13 = K->nb[3]; + + char * V_data = (char *) V->data; + size_t nb21 = V->nb[1]; + size_t nb22 = V->nb[2]; + size_t nb23 = V->nb[3]; + + if (need_f16_K && K->type != GGML_TYPE_F16) { + K_f16.alloc(ggml_nelements(K)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type); + to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream); + K_data = (char *) K_f16.ptr; + + const size_t bs = ggml_blck_size(K->type); + const size_t ts = ggml_type_size(K->type); + + nb11 = nb11*bs*sizeof(half)/ts; + nb12 = nb12*bs*sizeof(half)/ts; + nb13 = nb13*bs*sizeof(half)/ts; + } + + if (need_f16_V && V->type != GGML_TYPE_F16) { + V_f16.alloc(ggml_nelements(V)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); + to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); + V_data = (char *) V_f16.ptr; + + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); + + nb21 = nb21*bs*sizeof(half)/ts; + nb22 = nb22*bs*sizeof(half)/ts; + nb23 = nb23*bs*sizeof(half)/ts; + } + if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); @@ -133,8 +711,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern fattn_kernel<<>>( (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, + K_data, + 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, @@ -142,7 +720,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern 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], + nb11, nb12, nb13, + nb21, nb22, nb23, KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] ); CUDA_CHECK(cudaGetLastError()); diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index 4a07ac6ad..cb11d7212 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f16( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, @@ -83,7 +86,7 @@ static __global__ void flash_attn_tile_ext_f16( 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]; + const float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); } } @@ -238,6 +241,10 @@ static __global__ void flash_attn_tile_ext_f16( for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { const int j_VKQ = j_VKQ_0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]); kqsum_j = warp_reduce_sum(kqsum_j); @@ -271,13 +278,13 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 130e7cbdb..15e22f495 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, @@ -79,7 +82,7 @@ static __global__ void flash_attn_tile_ext_f32( #pragma unroll for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) { - float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x]; + float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x] : make_float2(0.0f, 0.0f); Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale; Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale; } @@ -237,6 +240,10 @@ static __global__ void flash_attn_tile_ext_f32( for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { const int j_VKQ = j_VKQ_0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + float kqsum_j = kqsum[j_VKQ_0/nwarps]; kqsum_j = warp_reduce_sum(kqsum_j); @@ -268,13 +275,13 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); @@ -283,11 +290,7 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * } void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); + const ggml_tensor * Q = dst->src[0]; if (Q->ne[1] <= 16) { constexpr int cols_per_block = 16; diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu deleted file mode 100644 index 54e1ac5d1..000000000 --- a/ggml-cuda/fattn-vec-f16.cu +++ /dev/null @@ -1,326 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f16.cuh" - -template // 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); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - - 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(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]; -#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 < ncols) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - ggml_tensor * KQV = dst; - ggml_tensor * Q = dst->src[0]; - - 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: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 256: { - constexpr int D = 256; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: - GGML_ASSERT(false); - break; - } -} - -template -void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - - if (Q->ne[1] == 1) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f16_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh index c7023610a..9e1aa2c6b 100644 --- a/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -1,5 +1,397 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // 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 nb21, + const int nb22, + const int nb23, + 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. -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V); + + 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. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); + + const half * maskh = (const half *) mask + ne11*ic0; + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + 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]; + 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 (f16 K) or q8_1 (quantized K) and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)]; + half2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + } + + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + + 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; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + 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(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid); + reinterpret_cast(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, 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) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + 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 < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu deleted file mode 100644 index 5bcabd092..000000000 --- a/ggml-cuda/fattn-vec-f32.cu +++ /dev/null @@ -1,275 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f32.cuh" - -template // 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); - - const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - - 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 < ncols) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -} - -template -void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f32_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh index 614d54ae3..ddf0c8374 100644 --- a/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -1,3 +1,374 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // 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 nb21, + const int nb22, + const int nb23, + 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. + + constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V); + + 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. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + 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 float2 (f16 K) or q8_1 (quantized K) and store in registers: + float2 Q_f2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)]; + float2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_f2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_f2[j][i0/WARP_SIZE].x *= scale; + Q_f2[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; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#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 = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, 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) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + 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 < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f32_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-wmma-f16.cuh b/ggml-cuda/fattn-wmma-f16.cuh new file mode 100644 index 000000000..59cd30d78 --- /dev/null +++ b/ggml-cuda/fattn-wmma-f16.cuh @@ -0,0 +1,490 @@ +#include "common.cuh" +#include "fattn-common.cuh" + +#if FP16_MMA_AVAILABLE +#include +#endif + +// D == head size, VKQ_stride == num VKQ rows calculated in parallel: +template +#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, + 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 nb21, + const int nb22, + const int nb23, + 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); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); + + 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(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); + + 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 ? 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)))); + 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 +} + +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 ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + constexpr int nwarps = 4; + + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; + const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + + if (4*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); + return; + } + if (2*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); + return; + } + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); +} + +#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \ + template void ggml_cuda_flash_attn_ext_wmma_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, float); +// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 8, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index af7c95232..38d30b210 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -4,454 +4,295 @@ #include "fattn-tile-f32.cuh" #include "fattn-vec-f16.cuh" #include "fattn-vec-f32.cuh" +#include "fattn-wmma-f16.cuh" #include "fattn.cuh" #include -#if FP16_MMA_AVAILABLE -#include -#endif +static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; -// D == head size, VKQ_stride == num VKQ rows calculated in parallel: -template -#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, - 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_MMA_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + const int32_t precision = KQV->op_params[2]; - 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); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - const half2 slope2 = make_half2(slopef, slopef); - - 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(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); - - 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 ? 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)))); - 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) { + if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { + constexpr int cols_per_block = 16; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } + } else { + constexpr int cols_per_block = 32; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + break; + // case 256: + // ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + // break; + default: + GGML_ASSERT(false); 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(); + return; } -#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) { + if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) { + constexpr int cols_per_block = 8; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); 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; + return; } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 16; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 32; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } +} +#define FATTN_VEC_F16_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f16_case(ctx, dst); \ + return; \ + } \ + +static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 ) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) #else - NO_DEVICE_CODE; -#endif // FP16_MMA_AVAILABLE + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); } -constexpr int get_max_power_of_2(int x) { - return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; -} +#define FATTN_VEC_F32_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f32_case(ctx, dst); \ + return; \ + } \ -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."); +static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; -// 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; -} +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) -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."); + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) -template -void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) - constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; - const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) - if (4*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 4; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - if (2*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 2; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - constexpr int parallel_blocks = 1; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#else + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); } void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -464,8 +305,8 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst // On AMD the tile kernels perform poorly, use the vec kernel instead: if (cc >= CC_OFFSET_AMD) { - if (precision == GGML_PREC_DEFAULT) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); } @@ -483,156 +324,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst if (!fp16_mma_available(cc)) { if (Q->ne[1] <= 8) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); } return; } - if (precision != GGML_PREC_DEFAULT) { - if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { + if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + return; + } else if(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; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); - 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>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); - break; - // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); - // break; - default: - GGML_ASSERT(false); - break; - } - } - return; } - if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - 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>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - 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>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - 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>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - break; - default: - GGML_ASSERT(false); - break; - } - return; + ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst); } diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 933d799ce..ebe1dc5c8 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -386,7 +386,7 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat( u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; } - return vec_dot_q8_0_q8_1_impl + return vec_dot_q8_0_q8_1_impl (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); } @@ -547,7 +547,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat( const float * x_dmf = (const float *) x_dm; const float * y_df = (const float *) y_ds; - return vec_dot_q8_0_q8_1_impl + return vec_dot_q8_0_q8_1_impl (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0], y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]); } @@ -826,11 +826,7 @@ template static __device__ __forceinlin const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif } #pragma unroll @@ -933,9 +929,7 @@ template static __device__ __forceinlin const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif } #pragma unroll diff --git a/ggml-cuda/norm.cu b/ggml-cuda/norm.cu index 86f774534..30866d512 100644 --- a/ggml-cuda/norm.cu +++ b/ggml-cuda/norm.cu @@ -170,6 +170,8 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -188,6 +190,8 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -202,6 +206,8 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 4b0d2e5ad..0dd07977e 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -58,10 +58,10 @@ static __global__ void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +template static __global__ void rope_neox( const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors ) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); @@ -85,13 +85,13 @@ static __global__ void rope_neox( const int i = row*ncols + ib*n_dims + ic/2; const int i2 = row/p_delta_rows; - float cur_rot = inv_ndims * ic - ib; - const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f); + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + + const float theta_base = p*powf(theta_scale, col/2.0f)/freq_factor; float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + n_dims/2]; @@ -164,7 +164,7 @@ static void rope_cuda( template static void rope_neox_cuda( const T * x, T * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); @@ -172,18 +172,31 @@ static void rope_neox_cuda( const dim3 block_nums(nrows, num_blocks_x, 1); const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); + } } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, freq_factors + ); + } } } @@ -214,34 +227,37 @@ static void rope_cuda_f32( static void rope_neox_cuda_f16( const half * x, half * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream) { + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } static void rope_neox_cuda_f32( const float * x, float * dst, int ncols, int n_dims, int nrows, const int32_t * pos, float freq_scale, int p_delta_rows, - float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } void ggml_cuda_op_rope(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 = (const float *)src1->data; + float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); GGML_ASSERT(src0->type == dst->type); const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - const int64_t ne2 = dst->ne[2]; const int64_t nrows = ggml_nrows(src0); //const int n_past = ((int32_t *) dst->op_params)[0]; @@ -259,16 +275,22 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; const int32_t * pos = nullptr; - if ((mode & 1) == 0) { - GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(src1->ne[0] == ne2); - pos = (const int32_t *) src1_d; - } const bool is_neox = mode & 2; const bool is_glm = mode & 4; + pos = (const int32_t *) src1_d; + + if (is_neox) { + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } + rope_corr_dims corr_dims; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); @@ -280,12 +302,12 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { if (src0->type == GGML_TYPE_F32) { rope_neox_cuda_f32( (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_cuda_f16( (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else { GGML_ASSERT(false); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu new file mode 100644 index 000000000..d7f103475 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu new file mode 100644 index 000000000..f3d8d2eda --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu new file mode 100644 index 000000000..9beb05ca2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu new file mode 100644 index 000000000..0c163dcba --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu new file mode 100644 index 000000000..3980167b3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu new file mode 100644 index 000000000..fe099921d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu new file mode 100644 index 000000000..d4d5e7999 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 000000000..f08b10c4d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 000000000..e8c3f8adc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 000000000..c01416a13 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 000000000..46615f281 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 000000000..72dcc1a2f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu new file mode 100644 index 000000000..9fa8a377d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 000000000..20ea86c6d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 000000000..ed815957c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 000000000..bbe9e6a1c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 000000000..d12a61699 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 000000000..1e901afcb --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu new file mode 100644 index 000000000..a3f98ce37 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 000000000..1bae97243 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 000000000..7258e9775 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 000000000..08435c005 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 000000000..17864e8e9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 000000000..9239138c9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu new file mode 100644 index 000000000..e387d9c1d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 000000000..d69d3bbd6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 000000000..61a478816 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 000000000..89995080a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 000000000..9e6a58dff --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 000000000..153cbfd86 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu new file mode 100644 index 000000000..09d576558 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 000000000..3e3c91e68 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 000000000..7b973058f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 000000000..a43a475d4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 000000000..5b570c0a3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 000000000..bf2cc684e --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu new file mode 100644 index 000000000..7428e45ea --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu new file mode 100644 index 000000000..4aee830de --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu new file mode 100644 index 000000000..36acb6319 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu new file mode 100644 index 000000000..a4090c390 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu new file mode 100644 index 000000000..17b6b2d11 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu new file mode 100644 index 000000000..549e1cea1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu new file mode 100644 index 000000000..66bcd820f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu new file mode 100644 index 000000000..15933a299 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu new file mode 100644 index 000000000..8aa785583 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu new file mode 100644 index 000000000..bde3924fd --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu new file mode 100644 index 000000000..1708181c1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu new file mode 100644 index 000000000..30fa6fa4c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu new file mode 100644 index 000000000..69673d50f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu new file mode 100644 index 000000000..d8b2b2e18 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 000000000..01cce7ab5 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 000000000..fd5563b39 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 000000000..b13cc4a0c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 000000000..86f1fc637 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 000000000..26e7df4be --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu new file mode 100644 index 000000000..e4fda8952 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 000000000..bd15117b4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 000000000..cb6c6a760 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 000000000..201b6641d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 000000000..6da57a44a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 000000000..47623c9bf --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu new file mode 100644 index 000000000..82c6861d2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 000000000..24a80c2b0 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 000000000..b95eaf7e1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 000000000..275f2efcc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 000000000..3673f7fd5 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 000000000..2c4d59947 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu new file mode 100644 index 000000000..2457cdf3f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 000000000..b3b411ed3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 000000000..b7f308a4d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 000000000..739686697 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 000000000..708d03113 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 000000000..df891be60 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu new file mode 100644 index 000000000..f49b6d1f9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 000000000..1de92148b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 000000000..7a1ba7f8d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 000000000..25493e4ba --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 000000000..3cd650c7b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 000000000..88ffa43d6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu new file mode 100644 index 000000000..8c7bac6c2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu new file mode 100644 index 000000000..a28f62e7b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu new file mode 100644 index 000000000..d39838b96 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu new file mode 100644 index 000000000..834d40f6c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu new file mode 100644 index 000000000..f7d54668b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu new file mode 100644 index 000000000..59e00ad83 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu new file mode 100644 index 000000000..6e63893de --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu new file mode 100644 index 000000000..ca356ad6c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, float); +DECL_FATTN_WMMA_F16_CASE(80, 16, float); +DECL_FATTN_WMMA_F16_CASE(96, 16, float); +DECL_FATTN_WMMA_F16_CASE(112, 16, float); +DECL_FATTN_WMMA_F16_CASE(128, 16, float); +DECL_FATTN_WMMA_F16_CASE(256, 16, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu new file mode 100644 index 000000000..430ee64eb --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu @@ -0,0 +1,9 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, float); +DECL_FATTN_WMMA_F16_CASE(80, 32, float); +DECL_FATTN_WMMA_F16_CASE(96, 32, float); +DECL_FATTN_WMMA_F16_CASE(112, 32, float); +DECL_FATTN_WMMA_F16_CASE(128, 32, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu new file mode 100644 index 000000000..d421d17cc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, half); +DECL_FATTN_WMMA_F16_CASE(80, 16, half); +DECL_FATTN_WMMA_F16_CASE(96, 16, half); +DECL_FATTN_WMMA_F16_CASE(112, 16, half); +DECL_FATTN_WMMA_F16_CASE(128, 16, half); +DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu new file mode 100644 index 000000000..deacd5f58 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, half); +DECL_FATTN_WMMA_F16_CASE(80, 32, half); +DECL_FATTN_WMMA_F16_CASE(96, 32, half); +DECL_FATTN_WMMA_F16_CASE(112, 32, half); +DECL_FATTN_WMMA_F16_CASE(128, 32, half); +DECL_FATTN_WMMA_F16_CASE(256, 32, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu new file mode 100644 index 000000000..282896733 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu @@ -0,0 +1,8 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 8, half); +DECL_FATTN_WMMA_F16_CASE(96, 8, half); +DECL_FATTN_WMMA_F16_CASE(128, 8, half); +DECL_FATTN_WMMA_F16_CASE(256, 8, half); diff --git a/ggml-cuda/template-instances/generate_cu_files.py b/ggml-cuda/template-instances/generate_cu_files.py new file mode 100755 index 000000000..ee5b460e0 --- /dev/null +++ b/ggml-cuda/template-instances/generate_cu_files.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 + +from glob import glob +import os + +TYPES_KV = ["GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_F16"] + +SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f{vkq_size}.cuh" + +DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v}); +""" + +SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +""" + +SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n" + + +def get_short_name(long_quant_name): + return long_quant_name.replace("GGML_TYPE_", "").lower() + + +def get_head_sizes(type_k, type_v): + if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16": + return [64, 128, 256] + if type_k == "GGML_TYPE_F16": + return [64, 128] + return [128] + + +for filename in glob("*.cu"): + os.remove(filename) + +for vkq_size in [16, 32]: + for type_k in TYPES_KV: + for type_v in TYPES_KV: + for head_size in get_head_sizes(type_k, type_v): + with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f: + f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v)) + +for kq_acc_t in ["half", "float"]: + for cols_per_block in [8, 16, 32]: + if kq_acc_t == "float" and cols_per_block == 8: + continue + + with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f: + f.write(SOURCE_FATTN_WMMA_START) + + for head_size in [64, 80, 96, 112, 128, 256]: + if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32 + continue + if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance + continue + f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size)) diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh index 86b87fa93..df9752390 100644 --- a/ggml-cuda/vecdotq.cuh +++ b/ggml-cuda/vecdotq.cuh @@ -180,8 +180,8 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp #define VDR_Q8_0_Q8_1_MMVQ 2 #define VDR_Q8_0_Q8_1_MMQ 8 -template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl( - const int * v, const int * u, const float & d8_0, const float & d8_1) { +template static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl( + const int * v, const int * u, const T & d8_0, const T & d8_1) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; @@ -192,7 +192,7 @@ template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp sumi = __dp4a(v[i], u[i], sumi); } - return d8_0*d8_1 * sumi; + return d8_0*d8_1 * ((T) sumi); #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -656,7 +656,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1( u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); } - return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); + return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); } static __device__ __forceinline__ float vec_dot_q2_K_q8_1( @@ -712,7 +712,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( static __device__ __forceinline__ float vec_dot_q4_K_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#ifndef GGML_QKK_64 const block_q4_K * bq4_K = (const block_q4_K *) vbq; int v[2]; @@ -754,58 +753,11 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( } return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - - float sumf_d = 0.0f; - float sumf_m = 0.0f; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - const uint16_t * a = (const uint16_t *)bq4_K->scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const float dall = bq4_K->dm[0]; - const float dmin = bq4_K->dm[1]; - - const float d8_1 = __low2float(bq8_1[0].ds); - const float d8_2 = __low2float(bq8_1[1].ds); - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * q4 = (const int *)bq4_K->qs + (iqs/2); - const int v1 = q4[0]; - const int v2 = q4[4]; - - const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); - const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); - - sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); - sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); - - return dall * sumf_d - dmin * sumf_m; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ float vec_dot_q5_K_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#ifndef GGML_QKK_64 const block_q5_K * bq5_K = (const block_q5_K *) vbq; int vl[2]; @@ -847,48 +799,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( } return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_q5_K * bq5_K = (const block_q5_K *) vbq; - - const int8_t * s = bq5_K->scales; - - const float d = bq5_K->d; - - const float d8_1 = __low2half(bq8_1[0].ds); - const float d8_2 = __low2half(bq8_1[1].ds); - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * ql = (const int *)bq5_K->qs + (iqs/2); - const int vl1 = ql[0]; - const int vl2 = ql[4]; - - const int step = 4 * (iqs/2); // 0, 4, 8, 12 - const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 - const int in = step%8; // 0, 4, 0, 4 - const int vh = (*((const int *)(bq5_K->qh + in))) >> im; - - const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); - const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); - const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); - const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - - const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); - - return d * sumf_d; - -#else - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ float vec_dot_q6_K_q8_1( @@ -919,7 +829,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -960,15 +869,11 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( } return d * (sumi1 + sumi2); #endif -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; const int ib32 = iqs; @@ -1002,17 +907,12 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif } // TODO static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -1048,16 +948,11 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; const int ib32 = iqs; @@ -1082,16 +977,12 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } // TODO: don't use lookup table for signs static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -1114,14 +1005,10 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -1149,14 +1036,10 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const float d = d1q * __low2float (bq8_1[ib32].ds); const float m = d1q * __high2float(bq8_1[ib32].ds); return d * sumi + m * delta; -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -1192,9 +1075,6 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds); return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - NO_DEVICE_CODE; -#endif } #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics @@ -1250,9 +1130,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -1270,10 +1148,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( sumi2 = __dp4a(v2, q8[j+4], sumi2); } return d * (sumi1 + sumi2); - -#else - NO_DEVICE_CODE; -#endif #else return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); #endif diff --git a/ggml-impl.h b/ggml-impl.h index 362d40f4d..5e77471f3 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -144,6 +144,10 @@ extern "C" { #endif #endif +#if defined(__ARM_FEATURE_SVE) +#include +#endif + // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 3f033d58b..eabd70d5e 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -22,6 +22,7 @@ #include "shaderop_mul_mat_q4_1.h" #include "shaderop_mul_mat_q6_k.h" #include "shaderop_mul_mat_mat_f32.h" +#include "shaderop_getrows_f32.h" #include "shaderop_getrows_f16.h" #include "shaderop_getrows_q4_0.h" #include "shaderop_getrows_q4_1.h" @@ -1146,6 +1147,14 @@ static void ggml_vk_get_rows( seq.record(s_algo); } +template +static void ggml_vk_get_rows_f32(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f32_comp_spv, + kp::shader_data::op_getrows_f32_comp_spv_len); + + ggml_vk_get_rows(spirv, "f32", sizeof(float), 0, std::forward(args)...); +} + template static void ggml_vk_get_rows_f16(Args&&... args) { const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv, @@ -1371,6 +1380,7 @@ static bool ggml_vk_supports_op(const struct ggml_tensor * op) { return op->ne[3] == 1; case GGML_OP_GET_ROWS: switch (op->src[0]->type) { + case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -1597,7 +1607,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -1662,7 +1671,9 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_GET_ROWS: { - if (src0t == GGML_TYPE_F16) { + if (src0t == GGML_TYPE_F32) { + ggml_vk_get_rows_f32(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else if (src0t == GGML_TYPE_F16) { ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); } else if (src0t == GGML_TYPE_Q4_0) { ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); @@ -1677,6 +1688,10 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_ROPE: { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; diff --git a/ggml-metal.h b/ggml-metal.h index a5c542189..e7543ae79 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -1,7 +1,7 @@ // An interface allowing to compute ggml_cgraph with Metal // // This is a fully functional interface that extends ggml with GPU support for Apple devices. -// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, OpenCL, etc.) +// A similar interface can be created for other GPU backends (e.g. Vulkan, CUDA, etc.) // // How it works? // diff --git a/ggml-metal.m b/ggml-metal.m index b0b16dbf7..fddc44f78 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -35,6 +35,10 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_ROW, GGML_METAL_KERNEL_TYPE_DIV, GGML_METAL_KERNEL_TYPE_DIV_ROW, + GGML_METAL_KERNEL_TYPE_REPEAT_F32, + GGML_METAL_KERNEL_TYPE_REPEAT_F16, + GGML_METAL_KERNEL_TYPE_REPEAT_I32, + GGML_METAL_KERNEL_TYPE_REPEAT_I16, GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, GGML_METAL_KERNEL_TYPE_CLAMP, @@ -184,9 +188,9 @@ enum ggml_metal_kernel_type { 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_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, + //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, @@ -381,10 +385,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; -#ifdef GGML_QKK_64 - prep[@"GGML_QKK_64"] = @(1); -#endif - MTLCompileOptions* options = [MTLCompileOptions new]; options.preprocessorMacros = prep; @@ -489,6 +489,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { 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_REPEAT_F32, repeat_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, 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); @@ -638,9 +642,9 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { 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_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_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); @@ -750,6 +754,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ACC: case GGML_OP_MUL: case GGML_OP_DIV: + case GGML_OP_REPEAT: case GGML_OP_SCALE: case GGML_OP_CLAMP: case GGML_OP_SQR: @@ -774,6 +779,15 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: + if (op->src[1]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[2]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[0]->ne[0] == 256) { + return false; + } return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: @@ -927,22 +941,32 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t ne10 = src1 ? src1->ne[0] : 0; const int64_t ne11 = src1 ? src1->ne[1] : 0; const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); + const int64_t ne13 = src1 ? src1->ne[3] : 0; const uint64_t nb10 = src1 ? src1->nb[0] : 0; const uint64_t nb11 = src1 ? src1->nb[1] : 0; const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); + const uint64_t nb13 = src1 ? src1->nb[3] : 0; - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; + + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; + + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_t nb3 = dst ? dst->nb[3] : 0; const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; @@ -970,10 +994,10 @@ static enum ggml_status ggml_metal_graph_compute( switch (dst->op) { case GGML_OP_CONCAT: { - const int64_t nb = ne00; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + const int32_t dim = ((int32_t *) dst->op_params)[0]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1002,7 +1026,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; const int nth = MIN(1024, ne0); @@ -1012,11 +1036,14 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_MUL: case GGML_OP_DIV: { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + const size_t offs = 0; bool bcast_row = false; - int64_t nb = ne00; + int64_t nb = ne00; // used by the "row" kernels id pipeline = nil; @@ -1085,6 +1112,42 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } } break; + case GGML_OP_REPEAT: + { + id pipeline; + + switch (src0t) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; + case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; + default: GGML_ASSERT(false); + } + + [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(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ACC: { GGML_ASSERT(src0t == GGML_TYPE_F32); @@ -1462,7 +1525,6 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -1763,11 +1825,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -1785,16 +1843,6 @@ static enum ggml_status ggml_metal_graph_compute( const int n_as = src0->ne[2]; // src2 = ids - const int64_t ne20 = src2->ne[0]; - const int64_t ne21 = src2->ne[1]; - const int64_t ne22 = src2->ne[2]; GGML_UNUSED(ne22); - const int64_t ne23 = src2->ne[3]; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20); - const uint64_t nb21 = src2->nb[1]; - const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22); - const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23); - const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); GGML_ASSERT(src2t == GGML_TYPE_I32); @@ -2018,12 +2066,7 @@ static enum ggml_status ggml_metal_graph_compute( { nth0 = 4; nth1 = 16; - #if QK_K == 64 - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; - #else pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; - #endif - } break; default: { @@ -2088,11 +2131,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -2153,6 +2192,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_RMS_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous_1(src0)); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -2180,6 +2220,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_GROUP_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous(src0)); //float eps; //memcpy(&eps, dst->op_params, sizeof(float)); @@ -2213,6 +2254,8 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_NORM: { + GGML_ASSERT(ggml_is_contiguous_1(src0)); + float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -2244,7 +2287,13 @@ static enum ggml_status ggml_metal_graph_compute( // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); @@ -2252,6 +2301,15 @@ static enum ggml_status ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + GGML_ASSERT(!is_glm && "GLM RoPE not implemented in Metal"); + + if (!is_neox) { + GGML_ASSERT(id_src2 == nil && "TODO: freq_factors not implemented for !is_neox"); + } + id pipeline = nil; switch (src0->type) { @@ -2263,33 +2321,38 @@ 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_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + if (id_src2 != nil) { + [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( int64_t) atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; + [encoder setBytes:&mode length:sizeof( int) atIndex:22]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:23]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:24]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:25]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:28]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:29]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2535,11 +2598,6 @@ static enum ggml_status ggml_metal_graph_compute( 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 uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; - 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); @@ -2575,7 +2633,7 @@ static enum ggml_status ggml_metal_graph_compute( 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; + //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); @@ -2588,7 +2646,7 @@ static enum ggml_status ggml_metal_graph_compute( 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; + //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); diff --git a/ggml-metal.metal b/ggml-metal.metal index 386e9195f..0cb85e1a5 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -168,6 +168,53 @@ kernel void kernel_div( } } +template +kernel void kernel_repeat( + device const char * src0, + device char * 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, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 % ne03; + const int64_t i02 = i2 % ne02; + const int64_t i01 = i1 % ne01; + + device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; + device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + const int i00 = i0 % ne00; + *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); + } +} + +typedef decltype(kernel_repeat) kernel_repeat_t; + +template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat; + // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( @@ -1640,6 +1687,7 @@ static void rope_yarn_corr_dims( typedef void (rope_t)( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1675,6 +1723,7 @@ template kernel void kernel_rope( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1718,13 +1767,13 @@ kernel void kernel_rope( const int64_t p = pos[i2]; - const float theta_0 = (float)p; + const float theta_base = (float)p; const float inv_ndims = -1.f/n_dims; if (!is_neox) { for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { + const float theta = theta_base * pow(freq_base, inv_ndims*i0); - const float theta = theta_0 * pow(freq_base, inv_ndims*i0); float cos_theta, sin_theta; rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -1740,16 +1789,14 @@ kernel void kernel_rope( } else { for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - const float cur_rot = inv_ndims*ic - ib; + const float freq_factor = src2 != src0 ? src2[i0] : 1.0f; + + const float theta = theta_base * pow(freq_base, inv_ndims*ic); - const float theta = theta_0 * pow(freq_base, cur_rot); float cos_theta, sin_theta; - rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); - - const int64_t i0 = ib*n_dims + ic/2; + rope_yarn(theta/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta); device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -2204,11 +2251,7 @@ kernel void kernel_flash_attn_ext_f16( // pointer to the mask device const half * mp = (device const half *) (mask + iq1*nb31); - // prepare diagonal scale matrix - simdgroup_float8x8 mscale(scale); - - // prepare diagonal slope matrix - simdgroup_float8x8 mslope(1.0f); + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -2217,7 +2260,7 @@ kernel void kernel_flash_attn_ext_f16( 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)); + slope = pow(base, exph); } // loop over the KV cache @@ -2242,18 +2285,20 @@ kernel void kernel_flash_attn_ext_f16( simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); } + simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + + const short tx = tiisg%4; + const short ty = tiisg/4; + if (mask != q) { // 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); + ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0]; + ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1]; } else { // mqk = mqk*scale - simdgroup_multiply(mqk, mscale, mqk); + ss[8*cc + ty*TF + 2*tx + 0] *= scale; + ss[8*cc + ty*TF + 2*tx + 1] *= scale; } - - simdgroup_store(mqk, ss + 8*cc, TF, 0, false); } } @@ -2416,7 +2461,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f 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 [[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( @@ -2694,7 +2739,7 @@ kernel void kernel_flash_attn_ext_vec_f16( } 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>; +//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, @@ -2816,8 +2861,7 @@ 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); - // TODO: is there a better way to handle -INFINITY? - dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0]; + dst_data[i00] = src[0]; } } @@ -3318,31 +3362,30 @@ kernel void kernel_concat( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, + constant int32_t & dim, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0; + device const float * x; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i02 < ne02) { - ((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0]; - src0_ptr += ntg.x*nb00; + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); } else { - ((device float *)dst_ptr)[0] = ((device float *)src1_ptr)[0]; - src1_ptr += ntg.x*nb10; + x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); } - dst_ptr += ntg.x*nb0; + + device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; } } @@ -3385,7 +3428,6 @@ void kernel_mul_mv_q2_K_f32_impl( const int step = sizeof(block_q2_K) * nb; -#if QK_K == 256 const int ix = tiisg/8; // 0...3 const int it = tiisg%8; // 0...7 const int iq = it/4; // 0 or 1 @@ -3437,57 +3479,6 @@ void kernel_mul_mv_q2_K_f32_impl( y4 += 4 * QK_K; } -#else - const int ix = tiisg/2; // 0...15 - const int it = tiisg%2; // 0...1 - - device const float * y4 = y + ix * QK_K + 8 * it; - - for (int ib = ix; ib < nb; ib += 16) { - - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0]; - yl[i+ 8] = y4[i+16]; sumy[1] += yl[i+ 8]; - yl[i+16] = y4[i+32]; sumy[2] += yl[i+16]; - yl[i+24] = y4[i+48]; sumy[3] += yl[i+24]; - } - - device const uint8_t * sc = (device const uint8_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = &x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+ 0] * (qs[i/2] & 0x0003); - acc2[0] += yl[i+ 1] * (qs[i/2] & 0x0300); - acc1[1] += yl[i+ 8] * (qs[i/2] & 0x000c); - acc2[1] += yl[i+ 9] * (qs[i/2] & 0x0c00); - acc1[2] += yl[i+16] * (qs[i/2] & 0x0030); - acc2[2] += yl[i+17] * (qs[i/2] & 0x3000); - acc1[3] += yl[i+24] * (qs[i/2] & 0x00c0); - acc2[3] += yl[i+25] * (qs[i/2] & 0xc000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc2[0]) * (sc[0] & 0xF) * 1.f/ 1.f + - (acc1[1] + 1.f/256.f * acc2[1]) * (sc[1] & 0xF) * 1.f/ 4.f + - (acc1[2] + 1.f/256.f * acc2[2]) * (sc[2] & 0xF) * 1.f/16.f + - (acc1[3] + 1.f/256.f * acc2[3]) * (sc[3] & 0xF) * 1.f/64.f) - - dmin * (sumy[0] * (sc[0] >> 4) + sumy[1] * (sc[1] >> 4) + sumy[2] * (sc[2] >> 4) + sumy[3] * (sc[3] >> 4)); - - qs += step/2; - sc += step; - dh += step/2; - } - - y4 += 16 * QK_K; - } -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -3525,7 +3516,6 @@ kernel void kernel_mul_mv_q2_K_f32( kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q3_K_f32_impl( device const void * src0, device const float * src1, @@ -3684,84 +3674,6 @@ void kernel_mul_mv_q3_K_f32_impl( } } } -#else -void kernel_mul_mv_q3_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - const int nb = ne00/QK_K; - - const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; - - const int row = 2 * r0 + sgitg; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - - device const block_q3_K * x = (device const block_q3_K *) src0 + row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - const int ix = tiisg/4; - const int il = 4 * (tiisg%4);// 0, 4, 8, 12 - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - float2 sum = {0.f, 0.f}; - - for (int i = ix; i < nb; i += 8) { - - const float d_all = (float)(x[i].d); - - device const uint16_t * q = (device const uint16_t *)(x[i].qs + il); - device const uint16_t * h = (device const uint16_t *)(x[i].hmask + in); - device const uint16_t * s = (device const uint16_t *)(x[i].scales); - device const float * y = yy + i * QK_K + il; - - const float d1 = d_all * ((int32_t)(s[0] & 0x000F) - 8); - const float d2 = d_all * ((int32_t)(s[0] & 0x00F0) - 128) * 1.f/64.f; - const float d3 = d_all * ((int32_t)(s[0] & 0x0F00) - 2048) * 1.f/4096.f; - const float d4 = d_all * ((int32_t)(s[0] & 0xF000) - 32768) * 1.f/262144.f; - - for (int l = 0; l < 4; l += 2) { - const uint16_t hm = h[l/2] >> iq; - sum[0] += y[l+ 0] * d1 * ((int32_t)(q[l/2] & 0x0003) - ((hm & 0x0001) ? 0 : 4)) - + y[l+16] * d2 * ((int32_t)(q[l/2] & 0x000c) - ((hm & 0x0004) ? 0 : 16)) - + y[l+32] * d3 * ((int32_t)(q[l/2] & 0x0030) - ((hm & 0x0010) ? 0 : 64)) - + y[l+48] * d4 * ((int32_t)(q[l/2] & 0x00c0) - ((hm & 0x0040) ? 0 : 256)); - sum[1] += y[l+ 1] * d1 * ((int32_t)(q[l/2] & 0x0300) - ((hm & 0x0100) ? 0 : 1024)) - + y[l+17] * d2 * ((int32_t)(q[l/2] & 0x0c00) - ((hm & 0x0400) ? 0 : 4096)) - + y[l+33] * d3 * ((int32_t)(q[l/2] & 0x3000) - ((hm & 0x1000) ? 0 : 16384)) - + y[l+49] * d4 * ((int32_t)(q[l/2] & 0xc000) - ((hm & 0x4000) ? 0 : 65536)); - } - - } - const float sumf = sum[0] + sum[1] * 1.f/256.f; - - const float tot = simd_sum(sumf); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; - } - -} -#endif [[host_name("kernel_mul_mv_q3_K_f32")]] kernel void kernel_mul_mv_q3_K_f32( @@ -3791,7 +3703,6 @@ kernel void kernel_mul_mv_q3_K_f32( kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q4_K_f32_impl( device const void * src0, device const float * src1, @@ -3905,103 +3816,6 @@ void kernel_mul_mv_q4_K_f32_impl( } } } -#else -void kernel_mul_mv_q4_K_f32_impl( - device const void * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, - constant int64_t & ne01, - constant int64_t & ne02, - constant int64_t & ne10, - constant int64_t & ne12, - constant int64_t & ne0, - constant int64_t & ne1, - constant uint & r2, - constant uint & r3, - threadgroup int8_t * shared_values [[threadgroup(0)]], - uint3 tgpig[[threadgroup_position_in_grid]], - uint tiisg[[thread_index_in_simdgroup]], - uint sgitg[[simdgroup_index_in_threadgroup]]) { - - const int ix = tiisg/4; // 0...7 - const int it = tiisg%4; // 0...3 - - const int nb = ne00/QK_K; - const int r0 = tgpig.x; - const int r1 = tgpig.y; - const int im = tgpig.z; - const int first_row = r0 * N_DST; - const int ib_row = first_row * nb; - - const uint i12 = im%ne12; - const uint i13 = im/ne12; - - const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); - - device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - float yl[8]; - float yh[8]; - float sumf[N_DST]={0.f}, all_sum; - - const int step = sizeof(block_q4_K) * nb / 2; - - device const float * y4 = y + ix * QK_K + 8 * it; - - uint16_t sc16[4]; - - for (int ib = ix; ib < nb; ib += 8) { - - float2 sumy = {0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i] = y4[i+ 0]; sumy[0] += yl[i]; - yh[i] = y4[i+32]; sumy[1] += yh[i]; - } - - device const uint16_t * sc = (device const uint16_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - sc16[0] = sc[0] & 0x000f; - sc16[1] = sc[0] & 0x0f00; - sc16[2] = sc[0] & 0x00f0; - sc16[3] = sc[0] & 0xf000; - - float2 acc1 = {0.f, 0.f}; - float2 acc2 = {0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+0] * (qs[i/2] & 0x000F); - acc1[1] += yl[i+1] * (qs[i/2] & 0x0F00); - acc2[0] += yh[i+0] * (qs[i/2] & 0x00F0); - acc2[1] += yh[i+1] * (qs[i/2] & 0xF000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc16[0] + - (acc2[0] + 1.f/256.f * acc2[1]) * sc16[1] * 1.f/4096.f) - - dmin * 1.f/16.f * (sumy[0] * sc16[2] + sumy[1] * sc16[3] * 1.f/256.f); - - qs += step; - sc += step; - dh += step; - } - - y4 += 8 * QK_K; - } - - for (int row = 0; row < N_DST; ++row) { - all_sum = simd_sum(sumf[row]); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum; - } - } -} -#endif [[host_name("kernel_mul_mv_q4_K_f32")]] kernel void kernel_mul_mv_q4_K_f32( @@ -4069,8 +3883,6 @@ void kernel_mul_mv_q5_K_f32_impl( const int step = sizeof(block_q5_K) * nb; -#if QK_K == 256 -# float yl[16], yh[16]; const uint16_t kmask1 = 0x3f3f; @@ -4153,54 +3965,6 @@ void kernel_mul_mv_q5_K_f32_impl( y1 += 4 * QK_K; } -#else - float yl[8], yh[8]; - - const int il = 4 * (tiisg/8); // 0, 4, 8, 12 - const int ix = tiisg%8; - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - device const float * y = yy + ix*QK_K + il; - - for (int i = ix; i < nb; i += 8) { - - for (int l = 0; l < 4; ++l) { - yl[l+0] = y[l+ 0]; - yl[l+4] = y[l+16]; - yh[l+0] = y[l+32]; - yh[l+4] = y[l+48]; - } - - device const half * dh = &x[i].d; - device const uint8_t * q = x[i].qs + il; - device const uint8_t * h = x[i].qh + in; - device const int8_t * s = x[i].scales; - - for (int row = 0; row < 2; ++row) { - - const float d = dh[0]; - - float2 acc = {0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - const uint8_t hl = h[l] >> iq; - acc[0] += yl[l+0] * s[0] * ((int16_t)(q[l+ 0] & 0x0F) - (hl & 0x01 ? 0 : 16)) - + yl[l+4] * s[1] * ((int16_t)(q[l+16] & 0x0F) - (hl & 0x04 ? 0 : 16)); - acc[1] += yh[l+0] * s[2] * ((int16_t)(q[l+ 0] & 0xF0) - (hl & 0x10 ? 0 : 256)) - + yh[l+4] * s[3] * ((int16_t)(q[l+16] & 0xF0) - (hl & 0x40 ? 0 : 256)); - } - sumf[row] += d * (acc[0] + 1.f/16.f * acc[1]); - - q += step; - h += step; - s += step; - dh += step/2; - - } - - y += 8 * QK_K; - } -#endif for (int row = 0; row < 2; ++row) { const float tot = simd_sum(sumf[row]); @@ -4279,7 +4043,6 @@ void kernel_mul_mv_q6_K_f32_impl( float sumf = 0; -#if QK_K == 256 const int tid = tiisg/2; const int ix = tiisg%2; const int ip = tid/8; // 0 or 1 @@ -4315,30 +4078,6 @@ void kernel_mul_mv_q6_K_f32_impl( } -#else - const int ix = tiisg/4; - const int il = 4*(tiisg%4); - - for (int i = ix; i < nb; i += 8) { - device const float * y = yy + i * QK_K + il; - device const uint8_t * ql = x[i].ql + il; - device const uint8_t * qh = x[i].qh + il; - device const int8_t * s = x[i].scales; - - const float d = x[i].d; - - float4 sums = {0.f, 0.f, 0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); - sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); - sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) >> 0)) - 32); - sums[3] += y[l+48] * ((int8_t)((ql[l+16] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); - } - sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]); - } - -#endif - const float tot = simd_sum(sumf); if (tiisg == 0) { dst[r1*ne0 + im*ne0*ne1 + row] = tot; @@ -5172,9 +4911,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const float * y4 = y + 32 * ix; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int ib32 = ix; ib32 < nb32; ib32 += 32) { @@ -5195,10 +4932,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const uint16_t * sc = (device const uint16_t *)xr->scales; for (int row = 0; row < N_DST; row++) { - -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); @@ -5214,14 +4948,9 @@ void kernel_mul_mv_iq1_m_f32_impl( } const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); -#if QK_K == 64 - const float d = (float) *((device const half *)(sc - 1)); - sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) + - (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1)); -#else + sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); -#endif sc += nb*sizeof(block_iq1_m)/2; qs += nb*sizeof(block_iq1_m); @@ -5333,7 +5062,6 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } -#if QK_K != 64 void kernel_mul_mv_iq4_xs_f32_impl( device const void * src0, device const float * src1, @@ -5428,7 +5156,6 @@ void kernel_mul_mv_iq4_xs_f32_impl( } } } -#endif [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( @@ -5541,11 +5268,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { -#if QK_K == 64 - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#else kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#endif } //============================= templates and their specializations ============================= @@ -5671,10 +5394,9 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg float dl, ml; uint8_t sc = xb->scales[il]; -#if QK_K == 256 q = q + 32*(il/8) + 16*(il&1); il = (il/2)%4; -#endif + half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); @@ -5690,7 +5412,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg device const uint8_t * h = (device const uint8_t *)xb->hmask; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 q = q + 32 * (il/8) + 16 * (il&1); h = h + 16 * (il&1); uint8_t m = 1 << (il/2); @@ -5711,17 +5432,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); } -#else - float kcoef = il&1 ? 1.f/16.f : 1.f; - uint16_t kmask = il&1 ? 0xF0 : 0x0F; - float dl = d_all * ((scales[il/2] & kmask) * kcoef - 8); - float coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - uint8_t m = 1<<(il*2); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i%8] & (m * (1 + i/8))) ? 0 : 4.f/coef)); - } -#endif } static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { @@ -5733,7 +5443,6 @@ template void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { device const uchar * q = xb->qs; -#if QK_K == 256 short is = (il/4) * 2; q = q + (il/4) * 32 + 16 * (il&1); il = il & 3; @@ -5742,16 +5451,7 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg const float min = xb->dmin; const float dl = d * sc[0]; const float ml = min * sc[1]; -#else - (void) get_scale_min_k4_just2; - q = q + 16 * (il&1); - device const uint8_t * s = xb->scales; - device const half2 * dh = (device const half2 *)xb->d; - const float2 d = (float2)dh[0]; - const float dl = il<2 ? d[0] * (s[0]&0xF) : d[0] * (s[1]&0xF)/16.h; - const float ml = il<2 ? d[1] * (s[0]>>4) : d[1] * (s[1]>>4); -#endif const ushort mask = il<2 ? 0x0F : 0xF0; for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - ml; @@ -5763,7 +5463,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg device const uint8_t * q = xb->qs; device const uint8_t * qh = xb->qh; -#if QK_K == 256 short is = (il/4) * 2; q = q + 32 * (il/4) + 16 * (il&1); qh = qh + 16 * (il&1); @@ -5780,17 +5479,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; } -#else - q = q + 16 * (il&1); - device const int8_t * s = xb->scales; - const float dl = xb->d * s[il]; - uint8_t m = 1<<(il*2); - const float coef = il<2 ? 1.f : 1.f/16.f; - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - (qh[i%8] & (m*(1+i/8)) ? 0.f : 16.f/coef)); - } -#endif } template @@ -5800,15 +5488,11 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg device const uint8_t * qh = (device const uint8_t *)xb->qh; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); qh = qh + 32*(il/8) + 16*(il&1); float sc = scales[(il%2) + 2 * ((il/2))]; il = (il/2) & 3; -#else - ql = ql + 16 * (il&1); - float sc = scales[il]; -#endif + const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; const float coef = il>1 ? 1.f/16.f : 1.f; @@ -5965,20 +5649,15 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & const int ib32 = il/2; il = il%2; device const uint16_t * sc = (device const uint16_t *)xb->scales; -#if QK_K == 64 - const float d = xb->d; -#else + iq1m_scale_t scale; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = scale.f16; -#endif + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; device const uint8_t * qh = xb->qh + 2*ib32 + il; -#if QK_K == 64 - const float dl = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1); -#else + const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); -#endif const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); @@ -6008,9 +5687,6 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 template void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { -#if QK_K == 64 - dequantize_iq4_nl(xb, il, reg); -#else // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 const int ib32 = il/2; il = il%2; @@ -6027,7 +5703,6 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; } -#endif } template @@ -6532,11 +6207,7 @@ kernel void kernel_mul_mm_id( sgitg); } -#if QK_K == 256 #define QK_NL 16 -#else -#define QK_NL 4 -#endif // // get rows @@ -6576,11 +6247,7 @@ template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; -#if QK_K == 64 -template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#else template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#endif // // matrix-matrix multiplication @@ -6608,11 +6275,7 @@ template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#else template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#endif // // indirect matrix-matrix multiplication @@ -6640,11 +6303,7 @@ template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#else template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#endif // // matrix-vector multiplication @@ -6853,7 +6512,5 @@ template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#if QK_K != 64 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#endif diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp deleted file mode 100644 index 922f24837..000000000 --- a/ggml-opencl.cpp +++ /dev/null @@ -1,2305 +0,0 @@ -#include "ggml.h" -#include "ggml-opencl.h" -#include "ggml-backend-impl.h" - -#include -#include -#include -#include -#include -#include -#include -#include - -#define CL_TARGET_OPENCL_VERSION 120 -#include - -#if defined(_MSC_VER) -#pragma warning(disable: 4244 4267) // possible loss of data -#endif - -#define CL_DMMV_LOCAL_SIZE 32 - -#ifndef K_QUANTS_PER_ITERATION -#define K_QUANTS_PER_ITERATION 1 -#else -static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); -#endif - -#define MULTILINE_QUOTE(...) #__VA_ARGS__ -static std::string program_source = MULTILINE_QUOTE( - -typedef char int8_t; -typedef uchar uint8_t; -typedef short int16_t; -typedef ushort uint16_t; -typedef int int32_t; -typedef uint uint32_t; - -struct __attribute__ ((packed)) block_q4_0 -{ - half d; - uint8_t qs[QK4_0 / 2]; -}; - -struct __attribute__ ((packed)) block_q4_1 -{ - half d; - half m; - uint8_t qs[QK4_1 / 2]; -}; - -struct __attribute__ ((packed)) block_q5_0 -{ - half d; - uint32_t qh; - uint8_t qs[QK5_0 / 2]; -}; - -struct __attribute__ ((packed)) block_q5_1 -{ - half d; - half m; - uint32_t qh; - uint8_t qs[QK5_1 / 2]; -}; - -struct __attribute__ ((packed)) block_q8_0 -{ - half d; - int8_t qs[QK8_0]; -}; - -struct __attribute__((packed)) block_q2_K -{ - uint8_t scales[16]; - uint8_t qs[64]; - half d; - half dmin; -}; - -struct __attribute__((packed)) block_q3_K -{ - uint8_t hmask[32]; - uint8_t qs[64]; - uint8_t scales[12]; - half d; -}; - -struct __attribute__((packed)) block_q4_K -{ - half d; - half dmin; - uint8_t scales[12]; - uint8_t qs[128]; -}; - -struct __attribute__((packed)) block_q5_K -{ - half d; - half dmin; - uint8_t scales[12]; - uint8_t qh[32]; - uint8_t qs[128]; -}; - -struct __attribute__((packed)) block_q6_K -{ - uint8_t ql[128]; - uint8_t qh[64]; - int8_t scales[16]; - half d; -}; - -__kernel void convert_fp16_to_fp32(__global half* x, __global float* y) { - const uint i = get_global_id(0); - - y[i] = vload_half(0, &x[i]); -} - -void dequantize_q4_0(__global const struct block_q4_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - const uint8_t vui = x[ib].qs[iqs]; - - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; - - *v0 = (vi0 - 8)*d; - *v1 = (vi1 - 8)*d; -} -void dequantize_q4_1(__global const struct block_q4_1* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - const float m = vload_half(0, &x[ib].m); - - const uint8_t vui = x[ib].qs[iqs]; - - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; - - *v0 = vi0*d + m; - *v1 = vi1*d + m; -} -void dequantize_q5_0(__global const struct block_q5_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - uint32_t qh = x[ib].qh; - - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16; - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1) - 16; - - *v0 = x0*d; - *v1 = x1*d; -} -void dequantize_q5_1(__global const struct block_q5_1* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - const float m = vload_half(0, &x[ib].m); - - uint32_t qh = x[ib].qh; - - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0); - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1); - - *v0 = x0*d + m; - *v1 = x1*d + m; -} -void dequantize_q8_0(__global const struct block_q8_0* x, const int ib, const int iqs, float* v0, float* v1) { - const float d = vload_half(0, &x[ib].d); - - const int8_t vi0 = x[ib].qs[iqs + 0]; - const int8_t vi1 = x[ib].qs[iqs + 1]; - - *v0 = vi0*d; - *v1 = vi1*d; -} -void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float* v1){ - *v0 = vload_half(0, &x[ib + 0]); - *v1 = vload_half(0, &x[ib + 1]); -} -); - -static std::string k_quants_source = MULTILINE_QUOTE( -inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m) -{ - if (j < 4) - { - *d = q[j] & 63; - *m = q[j + 4] & 63; - } - else - { - *d = (q[j + 4] & 0xF) | ((q[j - 4] >> 6) << 4); - *m = (q[j + 4] >> 4) | ((q[j - 0] >> 6) << 4); - } -} - -__kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int n = tid / 32; - const int l = tid - 32 * n; - const int is = 8 * n + l / 16; - - const uint8_t q = x[i].qs[32 * n + l]; - __global float *y = yy + get_group_id(0) * QK_K + 128 * n; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - y[l + 0] = dall * (x[i].scales[is + 0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is + 0] >> 4); - y[l + 32] = dall * (x[i].scales[is + 2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is + 2] >> 4); - y[l + 64] = dall * (x[i].scales[is + 4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is + 4] >> 4); - y[l + 96] = dall * (x[i].scales[is + 6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is + 6] >> 4); -} - -__kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __global float *yy) -{ - int r = get_local_id(0) / 4; - int i = get_group_id(0) + get_global_offset(0); - int tid = r / 2; - int is0 = r % 2; - int l0 = 16 * is0 + 4 * (get_local_id(0) % 4); - int n = tid / 4; - int j = tid - 4 * n; - - uint8_t m = 1 << (4 * n + j); - int is = 8 * n + 2 * j + is0; - int shift = 2 * j; - - int8_t us = is < 4 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 8] >> 0) & 3) << 4) - : is < 8 ? (x[i].scales[is - 0] & 0xF) | (((x[i].scales[is + 4] >> 2) & 3) << 4) - : is < 12 ? (x[i].scales[is - 8] >> 4) | (((x[i].scales[is + 0] >> 4) & 3) << 4) - : (x[i].scales[is - 8] >> 4) | (((x[i].scales[is - 4] >> 6) & 3) << 4); - float d_all = vload_half(0, &x[i].d); - float dl = d_all * (us - 32); - - __global float *y = yy + get_group_id(0) * QK_K + 128 * n + 32 * j; - const __global uint8_t *q = x[i].qs + 32 * n; - const __global uint8_t *hm = x[i].hmask; - - for (int l = l0; l < l0 + 4; ++l) - y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -} - -__kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int il = tid / 8; - const int ir = tid % 8; - const int is = 2 * il; - const int n = 4; - - __global float *y = yy + get_group_id(0) * QK_K + 64 * il + n * ir; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint8_t *q = x[i].qs + 32 * il + n * ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, &sc, &m); - float d1 = dall * sc; - float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, &sc, &m); - float d2 = dall * sc; - float m2 = dmin * m; - for (int l = 0; l < n; ++l) - { - y[l + 0] = d1 * (q[l] & 0xF) - m1; - y[l + 32] = d2 * (q[l] >> 4) - m2; - } -} - -__kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int il = tid / 16; - const int ir = tid % 16; - const int is = 2 * il; - - __global float *y = yy + get_group_id(0) * QK_K + 64 * il + 2 * ir; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint8_t *ql = x[i].qs + 32 * il + 2 * ir; - __global const uint8_t *qh = x[i].qh + 2 * ir; - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[i].scales, &sc, &m); - const float d1 = dall * sc; - const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[i].scales, &sc, &m); - const float d2 = dall * sc; - const float m2 = dmin * m; - - uint8_t hm = 1 << (2 * il); - y[0] = d1 * ((ql[0] & 0xF) + (qh[0] & hm ? 16 : 0)) - m1; - y[1] = d1 * ((ql[1] & 0xF) + (qh[1] & hm ? 16 : 0)) - m1; - hm <<= 1; - y[32] = d2 * ((ql[0] >> 4) + (qh[0] & hm ? 16 : 0)) - m2; - y[33] = d2 * ((ql[1] >> 4) + (qh[1] & hm ? 16 : 0)) - m2; -} - -__kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __global float *yy) -{ - const int i = get_group_id(0) + get_global_offset(0); - const int tid = get_local_id(0); - const int ip = tid / 32; - const int il = tid - 32 * ip; - const int is = 8 * ip + il / 16; - - __global float *y = yy + get_group_id(0) * QK_K + 128 * ip + il; - - const float d = vload_half(0, &x[i].d); - - __global const uint8_t *ql = x[i].ql + 64 * ip + il; - const uint8_t qh = x[i].qh[32 * ip + il]; - __global const int8_t *sc = x[i].scales + is; - - y[0] = d * sc[0] * ((int8_t)((ql[0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); - y[64] = d * sc[4] * ((int8_t)((ql[0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); - y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -} - -__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q2_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int step = 16/K_QUANTS_PER_ITERATION; - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int s_offset = 8*im; - const int y_offset = 128*im + l0; - - tmp[16 * ix + tid] = 0; - - uint32_t aux[4]; - const uint8_t * d = (const uint8_t *)aux; - const uint8_t * m = (const uint8_t *)(aux + 2); - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * q = x[i].qs + q_offset; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset); - aux[0] = a[0] & 0x0f0f0f0f; - aux[1] = a[1] & 0x0f0f0f0f; - aux[2] = (a[0] >> 4) & 0x0f0f0f0f; - aux[3] = (a[1] >> 4) & 0x0f0f0f0f; - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) - + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) - + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) - + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) - + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) - + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) - + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) - +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); - sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] - + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - - } - tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q3_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 - - const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop - const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0....15 or 0...7 - - const uint8_t m = 1 << (4*im); - - const int l0 = n*in; // 0...15 or 0...14 in steps of 2 - const int q_offset = 32*im + l0; - const int y_offset = 128*im + l0; - - uint16_t utmp[4]; - const int8_t * s = (const int8_t *)utmp; - - const uint16_t s_shift = 4*im; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * q = x[i].qs + q_offset; - __global const uint8_t * h = x[i].hmask + l0; - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); - utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); - utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); - utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); - - const float d = vload_half(0, &x[i].d); - - float sum = 0; - for (int l = 0; l < n; ++l) { - sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) - + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) - + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) - + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); - sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) - + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) - + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) - + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); - } - tmp[16 * ix + tid] += d * sum; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - //to rename it later, just to test now - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int row = get_group_id(0); - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...15 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; - - const int step = 8/K_QUANTS_PER_ITERATION; - - const int il = tid/step; // 0...3 - const int ir = tid - step*il;// 0...3 - const int n = 2*K_QUANTS_PER_ITERATION; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - __global const struct block_q4_K * x = xx + ib0; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const uint8_t * q1 = x[i].qs + q_offset; - __global const uint8_t * q2 = q1 + 64; - __global const float * y1 = yy + i*QK_K + y_offset; - __global const float * y2 = y1 + 128; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 s = (float4)(0.f); - float smin = 0; - for (int l = 0; l < n; ++l) { - s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4); - s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4); - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; - } - tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { - - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - const int row = get_group_id(0); - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - const int tid = get_local_id(0)/2; // 0...15 - const int ix = get_local_id(0)%2; - - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 2; - - const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 - const int in = il%2; - - const int l0 = n*(2*ir + in); - const int q_offset = 32*im + l0; - const int y_offset = 64*im + l0; - - const uint8_t hm1 = 1 << (2*im); - const uint8_t hm2 = hm1 << 4; - - uint16_t aux[4]; - const uint8_t * sc = (const uint8_t *)aux; - - __global const struct block_q5_K * x = xx + ib0; - - tmp[16 * ix + tid] = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2) { - - __global const uint8_t * ql1 = x[i].qs + q_offset; - __global const uint8_t * ql2 = ql1 + 64; - __global const uint8_t * qh = x[i].qh + l0; - __global const float * y1 = yy + i*QK_K + y_offset; - __global const float * y2 = y1 + 128; - - const float dall = vload_half(0, &x[i].d); - const float dmin = vload_half(0, &x[i].dmin); - - __global const uint16_t * a = (__global const uint16_t *)x[i].scales; - aux[0] = a[im+0] & kmask1; - aux[1] = a[im+2] & kmask1; - aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); - aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); - - float4 sum = (float4)(0.f); - float smin = 0; - for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) - + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) - + y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) - + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) - + y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0)); - smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] - + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; - } - tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) { - - const int row = get_group_id(0); - - const int num_blocks_per_row = ncols / QK_K; - const int ib0 = row*num_blocks_per_row + get_global_offset(0); - - __global const struct block_q6_K * x = xx + ib0; - - const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 - const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 - - const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 - - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...15 or 0...7 - -\n#if K_QUANTS_PER_ITERATION == 1\n - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 - const int is = 0; - -\n#else\n - - const int l0 = 4 * in; // 0, 4, 8, ..., 28 - const int is = in / 4; - -\n#endif\n - - const int ql_offset = 64*im + l0; - const int qh_offset = 32*im + l0; - const int s_offset = 8*im + is; - const int y_offset = 128*im + l0; - - tmp[16 * ix + tid] = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - - __global const float * y = yy + i * QK_K + y_offset; - __global const uint8_t * ql = x[i].ql + ql_offset; - __global const uint8_t * qh = x[i].qh + qh_offset; - __global const int8_t * s = x[i].scales + s_offset; - - const float d = vload_half(0, &x[i].d); - -\n#if K_QUANTS_PER_ITERATION == 1\n - float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) - + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) - + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) - + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) - + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) - + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) - + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) - +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); - tmp[16 * ix + tid] += sum; -\n#else\n - float sum = 0; - for (int l = 0; l < 4; ++l) { - sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) - + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) - + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) - + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); - } - tmp[16 * ix + tid] += sum; -\n#endif\n - - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=16; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} -); - - -std::string dequant_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global X_TYPE* x, __global float* y) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0)*2; - - if (i >= get_global_size(0)) { - return; - } - - const uint qk = QUANT_K; - const uint qr = QUANT_R; - - const int ib = i/qk + get_global_offset(0); // block index - const int iqs = (i%qk)/qr; // quant index - const int iybs = i - i%qk; // y block start index - const int y_offset = qr == 1 ? 1 : qk/2; - - // dequantize - float v0, v1; - DEQUANT_FUNC(x, ib, iqs, &v0, &v1); - y[iybs + iqs + 0] = v0; - y[iybs + iqs + y_offset] = v1; -} -); - -std::string dequant_mul_mat_vec_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) { - const int local_size = get_local_size(0); - const int row = get_group_id(0); - const int tid = get_local_id(0); - - const uint qk = QUANT_K; - const uint qr = QUANT_R; - - const int col_step = local_size * 2; - const int y_offset = qr == 1 ? 1 : qk/2; - - x += get_global_offset(0); - - tmp[tid] = 0; - - for (int col = tid*2; col < ncols; col += col_step) { - const int ib = (row*ncols + col)/qk; // block index - const int iqs = (col%qk)/qr; // quant index - const int iybs = col - col%qk; // y block start index - - // dequantize - float v0, v1; - DEQUANT_FUNC(x, ib, iqs, &v0, &v1); - - // matrix multiplication - tmp[tid] += v0 * y[iybs + iqs + 0]; - tmp[tid] += v1 * y[iybs + iqs + y_offset]; - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=local_size/2; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} - -); - - -std::string mul_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0); - - if (i >= get_global_size(0)) { - return; - } - - dst[dst_offset + i] = x[x_offset + i] * y[y_offset + i%ky]; -} -); - -std::string add_template = MULTILINE_QUOTE( -__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) { - const int i = get_group_id(0)*get_local_size(0) + get_local_id(0); - - if (i >= get_global_size(0)) { - return; - } - - dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky]; -} -); - -#define CL_CHECK(err) \ - do { \ - cl_int err_ = (err); \ - if (err_ != CL_SUCCESS) { \ - fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \ - #err, err_, __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -#define CLBLAST_CHECK(err) \ - do { \ - CLBlastStatusCode err_ = (err); \ - if (err_ != CLBlastSuccess) { \ - fprintf(stderr, "ggml_opencl: %s error %d at %s:%d\n", \ - #err, err_, __FILE__, __LINE__); \ - exit(1); \ - } \ - } while (0) - -std::array dequant_str_keys = { - "KERNEL_NAME", "X_TYPE", "QUANT_K", "QUANT_R", "DEQUANT_FUNC" -}; - -std::array dequant_str_values = { - "dequantize_row_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0", - "dequantize_row_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1", - "dequantize_row_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0", - "dequantize_row_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1", - "dequantize_row_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0", - "convert_row_f16", "half", "1", "1", "convert_f16" -}; - -std::array dequant_mul_mat_vec_str_values = { - "dequantize_mul_mat_vec_q4_0", "struct block_q4_0", "QK4_0", "QR4_0", "dequantize_q4_0", - "dequantize_mul_mat_vec_q4_1", "struct block_q4_1", "QK4_1", "QR4_1", "dequantize_q4_1", - "dequantize_mul_mat_vec_q5_0", "struct block_q5_0", "QK5_0", "QR5_0", "dequantize_q5_0", - "dequantize_mul_mat_vec_q5_1", "struct block_q5_1", "QK5_1", "QR5_1", "dequantize_q5_1", - "dequantize_mul_mat_vec_q8_0", "struct block_q8_0", "QK8_0", "QR8_0", "dequantize_q8_0", - "convert_mul_mat_vec_f16", "half", "1", "1", "convert_f16" -}; - -std::array mul_str_keys = { - "KERNEL_NAME", "TYPE" -}; -std::array mul_str_values = { - "mul_f32", "float" -}; - -static std::string& replace(std::string& s, const std::string& from, const std::string& to) { - size_t pos = 0; - while ((pos = s.find(from, pos)) != std::string::npos) { - s.replace(pos, from.length(), to); - pos += to.length(); - } - return s; -} - -static std::string generate_kernels() { - std::stringstream src; - src << program_source << '\n'; - src << k_quants_source << '\n'; - for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) { - std::string dequant_kernel = dequant_template; - std::string dmmv_kernel = dequant_mul_mat_vec_template; - for (size_t j = 0; j < dequant_str_keys.size(); j++) { - replace(dequant_kernel, dequant_str_keys[j], dequant_str_values[i + j]); - replace(dmmv_kernel, dequant_str_keys[j], dequant_mul_mat_vec_str_values[i + j]); - } - src << dequant_kernel << '\n'; - src << dmmv_kernel << '\n'; - } - for (size_t i = 0; i < mul_str_values.size(); i += mul_str_keys.size()) { - std::string mul_kernel = mul_template; - for (size_t j = 0; j < mul_str_keys.size(); j++) { - replace(mul_kernel, mul_str_keys[j], mul_str_values[i + j]); - } - src << mul_kernel << '\n'; - } - src << add_template << '\n'; - - return src.str(); -} - -static cl_platform_id platform; -static cl_device_id device; -static cl_context context; -static cl_command_queue queue; -static cl_program program; -static cl_kernel convert_row_f16_cl; -static cl_kernel dequantize_row_q4_0_cl, dequantize_row_q4_1_cl, dequantize_row_q5_0_cl, dequantize_row_q5_1_cl, dequantize_row_q8_0_cl; -static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, dequantize_mul_mat_vec_q5_0_cl, dequantize_mul_mat_vec_q5_1_cl, dequantize_mul_mat_vec_q8_0_cl, convert_mul_mat_vec_f16_cl; -static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl; -static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl; -static cl_kernel mul_f32_cl; -static cl_kernel add_f32_cl; -static bool fp16_support; - -static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) { - cl_program p; - char *program_log; - size_t program_size; - size_t log_size; - int err; - - program_size = strlen(program_buffer); - - p = clCreateProgramWithSource(ctx, 1, (const char**)&program_buffer, &program_size, &err); - if(err < 0) { - fprintf(stderr, "OpenCL error creating program"); - exit(1); - } - - std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math " - "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 " - "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION); - - err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL); - if(err < 0) { - - clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size); - program_log = (char*) malloc(log_size + 1); - program_log[log_size] = '\0'; - clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, log_size + 1, program_log, NULL); - fprintf(stderr, "ggml_opencl: kernel compile error:\n\n%s\n", program_log); - free(program_log); - exit(1); - } - - return p; -} - -void ggml_cl_init(void) { - static bool initialized = false; - if (initialized) { - return; - } - initialized = true; - - cl_int err; - - struct cl_device; - struct cl_platform { - cl_platform_id id; - unsigned number; - char name[128]; - char vendor[128]; - struct cl_device * devices; - unsigned n_devices; - struct cl_device * default_device; - }; - - struct cl_device { - struct cl_platform * platform; - cl_device_id id; - unsigned number; - cl_device_type type; - char name[128]; - }; - - enum { NPLAT = 16, NDEV = 16 }; - - struct cl_platform platforms[NPLAT]; - unsigned n_platforms = 0; - struct cl_device devices[NDEV]; - unsigned n_devices = 0; - struct cl_device * default_device = NULL; - - platform = NULL; - device = NULL; - - cl_platform_id platform_ids[NPLAT]; - CL_CHECK(clGetPlatformIDs(NPLAT, platform_ids, &n_platforms)); - - for (unsigned i = 0; i < n_platforms; i++) { - struct cl_platform * p = &platforms[i]; - p->number = i; - p->id = platform_ids[i]; - CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_NAME, sizeof(p->name), &p->name, NULL)); - CL_CHECK(clGetPlatformInfo(p->id, CL_PLATFORM_VENDOR, sizeof(p->vendor), &p->vendor, NULL)); - - cl_device_id device_ids[NDEV]; - cl_int clGetDeviceIDsError = clGetDeviceIDs(p->id, CL_DEVICE_TYPE_ALL, NDEV, device_ids, &p->n_devices); - if (clGetDeviceIDsError == CL_DEVICE_NOT_FOUND) { - p->n_devices = 0; - } else { - CL_CHECK(clGetDeviceIDsError); - } - p->devices = p->n_devices > 0 ? &devices[n_devices] : NULL; - p->default_device = NULL; - - for (unsigned j = 0; j < p->n_devices; j++) { - struct cl_device * d = &devices[n_devices]; - d->number = n_devices++; - d->id = device_ids[j]; - d->platform = p; - CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_NAME, sizeof(d->name), &d->name, NULL)); - CL_CHECK(clGetDeviceInfo(d->id, CL_DEVICE_TYPE, sizeof(d->type), &d->type, NULL)); - - if (p->default_device == NULL && d->type == CL_DEVICE_TYPE_GPU) { - p->default_device = d; - } - } - - if (default_device == NULL && p->default_device != NULL) { - default_device = p->default_device; - } - } - - if (n_devices == 0) { - fprintf(stderr, "ggml_opencl: could find any OpenCL devices.\n"); - exit(1); - } - - char * user_platform_string = getenv("GGML_OPENCL_PLATFORM"); - char * user_device_string = getenv("GGML_OPENCL_DEVICE"); - int user_platform_number = -1; - int user_device_number = -1; - - unsigned n; - if (user_platform_string != NULL && sscanf(user_platform_string, " %u", &n) == 1 && n < n_platforms) { - user_platform_number = (int)n; - } - if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1 && n < n_devices) { - user_device_number = (int)n; - } - if (user_platform_number != -1 && user_device_number != -1) { - cl_platform* platform = &platforms[user_platform_number]; - if ((unsigned)user_device_number >= platform->n_devices) { - fprintf(stderr, "ggml_opencl: invalid device number %d\n", user_device_number); - exit(1); - } - default_device = &platform->devices[user_device_number]; - } else { - - struct cl_device * selected_devices = devices; - unsigned n_selected_devices = n_devices; - - if (user_platform_number == -1 && user_platform_string != NULL && user_platform_string[0] != 0) { - for (unsigned i = 0; i < n_platforms; i++) { - struct cl_platform * p = &platforms[i]; - if (strstr(p->name, user_platform_string) != NULL || - strstr(p->vendor, user_platform_string) != NULL) { - user_platform_number = (int)i; - break; - } - } - if (user_platform_number == -1) { - fprintf(stderr, "ggml_opencl: no platform matching '%s' was found.\n", user_platform_string); - exit(1); - } - } - if (user_platform_number != -1) { - struct cl_platform * p = &platforms[user_platform_number]; - selected_devices = p->devices; - n_selected_devices = p->n_devices; - default_device = p->default_device; - if (n_selected_devices == 0) { - fprintf(stderr, "ggml_opencl: selected platform '%s' does not have any devices.\n", p->name); - exit(1); - } - } - - if (user_device_number == -1 && user_device_string != NULL && user_device_string[0] != 0) { - for (unsigned i = 0; i < n_selected_devices; i++) { - struct cl_device * d = &selected_devices[i]; - if (strstr(d->name, user_device_string) != NULL) { - user_device_number = d->number; - break; - } - } - if (user_device_number == -1) { - fprintf(stderr, "ggml_opencl: no device matching '%s' was found.\n", user_device_string); - exit(1); - } - } - if (user_device_number != -1) { - selected_devices = &devices[user_device_number]; - n_selected_devices = 1; - default_device = &selected_devices[0]; - } - - GGML_ASSERT(n_selected_devices > 0); - - if (default_device == NULL) { - default_device = &selected_devices[0]; - } - } - - fprintf(stderr, "ggml_opencl: selecting platform: '%s'\n", default_device->platform->name); - fprintf(stderr, "ggml_opencl: selecting device: '%s'\n", default_device->name); - if (default_device->type != CL_DEVICE_TYPE_GPU) { - fprintf(stderr, "ggml_opencl: warning, not a GPU: '%s'.\n", default_device->name); - } - - platform = default_device->platform->id; - device = default_device->id; - - size_t ext_str_size; - clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, 0, NULL, &ext_str_size); - char *ext_buffer = (char *)alloca(ext_str_size + 1); - clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL); - ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated - // Disabled due to faulty outputs - // Check if ext_buffer contains cl_khr_fp16 - fp16_support = false; // strstr(ext_buffer, "cl_khr_fp16") != NULL; - // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false"); - - cl_context_properties properties[] = { - (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0 - }; - - CL_CHECK((context = clCreateContext(properties, 1, &device, NULL, NULL, &err), err)); - - CL_CHECK((queue = clCreateCommandQueue(context, device, CL_QUEUE_OUT_OF_ORDER_EXEC_MODE_ENABLE, &err), - (err != CL_INVALID_QUEUE_PROPERTIES && err != CL_INVALID_VALUE ? err : - (queue = clCreateCommandQueue(context, device, 0, &err), err) - ))); - - const std::string kernel_src = generate_kernels(); - - program = build_program_from_source(context, device, kernel_src.c_str()); - - // FP16 to FP32 kernel - CL_CHECK((convert_row_f16_cl = clCreateKernel(program, "convert_row_f16", &err), err)); - - // Dequantize kernels - CL_CHECK((dequantize_row_q4_0_cl = clCreateKernel(program, "dequantize_row_q4_0", &err), err)); - CL_CHECK((dequantize_row_q4_1_cl = clCreateKernel(program, "dequantize_row_q4_1", &err), err)); - CL_CHECK((dequantize_row_q5_0_cl = clCreateKernel(program, "dequantize_row_q5_0", &err), err)); - CL_CHECK((dequantize_row_q5_1_cl = clCreateKernel(program, "dequantize_row_q5_1", &err), err)); - CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err)); - CL_CHECK((dequantize_row_q8_0_cl = clCreateKernel(program, "dequantize_row_q8_0", &err), err)); - CL_CHECK((dequantize_block_q2_k_cl = clCreateKernel(program, "dequantize_block_q2_K", &err), err)); - CL_CHECK((dequantize_block_q3_k_cl = clCreateKernel(program, "dequantize_block_q3_K", &err), err)); - CL_CHECK((dequantize_block_q4_k_cl = clCreateKernel(program, "dequantize_block_q4_K", &err), err)); - CL_CHECK((dequantize_block_q5_k_cl = clCreateKernel(program, "dequantize_block_q5_K", &err), err)); - CL_CHECK((dequantize_block_q6_k_cl = clCreateKernel(program, "dequantize_block_q6_K", &err), err)); - - // dequant mul mat kernel - CL_CHECK((dequantize_mul_mat_vec_q4_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_0", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q4_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_1", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_0", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_1_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_1", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q8_0_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q8_0", &err), err)); - CL_CHECK((convert_mul_mat_vec_f16_cl = clCreateKernel(program, "convert_mul_mat_vec_f16", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q2_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q2_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q3_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q3_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q4_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q4_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q5_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q5_K", &err), err)); - CL_CHECK((dequantize_mul_mat_vec_q6_K_cl = clCreateKernel(program, "dequantize_mul_mat_vec_q6_K", &err), err)); - - // mul kernel - CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err)); - - CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err)); -} - -static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - return &dequantize_row_q4_0_cl; - case GGML_TYPE_Q4_1: - return &dequantize_row_q4_1_cl; - case GGML_TYPE_Q5_0: - return &dequantize_row_q5_0_cl; - case GGML_TYPE_Q5_1: - return &dequantize_row_q5_1_cl; - case GGML_TYPE_Q8_0: - return &dequantize_row_q8_0_cl; - case GGML_TYPE_Q2_K: - return &dequantize_block_q2_k_cl; - case GGML_TYPE_Q3_K: - return &dequantize_block_q3_k_cl; - case GGML_TYPE_Q4_K: - return &dequantize_block_q4_k_cl; - case GGML_TYPE_Q5_K: - return &dequantize_block_q5_k_cl; - case GGML_TYPE_Q6_K: - return &dequantize_block_q6_k_cl; - case GGML_TYPE_F16: - return &convert_row_f16_cl; - default: - return nullptr; - } -} - -static size_t ggml_cl_global_denom(ggml_type type) { - switch (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: - return 1; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - return 4; - case GGML_TYPE_Q4_K: - return 8; - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - return 4; - case GGML_TYPE_F16: - default: - return 1; - } -} - -static size_t ggml_cl_local_size(ggml_type type) { - switch (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: - return 0; - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - return 64; - case GGML_TYPE_Q4_K: - return 32; - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - return 64; - case GGML_TYPE_F16: - default: - return 0; - } -} - -static cl_kernel* ggml_get_dequantize_mul_mat_vec_cl(ggml_type type) { - switch (type) { - case GGML_TYPE_Q4_0: - return &dequantize_mul_mat_vec_q4_0_cl; - case GGML_TYPE_Q4_1: - return &dequantize_mul_mat_vec_q4_1_cl; - case GGML_TYPE_Q5_0: - return &dequantize_mul_mat_vec_q5_0_cl; - case GGML_TYPE_Q5_1: - return &dequantize_mul_mat_vec_q5_1_cl; - case GGML_TYPE_Q8_0: - return &dequantize_mul_mat_vec_q8_0_cl; - case GGML_TYPE_F16: - return &convert_mul_mat_vec_f16_cl; - case GGML_TYPE_Q2_K: - return &dequantize_mul_mat_vec_q2_K_cl; - case GGML_TYPE_Q3_K: - return &dequantize_mul_mat_vec_q3_K_cl; - case GGML_TYPE_Q4_K: - return &dequantize_mul_mat_vec_q4_K_cl; - case GGML_TYPE_Q5_K: - return &dequantize_mul_mat_vec_q5_K_cl; - case GGML_TYPE_Q6_K: - return &dequantize_mul_mat_vec_q6_K_cl; - default: - return nullptr; - } -} - -// buffer pool for cl -#define MAX_CL_BUFFERS 256 - -struct scoped_spin_lock { - std::atomic_flag& lock; - scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { - while (lock.test_and_set(std::memory_order_acquire)) { - ; // spin - } - } - ~scoped_spin_lock() { - lock.clear(std::memory_order_release); - } - scoped_spin_lock(const scoped_spin_lock&) = delete; - scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; -}; - -struct cl_buffer { - cl_mem mem; - size_t size = 0; -}; - -static cl_buffer g_cl_buffer_pool[MAX_CL_BUFFERS]; -static std::atomic_flag g_cl_pool_lock = ATOMIC_FLAG_INIT; - -static cl_mem ggml_cl_pool_malloc(size_t size, size_t * actual_size) { - scoped_spin_lock lock(g_cl_pool_lock); - cl_int err; - - int best_i = -1; - size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs - int worst_i = -1; - size_t worst_size = 0; //largest unused buffer seen so far - for (int i = 0; i < MAX_CL_BUFFERS; ++i) { - cl_buffer &b = g_cl_buffer_pool[i]; - if (b.size > 0 && b.size >= size && b.size < best_size) - { - best_i = i; - best_size = b.size; - } - if (b.size > 0 && b.size > worst_size) - { - worst_i = i; - worst_size = b.size; - } - } - if(best_i!=-1) //found the smallest buffer that fits our needs - { - cl_buffer& b = g_cl_buffer_pool[best_i]; - cl_mem mem = b.mem; - *actual_size = b.size; - b.size = 0; - return mem; - } - if(worst_i!=-1) //no buffer that fits our needs, resize largest one to save memory - { - cl_buffer& b = g_cl_buffer_pool[worst_i]; - cl_mem mem = b.mem; - b.size = 0; - clReleaseMemObject(mem); - } - cl_mem mem; - CL_CHECK((mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err), err)); - *actual_size = size; - return mem; -} - -static void ggml_cl_pool_free(cl_mem mem, size_t size) { - scoped_spin_lock lock(g_cl_pool_lock); - - for (int i = 0; i < MAX_CL_BUFFERS; ++i) { - cl_buffer& b = g_cl_buffer_pool[i]; - if (b.size == 0) { - b.mem = mem; - b.size = size; - return; - } - } - fprintf(stderr, "WARNING: cl buffer pool full, increase MAX_CL_BUFFERS\n"); - clReleaseMemObject(mem); -} - -void ggml_cl_free_data(const struct ggml_tensor* tensor) { - if (tensor->backend != GGML_BACKEND_TYPE_GPU) { - return; - } - - cl_mem mem = (cl_mem)tensor->extra; - clReleaseMemObject(mem); -} - -static cl_int ggml_cl_h2d_tensor_2d(cl_command_queue queue, cl_mem dst, size_t offset, const struct ggml_tensor * src, uint64_t i3, uint64_t i2, cl_event* ev) { - cl_int err; - const uint64_t ne0 = src->ne[0]; - const uint64_t ne1 = src->ne[1]; - const uint64_t nb0 = src->nb[0]; - const uint64_t nb1 = src->nb[1]; - const uint64_t nb2 = src->nb[2]; - const uint64_t nb3 = src->nb[3]; - const enum ggml_type type = src->type; - const size_t ts = ggml_type_size(type); - const size_t bs = ggml_blck_size(type); - const uint64_t row_size = ts*ne0/bs; - - const char * x = (const char *) src->data + i2*nb2 + i3*nb3; - if (nb0 == ts && nb1 == row_size) { - return clEnqueueWriteBuffer(queue, dst, CL_FALSE, offset, ne1*row_size, x, 0, NULL, ev); - } - if (nb0 == ts) { - const size_t buffer_origin[3] = { offset, 0, 0 }; - const size_t host_origin[3] = { 0, 0, 0 }; - const size_t region[3] = { row_size, ne1, 1 }; - return clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, row_size, 0, nb1, 0, x, 0, NULL, ev); - } - std::vector events; - if (ev && ne1>1) events.reserve(ne1-1); - for (uint64_t i1 = 0; i1 < ne1; i1++) { - // pretend the row is a matrix with cols=1 - const size_t buffer_origin[3] = { offset + i1*row_size, 0, 0 }; - const size_t host_origin[3] = { 0, 0, 0 }; - const size_t region[3] = { ts, ne0/bs, 1 }; - // if an event is requested, make the last write wait for all previous writes to complete - if (ev && i1) { - events.push_back(*ev); - } - cl_uint nevents = i1 == ne1-1 ? events.size() : 0U; - err = clEnqueueWriteBufferRect(queue, dst, CL_FALSE, buffer_origin, host_origin, region, ts, 0, nb0, 0, x + i1*nb1, nevents, nevents ? events.data() : nullptr, ev); - if (err != CL_SUCCESS) { - for (auto event : events) { - clReleaseEvent(event); - } - return err; - } - } - for (auto event : events) { - CL_CHECK(clReleaseEvent(event)); - } - return CL_SUCCESS; -} - -static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - size_t x_size; - size_t d_size; - - cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 - cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. - cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst - - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - cl_event ev; - - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); - - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int i1 = i13*ne12*ne11 + i12*ne11; - - cl_int x_offset = 0; - cl_int y_offset = i1*ne10; - cl_int d_offset = 0; - - size_t global = ne00 * ne01; - cl_int ky = ne10 * ne11; - - CL_CHECK(clSetKernelArg(mul_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(mul_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, mul_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - - CL_CHECK(clReleaseEvent(ev)); - CL_CHECK(clFinish(queue)); - - // copy dst to host - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL)); - } - } - ggml_cl_pool_free(d_X, x_size); - ggml_cl_pool_free(d_D, d_size); -} - -void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - ggml_cl_mul_f32(src0, src1, dst); -} - -static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src1->backend == GGML_BACKEND_TYPE_GPU); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - size_t x_size; - size_t d_size; - - cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 - cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. - cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst - - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - cl_event ev; - - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); - - const int64_t i13 = i03%ne13; - const int64_t i12 = i02%ne12; - const int i1 = i13*ne12*ne11 + i12*ne11; - - cl_int x_offset = 0; - cl_int y_offset = i1*ne10; - cl_int d_offset = 0; - - size_t global = ne00 * ne01; - cl_int ky = ne10 * ne11; - - CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X)); - CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset)); - CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky)); - CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); - - CL_CHECK(clReleaseEvent(ev)); - CL_CHECK(clFinish(queue)); - - // copy dst to host - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL)); - } - } - ggml_cl_pool_free(d_X, x_size); - ggml_cl_pool_free(d_D, d_size); -} - -void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - ggml_cl_add_f32(src0, src1, dst); -} - -static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const float alpha = 1.0f; - const float beta = 0.0f; - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - - size_t x_size; - size_t y_size; - size_t d_size; - cl_mem d_X; - if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT - d_X = (cl_mem) src0->extra; - } else { - d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); - } - cl_mem d_Y = src1->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) src1->extra : ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = dst->backend == GGML_BACKEND_TYPE_GPU ? (cl_mem) dst->extra : ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); - - size_t x_offset = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_TYPE_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else { - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - } - - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { - // copy src1 to device - if (src1->backend == GGML_BACKEND_TYPE_CPU) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - } - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // copy dst to host - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &ev_sgemm, NULL)); - } - } - } - } - } - - if (src0->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_X, x_size); - } - if (src1->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_Y, y_size); - } - if (dst->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_D, d_size); - } -} - -static void ggml_cl_mul_mat_f16(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, void * wdata, size_t wsize) { - GGML_ASSERT(fp16_support); - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const ggml_fp16_t alpha = ggml_fp32_to_fp16(1.0f); - const ggml_fp16_t beta = ggml_fp32_to_fp16(0.0f); - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - - GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * y_ne); - GGML_ASSERT(wsize >= sizeof(ggml_fp16_t) * d_ne); - ggml_fp16_t * const tmp = (ggml_fp16_t *) wdata; - - size_t x_size; - size_t y_size; - size_t d_size; - cl_mem d_X; - if (src0->backend == GGML_BACKEND_TYPE_GPU) { // NOLINT - d_X = (cl_mem) src0->extra; - } else { - d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); - } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * d_ne, &d_size); - - bool src1_cont_rows = nb10 == sizeof(float); - bool src1_cont_cols = (size_t)nb11 == ne11*sizeof(float); - - size_t x_offset = 0; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - if (src0->backend == GGML_BACKEND_TYPE_GPU) { - x_offset = (i03 * ne02 + i02) * x_ne; - } else { - // copy src0 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, NULL)); - } - - // FIXME: convert on device - - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { - // convert src1 to fp16 - // TODO: use multiple threads - char * src1i = (char *) src1->data + i13*nb13 + i12*nb12; - if (src1_cont_rows) { - if (src1_cont_cols) { - ggml_fp32_to_fp16_row((float *) src1i, tmp, ne10*ne11); - } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - ggml_fp32_to_fp16_row((float *) (src1i + i11*nb11), tmp + i11*ne10, ne10); - } - } - } - else { - for (int64_t i11 = 0; i11 < ne11; i11++) { - for (int64_t i10 = 0; i10 < ne10; i10++) { - // very slow due to no inlining - tmp[i11*ne10 + i10] = ggml_fp32_to_fp16(*(float *) (src1i + i11*nb11 + i10*nb10)); - } - } - } - - // copy src1 to device - CL_CHECK(clEnqueueWriteBuffer(queue, d_Y, false, 0, sizeof(ggml_fp16_t) * y_ne, tmp, 0, NULL, NULL)); - - CL_CHECK(clFinish(queue)); - - // compute - cl_event ev_sgemm; - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, x_offset, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, &ev_sgemm); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - - // copy dst to host, then convert to float - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(ggml_fp16_t) * d_ne, tmp, 1, &ev_sgemm, NULL)); - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_fp16_to_fp32_row(tmp, d, d_ne); - } else { - // FIXME: convert dst to fp32 on device - } - } - } - } - } - - if (src0->backend != GGML_BACKEND_TYPE_GPU) { - ggml_cl_pool_free(d_X, x_size); - } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); -} - -static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - const ggml_type type = src0->type; - const bool mul_mat_vec = ne11 == 1 && ne00%2 == 0; - - const int64_t r2 = ne12 / ne02; - const int64_t r3 = ne13 / ne03; - - const float alpha = 1.0f; - const float beta = 0.0f; - const int x_ne = ne01 * ne00; - const int y_ne = ne11 * ne10; - const int d_ne = ne11 * ne01; - const int x_bps = x_ne / ggml_blck_size(type); // blocks per 2D slice - const size_t q_sz = ggml_type_size(type) * x_bps; - - size_t x_size; - size_t y_size; - size_t d_size; - size_t q_size; - cl_mem d_X; - if (!mul_mat_vec) { - d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); - } - cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); - cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); - cl_mem d_Q; - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - d_Q = ggml_cl_pool_malloc(q_sz, &q_size); - } - - cl_kernel* to_fp32_cl = ggml_get_to_fp32_cl(type); - cl_kernel* dmmv = ggml_get_dequantize_mul_mat_vec_cl(type); - GGML_ASSERT(to_fp32_cl != nullptr); - - const size_t global_denom = ggml_cl_global_denom(type); - const size_t local = mul_mat_vec ? CL_DMMV_LOCAL_SIZE : ggml_cl_local_size(type); - - size_t ev_idx = 0; - std::vector events; - - for (int64_t i03 = 0; i03 < ne03; i03++) { - // TODO: copy and dequantize src0 here when r3>1 - for (int64_t i13 = i03 * r3, e13 = i13 + r3; i13 < e13; i13++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - // copy src0 to device if necessary - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - events.emplace_back(); - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Q, 0, src0, i03, i02, events.data() + ev_idx++)); - } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { - d_Q = (cl_mem) src0->extra; - } else { - GGML_ASSERT(false); - } - - if (!mul_mat_vec) { - // convert src0 to fp32 on device - const size_t global = x_ne / global_denom; - const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; - CL_CHECK(clSetKernelArg(*to_fp32_cl, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*to_fp32_cl, 1, sizeof(cl_mem), &d_X)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); - } - - int64_t i12 = i02 * r2; - int64_t e12 = i12 + r2; - events.reserve(e12 - i12); - for (; i12 < e12; i12++) { - if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel - // copy src1 to device - events.emplace_back(); - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, events.data() + ev_idx++)); - - // compute - const size_t global = ne01 * local; - const size_t offset = src0->backend == GGML_BACKEND_TYPE_GPU ? (i03 * ne02 + i02) * x_bps : 0; - const cl_int ncols = ne00; - events.emplace_back(); - CL_CHECK(clSetKernelArg(*dmmv, 0, sizeof(cl_mem), &d_Q)); - CL_CHECK(clSetKernelArg(*dmmv, 1, sizeof(float) * local, NULL)); - CL_CHECK(clSetKernelArg(*dmmv, 2, sizeof(cl_mem), &d_Y)); - CL_CHECK(clSetKernelArg(*dmmv, 3, sizeof(cl_mem), &d_D)); - CL_CHECK(clSetKernelArg(*dmmv, 4, sizeof(cl_int), &ncols)); - CL_CHECK(clEnqueueNDRangeKernel(queue, *dmmv, 1, &offset, &global, &local, events.size() - 1, events.data(), events.data() + ev_idx++)); - } else { // CLBlast matrix matrix multiplication - // copy src1 to device - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_Y, 0, src1, i13, i12, NULL)); - - // wait for conversion - CL_CHECK(clFinish(queue)); - - // compute - events.emplace_back(); - clblast::StatusCode status = clblast::Gemm(clblast::Layout::kColMajor, - clblast::Transpose::kYes, clblast::Transpose::kNo, - ne01, ne11, ne10, - alpha, - d_X, 0, ne00, - d_Y, 0, ne10, - beta, - d_D, 0, ne01, - &queue, events.data() + ev_idx++); - - if (status != clblast::StatusCode::kSuccess) { - GGML_ASSERT(false); - } - } - - // copy dst to host - float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * d_ne, d, 1, &events[events.size() - 1], NULL)); - for (auto *event : events) { - clReleaseEvent(event); - } - - ev_idx = 0; - events.clear(); - } - } - } - } - - if (!mul_mat_vec) { - ggml_cl_pool_free(d_X, x_size); - } - ggml_cl_pool_free(d_Y, y_size); - ggml_cl_pool_free(d_D, d_size); - if (src0->backend == GGML_BACKEND_TYPE_CPU) { - ggml_cl_pool_free(d_Q, q_size); - } -} - - -bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst) { - const int64_t ne10 = src1->ne[0]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - - // TODO: find the optimal values for these - if ((src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && - src1->type == GGML_TYPE_F32 && - dst->type == GGML_TYPE_F32 && - ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_TYPE_GPU)) { - return true; - } - - return false; -} - -static bool ggml_cl_mul_mat_use_f16(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * /* dst */) { - // If device doesn't support FP16 - if (!fp16_support) { - return false; - } - - size_t src0_sz = ggml_nbytes(src0); - size_t src1_sz = ggml_nbytes(src1); - - // mul_mat_q: src0 is converted to fp32 on device - size_t mul_mat_q_transfer = src0_sz + src1_sz; - - // mul_mat_f16: src1 is converted to fp16 on cpu - size_t mul_mat_f16_transfer = src0_sz + sizeof(ggml_fp16_t) * ggml_nelements(src1); - - // choose the smaller one to transfer to the device - // TODO: this is not always the best choice due to the overhead of converting to fp16 - return mul_mat_f16_transfer < mul_mat_q_transfer; -} - -void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize) { - GGML_ASSERT(ggml_cl_can_mul_mat(src0, src1, dst)); - - if (src0->type == GGML_TYPE_F32) { - ggml_cl_mul_mat_f32(src0, src1, dst); - } - else if (src0->type == GGML_TYPE_F16) { - if (ggml_cl_mul_mat_use_f16(src0, src1, dst)) { - ggml_cl_mul_mat_f16(src0, src1, dst, wdata, wsize); - } - else { - ggml_cl_mul_mat_q_f32(src0, src1, dst); - } - } - else if (ggml_is_quantized(src0->type)) { - ggml_cl_mul_mat_q_f32(src0, src1, dst); - } - else { - GGML_ASSERT(false); - } -} - -size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - if (src0->type == GGML_TYPE_F16 && ggml_cl_mul_mat_use_f16(src0, src1, dst)) { - return sizeof(ggml_fp16_t) * std::max(src1->ne[0] * src1->ne[1], dst->ne[0] * dst->ne[1]); - } - return 0; -} - -void ggml_cl_transform_tensor(void * data, ggml_tensor * tensor) { - const int64_t ne0 = tensor->ne[0]; - const int64_t ne1 = tensor->ne[1]; - const int64_t ne2 = tensor->ne[2]; - const int64_t ne3 = tensor->ne[3]; - - const ggml_type type = tensor->type; - const size_t s_sz = ggml_type_size(type) * (size_t) (ne0 * ne1 / ggml_blck_size(type)); - const size_t q_sz = s_sz * (size_t) (ne2 * ne3); - - size_t q_size; - cl_mem dst = ggml_cl_pool_malloc(q_sz, &q_size); - - tensor->data = data; - // copy tensor to device - size_t offset = 0; - for (int64_t i3 = 0; i3 < ne3; i3++) { - for (int64_t i2 = 0; i2 < ne2; i2++) { - CL_CHECK(ggml_cl_h2d_tensor_2d(queue, dst, offset, tensor, i3, i2, NULL)); - offset += s_sz; - } - } - - CL_CHECK(clFinish(queue)); - - tensor->extra = dst; - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_GPU); -} - -// ggml-backend - -// buffer - -struct ggml_backend_opencl_buffer_context { - ~ggml_backend_opencl_buffer_context() { - if (buffer) { - clReleaseMemObject(buffer); - } - for (auto * sub_buffer : sub_buffers) { - clReleaseMemObject(sub_buffer); - } - } - - cl_mem buffer; - std::vector sub_buffers; -}; - -static void * const cl_ptr_base = (void *)(uintptr_t) 0x1000; - -static const char * ggml_backend_opencl_buffer_get_name(ggml_backend_buffer_t buffer) { - return "OpenCL"; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - delete ctx; -} - -static void * ggml_backend_opencl_buffer_get_base(ggml_backend_buffer_t buffer) { - return cl_ptr_base; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { - if (tensor->view_src != NULL && tensor->view_offs == 0) { - tensor->extra = tensor->view_src->extra; - } else { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - cl_buffer_region region = {(size_t)((char *)tensor->data - (char *)cl_ptr_base), ggml_nbytes(tensor)}; - cl_int err; - cl_mem sub_buffer = clCreateSubBuffer(ctx->buffer, CL_MEM_READ_WRITE, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &err); - CL_CHECK(err); - ctx->sub_buffers.push_back(sub_buffer); - tensor->extra = sub_buffer; - } - tensor->backend = GGML_BACKEND_TYPE_GPU; -} - -static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { - cl_mem tensor_buffer = (cl_mem) tensor->extra; - CL_CHECK(clEnqueueWriteBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { - cl_mem tensor_buffer = (cl_mem) tensor->extra; - CL_CHECK(clEnqueueReadBuffer(queue, tensor_buffer, true, offset, size, data, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - CL_CHECK(clEnqueueFillBuffer(queue, ctx->buffer, &value, sizeof(value), 0, buffer->size, 0, NULL, NULL)); - CL_CHECK(clFinish(queue)); -} - -static void ggml_backend_opencl_buffer_reset(ggml_backend_buffer_t buffer) { - ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context; - for (auto * sub_buffer : ctx->sub_buffers) { - clReleaseMemObject(sub_buffer); - } - ctx->sub_buffers.clear(); -} - -static ggml_backend_buffer_i ggml_backend_opencl_buffer_interface = { - /* .get_name = */ ggml_backend_opencl_buffer_get_name, - /* .free_buffer = */ ggml_backend_opencl_buffer_free_buffer, - /* .get_base = */ ggml_backend_opencl_buffer_get_base, - /* .init_tensor = */ ggml_backend_opencl_buffer_init_tensor, - /* .set_tensor = */ ggml_backend_opencl_buffer_set_tensor, - /* .get_tensor = */ ggml_backend_opencl_buffer_get_tensor, - /* .cpy_tensor = */ NULL, - /* .clear = */ ggml_backend_opencl_buffer_clear, - /* .reset = */ ggml_backend_opencl_buffer_reset, -}; - -// buffer type - -static const char * ggml_backend_opencl_buffer_type_name(ggml_backend_buffer_type_t buffer_type) { - return "OpenCL"; - - GGML_UNUSED(buffer_type); -} - -static ggml_backend_buffer_t ggml_backend_opencl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buffer_type, size_t size) { - ggml_cl_init(); - - cl_int err; - cl_mem mem = clCreateBuffer(context, CL_MEM_READ_WRITE, size, NULL, &err); - if (err != CL_SUCCESS) { - fprintf(stderr, "%s: failed to allocate %.2f MiB\n", __func__, size / 1024.0 / 1024.0); - return nullptr; - } - - ggml_backend_opencl_buffer_context * ctx = new ggml_backend_opencl_buffer_context{mem, {}}; - - return ggml_backend_buffer_init(buffer_type, ggml_backend_opencl_buffer_interface, ctx, size); -} - -static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_type_t buffer_type) { - // FIXME: not thread safe, device may not be initialized yet - static cl_uint alignment = -1; - 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; - - GGML_UNUSED(buffer_type); -} - -static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) { - static size_t max_size = -1; - if (max_size == (size_t)-1) { - ggml_cl_init(); - clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL); - } - return max_size; -} - -static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) { - //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend - return ggml_backend_is_cpu(backend); - - GGML_UNUSED(buffer_type); -} - -static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { - /* .get_name = */ ggml_backend_opencl_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, - /* .get_max_size = */ ggml_backend_opencl_buffer_type_get_max_size, - /* .get_alloc_size = */ NULL, - /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, - /* .is_host = */ NULL, -}; - - -ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type() { - static ggml_backend_buffer_type buffer_type = { - /* .iface = */ ggml_backend_opencl_buffer_type_interface, - /* .context = */ nullptr, - }; - - return &buffer_type; -} - -#if 0 -// host buffer type - -static const char * ggml_backend_opencl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { - return "CL_Host"; - - GGML_UNUSED(buft); -} - -static const char * ggml_backend_opencl_host_buffer_name(ggml_backend_buffer_t buffer) { - return "CL_Host"; - - GGML_UNUSED(buffer); -} - -static void ggml_backend_opencl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_cl_host_free(buffer->context); -} - -static ggml_backend_buffer_t ggml_backend_opencl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_cl_host_malloc(size); - - if (ptr == nullptr) { - // fallback to cpu buffer - return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); - } - - ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); - buffer->buft = buft; - buffer->iface.get_name = ggml_backend_opencl_host_buffer_name; - buffer->iface.free_buffer = ggml_backend_opencl_host_buffer_free_buffer; - - return buffer; -} - -ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() { - static struct ggml_backend_buffer_type ggml_backend_opencl_buffer_type_host = { - /* .iface = */ { - /* .get_name = */ ggml_backend_opencl_host_buffer_type_name, - /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer, - /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, - /* .get_max_size = */ NULL, // defaults to SIZE_MAX - /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, - /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, - /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, - }, - /* .context = */ nullptr, - }; - - return &ggml_backend_opencl_buffer_type_host; -} - -// backend - -static const char * ggml_backend_opencl_name(ggml_backend_t backend) { - return "OpenCL"; - - GGML_UNUSED(backend); -} - -static void ggml_backend_opencl_free(ggml_backend_t backend) { - GGML_UNUSED(backend); -} - -static ggml_backend_buffer_type_t ggml_backend_opencl_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_opencl_buffer_type(); - - GGML_UNUSED(backend); -} - -static ggml_status ggml_backend_opencl_graph_compute(ggml_backend_t backend, ggml_cgraph * graph) { - for (int i = 0; i < graph->n_nodes; ++i) { - ggml_tensor * node = graph->nodes[i]; - - if (ggml_is_empty(node)) { - continue; - } - - switch (node->op) { - case GGML_OP_MUL_MAT: - ggml_cl_mul_mat(node->src[0], node->src[1], node, nullptr, 0); - break; - case GGML_OP_MUL: - ggml_cl_mul(node->src[0], node->src[1], node); - break; - default: - GGML_ASSERT(false); - } - } - - return GGML_STATUS_SUCCESS; - - GGML_UNUSED(backend); -} - -static bool ggml_backend_opencl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { - switch (op->op) { - case GGML_OP_MUL_MAT: - return ggml_cl_can_mul_mat(op->src[0], op->src[1], op); - case GGML_OP_MUL: - // return ggml_can_repeat_rows(op->src[1], op->src[0]); - return true; - default: - return false; - } - - GGML_UNUSED(backend); -} - -static ggml_backend_i opencl_backend_i = { - /* .get_name = */ ggml_backend_opencl_name, - /* .free = */ ggml_backend_opencl_free, - /* .get_default_buffer_type = */ ggml_backend_opencl_get_default_buffer_type, - /* .set_tensor_async = */ NULL, - /* .get_tensor_async = */ NULL, - /* .cpy_tensor_from_async = */ NULL, - /* .cpy_tensor_to_async = */ NULL, - /* .synchronize = */ NULL, - /* .graph_plan_create = */ NULL, - /* .graph_plan_free = */ NULL, - /* .graph_plan_compute = */ NULL, - /* .graph_compute = */ ggml_backend_opencl_graph_compute, - /* .supports_op = */ ggml_backend_opencl_supports_op, -}; - -ggml_backend_t ggml_backend_opencl_init() { - ggml_backend_t backend = new ggml_backend { - /* .interface = */ opencl_backend_i, - /* .context = */ nullptr - }; - - return backend; -} - -bool ggml_backend_is_opencl(ggml_backend_t backend) { - return backend && backend->iface.get_name == ggml_backend_opencl_name; -} -#endif diff --git a/ggml-opencl.h b/ggml-opencl.h deleted file mode 100644 index 257a6be6a..000000000 --- a/ggml-opencl.h +++ /dev/null @@ -1,36 +0,0 @@ -#pragma once - -#include "ggml.h" -#include "ggml-backend.h" - -#ifdef __cplusplus -extern "C" { -#endif - -GGML_API void ggml_cl_init(void); - -GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst); -GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); -GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); - -// GGML_API void * ggml_cl_host_malloc(size_t size); -// GGML_API void ggml_cl_host_free(void * ptr); - -GGML_API void ggml_cl_free_data(const struct ggml_tensor* tensor); - -GGML_API void ggml_cl_transform_tensor(void * data, struct ggml_tensor * tensor); - -// backend API - -// GGML_API ggml_backend_t ggml_backend_opencl_init(void); - -// GGML_API bool ggml_backend_is_opencl(ggml_backend_t backend); - -GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_buffer_type(void); -// GGML_API ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type(void); - -#ifdef __cplusplus -} -#endif diff --git a/ggml-quants.c b/ggml-quants.c index ed40ca74a..9f864e5c4 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1888,7 +1888,6 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f return scale; } -#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { if (j < 4) { *d = q[j] & 63; *m = q[j + 4] & 63; @@ -1897,7 +1896,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif //========================- 2-bit (de)-quantization @@ -1961,20 +1959,13 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -1989,7 +1980,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 const uint8_t * q = x[i].qs; -#if QK_K == 256 int is = 0; float dl, ml; for (int n = 0; n < QK_K; n += 128) { @@ -2008,19 +1998,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 } q += 32; } -#else - float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4); - float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4); - float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4); - float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4); - for (int l = 0; l < 16; ++l) { - y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1; - y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2; - y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3; - y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4; - } - y += QK_K; -#endif } } @@ -2211,36 +2188,9 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } float dm, mm; -#if QK_K == 64 - float max_scale = 0, max_min = 0; - for (int j = 0; j < QK_K/16; ++j) { - max_scale = MAX(max_scale, scales[j]); - max_min = MAX(max_min, mins[j]); - } - dm = max_scale/15; - mm = max_min/15; - if (max_scale) { - float id = 1/dm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*scales[j]); - Ls[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Ls, 0, QK_K/16); - } - if (max_min) { - float id = 1/mm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*mins[j]); - Lm[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Lm, 0, QK_K/16); - } -#else dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); -#endif + y[i].d = GGML_FP32_TO_FP16(dm); y[i].dmin = GGML_FP32_TO_FP16(mm); dm = GGML_FP16_TO_FP32(y[i].d); @@ -2263,20 +2213,13 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -2317,7 +2260,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict } } -#if QK_K == 256 memset(y[i].scales, 0, 12); if (max_scale) { float iscale = -32.f/max_scale; @@ -2351,36 +2293,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict L[16*j + ii] = l + 4; } } -#else - if (max_scale) { - float iscale = -8.f/max_scale; - for (int j = 0; j < QK_K/16; j+=2) { - int l1 = nearest_int(iscale*scales[j]); - l1 = 8 + MAX(-8, MIN(7, l1)); - int l2 = nearest_int(iscale*scales[j+1]); - l2 = 8 + MAX(-8, MIN(7, l2)); - y[i].scales[j/2] = l1 | (l2 << 4); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - } else { - for (int j = 0; j < QK_K/16; j+=2) { - y[i].scales[j/2] = 0; - } - y[i].d = GGML_FP32_TO_FP16(0.f); - } - for (int j = 0; j < QK_K/16; ++j) { - int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4; - float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8); - if (!d) { - continue; - } - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-4, MIN(3, l)); - L[16*j + ii] = l + 4; - } - } -#endif memset(y[i].hmask, 0, QK_K/8); // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. @@ -2395,23 +2307,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict m = 0; hm <<= 1; } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; } } -#if QK_K == 256 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2461,49 +2366,12 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6 } } -#else -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { - assert(k % QK_K == 0); - assert(QK_K == 64); - const int nb = k / QK_K; - - for (int i = 0; i < nb; i++) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - - const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); - const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); - const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); - const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); - - for (int l=0; l<8; ++l) { - uint8_t h = hm[l]; - y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4)); - y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4)); - y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4)); - y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4)); - y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4)); - y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4)); - y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4)); - y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4)); - } - y += QK_K; - } -} -#endif void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { quantize_row_q3_K_reference(x, vy, k); } static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q3_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int nb = n_per_row / QK_K; @@ -2585,7 +2453,6 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri x += QK_K; } -#endif } size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2617,7 +2484,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict float scales[QK_K/32]; for (int i = 0; i < nb; i++) { - float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -2637,7 +2503,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } -#if QK_K == 256 float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float inv_min = max_min > 0 ? 63.f/max_min : 0.f; for (int j = 0; j < QK_K/32; ++j) { @@ -2669,39 +2534,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict L[32*j + ii] = l; } } -#else - const float s_factor = 15.f; - float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f; - float inv_min = max_min > 0 ? s_factor/max_min : 0.f; - int d1 = nearest_int(inv_scale*scales[0]); - int m1 = nearest_int(inv_min*mins[0]); - int d2 = nearest_int(inv_scale*scales[1]); - int m2 = nearest_int(inv_min*mins[1]); - y[i].scales[0] = d1 | (m1 << 4); - y[i].scales[1] = d2 | (m2 << 4); - y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor); - y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor); - float sumlx = 0; - int suml2 = 0; - for (int j = 0; j < QK_K/32; ++j) { - const uint8_t sd = y[i].scales[j] & 0xF; - const uint8_t sm = y[i].scales[j] >> 4; - const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd; - if (!d) continue; - const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm; - for (int ii = 0; ii < 32; ++ii) { - int l = nearest_int((x[32*j + ii] + m)/d); - l = MAX(0, MIN(15, l)); - L[32*j + ii] = l; - sumlx += (x[32*j + ii] + m)*l*sd; - suml2 += l*l*sd*sd; - } - } - if (suml2) { - y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2); - } -#endif uint8_t * q = y[i].qs; for (int j = 0; j < QK_K; j += 64) { for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); @@ -2709,7 +2542,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } x += QK_K; - } } @@ -2718,11 +2550,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 const int nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * q = x[i].qs; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -2737,18 +2566,6 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; q += 32; is += 2; } -#else - const float dall = GGML_FP16_TO_FP32(x[i].d[0]); - const float mall = GGML_FP16_TO_FP32(x[i].d[1]); - const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4); - const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4); - for (int l = 0; l < 32; ++l) { - y[l+ 0] = d1 * (q[l] & 0xF) - m1; - y[l+32] = d2 * (q[l] >> 4) - m2; - } - y += QK_K; -#endif - } } @@ -2759,10 +2576,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q4_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2833,7 +2646,6 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri x += QK_K; } -#endif } size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2858,21 +2670,13 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict assert(k % QK_K == 0); const int64_t nb = k / QK_K; -#if QK_K == 256 uint8_t L[QK_K]; float mins[QK_K/32]; float scales[QK_K/32]; float weights[32]; uint8_t Laux[32]; -#else - int8_t L[QK_K]; - float scales[QK_K/16]; -#endif for (int i = 0; i < nb; i++) { - -#if QK_K == 256 - float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -2944,55 +2748,8 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict m1 <<= 2; m2 <<= 2; ql += 32; } -#else - float max_scale = 0, amax = 0; - for (int j = 0; j < QK_K/16; ++j) { - scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL); - float abs_scale = fabsf(scales[j]); - if (abs_scale > amax) { - amax = abs_scale; - max_scale = scales[j]; - } - } - - float iscale = -128.f/max_scale; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(iscale*scales[j]); - y[i].scales[j] = MAX(-128, MIN(127, l)); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - - for (int j = 0; j < QK_K/16; ++j) { - const float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; - if (!d) continue; - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-16, MIN(15, l)); - L[16*j + ii] = l + 16; - } - } - - uint8_t * restrict qh = y[i].qh; - uint8_t * restrict ql = y[i].qs; - memset(qh, 0, QK_K/8); - - for (int j = 0; j < 32; ++j) { - int jm = j%8; - int is = j/8; - int l1 = L[j]; - if (l1 > 15) { - l1 -= 16; qh[jm] |= (1 << is); - } - int l2 = L[j + 32]; - if (l2 > 15) { - l2 -= 16; qh[jm] |= (1 << (4 + is)); - } - ql[j] = l1 | (l2 << 4); - } -#endif x += QK_K; - } } @@ -3001,12 +2758,9 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * ql = x[i].qs; const uint8_t * qh = x[i].qh; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -3023,21 +2777,6 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 ql += 32; is += 2; u1 <<= 2; u2 <<= 2; } -#else - float d = GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict s = x[i].scales; - for (int l = 0; l < 8; ++l) { - y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); - y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16)); - y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16)); - y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16)); - y[l+32] = d * s[2] * ((ql[l+ 0] >> 4) - (qh[l] & 0x10 ? 0 : 16)); - y[l+40] = d * s[2] * ((ql[l+ 8] >> 4) - (qh[l] & 0x20 ? 0 : 16)); - y[l+48] = d * s[3] * ((ql[l+16] >> 4) - (qh[l] & 0x40 ? 0 : 16)); - y[l+56] = d * s[3] * ((ql[l+24] >> 4) - (qh[l] & 0x80 ? 0 : 16)); - } - y += QK_K; -#endif } } @@ -3048,10 +2787,6 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q5_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3142,7 +2877,6 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri x += QK_K; } -#endif } size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -3215,7 +2949,6 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict uint8_t * restrict ql = y[i].ql; uint8_t * restrict qh = y[i].qh; -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { const uint8_t q1 = L[j + l + 0] & 0xF; @@ -3229,19 +2962,8 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict ql += 64; qh += 32; } -#else - for (int l = 0; l < 32; ++l) { - const uint8_t q1 = L[l + 0] & 0xF; - const uint8_t q2 = L[l + 32] & 0xF; - ql[l] = q1 | (q2 << 4); - } - for (int l = 0; l < 16; ++l) { - qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6); - } -#endif x += QK_K; - } } @@ -3250,14 +2972,12 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict ql = x[i].ql; const uint8_t * restrict qh = x[i].qh; const int8_t * restrict sc = x[i].scales; -#if QK_K == 256 for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -3275,20 +2995,6 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 qh += 32; sc += 8; } -#else - for (int l = 0; l < 16; ++l) { - const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - const int8_t q3 = (int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - const int8_t q4 = (int8_t)((ql[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - y[l+ 0] = d * sc[0] * q1; - y[l+16] = d * sc[1] * q2; - y[l+32] = d * sc[2] * q3; - y[l+48] = d * sc[3] * q4; - } - y += 64; -#endif - } } @@ -3299,10 +3005,6 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q6_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3384,7 +3086,6 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri x += QK_K; } -#endif } size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -3801,30 +3502,21 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in float delta[4]; uint16_t idx[4]; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int i = 0; i < nb; i++) { const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K == 64 - const float d = GGML_FP16_TO_FP32(x[i].d); -#else scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = GGML_FP16_TO_FP32(scale.f16); -#endif + const uint8_t * qs = x[i].qs; const uint8_t * qh = x[i].qh; for (int ib = 0; ib < QK_K/32; ++ib) { -#if QK_K == 64 - const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1); - const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1); -#else const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1); const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1); -#endif + idx[0] = qs[0] | ((qh[0] << 8) & 0x700); idx[1] = qs[1] | ((qh[0] << 4) & 0x700); idx[2] = qs[2] | ((qh[1] << 8) & 0x700); @@ -3875,9 +3567,6 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); -#if QK_K == 64 - dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); -#else const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3897,7 +3586,6 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, qs += 16; } } -#endif } //===================================== Q8_K ============================================== @@ -4125,7 +3813,44 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + const svbool_t ptrueh = svptrue_pat_b8(SV_VL16); + const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh); + + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q4_0 * restrict x0 = &x[i + 0]; + const block_q4_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -5696,7 +5421,32 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q8_0 * restrict x0 = &x[i + 0]; + const block_q8_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); + const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -5849,7 +5599,6 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -#if QK_K == 256 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); @@ -6339,6 +6088,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); @@ -6433,410 +6183,6 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q2_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); - - const int32x4_t vzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q2bytes; - - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; - - sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); - - int isum1 = 0, isum2 = 0; - - const uint8x16_t q2bits = vld1q_u8(q2); - - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); - q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); - q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); - - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; - - sum += d * (isum1 + isum2); - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3); - const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); - const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); - - const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0)); - const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1)); - const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0)); - const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1)); - - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m128i q2_0 = _mm_and_si128(q2bits, m3); - const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0)); - const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1)); - const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2)); - const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __riscv_v_intrinsic - - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; - - sumf += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); - - int isum1 = 0; - int isum2 = 0; - - size_t vl = 16; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - // load Q2 - vuint8mf2_t q2_x = __riscv_vle8_v_u8mf2(q2, vl); - - vint8mf2_t q2_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q2_x, 0x03, vl)); - vint8mf2_t q2_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x2, vl), 0x03 , vl)); - vint8mf2_t q2_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x4, vl), 0x03 , vl)); - vint8mf2_t q2_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x6, vl), 0x03 , vl)); - - // load Q8, and take product with Q2 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q2_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q2_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q2_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q2_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint16m1_t vs_0 = __riscv_vredsum_vs_i16m1_i16m1(p0, vzero, vl); - vint16m1_t vs_1 = __riscv_vredsum_vs_i16m1_i16m1(p1, vzero, vl); - vint16m1_t vs_2 = __riscv_vredsum_vs_i16m1_i16m1(p2, vzero, vl); - vint16m1_t vs_3 = __riscv_vredsum_vs_i16m1_i16m1(p3, vzero, vl); - - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[0]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[1]; - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_2) * scales[2]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_3) * scales[3]; - - sumf += d * (isum1 + isum2); - - } - - *s = sumf; - - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowScaleMask = vec_splats((signed char)0xF); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl_len(y[i].bsums, 8); - - vector signed char q2xmins = (vector signed char)vec_xl_len(x[i].scales, 4); - vector signed char vscales = vec_and(q2xmins, lowScaleMask); - - q2xmins = vec_sr(q2xmins, v4); - vector signed short q2xmins0 = vec_unpackh((vector signed char)q2xmins); - - vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char q2x00 = vec_and(qxs0, lowMask); - vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y01 = vec_xl( 16, y[i].qs); - vector signed char q8y02 = vec_xl( 32, y[i].qs); - vector signed char q8y03 = vec_xl( 48, y[i].qs); - - vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); - vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); - vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); - vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv0, vs0), vec_mulo(qv0, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv1, vs1), vec_mulo(qv1, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv2, vs2), vec_mulo(qv2, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv3, vs3), vec_mulo(qv3, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = __lsx_vld((const __m128i*)q2, 0); - const __m256i q2_0 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 2), q2bits), m3); - const __m256i q2_1 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 6), __lsx_vsrli_h(q2bits, 4)), m3); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p0 = lasx_maddubs_h(q2_0, q8_0); - const __m256i p1 = lasx_maddubs_h(q2_1, q8_1); - - const __m256i p_0 = lasx_ext16_32(lasx_extracti128(p0, 0)); - const __m256i p_1 = lasx_ext16_32(lasx_extracti128(p0, 1)); - const __m256i p_2 = lasx_ext16_32(lasx_extracti128(p1, 0)); - const __m256i p_3 = lasx_ext16_32(lasx_extracti128(p1, 1)); - - ft_union t0, t1, t2, t3; - t0.f = d * db[0]; - t1.f = d * db[1]; - t2.f = d * db[2]; - t3.f = d * db[3]; - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t0.i), __lasx_xvffint_s_w(p_0), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t1.i), __lasx_xvffint_s_w(p_1), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t2.i), __lasx_xvffint_s_w(p_2), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t3.i), __lasx_xvffint_s_w(p_3), acc); - } - - *s = hsum_float_8(acc) + summs; - -#else - - float sumf = 0; - - int isum[QK_K/16]; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - int summs = 0; - for (int j = 0; j < QK_K/16; ++j) { - summs += y[i].bsums[j] * (sc[j] >> 4); - } - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memset(isum, 0, (QK_K/16)*sizeof(int)); - for (int l = 0; l < 16; ++l) { - isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); - isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); - isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); - isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); - } - for (int l = 0; l < QK_K/16; ++l) { - isum[l] *= (sc[l] & 0xF); - } - sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; - } - *s = sumf; -#endif -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -7462,6 +6808,8 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; // Set up scales memcpy(aux, x[i].scales, 12); __m128i scales128 = lsx_set_w( @@ -7483,29 +6831,32 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int bit = 0; int is = 0; + __m256i xvbit; - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; for (int j = 0; j < QK_K/128; ++j) { // load low 2 bits const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit); // prepare low and high bits const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); - const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); - const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); - const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); - const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; // load Q8 quants @@ -7616,512 +6967,6 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r } -#else - -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const uint8x16_t mh = vdupq_n_u8(4); - - ggml_int8x16x4_t q3bytes; - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - ggml_uint8x16x4_t q3h; - - const uint8x8_t hbits = vld1_u8(x[i].hmask); - const uint8x16_t q3bits = vld1q_u8(x[i].qs); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs); - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; - - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1)); - q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2)); - q3h.val[1] = vandq_u8(mh, htmp); - q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2)); - q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4)); - - q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b), q3h.val[0])); - q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1])); - q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); - q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; - - sum += d * isum; - - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m256i m1 = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8)); - const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8)); - - memcpy(&aux64, x[i].hmask, 8); - - const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux); - __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4); - q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2); - q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2); - - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); - - // prepare low and high bits - const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits); - const __m256i q3l_0 = _mm256_and_si256(q3aux, m3); - const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3); - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); - const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); - - __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - - // multiply with scales - p16_0 = _mm256_madd_epi16(scale_0, p16_0); - p16_1 = _mm256_madd_epi16(scale_1, p16_1); - - p16_0 = _mm256_add_epi32(p16_0, p16_1); - - // multiply with block scale and accumulate - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - const __m128i m1 = _mm_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8); - const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8); - const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8); - const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8); - - memcpy(&aux64, x[i].hmask, 8); - - __m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m128i q3h_1 = _mm_srli_epi16(q3h_0, 2); - __m128i q3h_2 = _mm_srli_epi16(q3h_0, 4); - __m128i q3h_3 = _mm_srli_epi16(q3h_0, 6); - q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2); - q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2); - q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2); - q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2); - - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); - - // prepare low and high bits - const __m128i q3l_0 = _mm_and_si128(q3bits, m3); - const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3); - const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3); - const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3); - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - // multiply with scales - p16_0 = _mm_madd_epi16(scale_0, p16_0); - p16_1 = _mm_madd_epi16(scale_1, p16_1); - p16_2 = _mm_madd_epi16(scale_2, p16_2); - p16_3 = _mm_madd_epi16(scale_3, p16_3); - - p16_0 = _mm_add_epi32(p16_0, p16_2); - p16_1 = _mm_add_epi32(p16_1, p16_3); - __m256i p16 = MM256_SET_M128I(p16_1, p16_0); - - // multiply with block scale and accumulate - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; - - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(x[i].hmask, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); - - size_t vl = 16; - - // extend and combine both qh_x1 and qh_x2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); - - vuint8mf2_t qh_0 = __riscv_vand_vx_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_1 = __riscv_vand_vx_u8mf2(qh_x, 0x4, vl); - vuint8mf2_t qh_2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_3 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), 0x4, vl); - - // load Q3 - vuint8mf2_t q3_x = __riscv_vle8_v_u8mf2(q3, vl); - - vuint8mf2_t q3h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q3_x, 0x3, vl), qh_0, vl); - vuint8mf2_t q3h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 2, vl), 0x3, vl), qh_1, vl); - vuint8mf2_t q3h_2 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 4, vl), 0x3, vl), qh_2, vl); - vuint8mf2_t q3h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 0x6, vl), qh_3, vl); - - vint8mf2_t q3_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_0); - vint8mf2_t q3_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_1); - vint8mf2_t q3_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_2); - vint8mf2_t q3_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_3); - - // load Q8 and take product with Q3 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q3_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q3_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q3_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q3_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scales[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scales[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scales[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scales[3]; - - sumf += d * isum; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char v1 = vec_splats((signed char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x8); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - vector signed char vscales = (vector signed char)vec_xl_len(scales, 8); - vector signed char qxhs0 = (vector signed char)vec_xl_len(x[i].hmask, 8); - qxhs0 = vec_or(qxhs0, vec_sr(vec_sld(qxhs0, qxhs0, 8), (vector unsigned char)v1)); - - vscales = vec_sub(vscales, off); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char qxs10 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char qxs11 = vec_and(vec_sr(qxs0, v6), lowMask); - - //the 3rd bit - vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); - vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); - vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v4)), v2); - vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v6)), v2); - qxhs0 = vec_sr(qxhs0, v4); - - vector signed char q3x00 = vec_sub(qxs00, qxh00); - vector signed char q3x01 = vec_sub(qxs01, qxh01); - vector signed char q3x10 = vec_sub(qxs10, qxh02); - vector signed char q3x11 = vec_sub(qxs11, qxh03); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y01 = vec_xl( 16, y[i].qs); - vector signed char q8y10 = vec_xl( 32, y[i].qs); - vector signed char q8y11 = vec_xl( 48, y[i].qs); - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - - vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); - vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m256i m1 = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const __m256i scale_0 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[2] - 8), __lasx_xvreplgr2vr_h(aux8[0] - 8)); - const __m256i scale_1 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[3] - 8), __lasx_xvreplgr2vr_h(aux8[1] - 8)); - - memcpy(&aux64, x[i].hmask, 8); - - __m128i haux = __lsx_vinsgr2vr_d(haux, aux64, 0); - haux = __lsx_vinsgr2vr_d(haux, aux64 >> 1, 1); - __m256i q3h_0 = lasx_insertf128(__lsx_vsrli_h(haux, 2), haux); - __m256i q3h_1 = __lasx_xvsrli_h(q3h_0, 4); - q3h_0 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_0, m1), 2); - q3h_1 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_1, m1), 2); - - // load low 2 bits - const __m128i q3bits = __lsx_vld((const __m128i*)q3, 0); - - // prepare low and high bits - const __m256i q3aux = lasx_insertf128(__lsx_vsrli_h(q3bits, 2), q3bits); - const __m256i q3l_0 = __lasx_xvand_v(q3aux, m3); - const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3aux, 4), m3); - - // load Q8 quants - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); - const __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); - - __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - - // multiply with scales - p16_0 = lasx_madd_h(scale_0, p16_0); - p16_1 = lasx_madd_h(scale_1, p16_1); - - p16_0 = __lasx_xvadd_w(p16_0, p16_1); - - // multiply with block scale and accumulate - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(p16_0), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - int32_t scales[4]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - int8_t * restrict a = aux8; - for (int l = 0; l < 8; ++l) { - a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4); - a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4); - a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4); - a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4); - a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4); - a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4); - a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4); - a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4); - } - - scales[0] = (x[i].scales[0] & 0xF) - 8; - scales[1] = (x[i].scales[0] >> 4) - 8; - scales[2] = (x[i].scales[1] & 0xF) - 8; - scales[3] = (x[i].scales[1] >> 4) - 8; - - memset(aux32, 0, 8*sizeof(int32_t)); - for (int j = 0; j < QK_K/16; ++j) { - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l]; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; - -#endif - -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -8560,6 +7405,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); @@ -8572,6 +7420,11 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -8611,16 +7464,17 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + ft_union fi; fi.i = __lsx_vpickve2gr_w(acc_m, 0); *s = hsum_float_8(acc) + fi.f ; - #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -8679,381 +7533,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; #endif } -#else -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - const block_q4_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - - const int32x4_t mzero = vdupq_n_s32(0); - - float sumf = 0; - - ggml_int8x16x2_t q4bytes; - ggml_int8x16x4_t q8bytes; - - float sum_mins = 0.f; - - uint16_t aux16[2]; - const uint8_t * restrict scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t * restrict a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]); - sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); - - q8bytes = ggml_vld1q_s8_x4(q8); - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; - - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); - const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; - - sumf += d * (sumi1 + sumi2); - } - - *s = sumf - sum_mins; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m256i q4l = _mm256_and_si256(q4bits, m4); - const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); - - const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); - const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); - - const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc); - - const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc); - - } - - *s = hsum_float_8(acc) - summs; - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0); - const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1); - const __m128i q4_0 = _mm_and_si128(q4bits_0, m4); - const __m128i q4_1 = _mm_and_si128(q4bits_1, m4); - const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4); - const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0); - const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc); - - const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2); - const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc); - - } - - *s = hsum_float_8(acc) - summs; - -#elif defined __riscv_v_intrinsic - - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - size_t vl = 32; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - // load Q4 - vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); - - // load Q8 and multiply it with lower Q4 nibble - vint8m1_t q4_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); - vint16m2_t va_0 = __riscv_vwmul_vv_i16m2(q4_a, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m1_t aux1 = __riscv_vredsum_vs_i16m2_i16m1(va_0, vzero, vl); - - sumf += d*scales[0]*__riscv_vmv_x_s_i16m1_i16(aux1); - - // load Q8 and multiply it with upper Q4 nibble - vint8m1_t q4_s = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); - vint16m2_t va_1 = __riscv_vwmul_vv_i16m2(q4_s, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m1_t aux2 = __riscv_vredsum_vs_i16m2_i16m1(va_1, vzero, vl); - - sumf += d*scales[1]*__riscv_vmv_x_s_i16m1_i16(aux2); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d[1])); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - uint16_t s16[2]; - const uint8_t * scales = (const uint8_t *)s16; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - vector signed char utmps = (vector signed char)vec_xl_len(scales, 4); - vector signed short vscales = (vector signed short)vec_unpackh(utmps); - vector signed short q4xmins0 = vec_mergeh(vscales, vscales); - q4xmins0 = vec_sld(q4xmins0, q4xmins0, 8); - - vector signed short q8ysums0 = vec_xl_len((const int16_t *)(y[i].bsums), 8); - - vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q4xmins0, q8ysums0); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vd, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vd, vsumf1); - - vd = vec_mul(vyd, vec_splats(GGML_FP16_TO_FP32(x[i].d[0]))); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); - vector signed char q4x00 = vec_and(qxs0, lowMask); - vector signed char q4x01 = vec_sr(qxs0, v4); - vector signed char q4x10 = vec_and(qxs1, lowMask); - vector signed char q4x11 = vec_sr(qxs1, v4); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); - - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs0), vec_mulo(qv10, vs0)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs1), vec_mulo(qv01, vs1)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs1), vec_mulo(qv11, vs1)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = __lasx_xvreplfr2vr_s(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); - const __m256i q4l = __lasx_xvand_v(q4bits, m4); - const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); - - const __m256i q8l = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8h = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p16l = lasx_maddubs_h(q4l, q8l); - const __m256i p16h = lasx_maddubs_h(q4h, q8h); - - const __m256i p32l = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[0]), p16l); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32l), acc); - - const __m256i p32h = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[1]), p16h); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32h), acc); - } - - *s = hsum_float_8(acc) - summs; - -#else - - uint8_t aux8[QK_K]; - int16_t aux16[16]; - float sums [8]; - memset(sums, 0, 8*sizeof(float)); - - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - uint8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF; - for (int l = 0; l < 32; ++l) a[l+32] = q4[l] >> 4; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - for (int j = 0; j < QK_K/32; ++j) { - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - q8 += 16; a += 16; - for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l]; - q8 += 16; a += 16; - const float dl = d * scales[j]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]); - } - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -9151,12 +7631,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r float summs = 0.f; - for (int i = 0; i < nb; ++i) { - + for (int i = 0; i < nb; ++i) { const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); @@ -9166,10 +7644,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); utmp[2] = uaux; utmp[0] &= kmask1; -#else - // TODO - const float d = 0, dmin = 0; -#endif const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -9538,6 +8012,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); const __m128i mzero = __lsx_vldi(0); @@ -9552,15 +8029,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); -#else - // TODO - const float d = 0, dmin = 0; -#endif + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -9579,6 +8056,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256i sumi = __lasx_xvldi(0); int bit = 0; + __m256i xvbit; for (int j = 0; j < QK_K/64; ++j) { @@ -9587,13 +8065,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); hmask = __lasx_xvslli_h(hmask, 1); + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); hmask = __lasx_xvslli_h(hmask, 1); @@ -9607,10 +8087,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r p16_1 = lasx_madd_h(scale_1, p16_1); sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + } __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } *s = hsum_float_8(acc) + summs; @@ -9679,402 +8161,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const uint8x16_t mh = vdupq_n_u8(16); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q5bytes; - ggml_uint8x16x4_t q5h; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const uint8x8_t qhbits = vld1_u8(qh); - - const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); - q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); - q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2)); - q5h.val[2] = vbicq_u8(mh, htmp); - q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2)); - - q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0])); - q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1])); - q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); - q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); - - int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); - int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); - int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); - int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); - - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i mone = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); - - const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0])); - const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2])); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128); - - const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4); - const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4); - - const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); - const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0)); - const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1)); - const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0)); - const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1)); - - const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1)); - - acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i mone = _mm_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); - - const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]); - const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]); - const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]); - const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2); - - const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4); - const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4); - const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4); - const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4); - - const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4); - const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4); - const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4); - const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1))); - const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1))); - - const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2)); - const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(qh, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); - - size_t vl = 16; - - // combine both qh_1 and qh_2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); - - vuint8mf2_t qh_h0 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); - vuint8mf2_t qh_h1 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), vl), 16, vl); - vuint8mf2_t qh_h2 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(qh_x, vl), 16, vl); - vuint8mf2_t qh_h3 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); - - vint8mf2_t qh_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h0); - vint8mf2_t qh_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h1); - vint8mf2_t qh_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h2); - vint8mf2_t qh_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h3); - - // load q5 - vuint8mf2_t q5_x1 = __riscv_vle8_v_u8mf2(q5, vl); - vuint8mf2_t q5_x2 = __riscv_vle8_v_u8mf2(q5+16, vl); - - vint8mf2_t q5s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x1, 0xF, vl)); - vint8mf2_t q5s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x2, 0xF, vl)); - vint8mf2_t q5s_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x1, 0x4, vl)); - vint8mf2_t q5s_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x2, 0x4, vl)); - - vint8mf2_t q5_0 = __riscv_vsub_vv_i8mf2(q5s_0, qh_0, vl); - vint8mf2_t q5_1 = __riscv_vsub_vv_i8mf2(q5s_1, qh_1, vl); - vint8mf2_t q5_2 = __riscv_vsub_vv_i8mf2(q5s_2, qh_2, vl); - vint8mf2_t q5_3 = __riscv_vsub_vv_i8mf2(q5s_3, qh_3, vl); - - // load Q8 and multiply it with Q5 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q5_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q5_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q5_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q5_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - int32_t sumi1 = sc[0] * __riscv_vmv_x_s_i32m1_i32(vs_0); - int32_t sumi2 = sc[1] * __riscv_vmv_x_s_i32m1_i32(vs_1); - int32_t sumi3 = sc[2] * __riscv_vmv_x_s_i32m1_i32(vs_2); - int32_t sumi4 = sc[3] * __riscv_vmv_x_s_i32m1_i32(vs_3); - - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v1 = vec_splats((unsigned char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); - vector signed char qxs00 = (vector signed char)vec_and(qxs0, lowMask); - vector signed char qxs01 = (vector signed char)vec_sr(qxs0, v4); - vector signed char qxs10 = (vector signed char)vec_and(qxs1, lowMask); - vector signed char qxs11 = (vector signed char)vec_sr(qxs1, v4); - - vector signed char qxhs = (vector signed char)vec_xl_len(x[i].qh, 8); - vector signed char qxhs0 = vec_or(qxhs, vec_sr(vec_sld(qxhs, qxhs, 8), v1)); - vector signed char qxhs1 = vec_sr(qxhs0, v2); - vector signed char qxh00 = vec_sl(vec_andc((vector signed char)v1, qxhs0), v4); - vector signed char qxh10 = vec_sl(vec_andc((vector signed char)v1, qxhs1), v4); - vector signed char qxh01 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs0, v4)), v4); - vector signed char qxh11 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs1, v4)), v4); - - vector signed char q5x00 = vec_sub(qxs00, qxh00); - vector signed char q5x10 = vec_sub(qxs10, qxh10); - vector signed char q5x01 = vec_sub(qxs01, qxh01); - vector signed char q5x11 = vec_sub(qxs11, qxh11); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); - - vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); - vector signed short vs0 = vec_splat(vs, 0); - vector signed short vs1 = vec_splat(vs, 1); - vector signed short vs2 = vec_splat(vs, 2); - vector signed short vs3 = vec_splat(vs, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); - - const __m256i scale_l = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[1]), __lsx_vreplgr2vr_h(x[i].scales[0])); - const __m256i scale_h = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[3]), __lsx_vreplgr2vr_h(x[i].scales[2])); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - __m128i haux128 = __lsx_vinsgr2vr_d(haux128, aux64, 0); - haux128 = __lsx_vinsgr2vr_d(haux128, aux64 >> 1, 1); - const __m256i haux256 = lasx_insertf128(__lsx_vsrli_h(haux128, 2), haux128); - - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvandn_v(haux256, mone), 4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvandn_v(__lasx_xvsrli_h(haux256, 4), mone), 4); - - const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5l_0, q8_0)); - const __m256i p16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5l_1, q8_1)); - const __m256i s16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5h_0, q8_0)); - const __m256i s16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5h_1, q8_1)); - - const __m256i dot = __lasx_xvsub_w(__lasx_xvadd_w(p16_0, p16_1), __lasx_xvadd_w(s16_0, s16_1)); - - acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(dot), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[16]; - float sums [8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - int8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) { - a[l+ 0] = q4[l] & 0xF; - a[l+32] = q4[l] >> 4; - } - for (int is = 0; is < 8; ++is) { - uint8_t m = 1 << is; - for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16); - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict sc = x[i].scales; - - for (int j = 0; j < QK_K/16; ++j) { - const float dl = d * sc[j]; - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]); - q8 += 16; a += 16; - } - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} -#endif - - -#if QK_K == 256 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -10733,446 +8819,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q6_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - float sum = 0; - - const uint8x16_t m4b = vdupq_n_u8(0xF); - const int8x16_t m32s = vdupq_n_s8(32); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t mone = vdupq_n_u8(3); - - ggml_int8x16x4_t q6bytes; - ggml_uint8x16x4_t q6h; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - int32_t isum = 0; - - uint8x16_t qhbits = vld1q_u8(qh); - ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6); - ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); - uint8x16_t shifted = vshrq_n_u8(qhbits, 2); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 4); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 6); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); - q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); - q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); - q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - - sum += isum * d_all * y[i].d; - - } - *s = sum; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i m2 = _mm256_set1_epi8(3); - const __m256i m32s = _mm256_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m256i sumi = _mm256_setzero_si256(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4); - const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4); - - const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); - const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); - - __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - - p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(3); - const __m128i m32s = _mm_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4); - const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4); - const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4); - const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4); - - const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0); - const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1); - const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2); - const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0)); - __m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1)); - __m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0)); - __m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); - p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); - p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - int32_t isum = 0; - - size_t vl = 16; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load Q6 - vuint8mf2_t q6_0 = __riscv_vle8_v_u8mf2(q6, vl); - vuint8mf2_t q6_1 = __riscv_vle8_v_u8mf2(q6+16, vl); - - // load qh - vuint8mf2_t qh_x = __riscv_vle8_v_u8mf2(qh, vl); - - vuint8mf2_t qh0 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh1 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh2 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh3 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - - vuint8mf2_t q6h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_0, 0xF, vl), qh0, vl); - vuint8mf2_t q6h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_1, 0xF, vl), qh1, vl); - vuint8mf2_t q6h_2 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_0, 0x4, vl), qh2, vl); - vuint8mf2_t q6h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_1, 0x4, vl), qh3, vl); - - vint8mf2_t q6v_0 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_0), 32, vl); - vint8mf2_t q6v_1 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_1), 32, vl); - vint8mf2_t q6v_2 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_2), 32, vl); - vint8mf2_t q6v_3 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_3), 32, vl); - - // load Q8 and take product - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q6v_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q6v_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q6v_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q6v_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scale[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scale[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scale[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scale[3]; - - sumf += isum * d_all * y[i].d; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].ql, 0, 1); - __builtin_prefetch(x[i].qh, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].ql); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].ql); - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); - - vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); - vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); - vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); - vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); - - vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); - vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); - vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); - vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); - vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); - - vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); - vector signed short vs0 = vec_splat(vs, 0); - vector signed short vs1 = vec_splat(vs, 1); - vector signed short vs2 = vec_splat(vs, 2); - vector signed short vs3 = vec_splat(vs, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i m2 = __lasx_xvreplgr2vr_b(3); - const __m256i m32s = __lasx_xvreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = __lasx_xvreplgr2vr_b(x[i].scales[0]); - const __m64 scales_2 = __lasx_xvreplgr2vr_b(x[i].scales[1]); - const __m64 scales_3 = __lasx_xvreplgr2vr_b(x[i].scales[2]); - const __m64 scales_4 = __lasx_xvreplgr2vr_b(x[i].scales[3]); - - __m256i sumi = __lasx_xvldi(0); - - __m128i scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_1, 0); - scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_2, 1); - __m128i scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_3, 0); - scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_4, 1); - - const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); - const __m128i q4bitsH = __lsx_vld((const __m128i*)qh, 0); - - const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 2), q4bitsH), m2), 4); - const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 6), __lasx_xvsrli_h(q4bitsH, 4)), m2), 4); - - const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); - const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_1); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); - __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); - - __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - - p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); - p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int l = 0; l < 16; ++l) { - a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l+32] = (int8_t)((q4[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l+48] = (int8_t)((q4[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - } - int is = 0; - for (int j = 0; j < QK_K/16; ++j) { - int scale = x[i].scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#endif - #if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, @@ -11564,64 +9210,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); -#if QK_K == 64 - static const uint8_t k_bit_helper[16] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper); - const __m128i m511 = _mm_set1_epi16(511); - typedef union { - __m128i vec_index; - uint16_t index[8]; - } index_t; - - index_t idx; - __m256 accumf = _mm256_setzero_ps(); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs); - idx.vec_index = _mm_and_si128(q2_data, m511); - - const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9); - const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13); - const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper); - - const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); - const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); - const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits); - - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); - - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], - iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], - iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); - - __m256i signs; - signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_1); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); - - signs = _mm256_shuffle_epi8(full_signs, block_sign_shuffle_2); - signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - - const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); - - const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); -#else - static const uint8_t k_bit_helper[32] = { 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, @@ -11719,7 +9307,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); -#endif #elif defined(__loongarch_asx) const __m256i mone = __lasx_xvreplgr2vr_b(1); @@ -11740,62 +9327,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); -#if QK_K == 64 - static const uint8_t k_bit_helper[16] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper = __lsx_vld((const __m128i*)k_bit_helper, 0); - const __m128i m511 = __lsx_vreplgr2vr_h(511); - typedef union { - __m128i vec_index; - uint16_t index[8]; - } index_t; - - index_t idx; - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const __m128i q2_data = __lsx_vld((const __m128i*)x[i].qs, 0); - idx.vec_index = __lsx_vand_v(q2_data, m511); - - const __m128i partial_sign_bits = __lsx_vsrli_h(q2_data, 9); - const __m128i partial_sign_bits_upper = __lsx_vsrli_h(q2_data, 13); - const __m128i partial_sign_bits_for_counting = __lsx_vxor_v(partial_sign_bits, partial_sign_bits_upper); - - const __m128i odd_bits = lsx_shuffle_b(bit_helper, partial_sign_bits_for_counting); - const __m128i full_sign_bits = __lsx_vor_v(partial_sign_bits, odd_bits); - const __m256i full_signs = lasx_insertf128(full_sign_bits, full_sign_bits); - - const __m256i q8_1 = __lasx_xvld((const __m256i *)y[i].qs, 0); - const __m256i q8_2 = __lasx_xvld((const __m256i *)(y[i].qs+32), 0); - - const __m256i q2_1 = lasx_set_d(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], - iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); - const __m256i q2_2 = lasx_set_d(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], - iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); - __m256i signs; - signs = lasx_shuffle_b(full_signs, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); - - signs = lasx_shuffle_b(full_signs, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - - const __m256i sc1 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[1] & 0xf)+1)); - - const __m256i sum = __lasx_xvadd_w(lasx_madd_h(sc1, dot1), lasx_madd_h(sc2, dot2)); - - accumf = __lasx_vfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sum), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); -#else - static const uint8_t k_bit_helper[32] = { 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, @@ -11893,9 +9424,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); -#endif - - #elif defined(__POWER9_VECTOR__) vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); @@ -12748,10 +10276,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * ggml_int8x16x4_t q8b; vec_index_t idx; -#if QK_K == 256 uint32_t scales32[2]; const uint8_t * scales8 = (const uint8_t *)scales32; -#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -12761,11 +10287,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 memcpy(scales32, x[i].scales, 4); scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; -#endif int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { @@ -12806,13 +10330,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); -#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; -#else - sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); - sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); -#endif } sumf += d*(sumi1 + sumi2); } @@ -13476,17 +10996,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int nb = n / QK_K; -#if QK_K != 64 iq1m_scale_t scale; -#endif #if defined __ARM_NEON - -#if QK_K == 64 - const int32x4_t mask = vdupq_n_s32(0xf); -#else const int32x4_t mask = vdupq_n_s32(0x7); -#endif const int32x4_t mone = vdupq_n_s32(1); const int32x4_t mzero = vdupq_n_s32(0); @@ -13510,9 +11023,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int32x4_t sumi1 = mzero; int32x4_t sumi2 = mzero; @@ -13541,11 +11052,8 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); const int32x4_t p34 = vpaddq_s32(p3, p4); -#if QK_K == 64 - int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12); -#else int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); -#endif + scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); sumi1 = vmlaq_s32(sumi1, scales_4, p12); @@ -13555,22 +11063,14 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void } -#if QK_K == 64 - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#else sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#endif } *s = sumf; #elif defined __AVX2__ -#if QK_K == 64 - const __m256i mask = _mm256_set1_epi16(0xf); -#else const __m256i mask = _mm256_set1_epi16(0x7); -#endif const __m256i mone = _mm256_set1_epi16(1); __m256 accum1 = _mm256_setzero_ps(); @@ -13582,9 +11082,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif __m256i sumi1 = _mm256_setzero_si256(); __m256i sumi2 = _mm256_setzero_si256(); @@ -13614,13 +11112,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const __m256i dot3 = mul_add_epi8(delta1, q8b_1); const __m256i dot4 = mul_add_epi8(delta2, q8b_2); -#if QK_K == 64 - __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 4), _mm_set1_epi16(sc[0] >> 0)); - __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8)); -#else + __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); -#endif + scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); const __m256i p1 = _mm256_madd_epi16(dot1, scale1); @@ -13634,14 +11129,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void qs += 8; qh += 4; } -#if QK_K == 64 - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); -#else const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); -#endif + accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); - } *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); @@ -13658,9 +11149,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int sumi1 = 0, sumi2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { @@ -13680,24 +11169,17 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void sum1[l/2] += lsum1; sum2[l/2] += lsum2*delta[l]; } -#if QK_K == 64 - const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1; - const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1; -#else + const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; -#endif + sumi1 += sum1[0] * ls1 + sum1[1] * ls2; sumi2 += sum2[0] * ls1 + sum2[1] * ls2; qs += 4; qh += 2; } -#if QK_K == 64 - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#else sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#endif } *s = sumf; @@ -13885,9 +11367,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * UNUSED(by); UNUSED(bs); assert(n % QK_K == 0); -#if QK_K == 64 - ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc); -#else const block_iq4_xs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -14180,7 +11659,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = sumf; #endif -#endif } // ================================ IQ2 quantization ============================================= @@ -14756,7 +12234,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict printf("\n"); GGML_ASSERT(false); } - q2[2*ib+0] |= (grid_index << 8*k); + q2[2*ib+0] |= ((uint32_t) grid_index << 8*k); q2[2*ib+1] |= (block_signs[k] << 7*k); } GGML_ASSERT(scale >= 0); @@ -15998,10 +13476,6 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy const float * xx; for (int ibl = 0; ibl < nbl; ++ibl) { - -#if QK_K == 64 - y[ibl].d = GGML_FP32_TO_FP16(0.f); -#endif memset(y[ibl].qs, 0, QK_K/8); memset(y[ibl].qh, 0, QK_K/16); memset(y[ibl].scales, 0, QK_K/32); @@ -16176,22 +13650,13 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } uint16_t * sc = (uint16_t *)y[ibl].scales; -#if QK_K == 64 - float d = max_scale/31; -#else float d = max_scale/15; -#endif float id = 1/d; float sumqx_f = 0, sumq2_f = 0; for (int ib = 0; ib < QK_K/block_size; ++ib) { int l = nearest_int(0.5f*(id*scales[ib+0]-1)); -#if QK_K == 64 - l = MAX(0, MIN(15, l)); - sc[ib/4] |= (l << 4*(ib%4)); -#else l = MAX(0, MIN(7, l)); sc[ib/4] |= (l << 3*(ib%4)); -#endif y[ibl].qh[ib] |= masks[shifts[ib]]; const float * xb = xbl + block_size*ib; if (quant_weights) { @@ -16214,14 +13679,10 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } if (sumq2_f > 0) d = sumqx_f/sumq2_f; s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed. -#if QK_K == 64 - y[ibl].d = s.f16; -#else sc[0] |= ((s.u16 & 0x000f) << 12); sc[1] |= ((s.u16 & 0x00f0) << 8); sc[2] |= ((s.u16 & 0x0f00) << 4); sc[3] |= ((s.u16 & 0xf000) << 0); -#endif } } @@ -16410,9 +13871,6 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest } size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { -#if QK_K == 64 - return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); -#else GGML_ASSERT(n_per_row%QK_K == 0); int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -16430,7 +13888,6 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t qrow += nblock*sizeof(block_iq4_xs); } return nrow * nblock * sizeof(block_iq4_xs); -#endif } void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { @@ -16842,19 +14299,11 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte } break; case GGML_TYPE_Q4_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q5_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q6_K: { @@ -16877,18 +14326,12 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte { const block_iq1_m * q = (const block_iq1_m *) data; for (size_t i = 0; i < nb; ++i) { - #if QK_K == 64 - if (!validate_fp16(q[i].d, i)) { - return false; - } - #else iq1m_scale_t scale; const uint16_t * sc = (const uint16_t *)q[i].scales; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); if (!validate_fp16(scale.f16, i)) { return false; } - #endif } } break; case GGML_TYPE_IQ2_XXS: @@ -16913,12 +14356,9 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb); } break; case GGML_TYPE_IQ4_XS: - #if QK_K != 64 { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb); } break; - #endif - // with QK_K == 64, iq4_xs is iq4_nl case GGML_TYPE_IQ4_NL: { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb); diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index cc1d3ace1..679ce4f28 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #ifdef _WIN32 @@ -47,6 +48,7 @@ struct socket_t { sockfd_t fd; socket_t(sockfd_t fd) : fd(fd) {} ~socket_t() { + GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd); #ifdef _WIN32 closesocket(this->fd); #else @@ -97,7 +99,7 @@ static ggml_guid_t ggml_backend_rpc_guid() { } struct ggml_backend_rpc_buffer_type_context { - std::shared_ptr sock; + std::string endpoint; std::string name; size_t alignment; size_t max_size; @@ -106,8 +108,6 @@ struct ggml_backend_rpc_buffer_type_context { struct ggml_backend_rpc_context { std::string endpoint; std::string name; - std::shared_ptr sock; - ggml_backend_buffer_type_t buft; }; struct ggml_backend_rpc_buffer_context { @@ -231,14 +231,13 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) { return true; } -static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { - std::string str(endpoint); - size_t pos = str.find(':'); +static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { + size_t pos = endpoint.find(':'); if (pos == std::string::npos) { return false; } - host = str.substr(0, pos); - port = std::stoi(str.substr(pos + 1)); + host = endpoint.substr(0, pos); + port = std::stoi(endpoint.substr(pos + 1)); return true; } @@ -273,6 +272,44 @@ static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cm // RPC client-side implementation +static std::shared_ptr get_socket(const std::string & endpoint) { + static std::mutex mutex; + std::lock_guard lock(mutex); + static std::unordered_map> sockets; + static bool initialized = false; + + auto it = sockets.find(endpoint); + if (it != sockets.end()) { + if (auto sock = it->second.lock()) { + return sock; + } + } + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return nullptr; + } +#ifdef _WIN32 + if (!initialized) { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + return nullptr; + } + initialized = true; + } +#else + UNUSED(initialized); +#endif + auto sock = socket_connect(host.c_str(), port); + if (sock == nullptr) { + return nullptr; + } + GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd); + sockets[endpoint] = sock; + return sock; +} + GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; return ctx->name.c_str(); @@ -442,7 +479,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer std::vector input(input_size, 0); memcpy(input.data(), &size, sizeof(size)); std::vector output; - bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); + auto sock = get_socket(buft_ctx->endpoint); + bool status = send_rpc_cmd(sock, ALLOC_BUFFER, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | @@ -453,7 +491,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer if (remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC[" + std::string(buft_ctx->endpoint) + "]"}, remote_size); return buffer; } else { @@ -508,7 +546,7 @@ GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend } ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - return buft_ctx->sock == rpc_ctx->sock; + return buft_ctx->endpoint == rpc_ctx->endpoint; } static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { @@ -521,7 +559,6 @@ static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { /* .is_host = */ NULL, }; - GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; @@ -530,16 +567,13 @@ GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context; - delete buft_ctx; - delete rpc_ctx->buft; delete rpc_ctx; delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - return ctx->buft; + return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); } GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { @@ -590,7 +624,8 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t std::vector input; serialize_graph(cgraph, input); std::vector output; - bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); + auto sock = get_socket(rpc_ctx->endpoint); + bool status = send_rpc_cmd(sock, GRAPH_COMPUTE, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 1); return (enum ggml_status)output[0]; @@ -624,65 +659,48 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .event_synchronize = */ NULL, }; -static std::unordered_map instances; - GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; -} - -GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { - std::string endpoint_str(endpoint); - if (instances.find(endpoint_str) != instances.end()) { - return instances[endpoint_str]; + static std::mutex mutex; + std::lock_guard lock(mutex); + // NOTE: buffer types are allocated and never freed; this is by design + static std::unordered_map buft_map; + auto it = buft_map.find(endpoint); + if (it != buft_map.end()) { + return it->second; } -#ifdef _WIN32 - { - WSADATA wsaData; - int res = WSAStartup(MAKEWORD(2, 2), &wsaData); - if (res != 0) { - return nullptr; - } - } -#endif - fprintf(stderr, "Connecting to %s\n", endpoint); - std::string host; - int port; - if (!parse_endpoint(endpoint, host, port)) { - return nullptr; - } - auto sock = socket_connect(host.c_str(), port); + auto sock = get_socket(endpoint); if (sock == nullptr) { return nullptr; } size_t alignment = get_alignment(sock); size_t max_size = get_max_size(sock); ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { - /* .sock = */ sock, - /* .name = */ "RPC" + std::to_string(sock->fd), + /* .endpoint = */ endpoint, + /* .name = */ "RPC[" + std::string(endpoint) + "]", /* .alignment = */ alignment, - /* .max_size = */ max_size + /* .max_size = */ max_size }; ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { /* .iface = */ ggml_backend_rpc_buffer_type_interface, /* .context = */ buft_ctx }; + buft_map[endpoint] = buft; + return buft; +} +GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC" + std::to_string(sock->fd), - /* .sock = */ sock, - /* .buft = */ buft + /* .endpoint = */ endpoint, + /* .name = */ "RPC[" + std::string(endpoint) + "]", }; - instances[endpoint] = new ggml_backend { + ggml_backend_t backend = new ggml_backend { /* .guid = */ ggml_backend_rpc_guid(), /* .interface = */ ggml_backend_rpc_interface, /* .context = */ ctx }; - - return instances[endpoint]; + return backend; } GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { @@ -706,14 +724,13 @@ static void get_device_memory(const std::shared_ptr & sock, size_t * f } GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - if (backend == nullptr) { + auto sock = get_socket(endpoint); + if (sock == nullptr) { *free = 0; *total = 0; return; } - ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - get_device_memory(ctx->sock, free, total); + get_device_memory(sock, free, total); } // RPC server-side implementation diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index eac8f5579..5cd97e4ff 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2944,6 +2944,57 @@ namespace dpct using shared_memory = detail::device_memory; + template + inline T atomic_fetch_add(T *addr, T operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T atomic_fetch_add(T *addr, T operand, + sycl::memory_order memoryOrder) { + switch (memoryOrder) { + case sycl::memory_order::relaxed: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::acq_rel: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::seq_cst: + return atomic_fetch_add(addr, operand); + default: + assert(false && "Invalid memory_order for atomics. Valid memory_order for " + "atomics are: sycl::memory_order::relaxed, " + "sycl::memory_order::acq_rel, sycl::memory_order::seq_cst!"); + } + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand, + sycl::memory_order memoryOrder) { + atomic_fetch_add(addr, operand, memoryOrder); + } + } // COPY from DPCT head files #define GGML_COMMON_DECL_SYCL @@ -2971,20 +3022,19 @@ static int g_work_group_size = 0; // typedef sycl::half ggml_fp16_t; #define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP -#define VER_4VEC 610 //todo for hardward optimize. +#define VER_4VEC 130 //todo for hardward optimize. #define VER_GEN9 700 //todo for hardward optimize. #define VER_GEN12 1000000 //todo for hardward optimize. #define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize. #define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares - -//define for XMX in Intel GPU -//TODO: currently, it's not used for XMX really. -#define SYCL_USE_XMX +#if !defined(GGML_SYCL_FORCE_MMQ) + #define SYCL_USE_XMX +#endif // max batch size to use MMQ kernels when tensor cores are available -#define XMX_MAX_BATCH_SIZE 32 +#define MMQ_MAX_BATCH_SIZE 32 #if defined(_MSC_VER) @@ -3060,6 +3110,7 @@ void ggml_sycl_get_device_description(int device, char * description, size_t d bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); int get_main_device(); +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); void print_ggml_tensor(const char*name, struct ggml_tensor *src); void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); @@ -4197,7 +4248,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri const block_q2_K * x = (const block_q2_K *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int n = tid/32; const int l = tid - 32*n; const int is = 8*n + l/16; @@ -4211,18 +4261,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); -#else - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const uint8_t q = x[i].qs[il] >> (2*is); - dst_t * y = yy + i*QK_K + 16*is + il; - - float dall = x[i].dm[0]; - float dmin = x[i].dm[1]; - y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); - y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); -#endif - } template @@ -4232,7 +4270,6 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); const block_q3_K * x = (const block_q3_K *) vx; -#if QK_K == 256 const int r = item_ct1.get_local_id(2) / 4; const int tid = r/2; const int is0 = r%2; @@ -4256,31 +4293,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri const uint8_t * hm = x[i].hmask; for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); -#else - const int tid = item_ct1.get_local_id(2); - const int is = tid/16; // 0 or 1 - const int il = tid%16; // 0...15 - const int im = il/8; // 0...1 - const int in = il%8; // 0...7 - - dst_t * y = yy + i*QK_K + 16*is + il; - - const uint8_t q = x[i].qs[il] >> (2*is); - const uint8_t h = x[i].hmask[in] >> (2*is + im); - const float d = (float)x[i].d; - - if (is == 0) { - y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } else { - y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); - y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); - } -#endif - } -#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -4289,7 +4303,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8 m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif template static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy, @@ -4298,7 +4311,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 32 threads const int tid = item_ct1.get_local_id(2); const int il = tid/8; @@ -4322,15 +4334,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int tid = item_ct1.get_local_id(2); - const uint8_t * q = x[i].qs; - dst_t * y = yy + i*QK_K; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); - y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); -#endif } template @@ -4340,7 +4343,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = item_ct1.get_local_id(2); const int il = tid/16; // il is in 0...3 @@ -4367,18 +4369,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri hm <<= 1; y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; -#else - const int tid = item_ct1.get_local_id(2); - const uint8_t q = x[i].qs[tid]; - const int im = tid/8; // 0...3 - const int in = tid%8; // 0...7 - const int is = tid/16; // 0 or 1 - const uint8_t h = x[i].qh[in] >> im; - const float d = x[i].d; - dst_t * y = yy + i*QK_K + tid; - y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); - y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); -#endif } template @@ -4387,7 +4377,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri const block_q6_K * x = (const block_q6_K *) vx; const int i = item_ct1.get_group(2); -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = item_ct1.get_local_id(2); @@ -4407,24 +4396,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); -#else - - // assume 32 threads - const int tid = item_ct1.get_local_id(2); - const int ip = tid/16; // 0 or 1 - const int il = tid - 16*ip; // 0...15 - - dst_t * y = yy + i*QK_K + 16*ip + il; - - const float d = x[i].d; - - const uint8_t ql = x[i].ql[16*ip + il]; - const uint8_t qh = x[i].qh[il] >> (2*ip); - const int8_t * sc = x[i].scales; - - y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); - y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); -#endif } template @@ -4438,7 +4409,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4449,10 +4419,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4466,7 +4432,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const block_iq2_xs * x = (const block_iq2_xs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4475,10 +4440,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4490,7 +4451,6 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq2_s * x = (const block_iq2_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4498,13 +4458,9 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; #pragma unroll - for (int j = 0; j < 8; ++j) + for (int j = 0; j < 8; ++j) { y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); - -#endif - + } } template @@ -4518,7 +4474,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4533,10 +4488,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - assert(false); -#endif - } template @@ -4549,7 +4500,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq3_s * x = (const block_iq3_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4563,10 +4513,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); } -#else - assert(false); -#endif - } template @@ -4579,7 +4525,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_s * x = (const block_iq1_s *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4593,10 +4538,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4609,7 +4550,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_m * x = (const block_iq1_m *) vx; const int tid = item_ct1.get_local_id(2); -#if QK_K == 256 const int il = tid/8; // 0...3 const int ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -4627,10 +4567,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4704,7 +4640,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const int tid = item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = @@ -4755,42 +4690,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, tmp += dall * sum1 - dmin * sum2; } -#else - const int tid = item_ct1.get_local_id(2) / - (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2) % - (2 * K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; - - uint32_t uaux[2]; - const uint8_t * d = (const uint8_t *)uaux; - - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint32_t * s = (const uint32_t *)x[i].scales; - - uaux[0] = s[0] & 0x0f0f0f0f; - uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; - - const sycl::float2 dall = - x[i].dm.convert(); - - float sum1 = 0, sum2 = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t ql = q[l]; - sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) - + y[l+16] * d[1] * ((ql >> 2) & 3) - + y[l+32] * d[2] * ((ql >> 4) & 3) - + y[l+48] * d[3] * ((ql >> 6) & 3); - sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; - } - tmp += dall.x() * sum1 - dall.y() * sum2; - } - -#endif // sum up partial sums and write back result #pragma unroll @@ -4828,8 +4727,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -4882,34 +4779,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, tmp += d * sum; } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 - const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 - const int in = offset/8; // 0 or 1 - const int im = offset%8; // 0...7 - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + offset; - const uint8_t * q = x[i].qs + offset; - const uint8_t * s = x[i].scales; - - const float dall = (float)x[i].d; - - float sum = 0; - for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { - const uint8_t hl = x[i].hmask[im+l] >> in; - const uint8_t ql = q[l]; - sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) - + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) - + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) - + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); - } - tmp += sum; - } -#endif // sum up partial sums and write back result #pragma unroll @@ -4944,7 +4813,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, const block_q4_K * x = (const block_q4_K *)vx + ib0; -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -5033,36 +4901,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, #endif } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); - - const int step = tid * K_QUANTS_PER_ITERATION; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - float tmp = 0; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const float * y = yy + i*QK_K + step; - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].dm[0]; - const float m = (float)x[i].dm[1]; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) - + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) - + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) - + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); - } - tmp += sum; - } - -#endif // sum up partial sums and write back result #pragma unroll @@ -5097,7 +4935,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; @@ -5174,30 +5011,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, dmin * smin; } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); - const int step = tid * K_QUANTS_PER_ITERATION; - const int im = step/8; - const int in = step%8; - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - const uint8_t * q = x[i].qs + step; - const int8_t * s = x[i].scales; - const float * y = yy + i*QK_K + step; - const float d = x[i].d; - float sum = 0.f; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - const uint8_t h = x[i].qh[in+j] >> im; - sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) - + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) - + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) - + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); - } - tmp += sum; - } -#endif - // sum up partial sums and write back result #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -5224,8 +5037,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa const block_q6_K * x = (const block_q6_K *)vx + ib0; -#if QK_K == 256 - const int tid = item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = @@ -5282,37 +5093,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = item_ct1.get_local_id(2)%(2*K_QUANTS_PER_ITERATION); // 0...3 - - const int step = tid * K_QUANTS_PER_ITERATION; - - float tmp = 0; // partial sum for thread in warp - - for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { - - const float * y = yy + i * QK_K + step; - const uint8_t * ql = x[i].ql + step; - const uint8_t * qh = x[i].qh + step; - const int8_t * s = x[i].scales; - - const float d = x[i+0].d; - - float sum = 0; - for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { - sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) - + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) - + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) - + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); - } - tmp += sum; - - } - -#endif - // sum up partial sums and write back result #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -6857,7 +6637,6 @@ static __dpct_inline__ float vec_dot_q4_K_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#ifndef GGML_QKK_64 const block_q4_K * bq4_K = (const block_q4_K *) vbq; int v[2]; @@ -6899,52 +6678,6 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, } return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); - -#else - -#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics - const block_q4_K * bq4_K = (const block_q4_K *) vbq; - - float sumf_d = 0.0f; - float sumf_m = 0.0f; - - uint16_t aux16[2]; - const uint8_t * s = (const uint8_t *)aux16; - - const uint16_t * a = (const uint16_t *)bq4_K->scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const float dall = bq4_K->dm[0]; - const float dmin = bq4_K->dm[1]; - - const float d8_1 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * q4 = (const int *)bq4_K->qs + (iqs/2); - const int v1 = q4[0]; - const int v2 = q4[4]; - - const int dot1 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0)); - const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::dp4a(0x01010101, ui3, 0)); - - sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); - sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); - - return dall * sumf_d - dmin * sumf_m; - -#else - bad_arch(); -#endif // __SYCL_ARCH__ >= VER_4VEC - -#endif } template @@ -7003,11 +6736,7 @@ load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql, const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif } #pragma unroll @@ -7050,7 +6779,6 @@ static __dpct_inline__ float vec_dot_q5_K_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#ifndef GGML_QKK_64 const block_q5_K * bq5_K = (const block_q5_K *) vbq; int vl[2]; @@ -7092,48 +6820,6 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, } return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - -#else - -#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics - const block_q5_K * bq5_K = (const block_q5_K *) vbq; - - const int8_t * s = bq5_K->scales; - - const float d = bq5_K->d; - - const float d8_1 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); - const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); - const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); - const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); - - const int * ql = (const int *)bq5_K->qs + (iqs/2); - const int vl1 = ql[0]; - const int vl2 = ql[4]; - - const int step = 4 * (iqs/2); // 0, 4, 8, 12 - const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 - const int in = step%8; // 0, 4, 0, 4 - const int vh = (*((const int *)(bq5_K->qh + in))) >> im; - - const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); - const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); - const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); - const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); - - const float sumf_d = d8_1 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]); - - return d * sumf_d; - -#else - bad_arch(); -#endif // __SYCL_ARCH__ >= VER_4VEC - -#endif } template @@ -7205,9 +6891,7 @@ load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql, const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif } #pragma unroll @@ -7387,7 +7071,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs, const uint8_t *kmask_iq2xs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -7428,10 +7111,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, } return d * (sumi1 + sumi2); #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float @@ -7440,7 +7119,6 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, const uint64_t *iq2xs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; const int ib32 = iqs; @@ -7478,16 +7156,11 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq2_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -7531,9 +7204,6 @@ vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, } const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f; return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - assert(false); -#endif } static __dpct_inline__ float @@ -7542,7 +7212,6 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; const int ib32 = iqs; @@ -7570,17 +7239,12 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq3s_grid) { -#if QK_K == 256 const block_iq3_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -7609,16 +7273,12 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) * bq8_1[ib32].ds[0]; return d * sumi; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq1s_grid_gpu) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -7637,15 +7297,11 @@ vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const float d = d1q * bq8_1[ib32].ds[0]; const float m = d1q * bq8_1[ib32].ds[1]; return d * sumi + m * delta; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -7670,9 +7326,6 @@ vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * bq8_1[ib32].ds[0]; return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - assert(false); -#endif } static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4, @@ -7720,7 +7373,6 @@ static __dpct_inline__ float vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -7738,9 +7390,6 @@ vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2); } return d * (sumi1 + sumi2); -#else - assert(false); -#endif } template +template static void rope_neox( const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, - float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims -, - const sycl::nd_item<3> &item_ct1) { + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, + const float * freq_factors, const sycl::nd_item<3> &item_ct1) { const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + item_ct1.get_local_id(1)); @@ -9265,8 +8913,10 @@ static void rope_neox( float cur_rot = inv_ndims * ic - ib; const int p = has_pos ? pos[i2] : 0; + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + const float theta_base = - p * freq_scale * dpct::pow(theta_scale, col / 2.0f); + p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -10203,7 +9853,6 @@ template static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10215,27 +9864,12 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q2_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q2_K(vx, y, item_ct1); - }); - } - -#endif } template static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10247,19 +9881,6 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q3_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q3_K(vx, y, item_ct1); - }); - } -#endif } template @@ -10320,7 +9941,6 @@ template static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10332,27 +9952,12 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q5_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q5_K(vx, y, item_ct1); - }); - } - -#endif } template static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = k / QK_K; -#if QK_K == 256 { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10364,20 +9969,6 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q6_K(vx, y, item_ct1); }); } -#else - { - dpct::has_capability_or_fail(stream->get_device(), - {sycl::aspect::fp16}); - - stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * - sycl::range<3>(1, 1, 32), - sycl::range<3>(1, 1, 32)), - [=](sycl::nd_item<3> item_ct1) { - dequantize_block_q6_K(vx, y, item_ct1); - }); - } - -#endif } template @@ -10529,9 +10120,6 @@ template static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_row_iq4_nl_sycl(vx, y, k, stream); -#else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10546,7 +10134,6 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, }); }); } -#endif } @@ -12051,8 +11638,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, const int nrows_y, const int nrows_dst, dpct::queue_ptr stream) try { -#if QK_K == 256 - int id; SYCL_CHECK( CHECK_TRY_ERROR(id = get_current_device_id())); @@ -12167,7 +11752,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, }); } } -#endif } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -12881,7 +12465,7 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, const int32_t *pos, float freq_scale, int p_delta_rows, float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, - dpct::queue_ptr stream) { + const float * freq_factors, dpct::queue_ptr stream) { GGML_ASSERT(ncols % 2 == 0); const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); @@ -12891,38 +12475,48 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { - /* - DPCT1049:42: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, - item_ct1); - }); + if (freq_factors == nullptr) { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, + item_ct1); + }); + } else { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, + item_ct1); + }); + } } else { - /* - DPCT1049:43: The work-group size passed to the SYCL kernel may exceed - the limit. To get the device limit, query - info::device::max_work_group_size. Adjust the work-group size if needed. - */ dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); - stream->parallel_for( - sycl::nd_range<3>(block_nums * block_dims, block_dims), - [=](sycl::nd_item<3> item_ct1) { - rope_neox(x, dst, ncols, n_dims, pos, freq_scale, - p_delta_rows, ext_factor, attn_factor, - corr_dims, theta_scale, inv_ndims, item_ct1); - }); + if (freq_factors == nullptr) { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); + }); + } else { + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, freq_factors, item_ct1); + }); + } } } @@ -13969,6 +13563,10 @@ inline void ggml_sycl_op_concat(const ggml_tensor *src0, const float *src0_dd, const float *src1_dd, float *dst_dd, const dpct::queue_ptr &main_stream) { +#pragma message("TODO: generalize concat kernel for dim != 2") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7563") + int dim = dst->op_params[0]; + GGML_ASSERT(dim == 2); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -14454,6 +14052,7 @@ inline void ggml_sycl_op_rope(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) { + const ggml_tensor * src2 = dst->src[2]; GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); @@ -14479,6 +14078,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; const int32_t * pos = nullptr; if ((mode & 1) == 0) { GGML_ASSERT(src1->type == GGML_TYPE_I32); @@ -14489,6 +14089,16 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, const bool is_neox = mode & 2; const bool is_glm = mode & 4; + if (is_neox) { + pos = (const int32_t *) src1_dd; + + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } + rope_corr_dims corr_dims; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); @@ -14500,13 +14110,13 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, if (src0->type == GGML_TYPE_F32) { rope_neox_sycl( (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, main_stream + attn_factor, corr_dims, freq_factors, main_stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, attn_factor, corr_dims, - main_stream); + freq_factors, main_stream); } else { GGML_ASSERT(false); } @@ -15573,7 +15183,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, @@ -15638,6 +15248,29 @@ catch (sycl::exception const &exc) { std::exit(1); } +inline bool ggml_sycl_supports_mmq(enum ggml_type type) { + // TODO: accuracy issues in MMQ + return false; +} + +bool ggml_sycl_supports_dmmv(enum ggml_type type) { + switch (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: + case GGML_TYPE_F16: + return true; + default: + return false; + } +} static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = @@ -15654,75 +15287,42 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } + // check data types and tensor shapes for custom matrix multiplication kernels: + bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1; + + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; + + bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + + // mmvq and mmq need the __dp4a instruction which is available for gen12+ + // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e + use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); #ifdef SYCL_USE_XMX - const bool use_xmx = true; -#else - const bool use_xmx = false; -#endif + use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); +#endif // SYCL_USE_XMX - // debug helpers - //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); - //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); - //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); - //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); - //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); - //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); - - if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (!split && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { // KQ single-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n"); ggml_sycl_mul_mat_vec_p021(src0, src1, dst); - } else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (!split && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { // KQV single-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); ggml_sycl_mul_mat_vec_nc(src0, src1, dst); - } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); ggml_sycl_mul_mat_batched_sycl(src0, src1, dst); - } else if (src0->type == GGML_TYPE_F32) { - // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); - } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - // GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n"); - if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) { -#ifdef GGML_SYCL_FORCE_DMMV - const bool use_mul_mat_vec_q = false; -#else - bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); - use_mul_mat_vec_q = use_mul_mat_vec_q || - (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) || - (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) || - (src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) || - (src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M); - - -#endif // GGML_SYCL_FORCE_DMMV - - if (use_mul_mat_vec_q) { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); - } else { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); - } - } else { - bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); - - if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) { - use_mul_mat_q = false; - } - - if (use_mul_mat_q) { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); - } else { - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n"); - ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); - } - } + } else if (use_dequantize_mul_mat_vec) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); + } else if (use_mul_mat_vec_q) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); } else { - GGML_ASSERT(false); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); } } @@ -15899,22 +15499,86 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { } #endif +struct mmid_row_mapping { + int32_t i1; + int32_t i2; +}; + +__dpct_inline__ static void k_copy_src1_to_contiguous( + const char *__restrict__ src1_original, char *__restrict__ src1_contiguous, + int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping, + const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, + int64_t ne11, int64_t ne10, size_t nb11, size_t nb12, + const sycl::nd_item<3> &item_ct1, int &src1_row) { + int32_t iid1 = item_ct1.get_group(2); + int32_t id = item_ct1.get_group(1); + + const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); + + if (row_id_i != i02) { + return; + } + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + if (item_ct1.get_local_id(2) == 0) { + src1_row = + dpct::atomic_fetch_add( + cur_src1_row, 1); + row_mapping[src1_row] = {id, iid1}; + } + /* + DPCT1065:194: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better + performance if there is no access to global memory. + */ + item_ct1.barrier(); + + const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); + float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); + +#pragma unroll + for (int i = item_ct1.get_local_id(2); i < ne10; + i += item_ct1.get_local_range(2)) { + src1_row_contiguous[i] = src1_row_original[i]; + } +} + +__dpct_inline__ static void k_copy_dst_from_contiguous( + char *__restrict__ dst_original, const char *__restrict__ dst_contiguous, + const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1, + size_t nb2, const sycl::nd_item<3> &item_ct1) { + int32_t i = item_ct1.get_group(2); + + const int32_t i1 = row_mapping[i].i1; + const int32_t i2 = row_mapping[i].i2; + + const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); + float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); + +#pragma unroll + for (int j = item_ct1.get_local_id(2); j < ne0; + j += item_ct1.get_local_range(2)) { + dst_row_original[j] = dst_row_contiguous[j]; + } +} + static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && - "mul_mat_id does not support split buffers"); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers"); + const ggml_tensor *ids = dst->src[2]; + GGML_TENSOR_BINARY_OP_LOCALS + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - const size_t nb11 = src1->nb[1]; - const size_t nb1 = dst->nb[1]; - - const int32_t id = ((int32_t *)dst->op_params)[0]; - const int32_t n_as = src0->ne[2]; + const int64_t n_as = ne02; + const int64_t n_ids = ids->ne[0]; std::vector ids_host(ggml_nbytes(ids)); - const char *ids_dev = (const char *)ids->data; + const char * ids_dev = (const char *) ids->data; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); @@ -15954,24 +15618,40 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src0_row.ne[2] = 1; src0_row.ne[3] = 1; - src0_row.nb[3] = src0->nb[2]; + src0_row.nb[3] = nb02; - if (src1->ne[1] == 1) { - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = - *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] + - id * ids->nb[0]); + src1_row.ne[1] = 1; + src1_row.ne[2] = 1; + src1_row.ne[3] = 1; + src1_row.nb[2] = nb11; + src1_row.nb[3] = nb11; - GGML_ASSERT(row_id >= 0 && row_id < n_as); + dst_row.ne[1] = 1; + dst_row.ne[2] = 1; + dst_row.ne[3] = 1; + dst_row.nb[2] = nb1; + dst_row.nb[3] = nb1; + if (ne12 == 1) { + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + GGML_ASSERT(i02 >= 0 && i02 < n_as); + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + const int64_t i1 = id; + const int64_t i2 = i12; src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; + src0_original + i02*nb02; src1_row_extra.data_device[g_main_device] = - src1_original + i01 * src1->nb[1]; + src1_original + + i11*nb11 + i12*nb12; dst_row_extra.data_device[g_main_device] = - dst_original + i01 * dst->nb[1]; + dst_original + i1*nb1 + i2*nb2; ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); + } } } else { sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); @@ -15980,64 +15660,98 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); - for (int32_t row_id = 0; row_id < n_as; ++row_id) { + for (int64_t i02 = 0; i02 < n_as; i02++) { int64_t num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - if (row_id_i != row_id) { - continue; + GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); + + if (row_id_i != i02) { + continue; + } + + num_src1_rows++; } - - GGML_ASSERT(row_id >= 0 && row_id < n_as); - - SYCL_CHECK(CHECK_TRY_ERROR( - stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, - src1_original + i01 * nb11, nb11))); - num_src1_rows++; } if (num_src1_rows == 0) { continue; } - src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; + sycl_pool_alloc dev_cur_src1_row(1); + sycl_pool_alloc dev_row_mapping(num_src1_rows); + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memset(dev_cur_src1_row.get(), 0, sizeof(int)))); + + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u)); + sycl::range<3> grid_dims(1, n_ids, ids->ne[1]); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor src1_row_acc(cgh); + + char *__restrict src1_contiguous_get = + src1_contiguous.get(); + int *__restrict dev_cur_src1_row_get = + dev_cur_src1_row.get(); + mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); + size_t ids_nb_ct6 = ids->nb[1]; + size_t ids_nb_ct7 = ids->nb[0]; + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_src1_to_contiguous( + src1_original, src1_contiguous_get, + dev_cur_src1_row_get, + dev_row_mapping_get, ids_dev, i02, + ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12, + item_ct1, src1_row_acc); + }); + }); + } + + src0_row_extra.data_device[g_main_device] = src0_original + i02*nb02; + + GGML_ASSERT(nb11 == sizeof(float)*ne10); + GGML_ASSERT(nb1 == sizeof(float)*ne0); src1_row.ne[1] = num_src1_rows; - dst_row.ne[1] = num_src1_rows; src1_row.nb[1] = nb11; src1_row.nb[2] = num_src1_rows*nb11; src1_row.nb[3] = num_src1_rows*nb11; + dst_row.ne[1] = num_src1_rows; dst_row.nb[1] = nb1; dst_row.nb[2] = num_src1_rows*nb1; dst_row.nb[3] = num_src1_rows*nb1; ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); - num_src1_rows = 0; - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u)); + sycl::range<3> grid_dims(1, 1, num_src1_rows); + stream->submit([&](sycl::handler &cgh) { + const char *__restrict dst_contiguous_get = + dst_contiguous.get(); + const mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); - if (row_id_i != row_id) { - continue; - } - - GGML_ASSERT(row_id >= 0 && row_id < n_as); - - SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( - dst_original + i01 * nb1, - dst_contiguous.get() + num_src1_rows * nb1, nb1))); - num_src1_rows++; + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_dst_from_contiguous(dst_original, + dst_contiguous_get, + dev_row_mapping_get, + ne0, nb1, nb2, item_ct1); + }); + }); } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -17020,10 +16734,9 @@ GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backe UNUSED(buffer); } -// unused at the moment -//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { -// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -//} +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +} GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 70c4043d3..b50f55860 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -6720,6 +6720,367 @@ unsigned char diag_mask_inf_f32_data[] = { }; const uint64_t diag_mask_inf_f32_len = 1504; +unsigned char div_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x67,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -11260,12 +11621,12 @@ unsigned char matmul_f16_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -11299,12 +11660,12 @@ unsigned char matmul_f16_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -11419,7 +11780,7 @@ unsigned char matmul_f16_data[] = { 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, -0x30,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x30,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x38,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x7e,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x7f,0x02,0x00,0x00,0x7e,0x02,0x00,0x00, @@ -12130,12 +12491,12 @@ unsigned char matmul_f16_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -12170,13 +12531,13 @@ unsigned char matmul_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -12294,7 +12655,7 @@ unsigned char matmul_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x2c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x60,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x68,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xae,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xaf,0x02,0x00,0x00, @@ -13061,12 +13422,12 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -13102,12 +13463,12 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -13218,7 +13579,7 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x30,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x38,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x7e,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x7f,0x02,0x00,0x00,0x7e,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -13924,12 +14285,12 @@ unsigned char matmul_f16_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -13963,12 +14324,12 @@ unsigned char matmul_f16_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -14084,7 +14445,7 @@ unsigned char matmul_f16_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x80,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x81,0x02,0x00,0x00, @@ -14796,12 +15157,12 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -14836,13 +15197,13 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -14964,7 +15325,7 @@ unsigned char matmul_f16_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x73,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb9,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xba,0x02,0x00,0x00, @@ -15740,12 +16101,12 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -15781,12 +16142,12 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -15899,7 +16260,7 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 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, +0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x36,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x7c,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x7d,0x02,0x00,0x00, @@ -16598,12 +16959,12 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -16637,13 +16998,13 @@ unsigned char matmul_f16_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -16756,7 +17117,7 @@ unsigned char matmul_f16_f32_fp32_data[] = { 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, +0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,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, @@ -17458,12 +17819,12 @@ unsigned char matmul_f16_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -17497,13 +17858,13 @@ unsigned char matmul_f16_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -17615,7 +17976,7 @@ unsigned char matmul_f16_fp32_data[] = { 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,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,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, @@ -18319,12 +18680,12 @@ unsigned char matmul_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -18358,13 +18719,13 @@ unsigned char matmul_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -18478,7 +18839,7 @@ unsigned char matmul_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x80,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x81,0x02,0x00,0x00, @@ -19190,12 +19551,12 @@ unsigned char matmul_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -19231,12 +19592,12 @@ unsigned char matmul_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -19355,7 +19716,7 @@ unsigned char matmul_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3c,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,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, @@ -20140,13 +20501,13 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -20181,12 +20542,12 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -20296,7 +20657,7 @@ unsigned char matmul_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf5,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x27,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x27,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x2f,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x75,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x76,0x02,0x00,0x00,0x75,0x02,0x00,0x00, @@ -20990,12 +21351,12 @@ unsigned char matmul_f32_f16_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -21029,12 +21390,12 @@ unsigned char matmul_f32_f16_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -21150,7 +21511,7 @@ unsigned char matmul_f32_f16_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xfe,0x01,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x3a,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x80,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x81,0x02,0x00,0x00, @@ -21862,12 +22223,12 @@ unsigned char matmul_f32_f16_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -21902,13 +22263,13 @@ unsigned char matmul_f32_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -22030,7 +22391,7 @@ unsigned char matmul_f32_f16_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x6b,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x73,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb9,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xba,0x02,0x00,0x00, @@ -22806,12 +23167,12 @@ unsigned char matmul_f32_f16_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -22847,12 +23208,12 @@ unsigned char matmul_f32_f16_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -22965,7 +23326,7 @@ unsigned char matmul_f32_f16_aligned_fp32_data[] = { 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, +0x15,0x00,0x00,0x00,0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x36,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x7c,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x7d,0x02,0x00,0x00, @@ -23664,12 +24025,12 @@ unsigned char matmul_f32_f16_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -23703,13 +24064,13 @@ unsigned char matmul_f32_f16_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -23822,7 +24183,7 @@ unsigned char matmul_f32_f16_fp32_data[] = { 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, +0x2e,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,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, @@ -24523,13 +24884,13 @@ unsigned char matmul_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -24563,12 +24924,12 @@ unsigned char matmul_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -24679,7 +25040,7 @@ unsigned char matmul_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xf9,0x01,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2b,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x2b,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x33,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x79,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x7a,0x02,0x00,0x00,0x79,0x02,0x00,0x00, @@ -25277,6 +25638,51749 @@ unsigned char matmul_f32_fp32_data[] = { }; const uint64_t matmul_f32_fp32_len = 10208; +unsigned char matmul_id_f16_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x30,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x10,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x10,0x01,0x00,0x00,0x4e,0x01,0x00,0x00, +0x59,0x01,0x00,0x00,0x84,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_id_f16_f32_len = 10804; + +unsigned char matmul_id_f16_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x68,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x10,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00, +0x08,0x01,0x00,0x00,0x0f,0x01,0x00,0x00,0x86,0x01,0x00,0x00, +0x8e,0x01,0x00,0x00,0xcc,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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10732; + +unsigned char matmul_id_f16_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x2e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00, +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,0x00,0x10,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00, +0x90,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x10,0x01,0x00,0x00, +0x4e,0x01,0x00,0x00,0x59,0x01,0x00,0x00,0x82,0x02,0x00,0x00, +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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+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_id_f16_fp32_len = 10748; + +unsigned char matmul_id_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x31,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x0b,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,0x00,0x10,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7b,0x00,0x00,0x00, +0x90,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x10,0x01,0x00,0x00, +0x4f,0x01,0x00,0x00,0x5a,0x01,0x00,0x00,0x86,0x02,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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+const uint64_t matmul_id_q2_k_f32_len = 11712; + +unsigned char matmul_id_q2_k_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x9e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x29,0x01,0x00,0x00, +0x63,0x01,0x00,0x00,0xb0,0x01,0x00,0x00,0xb8,0x01,0x00,0x00, +0xfa,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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matmul_id_q3_k_f32_fp32_len = 13496; + +unsigned char matmul_id_q4_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x42,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x7b,0x00,0x00,0x00,0x90,0x00,0x00,0x00,0x11,0x01,0x00,0x00, +0x32,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6e,0x01,0x00,0x00, +0x9b,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_id_q5_1_f32_len = 11428; + +unsigned char matmul_id_q5_1_f32_aligned_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x8f,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x16,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x10,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x13,0x00,0x00,0x00,0x1f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x7c,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x12,0x01,0x00,0x00, +0x57,0x01,0x00,0x00,0xa4,0x01,0x00,0x00,0xac,0x01,0x00,0x00, +0xef,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 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+0x06,0x00,0x00,0x00,0xba,0x02,0x00,0x00,0xd2,0x02,0x00,0x00, +0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x75,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x77,0x02,0x00,0x00,0xf9,0x00,0x02,0x00, +0x60,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x60,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xbc,0x02,0x00,0x00, +0xd0,0x02,0x00,0x00,0x9a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x5d,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x5f,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x58,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x58,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xbe,0x02,0x00,0x00,0xcf,0x02,0x00,0x00,0x9a,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x55,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x57,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0xbf,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0xbf,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_id_q8_0_f32_fp32_len = 10960; + unsigned char matmul_q2_k_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4e,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -25388,12 +77492,12 @@ unsigned char matmul_q2_k_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -25427,13 +77531,13 @@ unsigned char matmul_q2_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -25567,7 +77671,7 @@ unsigned char matmul_q2_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x44,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x78,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x78,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x80,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xc6,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xc7,0x02,0x00,0x00,0xc6,0x02,0x00,0x00, @@ -26332,12 +78436,12 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -26373,12 +78477,12 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -26516,7 +78620,7 @@ unsigned char matmul_q2_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x65,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xa1,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe7,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe8,0x02,0x00,0x00,0xe7,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -27316,13 +79420,13 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -27357,12 +79461,12 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -27493,7 +79597,7 @@ unsigned char matmul_q2_k_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3e,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 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, @@ -28243,13 +80347,13 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -28283,12 +80387,12 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -28420,7 +80524,7 @@ unsigned char matmul_q2_k_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x3f,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x71,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x79,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xbf,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xc0,0x02,0x00,0x00,0xbf,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -29179,12 +81283,12 @@ unsigned char matmul_q3_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -29218,12 +81322,12 @@ unsigned char matmul_q3_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -29359,7 +81463,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0xc4,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xf8,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x00,0x03,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x46,0x03,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x47,0x03,0x00,0x00, @@ -29665,7 +81769,7 @@ unsigned char matmul_q3_k_f32_data[] = { 0xfb,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x8d,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x30,0x01,0x00,0x00,0x8f,0x01,0x00,0x00, 0x8e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x30,0x01,0x00,0x00, -0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x81,0x00,0x00,0x00, +0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x91,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00, 0x90,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x92,0x01,0x00,0x00,0x91,0x01,0x00,0x00,0xc7,0x00,0x05,0x00, @@ -30282,12 +82386,12 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -30322,13 +82426,13 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -30467,7 +82571,7 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xe5,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x19,0x03,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x19,0x03,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x21,0x03,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x67,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x68,0x03,0x00,0x00,0x67,0x03,0x00,0x00, @@ -30773,7 +82877,7 @@ unsigned char matmul_q3_k_f32_aligned_data[] = { 0x87,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x30,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x30,0x01,0x00,0x00,0x90,0x01,0x00,0x00, -0x8f,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0x71,0x00,0x04,0x00, +0x8f,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00, 0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x01,0x00,0x00, 0x91,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00, @@ -31425,12 +83529,12 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -31466,12 +83570,12 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -31604,7 +83708,7 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xbe,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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 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, @@ -31910,7 +84014,7 @@ unsigned char matmul_q3_k_f32_aligned_fp32_data[] = { 0x8d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x30,0x01,0x00,0x00, 0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x30,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x8f,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x92,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x92,0x01,0x00,0x00,0x91,0x01,0x00,0x00, 0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00,0x93,0x01,0x00,0x00, @@ -32511,12 +84615,12 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -32550,13 +84654,13 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -32689,7 +84793,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0xbf,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xf1,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xf1,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xf9,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x3f,0x03,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x40,0x03,0x00,0x00,0x3f,0x03,0x00,0x00, @@ -32995,7 +85099,7 @@ unsigned char matmul_q3_k_f32_fp32_data[] = { 0x86,0x00,0x00,0x00,0x8d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x30,0x01,0x00,0x00,0x8f,0x01,0x00,0x00,0x8e,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x30,0x01,0x00,0x00,0x90,0x01,0x00,0x00, -0x8f,0x01,0x00,0x00,0x81,0x00,0x00,0x00,0x71,0x00,0x04,0x00, +0x8f,0x01,0x00,0x00,0x91,0x00,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x91,0x01,0x00,0x00,0x90,0x01,0x00,0x00, 0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x01,0x00,0x00, 0x91,0x01,0x00,0x00,0xc7,0x00,0x05,0x00,0x15,0x00,0x00,0x00, @@ -33595,12 +85699,12 @@ unsigned char matmul_q4_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -33634,13 +85738,13 @@ unsigned char matmul_q4_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -33764,7 +85868,7 @@ unsigned char matmul_q4_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x47,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x4f,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x95,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x96,0x02,0x00,0x00, @@ -34483,12 +86587,12 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -34524,12 +86628,12 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -34660,7 +86764,7 @@ unsigned char matmul_q4_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x36,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x6a,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x72,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb8,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xb8,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -35413,13 +87517,13 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -35454,12 +87558,12 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -35582,7 +87686,7 @@ unsigned char matmul_q4_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x40,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x40,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00, @@ -36285,13 +88389,13 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -36325,12 +88429,12 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -36452,7 +88556,7 @@ unsigned char matmul_q4_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x40,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x40,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8e,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0x8e,0x02,0x00,0x00, @@ -37163,12 +89267,12 @@ unsigned char matmul_q4_1_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -37202,12 +89306,12 @@ unsigned char matmul_q4_1_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -37331,7 +89435,7 @@ unsigned char matmul_q4_1_f32_data[] = { 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, +0x4c,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,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, @@ -38057,12 +90161,12 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -38097,13 +90201,13 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -38232,7 +90336,7 @@ unsigned char matmul_q4_1_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3b,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x6f,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 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, @@ -38993,12 +91097,12 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -39034,12 +91138,12 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -39161,7 +91265,7 @@ unsigned char matmul_q4_1_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x4d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x93,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -39871,12 +91975,12 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -39910,13 +92014,13 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -40037,7 +92141,7 @@ unsigned char matmul_q4_1_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x13,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x4d,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x93,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x94,0x02,0x00,0x00,0x93,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -40755,12 +92859,12 @@ unsigned char matmul_q4_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -40794,12 +92898,12 @@ unsigned char matmul_q4_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -40934,7 +93038,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7f,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xb3,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xbb,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x01,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x02,0x03,0x00,0x00,0x01,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -41177,7 +93281,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0x51,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0x53,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00, -0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, +0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00,0xa8,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00, 0x55,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -41194,7 +93298,7 @@ unsigned char matmul_q4_k_f32_data[] = { 0xcf,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x65,0x01,0x00,0x00, -0x64,0x01,0x00,0x00,0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x64,0x01,0x00,0x00,0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0x65,0x01,0x00,0x00, 0xa8,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x67,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x66,0x01,0x00,0x00, @@ -41769,12 +93873,12 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -41809,13 +93913,13 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -41954,7 +94058,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa1,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xdd,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x23,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x24,0x03,0x00,0x00,0x23,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -42197,7 +94301,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0x52,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x1a,0x01,0x00,0x00, 0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, +0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, 0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0xa9,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x57,0x01,0x00,0x00, 0x56,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -42214,7 +94318,7 @@ unsigned char matmul_q4_k_f32_aligned_data[] = { 0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x66,0x01,0x00,0x00, -0x65,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x65,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x67,0x01,0x00,0x00,0x66,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x1a,0x01,0x00,0x00, 0x68,0x01,0x00,0x00,0x61,0x01,0x00,0x00,0x67,0x01,0x00,0x00, @@ -42824,12 +94928,12 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -42865,12 +94969,12 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -43001,7 +95105,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x79,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xab,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xab,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xb3,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xf9,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xfa,0x02,0x00,0x00,0xf9,0x02,0x00,0x00, @@ -43244,7 +95348,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 0xd0,0x00,0x00,0x00,0x51,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x1a,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x52,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x1a,0x01,0x00,0x00,0x54,0x01,0x00,0x00, -0x53,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x53,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x1a,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x54,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -43261,7 +95365,7 @@ unsigned char matmul_q4_k_f32_aligned_fp32_data[] = { 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, -0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x82,0x00,0x00,0x00, +0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00,0x92,0x00,0x00,0x00, 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, @@ -43820,12 +95924,12 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -43859,13 +95963,13 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -43996,7 +96100,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7a,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xac,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 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, @@ -44239,7 +96343,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x51,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00,0x53,0x01,0x00,0x00, 0x52,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00, -0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x81,0x00,0x00,0x00, +0x54,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x91,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x55,0x01,0x00,0x00, 0x54,0x01,0x00,0x00,0xa8,0x00,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00,0x55,0x01,0x00,0x00, @@ -44257,7 +96361,7 @@ unsigned char matmul_q4_k_f32_fp32_data[] = { 0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0x64,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0x64,0x01,0x00,0x00, -0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, +0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x66,0x01,0x00,0x00,0x65,0x01,0x00,0x00,0xa8,0x00,0x00,0x00, 0xc5,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x67,0x01,0x00,0x00, 0x60,0x01,0x00,0x00,0x66,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, @@ -44821,12 +96925,12 @@ unsigned char matmul_q5_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -44860,13 +96964,13 @@ unsigned char matmul_q5_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -44997,7 +97101,7 @@ unsigned char matmul_q5_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x3c,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x70,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x78,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xbe,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xbf,0x02,0x00,0x00, @@ -45752,12 +97856,12 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -45793,12 +97897,12 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -45936,7 +98040,7 @@ unsigned char matmul_q5_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x5f,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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x9b,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe1,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe2,0x02,0x00,0x00,0xe1,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -46725,13 +98829,13 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -46766,12 +98870,12 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -46901,7 +99005,7 @@ unsigned char matmul_q5_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x69,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x69,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xb7,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00, @@ -47640,13 +99744,13 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -47680,12 +99784,12 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -47814,7 +99918,7 @@ unsigned char matmul_q5_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x37,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x69,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x69,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x71,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xb7,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xb8,0x02,0x00,0x00,0xb7,0x02,0x00,0x00, @@ -48559,13 +100663,13 @@ unsigned char matmul_q5_1_f32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -48599,12 +100703,12 @@ unsigned char matmul_q5_1_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -48731,7 +100835,7 @@ unsigned char matmul_q5_1_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x73,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -49480,13 +101584,13 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -49521,12 +101625,12 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -49659,7 +101763,7 @@ unsigned char matmul_q5_1_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x5a,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x8e,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x8e,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x96,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xdc,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xdd,0x02,0x00,0x00,0xdc,0x02,0x00,0x00, @@ -50444,12 +102548,12 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -50484,13 +102588,13 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -50614,7 +102718,7 @@ unsigned char matmul_q5_1_f32_aligned_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb2,0x02,0x00,0x00, 0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xb3,0x02,0x00,0x00, @@ -51349,12 +103453,12 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -51388,12 +103492,12 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -51517,7 +103621,7 @@ unsigned char matmul_q5_1_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x32,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x64,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x6c,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xb2,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xb3,0x02,0x00,0x00, @@ -52261,12 +104365,12 @@ unsigned char matmul_q5_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -52300,12 +104404,12 @@ unsigned char matmul_q5_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -52443,7 +104547,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xa4,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd8,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0xe0,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x26,0x03,0x00,0x00,0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x27,0x03,0x00,0x00,0x26,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -52692,7 +104796,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0x5c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x18,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00, -0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x18,0x01,0x00,0x00, +0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x18,0x01,0x00,0x00, 0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00,0xa8,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00, 0x60,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -52709,7 +104813,7 @@ unsigned char matmul_q5_k_f32_data[] = { 0xcf,0x00,0x00,0x00,0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x18,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x6e,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x18,0x01,0x00,0x00,0x70,0x01,0x00,0x00, -0x6f,0x01,0x00,0x00,0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x6f,0x01,0x00,0x00,0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x18,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00, 0xa8,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x18,0x01,0x00,0x00, 0x72,0x01,0x00,0x00,0x6b,0x01,0x00,0x00,0x71,0x01,0x00,0x00, @@ -53318,12 +105422,12 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -53358,13 +105462,13 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -53506,7 +105610,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xc6,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xfa,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x02,0x03,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0x48,0x03,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0x49,0x03,0x00,0x00,0x48,0x03,0x00,0x00,0x20,0x00,0x04,0x00, @@ -53755,7 +105859,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0x5d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x19,0x01,0x00,0x00, 0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, -0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, +0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0xa9,0x00,0x00,0x00, 0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x62,0x01,0x00,0x00, 0x61,0x01,0x00,0x00,0x7c,0x00,0x04,0x00,0x15,0x00,0x00,0x00, @@ -53772,7 +105876,7 @@ unsigned char matmul_q5_k_f32_aligned_data[] = { 0xd0,0x00,0x00,0x00,0x0d,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x71,0x01,0x00,0x00, -0x70,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x70,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x72,0x01,0x00,0x00,0x71,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x19,0x01,0x00,0x00, 0x73,0x01,0x00,0x00,0x6c,0x01,0x00,0x00,0x72,0x01,0x00,0x00, @@ -54416,12 +106520,12 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -54457,12 +106561,12 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -54596,7 +106700,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x9e,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xd0,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xd0,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xd8,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x1e,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x1f,0x03,0x00,0x00,0x1e,0x03,0x00,0x00, @@ -54845,7 +106949,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 0xd0,0x00,0x00,0x00,0x5c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00, 0x19,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x5d,0x01,0x00,0x00, 0xc2,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, -0x5e,0x01,0x00,0x00,0x82,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x5e,0x01,0x00,0x00,0x92,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x5f,0x01,0x00,0x00, 0xa9,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -54862,7 +106966,7 @@ unsigned char matmul_q5_k_f32_aligned_fp32_data[] = { 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,0x01,0x00,0x00,0x82,0x00,0x00,0x00, +0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00,0x92,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x19,0x01,0x00,0x00,0x71,0x01,0x00,0x00, 0x70,0x01,0x00,0x00,0xa9,0x00,0x00,0x00,0xc5,0x00,0x05,0x00, 0x19,0x01,0x00,0x00,0x72,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, @@ -55456,12 +107560,12 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -55495,13 +107599,13 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -55635,7 +107739,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x9f,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xd1,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xd9,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x1f,0x03,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x20,0x03,0x00,0x00, @@ -55884,7 +107988,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0xfb,0x00,0x00,0x00,0xcf,0x00,0x00,0x00,0x5c,0x01,0x00,0x00, 0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00,0x5e,0x01,0x00,0x00, 0x5d,0x01,0x00,0x00,0xc2,0x00,0x05,0x00,0x18,0x01,0x00,0x00, -0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x81,0x00,0x00,0x00, +0x5f,0x01,0x00,0x00,0x5e,0x01,0x00,0x00,0x91,0x00,0x00,0x00, 0xc4,0x00,0x05,0x00,0x18,0x01,0x00,0x00,0x60,0x01,0x00,0x00, 0x5f,0x01,0x00,0x00,0xa8,0x00,0x00,0x00,0x71,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x61,0x01,0x00,0x00,0x60,0x01,0x00,0x00, @@ -55902,7 +108006,7 @@ unsigned char matmul_q5_k_f32_fp32_data[] = { 0x0c,0x01,0x00,0x00,0x3d,0x00,0x04,0x00,0x18,0x01,0x00,0x00, 0x6f,0x01,0x00,0x00,0x6e,0x01,0x00,0x00,0xc2,0x00,0x05,0x00, 0x18,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0x6f,0x01,0x00,0x00, -0x81,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x18,0x01,0x00,0x00, +0x91,0x00,0x00,0x00,0xc4,0x00,0x05,0x00,0x18,0x01,0x00,0x00, 0x71,0x01,0x00,0x00,0x70,0x01,0x00,0x00,0xa8,0x00,0x00,0x00, 0xc5,0x00,0x05,0x00,0x18,0x01,0x00,0x00,0x72,0x01,0x00,0x00, 0x6b,0x01,0x00,0x00,0x71,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, @@ -56501,12 +108605,12 @@ unsigned char matmul_q6_k_f32_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -56540,12 +108644,12 @@ unsigned char matmul_q6_k_f32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -56681,7 +108785,7 @@ unsigned char matmul_q6_k_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x68,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x9c,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0xa4,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0xea,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0xeb,0x02,0x00,0x00, @@ -57489,12 +109593,12 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -57529,13 +109633,13 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb8,0x00,0x00,0x00, @@ -57674,7 +109778,7 @@ unsigned char matmul_q6_k_f32_aligned_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x89,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xbd,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0xbd,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0xc5,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x0b,0x03,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x0c,0x03,0x00,0x00,0x0b,0x03,0x00,0x00, @@ -58517,12 +110621,12 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -58558,12 +110662,12 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -58696,7 +110800,7 @@ unsigned char matmul_q6_k_f32_aligned_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x62,0x02,0x00,0x00, 0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x94,0x02,0x00,0x00, -0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x9c,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xe2,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xe3,0x02,0x00,0x00,0xe2,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -59488,12 +111592,12 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -59527,13 +111631,13 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -59666,7 +111770,7 @@ unsigned char matmul_q6_k_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x63,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x95,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x95,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x9d,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0xe3,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0xe4,0x02,0x00,0x00,0xe3,0x02,0x00,0x00, @@ -60457,12 +112561,12 @@ unsigned char matmul_q8_0_f32_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -60496,13 +112600,13 @@ unsigned char matmul_q8_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0xb7,0x00,0x00,0x00, @@ -60626,7 +112730,7 @@ unsigned char matmul_q8_0_f32_data[] = { 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x14,0x02,0x00,0x00,0x84,0x00,0x00,0x00, 0x61,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x0d,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x48,0x02,0x00,0x00,0x08,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00,0x50,0x02,0x00,0x00, 0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x96,0x02,0x00,0x00, 0xc3,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x97,0x02,0x00,0x00, @@ -61349,12 +113453,12 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x20,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x20,0x00,0x04,0x00, 0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x22,0x00,0x00,0x00, -0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x0a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x35,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -61390,12 +113494,12 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x98,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa9,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -61526,7 +113630,7 @@ unsigned char matmul_q8_0_f32_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x37,0x02,0x00,0x00, 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, +0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x0a,0x00,0x00,0x00, 0x73,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, 0xb9,0x02,0x00,0x00,0xc4,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, 0xba,0x02,0x00,0x00,0xb9,0x02,0x00,0x00,0x20,0x00,0x04,0x00, @@ -62283,13 +114387,13 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -62324,12 +114428,12 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x78,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x7e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x78,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x82,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x82,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x15,0x00,0x00,0x00,0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x92,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x98,0x00,0x00,0x00,0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0xa2,0x00,0x00,0x00,0x07,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xa7,0x00,0x00,0x00, 0x40,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0xa9,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00, @@ -62452,7 +114556,7 @@ unsigned char matmul_q8_0_f32_aligned_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x41,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x41,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0xc4,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0x8f,0x02,0x00,0x00, @@ -63159,13 +115263,13 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x3b,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x14,0x00,0x00,0x00, 0x09,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x08,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x17,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x22,0x00,0x00,0x00,0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x09,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x0d,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x15,0x00,0x00,0x00,0x28,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x07,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x0a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x35,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x38,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, @@ -63199,12 +115303,12 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x81,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, +0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x15,0x00,0x00,0x00,0x91,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x97,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0xa1,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x32,0x00,0x04,0x00, +0xa1,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x32,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0xa6,0x00,0x00,0x00,0x40,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, @@ -63326,7 +115430,7 @@ unsigned char matmul_q8_0_f32_fp32_data[] = { 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x0f,0x02,0x00,0x00,0x84,0x00,0x00,0x00,0x61,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, -0x41,0x02,0x00,0x00,0x0d,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, +0x41,0x02,0x00,0x00,0x08,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, 0x0a,0x00,0x00,0x00,0x49,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x1d,0x00,0x03,0x00,0x8f,0x02,0x00,0x00,0xc3,0x00,0x00,0x00, 0x1e,0x00,0x03,0x00,0x90,0x02,0x00,0x00,0x8f,0x02,0x00,0x00, @@ -64294,605 +116398,7856 @@ const uint64_t mul_f32_len = 4276; unsigned char mul_mat_vec_f16_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x09,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x32,0x00,0x00,0x00,0x36,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0xab,0x00,0x00,0x00, -0xeb,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x69,0x00,0x00,0x00, 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+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t mul_mat_vec_id_q6_K_f32_len = 11612; + +unsigned char mul_mat_vec_id_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x3a,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,0x00,0x0e,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x4b,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, +0x71,0x00,0x00,0x00,0x81,0x00,0x00,0x00,0xba,0x00,0x00,0x00, +0xf8,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t mul_mat_vec_id_q8_0_f32_len = 3808; + unsigned char mul_mat_vec_nc_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xb3,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -65370,7 +124725,7 @@ const uint64_t mul_mat_vec_p021_f16_f32_len = 2768; unsigned char mul_mat_vec_q2_K_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x11,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x49,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -65378,721 +124733,721 @@ unsigned char mul_mat_vec_q2_K_f16_f32_data[] = { 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, 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uint64_t mul_mat_vec_q4_1_f16_f32_len = 4044; unsigned char mul_mat_vec_q4_1_f32_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x37,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x6f,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,0x00,0x00,0x00,0xc8,0x00,0x00,0x00,0x06,0x01,0x00,0x00, +0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x5b,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x8b,0x00,0x00,0x00, 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0x38,0x00,0x01,0x00, }; const uint64_t mul_mat_vec_q4_1_f32_f32_len = 4012; unsigned char mul_mat_vec_q4_K_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x0c,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, @@ -69819,870 +129174,870 @@ unsigned char mul_mat_vec_q4_K_f16_f32_data[] = { 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, -0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00, -0x23,0x02,0x00,0x00,0xc6,0x03,0x00,0x00,0x10,0x00,0x06,0x00, 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0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t mul_mat_vec_q4_K_f16_f32_len = 10400; unsigned char mul_mat_vec_q4_K_f32_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xb4,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xec,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, @@ -70690,2320 +130045,2320 @@ unsigned char mul_mat_vec_q4_K_f32_f32_data[] = { 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, -0x59,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0xaf,0x00,0x00,0x00, -0x23,0x02,0x00,0x00,0xa7,0x03,0x00,0x00,0x10,0x00,0x06,0x00, 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0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, @@ -75192,1000 +134547,1000 @@ unsigned char mul_mat_vec_q6_K_f16_f32_data[] = { 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, -0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00, -0xc7,0x00,0x00,0x00,0xdb,0x01,0x00,0x00,0x10,0x00,0x06,0x00, +0x11,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, +0x66,0x00,0x00,0x00,0x99,0x00,0x00,0x00,0xbe,0x00,0x00,0x00, +0xd4,0x00,0x00,0x00,0xe8,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 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, -0x47,0x00,0x04,0x00,0x11,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; const uint64_t mul_mat_vec_q6_K_f16_f32_len = 11960; unsigned char mul_mat_vec_q6_K_f32_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xde,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x16,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -76193,1631 +135548,1631 @@ unsigned char mul_mat_vec_q6_K_f32_f32_data[] = { 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, -0x59,0x00,0x00,0x00,0x8c,0x00,0x00,0x00,0xb1,0x00,0x00,0x00, -0xc7,0x00,0x00,0x00,0xd8,0x01,0x00,0x00,0x10,0x00,0x06,0x00, 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0x38,0x00,0x01,0x00, }; const uint64_t mul_mat_vec_q8_0_f16_f32_len = 3916; unsigned char mul_mat_vec_q8_0_f32_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x26,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x5e,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,0x00,0x00,0x00,0x40,0x00,0x00,0x00, -0x46,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x51,0x00,0x00,0x00, -0x85,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x5b,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x82,0x00,0x00,0x00, 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uint64_t rope_f32_len = 3072; unsigned char rope_neox_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x69,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, 0x68,0x00,0x00,0x00,0x98,0x00,0x00,0x00,0x9e,0x00,0x00,0x00, -0xcf,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x00,0x01,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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-0x94,0x00,0x00,0x00,0x13,0x01,0x00,0x00,0x12,0x01,0x00,0x00, -0x41,0x00,0x06,0x00,0xa1,0x00,0x00,0x00,0x14,0x01,0x00,0x00, -0x98,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x14,0x01,0x00,0x00,0x13,0x01,0x00,0x00, -0xf9,0x00,0x02,0x00,0x17,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, -0x17,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t rope_neox_f16_len = 3876; - -unsigned char rope_neox_f32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -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,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x97,0x00,0x00,0x00, -0x9d,0x00,0x00,0x00,0xce,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0xc5,0x00,0x00,0x00,0xd5,0x00,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x29,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00, @@ -79237,51 +138265,61 @@ unsigned char rope_neox_f32_data[] = { 0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00, 0x08,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, 0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x68,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x94,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x95,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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0x84,0x00,0x05,0x00,0x07,0x00,0x00,0x00,0xb4,0x00,0x00,0x00, @@ -79399,135 +138790,140 @@ unsigned char rope_neox_f32_data[] = { 0xbb,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x07,0x00,0x00,0x00, 0xbd,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x86,0x00,0x05,0x00, 0x07,0x00,0x00,0x00,0xbe,0x00,0x00,0x00,0x71,0x00,0x00,0x00, -0xbd,0x00,0x00,0x00,0x41,0x00,0x05,0x00,0x2f,0x00,0x00,0x00, -0xc1,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0xc0,0x00,0x00,0x00, -0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0xc2,0x00,0x00,0x00, -0xc1,0x00,0x00,0x00,0x70,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0xc4,0x00,0x00,0x00,0x83,0x00,0x00,0x00,0x70,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0xc7,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, -0x7f,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x56,0x01,0x00,0x00, -0xc7,0x00,0x00,0x00,0x0c,0x00,0x08,0x00,0x06,0x00,0x00,0x00, -0xc8,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x32,0x00,0x00,0x00, -0xc2,0x00,0x00,0x00,0xc4,0x00,0x00,0x00,0x56,0x01,0x00,0x00, -0x41,0x00,0x06,0x00,0xd0,0x00,0x00,0x00,0xd1,0x00,0x00,0x00, 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+0x1b,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t rope_neox_f32_len = 3792; +const uint64_t rope_neox_f32_len = 4032; unsigned char scale_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -81140,3 +140536,184 @@ unsigned char sqr_f32_data[] = { }; const uint64_t sqr_f32_len = 3252; +unsigned char sum_rows_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x76,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x27,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x6c,0x00,0x00,0x00, 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+0x18,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x14,0x00,0x00,0x00,0x6f,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x3a,0x00,0x00,0x00,0x70,0x00,0x00,0x00, +0x6c,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x0f,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x70,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x68,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x68,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t sum_rows_f32_len = 2112; + diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index aff451b63..5e12ea9dd 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -137,6 +137,7 @@ struct vk_device { vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_div_f32; vk_pipeline pipeline_add_f32; vk_pipeline pipeline_scale_f32; vk_pipeline pipeline_sqr_f32; @@ -152,6 +153,7 @@ struct vk_device { vk_pipeline pipeline_rope_f32, pipeline_rope_f16; vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; vk_pipeline pipeline_argsort_f32; + vk_pipeline pipeline_sum_rows_f32; std::vector pipelines; @@ -226,17 +228,27 @@ typedef std::vector 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 stride_a; uint32_t stride_b; uint32_t stride_d; 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; + uint32_t k_split; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; }; - 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; + uint32_t ne02; uint32_t ne12; uint32_t broadcast2; uint32_t broadcast3; +}; + +struct vk_mat_mat_id_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 batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t nei0; uint32_t nei1; uint32_t nbi1; uint32_t ne11; +}; +struct vk_mat_vec_id_push_constants { + uint32_t ncols; uint32_t stride_a; uint32_t stride_b; uint32_t stride_d; + uint32_t batch_stride_a; uint32_t batch_stride_b; uint32_t batch_stride_d; + uint32_t nei0; uint32_t ne11; }; struct vk_op_push_constants { @@ -290,6 +302,7 @@ struct vk_op_rope_neox_push_constants { float corr_dims[4]; float theta_scale; float inv_ndims; + uint32_t has_freq_facs; }; struct vk_op_soft_max_push_constants { @@ -1027,7 +1040,7 @@ 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(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q6_K] = std::make_shared(); - /*ctx->device->pipeline_matmul_id_f32 = std::make_shared(); + ctx->device->pipeline_matmul_id_f32 = std::make_shared(); ctx->device->pipeline_matmul_id_f16_f32 = std::make_shared(); ctx->device->pipeline_matmul_id_f16 = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_0] = std::make_shared(); @@ -1039,7 +1052,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q3_K] = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q4_K] = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q5_K] = std::make_shared(); - ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared();*/ + ctx->device->pipeline_dequant_mul_mat_mat_id[GGML_TYPE_Q6_K] = std::make_shared(); 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, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); @@ -1077,12 +1090,12 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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, 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_Q4_1]->l, "matmul_q4_1_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_1_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_1_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_1_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_1_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_1_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, 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); @@ -1140,96 +1153,96 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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_f32->l, "matmul_id_f32_l", matmul_id_f32_len, matmul_id_f32_data, "main", 4, sizeof(vk_mat_mat_id_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_id_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_id_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_id_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_id_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_id_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->l, "matmul_id_f16_l", matmul_id_f16_len, matmul_id_f16_data, "main", 4, sizeof(vk_mat_mat_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_m", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_s", matmul_id_q4_1_f32_len, matmul_id_q4_1_f32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_aligned_l", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_aligned_m", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_aligned_s", matmul_id_q4_1_f32_aligned_len, matmul_id_q4_1_f32_aligned_data, "main", 4, sizeof(vk_mat_mat_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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);*/ + 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_id_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_id_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_id_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_id_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_id_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_id_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, 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); @@ -1329,99 +1342,100 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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_f32->l, "matmul_id_f32_l", matmul_id_f32_fp32_len, matmul_id_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_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_id_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_id_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_id_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_id_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_id_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->l, "matmul_id_f16_l", matmul_id_f16_fp32_len, matmul_id_f16_fp32_data, "main", 4, sizeof(vk_mat_mat_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_Q4_1]->l, "matmul_id_q4_1_f32_l", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_m", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_f32_s", matmul_id_q4_1_f32_fp32_len, matmul_id_q4_1_f32_fp32_data, "main", 4, sizeof(vk_mat_mat_id_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_1_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_id_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_1_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_id_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_1_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_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_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_id_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_id_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_id_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_id_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_id_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_id_push_constants), s_wg_denoms, warptile_mmq_s, s_align); } // mul mat vec + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_f32_f32[GGML_TYPE_F32 ], "mul_mat_vec_f32_f32_f32", mul_mat_vec_f32_f32_f32_len, mul_mat_vec_f32_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_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); @@ -1434,6 +1448,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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_F32 ], "mul_mat_vec_f32_f16_f32", mul_mat_vec_f32_f16_f32_len, mul_mat_vec_f32_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_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); @@ -1446,17 +1461,18 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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);*/ + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_dequant_mul_mat_vec_id_f32[GGML_TYPE_F32 ], "mul_mat_vec_id_f32_f32", mul_mat_vec_id_f32_f32_len, mul_mat_vec_id_f32_f32_data, "main", 4, sizeof(vk_mat_vec_id_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_id_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_id_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_id_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_id_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_id_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_id_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_id_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_id_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_id_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_id_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_id_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); @@ -1504,6 +1520,8 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_div_f32, "div_f32", div_f32_len, div_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {512, 1, 1}, {}, 1); @@ -1522,10 +1540,12 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { 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); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); + + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_sum_rows_f32, "sum_rows_f32", sum_rows_f32_len, sum_rows_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, { device->subgroup_size }, 1); } static void ggml_vk_print_gpu_info(size_t idx) { @@ -1948,6 +1968,33 @@ 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 a_type, ggml_type b_type) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; +#endif + GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); + + switch (a_type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + 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 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_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; @@ -1983,13 +2030,14 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_id_pipeline(ggml_backend_vk_co 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) { +static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec_id(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 - GGML_ASSERT(b_type == GGML_TYPE_F32 || b_type == GGML_TYPE_F16); + GGML_ASSERT(b_type == GGML_TYPE_F32); switch (a_type) { + case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: case GGML_TYPE_Q4_1: @@ -2006,7 +2054,7 @@ static vk_pipeline ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * return nullptr; } - 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]; + return ctx->device->pipeline_dequant_mul_mat_vec_id_f32[a_type]; } static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { @@ -2154,7 +2202,11 @@ static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context const uint32_t wg1 = CEIL_DIV(elements[1], pipeline->wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline->wg_denoms[2]); #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; + std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline->name << ", {"; + for (auto& buffer : buffers) { + std::cerr << "(" << buffer.buffer << ", " << buffer.offset << ", " << buffer.size << "), "; + } + std::cerr << "}, (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; std::vector write_descriptor_sets; @@ -2735,22 +2787,21 @@ 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) { + uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3) { #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 != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << 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; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << (split_k_buffer.buffer != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << 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 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ")" << 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 }; + const vk_mat_mat_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, k, ne02, ne12, broadcast2, broadcast3 }; 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 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 }; + const vk_mat_mat_push_constants pc1 = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3 }; // Make sure enough workgroups get assigned for split k to work 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); @@ -2760,29 +2811,20 @@ static void ggml_vk_matmul( 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, + vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& ids, 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) { + uint32_t n_as, uint32_t nei0, uint32_t nei1, uint32_t nbi1, uint32_t ne11) { #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; + std::cerr << "ggml_vk_matmul_id(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), d: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), ids: (" << ids.buffer->buffer << ", " << ids.offset << ", " << ids.size << "), " << + "m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", " << + "batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ", " << + "n_as: " << n_as << ", nei0: " << nei0 << ", nei1: " << nei1 << ", nbi1: " << nbi1 << ", ne11: " << ne11 << ")" << 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 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 }); + const vk_mat_mat_id_push_constants pc = { m, n, k, stride_a, stride_b, stride_d, batch_stride_a, batch_stride_b, batch_stride_d, + nei0, nei1, nbi1, ne11 }; + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d, ids }, sizeof(vk_mat_mat_id_push_constants), &pc, { m, nei1, n_as }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -2996,8 +3038,9 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su 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 + ne01, ne11, ne10, + ne10, ne10, ne01, stride_batch_x, stride_batch_y, ne20*ne21, + split_k, ne12*ne13, ne02, ne12, r2, r3 ); // NOLINT } @@ -3149,8 +3192,8 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context // 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), + (uint32_t)ne02, (uint32_t)ne12, (uint32_t)r2, (uint32_t)r3, }; 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}); @@ -3310,26 +3353,26 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif - if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && dst->ne[1] == 1) { ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); - } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && dst->ne[1] == 1) { ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); - } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + } else if (dst->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); } else { ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, 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) { +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; + std::cerr << "ggml_vk_mul_mat_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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 << ", 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 + GGML_ASSERT(ids->type == GGML_TYPE_I32); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3341,16 +3384,18 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const uint64_t ne12 = src1->ne[2]; const uint64_t ne13 = src1->ne[3]; - const uint32_t nb11 = src1->nb[1]; + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + GGML_ASSERT(nei0 * nei1 <= 2048); + + const uint32_t nbi1 = ids->nb[1]; + const uint32_t nbi2 = ids->nb[2]; 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; - - 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); @@ -3364,15 +3409,20 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, size_t qx_buf_offset = 0; vk_buffer d_Qy; size_t qy_buf_offset = 0; + vk_buffer d_ids; + size_t ids_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool ids_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); + ggml_vk_host_get(ctx, ids->data, d_ids, ids_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; + ids_uma = d_ids != nullptr; } const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); @@ -3392,27 +3442,25 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, // 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 uint64_t x_ne = ne01 * ne00; + const uint64_t y_ne = ne11 * ne10; + const uint64_t d_ne = ne21 * ne20; - 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 kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, mmp, ne01, nei1)); + const bool aligned = ne10 == kpad && ne01 > 8 && nei1 > 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); + vk_pipeline pipeline = ggml_vk_guess_matmul_pipeline(ctx, mmp, ne01, nei1, 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 ids_sz = nbi2; 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; @@ -3427,6 +3475,11 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } + if (!ids_uma) { + d_ids = extra_ids->buffer_gpu.lock(); + ids_buf_offset = extra_ids->offset; + GGML_ASSERT(d_ids != nullptr); + } if (qx_needs_dequant) { d_X = ctx->prealloc_x; GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); @@ -3468,9 +3521,6 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, 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 }); @@ -3495,23 +3545,26 @@ static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, } // compute - ggml_vk_matmul( + ggml_vk_matmul_id( 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 + { d_D, d_buf_offset, d_sz * ne22 * ne23 }, { d_ids, ids_buf_offset, ids_sz }, + ne01, ne21, ne10, ne10, ne10, ne01, + stride_batch_x, stride_batch_y, ne20*ne21, + n_as, nei0, nei1, nbi1 / ggml_type_size(ids->type), ne11 ); // 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) { +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, const ggml_tensor * ids, 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; + std::cerr << "ggml_vk_mul_mat_vec_id_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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 << ", 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 + GGML_ASSERT(ids->type == GGML_TYPE_I32); const uint64_t ne00 = src0->ne[0]; const uint64_t ne01 = src0->ne[1]; @@ -3523,36 +3576,41 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte const uint64_t ne12 = src1->ne[2]; const uint64_t ne13 = src1->ne[3]; - GGML_ASSERT(ne11 == 1); + const uint64_t nei0 = ids->ne[0]; + const uint64_t nei1 = ids->ne[1]; + + const uint64_t nbi2 = ids->nb[2]; + + GGML_ASSERT(nei1 == 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; + 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; + vk_buffer d_ids; + size_t ids_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool ids_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); + ggml_vk_host_get(ctx, ids->data, d_ids, ids_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; + ids_uma = d_ids != nullptr; } const bool x_non_contig = !ggml_vk_dim01_contiguous(src0); @@ -3568,12 +3626,13 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte const uint64_t x_ne = ne01 * ne00; const uint64_t y_ne = ne11 * ne10; - const uint64_t d_ne = ne11 * ne01; + const uint64_t d_ne = ne21 * ne20; 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 ids_sz = nbi2; const uint64_t d_sz = sizeof(float) * d_ne; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3593,6 +3652,11 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } + if(!ids_uma) { + d_ids = extra_ids->buffer_gpu.lock(); + ids_buf_offset = extra_ids->offset; + GGML_ASSERT(d_ids != nullptr); + } if (qx_needs_dequant) { d_X = ctx->prealloc_x; } else { @@ -3618,7 +3682,7 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte } 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); + vk_pipeline dmmv = ggml_vk_get_dequantize_mul_mat_vec_id(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); @@ -3641,27 +3705,34 @@ static void ggml_vk_mul_mat_vec_id_q_f16(ggml_backend_vk_context * ctx, vk_conte 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 = { + const vk_mat_vec_id_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 + (uint32_t)x_ne, stride_batch_y, (uint32_t)(ne20*ne21), + (uint32_t)nei0, (uint32_t)ne11, }; 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}); -}*/ + 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}, { d_ids, ids_buf_offset, ids_sz } }, + sizeof(vk_mat_vec_id_push_constants), &pc, { (uint32_t)ne01, (uint32_t)nei0, 1 }); +} + +static void ggml_vk_mul_mat_id(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_mul_mat_id(" << src0 << ", " << src1 << ", " << src2 << ", " << dst << ")" << std::endl; +#endif + if (src2->ne[1] == 1 && (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + ggml_vk_mul_mat_vec_id_q_f16(ctx, subctx, src0, src1, src2, dst); + } else { + ggml_vk_mul_mat_id_q_f16(ctx, subctx, src0, src1, src2, dst); + } +} 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 @@ -3732,7 +3803,7 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { @@ -3753,6 +3824,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_mul_f32; } return nullptr; + case GGML_OP_DIV: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_div_f32; + } + return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_scale_f32; @@ -3850,9 +3926,16 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const return ctx->device->pipeline_argsort_f32; } return nullptr; + case GGML_OP_SUM_ROWS: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return ctx->device->pipeline_sum_rows_f32; + } + return nullptr; default: return nullptr; } + + GGML_UNUSED(src2); } static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { @@ -3870,6 +3953,7 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { case GGML_OP_GET_ROWS: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: @@ -3880,22 +3964,26 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& 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 std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", 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]; } + if (src2 != nullptr) { + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + } std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", 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] << "), " << ggml_op_name(op) << ")" << std::endl; #endif GGML_ASSERT(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT - GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT + GGML_ASSERT(ggml_vk_op_supports_incontiguous(op) || ggml_vk_dim01_contiguous(src0)); // NOLINT GGML_ASSERT(dst->extra != nullptr); 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 ne0 = ne00 * ne01; + const bool use_src1 = src1 != nullptr; const uint64_t ne10 = use_src1 ? src1->ne[0] : 0; const uint64_t ne11 = use_src1 ? src1->ne[1] : 0; @@ -3904,7 +3992,20 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne1 = ne10 * ne11; // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; - vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); + const bool use_src2 = src2 != nullptr; + const uint64_t ne20 = use_src2 ? src2->ne[0] : 0; + const uint64_t ne21 = use_src2 ? src2->ne[1] : 0; + const uint64_t ne22 = use_src2 ? src2->ne[2] : 0; + const uint64_t ne23 = use_src2 ? src2->ne[3] : 0; + const uint64_t ne2 = ne20 * ne21; + + const uint64_t ned0 = dst->ne[0]; + const uint64_t ned1 = dst->ne[1]; + const uint64_t ned2 = dst->ne[2]; + const uint64_t ned3 = dst->ne[3]; + const uint64_t ned = ned0 * ned1; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3927,15 +4028,18 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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; + ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; vk_buffer d_Z = nullptr; + size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool src2_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); @@ -3944,11 +4048,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } + if (use_src2) { + ggml_vk_host_get(ctx, src2->data, d_Z, z_buf_offset); + src2_uma = d_Z != nullptr; + } } - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; - uint64_t d_sz = ggml_type_size(dst->type) * ne0; + uint64_t x_sz = ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0; + uint64_t y_sz = use_src1 ? ggml_type_size(src1->type) * ne1 : 0; + uint64_t z_sz = use_src2 ? ggml_type_size(src2->type) * ne2 : 0; + uint64_t d_sz = ggml_type_size(dst->type) * ned; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3970,10 +4079,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } + if (use_src2 && !src2_uma) { + d_Z = extra_src2->buffer_gpu.lock(); + z_buf_offset = extra_src2->offset; + GGML_ASSERT(d_Z != nullptr); + } if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; + z_sz = use_src2 ? ggml_nbytes(src2) : 0; d_sz = ggml_nbytes(dst); if (x_buf_offset + x_sz >= d_X->size) { @@ -3982,6 +4097,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_buf_offset + y_sz >= d_Y->size) { y_sz = VK_WHOLE_SIZE; } + if (use_src2 && z_buf_offset + z_sz >= d_Z->size) { + z_sz = VK_WHOLE_SIZE; + } if (d_buf_offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -3997,6 +4115,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_SOFT_MAX: + case GGML_OP_SUM_ROWS: elements = { (uint32_t)ggml_nrows(src0), 1, 1 }; break; case GGML_OP_DIAG_MASK_INF: @@ -4021,13 +4140,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_sz != VK_WHOLE_SIZE) { y_sz *= ne12 * ne13; } + if (use_src2 && z_sz != VK_WHOLE_SIZE) { + z_sz *= ne22 * ne23; + } if (d_sz != VK_WHOLE_SIZE) { - d_sz *= ne02 * ne03; + d_sz *= ned2 * ned3; } } if (op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer + // Empty src1 is possible in soft_max, but the shader needs a buffer vk_subbuffer subbuf_y; if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; @@ -4037,6 +4159,28 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (op == GGML_OP_ROPE) { + const int mode = ((int32_t *) dst->op_params)[2]; + const bool is_neox = mode & 2; + + if (is_neox) { + // Empty src2 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { d_X, 0, d_X->size }; + } + + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else { + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } + } else if (use_src2) { + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); @@ -4047,6 +4191,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); GGML_ASSERT(op != GGML_OP_ARGSORT); + GGML_ASSERT(!use_src2); ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); @@ -4088,7 +4233,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -4096,7 +4241,7 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4111,7 +4256,7 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4126,7 +4271,22 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, + (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, + 0, + 0.0f, 0.0f, + }); +} + +static void ggml_vk_div(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const uint32_t src0_type_size = ggml_type_size(src0->type); + const uint32_t src1_type_size = ggml_type_size(src1->type); + const uint32_t dst_type_size = ggml_type_size(dst->type); + + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_DIV, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4141,7 +4301,7 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4154,7 +4314,7 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4168,7 +4328,7 @@ static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4183,7 +4343,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t dst_type_size = ggml_type_size(dst->type); const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4195,21 +4355,21 @@ 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) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(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) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -4228,7 +4388,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, scale, max_bias, @@ -4237,7 +4397,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, }); } -static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -4260,12 +4420,13 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], - freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims, + src2 != nullptr, }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f} }); @@ -4284,17 +4445,17 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, GGML_ASSERT(ncols_pad <= 1024); - std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl; - - std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; - - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_ARGSORT, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { ncols, ncols_pad, op_params[0], }); } +static void ggml_vk_sum_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SUM_ROWS, { (uint32_t)src0->ne[0], 0, 0.0f, 0.0f }); +} + #ifdef GGML_VULKAN_RUN_TESTS static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { @@ -4490,7 +4651,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t 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, 0, 0, 0, 0, 1 + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1 ); ggml_vk_ctx_end(subctx); } @@ -4994,7 +5157,9 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, 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, 0, 0, 0, 0, 1 + m, n, k, + k, k, m, k*m, k*n, m*n, + split_k, batch, batch, batch, 1, 1 ); ggml_vk_ctx_end(subctx); } @@ -5179,12 +5344,14 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm case GGML_OP_CONT: case GGML_OP_DUP: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_NORM: case GGML_OP_RMS_NORM: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ROPE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: break; case GGML_OP_UNARY: switch (ggml_get_unary_op(node)) { @@ -5404,6 +5571,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; + const ggml_tensor * src2 = node->src[2]; switch (node->op) { case GGML_OP_UNARY: @@ -5420,6 +5588,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_GET_ROWS: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: @@ -5439,6 +5608,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_MUL_MAT_ID: case GGML_OP_NONE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: break; default: std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; @@ -5467,6 +5637,10 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod case GGML_OP_MUL: ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node); + break; + case GGML_OP_DIV: + ggml_vk_div(ctx, ctx->compute_ctx, src0, src1, node); + break; case GGML_OP_SCALE: ggml_vk_scale(ctx, ctx->compute_ctx, src0, node); @@ -5520,20 +5694,23 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_ROPE: - ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, src2, node); break; case GGML_OP_ARGSORT: ggml_vk_argsort(ctx, ctx->compute_ctx, src0, node); + + break; + case GGML_OP_SUM_ROWS: + ggml_vk_sum_rows(ctx, ctx->compute_ctx, src0, node); + break; case GGML_OP_MUL_MAT: ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL_MAT_ID: - //ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, node); - std::cerr << "ggml_vulkan: GGML_OP_MUL_MAT_ID not implemented yet." << std::endl; - GGML_ASSERT(false); + ggml_vk_mul_mat_id(ctx, ctx->compute_ctx, src0, src1, src2, node); break; default: @@ -5563,6 +5740,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_ADD: case GGML_OP_GET_ROWS: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_SCALE: case GGML_OP_SQR: case GGML_OP_CLAMP: @@ -5580,6 +5758,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ case GGML_OP_TRANSPOSE: case GGML_OP_NONE: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: extra = (ggml_tensor_extra_gpu *) tensor->extra; break; @@ -5953,6 +6132,8 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { + ggml_vk_instance_init(); + #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl; #endif @@ -6232,7 +6413,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } break; case GGML_OP_MUL_MAT: - // case GGML_OP_MUL_MAT_ID: + case GGML_OP_MUL_MAT_ID: { switch (op->src[0]->type) { case GGML_TYPE_F32: @@ -6316,6 +6497,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_NORM: case GGML_OP_ADD: case GGML_OP_MUL: + case GGML_OP_DIV: case GGML_OP_RMS_NORM: case GGML_OP_SCALE: case GGML_OP_SQR: @@ -6324,6 +6506,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: case GGML_OP_ARGSORT: + case GGML_OP_SUM_ROWS: return true; default: return false; @@ -6333,15 +6516,10 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } GGML_CALL static bool ggml_backend_vk_offload_op(ggml_backend_t backend, const ggml_tensor * op) { - const ggml_tensor * dst = op; - const int min_batch_size = 32; - if (dst->ne[1] > min_batch_size && dst->op != GGML_OP_GET_ROWS) { - return true; - } - - return false; + return (op->ne[1] >= min_batch_size && op->op != GGML_OP_GET_ROWS) || + (op->ne[2] >= min_batch_size && op->op == GGML_OP_MUL_MAT_ID); UNUSED(backend); } @@ -6496,7 +6674,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectorop) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl; + std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl; done.push_back(tensor); @@ -6546,7 +6724,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { const size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -6557,12 +6735,12 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; + std::cerr << "tensor=" << tensor << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; if (tensor->src[0] != nullptr) { - std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; + std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; } if (tensor->src[1] != nullptr) { - std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; + std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); @@ -6573,43 +6751,11 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso std::vector done; ggml_vk_print_graph_origin(tensor, done); - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } -static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { - return; - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_CPU); - if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { - return; - } - for (int i3 = 0; i3 < tensor->ne[3]; i3++) { - for (int i2 = 0; i2 < tensor->ne[2]; i2++) { - for (int i1 = 0; i1 < tensor->ne[1]; i1++) { - for (int i0 = 0; i0 < tensor->ne[0]; i0++) { - float val = 0.0f; - if (tensor->type == GGML_TYPE_F32) { - val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); - } else if (tensor->type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); - } - if (std::isnan(val)) { - std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl; - std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; - std::cerr << std::endl; - ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(tensor, done); - GGML_ASSERT(false); - } - } - } - } - } -} - void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; @@ -6633,6 +6779,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -6651,9 +6798,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ size_t src1_size; size_t src2_size; - void * src0_buffer; - void * src1_buffer; - void * src2_buffer; + void * src0_buffer = nullptr; + void * src1_buffer = nullptr; + void * src2_buffer = nullptr; if (src0 != nullptr) { src0_clone = ggml_dup_tensor(ggml_ctx, src0); @@ -6662,10 +6809,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_buffer = malloc(src0_size); src0_clone->data = src0_buffer; - if (src0->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src0->buffer)) { memcpy(src0_clone->data, src0->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src0->backend == GGML_BACKEND_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src0->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6696,8 +6843,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, src0, "src0"); } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); } if (src1 != nullptr) { src1_clone = ggml_dup_tensor(ggml_ctx, src1); @@ -6706,10 +6851,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_buffer = malloc(src1_size); src1_clone->data = src1_buffer; - if (src1->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src1->buffer)) { memcpy(src1_clone->data, src1->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src1->backend == GGML_BACKEND_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src1->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6740,12 +6885,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, src1, "src1"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; + std::cerr << "src1_clone=" << tensor << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; if (src1->src[0] != nullptr) { - std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl; + std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl; } if (src1->src[1] != nullptr) { - std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl; + std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0); @@ -6756,14 +6901,74 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ std::vector done; ggml_vk_print_graph_origin(src1_clone, done); } + } + if (src2 != nullptr) { + src2_clone = ggml_dup_tensor(ggml_ctx, src2); - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); + src2_size = ggml_nbytes(src2); + + src2_buffer = malloc(src2_size); + src2_clone->data = src2_buffer; + if (ggml_backend_buffer_is_host(src2->buffer)) { + memcpy(src2_clone->data, src2->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (ggml_backend_buffer_is_vk(src2->buffer)) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { + for (int i3 = 0; i3 < src2->ne[3]; i3++) { + for (int i2 = 0; i2 < src2->ne[2]; i2++) { + const int idx = i3*src2->ne[2] + i2; + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); + } + } + + src2_clone->nb[0] = src2->nb[0]; + src2_clone->nb[1] = src2->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; + } + } else { + if (offset + src2_size >= buffer_gpu->size) { + src2_size = buffer_gpu->size - offset; + } + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src2_clone->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ASSERT(false); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(ctx, src2, "src2"); + std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "src2_clone=" << tensor << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; + if (src2->src[0] != nullptr) { + std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; + } + if (src2->src[1] != nullptr) { + std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(src2_clone, done); + } } if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_MUL_MAT_ID) { + tensor_clone = ggml_mul_mat_id(ggml_ctx, src0_clone, src1_clone, src2_clone); } else if (tensor->op == GGML_OP_MUL) { tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_DIV) { + tensor_clone = ggml_div(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_SCALE) { tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); } else if (tensor->op == GGML_OP_SQR) { @@ -6795,7 +7000,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: @@ -6833,6 +7038,8 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ 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 if (tensor->op == GGML_OP_SUM_ROWS) { + tensor_clone = ggml_sum_rows(ggml_ctx, src0_clone); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); @@ -6843,7 +7050,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); } @@ -6881,10 +7087,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -6932,12 +7139,15 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) { std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " src2->name=" << src2->name << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -6973,12 +7183,15 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -6997,12 +7210,15 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ if (avg_err > 0.05 || std::isnan(avg_err)) { std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; if (src0 != nullptr) { - std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; } if (src1 != nullptr) { - std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + if (src2 != nullptr) { + std::cerr << "src2=" << src2 << " op=" << ggml_op_name(src2->op) << " type=" << ggml_type_name(src2->type) << " ne0=" << src2->ne[0] << " nb0=" << src2->nb[0] << " ne1=" << src2->ne[1] << " nb1=" << src2->nb[1] << " ne2=" << src2->ne[2] << " nb2=" << src2->nb[2] << " ne3=" << src2->ne[3] << " nb3=" << src2->nb[3] << " offset=" << src2->view_offs << std::endl; } std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; std::cerr << std::endl << "Result:" << std::endl; @@ -7014,14 +7230,14 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_print_graph_origin(tensor, done); GGML_ASSERT(false); } else { - std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl; + std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl; } free(comp_result); comp_result = nullptr; comp_size = 0; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } diff --git a/ggml.c b/ggml.c index 4bd911528..11e5c34ab 100644 --- a/ggml.c +++ b/ggml.c @@ -5,6 +5,7 @@ #include "ggml-quants.h" #include "ggml.h" + #if defined(_MSC_VER) || defined(__MINGW32__) #include // using malloc.h with MSC/MINGW #elif !defined(__FreeBSD__) && !defined(__NetBSD__) && !defined(__OpenBSD__) @@ -28,6 +29,10 @@ #include #endif +#ifdef GGML_USE_OPENMP +#include +#endif + #ifdef GGML_USE_METAL #include #endif @@ -60,6 +65,9 @@ typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; +typedef atomic_int atomic_flag; + +#define ATOMIC_FLAG_INIT 0 static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); @@ -73,6 +81,12 @@ static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } +static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { + return InterlockedExchange(ptr, 1); +} +static void atomic_flag_clear(atomic_flag * ptr) { + InterlockedExchange(ptr, 0); +} typedef HANDLE pthread_t; @@ -283,17 +297,12 @@ inline static void * ggml_calloc(size_t num, size_t size) { #if defined(GGML_USE_ACCELERATE) #include -#if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions -#include "ggml-opencl.h" -#endif #elif defined(GGML_USE_OPENBLAS) #if defined(GGML_BLAS_USE_MKL) #include #else #include #endif -#elif defined(GGML_USE_CLBLAST) -#include "ggml-opencl.h" #endif // floating point type used to accumulate sums @@ -871,22 +880,14 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", -#if QK_K == 64 - .blck_size = QK4_NL, -#else .blck_size = QK_K, -#endif .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, .from_float = quantize_row_iq4_xs, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, .vec_dot = ggml_vec_dot_iq4_xs_q8_K, -#if QK_K == 64 - .vec_dot_type = GGML_TYPE_Q8_0, -#else .vec_dot_type = GGML_TYPE_Q8_K, -#endif .nrows = 1, }, [GGML_TYPE_Q8_K] = { @@ -1575,11 +1576,11 @@ do { \ // F16 arithmetic is not supported by AVX, so we use F32 instead -#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) -static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { float tmp[8]; for (int i = 0; i < 8; i++) { @@ -1588,13 +1589,14 @@ static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { return (__m256)__lasx_xvld(tmp, 0); } -static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { float arr[8]; __lasx_xvst(y, arr, 0); - for (int i = 0; i < 8; i++) + for (int i = 0; i < 8; i++) { x[i] = GGML_FP32_TO_FP16(arr[i]); + } } #define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) @@ -1670,7 +1672,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 -static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); @@ -1681,7 +1683,7 @@ static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { return __lsx_vld(tmp, 0); } -static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) { +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { float arr[4]; __lsx_vst(y, arr, 0); @@ -1754,7 +1756,7 @@ struct ggml_compute_state_shared { int64_t perf_node_start_cycles; int64_t perf_node_start_time_us; - const int n_threads; + int n_threads; // synchronization primitives atomic_int n_active; // num active threads @@ -2265,6 +2267,11 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } +#if __FINITE_MATH_ONLY__ +#error "some routines in ggml.c require non-finite math arithmetics -- pass -fno-finite-math-only to the compiler to fix" +#error "ref: https://github.com/ggerganov/llama.cpp/pull/7154#issuecomment-2143844461" +#endif + #if defined(__ARM_NEON) && defined(__aarch64__) // adapted from arm limited optimized routine @@ -2314,32 +2321,27 @@ inline static __m512 ggml_v_expf(__m512 x) { const __m512 r = _mm512_set1_ps(0x1.8p23f); const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); - const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); - const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); - if (_mm512_kortestz(c, c)) - return _mm512_fmadd_ps(j, k, k); - const __m512i g = _mm512_and_si512( - _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), - _mm512_set1_epi32(0x82000000u)); - const __m512 s1 = - _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); - const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); const __mmask16 d = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - return _mm512_mask_blend_ps( - d, _mm512_mask_blend_ps( - c, _mm512_fmadd_ps(k, j, k), - _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), - _mm512_mul_ps(s1, s1)); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); } // computes silu x/(1+exp(-x)) in single precision vector @@ -2678,9 +2680,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ARGSORT", "LEAKY_RELU", - "FLASH_ATTN", "FLASH_ATTN_EXT", - "FLASH_FF", "FLASH_ATTN_BACK", "SSM_CONV", "SSM_SCAN", @@ -2706,7 +2706,7 @@ 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 == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2768,9 +2768,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "argsort(x)", "leaky_relu(x)", - "flash_attn(x)", "flash_attn_ext(x)", - "flash_ff(x)", "flash_attn_back(x)", "ssm_conv(x)", "ssm_scan(x)", @@ -2796,7 +2794,7 @@ 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 == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -2895,24 +2893,20 @@ struct ggml_state { // global state static struct ggml_state g_state; -static atomic_int g_state_barrier = 0; +static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; // barrier via spin lock inline static void ggml_critical_section_start(void) { - int processing = atomic_fetch_add(&g_state_barrier, 1); - - while (processing > 0) { - // wait for other threads to finish - atomic_fetch_sub(&g_state_barrier, 1); - sched_yield(); // TODO: reconsider this - processing = atomic_fetch_add(&g_state_barrier, 1); + while (atomic_flag_test_and_set(&g_state_critical)) { + // spin + sched_yield(); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { - atomic_fetch_sub(&g_state_barrier, 1); + atomic_flag_clear(&g_state_critical); } #if defined(__gnu_linux__) @@ -3228,7 +3222,11 @@ GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) { + return ggml_is_contiguous(tensor); +} + +GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return @@ -3237,6 +3235,14 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } +GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + tensor->nb[0] == ggml_type_size(tensor->type) && + tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +} + GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); @@ -3369,10 +3375,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { GGML_PRINT_DEBUG("%s: g_state initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } -#if defined(GGML_USE_CLBLAST) - ggml_cl_init(); -#endif - ggml_setup_op_has_task_pass(); is_first_call = false; @@ -4894,10 +4896,21 @@ struct ggml_tensor * ggml_repeat_back( // ggml_concat struct ggml_tensor * ggml_concat( - struct ggml_context* ctx, - struct ggml_tensor* a, - struct ggml_tensor* b) { - GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]); + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int dim) { + GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); + + int64_t ne[GGML_MAX_DIMS]; + for (int d = 0; d < GGML_MAX_DIMS; ++d) { + if (d == dim) { + ne[d] = a->ne[d] + b->ne[d]; + continue; + } + GGML_ASSERT(a->ne[d] == b->ne[d]); + ne[d] = a->ne[d]; + } bool is_node = false; @@ -4905,7 +4918,9 @@ struct ggml_tensor * ggml_concat( is_node = true; } - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]); + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); + + ggml_set_op_params_i32(result, 0, dim); result->op = GGML_OP_CONCAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -5025,6 +5040,7 @@ struct ggml_tensor * ggml_leaky_relu( } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); result->op = GGML_OP_LEAKY_RELU; @@ -6231,6 +6247,7 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -6244,10 +6261,17 @@ static struct ggml_tensor * ggml_rope_impl( float xpos_base, bool xpos_down, bool inplace) { + GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); + GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + if (c) { + GGML_ASSERT(c->type == GGML_TYPE_F32); + GGML_ASSERT(c->ne[0] >= n_dims / 2); + } + bool is_node = false; if (a->grad) { @@ -6271,6 +6295,7 @@ static struct ggml_tensor * ggml_rope_impl( result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; + result->src[2] = c; return result; } @@ -6283,7 +6308,7 @@ struct ggml_tensor * ggml_rope( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false ); } @@ -6295,7 +6320,49 @@ struct ggml_tensor * ggml_rope_inplace( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ); +} + +struct ggml_tensor * ggml_rope_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ); +} + +struct ggml_tensor * ggml_rope_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } @@ -6314,7 +6381,7 @@ struct ggml_tensor * ggml_rope_custom( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false ); } @@ -6334,7 +6401,7 @@ struct ggml_tensor * ggml_rope_custom_inplace( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } @@ -6346,7 +6413,7 @@ struct ggml_tensor * ggml_rope_xpos_inplace( int n_dims, float base, bool down) { - return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); + return ggml_rope_impl(ctx, a, b, NULL, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); } // ggml_rope_back @@ -6355,6 +6422,7 @@ struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -6370,6 +6438,7 @@ struct ggml_tensor * ggml_rope_back( GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + GGML_ASSERT(c == NULL && "freq factors not implemented yet"); GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); @@ -6913,38 +6982,6 @@ struct ggml_tensor * ggml_top_k( return result; } -// ggml_flash_attn - -struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked) { - GGML_ASSERT(ggml_can_mul_mat(k, q)); - // TODO: check if vT can be multiplied by (k*qT) - - bool is_node = false; - - if (q->grad || k->grad || v->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, q->ne); - - int32_t t = masked ? 1 : 0; - ggml_set_op_params(result, &t, sizeof(t)); - - result->op = GGML_OP_FLASH_ATTN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = q; - result->src[1] = k; - result->src[2] = v; - - return result; -} - // ggml_flash_attn_ext struct ggml_tensor * ggml_flash_attn_ext( @@ -7004,38 +7041,6 @@ void ggml_flash_attn_ext_set_prec( ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second } -// ggml_flash_ff - -struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1) { - GGML_ASSERT(ggml_can_mul_mat(b0, a)); - // TODO: more checks - - bool is_node = false; - - if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne); - - result->op = GGML_OP_FLASH_FF; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b0; - result->src[2] = b1; - result->src[3] = c0; - result->src[4] = c1; - - return result; -} - // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( @@ -7045,6 +7050,8 @@ struct ggml_tensor * ggml_flash_attn_back( struct ggml_tensor * v, struct ggml_tensor * d, bool masked) { + GGML_ASSERT(false && "TODO: adapt to ggml_flash_attn_ext() changes"); + GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) @@ -9037,17 +9044,6 @@ static void ggml_compute_forward_add_f32( const int ith = params->ith; const int nth = params->nth; -#ifdef GGML_USE_CLBLAST - if (src1->backend == GGML_BACKEND_TYPE_GPU) { - // TODO: OpenCL kernel support full broadcast - GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); - if (ith == 0) { - ggml_cl_add(src0, src1, dst); - } - return; - } -#endif - const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS @@ -10155,17 +10151,6 @@ static void ggml_compute_forward_mul_f32( const int ith = params->ith; const int nth = params->nth; -#if defined(GGML_USE_CLBLAST) - if (src1->backend == GGML_BACKEND_TYPE_GPU) { - // TODO: OpenCL kernel support full broadcast - GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); - if (ith == 0) { - ggml_cl_mul(src0, src1, dst); - } - return; - } -#endif - const int64_t nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS @@ -10998,26 +10983,29 @@ static void ggml_compute_forward_concat_f32( GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ith; i2 < ne2; i2 += nth) { - if (i2 < ne02) { // src0 - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03); - - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); } - } - } // src1 - else { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13); - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; - } + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; } } } @@ -11025,8 +11013,8 @@ static void ggml_compute_forward_concat_f32( } static void ggml_compute_forward_concat( - const struct ggml_compute_params* params, - struct ggml_tensor* dst) { + const struct ggml_compute_params * params, + struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; @@ -11419,8 +11407,8 @@ static void ggml_compute_forward_gelu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11482,8 +11470,8 @@ static void ggml_compute_forward_gelu_quick_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11545,8 +11533,8 @@ static void ggml_compute_forward_silu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11657,9 +11645,9 @@ static void ggml_compute_forward_silu_back_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * grad = dst->src[1]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(grad)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src0, grad)); @@ -12398,15 +12386,6 @@ static void ggml_compute_forward_mul_mat( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_TYPE_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(dst)) { const int64_t ne_plane = ne01*ne00; @@ -12854,8 +12833,6 @@ static void ggml_compute_forward_out_prod_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_CLBLAST) - #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) bool use_blas = ggml_is_matrix(src0) && ggml_is_matrix(src1) && @@ -13053,7 +13030,7 @@ static void ggml_compute_forward_out_prod_q_f32( // nb01 >= nb00 - src0 is not transposed // compute by src0 rows - // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CLBLAST) + // TODO: #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (params->type == GGML_TASK_TYPE_INIT) { if (ith != 0) { @@ -14304,6 +14281,7 @@ static void ggml_compute_forward_rope_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]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -14356,13 +14334,24 @@ static void ggml_compute_forward_rope_f32( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14394,7 +14383,7 @@ static void ggml_compute_forward_rope_f32( const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; - theta_base *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -14429,28 +14418,22 @@ static void ggml_compute_forward_rope_f32( dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta; } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; + const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta ); - sin_theta *= sin_sign; + sin_theta *= sin_sign; theta_base *= theta_scale; - const int64_t i0 = ib*n_dims + ic/2; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -14475,6 +14458,7 @@ static void ggml_compute_forward_rope_f32( } } +// TODO: deduplicate f16/f32 code static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst, @@ -14482,6 +14466,7 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -14527,13 +14512,24 @@ static void ggml_compute_forward_rope_f16( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14565,7 +14561,7 @@ static void ggml_compute_forward_rope_f16( const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; - theta_base *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -14596,28 +14592,22 @@ static void ggml_compute_forward_rope_f16( dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; + const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta ); - sin_theta *= sin_sign; + sin_theta *= sin_sign; theta_base *= theta_scale; - const int64_t i0 = ib*n_dims + ic/2; - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -15647,400 +15637,6 @@ static void ggml_compute_forward_argsort( } } -// ggml_compute_forward_flash_attn - -static void ggml_compute_forward_flash_attn_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - 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; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - 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); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - 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 = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SW values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - sum = ggml_vec_soft_max_f32(Mup, S, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < masked_begin; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f32(masked_begin, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S, 0, 1); - } - } -} - -static void ggml_compute_forward_flash_attn_f16( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - 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; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - 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); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - 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 = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { - for (int64_t ic = 0; ic < nek1; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16(neq0, - S + i1, 0, - (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - } else { - for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16_unroll(neq0, nbk1, - S + i1, - ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); - } - } - - // scale - ggml_vec_scale_f32(nek1, S, scale); - - if (masked) { - for (int64_t i = P; i < M; i++) { - if (i > P + iq1) { - S[i] = -INFINITY; - } - } - } - - // softmax - // todo: exclude known -INF S[..] values from max and loop, assuming their results to be zero. - // dont forget to set their S values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(M, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - sum = ggml_vec_soft_max_f32(Mup, S, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(M, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < M; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); - - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); - } - - // todo: exclude known zero S[..] values from dot (reducing nev0 and increasing begin of v and S16). - if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16(nev0, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S16, 0, 1); - } - } else { - for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16_unroll(nev0, nbv1, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - S16); - } - } - } -} - -static void ggml_compute_forward_flash_attn( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_attn_f16(params, masked, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_f32(params, masked, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_flash_attn_ext static void ggml_compute_forward_flash_attn_ext_f16( @@ -16274,165 +15870,6 @@ static void ggml_compute_forward_flash_attn_ext( } } -// ggml_compute_forward_flash_ff - -static void ggml_compute_forward_flash_ff_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; // F16 - const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w - const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b - const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w - const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, nea, a, ne) - GGML_TENSOR_LOCALS(size_t, nba, a, nb) - GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne) - GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb) - GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne) - GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb) - GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne) - GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb) - GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne) - GGML_TENSOR_LOCALS(size_t, nbc1, c1, 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 = nea0; - //const int64_t N = nea1; - const int64_t M = neb01; - - GGML_ASSERT(ne0 == nea0); - GGML_ASSERT(ne1 == nea1); - GGML_ASSERT(ne2 == nea2); - - GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb10 == sizeof(float)); - GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbc10 == sizeof(float)); - - GGML_ASSERT(neb00 == D); - GGML_ASSERT(neb01 == M); - GGML_ASSERT(neb10 == M); - GGML_ASSERT(neb11 == 1); - - GGML_ASSERT(nec00 == M); - GGML_ASSERT(nec01 == D); - GGML_ASSERT(nec10 == D); - GGML_ASSERT(nec11 == 1); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by a rows using ggml_vec_dot_f32 - - // total rows in a - const int nr = nea1*nea2*nea3; - - // 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) { - // a indices - const int ia3 = ir/(nea2*nea1); - const int ia2 = (ir - ia3*nea2*nea1)/nea1; - const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); - - float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); - - for (int64_t ic = 0; ic < neb01; ++ic) { - // b0 indices - const int ib03 = ia3; - const int ib02 = ia2; - const int ib01 = ic; - - // S indices - const int i1 = ib01; - - ggml_vec_dot_f16(nea0, - S + i1, 0, - (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0, - (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3)), 0, 1); - } - - ggml_vec_add_f32(neb01, S, S, (float *) b1->data); - //ggml_vec_gelu_f32(neb01, S, S); - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); - - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); - } - - ggml_vec_gelu_f16(neb01, S16, S16); - - { - // dst indices - const int i1 = ia1; - const int i2 = ia2; - const int i3 = ia3; - - for (int64_t ic = 0; ic < nec01; ++ic) { - - ggml_vec_dot_f16(neb01, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), 0, - S16, 0, 1); - } - - ggml_vec_add_f32(nec01, - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) c1->data); - } - } -} - -static void ggml_compute_forward_flash_ff( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * b0 = dst->src[1]; - - switch (b0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_ff_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(false); // TODO - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( @@ -18003,21 +17440,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_leaky_relu(params, tensor); } break; - case GGML_OP_FLASH_ATTN: - { - const int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - 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); - } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); @@ -18387,6 +17813,7 @@ static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct gg static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; + struct ggml_tensor * src2 = tensor->src[2]; switch (tensor->op) { case GGML_OP_DUP: @@ -18918,6 +18345,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_back(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -18957,6 +18385,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_impl(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -19021,7 +18450,6 @@ 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; @@ -19038,7 +18466,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor masked); } - struct ggml_tensor * src2 = tensor->src[2]; const int64_t elem_q = ggml_nelements(src0); const int64_t elem_k = ggml_nelements(src1); const int64_t elem_v = ggml_nelements(src2); @@ -19076,10 +18503,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; - case GGML_OP_FLASH_FF: - { - GGML_ASSERT(false); // not supported - } break; case GGML_OP_FLASH_ATTN_BACK: { GGML_ASSERT(false); // not supported @@ -19766,15 +19189,10 @@ 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; - case GGML_OP_FLASH_FF: - { - n_tasks = n_threads; - } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; @@ -20086,11 +19504,6 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa { const enum ggml_type vec_dot_type = type_traits[node->src[0]->type].vec_dot_type; -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(node->src[0], node->src[1], node)) { - cur = ggml_cl_mul_mat_get_wsize(node->src[0], node->src[1], node); - } else -#endif #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(node)) { if (node->src[0]->type != GGML_TYPE_F32) { @@ -20171,40 +19584,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; } break; - case GGML_OP_FLASH_ATTN: - { - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - - if (node->src[1]->type == GGML_TYPE_F32) { - 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_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: { const int64_t ne00 = node->src[0]->ne[0]; // D cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; - case GGML_OP_FLASH_FF: - { - if (node->src[1]->type == GGML_TYPE_F32) { - 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_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: { const int64_t D = node->src[0]->ne[0]; @@ -20248,6 +19633,59 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa return cplan; } +static enum ggml_status ggml_graph_compute_parallel(struct ggml_compute_state * workers, int n_threads) { + enum ggml_status compute_status = GGML_STATUS_SUCCESS; + +#ifdef GGML_USE_OPENMP + if (n_threads > 1) { + #pragma omp parallel num_threads(n_threads) + { + #pragma omp single + { + // update the number of threads from the actual number of threads that we got from OpenMP + n_threads = omp_get_num_threads(); + workers[0].shared->n_threads = n_threads; + workers[0].shared->n_active = n_threads; + } + ggml_graph_compute_thread(&workers[omp_get_thread_num()]); + } + } else { + ggml_graph_compute_thread(&workers[0]); + } +#else + // create thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; ++j) { + const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); + GGML_ASSERT(rc == 0); + UNUSED(rc); + } + } + + // this is a work thread too + ggml_graph_compute_thread(&workers[0]); + + // join or kill thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; j++) { + const int rc = ggml_thread_join(workers[j].thrd, NULL); + GGML_ASSERT(rc == 0); + UNUSED(rc); + } + } +#endif + // don't leave affinity set on the main thread + clear_numa_thread_affinity(); + + for (int j = 0; j < n_threads; j++) { + if (workers[j].ec != GGML_STATUS_SUCCESS) { + compute_status = workers[j].ec; + break; + } + } + return compute_status; +} + enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { { GGML_ASSERT(cplan); @@ -20258,7 +19696,11 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl } } - const int n_threads = cplan->n_threads; + int n_threads = cplan->n_threads; + +#if defined(GGML_USE_OPENMP) + n_threads = MIN(n_threads, omp_get_max_threads()); +#endif struct ggml_compute_state_shared state_shared = { /*.cgraph =*/ cgraph, @@ -20274,47 +19716,20 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); - - // create thread pool - if (n_threads > 1) { - for (int j = 1; j < n_threads; ++j) { - workers[j] = (struct ggml_compute_state) { - .thrd = 0, - .ith = j, - .shared = &state_shared, - .ec = GGML_STATUS_SUCCESS, - }; - - const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); - GGML_ASSERT(rc == 0); - UNUSED(rc); - } - } - - workers[0].ith = 0; - workers[0].shared = &state_shared; - workers[0].ec = GGML_STATUS_SUCCESS; - const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); - // this is a work thread too - ggml_graph_compute_thread(&workers[0]); - enum ggml_status compute_status = workers[0].ec; - - // don't leave affinity set on the main thread - clear_numa_thread_affinity(); - - // join or kill thread pool - if (n_threads > 1) { - for (int j = 1; j < n_threads; j++) { - const int rc = ggml_thread_join(workers[j].thrd, NULL); - GGML_ASSERT(rc == 0); - if (workers[j].ec != GGML_STATUS_SUCCESS) - compute_status = workers[j].ec; - } + for (int j = 0; j < n_threads; ++j) { + workers[j] = (struct ggml_compute_state) { + .thrd = 0, + .ith = j, + .shared = &state_shared, + .ec = GGML_STATUS_SUCCESS, + }; } + enum ggml_status compute_status = ggml_graph_compute_parallel(workers, n_threads); + // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; @@ -22045,11 +21460,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#if QK_K == 64 - case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#else case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#endif case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); @@ -23350,6 +22761,16 @@ int ggml_cpu_has_neon(void) { #endif } +int ggml_cpu_has_sve(void) { +#if defined(__ARM_FEATURE_SVE) + // TODO: Currently, SVE 256 bit is only supported. + GGML_ASSERT(svcntb() == QK8_0); + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; @@ -23391,7 +22812,7 @@ int ggml_cpu_has_wasm_simd(void) { } int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUDA) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) return 1; #else return 0; @@ -23406,14 +22827,6 @@ int ggml_cpu_has_cuda(void) { #endif } -int ggml_cpu_has_clblast(void) { -#if defined(GGML_USE_CLBLAST) - return 1; -#else - return 0; -#endif -} - int ggml_cpu_has_vulkan(void) { #if defined(GGML_USE_VULKAN) return 1; @@ -23438,9 +22851,16 @@ int ggml_cpu_has_sycl(void) { #endif } +int ggml_cpu_has_rpc(void) { +#if defined(GGML_USE_RPC) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || - ggml_cpu_has_sycl(); + return ggml_cpu_has_cuda() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index 774757101..addcf1bfe 100644 --- a/ggml.h +++ b/ggml.h @@ -481,9 +481,7 @@ extern "C" { GGML_OP_ARGSORT, 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, GGML_OP_SSM_SCAN, @@ -758,7 +756,6 @@ extern "C" { GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); - GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor); GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); @@ -767,6 +764,11 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous() + GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 + GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); @@ -1009,12 +1011,13 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); - // concat a and b on dim 2 + // concat a and b along dim // used in stable-diffusion GGML_API struct ggml_tensor * ggml_concat( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int dim); GGML_API struct ggml_tensor * ggml_abs( struct ggml_context * ctx, @@ -1460,11 +1463,12 @@ extern "C" { struct ggml_tensor * b); // rotary position embedding - // if mode & 1 == 1, skip n_past elements (DEPRECATED) + // if mode & 1 == 1, skip n_past elements (NOT SUPPORTED) // if mode & 2 == 1, GPT-NeoX style // if mode & 4 == 1, ChatGLM style // // b is an int32 vector with size a->ne[2], it contains the positions + // c is freq factors (e.g. phi3-128k), (optional) GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1483,10 +1487,11 @@ extern "C" { int n_ctx); // custom RoPE - GGML_API struct ggml_tensor * ggml_rope_custom( + GGML_API struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1499,7 +1504,23 @@ extern "C" { float beta_slow); // in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_custom_inplace( + GGML_API struct ggml_tensor * ggml_rope_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); + + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -1512,20 +1533,36 @@ extern "C" { float ext_factor, float attn_factor, float beta_fast, - float beta_slow); + float beta_slow), + "use ggml_rope_ext instead"); - // compute correction dims for YaRN RoPE scaling - GGML_CALL void ggml_rope_yarn_corr_dims( - int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); - - // xPos RoPE, in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - float base, - bool down); + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow), + "use ggml_rope_ext_inplace instead"); + + struct ggml_tensor * ggml_rope_xpos_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + float base, + bool down); + + // compute correction dims for YaRN RoPE scaling + GGML_CALL void ggml_rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2]); // rotary position embedding backward, i.e compute dx from dy // a - dy @@ -1533,6 +1570,7 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1734,13 +1772,6 @@ extern "C" { struct ggml_tensor * a, int k); - GGML_API struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked); - #define GGML_KQ_MASK_PAD 32 // q: [n_embd, n_batch, n_head, 1] @@ -1761,6 +1792,7 @@ extern "C" { struct ggml_tensor * a, enum ggml_prec prec); + // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, @@ -1769,14 +1801,6 @@ extern "C" { struct ggml_tensor * d, bool masked); - GGML_API struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1); - GGML_API struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, struct ggml_tensor * s, @@ -2393,6 +2417,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); + GGML_API int ggml_cpu_has_sve (void); GGML_API int ggml_cpu_has_arm_fma (void); GGML_API int ggml_cpu_has_metal (void); GGML_API int ggml_cpu_has_f16c (void); @@ -2400,13 +2425,13 @@ extern "C" { GGML_API int ggml_cpu_has_wasm_simd (void); GGML_API int ggml_cpu_has_blas (void); GGML_API int ggml_cpu_has_cuda (void); - GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_vulkan (void); GGML_API int ggml_cpu_has_kompute (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); GGML_API int ggml_cpu_has_ssse3 (void); GGML_API int ggml_cpu_has_sycl (void); + GGML_API int ggml_cpu_has_rpc (void); GGML_API int ggml_cpu_has_vsx (void); GGML_API int ggml_cpu_has_matmul_int8(void); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 8096c03b7..a905f570c 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -225,10 +225,7 @@ mulmat_head = """#version 450 #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 +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require #define EXPERT_COUNT 8 #endif @@ -260,30 +257,22 @@ layout (push_constant) uniform parameter uint stride_a; uint stride_b; uint stride_d; - uint k_split; - - 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_d; - - uint ids_stride; - - uint n_as; uint nei0; uint nei1; uint nbi1; uint ne11; +#else + uint k_split; + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; #endif } p; @@ -301,16 +290,14 @@ shared FLOAT_TYPE buf_a[BM * (BK+1)]; shared FLOAT_TYPE buf_b[BN * (BK+1)]; #ifdef MUL_MAT_ID -shared u8vec2 rowids[2048]; +shared u16vec2 row_ids[2048]; #endif void main() { #ifdef MUL_MAT_ID - const uint batch_idx = gl_GlobalInvocationID.z / p.n_as; - const uint expert_idx = gl_GlobalInvocationID.z % p.n_as; + const uint expert_idx = gl_GlobalInvocationID.z; #else const uint batch_idx = gl_GlobalInvocationID.z; -#endif const uint i13 = batch_idx / p.ne12; const uint i12 = batch_idx % p.ne12; @@ -319,6 +306,7 @@ void main() { const uint i02 = i12 / p.broadcast2; const uint batch_idx_a = i03 * p.ne02 + i02; +#endif const uint blocks_m = (p.M + BM - 1) / BM; const uint ir = gl_WorkGroupID.x % blocks_m; @@ -350,30 +338,38 @@ void main() { 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); + row_ids[_ne1] = u16vec2(ii0, ii1); _ne1++; } } } - const u8vec2 id = rowids[ir * BN + ic]; + barrier(); + + // Workgroup has no work + if (ic * BN >= _ne1) return; #endif +#ifdef MUL_MAT_ID + const uint start_k = 0; + const uint end_k = p.K; +#else const uint start_k = ik * p.k_split; const uint end_k = min(p.K, (ik + 1) * p.k_split); +#endif uint pos_a = ( #ifdef MUL_MAT_ID - expert_idx * p.expert_stride_a + + expert_idx * p.batch_stride_a + +#else + batch_idx_a * p.batch_stride_a + #endif - batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; - uint pos_b = ( + ir * BM * p.stride_a + start_k) / LOAD_VEC_A; #ifdef MUL_MAT_ID - id.y * p.expert_stride_b1 + - (id.x % p.ne11) * p.expert_stride_b0 + + uint pos_b = 0; +#else + uint pos_b = (batch_idx * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC_B; #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]; @@ -620,7 +616,12 @@ mulmat_body2 = """ } [[unroll]] for (uint l = 0; l < BN; l += loadstride_b) { #if LOAD_VEC_B == 8 +#ifdef MUL_MAT_ID + const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b; +#else const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; +#endif const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx][0].x); buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx][0].y); @@ -631,18 +632,31 @@ mulmat_body2 = """ buf_b[buf_idx + 6] = FLOAT_TYPE(data_b[idx][1].z); buf_b[buf_idx + 7] = FLOAT_TYPE(data_b[idx][1].w); #elif LOAD_VEC_B == 4 +#ifdef MUL_MAT_ID + const u16vec2 row_idx = row_ids[ic * BN + loadc_b + l]; + const uint idx = pos_b + row_idx.y * p.batch_stride_b / LOAD_VEC_B + (row_idx.x % p.ne11) * p.stride_b / LOAD_VEC_B + loadr_b; +#else const uint idx = pos_b + (loadc_b + l) * p.stride_b / LOAD_VEC_B + loadr_b; +#endif const uint buf_idx = (loadc_b + l) * (BK+1) + loadr_b * LOAD_VEC_B; buf_b[buf_idx + 0] = FLOAT_TYPE(data_b[idx].x); buf_b[buf_idx + 1] = FLOAT_TYPE(data_b[idx].y); buf_b[buf_idx + 2] = FLOAT_TYPE(data_b[idx].z); buf_b[buf_idx + 3] = FLOAT_TYPE(data_b[idx].w); -#else +#elif !MUL_MAT_ID if (ic * BN + loadc_b + l < p.N && block + loadr_b < end_k) { buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + (loadc_b + l) * p.stride_b + loadr_b]); } else { buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); } +#else + const uint row_i = ic * BN + loadc_b + l; + if (row_i < _ne1) { + const u16vec2 row_idx = row_ids[row_i]; + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(data_b[pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + loadr_b]); + } else { + buf_b[(loadc_b + l) * (BK+1) + loadr_b] = FLOAT_TYPE(0.0f); + } #endif } @@ -681,11 +695,9 @@ mulmat_body2 = """ const uint dr = ir * BM + warp_r * WM; const uint dc = ic * BN + warp_c * WN; - const uint offsets = -#ifdef MUL_MAT_ID - expert_idx * p.expert_stride_d + +#ifndef MUL_MAT_ID + const uint offsets = batch_idx * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; #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++) { @@ -693,10 +705,20 @@ mulmat_body2 = """ const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; [[unroll]] for (uint cc = 0; cc < TN; cc++) { +#ifdef MUL_MAT_ID + const uint row_i = dc_warp + cc; + if (row_i >= _ne1) break; + + const u16vec2 row_idx = row_ids[row_i]; +#endif [[unroll]] for (uint cr = 0; cr < TM; cr++) { +#ifdef MUL_MAT_ID + data_d[row_idx.y * p.batch_stride_d + row_idx.x * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); +#else if (dr_warp + cr < p.M && dc_warp + cc < p.N) { data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); } +#endif } } } @@ -1172,28 +1194,59 @@ layout (push_constant) uniform parameter 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; + uint ne11; +#else + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; #endif } p; + +void get_offsets(out uint a_offset, out uint b_offset, out uint d_offset) { +#ifdef MUL_MAT_ID + const uint expert_idx = gl_GlobalInvocationID.y; +#else + const uint batch_idx = gl_GlobalInvocationID.y; +#endif + +#ifndef MUL_MAT_ID + 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; +#else + const uint expert_id = data_ids[expert_idx]; +#endif + + a_offset = +#ifdef MUL_MAT_ID + expert_id * p.batch_stride_a; +#else + batch_idx_a * p.batch_stride_a; +#endif + b_offset = +#ifdef MUL_MAT_ID + (expert_idx % p.ne11) * p.stride_b; +#else + batch_idx * p.batch_stride_b; +#endif + d_offset = +#ifdef MUL_MAT_ID + expert_idx * p.stride_d; +#else + batch_idx * p.batch_stride_d; +#endif +} """ mul_mat_vec_body = """ @@ -1206,41 +1259,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; @@ -1281,41 +1302,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; @@ -1384,41 +1373,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; @@ -1480,41 +1437,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; @@ -1625,41 +1550,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; @@ -1766,41 +1659,9 @@ 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; + uint a_offset, b_offset, d_offset; + get_offsets(a_offset, b_offset, d_offset); const uint num_blocks_per_row = p.ncols / QUANT_K; const uint ib0 = a_offset / QUANT_K + row*num_blocks_per_row; @@ -2143,12 +2004,18 @@ void 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 mul_body = """ data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) * FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); } """ +# DIV +div_body = """ + data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) / FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); +} +""" + # ADD add_body = """ data_d[p.d_offset + dst_idx(gl_GlobalInvocationID.x)] = D_TYPE(FLOAT_TYPE(data_a[src0_idx(gl_GlobalInvocationID.x)]) + FLOAT_TYPE(data_b[src1_idx(gl_GlobalInvocationID.x)])); @@ -2609,7 +2476,8 @@ layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; layout (binding = 1) readonly buffer Y {int data_b[];}; -layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; +layout (binding = 2) readonly buffer Z {float data_freq_factors[];}; +layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; layout (push_constant) uniform parameter { uint ncols; @@ -2622,6 +2490,7 @@ layout (push_constant) uniform parameter { float corr_dims[4]; float theta_scale; float inv_ndims; + uint has_freq_facs; } p; float rope_yarn_ramp(const float low, const float high, const uint i0) { @@ -2668,13 +2537,12 @@ void main() { const uint i = row*p.ncols + ib*p.ndims + ic/2; const uint i2 = row/p.p_delta_rows; - const float cur_rot = p.inv_ndims * ic - ib; - const int pos = data_b[i2]; - const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f); + const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f; + const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor; float cos_theta, sin_theta; - rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); + rope_yarn(theta_base, ic, cos_theta, sin_theta); const float x0 = float(data_a[i + 0]); const float x1 = float(data_a[i + p.ndims/2]); @@ -2758,6 +2626,41 @@ void main() { } """ +sum_rows_src = """ +#extension GL_EXT_control_flow_attributes : enable +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) writeonly buffer D {D_TYPE data_d[];}; + +layout (constant_id = 0) const uint BLOCK_SIZE = 32; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.x; + const uint col = gl_LocalInvocationID.x; + + tmp[col] = FLOAT_TYPE(0.0f); + + for (uint i = col; i < p.KX; i += BLOCK_SIZE) { + tmp[col] += FLOAT_TYPE(data_a[row*p.KX + i]); + } + + barrier(); + [[unroll]] for (int s = int(BLOCK_SIZE) / 2; s > 0; s >>= 1) { + if (col < s) { + tmp[col] += tmp[col + s]; + } + barrier(); + } + + if (col == 0) { + data_d[row] = D_TYPE(tmp[0]); + } +} +""" + GLSLC = "glslc" VK_NUM_TYPES = 16 @@ -2939,66 +2842,66 @@ async def main(): 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)) + 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", "".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)) + 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_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_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_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_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_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_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_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_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_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)) + 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 @@ -3007,7 +2910,9 @@ async def main(): stream.clear() stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) - if i == GGML_TYPE_F16: + if i == GGML_TYPE_F32: + stream.extend((shader_f32_defines, mul_mat_vec_layout, shader_float_dequant_func, mul_mat_vec_body)) + elif i == GGML_TYPE_F16: 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, mul_mat_vec_layout, shader_q4_0_dequant_func, mul_mat_vec_body)) @@ -3035,7 +2940,7 @@ async def main(): 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})) + 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): @@ -3114,8 +3019,11 @@ async def main(): tasks.append(string_to_spv("add_f32", f"{generic_binary_op_combined}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) + tasks.append(string_to_spv("mul_f32", f"{generic_binary_op_combined}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("div_f32", f"{generic_binary_op_combined}\n{div_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) + tasks.append(string_to_spv("scale_f32", f"{generic_unary_op_combined}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) tasks.append(string_to_spv("sqr_f32", f"{generic_unary_op_combined}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float", "FLOAT_TYPE": "float"})) @@ -3139,6 +3047,8 @@ async def main(): tasks.append(string_to_spv("argsort_f32", argsort_src, {"A_TYPE": "float"})) + tasks.append(string_to_spv("sum_rows_f32", f"{generic_head}\n{shader_f32}\n{sum_rows_src}", {"A_TYPE": "float", "D_TYPE": "float"})) + # Helper to decorate tasks with semaphore acquisition. async def withSemaphore(sem, task): async with sem: diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 692120f4d..a3c024c89 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -33,17 +33,21 @@ class Keys: FILE_TYPE = "general.file_type" class LLM: - VOCAB_SIZE = "{arch}.vocab_size" - CONTEXT_LENGTH = "{arch}.context_length" - EMBEDDING_LENGTH = "{arch}.embedding_length" - BLOCK_COUNT = "{arch}.block_count" - FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" - USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" - TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - EXPERT_COUNT = "{arch}.expert_count" - EXPERT_USED_COUNT = "{arch}.expert_used_count" - POOLING_TYPE = "{arch}.pooling_type" - LOGIT_SCALE = "{arch}.logit_scale" + VOCAB_SIZE = "{arch}.vocab_size" + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + EXPERT_COUNT = "{arch}.expert_count" + EXPERT_USED_COUNT = "{arch}.expert_used_count" + EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" + EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -55,14 +59,18 @@ class Keys: LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" CAUSAL = "{arch}.attention.causal" + Q_LORA_RANK = "{arch}.attention.q_lora_rank" + KV_LORA_RANK = "{arch}.attention.kv_lora_rank" class Rope: - DIMENSION_COUNT = "{arch}.rope.dimension_count" - FREQ_BASE = "{arch}.rope.freq_base" - SCALING_TYPE = "{arch}.rope.scaling.type" - SCALING_FACTOR = "{arch}.rope.scaling.factor" - SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" - SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" @@ -138,6 +146,8 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + ARCTIC = auto() + DEEPSEEK2 = auto() class MODEL_TENSOR(IntEnum): @@ -148,6 +158,8 @@ class MODEL_TENSOR(IntEnum): OUTPUT = auto() OUTPUT_NORM = auto() ROPE_FREQS = auto() + ROPE_FACTORS_LONG = auto() + ROPE_FACTORS_SHORT = auto() ATTN_Q = auto() ATTN_K = auto() ATTN_V = auto() @@ -164,6 +176,7 @@ class MODEL_TENSOR(IntEnum): FFN_DOWN = auto() FFN_UP = auto() FFN_ACT = auto() + FFN_NORM_EXP = auto() FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() @@ -180,6 +193,12 @@ class MODEL_TENSOR(IntEnum): SSM_A = auto() SSM_D = auto() SSM_OUT = auto() + ATTN_Q_A = auto() + ATTN_Q_B = auto() + ATTN_KV_A_MQA = auto() + ATTN_KV_B = auto() + ATTN_Q_A_NORM = auto() + ATTN_KV_A_NORM = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -215,6 +234,8 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK2: "deepseek2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -225,6 +246,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.OUTPUT_NORM: "output_norm", MODEL_TENSOR.OUTPUT: "output", MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", + MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", @@ -246,6 +269,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", @@ -257,6 +281,12 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", + MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", + MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", + MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", + MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", + MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -615,6 +645,7 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { ], MODEL_ARCH.MINICPM: [ MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, MODEL_TENSOR.OUTPUT_NORM, MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_NORM, @@ -727,6 +758,54 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.ARCTIC: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], # TODO } @@ -760,6 +839,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # @@ -900,9 +983,8 @@ class GGUFValueType(IntEnum): raise ValueError(f"Unknown type: {type(val)}") -# Note: Does not support GGML_QKK_64 -QK_K = 256 # Items here are (block size, type size) +QK_K = 256 GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.F32: (1, 4), GGMLQuantizationType.F16: (1, 2), diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 21b089f8a..e48bc00c3 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -12,6 +12,8 @@ from typing import Any, Literal, NamedTuple, TypeVar, Union import numpy as np import numpy.typing as npt +from .quants import quant_shape_to_byte_shape + if __name__ == "__main__": import sys from pathlib import Path @@ -251,6 +253,7 @@ class GGUFReader: tensor_names.add(tensor_name) ggml_type = GGMLQuantizationType(raw_dtype[0]) n_elems = int(np.prod(dims)) + np_dims = tuple(reversed(dims.tolist())) 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]) @@ -279,6 +282,7 @@ class GGUFReader: else: item_count = n_bytes item_type = np.uint8 + np_dims = quant_shape_to_byte_shape(np_dims, ggml_type) tensors.append(ReaderTensor( name = tensor_name, tensor_type = ggml_type, @@ -286,7 +290,7 @@ class GGUFReader: n_elements = n_elems, n_bytes = n_bytes, data_offset = data_offs, - data = self._get(data_offs, item_type, item_count), + data = self._get(data_offs, item_type, item_count).reshape(np_dims), field = field, )) self.tensors = tensors diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d5e323a52..b93747aff 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -13,7 +13,6 @@ from string import ascii_letters, digits import numpy as np from .constants import ( - GGML_QUANT_SIZES, GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_VERSION, @@ -26,6 +25,8 @@ from .constants import ( TokenType, ) +from .quants import quant_shape_from_byte_shape + logger = logging.getLogger(__name__) @@ -229,10 +230,7 @@ class GGUFWriter: else: dtype = raw_dtype if tensor_dtype == np.uint8: - block_size, type_size = GGML_QUANT_SIZES[raw_dtype] - if tensor_shape[-1] % type_size != 0: - raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})") - tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,) + tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) n_dims = len(tensor_shape) self.ti_data += self._pack("I", n_dims) for i in range(n_dims): @@ -376,9 +374,15 @@ class GGUFWriter: def add_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + def add_leading_dense_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) + def add_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_expert_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_parallel_residual(self, use: bool) -> None: self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) @@ -409,6 +413,12 @@ class GGUFWriter: def add_expert_used_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) + def add_expert_shared_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) + + def add_expert_weights_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + def add_layer_norm_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) @@ -418,6 +428,12 @@ class GGUFWriter: def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + def add_q_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) + + def add_kv_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) + def add_pooling_type(self, value: PoolingType) -> None: self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) @@ -433,12 +449,18 @@ class GGUFWriter: def add_rope_scaling_factor(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_attn_factors(self, value: Sequence[float]) -> None: + self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + def add_rope_scaling_yarn_log_mul(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) + def add_ssm_conv_kernel(self, value: int) -> None: self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py index e7fc0eae3..b22eec166 100644 --- a/gguf-py/gguf/quants.py +++ b/gguf-py/gguf/quants.py @@ -1,5 +1,5 @@ from __future__ import annotations -from typing import Callable +from typing import Callable, Sequence from numpy.typing import DTypeLike @@ -9,6 +9,20 @@ from .lazy import LazyNumpyTensor import numpy as np +def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType): + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % block_size != 0: + raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})") + return (*shape[:-1], shape[-1] // block_size * type_size) + + +def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType): + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})") + return (*shape[:-1], shape[-1] // type_size * block_size) + + # same as ggml_compute_fp32_to_bf16 in ggml-impl.h def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray: n = n.astype(np.float32, copy=False).view(np.int32) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8e1cac915..83e3c4c33 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -244,6 +244,7 @@ class TensorNameMap: "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.layers.{bid}.residual_mlp.w3", # arctic ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -255,6 +256,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_UP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2 ), # AWQ-activation gate @@ -272,6 +274,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc12", # nomic-bert "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 "transformer.h.{bid}.mlp.linear_1", # refact + "model.layers.{bid}.residual_mlp.w1", # arctic ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -283,6 +286,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_GATE_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2 ), # Feed-forward down @@ -306,6 +310,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 + "model.layers.{bid}.residual_mlp.w2", # arctic ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -317,6 +322,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_DOWN_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -380,6 +386,42 @@ class TensorNameMap: "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), + + MODEL_TENSOR.ATTN_Q_A: ( + "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_B: ( + "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_MQA: ( + "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_B: ( + "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_A_NORM: ( + "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_NORM: ( + "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 + ), + } + + # architecture-specific block mappings + arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { + MODEL_ARCH.ARCTIC: { + MODEL_TENSOR.FFN_NORM: ( + "model.layers.{bid}.residual_layernorm", + ), + MODEL_TENSOR.FFN_NORM_EXP: ( + "model.layers.{bid}.post_attention_layernorm", + ), + }, } mapping: dict[str, tuple[MODEL_TENSOR, str]] @@ -393,12 +435,14 @@ class TensorNameMap: self.mapping[tensor_name] = (tensor, tensor_name) for key in keys: self.mapping[key] = (tensor, tensor_name) + if arch in self.arch_block_mappings_cfg: + self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch]) for bid in range(n_blocks): for tensor, keys in self.block_mappings_cfg.items(): if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 60 + n_experts = 160 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 3ba99be4f..dc5749913 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -1,10 +1,15 @@ from __future__ import annotations +import re import logging import json import os from pathlib import Path -from typing import Any, Callable, Sequence, Mapping, Iterable +from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable + +from sentencepiece import SentencePieceProcessor + +import gguf from .gguf_writer import GGUFWriter @@ -163,3 +168,298 @@ class SpecialVocab: for typ in self.special_token_types: self._set_special_token(typ, config.get(f'{typ}_token_id')) return True + + +@runtime_checkable +class BaseVocab(Protocol): + tokenizer_model: ClassVar[str] + name: ClassVar[str] + + +@runtime_checkable +class Vocab(BaseVocab, Protocol): + vocab_size: int + added_tokens_dict: dict[str, int] + added_tokens_list: list[str] + fname_tokenizer: Path + + def __init__(self, base_path: Path): ... + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... + + +class NoVocab(BaseVocab): + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +class BpeVocab(Vocab): + tokenizer_model = "gpt2" + name = "bpe" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + + if (fname_tokenizer := base_path / 'vocab.json').exists(): + # "slow" tokenizer + with open(fname_tokenizer, encoding="utf-8") as f: + self.vocab = json.load(f) + + try: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + else: + # "fast" tokenizer + fname_tokenizer = base_path / 'tokenizer.json' + + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding="utf-8") as f: + tokenizer_json = json.load(f) + + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + if ( + tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'ByteLevel' + ): + raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') + + self.vocab = tokenizer_model["vocab"] + + if (added := tokenizer_json.get('added_tokens')) is not None: + # Added tokens here can be duplicates of the main vocabulary. + added_tokens = {item['content']: item['id'] + for item in added + if item['content'] not in self.vocab} + + vocab_size = len(self.vocab) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " + f"{vocab_size} - {expected_end_id}; got {actual_ids}") + + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_dict = added_tokens + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} + + for i, _ in enumerate(self.vocab): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab(Vocab): + tokenizer_model = "llama" + name = "spm" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + if (fname_tokenizer := base_path / 'tokenizer.model').exists(): + # normal location + try: + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): + # not found in alternate location either + raise FileNotFoundError('Cannot find tokenizer.model') + + 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} + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") + + # Token pieces that were added to the base vocabulary. + self.added_tokens_dict = added_tokens + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(i) + text = piece.encode("utf-8") + score: float = tokenizer.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + 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.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class LlamaHfVocab(Vocab): + tokenizer_model = "llama" + name = "hfft" + + def __init__(self, base_path: Path): + fname_tokenizer = base_path / 'tokenizer.json' + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding='utf-8') as f: + tokenizer_json = json.load(f) + + # pre-check so we know if we need transformers + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( + tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'Sequence' + ): + raise FileNotFoundError('Cannot find Llama BPE tokenizer') + + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "To use LlamaHfVocab, please install the `transformers` package. " + "You can install it with `pip install transformers`." + ) from e + + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + base_path, + cache_dir=base_path, + local_files_only=True, + ) + assert self.tokenizer.is_fast # assume tokenizer.json is used + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { + tok: self.tokenizer.get_vocab()[tok] + for tok in self.tokenizer.all_special_tokens + } + self.special_ids = set(self.tokenizer.all_special_ids) + + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + + self.fname_tokenizer = fname_tokenizer + + def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: + continue + + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") + + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, token_text, self.special_ids # Reuse already stored special IDs + ) + + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + + # Determine token type based on whether it's a special token + return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL + + def get_token_score(self, token_id: int) -> float: + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + if text in self.specials: + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) + score = self.get_token_score(self.specials[text]) + else: + toktype = gguf.TokenType.USER_DEFINED + score = -1000.0 + + yield text.encode("utf-8"), score, toktype + + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.hf_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py index 63d3c5d8f..21e91180c 100755 --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -118,9 +118,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new 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) + writer.add_tensor_info(tensor.name, tensor.data.shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) @@ -146,6 +144,7 @@ def main() -> None: 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("--pre-tokenizer", type=str, help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"') 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()), '""')) parser.add_argument("--special-token-by-id", action="append", type=str, help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0')) @@ -174,6 +173,9 @@ def main() -> None: if template: new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) + if args.pre_tokenizer: + new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer) + if remove_metadata: logger.warning('*** Warning *** Warning *** Warning **') logger.warning('* Most metadata is required for a fully functional GGUF file,') diff --git a/kompute-shaders/op_getrows_f32.comp b/kompute-shaders/op_getrows_f32.comp new file mode 100644 index 000000000..9d7acdaf8 --- /dev/null +++ b/kompute-shaders/op_getrows_f32.comp @@ -0,0 +1,31 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { float inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +void dequantize_row_f32(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) { + for (int j = 0; j < k; j++) { + out_[y + j] = inA[x + j]; + } +} + +void main() { + const uint i = gl_WorkGroupID.x; + const int r = inB[i + pcs.inBOff]; + + dequantize_row_f32(r*pcs.nb01/4 + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00); +} diff --git a/llama.cpp b/llama.cpp index d26fe559a..06889126e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13,8 +13,6 @@ #ifdef GGML_USE_CUDA # include "ggml-cuda.h" -#elif defined(GGML_USE_CLBLAST) -# include "ggml-opencl.h" #elif defined(GGML_USE_VULKAN) # include "ggml-vulkan.h" #elif defined(GGML_USE_SYCL) @@ -26,13 +24,9 @@ #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif -#ifndef QK_K -# ifdef GGML_QKK_64 -# define QK_K 64 -# else -# define QK_K 256 -# endif -#endif + +// TODO: replace with ggml API call +#define QK_K 256 #ifdef __has_include #if __has_include() @@ -107,14 +101,14 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 60 +#define LLAMA_MAX_EXPERTS 160 // // logging // LLAMA_ATTRIBUTE_FORMAT(2, 3) -static void llama_log_internal (ggml_log_level level, const char* format, ...); +static void llama_log_internal (ggml_log_level level, const char * format, ...); static void llama_log_callback_default(ggml_log_level level, const char * text, void * user_data); #define LLAMA_LOG_INFO(...) llama_log_internal(GGML_LOG_LEVEL_INFO , __VA_ARGS__) @@ -225,6 +219,8 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_ARCTIC, + LLM_ARCH_DEEPSEEK2, LLM_ARCH_UNKNOWN, }; @@ -261,6 +257,8 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_ARCTIC, "arctic" }, + { LLM_ARCH_DEEPSEEK2, "deepseek2" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -281,11 +279,15 @@ enum llm_kv { LLM_KV_CONTEXT_LENGTH, LLM_KV_EMBEDDING_LENGTH, LLM_KV_BLOCK_COUNT, + LLM_KV_LEADING_DENSE_BLOCK_COUNT, LLM_KV_FEED_FORWARD_LENGTH, + LLM_KV_EXPERT_FEED_FORWARD_LENGTH, LLM_KV_USE_PARALLEL_RESIDUAL, LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, + LLM_KV_EXPERT_SHARED_COUNT, + LLM_KV_EXPERT_WEIGHTS_SCALE, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, @@ -298,14 +300,18 @@ enum llm_kv { LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, LLM_KV_ATTENTION_CAUSAL, + LLM_KV_ATTENTION_Q_LORA_RANK, + LLM_KV_ATTENTION_KV_LORA_RANK, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, LLM_KV_ROPE_SCALE_LINEAR, LLM_KV_ROPE_SCALING_TYPE, LLM_KV_ROPE_SCALING_FACTOR, + LLM_KV_ROPE_SCALING_ATTN_FACTOR, LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_ROPE_SCALING_YARN_LOG_MUL, LLM_KV_SPLIT_NO, LLM_KV_SPLIT_COUNT, @@ -354,17 +360,21 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, - { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, - { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, - { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, - { LLM_KV_BLOCK_COUNT, "%s.block_count" }, - { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, - { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, - { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, - { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, - { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, - { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, - { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, + { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, + { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, + { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, + { LLM_KV_BLOCK_COUNT, "%s.block_count" }, + { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, + { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, + { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" }, + { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, + { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, + { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, + { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, + { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, + { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, + { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, + { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -375,14 +385,18 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" }, + { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" }, + { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, + { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" }, { LLM_KV_SPLIT_NO, "split.no" }, { LLM_KV_SPLIT_COUNT, "split.count" }, @@ -436,6 +450,8 @@ enum llm_tensor { LLM_TENSOR_OUTPUT, LLM_TENSOR_OUTPUT_NORM, LLM_TENSOR_ROPE_FREQS, + LLM_TENSOR_ROPE_FACTORS_LONG, + LLM_TENSOR_ROPE_FACTORS_SHORT, LLM_TENSOR_ATTN_Q, LLM_TENSOR_ATTN_K, LLM_TENSOR_ATTN_V, @@ -455,6 +471,7 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility LLM_TENSOR_FFN_GATE_EXP, LLM_TENSOR_FFN_UP_EXP, + LLM_TENSOR_FFN_NORM_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, @@ -471,6 +488,12 @@ enum llm_tensor { LLM_TENSOR_SSM_A, LLM_TENSOR_SSM_D, LLM_TENSOR_SSM_OUT, + LLM_TENSOR_ATTN_Q_A, + LLM_TENSOR_ATTN_Q_B, + LLM_TENSOR_ATTN_KV_A_MQA, + LLM_TENSOR_ATTN_KV_B, + LLM_TENSOR_ATTN_Q_A_NORM, + LLM_TENSOR_ATTN_KV_A_NORM, }; static const std::map> LLM_TENSOR_NAMES = { @@ -803,18 +826,20 @@ static const std::map> LLM_TENSOR_NA { LLM_ARCH_PHI3, { - { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, - { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, - { LLM_TENSOR_OUTPUT, "output" }, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, - { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, - { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, - { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, - { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, - { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, - { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" }, + { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, { @@ -1030,6 +1055,57 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_ARCTIC, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_NORM_EXPS, "blk.%d.ffn_norm_exps" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, + { + LLM_ARCH_DEEPSEEK2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q_A_NORM, "blk.%d.attn_q_a_norm" }, + { LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" }, + { LLM_TENSOR_ATTN_Q_B, "blk.%d.attn_q_b" }, + { LLM_TENSOR_ATTN_KV_A_MQA, "blk.%d.attn_kv_a_mqa" }, + { LLM_TENSOR_ATTN_KV_B, "blk.%d.attn_kv_b" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1624,12 +1700,13 @@ struct llama_mlock { }; using llama_mlocks = std::vector>; -static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { +// NOTE: avoid ever using this except for building the token_to_piece caches +static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) { std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + int check = llama_token_to_piece(model, token, result.data(), result.size(), special); GGML_ASSERT(check == -n_tokens); } else { @@ -1690,23 +1767,31 @@ static llama_state g_state; // available llama models enum e_model { MODEL_UNKNOWN, + MODEL_14M, MODEL_17M, MODEL_22M, MODEL_33M, + MODEL_70M, MODEL_109M, MODEL_137M, + MODEL_160M, MODEL_335M, + MODEL_410M, MODEL_0_5B, MODEL_1B, + MODEL_1_4B, MODEL_2B, + MODEL_2_8B, MODEL_3B, MODEL_4B, + MODEL_6_9B, MODEL_7B, MODEL_8B, MODEL_12B, MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_16B, MODEL_20B, MODEL_30B, MODEL_34B, @@ -1714,6 +1799,7 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_236B, MODEL_314B, MODEL_SMALL, MODEL_MEDIUM, @@ -1723,6 +1809,7 @@ enum e_model { MODEL_8x7B, MODEL_8x22B, MODEL_16x12B, + MODEL_10B_128x3_66B, }; static const size_t kiB = 1024; @@ -1732,6 +1819,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; bool rope_finetuned; + bool use_par_res; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on @@ -1747,12 +1835,21 @@ struct llama_hparams { uint32_t n_expert_used = 0; uint32_t n_vocab_type = 0; // for BERT-style token types + uint32_t n_layer_dense_lead = 0; + uint32_t n_lora_q = 0; + uint32_t n_lora_kv = 0; + uint32_t n_ff_exp = 0; + uint32_t n_expert_shared = 0; + float expert_weights_scale = 0.0; + float f_norm_eps; float f_norm_rms_eps; + float rope_attn_factor = 1.0f; float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; + float rope_yarn_log_mul; // for State Space Models uint32_t ssm_d_conv = 0; @@ -1786,6 +1883,12 @@ struct llama_hparams { if (this->n_expert != other.n_expert) return true; if (this->n_expert_used != other.n_expert_used) return true; + if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; + if (this->n_lora_q != other.n_lora_q) return true; + if (this->n_lora_kv != other.n_lora_kv) return true; + if (this->n_ff_exp != other.n_ff_exp) return true; + if (this->n_expert_shared != other.n_expert_shared) return true; + if (this->rope_finetuned != other.rope_finetuned) return true; if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; @@ -1798,8 +1901,11 @@ struct llama_hparams { if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true; + if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; + if (!is_float_close(this->expert_weights_scale, other.expert_weights_scale, EPSILON)) return true; + if (!is_float_close(this->rope_yarn_log_mul, other.rope_yarn_log_mul, EPSILON)) return true; return false; } @@ -1875,6 +1981,8 @@ struct llama_layer { struct ggml_tensor * attn_k_norm_b; struct ggml_tensor * attn_out_norm; struct ggml_tensor * attn_out_norm_b; + struct ggml_tensor * attn_q_a_norm; + struct ggml_tensor * attn_kv_a_norm; // attention struct ggml_tensor * wq; @@ -1882,6 +1990,10 @@ struct llama_layer { struct ggml_tensor * wv; struct ggml_tensor * wo; struct ggml_tensor * wqkv; + struct ggml_tensor * wq_a; + struct ggml_tensor * wq_b; + struct ggml_tensor * wkv_a_mqa; + struct ggml_tensor * wkv_b; // attention bias struct ggml_tensor * bq; @@ -1895,6 +2007,7 @@ struct llama_layer { struct ggml_tensor * ffn_norm_b; struct ggml_tensor * layer_out_norm; struct ggml_tensor * layer_out_norm_b; + struct ggml_tensor * ffn_norm_exps; // ff struct ggml_tensor * ffn_gate; // w1 @@ -1914,8 +2027,9 @@ struct llama_layer { struct ggml_tensor * ffn_up_shexp; // ff bias - struct ggml_tensor * ffn_down_b; // b2 - struct ggml_tensor * ffn_up_b; // b3 + struct ggml_tensor * ffn_gate_b = nullptr; + struct ggml_tensor * ffn_down_b = nullptr; // b2 + struct ggml_tensor * ffn_up_b = nullptr; // b3 struct ggml_tensor * ffn_act; // mamba proj @@ -1932,6 +2046,10 @@ struct llama_layer { // mamba bias struct ggml_tensor * ssm_conv1d_b; struct ggml_tensor * ssm_dt_b; + + // long rope factors + struct ggml_tensor * rope_long = nullptr; + struct ggml_tensor * rope_short = nullptr; }; struct llama_kv_cell { @@ -2029,12 +2147,12 @@ struct llama_control_vector { struct llama_vocab { using id = int32_t; using token = std::string; - using ttype = llama_token_type; + using tattr = llama_token_attr; struct token_data { token text; float score; - ttype type; + tattr attr; }; enum llama_vocab_type type = LLAMA_VOCAB_TYPE_SPM; @@ -2043,7 +2161,8 @@ struct llama_vocab { std::unordered_map token_to_id; std::vector id_to_token; - std::unordered_map special_tokens_cache; + std::vector cache_special_tokens; + std::vector cache_token_to_piece; // llama_token_to_piece(special = true); std::map, int> bpe_ranks; @@ -2250,13 +2369,34 @@ struct llama_context { struct llama_control_vector cvec; }; +static size_t llama_get_device_count(const llama_model & model) { + size_t count = 1; +#if defined(GGML_USE_CUDA) + count = ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_SYCL) + count = ggml_backend_sycl_get_device_count(); +#elif defined(GGML_USE_VULKAN) + count = ggml_backend_vk_get_device_count(); +#endif +#if defined(GGML_USE_RPC) + count += model.rpc_servers.size(); +#endif + return count; + GGML_UNUSED(model); +} + static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { ggml_backend_buffer_type_t buft = nullptr; -#ifdef GGML_USE_RPC - std::string endpoint = model.rpc_servers[gpu]; - buft = ggml_backend_rpc_buffer_type(endpoint.c_str()); -#elif defined(GGML_USE_METAL) +#if defined(GGML_USE_RPC) + int dev_count = (int)llama_get_device_count(model); + int rpc_count = (int)model.rpc_servers.size(); + if (gpu >= dev_count - rpc_count) { + const char * endpoint = model.rpc_servers[gpu - dev_count + rpc_count].c_str(); + return ggml_backend_rpc_buffer_type(endpoint); + } +#endif +#if defined(GGML_USE_METAL) buft = ggml_backend_metal_buffer_type(); #elif defined(GGML_USE_CUDA) buft = ggml_backend_cuda_buffer_type(gpu); @@ -2264,8 +2404,6 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_ buft = ggml_backend_vk_buffer_type(gpu); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); -#elif defined(GGML_USE_CLBLAST) - buft = ggml_backend_opencl_buffer_type(); #elif defined(GGML_USE_KOMPUTE) buft = ggml_backend_kompute_buffer_type(gpu); if (buft == nullptr) { @@ -2304,29 +2442,19 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(const llama_mo GGML_UNUSED(tensor_split); } -static size_t llama_get_device_count(const llama_model & model) { -#if defined(GGML_USE_RPC) - return model.rpc_servers.size(); -#elif defined(GGML_USE_CUDA) - return ggml_backend_cuda_get_device_count(); -#elif defined(GGML_USE_SYCL) - return ggml_backend_sycl_get_device_count(); -#elif defined(GGML_USE_VULKAN) - return ggml_backend_vk_get_device_count(); -#else - return 1; -#endif - GGML_UNUSED(model); -} - static size_t llama_get_device_memory(const llama_model & model, int device) { #if defined(GGML_USE_RPC) - size_t total; - size_t free; - std::string endpoint = model.rpc_servers[device]; - ggml_backend_rpc_get_device_memory(endpoint.c_str(), &free, &total); - return free; -#elif defined(GGML_USE_CUDA) + int dev_count = (int)llama_get_device_count(model); + int rpc_count = (int)model.rpc_servers.size(); + if (device >= dev_count - rpc_count) { + size_t total; + size_t free; + const char * endpoint = model.rpc_servers[device - dev_count + rpc_count].c_str(); + ggml_backend_rpc_get_device_memory(endpoint, &free, &total); + return free; + } +#endif +#if defined(GGML_USE_CUDA) size_t total; size_t free; ggml_backend_cuda_get_device_memory(device, &free, &total); @@ -2398,10 +2526,6 @@ static bool llama_kv_cache_init( } } -#ifdef GGML_USE_CLBLAST - offload = false; -#endif - // count used buffer types std::map buft_layer_count; if (offload) { @@ -2467,7 +2591,6 @@ static bool llama_kv_cache_init( static bool llama_kv_cache_find_slot( struct llama_kv_cache & cache, const struct llama_batch & batch) { - const uint32_t n_ctx = cache.size; const uint32_t n_tokens = batch.n_tokens; if (cache.recurrent) { @@ -2518,16 +2641,16 @@ static bool llama_kv_cache_find_slot( } // otherwise, one cell per token. - if (n_tokens > n_ctx) { - LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx); + if (n_tokens > cache.size) { + LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size); return false; } uint32_t n_tested = 0; while (true) { - if (cache.head + n_tokens > n_ctx) { - n_tested += n_ctx - cache.head; + if (cache.head + n_tokens > cache.size) { + n_tested += cache.size - cache.head; cache.head = 0; continue; } @@ -2546,7 +2669,7 @@ static bool llama_kv_cache_find_slot( break; } - if (n_tested >= n_ctx) { + if (n_tested >= cache.size) { //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens); return false; } @@ -3306,6 +3429,39 @@ struct llama_model_loader { return get_arr_n(llm_kv(kid), result, required); } + template + bool get_arr(const std::string & key, std::vector & result, const bool required = true) { + const int kid = gguf_find_key(meta, key.c_str()); + + if (kid < 0) { + if (required) { + throw std::runtime_error(format("key not found in model: %s", key.c_str())); + } + return false; + } + + struct GGUFMeta::ArrayInfo arr_info = + GGUFMeta::GKV::get_kv(meta, kid); + + if (arr_info.gt != GGUF_TYPE_FLOAT32 && arr_info.gt != GGUF_TYPE_INT32) { + throw std::runtime_error(format("%s is not a float32 or int32 array", key.c_str())); + } + + // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_FLOAT32 || std::is_same::value)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_INT32 || std::is_same::value)); + + result.resize(arr_info.length); + result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length); + + return true; + } + + template + bool get_arr(const enum llm_kv kid, T& result, const bool required = true) { + return get_arr(llm_kv(kid), result, required); + } + template bool get_key(const std::string & key, T & result, const bool required = true) { auto it = kv_overrides.find(key); @@ -3380,11 +3536,15 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) { + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) { struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); ggml_set_name(tensor, ggml_get_name(cur)); - n_created++; + if (duplicated) { + size_data += ggml_nbytes(cur); + } else { + n_created++; + } return tensor; } @@ -3419,14 +3579,17 @@ struct llama_model_loader { return cur; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { - const struct ggml_tensor * cur = check_tensor_dims(name, ne, required); + static const int TENSOR_NOT_REQUIRED = 1; + static const int TENSOR_DUPLICATED = 2; + + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, int flags = 0) { + const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED)); if (cur == NULL) { return NULL; } - return create_tensor_for(ctx, cur); + return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED); } struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector & ne, size_t offset, bool required = true) { @@ -3726,37 +3889,50 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { - case MODEL_22M: return "22M"; - case MODEL_33M: return "33M"; - case MODEL_109M: return "109M"; - case MODEL_137M: return "137M"; - case MODEL_0_5B: return "0.5B"; - case MODEL_1B: return "1B"; - case MODEL_2B: return "2B"; - case MODEL_3B: return "3B"; - case MODEL_7B: return "7B"; - case MODEL_8B: return "8B"; - case MODEL_12B: return "12B"; - case MODEL_13B: return "13B"; - case MODEL_14B: return "14B"; - case MODEL_15B: return "15B"; - case MODEL_20B: return "20B"; - case MODEL_30B: return "30B"; - case MODEL_34B: return "34B"; - case MODEL_35B: return "35B"; - case MODEL_40B: return "40B"; - case MODEL_65B: return "65B"; - case MODEL_70B: return "70B"; - case MODEL_314B: return "314B"; - case MODEL_SMALL: return "0.1B"; - case MODEL_MEDIUM: return "0.4B"; - case MODEL_LARGE: return "0.8B"; - case MODEL_XL: return "1.5B"; - case MODEL_A2_7B: return "A2.7B"; - case MODEL_8x7B: return "8x7B"; - case MODEL_8x22B: return "8x22B"; - case MODEL_16x12B: return "16x12B"; - default: return "?B"; + case MODEL_14M: return "14M"; + case MODEL_17M: return "17M"; + case MODEL_22M: return "22M"; + case MODEL_33M: return "33M"; + case MODEL_70M: return "70M"; + case MODEL_109M: return "109M"; + case MODEL_137M: return "137M"; + case MODEL_160M: return "160M"; + case MODEL_335M: return "335M"; + case MODEL_410M: return "410M"; + case MODEL_0_5B: return "0.5B"; + case MODEL_1B: return "1B"; + case MODEL_1_4B: return "1.4B"; + case MODEL_2B: return "2B"; + case MODEL_2_8B: return "2.8B"; + case MODEL_3B: return "3B"; + case MODEL_4B: return "4B"; + case MODEL_6_9B: return "6.9B"; + case MODEL_7B: return "7B"; + case MODEL_8B: return "8B"; + case MODEL_12B: return "12B"; + case MODEL_13B: return "13B"; + case MODEL_14B: return "14B"; + case MODEL_15B: return "15B"; + case MODEL_16B: return "16B"; + case MODEL_20B: return "20B"; + case MODEL_30B: return "30B"; + case MODEL_34B: return "34B"; + case MODEL_35B: return "35B"; + case MODEL_40B: return "40B"; + case MODEL_65B: return "65B"; + case MODEL_70B: return "70B"; + case MODEL_236B: return "236B"; + case MODEL_314B: return "314B"; + case MODEL_SMALL: return "0.1B"; + case MODEL_MEDIUM: return "0.4B"; + case MODEL_LARGE: return "0.8B"; + case MODEL_XL: return "1.5B"; + case MODEL_A2_7B: return "A2.7B"; + case MODEL_8x7B: return "8x7B"; + case MODEL_8x22B: return "8x22B"; + case MODEL_16x12B: return "16x12B"; + case MODEL_10B_128x3_66B: return "10B+128x3.66B"; + default: return "?B"; } } @@ -3849,6 +4025,8 @@ static void llm_load_hparams( } hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale; + ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false); + // sanity check for n_rot (optional) { hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; @@ -3886,7 +4064,9 @@ 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_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; + // granite uses a vocab with len 49152 + case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break; + case 36: model.type = e_model::MODEL_8B; break; // granite 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; @@ -4089,6 +4269,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_14B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4155,6 +4336,8 @@ static void llm_load_hparams( case 30: model.type = e_model::MODEL_3B; break; case 32: model.type = e_model::MODEL_7B; break; case 40: model.type = e_model::MODEL_15B; break; + case 52: model.type = e_model::MODEL_20B; break; // granite + case 88: model.type = e_model::MODEL_34B; break; // granite default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4229,6 +4412,85 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GPTNEOX: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res); + switch (hparams.n_layer) { + case 6: + switch (hparams.n_ff) { + case 512: model.type = e_model::MODEL_14M; break; + case 2048: model.type = e_model::MODEL_70M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 12: + switch (hparams.n_ff) { + case 3072: model.type = e_model::MODEL_160M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 16: + switch (hparams.n_ff) { + case 8192: model.type = e_model::MODEL_1B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 24: + switch (hparams.n_ff) { + case 4096: model.type = e_model::MODEL_410M; break; + case 8192: model.type = e_model::MODEL_1_4B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 32: + switch (hparams.n_ff) { + case 10240: model.type = e_model::MODEL_2_8B; break; + case 16384: model.type = e_model::MODEL_6_9B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 36: + switch (hparams.n_ff) { + case 20480: model.type = e_model::MODEL_12B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 44: + switch (hparams.n_ff) { + case 24576: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; + case LLM_ARCH_ARCTIC: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + if (hparams.n_expert == 128) { + switch (hparams.n_layer) { + case 35: model.type = e_model::MODEL_10B_128x3_66B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + model.type = e_model::MODEL_UNKNOWN; + } + } break; + case LLM_ARCH_DEEPSEEK2: + { + bool is_lite = (hparams.n_layer == 27); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + if (!is_lite) { + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + } + ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul); + + switch (hparams.n_layer) { + case 27: model.type = e_model::MODEL_16B; break; + case 60: model.type = e_model::MODEL_236B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4335,15 +4597,14 @@ static void llm_load_vocab( vocab.special_cls_id = 101; vocab.special_mask_id = 103; vocab.add_space_prefix = false; - } else { - if (tokenizer_model == "gpt2") { - vocab.type = LLAMA_VOCAB_TYPE_BPE; - } else { - LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); - LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); - vocab.type = LLAMA_VOCAB_TYPE_SPM; - return; + } else if (tokenizer_model == "gpt2") { + vocab.type = LLAMA_VOCAB_TYPE_BPE; + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); } + // read bpe merges and populate bpe ranks const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str()); if (merges_keyidx == -1) { @@ -4377,6 +4638,8 @@ static void llm_load_vocab( vocab.special_pad_id = -1; vocab.special_cls_id = -1; vocab.special_mask_id = -1; + } else { + throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str())); } // for now, only BPE models have pre-tokenizers @@ -4438,6 +4701,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "dbrx") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX; + } else if ( + tokenizer_pre == "smaug-bpe") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -4476,7 +4742,20 @@ static void llm_load_vocab( auto & token_data = vocab.id_to_token[i]; token_data.text = std::move(word); token_data.score = scores ? scores[i] : 0.0f; - token_data.type = toktypes ? (llama_token_type) toktypes[i] : LLAMA_TOKEN_TYPE_NORMAL; + token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; + + if (toktypes) { //TODO: remove, required until per token attributes are available from GGUF file + switch(toktypes[i]) { + case LLAMA_TOKEN_TYPE_UNKNOWN: token_data.attr = LLAMA_TOKEN_ATTR_UNKNOWN; break; + case LLAMA_TOKEN_TYPE_UNUSED: token_data.attr = LLAMA_TOKEN_ATTR_UNUSED; break; + case LLAMA_TOKEN_TYPE_NORMAL: token_data.attr = LLAMA_TOKEN_ATTR_NORMAL; break; + case LLAMA_TOKEN_TYPE_CONTROL: token_data.attr = LLAMA_TOKEN_ATTR_CONTROL; break; + case LLAMA_TOKEN_TYPE_USER_DEFINED: token_data.attr = LLAMA_TOKEN_ATTR_USER_DEFINED; break; + case LLAMA_TOKEN_TYPE_BYTE: token_data.attr = LLAMA_TOKEN_ATTR_BYTE; break; + case LLAMA_TOKEN_TYPE_UNDEFINED: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break; + default: token_data.attr = LLAMA_TOKEN_ATTR_UNDEFINED; break; + } + } } GGML_ASSERT(vocab.id_to_token.size() == vocab.token_to_id.size()); @@ -4566,96 +4845,88 @@ static void llm_load_vocab( // build special tokens cache { - // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type, - // and will always be correctly labeled in 'added_tokens.json' etc. - // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed - // to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer - // are special tokens. - // From testing, this appears to correlate 1:1 with special tokens. - // - - // Counting special tokens and verifying in only one direction - // is sufficient to detect difference in those two sets. - // - uint32_t special_tokens_count_by_type = 0; - uint32_t special_tokens_count_from_verification = 0; - - bool special_tokens_definition_mismatch = false; - - for (const auto & t : vocab.token_to_id) { - const auto & token = t.first; - const auto & id = t.second; - - // Count all non-normal tokens in the vocab while iterating - if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) { - special_tokens_count_by_type++; - } - - // Skip single character tokens - if (token.length() > 1) { - bool is_tokenizable = false; - - // Split token string representation in two, in all possible ways - // and check if both halves can be matched to a valid token - for (unsigned i = 1; i < token.length();) { - const auto left = token.substr(0, i); - const auto right = token.substr(i); - - // check if we didnt partition in the middle of a utf sequence - auto utf = utf8_len(left.at(left.length() - 1)); - - if (utf == 1) { - if (vocab.token_to_id.find(left) != vocab.token_to_id.end() && - vocab.token_to_id.find(right) != vocab.token_to_id.end() ) { - is_tokenizable = true; - break; - } - i++; - } else { - // skip over the rest of multibyte utf sequence - i += utf - 1; - } - } - - if (!is_tokenizable) { - // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1 - // it's faster to re-filter them here, since there are way less candidates now - - // Calculate a total "utf" length of a token string representation - size_t utf8_str_len = 0; - for (unsigned i = 0; i < token.length();) { - utf8_str_len++; - i += utf8_len(token.at(i)); - } - - // And skip the ones which are one character - if (utf8_str_len > 1) { - // At this point what we have left are special tokens only - vocab.special_tokens_cache[token] = id; - - // Count manually found special tokens - special_tokens_count_from_verification++; - - // If this manually found special token is not marked as such, flag a mismatch - if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) { - special_tokens_definition_mismatch = true; - } - } - } + for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) { + if (!(vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL)) { + vocab.cache_special_tokens.push_back(id); } } - if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) { - LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n", - __func__, - special_tokens_count_from_verification, vocab.id_to_token.size(), - special_tokens_count_by_type, vocab.id_to_token.size() - ); - } else { - LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n", - __func__, - special_tokens_count_from_verification, vocab.id_to_token.size() - ); + std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(), + [&] (const llama_vocab::id a, const llama_vocab::id b) { + return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size(); + } + ); + + LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size()); + } + + // build token to piece cache + { + size_t size_cache = 0; + + std::vector cache_token_to_piece(n_vocab); + + for (uint32_t id = 0; id < n_vocab; ++id) { + cache_token_to_piece[id] = llama_token_to_piece(&model, id, true); + + size_cache += cache_token_to_piece[id].size(); + } + + std::swap(vocab.cache_token_to_piece, cache_token_to_piece); + + LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0); + } + + // Handle per token attributes + //NOTE: Each model customizes per token attributes. + //NOTE: Per token attributes are missing from the GGUF file. + //TODO: Extract attributes from GGUF file. + { + auto _contains_any = [] (const std::string &str, const std::vector &substrs) -> bool { + for (auto substr : substrs) { + if (str.find(substr) < std::string::npos) { + return true; + } + } + return false; + }; + + auto _set_tokenid_attr = [&] (const llama_vocab::id id, llama_token_attr attr, bool value) { + uint32_t current = vocab.id_to_token.at(id).attr; + current = value ? (current | attr) : (current & ~attr); + vocab.id_to_token[id].attr = (llama_token_attr) current; + }; + + auto _set_token_attr = [&] (const std::string & token, llama_token_attr attr, bool value) { + _set_tokenid_attr(vocab.token_to_id.at(token), attr, value); + }; + + std::string model_name; + std::string tokenizer_pre; + + ml.get_key(LLM_KV_GENERAL_NAME, model_name, false); + ml.get_key(LLM_KV_TOKENIZER_PRE, tokenizer_pre, false); + + // model name to lowercase + std::transform(model_name.begin(), model_name.end(), model_name.begin(), + [] (const std::string::value_type x) { + return std::tolower(x); + } + ); + + // set attributes by model/tokenizer name + if (_contains_any(tokenizer_pre, {"jina-v2-es", "jina-v2-de"})) { + _set_token_attr("", LLAMA_TOKEN_ATTR_LSTRIP, true); + } else if (_contains_any(model_name, {"phi-3", "phi3"})) { + for (auto id : vocab.cache_special_tokens) { + _set_tokenid_attr(id, LLAMA_TOKEN_ATTR_RSTRIP, true); + } + for (auto token : {""}) { + _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, true); + } + for (auto token : {"", "", "<|endoftext|>"}) { + _set_token_attr(token, LLAMA_TOKEN_ATTR_RSTRIP, false); + } } } } @@ -4737,6 +5008,16 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); } if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); } if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); } + + if (model.arch == LLM_ARCH_DEEPSEEK2) { + LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead); + LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q); + LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv); + LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); + LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared); + LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); + LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); + } } // Returns false if cancelled by progress_callback @@ -4880,6 +5161,7 @@ static bool llm_load_tensors( // create tensors for the weights { const int64_t n_embd = hparams.n_embd; + const int64_t n_embd_head = n_embd / hparams.n_head; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); const int64_t n_embd_gqa = n_embd_v_gqa; @@ -4892,8 +5174,6 @@ static bool llm_load_tensors( throw std::runtime_error("model has expert layers but no expert layers are used"); } - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); @@ -4913,14 +5193,10 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - if (model.arch != LLM_ARCH_MINICPM){ - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); - // if output is NULL, init from the input tok embed - if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); - } + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -4938,10 +5214,10 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); @@ -4949,10 +5225,15 @@ static bool llm_load_tensors( 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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + + // optional MLP bias + layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } else { layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -4994,12 +5275,10 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5022,7 +5301,7 @@ static bool llm_load_tensors( layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5124,11 +5403,9 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5141,8 +5418,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false); + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); @@ -5160,12 +5437,10 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { // needs to be on GPU - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5263,14 +5538,14 @@ static bool llm_load_tensors( 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.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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}); @@ -5332,18 +5607,16 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, false); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5354,31 +5627,31 @@ static bool llm_load_tensors( auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); - layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, false); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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}, false); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - 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.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - 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.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); + layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_STABLELM: @@ -5407,17 +5680,17 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors, present in Stable LM 2 1.6B - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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}); @@ -5460,12 +5733,10 @@ static bool llm_load_tensors( // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5563,8 +5834,8 @@ static bool llm_load_tensors( layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.wqkv == nullptr) { layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); @@ -5601,17 +5872,20 @@ static bool llm_load_tensors( ggml_context* ctx_layer = ctx_for_layer(i); ggml_context* ctx_split = ctx_for_layer_split(i); - auto& layer = model.layers[i]; + auto & layer = model.layers[i]; layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), { n_embd }); - layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, false); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), { n_embd }); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd }); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), { n_embd, 2 * n_ff }); + + layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_PLAMO: @@ -5780,9 +6054,7 @@ static bool llm_load_tensors( // output model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading const int64_t n_ff = hparams.n_ff; const int64_t n_embd_head_k = hparams.n_embd_head_k; @@ -5817,12 +6089,10 @@ static bool llm_load_tensors( model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5873,12 +6143,10 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed, duplicated to allow offloading if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5939,9 +6207,7 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { @@ -5973,12 +6239,10 @@ static bool llm_load_tensors( // output { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5998,6 +6262,145 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_GPTNEOX: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + 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]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + 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.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + } + } break; + case LLM_ARCH_ARCTIC: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + } + + 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]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + } + } break; + case LLM_ARCH_DEEPSEEK2: + { + bool is_lite = (hparams.n_layer == 27); + + const uint32_t n_embd_head_qk_rope = hparams.n_rot; + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t q_lora_rank = hparams.n_lora_q; + const uint32_t kv_lora_rank = hparams.n_lora_kv; + const uint32_t n_ff_exp = hparams.n_ff_exp; + + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + + 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]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + if (!is_lite) { + layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + } + layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + + if (!is_lite) { + layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); + layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k}); + } else { + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + } + layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}); + layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + if ((uint32_t) i < hparams.n_layer_dense_lead) { + 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_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } else { + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + + GGML_ASSERT(hparams.n_expert > 0); + GGML_ASSERT(hparams.n_expert_used > 0); + + // MoE branch + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + + // Shared expert branch + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * hparams.n_expert_shared, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); + } + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6452,6 +6855,8 @@ static struct ggml_tensor * llm_build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, + bool scale_w, + float w_scale, const llm_build_cb & cb, int il) { int64_t n_embd = cur->ne[0]; @@ -6483,6 +6888,10 @@ static struct ggml_tensor * llm_build_moe_ffn( weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens); } + if (scale_w) { + weights = ggml_scale(ctx, weights, w_scale); + cb(weights, "ffn_moe_weights_scaled", il); + } cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens); ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] @@ -6587,7 +6996,7 @@ static struct ggml_tensor * llm_build_kqv( 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) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); } @@ -6596,7 +7005,7 @@ static struct ggml_tensor * llm_build_kqv( 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) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { // 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); @@ -6821,17 +7230,20 @@ struct llm_build_context { cb(lctx.inp_K_shift, "K_shift", -1); ggml_set_input(lctx.inp_K_shift); + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * rope_factors = build_rope_factors(il); struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions - ggml_rope_custom_inplace(ctx0, + ggml_rope_ext_inplace(ctx0, ggml_view_3d(ctx0, kv_self.k_l[il], n_embd_head_k, n_head_kv, n_ctx, ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), 0), - lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + lctx.inp_K_shift, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + cb(tmp, "K_shifted", il); ggml_build_forward_expand(gf, tmp); } @@ -6934,6 +7346,17 @@ struct llm_build_context { return lctx.inp_pos; } + struct ggml_tensor * build_rope_factors(int il) { + // choose long/short freq factors based on the context size + const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max; + + if (n_ctx_pre_seq > hparams.n_yarn_orig_ctx) { + return model.layers[il].rope_long; + } + + return model.layers[il].rope_short; + } + struct ggml_tensor * build_inp_out_ids() { lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs); cb(lctx.inp_out_ids, "inp_out_ids", -1); @@ -7041,15 +7464,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7079,9 +7502,9 @@ struct llm_build_context { cb(cur, "ffn_norm", il); cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); @@ -7099,6 +7522,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); } @@ -7171,13 +7595,13 @@ struct llm_build_context { switch (model.type) { case MODEL_7B: - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7283,15 +7707,15 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7404,14 +7828,14 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7527,15 +7951,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7580,6 +8004,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_GELU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -7679,15 +8104,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7723,6 +8148,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -8032,15 +8458,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8472,15 +8898,15 @@ struct llm_build_context { } - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8592,14 +9018,14 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -8703,15 +9129,15 @@ struct llm_build_context { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8817,15 +9243,15 @@ struct llm_build_context { Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8861,6 +9287,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, false, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -8969,8 +9396,8 @@ 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); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -8980,8 +9407,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -9057,6 +9484,9 @@ struct llm_build_context { // self-attention { + // rope freq factors for 128k context + struct ggml_tensor * rope_factors = build_rope_factors(il); + struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, @@ -9088,8 +9518,8 @@ 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); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -9097,8 +9527,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -9204,14 +9634,14 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -9412,15 +9842,15 @@ struct llm_build_context { cb(tmpk, "tmpk", il); cb(Vcur, "Vcur", il); - struct ggml_tensor * Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, + struct ggml_tensor * Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - struct ggml_tensor * Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, + struct ggml_tensor * Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9528,15 +9958,15 @@ struct llm_build_context { // cb(Vcur, "Vcur", il); // } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9645,15 +10075,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9775,15 +10205,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9849,7 +10279,7 @@ struct llm_build_context { cb(cur, "lmhead_scaling", -1); // lm_head - cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cur = ggml_mul_mat(ctx0, model.output, cur); cb(cur, "result_output", -1); ggml_build_forward_expand(gf, cur); @@ -9895,8 +10325,8 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -9904,8 +10334,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); cb(Qcur, "Qcur_scaled", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -10015,15 +10445,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10305,15 +10735,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10436,15 +10866,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10508,6 +10938,508 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gptneox() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, + model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", 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, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // ffn + if (hparams.use_par_res) { + // attention and ffn are computed in parallel + // x = x + attn(ln1(x)) + ffn(ln2(x)) + + struct ggml_tensor * attn_out = cur; + + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, attn_out); + cb(inpL, "l_out", il); + } else { + // attention and ffn are computed sequentially + // x = x + attn(ln1(x)) + // x = x + ffn(ln2(x)) + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, + model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_arctic() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + 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, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp); + cb(ffn_out, "ffn_out", il); + + // MoE + cur = llm_build_norm(ctx0, inpSA, hparams, + model.layers[il].ffn_norm_exps, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm_exps", il); + + cur = llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + false, 0.0, + cb, il); + cb(cur, "ffn_moe_out", il); + + cur = ggml_add(ctx0, cur, ffn_out); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_deepseek2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + bool is_lite = (hparams.n_layer == 27); + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. + const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k)); + const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)); + + const uint32_t n_embd_head_qk_rope = hparams.n_rot; + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self_attention + { + struct ggml_tensor * q = NULL; + if (!is_lite) { + // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens} + q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); + cb(q, "q", il); + + q = llm_build_norm(ctx0, q, hparams, + model.layers[il].attn_q_a_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(q, "q", il); + + // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens} + q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q); + cb(q, "q", il); + } else { + q = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(q, "q", il); + } + + // split into {n_head * n_embd_head_qk_nope, n_tokens} + struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(q->type, hparams.n_embd_head_k), + ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + 0); + cb(q_nope, "q_nope", il); + + // and {n_head * n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, + ggml_row_size(q->type, hparams.n_embd_head_k), + ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, n_embd_head_qk_nope)); + cb(q_pe, "q_pe", il); + + // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); + cb(kv_pe_compresseed, "kv_pe_compresseed", il); + + // split into {kv_lora_rank, n_tokens} + struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens, + kv_pe_compresseed->nb[1], + 0); + cb(kv_compressed, "kv_compressed", il); + + // and {n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens, + kv_pe_compresseed->nb[1], + kv_pe_compresseed->nb[1], + ggml_row_size(kv_pe_compresseed->type, kv_lora_rank)); + cb(k_pe, "k_pe", il); + + kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm + kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams, + model.layers[il].attn_kv_a_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(kv_compressed, "kv_compressed", il); + + // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens} + struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed); + cb(kv, "kv", il); + + // split into {n_head * n_embd_head_qk_nope, n_tokens} + struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + 0); + cb(k_nope, "k_nope", il); + + // and {n_head * n_embd_head_v, n_tokens} + struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_row_size(kv->type, (n_embd_head_qk_nope))); + cb(v_states, "v_states", il); + + v_states = ggml_cont(ctx0, v_states); + cb(v_states, "v_states", il); + + v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, + ggml_row_size(kv->type, hparams.n_embd_head_v * n_head), + 0); + cb(v_states, "v_states", il); + + q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE + q_pe = ggml_rope_ext( + ctx0, q_pe, inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor_scaled, beta_fast, beta_slow + ); + cb(q_pe, "q_pe", il); + + // shared RoPE key + k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE + k_pe = ggml_rope_ext( + ctx0, k_pe, inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor_scaled, beta_fast, beta_slow + ); + cb(k_pe, "k_pe", il); + + struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0); + cb(q_states, "q_states", il); + + struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0); + cb(k_states, "k_states", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, NULL, + k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } else { + // MoE branch + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = + llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, false, + true, hparams.expert_weights_scale, + cb, il); + cb(moe_out, "ffn_moe_out", il); + + // FFN shared expert + { + ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up_shexp, NULL, + model.layers[il].ffn_gate_shexp, NULL, + model.layers[il].ffn_down_shexp, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + cb(cur, "ffn_out", il); + } + } + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + }; static struct ggml_cgraph * llama_build_graph_defrag(llama_context & lctx, const std::vector & ids) { @@ -10718,6 +11650,18 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_GPTNEOX: + { + result = llm.build_gptneox(); + } break; + case LLM_ARCH_ARCTIC: + { + result = llm.build_arctic(); + } break; + case LLM_ARCH_DEEPSEEK2: + { + result = llm.build_deepseek2(); + } break; default: GGML_ASSERT(false); } @@ -11734,27 +12678,27 @@ static enum llama_vocab_type llama_vocab_get_type(const llama_vocab & vocab) { static bool llama_is_normal_token(const llama_vocab & vocab, llama_token id) { GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); - return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL; + return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_NORMAL; } static bool llama_is_unknown_token(const llama_vocab & vocab, llama_token id) { GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); - return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_UNKNOWN; + return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_UNKNOWN; } static bool llama_is_control_token(const llama_vocab & vocab, llama_token id) { GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); - return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_CONTROL; + return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_CONTROL; } static bool llama_is_byte_token(const llama_vocab & vocab, llama_token id) { GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); - return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_BYTE; + return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_BYTE; } static bool llama_is_user_defined_token(const llama_vocab& vocab, llama_token id) { GGML_ASSERT(vocab.type != LLAMA_VOCAB_TYPE_NONE); - return vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_USER_DEFINED; + return vocab.id_to_token[id].attr & LLAMA_TOKEN_ATTR_USER_DEFINED; } static uint8_t llama_token_to_byte(const llama_vocab& vocab, llama_token id) { @@ -12007,6 +12951,7 @@ struct llm_tokenizer_bpe { }); break; case LLAMA_VOCAB_PRE_TYPE_DBRX: + case LLAMA_VOCAB_PRE_TYPE_SMAUG: 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+", @@ -12229,7 +13174,7 @@ struct llm_tokenizer_wpm { llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {} void tokenize(const std::string & text, std::vector & output) { - auto * token_map = &vocab.token_to_id; + const auto & token_map = vocab.token_to_id; // normalize and split by whitespace std::vector words = preprocess(text); @@ -12244,108 +13189,89 @@ struct llm_tokenizer_wpm { } // prepend phantom space - std::string word1 = "\xe2\x96\x81" + word; - int n = word1.size(); + const std::string word1 = "\xe2\x96\x81" + word; + const int n = word1.size(); + + const size_t current_tokens = output.size(); // we're at the start of a new word - int i = 0; - bool match_any = false; - // move through character position in word - while (i < n) { + for (int i = 0; i < n; ++i) { // loop through possible match length bool match = false; for (int j = n; j > i; j--) { - auto it = token_map->find(word1.substr(i, j - i)); - if (it != token_map->end()) { + auto it = token_map.find(word1.substr(i, j - i)); + if (it != token_map.end()) { output.push_back(it->second); match = true; - match_any = true; - i = j; + i = j - 1; break; } } - // must be an unknown character - if (!match) { - i++; + if (!match) { // discard all + output.resize(current_tokens); + break; // and discard next tokens } } // we didn't find any matches for this word - if (!match_any) { + if (current_tokens == output.size()) { output.push_back(vocab.special_unk_id); } } } std::vector preprocess(const std::string & text) { - std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); + const std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); + std::vector words(1, ""); - // strip accents, strip control, uniformize whitespace, - // to lowercase, pad chinese characters, pad punctuation - std::string new_str = ""; - for (uint32_t code : cpts_nfd) { - const codepoint_flags flags = unicode_cpt_flags(code); - if (flags.is_accent_mark || flags.is_control) { + for (const char32_t cpt : cpts_nfd) { + const auto flags = unicode_cpt_flags(cpt); + + if (flags.is_whitespace) { + if (words.back().size()) { // finish previous word if any + words.emplace_back(); + } continue; } - code = unicode_tolower(code); - if (flags.is_separator || flags.is_whitespace) { //####FIXME: is_separator ? - code = ' '; + + assert (!flags.is_separator); + if (cpt == 0 || cpt == 0xFFFD || flags.is_control) { + continue; } - std::string s = unicode_cpt_to_utf8(code); - if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) { - new_str += " "; - new_str += s; - new_str += " "; + + const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt)); + if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) { + if (words.back().size()) { // finish previous word if any + words.emplace_back(); + } + words.back() = s; // single char word + words.emplace_back(); // start a new word } else { - new_str += s; + words.back() += s; // append char to word } } - // split by whitespace - uint64_t l = 0; - uint64_t r = 0; - std::vector words; - while (r < new_str.size()) { - // if is whitespace - if (isspace(new_str[r], std::locale::classic())) { - if (r > l) words.push_back(new_str.substr(l, (r - l))); - l = r + 1; - r = l; - } else { - r += 1; - } - } - if (r > l) { - words.push_back(new_str.substr(l, (r - l))); + if (!words.back().size()) { + words.pop_back(); } + return words; } - bool is_ascii_punct(uint32_t code) { - if (code > 0xFF) { - return false; - } - auto c = char(static_cast(code)); - return ispunct(c, std::locale::classic()); - } - - bool is_chinese_char(uint32_t cpt) { - if ((cpt >= 0x4E00 && cpt <= 0x9FFF) || - (cpt >= 0x3400 && cpt <= 0x4DBF) || + static bool is_chinese_char(uint32_t cpt) { + return + (cpt >= 0x04E00 && cpt <= 0x09FFF) || + (cpt >= 0x03400 && cpt <= 0x04DBF) || (cpt >= 0x20000 && cpt <= 0x2A6DF) || (cpt >= 0x2A700 && cpt <= 0x2B73F) || (cpt >= 0x2B740 && cpt <= 0x2B81F) || (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920 - (cpt >= 0xF900 && cpt <= 0xFAFF) || - (cpt >= 0x2F800 && cpt <= 0x2FA1F) || - (cpt >= 0x3000 && cpt <= 0x303F) || - (cpt >= 0xFF00 && cpt <= 0xFFEF)) { - return true; // NOLINT - } - return false; + (cpt >= 0x0F900 && cpt <= 0x0FAFF) || + (cpt >= 0x2F800 && cpt <= 0x2FA1F); + //(cpt >= 0x3000 && cpt <= 0x303F) || + //(cpt >= 0xFF00 && cpt <= 0xFFEF); } const llama_vocab & vocab; @@ -12389,9 +13315,9 @@ struct fragment_buffer_variant { static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) { // for each special token - for (const auto & st: vocab.special_tokens_cache) { - const auto & special_token = st.first; - const auto & special_id = st.second; + for (const llama_vocab::id special_id : vocab.cache_special_tokens) { + const auto & data = vocab.id_to_token[special_id]; + const auto & special_token = data.text; // for each text fragment std::forward_list::iterator it = buffer.begin(); @@ -12400,7 +13326,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // if a fragment is text ( not yet processed ) if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { - auto * raw_text = &(fragment.raw_text); + auto & raw_text = fragment.raw_text; auto raw_text_base_offset = fragment.offset; auto raw_text_base_length = fragment.length; @@ -12410,7 +13336,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // find the first occurrence of a given special token in this fragment // passing offset argument only limit the "search area" but match coordinates // are still relative to the source full raw_text - auto match = raw_text->find(special_token, raw_text_base_offset); + auto match = raw_text.find(special_token, raw_text_base_offset); // no occurrences found, stop processing this fragment for a given special token if (match == std::string::npos) break; @@ -12428,13 +13354,22 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< if (match > raw_text_base_offset) { // left const int64_t left_reminder_offset = raw_text_base_offset + 0; - const int64_t left_reminder_length = match - raw_text_base_offset; - buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length); + int64_t left_reminder_length = match - raw_text_base_offset; + + if (data.attr & LLAMA_TOKEN_ATTR_LSTRIP) { + while (left_reminder_length > 0 && isspace(raw_text[left_reminder_offset + left_reminder_length - 1])) { + left_reminder_length--; + } + } + + if (left_reminder_length > 0) { + buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length); + it++; + } #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); #endif - it++; } // special token @@ -12443,16 +13378,25 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // right if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) { - const int64_t right_reminder_offset = match + special_token.length(); - const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length()); - buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length); + int64_t right_reminder_offset = match + special_token.length(); + int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length()); + + if (data.attr & LLAMA_TOKEN_ATTR_RSTRIP) { + while (right_reminder_length > 0 && isspace(raw_text[right_reminder_offset])) { + right_reminder_offset++; + right_reminder_length--; + } + } + + if (right_reminder_length > 0) { + buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length); + it++; + } #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); #endif - it++; - if (source == 0) { buffer.erase_after(buffer.before_begin()); } else { @@ -12498,9 +13442,7 @@ static std::vector llama_tokenize_internal(const llama_vocab & // tokenizer.encode('', add_special_tokens=True) returns [1] // tokenizer.encode('', add_special_tokens=False) returns [] - static const bool rtrim = true; //TODO: as param bool is_prev_special = false; - bool special_token_rtrim = false; if (add_special && vocab.special_add_bos != 0) { GGML_ASSERT(vocab.special_bos_id != -1); @@ -12510,25 +13452,8 @@ static std::vector llama_tokenize_internal(const llama_vocab & for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { - // without adding this leading whitespace, we do not get the same results as the original tokenizer - - // TODO: It's likely possible to get rid of this string copy entirely - // by modifying llm_tokenizer_x to operate with string offsets like pre-tokenizer - // and passing 'add space prefix' as bool argument - // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); - if (special_token_rtrim) { - size_t num_whitespaces = 0; - while (isspace(raw_text[num_whitespaces])) { - num_whitespaces++; - } - if (num_whitespaces == raw_text.size()) { - continue; // skip if all whitespaces - } - raw_text = raw_text.substr(num_whitespaces); - } - if (vocab.add_space_prefix) { if (!output.size() || is_prev_special) { // prefix with space if first token raw_text = " " + raw_text; @@ -12544,11 +13469,6 @@ static std::vector llama_tokenize_internal(const llama_vocab & } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); is_prev_special = true; - // phi-3 special tokens without rtrim, works fine for llama-spm too - special_token_rtrim = rtrim - && fragment.token != vocab.special_bos_id - && fragment.token != vocab.special_unk_id - && fragment.token != vocab.special_eos_id; } } @@ -13549,7 +14469,7 @@ void llama_sample_repetition_penalties( void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) { GGML_ASSERT(ctx); - const int64_t t_start_sample_us = ggml_time_us(); + int64_t t_start_sample_us = ggml_time_us(); bool allow_eog = false; for (const auto & stack : grammar->stacks) { @@ -13561,12 +14481,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c std::vector, llama_partial_utf8>> candidates_decoded; candidates_decoded.reserve(candidates->size); - std::vector candidates_grammar; + + std::vector candidates_grammar; candidates_grammar.reserve(candidates->size); for (size_t i = 0; i < candidates->size; ++i) { - const llama_token id = candidates->data[i].id; - const std::string piece = llama_token_to_piece(ctx, id, false); + const llama_token id = candidates->data[i].id; + const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id); if (llama_token_is_eog(&ctx->model, id)) { if (!allow_eog) { @@ -13766,7 +14687,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string piece = llama_token_to_piece(ctx, token, false); + const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token); // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece, grammar->partial_utf8); @@ -13782,260 +14703,6 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar ctx->t_sample_us += ggml_time_us() - t_start_sample_us; } -// -// Beam search -// - -struct llama_beam { - std::vector tokens; - float p; // Cumulative beam probability (renormalized relative to all beams) - bool eob; // Initialize end-of-beam to false. Callback sets this to true. - // Sort beams by probability. In case of ties, prefer beams at eob. - bool operator<(const llama_beam & rhs) const { - return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob); - } - // Shift off first n tokens and discard them. - void shift_tokens(const size_t n) { - if (n) { - std::copy(tokens.begin() + n, tokens.end(), tokens.begin()); - tokens.resize(tokens.size() - n); - } - } - llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; } -}; - -// A struct for calculating logit-related info. -struct llama_logit_info { - const float * const logits; - const int n_vocab; - const float max_l; - const float normalizer; - struct sum_exp { - float max_l; - float operator()(float sum, float l) const { return sum + std::exp(l - max_l); } - }; - llama_logit_info(llama_context * ctx) - : logits(llama_get_logits(ctx)) - , n_vocab(llama_n_vocab(llama_get_model(ctx))) - , max_l(*std::max_element(logits, logits + n_vocab)) - , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l})) - { } - llama_token_data get_token_data(const llama_token token_id) const { - constexpr auto p = std::numeric_limits::quiet_NaN(); // never used - return {token_id, logits[token_id], p}; - } - // Return top k token_data by logit. - std::vector top_k(size_t k) { - std::vector min_heap; // min-heap by logit - const llama_token k_min = std::min(static_cast(k), n_vocab); - min_heap.reserve(k_min); - for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) { - min_heap.push_back(get_token_data(token_id)); - } - auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; }; - std::make_heap(min_heap.begin(), min_heap.end(), comp); - for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) { - if (min_heap.front().logit < logits[token_id]) { - std::pop_heap(min_heap.begin(), min_heap.end(), comp); - min_heap.back().id = token_id; - min_heap.back().logit = logits[token_id]; - std::push_heap(min_heap.begin(), min_heap.end(), comp); - } - } - return min_heap; - } - float probability_from_logit(float logit) const { - return normalizer * std::exp(logit - max_l); - } -}; - -struct llama_beam_search_data { - llama_context * ctx; - size_t n_beams; - int n_past; - int n_predict; - std::vector beams; - std::vector next_beams; - - // Re-calculated on each loop iteration - size_t common_prefix_length; - - // Used to communicate to/from callback on beams state. - std::vector beam_views; - - llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict) - : ctx(ctx) - , n_beams(n_beams) - , n_past(n_past) - , n_predict(n_predict) - , beam_views(n_beams) { - beams.reserve(n_beams); - next_beams.reserve(n_beams); - } - - // Collapse beams to a single beam given by index. - void collapse_beams(const size_t beam_idx) { - if (0u < beam_idx) { - std::swap(beams[0], beams[beam_idx]); - } - beams.resize(1); - } - - // Min-heaps are used to efficiently collect the top-k elements (k=n_beams). - // The repetitive patterns below reflect the 2 stages of heaps: - // * Gather elements until the vector is full, then call std::make_heap() on it. - // * If the heap is full and a new element is found that should be included, pop the - // least element to the back(), replace it with the new, then push it into the heap. - void fill_next_beams_by_top_probabilities(llama_beam & beam) { - // Min-heaps use a greater-than comparator. - const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; }; - if (beam.eob) { - // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough. - if (next_beams.size() < n_beams) { - next_beams.push_back(std::move(beam)); - if (next_beams.size() == n_beams) { - std::make_heap(next_beams.begin(), next_beams.end(), comp); - } - } else if (next_beams.front().p < beam.p) { - std::pop_heap(next_beams.begin(), next_beams.end(), comp); - next_beams.back() = std::move(beam); - std::push_heap(next_beams.begin(), next_beams.end(), comp); - } - } else { - // beam is not at end-of-sentence, so branch with next top_k tokens. - if (!beam.tokens.empty()) { - llama_decode(ctx, llama_batch_get_one(beam.tokens.data(), beam.tokens.size(), n_past, 0)); - } - llama_logit_info logit_info(ctx); - std::vector next_tokens = logit_info.top_k(n_beams); - - // Clear the kv slot so that other beams may try different tokens at this position. The llama_decode() - // call in loop() will conclusively fill in the kv slot once the beams converge at this position. - llama_kv_cache_seq_rm(ctx, 0, n_past, -1); - - size_t i=0; - if (next_beams.size() < n_beams) { - for (; next_beams.size() < n_beams ; ++i) { - llama_beam next_beam = beam; - next_beam.tokens.push_back(next_tokens[i].id); - next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit); - next_beams.push_back(std::move(next_beam)); - } - std::make_heap(next_beams.begin(), next_beams.end(), comp); - } else { - for (; next_beams.front().p == 0.0f ; ++i) { - std::pop_heap(next_beams.begin(), next_beams.end(), comp); - next_beams.back() = beam; - next_beams.back().tokens.push_back(next_tokens[i].id); - next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit); - std::push_heap(next_beams.begin(), next_beams.end(), comp); - } - } - for (; i < n_beams ; ++i) { - const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit); - if (next_beams.front().p < next_p) { - std::pop_heap(next_beams.begin(), next_beams.end(), comp); - next_beams.back() = beam; - next_beams.back().tokens.push_back(next_tokens[i].id); - next_beams.back().p = next_p; - std::push_heap(next_beams.begin(), next_beams.end(), comp); - } - } - } - } - - // Find common_prefix_length based on beams. - // Requires beams is not empty. - size_t find_common_prefix_length() { - size_t common_prefix_length = beams[0].tokens.size(); - for (size_t i = 1 ; i < beams.size() ; ++i) { - common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size()); - for (size_t j = 0 ; j < common_prefix_length ; ++j) { - if (beams[0].tokens[j] != beams[i].tokens[j]) { - common_prefix_length = j; - break; - } - } - } - return common_prefix_length; - } - - // Construct beams_state to send back to caller via the callback function. - // Side effect: set common_prefix_length = find_common_prefix_length(); - llama_beams_state get_beams_state(const bool last_call) { - for (size_t i = 0 ; i < beams.size() ; ++i) { - beam_views[i] = beams[i].view(); - } - common_prefix_length = find_common_prefix_length(); - return {beam_views.data(), beams.size(), common_prefix_length, last_call}; - } - - // Loop: - // * while i < n_predict, AND - // * any of the beams have not yet reached end-of-beam (eob), AND - // * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence - // (since all other beam probabilities can only decrease) - void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) { - beams.push_back({{}, 1.0f, false}); // Start with one empty beam w/ probability = 1.0 and !eob. - const auto not_eob = [](const llama_beam & beam) { return !beam.eob; }; - for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) && - !beams[top_beam_index()].eob ; ++i) { - callback(callback_data, get_beams_state(false)); // Sets common_prefix_length - update_beams_from_beam_views(); // Update values (p,eob) that callback may have changed. - if (common_prefix_length) { - llama_decode(ctx, llama_batch_get_one(beams[0].tokens.data(), common_prefix_length, n_past, 0)); - n_past += common_prefix_length; - } - // Zero-out next_beam probabilities to place them last in following min-heap. - std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; }); - for (llama_beam & beam : beams) { - beam.shift_tokens(common_prefix_length); - fill_next_beams_by_top_probabilities(beam); - } - // next_beams become the beams of next/final iteration. Swap them to re-use memory. - beams.swap(next_beams); - renormalize_beam_probabilities(beams); - } - collapse_beams(top_beam_index()); - callback(callback_data, get_beams_state(true)); - } - - // As beams grow, the cumulative probabilities decrease. - // Renormalize them to avoid floating point underflow. - static void renormalize_beam_probabilities(std::vector & beams) { - const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; }; - const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p); - std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; }); - } - - // Assumes beams is non-empty. Uses llama_beam::operator<() for ordering. - size_t top_beam_index() { - return std::max_element(beams.begin(), beams.end()) - beams.begin(); - } - - // Copy (p,eob) for each beam which may have been changed by the callback. - void update_beams_from_beam_views() { - for (size_t i = 0 ; i < beams.size() ; ++i) { - beams[i].p = beam_views[i].p; - beams[i].eob = beam_views[i].eob; - } - } -}; - -void llama_beam_search(llama_context * ctx, - llama_beam_search_callback_fn_t callback, void * callback_data, - size_t n_beams, int n_past, int n_predict) { - assert(ctx); - const int64_t t_start_sample_us = ggml_time_us(); - - llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict); - - beam_search_data.loop(callback, callback_data); - - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - ctx->n_sample++; -} - // // quantization // @@ -14251,8 +14918,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; - else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && - (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; if (qs.model.type == MODEL_70B) { // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with @@ -15248,7 +15913,7 @@ bool llama_supports_mlock(void) { } bool llama_supports_gpu_offload(void) { -#if defined(GGML_USE_CUDA) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ +#if defined(GGML_USE_CUDA) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) || defined(GGML_USE_RPC) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. return true; @@ -15305,7 +15970,7 @@ struct llama_model * llama_load_model_from_file( return true; }; } - if (params.rpc_servers != nullptr) { + if (params.rpc_servers != nullptr && params.rpc_servers[0] != '\0') { // split the servers set them into model->rpc_servers std::string servers(params.rpc_servers); size_t pos = 0; @@ -15359,6 +16024,11 @@ struct llama_context * llama_new_context_with_model( params.flash_attn = false; } + if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) { + LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__); + return nullptr; + } + llama_context * ctx = new llama_context(*model); const auto & hparams = model->hparams; @@ -15417,6 +16087,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + cparams.yarn_attn_factor *= hparams.rope_attn_factor; cparams.causal_attn = hparams.causal_attn; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { @@ -15462,17 +16133,7 @@ struct llama_context * llama_new_context_with_model( if (!hparams.vocab_only) { // initialize backends -#if defined(GGML_USE_RPC) - for (auto & server : model->rpc_servers) { - ggml_backend_t backend = ggml_backend_rpc_init(server.c_str()); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to connect RPC backend to %s\n", __func__, server.c_str()); - llama_free(ctx); - return nullptr; - } - ctx->backends.push_back(backend); - } -#elif defined(GGML_USE_METAL) +#if defined(GGML_USE_METAL) if (model->n_gpu_layers > 0) { ctx->backend_metal = ggml_backend_metal_init(); if (ctx->backend_metal == nullptr) { @@ -15511,7 +16172,7 @@ struct llama_context * llama_new_context_with_model( return nullptr; } if (model->split_mode == LLAMA_SPLIT_MODE_NONE) { - ggml_backend_t backend = ggml_backend_vk_init(0); + ggml_backend_t backend = ggml_backend_vk_init(model->main_gpu); if (backend == nullptr) { LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); llama_free(ctx); @@ -15564,6 +16225,19 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(backend); } +#endif +#if defined(GGML_USE_RPC) + if (model->n_gpu_layers > 0) { + for (const auto & endpoint : model->rpc_servers) { + ggml_backend_t backend = ggml_backend_rpc_init(endpoint.c_str()); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize RPC to '%s'\n", __func__, endpoint.c_str()); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } + } #endif ctx->backend_cpu = ggml_backend_cpu_init(); if (ctx->backend_cpu == nullptr) { @@ -15711,7 +16385,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { // these models do not use RoPE case LLM_ARCH_GPT2: case LLM_ARCH_GPTJ: - case LLM_ARCH_GPTNEOX: case LLM_ARCH_MPT: case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: @@ -15731,6 +16404,8 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_XVERSE: case LLM_ARCH_COMMAND_R: case LLM_ARCH_OLMO: + case LLM_ARCH_ARCTIC: + case LLM_ARCH_DEEPSEEK2: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 @@ -15747,6 +16422,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_PHI3: case LLM_ARCH_GEMMA: case LLM_ARCH_STARCODER2: + case LLM_ARCH_GPTNEOX: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here @@ -17125,6 +17801,14 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_ ctx->cparams.n_threads_batch = n_threads_batch; } +uint32_t llama_n_threads(struct llama_context * ctx) { + return ctx->cparams.n_threads; +} + +uint32_t llama_n_threads_batch(struct llama_context * ctx) { + return ctx->cparams.n_threads_batch; +} + void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { ctx->abort_callback = abort_callback; ctx->abort_callback_data = abort_callback_data; @@ -17336,9 +18020,9 @@ float llama_token_get_score(const struct llama_model * model, llama_token token) return model->vocab.id_to_token[token].score; } -llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token) { +llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token) { GGML_ASSERT(model->vocab.type != LLAMA_VOCAB_TYPE_NONE); - return model->vocab.id_to_token[token].type; + return model->vocab.id_to_token[token].attr; } bool llama_token_is_eog(const struct llama_model * model, llama_token token) { @@ -17348,6 +18032,10 @@ bool llama_token_is_eog(const struct llama_model * model, llama_token token) { ); } +bool llama_token_is_control(const struct llama_model * model, llama_token token) { + return llama_is_control_token(model->vocab, token); +} + llama_token llama_token_bos(const struct llama_model * model) { return model->vocab.special_bos_id; } @@ -17419,7 +18107,16 @@ static std::string llama_decode_text(const std::string & text) { const auto cpts = unicode_cpts_from_utf8(text); for (const auto cpt : cpts) { - decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt)); + const auto utf8 = unicode_cpt_to_utf8(cpt); + try { + decoded_text += unicode_utf8_to_byte(utf8); + } catch (const std::out_of_range & e) { + decoded_text += "[UNK_BYTE_0x"; + for (const auto c : utf8) { + decoded_text += format("%02x", (uint8_t) c); + } + decoded_text += text + "]"; + } } return decoded_text; @@ -17427,69 +18124,88 @@ static std::string llama_decode_text(const std::string & text) { // does not write null-terminator to buf int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) { + // ref: https://github.com/ggerganov/llama.cpp/pull/7587#discussion_r1620983843 + if (!special && llama_is_control_token(model->vocab, token)) { + return 0; + } + + // if we have a cache - use it + { + const auto & cache = model->vocab.cache_token_to_piece; + + if (!cache.empty()) { + const auto & res = cache.at(token); + if (length < (int) res.size()) { + return -(int) res.size(); + } + memcpy(buf, res.c_str(), res.size()); + return res.size(); + } + } + if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { - case LLAMA_VOCAB_TYPE_WPM: - case LLAMA_VOCAB_TYPE_SPM: { - // NOTE: we accept all unsupported token types, - // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; - llama_unescape_whitespace(result); - if (length < (int) result.length()) { - return -(int) result.length(); + case LLAMA_VOCAB_TYPE_WPM: + case LLAMA_VOCAB_TYPE_SPM: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. + if (llama_is_normal_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + llama_unescape_whitespace(result); + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT + if (length < 3) { + return -3; + } + memcpy(buf, "\xe2\x96\x85", 3); + return 3; + } else if (llama_is_byte_token(model->vocab, token)) { + if (length < 1) { + return -1; + } + buf[0] = llama_token_to_byte(model->vocab, token); + return 1; } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT - if (length < 3) { - return -3; - } - memcpy(buf, "\xe2\x96\x85", 3); - return 3; - } else if (llama_is_byte_token(model->vocab, token)) { - if (length < 1) { - return -1; - } - buf[0] = llama_token_to_byte(model->vocab, token); - return 1; + break; } - break; - } - case LLAMA_VOCAB_TYPE_BPE: { - // NOTE: we accept all unsupported token types, - // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; - result = llama_decode_text(result); - if (length < (int) result.length()) { - return -(int) result.length(); + case LLAMA_VOCAB_TYPE_BPE: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. + if (llama_is_normal_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + result = llama_decode_text(result); + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); + break; } - break; - } - default: - GGML_ASSERT(false); + default: + GGML_ASSERT(false); } } return 0; @@ -17559,6 +18275,15 @@ static int32_t llama_chat_apply_template_internal( } } // llama2 templates seem to not care about "add_generation_prompt" + } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos)) { + // Phi 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << message->content << "<|end|>\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) { // zephyr template for (auto message : chat) { @@ -17691,15 +18416,6 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } - } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) { - // Phi 3 - for (auto message : chat) { - std::string role(message->role); - ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n"; - } - if (add_ass) { - ss << "<|assistant|>\n"; - } } else { // template not supported return -1; @@ -17824,6 +18540,7 @@ const char * llama_print_system_info(void) { s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; + s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | "; s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | "; s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | "; diff --git a/llama.h b/llama.h index b7bf2afcb..b2a302dad 100644 --- a/llama.h +++ b/llama.h @@ -85,6 +85,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, LLAMA_VOCAB_PRE_TYPE_OLMO = 12, LLAMA_VOCAB_PRE_TYPE_DBRX = 13, + LLAMA_VOCAB_PRE_TYPE_SMAUG = 14, }; // note: these values should be synchronized with ggml_rope @@ -96,7 +97,7 @@ extern "C" { LLAMA_ROPE_TYPE_GLM = 4, }; - enum llama_token_type { + enum llama_token_type { //TODO: remove, required until per token attributes are available from GGUF file LLAMA_TOKEN_TYPE_UNDEFINED = 0, LLAMA_TOKEN_TYPE_NORMAL = 1, LLAMA_TOKEN_TYPE_UNKNOWN = 2, @@ -106,6 +107,20 @@ extern "C" { LLAMA_TOKEN_TYPE_BYTE = 6, }; + enum llama_token_attr { + LLAMA_TOKEN_ATTR_UNDEFINED = 0, + LLAMA_TOKEN_ATTR_UNKNOWN = 1 << 1, + LLAMA_TOKEN_ATTR_UNUSED = 1 << 2, + LLAMA_TOKEN_ATTR_NORMAL = 1 << 3, + LLAMA_TOKEN_ATTR_CONTROL = 1 << 4, // SPECIAL? + LLAMA_TOKEN_ATTR_USER_DEFINED = 1 << 5, + LLAMA_TOKEN_ATTR_BYTE = 1 << 6, + LLAMA_TOKEN_ATTR_NORMALIZED = 1 << 7, + LLAMA_TOKEN_ATTR_LSTRIP = 1 << 8, + LLAMA_TOKEN_ATTR_RSTRIP = 1 << 9, + LLAMA_TOKEN_ATTR_SINGLE_WORD = 1 << 10, + }; + // model file types enum llama_ftype { LLAMA_FTYPE_ALL_F32 = 0, @@ -264,6 +279,8 @@ extern "C" { bool check_tensors; // validate model tensor data }; + // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations + // https://github.com/ggerganov/llama.cpp/pull/7544 struct llama_context_params { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model @@ -290,14 +307,14 @@ extern "C" { ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; - enum ggml_type type_k; // data type for K cache - enum ggml_type type_v; // data type for V cache + enum ggml_type type_k; // data type for K cache [EXPERIMENTAL] + enum ggml_type type_v; // data type for V cache [EXPERIMENTAL] // Keep the booleans together to avoid misalignment during copy-by-value. 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 + bool flash_attn; // whether to use flash attention [EXPERIMENTAL] // Abort callback // if it returns true, execution of llama_decode() will be aborted @@ -421,8 +438,8 @@ extern "C" { LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); - LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); - LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); + LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); + LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); @@ -759,6 +776,12 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); + // Get the number of threads used for generation of a single token. + LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx); + + // Get the number of threads used for prompt and batch processing (multiple token). + LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx); + // Set whether to use causal attention or not // If set to true, the model will only attend to the past tokens LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); @@ -812,11 +835,14 @@ extern "C" { LLAMA_API float llama_token_get_score(const struct llama_model * model, llama_token token); - LLAMA_API enum llama_token_type llama_token_get_type(const struct llama_model * model, llama_token token); + LLAMA_API enum llama_token_attr llama_token_get_attr(const struct llama_model * model, llama_token token); // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token); + // Identify if Token Id is a control token or a render-able token + LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token); + // Special tokens LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence @@ -1030,49 +1056,9 @@ extern "C" { llama_token token); // - // Beam search + // Model split // - struct llama_beam_view { - const llama_token * tokens; - - size_t n_tokens; - float p; // Cumulative beam probability (renormalized relative to all beams) - bool eob; // Callback should set this to true when a beam is at end-of-beam. - }; - - // Passed to beam_search_callback function. - // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams - // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks. - // These pointers are valid only during the synchronous callback, so should not be saved. - struct llama_beams_state { - struct llama_beam_view * beam_views; - - size_t n_beams; // Number of elements in beam_views[]. - size_t common_prefix_length; // Current max length of prefix tokens shared by all beams. - bool last_call; // True iff this is the last callback invocation. - }; - - // Type of pointer to the beam_search_callback function. - // void* callback_data is any custom data passed to llama_beam_search, that is subsequently - // passed back to beam_search_callback. This avoids having to use global variables in the callback. - typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state); - - /// @details Deterministically returns entire sentence constructed by a beam search. - /// @param ctx Pointer to the llama_context. - /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state. - /// @param callback_data A pointer that is simply passed back to callback. - /// @param n_beams Number of beams to use. - /// @param n_past Number of tokens already evaluated. - /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier. - LLAMA_API void llama_beam_search( - struct llama_context * ctx, - llama_beam_search_callback_fn_t callback, - void * callback_data, - size_t n_beams, - int32_t n_past, - int32_t n_predict); - /// @details Build a split GGUF final path for this chunk. /// llama_split_path(split_path, sizeof(split_path), "/models/ggml-model-q4_0", 2, 4) => split_path = "/models/ggml-model-q4_0-00002-of-00004.gguf" // Returns the split_path length. diff --git a/models/ggml-vocab-phi-3.gguf b/models/ggml-vocab-phi-3.gguf index f8022a385..745be416a 100644 Binary files a/models/ggml-vocab-phi-3.gguf and b/models/ggml-vocab-phi-3.gguf differ diff --git a/requirements.txt b/requirements.txt index 43f82dc2e..e5cfbf10b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ # Package versions must stay compatible across all top-level python scripts. # --r ./requirements/requirements-convert.txt +-r ./requirements/requirements-convert-legacy-llama.txt -r ./requirements/requirements-convert-hf-to-gguf.txt -r ./requirements/requirements-convert-hf-to-gguf-update.txt diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt index 6ac402610..6eacaf429 100644 --- a/requirements/requirements-convert-hf-to-gguf-update.txt +++ b/requirements/requirements-convert-hf-to-gguf-update.txt @@ -1,2 +1,2 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt torch~=2.1.1 diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt index 6ac402610..6eacaf429 100644 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -1,2 +1,2 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt torch~=2.1.1 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert-legacy-llama.txt similarity index 100% rename from requirements/requirements-convert.txt rename to requirements/requirements-convert-legacy-llama.txt diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt index a0f37cd1c..e80c29012 100644 --- a/requirements/requirements-convert-llama-ggml-to-gguf.txt +++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt @@ -1 +1 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in index 92e39708b..9311055d9 100644 --- a/scripts/LlamaConfig.cmake.in +++ b/scripts/LlamaConfig.cmake.in @@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) set(LLAMA_BLAS @LLAMA_BLAS@) set(LLAMA_CUDA @LLAMA_CUDA@) set(LLAMA_METAL @LLAMA_METAL@) -set(LLAMA_CLBLAST @LLAMA_CLBLAST@) set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@) set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@) @@ -36,10 +35,6 @@ if (LLAMA_METAL) find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) endif() -if (LLAMA_CLBLAST) - find_package(CLBlast REQUIRED) -endif() - if (LLAMA_HIPBLAS) find_package(hip REQUIRED) find_package(hipblas REQUIRED) diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh index 6a7400d3c..0c6afdd59 100755 --- a/scripts/check-requirements.sh +++ b/scripts/check-requirements.sh @@ -166,7 +166,7 @@ if (( do_cleanup )); then rm -rf -- "$all_venv" fi -check_convert_script convert.py +check_convert_script examples/convert-legacy-llama.py for py in convert-*.py; do # skip convert-hf-to-gguf-update.py # TODO: the check is failing for some reason: diff --git a/scripts/compare-commits.sh b/scripts/compare-commits.sh index fd0ee88b2..a45cd3962 100755 --- a/scripts/compare-commits.sh +++ b/scripts/compare-commits.sh @@ -10,16 +10,18 @@ set -x bench_args="${@:3}" -rm -f llama-bench.sqlite +rm -f llama-bench.sqlite > /dev/null # to test a backend, call the script with the corresponding environment variable (e.g. LLAMA_CUDA=1 ./scripts/compare-commits.sh ...) -git checkout $1 -make clean && make -j32 $make_opts llama-bench -./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite +git checkout $1 > /dev/null +make clean > /dev/null +make -j$(nproc) $make_opts llama-bench > /dev/null +./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite -git checkout $2 -make clean && make -j32 $make_opts llama-bench -./llama-bench -o sql $bench_args | tee /dev/tty | sqlite3 llama-bench.sqlite +git checkout $2 > /dev/null +make clean > /dev/null +make -j$(nproc) $make_opts llama-bench > /dev/null +./llama-bench -o sql -oe md $bench_args | sqlite3 llama-bench.sqlite ./scripts/compare-llama-bench.py -b $1 -c $2 diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 0ede9e67c..513dde5e1 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -19,22 +19,22 @@ 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", - "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", - "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", + "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads", + "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: -BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"] +BOOL_PROPERTIES = ["cuda", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"] # Header names for the table: PRETTY_NAMES = { - "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS", - "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", - "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", - "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", - "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", - "tensor_split": "Tensor split" + "cuda": "CUDA", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC", + "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", + "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", + "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode", + "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", + "use_mmap": "Use mmap", "embeddings": "Embeddings", } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh index 01fda16fd..8a0168432 100755 --- a/scripts/convert-gg.sh +++ b/scripts/convert-gg.sh @@ -3,20 +3,20 @@ set -e # LLaMA v1 -python3 convert.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 # LLaMA v2 -python3 convert.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 # Code Llama -python3 convert.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 # Falcon python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1 diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh index 2058ceabf..5dabbf60e 100644 --- a/scripts/pod-llama.sh +++ b/scripts/pod-llama.sh @@ -75,7 +75,7 @@ if [ "$1" -eq "1" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k @@ -90,7 +90,7 @@ if [ "$1" -eq "2" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k @@ -105,7 +105,7 @@ if [ "$1" -eq "3" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k @@ -120,7 +120,7 @@ if [ "$1" -eq "4" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k @@ -135,7 +135,7 @@ if [ "$1" -eq "5" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k @@ -150,7 +150,7 @@ if [ "$1" -eq "6" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k @@ -165,7 +165,7 @@ if [ "$1" -eq "7" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k diff --git a/scripts/run-with-preset.py b/scripts/run-with-preset.py index e986a3604..0d7219113 100755 --- a/scripts/run-with-preset.py +++ b/scripts/run-with-preset.py @@ -13,12 +13,12 @@ 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", - "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct", + "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base", "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", "model", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q", "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", - "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n", + "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "repeat-last-n", "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed", "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical", "verbose-prompt" diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index eb6ce458e..b3715e204 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -3,7 +3,7 @@ # Helper script for deploying llama.cpp server with a single Bash command # # - Works on Linux and macOS -# - Supports: CPU, CUDA, Metal, OpenCL +# - Supports: CPU, CUDA, Metal # - Can run all GGUF models from HuggingFace # - Can serve requests in parallel # - Always builds latest llama.cpp from GitHub @@ -19,7 +19,7 @@ # --port: port number, default is 8888 # --repo: path to a repo containing GGUF model files # --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input -# --backend: cpu, cuda, metal, opencl, depends on the OS +# --backend: cpu, cuda, metal, depends on the OS # --gpu-id: gpu id, default is 0 # --n-parallel: number of parallel requests, default is 8 # --n-kv: KV cache size, default is 4096 @@ -72,7 +72,7 @@ function print_usage { printf " --port: port number, default is 8888\n" printf " --repo: path to a repo containing GGUF model files\n" printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" - printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" + printf " --backend: cpu, cuda, metal, depends on the OS\n" printf " --gpu-id: gpu id, default is 0\n" printf " --n-parallel: number of parallel requests, default is 8\n" printf " --n-kv: KV cache size, default is 4096\n" @@ -387,9 +387,6 @@ elif [[ "$backend" == "cpu" ]]; then elif [[ "$backend" == "metal" ]]; then printf "[+] Building with Metal backend\n" make -j server $log -elif [[ "$backend" == "opencl" ]]; then - printf "[+] Building with OpenCL backend\n" - LLAMA_CLBLAST=1 make -j server $log else printf "[-] Unknown backend: %s\n" "$backend" exit 1 @@ -407,8 +404,6 @@ elif [[ "$backend" == "cpu" ]]; then args="-ngl 0" elif [[ "$backend" == "metal" ]]; then args="-ngl 999" -elif [[ "$backend" == "opencl" ]]; then - args="-ngl 999" else printf "[-] Unknown backend: %s\n" "$backend" exit 1 diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index cf22afc41..9e34dc8b9 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -106,10 +106,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-kompute.h -> ggml-kompute.h # src/ggml-metal.h -> ggml-metal.h # src/ggml-metal.m -> ggml-metal.m - # src/ggml-mpi.h -> ggml-mpi.h - # src/ggml-mpi.c -> ggml-mpi.c - # src/ggml-opencl.cpp -> ggml-opencl.cpp - # src/ggml-opencl.h -> ggml-opencl.h # src/ggml-quants.c -> ggml-quants.c # src/ggml-quants.h -> ggml-quants.h # src/ggml-rpc.cpp -> ggml-rpc.cpp @@ -145,10 +141,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \ -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \ -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \ - -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \ - -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \ - -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \ - -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \ -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \ -e 's/src\/ggml-quants\.h/ggml-quants.h/g' \ -e 's/src\/ggml-rpc\.cpp/ggml-rpc.cpp/g' \ diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 57bede67b..5042f82ae 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -126d34985705a5a2222723c145cb4e125ac689f3 +2aae01fd9b8f9399f343cf18f46f38996ef52e2c diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index ec47fb27c..4843f8a4a 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -14,10 +14,6 @@ cp -rpv ../ggml/src/ggml-kompute.h ./ggml-kompute.h cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal -cp -rpv ../ggml/src/ggml-mpi.h ./ggml-mpi.h -cp -rpv ../ggml/src/ggml-mpi.c ./ggml-mpi.c -cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp -cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c cp -rpv ../ggml/src/ggml-quants.h ./ggml-quants.h cp -rpv ../ggml/src/ggml-rpc.cpp ./ggml-rpc.cpp diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 766a01752..cfa707315 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -129,8 +129,11 @@ llama_target_and_test(test-rope.cpp) llama_target_and_test(test-model-load-cancel.cpp LABEL "model") llama_target_and_test(test-autorelease.cpp LABEL "model") -llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) -target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) +# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8 +if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") + llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) + target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) +endif() # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index c74e253db..8dc90a45d 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1138,24 +1138,37 @@ struct test_soft_max : public test_case { // GGML_OP_ROPE struct test_rope : public test_case { const ggml_type type; - const std::array ne; + const std::array ne_a; int n_dims; int mode; int n_ctx; + float fs; // freq_scale + float ef; // ext_factor + float af; // attn_factor + bool ff; + int v; // view (1 : non-contiguous a) std::string vars() override { - return VARS_TO_STR5(type, ne, n_dims, mode, n_ctx); + return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v); } test_rope(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 1}, - int n_dims = 10, int mode = 0, int n_ctx = 512) - : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx) {} + std::array ne_a = {10, 10, 10, 1}, + int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0) + : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]); - ggml_tensor * out = ggml_rope(ctx, a, pos, n_dims, mode, n_ctx); + ggml_tensor * a; + if (v & 1) { + auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + } else { + a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + } + ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]); + ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr; + ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, n_ctx, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); return out; } @@ -1163,13 +1176,18 @@ struct test_rope : public test_case { for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { if (t->type == GGML_TYPE_I32) { // pos - std::vector data(ne[2]); - for (int i = 0; i < ne[2]; i++) { + std::vector data(ne_a[2]); + for (int i = 0; i < ne_a[2]; i++) { data[i] = rand() % n_ctx; } - ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int)); + ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int)); } else { - init_tensor_uniform(t); + if (t->ne[0] == n_dims/2) { + // frequency factors in the range [0.9f, 1.1f] + init_tensor_uniform(t, 0.9f, 1.1f); + } else { + init_tensor_uniform(t); + } } } } @@ -1252,22 +1270,41 @@ struct test_im2col : public test_case { // GGML_OP_CONCAT struct test_concat : public test_case { const ggml_type type; - const std::array ne; - const int64_t b_ne2; + const std::array ne_a; + const int64_t ne_b_d; + const int dim; + const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b) std::string vars() override { - return VARS_TO_STR3(type, ne, b_ne2); + return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v); } test_concat(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 10}, - int64_t b_ne2 = 10) - : type(type), ne(ne), b_ne2(b_ne2) {} + std::array ne_a = {10, 10, 10, 10}, + int64_t ne_b_d = 10, + int dim = 2, int v = 0) + : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]); - ggml_tensor * out = ggml_concat(ctx, a, b); + auto ne_b = ne_a; + ne_b[dim] = ne_b_d; + ggml_tensor * a; + if (v & 1) { + auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + } else { + a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + } + ggml_tensor * b; + if (v & 2) { + auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4; + b = ggml_new_tensor(ctx, type, 4, ne.data()); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); + } else { + b = ggml_new_tensor(ctx, type, 4, ne_b.data()); + } + ggml_tensor * out = ggml_concat(ctx, a, b, dim); return out; } }; @@ -1533,21 +1570,25 @@ struct test_flash_attn_ext : public test_case { const float max_bias; // ALiBi + const ggml_type type_KV; + std::string vars() override { - return VARS_TO_STR6(hs, nh, kv, nb, mask, max_bias); + return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV); } 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, bool mask = true, float max_bias = 0.0f) - : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias) {} + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16) + : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {} 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); + const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV)); + + ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1); + ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1); + ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1); ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr; ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias); return out; @@ -1763,14 +1804,14 @@ struct test_llama : public test_llm { struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur); struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur); - Qcur = ggml_rope_custom( - ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos, nullptr, hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); - Kcur = ggml_rope_custom( - ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, nullptr, hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -1889,13 +1930,13 @@ struct test_falcon : public test_llm { Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx, Qcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx, + Qcur = ggml_rope_ext( + ctx, Qcur, inp_pos, nullptr, hp.n_rot, 2, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); - Kcur = ggml_rope_custom( - ctx, Kcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx, + Kcur = ggml_rope_ext( + ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -2187,21 +2228,47 @@ 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, {32, 2, 32, 1}, true, 0.1f, 0.0f)); 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}) { - test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B - test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512)); // llama 13B - test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512)); // llama 30B - test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512)); // llama 65B - test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2) + { + bool all = true; + + for (float v : { 0, 1 }) { + for (float fs : { 1.0f, 1.4245f }) { + for (float ef : { 0.0f, 0.7465f }) { + for (float af : { 1.0f, 1.4245f }) { + for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { + // TODO: ff not supported yet for !neox + test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 7B + if (all) { + test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 13B + test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 30B + test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 65B + } + + for (bool ff : {false, true}) { // freq_factors + if (all) { + test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2) + } + + test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B) + } + } + all = false; + } + } + } + } } - test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); - test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); + for (int v : { 0, 1, 2, 3 }) { + for (int dim : { 0, 1, 2, 3, }) { + test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v)); + test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v)); + } + } for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) { test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order)); @@ -2227,7 +2294,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op 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, mask, max_bias)); + for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { + test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV)); + } } } } diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 4fe9183b9..cef9a650b 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -49,8 +49,14 @@ int main(void) { "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}", // Llama-3 "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", - // Phi-3 - "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}" + //Phi-3-mini + "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}", + //Phi-3-small + "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}", + //Phi-3-medium + "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}", + //Phi-3-vision + "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}" }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -79,8 +85,14 @@ int main(void) { "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", // Llama 3 "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - // Phi 3 - "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-mini + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-small + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-medium + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-vision + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", }; std::vector formatted_chat(1024); int32_t res; diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 8ff76c891..21ca43be3 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -1515,90 +1515,50 @@ int main(int argc, const char ** argv) { } // flash_attn f32 - { - srand(seed); - const int nargs = 3; + // TODO: adapt to ggml_flash_attn_ext() changes + //{ + // srand(seed); + // const int nargs = 3; - int64_t ne2[4]; + // int64_t ne2[4]; - get_random_dims(ne2, 4); - int64_t D = ne2[0]; - int64_t N = ne2[1]; - int64_t M = ne2[2] + N; - int64_t B = ne2[3]; + // get_random_dims(ne2, 4); + // int64_t D = ne2[0]; + // int64_t N = ne2[1]; + // int64_t M = ne2[2] + N; + // int64_t B = ne2[3]; - for (int masked = 0; masked <= 1; ++masked) { - for (int ndims = 2; ndims <= 4; ++ndims) { - int max_nrep = (ndims >= 3) ? 2 : 1; - for (int nrep = 1; nrep < max_nrep; ++nrep) { - int64_t neq[4] = { D, N, B*nrep, ne[3] }; - int64_t nek[4] = { D, M, B, ne[3] }; - int64_t nev[4] = { M, D, B, ne[3] }; - if (ndims == 2) { - neq[2] = 1; neq[3] = 1; - nek[2] = 1; nek[3] = 1; - nev[2] = 1; nev[3] = 1; - } else if (ndims == 3) { - neq[3] = 1; - nek[3] = 1; - nev[3] = 1; - } - x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); - x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); - x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - ggml_set_param(ctx0, x[2]); + // for (int masked = 0; masked <= 1; ++masked) { + // for (int ndims = 2; ndims <= 4; ++ndims) { + // int max_nrep = (ndims >= 3) ? 2 : 1; + // for (int nrep = 1; nrep < max_nrep; ++nrep) { + // int64_t neq[4] = { D, N, B*nrep, ne[3] }; + // int64_t nek[4] = { D, M, B, ne[3] }; + // int64_t nev[4] = { M, D, B, ne[3] }; + // if (ndims == 2) { + // neq[2] = 1; neq[3] = 1; + // nek[2] = 1; nek[3] = 1; + // nev[2] = 1; nev[3] = 1; + // } else if (ndims == 3) { + // neq[3] = 1; + // nek[3] = 1; + // nev[3] = 1; + // } + // x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); + // x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); + // x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); + // ggml_set_param(ctx0, x[0]); + // ggml_set_param(ctx0, x[1]); + // ggml_set_param(ctx0, x[2]); - struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); + // struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); - } - } - } - } + // check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); + // } + // } + // } + //} - // flash_attn f16, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 3; - - int64_t ne2[4]; - - get_random_dims(ne2, 4); - int64_t D = ne2[0]; - int64_t N = ne2[1]; - int64_t M = ne2[2] + N; - int64_t B = ne2[3]; - - for (int masked = 0; masked <= 1; ++masked) { - for (int ndims = 2; ndims <= 4; ++ndims) { - int64_t neq[4] = { D, N, B, ne[3] }; - int64_t nek[4] = { D, M, B, ne[3] }; - int64_t nev[4] = { M, D, B, ne[3] }; - if (ndims == 2) { - neq[2] = 1; neq[3] = 1; - nek[2] = 1; nek[3] = 1; - nev[2] = 1; nev[3] = 1; - } else if (ndims == 3) { - neq[3] = 1; - nek[3] = 1; - nev[3] = 1; - } - x[0] = get_random_tensor_f16(ctx0, ndims, neq, -0.1250f, 0.1250f); - x[1] = get_random_tensor_f16(ctx0, ndims, nek, -0.1250f, 0.1250f); - x[2] = get_random_tensor_f16(ctx0, ndims, nev, -0.1250f, 0.1250f); - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - ggml_set_param(ctx0, x[2]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - - check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); - } - } - } ggml_free(ctx0); } diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh index 1fec8bbf1..4d2b83655 100755 --- a/tests/test-tokenizer-0.sh +++ b/tests/test-tokenizer-0.sh @@ -28,6 +28,8 @@ printf "Tokenizing using (cpp) llama.cpp ...\n" cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" +set +e + diff $input.tok $input.tokcpp > /dev/null 2>&1 if [ $? -eq 0 ]; then diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index 7e1b656e5..52f589511 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -156,28 +156,47 @@ def generator_custom_text_edge_cases() -> Iterator[str]: 'a', # Phi-3 fail '<|endoftext|>', # Phi-3 fail 'a\na', # TODO: Bert fail + 'a b', # rstrip phi-3 + 'a b', # lstrip jina-v2 ] -def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]: - special_tokens = set(tokenizer.all_special_tokens) - special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "", ""]) - special_tokens = list(sorted(special_tokens)) - rand = random.Random() - for m in range(iterations): - rand.seed(m) - words = rand.choices(special_tokens, k=500) - if tokenizer.add_bos_token: # skip spam warning of double BOS - while words and words[0] == tokenizer.bos_token: - words.pop(0) - yield "".join(words) - - def generator_vocab_words(vocab: list[str]) -> Iterator[str]: """Brute force check all vocab words""" yield from vocab +def generator_added_lr_strip(tokenizer) -> Iterator[str]: + WHITESPACES = ["", " ", " ", " "] + special_tokens = list(tokenizer.all_special_tokens) + added_tokens = list(tokenizer.added_tokens_encoder) + all_tokens = list(sorted(set(special_tokens + added_tokens))) + for token in all_tokens: + for lstrip in WHITESPACES: + for rstrip in WHITESPACES: + yield lstrip + token + rstrip + yield "a" + lstrip + token + rstrip + yield lstrip + token + rstrip + "z" + yield "a" + lstrip + token + rstrip + "z" + + +def generator_random_added_tokens(tokenizer, iterations=100) -> Iterator[str]: + special_tokens = list(tokenizer.all_special_tokens) + added_tokens = list(tokenizer.added_tokens_encoder) + separations = [" ", "\n", "\t", "-", "!", "one", "1", "", ""] + all_tokens = list(sorted(set(special_tokens + added_tokens + separations))) + rand = random.Random() + for m in range(iterations): + rand.seed(m) + words = rand.choices(all_tokens, k=500) + if words[0] == tokenizer.bos_token: # skip spam warning of double BOS + while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS + words.pop(0) + if tokenizer.add_bos_token: # drop all starting BOS + words.pop(0) + yield "".join(words) + + def generator_random_chars(iterations=100) -> Iterator[str]: """Brute force random text with simple characters""" @@ -272,8 +291,8 @@ def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, g ids2 = func_tokenize2(text) 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] + ids1 = list(ids1)[max(0, i - 2) : i + 5 + 1] + ids2 = list(ids2)[max(0, i - 2) : i + 5 + 1] logger.info(" TokenIDs: " + str(ids1)) logger.info(" Expected: " + str(ids2)) raise Exception() @@ -293,20 +312,23 @@ def main(argv: list[str] = None): model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True) - tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False) - def func_tokenize1(text: str): return model.tokenize(text, add_special=True, parse_special=True) def func_tokenize2(text: str): return tokenizer.encode(text, add_special_tokens=True) + ids = func_tokenize2("a") + assert 1 <= len(ids) <= 3 + add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0] + tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token) + vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) - test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_added_lr_strip(tokenizer)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_added_tokens(tokenizer, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000)) @@ -318,7 +340,7 @@ def main(argv: list[str] = None): if __name__ == "__main__": # main() - path_tokenizers = "./models/tokenizers/" + path_tokenizers = "./models/tokenizers/" path_vocab_format = "./models/ggml-vocab-%s.gguf" # import os @@ -326,6 +348,8 @@ if __name__ == "__main__": tokenizers = [ "llama-spm", # SPM "phi-3", # SPM + "jina-v2-en", # WPM + "bert-bge", # WPM ] for tokenizer in tokenizers: