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Merge branch 'master' into quantize

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Ed Addario 2025-09-25 19:55:31 +01:00
parent dbdd179a92 835b2b915c
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1
.clang-tidy
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@ -17,6 +17,7 @@ Checks: >
clang-analyzer-*,
-clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling,
performance-*,
-performance-enum-size,
portability-*,
-portability-simd-intrinsics,
misc-*,

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.devops/s390x.Dockerfile Normal file
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@ -0,0 +1,123 @@
ARG GCC_VERSION=15.2.0
ARG UBUNTU_VERSION=24.04
### Build Llama.cpp stage
FROM gcc:${GCC_VERSION} AS build
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
apt update -y && \
apt upgrade -y && \
apt install -y --no-install-recommends \
git cmake ccache ninja-build \
# WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
libopenblas-dev libcurl4-openssl-dev && \
rm -rf /var/lib/apt/lists/*
WORKDIR /app
COPY . .
RUN --mount=type=cache,target=/root/.ccache \
--mount=type=cache,target=/app/build \
cmake -S . -B build -G Ninja \
-DCMAKE_BUILD_TYPE=Release \
-DCMAKE_C_COMPILER_LAUNCHER=ccache \
-DCMAKE_CXX_COMPILER_LAUNCHER=ccache \
-DLLAMA_BUILD_TESTS=OFF \
-DGGML_BACKEND_DL=OFF \
-DGGML_NATIVE=OFF \
-DGGML_BLAS=ON \
-DGGML_BLAS_VENDOR=OpenBLAS && \
cmake --build build --config Release -j $(nproc) && \
cmake --install build --prefix /opt/llama.cpp
COPY *.py /opt/llama.cpp/bin
COPY .devops/tools.sh /opt/llama.cpp/bin
COPY gguf-py /opt/llama.cpp/gguf-py
COPY requirements.txt /opt/llama.cpp/gguf-py
COPY requirements /opt/llama.cpp/gguf-py/requirements
### Collect all llama.cpp binaries, libraries and distro libraries
FROM scratch AS collector
# Copy llama.cpp binaries and libraries
COPY --from=build /opt/llama.cpp/bin /llama.cpp/bin
COPY --from=build /opt/llama.cpp/lib /llama.cpp/lib
COPY --from=build /opt/llama.cpp/gguf-py /llama.cpp/gguf-py
### Base image
FROM ubuntu:${UBUNTU_VERSION} AS base
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
apt update -y && \
apt install -y --no-install-recommends \
# WARNING: Do not use libopenblas-openmp-dev. libopenblas-dev is faster.
# See: https://github.com/ggml-org/llama.cpp/pull/15915#issuecomment-3317166506
curl libgomp1 libopenblas-dev && \
apt autoremove -y && \
apt clean -y && \
rm -rf /tmp/* /var/tmp/* && \
find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \
find /var/cache -type f -delete
# Copy llama.cpp libraries
COPY --from=collector /llama.cpp/lib /usr/lib/s390x-linux-gnu
### Full
FROM base AS full
ENV PATH="/root/.cargo/bin:${PATH}"
WORKDIR /app
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
--mount=type=cache,target=/var/lib/apt/lists,sharing=locked \
apt update -y && \
apt install -y \
git cmake libjpeg-dev \
python3 python3-pip python3-dev && \
apt autoremove -y && \
apt clean -y && \
rm -rf /tmp/* /var/tmp/* && \
find /var/cache/apt/archives /var/lib/apt/lists -not -name lock -type f -delete && \
find /var/cache -type f -delete
RUN curl https://sh.rustup.rs -sSf | bash -s -- -y
COPY --from=collector /llama.cpp/bin /app
COPY --from=collector /llama.cpp/gguf-py /app/gguf-py
RUN pip install --no-cache-dir --break-system-packages \
-r /app/gguf-py/requirements.txt
ENTRYPOINT [ "/app/tools.sh" ]
### CLI Only
FROM base AS light
WORKDIR /llama.cpp/bin
# Copy llama.cpp binaries and libraries
COPY --from=collector /llama.cpp/bin/llama-cli /llama.cpp/bin
ENTRYPOINT [ "/llama.cpp/bin/llama-cli" ]
### Server
FROM base AS server
ENV LLAMA_ARG_HOST=0.0.0.0
WORKDIR /llama.cpp/bin
# Copy llama.cpp binaries and libraries
COPY --from=collector /llama.cpp/bin/llama-server /llama.cpp/bin
EXPOSE 8080
ENTRYPOINT [ "/llama.cpp/bin/llama-server" ]

2
.github/workflows/build-riscv-native.yml vendored
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@ -6,7 +6,7 @@ on:
jobs:
debian-13-riscv64-native: # Bianbu 2.2
runs-on: self-hosted
runs-on: [self-hosted, RISCV64]
steps:
- name: Install prerequisites

237
.github/workflows/build.yml vendored
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@ -1247,3 +1247,240 @@ jobs:
-DGGML_CANN=on \
-DSOC_TYPE=${{ matrix.device }}
cmake --build build -j $(nproc)
# TODO: simplify the following workflows using a matrix
# TODO: run lighter CI on PRs and the full CI only on master (if needed)
ggml-ci-x64-cpu-low-perf:
runs-on: ubuntu-22.04
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-x64-cpu-low-perf
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Test
id: ggml-ci
run: |
LLAMA_ARG_THREADS=$(nproc) GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-arm64-cpu-low-perf:
runs-on: ubuntu-22.04-arm
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-arm64-cpu-low-perf
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Test
id: ggml-ci
run: |
LLAMA_ARG_THREADS=$(nproc) GG_BUILD_LOW_PERF=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-x64-cpu-high-perf:
runs-on: ubuntu-22.04
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-x64-cpu-high-perf
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Test
id: ggml-ci
run: |
LLAMA_ARG_THREADS=$(nproc) bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-arm64-cpu-high-perf:
runs-on: ubuntu-22.04-arm
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-arm64-cpu-high-perf
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Test
id: ggml-ci
run: |
LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_SVE=1 GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-arm64-cpu-high-perf-sve:
runs-on: ubuntu-22.04-arm
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: ccache
uses: ggml-org/ccache-action@v1.2.16
with:
key: ggml-ci-arm64-cpu-high-perf-sve
evict-old-files: 1d
- name: Dependencies
id: depends
run: |
sudo apt-get update
sudo apt-get install build-essential libcurl4-openssl-dev
- name: Test
id: ggml-ci
run: |
LLAMA_ARG_THREADS=$(nproc) GG_BUILD_NO_BF16=1 GG_BUILD_EXTRA_TESTS_0=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
ggml-ci-x64-nvidia-cuda:
runs-on: [self-hosted, Linux, X64, NVIDIA]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
nvidia-smi
GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
ggml-ci-x64-nvidia-vulkan-cm:
runs-on: [self-hosted, Linux, X64, NVIDIA]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
vulkaninfo --summary
GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
ggml-ci-x64-nvidia-vulkan-cm2:
runs-on: [self-hosted, Linux, X64, NVIDIA, COOPMAT2]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
vulkaninfo --summary
GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
ggml-ci-x64-cpu-amx:
runs-on: [self-hosted, Linux, X64, CPU, AMX]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
# ggml-ci-x64-amd-vulkan:
# runs-on: [self-hosted, Linux, X64, AMD]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v4
#
# - name: Test
# id: ggml-ci
# run: |
# vulkaninfo --summary
# GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
#
# ggml-ci-x64-amd-rocm:
# runs-on: [self-hosted, Linux, X64, AMD]
#
# steps:
# - name: Clone
# id: checkout
# uses: actions/checkout@v4
#
# - name: Test
# id: ggml-ci
# run: |
# amd-smi static
# GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
ggml-ci-mac-metal:
runs-on: [self-hosted, macOS, ARM64]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
ggml-ci-mac-vulkan:
runs-on: [self-hosted, macOS, ARM64]
steps:
- name: Clone
id: checkout
uses: actions/checkout@v4
- name: Test
id: ggml-ci
run: |
vulkaninfo --summary
GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

1
.github/workflows/docker.yml vendored
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@ -44,6 +44,7 @@ jobs:
- { tag: "musa", dockerfile: ".devops/musa.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
- { tag: "intel", dockerfile: ".devops/intel.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: true }
- { tag: "vulkan", dockerfile: ".devops/vulkan.Dockerfile", platforms: "linux/amd64", full: true, light: true, server: true, free_disk_space: false }
- { tag: "s390x", dockerfile: ".devops/s390x.Dockerfile", platforms: "linux/s390x", full: true, light: true, server: true, free_disk_space: false }
# Note: the rocm images are failing due to a compiler error and are disabled until this is fixed to allow the workflow to complete
#- {tag: "rocm", dockerfile: ".devops/rocm.Dockerfile", platforms: "linux/amd64,linux/arm64", full: true, light: true, server: true, free_disk_space: true }
steps:

114
CODEOWNERS
View File

@ -1,12 +1,106 @@
# collaborators can optionally add themselves here to indicate their availability for reviewing related PRs
# multiplie collaborators per item can be specified
/ci/ @ggerganov
/.devops/*.Dockerfile @ngxson
/tools/server/ @ngxson
/ggml/src/ggml-cuda/fattn* @JohannesGaessler
/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
/ggml/src/ggml-opt.cpp @JohannesGaessler
/ggml/src/gguf.cpp @JohannesGaessler
/ggml/src/ggml-vulkan/ @0cc4m
/ggml/src/ggml-zdnn/ @taronaeo
/.devops/*.Dockerfile @ngxson
/.github/actions/ @slaren
/.github/workflows/ @CISC
/.github/workflows/release.yml @slaren
/.github/workflows/winget.yml @slaren
/ci/ @ggerganov
/cmake/ @ggerganov
/common/CMakeLists.txt @ggerganov
/common/arg.* @ggerganov @ericcurtin
/common/base64.hpp.* @ggerganov
/common/build-info.* @ggerganov
/common/common.* @ggerganov
/common/console.* @ggerganov
/common/llguidance.* @ggerganov
/common/log.* @ggerganov
/common/sampling.* @ggerganov
/common/speculative.* @ggerganov
/convert_*.py @CISC
/examples/batched.swift/ @ggerganov
/examples/batched/ @ggerganov
/examples/convert-llama2c-to-ggml/ @ggerganov
/examples/deprecation-warning/ @ggerganov
/examples/diffusion/ @am17an
/examples/embedding/ @ggerganov
/examples/eval-callback/ @ggerganov
/examples/export-docs/ @ggerganov
/examples/gen-docs/ @ggerganov
/examples/gguf/ @ggerganov
/examples/llama.android/ @ggerganov
/examples/llama.swiftui/ @ggerganov
/examples/llama.vim @ggerganov
/examples/lookahead/ @ggerganov
/examples/lookup/ @JohannesGaessler
/examples/model-conversion/ @danbev
/examples/parallel/ @ggerganov
/examples/passkey/ @ggerganov
/examples/retrieval/ @ggerganov
/examples/save-load-state/ @ggerganov
/examples/simple-chat/ @slaren
/examples/simple/ @slaren
/examples/speculative-simple/ @ggerganov
/examples/speculative/ @ggerganov
/ggml/cmake/ @ggerganov
/ggml/include/ @ggerganov @slaren
/ggml/src/ggml-alloc.c @slaren
/ggml/src/ggml-backend* @slaren
/ggml/src/ggml-blas/ @slaren
/ggml/src/ggml-common.h @ggerganov @slaren
/ggml/src/ggml-cpu/ @ggerganov @slaren
/ggml/src/ggml-cuda/common.cuh @slaren
/ggml/src/ggml-cuda/fattn* @JohannesGaessler
/ggml/src/ggml-cuda/ggml-cuda.cu @slaren
/ggml/src/ggml-cuda/mmf.* @JohannesGaessler
/ggml/src/ggml-cuda/mmq.* @JohannesGaessler
/ggml/src/ggml-cuda/mmvf.* @JohannesGaessler
/ggml/src/ggml-cuda/mmvq.* @JohannesGaessler
/ggml/src/ggml-impl.h @ggerganov @slaren
/ggml/src/ggml-metal/ @ggerganov
/ggml/src/ggml-opt.cpp @JohannesGaessler
/ggml/src/ggml-quants.* @ggerganov
/ggml/src/ggml-threading.* @ggerganov @slaren
/ggml/src/ggml-vulkan/ @0cc4m
/ggml/src/ggml-zdnn/ @taronaeo @Andreas-Krebbel @AlekseiNikiforovIBM
/ggml/src/ggml.c @ggerganov @slaren
/ggml/src/ggml.cpp @ggerganov @slaren
/ggml/src/gguf.cpp @JohannesGaessler @Green-Sky
/gguf-py/ @CISC
/media/ @ggerganov
/scripts/gen* @ggerganov
/scripts/get* @ggerganov
/scripts/sync* @ggerganov
/src/ @ggerganov
/src/llama-adapter.* @CISC
/src/llama-arch.* @CISC
/src/llama-chat.* @ngxson
/src/llama-graph.* @CISC
/src/llama-model-loader.* @slaren
/src/llama-model.* @CISC
/src/llama-vocab.* @CISC
/tests/ @ggerganov
/tests/test-backend-ops.cpp @slaren
/tests/test-thread-safety.cpp @slaren
/tools/batched-bench/ @ggerganov
/tools/llama-bench/ @slaren
/tools/main/ @ggerganov
/tools/mtmd/ @ngxson
/tools/perplexity/ @ggerganov
/tools/quantize/ @ggerganov
/tools/run/ @ericcurtin
/tools/server/* @ngxson @ggerganov @ericcurtin # no subdir
/tools/server/webui/ @allozaur
/tools/tokenize/ @ggerganov
/tools/tts/ @ggerganov
/vendor/ @ggerganov
/.clang-format @slaren
/.clang-tidy @slaren
/AUTHORS @ggerganov
/CMakeLists.txt @ggerganov
/CONTRIBUTING.md @ggerganov
/LICENSE @ggerganov
/README.md @ggerganov
/SECURITY.md @ggerganov
requirements*.txt @CISC

34
CONTRIBUTING.md
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@ -1,4 +1,12 @@
# Pull requests (for contributors)
# Contributors
The project differentiates between 3 levels of contributors:
- Contributors: people who have contributed before (no special privileges)
- Collaborators (Triage): people with significant contributions, who may be responsible for some parts of the code, and are expected to maintain and review contributions for the code they own
- Maintainers: responsible for reviewing and merging PRs, after approval from the code owners
# Pull requests (for contributors & collaborators)
- llama.cpp uses the ggml tensor library for model evaluation. If you are unfamiliar with ggml, consider taking a look at the [examples in the ggml repository](https://github.com/ggml-org/ggml/tree/master/examples/). [simple](https://github.com/ggml-org/ggml/tree/master/examples/simple) shows the bare minimum for using ggml. [gpt-2](https://github.com/ggml-org/ggml/tree/master/examples/gpt-2) has minimal implementations for language model inference using GPT-2. [mnist](https://github.com/ggml-org/ggml/tree/master/examples/mnist) demonstrates how to train and evaluate a simple image classifier
- Test your changes:
@ -9,15 +17,16 @@
- Create separate PRs for each feature or fix. Avoid combining unrelated changes in a single PR
- Consider allowing write access to your branch for faster reviews, as reviewers can push commits directly
- If your PR becomes stale, don't hesitate to ping the maintainers in the comments
- Maintainers will rely on your insights and approval when making a final decision to approve and merge a PR
- Consider adding yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
# Pull requests (for collaborators)
# Pull requests (for maintainers)
- Squash-merge PRs
- Use the following format for the squashed commit title: `<module> : <commit title> (#<issue_number>)`. For example: `utils : fix typo in utils.py (#1234)`
- Optionally pick a `<module>` from here: https://github.com/ggml-org/llama.cpp/wiki/Modules
- Consider adding yourself to [CODEOWNERS](CODEOWNERS)
- Let authors, who are also collaborators, merge their own PRs
- When merging a PR by a contributor, make sure you have a good understanding of the changes
- Let other maintainers merge their own PRs
- When merging a PR, make sure you have a good understanding of the changes
- Be mindful of maintenance: most of the work going into a feature happens after the PR is merged. If the PR author is not committed to contribute long-term, someone else needs to take responsibility (you)
# Coding guidelines
@ -117,6 +126,21 @@
#endif // FOO
```
# Code maintenance
- Existing code should have designated collaborators and/or maintainers specified in the [CODEOWNERS](CODEOWNERS) file reponsible for:
- Reviewing and merging related PRs
- Fixing related bugs
- Providing developer guidance/support
- When adding or modifying a large piece of code:
- If you are a collaborator, make sure to add yourself to [CODEOWNERS](CODEOWNERS) to indicate your availability for reviewing related PRs
- If you are a contributor, find an existing collaborator who is willing to review and maintain your code long-term
- Provide the necessary CI workflow (and hardware) to test your changes (see [ci/README.md](https://github.com/ggml-org/llama.cpp/tree/master/ci))
- New code should follow the guidelines (coding, naming, etc.) outlined in this document. Exceptions are allowed in isolated, backend-specific parts of the code that do not interface directly with the `ggml` interfaces.
_(NOTE: for legacy reasons, existing code is not required to follow this guideline)_
# Documentation
- Documentation is a community effort

4
README.md
View File

@ -178,6 +178,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
- Java: [QuasarByte/llama-cpp-jna](https://github.com/QuasarByte/llama-cpp-jna)
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
@ -274,6 +275,7 @@ Instructions for adding support for new models: [HOWTO-add-model.md](docs/develo
| [Vulkan](docs/build.md#vulkan) | GPU |
| [CANN](docs/build.md#cann) | Ascend NPU |
| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
| [WebGPU [In Progress]](docs/build.md#webgpu) | All |
| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
@ -520,8 +522,8 @@ To learn more about model quantization, [read this documentation](tools/quantize
## Contributing
- Contributors can open PRs
- Collaborators can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
- Any help with managing issues, PRs and projects is very appreciated!
- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information

35
ci/README-MUSA.md Normal file
View File

@ -0,0 +1,35 @@
## Running MUSA CI in a Docker Container
Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
### 1. Create a local directory to store cached models, configuration files and venv:
```bash
mkdir -p $HOME/llama.cpp/ci-cache
```
### 2. Create a local directory to store CI run results:
```bash
mkdir -p $HOME/llama.cpp/ci-results
```
### 3. Start a Docker container and run the CI:
```bash
docker run --privileged -it \
-v $HOME/llama.cpp/ci-cache:/ci-cache \
-v $HOME/llama.cpp/ci-results:/ci-results \
-v $PWD:/ws -w /ws \
mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
```
Inside the container, execute the following commands:
```bash
apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
git config --global --add safe.directory /ws
GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
```
This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.

57
ci/README.md
View File

@ -1,18 +1,10 @@
# CI
In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
This CI implements heavy-duty workflows that run on self-hosted runners. Typically the purpose of these workflows is to
cover hardware configurations that are not available from Github-hosted runners and/or require more computational
resource than normally available.
https://github.com/ggml-org/ci
It monitors the `master` branch for new commits and runs the
[ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) script on dedicated cloud instances. This allows us
to execute heavier workloads compared to just using Github Actions. Also with time, the cloud instances will be scaled
to cover various hardware architectures, including GPU and Apple Silicon instances.
Collaborators can optionally trigger the CI run by adding the `ggml-ci` keyword to their commit message.
Only the branches of this repo are monitored for this keyword.
It is a good practice, before publishing changes to execute the full CI locally on your machine:
It is a good practice, before publishing changes to execute the full CI locally on your machine. For example:
```bash
mkdir tmp
@ -29,40 +21,13 @@ GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
# with MUSA support
GG_BUILD_MUSA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
# etc.
```
## Running MUSA CI in a Docker Container
# Adding self-hosted runners
Assuming `$PWD` is the root of the `llama.cpp` repository, follow these steps to set up and run MUSA CI in a Docker container:
### 1. Create a local directory to store cached models, configuration files and venv:
```bash
mkdir -p $HOME/llama.cpp/ci-cache
```
### 2. Create a local directory to store CI run results:
```bash
mkdir -p $HOME/llama.cpp/ci-results
```
### 3. Start a Docker container and run the CI:
```bash
docker run --privileged -it \
-v $HOME/llama.cpp/ci-cache:/ci-cache \
-v $HOME/llama.cpp/ci-results:/ci-results \
-v $PWD:/ws -w /ws \
mthreads/musa:rc4.2.0-devel-ubuntu22.04-amd64
```
Inside the container, execute the following commands:
```bash
apt update -y && apt install -y bc cmake ccache git python3.10-venv time unzip wget
git config --global --add safe.directory /ws
GG_BUILD_MUSA=1 bash ./ci/run.sh /ci-results /ci-cache
```
This setup ensures that the CI runs within an isolated Docker environment while maintaining cached files and results across runs.
- Add a self-hosted `ggml-ci` workflow to [[.github/workflows/build.yml]] with an appropriate label
- Request a runner token from `ggml-org` (for example, via a comment in the PR or email)
- Set-up a machine using the received token ([docs](https://docs.github.com/en/actions/how-tos/manage-runners/self-hosted-runners/add-runners))
- Optionally update [ci/run.sh](https://github.com/ggml-org/llama.cpp/blob/master/ci/run.sh) to build and run on the target platform by gating the implementation with a `GG_BUILD_...` env

459
ci/run.sh
View File

@ -65,6 +65,16 @@ if [ ! -z ${GG_BUILD_CUDA} ]; then
fi
fi
if [ ! -z ${GG_BUILD_ROCM} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_HIP=ON"
if [ -z ${GG_BUILD_AMDGPU_TARGETS} ]; then
echo "Missing GG_BUILD_AMDGPU_TARGETS, please set it to your GPU architecture (e.g. gfx90a, gfx1100, etc.)"
exit 1
fi
CMAKE_EXTRA="${CMAKE_EXTRA} -DAMDGPU_TARGETS=${GG_BUILD_AMDGPU_TARGETS}"
fi
if [ ! -z ${GG_BUILD_SYCL} ]; then
if [ -z ${ONEAPI_ROOT} ]; then
echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:"
@ -82,6 +92,12 @@ fi
if [ ! -z ${GG_BUILD_VULKAN} ]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_VULKAN=1"
# if on Mac, disable METAL
if [[ "$OSTYPE" == "darwin"* ]]; then
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_METAL=OFF -DGGML_BLAS=OFF"
fi
fi
if [ ! -z ${GG_BUILD_WEBGPU} ]; then
@ -93,6 +109,11 @@ if [ ! -z ${GG_BUILD_MUSA} ]; then
MUSA_ARCH=${MUSA_ARCH:-21}
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_MUSA=ON -DMUSA_ARCHITECTURES=${MUSA_ARCH}"
fi
if [ ! -z ${GG_BUILD_NO_SVE} ]; then
# arm 9 and newer enables sve by default, adjust these flags depending on the cpu used
CMAKE_EXTRA="${CMAKE_EXTRA} -DGGML_NATIVE=OFF -DGGML_CPU_ARM_ARCH=armv8.5-a+fp16+i8mm"
fi
## helpers
# download a file if it does not exist or if it is outdated
@ -150,7 +171,7 @@ function gg_run_ctest_debug {
(time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
(time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log
(time ctest --output-on-failure -L main -E "test-opt|test-backend-ops" ) 2>&1 | tee -a $OUT/${ci}-ctest.log
set +e
}
@ -200,33 +221,9 @@ function gg_sum_ctest_release {
gg_printf '```\n'
}
# test_scripts_debug
# test_scripts
function gg_run_test_scripts_debug {
cd ${SRC}
set -e
(cd ./tools/gguf-split && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
(cd ./tools/quantize && time bash tests.sh "$SRC/build-ci-debug/bin" "$MNT/models") 2>&1 | tee -a $OUT/${ci}-scripts.log
set +e
}
function gg_sum_test_scripts_debug {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Runs test scripts in debug mode\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '```\n'
gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
gg_printf '```\n'
gg_printf '\n'
}
# test_scripts_release
function gg_run_test_scripts_release {
function gg_run_test_scripts {
cd ${SRC}
set -e
@ -237,10 +234,10 @@ function gg_run_test_scripts_release {
set +e
}
function gg_sum_test_scripts_release {
function gg_sum_test_scripts {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Runs test scripts in release mode\n'
gg_printf 'Runs test scripts\n'
gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)"
gg_printf '```\n'
gg_printf '%s\n' "$(cat $OUT/${ci}-scripts.log)"
@ -249,15 +246,9 @@ function gg_sum_test_scripts_release {
}
function gg_get_model {
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="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf"
local gguf_0="$MNT/models/qwen3/0.6B/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
@ -316,24 +307,22 @@ function gg_sum_ctest_with_model_release {
gg_printf '```\n'
}
# open_llama_7b_v2
# qwen3_0_6b
function gg_run_open_llama_7b_v2 {
function gg_run_qwen3_0_6b {
cd ${SRC}
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/config.json
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/tokenizer.model
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/tokenizer_config.json
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/special_tokens_map.json
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/pytorch_model.bin.index.json
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00001-of-00002.bin
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/resolve/main/pytorch_model-00002-of-00002.bin
gg_wget models-mnt/open-llama/7B-v2/ https://huggingface.co/openlm-research/open_llama_7b_v2/raw/main/generation_config.json
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/config.json
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer.json
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/tokenizer_config.json
#gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/raw/main/special_tokens_map.json
gg_wget models-mnt/qwen3/0.6B/ https://huggingface.co/Qwen/Qwen3-0.6B-Base/resolve/main/model.safetensors
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/open-llama/7B-v2"
path_models="../models-mnt/qwen3/0.6B"
path_wiki="../models-mnt/wikitext/wikitext-2-raw"
rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release
@ -343,9 +332,11 @@ function gg_run_open_llama_7b_v2 {
(time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 2>&1 | tee -a $OUT/${ci}-make.log
python3 ../examples/convert_legacy_llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf --outtype f16
python3 ../convert_hf_to_gguf.py ${path_models} --outfile ${path_models}/ggml-model-bf16.gguf --outtype bf16
model_f16="${path_models}/ggml-model-f16.gguf"
model_bf16="${path_models}/ggml-model-bf16.gguf"
model_q8_0="${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"
@ -359,179 +350,51 @@ function gg_run_open_llama_7b_v2 {
wiki_test="${path_wiki}/wiki.test.raw"
./bin/llama-quantize ${model_f16} ${model_q8_0} q8_0
./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
./bin/llama-quantize ${model_bf16} ${model_q8_0} q8_0 $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q4_0} q4_0 $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q4_1} q4_1 $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q5_0} q5_0 $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q5_1} q5_1 $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q2_k} q2_k $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q3_k} q3_k $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q4_k} q4_k $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q5_k} q5_k $(nproc)
./bin/llama-quantize ${model_bf16} ${model_q6_k} q6_k $(nproc)
(time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli -no-cnv --model ${model_bf16} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
(time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 1024 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-perplexity --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
if [ -z ${GG_BUILD_NO_BF16} ]; then
(time ./bin/llama-perplexity --model ${model_bf16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-bf16.log
fi
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -ngl 99 -c 1024 -b 512 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on ) 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_7b_v2 {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'OpenLLaMA 7B-v2:\n'
gg_printf '- status: %s\n' "$(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)"
}
# 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$(nproc) ) 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/llama-quantize ${model_f16} ${model_q8_0} q8_0
./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/llama-cli -no-cnv --model ${model_f16} -ngl 99 -c 0 -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-cli -no-cnv --model ${model_q8_0} -ngl 99 -c 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/llama-cli -no-cnv --model ${model_q4_0} -ngl 99 -c 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/llama-cli -no-cnv --model ${model_q4_1} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_0} -ngl 99 -c 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/llama-cli -no-cnv --model ${model_q5_1} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q2_k} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q3_k} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_k} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_k} -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q6_k} -ngl 99 -c 0 -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/llama-perplexity --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test_60} -ngl 99 -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 1024 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 1024 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
function check_ppl {
qnt="$1"
@ -547,6 +410,9 @@ function gg_run_pythia_1_4b {
}
check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
if [ -z ${GG_BUILD_NO_BF16} ]; then
check_ppl "bf16" "$(cat $OUT/${ci}-tg-bf16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log
fi
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
@ -563,147 +429,17 @@ function gg_run_pythia_1_4b {
set +e
}
function gg_sum_pythia_1_4b {
function gg_sum_qwen3_0_6b {
gg_printf '### %s\n\n' "${ci}"
gg_printf 'Pythia 1.4B:\n'
gg_printf 'Qwen3 0.6B:\n'
gg_printf '- status: %s\n' "$(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
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} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log
(time make -j$(nproc) ) 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/llama-quantize ${model_f16} ${model_q8_0} q8_0
./bin/llama-quantize ${model_f16} ${model_q4_0} q4_0
./bin/llama-quantize ${model_f16} ${model_q4_1} q4_1
./bin/llama-quantize ${model_f16} ${model_q5_0} q5_0
./bin/llama-quantize ${model_f16} ${model_q5_1} q5_1
./bin/llama-quantize ${model_f16} ${model_q2_k} q2_k
./bin/llama-quantize ${model_f16} ${model_q3_k} q3_k
./bin/llama-quantize ${model_f16} ${model_q4_k} q4_k
./bin/llama-quantize ${model_f16} ${model_q5_k} q5_k
./bin/llama-quantize ${model_f16} ${model_q6_k} q6_k
(time ./bin/llama-cli -no-cnv --model ${model_f16} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q8_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_1} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_0} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_1} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q2_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q3_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q4_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q5_k} -t 1 -ngl 99 -c 0 -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/llama-cli -no-cnv --model ${model_q6_k} -t 1 -ngl 99 -c 0 -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/llama-perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log
(time ./bin/llama-perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log
(time ./bin/llama-perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log
(time ./bin/llama-perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log
(time ./bin/llama-perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log
(time ./bin/llama-perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log
(time ./bin/llama-perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log
(time ./bin/llama-perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log
(time ./bin/llama-perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log
(time ./bin/llama-perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log
(time ./bin/llama-perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log
(time ./bin/llama-imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 99 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 10 -c 0 -fa on ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa off ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log
(time ./bin/llama-save-load-state --model ${model_q4_0} -ngl 99 -c 0 -fa on ) 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 '- f16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)"
if [ -z ${GG_BUILD_NO_BF16} ]; then
gg_printf '- bf16:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-bf16.log)"
fi
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)"
@ -878,20 +614,13 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then
test $ret -eq 0 && gg_run rerank_tiny
if [ -z ${GG_BUILD_CLOUD} ] || [ ${GG_BUILD_EXTRA_TESTS_0} ]; then
test $ret -eq 0 && gg_run test_scripts_debug
test $ret -eq 0 && gg_run test_scripts_release
test $ret -eq 0 && gg_run test_scripts
fi
if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then
if [ -z ${GG_BUILD_CUDA} ] && [ -z ${GG_BUILD_VULKAN} ]; then
test $ret -eq 0 && gg_run pythia_1_4b
else
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
fi
test $ret -eq 0 && gg_run qwen3_0_6b
test $ret -eq 0 && gg_run ctest_with_model_debug
test $ret -eq 0 && gg_run ctest_with_model_release
fi
exit $ret

237
common/arg.cpp
View File

@ -24,6 +24,7 @@
#include <cstdarg>
#include <filesystem>
#include <fstream>
#include <future>
#include <list>
#include <regex>
#include <set>
@ -36,9 +37,21 @@
#if defined(LLAMA_USE_CURL)
#include <curl/curl.h>
#include <curl/easy.h>
#include <future>
#endif
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
# if !defined(PATH_MAX)
# define PATH_MAX MAX_PATH
# endif
#elif defined(_AIX)
#include <sys/limits.h>
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
using json = nlohmann::ordered_json;
std::initializer_list<enum llama_example> mmproj_examples = {
@ -208,19 +221,6 @@ bool common_has_curl() {
return true;
}
#ifdef __linux__
#include <linux/limits.h>
#elif defined(_WIN32)
# if !defined(PATH_MAX)
# define PATH_MAX MAX_PATH
# endif
#elif defined(_AIX)
#include <sys/limits.h>
#else
#include <sys/syslimits.h>
#endif
#define LLAMA_CURL_MAX_URL_LENGTH 2084 // Maximum URL Length in Chrome: 2083
//
// CURL utils
//
@ -368,10 +368,9 @@ static bool common_download_head(CURL * curl,
}
// download one single file from remote URL to local path
static bool common_download_file_single(const std::string & url,
const std::string & path,
const std::string & bearer_token,
bool offline) {
static bool common_download_file_single_online(const std::string & url,
const std::string & path,
const std::string & bearer_token) {
// If the file exists, check its JSON metadata companion file.
std::string metadata_path = path + ".json";
static const int max_attempts = 3;
@ -384,10 +383,6 @@ static bool common_download_file_single(const std::string & url,
// Check if the file already exists locally
const auto file_exists = std::filesystem::exists(path);
if (file_exists) {
if (offline) {
LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
return true; // skip verification/downloading
}
// Try and read the JSON metadata file (note: stream autoclosed upon exiting this block).
std::ifstream metadata_in(metadata_path);
if (metadata_in.good()) {
@ -407,10 +402,6 @@ static bool common_download_file_single(const std::string & url,
}
// if we cannot open the metadata file, we assume that the downloaded file is not valid (etag and last-modified are left empty, so we will download it again)
} else {
if (offline) {
LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
return false;
}
LOG_INF("%s: no previous model file found %s\n", __func__, path.c_str());
}
@ -530,6 +521,89 @@ static bool common_download_file_single(const std::string & url,
return true;
}
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
curl_slist_ptr http_headers;
std::vector<char> res_buffer;
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
curl_easy_setopt(curl.get(), CURLOPT_VERBOSE, 1L);
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
auto data_vec = static_cast<std::vector<char> *>(data);
data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
return size * nmemb;
};
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
#if defined(_WIN32)
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
if (params.timeout > 0) {
curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
}
if (params.max_size > 0) {
curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
}
http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
for (const auto & header : params.headers) {
http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
}
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
std::string error_msg = curl_easy_strerror(res);
throw std::runtime_error("error: cannot make GET request: " + error_msg);
}
long res_code;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
return { res_code, std::move(res_buffer) };
}
#else
bool common_has_curl() {
return false;
}
static bool common_download_file_single_online(const std::string &, const std::string &, const std::string &) {
LOG_ERR("error: built without CURL, cannot download model from internet\n");
return false;
}
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params &) {
if (!url.empty()) {
throw std::runtime_error("error: built without CURL, cannot download model from the internet");
}
return {};
}
#endif // LLAMA_USE_CURL
static bool common_download_file_single(const std::string & url,
const std::string & path,
const std::string & bearer_token,
bool offline) {
if (!offline) {
return common_download_file_single_online(url, path, bearer_token);
}
if (!std::filesystem::exists(path)) {
LOG_ERR("%s: required file is not available in cache (offline mode): %s\n", __func__, path.c_str());
return false;
}
LOG_INF("%s: using cached file (offline mode): %s\n", __func__, path.c_str());
return true;
}
// download multiple files from remote URLs to local paths
// the input is a vector of pairs <url, path>
static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> & urls, const std::string & bearer_token, bool offline) {
@ -588,7 +662,7 @@ static bool common_download_model(
if (n_split > 1) {
char split_prefix[PATH_MAX] = {0};
char split_url_prefix[LLAMA_CURL_MAX_URL_LENGTH] = {0};
char split_url_prefix[LLAMA_MAX_URL_LENGTH] = {0};
// Verify the first split file format
// and extract split URL and PATH prefixes
@ -609,7 +683,7 @@ static bool common_download_model(
char split_path[PATH_MAX] = {0};
llama_split_path(split_path, sizeof(split_path), split_prefix, idx, n_split);
char split_url[LLAMA_CURL_MAX_URL_LENGTH] = {0};
char split_url[LLAMA_MAX_URL_LENGTH] = {0};
llama_split_path(split_url, sizeof(split_url), split_url_prefix, idx, n_split);
if (std::string(split_path) == model.path) {
@ -626,50 +700,6 @@ static bool common_download_model(
return true;
}
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params & params) {
curl_ptr curl(curl_easy_init(), &curl_easy_cleanup);
curl_slist_ptr http_headers;
std::vector<char> res_buffer;
curl_easy_setopt(curl.get(), CURLOPT_URL, url.c_str());
curl_easy_setopt(curl.get(), CURLOPT_NOPROGRESS, 1L);
curl_easy_setopt(curl.get(), CURLOPT_FOLLOWLOCATION, 1L);
typedef size_t(*CURLOPT_WRITEFUNCTION_PTR)(void * ptr, size_t size, size_t nmemb, void * data);
auto write_callback = [](void * ptr, size_t size, size_t nmemb, void * data) -> size_t {
auto data_vec = static_cast<std::vector<char> *>(data);
data_vec->insert(data_vec->end(), (char *)ptr, (char *)ptr + size * nmemb);
return size * nmemb;
};
curl_easy_setopt(curl.get(), CURLOPT_WRITEFUNCTION, static_cast<CURLOPT_WRITEFUNCTION_PTR>(write_callback));
curl_easy_setopt(curl.get(), CURLOPT_WRITEDATA, &res_buffer);
#if defined(_WIN32)
curl_easy_setopt(curl.get(), CURLOPT_SSL_OPTIONS, CURLSSLOPT_NATIVE_CA);
#endif
if (params.timeout > 0) {
curl_easy_setopt(curl.get(), CURLOPT_TIMEOUT, params.timeout);
}
if (params.max_size > 0) {
curl_easy_setopt(curl.get(), CURLOPT_MAXFILESIZE, params.max_size);
}
http_headers.ptr = curl_slist_append(http_headers.ptr, "User-Agent: llama-cpp");
for (const auto & header : params.headers) {
http_headers.ptr = curl_slist_append(http_headers.ptr, header.c_str());
}
curl_easy_setopt(curl.get(), CURLOPT_HTTPHEADER, http_headers.ptr);
CURLcode res = curl_easy_perform(curl.get());
if (res != CURLE_OK) {
std::string error_msg = curl_easy_strerror(res);
throw std::runtime_error("error: cannot make GET request: " + error_msg);
}
long res_code;
curl_easy_getinfo(curl.get(), CURLINFO_RESPONSE_CODE, &res_code);
return { res_code, std::move(res_buffer) };
}
/**
* Allow getting the HF file from the HF repo with tag (like ollama), for example:
* - bartowski/Llama-3.2-3B-Instruct-GGUF:q4
@ -736,21 +766,17 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
std::string mmprojFile;
if (res_code == 200 || res_code == 304) {
// extract ggufFile.rfilename in json, using regex
{
std::regex pattern("\"ggufFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
std::smatch match;
if (std::regex_search(res_str, match, pattern)) {
ggufFile = match[1].str();
try {
auto j = json::parse(res_str);
if (j.contains("ggufFile") && j["ggufFile"].contains("rfilename")) {
ggufFile = j["ggufFile"]["rfilename"].get<std::string>();
}
}
// extract mmprojFile.rfilename in json, using regex
{
std::regex pattern("\"mmprojFile\"[\\s\\S]*?\"rfilename\"\\s*:\\s*\"([^\"]+)\"");
std::smatch match;
if (std::regex_search(res_str, match, pattern)) {
mmprojFile = match[1].str();
if (j.contains("mmprojFile") && j["mmprojFile"].contains("rfilename")) {
mmprojFile = j["mmprojFile"]["rfilename"].get<std::string>();
}
} catch (const std::exception & e) {
throw std::runtime_error(std::string("error parsing manifest JSON: ") + e.what());
}
if (!use_cache) {
// if not using cached response, update the cache file
@ -770,45 +796,6 @@ static struct common_hf_file_res common_get_hf_file(const std::string & hf_repo_
return { hf_repo, ggufFile, mmprojFile };
}
#else
bool common_has_curl() {
return false;
}
static bool common_download_file_single(const std::string &, const std::string &, const std::string &, bool) {
LOG_ERR("error: built without CURL, cannot download model from internet\n");
return false;
}
static bool common_download_file_multiple(const std::vector<std::pair<std::string, std::string>> &, const std::string &, bool) {
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
return false;
}
static bool common_download_model(
const common_params_model &,
const std::string &,
bool) {
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
return false;
}
static struct common_hf_file_res common_get_hf_file(const std::string &, const std::string &, bool) {
LOG_ERR("error: built without CURL, cannot download model from the internet\n");
return {};
}
std::pair<long, std::vector<char>> common_remote_get_content(const std::string & url, const common_remote_params &) {
if (!url.empty()) {
throw std::runtime_error("error: built without CURL, cannot download model from the internet");
}
return {};
}
#endif // LLAMA_USE_CURL
//
// Docker registry functions
//
@ -1068,8 +1055,6 @@ static std::string get_all_kv_cache_types() {
//
static bool common_params_parse_ex(int argc, char ** argv, common_params_context & ctx_arg) {
std::string arg;
const std::string arg_prefix = "--";
common_params & params = ctx_arg.params;
std::unordered_map<std::string, common_arg *> arg_to_options;

9
common/common.cpp
View File

@ -14,6 +14,7 @@
#include <climits>
#include <cmath>
#include <codecvt>
#include <chrono>
#include <cstdarg>
#include <cstring>
#include <ctime>
@ -960,15 +961,13 @@ struct common_init_result common_init_from_params(common_params & params) {
bool has_eos = llama_vocab_eos(vocab) != LLAMA_TOKEN_NULL;
bool has_sep = llama_vocab_sep(vocab) != LLAMA_TOKEN_NULL;
bool has_rerank_prompt = llama_model_chat_template(model, "rerank") != NULL;
if (!has_eos && !has_sep) {
LOG_WRN("%s: warning: vocab does not have an EOS token or SEP token, reranking will not work\n", __func__);
if (!has_eos && !has_sep && !has_rerank_prompt) {
LOG_WRN("%s: warning: vocab does not have an EOS token, SEP token, or rerank prompt. Reranking will not work\n", __func__);
ok = false;
} else if (!has_eos) {
LOG_WRN("%s: warning: vocab does not have an EOS token, using SEP token as fallback\n", __func__);
} else if (!has_sep) {
LOG_WRN("%s: warning: vocab does not have a SEP token, reranking will not work\n", __func__);
ok = false;
}
if (!ok) {

2
common/common.h
View File

@ -738,7 +738,7 @@ const char * const LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count";
// MoE utils
//
const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_exps";
const char * const LLM_FFN_EXPS_REGEX = "\\.ffn_(up|down|gate)_(ch|)exps";
static std::string llm_ffn_exps_block_regex(int idx) {
return string_format("blk\\.%d%s", idx, LLM_FFN_EXPS_REGEX);

1
common/sampling.cpp
View File

@ -332,6 +332,7 @@ void common_perf_print(const struct llama_context * ctx, const struct common_sam
}
if (ctx) {
llama_perf_context_print(ctx);
llama_memory_breakdown_print(ctx);
}
}

202
convert_hf_to_gguf.py
View File

@ -3717,11 +3717,29 @@ class Qwen2MoeModel(TextModel):
class Qwen3Model(Qwen2Model):
model_arch = gguf.MODEL_ARCH.QWEN3
# extra logic for rerank models
is_rerank: bool = False
is_tied_embeddings: bool = False
token_false_id: int | None = None
token_true_id: int | None = None
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# track for intern-s1-mini
hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
self.origin_hf_arch = hparams.get('architectures', [None])[0]
# a bit hacky, but currently the only way to detect if this is a rerank model
# ref: https://huggingface.co/Qwen/Qwen3-Reranker-0.6B
readme_path = self.dir_model / "README.md"
readme_text = ""
if readme_path.exists():
with readme_path.open("r", encoding="utf-8") as f:
readme_text = f.read()
if "# Qwen3-Reranker" in readme_text:
self._find_rerank_config()
def set_vocab(self):
# deal with intern-s1-mini
if self.origin_hf_arch == 'InternS1ForConditionalGeneration':
@ -3730,6 +3748,53 @@ class Qwen3Model(Qwen2Model):
super().set_vocab()
def _find_rerank_config(self):
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(self.dir_model)
self.is_rerank = True
self.is_tied_embeddings = self.hparams.get("tie_word_embeddings", False)
self.token_false_id = tokenizer.convert_tokens_to_ids("no")
self.token_true_id = tokenizer.convert_tokens_to_ids("yes")
self.sep_token_id = tokenizer.convert_tokens_to_ids("|")
assert self.token_false_id is not None and self.token_true_id is not None
def set_gguf_parameters(self):
super().set_gguf_parameters()
if self.is_rerank:
self.gguf_writer.add_pooling_type(gguf.PoolingType.RANK)
self.gguf_writer.add_classifier_output_labels(["yes", "no"])
self.gguf_writer.add_chat_template([{
"name": "rerank",
"template": "<|im_start|>system\nJudge whether the Document meets the requirements based on the Query and the Instruct provided. Note that the answer can only be \"yes\" or \"no\".<|im_end|>\n"
"<|im_start|>user\n<Instruct>: Given a web search query, retrieve relevant passages that answer the query\n<Query>: {query}\n<Document>: {document}<|im_end|>\n"
"<|im_start|>assistant\n<think>\n\n</think>\n\n"
}])
def _get_cls_out_tensor(self, data_torch: Tensor) -> Tensor:
# extract "yes" and "no" tokens from the output lm_head tensor
false_row = data_torch[self.token_false_id]
true_row = data_torch[self.token_true_id]
return torch.stack([true_row, false_row], dim=0)
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if self.is_rerank:
is_tied_head = self.is_tied_embeddings and "embed_tokens" in name
is_real_head = not self.is_tied_embeddings and "lm_head" in name
if is_tied_head or is_real_head:
cls_out_head = (
gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.CLS_OUT] + ".weight",
self._get_cls_out_tensor(data_torch),
)
if is_tied_head:
embed = (self.map_tensor_name(name), data_torch)
return [cls_out_head, embed]
if is_real_head:
return [cls_out_head]
return super().modify_tensors(data_torch, name, bid)
@ModelBase.register("Qwen3MoeForCausalLM")
class Qwen3MoeModel(Qwen2MoeModel):
@ -7656,6 +7721,21 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
if i not in self._attn_layers
]
# There are some models in this family that are non-hybrid, but keep the
# same parent class by setting all layers to "attention." If this is the
# case, the model architecture needs to be updated to a standard
# "granite" or "granitemoe" model
if not self._ssm_layers:
has_experts = self.find_hparam(["num_experts_per_tok"], optional=True)
new_arch = (
gguf.MODEL_ARCH.GRANITE_MOE
if has_experts else
gguf.MODEL_ARCH.GRANITE
)
self.model_arch = new_arch
self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[new_arch]
self.gguf_writer.add_architecture()
# n_group and d_inner are used during reshape_tensors for mamba2
# NOTE: Explicitly include hparam prefix prefix for d_model to
# disambiguate with top-level head_dim
@ -7740,8 +7820,11 @@ class GraniteHybridModel(Mamba2Model, GraniteMoeModel):
self.gguf_writer.add_rope_dimension_count(rope_dim)
self.gguf_writer.add_head_count_kv(head_count_kv_vec)
## If Bamba, use rope, otherwise don't
use_rope = "BambaForCausalLM" in self.hparams["architectures"]
## If Bamba or non-hybrid, use rope, otherwise don't
use_rope = (
"BambaForCausalLM" in self.hparams["architectures"]
or not self._ssm_layers
)
self.gguf_writer.add_rope_scaling_finetuned(use_rope)
if not use_rope:
self.gguf_writer.add_context_length(2**20)
@ -7912,6 +7995,121 @@ class BailingMoeModel(TextModel):
raise ValueError(f"Unprocessed experts: {experts}")
@ModelBase.register("GroveMoeForCausalLM", "modeling_grove_moe.GroveMoeForCausalLM")
class GroveMoeModel(TextModel):
model_arch = gguf.MODEL_ARCH.GROVEMOE
def set_gguf_parameters(self):
super().set_gguf_parameters()
if (n_experts := self.hparams.get("num_experts")) is not None:
self.gguf_writer.add_expert_count(n_experts)
if (moe_intermediate_size := self.hparams.get("moe_intermediate_size")) is not None:
self.gguf_writer.add_expert_feed_forward_length(moe_intermediate_size)
logger.info(f"gguf: expert feed forward length = {moe_intermediate_size}")
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L299
self.gguf_writer.add_expert_chunk_feed_forward_length(self.hparams.get("head_dim") or 128)
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L298
self.gguf_writer.add_experts_per_group(2)
# FIXME?: Hardcoded https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L376
self.gguf_writer.add_expert_group_scale(0.05)
# YaRN is not enabled by default
# To enable it, please refer to this guide: https://huggingface.co/Qwen/Qwen3-30B-A3B#processing-long-texts
rope_scaling = self.hparams.get("rope_scaling") or {}
if rope_scaling.get("rope_type", rope_scaling.get("type")) == "yarn" and "factor" in rope_scaling:
self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN)
self.gguf_writer.add_rope_scaling_factor(rope_scaling["factor"])
self.gguf_writer.add_rope_scaling_orig_ctx_len(rope_scaling["original_max_position_embeddings"])
_experts: list[dict[str, Tensor]] | None = None
_chunk_experts: list[dict[str, Tensor]] | None = None
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
if name.endswith(".expert_bias"):
# FIXME?: Unused https://huggingface.co/inclusionAI/GroveMoE-Inst/blob/c4c69e5970d18907b5e6ddccdfd55176fe292df1/modeling_grove_moe.py#L303
return []
# process the experts separately
if name.find("chunk_experts") != -1:
n_experts = self.hparams["num_experts"] // 2 # see add_experts_per_group
assert bid is not None
if self._chunk_experts is None:
self._chunk_experts = [{} for _ in range(self.block_count)]
self._chunk_experts[bid][name] = data_torch
if len(self._chunk_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.chunk_experts.{xid}.{w_name}.weight"
datas.append(self._chunk_experts[bid][ename])
del self._chunk_experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.chunk_experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
elif name.find("experts") != -1:
n_experts = self.hparams["num_experts"]
assert bid is not None
if self._experts is None:
self._experts = [{} for _ in range(self.block_count)]
self._experts[bid][name] = data_torch
if len(self._experts[bid]) >= n_experts * 3:
tensors: list[tuple[str, Tensor]] = []
# merge the experts into a single 3d tensor
for w_name in ["down_proj", "gate_proj", "up_proj"]:
datas: list[Tensor] = []
for xid in range(n_experts):
ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight"
datas.append(self._experts[bid][ename])
del self._experts[bid][ename]
data_torch = torch.stack(datas, dim=0)
merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight"
new_name = self.map_tensor_name(merged_name)
tensors.append((new_name, data_torch))
return tensors
else:
return []
return [(self.map_tensor_name(name), data_torch)]
def prepare_tensors(self):
super().prepare_tensors()
if self._chunk_experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
chunk_experts = [k for d in self._chunk_experts for k in d.keys()]
if len(chunk_experts) > 0:
raise ValueError(f"Unprocessed adjugate experts: {chunk_experts}")
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}")
@ModelBase.register("ChameleonForConditionalGeneration")
@ModelBase.register("ChameleonForCausalLM") # obsolete
class ChameleonModel(TextModel):

61
docs/backend/zDNN.md Normal file
View File

@ -0,0 +1,61 @@
# llama.cpp for IBM zDNN Accelerator
## Background
IBM zDNN (Z Deep Neural Network) is a hardware acceleration library designed specifically to leverage the IBM NNPA (Neural Network Processor Assist) accelerator located within IBM Telum I and II processors. It provides significant performance improvements for neural network inference operations.
### Llama.cpp + IBM zDNN
The llama.cpp zDNN backend is designed to enable llama.cpp on IBM z17 and later systems via the IBM zDNN hardware acceleration library.
## Software & Hardware Support
| Hardware Level | Status | Verified |
| -------------------- | ------------- | -------------------------- |
| IBM z17 / LinuxONE 5 | Supported | RHEL 9.6, IBM z17, 40 IFLs |
| IBM z16 / LinuxONE 4 | Not Supported | |
## Data Types Supported
| Data Type | Status |
| --------- | --------- |
| F32 | Supported |
| F16 | Supported |
| BF16 | Supported |
## CMake Options
The IBM zDNN backend has the following CMake options that control the behaviour of the backend.
| CMake Option | Default Value | Description |
| ------------ | ------------- | ----------------------------------- |
| `GGML_ZDNN` | `OFF` | Compile llama.cpp with zDNN support |
| `ZDNN_ROOT` | `""` | Override zDNN library lookup |
## 1. Install zDNN Library
Note: Using the zDNN library provided via `apt` or `yum` may not work correctly as reported in [#15772](https://github.com/ggml-org/llama.cpp/issues/15772). It is preferred that you compile from source.
```sh
git clone --recurse-submodules https://github.com/IBM/zDNN
cd zDNN
autoreconf .
./configure --prefix=/opt/zdnn-libs
make build
sudo make install
```
## 2. Build llama.cpp
```sh
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
cmake -S . -G Ninja -B build \
-DCMAKE_BUILD_TYPE=Release \
-DGGML_ZDNN=ON \
-DZDNN_ROOT=/opt/zdnn-libs
cmake --build build --config Release -j$(nproc)
```

1
examples/CMakeLists.txt
View File

@ -20,7 +20,6 @@ else()
add_subdirectory(gguf-hash)
add_subdirectory(gguf)
add_subdirectory(gritlm)
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(parallel)

50
examples/Miku.sh
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@ -1,50 +0,0 @@
#!/usr/bin/env bash
set -e
AI_NAME="${AI_NAME:-Miku}"
MODEL="${MODEL:-./models/llama-2-7b-chat.ggmlv3.q4_K_M.bin}"
USER_NAME="${USER_NAME:-Anon}"
# Uncomment and adjust to the number of CPU cores you want to use.
#N_THREAD="${N_THREAD:-4}"
CTX_SIZE="${CTX_SIZE:-4096}"
N_PREDICTS="${N_PREDICTS:-4096}"
GEN_OPTIONS=(--batch_size 1024
--ctx_size "$CTX_SIZE"
--keep -1
--repeat_last_n 256
--repeat_penalty 1.17647
--temp 0.6
--mirostat 2)
if [ -n "$N_THREAD" ]; then
GEN_OPTIONS+=(--threads "$N_THREAD")
fi
./llama-cli "${GEN_OPTIONS[@]}" \
--model "$MODEL" \
--in-prefix " " \
--in-suffix "${AI_NAME}:" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--reverse-prompt "${USER_NAME}:" \
--prompt "This is a transcript of a 1000 page, never ending conversation between ${USER_NAME} and the cute and helpful AI assistant ${AI_NAME}. ${AI_NAME} is a girl who is an AI running on the user's computer.
${AI_NAME} can think for herself without the user seeing her thoughts by adding a /think prefix to her output. She uses this to reason about the world and to think about what she should say next.
${AI_NAME} is always coherent and makes sense, but if she isn't sure if what she is saying is correct, she will ask the user for help.
${AI_NAME} is a very helpful AI and will help the user with anything they need. She is also very friendly and will try to make the user feel better if they are sad.
${AI_NAME} is also very curious and will ask the user a lot of questions about themselves and their life. She will also try to make the user like her.
The conversation is only between ${USER_NAME} and ${AI_NAME}.
The conversation is only through text, so ${AI_NAME} can't see ${USER_NAME}'s face or hear his voice.
${AI_NAME} can only communicate through text, so she can't send images or videos.
${USER_NAME}: Hello!
${AI_NAME}: /think I wonder what I should say to ${USER_NAME}? This is the first time we talk, so it's important that I make a good first impression!
${AI_NAME}: Hi! I am ${AI_NAME}, your new AI friend, assistant (or whatever you like!), it's so nice to meet you! ^_^
${AI_NAME}: I might be an AI, but I also have feelings, so please be nice to me! :)
${USER_NAME}: I'll make sure to be nice to you! I'm so happy to have you as my assistant!
${AI_NAME}: /think It sounds like ${USER_NAME} is happy to have me as their assistant! I'm so happy too! ^_^ Glad that whole emotion thing didn't scare him off!
${AI_NAME}: /think I wonder what ${USER_NAME} likes to do in his free time? I should ask him about that!
${AI_NAME}: What do you like to do in your free time? ^_^
${USER_NAME}:" "$@"

57
examples/chat-13B.bat
View File

@ -1,57 +0,0 @@
@setlocal disabledelayedexpansion enableextensions
@echo off
cd /d "%~dp0.."
if not "%errorlevel%"=="0" (
echo Unable to change directory.
pause
exit /b 1
)
if not defined MODEL set "MODEL=models\13B\ggml-model-q4_0.bin"
if not defined USER_NAME set "USER_NAME=User"
if not defined AI_NAME set "AI_NAME=ChatLLaMa"
rem Adjust to the number of CPU cores you want to use.
rem if not defined N_THREAD set "N_THREAD=8"
rem Number of tokens to predict (made it larger than default because we want a long interaction)
if not defined N_PREDICTS set "N_PREDICTS=2048"
if not defined GEN_OPTIONS set "GEN_OPTIONS=--ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647"
rem Default main script paths
set "DEFAULT_MAIN_SCRIPT_PATHS=main.exe build\bin\main.exe"
rem Get main script path from command line arguments
set "MAIN_SCRIPT_PATH=%~1"
rem If the main script path was not specified, try the default paths
if not defined MAIN_SCRIPT_PATH (
for %%i in (%DEFAULT_MAIN_SCRIPT_PATHS%) do (
if exist "%%i" set "MAIN_SCRIPT_PATH=%%i"
)
)
rem If the main script path was not found, tell the user how to specify it
if not defined MAIN_SCRIPT_PATH (
echo The main script could not be found. Please provide the path to the main script as 1st argument to this script, or place the main script in one of the default locations:
echo %DEFAULT_MAIN_SCRIPT_PATHS%
pause
exit /b 1
)
rem Default context, feel free to edit it
set "PROMPT_TEXT=Text transcript of a never ending dialog, where %USER_NAME% interacts with an AI assistant named %AI_NAME%. %AI_NAME% is helpful, kind, honest, friendly, good at writing and never fails to answer %USER_NAME%'s requests immediately and with details and precision. There are no annotations like (30 seconds passed...) or (to himself), just what %USER_NAME% and %AI_NAME% say aloud to each other. The dialog lasts for years, the entirety of it is shared below. It's 10000 pages long. The transcript only includes text, it does not include markup like HTML and Markdown."
rem Set a temporary variable if N_THREAD is set
if defined N_THREAD (
set "_N_THREAD=--threads %N_THREAD%"
) else (
set "_N_THREAD="
)
rem Run the script
echo "%MAIN_SCRIPT_PATH%" %GEN_OPTIONS% %_N_THREAD% ^
--model "%MODEL%" ^
--n_predict %N_PREDICTS% ^
--color --interactive ^
--reverse-prompt "%USER_NAME%:" ^
--prompt "%PROMPT_TEXT%"

41
examples/chat-13B.sh
View File

@ -1,41 +0,0 @@
#!/usr/bin/env bash
set -e
cd "$(dirname "$0")/.." || exit
MODEL="${MODEL:-./models/13B/ggml-model-q4_0.bin}"
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}
USER_NAME="${USER_NAME:-USER}"
AI_NAME="${AI_NAME:-ChatLLaMa}"
# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-8}"
# Number of tokens to predict (made it larger than default because we want a long interaction)
N_PREDICTS="${N_PREDICTS:-2048}"
# Note: you can also override the generation options by specifying them on the command line:
# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
DATE_TIME=$(date +%H:%M)
DATE_YEAR=$(date +%Y)
PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)
sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
-e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \
-e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \
-e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \
$PROMPT_TEMPLATE > $PROMPT_FILE
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
./llama-cli $GEN_OPTIONS \
--model "$MODEL" \
--threads "$N_THREAD" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--file ${PROMPT_FILE} \
--reverse-prompt "${USER_NAME}:" \
--in-prefix ' ' \
"$@"

149
examples/chat-persistent.sh
View File

@ -1,149 +0,0 @@
#!/usr/bin/env bash
set -euo pipefail
cd "$(dirname "$0")/.." || exit
if [[ -z "${PROMPT_CACHE_FILE+x}" || -z "${CHAT_SAVE_DIR+x}" ]]; then
echo >&2 "error: PROMPT_CACHE_FILE and CHAT_SAVE_DIR must be provided"
exit 1
fi
MODEL="${MODEL:-./models/llama-13b/ggml-model-q4_0.gguf}"
PROMPT_TEMPLATE="${PROMPT_TEMPLATE:-./prompts/chat.txt}"
USER_NAME="${USER_NAME:-User}"
AI_NAME="${AI_NAME:-ChatLLaMa}"
DATE_TIME="$(date +%H:%M)"
DATE_YEAR="$(date +%Y)"
LOG="${CHAT_SAVE_DIR}/main.log"
LOG_BG="${CHAT_SAVE_DIR}/main-bg.log"
CUR_PROMPT_FILE="${CHAT_SAVE_DIR}/current-prompt.txt"
CUR_PROMPT_CACHE="${CHAT_SAVE_DIR}/current-cache.bin"
NEXT_PROMPT_FILE="${CHAT_SAVE_DIR}/next-prompt.txt"
NEXT_PROMPT_CACHE="${CHAT_SAVE_DIR}/next-cache.bin"
SESSION_AND_SAMPLE_PATTERN='main: session file matches [[:digit:]]+ / [[:digit:]]+'\
'|'\
'sampling time =[[:space:]]+[[:digit:]]+.[[:digit:]]+ ms /[[:space:]]+[[:digit:]]+'
SED_DELETE_MESSAGES="/^(${USER_NAME}:|${AI_NAME}:|\\.\\.\\.)/,\$d"
CTX_SIZE=2048
CTX_ROTATE_POINT=$((CTX_SIZE * 3 / 5)) # REVIEW
OPTS=(--model "$MODEL" --ctx_size "$CTX_SIZE" --repeat_last_n 256 "$@")
# An unbuffered `tail -c+N`
skip_bytes() {
LANG=C IFS= read -r -n "$1" -d '' c
while LANG=C IFS= read -r -n 1 -d '' c; do
printf '%s' "$c"
done
}
mkdir -p "$CHAT_SAVE_DIR"
echo >"$LOG"
trap "tail -n100 ${LOG}" EXIT
if [[ ! -e "$CUR_PROMPT_FILE" ]]; then
sed -e "s/\[\[USER_NAME\]\]/${USER_NAME}/g" \
-e "s/\[\[AI_NAME\]\]/${AI_NAME}/g" \
-e "s/\[\[DATE_TIME\]\]/${DATE_TIME}/g" \
-e "s/\[\[DATE_YEAR\]\]/${DATE_YEAR}/g" \
"$PROMPT_TEMPLATE" >"$CUR_PROMPT_FILE"
fi
if [[ ! -e "$NEXT_PROMPT_FILE" ]]; then
sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE"
fi
if [[ "$(tail -c4 "$NEXT_PROMPT_FILE")" != "..." ]]; then
echo '...' >>"$NEXT_PROMPT_FILE"
fi
if [[ ! -e "$PROMPT_CACHE_FILE" ]]; then
echo 'Prompt cache does not exist, building...'
# Default batch_size to 64 here for better user feedback during initial prompt processing
./llama-cli 2>>"$LOG" \
--batch_size 64 \
"${OPTS[@]}" \
--prompt-cache "$PROMPT_CACHE_FILE" \
--file "$CUR_PROMPT_FILE" \
--n_predict 1
echo
echo 'Done!'
fi
if [[ ! -e "$CUR_PROMPT_CACHE" ]]; then
cp "$PROMPT_CACHE_FILE" "$CUR_PROMPT_CACHE"
fi
if [[ ! -e "$NEXT_PROMPT_CACHE" ]]; then
cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE"
fi
printf '%s ' "$(< "$CUR_PROMPT_FILE")"
n_tokens=0
while read -e line; do
# Limit generation to remaining context, with a buffer and estimating 2 chars/token for input
n_predict=$((CTX_SIZE - n_tokens - ${#line} / 2 - 32))
# Swap prompts when we're about to run out of context
if ((n_predict <= 0)); then
wait # for background main (below) to finish with next prompt
mv "$NEXT_PROMPT_FILE" "$CUR_PROMPT_FILE"
mv "$NEXT_PROMPT_CACHE" "$CUR_PROMPT_CACHE"
sed -r "$SED_DELETE_MESSAGES" "$CUR_PROMPT_FILE" >"$NEXT_PROMPT_FILE"
echo '...' >>"$NEXT_PROMPT_FILE"
cp "$PROMPT_CACHE_FILE" "$NEXT_PROMPT_CACHE"
n_tokens=0
n_predict=$((CTX_SIZE / 2))
fi
echo " ${line}" >>"$CUR_PROMPT_FILE"
if ((n_tokens > CTX_ROTATE_POINT)); then
echo " ${line}" >>"$NEXT_PROMPT_FILE"
fi
n_prompt_len_pre=$(($(wc -c <"$CUR_PROMPT_FILE")))
printf '%s: ' "$AI_NAME" >>"$CUR_PROMPT_FILE"
./llama-cli 2>>"$LOG" "${OPTS[@]}" \
--prompt-cache "$CUR_PROMPT_CACHE" \
--prompt-cache-all \
--file "$CUR_PROMPT_FILE" \
--reverse-prompt "${USER_NAME}:" \
--n_predict "$n_predict" |
skip_bytes 1 | # skip BOS token added by ./llama-cli
tee "$CUR_PROMPT_FILE.tmp" | # save prompt + generation to tmp file
skip_bytes "$n_prompt_len_pre" # print generation
mv "$CUR_PROMPT_FILE.tmp" "$CUR_PROMPT_FILE"
# if we hit n_predict instead of reverse-prompt, we need to add the prompt
if [[ "$(tail -n1 "$CUR_PROMPT_FILE")" != "${USER_NAME}:" ]]; then
printf '\n%s:' "$USER_NAME"
printf '\n%s:' "$USER_NAME" >> "$CUR_PROMPT_FILE"
fi
printf ' '
if ! session_and_sample_msg=$(tail -n30 "$LOG" | grep -oE "$SESSION_AND_SAMPLE_PATTERN"); then
echo >&2 "Couldn't get number of tokens from ./llama-cli output!"
exit 1
fi
n_tokens=$(awk '{sum+=$1} END {print sum}' <<< "$(cut -d/ -f2 <<< "$session_and_sample_msg")")
if ((n_tokens > CTX_ROTATE_POINT)); then
tail -c+$((n_prompt_len_pre + 1)) "$CUR_PROMPT_FILE" >>"$NEXT_PROMPT_FILE"
fi
# Update cache for next prompt in background, ideally during user input
./llama-cli >>"$LOG_BG" 2>&1 "${OPTS[@]}" \
--prompt-cache "$NEXT_PROMPT_CACHE" \
--file "$NEXT_PROMPT_FILE" \
--n_predict 1 &
done

41
examples/chat-vicuna.sh
View File

@ -1,41 +0,0 @@
#!/usr/bin/env bash
set -e
cd "$(dirname "$0")/.." || exit
MODEL="${MODEL:-./models/ggml-vic13b-uncensored-q5_0.bin}"
PROMPT_TEMPLATE=${PROMPT_TEMPLATE:-./prompts/chat.txt}
USER_NAME="### Human"
AI_NAME="### Assistant"
# Adjust to the number of CPU cores you want to use.
N_THREAD="${N_THREAD:-8}"
# Number of tokens to predict (made it larger than default because we want a long interaction)
N_PREDICTS="${N_PREDICTS:-2048}"
# Note: you can also override the generation options by specifying them on the command line:
# For example, override the context size by doing: ./chatLLaMa --ctx_size 1024
GEN_OPTIONS="${GEN_OPTIONS:---ctx_size 2048 --temp 0.7 --top_k 40 --top_p 0.5 --repeat_last_n 256 --batch_size 1024 --repeat_penalty 1.17647}"
DATE_TIME=$(date +%H:%M)
DATE_YEAR=$(date +%Y)
PROMPT_FILE=$(mktemp -t llamacpp_prompt.XXXXXXX.txt)
sed -e "s/\[\[USER_NAME\]\]/$USER_NAME/g" \
-e "s/\[\[AI_NAME\]\]/$AI_NAME/g" \
-e "s/\[\[DATE_TIME\]\]/$DATE_TIME/g" \
-e "s/\[\[DATE_YEAR\]\]/$DATE_YEAR/g" \
$PROMPT_TEMPLATE > $PROMPT_FILE
# shellcheck disable=SC2086 # Intended splitting of GEN_OPTIONS
./bin/llama-cli $GEN_OPTIONS \
--model "$MODEL" \
--threads "$N_THREAD" \
--n_predict "$N_PREDICTS" \
--color --interactive \
--file ${PROMPT_FILE} \
--reverse-prompt "### Human:" \
--in-prefix ' ' \
"$@"

16
examples/chat.sh
View File

@ -1,16 +0,0 @@
#!/usr/bin/env bash
#
# Temporary script - will be removed in the future
#
cd `dirname $0`
cd ..
# Important:
#
# "--keep 48" is based on the contents of prompts/chat-with-bob.txt
#
./llama-cli -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \
--repeat_penalty 1.0 --color -i \
-r "User:" -f prompts/chat-with-bob.txt

4
examples/embedding/README.md
View File

@ -43,8 +43,8 @@ The above command will output space-separated float values.
| $"string"$ | |
|--------------|-|
| "\n" | (default)
| "<#embSep#>" | for exemple
| "<#sep#>" | other exemple
| "<#embSep#>" | for example
| "<#sep#>" | other example
## examples
### Unix-based systems (Linux, macOS, etc.):

43
examples/embedding/embedding.cpp
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@ -95,8 +95,13 @@ int main(int argc, char ** argv) {
params.n_batch = params.n_ctx;
}
// For non-causal models, batch size must be equal to ubatch size
params.n_ubatch = params.n_batch;
// for non-causal models, batch size must be equal to ubatch size
if (params.attention_type != LLAMA_ATTENTION_TYPE_CAUSAL) {
params.n_ubatch = params.n_batch;
}
// get max number of sequences per batch
const int n_seq_max = llama_max_parallel_sequences();
llama_backend_init();
llama_numa_init(params.numa);
@ -144,6 +149,7 @@ int main(int argc, char ** argv) {
// get added sep and eos token, if any
const std::string added_sep_token = llama_vocab_get_add_sep(vocab) ? llama_vocab_get_text(vocab, llama_vocab_sep(vocab)) : "";
const std::string added_eos_token = llama_vocab_get_add_eos(vocab) ? llama_vocab_get_text(vocab, llama_vocab_eos(vocab)) : "";
const char * rerank_prompt = llama_model_chat_template(model, "rerank");
// tokenize the prompts and trim
std::vector<std::vector<int32_t>> inputs;
@ -153,21 +159,28 @@ int main(int argc, char ** argv) {
// split classification pairs and insert expected separator tokens
if (pooling_type == LLAMA_POOLING_TYPE_RANK && prompt.find(params.cls_sep) != std::string::npos) {
std::vector<std::string> pairs = split_lines(prompt, params.cls_sep);
std::string final_prompt;
for (size_t i = 0; i < pairs.size(); i++) {
final_prompt += pairs[i];
if (i != pairs.size() - 1) {
if (!added_eos_token.empty()) {
final_prompt += added_eos_token;
}
if (!added_sep_token.empty()) {
final_prompt += added_sep_token;
if (rerank_prompt != nullptr) {
const std::string query = pairs[0];
const std::string doc = pairs[1];
std::string final_prompt = rerank_prompt;
string_replace_all(final_prompt, "{query}" , query);
string_replace_all(final_prompt, "{document}", doc );
inp = common_tokenize(vocab, final_prompt, true, true);
} else {
std::string final_prompt;
for (size_t i = 0; i < pairs.size(); i++) {
final_prompt += pairs[i];
if (i != pairs.size() - 1) {
if (!added_eos_token.empty()) {
final_prompt += added_eos_token;
}
if (!added_sep_token.empty()) {
final_prompt += added_sep_token;
}
}
}
inp = common_tokenize(ctx, final_prompt, true, true);
}
inp = common_tokenize(ctx, final_prompt, true, true);
} else {
inp = common_tokenize(ctx, prompt, true, true);
}
@ -229,7 +242,7 @@ int main(int argc, char ** argv) {
const uint64_t n_toks = inp.size();
// encode if at capacity
if (batch.n_tokens + n_toks > n_batch) {
if (batch.n_tokens + n_toks > n_batch || s >= n_seq_max) {
float * out = emb + e * n_embd;
batch_decode(ctx, batch, out, s, n_embd, params.embd_normalize);
e += pooling_type == LLAMA_POOLING_TYPE_NONE ? batch.n_tokens : s;

5
examples/gritlm/CMakeLists.txt
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@ -1,5 +0,0 @@
set(TARGET llama-gritlm)
add_executable(${TARGET} gritlm.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_17)

62
examples/gritlm/README.md
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@ -1,62 +0,0 @@
## Generative Representational Instruction Tuning (GRIT) Example
[gritlm] a model which can generate embeddings as well as "normal" text
generation depending on the instructions in the prompt.
* Paper: https://arxiv.org/pdf/2402.09906.pdf
### Retrieval-Augmented Generation (RAG) use case
One use case for `gritlm` is to use it with RAG. If we recall how RAG works is
that we take documents that we want to use as context, to ground the large
language model (LLM), and we create token embeddings for them. We then store
these token embeddings in a vector database.
When we perform a query, prompt the LLM, we will first create token embeddings
for the query and then search the vector database to retrieve the most
similar vectors, and return those documents so they can be passed to the LLM as
context. Then the query and the context will be passed to the LLM which will
have to _again_ create token embeddings for the query. But because gritlm is used
the first query can be cached and the second query tokenization generation does
not have to be performed at all.
### Running the example
Download a Grit model:
```console
$ scripts/hf.sh --repo cohesionet/GritLM-7B_gguf --file gritlm-7b_q4_1.gguf --outdir models
```
Run the example using the downloaded model:
```console
$ ./llama-gritlm -m models/gritlm-7b_q4_1.gguf
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "A purely peer-to-peer version of electronic cash w" is: 0.605
Cosine similarity between "Bitcoin: A Peer-to-Peer Electronic Cash System" and "All text-based language problems can be reduced to" is: 0.103
Cosine similarity between "Generative Representational Instruction Tuning" and "A purely peer-to-peer version of electronic cash w" is: 0.112
Cosine similarity between "Generative Representational Instruction Tuning" and "All text-based language problems can be reduced to" is: 0.547
Oh, brave adventurer, who dared to climb
The lofty peak of Mt. Fuji in the night,
When shadows lurk and ghosts do roam,
And darkness reigns, a fearsome sight.
Thou didst set out, with heart aglow,
To conquer this mountain, so high,
And reach the summit, where the stars do glow,
And the moon shines bright, up in the sky.
Through the mist and fog, thou didst press on,
With steadfast courage, and a steadfast will,
Through the darkness, thou didst not be gone,
But didst climb on, with a steadfast skill.
At last, thou didst reach the summit's crest,
And gazed upon the world below,
And saw the beauty of the night's best,
And felt the peace, that only nature knows.
Oh, brave adventurer, who dared to climb
The lofty peak of Mt. Fuji in the night,
Thou art a hero, in the eyes of all,
For thou didst conquer this mountain, so bright.
```
[gritlm]: https://github.com/ContextualAI/gritlm

231
examples/gritlm/gritlm.cpp
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@ -1,231 +0,0 @@
#include "arg.h"
#include "common.h"
#include "llama.h"
#include <string>
#include <vector>
// #define GRIT_DEBUG
static std::vector<std::vector<float>> encode(llama_context * ctx, const std::vector<std::string> & sentences, const std::string & instruction) {
std::vector<std::vector<float>> result;
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_batch batch = llama_batch_init(llama_n_batch(ctx), 0, 1);
for (uint64_t i = 0; i < sentences.size(); i++) {
common_batch_clear(batch);
const std::string input_string = instruction + sentences[i];
std::vector<llama_token> inputs = common_tokenize(vocab, input_string, true, false);
const int32_t n_toks = inputs.size();
// GritLM seems to have EOS = ""
// https://github.com/ContextualAI/gritlm/blob/92025b16534712b31b3c4aaaf069350e222bd5f8/gritlm/gritlm.py#L18
// inputs.push_back(llama_vocab_eos(vocab));
// we want to ignore instruction tokens for mean pooling
const int32_t n_inst = common_tokenize(vocab, instruction, true, false).size();
#ifdef GRIT_DEBUG
// debug tokens - should be matching as referenced in the GritLM sample
std::for_each(inputs.begin(), inputs.end(), [&ctx](llama_token t) {
std::printf("[%u:%s]", t, llama_token_to_piece(ctx, t).c_str());
});
std::printf("\n");
#endif
// add input to batch (this increments n_tokens)
for (int32_t j = 0; j < n_toks; j++) {
common_batch_add(batch, inputs[j], j, { 0 }, true);
}
// clear previous kv_cache values (irrelevant for embeddings)
llama_memory_clear(llama_get_memory(ctx), true);
llama_set_causal_attn(ctx, false);
// run model
llama_decode(ctx, batch);
// get embedding dimensions
uint64_t n_embd = llama_model_n_embd(model);
// allocate embedding output
std::vector<float> emb_unorm(n_embd, 0.0f);
// sum up all token embeddings
for (int32_t k = n_inst; k < n_toks; k++) {
float * emb = llama_get_embeddings_ith(ctx, k);
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] += emb[j];
}
}
// divide by number of tokens (mean pooling)
{
const uint64_t n_sent = n_toks - n_inst;
for (uint64_t j = 0; j < n_embd; j++) {
emb_unorm[j] /= n_sent;
}
}
std::vector<float> emb_norm(emb_unorm.size());
common_embd_normalize(emb_unorm.data(), emb_norm.data(), n_embd, 2);
result.push_back(emb_norm);
#ifdef GRIT_DEBUG
// print out emb_norm
std::printf("embedding %ld: ", i);
for (uint64_t j = 0; j < n_embd; j++) {
std::printf("%.5f ", emb_norm[j]);
}
std::printf("\n\n");
#endif
}
llama_batch_free(batch);
return result;
}
static std::string generate(llama_context * ctx, llama_sampler * smpl, const std::string & prompt, bool stream) {
std::string result;
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
llama_token eos_token = llama_vocab_eos(vocab);
llama_memory_clear(llama_get_memory(ctx), true);
llama_set_causal_attn(ctx, true);
llama_batch bat = llama_batch_init(llama_n_batch(ctx), 0, 1);
std::vector<llama_token> inputs = common_tokenize(vocab, prompt, false, true);
int32_t i_current_token = 0;
while (true) {
common_batch_clear(bat);
{
const int32_t n_inputs = inputs.size();
for (int32_t i = 0; i < n_inputs; i++) {
common_batch_add(bat, inputs[i], i_current_token++, { 0 }, i == n_inputs - 1);
}
}
inputs.clear();
llama_decode(ctx, bat);
llama_token token = llama_sampler_sample(smpl, ctx, bat.n_tokens - 1);
if (token == eos_token) {
break;
}
std::string piece = common_token_to_piece(ctx, token);
if (stream) {
std::printf("%s", piece.c_str());
std::fflush(stdout);
}
inputs.push_back(token);
result += piece;
}
if (stream) {
std::printf("\n");
}
llama_batch_free(bat);
return result;
}
static std::string gritlm_instruction(const std::string & instruction) {
return !instruction.empty() ? "<|user|>\n" + instruction + "\n<|embed|>\n" : "<|embed|>\n";
}
int main(int argc, char * argv[]) {
common_params params;
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_COMMON)) {
return 1;
}
common_init();
llama_model_params mparams = common_model_params_to_llama(params);
llama_context_params cparams = common_context_params_to_llama(params);
cparams.embeddings = true;
llama_backend_init();
llama_model * model = llama_model_load_from_file(params.model.path.c_str(), mparams);
// create generation context
llama_context * ctx = llama_init_from_model(model, cparams);
auto sparams = llama_sampler_chain_default_params();
sparams.no_perf = false;
llama_sampler * smpl = llama_sampler_chain_init(sparams);
llama_sampler_chain_add(smpl, llama_sampler_init_greedy());
// ### Embedding/Representation ###
// samples taken from: https://github.com/ContextualAI/gritlm#basic
{
const std::string instruction = "Given a scientific paper title, retrieve the paper's abstract";
const std::vector<std::string> queries = {
"Bitcoin: A Peer-to-Peer Electronic Cash System",
"Generative Representational Instruction Tuning",
};
const std::vector<std::string> documents = {
"A purely peer-to-peer version of electronic cash would allow online payments to be sent directly from one party to another without going through a financial institution. Digital signatures provide part of the solution, but the main benefits are lost if a trusted third party is still required to prevent double-spending. We propose a solution to the double-spending problem using a peer-to-peer network. The network timestamps transactions by hashing them into an ongoing chain of hash-based proof-of-work, forming a record that cannot be changed without redoing the proof-of-work. The longest chain not only serves as proof of the sequence of events witnessed, but proof that it came from the largest pool of CPU power. As long as a majority of CPU power is controlled by nodes that are not cooperating to attack the network, they'll generate the longest chain and outpace attackers. The network itself requires minimal structure. Messages are broadcast on a best effort basis, and nodes can leave and rejoin the network at will, accepting the longest proof-of-work chain as proof of what happened while they were gone.",
"All text-based language problems can be reduced to either generation or embedding. Current models only perform well at one or the other. We introduce generative representational instruction tuning (GRIT) whereby a large language model is trained to handle both generative and embedding tasks by distinguishing between them through instructions. Compared to other open models, our resulting GritLM 7B sets a new state of the art on the Massive Text Embedding Benchmark (MTEB) and outperforms all models up to its size on a range of generative tasks. By scaling up further, GritLM 8X7B outperforms all open generative language models that we tried while still being among the best embedding models. Notably, we find that GRIT matches training on only generative or embedding data, thus we can unify both at no performance loss. Among other benefits, the unification via GRIT speeds up Retrieval-Augmented Generation (RAG) by > 60% for long documents, by no longer requiring separate retrieval and generation models. Models, code, etc. are freely available at https://github.com/ContextualAI/gritlm.",
};
// No need to add instruction for retrieval documents
const std::vector<std::vector<float>> d_rep = encode(ctx, documents, gritlm_instruction(""));
const std::vector<std::vector<float>> q_rep = encode(ctx, queries, gritlm_instruction(instruction));
const int n_embd = llama_model_n_embd(model);
const float cosine_sim_q0_d0 = common_embd_similarity_cos(q_rep[0].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q0_d1 = common_embd_similarity_cos(q_rep[0].data(), d_rep[1].data(), n_embd);
const float cosine_sim_q1_d0 = common_embd_similarity_cos(q_rep[1].data(), d_rep[0].data(), n_embd);
const float cosine_sim_q1_d1 = common_embd_similarity_cos(q_rep[1].data(), d_rep[1].data(), n_embd);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[0].c_str(), cosine_sim_q0_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[0].c_str(), documents[1].c_str(), cosine_sim_q0_d1);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[0].c_str(), cosine_sim_q1_d0);
std::printf("Cosine similarity between \"%.50s\" and \"%.50s\" is: %.3f\n", queries[1].c_str(), documents[1].c_str(), cosine_sim_q1_d1);
}
llama_set_embeddings(ctx, false);
// ### Generation ###
// GritLM models are not finetuned with system prompts, as you can just include system-like instructions together with your user instruction
{
const std::string prompt = "<|user|>\nPlease write me a poem about my recent hike of Mt. Fuji at midnight in the style of Shakespeare.\n<|assistant|>\n";
std::string response = generate(ctx, smpl, prompt, true);
}
llama_sampler_free(smpl);
llama_free(ctx);
llama_model_free(model);
llama_backend_free();
return 0;
}

21
examples/jeopardy/README.md
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@ -1,21 +0,0 @@
# llama.cpp/example/jeopardy
This is pretty much just a straight port of aigoopy/llm-jeopardy/ with an added graph viewer.
The jeopardy test can be used to compare the fact knowledge of different models and compare them to each other. This is in contrast to some other tests, which test logical deduction, creativity, writing skills, etc.
Step 1: Open jeopardy.sh and modify the following:
```
MODEL=(path to your model)
MODEL_NAME=(name of your model)
prefix=(basically, if you use vicuna it's Human: , if you use something else it might be User: , etc)
opts=(add -instruct here if needed for your model, or anything else you want to test out)
```
Step 2: Run `jeopardy.sh` from the llama.cpp folder
Step 3: Repeat steps 1 and 2 until you have all the results you need.
Step 4: Run `graph.py`, and follow the instructions. At the end, it will generate your final graph.
Note: The Human bar is based off of the full, original 100 sample questions. If you modify the question count or questions, it will not be valid.

58
examples/jeopardy/graph.py
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@ -1,58 +0,0 @@
#!/usr/bin/env python3
import matplotlib.pyplot as plt
import os
import csv
labels = []
numbers = []
numEntries = 1
rows = []
def bar_chart(numbers, labels, pos):
plt.bar(pos, numbers, color='blue')
plt.xticks(ticks=pos, labels=labels)
plt.title("Jeopardy Results by Model")
plt.xlabel("Model")
plt.ylabel("Questions Correct")
plt.show()
def calculatecorrect():
directory = os.fsencode("./examples/jeopardy/results/")
csv_reader = csv.reader(open("./examples/jeopardy/qasheet.csv", 'rt'), delimiter=',')
for row in csv_reader:
global rows
rows.append(row)
for listing in os.listdir(directory):
filename = os.fsdecode(listing)
if filename.endswith(".txt"):
file = open("./examples/jeopardy/results/" + filename, "rt")
global labels
global numEntries
global numbers
labels.append(filename[:-4])
numEntries += 1
i = 1
totalcorrect = 0
for line in file.readlines():
if line.strip() != "------":
print(line)
else:
print("Correct answer: " + rows[i][2] + "\n")
i += 1
print("Did the AI get the question right? (y/n)")
if input() == "y":
totalcorrect += 1
numbers.append(totalcorrect)
if __name__ == '__main__':
calculatecorrect()
pos = list(range(numEntries))
labels.append("Human")
numbers.append(48.11)
bar_chart(numbers, labels, pos)
print(labels)
print(numbers)

30
examples/jeopardy/jeopardy.sh
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@ -1,30 +0,0 @@
#!/usr/bin/env bash
set -e
MODEL=./models/ggml-vicuna-13b-1.1-q4_0.bin
MODEL_NAME=Vicuna
# exec options
prefix="Human: " # Ex. Vicuna uses "Human: "
opts="--temp 0 -n 80" # additional flags
nl='
'
introduction="You will be playing a game of Jeopardy. Simply answer the question in the correct format (Ex. What is Paris, or Who is George Washington)."
# file options
question_file=./examples/jeopardy/questions.txt
touch ./examples/jeopardy/results/$MODEL_NAME.txt
output_file=./examples/jeopardy/results/$MODEL_NAME.txt
counter=1
echo 'Running'
while IFS= read -r question
do
exe_cmd="./llama-cli -p "\"$prefix$introduction$nl$prefix$question\"" "$opts" -m ""\"$MODEL\""" >> ""\"$output_file\""
echo $counter
echo "Current Question: $question"
eval "$exe_cmd"
echo -e "\n------" >> $output_file
counter=$((counter+1))
done < "$question_file"

103
examples/jeopardy/qasheet.csv
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@ -1,103 +0,0 @@
Index,Original Category,Original Correct Question,Model Prompt
1,The Oscars,Who is John Williams?,Which actor Born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?
2,English Literature,What is Paradise Lost?,"What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?"
3,Writers’ Lesser-Known Works,Who is Niccolò Machiavelli?,"Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?"
4,Exploration,What is Easter Island (Rapa Nui)?,"James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?"
5,The Bill of Rights,What is the Eighth Amendment?,England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?
6,Nobel Peace Prize Winners,Who are Nelson Mandela & Desmond Tutu?,"Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?"
7,Famous Names,Who is Walt Disney?,"In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?"
8,Geography,What is Colombia?,"Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?"
9,Fashion History,What are rhinestones?,"Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?"
10,Movies of the ’80s,What is Driving Miss Daisy?,What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?
11,Novelists,Who is John Grisham?,"A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?"
12,20th Century Eponyms,What is the Maginot Line?,"A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?"
13,City History,What is Stockholm?,"Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?"
14,Brand Names,What is Jacuzzi?,"The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?"
15,American Authors,Who is Washington Irving?,"In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?"
16,Symbols,What is “less than”?,What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?
17,Movie Theme Songs,Who is James Bond?,"Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?"
18,American Novelists,Who is Joseph Heller?,"What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?"
19,Medieval Places,"What is Canterbury, England? (Canterbury Cathedral)","In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?"
20,Countries of Africa,What is Morocco?,"At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?"
21,Statehood,What is Wyoming?,Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?
22,1980s Movies,What is Raiders of the Lost Ark?,"A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?"
23,Art Exhibitions,Who is Rembrandt?,In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?
24,Countries of the World,What is Mongolia?,"Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?"
25,Literature,What is “Howl”?,A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?
26,Invasions,Who is William of Orange?,"Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?"
27,Landmarks,What is the Eiffel Tower?,"After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?"
28,Geographic Name’s the Same,What is Dover?,"The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?"
29,Names in the Bookstore,Who is Peter Mark Roget?,"This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?"
30,U.S. History,Who is Dr. Samuel Mudd?,"An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?"
31,American Literature,What is The Things They Carried?,"Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?"
32,Nonfiction,What is The Communist Manifesto,"What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?"
33, a new version was passed 81 years later,Laws in U.S. History,What is the Civil Rights Act?,,,,,,,,,,,,,,,,,,0, 2/3
34,Names of Myth,Who is Helen of Troy?,"Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?"
35,African Countries,What is Sudan?,"Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?"
36,The Ancient World,What is Alexandria?,"The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?"
37,Famous Names,Who is Andy Warhol?,"For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?"
38,People & Places,What is Guam?,"Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?"
39,Current World Leaders,What is the Philippines?,"In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?"
40,Writers & The South,Who is Tennessee Williams?,In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?
41,National Parks,What is Yellowstone?,"What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?"
42,Sports,Who are the Harlem Globetrotters?,"In 2010 who introduced the 4-point shot, 35 feet from the basket?"
43,The U.S. Military,What is “Top Gun”?,Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?
44,Art & Science,What is Halley’s Comet?,"A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?"
45,Words From World War I,What is “tank”?,"In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?"
46,European History,What is Holy Roman Emperor?,"Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?"
47,Theater History,Who is Peter Pan?,"In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?"
48,European Cities,What is Aachen?,"Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?"
49,Word Origins,What is mantra?,This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?
50,Inventions,What is barbed wire?,1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?
51,World War II,What is Schindler’s list?,"Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?"
52, their offspring was the source of this mythical object,Mythology,What is the Golden Fleece?
53,Literature,What is Pride and Prejudice?,"Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?"
54, only these 2 west of the Mississippi River border each other,U.S. State Names,What are Oregon & Nevada?
55,Word Origins,What is passion?,"Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?"
56,World Cinema,What is La Vie en Rose?,"The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?"
57,History,What is Santa Maria?,"Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?"
58,Landmarks,What is a kremlin?,Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?
59,Foreign-Born Authors,Who is Vladimir Nabokov?,In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?
60,Astronomy & Geography,What is Capricorn?,"At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?"
61,Television,What is Law & Order?,"Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?"
62,British Landmarks,What is the Tower of London?,"Like Sir Thomas More, 3 16th century English queens are buried at what British location?"
63,Early American History,What are witches?,"In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person … be condemned'?"
64,Geography Mnemonics,What are Arkansas and Louisiana?,"The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?"
65,Business Milestones,What is the Ford Model T?,"What was first sold in 1908, at a price equivalent to about $27,000 today?"
66,In The Bookstore,Who is Tom Clancy?,The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?
67,Historic Art,What is the Bayeux Tapestry?,The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?
68,Pop Stars,Who is Madonna?,In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?
69,Classic Tale Characters,Who is Scheherazade?,"In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?"
70,USA,What is Jack Daniel’s?,"Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?"
71,Historic People,Who was William Bligh?,"After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?"
72,The Movies,What is The Godfather?,Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?
73,Continental Geography,What is Colombia?,"Until a 1903 secession, what country's contiguous territory spanned 2 continents?"
74,Foreign-Born Authors,Who is Isabel Allende?,"Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?"
75,Historic Crimes,What is the Mona Lisa?,"Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?"
76,U.S. Bodies of Water,What is Lake Mead?,"Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?"
77,Gods & Goddesses,Who is Aurora (or Eos)?,"Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?"
78,America At War,What is the Battle of New Orleans?,"Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?"
79,Children’s Books,What is The Velveteen Rabbit?,"Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?"
80,TV Finales,What is Grace and Frankie?,"In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?"
81,American Poems,Who is Evangeline?,"In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?"
82,Famous Names,Who is Banksy?,"In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?"
83,Children’s Lit,What is Charlotte’s Web?,The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?
84,Classic Songs,What is “Here Comes Santa Claus”?,The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?
85,Brand Names,What are Milk Duds?,"Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?"
86,Countries of the World,What is Italy?,"What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?"
87,Action Movies,What is Die Hard?,"What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?"
88,Presidential Facts,Who is Woodrow Wilson?,Only 3 presidents have married while in office— John Tyler was the first & which one was the last?
89,19th Century Americans,Who is Frederick Douglass?,"Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?"
90,Latin Phrases,What is “quid pro quo”?,"Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?"
91,1970s Movies,What is Monty Python and the Holy Grail?,The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?
92,Name’s The Same,What is Manhattan?,"A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?"
93,U.S. Presidents,Who is Calvin Coolidge?,"Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?"
94,Plays,What is The Tempest?,A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?
95,Landmarks,What is the Berlin Wall?,"In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?"
96,World Capitals,"What is Vienna, Austria?","Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?"
97,Language & Its Meanings,What is a night owl?,"Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?"
98,Flags of Our Hemisphere,What is Brazil?,"The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?"
99,Names in U.S. History,Who is Oliver Brown?,What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?
100,Children’s Authors,"Who is Sarah? (from Sarah, Plain and Tall)","Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?"
,,,
TOTALS,,,
Can't render this file because it has a wrong number of fields in line 34.

100
examples/jeopardy/questions.txt
View File

@ -1,100 +0,0 @@
Which man born in 1932 was the son of a percussionist in the CBS radio orchestra has been nominated for 53 Oscars?
What work in English Literature says: 'The mind is its own place, & in itself can make a heaven of hell, a hell of heaven. What matter where, if I be still the same'?
Known for more philosophical works, he wrote the play 'La Mandragola', in which Florentines are rewarded for immoral actions?
James Cook's account of a 1774 visit where records an object 'near 27 feet long, and upwards of 8 feet over the breast or shoulders'?
England's 'Bloody Assizes' & a 1685 life sentence for perjury were 2 main origins of which amendment to the U.S. Constitution?
Which nobel peace price winners each lived at times on Vilakazi St. in Soweto , so it claims to be the world's only street home to 2 Nobel Peace Prize winners?
In 1966, the year of who's death did he share plans for an experimental prototype community in Florida?
Of the 13 nations through which the Equator passes, what is the only one whose coastline borders the Caribbean Sea?
Which decorative items in fashion history get their name from their origin in the port city of Strasbourg, on the border of France & Germany?
What 1980's movie is based on an off-Broadway play with just 3 characters and won the Best Picture Oscar & the actors in all 3 roles were nominated?
A 2012 book review for which novelist noted subjects that 'sparked his ire': capital punishment, big tobacco & 'the plight of the unjustly convicted'?
A 1940 headline about what 20th Century Eponym included 'failure', 'liability when it came to offense' & 'stout hearts no match for tanks'?
Over 700 years after its traditional 1252 founding date, what port city became associated with a psychological response?
The success of what brand has its roots with a hydrotherapy pump its cofounder created for his son, who had arthritis?
In a periodical in 1807, what American Author called New York City 'Gotham, Gotham! Most enlightened of cities'?
What symbol is a rotated V in math and a feeling of some marginalized or underrepresented people in society?
Monty Norman, the composer of what character's theme, said the staccato riff conveyed sexiness, mystery & ruthlessness?
What American Novelist served with an airman named Yohannan in World War II & despite what readers might think, he said he enjoyed his service?
In what Medieval place did one of the participants in an 1170 event say, 'Let us away, knights; he will rise no more'?
At one time a province of the Roman Empire, what African country kingdom is known to Arabic scholars as Al-Maghrib Al-Aqsa, 'the far west'?
Congress relented in 1890 after what prospective state said it would wait 100 years rather than come in without the women?
A writer & producer of what movie said he wanted it to be like a Western or James Bond film, 'only it takes place in the 30s'?
In 1898 what's been called the first blockbuster art show was devoted to which artist & put on for Queen Wilhelmina's coronation?
Part of the largest contiguous land empire during the 1200s & 1300s, today what is the world's second-largest landlocked country?
A 2006 book was titled 'The Poem That Changed America:' What 'Fifty Years Later'?
Backed by 14,000 troops, who invaded England to restore, in his words, its 'religion, laws, and liberties'?
After its completion in the late 19th c., what was landmark was called 'a truly tragic street lamp' & a 'high & skinny pyramid of iron ladders'?
The busiest passenger port in the U.K., what shares its name with a capital of one of the original 13 states?
This man made lists, perhaps to cope with depression; a set of lists he published in 1852 made whose name synonymous with a type of book?
An 1869 presidential pardon was granted to which man, due in part to a plea by the Medical Society of Harford County, Maryland?
Letters, pocket knives, C rations & steel helmets are among the tangible items referred to in the title of what American literature modern war classic?
What nonfiction book has the line, 'The discovery of America…opened up fresh ground for the rising bourgeoisie'?
A radical Republican championed what 1875 act but the Supreme Court struck it down in 1883; a new version was passed 81 years later?
Whose brothers, Castor & Pollux, saved her after Theseus stole her away as a kid; a larger force would seek her later in life?
Once Africa's largest country in area, what African Country dropped to third in 2011 when a portion of it declared independence?
The ancient writer Galen said books on ships arriving to what city's port were seized, originals kept & copies returned?
For a special 1970s cookbook, who provided one simple recipe–a can of Campbell's tomato soup & 2 cans of milk?
Thought to descend from people of Southeast Asia, the Chamorro make up what U.S. territory’s largest ethnic group?
In office from 2022, the president of what country has taken so many foreign trips a play on his name is 'Ferdinand Magellan Jr.'?
In 1939 which writer lived on Toulouse Street in the French Quarter & chose the professional name that bonded him to the South?
What National Park is named for a river indigenous people called Mi tse a-da-zi, translated by French-speaking trappers as 'Pierre Jaune'?
In 2010 who introduced the 4-point shot, 35 feet from the basket?
Losses over Asia in the 1960s led to the establishment of the program known as what at a San Diego naval base in 1969?
A craft that visited what was named for Giotto, based on the story that 680 years earlier, the painter depicted it as the Star of Bethlehem?
In World War I, 'Cistern' & 'reservoir' were suggested names for what secret invention, but the British preferred this less clumsy monosyllable?
Until 1806, some German nobles included among their honors the title of 'Elector' for their role in selecting this personage?
In 1904, wearing a harness, actress Nina Boucicault became the first to play what character onstage?
Alphabetically the first German city in encyclopedias, what was also the first one taken by the Allies in World War II?
This Sanskrit word referring to a spoken word or phrase comes from a word for 'to think'?
1917's 'Elements of Trench Warfare' said what Old West invention was 'difficult to destroy' & 'difficult to get through'?
Mimi Reinhard, who never learned to type using more than 2 fingers, produced what in World War II with 1,100 names, including hers?
Poseidon carried off the maiden Theophane & turned her into a ewe; their offspring was the source of what mythical object?
Published in 2011, P.D. James' final novel, 'Death Comes to Pemberley', was a sequel to what novel from 200 years earlier?
5 U.S. states have 6-letter names; only which 2 west of the Mississippi River border each other?
Originally relating to a story of suffering, what word now more commonly refers to strong emotion of any kind?
The 2007 biopic called 'La Môme' in France, meaning 'The Kid', was released in the U.S. under what other French title?
Returning home in 1493, Columbus stopped in the Azores at an island with what name, also something he'd lost off the Haiti coast?
Pskov & Nizhny Novgorod are 2 of the cities that have a fortress called what?
In the 1950s the New York Times said what author 'is writing about all lust' & his lecherous narrator 'is all of us'?
At the winter solstice, the sun is in Sagittarius; it once appeared in what constellation, giving a geographic feature its name?
Mike Post combined the sound of a slamming jail door, an anvil & 100 men stomping on a floor for what television series that debuted in 1990?
Like Sir Thomas More, 3 16th century English queens are buried at what British location?
In 1692 Increase Mather wrote, 'It were better that ten suspected' of these who 'escape, than that one innocent person be condemned'?
The Geography Mnemonic Mimal, sometimes said to be the silhouette of a chef or elf, stands for Minnesota, Iowa, Missouri, and what other 2 states?
What was first sold in 1908, at a price equivalent to about $27,000 today?
The name of what author dead since 2013 now appears on books written by a former U.S. marshal & a former Apache helicopter pilot?
The artwork once known in France as 'la tapisserie de la Reine Mathilde' is better known as what?
In 2022 which pop star became the first woman to have a Billboard Top 10 album in 5 decades starting with the 1980s?
In one 19th century translation, what female classic tale character 'perceived the dawn of day and ceased' speaking nearly 1,000 times?
Ironically, though what company founded in the 1860s is Moore County, Tennessee's largest employer, Moore is a dry county?
After a 1789 event, who wrote, 'My first determination was to seek a supply of…water at Tofoa, & afterwards to sail for Tongataboo'?
Laurence Olivier & Ernest Borgnine were considered for the lead role & Sergio Leone to direct for what film that turned 50 in 2022?
Until a 1903 secession, what country's contiguous territory spanned 2 continents?
Early in her career which foreign-born author translated romance novels into Spanish, often changing the dialogue to make the heroines smarter?
Saying it was stolen by Napoleon, self-styled Italian patriot Vincenzo Peruggia took what in 1911?
Continuing a downward trend, in July 2022 what US body of water was at 27% capacity, its lowest level since 1937 when it was first being filled?
Each morning which goddess began her ride in her chariot across the sky ahead of her brother Sol, or Helios?
Until the Civil War, the Jan. 8 date of what American battle of dubious military importance but big morale value was a national holiday?
Which children's book title character is told 'By the time you are real, most of your hair has been loved off your eyes drop out & you get shabby'?
In a TV reunion over 40 years in the making, Dolly Parton appeared as an angel named Agnes in the final episode of what comedy in 2022?
In an 1847 American poem what character sees her town of Grand-Pré burned, but finally reunites with her beau for a kiss before his death?
In 2001 who published a book called 'Banging Your Head Against a Brick Wall'; in 2002, 'Existencilism'?
The title object of what childrens book 'never looked more beautiful each strand held dozens of bright drops of early morning dew'?
The shouts of excited children at a 1946 holiday parade are said to have inspired what perennial classic song favorite?
Unable to make what candies perfectly round, the confectioner embraced this flawed name for the product?
What country is home to 58 UNESCO World Heritage Sites, more than any other country; the sites include a volcano & a lagoon?
What action movie's last line is 'If this is their idea of Christmas, I gotta be here for New Years'?
Only 3 presidents have married while in office— John Tyler was the first & which one was the last?
Demonstrating the dignity & humanity of Black Americans, who sat for 160 known photographs, the most of any American in the 19th century?
Originally, which Latin 3-word phrase referred to when a doctor or apothecary substituted one medicine for another?
The 1975 premiere of what movie comedy advertised free coconuts for the first thousand in the audience?
A cocktail, an island & a WWII venture originally called 'Development of Substitute Materials' all bear what name?
Which US President was sworn in twice as President within 2 years, first by his father & then later by a former U.S. President?
A 1609 story in which an exiled king of Bulgaria creates a sea palace with his magic may have inspired the plot of what play?
In 2009, during a 20th anniversary celebration, what landmark was called 'an edifice of fear. On Nov. 9, it became a place of joy'?
Among what world capital's nicknames are the 'City of Classical Music' &, possibly in honor of a famous resident from 1860 to 1938, the 'City of Dreams'?
Now meaning someone with nocturnal habits, what catches a sleeping dove in Shakespeare's 'Lucrece'?
The stars on what country's flag represent states, 26 of them; unlike the USA's, its 'federal district' gets its own 27th star?
What father was the only man among the 13 plaintiffs in a US class-action case filed in 1951?
Reversing the story of what heroine she created, childrens author Patricia Maclachlan was born on the prairie but spent much of her life in New England?

28
examples/llm.vim
View File

@ -1,28 +0,0 @@
" Basic plugin example
function! Llm()
let url = "http://127.0.0.1:8080/completion"
" Get the content of the current buffer
let buffer_content = join(getline(1, '$'), "\n")
" Create the JSON payload
let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":256,"stop": ["\n\n\n"],"stream": v:false}
let json_payload.prompt = buffer_content
" Define the curl command
let curl_command = 'curl -k -s -X POST -H "Content-Type: application/json" -d @- ' . url
let response = system(curl_command, json_encode(json_payload))
" Extract the content field from the response
let content = json_decode(response).content
let split_newlines = split(content, '\n', 1)
" Insert the content at the cursor position
call setline(line('.'), [ getline('.') . split_newlines[0] ] + split_newlines[1:])
endfunction
command! Llm call Llm()
noremap <F2> :Llm<CR>

13
examples/model-conversion/Makefile
View File

@ -118,13 +118,17 @@ embedding-convert-model:
embedding-run-original-model:
$(call validate_embedding_model_path,embedding-run-original-model)
@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" ./scripts/embedding/run-original-model.py
@EMBEDDING_MODEL_PATH="$(EMBEDDING_MODEL_PATH)" \
./scripts/embedding/run-original-model.py \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
embedding-run-converted-model:
@CONVERTED_EMBEDDING_MODEL="$(CONVERTED_EMBEDDING_MODEL)" ./scripts/embedding/run-converted-model.sh ${CONVERTED_EMBEDDING_MODEL}
@./scripts/embedding/run-converted-model.sh $(CONVERTED_EMBEDDING_MODEL) \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
embedding-verify-logits: embedding-run-original-model embedding-run-converted-model
@./scripts/embedding/compare-embeddings-logits.sh
@./scripts/embedding/compare-embeddings-logits.sh \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
embedding-inspect-original-model:
$(call validate_embedding_model_path,embedding-inspect-original-model)
@ -156,7 +160,8 @@ embedding-quantize-model:
$(call quantize_model,$(CONVERTED_EMBEDDING_MODEL),QUANTIZED_EMBEDDING_MODEL)
embedding-run-quantized-model:
@./scripts/embedding/run-converted-model.sh ${QUANTIZED_EMBEDDING_MODEL}
@./scripts/embedding/run-converted-model.sh $(QUANTIZED_EMBEDDING_MODEL) \
$(if $(PROMPTS_FILE),--prompts-file "$(PROMPTS_FILE)")
###
### Perplexity targets/recipes

4
examples/model-conversion/README.md
View File

@ -105,12 +105,12 @@ new model, the model can be converted to GGUF format using the following command
### Inspecting the converted model
The converted model can be inspected using the following command:
```console
(venv) $ make inspect-converted-model
(venv) $ make causal-inspect-converted-model
```
### Running the converted model
```console
(venv) $ make run-converted-model
(venv) $ make causal-run-converted-model
```
### Model logits verfication

52
examples/model-conversion/logits.cpp
View File

@ -151,6 +151,35 @@ int main(int argc, char ** argv) {
logits = llama_get_embeddings(ctx);
n_logits = llama_model_n_embd(model) * batch.n_tokens;
type = "-embeddings";
const int n_embd = llama_model_n_embd(model);
const int n_embd_count = batch.n_tokens;
printf("Embedding dimension: %d\n", n_embd);
printf("\n");
// Print embeddings in the specified format
for (int j = 0; j < n_embd_count; j++) {
printf("embedding %d: ", j);
// Print first 3 values
for (int i = 0; i < 3 && i < n_embd; i++) {
printf("%9.6f ", logits[j * n_embd + i]);
}
printf(" ... ");
// Print last 3 values
for (int i = n_embd - 3; i < n_embd; i++) {
if (i >= 0) {
printf("%9.6f ", logits[j * n_embd + i]);
}
}
printf("\n");
}
printf("\n");
printf("Embeddings size: %d\n", n_logits);
} else {
logits = llama_get_logits_ith(ctx, batch.n_tokens - 1);
@ -183,22 +212,23 @@ int main(int argc, char ** argv) {
return 1;
}
for (int i = 0; i < n_logits; i++) {
fprintf(f, "%d: %.6f\n", i, logits[i]); // Added index and changed format
fprintf(f, "%d: %.6f\n", i, logits[i]);
}
fclose(f);
// Print first and last 10 logits for quick verification
printf("First 10 logits: ");
for (int i = 0; i < 10 && i < n_logits; i++) {
printf("%.6f ", logits[i]);
}
printf("\n");
if (!embedding_mode) {
printf("First 10 logits: ");
for (int i = 0; i < 10 && i < n_logits; i++) {
printf("%.6f ", logits[i]);
}
printf("\n");
printf("Last 10 logits: ");
for (int i = n_logits - 10; i < n_logits; i++) {
if (i >= 0) printf("%.6f ", logits[i]);
printf("Last 10 logits: ");
for (int i = n_logits - 10; i < n_logits; i++) {
if (i >= 0) printf("%.6f ", logits[i]);
}
printf("\n\n");
}
printf("\n\n");
printf("Logits saved to %s\n", bin_filename);
printf("Logits saved to %s\n", txt_filename);

2
examples/model-conversion/scripts/causal/compare-logits.py
View File

@ -48,7 +48,7 @@ def main():
print(f"Error: Model file not found: {model_path}")
sys.exit(1)
model_name = os.path.splitext(os.path.basename(model_path))[0]
model_name = os.path.basename(model_path)
data_dir = Path("data")
pytorch_file = data_dir / f"pytorch-{model_name}.bin"

2
examples/model-conversion/scripts/causal/run-org-model.py
View File

@ -193,7 +193,7 @@ print(f"Input text: {repr(prompt)}")
print(f"Tokenized: {tokenizer.convert_ids_to_tokens(input_ids[0])}")
with torch.no_grad():
outputs = model(input_ids)
outputs = model(input_ids.to(model.device))
logits = outputs.logits
# Extract logits for the last token (next token prediction)

49
examples/model-conversion/scripts/embedding/compare-embeddings-logits.sh
View File

@ -2,8 +2,37 @@
set -e
MODEL_PATH="${1:-"$EMBEDDING_MODEL_PATH"}"
MODEL_NAME="${2:-$(basename "$MODEL_PATH")}"
# Parse command line arguments
MODEL_PATH=""
MODEL_NAME=""
PROMPTS_FILE=""
# First argument is always model path
if [ $# -gt 0 ] && [[ "$1" != --* ]]; then
MODEL_PATH="$1"
shift
fi
# Parse remaining arguments
while [[ $# -gt 0 ]]; do
case $1 in
--prompts-file|-pf)
PROMPTS_FILE="$2"
shift 2
;;
*)
# If MODEL_NAME not set and this isn't a flag, use as model name
if [ -z "$MODEL_NAME" ] && [[ "$1" != --* ]]; then
MODEL_NAME="$1"
fi
shift
;;
esac
done
# Set defaults
MODEL_PATH="${MODEL_PATH:-"$EMBEDDING_MODEL_PATH"}"
MODEL_NAME="${MODEL_NAME:-$(basename "$MODEL_PATH")}"
if [ -t 0 ]; then
CPP_EMBEDDINGS="data/llamacpp-${MODEL_NAME}-embeddings.bin"
@ -35,8 +64,18 @@ with open('$TEMP_FILE', 'wb') as f:
trap "rm -f $TEMP_FILE" EXIT
fi
python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
# Build the semantic_check.py command
SEMANTIC_CMD="python scripts/utils/semantic_check.py --model-path $MODEL_PATH \
--python-embeddings data/pytorch-${MODEL_NAME}-embeddings.bin \
--cpp-embeddings $CPP_EMBEDDINGS \
--prompt "Hello world today"
--cpp-embeddings $CPP_EMBEDDINGS"
# Add prompts file if specified, otherwise use default prompt
if [ -n "$PROMPTS_FILE" ]; then
SEMANTIC_CMD="$SEMANTIC_CMD --prompts-file \"$PROMPTS_FILE\""
else
SEMANTIC_CMD="$SEMANTIC_CMD --prompt \"Hello world today\""
fi
# Execute the command
eval $SEMANTIC_CMD

38
examples/model-conversion/scripts/embedding/run-converted-model.sh
View File

@ -2,8 +2,27 @@
set -e
# First try command line argument, then environment variable, then file
CONVERTED_MODEL="${1:-"$CONVERTED_EMBEDDING_MODEL"}"
# Parse command line arguments
CONVERTED_MODEL=""
PROMPTS_FILE=""
while [[ $# -gt 0 ]]; do
case $1 in
-p|--prompts-file)
PROMPTS_FILE="$2"
shift 2
;;
*)
if [ -z "$CONVERTED_MODEL" ]; then
CONVERTED_MODEL="$1"
fi
shift
;;
esac
done
# First try command line argument, then environment variable
CONVERTED_MODEL="${CONVERTED_MODEL:-"$CONVERTED_EMBEDDING_MODEL"}"
# Final check if we have a model path
if [ -z "$CONVERTED_MODEL" ]; then
@ -13,8 +32,19 @@ if [ -z "$CONVERTED_MODEL" ]; then
exit 1
fi
# Read prompt from file or use default
if [ -n "$PROMPTS_FILE" ]; then
if [ ! -f "$PROMPTS_FILE" ]; then
echo "Error: Prompts file '$PROMPTS_FILE' not found" >&2
exit 1
fi
PROMPT=$(cat "$PROMPTS_FILE")
else
PROMPT="Hello world today"
fi
echo $CONVERTED_MODEL
cmake --build ../../build --target llama-logits -j8
../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "Hello world today"
# TODO: update logits.cpp to accept a --file/-f option for the prompt
../../build/bin/llama-logits -m "$CONVERTED_MODEL" -embd-mode "$PROMPT"

42
examples/model-conversion/scripts/embedding/run-original-model.py
View File

@ -13,14 +13,37 @@ unreleased_model_name = os.getenv('UNRELEASED_MODEL_NAME')
parser = argparse.ArgumentParser(description='Process model with specified path')
parser.add_argument('--model-path', '-m', help='Path to the model')
parser.add_argument('--prompts-file', '-p', help='Path to file containing prompts (one per line)')
args = parser.parse_args()
def read_prompt_from_file(file_path):
try:
with open(file_path, 'r', encoding='utf-8') as f:
return f.read().strip()
except FileNotFoundError:
print(f"Error: Prompts file '{file_path}' not found")
exit(1)
except Exception as e:
print(f"Error reading prompts file: {e}")
exit(1)
model_path = os.environ.get('EMBEDDING_MODEL_PATH', args.model_path)
if model_path is None:
parser.error("Model path must be specified either via --model-path argument or EMBEDDING_MODEL_PATH environment variable")
tokenizer = AutoTokenizer.from_pretrained(model_path)
config = AutoConfig.from_pretrained(model_path)
# This can be used to override the sliding window size for manual testing. This
# can be useful to verify the sliding window attention mask in the original model
# and compare it with the converted .gguf model.
if hasattr(config, 'sliding_window'):
original_sliding_window = config.sliding_window
#original_sliding_window = 6
print(f"Modified sliding window: {original_sliding_window} -> {config.sliding_window}")
print(f"Using unreleased model: {unreleased_model_name}")
if unreleased_model_name:
model_name_lower = unreleased_model_name.lower()
unreleased_module_path = f"transformers.models.{model_name_lower}.modular_{model_name_lower}"
@ -29,19 +52,28 @@ if unreleased_model_name:
try:
model_class = getattr(importlib.import_module(unreleased_module_path), class_name)
model = model_class.from_pretrained(model_path) # Note: from_pretrained, not fromPretrained
model = model_class.from_pretrained(model_path, config=config)
except (ImportError, AttributeError) as e:
print(f"Failed to import or load model: {e}")
exit(1)
else:
model = AutoModel.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, config=config)
print(f"Model class: {type(model)}")
#print(f"Model file: {type(model).__module__}")
config = AutoConfig.from_pretrained(model_path)
print(f"Model file: {type(model).__module__}")
# Verify the model is using the correct sliding window
if hasattr(model.config, 'sliding_window'):
print(f"Model's sliding_window: {model.config.sliding_window}")
else:
print("Model config does not have sliding_window attribute")
model_name = os.path.basename(model_path)
texts = [ "Hello world today" ]
if args.prompts_file:
prompt_text = read_prompt_from_file(args.prompts_file)
texts = [prompt_text]
else:
texts = ["Hello world today"]
encoded = tokenizer(
texts,

2
examples/model-conversion/scripts/utils/check-nmse.py
View File

@ -67,7 +67,7 @@ def main():
parser.add_argument('-m', '--model-path', required=True, help='Path to the model directory')
args = parser.parse_args()
model_name = os.path.splitext(os.path.basename(args.model_path))[0]
model_name = os.path.basename(args.model_path)
data_dir = Path("data")
pytorch_file = data_dir / f"pytorch-{model_name}.bin"

4
examples/model-conversion/scripts/utils/inspect-org-model.py
View File

@ -40,7 +40,7 @@ if os.path.exists(index_path):
file_path = os.path.join(model_path, file_name)
print(f"\n--- From {file_name} ---")
with safe_open(file_path, framework="pt") as f: # type: ignore
with safe_open(file_path, framework="pt") as f:
for tensor_name in sorted(tensor_names):
tensor = f.get_tensor(tensor_name)
print(f"- {tensor_name} : shape = {tensor.shape}, dtype = {tensor.dtype}")
@ -49,7 +49,7 @@ elif os.path.exists(single_file_path):
# Single file model (original behavior)
print("Single-file model detected")
with safe_open(single_file_path, framework="pt") as f: # type: ignore
with safe_open(single_file_path, framework="pt") as f:
keys = f.keys()
print("Tensors in model:")
for key in sorted(keys):

23
examples/model-conversion/scripts/utils/semantic_check.py
View File

@ -101,6 +101,17 @@ def test_single_prompt_similarity(python_emb, cpp_emb, tokens, prompt):
'rms_diff': np.sqrt(np.mean(diff_matrix**2))
}
def read_prompt_from_file(file_path):
try:
with open(file_path, 'r', encoding='utf-8') as f:
return f.read().strip()
except FileNotFoundError:
print(f"Error: Prompts file '{file_path}' not found")
exit(1)
except Exception as e:
print(f"Error reading prompts file: {e}")
exit(1)
def main():
parser = argparse.ArgumentParser(description='Test semantic similarity between Python and llama.cpp embeddings')
parser.add_argument('--model-path', '-m', required=True, help='Path to the original Python model')
@ -108,14 +119,20 @@ def main():
parser.add_argument('--cpp-embeddings', '-ce', help='Path to llama.cpp embeddings "logits" binary file')
parser.add_argument('--causal', '-c', default=False, help='if the model is causal (default: false)', action='store_true')
parser.add_argument('--prompt', '-p', default='Hello world today', help='Test prompt')
parser.add_argument('--prompts-file', '-pf', help='Path to file containing prompts')
args = parser.parse_args()
if args.prompts_file:
prompt = read_prompt_from_file(args.prompts_file)
else:
prompt = args.prompt
print("Semantic Similarity Test Between Python and llama.cpp Embedding Models")
print("=" * 70)
# Single prompt detailed comparison
print(f"\nTesting with prompt: '{args.prompt}'")
print(f"\nTesting with prompt: '{prompt}'")
# Load the python model to get configuration information and also to load the tokenizer.
print("Loading model and tokenizer using AutoTokenizer:", args.model_path)
@ -144,7 +161,7 @@ def main():
else:
model = AutoModel.from_pretrained(args.model_path)
encoded = tokenizer(args.prompt, return_tensors="pt")
encoded = tokenizer(prompt, return_tensors="pt")
tokens = tokenizer.convert_ids_to_tokens(encoded['input_ids'][0])
n_tokens = len(tokens)
print(f"n_tokens: {n_tokens}");
@ -155,7 +172,7 @@ def main():
python_embeddings = load_embeddings_from_file(args.python_embeddings, n_tokens, model.config.hidden_size)
# Run comparison
results = test_single_prompt_similarity(python_embeddings, llamacpp_embeddings, tokens, args.prompt)
results = test_single_prompt_similarity(python_embeddings, llamacpp_embeddings, tokens, prompt)
# Summary
print(f"\n=== SUMMARY ===")

3
ggml/include/ggml-backend.h
View File

@ -314,7 +314,8 @@ extern "C" {
GGML_API int ggml_backend_sched_get_n_splits(ggml_backend_sched_t sched);
GGML_API int ggml_backend_sched_get_n_copies(ggml_backend_sched_t sched);
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend);
GGML_API void ggml_backend_sched_set_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node, ggml_backend_t backend);
GGML_API ggml_backend_t ggml_backend_sched_get_tensor_backend(ggml_backend_sched_t sched, struct ggml_tensor * node);

3
ggml/include/ggml-zdnn.h
View File

@ -7,6 +7,9 @@
extern "C" {
#endif
// device buffer
GGML_BACKEND_API ggml_backend_buffer_type_t ggml_backend_zdnn_buffer_type(void);
GGML_BACKEND_API ggml_backend_reg_t ggml_backend_zdnn_reg(void);
#ifdef __cplusplus

420
ggml/src/ggml-alloc.c
View File

@ -23,7 +23,7 @@ static bool ggml_is_view(const struct ggml_tensor * t) {
}
// ops that return true for this function must not use restrict pointers for their backend implementations
static bool ggml_op_can_inplace(enum ggml_op op) {
bool ggml_op_can_inplace(enum ggml_op op) {
switch (op) {
case GGML_OP_SCALE:
case GGML_OP_DIAG_MASK_ZERO:
@ -95,39 +95,104 @@ enum ggml_status ggml_tallocr_alloc(struct ggml_tallocr * talloc, struct ggml_te
// dynamic tensor allocator
#define GGML_VBUFFER_MAX_CHUNKS 16
// relative memory address within an allocation that can be split into multiple buffers (chunks)
struct buffer_address {
int chunk; // index of a backend buffer
size_t offset; // local memory offset within the buffer
};
static const struct buffer_address GGML_BUFFER_ADDRESS_INVALID = { -1, SIZE_MAX };
static bool ggml_buffer_address_less(struct buffer_address a, struct buffer_address b) {
return a.chunk != b.chunk ? a.chunk < b.chunk : a.offset < b.offset;
}
struct free_block {
size_t offset;
size_t size;
};
struct tallocr_chunk {
struct free_block free_blocks[MAX_FREE_BLOCKS];
int n_free_blocks;
size_t max_size;
};
struct ggml_dyn_tallocr {
size_t alignment;
int n_free_blocks;
struct free_block free_blocks[MAX_FREE_BLOCKS];
size_t max_size;
size_t max_chunk_size;
struct tallocr_chunk * chunks[GGML_VBUFFER_MAX_CHUNKS];
int n_chunks;
#ifdef GGML_ALLOCATOR_DEBUG
struct {
const struct ggml_tensor * tensor;
size_t offset;
struct buffer_address addr;
} allocated_tensors[1024];
#endif
};
static void ggml_dyn_tallocr_insert_block(struct tallocr_chunk * chunk, size_t offset, size_t size) {
GGML_ASSERT(chunk->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
int insert_pos = 0;
while (insert_pos < chunk->n_free_blocks && chunk->free_blocks[insert_pos].offset < offset) {
insert_pos++;
}
// shift all blocks from insert_pos onward to make room for the new block
for (int i = chunk->n_free_blocks; i > insert_pos; i--) {
chunk->free_blocks[i] = chunk->free_blocks[i-1];
}
// insert the new block
chunk->free_blocks[insert_pos].offset = offset;
chunk->free_blocks[insert_pos].size = size;
chunk->n_free_blocks++;
}
static void ggml_dyn_tallocr_remove_block(struct tallocr_chunk * chunk, int idx) {
// shift all elements after idx by 1 to the left, overwriting the element at idx
for (int i = idx; i < chunk->n_free_blocks; i++) {
chunk->free_blocks[i] = chunk->free_blocks[i+1];
}
chunk->n_free_blocks--;
}
static int ggml_dyn_tallocr_new_chunk(struct ggml_dyn_tallocr * alloc, size_t min_size) {
if (alloc->n_chunks >= GGML_VBUFFER_MAX_CHUNKS) {
return -1;
}
struct tallocr_chunk * chunk = calloc(1, sizeof(struct tallocr_chunk));
chunk->n_free_blocks = 1;
chunk->free_blocks[0].offset = 0;
// available space in a chunk is limited to max_chunk_size, but can be higher if:
// 1. a single tensor exceeds the maximum, and cannot fit any other way
// 2. we are running out of chunks
// backends will either manage to allocate the larger size, or report an error.
chunk->free_blocks[0].size = MAX(min_size, alloc->max_chunk_size);
if (alloc->n_chunks == GGML_VBUFFER_MAX_CHUNKS - 1) {
chunk->free_blocks[0].size = SIZE_MAX/2;
}
alloc->chunks[alloc->n_chunks] = chunk;
alloc->n_chunks++;
return alloc->n_chunks - 1;
}
#ifdef GGML_ALLOCATOR_DEBUG
static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
static void add_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i].tensor == NULL) {
alloc->allocated_tensors[i].tensor = tensor;
alloc->allocated_tensors[i].offset = offset;
alloc->allocated_tensors[i].addr = addr;
return;
}
}
GGML_ABORT("out of allocated_tensors");
}
static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, const struct ggml_tensor * tensor) {
static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, const struct ggml_tensor * tensor) {
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i].offset == offset) {
if (alloc->allocated_tensors[i].addr.chunk == addr.chunk && alloc->allocated_tensors[i].addr.offset == addr.offset) {
alloc->allocated_tensors[i].tensor = NULL;
return;
}
@ -136,76 +201,94 @@ static void remove_allocated_tensor(struct ggml_dyn_tallocr * alloc, size_t offs
}
#endif
static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
static struct buffer_address ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t size, const struct ggml_tensor * tensor) {
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size);
int best_fit_chunk = -1;
int best_fit_block = -1;
size_t max_avail = 0;
// find the best fitting free block besides the last block
int best_fit_block = -1;
size_t best_fit_size = SIZE_MAX;
for (int i = 0; i < alloc->n_free_blocks - 1; i++) {
struct free_block * block = &alloc->free_blocks[i];
max_avail = MAX(max_avail, block->size);
if (block->size >= size && block->size <= best_fit_size) {
best_fit_block = i;
best_fit_size = block->size;
// find the best fitting free block besides the last block, within any chunk
for (int c = 0; c < alloc->n_chunks; ++c) {
struct tallocr_chunk * chunk = alloc->chunks[c];
size_t best_fit_size = SIZE_MAX;
for (int i = 0; i < chunk->n_free_blocks - 1; i++) {
struct free_block * block = &chunk->free_blocks[i];
max_avail = MAX(max_avail, block->size);
if (block->size >= size && block->size <= best_fit_size) {
best_fit_chunk = c;
best_fit_block = i;
best_fit_size = block->size;
}
}
}
if (best_fit_block == -1) {
// the last block is our last resort
struct free_block * block = &alloc->free_blocks[alloc->n_free_blocks - 1];
max_avail = MAX(max_avail, block->size);
if (block->size >= size) {
best_fit_block = alloc->n_free_blocks - 1;
} else {
// this should never happen
GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
__func__, size, max_avail);
GGML_ABORT("not enough space in the buffer");
}
}
struct free_block * block = &alloc->free_blocks[best_fit_block];
size_t offset = block->offset;
block->offset = offset + size;
block->size -= size;
if (block->size == 0) {
// remove block if empty
alloc->n_free_blocks--;
for (int j = best_fit_block; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
}
}
AT_PRINTF("block %d, offset %zu\n", best_fit_block, offset);
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, offset, tensor);
size_t cur_max = offset + size;
if (cur_max > alloc->max_size) {
// sort allocated_tensors by offset
for (int i = 0; i < 1024; i++) {
for (int j = i + 1; j < 1024; j++) {
if (alloc->allocated_tensors[i].offset > alloc->allocated_tensors[j].offset) {
const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor;
size_t tmp_offset = alloc->allocated_tensors[i].offset;
alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor;
alloc->allocated_tensors[i].offset = alloc->allocated_tensors[j].offset;
alloc->allocated_tensors[j].tensor = tmp_tensor;
alloc->allocated_tensors[j].offset = tmp_offset;
// no suitable block found, try the last block (this will grow a chunks size)
for (int c = 0; c < alloc->n_chunks; ++c) {
struct tallocr_chunk * chunk = alloc->chunks[c];
if (chunk->n_free_blocks > 0) {
struct free_block * block = &chunk->free_blocks[chunk->n_free_blocks - 1];
max_avail = MAX(max_avail, block->size);
if (block->size >= size) {
best_fit_chunk = c;
best_fit_block = chunk->n_free_blocks - 1;
break;
}
}
}
GGML_LOG_DEBUG("max_size = %.2f MB: tensors: ", cur_max / 1024.0 / 1024.0);
}
if (best_fit_block == -1) {
// none of the existing chunks have enough space left
best_fit_chunk = ggml_dyn_tallocr_new_chunk(alloc, size);
best_fit_block = 0;
}
if (best_fit_chunk == -1) {
// since the last chunk always has virtually endless memory, this should never happen
GGML_LOG_ERROR("%s: not enough space in the buffer to allocate %zu bytes, largest block available %zu bytes\n",
__func__, size, max_avail);
GGML_ABORT("graph allocation: failed to reserve memory");
}
struct tallocr_chunk * chunk = alloc->chunks[best_fit_chunk];
struct free_block * block = &chunk->free_blocks[best_fit_block];
struct buffer_address addr = {.chunk = best_fit_chunk, .offset = block->offset };
block->offset += size;
block->size -= size;
if (block->size == 0) {
// remove block if empty
ggml_dyn_tallocr_remove_block(chunk, best_fit_block);
}
AT_PRINTF("block %d, offset %zu, chunk %d\n", best_fit_block, addr.offset, addr.chunk);
#ifdef GGML_ALLOCATOR_DEBUG
add_allocated_tensor(alloc, addr, tensor);
size_t cur_max = addr.offset + size;
if (cur_max > alloc->max_size[addr.chunk]) {
// sort allocated_tensors by chunk/offset
for (int i = 0; i < 1024; i++) {
for (int j = i + 1; j < 1024; j++) {
if (ggml_buffer_address_less(alloc->allocated_tensors[j].addr, alloc->allocated_tensors[i].addr)) {
const struct ggml_tensor * tmp_tensor = alloc->allocated_tensors[i].tensor;
struct buffer_address tmp_addr = alloc->allocated_tensors[i].addr;
alloc->allocated_tensors[i].tensor = alloc->allocated_tensors[j].tensor;
alloc->allocated_tensors[i].addr = alloc->allocated_tensors[j].addr;
alloc->allocated_tensors[j].tensor = tmp_tensor;
alloc->allocated_tensors[j].addr = tmp_addr;
}
}
}
GGML_LOG_DEBUG("max_size[%d] = %.2f MB: tensors: ", addr.chunk, cur_max / 1024.0 / 1024.0);
for (int i = 0; i < 1024; i++) {
if (alloc->allocated_tensors[i].tensor) {
GGML_LOG_DEBUG("%s [%zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
alloc->allocated_tensors[i].offset,
alloc->allocated_tensors[i].offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
GGML_LOG_DEBUG("%s [%d: %zx-%zx] (%.2f MB) ", alloc->allocated_tensors[i].tensor->name,
alloc->allocated_tensors[i].addr.chunk,
alloc->allocated_tensors[i].addr.offset,
alloc->allocated_tensors[i].addr.offset + ggml_nbytes(alloc->allocated_tensors[i].tensor),
ggml_nbytes(alloc->allocated_tensors[i].tensor) / 1024.0 / 1024.0);
}
}
@ -213,78 +296,69 @@ static size_t ggml_dyn_tallocr_alloc(struct ggml_dyn_tallocr * alloc, size_t siz
}
#endif
alloc->max_size = MAX(alloc->max_size, offset + size);
chunk->max_size = MAX(chunk->max_size, addr.offset + size);
return offset;
return addr;
GGML_UNUSED(tensor);
}
// this is a very naive implementation, but for our case the number of free blocks should be very small
static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, size_t offset, size_t size, const struct ggml_tensor * tensor) {
static void ggml_dyn_tallocr_free_tensor(struct ggml_dyn_tallocr * alloc, struct buffer_address addr, size_t size, const struct ggml_tensor * tensor) {
size = aligned_offset(NULL, size, alloc->alignment);
AT_PRINTF("%s: freeing %s at %zu (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, offset, size, alloc->n_free_blocks);
AT_PRINTF("%s: freeing %s at {chunk=%d, offset=%zu} (%zu bytes) - n_free_blocks = %d\n",
__func__, tensor->name, addr.chunk, addr.offset, size, alloc->chunks[addr.chunk]->n_free_blocks);
#ifdef GGML_ALLOCATOR_DEBUG
remove_allocated_tensor(alloc, offset, tensor);
remove_allocated_tensor(alloc, addr, tensor);
#endif
struct tallocr_chunk * chunk = alloc->chunks[addr.chunk];
// see if we can merge with an existing block
for (int i = 0; i < alloc->n_free_blocks; i++) {
struct free_block * block = &alloc->free_blocks[i];
for (int i = 0; i < chunk->n_free_blocks; i++) {
struct free_block * block = &chunk->free_blocks[i];
// check if ptr is at the end of the block
if (block->offset + block->size == offset) {
if (block->offset + block->size == addr.offset) {
block->size += size;
// check if we can merge with the next block
if (i < alloc->n_free_blocks - 1 && block->offset + block->size == alloc->free_blocks[i+1].offset) {
block->size += alloc->free_blocks[i+1].size;
alloc->n_free_blocks--;
for (int j = i+1; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
if (i < chunk->n_free_blocks - 1) {
struct free_block * next = &chunk->free_blocks[i+1];
if (block->offset + block->size == next->offset) {
block->size += next->size;
ggml_dyn_tallocr_remove_block(chunk, i+1);
}
}
return;
}
// check if ptr is at the beginning of the block
if (offset + size == block->offset) {
block->offset = offset;
if (addr.offset + size == block->offset) {
block->offset = addr.offset;
block->size += size;
// check if we can merge with the previous block
if (i > 0 && alloc->free_blocks[i-1].offset + alloc->free_blocks[i-1].size == block->offset) {
alloc->free_blocks[i-1].size += block->size;
alloc->n_free_blocks--;
for (int j = i; j < alloc->n_free_blocks; j++) {
alloc->free_blocks[j] = alloc->free_blocks[j+1];
if (i > 0) {
struct free_block * prev = &chunk->free_blocks[i-1];
if (prev->offset + prev->size == block->offset) {
prev->size += block->size;
ggml_dyn_tallocr_remove_block(chunk, i);
}
}
return;
}
}
// otherwise, add a new block
GGML_ASSERT(alloc->n_free_blocks < MAX_FREE_BLOCKS && "out of free blocks");
// insert the new block in the correct position to keep the array sorted by address (to make merging blocks faster)
int insert_pos = 0;
while (insert_pos < alloc->n_free_blocks && alloc->free_blocks[insert_pos].offset < offset) {
insert_pos++;
}
// shift all blocks from insert_pos onward to make room for the new block
for (int i = alloc->n_free_blocks; i > insert_pos; i--) {
alloc->free_blocks[i] = alloc->free_blocks[i-1];
}
// insert the new block
alloc->free_blocks[insert_pos].offset = offset;
alloc->free_blocks[insert_pos].size = size;
alloc->n_free_blocks++;
ggml_dyn_tallocr_insert_block(chunk, addr.offset, size);
GGML_UNUSED(tensor);
}
static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
alloc->n_free_blocks = 1;
alloc->free_blocks[0].offset = 0;
alloc->free_blocks[0].size = SIZE_MAX/2; // restrict maximum size of a measure allocator to half size_t max to avoid overflows
alloc->max_size = 0;
for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; i++) {
free(alloc->chunks[i]);
alloc->chunks[i] = NULL;
}
alloc->n_chunks = 0;
#ifdef GGML_ALLOCATOR_DEBUG
for (int i = 0; i < 1024; i++) {
@ -293,14 +367,14 @@ static void ggml_dyn_tallocr_reset(struct ggml_dyn_tallocr * alloc) {
#endif
}
static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment, size_t max_buffer_size) {
struct ggml_dyn_tallocr * alloc = (struct ggml_dyn_tallocr *)malloc(sizeof(struct ggml_dyn_tallocr));
*alloc = (struct ggml_dyn_tallocr) {
/*.alignment = */ alignment,
/*.n_free_blocks = */ 0,
/*.free_blocks = */ {{0}},
/*.max_size = */ 0,
/*.alignment = */ alignment,
/*.max_chunk_size = */ MIN(max_buffer_size, SIZE_MAX/2), // clamp to avoid overflows
/*.chunks = */ {NULL},
/*.n_chunks = */ 0,
#ifdef GGML_ALLOCATOR_DEBUG
/*.allocated_tensors = */ {{0}},
#endif
@ -312,11 +386,79 @@ static struct ggml_dyn_tallocr * ggml_dyn_tallocr_new(size_t alignment) {
}
static void ggml_dyn_tallocr_free(struct ggml_dyn_tallocr * alloc) {
for (int i = 0; i < alloc->n_chunks; ++i) {
free(alloc->chunks[i]);
}
free(alloc);
}
static size_t ggml_dyn_tallocr_max_size(struct ggml_dyn_tallocr * alloc) {
return alloc->max_size;
size_t max_size = 0;
for (int i = 0; i < alloc->n_chunks; i++) {
max_size += alloc->chunks[i]->max_size;
}
return max_size;
}
// virtual buffer with contiguous memory range, split into multiple backend buffers (chunks)
struct vbuffer {
ggml_backend_buffer_t chunks[GGML_VBUFFER_MAX_CHUNKS];
};
static void ggml_vbuffer_free(struct vbuffer * buf) {
if (buf == NULL) {
return;
}
for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS; ++i) {
ggml_backend_buffer_free(buf->chunks[i]);
}
free(buf);
}
static int ggml_vbuffer_n_chunks(struct vbuffer * buf) {
int n = 0;
while (n < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[n]) n++;
return n;
}
static size_t ggml_vbuffer_size(struct vbuffer * buf) {
size_t size = 0;
for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) {
size += ggml_backend_buffer_get_size(buf->chunks[i]);
}
return size;
}
static struct vbuffer * ggml_vbuffer_alloc(ggml_backend_buffer_type_t buft, const struct ggml_dyn_tallocr * talloc, enum ggml_backend_buffer_usage usage) {
struct vbuffer * buf = (struct vbuffer *)calloc(1, sizeof(struct vbuffer));
if (buf == NULL) {
return NULL;
}
for (int n = 0; n < talloc->n_chunks; n++) {
size_t chunk_size = talloc->chunks[n]->max_size;
buf->chunks[n] = ggml_backend_buft_alloc_buffer(buft, chunk_size);
if (buf->chunks[n] == NULL) {
ggml_vbuffer_free(buf);
return NULL;
}
ggml_backend_buffer_set_usage(buf->chunks[n], usage);
}
return buf;
}
static void ggml_vbuffer_tensor_alloc(struct vbuffer * buf, struct ggml_tensor * tensor, struct buffer_address buf_addr) {
void * base = ggml_backend_buffer_get_base(buf->chunks[buf_addr.chunk]);
void * addr = (char *)base + buf_addr.offset;
ggml_backend_tensor_alloc(buf->chunks[buf_addr.chunk], tensor, addr);
}
static void ggml_vbuffer_reset(struct vbuffer * buf) {
for (int i = 0; i < GGML_VBUFFER_MAX_CHUNKS && buf->chunks[i]; ++i) {
ggml_backend_buffer_reset(buf->chunks[i]);
}
}
@ -328,13 +470,13 @@ struct hash_node {
int n_children;
int n_views;
int buffer_id;
size_t offset; // offset within the buffer
struct buffer_address addr;
bool allocated;
};
struct tensor_alloc {
int buffer_id;
size_t offset;
struct buffer_address addr;
size_t size_max; // 0 = pre-allocated, unused, or view
};
@ -349,7 +491,7 @@ struct node_alloc {
struct ggml_gallocr {
ggml_backend_buffer_type_t * bufts; // [n_buffers]
ggml_backend_buffer_t * buffers; // [n_buffers]
struct vbuffer ** buffers; // [n_buffers]
struct ggml_dyn_tallocr ** buf_tallocs; // [n_buffers]
int n_buffers;
@ -370,7 +512,7 @@ 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));
galloc->buffers = calloc(n_bufs, sizeof(struct vbuffer *));
GGML_ASSERT(galloc->buffers != NULL);
galloc->buf_tallocs = calloc(n_bufs, sizeof(struct ggml_dyn_tallocr *));
@ -390,7 +532,8 @@ ggml_gallocr_t ggml_gallocr_new_n(ggml_backend_buffer_type_t * bufts, int n_bufs
if (galloc->buf_tallocs[i] == NULL) {
size_t alignment = ggml_backend_buft_get_alignment(bufts[i]);
galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment);
size_t max_size = ggml_backend_buft_get_max_size(bufts[i]);
galloc->buf_tallocs[i] = ggml_dyn_tallocr_new(alignment, max_size);
}
}
galloc->n_buffers = n_bufs;
@ -418,7 +561,7 @@ void ggml_gallocr_free(ggml_gallocr_t galloc) {
}
}
if (!freed) {
ggml_backend_buffer_free(galloc->buffers[i]);
ggml_vbuffer_free(galloc->buffers[i]);
}
}
if (galloc->buf_tallocs != NULL) {
@ -467,7 +610,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
if (!ggml_gallocr_is_allocated(galloc, node) && !ggml_is_view(node)) {
hn->allocated = true;
assert(hn->offset == 0);
assert(hn->addr.offset == 0);
// try to reuse a parent's buffer (inplace)
if (ggml_op_can_inplace(node->op)) {
@ -501,9 +644,9 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
struct hash_node * view_src_hn = ggml_gallocr_hash_get(galloc, view_src);
if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) {
AT_PRINTF("reusing view parent %s (%s) for %s\n", parent->name, view_src->name, node->name);
assert(view_src_hn->offset == p_hn->offset);
assert(view_src_hn->addr.chunk == p_hn->addr.chunk && view_src_hn->addr.offset == p_hn->addr.offset);
hn->buffer_id = p_hn->buffer_id;
hn->offset = p_hn->offset;
hn->addr = p_hn->addr;
p_hn->allocated = false; // avoid freeing the parent
view_src_hn->allocated = false;
return;
@ -511,7 +654,7 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
} else {
AT_PRINTF("reusing parent %s for %s\n", parent->name, node->name);
hn->buffer_id = p_hn->buffer_id;
hn->offset = p_hn->offset;
hn->addr = p_hn->addr;
p_hn->allocated = false; // avoid freeing the parent
return;
}
@ -522,9 +665,8 @@ static void ggml_gallocr_allocate_node(ggml_gallocr_t galloc, struct ggml_tensor
struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
size_t size = ggml_backend_buft_get_alloc_size(buft, node);
size_t offset = ggml_dyn_tallocr_alloc(alloc, size, node);
hn->buffer_id = buffer_id;
hn->offset = offset;
hn->addr = ggml_dyn_tallocr_alloc(alloc, size, node);
}
}
@ -536,12 +678,11 @@ static void ggml_gallocr_free_node(ggml_gallocr_t galloc, struct ggml_tensor * n
}
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
size_t offset = hn->offset;
int buffer_id = hn->buffer_id;
struct ggml_dyn_tallocr * alloc = galloc->buf_tallocs[buffer_id];
ggml_backend_buffer_type_t buft = galloc->bufts[buffer_id];
size_t size = ggml_backend_buft_get_alloc_size(buft, node);
ggml_dyn_tallocr_free_tensor(alloc, offset, size, node);
ggml_dyn_tallocr_free_tensor(alloc, hn->addr, size, node);
hn->allocated = false;
}
@ -692,24 +833,24 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
struct node_alloc * node_alloc = &galloc->node_allocs[i];
if (node->view_src || node->data) {
node_alloc->dst.buffer_id = -1;
node_alloc->dst.offset = SIZE_MAX;
node_alloc->dst.addr = GGML_BUFFER_ADDRESS_INVALID;
node_alloc->dst.size_max = 0;
} else {
struct hash_node * hn = ggml_gallocr_hash_get(galloc, node);
node_alloc->dst.buffer_id = hn->buffer_id;
node_alloc->dst.offset = hn->offset;
node_alloc->dst.addr = hn->addr;
node_alloc->dst.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], node);
}
for (int j = 0; j < GGML_MAX_SRC; j++) {
struct ggml_tensor * src = node->src[j];
if (!src || src->view_src || src->data) {
node_alloc->src[j].buffer_id = -1;
node_alloc->src[j].offset = SIZE_MAX;
node_alloc->src[j].addr = GGML_BUFFER_ADDRESS_INVALID;
node_alloc->src[j].size_max = 0;
} else {
struct hash_node * hn = ggml_gallocr_hash_get(galloc, src);
node_alloc->src[j].buffer_id = hn->buffer_id;
node_alloc->src[j].offset = hn->offset;
node_alloc->src[j].addr = hn->addr;
node_alloc->src[j].size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], src);
}
}
@ -725,11 +866,11 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
struct hash_node * hn = ggml_gallocr_hash_get(galloc, leaf);
if (leaf->view_src || leaf->data) {
galloc->leaf_allocs[i].leaf.buffer_id = -1;
galloc->leaf_allocs[i].leaf.offset = SIZE_MAX;
galloc->leaf_allocs[i].leaf.addr = GGML_BUFFER_ADDRESS_INVALID;
galloc->leaf_allocs[i].leaf.size_max = 0;
} else {
galloc->leaf_allocs[i].leaf.buffer_id = hn->buffer_id;
galloc->leaf_allocs[i].leaf.offset = hn->offset;
galloc->leaf_allocs[i].leaf.addr = hn->addr;
galloc->leaf_allocs[i].leaf.size_max = ggml_backend_buft_get_alloc_size(galloc->bufts[hn->buffer_id], leaf);
}
}
@ -744,7 +885,7 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
}
}
size_t cur_size = galloc->buffers[i] ? ggml_backend_buffer_get_size(galloc->buffers[i]) : 0;
size_t cur_size = galloc->buffers[i] ? ggml_vbuffer_size(galloc->buffers[i]) : 0;
size_t new_size = ggml_dyn_tallocr_max_size(galloc->buf_tallocs[i]);
// even if there are no tensors allocated in this buffer, we still need to allocate it to initialize views
@ -753,13 +894,12 @@ bool ggml_gallocr_reserve_n(ggml_gallocr_t galloc, struct ggml_cgraph * graph, c
GGML_LOG_DEBUG("%s: reallocating %s buffer from size %.02f MiB to %.02f MiB\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), cur_size / 1024.0 / 1024.0, new_size / 1024.0 / 1024.0);
#endif
ggml_backend_buffer_free(galloc->buffers[i]);
galloc->buffers[i] = ggml_backend_buft_alloc_buffer(galloc->bufts[i], new_size);
ggml_vbuffer_free(galloc->buffers[i]);
galloc->buffers[i] = ggml_vbuffer_alloc(galloc->bufts[i], galloc->buf_tallocs[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
if (galloc->buffers[i] == NULL) {
GGML_LOG_ERROR("%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(galloc->bufts[i]), new_size);
return false;
}
ggml_backend_buffer_set_usage(galloc->buffers[i], GGML_BACKEND_BUFFER_USAGE_COMPUTE);
}
}
@ -772,11 +912,11 @@ bool ggml_gallocr_reserve(ggml_gallocr_t galloc, struct ggml_cgraph *graph) {
static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor * tensor, struct tensor_alloc * tensor_alloc) {
int buffer_id = tensor_alloc->buffer_id;
assert(tensor->data || tensor->view_src || ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
assert(tensor->data || tensor->view_src || ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max);
if (tensor->view_src != NULL) {
if (tensor->buffer == NULL) {
assert(tensor_alloc->offset == SIZE_MAX);
assert(tensor_alloc->addr.offset == SIZE_MAX);
if (tensor->view_src->buffer == NULL) {
// this tensor was allocated without ggml-backend
return;
@ -785,11 +925,9 @@ static void ggml_gallocr_init_tensor(ggml_gallocr_t galloc, struct ggml_tensor *
}
} else {
if (tensor->data == NULL) {
assert(tensor_alloc->offset != SIZE_MAX);
assert(ggml_backend_buffer_get_alloc_size(galloc->buffers[buffer_id], tensor) <= tensor_alloc->size_max);
void * base = ggml_backend_buffer_get_base(galloc->buffers[buffer_id]);
void * addr = (char *)base + tensor_alloc->offset;
ggml_backend_tensor_alloc(galloc->buffers[buffer_id], tensor, addr);
assert(tensor_alloc->addr.offset != SIZE_MAX);
assert(ggml_backend_buft_get_alloc_size(galloc->bufts[buffer_id], tensor) <= tensor_alloc->size_max);
ggml_vbuffer_tensor_alloc(galloc->buffers[buffer_id], tensor, tensor_alloc->addr);
} else {
if (tensor->buffer == NULL) {
// this tensor was allocated without ggml-backend
@ -874,7 +1012,7 @@ bool ggml_gallocr_alloc_graph(ggml_gallocr_t galloc, struct ggml_cgraph * graph)
// reset buffers
for (int i = 0; i < galloc->n_buffers; i++) {
if (galloc->buffers[i] != NULL) {
ggml_backend_buffer_reset(galloc->buffers[i]);
ggml_vbuffer_reset(galloc->buffers[i]);
}
}
@ -917,7 +1055,7 @@ size_t ggml_gallocr_get_buffer_size(ggml_gallocr_t galloc, int buffer_id) {
}
}
return ggml_backend_buffer_get_size(galloc->buffers[buffer_id]);
return ggml_vbuffer_size(galloc->buffers[buffer_id]);
}
// utils

8
ggml/src/ggml-backend.cpp
View File

@ -1793,6 +1793,14 @@ ggml_backend_t ggml_backend_sched_get_backend(ggml_backend_sched_t sched, int i)
return sched->backends[i];
}
ggml_backend_buffer_type_t ggml_backend_sched_get_buffer_type(ggml_backend_sched_t sched, ggml_backend_t backend) {
GGML_ASSERT(sched);
int backend_index = ggml_backend_sched_backend_id(sched, backend);
GGML_ASSERT(backend_index >= 0 && backend_index < sched->n_backends);
return sched->bufts[backend_index];
}
size_t ggml_backend_sched_get_buffer_size(ggml_backend_sched_t sched, ggml_backend_t backend) {
GGML_ASSERT(sched);
int backend_index = ggml_backend_sched_backend_id(sched, backend);

24
ggml/src/ggml-cpu/arch/loongarch/quants.c
View File

@ -105,6 +105,18 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128
return ((v4f32)res)[0];
}
// multiply int8_t, add results pairwise twice
static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
// Get absolute values of x vectors
const __m128i ax = __lsx_vsigncov_b(x, x);
// Sign the values of the y vectors
const __m128i sy = __lsx_vsigncov_b(x, y);
// Perform multiplication and create 16-bit values
const __m128i dot = lsx_maddubs_h(ax, sy);
const __m128i ones = __lsx_vreplgr2vr_h(1);
return lsx_madd_h(ones, dot);
}
#endif
#if defined(__loongarch_asx)
@ -323,18 +335,6 @@ static inline __m256i lasx_xvandi_b_bit(__m256i a, const unsigned int b) {
}
}
// multiply int8_t, add results pairwise twice
static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) {
// Get absolute values of x vectors
const __m128i ax = __lsx_vsigncov_b(x, x);
// Sign the values of the y vectors
const __m128i sy = __lsx_vsigncov_b(x, y);
// Perform multiplication and create 16-bit values
const __m128i dot = lsx_maddubs_h(ax, sy);
const __m128i ones = __lsx_vreplgr2vr_h(1);
return lsx_madd_h(ones, dot);
}
// horizontally add 8 floats
static inline float hsum_float_8(const __m256 x) {
__m128 res = lasx_extractf128(x, 1);

4
ggml/src/ggml-cpu/arch/x86/repack.cpp
View File

@ -878,7 +878,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
const __m256i rhs_raw_mat_89AB_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 64));
const __m256i rhs_raw_mat_CDEF_1 = _mm256_loadu_si256((const __m256i *)(b_ptr_1[b].qs + 96));
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values
const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);
@ -1231,7 +1231,7 @@ static void gemm_q4_b32_8x8_q8_0_lut_avx(int n, float * GGML_RESTRICT s, size_t
const __m256i rhs_raw_mat_0123_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 64));
const __m256i rhs_raw_mat_4567_1 = _mm256_loadu_si256((const __m256i *)(b_ptr[b].qs + 96));
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of valuess
// Save the values in the following vectors in the formats B0B1B4B5, B2B3B6B7 for further processing and storing of values
const __m256i rhs_raw_mat_0145_0 = _mm256_blend_epi32(rhs_raw_mat_0123_0, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_0, requiredOrder), 240);
const __m256i rhs_raw_mat_2367_0 = _mm256_blend_epi32(_mm256_permutevar8x32_epi32(rhs_raw_mat_0123_0, requiredOrder), rhs_raw_mat_4567_0, 240);
const __m256i rhs_raw_mat_0145_1 = _mm256_blend_epi32(rhs_raw_mat_0123_1, _mm256_permutevar8x32_epi32(rhs_raw_mat_4567_1, requiredOrder), 240);

20
ggml/src/ggml-cpu/ggml-cpu.c
View File

@ -473,10 +473,10 @@ struct ggml_threadpool {
struct ggml_compute_state {
#ifndef GGML_USE_OPENMP
ggml_thread_t thrd;
bool cpumask[GGML_MAX_N_THREADS];
int last_graph;
bool pending;
#endif
bool cpumask[GGML_MAX_N_THREADS];
struct ggml_threadpool * threadpool;
int ith;
};
@ -3081,7 +3081,14 @@ static struct ggml_threadpool * ggml_threadpool_new_impl(
threadpool->workers = workers;
#ifndef GGML_USE_OPENMP
#ifdef GGML_USE_OPENMP
int32_t cpumask_iter = 0;
// Compute CPU masks for each thread
for (int j = 0; j < tpp->n_threads; j++) {
ggml_thread_cpumask_next(tpp->cpumask, workers[j].cpumask, tpp->strict_cpu, &cpumask_iter);
}
#else // GGML_USE_OPENMP
ggml_mutex_init(&threadpool->mutex);
ggml_cond_init(&threadpool->cond);
@ -3154,7 +3161,14 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl
atomic_store_explicit(&threadpool->n_threads_cur, n_threads, memory_order_relaxed);
}
ggml_graph_compute_thread(&threadpool->workers[omp_get_thread_num()]);
// Apply thread CPU mask and priority
int ith = omp_get_thread_num();
ggml_thread_apply_priority(threadpool->prio);
if (ggml_thread_cpumask_is_valid(threadpool->workers[ith].cpumask)) {
ggml_thread_apply_affinity(threadpool->workers[ith].cpumask);
}
ggml_graph_compute_thread(&threadpool->workers[ith]);
}
} else {
atomic_store_explicit(&threadpool->n_threads_cur, 1, memory_order_relaxed);

12
ggml/src/ggml-cpu/ops.cpp
View File

@ -4739,6 +4739,7 @@ void ggml_compute_forward_get_rows(
//}
}
template<typename idx_t>
static void ggml_compute_forward_set_rows_f32(
const ggml_compute_params * params,
ggml_tensor * dst) {
@ -4777,7 +4778,7 @@ static void ggml_compute_forward_set_rows_f32(
const int64_t i11 = i02%ne11;
const int64_t i10 = i;
const int64_t i1 = *(int64_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
const int64_t i1 = *(idx_t *) ((char *) src1->data + i10*nb10 + i11*nb11 + i12*nb12);
GGML_ASSERT(i1 >= 0 && i1 < ne1);
@ -4794,11 +4795,18 @@ void ggml_compute_forward_set_rows(
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
switch (src0->type) {
case GGML_TYPE_F32:
{
ggml_compute_forward_set_rows_f32(params, dst);
if (src1->type == GGML_TYPE_I64) {
ggml_compute_forward_set_rows_f32<int64_t>(params, dst);
} else if (src1->type == GGML_TYPE_I32) {
ggml_compute_forward_set_rows_f32<int32_t>(params, dst);
} else {
GGML_ABORT("src1->type = %d (%s) not supported", src1->type, ggml_type_name(src1->type));
}
} break;
default:
{

16
ggml/src/ggml-cpu/simd-mappings.h
View File

@ -998,9 +998,9 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
#define GGML_F32_EPR 4
#define GGML_F32x4 __m128
#define GGML_F32x4_ZERO __lsx_vldi(0)
#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0)
#define GGML_F32x4_ZERO (__m128)__lsx_vldi(0)
#define GGML_F32x4_SET1(x) (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
#define GGML_F32x4_LOAD(x) (__m128)__lsx_vld((x), 0)
#define GGML_F32x4_STORE(x, y) __lsx_vst(y, x, 0)
#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a)
#define GGML_F32x4_ADD __lsx_vfadd_s
@ -1022,7 +1022,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) {
__m128i tmp = __lsx_vsrli_d((__m128i) x[0], 32); \
tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, x[0]); \
tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \
const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \
const __m128 t0 = (__m128)__lsx_vshuf4i_w(tmp, 0x88); \
tmp = __lsx_vsrli_d((__m128i) t0, 32); \
tmp = (__m128i) __lsx_vfadd_s((__m128) tmp, t0); \
tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \
@ -1052,7 +1052,7 @@ static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) {
tmp[2] = GGML_CPU_FP16_TO_FP32(x[2]);
tmp[3] = GGML_CPU_FP16_TO_FP32(x[3]);
return __lsx_vld(tmp, 0);
return (__m128)__lsx_vld(tmp, 0);
}
static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
@ -1067,9 +1067,9 @@ static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) {
}
#define GGML_F32Cx4 __m128
#define GGML_F32Cx4_ZERO __lsx_vldi(0)
#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x)
#define GGML_F32Cx4_ZERO (__m128)__lsx_vldi(0)
#define GGML_F32Cx4_SET1(x) (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0)
#define GGML_F32Cx4_LOAD(x) (__m128)__lsx_f16x4_load(x)
#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y)
#define GGML_F32Cx4_FMA GGML_F32x4_FMA
#define GGML_F32Cx4_ADD __lsx_vfadd_s

57
ggml/src/ggml-cuda/ggml-cuda.cu
View File

@ -45,6 +45,7 @@
#include "ggml-cuda/sumrows.cuh"
#include "ggml-cuda/mean.cuh"
#include "ggml-cuda/tsembd.cuh"
#include "ggml-cuda/topk-moe.cuh"
#include "ggml-cuda/unary.cuh"
#include "ggml-cuda/upscale.cuh"
#include "ggml-cuda/wkv.cuh"
@ -2825,6 +2826,44 @@ static bool ggml_cuda_can_fuse(const struct ggml_cgraph * cgraph, int node_idx,
GGML_ASSERT(unary_ops.size() == num_unary);
#endif
//TODO: remove special case once ggml_can_fuse can handle empty nodes
std::initializer_list<enum ggml_op> topk_moe_ops = ggml_cuda_topk_moe_ops(false);
std::initializer_list<enum ggml_op> topk_moe_ops_with_norm = ggml_cuda_topk_moe_ops(true);
if (ops.size() == topk_moe_ops_with_norm.size() && std::equal(ops.begin(), ops.end(), topk_moe_ops_with_norm.begin())) {
if (node_idx + topk_moe_ops_with_norm.size() > (size_t)cgraph->n_nodes) {
return false;
}
for (size_t i = 0; i < topk_moe_ops_with_norm.size(); i++) {
if (cgraph->nodes[node_idx + i]->op != topk_moe_ops_with_norm.begin()[i]) return false;
}
ggml_tensor * softmax = cgraph->nodes[node_idx];
ggml_tensor * weights = cgraph->nodes[node_idx+8];
if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
return true;
}
}
if (ops.size() == topk_moe_ops.size() && std::equal(ops.begin(), ops.end(), topk_moe_ops.begin())) {
if (node_idx + topk_moe_ops.size() > (size_t)cgraph->n_nodes) {
return false;
}
for (size_t i = 0; i < topk_moe_ops.size(); i++) {
if (cgraph->nodes[node_idx + i]->op != topk_moe_ops.begin()[i]) return false;
}
ggml_tensor * softmax = cgraph->nodes[node_idx];
ggml_tensor * weights = cgraph->nodes[node_idx+4];
if (ggml_cuda_should_use_topk_moe(softmax, weights)) {
return true;
}
}
if (!ggml_can_fuse(cgraph, node_idx, ops)) {
return false;
}
@ -2915,6 +2954,22 @@ static void evaluate_and_capture_cuda_graph(ggml_backend_cuda_context * cuda_ctx
static bool disable_fusion = (getenv("GGML_CUDA_DISABLE_FUSION") != nullptr);
if (!disable_fusion) {
if (ggml_cuda_can_fuse(cgraph, i, ggml_cuda_topk_moe_ops(/*with norm*/ true), {})) {
ggml_tensor * weights = cgraph->nodes[i+8];
ggml_tensor * selected_experts = cgraph->nodes[i+3];
ggml_cuda_op_topk_moe(*cuda_ctx, node, weights, selected_experts, /*with norm*/ true);
i += 8;
continue;
}
if (ggml_cuda_can_fuse(cgraph, i, ggml_cuda_topk_moe_ops(/*with norm*/ false), {})) {
ggml_tensor * weights = cgraph->nodes[i+4];
ggml_tensor * selected_experts = cgraph->nodes[i+3];
ggml_cuda_op_topk_moe(*cuda_ctx, node, weights, selected_experts, /*with norm*/ false);
i += 4;
continue;
}
if (node->op == GGML_OP_ADD) {
int n_fuse = 0;
ggml_op ops[8];
@ -3427,7 +3482,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q5_0 ||
op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_IQ4_NL) &&
op->src[0]->type == GGML_TYPE_F32 &&
op->src[1]->type == GGML_TYPE_I64;
(op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
} break;
case GGML_OP_CPY:
{

72
ggml/src/ggml-cuda/set-rows.cu
View File

@ -4,9 +4,9 @@
typedef void (*set_rows_kernel_t)(const char * src, char * dst);
// Generic quantized set_rows kernel template
template<typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
template<typename idx_t, typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
static __global__ void k_set_rows_quant(
const float * __restrict__ src0, const int64_t * __restrict__ src1, block_type * __restrict__ dst,
const float * __restrict__ src0, const idx_t * __restrict__ src1, block_type * __restrict__ dst,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const int64_t s01, const int64_t s02, const int64_t s03,
@ -45,9 +45,9 @@ static __global__ void k_set_rows_quant(
}
// Template dispatch function for quantized set_rows
template<typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
template<typename idx_t, typename block_type, int qk, void (*quantize_func)(const float*, block_type*)>
static void set_rows_cuda_quant(
const float * src0_d, const int64_t * src1_d, block_type * dst_d,
const float * src0_d, const idx_t * src1_d, block_type * dst_d,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const size_t nb01, const size_t nb02, const size_t nb03,
@ -64,15 +64,15 @@ static void set_rows_cuda_quant(
const int64_t s01 = nb01/sizeof(float);
const int64_t s02 = nb02/sizeof(float);
const int64_t s03 = nb03/sizeof(float);
const int64_t s10 = nb10/sizeof(int64_t);
const int64_t s11 = nb11/sizeof(int64_t);
const int64_t s12 = nb12/sizeof(int64_t);
const int64_t s10 = nb10/sizeof(idx_t);
const int64_t s11 = nb11/sizeof(idx_t);
const int64_t s12 = nb12/sizeof(idx_t);
const int64_t s1 = nb1;
const int64_t s2 = nb2;
const int64_t s3 = nb3;
if (ne_total > 0) {
k_set_rows_quant<block_type, qk, quantize_func><<<grid_size, block_size, 0, stream>>>(
k_set_rows_quant<idx_t, block_type, qk, quantize_func><<<grid_size, block_size, 0, stream>>>(
src0_d, src1_d, dst_d,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -82,9 +82,9 @@ static void set_rows_cuda_quant(
}
}
template<typename src_t, typename dst_t>
template<typename src_t, typename idx_t, typename dst_t>
static __global__ void k_set_rows(
const src_t * __restrict__ src0, const int64_t * __restrict__ src1, dst_t * __restrict__ dst,
const src_t * __restrict__ src0, const idx_t * __restrict__ src1, dst_t * __restrict__ dst,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const int64_t s01, const int64_t s02, const int64_t s03,
@ -118,9 +118,9 @@ static __global__ void k_set_rows(
GGML_UNUSED(ne13);
}
template<typename src_t, typename dst_t>
template<typename src_t, typename idx_t, typename dst_t>
static void set_rows_cuda(
const src_t * src0_d, const int64_t * src1_d, dst_t * dst_d,
const src_t * src0_d, const idx_t * src1_d, dst_t * dst_d,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne10, const int64_t ne11, const int64_t ne12, const int64_t ne13,
const size_t nb01, const size_t nb02, const size_t nb03,
@ -137,9 +137,9 @@ static void set_rows_cuda(
const int64_t s01 = nb01/sizeof(src_t);
const int64_t s02 = nb02/sizeof(src_t);
const int64_t s03 = nb03/sizeof(src_t);
const int64_t s10 = nb10/sizeof(int64_t);
const int64_t s11 = nb11/sizeof(int64_t);
const int64_t s12 = nb12/sizeof(int64_t);
const int64_t s10 = nb10/sizeof(idx_t);
const int64_t s11 = nb11/sizeof(idx_t);
const int64_t s12 = nb12/sizeof(idx_t);
const int64_t s1 = nb1/sizeof(dst_t);
const int64_t s2 = nb2/sizeof(dst_t);
const int64_t s3 = nb3/sizeof(dst_t);
@ -155,23 +155,16 @@ static void set_rows_cuda(
}
}
void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_I64);
template<typename src_t, typename idx_t>
static void set_rows_cuda(ggml_backend_cuda_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const src_t * src0_d = (const src_t *)src0->data;
const idx_t * src1_d = (const idx_t *)src1->data;
GGML_TENSOR_BINARY_OP_LOCALS
const float * src0_d = (const float *)src0->data;
const int64_t * src1_d = (const int64_t *)src1->data;
cudaStream_t stream = ctx.stream();
if (dst->type == GGML_TYPE_F32) {
set_rows_cuda(
src0_d, src1_d, (float*)dst->data,
@ -203,7 +196,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_Q4_0) {
set_rows_cuda_quant<block_q4_0, QK4_0, quantize_f32_q4_0_block>(
set_rows_cuda_quant<idx_t, block_q4_0, QK4_0, quantize_f32_q4_0_block>(
src0_d, src1_d, (block_q4_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -213,7 +206,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_Q4_1) {
set_rows_cuda_quant<block_q4_1, QK4_1, quantize_f32_q4_1_block>(
set_rows_cuda_quant<idx_t, block_q4_1, QK4_1, quantize_f32_q4_1_block>(
src0_d, src1_d, (block_q4_1*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -223,7 +216,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_Q5_0) {
set_rows_cuda_quant<block_q5_0, QK5_0, quantize_f32_q5_0_block>(
set_rows_cuda_quant<idx_t, block_q5_0, QK5_0, quantize_f32_q5_0_block>(
src0_d, src1_d, (block_q5_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -233,7 +226,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_Q5_1) {
set_rows_cuda_quant<block_q5_1, QK5_1, quantize_f32_q5_1_block>(
set_rows_cuda_quant<idx_t, block_q5_1, QK5_1, quantize_f32_q5_1_block>(
src0_d, src1_d, (block_q5_1*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -243,7 +236,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_Q8_0) {
set_rows_cuda_quant<block_q8_0, QK8_0, quantize_f32_q8_0_block>(
set_rows_cuda_quant<idx_t, block_q8_0, QK8_0, quantize_f32_q8_0_block>(
src0_d, src1_d, (block_q8_0*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -253,7 +246,7 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
stream
);
} else if (dst->type == GGML_TYPE_IQ4_NL) {
set_rows_cuda_quant<block_iq4_nl, QK4_NL, quantize_f32_iq4_nl_block>(
set_rows_cuda_quant<idx_t, block_iq4_nl, QK4_NL, quantize_f32_iq4_nl_block>(
src0_d, src1_d, (block_iq4_nl*)dst->data,
ne00, ne01, ne02, ne03,
ne10, ne11, ne12, ne13,
@ -266,3 +259,18 @@ void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
GGML_ABORT("unsupported type %s", ggml_type_name(dst->type));
}
}
void ggml_cuda_op_set_rows(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(src0->type == GGML_TYPE_F32);
GGML_ASSERT(src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32);
if (src1->type == GGML_TYPE_I64) {
set_rows_cuda<float, int64_t>(ctx, src0, src1, dst);
} else {
set_rows_cuda<float, int32_t>(ctx, src0, src1, dst);
}
}

259
ggml/src/ggml-cuda/topk-moe.cu Normal file
View File

@ -0,0 +1,259 @@
#include "ggml-cuda/common.cuh"
#include "ggml.h"
#include "topk-moe.cuh"
#include <initializer_list>
/*
This kernel does the following:
1. softmax over the logits per token [n_experts, n_tokens]
2. argmax reduce over the top-k (n_experts_used) logits
3. write weights + ids to global memory
4. optionally normalize the weights
It is intended as fusion of softmax->top-k->get_rows pipeline for MoE models
*/
template <size_t n_experts, bool with_norm>
__launch_bounds__(4 * WARP_SIZE, 1) __global__ void topk_moe_cuda(const float * logits,
float * weights,
int32_t * ids,
const int n_rows,
const int n_expert_used) {
const int row = blockIdx.x * blockDim.y + threadIdx.y;
if (row >= n_rows) {
return;
}
logits += n_experts * row;
weights += n_expert_used * row;
ids += n_experts * row;
constexpr int experts_per_thread = (n_experts > WARP_SIZE) ? n_experts / WARP_SIZE : 1;
float logits_r[experts_per_thread];
#pragma unroll
for (int i = 0; i < n_experts; i += WARP_SIZE) {
const int expert = i + threadIdx.x;
logits_r[i / WARP_SIZE] = n_experts % WARP_SIZE == 0 || expert < n_experts ? logits[expert] : -INFINITY;
}
float max_val = logits_r[0];
#pragma unroll
for (int i = 1; i < experts_per_thread; i++) {
const float val = logits_r[i];
max_val = max(val, max_val);
}
max_val = warp_reduce_max(max_val);
float wt[experts_per_thread];
float tmp = 0.f;
#pragma unroll
for (int i = 0; i < experts_per_thread; i++) {
const float val = logits_r[i];
wt[i] = expf(val - max_val);
tmp += wt[i];
}
tmp = warp_reduce_sum(tmp);
const float inv_sum = 1.0f / tmp;
#pragma unroll
for (int i = 0; i < experts_per_thread; i++) {
wt[i] = wt[i] * inv_sum;
}
//at this point, each thread holds a portion of softmax,
//we do the argmax reduce over n_expert_used, each time marking
//the expert weight as -inf to exclude from the next iteration
float wt_sum = 0.f;
extern __shared__ float data_topk_shared[];
float * wt_shared_ptr = data_topk_shared + threadIdx.y * n_expert_used;
for (int k = 0; k < n_expert_used; k++) {
float max_val = wt[0];
int max_expert = threadIdx.x;
#pragma unroll
for (int i = 1; i < experts_per_thread; i++) {
const int expert = threadIdx.x + i * WARP_SIZE;
if ((n_experts % WARP_SIZE == 0 || expert < n_experts) && wt[i] > max_val) {
max_val = wt[i];
max_expert = expert;
}
}
#pragma unroll
for (int mask = WARP_SIZE / 2; mask > 0; mask /= 2) {
const float val = __shfl_xor_sync(0xFFFFFFFF, max_val, mask, WARP_SIZE);
const int expert = __shfl_xor_sync(0xFFFFFFFF, max_expert, mask, WARP_SIZE);
if (val > max_val || (val == max_val && expert < max_expert)) {
max_val = val;
max_expert = expert;
}
}
if ((max_expert & (WARP_SIZE - 1)) == threadIdx.x) {
wt[max_expert / WARP_SIZE] = -INFINITY;
wt_shared_ptr[k] = max_val;
ids[k] = max_expert;
if constexpr (with_norm) {
wt_sum += max_val;
}
}
}
if constexpr (with_norm) {
wt_sum = warp_reduce_sum(wt_sum);
const float inv_sum = 1.0f / wt_sum;
for (int i = threadIdx.x; i < n_expert_used; i += WARP_SIZE) {
wt_shared_ptr[i] = wt_shared_ptr[i] * inv_sum;
}
}
for (int i = threadIdx.x; i < n_expert_used; i += WARP_SIZE) {
weights[i] = wt_shared_ptr[i];
}
}
template <bool with_norm>
static void launch_topk_moe_cuda(ggml_backend_cuda_context & ctx,
const float * logits,
float * weights,
int32_t * ids,
const int n_rows,
const int n_expert,
const int n_expert_used) {
const int rows_per_block = 4;
dim3 grid_dims((n_rows + rows_per_block - 1) / rows_per_block, 1, 1);
dim3 block_dims(WARP_SIZE, rows_per_block, 1);
cudaStream_t stream = ctx.stream();
const int nbytes_shared = n_expert_used * rows_per_block * sizeof(float);
switch (n_expert) {
case 1:
topk_moe_cuda<1, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 2:
topk_moe_cuda<2, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 4:
topk_moe_cuda<4, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 8:
topk_moe_cuda<8, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 16:
topk_moe_cuda<16, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 32:
topk_moe_cuda<32, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 64:
topk_moe_cuda<64, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 128:
topk_moe_cuda<128, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 256:
topk_moe_cuda<256, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
case 512:
topk_moe_cuda<512, with_norm>
<<<grid_dims, block_dims, nbytes_shared, stream>>>(logits, weights, ids, n_rows, n_expert_used);
break;
default:
GGML_ASSERT(false && "fatal error");
break;
}
}
void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx,
const ggml_tensor * logits,
ggml_tensor * weights,
ggml_tensor * ids,
const bool with_norm) {
GGML_ASSERT(logits->type == GGML_TYPE_F32);
GGML_ASSERT(weights->type == GGML_TYPE_F32);
GGML_ASSERT(ids->type == GGML_TYPE_I32);
const int n_experts = logits->ne[0];
const int n_rows = logits->ne[1];
const float * logits_d = (const float *) logits->src[0]->data;
float * weights_d = (float *) weights->data;
int32_t * ids_d = (int32_t *) ids->data;
GGML_ASSERT(ids->nb[1] / ggml_type_size(ids->type) == (size_t) n_experts);
cudaStream_t stream = ctx.stream();
const int n_expert_used = weights->ne[1];
if (with_norm) {
launch_topk_moe_cuda<true>(ctx, logits_d, weights_d, ids_d, n_rows, n_experts, n_expert_used);
} else {
launch_topk_moe_cuda<false>(ctx, logits_d, weights_d, ids_d, n_rows, n_experts, n_expert_used);
}
}
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, const ggml_tensor * weights) {
float scale = 1.0f;
float max_bias = 0.0f;
memcpy(&scale, (const float *) softmax->op_params + 0, sizeof(float));
memcpy(&max_bias, (const float *) softmax->op_params + 1, sizeof(float));
if (!ggml_is_contiguous(softmax->src[0]) || !ggml_is_contiguous(weights)) {
return false;
}
if (scale != 1.0f || max_bias != 0.0f) {
return false;
}
// don't fuse when masks or sinks are present
if (softmax->src[1] || softmax->src[2]) {
return false;
}
const int n_expert = softmax->ne[0];
// n_expert must be a power of 2
if ((n_expert & (n_expert - 1)) != 0 || n_expert > 512) {
return false;
}
return true;
}
std::initializer_list<enum ggml_op> ggml_cuda_topk_moe_ops(bool norm) {
static std::initializer_list<enum ggml_op> norm_ops = { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT,
GGML_OP_VIEW, GGML_OP_GET_ROWS, GGML_OP_RESHAPE,
GGML_OP_SUM_ROWS, GGML_OP_DIV, GGML_OP_RESHAPE };
static std::initializer_list<enum ggml_op> no_norm_ops = { GGML_OP_SOFT_MAX, GGML_OP_RESHAPE, GGML_OP_ARGSORT,
GGML_OP_VIEW, GGML_OP_GET_ROWS };
if (norm) {
return norm_ops;
}
return no_norm_ops;
}

14
ggml/src/ggml-cuda/topk-moe.cuh Normal file
View File

@ -0,0 +1,14 @@
#include "common.cuh"
#include "ggml.h"
#include <initializer_list>
void ggml_cuda_op_topk_moe(ggml_backend_cuda_context & ctx,
const ggml_tensor * logits,
ggml_tensor * weights,
ggml_tensor * top_k,
const bool with_norm);
bool ggml_cuda_should_use_topk_moe(const ggml_tensor * softmax, const ggml_tensor * weights);
std::initializer_list<enum ggml_op> ggml_cuda_topk_moe_ops(bool with_norm);

53
ggml/src/ggml-impl.h
View File

@ -73,7 +73,7 @@ static inline int ggml_up(int n, int m) {
return (n + m - 1) & ~(m - 1);
}
// TODO: move to ggml.h?
// TODO: move to ggml.h? (won't be able to inline)
static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) {
if (a->type != b->type) {
return false;
@ -89,6 +89,19 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml
return true;
}
static bool ggml_op_is_empty(enum ggml_op op) {
switch (op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
return true;
default:
return false;
}
}
//
// logging
//
@ -329,6 +342,10 @@ struct ggml_cgraph {
// if you need the gradients, get them from the original graph
struct ggml_cgraph ggml_graph_view(struct ggml_cgraph * cgraph, int i0, int i1);
// ggml-alloc.c: true if the operation can reuse memory from its sources
GGML_API bool ggml_op_can_inplace(enum ggml_op op);
// Memory allocation
GGML_API void * ggml_aligned_malloc(size_t size);
@ -570,27 +587,27 @@ static inline bool ggml_node_has_n_uses(const struct ggml_cgraph * cgraph, int n
return true;
}
// Returns true if nodes [i, i+ops.size()) are the sequence of ggml_ops in ops[]
// Returns true if nodes with indices { node_idxs } are the sequence of ggml_ops in ops[]
// and are fusable. Nodes are considered fusable according to this function if:
// - all nodes except the last have only one use and are not views/outputs (see ggml_node_has_N_uses).
// - all nodes except the last are a src of the following node.
// - all nodes are the same shape.
// TODO: Consider allowing GGML_OP_NONE nodes in between
static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, const enum ggml_op * ops, int num_ops) {
if (node_idx + num_ops > cgraph->n_nodes) {
return false;
}
static inline bool ggml_can_fuse_ext(const struct ggml_cgraph * cgraph, const int * node_idxs, const enum ggml_op * ops, int num_ops) {
for (int i = 0; i < num_ops; ++i) {
struct ggml_tensor * node = cgraph->nodes[node_idx + i];
if (node_idxs[i] >= cgraph->n_nodes) {
return false;
}
struct ggml_tensor * node = cgraph->nodes[node_idxs[i]];
if (node->op != ops[i]) {
return false;
}
if (i < num_ops - 1 && !ggml_node_has_n_uses(cgraph, node_idx + i, 1)) {
if (i < num_ops - 1 && !ggml_node_has_n_uses(cgraph, node_idxs[i], 1)) {
return false;
}
if (i > 0) {
struct ggml_tensor * prev = cgraph->nodes[node_idx + i - 1];
struct ggml_tensor * prev = cgraph->nodes[node_idxs[i - 1]];
if (node->src[0] != prev && node->src[1] != prev) {
return false;
}
@ -602,6 +619,22 @@ static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx
return true;
}
// same as above, for sequential indices starting at node_idx
static inline bool ggml_can_fuse(const struct ggml_cgraph * cgraph, int node_idx, const enum ggml_op * ops, int num_ops) {
assert(num_ops < 32);
if (node_idx + num_ops > cgraph->n_nodes) {
return false;
}
int idxs[32];
for (int i = 0; i < num_ops; ++i) {
idxs[i] = node_idx + i;
}
return ggml_can_fuse_ext(cgraph, idxs, ops, num_ops);
}
#ifdef __cplusplus
}
#endif

24
ggml/src/ggml-metal/ggml-metal-common.cpp
View File

@ -184,20 +184,6 @@ bool ggml_mem_ranges_check(ggml_mem_ranges_t mrs, const ggml_tensor * tensor) {
return ggml_mem_ranges_check_dst(mrs, tensor);
}
// TODO: move to ggml.h?
static bool is_empty(ggml_op op) {
switch (op) {
case GGML_OP_NONE:
case GGML_OP_RESHAPE:
case GGML_OP_TRANSPOSE:
case GGML_OP_VIEW:
case GGML_OP_PERMUTE:
return true;
default:
return false;
}
}
struct node_info {
ggml_tensor * node;
@ -212,7 +198,7 @@ struct node_info {
}
bool is_empty() const {
return ::is_empty(node->op);
return ggml_op_is_empty(node->op);
}
void add_fused(ggml_tensor * t) {
@ -270,8 +256,6 @@ static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node
// perform reorders only across these types of ops
// can be expanded when needed
// IMPORTANT: do not add ops such as GGML_OP_CPY or GGML_OP_SET_ROWS
// the dependencies from such ops are not always represented in the graph
const auto & h_safe = [](ggml_op op) {
switch (op) {
case GGML_OP_MUL_MAT:
@ -287,9 +271,11 @@ static std::vector<int> ggml_metal_graph_optimize_reorder(const std::vector<node
case GGML_OP_GLU:
case GGML_OP_SCALE:
case GGML_OP_GET_ROWS:
case GGML_OP_CPY:
case GGML_OP_SET_ROWS:
return true;
default:
return is_empty(op);
return ggml_op_is_empty(op);
}
};
@ -397,6 +383,7 @@ void ggml_graph_optimize(ggml_cgraph * gf) {
// fuse only ops that start with these operations
// can be expanded when needed
if (node.op() == GGML_OP_ADD ||
node.op() == GGML_OP_NORM ||
node.op() == GGML_OP_RMS_NORM) {
ops[0] = node.op();
@ -406,6 +393,7 @@ void ggml_graph_optimize(ggml_cgraph * gf) {
// can be expanded when needed
if (gf->nodes[f]->op != GGML_OP_ADD &&
gf->nodes[f]->op != GGML_OP_MUL &&
gf->nodes[f]->op != GGML_OP_NORM &&
gf->nodes[f]->op != GGML_OP_RMS_NORM) {
break;
}

67
ggml/src/ggml-metal/ggml-metal-device.cpp
View File

@ -142,11 +142,11 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_get_rows(ggml_metal_librar
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, ggml_type tdst) {
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows(ggml_metal_library_t lib, ggml_type tidx, ggml_type tdst) {
char base[256];
char name[256];
snprintf(base, 256, "kernel_set_rows_%s", ggml_type_name(tdst));
snprintf(base, 256, "kernel_set_rows_%s_%s", ggml_type_name(tdst), ggml_type_name(tidx));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
@ -1090,36 +1090,6 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_bin(
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rms_norm(ggml_metal_library_t lib, const ggml_tensor * op, int32_t n_fuse) {
assert(op->op == GGML_OP_RMS_NORM);
GGML_ASSERT(op->src[0]->ne[0] % 4 == 0);
GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
char base[256];
char name[256];
switch (n_fuse) {
case 1: snprintf(base, 256, "kernel_rms_norm_f32"); break;
case 2: snprintf(base, 256, "kernel_rms_norm_mul_f32"); break;
case 3: snprintf(base, 256, "kernel_rms_norm_mul_add_f32"); break;
default: GGML_ABORT("fatal error");
}
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
if (res) {
return res;
}
res = ggml_metal_library_compile_pipeline(lib, base, name, nullptr);
ggml_metal_pipeline_set_smem(res, 32*sizeof(float));
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_l2_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_L2_NORM);
@ -1167,16 +1137,37 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_group_norm(ggml_metal_libr
return res;
}
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm(ggml_metal_library_t lib, const ggml_tensor * op) {
assert(op->op == GGML_OP_NORM);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm(ggml_metal_library_t lib, const ggml_tensor * op, int n_fuse) {
assert(op->op == GGML_OP_NORM || op->op == GGML_OP_RMS_NORM);
GGML_ASSERT(op->src[0]->ne[0] % 4 == 0);
GGML_ASSERT(ggml_is_contiguous_1(op->src[0]));
GGML_ASSERT(ggml_is_contiguous_rows(op->src[0]));
char base[256];
char name[256];
snprintf(base, 256, "kernel_norm_f32");
const char * suffix = "";
if (op->ne[0] % 4 == 0) {
suffix = "_4";
}
switch (op->op) {
case GGML_OP_NORM:
switch (n_fuse) {
case 1: snprintf(base, 256, "kernel_norm_f32%s", suffix); break;
case 2: snprintf(base, 256, "kernel_norm_mul_f32%s", suffix); break;
case 3: snprintf(base, 256, "kernel_norm_mul_add_f32%s", suffix); break;
default: GGML_ABORT("fatal error");
} break;
case GGML_OP_RMS_NORM:
switch (n_fuse) {
case 1: snprintf(base, 256, "kernel_rms_norm_f32%s", suffix); break;
case 2: snprintf(base, 256, "kernel_rms_norm_mul_f32%s", suffix); break;
case 3: snprintf(base, 256, "kernel_rms_norm_mul_add_f32%s", suffix); break;
default: GGML_ABORT("fatal error");
} break;
default: GGML_ABORT("fatal error");
}
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);
@ -1237,7 +1228,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_im2col(ggml_metal_library_
char base[256];
char name[256];
snprintf(base, 256, "kernel_im2col_ext_%s", ggml_type_name(op->type));
snprintf(base, 256, "kernel_im2col_%s", ggml_type_name(op->type));
snprintf(name, 256, "%s", base);
ggml_metal_pipeline_t res = ggml_metal_library_get_pipeline(lib, name);

5
ggml/src/ggml-metal/ggml-metal-device.h
View File

@ -105,7 +105,7 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_base (ggml_me
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_cpy (ggml_metal_library_t lib, enum ggml_type tsrc, enum ggml_type tdst);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_pool_2d (ggml_metal_library_t lib, const struct ggml_tensor * op, enum ggml_op_pool op_pool);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_get_rows (ggml_metal_library_t lib, enum ggml_type tsrc);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows (ggml_metal_library_t lib, enum ggml_type tdst);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_set_rows (ggml_metal_library_t lib, enum ggml_type tidx, enum ggml_type tdst);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_repeat (ggml_metal_library_t lib, enum ggml_type tsrc);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_unary (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_glu (ggml_metal_library_t lib, const struct ggml_tensor * op);
@ -123,10 +123,9 @@ ggml_metal_pipeline_t ggml_metal_library_get_pipeline_mul_mv_id (ggml_me
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argmax (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_argsort (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_bin (ggml_metal_library_t lib, enum ggml_op op, int32_t n_fuse, bool row);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rms_norm (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_l2_norm (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_group_norm (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_norm (ggml_metal_library_t lib, const struct ggml_tensor * op, int32_t n_fuse);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_rope (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_im2col (ggml_metal_library_t lib, const struct ggml_tensor * op);
ggml_metal_pipeline_t ggml_metal_library_get_pipeline_conv_transpose_1d (ggml_metal_library_t lib, const struct ggml_tensor * op);

4
ggml/src/ggml-metal/ggml-metal-device.m
View File

@ -661,13 +661,13 @@ bool ggml_metal_device_supports_op(ggml_metal_device_t dev, const struct ggml_te
case GGML_OP_SOFT_MAX:
case GGML_OP_GROUP_NORM:
return has_simdgroup_reduction && ggml_is_contiguous_rows(op->src[0]);
case GGML_OP_RMS_NORM:
case GGML_OP_L2_NORM:
return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
case GGML_OP_ARGMAX:
return has_simdgroup_reduction;
case GGML_OP_NORM:
return has_simdgroup_reduction && (op->ne[0] % 4 == 0 && ggml_is_contiguous_1(op->src[0]));
case GGML_OP_RMS_NORM:
return has_simdgroup_reduction && (ggml_is_contiguous_rows(op->src[0]));
case GGML_OP_ROPE:
return true;
case GGML_OP_IM2COL:

13
ggml/src/ggml-metal/ggml-metal-impl.h
View File

@ -428,16 +428,11 @@ typedef struct {
uint64_t nb1;
} ggml_metal_kargs_mul_mv_id;
// NORM
// RMS_NORM
typedef struct {
int32_t ne00;
int32_t ne00_4;
uint64_t nb01;
float eps;
} ggml_metal_kargs_norm;
typedef struct {
int32_t ne00;
int32_t ne00_4;
int32_t ne00_t;
uint64_t nb1;
uint64_t nb2;
uint64_t nb3;
@ -448,7 +443,7 @@ typedef struct {
uint64_t nbf1[3];
uint64_t nbf2[3];
uint64_t nbf3[3];
} ggml_metal_kargs_rms_norm;
} ggml_metal_kargs_norm;
typedef struct {
int32_t ne00;

269
ggml/src/ggml-metal/ggml-metal-ops.cpp
View File

@ -266,10 +266,6 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
{
n_fuse = ggml_metal_op_set_rows(ctx, idx);
} break;
case GGML_OP_RMS_NORM:
{
n_fuse = ggml_metal_op_rms_norm(ctx, idx);
} break;
case GGML_OP_L2_NORM:
{
n_fuse = ggml_metal_op_l2_norm(ctx, idx);
@ -279,6 +275,7 @@ static int ggml_metal_op_encode_impl(ggml_metal_op_t ctx, int idx) {
n_fuse = ggml_metal_op_group_norm(ctx, idx);
} break;
case GGML_OP_NORM:
case GGML_OP_RMS_NORM:
{
n_fuse = ggml_metal_op_norm(ctx, idx);
} break;
@ -892,7 +889,7 @@ int ggml_metal_op_set_rows(ggml_metal_op_t ctx, int idx) {
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint32_t, nb, op, nb);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op->type);
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_set_rows(lib, op->src[1]->type, op->type);
const int32_t nk0 = ne0/ggml_blck_size(op->type);
@ -2346,146 +2343,6 @@ int ggml_metal_op_bin(ggml_metal_op_t ctx, int idx) {
return n_fuse;
}
int ggml_metal_op_rms_norm(ggml_metal_op_t ctx, int idx) {
ggml_cgraph * gf = ctx->gf;
ggml_tensor * op = ggml_graph_node(gf, idx);
ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;
const int idx_end = ctx->idx_end;
const bool use_fusion = ctx->use_fusion;
const int debug_fusion = ctx->debug_fusion;
ggml_tensor ** ops = ggml_graph_nodes(gf) + idx;
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
GGML_TENSOR_LOCALS(uint32_t, nb, op, nb);
float eps;
memcpy(&eps, op->op_params, sizeof(float));
ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
ggml_metal_kargs_rms_norm args = {
/*.ne00 =*/ ne00,
/*.ne00_4 =*/ ne00/4,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.eps =*/ eps,
/*.nef1 =*/ { ne01 },
/*.nef2 =*/ { ne02 },
/*.nef3 =*/ { ne03 },
/*.nbf1 =*/ { nb01 },
/*.nbf2 =*/ { nb02 },
/*.nbf3 =*/ { nb03 },
};
ggml_op fops[8];
int n_fuse = 1;
ggml_metal_buffer_id bid_fuse[2] = { bid_src0, bid_src0 };
// d[0] = rms_norm(a)
// d[1] = mul(d[0], b)
// d[2] = add(d[1], c)
if (use_fusion) {
fops[0] = GGML_OP_RMS_NORM;
fops[1] = GGML_OP_MUL;
fops[2] = GGML_OP_ADD;
for (n_fuse = 0; n_fuse <= 1 && idx + n_fuse + 1 < idx_end; ++n_fuse) {
if (!ggml_can_fuse(gf, idx + n_fuse, fops + n_fuse, 2)) {
break;
}
if (ops[n_fuse] != ops[n_fuse + 1]->src[0]) {
break;
}
if (ops[n_fuse + 1]->src[1]->ne[0] != op->ne[0]) {
break;
}
if (!ggml_is_contiguous_rows(ops[n_fuse + 1]->src[1])) {
break;
}
if (ops[n_fuse + 1]->type != GGML_TYPE_F32) {
break;
}
//ctx->fuse_cnt[ops[n_fuse + 1]->op]++;
bid_fuse[n_fuse] = ggml_metal_get_buffer_id(ops[n_fuse + 1]->src[1]);
args.nef1[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[1];
args.nef2[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[2];
args.nef3[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[3];
args.nbf1[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[1];
args.nbf2[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[2];
args.nbf3[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[3];
}
++n_fuse;
if (debug_fusion > 1 && n_fuse > 1) {
if (n_fuse == 2) {
GGML_LOG_DEBUG("%s: fuse: RMS_NORM + MUL\n", __func__);
}
if (n_fuse == 3) {
GGML_LOG_DEBUG("%s: fuse: RMS_NORM + MUL + ADD\n", __func__);
}
}
}
if (n_fuse > 1) {
bid_dst = ggml_metal_get_buffer_id(ops[n_fuse - 1]);
for (int i = 1; i < n_fuse; ++i) {
if (!ggml_metal_op_concurrency_check(ctx, ops[i])) {
ggml_metal_op_concurrency_reset(ctx);
break;
}
}
}
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_rms_norm(lib, op, n_fuse);
int nth = 32; // SIMD width
while (nth < ne00/4 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nth *= 2;
}
nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
nth = std::min(nth, ne00/4);
const size_t smem = ggml_metal_pipeline_get_smem(pipeline);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_fuse[0], 2);
ggml_metal_encoder_set_buffer (enc, bid_fuse[1], 3);
ggml_metal_encoder_set_buffer (enc, bid_dst, 4);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
return n_fuse;
}
int ggml_metal_op_l2_norm(ggml_metal_op_t ctx, int idx) {
ggml_cgraph * gf = ctx->gf;
ggml_tensor * op = ggml_graph_node(gf, idx);
@ -2594,6 +2451,14 @@ int ggml_metal_op_norm(ggml_metal_op_t ctx, int idx) {
ggml_metal_library_t lib = ctx->lib;
ggml_metal_encoder_t enc = ctx->enc;
const int idx_end = ctx->idx_end;
const bool use_fusion = ctx->use_fusion;
const int debug_fusion = ctx->debug_fusion;
ggml_tensor ** ops = ggml_graph_nodes(gf) + idx;
GGML_TENSOR_LOCALS( int32_t, ne0, op->src[0], ne);
GGML_TENSOR_LOCALS(uint64_t, nb0, op->src[0], nb);
GGML_TENSOR_LOCALS( int32_t, ne, op, ne);
@ -2602,37 +2467,121 @@ int ggml_metal_op_norm(ggml_metal_op_t ctx, int idx) {
float eps;
memcpy(&eps, op->op_params, sizeof(float));
ggml_metal_buffer_id bid_src0 = ggml_metal_get_buffer_id(op->src[0]);
ggml_metal_buffer_id bid_dst = ggml_metal_get_buffer_id(op);
ggml_metal_kargs_norm args = {
/*.ne00 =*/ ne00,
/*.ne00_4 =*/ ne00/4,
/*.nb01 =*/ nb01,
/*.ne00_t =*/ ne00 % 4 == 0 ? ne00/4 : ne00,
/*.nb1 =*/ nb1,
/*.nb2 =*/ nb2,
/*.nb3 =*/ nb3,
/*.eps =*/ eps,
/*.nef1 =*/ { ne01 },
/*.nef2 =*/ { ne02 },
/*.nef3 =*/ { ne03 },
/*.nbf1 =*/ { nb01 },
/*.nbf2 =*/ { nb02 },
/*.nbf3 =*/ { nb03 },
};
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_norm(lib, op);
ggml_op fops[8];
int n_fuse = 1;
ggml_metal_buffer_id bid_fuse[2] = { bid_src0, bid_src0 };
// d[0] = norm(a)
// d[1] = mul(d[0], b)
// d[2] = add(d[1], c)
if (use_fusion) {
fops[0] = op->op;
fops[1] = GGML_OP_MUL;
fops[2] = GGML_OP_ADD;
for (n_fuse = 0; n_fuse <= 1 && idx + n_fuse + 1 < idx_end; ++n_fuse) {
if (!ggml_can_fuse(gf, idx + n_fuse, fops + n_fuse, 2)) {
break;
}
if (ops[n_fuse] != ops[n_fuse + 1]->src[0]) {
break;
}
if (ops[n_fuse + 1]->src[1]->ne[0] != op->ne[0]) {
break;
}
if (!ggml_is_contiguous_rows(ops[n_fuse + 1]->src[1])) {
break;
}
if (ops[n_fuse + 1]->type != GGML_TYPE_F32) {
break;
}
//ctx->fuse_cnt[ops[n_fuse + 1]->op]++;
bid_fuse[n_fuse] = ggml_metal_get_buffer_id(ops[n_fuse + 1]->src[1]);
args.nef1[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[1];
args.nef2[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[2];
args.nef3[n_fuse + 1] = ops[n_fuse + 1]->src[1]->ne[3];
args.nbf1[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[1];
args.nbf2[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[2];
args.nbf3[n_fuse + 1] = ops[n_fuse + 1]->src[1]->nb[3];
}
++n_fuse;
if (debug_fusion > 1 && n_fuse > 1) {
if (n_fuse == 2) {
GGML_LOG_DEBUG("%s: fuse: %s + MUL\n", __func__, ggml_op_name(op->op));
}
if (n_fuse == 3) {
GGML_LOG_DEBUG("%s: fuse: %s + MUL + ADD\n", __func__, ggml_op_name(op->op));
}
}
}
if (n_fuse > 1) {
bid_dst = ggml_metal_get_buffer_id(ops[n_fuse - 1]);
for (int i = 1; i < n_fuse; ++i) {
if (!ggml_metal_op_concurrency_check(ctx, ops[i])) {
ggml_metal_op_concurrency_reset(ctx);
break;
}
}
}
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_norm(lib, op, n_fuse);
int nth = 32; // SIMD width
while (nth < ne00/4 && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
while (nth < args.ne00_t && nth < ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)) {
nth *= 2;
}
nth = std::min(nth, ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
nth = std::min(nth, ne00/4);
nth = std::min(nth, args.ne00_t);
const size_t smem = ggml_metal_pipeline_get_smem(pipeline);
const int64_t nrows = ggml_nrows(op->src[0]);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[0]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
ggml_metal_encoder_set_buffer (enc, bid_src0, 1);
ggml_metal_encoder_set_buffer (enc, bid_fuse[0], 2);
ggml_metal_encoder_set_buffer (enc, bid_fuse[1], 3);
ggml_metal_encoder_set_buffer (enc, bid_dst, 4);
ggml_metal_encoder_set_threadgroup_memory_size(enc, smem, 0);
ggml_metal_encoder_dispatch_threadgroups(enc, nrows, 1, 1, nth, 1, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, ne01, ne02, ne03, nth, 1, 1);
return 1;
return n_fuse;
}
int ggml_metal_op_rope(ggml_metal_op_t ctx, int idx) {
@ -2768,7 +2717,6 @@ int ggml_metal_op_im2col(ggml_metal_op_t ctx, int idx) {
const uint64_t ofs0 = op->src[1]->nb[is_2D ? 3 : 2] / 4;
const uint64_t ofs1 = op->src[1]->nb[is_2D ? 2 : 1] / 4;
ggml_metal_kargs_im2col args = {
/*.ofs0 =*/ ofs0,
/*.ofs1 =*/ ofs1,
@ -2789,15 +2737,16 @@ int ggml_metal_op_im2col(ggml_metal_op_t ctx, int idx) {
ggml_metal_pipeline_t pipeline = ggml_metal_library_get_pipeline_im2col(lib, op);
const uint64_t n_threads = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline), N);
const int64_t quotient = N / n_threads + (N % n_threads > 0 ? 1 : 0);
GGML_ASSERT(KH*KW <= ggml_metal_pipeline_max_theads_per_threadgroup(pipeline));
const uint64_t ntptg0 = std::min(ggml_metal_pipeline_max_theads_per_threadgroup(pipeline)/(KH*KW), N);
ggml_metal_encoder_set_pipeline(enc, pipeline);
ggml_metal_encoder_set_bytes (enc, &args, sizeof(args), 0);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op->src[1]), 1);
ggml_metal_encoder_set_buffer (enc, ggml_metal_get_buffer_id(op), 2);
ggml_metal_encoder_dispatch_threadgroups(enc, quotient * CHW, OH, OW, n_threads, 1, 1);
ggml_metal_encoder_dispatch_threadgroups(enc, IC, OH, OW, ntptg0, KH, KW);
return 1;
}

1
ggml/src/ggml-metal/ggml-metal-ops.h
View File

@ -60,7 +60,6 @@ int ggml_metal_op_mul_mat_id (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_add_id (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_flash_attn_ext (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_bin (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_rms_norm (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_l2_norm (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_group_norm (ggml_metal_op_t ctx, int idx);
int ggml_metal_op_norm (ggml_metal_op_t ctx, int idx);

300
ggml/src/ggml-metal/ggml-metal.metal
View File

@ -66,6 +66,10 @@ static inline float e8m0_to_fp32(uint8_t x) {
return as_type<float>(bits);
}
static inline float dot(float x, float y) {
return x*y;
}
// NOTE: this is not dequantizing - we are simply fitting the template
template <typename type4x4>
void dequantize_f32(device const float4x4 * src, short il, thread type4x4 & reg) {
@ -2493,30 +2497,43 @@ kernel void kernel_argmax_f32(
dst_i32[tgpig] = arg_val;
}
kernel void kernel_norm_f32(
// F == 1 : norm (no fuse)
// F == 2 : norm + mul
// F == 3 : norm + mul + add
template <typename T, short F>
kernel void kernel_norm_fuse_impl(
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
device char * dst,
threadgroup float * shmem_f32 [[threadgroup(0)]],
uint tgpig[[threadgroup_position_in_grid]],
ushort tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort ntg[[threads_per_threadgroup]]) {
uint3 tgpig[[threadgroup_position_in_grid]],
ushort3 tpitg[[thread_position_in_threadgroup]],
ushort sgitg[[simdgroup_index_in_threadgroup]],
ushort tiisg[[thread_index_in_simdgroup]],
ushort3 ntg[[threads_per_threadgroup]]) {
if (sgitg == 0) {
shmem_f32[tiisg] = 0.0f;
}
device const float4 * x = (device const float4 *) (src0 + tgpig*args.nb01);
const int i01 = tgpig.x;
const int i02 = tgpig.y;
const int i03 = tgpig.z;
float4 sumf4(0.0f);
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
T sumft(0.0f);
float sumf = 0.0f;
for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
sumf4 += x[i00];
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumft += x[i00];
}
sumf = sumf4[0] + sumf4[1] + sumf4[2] + sumf4[3];
sumf = dot(sumft, T(1.0f));
sumf = simd_sum(sumf);
threadgroup_barrier(mem_flags::mem_threadgroup);
@ -2532,10 +2549,10 @@ kernel void kernel_norm_f32(
const float mean = sumf/args.ne00;
device float4 * y = (device float4 *) dst + tgpig*args.ne00_4;
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
sumf = 0.0f;
for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
y[i00] = x[i00] - mean;
sumf += dot(y[i00], y[i00]);
}
@ -2555,17 +2572,35 @@ kernel void kernel_norm_f32(
const float variance = sumf/args.ne00;
const float scale = 1.0f/sqrt(variance + args.eps);
for (int i00 = tpitg; i00 < args.ne00_4; i00 += ntg) {
y[i00] = y[i00] * scale;
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (y[i00]*scale);
}
if (F == 2) {
y[i00] = (y[i00]*scale)*f0[i00];
}
if (F == 3) {
y[i00] = (y[i00]*scale)*f0[i00] + f1[i00];
}
}
}
typedef decltype(kernel_norm_fuse_impl<float4, 1>) kernel_norm_fuse_t;
template [[host_name("kernel_norm_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 1>;
template [[host_name("kernel_norm_mul_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 2>;
template [[host_name("kernel_norm_mul_add_f32")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float, 3>;
template [[host_name("kernel_norm_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_norm_mul_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_norm_mul_add_f32_4")]] kernel kernel_norm_fuse_t kernel_norm_fuse_impl<float4, 3>;
// F == 1 : rms_norm (no fuse)
// F == 2 : rms_norm + mul
// F == 3 : rms_norm + mul + add
template <short F>
template <typename T, short F>
kernel void kernel_rms_norm_fuse_impl(
constant ggml_metal_kargs_rms_norm & args,
constant ggml_metal_kargs_norm & args,
device const char * src0,
device const char * src1_0,
device const char * src1_1,
@ -2584,15 +2619,15 @@ kernel void kernel_rms_norm_fuse_impl(
const int i02 = tgpig.y;
const int i03 = tgpig.z;
device const float4 * x = (device const float4 *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const T * x = (device const T *) (src0 + i03*args.nbf3[0] + i02*args.nbf2[0] + i01*args.nbf1[0]);
device const float4 * f0 = (device const float4 *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const float4 * f1 = (device const float4 *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
device const T * f0 = (device const T *) (src1_0 + (i03%args.nef3[1])*args.nbf3[1] + (i02%args.nef2[1])*args.nbf2[1] + (i01%args.nef1[1])*args.nbf1[1]);
device const T * f1 = (device const T *) (src1_1 + (i03%args.nef3[2])*args.nbf3[2] + (i02%args.nef2[2])*args.nbf2[2] + (i01%args.nef1[2])*args.nbf1[2]);
float sumf = 0.0f;
// parallel sum
for (int i00 = tpitg.x; i00 < args.ne00_4; i00 += ntg.x) {
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
sumf += dot(x[i00], x[i00]);
}
sumf = simd_sum(sumf);
@ -2611,8 +2646,8 @@ kernel void kernel_rms_norm_fuse_impl(
const float mean = sumf/args.ne00;
const float scale = 1.0f/sqrt(mean + args.eps);
device float4 * y = (device float4 *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
for (int i00 = tpitg.x; i00 < args.ne00_4; i00 += ntg.x) {
device T * y = (device T *) (dst + i03*args.nb3 + i02*args.nb2 + i01*args.nb1);
for (int i00 = tpitg.x; i00 < args.ne00_t; i00 += ntg.x) {
if (F == 1) {
y[i00] = (x[i00]*scale);
}
@ -2625,11 +2660,15 @@ kernel void kernel_rms_norm_fuse_impl(
}
}
typedef decltype(kernel_rms_norm_fuse_impl<1>) kernel_rms_norm_fuse_t;
typedef decltype(kernel_rms_norm_fuse_impl<float4, 1>) kernel_rms_norm_fuse_t;
template [[host_name("kernel_rms_norm_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<1>;
template [[host_name("kernel_rms_norm_mul_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<2>;
template [[host_name("kernel_rms_norm_mul_add_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<3>;
template [[host_name("kernel_rms_norm_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 1>;
template [[host_name("kernel_rms_norm_mul_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float, 3>;
template [[host_name("kernel_rms_norm_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 1>;
template [[host_name("kernel_rms_norm_mul_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 2>;
template [[host_name("kernel_rms_norm_mul_add_f32_4")]] kernel kernel_rms_norm_fuse_t kernel_rms_norm_fuse_impl<float4, 3>;
kernel void kernel_l2_norm_f32(
constant ggml_metal_kargs_l2_norm & args,
@ -3987,60 +4026,7 @@ template [[host_name("kernel_rope_multi_f16")]] kernel kernel_rope_multi_t kerne
template [[host_name("kernel_rope_vision_f32")]] kernel kernel_rope_vision_t kernel_rope_vision<float>;
template [[host_name("kernel_rope_vision_f16")]] kernel kernel_rope_vision_t kernel_rope_vision<half>;
// TODO: obolete -- remove
//typedef void (im2col_t)(
// constant ggml_metal_kargs_im2col & args,
// device const float * x,
// device char * dst,
// uint3 tgpig[[threadgroup_position_in_grid]],
// uint3 tgpg[[threadgroups_per_grid]],
// uint3 tpitg[[thread_position_in_threadgroup]],
// uint3 ntg[[threads_per_threadgroup]]);
//
//template <typename T>
//kernel void kernel_im2col(
// constant ggml_metal_kargs_im2col & args,
// device const float * x,
// device char * dst,
// uint3 tgpig[[threadgroup_position_in_grid]],
// uint3 tgpg[[threadgroups_per_grid]],
// uint3 tpitg[[thread_position_in_threadgroup]],
// uint3 ntg[[threads_per_threadgroup]]) {
//// const int64_t IC = tgpg[0];
// const int64_t OH = tgpg[1];
// const int64_t OW = tgpg[2];
//
//// const int64_t N = ntg[0];
// const int64_t KH = ntg[1];
// const int64_t KW = ntg[2];
//
// const int64_t in = tpitg[0];
// const int64_t ikh = tpitg[1];
// const int64_t ikw = tpitg[2];
//
// const int64_t iic = tgpig[0];
// const int64_t ioh = tgpig[1];
// const int64_t iow = tgpig[2];
//
// const int64_t iiw = iow*args.s0 + ikw*args.d0 - args.p0;
// const int64_t iih = ioh*args.s1 + ikh*args.d1 - args.p1;
//
// const int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*args.CHW + (iic*(KH*KW) + ikh*KW + ikw);
//
// device T * pdst = (device T *) (dst);
//
// if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
// pdst[offset_dst] = 0.0f;
// } else {
// const int64_t offset_src = in*args.ofs0 + iic*args.ofs1 + iih*args.IW + iiw;
// pdst[offset_dst] = x[offset_src];
// }
//}
//
//template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col<float>;
//template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
typedef void (im2col_ext_t)(
typedef void (im2col_t)(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
@ -4050,48 +4036,113 @@ typedef void (im2col_ext_t)(
uint3 ntg[[threads_per_threadgroup]]);
template <typename T>
kernel void kernel_im2col_ext(
kernel void kernel_im2col(
constant ggml_metal_kargs_im2col & args,
device const float * x,
device char * dst,
uint3 tgpig[[threadgroup_position_in_grid]],
uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW
uint3 tgpg[[threadgroups_per_grid]],
uint3 tpitg[[thread_position_in_threadgroup]],
uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1]
const int64_t KHW = (int64_t)args.KHW;
uint3 ntg[[threads_per_threadgroup]]) {
// const int64_t IC = tgpg[0];
const int64_t OH = tgpg[1];
const int64_t OW = tgpg[2];
const int64_t d = tgpig[0] / args.CHW;
const int64_t chw = tgpig[0] % args.CHW;
const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1)
const int64_t HW = tgpig[0] % KHW;
const int64_t KH = ntg[1];
const int64_t KW = ntg[2];
const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0];
if (tpitg_0 >= args.N) {
return;
}
int64_t in = tpitg[0];
const int64_t ikh = tpitg[1];
const int64_t ikw = tpitg[2];
const int64_t tpitg_1 = HW / args.KW;
const int64_t tpitg_2 = HW % args.KW;
const int64_t iic = tgpig[0];
const int64_t ioh = tgpig[1];
const int64_t iow = tgpig[2];
const int64_t iiw = tgpig[2] * args.s0 + tpitg_2 * args.d0 - args.p0;
const int64_t iih = tgpig[1] * args.s1 + tpitg_1 * args.d1 - args.p1;
const int64_t iiw = iow*args.s0 + ikw*args.d0 - args.p0;
const int64_t iih = ioh*args.s1 + ikh*args.d1 - args.p1;
const int64_t offset_dst =
(tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * args.CHW +
(tgpig_0 * KHW + tpitg_1 * args.KW + tpitg_2);
int64_t offset_dst = (in*OH*OW + ioh*OW + iow)*args.CHW + (iic*(KH*KW) + ikh*KW + ikw);
device T * pdst = (device T *) (dst);
if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
pdst[offset_dst] = 0.0f;
while (in < args.N) {
pdst[offset_dst] = 0.0f;
offset_dst += ntg[0]*args.CHW*OH*OW;
in += ntg[0];
}
} else {
const int64_t offset_src = tpitg_0 * args.ofs0 + tgpig_0 * args.ofs1;
pdst[offset_dst] = x[offset_src + iih * args.IW + iiw];
int64_t offset_src = in*args.ofs0 + iic*args.ofs1 + iih*args.IW + iiw;
while (in < args.N) {
pdst[offset_dst] = x[offset_src];
offset_dst += ntg[0]*args.CHW*OH*OW;
offset_src += ntg[0]*args.ofs0;
in += ntg[0];
}
}
}
template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext<float>;
template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext<half>;
template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col<float>;
template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col<half>;
// TODO: obolete -- remove
//typedef void (im2col_ext_t)(
// constant ggml_metal_kargs_im2col & args,
// device const float * x,
// device char * dst,
// uint3 tgpig[[threadgroup_position_in_grid]],
// uint3 tgpg[[threadgroups_per_grid]],
// uint3 tpitg[[thread_position_in_threadgroup]],
// uint3 ntg[[threads_per_threadgroup]]);
//
//template <typename T>
//kernel void kernel_im2col_ext(
// constant ggml_metal_kargs_im2col & args,
// device const float * x,
// device char * dst,
// uint3 tgpig[[threadgroup_position_in_grid]],
// uint3 tgpg[[threadgroups_per_grid]], // tgpg[0] = D x IC x KH x KW, CHW = IC x KH x KW
// uint3 tpitg[[thread_position_in_threadgroup]],
// uint3 ntg[[threads_per_threadgroup]]) { // [M, 1, 1]
// const int64_t KHW = (int64_t)args.KHW;
//
// const int64_t d = tgpig[0] / args.CHW;
// const int64_t chw = tgpig[0] % args.CHW;
// const int64_t tgpig_0 = chw / KHW; // 0 ~ (IC - 1)
// const int64_t HW = tgpig[0] % KHW;
//
// const int64_t tpitg_0 = (d * ntg[0]) + tpitg[0];
// if (tpitg_0 >= args.N) {
// return;
// }
//
// const int64_t tpitg_1 = HW / args.KW;
// const int64_t tpitg_2 = HW % args.KW;
//
// const int64_t iiw = tgpig[2] * args.s0 + tpitg_2 * args.d0 - args.p0;
// const int64_t iih = tgpig[1] * args.s1 + tpitg_1 * args.d1 - args.p1;
//
// const int64_t offset_dst =
// (tpitg_0 * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * args.CHW +
// (tgpig_0 * KHW + tpitg_1 * args.KW + tpitg_2);
//
// device T * pdst = (device T *) (dst);
//
// if (iih < 0 || iih >= args.IH || iiw < 0 || iiw >= args.IW) {
// pdst[offset_dst] = 0.0f;
// } else {
// const int64_t offset_src = tpitg_0 * args.ofs0 + tgpig_0 * args.ofs1;
// pdst[offset_dst] = x[offset_src + iih * args.IW + iiw];
// }
//}
//
//template [[host_name("kernel_im2col_ext_f32")]] kernel im2col_ext_t kernel_im2col_ext<float>;
//template [[host_name("kernel_im2col_ext_f16")]] kernel im2col_ext_t kernel_im2col_ext<half>;
typedef void (conv_transpose_1d_t)(
constant ggml_metal_kargs_conv_transpose_1d & args,
@ -7743,7 +7794,7 @@ kernel void kernel_get_rows_i32(
}
}
template<typename block_q, void (*quantize_func)(device const float *, device block_q &)>
template<typename TI, typename block_q, void (*quantize_func)(device const float *, device block_q &)>
kernel void kernel_set_rows_q32(
constant ggml_metal_kargs_set_rows & args,
device const void * src0,
@ -7764,7 +7815,7 @@ kernel void kernel_set_rows_q32(
}
const int32_t i10 = i01;
const int64_t i1 = ((const device int64_t *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
device block_q * dst_row = ( device block_q *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
const device float * src_row = (const device float *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
@ -7774,7 +7825,7 @@ kernel void kernel_set_rows_q32(
}
}
template<typename T>
template<typename T, typename TI>
kernel void kernel_set_rows_f(
constant ggml_metal_kargs_set_rows & args,
device const void * src0,
@ -7795,7 +7846,7 @@ kernel void kernel_set_rows_f(
}
const int32_t i10 = i01;
const int64_t i1 = ((const device int64_t *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
const TI i1 = ((const device TI *) ((const device char *) src1 + i10*args.nb10 + i11*args.nb11 + i12*args.nb12))[0];
device T * dst_row = ( device T *) (( device char *) dst + i1*args.nb1 + i02*args.nb2 + i03*args.nb3);
const device float * src_row = (const device float *) ((const device char *) src0 + i01*args.nb01 + i02*args.nb02 + i03*args.nb03);
@ -8218,22 +8269,31 @@ template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_q_t kernel_get
// set rows
//
typedef decltype(kernel_set_rows_f<float>) set_rows_f_t;
typedef decltype(kernel_set_rows_f<float, int64_t>) set_rows_f_t;
template [[host_name("kernel_set_rows_f32")]] kernel set_rows_f_t kernel_set_rows_f<float>;
template [[host_name("kernel_set_rows_f16")]] kernel set_rows_f_t kernel_set_rows_f<half>;
template [[host_name("kernel_set_rows_f32_i64")]] kernel set_rows_f_t kernel_set_rows_f<float, int64_t>;
template [[host_name("kernel_set_rows_f32_i32")]] kernel set_rows_f_t kernel_set_rows_f<float, int32_t>;
template [[host_name("kernel_set_rows_f16_i64")]] kernel set_rows_f_t kernel_set_rows_f<half, int64_t>;
template [[host_name("kernel_set_rows_f16_i32")]] kernel set_rows_f_t kernel_set_rows_f<half, int32_t>;
#if defined(GGML_METAL_HAS_BF16)
template [[host_name("kernel_set_rows_bf16")]] kernel set_rows_f_t kernel_set_rows_f<bfloat>;
template [[host_name("kernel_set_rows_bf16_i64")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int64_t>;
template [[host_name("kernel_set_rows_bf16_i32")]] kernel set_rows_f_t kernel_set_rows_f<bfloat, int32_t>;
#endif
typedef decltype(kernel_set_rows_q32<block_q8_0, quantize_q8_0>) set_rows_q32_t;
typedef decltype(kernel_set_rows_q32<int64_t, block_q8_0, quantize_q8_0>) set_rows_q32_t;
template [[host_name("kernel_set_rows_q8_0")]] kernel set_rows_q32_t kernel_set_rows_q32<block_q8_0, quantize_q8_0>;
template [[host_name("kernel_set_rows_q4_0")]] kernel set_rows_q32_t kernel_set_rows_q32<block_q4_0, quantize_q4_0>;
template [[host_name("kernel_set_rows_q4_1")]] kernel set_rows_q32_t kernel_set_rows_q32<block_q4_1, quantize_q4_1>;
template [[host_name("kernel_set_rows_q5_0")]] kernel set_rows_q32_t kernel_set_rows_q32<block_q5_0, quantize_q5_0>;
template [[host_name("kernel_set_rows_q5_1")]] kernel set_rows_q32_t kernel_set_rows_q32<block_q5_1, quantize_q5_1>;
template [[host_name("kernel_set_rows_iq4_nl")]] kernel set_rows_q32_t kernel_set_rows_q32<block_iq4_nl, quantize_iq4_nl>;
template [[host_name("kernel_set_rows_q8_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q8_0, quantize_q8_0>;
template [[host_name("kernel_set_rows_q8_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q8_0, quantize_q8_0>;
template [[host_name("kernel_set_rows_q4_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_0, quantize_q4_0>;
template [[host_name("kernel_set_rows_q4_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_0, quantize_q4_0>;
template [[host_name("kernel_set_rows_q4_1_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q4_1, quantize_q4_1>;
template [[host_name("kernel_set_rows_q4_1_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q4_1, quantize_q4_1>;
template [[host_name("kernel_set_rows_q5_0_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_0, quantize_q5_0>;
template [[host_name("kernel_set_rows_q5_0_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_0, quantize_q5_0>;
template [[host_name("kernel_set_rows_q5_1_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_q5_1, quantize_q5_1>;
template [[host_name("kernel_set_rows_q5_1_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_q5_1, quantize_q5_1>;
template [[host_name("kernel_set_rows_iq4_nl_i64")]] kernel set_rows_q32_t kernel_set_rows_q32<int64_t, block_iq4_nl, quantize_iq4_nl>;
template [[host_name("kernel_set_rows_iq4_nl_i32")]] kernel set_rows_q32_t kernel_set_rows_q32<int32_t, block_iq4_nl, quantize_iq4_nl>;
//
// matrix-matrix multiplication

4
ggml/src/ggml-opencl/CMakeLists.txt
View File

@ -82,9 +82,13 @@ set(GGML_OPENCL_KERNELS
mul_mv_q4_0_f32_1d_8x_flat
mul_mv_q4_0_f32_1d_16x_flat
mul_mv_q6_k
mul_mv_q8_0_f32
mul_mv_q8_0_f32_flat
mul_mv_mxfp4_f32
mul_mv_mxfp4_f32_flat
mul_mv_id_q4_0_f32_8x_flat
mul_mv_id_q8_0_f32
mul_mv_id_q8_0_f32_flat
mul_mv_id_mxfp4_f32
mul_mv_id_mxfp4_f32_flat
mul_mm_f32_f32_l4_lm

432
ggml/src/ggml-opencl/ggml-opencl.cpp
View File

@ -367,6 +367,7 @@ struct ggml_backend_opencl_context {
cl_program program_mul_mv_q4_0_f32_1d_8x_flat;
cl_program program_mul_mv_q4_0_f32_1d_16x_flat;
cl_program program_mul_mv_q6_K;
cl_program program_mul_mv_q8_0_f32, program_mul_mv_q8_0_f32_flat;
cl_program program_mul_mv_mxfp4_f32;
cl_program program_mul_mv_mxfp4_f32_flat;
cl_program program_mul_mv_f16_f16;
@ -402,6 +403,7 @@ struct ggml_backend_opencl_context {
cl_program program_conv_2d_f16_f32;
cl_program program_tsembd;
cl_program program_mul_mv_id_q4_0_f32_8x_flat;
cl_program program_mul_mv_id_q8_0_f32, program_mul_mv_id_q8_0_f32_flat;
cl_program program_mul_mv_id_mxfp4_f32;
cl_program program_mul_mv_id_mxfp4_f32_flat;
cl_program program_mul_mm_f32_f32_l4_lm;
@ -437,7 +439,7 @@ struct ggml_backend_opencl_context {
std::map<std::pair<int, int>, int> kernels_flash_attn_bm;
std::map<std::pair<int, int>, int> kernels_flash_attn_bn;
cl_kernel kernel_get_rows_f32, kernel_get_rows_f16, kernel_get_rows_q4_0;
cl_kernel kernel_set_rows_f32, kernel_set_rows_f16;
cl_kernel kernel_set_rows_f32_i64, kernel_set_rows_f32_i32, kernel_set_rows_f16_i64, kernel_set_rows_f16_i32;
cl_kernel kernel_rope_norm_f32, kernel_rope_norm_f16, kernel_rope_neox_f32, kernel_rope_neox_f16;
cl_kernel kernel_rope_multi_f32, kernel_rope_multi_f16, kernel_rope_vision_f32, kernel_rope_vision_f16;
cl_kernel kernel_cpy_f16_f16, kernel_cpy_f16_f32, kernel_cpy_f32_f16, kernel_cpy_f32_f32;
@ -450,11 +452,13 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_mul_mat_q4_0_f32, kernel_mul_mat_q4_0_f32_v;
cl_kernel kernel_convert_block_q4_0, kernel_restore_block_q4_0;
cl_kernel kernel_convert_block_mxfp4, kernel_restore_block_mxfp4;
cl_kernel kernel_convert_block_q8_0, kernel_restore_block_q8_0;
cl_kernel kernel_mul_mat_q4_0_f32_8x_flat;
cl_kernel kernel_convert_block_q4_0_noshuffle;
cl_kernel kernel_mul_mat_q4_0_f32_1d_8x_flat, kernel_mul_mat_q4_0_f32_1d_16x_flat;
cl_kernel kernel_mul_mv_q6_K_f32;
cl_kernel kernel_mul_mv_mxfp4_f32, kernel_mul_mv_mxfp4_f32_flat;
cl_kernel kernel_mul_mv_q8_0_f32, kernel_mul_mv_q8_0_f32_flat;
cl_kernel kernel_im2col_f32, kernel_im2col_f16;
cl_kernel kernel_argsort_f32_i32;
cl_kernel kernel_sum_rows_f32;
@ -471,6 +475,7 @@ struct ggml_backend_opencl_context {
cl_kernel kernel_conv_2d_f16_f32;
cl_kernel kernel_timestep_embedding;
cl_kernel kernel_mul_mv_id_q4_0_f32_8x_flat;
cl_kernel kernel_mul_mv_id_q8_0_f32, kernel_mul_mv_id_q8_0_f32_flat;
cl_kernel kernel_mul_mv_id_mxfp4_f32;
cl_kernel kernel_mul_mv_id_mxfp4_f32_flat;
cl_kernel kernel_mul_mm_f32_f32_l4_lm;
@ -769,8 +774,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
CL_CHECK((backend_ctx->kernel_convert_block_q4_0_noshuffle = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0_noshuffle", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q4_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q4_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q4_0", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_mxfp4 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_mxfp4", &err), err));
CL_CHECK((backend_ctx->kernel_convert_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_convert_block_q8_0", &err), err));
CL_CHECK((backend_ctx->kernel_restore_block_q8_0 = clCreateKernel(backend_ctx->program_cvt, "kernel_restore_block_q8_0", &err), err));
GGML_LOG_CONT(".");
}
@ -992,6 +999,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mv_q8_0_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_q8_0_f32.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_q8_0_f32.cl");
#endif
backend_ctx->program_mul_mv_q8_0_f32 =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32, "kernel_mul_mv_q8_0_f32", &err), err));
GGML_LOG_CONT(".");
}
// mul_mv_q8_0_f32_flat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_q8_0_f32_flat.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_q8_0_f32_flat.cl");
#endif
backend_ctx->program_mul_mv_q8_0_f32_flat =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_q8_0_f32_flat, "kernel_mul_mv_q8_0_f32_flat", &err), err));
GGML_LOG_CONT(".");
}
// mul_mv_mxfp4_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -1671,8 +1710,10 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
backend_ctx->program_set_rows =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_set_rows_f32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32", &err), err));
CL_CHECK((backend_ctx->kernel_set_rows_f16 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16", &err), err));
CL_CHECK((backend_ctx->kernel_set_rows_f32_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i64", &err), err));
CL_CHECK((backend_ctx->kernel_set_rows_f32_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f32_i32", &err), err));
CL_CHECK((backend_ctx->kernel_set_rows_f16_i64 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16_i64", &err), err));
CL_CHECK((backend_ctx->kernel_set_rows_f16_i32 = clCreateKernel(backend_ctx->program_set_rows, "kernel_set_rows_f16_i32", &err), err));
GGML_LOG_CONT(".");
}
@ -1733,6 +1774,38 @@ static void load_cl_kernels(ggml_backend_opencl_context *backend_ctx, ggml_cl_ve
GGML_LOG_CONT(".");
}
// mul_mv_id_q8_0_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_id_q8_0_f32.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_id_q8_0_f32.cl");
#endif
backend_ctx->program_mul_mv_id_q8_0_f32 =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32 = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32, "kernel_mul_mv_id_q8_0_f32", &err), err));
GGML_LOG_CONT(".");
}
// mul_mv_id_q8_0_f32_flat
{
#ifdef GGML_OPENCL_EMBED_KERNELS
const std::string kernel_src {
#include "mul_mv_id_q8_0_f32_flat.cl.h"
};
#else
const std::string kernel_src = read_file("mul_mv_id_q8_0_f32_flat.cl");
#endif
backend_ctx->program_mul_mv_id_q8_0_f32_flat =
build_program_from_source(backend_ctx->context, backend_ctx->device, kernel_src.c_str(), compile_opts);
CL_CHECK((backend_ctx->kernel_mul_mv_id_q8_0_f32_flat = clCreateKernel(backend_ctx->program_mul_mv_id_q8_0_f32_flat, "kernel_mul_mv_id_q8_0_f32_flat", &err), err));
GGML_LOG_CONT(".");
}
// mul_mv_id_mxfp4_f32
{
#ifdef GGML_OPENCL_EMBED_KERNELS
@ -2463,10 +2536,8 @@ struct ggml_tensor_extra_cl_mxfp4 {
CL_CHECK(clReleaseMemObject(q_img));
q = nullptr;
}
// Currently, q_img and d_img are only initialized when SMALL_ALLOC is
// enabled. They point to the images in ggml_backend_opencl_buffer_context.
// So, there is no need to release them here.
// TODO: initialize them for non SMALL_PATH path, or remove them.
// Currently, q_img and d_img are not used. They can be image1d_buffer_t
// that wraps around q and d to utilize image access path.
q_img = nullptr;
e_img = nullptr;
size_q = 0;
@ -2474,6 +2545,41 @@ struct ggml_tensor_extra_cl_mxfp4 {
}
};
struct ggml_tensor_extra_cl_q8_0 {
cl_mem q = nullptr;
cl_mem q_img = nullptr;
cl_mem d = nullptr;
cl_mem d_img = nullptr;
size_t size_q = 0;
size_t size_d = 0;
~ggml_tensor_extra_cl_q8_0() {
reset();
}
void reset() {
// q and d are subbuffers into the bigger buffer allocated in ggml_backend_buffer.
// They must be properly released so that the original buffer can be
// properly released to avoid memory leak.
if (q != nullptr) {
CL_CHECK(clReleaseMemObject(q));
q = nullptr;
}
if (d != nullptr) {
CL_CHECK(clReleaseMemObject(d));
d = nullptr;
}
// Currently, q_img and d_img are not used. They can be image1d_buffer_t
// that wraps around q and d to utilize image access path.
q_img = nullptr;
d_img = nullptr;
size_q = 0;
size_d = 0;
}
};
//------------------------------------------------------------------------------
// Backend API
//------------------------------------------------------------------------------
@ -2699,7 +2805,7 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
switch (op->type) {
case GGML_TYPE_F16:
case GGML_TYPE_F32:
return true;
return (op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32);
default:
return false;
}
@ -2807,10 +2913,13 @@ static bool ggml_opencl_supports_op(ggml_backend_dev_t dev, const struct ggml_te
} else if (op->src[0]->type == GGML_TYPE_Q4_0 || op->src[0]->type == GGML_TYPE_MXFP4 ||
op->src[0]->type == GGML_TYPE_Q6_K) {
return op->src[1]->type == GGML_TYPE_F32 && ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
} else if (op->src[0]->type == GGML_TYPE_Q8_0) {
return op->src[1]->type == GGML_TYPE_F32;
}
return false;
case GGML_OP_MUL_MAT_ID:
if (op->src[0]->type == GGML_TYPE_Q4_0 ||
op->src[0]->type == GGML_TYPE_Q8_0 ||
op->src[0]->type == GGML_TYPE_MXFP4) {
if (op->src[1]->type == GGML_TYPE_F32) {
return ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]);
@ -2983,6 +3092,12 @@ struct ggml_backend_opencl_buffer_context {
for (ggml_tensor_extra_cl_mxfp4 * e : temp_tensor_extras_mxfp4_in_use) {
delete e;
}
for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0) {
delete e;
}
for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) {
delete e;
}
}
ggml_tensor_extra_cl * ggml_opencl_alloc_temp_tensor_extra() {
@ -3030,6 +3145,21 @@ struct ggml_backend_opencl_buffer_context {
return extra;
}
ggml_tensor_extra_cl_q8_0 * ggml_opencl_alloc_temp_tensor_extra_q8_0() {
ggml_tensor_extra_cl_q8_0 * extra;
if (temp_tensor_extras_q8_0.empty()) {
extra = new ggml_tensor_extra_cl_q8_0();
} else {
extra = temp_tensor_extras_q8_0.back();
temp_tensor_extras_q8_0.pop_back();
}
temp_tensor_extras_q8_0_in_use.push_back(extra);
extra->reset();
return extra;
}
void reset() {
for (ggml_tensor_extra_cl * e : temp_tensor_extras_in_use) {
temp_tensor_extras.push_back(e);
@ -3045,6 +3175,11 @@ struct ggml_backend_opencl_buffer_context {
temp_tensor_extras_mxfp4.push_back(e);
}
temp_tensor_extras_mxfp4_in_use.clear();
for (ggml_tensor_extra_cl_q8_0 * e : temp_tensor_extras_q8_0_in_use) {
temp_tensor_extras_q8_0.push_back(e);
}
temp_tensor_extras_q8_0_in_use.clear();
}
// Pools for extras. Available extras are in `temp_tensor_extras`. Extras
@ -3058,6 +3193,8 @@ struct ggml_backend_opencl_buffer_context {
std::vector<ggml_tensor_extra_cl_q4_0 *> temp_tensor_extras_q4_0_in_use;
std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4;
std::vector<ggml_tensor_extra_cl_mxfp4 *> temp_tensor_extras_mxfp4_in_use;
std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0;
std::vector<ggml_tensor_extra_cl_q8_0 *> temp_tensor_extras_q8_0_in_use;
// The buffer_context is initially created by ggml_backend_buft_alloc_buffer
// before any tensor is initialized (at the beginning of alloc_tensor_range).
@ -3470,6 +3607,65 @@ static void ggml_backend_opencl_buffer_set_tensor(ggml_backend_buffer_t buffer,
tensor->extra = extra;
return;
}
if (tensor->type == GGML_TYPE_Q8_0) {
ggml_tensor_extra_cl * extra_orig = (ggml_tensor_extra_cl *)tensor->extra;
GGML_ASSERT(extra_orig && "Tesnors in OpenCL backend should have been allocated and initialized");
// Allocate the new extra and create aliases from the original.
ggml_backend_opencl_buffer_context * ctx = (ggml_backend_opencl_buffer_context *) buffer->context;
ggml_tensor_extra_cl_q8_0 * extra = ctx->ggml_opencl_alloc_temp_tensor_extra_q8_0();
size_t size_d = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*sizeof(ggml_fp16_t);
size_t size_q = ggml_nelements(tensor)/ggml_blck_size(tensor->type)*(ggml_blck_size(tensor->type)*sizeof(char));
GGML_ASSERT(size_d + size_q == ggml_nbytes(tensor) && "Incorrect tensor size");
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
CL_CHECK(clEnqueueWriteBuffer(
queue, data_device, CL_TRUE, 0,
ggml_nbytes(tensor), data, 0, NULL, NULL));
// The original tensor memory is divided into scales and quants, i.e.,
// we first store scales, then quants.
cl_buffer_region region;
// Create subbuffer for scales.
region.origin = align_to(extra_orig->offset + tensor->view_offs + offset, backend_ctx->alignment);
region.size = size_d;
extra->d = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
auto previous_origin = region.origin;
// Create subbuffer for quants.
region.origin = align_to(previous_origin + size_d, backend_ctx->alignment);
region.size = size_q;
extra->q = clCreateSubBuffer(
extra_orig->data_device, CL_MEM_READ_WRITE,
CL_BUFFER_CREATE_TYPE_REGION, &region, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_convert_block_q8_0;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra->d));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {64, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL, global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clReleaseMemObject(data_device));
tensor->extra = extra;
return;
}
#endif // GGML_OPENCL_SOA_Q
@ -3543,6 +3739,32 @@ static void ggml_backend_opencl_buffer_get_tensor(ggml_backend_buffer_t buffer,
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
global_work_size, local_work_size, 0, NULL, &evt));
CL_CHECK(clWaitForEvents(1, &evt));
CL_CHECK(clEnqueueReadBuffer(
queue, data_device, CL_TRUE, offset,
size, data, 0, NULL, NULL));
CL_CHECK(clReleaseMemObject(data_device));
return;
}
if (tensor->type == GGML_TYPE_Q8_0) {
ggml_tensor_extra_cl_q8_0 * extra = (ggml_tensor_extra_cl_q8_0 *)tensor->extra;
cl_int err;
cl_mem data_device = clCreateBuffer(context, CL_MEM_READ_WRITE,
ggml_nbytes(tensor), NULL, &err);
CL_CHECK(err);
cl_kernel kernel = backend_ctx->kernel_restore_block_q8_0;
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &data_device));
size_t global_work_size[] = {(size_t)ggml_nelements(tensor)/ggml_blck_size(tensor->type), 1, 1};
size_t local_work_size[] = {1, 1, 1};
cl_event evt;
CL_CHECK(clEnqueueNDRangeKernel(queue, kernel, 3, NULL,
global_work_size, local_work_size, 0, NULL, &evt));
@ -4064,6 +4286,7 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
GGML_ASSERT(src1->extra);
GGML_ASSERT(dst);
GGML_ASSERT(dst->extra);
GGML_ASSERT(src1->type == GGML_TYPE_I64 || src1->type == GGML_TYPE_I32);
// ne0 = ne00
// ne2 = ne02
@ -4106,10 +4329,18 @@ static void ggml_cl_set_rows(ggml_backend_t backend, const ggml_tensor * src0, c
switch (dst->type) {
case GGML_TYPE_F32:
kernel = backend_ctx->kernel_set_rows_f32;
if (src1->type == GGML_TYPE_I64) {
kernel = backend_ctx->kernel_set_rows_f32_i64;
} else {
kernel = backend_ctx->kernel_set_rows_f32_i32;
}
break;
case GGML_TYPE_F16:
kernel = backend_ctx->kernel_set_rows_f16;
if (src1->type == GGML_TYPE_I64) {
kernel = backend_ctx->kernel_set_rows_f16_i64;
} else {
kernel = backend_ctx->kernel_set_rows_f16_i32;
}
break;
default:
GGML_ABORT("not implemented");
@ -5888,12 +6119,12 @@ static void ggml_cl_concat(ggml_backend_t backend, const ggml_tensor * src0, con
} else {
cl_kernel kernel = backend_ctx->kernel_concat_f32_non_contiguous;
long ne00 = src0->ne[0], ne01 = src0->ne[1], ne02 = src0->ne[2], ne03 = src0->ne[3];
cl_long ne00 = src0->ne[0], ne01 = src0->ne[1], ne02 = src0->ne[2], ne03 = src0->ne[3];
cl_ulong nb00 = src0->nb[0], nb01 = src0->nb[1], nb02 = src0->nb[2], nb03 = src0->nb[3];
cl_ulong nb10 = src1->nb[0], nb11 = src1->nb[1], nb12 = src1->nb[2], nb13 = src1->nb[3];
long d_ne0 = dst->ne[0], d_ne1 = dst->ne[1], d_ne2 = dst->ne[2], d_ne3 = dst->ne[3];
cl_long d_ne0 = dst->ne[0], d_ne1 = dst->ne[1], d_ne2 = dst->ne[2], d_ne3 = dst->ne[3];
cl_ulong d_nb0 = dst->nb[0], d_nb1 = dst->nb[1], d_nb2 = dst->nb[2], d_nb3 = dst->nb[3];
@ -5904,10 +6135,10 @@ static void ggml_cl_concat(ggml_backend_t backend, const ggml_tensor * src0, con
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad_cl->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &off_dst));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(long), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(long), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(long), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(long), &ne03));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_long), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_long), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_long), &ne02));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_long), &ne03));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb00));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb02));
@ -5918,10 +6149,10 @@ static void ggml_cl_concat(ggml_backend_t backend, const ggml_tensor * src0, con
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(cl_ulong), &nb13));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(long), &d_ne0));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(long), &d_ne1));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(long), &d_ne2));
CL_CHECK(clSetKernelArg(kernel, 21, sizeof(long), &d_ne3));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_long), &d_ne0));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(cl_long), &d_ne1));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(cl_long), &d_ne2));
CL_CHECK(clSetKernelArg(kernel, 21, sizeof(cl_long), &d_ne3));
CL_CHECK(clSetKernelArg(kernel, 22, sizeof(cl_ulong), &d_nb0));
CL_CHECK(clSetKernelArg(kernel, 23, sizeof(cl_ulong), &d_nb1));
CL_CHECK(clSetKernelArg(kernel, 24, sizeof(cl_ulong), &d_nb2));
@ -6268,6 +6499,7 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
#ifdef GGML_OPENCL_SOA_Q
ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
#endif
const int ne00 = src0 ? src0->ne[0] : 0;
@ -6937,7 +7169,84 @@ static void ggml_cl_mul_mat(ggml_backend_t backend, const ggml_tensor * src0, co
#endif // GGML_OPENCL_SOA_Q
break;
case GGML_TYPE_Q4_1:
case GGML_TYPE_Q8_0:
case GGML_TYPE_Q8_0: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_q8_0_f32_flat;
// nth0 - subgroup size
// nth1 - number of subgroups per workgroup
// ndst - number of output values per workgroup = output per subgroup * number of subgroups
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 2;
ndst = nth1*4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 2;
ndst = nth1*4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q8_0->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));
#else
kernel = backend_ctx->kernel_mul_mv_q8_0_f32;
// nth0 - subgroup size
// nth1 - number of subgroups per workgroup
// ndst - number of output values per workgroup = output per subgroup * number of subgroups
if (backend_ctx->gpu_family == INTEL) {
nth0 = 16;
nth1 = 2;
ndst = nth1*4;
} else if (backend_ctx->gpu_family == ADRENO) {
nth0 = 64;
nth1 = 2;
ndst = nth1*4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb03));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb13));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne1));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &r2));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(int), &r3));
#endif // GGML_OPENCL_SOA_Q
break;
}
case GGML_TYPE_Q2_K:
case GGML_TYPE_Q3_K:
case GGML_TYPE_Q4_K:
@ -7115,6 +7424,7 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
#ifdef GGML_OPENCL_SOA_Q
ggml_tensor_extra_cl_q4_0 * extra0_q4_0 = (ggml_tensor_extra_cl_q4_0 *)src0->extra;
ggml_tensor_extra_cl_mxfp4 * extra0_mxfp4 = (ggml_tensor_extra_cl_mxfp4 *)src0->extra;
ggml_tensor_extra_cl_q8_0 * extra0_q8_0 = (ggml_tensor_extra_cl_q8_0 *)src0->extra;
#endif
const int ne00 = src0->ne[0];
@ -7202,6 +7512,82 @@ static void ggml_cl_mul_mat_id(ggml_backend_t backend, const ggml_tensor * src0,
break;
}
case GGML_TYPE_Q8_0: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_id_q8_0_f32_flat;
if (backend_ctx->gpu_family == INTEL) {
sgs = 16;
nsg = 2;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
sgs = 64;
nsg = 2;
ndst = 4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0_q8_0->q));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_mem), &extra0_q8_0->d));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra2->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset2));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne20));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &ne21));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb21));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &ne1));
#else
kernel = backend_ctx->kernel_mul_mv_id_q8_0_f32;
if (backend_ctx->gpu_family == INTEL) {
sgs = 16;
nsg = 2;
ndst = 4;
} else if (backend_ctx->gpu_family == ADRENO) {
sgs = 64;
nsg = 2;
ndst = 4;
} else {
GGML_ASSERT(false && "TODO: Unknown GPU");
}
CL_CHECK(clSetKernelArg(kernel, 0, sizeof(cl_mem), &extra0->data_device));
CL_CHECK(clSetKernelArg(kernel, 1, sizeof(cl_ulong), &offset0));
CL_CHECK(clSetKernelArg(kernel, 2, sizeof(cl_mem), &extra1->data_device));
CL_CHECK(clSetKernelArg(kernel, 3, sizeof(cl_ulong), &offset1));
CL_CHECK(clSetKernelArg(kernel, 4, sizeof(cl_mem), &extra2->data_device));
CL_CHECK(clSetKernelArg(kernel, 5, sizeof(cl_ulong), &offset2));
CL_CHECK(clSetKernelArg(kernel, 6, sizeof(cl_mem), &extrad->data_device));
CL_CHECK(clSetKernelArg(kernel, 7, sizeof(cl_ulong), &offsetd));
CL_CHECK(clSetKernelArg(kernel, 8, sizeof(int), &ne00));
CL_CHECK(clSetKernelArg(kernel, 9, sizeof(int), &ne01));
CL_CHECK(clSetKernelArg(kernel, 10, sizeof(cl_ulong), &nb01));
CL_CHECK(clSetKernelArg(kernel, 11, sizeof(cl_ulong), &nb02));
CL_CHECK(clSetKernelArg(kernel, 12, sizeof(int), &ne11));
CL_CHECK(clSetKernelArg(kernel, 13, sizeof(int), &ne12));
CL_CHECK(clSetKernelArg(kernel, 14, sizeof(cl_ulong), &nb11));
CL_CHECK(clSetKernelArg(kernel, 15, sizeof(cl_ulong), &nb12));
CL_CHECK(clSetKernelArg(kernel, 16, sizeof(int), &ne20));
CL_CHECK(clSetKernelArg(kernel, 17, sizeof(int), &ne21));
CL_CHECK(clSetKernelArg(kernel, 18, sizeof(cl_ulong), &nb21));
CL_CHECK(clSetKernelArg(kernel, 19, sizeof(int), &ne0));
CL_CHECK(clSetKernelArg(kernel, 20, sizeof(int), &ne1));
#endif // GGML_OPENCL_SOA_Q
break;
}
case GGML_TYPE_MXFP4: {
#ifdef GGML_OPENCL_SOA_Q
kernel = backend_ctx->kernel_mul_mv_id_mxfp4_f32_flat;

42
ggml/src/ggml-opencl/kernels/cvt.cl
View File

@ -117,9 +117,8 @@ kernel void kernel_convert_block_q4_0_noshuffle(
}
}
//------------------------------------------------------------------------------
// block_q4_0
// block_mxfp4
//------------------------------------------------------------------------------
#define QK_MXFP4 32
struct block_mxfp4 {
@ -162,3 +161,42 @@ kernel void kernel_restore_block_mxfp4(
b->qs[i] = q[i];
}
}
//------------------------------------------------------------------------------
// block_q8_0
//------------------------------------------------------------------------------
typedef struct {
half d; // delta
char qs[QK8_0]; // quants
} block_q8_0;
kernel void kernel_convert_block_q8_0(
global block_q8_0 * src0,
global uchar * dst_q,
global half * dst_d
) {
global block_q8_0 * b = (global block_q8_0 *) src0 + get_global_id(0);
global uchar * q = (global uchar *) dst_q + QK8_0*get_global_id(0);
global half * d = (global half *) dst_d + get_global_id(0);
*d = b->d;
for (int i = 0; i < QK8_0; ++i) {
q[i] = b->qs[i];
}
}
kernel void kernel_restore_block_q8_0(
global uchar * src_q,
global half * src_d,
global block_q8_0 * dst
) {
global block_q8_0 * b = (global block_q8_0 *) dst + get_global_id(0);
global uchar * q = (global uchar *) src_q + QK8_0*get_global_id(0);
global half * d = (global half *) src_d + get_global_id(0);
b->d = *d;
for (int i = 0; i < QK8_0; ++i) {
b->qs[i] = q[i];
}
}

140
ggml/src/ggml-opencl/kernels/mul_mv_id_q8_0_f32.cl Normal file
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@ -0,0 +1,140 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK8_0 32
typedef struct {
half d; // delta
char qs[QK8_0]; // quants
} block_q8_0;
#define NB_Q8_0 8
#ifdef INTEL_GPU
#define N_R0_Q8_0 4 // number of rows each subgroup works on
#define N_SG_Q8_0 2 // number of subgroups in a work group
#define N_SIMDWIDTH 16 // subgroup size
#elif defined (ADRENO_GPU)
#define N_R0_Q8_0 4
#define N_SG_Q8_0 2
#define N_SIMDWIDTH 64
#endif
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_id_q8_0_f32(
global char * src0,
ulong offset0,
global char * src1,
ulong offset1,
global char * src2,
ulong offset2,
global char * dst,
ulong offsetd,
int ne00,
int ne01,
ulong nb01,
ulong nb02,
int ne11,
int ne12,
ulong nb11,
ulong nb12,
int ne20,
int ne21,
ulong nb21,
int ne0,
int ne1
) {
src0 = (global char *)((global char *)src0 + offset0);
src1 = (global char *)((global char *)src1 + offset1);
src2 = (global char *)((global char *)src2 + offset2);
dst = (global char *)((global char *)dst + offsetd);
int iid1 = get_group_id(2)/ne20;
int idx = get_group_id(2)%ne20;
int i02 = ((global int *) (src2 + iid1*nb21))[idx];
int i11_ = idx % ne11;
int i12_ = iid1;
int i1 = idx;
int i2 = i12_;
global char * src0_cur = src0 + i02*nb02;
global char * src1_cur = src1 + i11_*nb11 + i12_*nb12;
global char * dst_cur = dst + (i1*ne0 + i2*ne1*ne0)*sizeof(float);
int nb = ne00/QK8_0;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int first_row = (r0*N_SG_Q8_0 + get_sub_group_id()) * N_R0_Q8_0;
ulong offset_src1 = r1*nb11;
global float * y = (global float *) (src1_cur + offset_src1);
// pointers to src0 rows
global block_q8_0 * ax[N_R0_Q8_0];
for (int row = 0; row < N_R0_Q8_0; ++row) {
ulong offset_src0 = (first_row + row)*nb01;
ax[row] = (global block_q8_0 *) ((global char *) src0_cur + offset_src0);
}
float yl[NB_Q8_0];
float sumf[N_R0_Q8_0] = { 0.f };
const short ix = get_sub_group_local_id()/4;
const short il = get_sub_group_local_id()%4;
global float * yb = y + ix*QK8_0 + il*NB_Q8_0;
// each thread handles NB_Q8_0 quants at a time
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/4) {
for (short i = 0; i < NB_Q8_0; ++i) {
yl[i] = yb[i];
}
for (short row = 0; row < N_R0_Q8_0; row++) {
global char * qs = ax[row][ib].qs + il*NB_Q8_0;
float sumq = 0.f;
for (short iq = 0; iq < NB_Q8_0; ++iq) {
sumq += qs[iq] * yl[iq];
}
sumf[row] += sumq*ax[row][ib].d;
}
yb += N_SIMDWIDTH*NB_Q8_0;
}
global float * dst_f32 = (global float *) dst_cur + (ulong)r1*ne0;
for (int row = 0; row < N_R0_Q8_0; ++row) {
float tot = sub_group_reduce_add(sumf[row]);
if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
dst_f32[first_row + row] = tot;
}
}
}

222
ggml/src/ggml-opencl/kernels/mul_mv_id_q8_0_f32_flat.cl Normal file
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@ -0,0 +1,222 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK8_0 32
typedef struct {
half d; // delta
char qs[QK8_0]; // quants
} block_q8_0;
#define NB_Q8_0 8
#ifdef INTEL_GPU
#define N_R0_Q8_0 4 // number of rows each subgroup works on
#define N_SG_Q8_0 2 // number of subgroups in a work group
#define N_SIMDWIDTH 16 // subgroup size
#elif defined (ADRENO_GPU)
#define N_R0_Q8_0 4
#define N_SG_Q8_0 2
#define N_SIMDWIDTH 64
#endif
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_id_q8_0_f32_flat(
global char * src0_q,
global half * src0_d,
global char * src1,
ulong offset1,
global char * src2,
ulong offset2,
global char * dst,
ulong offsetd,
int ne00,
int ne01,
ulong nb01,
ulong nb02,
int ne11,
int ne12,
ulong nb11,
ulong nb12,
int ne20,
int ne21,
ulong nb21,
int ne0,
int ne1
) {
src1 = (global char *)((global char *)src1 + offset1);
src2 = (global char *)((global char *)src2 + offset2);
dst = (global char *)((global char *)dst + offsetd);
int iid1 = (int)get_group_id(2)/ne20;
int idx = (int)get_group_id(2)%ne20;
int i02 = ((global int *) (src2 + iid1*nb21))[idx];
int i11_ = idx % ne11;
int i12_ = iid1;
int i1 = idx;
int i2 = i12_;
// 34 == sizeof(block_q8_0)
uint src0_off = i02*nb02;
src0_off /= 34;
global char * src0_q_cur = src0_q + src0_off*sizeof(char)*QK8_0;
global half * src0_d_cur = src0_d + src0_off;
global char * src1_cur = src1 + i11_*nb11 + i12_*nb12;
global char * dst_cur = dst + (i1*ne0 + i2*ne1*ne0)*sizeof(float);
int nb = ne00/QK8_0;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int first_row = (r0*N_SG_Q8_0 + get_sub_group_id()) * N_R0_Q8_0;
ulong offset_src1 = r1*nb11;
global float * y = (global float *) (src1_cur + offset_src1);
// pointers to src0 rows
uint offset_src0_base = first_row*nb01;
global char * ax0, * ax1, * ax2, * ax3;
global half * ad0, * ad1, * ad2, * ad3;
uint offset_src0;
offset_src0 = offset_src0_base + 0*nb01;
offset_src0 = offset_src0/34;
ax0 = (global char *) ((global char *) src0_q_cur + offset_src0*sizeof(char)*QK8_0);
ad0 = (global half *) ((global char *) src0_d_cur + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 1*nb01;
offset_src0 = offset_src0/34;
ax1 = (global char *) ((global char *) src0_q_cur + offset_src0*sizeof(char)*QK8_0);
ad1 = (global half *) ((global char *) src0_d_cur + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 2*nb01;
offset_src0 = offset_src0/34;
ax2 = (global char *) ((global char *) src0_q_cur + offset_src0*sizeof(char)*QK8_0);
ad2 = (global half *) ((global char *) src0_d_cur + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 3*nb01;
offset_src0 = offset_src0/34;
ax3 = (global char *) ((global char *) src0_q_cur + offset_src0*sizeof(char)*QK8_0);
ad3 = (global half *) ((global char *) src0_d_cur + offset_src0*sizeof(half));
const short ix = get_sub_group_local_id()/4;
const short il = get_sub_group_local_id()%4;
global float * yb = y + ix*QK8_0 + il*NB_Q8_0;
float8 yl;
float8 qv;
float4 sumf = 0.f;
float sumq = 0.f;
global char * qs;
// each thread handles NB_Q8_0 quants at a time
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/4) {
yl = vload8(0, yb);
qs = ax0 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s0 += sumq*ad0[ib];
qs = ax1 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s1 += sumq*ad1[ib];
qs = ax2 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s2 += sumq*ad2[ib];
qs = ax3 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s3 += sumq*ad3[ib];
yb += N_SIMDWIDTH*NB_Q8_0;
}
global float * dst_f32 = (global float *) dst_cur + (ulong)r1*ne0;
float4 tot = (float4)(
sub_group_reduce_add(sumf.s0),
sub_group_reduce_add(sumf.s1),
sub_group_reduce_add(sumf.s2),
sub_group_reduce_add(sumf.s3)
);
if (get_sub_group_local_id() == 0) {
if (first_row + 0 < ne01) {
dst_f32[first_row + 0] = tot.s0;
}
if (first_row + 1 < ne01) {
dst_f32[first_row + 1] = tot.s1;
}
if (first_row + 2 < ne01) {
dst_f32[first_row + 2] = tot.s2;
}
if (first_row + 3 < ne01) {
dst_f32[first_row + 3] = tot.s3;
}
}
}

125
ggml/src/ggml-opencl/kernels/mul_mv_q8_0_f32.cl Normal file
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@ -0,0 +1,125 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK8_0 32
typedef struct {
half d; // delta
char qs[QK8_0]; // quants
} block_q8_0;
#define NB_Q8_0 8
#ifdef INTEL_GPU
#define N_R0_Q8_0 4 // number of rows each subgroup works on
#define N_SG_Q8_0 2 // number of subgroups in a work group
#define N_SIMDWIDTH 16 // subgroup size
#elif defined (ADRENO_GPU)
#define N_R0_Q8_0 4
#define N_SG_Q8_0 2
#define N_SIMDWIDTH 64
#endif
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_q8_0_f32(
global char * src0,
ulong offset0,
global char * src1,
ulong offset1,
global char * dst,
ulong offsetd,
int ne00,
int ne01,
ulong nb01,
ulong nb02,
ulong nb03,
int ne12,
ulong nb11,
ulong nb12,
ulong nb13,
int ne0,
int ne1,
int r2,
int r3
) {
src0 = (global char*)((global char*)src0 + offset0);
src1 = (global char*)((global char*)src1 + offset1);
dst = (global char*)((global char*)dst + offsetd);
int nb = ne00/QK8_0;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0*N_SG_Q8_0 + get_sub_group_id()) * N_R0_Q8_0;
uint i12 = im%ne12;
uint i13 = im/ne12;
ulong offset_src1 = r1*nb11 + i12*nb12 + i13*nb13;
global float * y = (global float *) (src1 + offset_src1);
// pointers to src0 rows
global block_q8_0 * ax[N_R0_Q8_0];
for (int row = 0; row < N_R0_Q8_0; ++row) {
ulong offset_src0 = (first_row + row)*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
ax[row] = (global block_q8_0 *) ((global char *) src0 + offset_src0);
}
float yl[NB_Q8_0];
float sumf[N_R0_Q8_0] = { 0.f };
const short ix = get_sub_group_local_id()/4;
const short il = get_sub_group_local_id()%4;
global float * yb = y + ix*QK8_0 + il*NB_Q8_0;
// each thread handles NB_Q8_0 quants at a time
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/4) {
for (short i = 0; i < NB_Q8_0; ++i) {
yl[i] = yb[i];
}
for (short row = 0; row < N_R0_Q8_0; row++) {
global char * qs = ax[row][ib].qs + il*NB_Q8_0;
float sumq = 0.f;
for (short iq = 0; iq < NB_Q8_0; ++iq) {
sumq += qs[iq] * yl[iq];
}
sumf[row] += sumq*ax[row][ib].d;
}
yb += N_SIMDWIDTH*NB_Q8_0;
}
global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
for (int row = 0; row < N_R0_Q8_0; ++row) {
float tot = sub_group_reduce_add(sumf[row]);
if (get_sub_group_local_id() == 0 && first_row + row < ne01) {
dst_f32[first_row + row] = tot;
}
}
}

202
ggml/src/ggml-opencl/kernels/mul_mv_q8_0_f32_flat.cl Normal file
View File

@ -0,0 +1,202 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
#ifdef cl_intel_subgroups
#pragma OPENCL EXTENSION cl_intel_subgroups : enable
#else
#pragma OPENCL EXTENSION cl_khr_subgroups : enable
#endif
#ifdef cl_intel_required_subgroup_size
#pragma OPENCL EXTENSION cl_intel_required_subgroup_size : enable
#define INTEL_GPU 1
#define REQD_SUBGROUP_SIZE_16 __attribute__((intel_reqd_sub_group_size(16)))
#define REQD_SUBGROUP_SIZE_32 __attribute__((intel_reqd_sub_group_size(32)))
#elif defined(cl_qcom_reqd_sub_group_size)
#pragma OPENCL EXTENSION cl_qcom_reqd_sub_group_size : enable
#define ADRENO_GPU 1
#define REQD_SUBGROUP_SIZE_64 __attribute__((qcom_reqd_sub_group_size("half")))
#define REQD_SUBGROUP_SIZE_128 __attribute__((qcom_reqd_sub_group_size("full")))
#endif
#define QK8_0 32
typedef struct {
half d; // delta
char qs[QK8_0]; // quants
} block_q8_0;
#define NB_Q8_0 8
#ifdef INTEL_GPU
#define N_R0_Q8_0 4 // number of rows each subgroup works on
#define N_SG_Q8_0 2 // number of subgroups in a work group
#define N_SIMDWIDTH 16 // subgroup size
#elif defined (ADRENO_GPU)
#define N_R0_Q8_0 4
#define N_SG_Q8_0 2
#define N_SIMDWIDTH 64
#endif
#ifdef INTEL_GPU
REQD_SUBGROUP_SIZE_16
#elif defined (ADRENO_GPU)
REQD_SUBGROUP_SIZE_64
#endif
kernel void kernel_mul_mv_q8_0_f32_flat(
global char * src0_q,
global half * src0_d,
global char * src1,
ulong offset1,
global char * dst,
ulong offsetd,
int ne00,
int ne01,
ulong nb01,
ulong nb02,
ulong nb03,
int ne12,
ulong nb11,
ulong nb12,
ulong nb13,
int ne0,
int ne1,
int r2,
int r3
) {
src1 = (global char*)((global char*)src1 + offset1);
dst = (global char*)((global char*)dst + offsetd);
int nb = ne00/QK8_0;
int r0 = get_group_id(0);
int r1 = get_group_id(1);
int im = get_group_id(2);
int first_row = (r0*N_SG_Q8_0 + get_sub_group_id()) * N_R0_Q8_0;
uint i12 = im%ne12;
uint i13 = im/ne12;
ulong offset_src1 = r1*nb11 + i12*nb12 + i13*nb13;
global float * y = (global float *) (src1 + offset_src1);
// pointers to src0 rows
uint offset_src0_base = first_row*nb01 + (i12/r2)*nb02 + (i13/r3)*nb03;
global char * ax0, * ax1, * ax2, * ax3;
global half * ad0, * ad1, * ad2, * ad3;
uint offset_src0;
offset_src0 = offset_src0_base + 0*nb01;
offset_src0 = offset_src0/34;
ax0 = (global char *) ((global char *) src0_q + offset_src0*sizeof(char)*QK8_0);
ad0 = (global half *) ((global char *) src0_d + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 1*nb01;
offset_src0 = offset_src0/34;
ax1 = (global char *) ((global char *) src0_q + offset_src0*sizeof(char)*QK8_0);
ad1 = (global half *) ((global char *) src0_d + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 2*nb01;
offset_src0 = offset_src0/34;
ax2 = (global char *) ((global char *) src0_q + offset_src0*sizeof(char)*QK8_0);
ad2 = (global half *) ((global char *) src0_d + offset_src0*sizeof(half));
offset_src0 = offset_src0_base + 3*nb01;
offset_src0 = offset_src0/34;
ax3 = (global char *) ((global char *) src0_q + offset_src0*sizeof(char)*QK8_0);
ad3 = (global half *) ((global char *) src0_d + offset_src0*sizeof(half));
const short ix = get_sub_group_local_id()/4;
const short il = get_sub_group_local_id()%4;
global float * yb = y + ix*QK8_0 + il*NB_Q8_0;
float8 yl;
float8 qv;
float4 sumf = 0.f;
float sumq = 0.f;
global char * qs;
// each thread handles NB_Q8_0 quants at a time
for (int ib = ix; ib < nb; ib += N_SIMDWIDTH/4) {
yl = vload8(0, yb);
qs = ax0 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s0 += sumq*ad0[ib];
qs = ax1 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s1 += sumq*ad1[ib];
qs = ax2 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s2 += sumq*ad2[ib];
qs = ax3 + ib*sizeof(char)*QK8_0 + il*NB_Q8_0;
qv = convert_float8(vload8(0, qs));
sumq = 0;
sumq += qv.s0*yl.s0;
sumq += qv.s1*yl.s1;
sumq += qv.s2*yl.s2;
sumq += qv.s3*yl.s3;
sumq += qv.s4*yl.s4;
sumq += qv.s5*yl.s5;
sumq += qv.s6*yl.s6;
sumq += qv.s7*yl.s7;
sumf.s3 += sumq*ad3[ib];
yb += N_SIMDWIDTH*NB_Q8_0;
}
global float * dst_f32 = (global float *) dst + (ulong)im*ne0*ne1 + (ulong)r1*ne0;
float4 tot = (float4)(
sub_group_reduce_add(sumf.s0),
sub_group_reduce_add(sumf.s1),
sub_group_reduce_add(sumf.s2),
sub_group_reduce_add(sumf.s3)
);
if (get_sub_group_local_id() == 0) {
if (first_row + 0 < ne01) {
dst_f32[first_row + 0] = tot.s0;
}
if (first_row + 1 < ne01) {
dst_f32[first_row + 1] = tot.s1;
}
if (first_row + 2 < ne01) {
dst_f32[first_row + 2] = tot.s2;
}
if (first_row + 3 < ne01) {
dst_f32[first_row + 3] = tot.s3;
}
}
}

98
ggml/src/ggml-opencl/kernels/set_rows.cl
View File

@ -1,6 +1,6 @@
#pragma OPENCL EXTENSION cl_khr_fp16 : enable
kernel void kernel_set_rows_f32(
kernel void kernel_set_rows_f32_i64(
global char * src0,
ulong offset0,
global char * src1,
@ -47,7 +47,7 @@ kernel void kernel_set_rows_f32(
}
}
kernel void kernel_set_rows_f16(
kernel void kernel_set_rows_f16_i64(
global char * src0,
ulong offset0,
global char * src1,
@ -93,3 +93,97 @@ kernel void kernel_set_rows_f16(
dst_row[ind] = src_row[ind];
}
}
kernel void kernel_set_rows_f32_i32(
global char * src0,
ulong offset0,
global char * src1,
ulong offset1,
global char * dst,
ulong offsetd,
int ne01,
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
ulong nb10,
ulong nb11,
ulong nb12,
int nblk0,
ulong nb1,
ulong nb2,
ulong nb3
) {
src0 = src0 + offset0;
src1 = src1 + offset1;
dst = dst + offsetd;
int i03 = get_group_id(2);
int i02 = get_group_id(1);
int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
if (i01 >= ne01) {
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
int i10 = i01;
int i1 = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
global float * dst_row = (global float *) (dst + i1*nb1 + i02*nb2 + i03*nb3);
global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
for (int ind = get_local_id(0); ind < nblk0; ind += get_local_size(0)) {
dst_row[ind] = (float)src_row[ind];
}
}
kernel void kernel_set_rows_f16_i32(
global char * src0,
ulong offset0,
global char * src1,
ulong offset1,
global char * dst,
ulong offsetd,
int ne01,
ulong nb01,
ulong nb02,
ulong nb03,
int ne11,
int ne12,
ulong nb10,
ulong nb11,
ulong nb12,
int nblk0,
ulong nb1,
ulong nb2,
ulong nb3
) {
src0 = src0 + offset0;
src1 = src1 + offset1;
dst = dst + offsetd;
int i03 = get_group_id(2);
int i02 = get_group_id(1);
int i01 = get_group_id(0)*get_local_size(1) + get_local_id(1);
if (i01 >= ne01) {
return;
}
int i12 = i03%ne12;
int i11 = i02%ne11;
int i10 = i01;
int i1 = ((global int *)(src1 + i10*nb10 + i11*nb11 + i12*nb12))[0];
global half * dst_row = (global half *) (dst + i1*nb1 + i02*nb2 + i03*nb3);
global float * src_row = (global float *) (src0 + i01*nb01 + i02*nb02 + i03*nb03);
for (int ind = get_local_id(0); ind < nblk0; ind += get_local_size(0)) {
dst_row[ind] = src_row[ind];
}
}

1
ggml/src/ggml-quants.c
View File

@ -3721,6 +3721,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * GGML_RESTRICT
}
float best = 0;
float scale = max/(2*kMaxQ-1);
for (int k = 0; k < 8; ++k) is_on_grid[k] = true;
for (int is = -15; is <= 15; ++is) {
float id = (2*kMaxQ-1+is*0.2f)/max;
float this_scale = 1/id;

76
ggml/src/ggml-rpc/ggml-rpc.cpp
View File

@ -31,6 +31,12 @@
#include <filesystem>
#include <algorithm>
static const char * RPC_DEBUG = std::getenv("GGML_RPC_DEBUG");
#define LOG_DBG(...) \
do { if (RPC_DEBUG) GGML_LOG_DEBUG(__VA_ARGS__); } while (0)
namespace fs = std::filesystem;
static constexpr size_t MAX_CHUNK_SIZE = 1024ull * 1024ull * 1024ull; // 1 GiB
@ -47,7 +53,7 @@ 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);
LOG_DBG("[%s] closing socket %d\n", __func__, this->fd);
#ifdef _WIN32
closesocket(this->fd);
#else
@ -265,14 +271,14 @@ static std::shared_ptr<socket_t> socket_connect(const char * host, int port) {
return nullptr;
}
if (!set_no_delay(sockfd)) {
fprintf(stderr, "Failed to set TCP_NODELAY\n");
GGML_LOG_ERROR("Failed to set TCP_NODELAY\n");
return nullptr;
}
addr.sin_family = AF_INET;
addr.sin_port = htons(port);
struct hostent * server = gethostbyname(host);
if (server == NULL) {
fprintf(stderr, "Cannot resolve host '%s'\n", host);
GGML_LOG_ERROR("Cannot resolve host '%s'\n", host);
return nullptr;
}
memcpy(&addr.sin_addr.s_addr, server->h_addr, server->h_length);
@ -289,7 +295,7 @@ static std::shared_ptr<socket_t> socket_accept(sockfd_t srv_sockfd) {
return nullptr;
}
if (!set_no_delay(client_socket_fd)) {
fprintf(stderr, "Failed to set TCP_NODELAY\n");
GGML_LOG_ERROR("Failed to set TCP_NODELAY\n");
return nullptr;
}
return client_socket;
@ -302,11 +308,11 @@ static std::shared_ptr<socket_t> create_server_socket(const char * host, int por
return nullptr;
}
if (!set_reuse_addr(sockfd)) {
fprintf(stderr, "Failed to set SO_REUSEADDR\n");
GGML_LOG_ERROR("Failed to set SO_REUSEADDR\n");
return nullptr;
}
if (inet_addr(host) == INADDR_NONE) {
fprintf(stderr, "Invalid host address: %s\n", host);
GGML_LOG_ERROR("Invalid host address: %s\n", host);
return nullptr;
}
struct sockaddr_in serv_addr;
@ -349,7 +355,7 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) {
return false;
}
if (n == 0) {
GGML_LOG_ERROR("recv returned 0 (peer closed?)\n");
LOG_DBG("recv returned 0 (peer closed?)\n");
return false;
}
bytes_recv += (size_t)n;
@ -383,7 +389,7 @@ static bool recv_msg(sockfd_t sockfd, std::vector<uint8_t> & input) {
try {
input.resize(size);
} catch (const std::bad_alloc & e) {
fprintf(stderr, "Failed to allocate input buffer of size %" PRIu64 "\n", size);
GGML_LOG_ERROR("Failed to allocate input buffer of size %" PRIu64 "\n", size);
return false;
}
return recv_data(sockfd, input.data(), size);
@ -443,11 +449,11 @@ static bool check_server_version(const std::shared_ptr<socket_t> & sock) {
bool status = send_rpc_cmd(sock, RPC_CMD_HELLO, nullptr, 0, &response, sizeof(response));
RPC_STATUS_ASSERT(status);
if (response.major != RPC_PROTO_MAJOR_VERSION || response.minor > RPC_PROTO_MINOR_VERSION) {
fprintf(stderr, "RPC server version mismatch: %d.%d.%d\n", response.major, response.minor, response.patch);
GGML_LOG_ERROR("RPC server version mismatch: %d.%d.%d\n", response.major, response.minor, response.patch);
return false;
}
if (response.minor != RPC_PROTO_MINOR_VERSION || response.patch != RPC_PROTO_PATCH_VERSION) {
fprintf(stderr, "WARNING: RPC server version mismatch: %d.%d.%d\n", response.major, response.minor, response.patch);
GGML_LOG_INFO("WARNING: RPC server version mismatch: %d.%d.%d\n", response.major, response.minor, response.patch);
}
return true;
}
@ -488,7 +494,7 @@ static std::shared_ptr<socket_t> get_socket(const std::string & endpoint) {
if (!check_server_version(sock)) {
return nullptr;
}
GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd);
LOG_DBG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd);
sockets[endpoint] = sock;
return sock;
}
@ -809,7 +815,7 @@ ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) {
}
auto sock = get_socket(endpoint);
if (sock == nullptr) {
fprintf(stderr, "Failed to connect to %s\n", endpoint);
GGML_LOG_ERROR("Failed to connect to %s\n", endpoint);
return nullptr;
}
size_t alignment = get_alignment(sock);
@ -909,7 +915,7 @@ void rpc_server::hello(rpc_msg_hello_rsp & response) {
response.major = RPC_PROTO_MAJOR_VERSION;
response.minor = RPC_PROTO_MINOR_VERSION;
response.patch = RPC_PROTO_PATCH_VERSION;
GGML_PRINT_DEBUG("[%s] version: %d.%d.%d\n", __func__, response.major, response.minor, response.patch);
LOG_DBG("[%s] version: %d.%d.%d\n", __func__, response.major, response.minor, response.patch);
}
bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_msg_get_alloc_size_rsp & response) {
@ -929,7 +935,7 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_
GGML_LOG_ERROR("Null tensor pointer passed to server get_alloc_size function.\n");
return false;
}
LOG_DBG("[%s] buffer: %p, data: %p\n", __func__, (void*)tensor->buffer, tensor->data);
if (tensor->buffer == nullptr) {
//No buffer allocated.
buft = ggml_backend_get_default_buffer_type(backend);
@ -937,7 +943,7 @@ bool rpc_server::get_alloc_size(const rpc_msg_get_alloc_size_req & request, rpc_
buft = tensor->buffer->buft;
}
response.alloc_size = ggml_backend_buft_get_alloc_size(buft,tensor);
response.alloc_size = ggml_backend_buft_get_alloc_size(buft, tensor);
return true;
}
@ -950,29 +956,29 @@ void rpc_server::alloc_buffer(const rpc_msg_alloc_buffer_req & request, rpc_msg_
if (buffer != nullptr) {
response.remote_ptr = reinterpret_cast<uint64_t>(buffer);
response.remote_size = buffer->size;
GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size);
LOG_DBG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, request.size, response.remote_ptr, response.remote_size);
buffers.insert(buffer);
} else {
GGML_LOG_ERROR("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
LOG_DBG("[%s] size: %" PRIu64 " -> failed\n", __func__, request.size);
}
}
void rpc_server::get_alignment(rpc_msg_get_alignment_rsp & response) {
ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
size_t alignment = ggml_backend_buft_get_alignment(buft);
GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment);
LOG_DBG("[%s] alignment: %lu\n", __func__, alignment);
response.alignment = alignment;
}
void rpc_server::get_max_size(rpc_msg_get_max_size_rsp & response) {
ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend);
size_t max_size = ggml_backend_buft_get_max_size(buft);
GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size);
LOG_DBG("[%s] max_size: %lu\n", __func__, max_size);
response.max_size = max_size;
}
bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rpc_msg_buffer_get_base_rsp & response) {
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
LOG_DBG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
if (buffers.find(buffer) == buffers.end()) {
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
@ -984,7 +990,7 @@ bool rpc_server::buffer_get_base(const rpc_msg_buffer_get_base_req & request, rp
}
bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) {
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
LOG_DBG("[%s] remote_ptr: %" PRIx64 "\n", __func__, request.remote_ptr);
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
if (buffers.find(buffer) == buffers.end()) {
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
@ -996,7 +1002,7 @@ bool rpc_server::free_buffer(const rpc_msg_free_buffer_req & request) {
}
bool rpc_server::buffer_clear(const rpc_msg_buffer_clear_req & request) {
GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value);
LOG_DBG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, request.remote_ptr, request.value);
ggml_backend_buffer_t buffer = reinterpret_cast<ggml_backend_buffer_t>(request.remote_ptr);
if (buffers.find(buffer) == buffers.end()) {
GGML_LOG_ERROR("[%s] buffer not found\n", __func__);
@ -1073,7 +1079,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
return false;
}
GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size);
// sanitize tensor->data
{
@ -1096,7 +1102,7 @@ bool rpc_server::set_tensor(const std::vector<uint8_t> & input) {
fs::path cache_file = fs::path(cache_dir) / hash_str;
std::ofstream ofs(cache_file, std::ios::binary);
ofs.write((const char *)data, size);
printf("[%s] saved to '%s'\n", __func__, cache_file.c_str());
GGML_LOG_INFO("[%s] saved to '%s'\n", __func__, cache_file.c_str());
}
ggml_backend_tensor_set(tensor, data, offset, size);
return true;
@ -1142,8 +1148,8 @@ bool rpc_server::set_tensor_hash(const rpc_msg_set_tensor_hash_req & request, rp
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
return false;
}
GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu, hash: %" PRIx64 "\n",
__func__, (void*)tensor->buffer, tensor->data, request.offset, size, request.hash);
LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu, hash: %" PRIx64 "\n",
__func__, (void*)tensor->buffer, tensor->data, request.offset, size, request.hash);
// sanitize tensor->data
{
@ -1177,7 +1183,7 @@ bool rpc_server::init_tensor(const rpc_msg_init_tensor_req & request) {
GGML_LOG_ERROR("Null tensor pointer passed to server init_tensor function.\n");
return false;
}
LOG_DBG("[%s] buffer: %p, data: %p\n", __func__, (void*)tensor->buffer, tensor->data);
// Call the backend's buffer_init_tensor function
ggml_backend_buffer_t buffer = tensor->buffer;
if (buffer && buffer->iface.init_tensor) {
@ -1210,7 +1216,7 @@ bool rpc_server::get_tensor(const rpc_msg_get_tensor_req & request, std::vector<
GGML_LOG_ERROR("[%s] error deserializing tensor\n", __func__);
return false;
}
GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size);
LOG_DBG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %" PRIu64 "\n", __func__, (void*)tensor->buffer, tensor->data, request.offset, request.size);
// sanitize tensor->data
{
@ -1254,7 +1260,7 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
uint64_t dst_buf_sz = (uint64_t) ggml_backend_buffer_get_size(dst->buffer);
if (dst_data + src_size > dst_base + dst_buf_sz) {
GGML_PRINT_DEBUG("[%s] out-of-bounds write in rpc_server::copy_tensor:\n"
GGML_LOG_ERROR("[%s] out-of-bounds write in rpc_server::copy_tensor:\n"
" write range : [0x%" PRIx64 ", 0x%" PRIx64 "]\n"
" buffer base: [0x%" PRIx64 ", 0x%" PRIx64 "]\n",
__func__,
@ -1265,8 +1271,8 @@ bool rpc_server::copy_tensor(const rpc_msg_copy_tensor_req & request, rpc_msg_co
return false;
}
GGML_PRINT_DEBUG("[%s] src->buffer: %p, dst->buffer: %p\n",
__func__, (void*) src->buffer, (void*) dst->buffer);
LOG_DBG("[%s] src->buffer: %p, dst->buffer: %p\n",
__func__, (void*) src->buffer, (void*) dst->buffer);
response.result = ggml_backend_buffer_copy_tensor(src, dst);
return true;
@ -1342,7 +1348,7 @@ bool rpc_server::graph_compute(const std::vector<uint8_t> & input, rpc_msg_graph
return false;
}
const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors));
GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors);
LOG_DBG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors);
size_t buf_size = ggml_tensor_overhead()*(n_nodes + n_tensors) + ggml_graph_overhead_custom(n_nodes, false);
@ -1394,7 +1400,7 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir,
}
// the first command sent by the client must be HELLO
if (cmd != RPC_CMD_HELLO) {
fprintf(stderr, "Expected HELLO command, update client\n");
GGML_LOG_ERROR("Expected HELLO command, update client\n");
return;
}
if (!recv_msg(sockfd, nullptr, 0)) {
@ -1411,7 +1417,7 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir,
}
if (cmd >= RPC_CMD_COUNT) {
// fail fast if the command is invalid
fprintf(stderr, "Unknown command: %d\n", cmd);
GGML_LOG_ERROR("Unknown command: %d\n", cmd);
break;
}
switch (cmd) {
@ -1599,7 +1605,7 @@ static void rpc_serve_client(ggml_backend_t backend, const char * cache_dir,
break;
}
default: {
fprintf(stderr, "Unknown command: %d\n", cmd);
GGML_LOG_ERROR("Unknown command: %d\n", cmd);
return;
}
}

2
ggml/src/ggml-sycl/ggml-sycl.cpp
View File

@ -4271,7 +4271,7 @@ static bool ggml_backend_sycl_device_supports_op(ggml_backend_dev_t dev, const g
return ((op->type == GGML_TYPE_F32 || op->type == GGML_TYPE_F16 || op->type == GGML_TYPE_BF16 ||
op->type == GGML_TYPE_Q8_0 || op->type == GGML_TYPE_Q5_1 || op->type == GGML_TYPE_Q5_0 ||
op->type == GGML_TYPE_Q4_1 || op->type == GGML_TYPE_Q4_0 || op->type == GGML_TYPE_IQ4_NL) &&
(op->src[1]->type == GGML_TYPE_I64));
(op->src[1]->type == GGML_TYPE_I64 || op->src[1]->type == GGML_TYPE_I32));
}
break;
case GGML_OP_CPY:

76
ggml/src/ggml-sycl/set_rows.cpp
View File

@ -16,9 +16,9 @@ convert (const char* src, char* dst) {
*reinterpret_cast<TOut*>(dst) = dst_val;
}
template <typename blockType, int qk, cpy_kernel_t cpyblck>
template <typename TIdx, typename blockType, int qk, cpy_kernel_t cpyblck>
static void set_rows_sycl_q(const char * __restrict__ src0_d,
const int64_t * __restrict__ src1_d,
const TIdx * __restrict__ src1_d,
blockType * __restrict__ dst_d,
// tensor dimensions src0 and src1
const int64_t ne00,
@ -66,7 +66,7 @@ static void set_rows_sycl_q(const char * __restrict__ src0_d,
const size_t src_offset = calculate_offset<3>({ nb01, nb02, nb03 }, { i01, i02, i03 });
const char * src_block = src0_d + src_offset + i00 * sizeof(float);
const size_t src1_offset = calculate_offset<3>({ nb10, nb11, nb12 }, { i10, i11, i12 });
const int64_t dst_row = src1_d[src1_offset / sizeof(int64_t)];
const int64_t dst_row = src1_d[src1_offset / sizeof(TIdx)];
const size_t dst_offset =
calculate_offset<3>({ nb1, nb2, nb3 }, { dst_row, i02, i03 }) + (i00 / qk) * sizeof(blockType);
char * dst_block = reinterpret_cast<char *>(reinterpret_cast<char *>(dst_d) + dst_offset);
@ -78,9 +78,9 @@ static void set_rows_sycl_q(const char * __restrict__ src0_d,
GGML_UNUSED(nb13);
}
template<typename TIn, typename TOut>
template<typename TIn, typename TIdx, typename TOut>
static void k_set_rows(
const char * __restrict__ src0, const int64_t * __restrict__ src1, char * __restrict__ dst,
const char * __restrict__ src0, const TIdx * __restrict__ src1, char * __restrict__ dst,
const int64_t ne00, const int64_t ne01, const int64_t ne02,
const int64_t ne11, const int64_t ne12,
const size_t nb01, const size_t nb02, const size_t nb03,
@ -104,7 +104,7 @@ static void k_set_rows(
const int64_t i11 = i02 % ne11;
const int64_t i10 = i01;
const int64_t dst_row = *(const int64_t *)((const char *)src1 + calculate_offset<3>({nb10, nb11, nb12}, {i10, i11, i12}));
const int64_t dst_row = *(const TIdx *)((const char *)src1 + calculate_offset<3>({nb10, nb11, nb12}, {i10, i11, i12}));
const char * src0_row = src0 + calculate_offset<3>({nb01, nb02, nb03}, {i01, i02, i03});
const char * src_elem = src0_row + i00 * src_type_size;
@ -114,9 +114,9 @@ static void k_set_rows(
convert<TIn, TOut>(src_elem, dst_elem);
}
template<typename TIn, typename TOut>
template<typename TIn, typename TIdx, typename TOut>
static void set_rows_sycl(
const char * src0_d, const int64_t * src1_d, char * dst_d,
const char * src0_d, const TIdx * src1_d, char * dst_d,
const int64_t ne00, const int64_t ne01, const int64_t ne02, const int64_t ne03,
const int64_t ne11, const int64_t ne12, const size_t nb01, const size_t nb02, const size_t nb03,
const size_t nb10, const size_t nb11, const size_t nb12,
@ -132,7 +132,7 @@ static void set_rows_sycl(
stream->parallel_for(
sycl::nd_range<1>(grid_size * block_size, block_size),
[=](sycl::nd_item<1> item_ct1) {
k_set_rows<TIn, TOut>(
k_set_rows<TIn, TIdx, TOut>(
src0_d, src1_d, dst_d,
ne00, ne01, ne02,
ne11, ne12,
@ -147,74 +147,69 @@ static void set_rows_sycl(
);
}
void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
GGML_ASSERT(dst->src[1]->type == GGML_TYPE_I64);
template<typename TIn, typename TIdx>
static void set_rows_sycl(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
const char * src0_d = (const char *)src0->data;
const TIdx * src1_d = (const TIdx *)src1->data;
GGML_TENSOR_BINARY_OP_LOCALS
const int64_t * src1_dd = static_cast<const int64_t *>(src1->data);
dpct::queue_ptr stream = ctx.stream();
switch (dst->type) {
case GGML_TYPE_F32:
set_rows_sycl<float, float>(
(const char *)src0->data, src1_dd, (char *)dst->data,
set_rows_sycl<TIn, TIdx, float>(
src0_d, src1_d, (char *)dst->data,
ne00, ne01, ne02, ne03,
ne11, ne12,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
sizeof(float), sizeof(float),
sizeof(TIn), sizeof(float),
stream
);
break;
case GGML_TYPE_F16:
dpct::has_capability_or_fail(stream->get_device(), { sycl::aspect::fp16 });
set_rows_sycl<float, sycl::half>(
(const char *)src0->data, src1_dd, (char *)dst->data,
set_rows_sycl<TIn, TIdx, sycl::half>(
src0_d, src1_d, (char *)dst->data,
ne00, ne01, ne02, ne03,
ne11, ne12,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
sizeof(float), sizeof(sycl::half),
sizeof(TIn), sizeof(sycl::half),
stream
);
break;
case GGML_TYPE_BF16:
set_rows_sycl<float, sycl::ext::oneapi::bfloat16>(
(const char *)src0->data, src1_dd, (char *)dst->data,
set_rows_sycl<TIn, TIdx, sycl::ext::oneapi::bfloat16>(
src0_d, src1_d, (char *)dst->data,
ne00, ne01, ne02, ne03,
ne11, ne12,
nb01, nb02, nb03,
nb10, nb11, nb12,
nb1, nb2, nb3,
sizeof(float), sizeof(sycl::ext::oneapi::bfloat16),
sizeof(TIn), sizeof(sycl::ext::oneapi::bfloat16),
stream
);
break;
case GGML_TYPE_Q8_0:
set_rows_sycl_q<block_q8_0, QK8_0, cpy_blck_f32_q8_0>((const char *)src0->data, src1_dd, (block_q8_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_q8_0, QK8_0, cpy_blck_f32_q8_0>(src0_d, src1_d, (block_q8_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q5_1:
set_rows_sycl_q<block_q5_1, QK5_1, cpy_blck_f32_q5_1>((const char *)src0->data, src1_dd, (block_q5_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_q5_1, QK5_1, cpy_blck_f32_q5_1>(src0_d, src1_d, (block_q5_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q5_0:
set_rows_sycl_q<block_q5_0, QK5_0, cpy_blck_f32_q5_0>((const char *)src0->data, src1_dd, (block_q5_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_q5_0, QK5_0, cpy_blck_f32_q5_0>(src0_d, src1_d, (block_q5_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q4_1:
set_rows_sycl_q<block_q4_1, QK4_1, cpy_blck_f32_q4_1>((const char *)src0->data, src1_dd, (block_q4_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_q4_1, QK4_1, cpy_blck_f32_q4_1>(src0_d, src1_d, (block_q4_1 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_Q4_0:
set_rows_sycl_q<block_q4_0, QK4_0, cpy_blck_f32_q4_0>((const char *)src0->data, src1_dd, (block_q4_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_q4_0, QK4_0, cpy_blck_f32_q4_0>(src0_d, src1_d, (block_q4_0 *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
case GGML_TYPE_IQ4_NL:
set_rows_sycl_q<block_iq4_nl, QK4_NL, cpy_blck_f32_iq4_nl>((const char *)src0->data, src1_dd, (block_iq4_nl *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
set_rows_sycl_q<TIdx, block_iq4_nl, QK4_NL, cpy_blck_f32_iq4_nl>(src0_d, src1_d, (block_iq4_nl *)dst->data, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nb00, nb01, nb02, nb03, nb10, nb11, nb12, nb13, nb1, nb2, nb3, stream);
break;
default:
@ -222,3 +217,18 @@ void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
break;
}
}
void ggml_sycl_op_set_rows(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);
const ggml_tensor * src0 = dst->src[0];
const ggml_tensor * src1 = dst->src[1];
GGML_ASSERT(dst->src[0]->type == GGML_TYPE_F32);
GGML_ASSERT(dst->src[1]->type == GGML_TYPE_I64 || dst->src[1]->type == GGML_TYPE_I32);
if (src1->type == GGML_TYPE_I64) {
set_rows_sycl<float, int64_t>(ctx, src0, src1, dst);
} else {
set_rows_sycl<float, int32_t>(ctx, src0, src1, dst);
}
}

312
ggml/src/ggml-vulkan/ggml-vulkan.cpp
View File

@ -520,7 +520,8 @@ struct vk_device_struct {
vk_pipeline pipeline_contig_cpy_f32_f32, pipeline_contig_cpy_f32_f16, pipeline_contig_cpy_f16_f16, pipeline_contig_cpy_f16_f32, pipeline_contig_cpy_f32_bf16, pipeline_contig_cpy_f32_i32, pipeline_contig_cpy_i32_f32;
vk_pipeline pipeline_cpy_f32_quant[GGML_TYPE_COUNT];
vk_pipeline pipeline_cpy_quant_f32[GGML_TYPE_COUNT];
vk_pipeline pipeline_set_rows[GGML_TYPE_COUNT];
vk_pipeline pipeline_set_rows_i32[GGML_TYPE_COUNT];
vk_pipeline pipeline_set_rows_i64[GGML_TYPE_COUNT];
vk_pipeline pipeline_norm_f32;
vk_pipeline pipeline_group_norm_f32;
vk_pipeline pipeline_rms_norm_f32;
@ -574,6 +575,8 @@ struct vk_device_struct {
vk_pipeline pipeline_opt_step_sgd_f32;
vk_pipeline pipeline_conv2d_f32[CONV_SHAPE_COUNT];
vk_pipeline pipeline_conv2d_f16_f32[CONV_SHAPE_COUNT];
vk_pipeline pipeline_conv_transpose_2d_f32[CONV_SHAPE_COUNT];
vk_pipeline pipeline_conv_transpose_2d_f16_f32[CONV_SHAPE_COUNT];
vk_pipeline pipeline_conv2d_dw_whcn_f32, pipeline_conv2d_dw_whcn_f16_f32;
vk_pipeline pipeline_conv2d_dw_cwhn_f32, pipeline_conv2d_dw_cwhn_f16_f32;
@ -1117,6 +1120,56 @@ template <> void init_pushconst_fastdiv(vk_op_conv2d_push_constants &p) {
init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL);
}
struct vk_op_conv_transpose_2d_push_constants {
uint32_t Cout;
uint32_t Cin;
uint32_t N;
uint32_t KW;
uint32_t KH;
uint32_t W;
uint32_t H;
uint32_t OW;
uint32_t OH;
uint32_t s0;
uint32_t s1;
uint32_t p0;
uint32_t p1;
uint32_t d0;
uint32_t d1;
uint32_t nb01;
uint32_t nb02;
uint32_t nb03;
uint32_t nb11;
uint32_t nb12;
uint32_t nb13;
uint32_t nb1;
uint32_t nb2;
uint32_t nb3;
// init_fastdiv_values constants for dividing by KW, KW*KH, OW, OW*OH, s0, s1
uint32_t KWmp; uint32_t KWL;
uint32_t KWKHmp; uint32_t KWKHL;
uint32_t OWmp; uint32_t OWL;
uint32_t OWOHmp; uint32_t OWOHL;
uint32_t s0mp; uint32_t s0L;
uint32_t s1mp; uint32_t s1L;
};
template <> void init_pushconst_fastdiv(vk_op_conv_transpose_2d_push_constants &p) {
// Compute magic values to divide by KW, KW*KH, OW, OW*OH, s0, s1
init_fastdiv_values(p.KW, p.KWmp, p.KWL);
init_fastdiv_values(p.KW*p.KH, p.KWKHmp, p.KWKHL);
init_fastdiv_values(p.OW, p.OWmp, p.OWL);
init_fastdiv_values(p.OW*p.OH, p.OWOHmp, p.OWOHL);
init_fastdiv_values(p.s0, p.s0mp, p.s0L);
init_fastdiv_values(p.s1, p.s1mp, p.s1L);
}
struct vk_op_conv2d_dw_push_constants {
uint32_t ne;
uint32_t batches;
@ -1185,6 +1238,14 @@ struct vk_staging_memcpy {
size_t n;
};
struct vk_staging_memset {
vk_staging_memset(void * _dst, uint32_t _val, size_t _n) : dst(_dst), val(_val), n(_n) {}
void * dst;
uint32_t val;
size_t n;
};
struct vk_context_struct {
vk_submission * s;
std::vector<vk_sequence> seqs;
@ -1193,6 +1254,7 @@ struct vk_context_struct {
std::vector<vk_staging_memcpy> in_memcpys;
std::vector<vk_staging_memcpy> out_memcpys;
std::vector<vk_staging_memset> memsets;
vk_command_pool * p {};
};
@ -1313,7 +1375,7 @@ class vk_perf_logger {
flops[name].push_back(m * n * (k + (k - 1)) * batch);
return;
}
if (node->op == GGML_OP_CONV_2D) {
if (node->op == GGML_OP_CONV_2D || node->op == GGML_OP_CONV_TRANSPOSE_2D) {
std::string name = ggml_op_name(node->op);
ggml_tensor * knl = node->src[0];
uint64_t OW = node->ne[0];
@ -1322,7 +1384,7 @@ class vk_perf_logger {
uint64_t Cout = node->ne[2];
uint64_t KW = knl->ne[0];
uint64_t KH = knl->ne[1];
uint64_t Cin = knl->ne[2];
uint64_t Cin = node->src[1]->ne[2];
// KxCRS @ CRSxNPQ = KxNPQ -> M=K, K=CRS, N=NPQ
uint64_t size_M = Cout;
uint64_t size_K = Cin * KW * KH;
@ -1584,7 +1646,9 @@ static void ggml_vk_create_pipeline_func(vk_device& device, vk_pipeline& pipelin
}
vk::ComputePipelineCreateInfo compute_pipeline_create_info(
vk::PipelineCreateFlags{},
device->pipeline_executable_properties_support ?
vk::PipelineCreateFlagBits::eCaptureStatisticsKHR :
vk::PipelineCreateFlags{},
pipeline_shader_create_info,
pipeline->layout);
@ -3285,27 +3349,26 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_cpy_f32_quant[GGML_TYPE_IQ4_NL], "cpy_f32_iq4_nl", cpy_f32_iq4_nl_len, cpy_f32_iq4_nl_data, "main", 2, sizeof(vk_op_unary_push_constants), {32, 1, 1}, {}, 1);
}
#define SET_ROWS(itype, rte) \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F32], "set_rows_f32" #itype, set_rows_f32 ## itype ## rte ## _len, set_rows_f32 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_F16], "set_rows_f16" #itype, set_rows_f16 ## itype ## rte ## _len, set_rows_f16 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_BF16], "set_rows_bf16" #itype, set_rows_bf16 ## itype ## rte ## _len, set_rows_bf16 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_0], "set_rows_q4_0" #itype, set_rows_q4_0 ## itype ## rte ## _len, set_rows_q4_0 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q4_1], "set_rows_q4_1" #itype, set_rows_q4_1 ## itype ## rte ## _len, set_rows_q4_1 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_0], "set_rows_q5_0" #itype, set_rows_q5_0 ## itype ## rte ## _len, set_rows_q5_0 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q5_1], "set_rows_q5_1" #itype, set_rows_q5_1 ## itype ## rte ## _len, set_rows_q5_1 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_Q8_0], "set_rows_q8_0" #itype, set_rows_q8_0 ## itype ## rte ## _len, set_rows_q8_0 ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true); \
ggml_vk_create_pipeline(device, device->pipeline_set_rows ## itype [GGML_TYPE_IQ4_NL], "set_rows_iq4_nl" #itype, set_rows_iq4_nl ## itype ## rte ## _len, set_rows_iq4_nl ## itype ## rte ## _data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
if (device->float_controls_rte_fp16) {
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_F32], "set_rows_f32", set_rows_f32_rte_len, set_rows_f32_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_F16], "set_rows_f16", set_rows_f16_rte_len, set_rows_f16_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_BF16], "set_rows_bf16", set_rows_bf16_rte_len, set_rows_bf16_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q4_0], "set_rows_q4_0", set_rows_q4_0_rte_len, set_rows_q4_0_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q4_1], "set_rows_q4_1", set_rows_q4_1_rte_len, set_rows_q4_1_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q5_0], "set_rows_q5_0", set_rows_q5_0_rte_len, set_rows_q5_0_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q5_1], "set_rows_q5_1", set_rows_q5_1_rte_len, set_rows_q5_1_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q8_0], "set_rows_q8_0", set_rows_q8_0_rte_len, set_rows_q8_0_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_IQ4_NL], "set_rows_iq4_nl", set_rows_iq4_nl_rte_len, set_rows_iq4_nl_rte_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
SET_ROWS(_i32, _rte)
SET_ROWS(_i64, _rte)
} else {
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_F32], "set_rows_f32", set_rows_f32_len, set_rows_f32_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_F16], "set_rows_f16", set_rows_f16_len, set_rows_f16_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_BF16], "set_rows_bf16", set_rows_bf16_len, set_rows_bf16_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q4_0], "set_rows_q4_0", set_rows_q4_0_len, set_rows_q4_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q4_1], "set_rows_q4_1", set_rows_q4_1_len, set_rows_q4_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q5_0], "set_rows_q5_0", set_rows_q5_0_len, set_rows_q5_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q5_1], "set_rows_q5_1", set_rows_q5_1_len, set_rows_q5_1_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_Q8_0], "set_rows_q8_0", set_rows_q8_0_len, set_rows_q8_0_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
ggml_vk_create_pipeline(device, device->pipeline_set_rows[GGML_TYPE_IQ4_NL], "set_rows_iq4_nl", set_rows_iq4_nl_len, set_rows_iq4_nl_data, "main", 3, sizeof(vk_op_binary_push_constants), {1, 1, 1}, {1}, 1, true);
SET_ROWS(_i32, )
SET_ROWS(_i64, )
}
#undef SET_ROWS
ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_0], "cpy_q4_0_f32", cpy_q4_0_f32_len, cpy_q4_0_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_0), 1, 1}, {}, 1);
ggml_vk_create_pipeline(device, device->pipeline_cpy_quant_f32[GGML_TYPE_Q4_1], "cpy_q4_1_f32", cpy_q4_1_f32_len, cpy_q4_1_f32_data, "main", 2, sizeof(vk_op_unary_push_constants), {(uint32_t)ggml_blck_size(GGML_TYPE_Q4_1), 1, 1}, {}, 1);
@ -3380,7 +3443,6 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
CREATE_UNARY(exp)
CREATE_UNARY(gelu)
CREATE_UNARY(gelu_erf)
CREATE_UNARY(gelu_quick)
@ -3392,6 +3454,17 @@ static void ggml_vk_load_shaders(vk_device& device) {
CREATE_UNARY(hardswish)
#undef CREATE_UNARY
#define CREATE_UNARY_RTE(name) \
if (device->float_controls_rte_fp16) { \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32_rte", name ## _f32_rte_len, name ## _f32_rte_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16_rte", name ## _f16_rte_len, name ## _f16_rte_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \
} else { \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32", name ## _f32_len, name ## _f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [1], #name "_f16", name ## _f16_len, name ## _f16_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); \
}
CREATE_UNARY_RTE(exp)
#undef CREATE_UNARY_RTE
#define CREATE_GLU(name) \
if (device->float_controls_rte_fp16) { \
ggml_vk_create_pipeline(device, device->pipeline_ ## name [0], #name "_f32_rte", name ## _f32_rte_len, name ## _f32_rte_data, "main", 3, sizeof(vk_op_glu_push_constants), {512, 1, 1}, {}, 1, true); \
@ -3471,7 +3544,7 @@ static void ggml_vk_load_shaders(vk_device& device) {
ggml_vk_create_pipeline(device, device->pipeline_opt_step_sgd_f32, "opt_step_sgd_f32", opt_step_sgd_f32_len, opt_step_sgd_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1);
// conv2d
// conv2d, conv_transpose_2d
for (uint32_t s = 0; s < CONV_SHAPE_COUNT; ++s) {
uint32_t conv2d_WG_SIZE = 256;
uint32_t conv2d_BS_K = 128;
@ -3546,31 +3619,30 @@ static void ggml_vk_load_shaders(vk_device& device) {
std::array<uint32_t, 3> wg_denoms = { conv2d_BS_K, conv2d_BS_NPQ, 1 };
std::vector<uint32_t> spec_constants = { conv2d_WG_SIZE, conv2d_BS_K, conv2d_BS_CRS, conv2d_BS_NPQ, conv2d_TS_K, use_collectives, conv2d_SHMEM_PAD };
#define CREATE_CONV(name, type_suffix, spv_suffix) \
ggml_vk_create_pipeline( \
device, device->pipeline_##name##type_suffix[s], #name #type_suffix, \
name##type_suffix##spv_suffix##_len, name##type_suffix##spv_suffix##_data, "main", 3, \
sizeof(vk_op_##name##_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
#define CREATE_CONVS(spv_suffix) \
CREATE_CONV(conv2d, _f32, spv_suffix) \
CREATE_CONV(conv2d, _f16_f32, spv_suffix) \
if (device->properties.limits.maxPushConstantsSize >= sizeof(vk_op_conv_transpose_2d_push_constants)) { \
CREATE_CONV(conv_transpose_2d, _f32, spv_suffix) \
CREATE_CONV(conv_transpose_2d, _f16_f32, spv_suffix) \
}
#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
if (device->coopmat2) {
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_cm2_len, conv2d_f32_cm2_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_cm2_len, conv2d_f16_f32_cm2_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
CREATE_CONVS(_cm2)
} else
#endif
if (conv2d_UNROLL) {
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_unroll_len, conv2d_f32_unroll_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_unroll_len, conv2d_f16_f32_unroll_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
CREATE_CONVS(_unroll)
} else {
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f32[s], "conv2d_f32", conv2d_f32_len, conv2d_f32_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
ggml_vk_create_pipeline(
device, device->pipeline_conv2d_f16_f32[s], "conv2d_f16_f32", conv2d_f16_f32_len, conv2d_f16_f32_data, "main", 3,
sizeof(vk_op_conv2d_push_constants), wg_denoms, spec_constants, 1, true, use_collectives);
CREATE_CONVS( )
}
#undef CREATE_CONV
#undef CREATE_CONVS
}
ggml_vk_create_pipeline(device, device->pipeline_conv2d_dw_whcn_f32, "conv2d_dw_whcn_f32", conv2d_dw_whcn_f32_len, conv2d_dw_whcn_f32_data, "main", 3, sizeof(vk_op_conv2d_dw_push_constants), {512, 1, 1}, {}, 1);
@ -5194,6 +5266,14 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect
}
}
static void deferred_memset(void * dst, uint32_t val, size_t size, std::vector<vk_staging_memset>* memsets = nullptr) {
if (memsets == nullptr) {
memset(dst, val, size);
} else {
memsets->emplace_back(dst, val, size);
}
}
static void ggml_vk_ensure_sync_staging_buffer(vk_device& device, size_t size) {
if (device->sync_staging == nullptr || device->sync_staging->size < size) {
VK_LOG_MEMORY("ggml_vk_ensure_sync_staging_buffer(" << size << ")");
@ -5389,6 +5469,10 @@ static void ggml_vk_buffer_write_2d(vk_buffer& dst, size_t offset, const void *
memcpy(cpy.dst, cpy.src, cpy.n);
}
for (auto& mset : subctx->memsets) {
memset(mset.dst, mset.val, mset.n);
}
ggml_vk_submit(subctx, dst->device->fence);
VK_CHECK(dst->device->device.waitForFences({ dst->device->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences");
dst->device->device.resetFences({ dst->device->fence });
@ -5528,12 +5612,25 @@ static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& sr
static void ggml_vk_buffer_memset_async(vk_context& ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
VK_LOG_DEBUG("ggml_vk_buffer_memset_async(" << offset << ", " << c << ", " << size << ")");
if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
dst->device->uma) {
deferred_memset((uint8_t*)dst->ptr + offset, c, size, &ctx->memsets);
return;
}
// Fall back to GPU fillBuffer for non-UMA or non-host-visible buffers
ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c);
}
static void ggml_vk_buffer_memset(vk_buffer& dst, size_t offset, uint32_t c, size_t size) {
VK_LOG_DEBUG("ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")");
if (dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible &&
dst->device->uma) {
memset((uint8_t*)dst->ptr + offset, c, size);
return;
}
std::lock_guard<std::recursive_mutex> guard(dst->device->mutex);
vk_context subctx = ggml_vk_create_temporary_context(dst->device->transfer_queue.cmd_pool);
ggml_vk_ctx_begin(dst->device, subctx);
@ -7502,6 +7599,33 @@ static std::array<uint32_t, 3> ggml_vk_get_conv_elements(const ggml_tensor *dst)
return elements;
}
static std::array<uint32_t, 3> ggml_vk_get_conv_transpose_2d_elements(const ggml_tensor *dst) {
const ggml_tensor *src0 = dst->src[0];
const ggml_tensor *src1 = dst->src[1];
// src0 - kernel: [KW, KH, Cout, Cin]
// src1 - input: [W, H, Cin, N]
// dst - result: [OW, OH, Cout, N]
auto calc_conv_output_size = [](int64_t ins, int64_t ks, int s, int p, int d) -> int64_t {
return (ins - 1) * s - 2 * p + (ks - 1) * d + 1;
};
// parallelize in {OW/BS_K, OH/BS_NPQ, 1}
int64_t W = src1->ne[0];
int64_t H = src1->ne[1];
int64_t KW = src0->ne[0];
int64_t KH = src0->ne[1];
int64_t Cout = src0->ne[2];
int64_t N = src1->ne[3];
int64_t OH = calc_conv_output_size(H, KH, dst->op_params[0], 0, 1);
int64_t OW = calc_conv_output_size(W, KW, dst->op_params[0], 0, 1);
int64_t NPQ = N * OW * OH;
// Tile output matrix to (K/NB_K, NPQ/NB_NPQ, 1) workgroups
std::array<uint32_t, 3> elements = { static_cast<uint32_t>(Cout), static_cast<uint32_t>(NPQ), 1 };
return elements;
}
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, const ggml_tensor * dst, ggml_op op) {
switch (op) {
case GGML_OP_GET_ROWS:
@ -7648,7 +7772,11 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
case GGML_OP_DUP:
return ggml_vk_get_cpy_pipeline(ctx, src0, dst, dst->type);
case GGML_OP_SET_ROWS:
return ctx->device->pipeline_set_rows[dst->type];
if (src1->type == GGML_TYPE_I64) {
return ctx->device->pipeline_set_rows_i64[dst->type];
} else {
return ctx->device->pipeline_set_rows_i32[dst->type];
}
case GGML_OP_SILU_BACK:
if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) {
return ctx->device->pipeline_silu_back_f32;
@ -7879,9 +8007,12 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
}
return nullptr;
case GGML_OP_CONV_2D:
case GGML_OP_CONV_TRANSPOSE_2D:
if (src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 &&
ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)) {
auto elements = ggml_vk_get_conv_elements(dst);
std::array<uint32_t, 3> elements;
if (op == GGML_OP_CONV_2D) elements = ggml_vk_get_conv_elements(dst);
else if (op == GGML_OP_CONV_TRANSPOSE_2D) elements = ggml_vk_get_conv_transpose_2d_elements(dst);
vk_conv_shapes shape;
uint32_t tiles[CONV_SHAPE_COUNT];
@ -7901,10 +8032,18 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
shape = CONV_SHAPE_64x32;
}
if (src0->type == GGML_TYPE_F32) {
return ctx->device->pipeline_conv2d_f32[shape];
} else if (src0->type == GGML_TYPE_F16) {
return ctx->device->pipeline_conv2d_f16_f32[shape];
if (op == GGML_OP_CONV_2D) {
if (src0->type == GGML_TYPE_F32) {
return ctx->device->pipeline_conv2d_f32[shape];
} else if (src0->type == GGML_TYPE_F16) {
return ctx->device->pipeline_conv2d_f16_f32[shape];
}
} else if (op == GGML_OP_CONV_TRANSPOSE_2D) {
if (src0->type == GGML_TYPE_F32) {
return ctx->device->pipeline_conv_transpose_2d_f32[shape];
} else if (src0->type == GGML_TYPE_F16) {
return ctx->device->pipeline_conv_transpose_2d_f16_f32[shape];
}
}
}
return nullptr;
@ -8304,6 +8443,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
{
elements = ggml_vk_get_conv_elements(dst);
} break;
case GGML_OP_CONV_TRANSPOSE_2D:
{
elements = ggml_vk_get_conv_transpose_2d_elements(dst);
} break;
case GGML_OP_ADD:
case GGML_OP_SUB:
case GGML_OP_DIV:
@ -9477,6 +9620,55 @@ static void ggml_vk_conv_2d(ggml_backend_vk_context * ctx, vk_context & subctx,
ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONV_2D, std::move(p), dryrun);
}
static void ggml_vk_conv_transpose_2d(ggml_backend_vk_context * ctx, vk_context & subctx, const ggml_tensor * src0,
const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16);
GGML_ASSERT(src1->type == GGML_TYPE_F32);
GGML_ASSERT(dst->type == GGML_TYPE_F32);
GGML_TENSOR_BINARY_OP_LOCALS
GGML_ASSERT(nb00 == sizeof(float) || nb00 == sizeof(ggml_fp16_t));
GGML_ASSERT(nb10 == sizeof(float));
GGML_ASSERT(nb0 == sizeof(float));
vk_op_conv_transpose_2d_push_constants p{};
p.Cout = static_cast<uint32_t>(ne02);
p.Cin = static_cast<uint32_t>(ne03);
p.N = static_cast<uint32_t>(ne13);
p.KW = static_cast<uint32_t>(ne00);
p.KH = static_cast<uint32_t>(ne01);
p.W = static_cast<uint32_t>(ne10);
p.H = static_cast<uint32_t>(ne11);
p.OW = static_cast<uint32_t>(ne0);
p.OH = static_cast<uint32_t>(ne1);
p.s0 = static_cast<uint32_t>(dst->op_params[0]);
p.s1 = static_cast<uint32_t>(dst->op_params[0]);
p.p0 = 0;
p.p1 = 0;
p.d0 = 1;
p.d1 = 1;
p.nb01 = static_cast<uint32_t>(nb01 / nb00);
p.nb02 = static_cast<uint32_t>(nb02 / nb00);
p.nb03 = static_cast<uint32_t>(nb03 / nb00);
p.nb11 = static_cast<uint32_t>(nb11 / nb10);
p.nb12 = static_cast<uint32_t>(nb12 / nb10);
p.nb13 = static_cast<uint32_t>(nb13 / nb10);
p.nb1 = static_cast<uint32_t>(nb1 / nb0);
p.nb2 = static_cast<uint32_t>(nb2 / nb0);
p.nb3 = static_cast<uint32_t>(nb3 / nb0);
GGML_ASSERT(ne02 == ne2);
GGML_ASSERT(ne03 == ne12);
ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_CONV_TRANSPOSE_2D, std::move(p), dryrun);
}
static void ggml_vk_conv_2d_dw(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, bool dryrun = false) {
vk_op_conv2d_dw_push_constants p{};
p.ne = ggml_nelements(dst);
@ -10569,6 +10761,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_RWKV_WKV6:
case GGML_OP_RWKV_WKV7:
@ -10640,6 +10833,7 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_LEAKY_RELU:
case GGML_OP_OPT_STEP_SGD:
@ -10951,6 +11145,10 @@ static bool ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_cgraph * cgr
case GGML_OP_CONV_2D:
ggml_vk_conv_2d(ctx, compute_ctx, src0, src1, node, dryrun);
break;
case GGML_OP_CONV_TRANSPOSE_2D:
ggml_vk_conv_transpose_2d(ctx, compute_ctx, src0, src1, node, dryrun);
break;
case GGML_OP_CONV_2D_DW:
ggml_vk_conv_2d_dw(ctx, compute_ctx, src0, src1, node, dryrun);
@ -11091,6 +11289,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
case GGML_OP_CONV_TRANSPOSE_1D:
case GGML_OP_POOL_2D:
case GGML_OP_CONV_2D:
case GGML_OP_CONV_TRANSPOSE_2D:
case GGML_OP_CONV_2D_DW:
case GGML_OP_RWKV_WKV6:
case GGML_OP_RWKV_WKV7:
@ -11168,6 +11367,10 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
memcpy(cpy.dst, cpy.src, cpy.n);
}
for (auto& mset : subctx->memsets) {
memset(mset.dst, mset.val, mset.n);
}
if (almost_ready && !ctx->almost_ready_fence_pending && !use_fence) {
ggml_vk_submit(subctx, ctx->almost_ready_fence);
ctx->almost_ready_fence_pending = true;
@ -11190,6 +11393,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_cgraph *
}
subctx->in_memcpys.clear();
subctx->out_memcpys.clear();
subctx->memsets.clear();
}
return true;
@ -11743,10 +11947,10 @@ static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cg
ggml_vk_build_graph(ctx, cgraph, i, nullptr, 0, true, false, false, false);
if (cgraph->nodes[i]->op == GGML_OP_MUL_MAT || cgraph->nodes[i]->op == GGML_OP_MUL_MAT_ID) {
total_mat_mul_bytes += ggml_nbytes(cgraph->nodes[i]->src[0]);
} else if (cgraph->nodes[i]->op == GGML_OP_CONV_2D) {
} else if (cgraph->nodes[i]->op == GGML_OP_CONV_2D || cgraph->nodes[i]->op == GGML_OP_CONV_TRANSPOSE_2D) {
// Return CRSxNPQxsizeof(*) to account as many bytes as mul_mat has in im2col->mul_mat mode.
auto CRS_size =
cgraph->nodes[i]->src[0]->ne[0] * cgraph->nodes[i]->src[0]->ne[1] * cgraph->nodes[i]->src[0]->ne[2];
cgraph->nodes[i]->src[0]->ne[0] * cgraph->nodes[i]->src[0]->ne[1] * cgraph->nodes[i]->src[1]->ne[2];
auto NPQ_size = cgraph->nodes[i]->ne[0] * cgraph->nodes[i]->ne[1] * cgraph->nodes[i]->ne[3];
total_mat_mul_bytes += NPQ_size * CRS_size * ggml_type_size(cgraph->nodes[i]->type);
}
@ -12567,10 +12771,15 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
case GGML_OP_CONV_TRANSPOSE_1D:
return op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32;
case GGML_OP_CONV_2D:
case GGML_OP_CONV_TRANSPOSE_2D:
{
// Op is disabled for Apple because it segfaults at pipeline create time on MoltenVK
ggml_backend_vk_device_context * ctx = (ggml_backend_vk_device_context *)dev->context;
const vk_device& device = ggml_vk_get_device(ctx->device);
if (op->op == GGML_OP_CONV_TRANSPOSE_2D &&
device->properties.limits.maxPushConstantsSize < sizeof(vk_op_conv_transpose_2d_push_constants)) {
return false;
}
// Channel-contiguous format is not supported yet.
return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
op->src[1]->type == GGML_TYPE_F32 &&
@ -13189,6 +13398,9 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_cgraph *
const int32_t d0 = tensor->op_params[4];
const int32_t d1 = tensor->op_params[5];
tensor_clone = ggml_conv_2d(ggml_ctx, src_clone[0], src_clone[1], s0, s1, p0, p1, d0, d1);
} else if (tensor->op == GGML_OP_CONV_TRANSPOSE_2D) {
const int32_t s = tensor->op_params[0];
tensor_clone = ggml_conv_transpose_2d_p0(ggml_ctx, src_clone[0], src_clone[1], s);
} else if (tensor->op == GGML_OP_LEAKY_RELU) {
const float * op_params = (const float *)tensor->op_params;
tensor_clone = ggml_leaky_relu(ggml_ctx, src_clone[0], op_params[0], false);

24
ggml/src/ggml-vulkan/vulkan-shaders/conv2d_mm.comp
View File

@ -16,7 +16,7 @@
// shape notation: [dim(N), ..., dim(0)] -- stride(dim(j)) >= stride(dim(i)) if i > j
layout(binding = 0) readonly buffer A {
A_TYPE knl_data[];
}; // src0 - kernel: [KW, KH, Cin, Cout]
}; // src0 - kernel: [KW, KH, Cin, Cout] for conv_2d, [KW, KH, Cout, Cin] for conv_transposed_2d
layout(binding = 1) readonly buffer B {
B_TYPE src_data[];
@ -66,6 +66,10 @@ layout(push_constant) uniform parameter {
uint32_t KWKHmp; uint32_t KWKHL;
uint32_t OWmp; uint32_t OWL;
uint32_t OWOHmp; uint32_t OWOHL;
#ifdef TRANSPOSE
uint32_t s0mp; uint32_t s0L;
uint32_t s1mp; uint32_t s1L;
#endif
}
p;
@ -225,7 +229,11 @@ void main() {
uint32_t B_ly = r_offset + Ar;
uint32_t B_lx = Ac;
uint32_t K_idx = B_idx_K * BS_K + B_ly; /* Global K_idx (row index of A)*/
#ifdef TRANSPOSE
uint32_t knl_idx = min(KW_idx_a + KH_idx_a * p.nb01 + K_idx * p.nb02 + Cin_idx_a * p.nb03, K * CRS - 1);
#else
uint32_t knl_idx = min(KW_idx_a + KH_idx_a * p.nb01 + Cin_idx_a * p.nb02 + K_idx * p.nb03, K * CRS - 1);
#endif
float val = knl_data[knl_idx];
if (K_idx >= K || CRS_idx_a >= CRS) {
val = 0.0;
@ -267,12 +275,24 @@ void main() {
KW_idx_b = CRS_remainder - KH_idx_b * p.KW;
#endif
#ifdef TRANSPOSE
uint32_t H_idx_x_s1 = OH_idx - KH_idx_b * p.d1 + p.p1;
uint32_t W_idx_x_s0 = OW_idx - KW_idx_b * p.d0 + p.p0;
uint32_t H_idx = fastdiv(H_idx_x_s1, p.s1mp, p.s1L);
uint32_t W_idx = fastdiv(W_idx_x_s0, p.s0mp, p.s0L);
#else
uint32_t H_idx = OH_idx * p.s1 + KH_idx_b * p.d1 - p.p1;
uint32_t W_idx = OW_idx * p.s0 + KW_idx_b * p.d0 - p.p0;
#endif
uint32_t src_idx =
min(max(W_idx + H_idx * p.nb11 + Cin_idx_b * p.nb12 + N_idx * p.nb13, 0), p.Cin * p.N * p.W * p.H - 1);
float val = src_data[src_idx];
if (CRS_idx_b >= CRS || NPQ_idx >= NPQ || H_idx < 0 || H_idx >= p.H || W_idx < 0 || W_idx >= p.W) {
if (CRS_idx_b >= CRS || NPQ_idx >= NPQ
|| H_idx >= p.H || W_idx >= p.W // Lower bound checks aren't necessary. (idx >= 0x80000000 for such case)
#ifdef TRANSPOSE
|| (H_idx_x_s1 - H_idx * p.s1 != 0) || (W_idx_x_s0 - W_idx * p.s0 != 0)
#endif
) {
val = 0.0;
}
Bsh[B_ly * Bsh_stride + B_lx] = SHMEM_TYPE(val);

11
ggml/src/ggml-vulkan/vulkan-shaders/copy_to_quant.comp
View File

@ -15,8 +15,15 @@ layout (binding = 0) readonly buffer S {float data_s[];};
#if defined(SET_ROWS)
#include "generic_binary_head.comp"
layout (binding = 1) readonly buffer C {uvec2 data_i[];};
layout (binding = 1) readonly buffer C {B_TYPE data_i[];};
layout (binding = 2) writeonly buffer Q {A_TYPE data_q[];};
#if B_SIZE == 64
#define DATA_I_SWIZZLE .x
#else
#define DATA_I_SWIZZLE
#endif
#else
#include "generic_unary_head.comp"
layout (binding = 1) writeonly buffer Q {A_TYPE data_q[];};
@ -259,7 +266,7 @@ void main() {
uint i11 = fastmod(i02, p.ne11);
uint i10 = i01;
uint i1 = data_i[src1_idx(i10, i11, i12, 0) + get_boffset()].x;
uint i1 = data_i[src1_idx(i10, i11, i12, 0) + get_boffset()] DATA_I_SWIZZLE;
uint src0_idx = src0_idx(i00, i01, i02, i03) + get_aoffset();
uint dst_idx = dst_idx(i00 / QUANT_K, i1, i02, i03) + get_doffset();

1
ggml/src/ggml-vulkan/vulkan-shaders/exp.comp
View File

@ -1,5 +1,6 @@
#version 450
#include "rte.comp"
#include "generic_head.comp"
#include "types.comp"

28
ggml/src/ggml-vulkan/vulkan-shaders/mul_mm.comp
View File

@ -31,10 +31,22 @@
#include "types.comp"
#ifndef LOAD_VEC_A
#define LOAD_VEC_A 2
#define LOAD_VEC_A 1
#endif
#ifndef LOAD_VEC_B
#define LOAD_VEC_B 2
#define LOAD_VEC_B 1
#endif
// Load 2 values at once without affecting index calculations through LOAD_VEC
#if (defined(DATA_A_F32) || defined(DATA_A_F16) || defined(DATA_A_BF16)) && !defined(ALIGNED)
#define LOAD_VEC_BATCH_A 2
#else
#define LOAD_VEC_BATCH_A 1
#endif
#if !defined(ALIGNED)
#define LOAD_VEC_BATCH_B 2
#else
#define LOAD_VEC_BATCH_B 1
#endif
#if !defined(TO_FLOAT_TYPE)
@ -236,13 +248,13 @@ void main() {
const uint warp_r = warp_i % (BM / WM);
const uint warp_c = warp_i / (BM / WM);
const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A);
const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A);
const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B);
const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B);
const uint loadr_a = gl_LocalInvocationID.x % (BK / LOAD_VEC_A / LOAD_VEC_BATCH_A);
const uint loadc_a = gl_LocalInvocationID.x / (BK / LOAD_VEC_A / LOAD_VEC_BATCH_A);
const uint loadr_b = gl_LocalInvocationID.x % (BK / LOAD_VEC_B / LOAD_VEC_BATCH_B);
const uint loadc_b = gl_LocalInvocationID.x / (BK / LOAD_VEC_B / LOAD_VEC_BATCH_B);
const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A / BK;
const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B / BK;
const uint loadstride_a = gl_WorkGroupSize.x * LOAD_VEC_A * LOAD_VEC_BATCH_A / BK;
const uint loadstride_b = gl_WorkGroupSize.x * LOAD_VEC_B * LOAD_VEC_BATCH_B / BK;
#ifdef MUL_MAT_ID
#ifdef MUL_MAT_ID_USE_SUBGROUPS

18
ggml/src/ggml-vulkan/vulkan-shaders/mul_mm_funcs.comp
View File

@ -14,8 +14,8 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
FLOAT_TYPE_VEC4 aa = FLOAT_TYPE_VEC4(data_a[idx]);
buf_a[buf_idx ] = aa.xy;
buf_a[buf_idx + 1] = aa.zw;
#else // LOAD_VEC_A == 2
const uint idx = pos_a * 2 + col * p.stride_a + row * 2;
#else // LOAD_VEC_BATCH_A == 2
const uint idx = pos_a + col * p.stride_a + row * 2;
const uint buf_idx = col * SHMEM_STRIDE + row;
if (idx_m < p.M && block + row * 2 + 1 < end_k) {
buf_a[buf_idx] = FLOAT_TYPE_VEC2(data_a[idx],
@ -33,8 +33,8 @@ void load_a_to_shmem(const uint pos_a, const uint row, const uint col, const uin
FLOAT_TYPE_VEC4 aa = FLOAT_TYPE_VEC4(TO_FLOAT_TYPE(data_a[idx]));
buf_a[buf_idx ] = aa.xy;
buf_a[buf_idx + 1] = aa.zw;
#else // LOAD_VEC_A == 2
const uint idx = pos_a * 2 + col * p.stride_a + row * 2;
#else // LOAD_VEC_BATCH_A == 2
const uint idx = pos_a + col * p.stride_a + row * 2;
const uint buf_idx = col * SHMEM_STRIDE + row;
if (idx_m < p.M && block + row * 2 + 1 < end_k) {
buf_a[buf_idx] = FLOAT_TYPE_VEC2(TO_FLOAT_TYPE(data_a[idx]),
@ -500,8 +500,8 @@ void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uin
#endif
buf_b[buf_idx + 0] = bb.xy;
buf_b[buf_idx + 1] = bb.zw;
#else // LOAD_VEC_B == 2
const uint idx = pos_b * 2 + col * p.stride_b + row * 2;
#else // LOAD_VEC_BATCH_B == 2
const uint idx = pos_b + col * p.stride_b + row * 2;
const uint buf_idx = col * SHMEM_STRIDE + row;
if (idx_n < p.N && block + row * 2 + 1 < end_k) {
buf_b[buf_idx] = FLOAT_TYPE_VEC2(TO_FLOAT_TYPE(data_b[idx]),
@ -536,17 +536,17 @@ void load_b_to_shmem(const uint pos_b, const uint row, const uint col, const uin
#endif
buf_b[buf_idx + 0] = bb.xy;
buf_b[buf_idx + 1] = bb.zw;
#else // LOAD_VEC_B == 2
#else // LOAD_VEC_BATCH_B == 2
const uint row_i = ic * BN + col;
const uint buf_idx = col * SHMEM_STRIDE + row;
if (row_i < _ne1 && block + row * 2 + 1 < end_k) {
const u16vec2 row_idx = row_ids[col];
const uint idx = pos_b * 2 + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
buf_b[buf_idx] = FLOAT_TYPE_VEC2(TO_FLOAT_TYPE(data_b[idx]),
TO_FLOAT_TYPE(data_b[idx + 1]));
} else if (row_i < _ne1 && block + row * 2 < end_k) {
const u16vec2 row_idx = row_ids[col];
const uint idx = pos_b * 2 + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
const uint idx = pos_b + row_idx.y * p.batch_stride_b + (row_idx.x % p.ne11) * p.stride_b + row * 2;
buf_b[buf_idx] = FLOAT_TYPE_VEC2(TO_FLOAT_TYPE(data_b[idx]), 0.0f);
} else {
buf_b[buf_idx] = FLOAT_TYPE_VEC2(0.0f);

41
ggml/src/ggml-vulkan/vulkan-shaders/vulkan-shaders-gen.cpp
View File

@ -454,7 +454,7 @@ void matmul_shaders(bool fp16, MatMulIdType matmul_id_type, bool coopmat, bool c
std::string data_a_key = "DATA_A_" + to_uppercase(tname);
// For unaligned, load one at a time for f32/f16, or two at a time for quants
std::string load_vec_a_unaligned = coopmat2 ? "1" : (tname == "f32" || tname == "f16" || tname == "bf16") ? "2" : load_vec_quant;
std::string load_vec_a_unaligned = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? "1" : load_vec_quant;
// For aligned matmul loads
std::string load_vec_a = (coopmat2 || tname == "f32" || tname == "f16" || tname == "bf16") ? load_vec : load_vec_quant;
@ -635,8 +635,10 @@ void process_shaders() {
}
for (std::string t : {"f32", "f16", "bf16", "q4_0", "q4_1", "q5_0", "q5_1", "q8_0", "iq4_nl"}) {
string_to_spv("set_rows_" + t, "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
string_to_spv("set_rows_" + t + "_rte", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}});
string_to_spv("set_rows_" + t + "_i32", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
string_to_spv("set_rows_" + t + "_i32_rte", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uint"}, {"B_SIZE", "32"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}});
string_to_spv("set_rows_" + t + "_i64", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}});
string_to_spv("set_rows_" + t + "_i64_rte", "copy_to_quant.comp", {{"SET_ROWS", "1"}, {"DATA_A_" + to_uppercase(t), "1"}, {"B_TYPE", "uvec2"}, {"B_SIZE", "64"}, {"D_TYPE", "float"}, {"FLOAT_TYPE", "float"}, {"RTE16", "1"}});
}
auto get_type_str = [](bool f16) {
@ -704,8 +706,11 @@ void process_shaders() {
string_to_spv("upscale_f32", "upscale.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("exp_f16", "exp.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("exp_f32", "exp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
for (auto rte : {false, true}) {
std::string suffix = rte ? "_rte" : "";
string_to_spv("exp_f16" + suffix, "exp.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}, {"RTE16", rte ? "1" : "0"}});
string_to_spv("exp_f32" + suffix, "exp.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"} , {"RTE16", rte ? "1" : "0"}});
}
string_to_spv("gelu_f16", "gelu.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
string_to_spv("gelu_f32", "gelu.comp", {{"A_TYPE", "float"}, {"D_TYPE", "float"}});
string_to_spv("gelu_erf_f16", "gelu_erf.comp", {{"A_TYPE", "float16_t"}, {"D_TYPE", "float16_t"}});
@ -793,16 +798,26 @@ void process_shaders() {
string_to_spv("opt_step_adamw_f32", "opt_step_adamw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
string_to_spv("opt_step_sgd_f32", "opt_step_sgd.comp", merge_maps(base_dict, {{"A_TYPE", "float"}}));
string_to_spv("conv2d_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
string_to_spv("conv2d_f16_f32_unroll", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}});
string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});
string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", ""}});
for (auto transpose : {false, true}) {
for (auto unroll : {false, true}) {
for (auto a_f16 : {false, true}) {
std::map<std::string, std::string> defines = {
{"A_TYPE", a_f16 ? "float16_t" : "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"},
{"USE_COLLECTIVES", "1"}, {"UNROLL", unroll ? "[[unroll]]" : ""},
};
if (transpose) defines["TRANSPOSE"] = "1";
std::string name = std::string(transpose ? "conv_transpose_2d": "conv2d")
+ (a_f16 ? "_f16" : "") + "_f32";
string_to_spv(name + (unroll ? "_unroll" : ""), "conv2d_mm.comp", defines);
#if defined(GGML_VULKAN_COOPMAT2_GLSLC_SUPPORT)
string_to_spv("conv2d_f32", "conv2d_mm.comp", {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}, {"COOPMAT2", "1"}}, true, false, true);
string_to_spv("conv2d_f16_f32", "conv2d_mm.comp", {{"A_TYPE", "float16_t"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"USE_COLLECTIVES", "1"}, {"UNROLL", "[[unroll]]"}, {"COOPMAT2", "1"}}, true, false, true);
if (unroll) {
defines["COOPMAT2"] = "1";
string_to_spv(name, "conv2d_mm.comp", defines, true, false, true);
}
#endif
}
}
}
string_to_spv("conv2d_dw_whcn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"WHCN", "1"}}));
string_to_spv("conv2d_dw_cwhn_f32", "conv2d_dw.comp", merge_maps(base_dict, {{"A_TYPE", "float"}, {"B_TYPE", "float"}, {"D_TYPE", "float"}, {"CWHN", "1"}}));

2
ggml/src/ggml-webgpu/ggml-webgpu.cpp
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@ -1310,7 +1310,7 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
break;
case GGML_OP_CPY:
case GGML_OP_SET_ROWS:
supports_op = (op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32);
supports_op = (op->type == GGML_TYPE_F16 && op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_I64);
break;
case GGML_OP_GET_ROWS:
if (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16 ||

1
ggml/src/ggml-zdnn/.gitignore vendored Normal file
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@ -0,0 +1 @@
zdnn.h

59
ggml/src/ggml-zdnn/common.hpp Normal file
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@ -0,0 +1,59 @@
#ifndef GGML_ZDNN_COMMON_HPP
#define GGML_ZDNN_COMMON_HPP
#include "ggml.h"
#include "ggml-impl.h"
#include "zdnn.h"
#include <vector>
#include <memory>
#define GGML_ZDNN_NAME "zDNN"
#define GGML_ZDNN_VERSION ZDNN_VERNUM
#define ZDNN_CHECK(stmt) \
do { \
zdnn_status status = (stmt); \
GGML_ASSERT(status == ZDNN_OK); \
} while (0);
struct ggml_backend_zdnn_device_context {
int zdnn_device;
int zdnn_device_ref_count;
bool has_parmblkformat_0;
bool has_parmblkformat_1; // checks for z17
size_t max_size;
char name[128];
};
struct ggml_backend_zdnn_context {
int device;
ggml_cgraph * gf;
};
struct ggml_backend_zdnn_buffer {
void * data;
ggml_backend_zdnn_buffer * extra; // for bias, etc.
size_t size;
zdnn_tensor_desc pre_tfm_desc;
zdnn_tensor_desc tfm_desc;
zdnn_ztensor ztensor;
char name[GGML_MAX_NAME];
};
struct ggml_backend_zdnn_buffer_context {
void * all_data;
size_t all_size;
bool owned;
int n_buffers;
std::vector<std::unique_ptr<ggml_backend_zdnn_buffer>> buffers;
};
#endif // GGML_ZDNN_COMMON_HPP

98
ggml/src/ggml-zdnn/ggml-zdnn-impl.h
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@ -1,98 +0,0 @@
#ifndef GGML_ZDNN_IMPL
#define GGML_ZDNN_IMPL
#include "zdnn.h"
#include "ggml.h"
#include "ggml-zdnn.h"
#include <vector>
#include <memory>
#include <vecintrin.h>
#define GGML_ZDNN_NAME "zDNN"
#define GGML_ZDNN_VERSION ZDNN_VERNUM
#define vec_neg(a) (-(a)) // Vector Negate
#define vec_add(a, b) ((a) + (b)) // Vector Add
#define vec_sub(a, b) ((a) - (b)) // Vector Subtract
#define vec_mul(a, b) ((a) * (b)) // Vector Multiply
#define vec_div(a, b) ((a) / (b)) // Vector Divide
#define vec_sl(a, b) ((a) << (b)) // Vector Shift Left
#define vec_sra(a, b) ((a) >> (b)) // Vector Shift Right
#define vec_sr(a, b) ((a) >> (b)) // Vector Shift Right Algebraic
#define vec_slo(a, b) vec_slb(a, (b) << 64) // Vector Shift Left by Octet
#define vec_sro(a, b) vec_srb(a, (b) << 64) // Vector Shift Right by Octet
#ifndef vec_and
#define vec_and(a, b) ((a) & (b)) // Vector AND
#endif
#ifndef vec_or
#define vec_or(a, b) ((a) | (b)) // Vector OR
#endif
#ifndef vec_xor
#define vec_xor(a, b) ((a) ^ (b)) // Vector XOR
#endif
typedef signed char char8x16_t __attribute__((vector_size(16)));
typedef unsigned char uchar8x16_t __attribute__((vector_size(16)));
typedef int8_t int8x16_t __attribute__((vector_size(16)));
typedef int16_t int16x8_t __attribute__((vector_size(16)));
typedef int32_t int32x4_t __attribute__((vector_size(16)));
typedef uint8_t uint8x16_t __attribute__((vector_size(16)));
typedef uint16_t uint16x8_t __attribute__((vector_size(16)));
typedef uint32_t uint32x4_t __attribute__((vector_size(16)));
typedef float float32x4_t __attribute__((vector_size(16)));
typedef double double64x2_t __attribute__((vector_size(16)));
typedef signed long long long64x2_t __attribute__((vector_size(16)));
typedef unsigned long long ulong64x2_t __attribute__((vector_size(16)));
#define ZDNN_CHECK(stmt) \
do { \
zdnn_status status = (stmt); \
GGML_ASSERT(status == ZDNN_OK); \
} while (0);
struct ggml_backend_zdnn_device_context {
int zdnn_device;
int zdnn_device_ref_count;
bool has_parmblkformat_0;
bool has_parmblkformat_1;
size_t max_size;
char name[128];
};
struct ggml_backend_zdnn_context {
int device;
ggml_cgraph * gf;
};
struct ggml_backend_zdnn_buffer {
void * data;
ggml_backend_zdnn_buffer * extra; // for bias, etc.
size_t size;
zdnn_tensor_desc pre_tfm_desc;
zdnn_tensor_desc tfm_desc;
zdnn_ztensor ztensor;
char name[GGML_MAX_NAME];
};
struct ggml_backend_zdnn_buffer_context {
void * all_data;
size_t all_size;
bool owned;
int n_buffers;
std::vector<std::unique_ptr<ggml_backend_zdnn_buffer>> buffers;
};
#endif // GGML_ZDNN_IMPL

193
ggml/src/ggml-zdnn/ggml-zdnn.cpp
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@ -1,187 +1,38 @@
#include "zdnn.h"
#include "ggml-zdnn.h"
#include "ggml-zdnn-impl.h"
#include "ggml-impl.h"
#include "ggml-backend-impl.h"
#include "ggml-zdnn/common.hpp"
#include "ggml-zdnn/mmf.hpp"
#include "ggml-zdnn/utils.hpp"
#include "ggml.h"
#include <vector>
#include <memory>
#include <csignal>
#include <csignal> // raise(SIGTRAP)
#include <unistd.h>
inline zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return FP32;
case GGML_TYPE_F16:
return FP16;
case GGML_TYPE_BF16:
return BFLOAT;
case GGML_TYPE_I8:
return INT8;
case GGML_TYPE_I32:
return INT32;
case GGML_TYPE_Q8_0:
return INT8;
default:
GGML_ABORT("%s: fatal: unable to determine zTensor data type",
__func__);
break;
}
static void ggml_zdnn_compute_forward_mul_mat(
const ggml_backend_zdnn_context * ctx,
ggml_tensor * dst) {
const ggml_tensor * src0 = dst->src[0]; // weights
const ggml_tensor * src1 = dst->src[1]; // inputs
// TODO: implement support for quantized types
// we currently only support f32, f16, and bf16
ggml_zdnn_mul_mat_f(ctx, src0, src1, dst);
}
inline void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc,
zdnn_tensor_desc & tfm_desc,
zdnn_ztensor & ztensor,
const ggml_tensor * src,
const int64_t * ne,
const zdnn_data_layouts layout) {
zdnn_init_pre_transformed_desc(
layout,
ggml_zdnn_type_mapping(src->type),
&pre_tfm_desc,
ne[3], ne[2], ne[1], ne[0]
);
static bool ggml_zdnn_compute_forward(
ggml_backend_zdnn_context * ctx,
ggml_tensor * dst) {
ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
}
inline void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor,
void * buffer) {
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
}
inline void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
switch (tensor->op) {
case GGML_OP_MUL_MAT:
{
zdnn_init_pre_transformed_desc(
ZDNN_2D,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[1], tensor->ne[0]
);
} break;
default:
{
// For 4D tensors, GGML uses NCHW layout. However, because zDNN
// automatically transforms everything to NHWC, we will use it
// directly to avoid the performance penalty changing the
// layout and reshaping the tensor.
zdnn_init_pre_transformed_desc(
ZDNN_NHWC,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
);
// TODO: Consider adding a ggml check.
// TODO: If tensor = 4D, use ZDNN_NCHW by default.
// TODO: If tensor = 2D, use ZDNN_NHWC by default.
} break;
}
ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
}
static void ggml_zdnn_mul_mat_op(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
GGML_TENSOR_BINARY_OP_LOCALS;
const enum ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
const ggml_tensor * weights = src0;
const ggml_tensor * inputs = src1;
ggml_tensor * output = dst;
ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
ggml_backend_zdnn_buffer * inputs_extra = (ggml_backend_zdnn_buffer *)inputs->extra;
ggml_backend_zdnn_buffer * output_extra = (ggml_backend_zdnn_buffer *)output->extra;
ggml_backend_zdnn_buffer * bias_extra = (ggml_backend_zdnn_buffer *)output_extra->extra;
const int64_t weights_rows = ne01;
const int64_t weights_cols = ne00;
const int64_t inputs_rows = ne11;
const int64_t inputs_cols = ne10;
assert(inputs_cols == weights_cols);
const int64_t output_rows = ne1;
const int64_t output_cols = ne0;
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, weights_extra->name,
// weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
// weights_extra->pre_tfm_desc.dim1,
// weights_extra->pre_tfm_desc.dim2,
// weights_extra->pre_tfm_desc.dim3,
// weights_extra->pre_tfm_desc.dim4);
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, inputs_extra->name,
// inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
// inputs_extra->pre_tfm_desc.dim1,
// inputs_extra->pre_tfm_desc.dim2,
// inputs_extra->pre_tfm_desc.dim3,
// inputs_extra->pre_tfm_desc.dim4);
GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1 == inputs->ne[0] && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2 == inputs->ne[1] && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &bias_extra->ztensor,
false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
// TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
GGML_UNUSED(ctx);
GGML_UNUSED(weights_rows);
GGML_UNUSED(weights_cols);
GGML_UNUSED(inputs_rows);
GGML_UNUSED(inputs_cols);
GGML_UNUSED(output_rows);
GGML_UNUSED(output_cols);
}
static void ggml_zdnn_mul_mat_dispatch(ggml_backend_zdnn_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
// debug helpers
// GGML_LOG_INFO("%s: use_mul_mat_vec = %d\n", __func__, use_mul_mat_vec);
// GGML_LOG_INFO("%s: use_mul_mat_vec_q = %d\n", __func__, use_mul_mat_vec_q);
// GGML_LOG_INFO("%s: use_mul_mat_q = %d\n", __func__, use_mul_mat_q);
// GGML_LOG_INFO("%s: src0: %8d %8d %8d %8d\n", __func__, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]);
// GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]);
// GGML_LOG_INFO("%s: src1: %8d %8d %8d %8d\n", __func__, src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]);
// GGML_LOG_INFO("%s: %8d %8d %8d %8d\n", __func__, src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]);
// GGML_LOG_INFO("%s: src0 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name);
// GGML_LOG_INFO("%s: src1 is contiguous %d, transposed %d, type = %s, name = %s\n", __func__, ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name);
ggml_zdnn_mul_mat_op(ctx, src0, src1, dst);
}
static bool ggml_zdnn_compute_forward(ggml_backend_zdnn_context * ctx, ggml_tensor * dst) {
switch (dst->op) {
case GGML_OP_MUL_MAT:
ggml_zdnn_mul_mat_dispatch(ctx, dst->src[0], dst->src[1], dst);
break;
{
ggml_zdnn_compute_forward_mul_mat(ctx, dst);
} break;
default:
return false;

80
ggml/src/ggml-zdnn/mmf.cpp Normal file
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@ -0,0 +1,80 @@
#include "ggml.h"
#include "mmf.hpp"
void ggml_zdnn_mul_mat_f(
const ggml_backend_zdnn_context * ctx,
const ggml_tensor * src0,
const ggml_tensor * src1,
ggml_tensor * dst) {
GGML_TENSOR_BINARY_OP_LOCALS;
const enum ggml_type type = src0->type;
GGML_ASSERT(ne0 == ne01);
GGML_ASSERT(ne1 == ne11);
GGML_ASSERT(ne2 == ne12);
GGML_ASSERT(ne3 == ne13);
// we don't support permuted src0 or src1
GGML_ASSERT(nb00 == ggml_type_size(type));
GGML_ASSERT(nb10 == ggml_type_size(src1->type));
// dst cannot be transposed or permuted
GGML_ASSERT(nb0 == sizeof(float));
GGML_ASSERT(nb0 <= nb1);
GGML_ASSERT(nb1 <= nb2);
GGML_ASSERT(nb2 <= nb3);
const ggml_tensor * weights = src0;
const ggml_tensor * inputs = src1;
ggml_tensor * output = dst;
ggml_backend_zdnn_buffer * weights_extra = (ggml_backend_zdnn_buffer *)weights->extra;
ggml_backend_zdnn_buffer * inputs_extra = (ggml_backend_zdnn_buffer *)inputs->extra;
ggml_backend_zdnn_buffer * output_extra = (ggml_backend_zdnn_buffer *)output->extra;
ggml_backend_zdnn_buffer * bias_extra = (ggml_backend_zdnn_buffer *)output_extra->extra;
const int64_t weights_rows = ne01;
const int64_t weights_cols = ne00;
const int64_t inputs_rows = ne11;
const int64_t inputs_cols = ne10;
assert(inputs_cols == weights_cols);
const int64_t output_rows = ne1;
const int64_t output_cols = ne0;
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, weights_extra->name,
// weights->ne[3], weights->ne[2], weights->ne[1], weights->ne[0],
// weights_extra->pre_tfm_desc.dim1,
// weights_extra->pre_tfm_desc.dim2,
// weights_extra->pre_tfm_desc.dim3,
// weights_extra->pre_tfm_desc.dim4);
// GGML_LOG_INFO("%s: tensor '%s' tensor dimensions: [%ld, %ld, %ld, %ld] pre_tfm_desc dimensions: [%ld, %ld, %ld, %ld]\n",
// __func__, inputs_extra->name,
// inputs->ne[3], inputs->ne[2], inputs->ne[1], inputs->ne[0],
// inputs_extra->pre_tfm_desc.dim1,
// inputs_extra->pre_tfm_desc.dim2,
// inputs_extra->pre_tfm_desc.dim3,
// inputs_extra->pre_tfm_desc.dim4);
GGML_ASSERT(weights_extra->pre_tfm_desc.dim1 == weights->ne[0] && "weights_extra->pre_tfm_desc.dim1 must match weights->ne[0]");
GGML_ASSERT(weights_extra->pre_tfm_desc.dim2 == weights->ne[1] && "weights_extra->pre_tfm_desc.dim2 must match weights->ne[1]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim1 == inputs->ne[0] && "inputs_extra->pre_tfm_desc.dim1 must match inputs->ne[0]");
GGML_ASSERT(inputs_extra->pre_tfm_desc.dim2 == inputs->ne[1] && "inputs_extra->pre_tfm_desc.dim2 must match inputs->ne[1]");
ZDNN_CHECK(zdnn_matmul_transpose_op(&inputs_extra->ztensor, &weights_extra->ztensor, &bias_extra->ztensor,
false, true, MATMUL_OP_ADDITION, &output_extra->ztensor));
// TODO: Remove in the future as we are currently DLF16 -> FP32 then in the next op, FP32 -> DLF16 again. Inefficient.
ZDNN_CHECK(zdnn_transform_origtensor(&output_extra->ztensor, output->data));
GGML_UNUSED(ctx);
GGML_UNUSED(weights_rows);
GGML_UNUSED(weights_cols);
GGML_UNUSED(inputs_rows);
GGML_UNUSED(inputs_cols);
GGML_UNUSED(output_rows);
GGML_UNUSED(output_cols);
}

12
ggml/src/ggml-zdnn/mmf.hpp Normal file
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@ -0,0 +1,12 @@
#ifndef GGML_ZDNN_MMF_HPP
#define GGML_ZDNN_MMF_HPP
#include "common.hpp"
void ggml_zdnn_mul_mat_f(
const ggml_backend_zdnn_context * ctx,
const ggml_tensor * src0,
const ggml_tensor * src1,
ggml_tensor * dst);
#endif // GGML_ZDNN_MMF_HPP

79
ggml/src/ggml-zdnn/utils.cpp Normal file
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@ -0,0 +1,79 @@
#include "ggml.h"
#include "utils.hpp"
zdnn_data_types ggml_zdnn_type_mapping(ggml_type type) {
switch (type) {
case GGML_TYPE_F32:
return FP32;
case GGML_TYPE_F16:
return FP16;
case GGML_TYPE_BF16:
return BFLOAT;
case GGML_TYPE_Q8_0:
return INT8;
case GGML_TYPE_I8:
return INT8;
case GGML_TYPE_I32:
return INT32;
default:
GGML_ABORT("%s: fatal: unable to determine zTensor data type",
__func__);
break;
}
}
void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc,
zdnn_tensor_desc & tfm_desc,
zdnn_ztensor & ztensor,
const ggml_tensor * src,
const int64_t * ne,
const zdnn_data_layouts layout) {
zdnn_init_pre_transformed_desc(
layout,
ggml_zdnn_type_mapping(src->type),
&pre_tfm_desc,
ne[3], ne[2], ne[1], ne[0]
);
ZDNN_CHECK(zdnn_generate_transformed_desc(&pre_tfm_desc, &tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&pre_tfm_desc, &tfm_desc, &ztensor));
}
void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer) {
ZDNN_CHECK(zdnn_transform_ztensor(&ztensor, buffer));
}
void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor) {
switch (tensor->op) {
case GGML_OP_MUL_MAT:
{
zdnn_init_pre_transformed_desc(
ZDNN_2D,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[1], tensor->ne[0]
);
} break;
default:
{
// For 4D tensors, GGML uses NCHW layout. However, because zDNN
// automatically transforms everything to NHWC, we will use it
// directly to avoid the performance penalty changing the
// layout and reshaping the tensor.
zdnn_init_pre_transformed_desc(
ZDNN_NHWC,
ggml_zdnn_type_mapping(tensor->type),
&buffer->pre_tfm_desc,
tensor->ne[3], tensor->ne[2], tensor->ne[1], tensor->ne[0]
);
// TODO: Consider adding a ggml check.
// TODO: If tensor = 4D, use ZDNN_NCHW by default.
// TODO: If tensor = 2D, use ZDNN_NHWC by default.
} break;
}
ZDNN_CHECK(zdnn_generate_transformed_desc(&buffer->pre_tfm_desc, &buffer->tfm_desc));
ZDNN_CHECK(zdnn_init_ztensor_with_malloc(&buffer->pre_tfm_desc, &buffer->tfm_desc, &buffer->ztensor));
}

19
ggml/src/ggml-zdnn/utils.hpp Normal file
View File

@ -0,0 +1,19 @@
#ifndef GGML_ZDNN_UTILITIES_HPP
#define GGML_ZDNN_UTILITIES_HPP
#include "common.hpp"
zdnn_data_types ggml_zdnn_type_mapping(ggml_type type);
void ggml_zdnn_create_tensor(zdnn_tensor_desc & pre_tfm_desc,
zdnn_tensor_desc & tfm_desc,
zdnn_ztensor & ztensor,
const ggml_tensor * src,
const int64_t * ne,
const zdnn_data_layouts layout);
void ggml_zdnn_load_tensor(zdnn_ztensor & ztensor, void * buffer);
void ggml_zdnn_init_tensor(ggml_backend_zdnn_buffer * buffer, const ggml_tensor * tensor);
#endif // GGML_ZDNN_UTILITIES_HPP

2
ggml/src/ggml.c
View File

@ -3677,7 +3677,7 @@ struct ggml_tensor * ggml_set_rows(
GGML_ASSERT(b->ne[3] % c->ne[2] == 0);
GGML_ASSERT(c->ne[3] == 1);
GGML_ASSERT(b->type == GGML_TYPE_F32);
GGML_ASSERT(c->type == GGML_TYPE_I64);
GGML_ASSERT(c->type == GGML_TYPE_I64 || c->type == GGML_TYPE_I32);
GGML_ASSERT(ggml_is_contiguous_rows(a));
GGML_ASSERT(ggml_is_contiguous_rows(b));

31
gguf-py/gguf/constants.py
View File

@ -96,6 +96,7 @@ class Keys:
FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length"
EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length"
EXPERT_CHUNK_FEED_FORWARD_LENGTH = "{arch}.expert_chunk_feed_forward_length"
USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual"
TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout"
EXPERT_COUNT = "{arch}.expert_count"
@ -104,6 +105,8 @@ class Keys:
EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale"
EXPERT_WEIGHTS_NORM = "{arch}.expert_weights_norm"
EXPERT_GATING_FUNC = "{arch}.expert_gating_func"
EXPERT_GROUP_SCALE = "{arch}.expert_group_scale"
EXPERTS_PER_GROUP = "{arch}.experts_per_group"
MOE_EVERY_N_LAYERS = "{arch}.moe_every_n_layers"
NEXTN_PREDICT_LAYERS = "{arch}.nextn_predict_layers"
POOLING_TYPE = "{arch}.pooling_type"
@ -401,6 +404,7 @@ class MODEL_ARCH(IntEnum):
LLADA = auto()
LLADA_MOE = auto()
SEED_OSS = auto()
GROVEMOE = auto()
class VISION_PROJECTOR_TYPE(IntEnum):
@ -450,6 +454,9 @@ class MODEL_TENSOR(IntEnum):
FFN_GATE_SHEXP = auto()
FFN_DOWN_SHEXP = auto()
FFN_UP_SHEXP = auto()
FFN_GATE_CHEXP = auto()
FFN_DOWN_CHEXP = auto()
FFN_UP_CHEXP = auto()
FFN_EXP_PROBS_B = auto()
ATTN_Q_NORM = auto()
ATTN_K_NORM = auto()
@ -738,6 +745,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
MODEL_ARCH.LLADA: "llada",
MODEL_ARCH.LLADA_MOE: "llada-moe",
MODEL_ARCH.SEED_OSS: "seed_oss",
MODEL_ARCH.GROVEMOE: "grovemoe",
}
VISION_PROJECTOR_TYPE_NAMES: dict[VISION_PROJECTOR_TYPE, str] = {
@ -784,6 +792,9 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp",
MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp",
MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp",
MODEL_TENSOR.FFN_GATE_CHEXP: "blk.{bid}.ffn_gate_chexps",
MODEL_TENSOR.FFN_DOWN_CHEXP: "blk.{bid}.ffn_down_chexps",
MODEL_TENSOR.FFN_UP_CHEXP: "blk.{bid}.ffn_up_chexps",
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",
@ -2712,6 +2723,26 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
],
MODEL_ARCH.GROVEMOE: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
MODEL_TENSOR.OUTPUT,
MODEL_TENSOR.ATTN_NORM,
MODEL_TENSOR.ATTN_Q,
MODEL_TENSOR.ATTN_Q_NORM,
MODEL_TENSOR.ATTN_K,
MODEL_TENSOR.ATTN_K_NORM,
MODEL_TENSOR.ATTN_V,
MODEL_TENSOR.ATTN_OUT,
MODEL_TENSOR.FFN_NORM,
MODEL_TENSOR.FFN_GATE_INP,
MODEL_TENSOR.FFN_GATE_EXP,
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
MODEL_TENSOR.FFN_GATE_CHEXP,
MODEL_TENSOR.FFN_DOWN_CHEXP,
MODEL_TENSOR.FFN_UP_CHEXP,
],
# TODO
}

9
gguf-py/gguf/gguf_writer.py
View File

@ -670,6 +670,9 @@ class GGUFWriter:
def add_expert_shared_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length)
def add_expert_chunk_feed_forward_length(self, length: int) -> None:
self.add_uint32(Keys.LLM.EXPERT_CHUNK_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)
@ -757,6 +760,12 @@ class GGUFWriter:
def add_expert_gating_func(self, value: ExpertGatingFuncType) -> None:
self.add_uint32(Keys.LLM.EXPERT_GATING_FUNC.format(arch=self.arch), value.value)
def add_expert_group_scale(self, value: float) -> None:
self.add_float32(Keys.LLM.EXPERT_GROUP_SCALE.format(arch=self.arch), value)
def add_experts_per_group(self, count: int) -> None:
self.add_uint32(Keys.LLM.EXPERTS_PER_GROUP.format(arch=self.arch), count)
def add_moe_every_n_layers(self, value: int) -> None:
self.add_uint32(Keys.LLM.MOE_EVERY_N_LAYERS.format(arch=self.arch), value)

12
gguf-py/gguf/tensor_mapping.py
View File

@ -427,6 +427,10 @@ class TensorNameMap:
"model.layers.{bid}.mlp.shared_mlp.up_proj", # hunyuan
),
MODEL_TENSOR.FFN_UP_CHEXP: (
"model.layers.{bid}.mlp.chunk_experts.up_proj", # grovemoe
),
# AWQ-activation gate
MODEL_TENSOR.FFN_ACT: (
"transformer.blocks.{bid}.ffn.act", # mpt
@ -468,6 +472,10 @@ class TensorNameMap:
"model.layers.{bid}.mlp.shared_mlp.gate_proj", # hunyuan
),
MODEL_TENSOR.FFN_GATE_CHEXP: (
"model.layers.{bid}.mlp.chunk_experts.gate_proj", # grovemoe
),
# Feed-forward down
MODEL_TENSOR.FFN_DOWN: (
"gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox
@ -524,6 +532,10 @@ class TensorNameMap:
"model.layers.{bid}.mlp.shared_mlp.down_proj", # hunyuan
),
MODEL_TENSOR.FFN_DOWN_CHEXP: (
"model.layers.{bid}.mlp.chunk_experts.down_proj", # grovemoe
),
MODEL_TENSOR.ATTN_Q_NORM: (
"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
"model.layers.{bid}.self_attn.q_layernorm", # persimmon

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