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Merge branch 'ggml-org:master' into master
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‎.github/workflows/build-riscv-native.yml‎

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jobs:
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debian-13-riscv64-native: # Bianbu 2.2
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runs-on: self-hosted
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runs-on: [self-hosted, RISCV64]
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steps:
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- name: Install prerequisites

‎.github/workflows/build.yml‎

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-DGGML_CANN=on \
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-DSOC_TYPE=${{ matrix.device }}
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cmake --build build -j $(nproc)
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# TODO: simplify the following workflows using a matrix
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# TODO: run lighter CI on PRs and the full CI only on master (if needed)
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ggml-ci-x64-cpu-low-perf:
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runs-on: [self-hosted, Linux, X64, CPU, low-perf]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-arm64-cpu-low-perf:
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runs-on: [self-hosted, Linux, ARM64, CPU, low-perf]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-cpu-high-perf:
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runs-on: [self-hosted, Linux, X64, CPU, high-perf]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-arm64-cpu-high-perf:
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runs-on: [self-hosted, Linux, ARM64, CPU, high-perf]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-nvidia-v100-cuda:
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runs-on: [self-hosted, Linux, X64, NVIDIA, V100]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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nvidia-smi
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GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-nvidia-v100-vulkan:
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runs-on: [self-hosted, Linux, X64, NVIDIA, V100]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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vulkaninfo
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GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-nvidia-t4-cuda:
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runs-on: [self-hosted, Linux, X64, NVIDIA, T4]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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nvidia-smi
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GG_BUILD_CUDA=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-nvidia-t4-vulkan:
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runs-on: [self-hosted, Linux, X64, NVIDIA, T4]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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vulkaninfo
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GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-nvidia-t4-vulkan-coopmat1:
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runs-on: [self-hosted, Linux, X64, NVIDIA, T4]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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vulkaninfo
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GG_BUILD_VULKAN=1 GGML_VK_DISABLE_COOPMAT2=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-cpu-amx:
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runs-on: [self-hosted, Linux, X64, CPU, AMX]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-amd-v710-vulkan:
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runs-on: [self-hosted, Linux, X64, AMD, V710]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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vulkaninfo
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GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-x64-amd-v710-rocm:
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runs-on: [self-hosted, Linux, X64, AMD, V710]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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vulkaninfo
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GG_BUILD_ROCM=1 GG_BUILD_AMDGPU_TARGETS="gfx1101" bash ./ci/run.sh ~/results/llama.cpp /mnt/llama.cpp
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ggml-ci-mac-metal:
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runs-on: [self-hosted, macOS, ARM64]
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steps:
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- name: Clone
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id: checkout
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uses: actions/checkout@v4
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- name: Test
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id: ggml-ci
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run: |
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GG_BUILD_METAL=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp
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# TODO: install vulkan drivers
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# ggml-ci-mac-vulkan:
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# runs-on: [self-hosted, macOS, ARM64]
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#
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# steps:
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# - name: Clone
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# id: checkout
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# uses: actions/checkout@v4
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#
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# - name: Test
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# id: ggml-ci
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# run: |
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# GG_BUILD_VULKAN=1 bash ./ci/run.sh ~/results/llama.cpp ~/mnt/llama.cpp

‎ci/README-MUSA.md‎

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

‎ci/README.md‎

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

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