mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
33983057d0
* musa: fix build warning (unused parameter) Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * musa: upgrade MUSA SDK version to rc4.0.1 Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * musa: use mudnn::Unary::IDENTITY op to accelerate D2D memory copy Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> * Update ggml/src/ggml-cuda/cpy.cu Co-authored-by: Johannes Gäßler <johannesg@5d6.de> * musa: remove MUDNN_CHECK_GEN and use CUDA_CHECK_GEN instead in MUDNN_CHECK Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> --------- Signed-off-by: Xiaodong Ye <xiaodong.ye@mthreads.com> Co-authored-by: Johannes Gäßler <johannesg@5d6.de>
69 lines
2.2 KiB
Markdown
69 lines
2.2 KiB
Markdown
# CI
|
|
|
|
In addition to [Github Actions](https://github.com/ggml-org/llama.cpp/actions) `llama.cpp` uses a custom CI framework:
|
|
|
|
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:
|
|
|
|
```bash
|
|
mkdir tmp
|
|
|
|
# CPU-only build
|
|
bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
|
|
|
# with CUDA support
|
|
GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt
|
|
|
|
# with SYCL support
|
|
source /opt/intel/oneapi/setvars.sh
|
|
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
|
|
```
|
|
|
|
## 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:rc4.0.1-mudnn-devel-ubuntu22.04
|
|
```
|
|
|
|
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.
|