diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile new file mode 100644 index 000000000..4f83904bc --- /dev/null +++ b/.devops/server-cuda.Dockerfile @@ -0,0 +1,32 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=11.7.1 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} +# Target the CUDA runtime image +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} as build + +# Unless otherwise specified, we make a fat build. +ARG CUDA_DOCKER_ARCH=all + +RUN apt-get update && \ + apt-get install -y build-essential git + +WORKDIR /app + +COPY . . + +# Set nvcc architecture +ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} +# Enable cuBLAS +ENV LLAMA_CUBLAS=1 + +RUN make + +FROM ${BASE_CUDA_RUN_CONTAINER} as runtime + +COPY --from=build /app/server /server + +ENTRYPOINT [ "/server" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile new file mode 100644 index 000000000..e343d278c --- /dev/null +++ b/.devops/server-intel.Dockerfile @@ -0,0 +1,25 @@ +ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 +ARG UBUNTU_VERSION=22.04 + +FROM intel/hpckit:$ONEAPI_VERSION as build + +RUN apt-get update && \ + apt-get install -y git + +WORKDIR /app + +COPY . . + +# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance +RUN mkdir build && \ + cd build && \ + cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ + cmake --build . --config Release --target main server + +FROM ubuntu:$UBUNTU_VERSION as runtime + +COPY --from=build /app/build/bin/server /server + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile new file mode 100644 index 000000000..e9a31647c --- /dev/null +++ b/.devops/server-rocm.Dockerfile @@ -0,0 +1,45 @@ +ARG UBUNTU_VERSION=22.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=5.6 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +FROM ${BASE_ROCM_DEV_CONTAINER} as build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +ARG ROCM_DOCKER_ARCH=\ + gfx803 \ + gfx900 \ + gfx906 \ + gfx908 \ + gfx90a \ + gfx1010 \ + gfx1030 \ + gfx1100 \ + gfx1101 \ + gfx1102 + +COPY requirements.txt requirements.txt +COPY requirements requirements + +RUN pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt + +WORKDIR /app + +COPY . . + +# Set nvcc architecture +ENV GPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +ENV LLAMA_HIPBLAS=1 +ENV CC=/opt/rocm/llvm/bin/clang +ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN make + +ENTRYPOINT [ "/app/server" ] diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile new file mode 100644 index 000000000..134588fe2 --- /dev/null +++ b/.devops/server.Dockerfile @@ -0,0 +1,20 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION as build + +RUN apt-get update && \ + apt-get install -y build-essential git + +WORKDIR /app + +COPY . . + +RUN make + +FROM ubuntu:$UBUNTU_VERSION as runtime + +COPY --from=build /app/server /server + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 825b8f503..94f9161fc 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -28,14 +28,18 @@ jobs: config: - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" } # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I # have disabled them for now until the reason why # is understood. - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" } steps: - name: Check out the repo uses: actions/checkout@v3 diff --git a/README.md b/README.md index 76e48ce8a..cd95f8144 100644 --- a/README.md +++ b/README.md @@ -931,17 +931,20 @@ Place your desired model into the `~/llama.cpp/models/` directory and execute th * Create a folder to store big models & intermediate files (ex. /llama/models) #### Images -We have two Docker images available for this project: +We have three Docker images available for this project: 1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executabhle file. (platforms: `linux/amd64`, `linux/arm64`) Additionally, there the following images, similar to the above: - `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) - `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) - `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) - `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). @@ -967,6 +970,12 @@ or with a light image: docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 ``` +or with a server image: + +```bash +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +``` + ### Docker With CUDA Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. @@ -976,6 +985,7 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia ```bash docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile . ``` You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. @@ -989,6 +999,7 @@ The resulting images, are essentially the same as the non-CUDA images: 1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. 2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. +3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. #### Usage @@ -997,6 +1008,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne ```bash docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 ``` ### Contributing diff --git a/examples/server/README.md b/examples/server/README.md index 1c92a2041..dce4ec47c 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -66,6 +66,14 @@ server.exe -m models\7B\ggml-model.gguf -c 2048 The above command will start a server that by default listens on `127.0.0.1:8080`. You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url. +### Docker: +```bash +docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 + +# or, with CUDA: +docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 +``` + ## Testing with CURL Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.