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## Development
### Code Generation
The backend uses code generation from YAML configuration:
```bash
# Regenerate protocol code
cd ggml-virtgpu/
python regenerate_remoting.py
```
### Adding New Operations
1. Add function definition to `ggmlremoting_functions.yaml`
2. Regenerate code with `regenerate_remoting.py`
3. Implement guest-side forwarding in `virtgpu-forward-*.cpp`
4. Implement host-side handling in `backend-dispatched-*.cpp`
## Testing
This document provides instructions for building and testing the GGML-VirtGPU backend on macOS with containers.
### Prerequisites
The testing setup requires:
- macOS host system
- Container runtime with `libkrun` provider (podman machine)
- Access to development patchset for VirglRenderer
### Required Patchsets
The backend requires patches that are currently under review:
- **Virglrenderer APIR upstream PR**: https://gitlab.freedesktop.org/virgl/virglrenderer/-/merge_requests/1590 (for reference)
- **MacOS Virglrenderer (for krunkit)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-macos
- **Linux Virglrenderer (for krun)**: https://gitlab.freedesktop.org/kpouget/virglrenderer/-/tree/main-linux
### Build Instructions
#### 1. Build ggml-virtgpu-backend (Host-side, macOS)
```bash
# Build the backend that runs natively on macOS
mkdir llama.cpp
cd llama.cpp
git clone https://github.com/ggml-org/llama.cpp.git src
cd src
LLAMA_MAC_BUILD=$PWD/build/ggml-virtgpu-backend
cmake -S . -B $LLAMA_MAC_BUILD \
-DGGML_NATIVE=OFF \
-DLLAMA_CURL=ON \
-DGGML_REMOTINGBACKEND=ONLY \
-DGGML_METAL=ON
TARGETS="ggml-metal"
cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $TARGETS
# Build additional tools for native benchmarking
EXTRA_TARGETS="llama-run llama-bench"
cmake --build $LLAMA_MAC_BUILD --parallel 8 --target $EXTRA_TARGETS
```
#### 2. Build virglrenderer (Host-side, macOS)
```bash
# Build virglrenderer with APIR support
mkdir virglrenderer
git clone https://gitlab.freedesktop.org/kpouget/virglrenderer -b main-macos src
cd src
VIRGL_BUILD_DIR=$PWD/build
# -Dvenus=true and VIRGL_ROUTE_VENUS_TO_APIR=1 route the APIR requests via the Venus backend, for easier testing without a patched hypervisor
meson setup $VIRGL_BUILD_DIR \
-Dvenus=true \
-Dapir=true
ninja -C $VIRGL_BUILD_DIR
```
#### 3. Build ggml-virtgpu (Guest-side, Linux)
Option A: Build from a script:
```bash
# Inside a Linux container
mkdir llama.cpp
git clone https://github.com/ggml-org/llama.cpp.git src
cd src
LLAMA_LINUX_BUILD=$PWD//build-virtgpu
cmake -S . -B $LLAMA_LINUX_BUILD \
-DGGML_VIRTGPU=ON
ninja -C $LLAMA_LINUX_BUILD
```
Option B: Build container image with frontend:
```bash
cat << EOF > remoting.containerfile
FROM quay.io/fedora/fedora:43
USER 0
WORKDIR /app/remoting
ARG LLAMA_CPP_REPO="https://github.com/ggml-org/llama.cpp.git"
ARG LLAMA_CPP_VERSION="master"
ARG LLAMA_CPP_CMAKE_FLAGS="-DGGML_VIRTGPU=ON"
ARG LLAMA_CPP_CMAKE_BUILD_FLAGS="--parallel 4"
RUN dnf install -y git cmake gcc gcc-c++ libcurl-devel libdrm-devel
RUN git clone "\${LLAMA_CPP_REPO}" src \\
&& git -C src fetch origin \${LLAMA_CPP_VERSION} \\
&& git -C src reset --hard FETCH_HEAD
RUN mkdir -p build \\
&& cd src \\
&& set -o pipefail \\
&& cmake -S . -B ../build \${LLAMA_CPP_CMAKE_FLAGS} \\
&& cmake --build ../build/ \${LLAMA_CPP_CMAKE_BUILD_FLAGS}
ENTRYPOINT ["/app/remoting/src/build/bin/llama-server"]
EOF
mkdir -p empty_dir
podman build -f remoting.containerfile ./empty_dir -t localhost/llama-cpp.virtgpu
```
### Environment Setup
#### Set krunkit Environment Variables
```bash
# Define the base directories (adapt these paths to your system)
VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build
LLAMA_MAC_BUILD=$HOME/remoting/llama.cpp/build-backend
# For krunkit to load the custom virglrenderer library
export DYLD_LIBRARY_PATH=$VIRGL_BUILD_DIR/src
# For Virglrenderer to load the ggml-remotingbackend library
export VIRGL_APIR_BACKEND_LIBRARY="$LLAMA_MAC_BUILD/bin/libggml-virtgpu-backend.dylib"
# For llama.cpp remotingbackend to load the ggml-metal backend
export APIR_LLAMA_CPP_GGML_LIBRARY_PATH="$LLAMA_MAC_BUILD/bin/libggml-metal.dylib"
export APIR_LLAMA_CPP_GGML_LIBRARY_REG=ggml_backend_metal_reg
```
#### Launch Container Environment
```bash
# Set container provider to libkrun
export CONTAINERS_MACHINE_PROVIDER=libkrun
podman machine start
```
#### Verify Environment
Confirm that krunkit is using the correct virglrenderer library:
```bash
lsof -c krunkit | grep virglrenderer
# Expected output:
# krunkit 50574 user txt REG 1,14 2273912 10849442 ($VIRGL_BUILD_DIR/src)/libvirglrenderer.1.dylib
```
### Running Tests
#### Launch Test Container
```bash
# Optional model caching
mkdir -p models
PODMAN_CACHE_ARGS="-v models:/models --user root:root --cgroupns host --security-opt label=disable -w /models"
podman run $PODMAN_CACHE_ARGS -it --rm --device /dev/dri localhost/llama-cpp.virtgpu
```
#### Test llama.cpp in Container
```bash
# Run performance benchmark
/app/remoting/build/bin/llama-bench -m ./llama3.2
```
Expected output (performance may vary):
```
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | ------------: | -------------------: |
| llama 3B Q4_K - Medium | 1.87 GiB | 3.21 B | ggml-virtgpu | 99 | pp512 | 991.30 ± 0.66 |
| llama 3B Q4_K - Medium | 1.87 GiB | 3.21 B | ggml-virtgpu | 99 | tg128 | 85.71 ± 0.11 |
```
### Troubleshooting
#### SSH Environment Variable Issues
⚠️ **Warning**: Setting `DYLD_LIBRARY_PATH` from SSH doesn't work on macOS. Here is a workaround:
**Workaround 1: Replace system library**
```bash
VIRGL_BUILD_DIR=$HOME/remoting/virglrenderer/build # ⚠️ adapt to your system
BREW_VIRGL_DIR=/opt/homebrew/Cellar/virglrenderer/0.10.4d/lib
VIRGL_LIB=libvirglrenderer.1.dylib
cd $BREW_VIRGL_DIR
mv $VIRGL_LIB ${VIRGL_LIB}.orig
ln -s $VIRGL_BUILD_DIR/src/$VIRGL_LIB
```
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