101 lines
3.5 KiB
Markdown
101 lines
3.5 KiB
Markdown
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# AMD Configuration and Setup for SGLang
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## Introduction
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This document describes how to set up an AMD-based environment for [SGLang](https://github.com/sgl-project/sglang). If you encounter issues or have questions, please [open an issue](https://github.com/sgl-project/sglang/issues) on the SGLang repository.
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## System Configure
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When using AMD GPUs (such as MI300X), certain system-level optimizations help ensure stable performance. Here we take MI300X as an example. AMD provides official documentation for MI300X optimization and system tuning:
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- [AMD MI300X Tuning Guides](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/index.html)
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- [LLM inference performance validation on AMD Instinct MI300X](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html)
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- [AMD Instinct MI300X System Optimization](https://rocm.docs.amd.com/en/latest/how-to/system-optimization/mi300x.html)
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- [AMD Instinct MI300X Workload Optimization](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference-optimization/workload.html)
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**NOTE:** We strongly recommend reading theses docs entirely guide to fully utilize your system.
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Below are a few key settings to confirm or enable:
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### Update GRUB Settings
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In `/etc/default/grub`, append the following to `GRUB_CMDLINE_LINUX`:
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```text
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pci=realloc=off iommu=pt
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```
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Afterward, run `sudo update-grub` (or your distro’s equivalent) and reboot.
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### Disable NUMA Auto-Balancing
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```bash
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sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
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```
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You can automate or verify this change using [this helpful script](https://github.com/ROCm/triton/blob/rocm_env/scripts/amd/env_check.sh).
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Again, please go through the entire documentation to confirm your system is using the recommended configuration.
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## Installing SGLang
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For general installation instructions, see the official [SGLang Installation Docs](https://docs.sglang.ai/start/install.html). Below are the AMD-specific steps summarized for convenience.
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### Install from Source
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```bash
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git clone https://github.com/sgl-project/sglang.git
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cd sglang
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pip install --upgrade pip
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pip install sgl-kernel --force-reinstall --no-deps
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pip install -e "python[all_hip]"
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```
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### Install Using Docker (Recommended)
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1. Build the docker image.
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```bash
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docker build -t sglang_image -f Dockerfile.rocm .
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```
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2. Create a convenient alias.
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```bash
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alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri \
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--ipc=host --shm-size 16G --group-add video --cap-add=SYS_PTRACE \
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--security-opt seccomp=unconfined \
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-v $HOME/dockerx:/dockerx \
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-v /data:/data'
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```
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3. Launch the server.
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**NOTE:** Replace `<secret>` below with your [huggingface hub token](https://huggingface.co/docs/hub/en/security-tokens).
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```bash
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drun -p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<secret>" \
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sglang_image \
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python3 -m sglang.launch_server \
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--model-path NousResearch/Meta-Llama-3.1-8B \
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--host 0.0.0.0 \
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--port 30000
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```
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4. To verify the utility, you can run a benchmark in another terminal or refer to [other docs](https://docs.sglang.ai/backend/openai_api_completions.html) to send requests to the engine.
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```bash
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drun sglang_image \
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python3 -m sglang.bench_serving \
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--backend sglang \
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--dataset-name random \
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--num-prompts 4000 \
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--random-input 128 \
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--random-output 128
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```
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With your AMD system properly configured and SGLang installed, you can now fully leverage AMD hardware to power SGLang’s machine learning capabilities.
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