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chore: bump v0.4.2.post2 (#3313)

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This commit is contained in:
Yineng Zhang
2025-02-05 17:35:02 +08:00
committed by GitHub
parent 76fa2d152c
commit 7aad8d1854
5 changed files with 11 additions and 11 deletions
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2
docker/Dockerfile.rocm
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@@ -1,5 +1,5 @@
# Usage (to build SGLang ROCm docker image):
# docker build --build-arg SGL_BRANCH=v0.4.2.post1 -t v0.4.2.post1-rocm630 -f Dockerfile.rocm .
# docker build --build-arg SGL_BRANCH=v0.4.2.post2 -t v0.4.2.post2-rocm630 -f Dockerfile.rocm .
# default base image
ARG BASE_IMAGE="rocm/vllm-dev:20250114"

4
docs/developer/setup_github_runner.md
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@@ -11,9 +11,9 @@ docker pull nvidia/cuda:12.1.1-devel-ubuntu22.04
# Nvidia
docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-devel-ubuntu22.04 /bin/bash
# AMD
docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.2.post1-rocm630 /bin/bash
docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.2.post2-rocm630 /bin/bash
# AMD just the last 2 GPUs
docker run --rm --device=/dev/kfd --device=/dev/dri/renderD176 --device=/dev/dri/renderD184 --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.2.post1-rocm630 /bin/bash
docker run --rm --device=/dev/kfd --device=/dev/dri/renderD176 --device=/dev/dri/renderD184 --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.2.post2-rocm630 /bin/bash
```
### Step 2: Configure the runner by `config.sh`

12
docs/start/install.md
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@@ -6,7 +6,7 @@ You can install SGLang using any of the methods below.
```
pip install --upgrade pip
pip install sgl-kernel --force-reinstall --no-deps
pip install "sglang[all]" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer/
pip install "sglang[all]>=0.4.2.post2" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer/
```
Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/installation.html) to install the proper version according to your PyTorch and CUDA versions.
@@ -14,7 +14,7 @@ Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/
## Method 2: From source
```
# Use the last release branch
git clone -b v0.4.2.post1 https://github.com/sgl-project/sglang.git
git clone -b v0.4.2.post2 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
@@ -28,7 +28,7 @@ Note: To AMD ROCm system with Instinct/MI GPUs, do following instead:
```
# Use the last release branch
git clone -b v0.4.2.post1 https://github.com/sgl-project/sglang.git
git clone -b v0.4.2.post2 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
@@ -56,7 +56,7 @@ docker run --gpus all \
Note: To AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
```bash
docker build --build-arg SGL_BRANCH=v0.4.2.post1 -t v0.4.2.post1-rocm630 -f Dockerfile.rocm .
docker build --build-arg SGL_BRANCH=v0.4.2.post2 -t v0.4.2.post2-rocm630 -f Dockerfile.rocm .
alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
--shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
@@ -65,11 +65,11 @@ alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/d
drun -p 30000:30000 \
-v ~/.cache/huggingface:/root/.cache/huggingface \
--env "HF_TOKEN=<secret>" \
v0.4.2.post1-rocm630 \
v0.4.2.post2-rocm630 \
python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
drun v0.4.2.post1-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
drun v0.4.2.post2-rocm630 python3 -m sglang.bench_one_batch --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
```
## Method 4: Using docker compose

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python/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "sglang"
version = "0.4.2.post1"
version = "0.4.2.post2"
description = "SGLang is yet another fast serving framework for large language models and vision language models."
readme = "README.md"
requires-python = ">=3.8"

2
python/sglang/version.py
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@@ -1 +1 @@
__version__ = "0.4.2.post1"
__version__ = "0.4.2.post2"