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chore: bump v0.4.0.post2 (#2525)

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This commit is contained in:
Yineng Zhang
2024-12-21 21:16:34 +08:00
committed by GitHub
parent feb2b768ba
commit 8f4d04e540
6 changed files with 11 additions and 11 deletions
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2
.github/workflows/release-docker.yml vendored
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@@ -58,7 +58,7 @@ jobs:
docker build . -f docker/Dockerfile --build-arg CUDA_VERSION=${{ matrix.cuda_version }} --build-arg BUILD_TYPE=${{ matrix.build_type }} -t lmsysorg/sglang:${tag}${tag_suffix} --no-cache
docker push lmsysorg/sglang:${tag}${tag_suffix}
if [ "${{ matrix.cuda_version }}" = "12.1.1" ]; then
if [ "${{ matrix.cuda_version }}" = "12.4.1" ]; then
docker tag lmsysorg/sglang:${tag}${tag_suffix} lmsysorg/sglang:latest${tag_suffix}
docker push lmsysorg/sglang:latest${tag_suffix}
fi

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=v0.4.0.post1 -t v0.4.0.post1-rocm620 -f Dockerfile.rocm .
# docker build --build-arg SGL_BRANCH=v0.4.0.post2 -t v0.4.0.post2-rocm620 -f Dockerfile.rocm .
# default base image
ARG BASE_IMAGE="rocm/vllm-dev:20241022"

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:v0.4.0.post1-rocm620 /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:v0.4.0.post2-rocm620 /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:v0.4.0.post1-rocm620 /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:v0.4.0.post2-rocm620 /bin/bash
```
### Step 2: Configure the runner by `config.sh`

10
docs/start/install.md
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@@ -13,7 +13,7 @@ Note: Please check the [FlashInfer installation doc](https://docs.flashinfer.ai/
## Method 2: From source
```
# Use the last release branch
git clone -b v0.4.0.post1 https://github.com/sgl-project/sglang.git
git clone -b v0.4.0.post2 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
@@ -26,7 +26,7 @@ Note: To AMD ROCm system with Instinct/MI GPUs, do following instead:
```
# Use the last release branch
git clone -b v0.4.0.post1 https://github.com/sgl-project/sglang.git
git clone -b v0.4.0.post2 https://github.com/sgl-project/sglang.git
cd sglang
pip install --upgrade pip
@@ -51,7 +51,7 @@ 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=v0.4.0.post1 -t v0.4.0.post1-rocm620 -f Dockerfile.rocm .
docker build --build-arg SGL_BRANCH=v0.4.0.post2 -t v0.4.0.post2-rocm620 -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 \
@@ -60,11 +60,11 @@ 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>" \
v0.4.0.post1-rocm620 \
v0.4.0.post2-rocm620 \
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 v0.4.0.post1-rocm620 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 v0.4.0.post2-rocm620 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

2
python/pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "sglang"
version = "0.4.0.post1"
version = "0.4.0.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__ = "0.4.0.post1"
__version__ = "0.4.0.post2"