chore: bump v0.4.1.post5 (#2840)

benchmark/deepseek_v3/README.md

@@ -4,6 +4,8 @@ The SGLang and DeepSeek teams collaborated to get DeepSeek V3 FP8 running on NVI
 
 Special thanks to Meituan's Search & Recommend Platform Team and Baseten's Model Performance Team for implementing the model, and DataCrunch for providing GPU resources.
 
+For optimizations made on the DeepSeek series models regarding SGLang, please refer to https://sgl-project.github.io/references/deepseek.html
+
 ## Hardware Recommendation
 - 8 x NVIDIA H200 GPUs
 
@@ -29,7 +31,7 @@ For high QPS scenarios, add the `--enable-dp-attention` argument to boost throug
 ### Using pip
 ```bash
 # Installation
-pip install "sglang[all]>=0.4.1.post3" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer
+pip install "sglang[all]>=0.4.1.post5" --find-links https://flashinfer.ai/whl/cu124/torch2.4/flashinfer
 
 # Launch
 python3 -m sglang.launch_server --model deepseek-ai/DeepSeek-V3 --tp 8 --trust-remote-code

docker/Dockerfile.rocm

 # Usage (to build SGLang ROCm docker image):
-#   docker build --build-arg SGL_BRANCH=v0.4.1.post4 -t v0.4.1.post4-rocm620 -f Dockerfile.rocm .
+#   docker build --build-arg SGL_BRANCH=v0.4.1.post5 -t v0.4.1.post5-rocm620 -f Dockerfile.rocm .
 
 # default base image
 ARG BASE_IMAGE="rocmshared/vllm-rocm:20241031-tuned"

docs/developer/setup_github_runner.md

@@ -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:v0.4.1.post4-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:v0.4.1.post5-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:v0.4.1.post4-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:v0.4.1.post5-rocm620 /bin/bash
 ```
 
 ### Step 2: Configure the runner by `config.sh`

docs/start/install.md

@@ -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 v0.4.1.post4 https://github.com/sgl-project/sglang.git
+git clone -b v0.4.1.post5 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 v0.4.1.post4 https://github.com/sgl-project/sglang.git
+git clone -b v0.4.1.post5 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=v0.4.1.post4 -t v0.4.1.post4-rocm620 -f Dockerfile.rocm .
+docker build --build-arg SGL_BRANCH=v0.4.1.post5 -t v0.4.1.post5-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>" \
-    v0.4.1.post4-rocm620 \
+    v0.4.1.post5-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 v0.4.1.post4-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 v0.4.1.post5-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

python/pyproject.toml

@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "sglang"
-version = "0.4.1.post4"
+version = "0.4.1.post5"
 description = "SGLang is yet another fast serving framework for large language models and vision language models."
 readme = "README.md"
 requires-python = ">=3.8"

python/sglang/version.py

-__version__ = "0.4.1.post4"
+__version__ = "0.4.1.post5"