Release v0.4.5.post2 (#5582)
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@@ -1,5 +1,5 @@
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# Usage (to build SGLang ROCm docker image):
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# Usage (to build SGLang ROCm docker image):
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# docker build --build-arg SGL_BRANCH=v0.4.5.post1 -t v0.4.5.post1-rocm630 -f Dockerfile.rocm .
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# docker build --build-arg SGL_BRANCH=v0.4.5.post2 -t v0.4.5.post2-rocm630 -f Dockerfile.rocm .
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# default base image
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# default base image
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ARG BASE_IMAGE="rocm/sgl-dev:vllm20250114"
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ARG BASE_IMAGE="rocm/sgl-dev:vllm20250114"
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@@ -11,9 +11,9 @@ docker pull nvidia/cuda:12.1.1-devel-ubuntu22.04
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# Nvidia
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# Nvidia
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docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-devel-ubuntu22.04 /bin/bash
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docker run --shm-size 128g -it -v /tmp/huggingface:/hf_home --gpus all nvidia/cuda:12.1.1-devel-ubuntu22.04 /bin/bash
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# AMD
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# AMD
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docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.5.post1-rocm630 /bin/bash
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docker run --rm --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 128g -it -v /tmp/huggingface:/hf_home lmsysorg/sglang:v0.4.5.post2-rocm630 /bin/bash
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# AMD just the last 2 GPUs
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# AMD just the last 2 GPUs
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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:v0.4.5.post1-rocm630 /bin/bash
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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:v0.4.5.post2-rocm630 /bin/bash
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```
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```
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### Step 2: Configure the runner by `config.sh`
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### Step 2: Configure the runner by `config.sh`
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@@ -11,7 +11,7 @@ It is recommended to use uv to install the dependencies for faster installation:
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```bash
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```bash
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pip install --upgrade pip
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pip install --upgrade pip
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pip install uv
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pip install uv
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uv pip install "sglang[all]>=0.4.5.post1"
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uv pip install "sglang[all]>=0.4.5.post2"
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```
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```
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**Quick Fixes to Common Problems**
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**Quick Fixes to Common Problems**
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@@ -29,7 +29,7 @@ uv pip install "sglang[all]>=0.4.5.post1"
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```bash
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```bash
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# Use the last release branch
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# Use the last release branch
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git clone -b v0.4.5.post1 https://github.com/sgl-project/sglang.git
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git clone -b v0.4.5.post2 https://github.com/sgl-project/sglang.git
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cd sglang
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cd sglang
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pip install --upgrade pip
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pip install --upgrade pip
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@@ -44,7 +44,7 @@ Note: For AMD ROCm system with Instinct/MI GPUs, do following instead:
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```bash
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```bash
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# Use the last release branch
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# Use the last release branch
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git clone -b v0.4.5.post1 https://github.com/sgl-project/sglang.git
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git clone -b v0.4.5.post2 https://github.com/sgl-project/sglang.git
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cd sglang
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cd sglang
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pip install --upgrade pip
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pip install --upgrade pip
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@@ -73,7 +73,7 @@ docker run --gpus all \
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Note: For AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
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Note: For AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
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```bash
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```bash
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docker build --build-arg SGL_BRANCH=v0.4.5.post1 -t v0.4.5.post1-rocm630 -f Dockerfile.rocm .
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docker build --build-arg SGL_BRANCH=v0.4.5.post2 -t v0.4.5.post2-rocm630 -f Dockerfile.rocm .
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alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
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alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
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--shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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--shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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@@ -82,11 +82,11 @@ alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/d
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drun -p 30000:30000 \
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drun -p 30000:30000 \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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-v ~/.cache/huggingface:/root/.cache/huggingface \
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--env "HF_TOKEN=<secret>" \
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--env "HF_TOKEN=<secret>" \
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v0.4.5.post1-rocm630 \
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v0.4.5.post2-rocm630 \
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python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
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# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
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# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
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drun v0.4.5.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
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drun v0.4.5.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
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```
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```
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## Method 4: Using docker compose
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## Method 4: Using docker compose
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
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[project]
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[project]
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name = "sglang"
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name = "sglang"
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version = "0.4.5.post1"
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version = "0.4.5.post2"
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description = "SGLang is yet another fast serving framework for large language models and vision language models."
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description = "SGLang is yet another fast serving framework for large language models and vision language models."
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readme = "README.md"
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readme = "README.md"
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requires-python = ">=3.8"
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requires-python = ">=3.8"
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@@ -1 +1 @@
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__version__ = "0.4.5.post1"
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__version__ = "0.4.5.post2"
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