ROCm: bump 6.3.0 (#3259)

.github/workflows/release-docker-amd.yml

@@ -14,7 +14,7 @@ jobs:
     environment: 'prod'
     strategy:
       matrix:
-        rocm_version: ['6.2.0']
+        rocm_version: ['6.3.0']
         build_type: ['all', 'srt']
     steps:
       - name: Checkout repository
@@ -41,8 +41,8 @@ jobs:
         run: |
           version=$(cat python/sglang/version.py | cut -d'"' -f2)
 
-          if [ "${{ matrix.rocm_version }}" = "6.2.0" ]; then
-            rocm_tag="rocm620"
+          if [ "${{ matrix.rocm_version }}" = "6.3.0" ]; then
+            rocm_tag="rocm630"
           else
             echo "Unsupported ROCm version"
             exit 1

docker/Dockerfile.rocm

 # Usage (to build SGLang ROCm docker image):
-#   docker build --build-arg SGL_BRANCH=v0.4.2.post1 -t v0.4.2.post1-rocm620 -f Dockerfile.rocm .
+#   docker build --build-arg SGL_BRANCH=v0.4.2.post1 -t v0.4.2.post1-rocm630 -f Dockerfile.rocm .
 
 # default base image
-ARG BASE_IMAGE="rocmshared/vllm-rocm:20250114-tuned-elementwise-layernorm"
+ARG BASE_IMAGE="rocm/vllm-dev:20250114"
 
 FROM $BASE_IMAGE AS base
 USER root

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:v0.4.2.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:v0.4.2.post1-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-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:v0.4.2.post1-rocm630 /bin/bash
 ```
 
 ### Step 2: Configure the runner by `config.sh`

docs/start/install.md

@@ -54,7 +54,7 @@ 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-rocm620 -f Dockerfile.rocm .
+docker build --build-arg SGL_BRANCH=v0.4.2.post1 -t v0.4.2.post1-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 \
@@ -63,11 +63,11 @@ 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-rocm620 \
+    v0.4.2.post1-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-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 v0.4.2.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
 ```
 
 ## Method 4: Using docker compose

python/pyproject.toml

@@ -19,31 +19,29 @@ dependencies = ["requests", "tqdm", "numpy", "IPython", "setproctitle"]
 runtime_common = [
     "aiohttp", "decord", "fastapi",
     "hf_transfer", "huggingface_hub", "interegular", "modelscope",
-    "orjson", "outlines>=0.0.44,<0.1.0",
-    "packaging", "pillow", "prometheus-client>=0.20.0",
-    "psutil", "pydantic", "python-multipart",
-    "pyzmq>=25.1.2", "torchao>=0.7.0", "uvicorn", "uvloop",
-    "xgrammar>=0.1.10"
+    "orjson", "packaging", "pillow", "prometheus-client>=0.20.0",
+    "psutil", "pydantic", "python-multipart", "pyzmq>=25.1.2",
+    "torchao>=0.7.0", "uvicorn", "uvloop", "xgrammar>=0.1.10"
 ]
 srt = [
     "sglang[runtime_common]", "cuda-python",
     "sgl-kernel>=0.0.3.post1", "torch", "vllm==0.6.4.post1",
-    "flashinfer==0.1.6"
+    "flashinfer==0.1.6", "outlines>=0.0.44,<0.1.0"
 ]
 
 # HIP (Heterogeneous-computing Interface for Portability) for AMD
 # => base docker rocm/vllm-dev:20241022, not from public vllm whl
-srt_hip = ["sglang[runtime_common]", "torch", "vllm==0.6.3.post2.dev1"]
+srt_hip = ["sglang[runtime_common]", "torch", "vllm==0.6.7.dev2", "outlines==0.1.11"]
 # xpu is not enabled in public vllm and torch whl,
 # need to follow https://docs.vllm.ai/en/latest/getting_started/xpu-installation.htmlinstall vllm
-srt_xpu = ["sglang[runtime_common]"]
+srt_xpu = ["sglang[runtime_common]", "outlines>=0.0.44,<0.1.0"]
 #For Intel Gaudi(device : hpu) follow the installation guide
 #https://docs.vllm.ai/en/latest/getting_started/gaudi-installation.html
-srt_hpu = ["sglang[runtime_common]"]
+srt_hpu = ["sglang[runtime_common]", "outlines>=0.0.44,<0.1.0"]
 # CPU: currently, there are no pre-built vllm wheels for CPU.
 # To install vllm for CPU, please follow the instruction here:
 # https://docs.vllm.ai/en/latest/getting_started/installation/cpu/index.html
-srt_cpu = ["sglang[runtime_common]", "torch"]
+srt_cpu = ["sglang[runtime_common]", "torch", "outlines>=0.0.44,<0.1.0"]
 
 openai = ["openai>=1.0", "tiktoken"]
 anthropic = ["anthropic>=0.20.0"]

python/sglang/srt/constrained/outlines_backend.py

@@ -20,7 +20,6 @@ from typing import Dict, List, Optional, Tuple, Union
 import interegular
 import torch
 from outlines.fsm.guide import RegexGuide
-from outlines.fsm.json_schema import build_regex_from_schema
 from outlines.models.transformers import TransformerTokenizer
 from pydantic import BaseModel
 
@@ -29,6 +28,15 @@ from sglang.srt.constrained.base_grammar_backend import (
     BaseGrammarObject,
 )
 from sglang.srt.constrained.outlines_jump_forward import OutlinesJumpForwardMap
+from sglang.srt.utils import is_hip
+
+is_hip_ = is_hip()
+
+if is_hip_:
+    from outlines_core.fsm.json_schema import build_regex_from_schema
+else:
+    from outlines.fsm.json_schema import build_regex_from_schema
+
 
 logger = logging.getLogger(__name__)
 

python/sglang/srt/custom_op.py

@@ -20,7 +20,7 @@ class CustomOp(nn.Module):
         raise NotImplementedError
 
     def forward_hip(self, *args, **kwargs):
-        raise NotImplementedError
+        return self.forward_native(*args, **kwargs)
 
     def forward_xpu(self, *args, **kwargs):
         return self.forward_native(*args, **kwargs)