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Commit 118f1fc7 authored by maxiao1's avatar maxiao1
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sglangv0.5.2 & support Qwen3-Next-80B-A3B-Instruct

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.github/workflows/release-pypi.yml 0 → 100644
View file @ 118f1fc7
name: Release PyPI
on:
push:
branches:
- main
paths:
- "python/sglang/version.py"
workflow_dispatch:
jobs:
publish:
if: github.repository == 'sgl-project/sglang'
runs-on: ubuntu-latest
environment: "prod"
steps:
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: "3.10"
- name: Checkout repository
uses: actions/checkout@v4
- name: Upload to pypi
run: |
cd python
cp ../README.md ../LICENSE .
pip install build
python3 -m build
pip install twine
python3 -m twine upload dist/* -u __token__ -p ${{ secrets.PYPI_TOKEN }}
.github/workflows/release-whl-kernel-cu118.yml 0 → 100644
View file @ 118f1fc7
name: Release SGLang Kernel Wheel (cu118)
on:
workflow_dispatch:
inputs:
tag_name:
type: string
push:
branches:
- main
paths:
- sgl-kernel/python/sgl_kernel/version.py
jobs:
build-wheels:
if: github.repository == 'sgl-project/sglang'
runs-on: sgl-kernel-release-node
strategy:
matrix:
python-version: ["3.9"]
cuda-version: ["11.8"]
steps:
- uses: actions/checkout@v4
with:
submodules: "recursive"
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Build wheels for Python ${{ matrix.python-version }} and CUDA ${{ matrix.cuda-version }}
run: |
cd sgl-kernel
chmod +x ./build.sh
./build.sh "${{ matrix.python-version }}" "${{ matrix.cuda-version }}"
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: wheel-python${{ matrix.python-version }}-cuda${{ matrix.cuda-version }}
path: sgl-kernel/dist/*
release:
needs: build-wheels
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Download artifacts
uses: actions/download-artifact@v4
with:
path: sgl-kernel/dist/
merge-multiple: true
pattern: wheel-*
- name: Set tag name
id: set_tag_name
run: |
if [ -z "${{ inputs.tag_name }}" ]; then
TAG_NAME="v$(cat sgl-kernel/python/sgl_kernel/version.py | cut -d'"' -f2)"
echo "tag_name=$TAG_NAME" >> $GITHUB_OUTPUT
else
echo "tag_name=${{ inputs.tag_name }}" >> $GITHUB_OUTPUT
fi
- name: Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.set_tag_name.outputs.tag_name }}
repository: sgl-project/whl
token: ${{ secrets.WHL_TOKEN }}
files: |
sgl-kernel/dist/*
- name: Clone wheel index
run: git clone https://oauth2:${WHL_TOKEN}@github.com/sgl-project/whl.git sgl-whl
env:
WHL_TOKEN: ${{ secrets.WHL_TOKEN }}
- name: Update wheel index
run: python3 scripts/update_kernel_whl_index.py
- name: Push wheel index
run: |
cd sgl-whl
git config --local user.name "github-actions[bot]"
git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add -A
git commit -m "update whl index"
git push
.github/workflows/release-whl-kernel.yml 0 → 100644
View file @ 118f1fc7
name: Release SGLang Kernels
on:
push:
branches:
- main
paths:
- sgl-kernel/python/sgl_kernel/version.py
workflow_dispatch:
inputs:
tag_name:
type: string
required: false
concurrency:
group: release-sglang-kernels-${{ github.ref }}
cancel-in-progress: true
jobs:
build-cu129:
if: github.repository == 'sgl-project/sglang'
runs-on: sgl-kernel-release-node
strategy:
matrix:
python-version: ["3.10"]
cuda-version: ["12.9"]
steps:
- uses: actions/checkout@v4
with:
submodules: "recursive"
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Build wheels
run: |
cd sgl-kernel
chmod +x ./build.sh
./build.sh "${{ matrix.python-version }}" "${{ matrix.cuda-version }}"
- name: Upload to PyPI
working-directory: sgl-kernel
run: |
pip install twine
python3 -m twine upload dist/* -u __token__ -p ${{ secrets.PYPI_TOKEN }}
build-cu124:
if: github.repository == 'sgl-project/sglang'
needs: build-cu129
runs-on: sgl-kernel-release-node
strategy:
matrix:
python-version: ["3.10"]
cuda-version: ["12.4"]
steps:
- uses: actions/checkout@v4
with:
submodules: "recursive"
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Build wheels
run: |
cd sgl-kernel
chmod +x ./build.sh
./build.sh "${{ matrix.python-version }}" "${{ matrix.cuda-version }}"
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: wheel-python${{ matrix.python-version }}-cuda${{ matrix.cuda-version }}
path: sgl-kernel/dist/*
release-cu124:
needs: build-cu124
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Download artifacts
uses: actions/download-artifact@v4
with:
path: sgl-kernel/dist/
merge-multiple: true
pattern: wheel-*
- name: Set tag name
id: set_tag_name
run: |
if [ -z "${{ inputs.tag_name }}" ]; then
TAG_NAME="v$(cat sgl-kernel/python/sgl_kernel/version.py | cut -d'"' -f2)"
echo "tag_name=$TAG_NAME" >> $GITHUB_OUTPUT
else
echo "tag_name=${{ inputs.tag_name }}" >> $GITHUB_OUTPUT
fi
- name: Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.set_tag_name.outputs.tag_name }}
repository: sgl-project/whl
token: ${{ secrets.WHL_TOKEN }}
files: |
sgl-kernel/dist/*
- name: Clone wheel index
run: git clone https://oauth2:${WHL_TOKEN}@github.com/sgl-project/whl.git sgl-whl
env:
WHL_TOKEN: ${{ secrets.WHL_TOKEN }}
- name: Update wheel index
run: python3 scripts/update_kernel_whl_index.py --cuda 124
- name: Push wheel index
run: |
cd sgl-whl
git config --local user.name "github-actions[bot]"
git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add -A
git commit -m "update whl index"
git push
build-cu128:
if: github.repository == 'sgl-project/sglang'
needs: build-cu129
runs-on: sgl-kernel-release-node
strategy:
matrix:
python-version: ["3.10"]
cuda-version: ["12.8"]
steps:
- uses: actions/checkout@v4
with:
submodules: "recursive"
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Build wheels
run: |
cd sgl-kernel
chmod +x ./build.sh
./build.sh "${{ matrix.python-version }}" "${{ matrix.cuda-version }}"
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: wheel-python${{ matrix.python-version }}-cuda${{ matrix.cuda-version }}
path: sgl-kernel/dist/*
release-cu128:
needs: build-cu128
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Download artifacts
uses: actions/download-artifact@v4
with:
path: sgl-kernel/dist/
merge-multiple: true
pattern: wheel-*
- name: Set tag name
id: set_tag_name
run: |
if [ -z "${{ inputs.tag_name }}" ]; then
TAG_NAME="v$(cat sgl-kernel/python/sgl_kernel/version.py | cut -d'"' -f2)"
echo "tag_name=$TAG_NAME" >> $GITHUB_OUTPUT
else
echo "tag_name=${{ inputs.tag_name }}" >> $GITHUB_OUTPUT
fi
- name: Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.set_tag_name.outputs.tag_name }}
repository: sgl-project/whl
token: ${{ secrets.WHL_TOKEN }}
files: |
sgl-kernel/dist/*
- name: Clone wheel index
run: git clone https://oauth2:${WHL_TOKEN}@github.com/sgl-project/whl.git sgl-whl
env:
WHL_TOKEN: ${{ secrets.WHL_TOKEN }}
- name: Update wheel index
run: python3 scripts/update_kernel_whl_index.py --cuda 128
- name: Push wheel index
run: |
cd sgl-whl
git config --local user.name "github-actions[bot]"
git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add -A
git commit -m "update whl index"
git push
build-cu129-aarch64:
if: github.repository == 'sgl-project/sglang'
runs-on: sgl-kernel-release-node-arm
strategy:
matrix:
python-version: ["3.10"]
cuda-version: ["12.9"]
steps:
- uses: actions/checkout@v4
with:
submodules: "recursive"
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
- name: Build wheels
run: |
cd sgl-kernel
chmod +x ./build.sh
./build.sh "${{ matrix.python-version }}" "${{ matrix.cuda-version }}" aarch64
- name: Upload artifacts
uses: actions/upload-artifact@v4
with:
name: wheel-python${{ matrix.python-version }}-cuda${{ matrix.cuda-version }}-aarch64
path: sgl-kernel/dist/*
release-cu129-aarch64:
needs: build-cu129-aarch64
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v4
- name: Download artifacts
uses: actions/download-artifact@v4
with:
path: sgl-kernel/dist/
merge-multiple: true
pattern: wheel-*
- name: Set tag name
id: set_tag_name
run: |
if [ -z "${{ inputs.tag_name }}" ]; then
TAG_NAME="v$(cat sgl-kernel/python/sgl_kernel/version.py | cut -d'"' -f2)"
echo "tag_name=$TAG_NAME" >> $GITHUB_OUTPUT
else
echo "tag_name=${{ inputs.tag_name }}" >> $GITHUB_OUTPUT
fi
- name: Release
uses: softprops/action-gh-release@v2
with:
tag_name: ${{ steps.set_tag_name.outputs.tag_name }}
repository: sgl-project/whl
token: ${{ secrets.WHL_TOKEN }}
files: |
sgl-kernel/dist/*
- name: Clone wheel index
run: git clone https://oauth2:${WHL_TOKEN}@github.com/sgl-project/whl.git sgl-whl
env:
WHL_TOKEN: ${{ secrets.WHL_TOKEN }}
- name: Update wheel index
run: python3 scripts/update_kernel_whl_index.py --cuda 129
- name: Push wheel index
run: |
cd sgl-whl
git config --local user.name "github-actions[bot]"
git config --local user.email "41898282+github-actions[bot]@users.noreply.github.com"
git add -A
git commit -m "update whl index"
git push
.github/workflows/vllm-dependency-test.yml 0 → 100644
View file @ 118f1fc7
name: VLLM Dependency Test
on:
push:
branches: [ main ]
paths:
- "python/**"
- "scripts/ci/**"
- "test/**"
pull_request:
branches: [ main ]
paths:
- "python/**"
- "scripts/ci/**"
- "test/**"
concurrency:
group: vllm-dependency-test-${{ github.ref }}
cancel-in-progress: true
jobs:
vllm-dependency-test:
if: (github.repository == 'sgl-project/sglang' || github.event_name == 'pull_request') &&
github.event.pull_request.draft == false
runs-on: 1-gpu-runner
steps:
- name: Checkout code
uses: actions/checkout@v4
- name: Install dependencies
run: |
bash scripts/ci/ci_install_dependency.sh
pip install "bitsandbytes>=0.44.0"
pip install "sgl-kernel==0.3.7"
- name: Run vLLM dependency tests
timeout-minutes: 60
run: |
export SGLANG_SKIP_SGL_KERNEL_VERSION_CHECK=1
cd test/srt
python3 run_suite.py --suite vllm_dependency_test --timeout-per-file 3600
.gitignore 0 → 100644
View file @ 118f1fc7
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
# Tokenizer cache for tests
.tokenizer_cache/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
.idea/
# MacOS
.DS_Store
# Vim
*.swp
# Documentation
docs/_build
# SGL
benchmark/mmlu/data
benchmark/mmlu/data.tar
benchmark/llava_bench/images
benchmark/llava_bench/mme_pack
*.jsonl
tmp*.txt
# Plots
*.png
*.pdf
# personnal
work_dirs/
*.csv
!logo.png
# Prerequisites
*.d
# Compiled Object files
*.slo
*.lo
*.o
*.obj
# Precompiled Headers
*.gch
*.pch
# Compiled Dynamic libraries
*.so
*.dylib
*.dll
# Fortran module files
*.mod
*.smod
# Compiled Static libraries
*.lai
*.la
*.a
*.lib
# Executables
*.exe
*.out
*.app
compile_commands.json
*.iml
# VSCode
.vscode
1
# Autoenv
.env.leave
# Rust lib
Cargo.lock
lmms-eval
.isort.cfg 0 → 100644
View file @ 118f1fc7
[settings]
profile=black
known_first_party=sglang
.pre-commit-config.yaml 0 → 100644
View file @ 118f1fc7
default_stages: [pre-commit, pre-push, manual]
repos:
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v5.0.0
hooks:
- id: check-symlinks
- id: destroyed-symlinks
- id: trailing-whitespace
- id: end-of-file-fixer
- id: check-yaml
args: [--allow-multiple-documents]
- id: check-toml
- id: check-ast
- id: check-added-large-files
- id: check-merge-conflict
- id: check-shebang-scripts-are-executable
- id: detect-private-key
- id: debug-statements
- id: no-commit-to-branch
- repo: https://github.com/PyCQA/isort
rev: 5.13.2
hooks:
- id: isort
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.11.7
hooks:
- id: ruff
args: [--select=F401, --fixable=F401]
files: ^(benchmark/|docs/|examples/)
exclude: \.ipynb$
- repo: https://github.com/psf/black
rev: 24.10.0
hooks:
- id: black-jupyter
- repo: https://github.com/codespell-project/codespell
rev: v2.4.1
hooks:
- id: codespell
additional_dependencies: ['tomli']
args: ['--toml', 'python/pyproject.toml', '-L', 'cann,thi,makro,wil,rouge']
exclude: |
(?x)^(
test/srt/test_reasoning_parser\.py|
docs/advanced_features/vlm_query\.ipynb
)$
- repo: https://github.com/pre-commit/mirrors-clang-format
rev: v18.1.8
hooks:
- id: clang-format
types_or: [c++, cuda]
args: [--style=file, --verbose]
- repo: https://github.com/kynan/nbstripout
rev: 0.8.1
hooks:
- id: nbstripout
args:
- '--keep-output'
- '--extra-keys=metadata.kernelspec metadata.language_info.version'
3rdparty/amd/profiling/PROFILING.md 0 → 100644
View file @ 118f1fc7
## Profiling SGLang Infer System with AMD GPUs
This AppNote describes the SGLang profiling technical, code augment and running steps for systems with AMD Instinct GPUs, nevertheless the same procedure may work with Nvidia GPUs too.
Examples and steps are provided in detail, to facilitate easy reproduce and use to localize performance problem towards optimizations.
Two primary methods are covered:
- [RPD](https://github.com/ROCm/rocmProfileData.git)
- [PyTorch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html)
### Profiling SGLang Infer System with RPD Profiler
RPD profiler is a low-overhead cross-platform profiler. Therefore, the same RPD code augment not only works for profiling on ROCm/AMD GPUs, but also works for profiling on CUDA/Nvidia GPUs as well. To do RPD profiling on SGLang repository, please use scripts and patch files included in this directory and follow the steps below:
1. Install RPD with rpd.patch applied during installation using install_rpd.sh, both files are in this directory.
install_rpd.sh
```bash
# download and install RPD
apt update && apt install -y sqlite3 libsqlite3-dev libfmt-dev
# install rpd module
git clone https://github.com/ROCmSoftwarePlatform/rocmProfileData
cd rocmProfileData
git checkout 976899e9c6dbc6dd2bccf770818e4e44125590ac
git apply rpd.patch
make && make install
cd rocpd_python && python setup.py install && cd ..
cd rpd_tracer && make clean;make install && python setup.py install && cd ..
```
rpd.patch
```bash
diff --git a/rpd_tracer/Makefile b/rpd_tracer/Makefile
index e9d9feb..b2e9e1a 100644
--- a/rpd_tracer/Makefile
+++ b/rpd_tracer/Makefile
@@ -16,7 +16,7 @@ ifneq (,$(HIP_PATH))
$(info Building with roctracer)
RPD_LIBS += -L/opt/rocm/lib -lroctracer64 -lroctx64 -lamdhip64 -lrocm_smi64
RPD_INCLUDES += -I/opt/rocm/include -I/opt/rocm/include/roctracer -I/opt/rocm/include/hsa
- RPD_SRCS += RoctracerDataSource.cpp RocmSmiDataSource.cpp
+ RPD_SRCS += RoctracerDataSource.cpp
RPD_INCLUDES += -D__HIP_PLATFORM_AMD__
endif
```
2. Add loadTracer.sh file included in this directory to /sglang/python/sglang.
loadTracer.sh
```bash
#!/bin/bash
################################################################################
# Copyright (c) 2021 - 2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
################################################################################
OUTPUT_FILE="trace.rpd"
if [ "$1" = "-o" ] ; then
OUTPUT_FILE=$2
shift
shift
fi
if [ -e ${OUTPUT_FILE} ] ; then
rm ${OUTPUT_FILE}
fi
python3 -m rocpd.schema --create ${OUTPUT_FILE}
if [ $? != 0 ] ; then
echo "Error: Could not create rpd file. Please run 'python setup.py install' from the rocpd_python dir"
exit
fi
export RPDT_FILENAME=${OUTPUT_FILE}
export RPDT_AUTOSTART=0
LD_PRELOAD=librocm-smi_64:librpd_tracer.so "$@"
```
3. Apply patch (provided in this directory) with "git apply rpd_profile_server_enable.patch" if the main profiling purpose is to get info on gpu kernels as well as limited cpu activity info.
#### Common Notes 1
Please note that although we are doing TP=8 in the example, we purposely only log RPD profiling on 2 ranks in the patch file (i.e.tp_rank=0/1) for profiling/visualization convenience, as even Perfetto streaming mode can only load maximal 8GB json file for visualization. With 2 ranks logged in RPD profiling, we could still check whether there are issues among ranks (e.g. load imbalance issue, nccl issue), and at the same time, we could log relatively longer time duration before the json file generated from RPD file hits 8GB size.
rpd_profile_server_enable.patch
```bash
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..9021c01 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -71,6 +71,8 @@ from sglang.srt.utils import (
suppress_other_loggers,
)
from sglang.utils import get_exception_traceback
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
logger = logging.getLogger(__name__)
@@ -245,6 +247,7 @@ class Scheduler:
],
with_stack=True,
)
+ self.rpd = rpdTracerControl()
@torch.inference_mode()
def event_loop(self):
@@ -1027,15 +1030,24 @@ class Scheduler:
def start_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.start()
+ #self.profiler.start() #block pytorch profiler for rpd profiler enabling
+ if self.tp_rank == 0 or self.tp_rank == 1:
+ self.rpd.start()
+ self.rpd.rangePush("", "rpd profile range", "")
+ logger.info("rpd is enabled")
def stop_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ #self.profiler.stop()
+ #self.profiler.export_chrome_trace(
+ # self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
+ #)
+ if self.tp_rank ==0 or self.tp_rank ==1:
+ self.rpd.rangePop()
+ self.rpd.stop()
+ self.rpd.flush()
+ logger.info("rpd is done")
logger.info("Profiler is done")
```
#### Advanced Debugging with RPD Profiler
Sometimes, we want to use rpd profiler to capture more CPU and python activities in order to debug some challenging issues (e.g. root cause of load imbalance across gpu processes, root cause of bubbles, etc). Only in such cases, we need to apply patch "git apply rpd_profile_server_enable_wCPU_activities.patch", where 3 files are modified.
rpd_profile_server_enable_wCPU_activities.patch
```bash
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..2edb427 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -71,6 +71,8 @@ from sglang.srt.utils import (
suppress_other_loggers,
)
from sglang.utils import get_exception_traceback
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
logger = logging.getLogger(__name__)
@@ -245,6 +247,7 @@ class Scheduler:
],
with_stack=True,
)
+ self.rpd = rpdTracerControl()
@torch.inference_mode()
def event_loop(self):
@@ -1027,15 +1030,26 @@ class Scheduler:
def start_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.start()
+ #self.profiler.start()
+ logger.info("torch profiler is disabled")
+ if self.tp_rank == 0 or self.tp_rank == 1:
+ self.rpd.setPythonTrace(True)
+ self.rpd.start()
+ self.rpd.rangePush("", "scheduler", "")
+ logger.info("rpd is enabled inside scheduler profiling")
def stop_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ #self.profiler.stop()
+ #self.profiler.export_chrome_trace(
+ # self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
+ #)
+ if self.tp_rank ==0 or self.tp_rank ==1:
+ self.rpd.rangePop()
+ self.rpd.stop()
+ self.rpd.flush()
+ logger.info("rpd is done inside scheduler")
logger.info("Profiler is done")
diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py
index 2621ccd..181df85 100644
--- a/python/sglang/srt/managers/tokenizer_manager.py
+++ b/python/sglang/srt/managers/tokenizer_manager.py
@@ -58,6 +58,10 @@ from sglang.srt.sampling.sampling_params import SamplingParams
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import is_generation_model, is_multimodal_model
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
+
+
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
logger = logging.getLogger(__name__)
@@ -514,10 +518,20 @@ class TokenizerManager:
self.send_to_scheduler.send_pyobj(req)
def start_profile(self):
+ rpd = rpdTracerControl()
+ rpd.setPythonTrace(True)
+ rpd.start()
+ rpd.rangePush("", "tokenizer_manager", "")
+ logger.info("tokenizer_manager rpd profiling started!")
req = ProfileReq.START_PROFILE
self.send_to_scheduler.send_pyobj(req)
def stop_profile(self):
+ rpd = rpdTracerControl()
+ rpd.rangePop()
+ rpd.stop()
+ rpd.flush()
+ logger.info("rpd profiling is done inside tokenizer_manager!")
req = ProfileReq.STOP_PROFILE
self.send_to_scheduler.send_pyobj(req)
diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py
index 7111c93..2bd722c 100644
--- a/python/sglang/srt/server.py
+++ b/python/sglang/srt/server.py
@@ -30,6 +30,8 @@ import threading
import time
from http import HTTPStatus
from typing import Dict, List, Optional, Union
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
# Fix a bug of Python threading
setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
@@ -152,6 +154,11 @@ async def flush_cache():
@app.post("/start_profile")
async def start_profile():
"""Start profiling."""
+ rpd = rpdTracerControl()
+ rpd.setPythonTrace(True)
+ rpd.start()
+ rpd.rangePush("", "server rpd profile range", "")
+ logger.info("rpd profiling started in server.py!")
tokenizer_manager.start_profile()
return Response(
content="Start profiling.\n",
@@ -164,6 +171,11 @@ async def start_profile():
async def stop_profile():
"""Stop profiling."""
tokenizer_manager.stop_profile()
+ rpd = rpdTracerControl()
+ rpd.rangePop()
+ rpd.stop()
+ rpd.flush()
+ logger.info("rpd profiling is done in server.py!")
return Response(
content="Stop profiling. This will take some time.\n",
status_code=200,
```
4. As an example for grok1 profiling, we create a dummy_grok1 directory with config.json (see content below) inside this directory and copy this directory to the right path for "--model-path" if you want to use the example server.sh file provided.
```bash
cat ../dummy_grok1/config.json
{
"architectures": [
"Grok1ModelForCausalLM"
],
"embedding_multiplier_scale": 78.38367176906169,
"output_multiplier_scale": 0.5773502691896257,
"vocab_size": 131072,
"hidden_size": 6144,
"intermediate_size": 32768,
"max_position_embeddings": 8192,
"num_experts_per_tok": 2,
"num_local_experts": 8,
"num_attention_heads": 48,
"num_hidden_layers": 64,
"num_key_value_heads": 8,
"head_dim": 128,
"rms_norm_eps": 1e-05,
"rope_theta": 10000.0,
"model_type": "mixtral",
"torch_dtype": "bfloat16"
}
```
5. Launch server with rpd enabled script ./server.sh in one terminal inside the docker container.
#### Common Notes 2
- Remember to change model-path to the correct path
- loadTracer.sh is needed to conduct profiling
- SGLANG_TORCH_PROFILER_DIR is used for default torch profiler
- Do not use loadTracer.sh if you are using the torch profiler, simply use python3 -m sglang.launch_server.
server.sh
```bash
#!/bin/bash
# export SGLANG_TORCH_PROFILER_DIR=/data/sglang/
export SGLANG_TORCH_PROFILER_DIR=/sgl-workspace/sglang/profile/
# Get the current timestamp
TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
# Define the log file with a timestamp
LOGFILE="sglang_server_log_$TIMESTAMP.json"
# Run the Python command and save the output to the log file
loadTracer.sh python3 -m sglang.launch_server \
--model-path /sgl-workspace/sglang/dummy_grok1 \
--tokenizer-path Xenova/grok-1-tokenizer \
--load-format dummy \
--quantization fp8 \
--tp 8 \
--port 30000 \
--disable-radix-cache 2>&1 | tee "$LOGFILE"
```
6. Open another terminal for the same docker container, and run the rpd enabled ./client.sh after you see "The server is fired up and is ready to roll!" message from server side terminal.
#### Common Notes 3
- Use curl http://localhost:30000/start_profile & curl http://localhost:30000/stop_profile to control the start and end of profiling. Check sglang/python/sglang/srt/managers/scheduler.py for more details.
- Please don't use RPD profiler together with PyTorch profiler to avoid interference.
- The rocmProfileData/tools/rpd2tracing.py file is used to generate json file from RPD file.
client.sh
```bash
#!/bin/bash
# Start profiling via API
curl http://localhost:30000/start_profile -H "Content-Type: application/json"
# Benchmark serving using sglang with random dataset and tokenizer
# Define the log file with a timestamp
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
LOGFILE="sglang_client_log_$TIMESTAMP.json"
# Run the benchmark with specified parameters and save logs
python3 -m sglang.bench_serving \
--backend sglang \
--tokenizer Xenova/grok-1-tokenizer \
--dataset-name random \
--random-input 1024\
--random-output 1024 \
--num-prompts 120 \
--request-rate 8 \
--output-file online.jsonl 2>&1 | tee "$LOGFILE"
# Stop profiling via API
curl http://localhost:30000/stop_profile -H "Content-Type: application/json"
# Convert tracing file to csv & json
sqlite3 trace.rpd ".mode csv" ".header on" ".output trace.csv" "select * from top;" ".output stdout"
python3 ./rocmProfileData/tools/rpd2tracing.py trace.rpd trace.json
```
7. Follow [Perfetto docs](https://perfetto.dev/docs/visualization/large-traces) to visualize large json files. Try to adjust parameters so that the trace.json file size is less than 9GB.
### Profiling SGLang Infer System with PyTorch Profiler
Please use the steps as follows:
1. Apply the patch torch_profiler.patch. Note that you can modify "if self.tp_rank == 0" in the patch to allow more ranks be recorded in profiling.
torch_profiler.patch
```bash
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..6ecd78c 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -240,7 +240,6 @@ class Scheduler:
)
self.profiler = torch.profiler.profile(
activities=[
- torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
with_stack=True,
@@ -1033,9 +1032,11 @@ class Scheduler:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ if self.tp_rank == 0:
+ with open(f"stats_repro_{int(time.time())}.txt", "w") as f:
+ print(self.profiler.key_averages(group_by_input_shape=True).table(sort_by="cuda_time_total", row_limit=-1), file=f)
+ print("Profiling stats done.")
+
logger.info("Profiler is done")
```
2. Create the model path directory and copy it to the right path for "--model-path" if you want to use the server.sh file provided.
3. Modify the included server.sh by removing "loadTracer.sh" before python command and launch script ./server.sh in one terminal inside the docker container.
4. Similar to step 6 in RPD profiling section, but remove the last 2 lines in client.sh, which converted rpd file into csv and json files. Run modified client.sh for PyTorch profiling.
-------
- [Torch Profiler](https://pytorch.org/tutorials/recipes/recipes/profiler_recipe.html)
3rdparty/amd/profiling/client.sh 0 → 100755
View file @ 118f1fc7
#!/bin/bash
# Start profiling via API
curl http://localhost:30000/start_profile -H "Content-Type: application/json"
# Benchmark serving using sglang with random dataset and tokenizer
# Define the log file with a timestamp
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
LOGFILE="sglang_client_log_$TIMESTAMP.json"
# Run the benchmark with specified parameters and save logs
python3 -m sglang.bench_serving \
--backend sglang \
--tokenizer Xenova/grok-1-tokenizer \
--dataset-name random \
--random-input 1024\
--random-output 1024 \
--num-prompts 240 \
--request-rate 8 \
--output-file online.jsonl 2>&1 | tee "$LOGFILE"
# Stop profiling via API
curl http://localhost:30000/stop_profile -H "Content-Type: application/json"
# Convert tracing file to csv & json
sqlite3 trace.rpd ".mode csv" ".header on" ".output trace.csv" "select * from top;" ".output stdout"
python3 /sgl-workspace/rocmProfileData/tools/rpd2tracing.py trace.rpd trace.json
3rdparty/amd/profiling/install_rpd.sh 0 → 100644
View file @ 118f1fc7
# download and install RPD
apt update && apt install -y sqlite3 libsqlite3-dev libfmt-dev
# install rpd module
git clone https://github.com/ROCmSoftwarePlatform/rocmProfileData
cd rocmProfileData
git apply rpd.patch
make && make install
cd rocpd_python && python setup.py install && cd ..
cd rpd_tracer && make clean;make install && python setup.py install && cd ..
3rdparty/amd/profiling/loadTracer.sh 0 → 100755
View file @ 118f1fc7
#!/bin/bash
################################################################################
# Copyright (c) 2021 - 2023 Advanced Micro Devices, Inc. All rights reserved.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
################################################################################
OUTPUT_FILE="trace.rpd"
if [ "$1" = "-o" ] ; then
OUTPUT_FILE=$2
shift
shift
fi
if [ -e ${OUTPUT_FILE} ] ; then
rm ${OUTPUT_FILE}
fi
python3 -m rocpd.schema --create ${OUTPUT_FILE}
if [ $? != 0 ] ; then
echo "Error: Could not create rpd file. Please run 'python setup.py install' from the rocpd_python dir"
exit
fi
export RPDT_FILENAME=${OUTPUT_FILE}
export RPDT_AUTOSTART=0
LD_PRELOAD=librocm-smi_64:librpd_tracer.so "$@"
3rdparty/amd/profiling/rpd.patch 0 → 100644
View file @ 118f1fc7
diff --git a/rpd_tracer/Makefile b/rpd_tracer/Makefile
index e9d9feb..b2e9e1a 100644
--- a/rpd_tracer/Makefile
+++ b/rpd_tracer/Makefile
@@ -16,7 +16,7 @@ ifneq (,$(HIP_PATH))
$(info Building with roctracer)
RPD_LIBS += -L/opt/rocm/lib -lroctracer64 -lroctx64 -lamdhip64 -lrocm_smi64
RPD_INCLUDES += -I/opt/rocm/include -I/opt/rocm/include/roctracer -I/opt/rocm/include/hsa
- RPD_SRCS += RoctracerDataSource.cpp RocmSmiDataSource.cpp
+ RPD_SRCS += RoctracerDataSource.cpp
RPD_INCLUDES += -D__HIP_PLATFORM_AMD__
endif
3rdparty/amd/profiling/rpd_profile_server_enable.patch 0 → 100644
View file @ 118f1fc7
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..9021c01 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -71,6 +71,8 @@ from sglang.srt.utils import (
suppress_other_loggers,
)
from sglang.utils import get_exception_traceback
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
logger = logging.getLogger(__name__)
@@ -245,6 +247,7 @@ class Scheduler:
],
with_stack=True,
)
+ self.rpd = rpdTracerControl()
@torch.inference_mode()
def event_loop(self):
@@ -1027,15 +1030,24 @@ class Scheduler:
def start_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.start()
+ #self.profiler.start() #block pytorch profiler for rpd profiler enabling
+ if self.tp_rank == 0 or self.tp_rank == 1:
+ self.rpd.start()
+ self.rpd.rangePush("", "rpd profile range", "")
+ logger.info("rpd is enabled")
def stop_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ #self.profiler.stop()
+ #self.profiler.export_chrome_trace(
+ # self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
+ #)
+ if self.tp_rank ==0 or self.tp_rank ==1:
+ self.rpd.rangePop()
+ self.rpd.stop()
+ self.rpd.flush()
+ logger.info("rpd is done")
logger.info("Profiler is done")
3rdparty/amd/profiling/rpd_profile_server_enable_wCPU_activities.patch 0 → 100644
View file @ 118f1fc7
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..2edb427 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -71,6 +71,8 @@ from sglang.srt.utils import (
suppress_other_loggers,
)
from sglang.utils import get_exception_traceback
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
logger = logging.getLogger(__name__)
@@ -245,6 +247,7 @@ class Scheduler:
],
with_stack=True,
)
+ self.rpd = rpdTracerControl()
@torch.inference_mode()
def event_loop(self):
@@ -1027,15 +1030,26 @@ class Scheduler:
def start_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.start()
+ #self.profiler.start()
+ logger.info("torch profiler is disabled")
+ if self.tp_rank == 0 or self.tp_rank == 1:
+ self.rpd.setPythonTrace(True)
+ self.rpd.start()
+ self.rpd.rangePush("", "scheduler", "")
+ logger.info("rpd is enabled inside scheduler profiling")
def stop_profile(self) -> None:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
- self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ #self.profiler.stop()
+ #self.profiler.export_chrome_trace(
+ # self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
+ #)
+ if self.tp_rank ==0 or self.tp_rank ==1:
+ self.rpd.rangePop()
+ self.rpd.stop()
+ self.rpd.flush()
+ logger.info("rpd is done inside scheduler")
logger.info("Profiler is done")
diff --git a/python/sglang/srt/managers/tokenizer_manager.py b/python/sglang/srt/managers/tokenizer_manager.py
index 2621ccd..181df85 100644
--- a/python/sglang/srt/managers/tokenizer_manager.py
+++ b/python/sglang/srt/managers/tokenizer_manager.py
@@ -58,6 +58,10 @@ from sglang.srt.sampling.sampling_params import SamplingParams
from sglang.srt.server_args import PortArgs, ServerArgs
from sglang.srt.utils import is_generation_model, is_multimodal_model
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
+
+
asyncio.set_event_loop_policy(uvloop.EventLoopPolicy())
logger = logging.getLogger(__name__)
@@ -514,10 +518,20 @@ class TokenizerManager:
self.send_to_scheduler.send_pyobj(req)
def start_profile(self):
+ rpd = rpdTracerControl()
+ rpd.setPythonTrace(True)
+ rpd.start()
+ rpd.rangePush("", "tokenizer_manager", "")
+ logger.info("tokenizer_manager rpd profiling started!")
req = ProfileReq.START_PROFILE
self.send_to_scheduler.send_pyobj(req)
def stop_profile(self):
+ rpd = rpdTracerControl()
+ rpd.rangePop()
+ rpd.stop()
+ rpd.flush()
+ logger.info("rpd profiling is done inside tokenizer_manager!")
req = ProfileReq.STOP_PROFILE
self.send_to_scheduler.send_pyobj(req)
diff --git a/python/sglang/srt/server.py b/python/sglang/srt/server.py
index 7111c93..2bd722c 100644
--- a/python/sglang/srt/server.py
+++ b/python/sglang/srt/server.py
@@ -30,6 +30,8 @@ import threading
import time
from http import HTTPStatus
from typing import Dict, List, Optional, Union
+from rpdTracerControl import rpdTracerControl
+rpdTracerControl.skipCreate()
# Fix a bug of Python threading
setattr(threading, "_register_atexit", lambda *args, **kwargs: None)
@@ -152,6 +154,11 @@ async def flush_cache():
@app.post("/start_profile")
async def start_profile():
"""Start profiling."""
+ rpd = rpdTracerControl()
+ rpd.setPythonTrace(True)
+ rpd.start()
+ rpd.rangePush("", "server rpd profile range", "")
+ logger.info("rpd profiling started in server.py!")
tokenizer_manager.start_profile()
return Response(
content="Start profiling.\n",
@@ -164,6 +171,11 @@ async def start_profile():
async def stop_profile():
"""Stop profiling."""
tokenizer_manager.stop_profile()
+ rpd = rpdTracerControl()
+ rpd.rangePop()
+ rpd.stop()
+ rpd.flush()
+ logger.info("rpd profiling is done in server.py!")
return Response(
content="Stop profiling. This will take some time.\n",
status_code=200,
3rdparty/amd/profiling/server.sh 0 → 100755
View file @ 118f1fc7
#!/bin/bash
# export SGLANG_TORCH_PROFILER_DIR=/data/sglang/
export SGLANG_TORCH_PROFILER_DIR=/sgl-workspace/sglang/profile/
# Get the current timestamp
TIMESTAMP=$(date +"%Y%m%d_%H%M%S")
# Define the log file with a timestamp
LOGFILE="sglang_server_log_$TIMESTAMP.json"
# Run the Python command and save the output to the log file
loadTracer.sh python3 -m sglang.launch_server \
--model-path /sgl-workspace/sglang/dummy_grok1 \
--tokenizer-path Xenova/grok-1-tokenizer \
--load-format dummy \
--quantization fp8 \
--tp 8 \
--port 30000 \
--disable-radix-cache 2>&1 | tee "$LOGFILE"
3rdparty/amd/profiling/torch_profiler.patch 0 → 100644
View file @ 118f1fc7
diff --git a/python/sglang/srt/managers/scheduler.py b/python/sglang/srt/managers/scheduler.py
index 62d1ff9..6ecd78c 100644
--- a/python/sglang/srt/managers/scheduler.py
+++ b/python/sglang/srt/managers/scheduler.py
@@ -240,7 +240,6 @@ class Scheduler:
)
self.profiler = torch.profiler.profile(
activities=[
- torch.profiler.ProfilerActivity.CPU,
torch.profiler.ProfilerActivity.CUDA,
],
with_stack=True,
@@ -1033,9 +1032,11 @@ class Scheduler:
if self.profiler is None:
raise RuntimeError("Profiler is not enabled.")
self.profiler.stop()
- self.profiler.export_chrome_trace(
- self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
- )
+ if self.tp_rank == 0:
+ with open(f"stats_repro_{int(time.time())}.txt", "w") as f:
+ print(self.profiler.key_averages(group_by_input_shape=True).table(sort_by="cuda_time_total", row_limit=-1), file=f)
+ print("Profiling stats done.")
+
logger.info("Profiler is done")
3rdparty/amd/tuning/TUNING.md 0 → 100644
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## Tuning SGLang Infer System with AMD GPUs
This AppNote describes the SGLang performance tuning technical, code harness and running steps for systems with AMD Instinct GPUs.
Harness code, examples and steps are provided in detail, to facilitate easy reproduce & use to tune performance towards workloads.
Three primary runtime areas are covered:
## 1. Triton Kernels
To maximize Triton kernel efficiency, several strategies can be employed:
### Key Environment Variables:
- **num_stages**: Adjusts the number of pipeline stages to optimize kernel efficiency based on the specific type of operations (e.g., General Matrix Multiplication - GEMM).
- **waves_per_eu**: Controls the usage of Vector General Purpose Registers (VGPR) to enhance occupancy, thereby improving latency or throughput.
- **BLOCK_M, BLOCK_N, BLOCK_K**: Tunable tile sizes that assist in balancing memory transfer and computational efficiency.
- **matrix_instr_nonkdim**: Optimizes the usage of Matrix-Fused Multiply-Add (MFMA) instructions for specific kernel types, such as Flash Attention.
- **OPTIMIZE_EPILOGUE**: An environment variable that can be set to `1` to enhance performance by eliminating the `convert_layout` operation in the kernel's epilogue.
```python
@triton.autotune(configs=[
triton.Config({'waves_per_eu': 1}, num_warps=4, num_stages=1),
triton.Config({'waves_per_eu': 1}, num_warps=8, num_stages=1),
triton.Config({'waves_per_eu': 1}, num_warps=16, num_stages=1),
triton.Config({'waves_per_eu': 2}, num_warps=4, num_stages=1),
triton.Config({'waves_per_eu': 2}, num_warps=8, num_stages=1),
triton.Config({'waves_per_eu': 2}, num_warps=16, num_stages=1),
triton.Config({'waves_per_eu': 4}, num_warps=4, num_stages=1),
triton.Config({'waves_per_eu': 4}, num_warps=8, num_stages=1),
triton.Config({'waves_per_eu': 4}, num_warps=16, num_stages=1),
], key=['BLOCK_N', 'NUM_TOKEN_BLKS'], use_cuda_graph=True)
@triton.jit
def _triton_kernel_funtion():
...
```
## 2. Torch Tunable Operations
**TunableOp** is a feature in PyTorch that allows for the definition and optimization of custom kernels with tunable parameters. This feature is particularly useful for enhancing the performance of kernels by experimenting with different configurations.
### Key Environment Variables:
1. **PYTORCH_TUNABLEOP_ENABLED**:
- Default: `0`
- Set to `1` to enable TunableOp.
2. **PYTORCH_TUNABLEOP_TUNING**:
- Default: `1`
- Set to `0` to disable tuning. If a tuned entry is not found, it will run the tuning step and record the entry when PYTORCH_TUNABLEOP_ENABLED is enabled.
3. **PYTORCH_TUNABLEOP_VERBOSE**:
- Default: `0`
- Set to `1` to enable verbose output for TunableOp.
### Usage Example:
To enable TunableOp and tuning, and optionally enable verbose mode, you can run the following command in your terminal:
```bash
#Tuning
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=1 your_script.sh
#Inference with tuning op
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=0 your_script.sh
#Print out the log
PYTORCH_TUNABLEOP_ENABLED=1 PYTORCH_TUNABLEOP_TUNING=0 PYTORCH_TUNABLEOP_VERBOSE=1 your_script.sh
```
## 3. Torch Compilation
The following are suggestions for optimizing matrix multiplication (GEMM) and convolution (conv) operations in PyTorch using Inductor, a part of the PyTorch compilation framework. The goal is to leverage Triton to achieve better performance.
To tune Triton kernels with GEMM and convolution ops (conv), use the `torch.compile` function with the max-autotune mode. This benchmarks a predefined list of Triton configurations and selects the fastest one for each shape.
### Key Configurations:
1. **Max Autotune**:
- Set `torch._inductor.config.max_autotune = True` or `TORCHINDUCTOR_MAX_AUTOTUNE=1`.
2. **Fine-Grained Control**:
- Enable GEMM tuning: `torch._inductor.config.max_autotune_gemm = True`.
- Enable tuning for pointwise/reduction ops: `torch._inductor.config.max_autotune.pointwise = True`.
3. **Backend Selection**:
- Use `torch._inductor.max_autotune_gemm_backends` to limit backends to TRITON for better performance.
4. **Freezing for Inference**:
- Use `torch._inductor.config.freezing=True` to enable constant folding optimizations.
5. **Debugging**:
- Set `TORCH_COMPILE_DEBUG=1` to extract Triton kernels generated by Inductor.
### Example Code Block:
```bash
#Gemm Tuning
TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 your_script.sh
#Specify your backend to TRITON for Gemm Tuning
TORCHINDUCTOR_MAX_AUTOTUNE=1 TORCHINDUCTOR_COORDINATE_DESCENT_TUNING=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM_BACKENDS=TRITON your_script.sh
#Inference with large improvement on AMD GPU
TORCHINDUCTOR_FREEZING=1 your_script.sh
```
## 4. Fused MOE kernel
To maximize moe kernel efficiency, need to use below scripts to find out the best launch configuration
### Key parameters:
- **--model**: what moe model type to do tuning, it will automatically decide the size of d_model, model_intermediate_size, num_layers
- **--tp-size**: simulate the whole model run configuration to set the dimension size using tp correctly
- **--batch**: M dimension size of moe kernel, for prefill moe kernel the value is batch*input_len, for decode moe kernel the value is batch
- **--dtype**: computation type
```bash
#Tuning
#for example, we have one case like this "python3 -m sglang.bench_latency --model dummy_grok1/ --load-format dummy --tokenizer-path Xenova/grok-1-tokenizer --tp 8 --batch-size 32 --input 1024 --output 8 --attention-backend triton --sampling-backend pytorch --quantization fp8" to run, it defined batch-size 32 input length 1024 and output length 8, from "--batch" in moe view point, the prefill batch is 32*1024 = 32768, the decode batch is 32*1(only one output token generated in each run).
#so we can tune decode moe use below command
python benchmark_moe_rocm.py --model grok1 --tp-size 8 --dtype float8 --batch "32"
# and use this command to tune prefill moe
python benchmark_moe_rocm.py --model grok1 --tp-size 8 --dtype float8 --batch "32768"
```
## Reference
For more detailed information on tuning SGLang performance with AMD GPUs, please refer to the following link:
[ROCm Documentation: Triton Kernel Performance Optimization](https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html#triton-kernel-performance-optimization)
3rdparty/amd/tuning/benchmark_moe_rocm.py 0 → 100644
View file @ 118f1fc7
import argparse
import json
import os
import sys
import torch
import torch.nn.functional as F
import triton
import triton.language as tl
from tqdm import tqdm
from transformers import AutoConfig
from sglang.srt.layers.moe.fused_moe_triton.fused_moe import (
fused_moe,
get_config_file_name,
)
padding_size = 128 if bool(int(os.getenv("SGLANG_MOE_PADDING", "0"))) else 0
def main(model, tp_size, dtype: str, batches):
method = fused_moe
for bs in batches:
run_grid(int(bs), model=model, method=method, tp_size=tp_size, dtype=dtype)
def prune_configs(M, N, K, configs):
pruned_configs = []
elemBytes_a = 1 # [DV Note] Hard-coded for float16 (2 bytes)
elemBytes_b = 1 # [DV Note] Hard-coded for float16 (2 bytes)
mfma = 16 if M < 32 or N < 32 else 32
# TODO (zhanglx): figure out the boundary between large and small gemms
large_gemm = False
if M >= 2048 and N >= 2048:
large_gemm = True
for config in configs:
BLOCK_SIZE_M = config.get("BLOCK_SIZE_M")
BLOCK_SIZE_N = config.get("BLOCK_SIZE_N")
BLOCK_SIZE_K = config.get("BLOCK_SIZE_K")
num_warps = config.get("num_warps")
matrix_instr_nonkdim = config.get("matrix_instr_nonkdim")
# kpack = config.get("kpack")
if matrix_instr_nonkdim > mfma:
continue
if mfma == 4 and BLOCK_SIZE_K < 64:
continue
# some layouts could not work properly in case
# number elements per thread is less 1
if BLOCK_SIZE_M * BLOCK_SIZE_N < 64:
continue
SPLIT_K = 1 # config.get("SPLIT_K")
GROUP_M = config.get("GROUP_SIZE_M")
if matrix_instr_nonkdim > BLOCK_SIZE_M or matrix_instr_nonkdim > BLOCK_SIZE_N:
continue
if matrix_instr_nonkdim >= M and matrix_instr_nonkdim != BLOCK_SIZE_M:
continue
if matrix_instr_nonkdim >= N and matrix_instr_nonkdim != BLOCK_SIZE_N:
continue
# Skip BLOCK_SIZE that is too large compare to M/N
# unless BLOCK_SIZE is already small enough
if M * 2 < BLOCK_SIZE_M and BLOCK_SIZE_M != 16:
continue
if N * 2 < BLOCK_SIZE_N and BLOCK_SIZE_N != 16:
continue
# skip large split_k when not necessary
if SPLIT_K != 1 and not need_split_k(M, N, K):
continue
# skip split_k that leads to EVEN_K = false
leap = SPLIT_K * BLOCK_SIZE_K
modv = K % leap
if modv != 0:
continue
# skip large GROUP_M
if GROUP_M * BLOCK_SIZE_M > M and GROUP_M != 1:
continue
# out of shared memory resource
# TODO (zhanglx): This does not consider the LDS usage in the epilogue
LDS = (
BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a
+ BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b
)
if LDS > 65536:
continue
# Skip small block sizes and num_warps for large gemm
# For fp16 and f8, we want to only use BLOCK_SIZE >= 64
if large_gemm:
if BLOCK_SIZE_M < 64 or BLOCK_SIZE_N < 64:
continue
if BLOCK_SIZE_K < 64:
continue
if num_warps < 4:
continue
pruned_configs.append(config)
return pruned_configs
def union_of_list_of_dicts(l1, l2):
result = []
temp_list = l1.copy()
temp_list.extend(l2)
for myDict in temp_list:
if myDict not in result:
result.append(myDict)
return result
def run_grid(bs, model, method, tp_size, dtype: str):
config = AutoConfig.from_pretrained(model)
top_k = config.num_experts_per_tok
d_model = config.hidden_size
model_intermediate_size = config.intermediate_size
num_layers = config.num_hidden_layers
hidden_states_dtype = config.torch_dtype
if config.num_experts_per_tok:
if config.architectures[0] == "Grok1ModelForCausalLM":
num_total_experts = config.num_experts
else:
num_total_experts = config.num_local_experts
else:
raise ValueError(f"Unsupported Mixtral model {model}")
# tp_size = 2
num_warmup_calls = 10
num_calls = 30
num_warmup_trials = 1
num_trials = 1
full_configs = []
block_m_range = [16, 32, 64, 128, 256]
block_n_range = [16, 32, 64, 128, 256]
block_k_range = [32, 64, 128, 256] # MUST >= 32
num_warps_range = [1, 2, 4, 8]
group_m_range = [1, 4, 8, 16, 32]
# For now we see better perf with num_stages=0 for all gemm configs we care
# But keep this explicit so that we do not forget we may need to set it to
# other values in the future
num_stage_range = [2]
waves_per_eu_range = [0, 1, 2, 4, 8]
# Remove 32 because of triton compiling error
matrix_instr_nonkdim_range = [16]
kpack_range = [1, 2]
for block_size_m in block_m_range:
for block_size_n in block_n_range:
for block_size_k in block_k_range:
for group_size_m in group_m_range:
for num_warps in num_warps_range:
for num_stages in num_stage_range:
for waves_per_eu in waves_per_eu_range:
for matrix_instr_nonkdim in matrix_instr_nonkdim_range:
for kpack in kpack_range:
full_configs.append(
{
"BLOCK_SIZE_M": block_size_m,
"BLOCK_SIZE_N": block_size_n,
"BLOCK_SIZE_K": block_size_k,
"GROUP_SIZE_M": group_size_m,
"num_warps": num_warps,
"num_stages": num_stages,
"waves_per_eu": waves_per_eu,
"matrix_instr_nonkdim": matrix_instr_nonkdim,
"kpack": kpack,
}
)
M1 = bs * 2
N1 = model_intermediate_size * 2 // tp_size
K1 = d_model
prune_configs_1 = prune_configs(M1, N1, K1, full_configs)
M2 = bs * 2
N2 = d_model
K2 = model_intermediate_size // tp_size
prune_configs_2 = prune_configs(M2, N2, K2, full_configs)
configs = union_of_list_of_dicts(prune_configs_1, prune_configs_2)
print(
f"{bs=} || {len(full_configs)=} | {len(prune_configs_1)=} | \
{len(prune_configs_2)=} | {len(configs)=}"
)
best_config = None
best_time_us = 1e20
print(f"{tp_size=} {bs=}")
for config in tqdm(configs):
# warmup
try:
print(config)
for _ in range(num_warmup_trials):
run_timing(
num_calls=num_warmup_calls,
bs=bs,
d_model=d_model,
num_total_experts=num_total_experts,
top_k=top_k,
tp_size=tp_size,
model_intermediate_size=model_intermediate_size,
method=method,
config=config,
dtype=dtype,
hidden_states_dtype=hidden_states_dtype,
)
except triton.runtime.autotuner.OutOfResources:
continue
# trial
for _ in range(num_trials):
kernel_dur_ms = run_timing(
num_calls=num_calls,
bs=bs,
d_model=d_model,
num_total_experts=num_total_experts,
top_k=top_k,
tp_size=tp_size,
model_intermediate_size=model_intermediate_size,
method=method,
config=config,
dtype=dtype,
hidden_states_dtype=hidden_states_dtype,
)
kernel_dur_us = 1000 * kernel_dur_ms
model_dur_ms = kernel_dur_ms * num_layers
if kernel_dur_us < best_time_us:
best_config = config
best_time_us = kernel_dur_us
tqdm.write(
f"{kernel_dur_us=:.1f} {model_dur_ms=:.1f}"
f" {bs=} {tp_size=} {top_k=} {num_total_experts=} "
f"{d_model=} {model_intermediate_size=} {num_layers=}"
)
print("best_time_us", best_time_us)
print("best_config", best_config)
# holds Dict[str, Dict[str, int]]
filename = get_config_file_name(
num_total_experts,
model_intermediate_size // tp_size,
"float8" if dtype == "float8" else None,
)
print(f"writing config to file {filename}")
existing_content = {}
if os.path.exists(filename):
with open(filename, "r") as f:
existing_content = json.load(f)
existing_content[str(bs)] = best_config
with open(filename, "w") as f:
json.dump(existing_content, f, indent=4)
f.write("\n")
def run_timing(
num_calls: int,
bs: int,
d_model: int,
num_total_experts: int,
top_k: int,
tp_size: int,
model_intermediate_size: int,
method,
config,
dtype: str,
hidden_states_dtype,
) -> float:
shard_intermediate_size = model_intermediate_size // tp_size
hidden_states = torch.rand(
(bs, d_model),
device="cuda:0",
dtype=hidden_states_dtype,
)
w1 = torch.rand(
(num_total_experts, 2 * shard_intermediate_size, d_model + padding_size),
device=hidden_states.device,
dtype=hidden_states.dtype,
)
w2 = torch.rand(
(num_total_experts, d_model, shard_intermediate_size + padding_size),
device=hidden_states.device,
dtype=hidden_states.dtype,
)
w1_scale = None
w2_scale = None
a1_scale = None
a2_scale = None
if dtype == "float8":
w1 = w1.to(torch.float8_e4m3fnuz)
w2 = w2.to(torch.float8_e4m3fnuz)
w1_scale = torch.ones(
num_total_experts, device=hidden_states.device, dtype=torch.float32
)
w2_scale = torch.ones(
num_total_experts, device=hidden_states.device, dtype=torch.float32
)
a1_scale = torch.ones(1, device=hidden_states.device, dtype=torch.float32)
a2_scale = torch.ones(1, device=hidden_states.device, dtype=torch.float32)
gating_output = F.softmax(
torch.rand(
(num_calls, bs, num_total_experts),
device=hidden_states.device,
dtype=torch.float32,
),
dim=-1,
)
##################################
start_event = torch.cuda.Event(enable_timing=True)
end_event = torch.cuda.Event(enable_timing=True)
start_event.record()
for i in range(num_calls):
hidden_states = method(
hidden_states=hidden_states,
w1=w1,
w2=w2,
w1_scale=w1_scale,
w2_scale=w2_scale,
a1_scale=a1_scale,
a2_scale=a2_scale,
gating_output=gating_output[0],
topk=top_k,
renormalize=True,
inplace=True,
override_config=config,
use_fp8=dtype == "float8",
)
end_event.record()
end_event.synchronize()
dur_ms = start_event.elapsed_time(end_event) / num_calls
return dur_ms
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog="benchmark_mixtral_moe",
description="Benchmark and tune the fused_moe kernel",
)
parser.add_argument(
"--dtype",
type=str,
default="auto",
choices=["float8", "float16", "bfloat16"],
help="Data type used for fused_moe kernel computations",
)
parser.add_argument("--model", type=str, default="hpcai-tech/grok-1")
parser.add_argument("--tp-size", type=int, default=2, help="Tensor paralleli size")
parser.add_argument("-b", "--batches", type=str)
args = parser.parse_args()
batches = args.batches.split(",")
sys.exit(main(args.model, args.tp_size, args.dtype, batches))
LICENSE 0 → 100644
View file @ 118f1fc7
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Makefile 0 → 100644
View file @ 118f1fc7
.PHONY: check-deps install-deps format update help
# Show help for each target
help:
@echo "Available targets:"
@grep -E '^[a-zA-Z0-9_-]+:.*?## .*$$' $(MAKEFILE_LIST) | sort | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[36m%-20s\033[0m %s\n", $$1, $$2}'
check-deps: ## Check and install required Python formatting dependencies
@command -v isort >/dev/null 2>&1 || (echo "Installing isort..." && pip install isort)
@command -v black >/dev/null 2>&1 || (echo "Installing black..." && pip install black)
install-deps: ## Install Python formatting tools (isort and black)
pip install isort black
format: check-deps ## Format modified Python files using isort and black
@echo "Formatting modified Python files..."
git diff --name-only --diff-filter=M | grep '\.py$$' | xargs -I {} sh -c 'isort {} && black {}'
FILES_TO_UPDATE = docker/Dockerfile.rocm \
python/pyproject.toml \
python/sglang/version.py \
docs/developer_guide/setup_github_runner.md \
docs/get_started/install.md \
docs/platforms/amd_gpu.md \
docs/platforms/ascend_npu.md \
benchmark/deepseek_v3/README.md
update: ## Update version numbers across project files. Usage: make update <new_version>
@if [ -z "$(filter-out $@,$(MAKECMDGOALS))" ]; then \
echo "Version required. Usage: make update <new_version>"; \
exit 1; \
fi
@OLD_VERSION=$$(grep "version" python/sglang/version.py | cut -d '"' -f2); \
NEW_VERSION=$(filter-out $@,$(MAKECMDGOALS)); \
echo "Updating version from $$OLD_VERSION to $$NEW_VERSION"; \
for file in $(FILES_TO_UPDATE); do \
if [ "$(shell uname)" = "Darwin" ]; then \
sed -i '' -e "s/$$OLD_VERSION/$$NEW_VERSION/g" $$file; \
else \
sed -i -e "s/$$OLD_VERSION/$$NEW_VERSION/g" $$file; \
fi \
done; \
echo "Version update complete"
%:
@:
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