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Merge branch 'develop' into develop-internal

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This commit is contained in:
Parag Bhandari
2025-09-16 05:12:42 -04:00
parent b800801427 b07ae4ba6c
commit 60e3a8107c
89 changed files with 7608 additions and 1892 deletions
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154
.azuredevops/components/HIP.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: hip_clr_combined
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -35,93 +54,24 @@ parameters:
type: object
default:
- llvm-project
# hip and clr are tightly-coupled
# run this same template for both repos
# any changes for clr should just trigger HIP pipeline
# similarly for hipother repo, for Nvidia backend
- ROCR-Runtime
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt }
- { os: almalinux8, packageManager: dnf }
- { os: ubuntu2204, packageManager: apt, platform: amd }
- { os: ubuntu2204, packageManager: apt, platform: nvidia }
- { os: almalinux8, packageManager: dnf, platform: amd }
- { os: almalinux8, packageManager: dnf, platform: nvidia }
# HIP with AMD backend
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: hip_clr_combined_${{ job.os }}_amd
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
variables:
- group: common
- template: /.azuredevops/variables-global.yml
workspace:
clean: all
steps:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
# checkout triggering repo (either HIP or clr)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
# if this is triggered by HIP repo, matching repo is clr
# if this is triggered by clr repo, matching repo is HIP
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: matching_repo
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: hipother_repo
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependenciesAMD }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
# compile clr
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: clr
cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
-DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
-DHIP_PLATFORM=amd
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DHIPCC_BIN_DIR=$(Agent.BuildDirectory)/rocm/bin
-DCLR_BUILD_HIP=ON
-DCLR_BUILD_OCL=ON
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
artifactName: amd
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
artifactName: amd
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
# parameters:
# aptPackages: ${{ parameters.aptPackages }}
# pipModules: ${{ parameters.pipModules }}
# environment: amd
# HIP with Nvidia backend
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: hip_clr_combined_${{ job.os }}_nvidia
- job: ${{ parameters.componentName }}_${{ job.os }}_${{ job.platform }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
@@ -140,49 +90,45 @@ jobs:
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
# checkout triggering repo (either HIP or clr)
# full checkout of rocm-systems superrepo, we need clr, hip, and hipother
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
# if this is triggered by HIP repo, matching repo is clr
# if this is triggered by clr repo, matching repo is HIP
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: matching_repo
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: hipother_repo
# sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependenciesNvidia }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
- script: 'ls -1R $(Agent.BuildDirectory)/rocm'
displayName: 'Artifact listing'
# compile clr
${{ if eq(job.platform, 'amd') }}:
dependencyList: ${{ parameters.rocmDependenciesAMD }}
${{ elseif eq(job.platform, 'nvidia') }}:
dependencyList: ${{ parameters.rocmDependenciesNvidia }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: clr
cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
cmakeBuildDir: $(Agent.BuildDirectory)/s/projects/clr/build
cmakeSourceDir: $(Agent.BuildDirectory)/s/projects/clr
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
-DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
-DHIP_PLATFORM=nvidia
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DHIPCC_BIN_DIR=$(Agent.BuildDirectory)/rocm/bin
-DHIP_COMMON_DIR=$(Agent.BuildDirectory)/s/projects/hip
-DHIPNV_DIR=$(Agent.BuildDirectory)/s/projects/hipother/hipnv
-DHIP_PLATFORM=${{ job.platform }}
-DCLR_BUILD_HIP=ON
-DCLR_BUILD_OCL=OFF
-DHIPNV_DIR=$(Build.SourcesDirectory)/hipother/hipnv
-DCLR_BUILD_OCL=ON
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
artifactName: ${{ job.platform }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
artifactName: nvidia
artifactName: ${{ job.platform }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
# parameters:
# aptPackages: ${{ parameters.aptPackages }}
# pipModules: ${{ parameters.pipModules }}
# environment: nvidia

4
.azuredevops/components/MIOpen.yml
View File

@@ -131,6 +131,7 @@ jobs:
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
@@ -149,6 +150,7 @@ jobs:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Build and install other dependencies
retryCountOnTaskFailure: 3
inputs:
targetType: inline
workingDirectory: $(Agent.BuildDirectory)/s
@@ -210,6 +212,7 @@ jobs:
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
@@ -228,6 +231,7 @@ jobs:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Build and install other dependencies
retryCountOnTaskFailure: 3
inputs:
targetType: inline
workingDirectory: $(Agent.BuildDirectory)/s

48
.azuredevops/components/ROCR-Runtime.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: ROCR-Runtime
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -45,6 +64,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ROCR_Runtime_build_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
@@ -65,14 +88,18 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
@@ -82,22 +109,26 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
# parameters:
# aptPackages: ${{ parameters.aptPackages }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ROCR_Runtime_test_${{ job.os }}_${{ job.target }}
dependsOn: ROCR_Runtime_build_${{ job.os }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -122,9 +153,12 @@ jobs:
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
parameters:
runRocminfo: false
@@ -132,7 +166,7 @@ jobs:
displayName: Build kfdtest
inputs:
targetType: 'inline'
workingDirectory: $(Build.SourcesDirectory)/libhsakmt/tests/kfdtest
workingDirectory: $(Agent.BuildDirectory)/s/libhsakmt/tests/kfdtest
script: |
if [ -e /opt/rh/gcc-toolset-14/enable ]; then
source /opt/rh/gcc-toolset-14/enable
@@ -143,17 +177,17 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: kfdtest
testExecutable: BIN_DIR=$(Build.SourcesDirectory)/libhsakmt/tests/kfdtest/build ./run_kfdtest.sh
testExecutable: BIN_DIR=$(Agent.BuildDirectory)/s/libhsakmt/tests/kfdtest/build ./run_kfdtest.sh
testParameters: '-p core --gtest_output=xml:./test_output.xml --gtest_color=yes'
testDir: $(Build.SourcesDirectory)/libhsakmt/tests/kfdtest/scripts
testDir: $(Agent.BuildDirectory)/s/libhsakmt/tests/kfdtest/scripts
os: ${{ job.os }}
- task: Bash@3
displayName: Build rocrtst
inputs:
targetType: 'inline'
workingDirectory: $(Build.SourcesDirectory)/rocrtst/suites/test_common
workingDirectory: $(Agent.BuildDirectory)/s/rocrtst/suites/test_common
script: |
echo $(Build.SourcesDirectory)/rocrtst/thirdparty/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/s/rocrtst/thirdparty/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
sudo cat /etc/ld.so.conf.d/rocm-ci.conf
sudo ldconfig -v
ldconfig -p
@@ -176,7 +210,7 @@ jobs:
componentName: rocrtst
testExecutable: ./rocrtst64
testParameters: '--gtest_filter="-rocrtstNeg.Memory_Negative_Tests:rocrtstFunc.Memory_Max_Mem" --gtest_output=xml:./test_output.xml --gtest_color=yes'
testDir: $(Build.SourcesDirectory)/rocrtst/suites/test_common/build/${{ job.target }}
testDir: $(Agent.BuildDirectory)/s//rocrtst/suites/test_common/build/${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:

1
.azuredevops/components/Tensile.yml
View File

@@ -171,6 +171,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: DownloadPipelineArtifact@2
displayName: 'Download Pipeline Wheel Files'
retryCountOnTaskFailure: 3
inputs:
itemPattern: '**/*${{ job.os }}*.whl'
targetPath: $(Agent.BuildDirectory)

39
.azuredevops/components/hip-tests.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: hip-tests
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -60,6 +79,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: hip_tests_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -76,15 +99,18 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
# compile hip-tests
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: hip-tests
componentName: ${{ parameters.componentName }}
cmakeSourceDir: '../catch'
customBuildTarget: build_tests
extraBuildFlags: >-
@@ -96,9 +122,12 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -108,6 +137,7 @@ jobs:
extraEnvVars:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hip_tests_test_${{ job.target }}
timeoutInMinutes: 240
@@ -115,7 +145,7 @@ jobs:
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -125,6 +155,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -138,6 +169,8 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Symlink rocm_agent_enumerator
inputs:
@@ -149,7 +182,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: hip_tests
componentName: ${{ parameters.componentName }}
testDir: $(Agent.BuildDirectory)/rocm/share/hip
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:

6
.azuredevops/components/hipBLASLt.yml
View File

@@ -35,6 +35,8 @@ parameters:
- ccache
- gfortran
- git
- libboost-filesystem-dev
- libboost-program-options-dev
- libdrm-dev
- liblapack-dev
- libmsgpack-dev
@@ -176,7 +178,7 @@ jobs:
mkdir -p $(Agent.BuildDirectory)/temp-deps
cd $(Agent.BuildDirectory)/temp-deps
# position-independent LAPACK is required for almalinux8 builds
cmake -DBUILD_GTEST=OFF -DBUILD_LAPACK=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/s/deps
cmake -DBUILD_GTEST=OFF -DBUILD_LAPACK=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/sparse/projects/hipblaslt/deps
make -j
sudo make install
- script: |
@@ -195,6 +197,8 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeSourceDir: $(Agent.BuildDirectory)/sparse/projects/hipblaslt
cmakeBuildDir: $(Agent.BuildDirectory)/sparse/projects/hipblaslt/build
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_INCLUDE_PATH=$(Agent.BuildDirectory)/rocm/llvm/include

2
.azuredevops/components/hipSPARSELt.yml
View File

@@ -44,6 +44,7 @@ parameters:
type: object
default:
- joblib
- msgpack
- name: rocmDependencies
type: object
default:
@@ -158,6 +159,7 @@ jobs:
-DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm"
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DBUILD_CLIENTS_TESTS=ON
-DBUILD_USE_LOCAL_TENSILE=OFF
-GNinja
${{ if ne(parameters.sparseCheckoutDir, '') }}:
cmakeSourceDir: $(Build.SourcesDirectory)/projects/hipsparselt

236
.azuredevops/components/origami.yml Normal file
View File

@@ -0,0 +1,236 @@
parameters:
- name: componentName
type: string
default: origami
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
type: boolean
default: false
- name: aptPackages
type: object
default:
- cmake
- git
- ninja-build
- wget
- python3
- python3-dev
- python3-pip
- name: pipModules
type: object
default:
- nanobind>=2.0.0
- name: rocmDependencies
type: object
default:
- clr
- llvm-project
- rocm-cmake
- rocminfo
- ROCR-Runtime
- rocprofiler-register
- name: rocmTestDependencies
type: object
default:
- clr
- llvm-project
- rocm-cmake
- rocminfo
- ROCR-Runtime
- rocprofiler-register
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt }
- { os: almalinux8, packageManager: dnf }
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- name: downstreamComponentMatrix
type: object
default:
- hipBLASLt:
name: hipBLASLt
sparseCheckoutDir: projects/hipblaslt
skipUnifiedBuild: 'false'
buildDependsOn:
- origami_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: origami_build_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
- name: ROCM_PATH
value: $(Agent.BuildDirectory)/rocm
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
os: ${{ job.os }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DORIGAMI_BUILD_SHARED_LIBS=ON
-DORIGAMI_ENABLE_PYTHON=ON
-DORIGAMI_BUILD_TESTING=ON
-GNinja
- ${{ if ne(job.os, 'almalinux8') }}:
- task: PublishPipelineArtifact@1
displayName: 'Publish Build Directory Artifact'
inputs:
targetPath: '$(Agent.BuildDirectory)/s/build'
artifact: '${{ parameters.componentName }}_${{ job.os }}_build_dir'
publishLocation: 'pipeline'
- task: PublishPipelineArtifact@1
displayName: 'Publish Python Source Artifact'
inputs:
targetPath: '$(Agent.BuildDirectory)/s/python'
artifact: '${{ parameters.componentName }}_${{ job.os }}_python_src'
publishLocation: 'pipeline'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
os: ${{ job.os }}
componentName: ${{ parameters.componentName }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: origami_test_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 120
dependsOn: origami_build_${{ job.os }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool: ${{ job.target }}_test_pool
workspace:
clean: all
steps:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
os: ${{ job.os }}
- task: DownloadPipelineArtifact@2
displayName: 'Download Build Directory Artifact'
inputs:
artifact: '${{ parameters.componentName }}_${{ job.os }}_build_dir'
path: '$(Agent.BuildDirectory)/s/build'
- task: DownloadPipelineArtifact@2
displayName: 'Download Python Source Artifact'
inputs:
artifact: '${{ parameters.componentName }}_${{ job.os }}_python_src'
path: '$(Agent.BuildDirectory)/s/python'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- script: |
export PYTHONPATH=$(Agent.BuildDirectory)/s/build/python:$PYTHONPATH
echo "--- Running origami_test.py ---"
python3 $(Agent.BuildDirectory)/s/python/origami_test.py
echo "--- Running origami_grid_test.py ---"
python3 $(Agent.BuildDirectory)/s/python/origami_grid_test.py
displayName: 'Run Python Binding Tests'
condition: succeeded()
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
environment: test
gpuTarget: ${{ job.target }}
- ${{ if parameters.triggerDownstreamJobs }}:
- ${{ each component in parameters.downstreamComponentMatrix }}:
- ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
- template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
buildDependsOn: ${{ component.buildDependsOn }}
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
triggerDownstreamJobs: true
unifiedBuild: ${{ parameters.unifiedBuild }}

9
.azuredevops/components/rocBLAS.yml
View File

@@ -115,6 +115,13 @@ parameters:
# buildDependsOn:
# - rocBLAS_build
# - rocPRIM_build
# temporary rocblas->hipblas downstream path while the SOLVERs are disabled
- hipBLAS:
name: hipBLAS
sparseCheckoutDir: projects/hipblas
skipUnifiedBuild: 'false'
buildDependsOn:
- rocBLAS_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -172,6 +179,8 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeSourceDir: $(Agent.BuildDirectory)/sparse/projects/rocblas
cmakeBuildDir: $(Agent.BuildDirectory)/sparse/projects/rocblas/build
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_BUILD_TYPE=Release

47
.azuredevops/components/rocDecode.yml
View File

@@ -8,6 +8,25 @@ parameters:
- name: checkoutRef
type: string
default: ''
- name: rocPyDecodeRepo
type: string
default: rocpydecode_repo
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -56,10 +75,23 @@ parameters:
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- name: downstreamComponentMatrix
type: object
default:
- rocPyDecode:
name: rocPyDecode
sparseCheckoutDir: ''
skipUnifiedBuild: 'false'
buildDependsOn:
- rocDecode_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -83,12 +115,15 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
os: ${{ job.os }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
@@ -169,3 +204,15 @@ jobs:
registerROCmPackages: true
environment: test
gpuTarget: ${{ job.target }}
- ${{ if parameters.triggerDownstreamJobs }}:
- ${{ each component in parameters.downstreamComponentMatrix }}:
- ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
- template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
parameters:
checkoutRepo: ${{ parameters.rocPyDecodeRepo }}
sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
buildDependsOn: ${{ component.buildDependsOn }}
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
triggerDownstreamJobs: true
unifiedBuild: ${{ parameters.unifiedBuild }}

37
.azuredevops/components/rocPyDecode.yml
View File

@@ -5,6 +5,22 @@ parameters:
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -47,19 +63,19 @@ parameters:
type: object
default:
buildJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
testJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocPyDecode_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -74,16 +90,20 @@ jobs:
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: 'Save Python Package Paths'
inputs:
@@ -190,6 +210,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: DownloadPipelineArtifact@2
displayName: 'Download Pipeline Wheel Files'
retryCountOnTaskFailure: 3
inputs:
itemPattern: '**/*.whl'
targetPath: $(Agent.BuildDirectory)

28
.azuredevops/components/rocm-core.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocm-core
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -27,6 +46,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocm_core_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
pool:
${{ if eq(job.os, 'ubuntu2404') }}:
vmImage: 'ubuntu-24.04'
@@ -50,8 +73,10 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
@@ -65,9 +90,12 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml

22
.azuredevops/components/rocm-libraries.yml
View File

@@ -36,8 +36,10 @@ parameters:
- gfortran
- git
- libdrm-dev
- liblapack-dev
- libmsgpack-dev
- libnuma-dev
- libopenblas-dev
- ninja-build
- python3-pip
- python3-venv
@@ -46,6 +48,8 @@ parameters:
default:
- joblib
- "packaging>=22.0"
- pytest
- pytest-cmake
- --upgrade
- name: rocmDependencies
type: object
@@ -98,12 +102,12 @@ jobs:
workspace:
clean: all
steps:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
@@ -134,11 +138,25 @@ jobs:
rocm-libraries | ${{ job.os }} | ${{ job.target }} | $(DAY_STRING)
rocm-libraries | ${{ job.os }} | ${{ job.target }}
rocm-libraries | ${{ job.os }}
- task: Bash@3
displayName: Add paths for CMake and Python site-packages binaries
inputs:
targetType: inline
script: |
USER_BASE=$(python3 -m site --user-base)
echo "##vso[task.prependpath]$USER_BASE/bin"
echo "##vso[task.setvariable variable=PytestCmakePath]$USER_BASE/share/Pytest/cmake"
displayName: Set cmake configure paths
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DROCM_LIBRARIES_SUPERBUILD=ON
-D CMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor;$(PytestCmakePath)
-D CMAKE_INCLUDE_PATH=$(Agent.BuildDirectory)/rocm/llvm/include
-D CMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-D CMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-D CMAKE_CXX_COMPILER_LAUNCHER=ccache
-D CMAKE_C_COMPILER_LAUNCHER=ccache
-G Ninja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:

34
.azuredevops/components/rocm_smi_lib.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocm_smi_lib
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -32,6 +51,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocm_smi_lib_build_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
pool:
${{ if eq(job.os, 'ubuntu2404') }}:
vmImage: 'ubuntu-24.04'
@@ -55,8 +78,10 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
@@ -65,22 +90,26 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
# parameters:
# aptPackages: ${{ parameters.aptPackages }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocm_smi_lib_test_${{ job.os }}_${{ job.target }}
dependsOn: rocm_smi_lib_build_${{ job.os }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -90,6 +119,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -103,7 +133,7 @@ jobs:
runRocminfo: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocm_smi_lib
componentName: ${{ parameters.componentName }}
testDir: '$(Agent.BuildDirectory)'
testExecutable: 'sudo ./rocm/share/rocm_smi/rsmitst_tests/rsmitst'
testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'

39
.azuredevops/components/rocminfo.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocminfo
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -40,7 +59,11 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocminfo_build_${{ job.os }}
- job: ${{ parameters.componentName }}_build_${{ job.os }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
@@ -62,14 +85,18 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
useAmdclang: false
extraBuildFlags: >-
@@ -78,19 +105,23 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocminfo_test_${{ job.target }}
dependsOn: rocminfo_build_${{ job.os }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -115,12 +146,14 @@ jobs:
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
parameters:
runRocminfo: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocminfo
componentName: ${{ parameters.componentName }}
testDir: '$(Agent.BuildDirectory)'
testExecutable: './rocm/bin/rocminfo'
testParameters: ''

37
.azuredevops/components/rocprofiler-compute.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocprofiler-compute
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -36,6 +55,7 @@ parameters:
- pymongo
- pyyaml
- setuptools
- sqlalchemy
- tabulate
- textual
- textual_plotext
@@ -78,6 +98,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocprofiler_compute_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -94,15 +118,19 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
extraBuildFlags: >-
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -111,6 +139,7 @@ jobs:
# pipModules: ${{ parameters.pipModules }}
# gpuTarget: ${{ job.target }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocprofiler_compute_test_${{ job.target }}
timeoutInMinutes: 120
@@ -118,7 +147,7 @@ jobs:
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -138,8 +167,10 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
@@ -147,6 +178,8 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Add en_US.UTF-8 locale
inputs:
@@ -177,7 +210,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocprofiler-compute
componentName: ${{ parameters.componentName }}
testDir: $(Build.BinariesDirectory)/libexec/rocprofiler-compute
testExecutable: ROCM_PATH=$(Agent.BuildDirectory)/rocm ctest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml

39
.azuredevops/components/rocprofiler-sdk.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocprofiler-sdk
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -73,6 +92,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocprofiler_sdk_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -89,6 +112,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
@@ -96,6 +120,8 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Add Python site-packages binaries to path
inputs:
@@ -105,6 +131,7 @@ jobs:
echo "##vso[task.prependpath]$USER_BASE/bin"
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DROCPROFILER_BUILD_TESTS=ON
@@ -114,9 +141,12 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -126,13 +156,14 @@ jobs:
# gpuTarget: ${{ job.target }}
# registerROCmPackages: true
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocprofiler_sdk_test_${{ job.target }}
dependsOn: rocprofiler_sdk_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -150,6 +181,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
checkoutRepo: ${{ parameters.checkoutRepo }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
@@ -157,6 +189,8 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Add Python and ROCm binaries to path
inputs:
@@ -167,6 +201,7 @@ jobs:
echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/bin"
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: ${{ parameters.componentName }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DROCPROFILER_BUILD_TESTS=ON
@@ -177,7 +212,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH}}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocprofiler-sdk
componentName: ${{ parameters.componentName }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

35
.azuredevops/components/rocprofiler-systems.yml
View File

@@ -6,6 +6,25 @@ parameters:
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: componentName
type: string
default: rocprofiler-systems
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -87,6 +106,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocprofiler_systems_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -105,6 +128,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
@@ -136,12 +160,16 @@ jobs:
-DCMAKE_CXX_FLAGS=-I$(Agent.BuildDirectory)/rocm/include/rocjpeg
-DGPU_TARGETS=${{ job.target }}
-GNinja
componentName: ${{ parameters.componentName }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
gpuTarget: ${{ job.target }}
componentName: ${{ parameters.componentName }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
@@ -151,13 +179,14 @@ jobs:
registerROCmPackages: true
extraPaths: /home/user/workspace/rocm/bin:/home/user/workspace/rocm/llvm/bin
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocprofiler_systems_test_${{ job.target }}
dependsOn: rocprofiler_systems_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
timeoutInMinutes: 180
@@ -186,6 +215,8 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Add ROCm binaries to PATH
inputs:
@@ -218,7 +249,7 @@ jobs:
workingDirectory: build
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocprofiler-systems
componentName: ${{ parameters.componentName }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}

32
.azuredevops/components/rocprofiler.yml
View File

@@ -8,6 +8,22 @@ parameters:
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -70,6 +86,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -94,6 +114,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
@@ -108,6 +129,8 @@ jobs:
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
@@ -115,6 +138,7 @@ jobs:
extraBuildFlags: >-
-DCMAKE_MODULE_PATH=$(Build.SourcesDirectory)/cmake_modules;$(Agent.BuildDirectory)/rocm/lib/cmake;$(Agent.BuildDirectory)/rocm/lib/cmake/hip;$(Agent.BuildDirectory)/rocm/lib64/cmake;$(Agent.BuildDirectory)/rocm/lib64/cmake/hip
-DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor"
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DENABLE_LDCONFIG=OFF
-DUSE_PROF_API=1
@@ -122,10 +146,13 @@ jobs:
multithreadFlag: -- -j32
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
@@ -139,6 +166,7 @@ jobs:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- ROCM_PATH:::/home/user/workspace/rocm
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
@@ -159,6 +187,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -166,6 +195,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
@@ -177,6 +207,8 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:

34
.azuredevops/components/roctracer.yml
View File

@@ -8,6 +8,22 @@ parameters:
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -65,6 +81,10 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -87,6 +107,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
@@ -94,6 +115,8 @@ jobs:
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
# the linker flags will not affect ubuntu2204 builds as the paths do not exist
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
@@ -109,10 +132,13 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
@@ -123,13 +149,14 @@ jobs:
# gpuTarget: ${{ job.target }}
# registerROCmPackages: true
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -148,6 +175,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
@@ -157,10 +185,12 @@ jobs:
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: roctracer
componentName: ${{ parameters.componentName }}
testExecutable: $(Agent.BuildDirectory)/rocm/share/roctracer/run_tests.sh
testParameters: ''
testDir: $(Agent.BuildDirectory)

63
.azuredevops/dependencies/catch2.yml Normal file
View File

@@ -0,0 +1,63 @@
parameters:
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
- name: catch2Version
type: string
default: ''
- name: aptPackages
type: object
default:
- cmake
- git
- ninja-build
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt}
- { os: almalinux8, packageManager: dnf}
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: catch2_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: Bash@3
displayName: Clone catch2 ${{ parameters.catch2Version }}
inputs:
targetType: inline
script: git clone https://github.com/catchorg/Catch2.git -b ${{ parameters.catch2Version }}
workingDirectory: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeBuildDir: $(Agent.BuildDirectory)/Catch2/build
cmakeSourceDir: $(Agent.BuildDirectory)/Catch2
useAmdclang: false
extraBuildFlags: >-
-DCMAKE_BUILD_TYPE=Release
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
os: ${{ job.os }}

67
.azuredevops/dependencies/fmtlib.yml Normal file
View File

@@ -0,0 +1,67 @@
parameters:
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
- name: fmtlibVersion
type: string
default: ''
- name: aptPackages
type: object
default:
- cmake
- git
- ninja-build
- libfmt-dev
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt}
- { os: almalinux8, packageManager: dnf}
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: fmtlib_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: Bash@3
displayName: Clone fmtlib ${{ parameters.fmtlibVersion }}
inputs:
targetType: inline
script: git clone https://github.com/fmtlib/fmt.git -b ${{ parameters.fmtlibVersion }}
workingDirectory: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeBuildDir: $(Agent.BuildDirectory)/fmt/build
cmakeSourceDir: $(Agent.BuildDirectory)/fmt
useAmdclang: false
extraBuildFlags: >-
-DCMAKE_BUILD_TYPE=Release
-DFMT_SYSTEM_HEADERS=ON
-DFMT_INSTALL=ON
-DFMT_TEST=OFF
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
os: ${{ job.os }}

64
.azuredevops/dependencies/libdivide.yml Normal file
View File

@@ -0,0 +1,64 @@
parameters:
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
- name: libdivideVersion
type: string
default: ''
- name: aptPackages
type: object
default:
- cmake
- git
- ninja-build
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt}
- { os: almalinux8, packageManager: dnf}
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: libdivide_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: Bash@3
displayName: Clone libdivide ${{ parameters.libdivideVersion }}
inputs:
targetType: inline
script: git clone https://github.com/ridiculousfish/libdivide.git -b ${{ parameters.libdivideVersion }}
workingDirectory: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeBuildDir: $(Agent.BuildDirectory)/libdivide/build
cmakeSourceDir: $(Agent.BuildDirectory)/libdivide
useAmdclang: false
extraBuildFlags: >-
-DCMAKE_BUILD_TYPE=Release
-DLIBDIVIDE_BUILD_TESTS=OFF
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
os: ${{ job.os }}

71
.azuredevops/dependencies/spdlog.yml Normal file
View File

@@ -0,0 +1,71 @@
parameters:
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
- name: spdlogVersion
type: string
default: ''
- name: aptPackages
type: object
default:
- cmake
- git
- ninja-build
- name: jobMatrix
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt}
- { os: almalinux8, packageManager: dnf}
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: spdlog_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: 'ubuntu-22.04'
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- fmtlib
- task: Bash@3
displayName: Clone spdlog ${{ parameters.spdlogVersion }}
inputs:
targetType: inline
script: git clone https://github.com/gabime/spdlog.git -b ${{ parameters.spdlogVersion }}
workingDirectory: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
cmakeBuildDir: $(Agent.BuildDirectory)/spdlog/build
cmakeSourceDir: $(Agent.BuildDirectory)/spdlog
useAmdclang: false
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/vendor
-DCMAKE_BUILD_TYPE=Release
-DSPDLOG_USE_STD_FORMAT=OFF
-DSPDLOG_FMT_EXTERNAL_HO=ON
-DSPDLOG_INSTALL=ON
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
os: ${{ job.os }}

1
.azuredevops/nightly/pytorch.yml
View File

@@ -397,6 +397,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- task: DownloadPipelineArtifact@2
displayName: 'Download Pipeline Wheel Files'
retryCountOnTaskFailure: 3
inputs:
itemPattern: '**/*.whl'
targetPath: $(Agent.BuildDirectory)

3
.azuredevops/nightly/rocm-nightly.yml
View File

@@ -93,7 +93,7 @@ schedules:
jobs:
- ${{ each job in parameters.jobList }}:
- job: nightly_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 90
timeoutInMinutes: 120
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -226,6 +226,7 @@ jobs:
cat Dockerfile
- task: Docker@2
displayName: Build and upload Docker image
retryCountOnTaskFailure: 3
inputs:
containerRegistry: ContainerService3
repository: 'nightly-${{ job.os }}-${{ job.target }}'

23
.azuredevops/tag-builds/catch2.yml Normal file
View File

@@ -0,0 +1,23 @@
variables:
- group: common
- template: /.azuredevops/variables-global.yml
parameters:
- name: catch2Version
type: string
default: "v3.7.0"
resources:
repositories:
- repository: pipelines_repo
type: github
endpoint: ROCm
name: ROCm/ROCm
trigger: none
pr: none
jobs:
- template: ${{ variables.CI_DEPENDENCIES_PATH }}/catch2.yml
parameters:
catch2Version: ${{ parameters.catch2Version }}

23
.azuredevops/tag-builds/fmtlib.yml Normal file
View File

@@ -0,0 +1,23 @@
variables:
- group: common
- template: /.azuredevops/variables-global.yml
parameters:
- name: fmtlibVersion
type: string
default: "11.1.3"
resources:
repositories:
- repository: pipelines_repo
type: github
endpoint: ROCm
name: ROCm/ROCm
trigger: none
pr: none
jobs:
- template: ${{ variables.CI_DEPENDENCIES_PATH }}/fmtlib.yml
parameters:
fmtlibVersion: ${{ parameters.fmtlibVersion }}

23
.azuredevops/tag-builds/libdivide.yml Normal file
View File

@@ -0,0 +1,23 @@
variables:
- group: common
- template: /.azuredevops/variables-global.yml
parameters:
- name: libdivideVersion
type: string
default: master
resources:
repositories:
- repository: pipelines_repo
type: github
endpoint: ROCm
name: ROCm/ROCm
trigger: none
pr: none
jobs:
- template: ${{ variables.CI_DEPENDENCIES_PATH }}/libdivide.yml
parameters:
libdivideVersion: ${{ parameters.libdivideVersion }}

23
.azuredevops/tag-builds/spdlog.yml Normal file
View File

@@ -0,0 +1,23 @@
variables:
- group: common
- template: /.azuredevops/variables-global.yml
parameters:
- name: spdlogVersion
type: string
default: "v1.15.1"
resources:
repositories:
- repository: pipelines_repo
type: github
endpoint: ROCm
name: ROCm/ROCm
trigger: none
pr: none
jobs:
- template: ${{ variables.CI_DEPENDENCIES_PATH }}/spdlog.yml
parameters:
spdlogVersion: ${{ parameters.spdlogVersion }}

4
.azuredevops/templates/steps/artifact-download.yml
View File

@@ -24,7 +24,11 @@ parameters:
steps:
- task: DownloadPipelineArtifact@2
displayName: Download ${{ parameters.componentName }}
retryCountOnTaskFailure: 3
inputs:
${{ if eq(parameters.componentName, 'clr') }}:
itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*amd*' # filter out nvidia clr artifacts
${{ else }}:
itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*'
targetPath: '$(Pipeline.Workspace)/d'
allowPartiallySucceededBuilds: true

2
.azuredevops/templates/steps/checkout.yml
View File

@@ -20,7 +20,7 @@ steps:
retryCountOnTaskFailure: 3
fetchFilter: blob:none
${{ if ne(parameters.sparseCheckoutDir, '') }}:
sparseCheckoutDirectories: ${{ parameters.sparseCheckoutDir }}
sparseCheckoutDirectories: ${{ parameters.sparseCheckoutDir }} shared
path: sparse
- ${{ if ne(parameters.sparseCheckoutDir, '') }}:
- task: Bash@3

19
.azuredevops/templates/steps/dependencies-apt.yml
View File

@@ -10,6 +10,7 @@ steps:
- ${{ if eq(parameters.registerROCmPackages, true) }}:
- task: Bash@3
displayName: 'Register AMDGPU & ROCm repos (apt)'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
@@ -20,7 +21,8 @@ steps:
echo -e 'Package: *\nPin: release o=repo.radeon.com\nPin-Priority: 600' | sudo tee /etc/apt/preferences.d/rocm-pin-600
sudo apt update
- task: Bash@3
displayName: 'sudo apt-get update'
displayName: 'APT update and install packages'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
@@ -28,15 +30,6 @@ steps:
echo "deb http://archive.ubuntu.com/ubuntu/ jammy-updates main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
echo "deb http://archive.ubuntu.com/ubuntu/ jammy-backports main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
echo "deb http://archive.ubuntu.com/ubuntu/ jammy-security main restricted universe multiverse" | sudo tee -a /etc/apt/sources.list.d/default.list
sudo DEBIAN_FRONTEND=noninteractive apt-get --yes update
- task: Bash@3
displayName: 'sudo apt-get fix'
inputs:
targetType: inline
script: sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-broken install
- ${{ if gt(length(parameters.aptPackages), 0) }}:
- task: Bash@3
displayName: 'sudo apt-get install ...'
inputs:
targetType: inline
script: sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-missing install ${{ join(' ', parameters.aptPackages) }}
sudo DEBIAN_FRONTEND=noninteractive apt-get --yes update && \
sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-broken install && \
sudo DEBIAN_FRONTEND=noninteractive apt-get --yes --fix-missing install ${{ join(' ', parameters.aptPackages) }}

67
.azuredevops/templates/steps/dependencies-aqlprofile.yml
View File

@@ -5,51 +5,28 @@ parameters:
steps:
- task: Bash@3
displayName: Get aqlprofile package name
displayName: Download and install aqlprofile
retryCountOnTaskFailure: 3
inputs:
targetType: inline
${{ if eq(parameters.os, 'ubuntu2204') }}:
workingDirectory: $(Agent.BuildDirectory)
script: |
export packageName=$(curl -s https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb")
echo "##vso[task.setvariable variable=packageName;isreadonly=true]$packageName"
${{ if eq(parameters.os, 'almalinux8') }}:
script: |
export packageName=$(curl -s https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/ | grep -oP "hsa-amd-aqlprofile-[^\"]+\.rpm" | head -n1)
echo "##vso[task.setvariable variable=packageName;isreadonly=true]$packageName"
- task: Bash@3
displayName: 'Download aqlprofile'
inputs:
targetType: inline
workingDirectory: '$(Pipeline.Workspace)'
${{ if eq(parameters.os, 'ubuntu2204') }}:
script: wget -nv https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/$(packageName)
${{ if eq(parameters.os, 'almalinux8') }}:
script: wget -nv https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/$(packageName)
- task: Bash@3
displayName: 'Extract aqlprofile'
inputs:
targetType: inline
workingDirectory: '$(Pipeline.Workspace)'
${{ if eq(parameters.os, 'ubuntu2204') }}:
script: |
mkdir hsa-amd-aqlprofile
dpkg-deb -R $(packageName) hsa-amd-aqlprofile
${{ if eq(parameters.os, 'almalinux8') }}:
script: |
mkdir hsa-amd-aqlprofile
sudo dnf -y install rpm-build cpio
rpm2cpio $(packageName) | (cd hsa-amd-aqlprofile && cpio -idmv)
- task: Bash@3
displayName: 'Copy aqlprofile files'
inputs:
targetType: inline
script: |
mkdir -p $(Agent.BuildDirectory)/rocm
cp -R hsa-amd-aqlprofile/opt/rocm-*/* $(Agent.BuildDirectory)/rocm
workingDirectory: '$(Pipeline.Workspace)'
- task: Bash@3
displayName: 'Clean up aqlprofile'
inputs:
targetType: inline
script: rm -rf hsa-amd-aqlprofile $(packageName)
workingDirectory: '$(Pipeline.Workspace)'
set -e
if [ "${{ parameters.os }}" = "ubuntu2204" ]; then
packageName=$(curl -s https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb") && \
wget -nv https://repo.radeon.com/rocm/apt/$(REPO_RADEON_VERSION)/pool/main/h/hsa-amd-aqlprofile/$packageName && \
mkdir -p hsa-amd-aqlprofile && \
dpkg-deb -R $packageName hsa-amd-aqlprofile
elif [ "${{ parameters.os }}" = "almalinux8" ]; then
sudo dnf -y install rpm-build cpio && \
packageName=$(curl -s https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/ | grep -oP "hsa-amd-aqlprofile-[^\"]+\.rpm" | head -n1) && \
wget -nv https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/$packageName && \
mkdir -p hsa-amd-aqlprofile && \
rpm2cpio $packageName | (cd hsa-amd-aqlprofile && cpio -idmv)
else
echo "Unsupported OS: ${{ parameters.os }}"
exit 1
fi && \
mkdir -p $(Agent.BuildDirectory)/rocm && \
cp -R hsa-amd-aqlprofile/opt/rocm-*/* $(Agent.BuildDirectory)/rocm && \
rm -rf hsa-amd-aqlprofile $packageName

19
.azuredevops/templates/steps/dependencies-dnf.yml
View File

@@ -89,6 +89,7 @@ steps:
- ${{ if eq(parameters.registerROCmPackages, true) }}:
- task: Bash@3
displayName: 'Register AMDGPU & ROCm repos (dnf)'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
@@ -109,12 +110,13 @@ steps:
sudo dnf makecache
- task: Bash@3
displayName: 'Install base dnf packages'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
sudo dnf config-manager --set-enabled powertools
# rpm fusion free repo for some dependencies
sudo dnf -y install https://download1.rpmfusion.org/free/el/rpmfusion-free-release-8.noarch.rpm
sudo dnf config-manager --set-enabled powertools && \
sudo dnf -y install https://download1.rpmfusion.org/free/el/rpmfusion-free-release-8.noarch.rpm && \
sudo dnf -y install ${{ join(' ', parameters.basePackages) }}
- task: Bash@3
displayName: 'Check gcc environment'
@@ -128,6 +130,7 @@ steps:
g++ -print-file-name=libstdc++.so
- task: Bash@3
displayName: 'Set python 3.11 as default'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
@@ -142,18 +145,20 @@ steps:
- ${{ if eq(pkg, 'ninja-build') }}:
- task: Bash@3
displayName: 'Install ninja 1.11.1'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |
curl -LO https://github.com/ninja-build/ninja/releases/download/v1.11.1/ninja-linux.zip
sudo dnf -y install unzip
unzip ninja-linux.zip
sudo mv ninja /usr/local/bin/ninja
sudo chmod +x /usr/local/bin/ninja
sudo dnf -y install unzip && \
curl -LO https://github.com/ninja-build/ninja/releases/download/v1.11.1/ninja-linux.zip && \
unzip ninja-linux.zip && \
sudo mv ninja /usr/local/bin/ninja && \
sudo chmod +x /usr/local/bin/ninja && \
echo "##vso[task.prependpath]/usr/local/bin"
- ${{ if ne(parameters.aptToDnfMap[pkg], '') }}:
- task: Bash@3
displayName: 'dnf install ${{ parameters.aptToDnfMap[pkg] }}'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: |

1
.azuredevops/templates/steps/dependencies-other.yml
View File

@@ -27,6 +27,7 @@ steps:
- ${{ if gt(length(parameters.pipModules), 0) }}:
- task: Bash@3
displayName: 'pip install ...'
retryCountOnTaskFailure: 3
inputs:
targetType: inline
script: python3 -m pip install -v --force-reinstall ${{ join(' ', parameters.pipModules) }}

42
.azuredevops/templates/steps/dependencies-rocm.yml
View File

@@ -47,8 +47,8 @@ parameters:
developBranch: aomp-dev
hasGpuTarget: false
clr:
pipelineId: 145
developBranch: amd-staging
pipelineId: 335
developBranch: develop
hasGpuTarget: false
composable_kernel:
pipelineId: 86
@@ -59,12 +59,12 @@ parameters:
developBranch: rocm
hasGpuTarget: false
HIP:
pipelineId: 93
developBranch: amd-staging
pipelineId: 335
developBranch: develop
hasGpuTarget: false
hip-tests:
pipelineId: 233
developBranch: amd-staging
pipelineId: 362
developBranch: develop
hasGpuTarget: false
hipBLAS:
pipelineId: 317
@@ -171,16 +171,16 @@ parameters:
developBranch: develop
hasGpuTarget: false
rocm-core:
pipelineId: 103
developBranch: master
pipelineId: 349
developBranch: develop
hasGpuTarget: false
rocm-examples:
pipelineId: 216
developBranch: amd-staging
hasGpuTarget: true
rocminfo:
pipelineId: 91
developBranch: amd-staging
pipelineId: 356
developBranch: develop
hasGpuTarget: false
rocMLIR:
pipelineId: 229
@@ -195,19 +195,19 @@ parameters:
developBranch: master
hasGpuTarget: false
rocm_smi_lib:
pipelineId: 96
developBranch: amd-staging
pipelineId: 358
developBranch: develop
hasGpuTarget: false
rocPRIM:
pipelineId: 273
developBranch: develop
hasGpuTarget: true
rocprofiler:
pipelineId: 143
developBranch: amd-staging
pipelineId: 329
developBranch: develop
hasGpuTarget: true
rocprofiler-compute:
pipelineId: 257
pipelineId: 344
developBranch: develop
hasGpuTarget: true
rocprofiler-register:
@@ -215,8 +215,8 @@ parameters:
developBranch: develop
hasGpuTarget: false
rocprofiler-sdk:
pipelineId: 246
developBranch: amd-staging
pipelineId: 347
developBranch: develop
hasGpuTarget: true
rocprofiler-systems:
pipelineId: 255
@@ -227,8 +227,8 @@ parameters:
developBranch: develop
hasGpuTarget: true
ROCR-Runtime:
pipelineId: 10
developBranch: amd-staging
pipelineId: 354
developBranch: develop
hasGpuTarget: false
rocRAND:
pipelineId: 274
@@ -251,8 +251,8 @@ parameters:
developBranch: develop
hasGpuTarget: true
roctracer:
pipelineId: 141
developBranch: amd-staging
pipelineId: 331
developBranch: develop
hasGpuTarget: true
rocWMMA:
pipelineId: 109

7
.azuredevops/templates/steps/dependencies-vendor.yml
View File

@@ -8,15 +8,20 @@ parameters:
type: object
default:
boost: 250
catch2: 343
fmtlib: 341
grpc: 72
gtest: 73
half560: 68
lapack: 69
libdivide: 342
spdlog: 340
steps:
- ${{ each dependency in parameters.dependencyList }}:
- task: DownloadPipelineArtifact@2
displayName: Download ${{ dependency }}
retryCountOnTaskFailure: 3
inputs:
project: ROCm-CI
buildType: specific
@@ -28,7 +33,7 @@ steps:
inputs:
archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
destinationFolder: $(Agent.BuildDirectory)/vendor
cleanDestinationFolder: true
cleanDestinationFolder: false
overwriteExistingFiles: true
- task: DeleteFiles@1
displayName: Clean up ${{ dependency }}

1
.azuredevops/templates/steps/local-artifact-download.yml
View File

@@ -33,6 +33,7 @@ parameters:
steps:
- task: DownloadPipelineArtifact@2
displayName: Download ${{ parameters.preTargetFilter}}*${{ parameters.os }}_${{ parameters.gpuTarget}}*${{ parameters.postTargetFilter}}
retryCountOnTaskFailure: 3
inputs:
${{ if eq(parameters.buildType, 'specific') }}:
buildType: specific

1
.azuredevops/templates/steps/miopen-get-ck-build.yml
View File

@@ -7,6 +7,7 @@ steps:
- task: Bash@3
name: downloadCKBuild
displayName: Download specific CK build
retryCountOnTaskFailure: 3
env:
CXX: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
CC: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang

6
.azuredevops/variables-global.yml
View File

@@ -28,13 +28,13 @@ variables:
- name: GFX90A_TEST_POOL
value: gfx90a_test_pool
- name: LATEST_RELEASE_VERSION
value: 6.4.2
value: 6.4.3
- name: REPO_RADEON_VERSION
value: 6.4.2
value: 6.4.3
- name: NEXT_RELEASE_VERSION
value: 7.0.0
- name: LATEST_RELEASE_TAG
value: rocm-6.4.2
value: rocm-6.4.3
- name: DOCKER_SKIP_GFX
value: gfx90a
- name: COMPOSABLE_KERNEL_PIPELINE_ID

17
.wordlist.txt
View File

@@ -5,6 +5,7 @@ ACEs
ACS
AccVGPR
AccVGPRs
AITER
ALU
AllReduce
AMD
@@ -118,6 +119,7 @@ Deprecations
DevCap
DirectX
Dockerfile
Dockerized
Doxygen
dropless
ELMo
@@ -125,6 +127,7 @@ ENDPGM
EPYC
ESXi
EoS
fas
FBGEMM
FIFOs
FFT
@@ -157,6 +160,7 @@ GEMMs
GFLOPS
GFortran
GFXIP
GGUF
Gemma
GiB
GIM
@@ -198,6 +202,7 @@ HWE
HWS
Haswell
Higgs
href
Hyperparameters
Huggingface
IB
@@ -301,6 +306,7 @@ Multicore
Multithreaded
MyEnvironment
MyST
NANOO
NBIO
NBIOs
NCCL
@@ -373,6 +379,7 @@ PowerEdge
PowerShell
Pretrained
Pretraining
Primus
Profiler's
PyPi
Pytest
@@ -515,6 +522,7 @@ unwindowed
VALU
VBIOS
VCN
verl's
VGPR
VGPRs
VM
@@ -546,6 +554,7 @@ Xilinx
Xnack
Xteam
YAML
YAMLs
YML
YModel
ZeRO
@@ -610,6 +619,7 @@ completers
composable
concretization
config
configs
conformant
const
constructible
@@ -692,6 +702,7 @@ github
globals
gnupg
grayscale
gx
gzip
heterogenous
hipBLAS
@@ -764,6 +775,7 @@ logits
lossy
macOS
matchers
maxtext
megatron
microarchitecture
migraphx
@@ -802,6 +814,7 @@ parallelizing
param
parameterization
passthrough
pe
perfcounter
performant
perl
@@ -824,11 +837,14 @@ preprocessing
preprocessor
prequantized
prerequisites
pretrain
pretraining
primus
profiler
profilers
protobuf
pseudorandom
px
py
pytorch
recommender
@@ -944,6 +960,7 @@ toolchain
toolchains
toolset
toolsets
torchtitan
torchvision
tqdm
tracebacks

12
docs/about/license.md
View File

@@ -29,7 +29,7 @@ additional licenses. Please review individual repositories for more information.
| [AMD SMI](https://github.com/ROCm/amdsmi) | [MIT](https://github.com/ROCm/amdsmi/blob/amd-staging/LICENSE) |
| [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
| [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
| [AQLprofile] | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE) |
| [AQLprofile] | [MIT](https://github.com/ROCm/aqlprofile/blob/amd-staging/LICENSE.md) |
| [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
| [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
| [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
@@ -50,7 +50,7 @@ additional licenses. Please review individual repositories for more information.
| [llvm-project](https://github.com/ROCm/llvm-project/) | [Apache](https://github.com/ROCm/llvm-project/blob/amd-staging/LICENSE.TXT) |
| [llvm-project/flang](https://github.com/ROCm/llvm-project/tree/amd-staging/flang) | [Apache 2.0](https://github.com/ROCm/llvm-project/blob/amd-staging/flang/LICENSE.TXT) |
| [MIGraphX](https://github.com/ROCm/AMDMIGraphX/) | [MIT](https://github.com/ROCm/AMDMIGraphX/blob/develop/LICENSE) |
| [MIOpen](https://github.com/ROCm/MIOpen/) | [MIT](https://github.com/ROCm/MIOpen/blob/develop/LICENSE.txt) |
| [MIOpen](https://github.com/ROCm/MIOpen/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/miopen/LICENSE.md) |
| [MIVisionX](https://github.com/ROCm/MIVisionX/) | [MIT](https://github.com/ROCm/MIVisionX/blob/develop/LICENSE.txt) |
| [rocAL](https://github.com/ROCm/rocAL) | [MIT](https://github.com/ROCm/rocAL/blob/develop/LICENSE.txt) |
| [rocALUTION](https://github.com/ROCm/rocALUTION/) | [MIT](https://github.com/ROCm/rocALUTION/blob/develop/LICENSE.md) |
@@ -67,15 +67,15 @@ additional licenses. Please review individual repositories for more information.
| [ROCm Communication Collectives Library (RCCL)](https://github.com/ROCm/rccl/) | [Custom](https://github.com/ROCm/rccl/blob/develop/LICENSE.txt) |
| [ROCm-Core](https://github.com/ROCm/rocm-core) | [MIT](https://github.com/ROCm/rocm-core/blob/master/copyright) |
| [ROCm Compute Profiler](https://github.com/ROCm/rocprofiler-compute) | [MIT](https://github.com/ROCm/rocprofiler-compute/blob/amd-staging/LICENSE) |
| [ROCm Data Center (RDC)](https://github.com/ROCm/rdc/) | [MIT](https://github.com/ROCm/rdc/blob/amd-staging/LICENSE) |
| [ROCm Data Center (RDC)](https://github.com/ROCm/rdc/) | [MIT](https://github.com/ROCm/rdc/blob/amd-staging/LICENSE.md) |
| [ROCm-Device-Libs](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/device-libs) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/device-libs/LICENSE.TXT) |
| [ROCm-OpenCL-Runtime](https://github.com/ROCm/clr/tree/amd-staging/opencl) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/opencl/LICENSE.txt) |
| [ROCm Performance Primitives (RPP)](https://github.com/ROCm/rpp) | [MIT](https://github.com/ROCm/rpp/blob/develop/LICENSE) |
| [ROCm SMI Lib](https://github.com/ROCm/rocm_smi_lib/) | [MIT](https://github.com/ROCm/rocm_smi_lib/blob/amd-staging/License.txt) |
| [ROCm Systems Profiler](https://github.com/ROCm/rocprofiler-systems) | [MIT](https://github.com/ROCm/rocprofiler-systems/blob/amd-staging/LICENSE) |
| [ROCm SMI Lib](https://github.com/ROCm/rocm_smi_lib/) | [MIT](https://github.com/ROCm/rocm_smi_lib/blob/amd-staging/LICENSE.md) |
| [ROCm Systems Profiler](https://github.com/ROCm/rocprofiler-systems) | [MIT](https://github.com/ROCm/rocprofiler-systems/blob/amd-staging/LICENSE.md) |
| [ROCm Validation Suite](https://github.com/ROCm/ROCmValidationSuite/) | [MIT](https://github.com/ROCm/ROCmValidationSuite/blob/master/LICENSE) |
| [rocPRIM](https://github.com/ROCm/rocPRIM/) | [MIT](https://github.com/ROCm/rocPRIM/blob/develop/LICENSE.txt) |
| [ROCProfiler](https://github.com/ROCm/rocprofiler/) | [MIT](https://github.com/ROCm/rocprofiler/blob/amd-staging/LICENSE) |
| [ROCProfiler](https://github.com/ROCm/rocprofiler/) | [MIT](https://github.com/ROCm/rocprofiler/blob/amd-staging/LICENSE.md) |
| [ROCprofiler-SDK](https://github.com/ROCm/rocprofiler-sdk) | [MIT](https://github.com/ROCm/rocprofiler-sdk/blob/amd-mainline/LICENSE) |
| [rocPyDecode](https://github.com/ROCm/rocPyDecode) | [MIT](https://github.com/ROCm/rocPyDecode/blob/develop/LICENSE.txt) |
| [rocRAND](https://github.com/ROCm/rocRAND/) | [MIT](https://github.com/ROCm/rocRAND/blob/develop/LICENSE.txt) |

6
docs/compatibility/compatibility-matrix.rst
View File

@@ -253,8 +253,12 @@ Expand for full historical view of:
.. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
.. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
.. [#verl_compat] verl is only supported on ROCm 6.2.0.
.. [#stanford-megatron-lm_compat] Stanford Megatron-LM is only supported on ROCm 6.3.0.
.. [#dgl_compat] DGL is only supported on ROCm 6.4.0.
.. [#megablocks_compat] Megablocks is only supported on ROCm 6.3.0.
.. [#taichi_compat] Taichi is only supported on ROCm 6.3.2.
.. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
.. [#ray_compat] Ray is only supported on ROCm 6.4.1.
.. [#llama-cpp_compat] llama.cpp is only supported on ROCm 6.4.0.
.. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
.. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.

156
docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst Normal file
View File

@@ -0,0 +1,156 @@
:orphan:
.. meta::
:description: llama.cpp deep learning framework compatibility
:keywords: GPU, GGML, llama.cpp compatibility
.. version-set:: rocm_version latest
********************************************************************************
llama.cpp compatibility
********************************************************************************
`llama.cpp <https://github.com/ggml-org/llama.cpp>`__ is an open-source framework
for Large Language Model (LLM) inference that runs on both central processing units
(CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing
a simple, dependency-free setup.
The framework supports multiple quantization options, from 1.5-bit to 8-bit integers,
to speed up inference and reduce memory usage. Originally built as a CPU-first library,
llama.cpp is easy to integrate with other programming environments and is widely
adopted across diverse platforms, including consumer devices.
ROCm support for llama.cpp is upstreamed, and you can build the official source code
with ROCm support:
- ROCm support for llama.cpp is hosted in the official `https://github.com/ROCm/llama.cpp
<https://github.com/ROCm/llama.cpp>`_ repository.
- Due to independent compatibility considerations, this location differs from the
`https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`_ upstream repository.
- To install llama.cpp, use the prebuilt :ref:`Docker image <llama-cpp-docker-compat>`,
which includes ROCm, llama.cpp, and all required dependencies.
- See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>`
to install and get started.
- See the `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md#hip>`__
in the upstream llama.cpp documentation.
.. note::
llama.cpp is supported on ROCm 6.4.0.
Supported devices
================================================================================
**Officially Supported**: AMD Instinct™ MI300X, MI210
Use cases and recommendations
================================================================================
llama.cpp can be applied in a variety of scenarios, particularly when you need to meet one or more of the following requirements:
- Plain C/C++ implementation with no external dependencies
- Support for 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory usage
- Custom HIP (Heterogeneous-compute Interface for Portability) kernels for running large language models (LLMs) on AMD GPUs (graphics processing units)
- CPU (central processing unit) + GPU (graphics processing unit) hybrid inference for partially accelerating models larger than the total available VRAM (video random-access memory)
llama.cpp is also used in a range of real-world applications, including:
- Games such as `Lucy's Labyrinth <https://github.com/MorganRO8/Lucys_Labyrinth>`__:
A simple maze game where AI-controlled agents attempt to trick the player.
- Tools such as `Styled Lines <https://marketplace.unity.com/packages/tools/ai-ml-integration/style-text-webgl-ios-stand-alone-llm-llama-cpp-wrapper-292902>`__:
A proprietary, asynchronous inference wrapper for Unity3D game development, including pre-built mobile and web platform wrappers and a model example.
- Various other AI applications use llama.cpp as their inference engine;
for a detailed list, see the `user interfaces (UIs) section <https://github.com/ggml-org/llama.cpp?tab=readme-ov-file#description>`__.
For more use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs.
- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__,
blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``,
server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for
AMD Instinct GPUs within the ROCm ecosystem.
.. _llama-cpp-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp>`__
with ROCm backends on Docker Hub. The following Docker image tags and associated
inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
Click |docker-icon| to view the image on Docker Hub.
.. important::
Tag endings of ``_full``, ``_server``, and ``_light`` serve different purposes for entrypoints as follows:
- Full: This image includes both the main executable file and the tools to convert ``LLaMA`` models into ``ggml`` and convert into 4-bit quantization.
- Server: This image only includes the server executable file.
- Light: This image only includes the main executable file.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Full Docker
- Server Docker
- Light Docker
- llama.cpp
- Ubuntu
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_full/images/sha256-f78f6c81ab2f8e957469415fe2370a1334fe969c381d1fe46050c85effaee9d5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_server/images/sha256-275ad9e18f292c26a00a2de840c37917e98737a88a3520bdc35fd3fc5c9a6a9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_light/images/sha256-cc324e6faeedf0e400011f07b49d2dc41a16bae257b2b7befa0f4e2e97231320"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b5997 <https://github.com/ROCm/llama.cpp/tree/release/b5997>`__
- 24.04
Key ROCm libraries for llama.cpp
================================================================================
llama.cpp functionality on ROCm is determined by its underlying library
dependencies. These ROCm components affect the capabilities, performance, and
feature set available to developers.
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- Purpose
- Usage
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
- Supports operations such as matrix multiplication, matrix-vector
products, and tensor contractions. Utilized in both dense and batched
linear algebra operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- :version-ref:`hipBLASLt rocm_version`
- hipBLASLt is an extension of the hipBLAS library, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
- By setting the flag ``ROCBLAS_USE_HIPBLASLT``, you can dispatch hipblasLt
kernels where possible.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
- :version-ref:`rocWMMA rocm_version`
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
multiplication (GEMM) and accumulation operations with mixed precision
support.
- Can be used to enhance the flash attention performance on AMD compute, by enabling
the flag during compile time.

111
docs/compatibility/ml-compatibility/ray-compatibility.rst Normal file
View File

@@ -0,0 +1,111 @@
:orphan:
.. meta::
:description: Ray deep learning framework compatibility
:keywords: GPU, Ray compatibility
.. version-set:: rocm_version latest
*******************************************************************************
Ray compatibility
*******************************************************************************
Ray is a unified framework for scaling AI and Python applications from your laptop
to a full cluster, without changing your code. Ray consists of `a core distributed
runtime <https://docs.ray.io/en/latest/ray-core/walkthrough.html>`_ and a set of
`AI libraries <https://docs.ray.io/en/latest/ray-air/getting-started.html>`_ for
simplifying machine learning computations.
Ray is a general-purpose framework that runs many types of workloads efficiently.
Any Python application can be scaled with Ray, without extra infrastructure.
ROCm support for Ray is upstreamed, and you can build the official source code
with ROCm support:
- ROCm support for Ray is hosted in the official `https://github.com/ROCm/ray
<https://github.com/ROCm/ray>`_ repository.
- Due to independent compatibility considerations, this location differs from the
`https://github.com/ray-project/ray <https://github.com/ray-project/ray>`_ upstream repository.
- To install Ray, use the prebuilt :ref:`Docker image <ray-docker-compat>`
which includes ROCm, Ray, and all required dependencies.
- See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>`
for instructions to get started.
- See the `Installation section <https://docs.ray.io/en/latest/ray-overview/installation.html>`_
in the upstream Ray documentation.
- The Docker image provided is based on the upstream Ray `Daily Release (Nightly) wheels <https://docs.ray.io/en/latest/ray-overview/installation.html#daily-releases-nightlies>`__
corresponding to commit `005c372 <https://github.com/ray-project/ray/commit/005c372262e050d5745f475e22e64305fa07f8b8>`__.
.. note::
Ray is supported on ROCm 6.4.1.
Supported devices
================================================================================
**Officially Supported**: AMD Instinct™ MI300X, MI210
Use cases and recommendations
================================================================================
* The `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm
Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__
blog provides an overview of Volcano Engine Reinforcement Learning (verl)
for large language models (LLMs) and discusses its benefits in large-scale
reinforcement learning from human feedback (RLHF). It uses Ray as part of a
hybrid orchestration engine to schedule and coordinate training and inference
tasks in parallel, enabling optimized resource utilization and potential overlap
between these phases. This dynamic resource allocation strategy significantly
improves overall system efficiency. The blog presents verl’s performance results,
focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X
GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and
accelerate your RLHF training with ROCm-optimized performance.
* The `Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows
<https://rocm.blogs.amd.com/artificial-intelligence/rocm-ray/README.html>`__
blog post describes key use cases such as training and inference for large language models (LLMs),
model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale
workloads using Ray in the ROCm environment.
For more use cases and recommendations, see the AMD GPU tabs in the `Accelerator Support
topic <https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html#accelerator-support>`__
of the Ray core documentation and refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs.
.. _ray-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes ready-made `ROCm Ray Docker images <https://hub.docker.com/r/rocm/ray/tags>`__
with ROCm backends on Docker Hub. The following Docker image tags and
associated inventories represent the latest Ray version from the official Docker Hub and are validated for
`ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click the |docker-icon|
icon to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- Ray
- Pytorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/ray/ray-2.48.0.post0_rocm6.4.1_ubuntu24.04_py3.12_pytorch2.6.0/images/sha256-0d166fe6bdced38338c78eedfb96eff92655fb797da3478a62dd636365133cc0"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
- `2.48.0.post0 <https://github.com/ROCm/ray/tree/release/2.48.0.post0>`_
- 2.6.0+git684f6f2
- 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_

29
docs/conf.py
View File

@@ -9,17 +9,21 @@ import shutil
import sys
from pathlib import Path
shutil.copy2("../RELEASE.md", "./about/release-notes.md")
shutil.copy2("../CHANGELOG.md", "./release/changelog.md")
gh_release_path = os.path.join("..", "RELEASE.md")
gh_changelog_path = os.path.join("..", "CHANGELOG.md")
sphinx_release_path = os.path.join("about", "release-notes.md")
sphinx_changelog_path = os.path.join("release", "changelog.md")
shutil.copy2(gh_release_path, sphinx_release_path)
shutil.copy2(gh_changelog_path, sphinx_changelog_path)
# Mark the consolidated changelog as orphan to prevent Sphinx from warning about missing toctree entries
with open("./release/changelog.md", "r+") as file:
with open(sphinx_changelog_path, "r+", encoding="utf-8") as file:
content = file.read()
file.seek(0)
file.write(":orphan:\n" + content)
# Replace GitHub-style [!ADMONITION]s with Sphinx-compatible ```{admonition} blocks
with open("./release/changelog.md", "r") as file:
with open(sphinx_changelog_path, "r", encoding="utf-8") as file:
lines = file.readlines()
modified_lines = []
@@ -57,11 +61,14 @@ with open("./release/changelog.md", "r") as file:
file.close()
with open("./release/changelog.md", 'w') as file:
with open(sphinx_changelog_path, "w", encoding="utf-8") as file:
file.writelines(modified_lines)
os.system("mkdir -p ../_readthedocs/html/downloads")
os.system("cp compatibility/compatibility-matrix-historical-6.0.csv ../_readthedocs/html/downloads/compatibility-matrix-historical-6.0.csv")
matrix_path = os.path.join("compatibility", "compatibility-matrix-historical-6.0.csv")
rtd_path = os.path.join("..", "_readthedocs", "html", "downloads")
if not os.path.exists(rtd_path):
os.makedirs(rtd_path)
shutil.copy2(matrix_path, rtd_path)
latex_engine = "xelatex"
latex_elements = {
@@ -101,6 +108,8 @@ article_pages = [
{"file": "compatibility/ml-compatibility/dgl-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/megablocks-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/taichi-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/ray-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/llama-cpp-compatibility", "os": ["linux"]},
{"file": "how-to/deep-learning-rocm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
@@ -117,11 +126,15 @@ article_pages = [
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.6", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-primus-migration-guide", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-megatron", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
@@ -147,6 +160,8 @@ article_pages = [
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250521", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250605", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250702", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250702", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250715", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},

18
docs/contribute/building.md
View File

@@ -28,6 +28,10 @@ See the [Python requirements file](https://github.com/ROCm/ROCm/blob/develop/doc
Use the Python Virtual Environment (`venv`) and run the following commands from the project root:
::::{tab-set}
:::{tab-item} Linux and WSL
:sync: linux
```sh
python3 -mvenv .venv
@@ -35,6 +39,20 @@ python3 -mvenv .venv
.venv/bin/python -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
```
:::
:::{tab-item} Windows
:sync: windows
```powershell
python -mvenv .venv
.venv\Scripts\python.exe -m pip install -r docs/sphinx/requirements.txt
.venv\Scripts\python.exe -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
```
:::
::::
Navigate to `_build/html/index.html` and open this file in a web browser.
## Visual Studio Code

91
docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.0_20250812-benchmark-models.yaml Normal file
View File

@@ -0,0 +1,91 @@
vllm_benchmark:
unified_docker:
latest:
pull_tag: rocm/vllm:rocm6.4.1_vllm_0.10.0_20250812
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.0_20250812/images/sha256-4c277ad39af3a8c9feac9b30bf78d439c74d9b4728e788a419d3f1d0c30cacaa
rocm_version: 6.4.1
vllm_version: 0.10.0 (0.10.1.dev395+g340ea86df.rocm641)
pytorch_version: 2.7.0+gitf717b2a
hipblaslt_version: 0.15
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.1 8B
mad_tag: pyt_vllm_llama-3.1-8b
model_repo: meta-llama/Llama-3.1-8B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: float16
- model: Llama 3.1 70B
mad_tag: pyt_vllm_llama-3.1-70b
model_repo: meta-llama/Llama-3.1-70B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: float16
- model: Llama 3.1 405B
mad_tag: pyt_vllm_llama-3.1-405b
model_repo: meta-llama/Llama-3.1-405B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
precision: float16
- model: Llama 2 70B
mad_tag: pyt_vllm_llama-2-70b
model_repo: meta-llama/Llama-2-70b-chat-hf
url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
precision: float16
- model: Llama 3.1 8B FP8
mad_tag: pyt_vllm_llama-3.1-8b_fp8
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
precision: float8
- model: Llama 3.1 70B FP8
mad_tag: pyt_vllm_llama-3.1-70b_fp8
model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
precision: float8
- model: Llama 3.1 405B FP8
mad_tag: pyt_vllm_llama-3.1-405b_fp8
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
precision: float8
- group: Mistral AI
tag: mistral
models:
- model: Mixtral MoE 8x7B
mad_tag: pyt_vllm_mixtral-8x7b
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
precision: float16
- model: Mixtral MoE 8x22B
mad_tag: pyt_vllm_mixtral-8x22b
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
precision: float16
- model: Mixtral MoE 8x7B FP8
mad_tag: pyt_vllm_mixtral-8x7b_fp8
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
precision: float8
- model: Mixtral MoE 8x22B FP8
mad_tag: pyt_vllm_mixtral-8x22b_fp8
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
precision: float8
- group: Qwen
tag: qwen
models:
- model: QwQ-32B
mad_tag: pyt_vllm_qwq-32b
model_repo: Qwen/QwQ-32B
url: https://huggingface.co/Qwen/QwQ-32B
precision: float16
- model: Qwen3 30B A3B
mad_tag: pyt_vllm_qwen3-30b-a3b
model_repo: Qwen/Qwen3-30B-A3B
url: https://huggingface.co/Qwen/Qwen3-30B-A3B
precision: float16
- group: Microsoft Phi
tag: phi
models:
- model: Phi-4
mad_tag: pyt_vllm_phi-4
model_repo: microsoft/phi-4
url: https://huggingface.co/microsoft/phi-4

163
docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml Normal file
View File

@@ -0,0 +1,163 @@
vllm_benchmark:
unified_docker:
latest:
# TODO: update me
pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea
rocm_version: 6.4.1
vllm_version: 0.9.1 (0.9.2.dev364+gb432b7a28.rocm641)
pytorch_version: 2.7.0+gitf717b2a
hipblaslt_version: 0.15
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.1 8B
mad_tag: pyt_vllm_llama-3.1-8b
model_repo: meta-llama/Llama-3.1-8B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: float16
- model: Llama 3.1 70B
mad_tag: pyt_vllm_llama-3.1-70b
model_repo: meta-llama/Llama-3.1-70B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: float16
- model: Llama 3.1 405B
mad_tag: pyt_vllm_llama-3.1-405b
model_repo: meta-llama/Llama-3.1-405B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
precision: float16
- model: Llama 2 7B
mad_tag: pyt_vllm_llama-2-7b
model_repo: meta-llama/Llama-2-7b-chat-hf
url: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
precision: float16
- model: Llama 2 70B
mad_tag: pyt_vllm_llama-2-70b
model_repo: meta-llama/Llama-2-70b-chat-hf
url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
precision: float16
- model: Llama 3.1 8B FP8
mad_tag: pyt_vllm_llama-3.1-8b_fp8
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
precision: float8
- model: Llama 3.1 70B FP8
mad_tag: pyt_vllm_llama-3.1-70b_fp8
model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
precision: float8
- model: Llama 3.1 405B FP8
mad_tag: pyt_vllm_llama-3.1-405b_fp8
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
precision: float8
- group: Mistral AI
tag: mistral
models:
- model: Mixtral MoE 8x7B
mad_tag: pyt_vllm_mixtral-8x7b
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
precision: float16
- model: Mixtral MoE 8x22B
mad_tag: pyt_vllm_mixtral-8x22b
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
precision: float16
- model: Mistral 7B
mad_tag: pyt_vllm_mistral-7b
model_repo: mistralai/Mistral-7B-Instruct-v0.3
url: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
precision: float16
- model: Mixtral MoE 8x7B FP8
mad_tag: pyt_vllm_mixtral-8x7b_fp8
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
precision: float8
- model: Mixtral MoE 8x22B FP8
mad_tag: pyt_vllm_mixtral-8x22b_fp8
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
precision: float8
- model: Mistral 7B FP8
mad_tag: pyt_vllm_mistral-7b_fp8
model_repo: amd/Mistral-7B-v0.1-FP8-KV
url: https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV
precision: float8
- group: Qwen
tag: qwen
models:
- model: Qwen2 7B
mad_tag: pyt_vllm_qwen2-7b
model_repo: Qwen/Qwen2-7B-Instruct
url: https://huggingface.co/Qwen/Qwen2-7B-Instruct
precision: float16
- model: Qwen2 72B
mad_tag: pyt_vllm_qwen2-72b
model_repo: Qwen/Qwen2-72B-Instruct
url: https://huggingface.co/Qwen/Qwen2-72B-Instruct
precision: float16
- model: QwQ-32B
mad_tag: pyt_vllm_qwq-32b
model_repo: Qwen/QwQ-32B
url: https://huggingface.co/Qwen/QwQ-32B
precision: float16
tunableop: true
- group: Databricks DBRX
tag: dbrx
models:
- model: DBRX Instruct
mad_tag: pyt_vllm_dbrx-instruct
model_repo: databricks/dbrx-instruct
url: https://huggingface.co/databricks/dbrx-instruct
precision: float16
- model: DBRX Instruct FP8
mad_tag: pyt_vllm_dbrx_fp8
model_repo: amd/dbrx-instruct-FP8-KV
url: https://huggingface.co/amd/dbrx-instruct-FP8-KV
precision: float8
- group: Google Gemma
tag: gemma
models:
- model: Gemma 2 27B
mad_tag: pyt_vllm_gemma-2-27b
model_repo: google/gemma-2-27b
url: https://huggingface.co/google/gemma-2-27b
precision: float16
- group: Cohere
tag: cohere
models:
- model: C4AI Command R+ 08-2024
mad_tag: pyt_vllm_c4ai-command-r-plus-08-2024
model_repo: CohereForAI/c4ai-command-r-plus-08-2024
url: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024
precision: float16
- model: C4AI Command R+ 08-2024 FP8
mad_tag: pyt_vllm_command-r-plus_fp8
model_repo: amd/c4ai-command-r-plus-FP8-KV
url: https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV
precision: float8
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek MoE 16B
mad_tag: pyt_vllm_deepseek-moe-16b-chat
model_repo: deepseek-ai/deepseek-moe-16b-chat
url: https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat
precision: float16
- group: Microsoft Phi
tag: phi
models:
- model: Phi-4
mad_tag: pyt_vllm_phi-4
model_repo: microsoft/phi-4
url: https://huggingface.co/microsoft/phi-4
- group: TII Falcon
tag: falcon
models:
- model: Falcon 180B
mad_tag: pyt_vllm_falcon-180b
model_repo: tiiuae/falcon-180B
url: https://huggingface.co/tiiuae/falcon-180B
precision: float16

13
docs/data/how-to/rocm-for-ai/inference/sglang-benchmark-models.yaml
View File

@@ -1,11 +1,10 @@
sglang_benchmark:
unified_docker:
latest:
pull_tag: lmsysorg/sglang:v0.4.5-rocm630
dockers:
- pull_tag: lmsysorg/sglang:v0.4.5-rocm630
docker_hub_url: https://hub.docker.com/layers/lmsysorg/sglang/v0.4.5-rocm630/images/sha256-63d2cb760a237125daf6612464cfe2f395c0784e21e8b0ea37d551cd10d3c951
rocm_version: 6.3.0
sglang_version: 0.4.5 (0.4.5-rocm)
pytorch_version: 2.6.0a0+git8d4926e
components:
ROCm: 6.3.0
SGLang: 0.4.5 (0.4.5-rocm)
PyTorch: 2.6.0a0+git8d4926e
model_groups:
- group: DeepSeek
tag: deepseek

195
docs/data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
View File

@@ -1,13 +1,11 @@
vllm_benchmark:
unified_docker:
latest:
# TODO: update me
pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea
rocm_version: 6.4.1
vllm_version: 0.9.1 (0.9.2.dev364+gb432b7a28.rocm641)
pytorch_version: 2.7.0+gitf717b2a
hipblaslt_version: 0.15
dockers:
- pull_tag: rocm/vllm:rocm6.4.1_vllm_0.10.1_20250909
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c
components:
ROCm: 6.4.1
vLLM: 0.10.1 (0.10.1rc2.dev409+g0b6bf6691.rocm641)
PyTorch: 2.7.0+gitf717b2a
hipBLASLt: 0.15
model_groups:
- group: Meta Llama
tag: llama
@@ -17,41 +15,85 @@ vllm_benchmark:
model_repo: meta-llama/Llama-3.1-8B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: float16
config:
tp: 1
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Llama 3.1 70B
mad_tag: pyt_vllm_llama-3.1-70b
model_repo: meta-llama/Llama-3.1-70B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: float16
config:
tp: 8
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Llama 3.1 405B
mad_tag: pyt_vllm_llama-3.1-405b
model_repo: meta-llama/Llama-3.1-405B-Instruct
url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
precision: float16
- model: Llama 2 7B
mad_tag: pyt_vllm_llama-2-7b
model_repo: meta-llama/Llama-2-7b-chat-hf
url: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
precision: float16
config:
tp: 8
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Llama 2 70B
mad_tag: pyt_vllm_llama-2-70b
model_repo: meta-llama/Llama-2-70b-chat-hf
url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
precision: float16
config:
tp: 8
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 4096
max_num_batched_tokens: 4096
max_model_len: 4096
- model: Llama 3.1 8B FP8
mad_tag: pyt_vllm_llama-3.1-8b_fp8
model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
precision: float8
config:
tp: 1
dtype: auto
kv_cache_dtype: fp8
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Llama 3.1 70B FP8
mad_tag: pyt_vllm_llama-3.1-70b_fp8
model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
precision: float8
config:
tp: 8
dtype: auto
kv_cache_dtype: fp8
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Llama 3.1 405B FP8
mad_tag: pyt_vllm_llama-3.1-405b_fp8
model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
precision: float8
config:
tp: 8
dtype: auto
kv_cache_dtype: fp8
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- group: Mistral AI
tag: mistral
models:
@@ -60,92 +102,76 @@ vllm_benchmark:
model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
precision: float16
config:
tp: 8
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 32768
max_num_batched_tokens: 32768
max_model_len: 8192
- model: Mixtral MoE 8x22B
mad_tag: pyt_vllm_mixtral-8x22b
model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
precision: float16
- model: Mistral 7B
mad_tag: pyt_vllm_mistral-7b
model_repo: mistralai/Mistral-7B-Instruct-v0.3
url: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
precision: float16
config:
tp: 8
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 65536
max_num_batched_tokens: 65536
max_model_len: 8192
- model: Mixtral MoE 8x7B FP8
mad_tag: pyt_vllm_mixtral-8x7b_fp8
model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
precision: float8
config:
tp: 8
dtype: auto
kv_cache_dtype: fp8
max_seq_len_to_capture: 32768
max_num_batched_tokens: 32768
max_model_len: 8192
- model: Mixtral MoE 8x22B FP8
mad_tag: pyt_vllm_mixtral-8x22b_fp8
model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
precision: float8
- model: Mistral 7B FP8
mad_tag: pyt_vllm_mistral-7b_fp8
model_repo: amd/Mistral-7B-v0.1-FP8-KV
url: https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV
precision: float8
config:
tp: 8
dtype: auto
kv_cache_dtype: fp8
max_seq_len_to_capture: 65536
max_num_batched_tokens: 65536
max_model_len: 8192
- group: Qwen
tag: qwen
models:
- model: Qwen2 7B
mad_tag: pyt_vllm_qwen2-7b
model_repo: Qwen/Qwen2-7B-Instruct
url: https://huggingface.co/Qwen/Qwen2-7B-Instruct
precision: float16
- model: Qwen2 72B
mad_tag: pyt_vllm_qwen2-72b
model_repo: Qwen/Qwen2-72B-Instruct
url: https://huggingface.co/Qwen/Qwen2-72B-Instruct
precision: float16
- model: QwQ-32B
mad_tag: pyt_vllm_qwq-32b
model_repo: Qwen/QwQ-32B
url: https://huggingface.co/Qwen/QwQ-32B
precision: float16
tunableop: true
- group: Databricks DBRX
tag: dbrx
models:
- model: DBRX Instruct
mad_tag: pyt_vllm_dbrx-instruct
model_repo: databricks/dbrx-instruct
url: https://huggingface.co/databricks/dbrx-instruct
precision: float16
- model: DBRX Instruct FP8
mad_tag: pyt_vllm_dbrx_fp8
model_repo: amd/dbrx-instruct-FP8-KV
url: https://huggingface.co/amd/dbrx-instruct-FP8-KV
precision: float8
- group: Google Gemma
tag: gemma
models:
- model: Gemma 2 27B
mad_tag: pyt_vllm_gemma-2-27b
model_repo: google/gemma-2-27b
url: https://huggingface.co/google/gemma-2-27b
precision: float16
- group: Cohere
tag: cohere
models:
- model: C4AI Command R+ 08-2024
mad_tag: pyt_vllm_c4ai-command-r-plus-08-2024
model_repo: CohereForAI/c4ai-command-r-plus-08-2024
url: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024
precision: float16
- model: C4AI Command R+ 08-2024 FP8
mad_tag: pyt_vllm_command-r-plus_fp8
model_repo: amd/c4ai-command-r-plus-FP8-KV
url: https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV
precision: float8
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek MoE 16B
mad_tag: pyt_vllm_deepseek-moe-16b-chat
model_repo: deepseek-ai/deepseek-moe-16b-chat
url: https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat
config:
tp: 1
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 131072
max_num_batched_tokens: 131072
max_model_len: 8192
- model: Qwen3 30B A3B
mad_tag: pyt_vllm_qwen3-30b-a3b
model_repo: Qwen/Qwen3-30B-A3B
url: https://huggingface.co/Qwen/Qwen3-30B-A3B
precision: float16
config:
tp: 1
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 32768
max_num_batched_tokens: 32768
max_model_len: 8192
- group: Microsoft Phi
tag: phi
models:
@@ -153,11 +179,10 @@ vllm_benchmark:
mad_tag: pyt_vllm_phi-4
model_repo: microsoft/phi-4
url: https://huggingface.co/microsoft/phi-4
- group: TII Falcon
tag: falcon
models:
- model: Falcon 180B
mad_tag: pyt_vllm_falcon-180b
model_repo: tiiuae/falcon-180B
url: https://huggingface.co/tiiuae/falcon-180B
precision: float16
config:
tp: 1
dtype: auto
kv_cache_dtype: auto
max_seq_len_to_capture: 16384
max_num_batched_tokens: 16384
max_model_len: 8192

72
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dockers:
- pull_tag: rocm/jax-training:maxtext-v25.7
docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025
components:
ROCm: 6.4.1
JAX: 0.5.0
Python: 3.10.12
Transformer Engine: 2.1.0+90d703dd
hipBLASLt: 1.x.x
- pull_tag: rocm/jax-training:maxtext-v25.7-jax060
docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025
components:
ROCm: 6.4.1
JAX: 0.6.0
Python: 3.10.12
Transformer Engine: 2.1.0+90d703dd
hipBLASLt: 1.1.0-499ece1c21
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.3 70B
mad_tag: jax_maxtext_train_llama-3.3-70b
model_repo: Llama-3.3-70B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3.1 8B
mad_tag: jax_maxtext_train_llama-3.1-8b
model_repo: Llama-3.1-8B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3.1 70B
mad_tag: jax_maxtext_train_llama-3.1-70b
model_repo: Llama-3.1-70B
precision: bf16
doc_options: ["single-node"]
- model: Llama 3 8B
mad_tag: jax_maxtext_train_llama-3-8b
multinode_training_script: llama3_8b_multinode.sh
doc_options: ["multi-node"]
- model: Llama 3 70B
mad_tag: jax_maxtext_train_llama-3-70b
multinode_training_script: llama3_70b_multinode.sh
doc_options: ["multi-node"]
- model: Llama 2 7B
mad_tag: jax_maxtext_train_llama-2-7b
model_repo: Llama-2-7B
precision: bf16
multinode_training_script: llama2_7b_multinode.sh
doc_options: ["single-node", "multi-node"]
- model: Llama 2 70B
mad_tag: jax_maxtext_train_llama-2-70b
model_repo: Llama-2-70B
precision: bf16
multinode_training_script: llama2_70b_multinode.sh
doc_options: ["single-node", "multi-node"]
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek-V2-Lite (16B)
mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
model_repo: DeepSeek-V2-lite
precision: bf16
doc_options: ["single-node"]
- group: Mistral AI
tag: mistral
models:
- model: Mixtral 8x7B
mad_tag: jax_maxtext_train_mixtral-8x7b
model_repo: Mixtral-8x7B
precision: bf16
doc_options: ["single-node"]

27
docs/data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
View File

@@ -1,26 +1,15 @@
dockers:
- pull_tag: rocm/megatron-lm:v25.6_py312
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0
- pull_tag: rocm/megatron-lm:v25.7_py310
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
components:
ROCm: 6.4.1
PyTorch: 2.8.0a0+git7d205b2
Python: 3.12
Transformer Engine: 2.1.0.dev0+8c4a512
hipBLASLt: 393e413
Triton: 3.3.0
RCCL: 2.23.4.7a84c5d
doc_name: Ubuntu 24.04 + Python 3.12
- pull_tag: rocm/megatron-lm:v25.6_py310
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6
components:
ROCm: 6.4.1
PyTorch: 2.8.0a0+git7d205b2
ROCm: 6.4.2
Primus: v0.1.0-rc1
PyTorch: 2.8.0a0+gitd06a406
Python: "3.10"
Transformer Engine: 2.1.0.dev0+8c4a512
hipBLASLt: 393e413
Transformer Engine: 2.1.0.dev0+ba586519
hipBLASLt: 37ba1d36
Triton: 3.3.0
RCCL: 2.23.4.7a84c5d
doc_name: Ubuntu 22.04 + Python 3.10
RCCL: 2.22.3
model_groups:
- group: Meta Llama
tag: llama

60
docs/data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.6-benchmark-models.yaml Normal file
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@@ -0,0 +1,60 @@
dockers:
- pull_tag: rocm/megatron-lm:v25.6_py312
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0
components:
ROCm: 6.4.1
PyTorch: 2.8.0a0+git7d205b2
Python: 3.12
Transformer Engine: 2.1.0.dev0+8c4a512
hipBLASLt: 393e413
Triton: 3.3.0
RCCL: 2.23.4.7a84c5d
doc_name: Ubuntu 24.04 + Python 3.12
- pull_tag: rocm/megatron-lm:v25.6_py310
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6
components:
ROCm: 6.4.1
PyTorch: 2.8.0a0+git7d205b2
Python: "3.10"
Transformer Engine: 2.1.0.dev0+8c4a512
hipBLASLt: 393e413
Triton: 3.3.0
RCCL: 2.23.4.7a84c5d
doc_name: Ubuntu 22.04 + Python 3.10
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.3 70B
mad_tag: pyt_megatron_lm_train_llama-3.3-70b
- model: Llama 3.1 8B
mad_tag: pyt_megatron_lm_train_llama-3.1-8b
- model: Llama 3.1 70B
mad_tag: pyt_megatron_lm_train_llama-3.1-70b
- model: Llama 3.1 70B (proxy)
mad_tag: pyt_megatron_lm_train_llama-3.1-70b-proxy
- model: Llama 2 7B
mad_tag: pyt_megatron_lm_train_llama-2-7b
- model: Llama 2 70B
mad_tag: pyt_megatron_lm_train_llama-2-70b
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek-V3 (proxy)
mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
- model: DeepSeek-V2-Lite
mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
- group: Mistral AI
tag: mistral
models:
- model: Mixtral 8x7B
mad_tag: pyt_megatron_lm_train_mixtral-8x7b
- model: Mixtral 8x22B (proxy)
mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
- group: Qwen
tag: qwen
models:
- model: Qwen 2.5 7B
mad_tag: pyt_megatron_lm_train_qwen2.5-7b
- model: Qwen 2.5 72B
mad_tag: pyt_megatron_lm_train_qwen2.5-72b

120
docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.6-benchmark-models.yaml Normal file
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@@ -0,0 +1,120 @@
unified_docker:
latest:
pull_tag: rocm/pytorch-training:v25.6
docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
rocm_version: 6.4.1
pytorch_version: 2.8.0a0+git7d205b2
python_version: 3.10.17
transformer_engine_version: 1.14.0+2f85f5f2
flash_attention_version: 3.0.0.post1
hipblaslt_version: 0.15.0-8c6919d
triton_version: 3.3.0
model_groups:
- group: Pre-training
tag: pre-training
models:
- model: Llama 3.1 8B
mad_tag: pyt_train_llama-3.1-8b
model_repo: Llama-3.1-8B
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: BF16
training_modes: [pretrain]
- model: Llama 3.1 70B
mad_tag: pyt_train_llama-3.1-70b
model_repo: Llama-3.1-70B
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: BF16
training_modes: [pretrain]
- model: FLUX.1-dev
mad_tag: pyt_train_flux
model_repo: Flux
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
precision: BF16
training_modes: [pretrain]
- group: Fine-tuning
tag: fine-tuning
models:
- model: Llama 4 Scout 17B-16E
mad_tag: pyt_train_llama-4-scout-17b-16e
model_repo: Llama-4-17B_16E
url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 3.3 70B
mad_tag: pyt_train_llama-3.3-70b
model_repo: Llama-3.3-70B
url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
precision: BF16
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
- model: Llama 3.2 1B
mad_tag: pyt_train_llama-3.2-1b
model_repo: Llama-3.2-1B
url: https://huggingface.co/meta-llama/Llama-3.2-1B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 3.2 3B
mad_tag: pyt_train_llama-3.2-3b
model_repo: Llama-3.2-3B
url: https://huggingface.co/meta-llama/Llama-3.2-3B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 3.2 Vision 11B
mad_tag: pyt_train_llama-3.2-vision-11b
model_repo: Llama-3.2-Vision-11B
url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
precision: BF16
training_modes: [finetune_fw]
- model: Llama 3.2 Vision 90B
mad_tag: pyt_train_llama-3.2-vision-90b
model_repo: Llama-3.2-Vision-90B
url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
precision: BF16
training_modes: [finetune_fw]
- model: Llama 3.1 8B
mad_tag: pyt_train_llama-3.1-8b
model_repo: Llama-3.1-8B
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 3.1 70B
mad_tag: pyt_train_llama-3.1-70b
model_repo: Llama-3.1-70B
url: https://huggingface.co/meta-llama/Llama-3.1-70B
precision: BF16
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
- model: Llama 3.1 405B
mad_tag: pyt_train_llama-3.1-405b
model_repo: Llama-3.1-405B
url: https://huggingface.co/meta-llama/Llama-3.1-405B
precision: BF16
training_modes: [finetune_qlora, HF_finetune_lora]
- model: Llama 3 8B
mad_tag: pyt_train_llama-3-8b
model_repo: Llama-3-8B
url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 3 70B
mad_tag: pyt_train_llama-3-70b
model_repo: Llama-3-70B
url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 2 7B
mad_tag: pyt_train_llama-2-7b
model_repo: Llama-2-7B
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
precision: BF16
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
- model: Llama 2 13B
mad_tag: pyt_train_llama-2-13b
model_repo: Llama-2-13B
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Llama 2 70B
mad_tag: pyt_train_llama-2-70b
model_repo: Llama-2-70B
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
precision: BF16
training_modes: [finetune_lora, finetune_qlora, HF_finetune_lora]

58
docs/data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml Normal file
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@@ -0,0 +1,58 @@
dockers:
- pull_tag: rocm/megatron-lm:v25.7_py310
docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a
components:
ROCm: 6.4.2
Primus: v0.1.0-rc1
PyTorch: 2.8.0a0+gitd06a406
Python: "3.10"
Transformer Engine: 2.1.0.dev0+ba586519
hipBLASLt: 37ba1d36
Triton: 3.3.0
RCCL: 2.22.3
model_groups:
- group: Meta Llama
tag: llama
models:
- model: Llama 3.3 70B
mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
config_name: llama3.3_70B-pretrain.yaml
- model: Llama 3.1 70B
mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
config_name: llama3.1_70B-pretrain.yaml
- model: Llama 3.1 8B
mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
config_name: llama3.1_8B-pretrain.yaml
- model: Llama 2 7B
mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
config_name: llama2_7B-pretrain.yaml
- model: Llama 2 70B
mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
config_name: llama2_70B-pretrain.yaml
- group: DeepSeek
tag: deepseek
models:
- model: DeepSeek-V3 (proxy)
mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
config_name: deepseek_v3-pretrain.yaml
- model: DeepSeek-V2-Lite
mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
config_name: deepseek_v2_lite-pretrain.yaml
- group: Mistral AI
tag: mistral
models:
- model: Mixtral 8x7B
mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
config_name: mixtral_8x7B_v0.1-pretrain.yaml
- model: Mixtral 8x22B (proxy)
mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
config_name: mixtral_8x22B_v0.1-pretrain.yaml
- group: Qwen
tag: qwen
models:
- model: Qwen 2.5 7B
mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
config_name: primus_qwen2.5_7B-pretrain.yaml
- model: Qwen 2.5 72B
mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
config_name: qwen2.5_72B-pretrain.yaml

120
docs/data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
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@@ -1,38 +1,17 @@
unified_docker:
latest:
pull_tag: rocm/pytorch-training:v25.6
docker_hub_url: https://hub.docker.com/r/rocm/pytorch-training/tags
rocm_version: 6.4.1
pytorch_version: 2.8.0a0+git7d205b2
python_version: 3.10.17
transformer_engine_version: 1.14.0+2f85f5f2
flash_attention_version: 3.0.0.post1
hipblaslt_version: 0.15.0-8c6919d
triton_version: 3.3.0
dockers:
- pull_tag: rocm/pytorch-training:v25.7
docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
components:
ROCm: 6.4.2
PyTorch: 2.8.0a0+gitd06a406
Python: 3.10.18
Transformer Engine: 2.2.0.dev0+94e53dd8
Flash Attention: 3.0.0.post1
hipBLASLt: 1.1.0-4b9a52edfc
Triton: 3.3.0
model_groups:
- group: Pre-training
tag: pre-training
models:
- model: Llama 3.1 8B
mad_tag: pyt_train_llama-3.1-8b
model_repo: Llama-3.1-8B
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: BF16
training_modes: [pretrain]
- model: Llama 3.1 70B
mad_tag: pyt_train_llama-3.1-70b
model_repo: Llama-3.1-70B
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: BF16
training_modes: [pretrain]
- model: FLUX.1-dev
mad_tag: pyt_train_flux
model_repo: Flux
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
precision: BF16
training_modes: [pretrain]
- group: Fine-tuning
tag: fine-tuning
- group: Meta Llama
tag: llama
models:
- model: Llama 4 Scout 17B-16E
mad_tag: pyt_train_llama-4-scout-17b-16e
@@ -75,19 +54,19 @@ model_groups:
model_repo: Llama-3.1-8B
url: https://huggingface.co/meta-llama/Llama-3.1-8B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
- model: Llama 3.1 70B
mad_tag: pyt_train_llama-3.1-70b
model_repo: Llama-3.1-70B
url: https://huggingface.co/meta-llama/Llama-3.1-70B
url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
precision: BF16
training_modes: [finetune_fw, finetune_lora, finetune_qlora]
training_modes: [pretrain, finetune_fw, finetune_lora]
- model: Llama 3.1 405B
mad_tag: pyt_train_llama-3.1-405b
model_repo: Llama-3.1-405B
url: https://huggingface.co/meta-llama/Llama-3.1-405B
precision: BF16
training_modes: [finetune_qlora, HF_finetune_lora]
training_modes: [finetune_qlora]
- model: Llama 3 8B
mad_tag: pyt_train_llama-3-8b
model_repo: Llama-3-8B
@@ -117,4 +96,67 @@ model_groups:
model_repo: Llama-2-70B
url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
precision: BF16
training_modes: [finetune_lora, finetune_qlora, HF_finetune_lora]
training_modes: [finetune_lora, finetune_qlora]
- group: OpenAI
tag: openai
models:
- model: GPT OSS 20B
mad_tag: pyt_train_gpt_oss_20b
model_repo: GPT-OSS-20B
url: https://huggingface.co/openai/gpt-oss-20b
precision: BF16
training_modes: [HF_finetune_lora]
- model: GPT OSS 120B
mad_tag: pyt_train_gpt_oss_120b
model_repo: GPT-OSS-120B
url: https://huggingface.co/openai/gpt-oss-120b
precision: BF16
training_modes: [HF_finetune_lora]
- group: Qwen
tag: qwen
models:
- model: Qwen 3 8B
mad_tag: pyt_train_qwen3-8b
model_repo: Qwen3-8B
url: https://huggingface.co/Qwen/Qwen3-8B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Qwen 3 32B
mad_tag: pyt_train_qwen3-32b
model_repo: Qwen3-32
url: https://huggingface.co/Qwen/Qwen3-32B
precision: BF16
training_modes: [finetune_lora]
- model: Qwen 2.5 32B
mad_tag: pyt_train_qwen2.5-32b
model_repo: Qwen2.5-32B
url: https://huggingface.co/Qwen/Qwen2.5-32B
precision: BF16
training_modes: [finetune_lora]
- model: Qwen 2.5 72B
mad_tag: pyt_train_qwen2.5-72b
model_repo: Qwen2.5-72B
url: https://huggingface.co/Qwen/Qwen2.5-72B
precision: BF16
training_modes: [finetune_lora]
- model: Qwen 2 1.5B
mad_tag: pyt_train_qwen2-1.5b
model_repo: Qwen2-1.5B
url: https://huggingface.co/Qwen/Qwen2-1.5B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- model: Qwen 2 7B
mad_tag: pyt_train_qwen2-7b
model_repo: Qwen2-7B
url: https://huggingface.co/Qwen/Qwen2-7B
precision: BF16
training_modes: [finetune_fw, finetune_lora]
- group: Flux
tag: flux
models:
- model: FLUX.1-dev
mad_tag: pyt_train_flux
model_repo: Flux
url: https://huggingface.co/black-forest-labs/FLUX.1-dev
precision: BF16
training_modes: [pretrain]

160
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@@ -2,58 +2,146 @@
:description: How to install deep learning frameworks for ROCm
:keywords: deep learning, frameworks, ROCm, install, PyTorch, TensorFlow, JAX, MAGMA, DeepSpeed, ML, AI
********************************************
Installing deep learning frameworks for ROCm
********************************************
**********************************
Deep learning frameworks for ROCm
**********************************
ROCm provides a comprehensive ecosystem for deep learning development, including
:ref:`libraries <artificial-intelligence-apis>` for optimized deep learning operations and ROCm-aware versions of popular
deep learning frameworks and libraries such as PyTorch, TensorFlow, and JAX. ROCm works closely with these
frameworks to ensure that framework-specific optimizations take advantage of AMD accelerator and GPU architectures.
Deep learning frameworks provide environments for machine learning, training, fine-tuning, inference, and performance optimization.
The following guides provide information on compatibility and supported
features for these ROCm-enabled deep learning frameworks.
ROCm offers a complete ecosystem for developing and running deep learning applications efficiently. It also provides ROCm-compatible versions of popular frameworks and libraries, such as PyTorch, TensorFlow, JAX, and others.
* :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>`
* :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
* :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>`
* :doc:`verl compatibility <../compatibility/ml-compatibility/verl-compatibility>`
* :doc:`Stanford Megatron-LM compatibility <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
* :doc:`DGL compatibility <../compatibility/ml-compatibility/dgl-compatibility>`
* :doc:`Megablocks compatibility <../compatibility/ml-compatibility/megablocks-compatibility>`
* :doc:`Taichi compatibility <../compatibility/ml-compatibility/taichi-compatibility>`
The AMD ROCm organization actively contributes to open-source development and collaborates closely with framework organizations. This collaboration ensures that framework-specific optimizations effectively leverage AMD GPUs and accelerators.
This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
The table below summarizes information about ROCm-enabled deep learning frameworks. It includes details on ROCm compatibility and third-party tool support, installation steps and options, and links to GitHub resources. For a complete list of supported framework versions on ROCm, see the :doc:`Compatibility matrix <../compatibility/compatibility-matrix>` topic.
.. image:: ../data/how-to/framework_install_2024_07_04.png
:alt: Flowchart for installing ROCm-aware machine learning frameworks
:align: center
.. list-table::
:header-rows: 1
:widths: 5 3 6 3
See the installation instructions to get started.
* - Framework
- Installation
- Installation options
- GitHub
* :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
* :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
* :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/dgl-install>`
* :doc:`Megablocks for ROCm <rocm-install-on-linux:install/3rd-party/megablocks-install>`
* :doc:`Taichi for ROCm <rocm-install-on-linux:install/3rd-party/taichi-install>`
* - `PyTorch <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/pytorch-compatibility.html>`__
- .. raw:: html
.. note::
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-docker-image-with-pytorch-pre-installed>`__
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-a-wheels-package>`__
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-rocm-base-docker-image>`__
- `Upstream Docker file <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html#using-the-pytorch-upstream-dockerfile>`__
- .. raw:: html
For guidance on installing ROCm itself, refer to :doc:`ROCm installation for Linux <rocm-install-on-linux:index>`.
<a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>
* - `TensorFlow <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/tensorflow-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-docker-image-with-tensorflow-pre-installed>`__
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/tensorflow-install.html#using-a-wheels-package>`__
- .. raw:: html
<a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a>
* - `JAX <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/jax-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/jax-install.html#using-a-prebuilt-docker-image>`__
- .. raw:: html
<a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
* - `verl <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/verl-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/verl-install.html#use-a-prebuilt-docker-image-with-verl-pre-installed>`__
- .. raw:: html
<a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
* - `Stanford Megatron-LM <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/stanford-megatron-lm-install.html#use-a-prebuilt-docker-image-with-stanford-megatron-lm-pre-installed>`__
- .. raw:: html
<a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
* - `DGL <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/dgl-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/dgl-install.html#use-a-prebuilt-docker-image-with-dgl-pre-installed>`__
- .. raw:: html
<a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a>
* - `Megablocks <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/megablocks-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/megablocks-install.html#using-a-prebuilt-docker-image-with-megablocks-pre-installed>`__
- .. raw:: html
<a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
* - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
- .. raw:: html
<a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
* - `Ray <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/ray-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#using-a-prebuilt-docker-image-with-ray-pre-installed>`__
- `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#install-ray-on-bare-metal-or-a-custom-container>`__
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/ray-install.html#build-your-own-docker-image>`__
- .. raw:: html
<a href="https://github.com/ROCm/ray"><i class="fab fa-github fa-lg"></i></a>
* - `llama.cpp <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/llama-cpp-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html"><i class="fas fa-link fa-lg"></i></a>
-
- `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#use-a-prebuilt-docker-image-with-llama-cpp-pre-installed>`__
- .. raw:: html
<a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>
Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
through the following guides.
* :doc:`rocm-for-ai/index`
* :doc:`Training <rocm-for-ai/training/index>`
* :doc:`Use ROCm for training <rocm-for-ai/training/index>`
* :doc:`Fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
* :doc:`Use ROCm for fine-tuning LLMs <rocm-for-ai/fine-tuning/index>`
* :doc:`Inference <rocm-for-ai/inference/index>`
* :doc:`Use ROCm for AI inference <rocm-for-ai/inference/index>`
* :doc:`Inference optimization <rocm-for-ai/inference-optimization/index>`
* :doc:`Use ROCm for AI inference optimization <rocm-for-ai/inference-optimization/index>`

2
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@@ -939,7 +939,7 @@ hipBLASLt benchmarking
The GEMM library
`hipBLASLt <https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/index.html>`_
provides a benchmark tool for its supported operations. Refer to the
`documentation <https://github.com/ROCm/hipBLASLt/blob/develop/clients/benchmarks/README.md>`_
`documentation <https://github.com/ROCm/hipBLASLt/blob/develop/clients/bench/README.md>`_
for details.
* Example 1: Benchmark mix fp8 GEMM

445
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View File

@@ -0,0 +1,445 @@
:orphan:
.. meta::
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
ROCm vLLM Docker image.
:keywords: model, MAD, automation, dashboarding, validate
**********************************
vLLM inference performance testing
**********************************
.. caution::
This documentation does not reflect the latest version of ROCm vLLM
inference performance documentation. See :doc:`../vllm` for the latest version.
.. _vllm-benchmark-unified-docker-812:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.0_20250812-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
The `ROCm vLLM Docker <{{ unified_docker.docker_hub_url }}>`_ image offers
a prebuilt, optimized environment for validating large language model (LLM)
inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
accelerators and includes the following components:
.. list-table::
:header-rows: 1
* - Software component
- Version
* - `ROCm <https://github.com/ROCm/ROCm>`__
- {{ unified_docker.rocm_version }}
* - `vLLM <https://docs.vllm.ai/en/latest>`__
- {{ unified_docker.vllm_version }}
* - `PyTorch <https://github.com/ROCm/pytorch>`__
- {{ unified_docker.pytorch_version }}
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- {{ unified_docker.hipblaslt_version }}
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements-812>` for
MI300X series accelerators.
What's new
==========
The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <vllm-history>`.
* Upgraded to vLLM v0.10.
* FP8 KV cache support via AITER.
* Full graph capture support via AITER.
Supported models
================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.0_20250812-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
.. _vllm-benchmark-available-models-812:
The following models are supported for inference performance benchmarking
with vLLM and ROCm. Some instructions, commands, and recommendations in this
documentation might vary by model -- select one to get started.
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model group</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. _vllm-benchmark-vllm-812:
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. note::
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
Some models require access authorization prior to use via an external license agreement through a third party.
{% endfor %}
{% endfor %}
.. note::
vLLM is a toolkit and library for LLM inference and serving. AMD implements
high-performance custom kernels and modules in vLLM to enhance performance.
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements-812:
Performance measurements
========================
To evaluate performance, the
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
page provides reference throughput and serving measurements for inferencing popular AI models.
.. important::
The performance data presented in
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
only reflects the latest version of this inference benchmarking environment.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting training.
To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.10.0_20250812-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
Pull the Docker image
=====================
Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
Benchmarking
============
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. _vllm-benchmark-mad-812:
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. tab-set::
.. tab-item:: MAD-integrated benchmarking
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{model.mad_tag}} \
--keep-model-dir \
--live-output \
--timeout 28800
MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
and ``{{ model.mad_tag }}_serving.csv``.
Although the :ref:`available models
<vllm-benchmark-available-models-812>` are preconfigured to collect
offline throughput and online serving performance data, you can
also change the benchmarking parameters. See the standalone
benchmarking tab for more information.
{% if model.tunableop %}
.. note::
For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
TunableOp automatically explores different implementations and configurations of certain PyTorch
operators to find the fastest one for your hardware.
By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
`<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
the ``--tunableop on`` argument in your run.
Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
performance-collection run.
{% endif %}
.. tab-item:: Standalone benchmarking
.. rubric:: Download the Docker image and required scripts
1. Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
docker run -it \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--shm-size 16G \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--cap-add=SYS_PTRACE \
-v $(pwd):/workspace \
--env HUGGINGFACE_HUB_CACHE=/workspace \
--name test \
{{ unified_docker.pull_tag }}
2. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
3. To start the benchmark, use the following command with the appropriate options.
.. code-block::
./run.sh \
--config $CONFIG_CSV \
--model_repo {{ model.model_repo }} \
<overrides>
.. dropdown:: Benchmark options
:open:
.. list-table::
:header-rows: 1
:align: center
* - Name
- Options
- Description
* - ``--config``
- ``configs/default.csv``
- Run configs from the CSV for the chosen model repo and benchmark.
* -
- ``configs/extended.csv``
-
* -
- ``configs/performance.csv``
-
* - ``--benchmark``
- ``throughput``
- Measure offline end-to-end throughput.
* -
- ``serving``
- Measure online serving performance.
* -
- ``all``
- Measure both throughput and serving.
* - `<overrides>`
- See `run.sh <https://github.com/ROCm/MAD/blob/develop/scripts/vllm/run.sh>`__ for more info.
- Additional overrides to the config CSV.
The input sequence length, output sequence length, and tensor parallel (TP) are
already configured. You don't need to specify them with this script.
.. note::
For best performance, it's recommended to run with ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1``.
If you encounter the following error, pass your access-authorized Hugging
Face token to the gated models.
.. code-block::
OSError: You are trying to access a gated repo.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. rubric:: Benchmarking examples
Here are some examples of running the benchmark with various options:
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
export MAD_MODEL_NAME={{ model.mad_tag }}
./run.sh \
--config configs/default.csv \
--model_repo {{model.model_repo}} \
--benchmark throughput
Find the throughput benchmark report at ``./{{ model.mad_tag }}_throughput.csv``.
* Serving benchmark
Use this command to benchmark the serving performance of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
export MAD_MODEL_NAME={{ model.mad_tag }}
./run.sh \
--config configs/default.csv \
--model_repo {{model.model_repo}} \
--benchmark serving
Find the serving benchmark report at ``./{{ model.mad_tag }}_serving.csv``.
.. raw:: html
<style>
mjx-container[jax="CHTML"][display="true"] {
text-align: left;
margin: 0;
}
</style>
.. note::
Throughput is calculated as:
- .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
- .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
{% endfor %}
{% endfor %}
Advanced usage
==============
For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
see the developer's guide at `<https://github.com/ROCm/vllm/tree/f94ec9beeca1071cc34f9d1e206d8c7f3ac76129/docs/dev-docker>`__.
Reproducing the Docker image
----------------------------
To reproduce this ROCm/vLLM Docker image release, follow these steps:
1. Clone the `vLLM repository <https://github.com/ROCm/vllm>`__.
.. code-block:: shell
git clone https://github.com/ROCm/vllm.git
2. Checkout the specific release commit.
.. code-block:: shell
cd vllm
git checkout 340ea86dfe5955d6f9a9e767d6abab5aacf2c978
3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
.. code-block:: shell
docker build -f docker/Dockerfile.rocm -t vllm-rocm .
Further reading
===============
- To learn more about the options for latency and throughput benchmark scripts,
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run community models from Hugging Face on AMD GPUs, see
:doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
- To learn how to fine-tune LLMs and optimize inference, see
:doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
- For a list of other ready-made Docker images for AI with ROCm, see
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
Previous versions
=================
See :doc:`vllm-history` to find documentation for previous releases
of the ``ROCm/vllm`` Docker image.

6
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View File

@@ -14,7 +14,7 @@ vLLM inference performance testing
This documentation does not reflect the latest version of ROCm vLLM
inference performance documentation. See :doc:`../vllm` for the latest version.
.. _vllm-benchmark-unified-docker:
.. _vllm-benchmark-unified-docker-702:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250702-benchmark-models.yaml
@@ -77,7 +77,7 @@ vLLM inference performance testing
</div>
</div>
.. _vllm-benchmark-vllm:
.. _vllm-benchmark-vllm-702:
{% for model_group in model_groups %}
{% for model in model_group.models %}
@@ -159,7 +159,7 @@ vLLM inference performance testing
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-702:
{% for model_group in model_groups %}
{% for model in model_group.models %}

450
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View File

@@ -0,0 +1,450 @@
:orphan:
.. meta::
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
ROCm vLLM Docker image.
:keywords: model, MAD, automation, dashboarding, validate
**********************************
vLLM inference performance testing
**********************************
.. caution::
This documentation does not reflect the latest version of ROCm vLLM
inference performance documentation. See :doc:`../vllm` for the latest version.
.. _vllm-benchmark-unified-docker-715:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
The `ROCm vLLM Docker <{{ unified_docker.docker_hub_url }}>`_ image offers
a prebuilt, optimized environment for validating large language model (LLM)
inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
accelerators and includes the following components:
.. list-table::
:header-rows: 1
* - Software component
- Version
* - `ROCm <https://github.com/ROCm/ROCm>`__
- {{ unified_docker.rocm_version }}
* - `vLLM <https://docs.vllm.ai/en/latest>`__
- {{ unified_docker.vllm_version }}
* - `PyTorch <https://github.com/ROCm/pytorch>`__
- {{ unified_docker.pytorch_version }}
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- {{ unified_docker.hipblaslt_version }}
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements-715>` for
MI300X series accelerators.
What's new
==========
The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <vllm-history>`.
* The ``--compilation-config-parameter`` is no longer required as its options are now enabled by default.
This parameter has been removed from the benchmarking script.
* Resolved Llama 3.1 405 B custom all-reduce issue, eliminating the need for ``--disable-custom-all-reduce``.
This parameter has been removed from the benchmarking script.
* Fixed a ``+rms_norm`` custom kernel issue.
* Added quick reduce functionality. Set ``VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=FP`` to enable; supported modes are ``FP``, ``INT8``, ``INT6``, ``INT4``.
* Implemented a workaround to potentially mitigate GPU crashes experienced with the Command R+ model, pending a driver fix.
Supported models
================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
.. _vllm-benchmark-available-models-715:
The following models are supported for inference performance benchmarking
with vLLM and ROCm. Some instructions, commands, and recommendations in this
documentation might vary by model -- select one to get started.
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model group</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. _vllm-benchmark-vllm-715:
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. note::
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
Some models require access authorization prior to use via an external license agreement through a third party.
{% endfor %}
{% endfor %}
.. note::
vLLM is a toolkit and library for LLM inference and serving. AMD implements
high-performance custom kernels and modules in vLLM to enhance performance.
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements-715:
Performance measurements
========================
To evaluate performance, the
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
page provides reference throughput and latency measurements for inferencing popular AI models.
.. important::
The performance data presented in
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
only reflects the latest version of this inference benchmarking environment.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting training.
To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
Pull the Docker image
=====================
Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
Benchmarking
============
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. _vllm-benchmark-mad-715:
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. tab-set::
.. tab-item:: MAD-integrated benchmarking
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{model.mad_tag}} \
--keep-model-dir \
--live-output \
--timeout 28800
MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
Although the :ref:`available models <vllm-benchmark-available-models-715>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
{% if model.tunableop %}
.. note::
For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
TunableOp automatically explores different implementations and configurations of certain PyTorch
operators to find the fastest one for your hardware.
By default, ``{{model.mad_tag}}`` runs with TunableOp disabled
(see
`<https://github.com/ROCm/MAD/blob/develop/models.json>`__).
To enable it, include the ``--tunableop on`` argument in your
run.
Enabling TunableOp triggers a two-pass run -- a warm-up followed
by the performance-collection run.
{% endif %}
.. tab-item:: Standalone benchmarking
.. rubric:: Download the Docker image and required scripts
1. Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
docker run -it \
--device=/dev/kfd \
--device=/dev/dri \
--group-add video \
--shm-size 16G \
--security-opt seccomp=unconfined \
--security-opt apparmor=unconfined \
--cap-add=SYS_PTRACE \
-v $(pwd):/workspace \
--env HUGGINGFACE_HUB_CACHE=/workspace \
--name test \
{{ unified_docker.pull_tag }}
2. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
3. To start the benchmark, use the following command with the appropriate options.
.. dropdown:: Benchmark options
:open:
.. list-table::
:header-rows: 1
:align: center
* - Name
- Options
- Description
* - ``$test_option``
- latency
- Measure decoding token latency
* -
- throughput
- Measure token generation throughput
* -
- all
- Measure both throughput and latency
* - ``$num_gpu``
- 1 or 8
- Number of GPUs
* - ``$datatype``
- ``float16`` or ``float8``
- Data type
The input sequence length, output sequence length, and tensor parallel (TP) are
already configured. You don't need to specify them with this script.
Command:
.. code-block::
./vllm_benchmark_report.sh \
-s $test_option \
-m {{model.model_repo}} \
-g $num_gpu \
-d {{model.precision}}
.. note::
For best performance, it's recommend to run with ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1``.
If you encounter the following error, pass your access-authorized Hugging
Face token to the gated models.
.. code-block::
OSError: You are trying to access a gated repo.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. rubric:: Benchmarking examples
Here are some examples of running the benchmark with various options:
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
./vllm_benchmark_report.sh \
-s latency \
-m {{model.model_repo}} \
-g 8 \
-d {{model.precision}}
Find the latency report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
./vllm_benchmark_report.sh \
-s throughput \
-m {{model.model_repo}} \
-g 8 \
-d {{model.precision}}
Find the throughput report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
.. raw:: html
<style>
mjx-container[jax="CHTML"][display="true"] {
text-align: left;
margin: 0;
}
</style>
.. note::
Throughput is calculated as:
- .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
- .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
{% endfor %}
{% endfor %}
Advanced usage
==============
For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
see the developer's guide at `<https://github.com/ROCm/vllm/tree/f94ec9beeca1071cc34f9d1e206d8c7f3ac76129/docs/dev-docker>`__.
Reproducing the Docker image
----------------------------
To reproduce this ROCm/vLLM Docker image release, follow these steps:
1. Clone the `vLLM repository <https://github.com/ROCm/vllm>`__.
.. code-block:: shell
git clone https://github.com/ROCm/vllm.git
2. Checkout the specific release commit.
.. code-block:: shell
cd vllm
git checkout b432b7a285aa0dcb9677380936ffa74931bb6d6f
3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
.. code-block:: shell
docker build -f docker/Dockerfile.rocm -t vllm-rocm .
Known issues and workarounds
============================
AITER does not support FP8 KV cache yet.
Further reading
===============
- To learn more about the options for latency and throughput benchmark scripts,
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run community models from Hugging Face on AMD GPUs, see
:doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
- To learn how to fine-tune LLMs and optimize inference, see
:doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
- For a list of other ready-made Docker images for AI with ROCm, see
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
Previous versions
=================
See :doc:`vllm-history` to find documentation for previous releases
of the ``ROCm/vllm`` Docker image.

15
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View File

@@ -7,7 +7,7 @@ vLLM inference performance testing version history
This table lists previous versions of the ROCm vLLM inference Docker image for
inference performance testing. For detailed information about available models
for benchmarking, see the version-specific documentation. You can find tagged
previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/vllm/tags>`__.
previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.1_20250909/images/sha256-1113268572e26d59b205792047bea0e61e018e79aeadceba118b7bf23cb3715c>`__.
.. list-table::
:header-rows: 1
@@ -16,14 +16,23 @@ previous releases of the ``ROCm/vllm`` Docker image on `Docker Hub <https://hub.
- Components
- Resources
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715``
* - ``rocm/vllm:rocm6.4.1_vllm_0.10.0_20250812``
(latest)
-
* ROCm 6.4.1
* vLLM 0.10.0
* PyTorch 2.7.0
-
* :doc:`Documentation <../vllm>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.10.0_20250812/images/sha256-4c277ad39af3a8c9feac9b30bf78d439c74d9b4728e788a419d3f1d0c30cacaa>`__
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715``
-
* ROCm 6.4.1
* vLLM 0.9.1
* PyTorch 2.7.0
-
* :doc:`Documentation <../vllm>`
* :doc:`Documentation <vllm-0.9.1-20250715>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea>`__
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250702``

20
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View File

@@ -31,22 +31,26 @@ PyTorch inference performance testing
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1" style="display: none;">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
<div class="row gx-0 pt-1" style="display: none;">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
<div class="col-12 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% if models|length % 3 == 0 %}
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>

26
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View File

@@ -2,19 +2,19 @@
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and SGLang
:keywords: model, MAD, automation, dashboarding, validate
************************************
SGLang inference performance testing
************************************
*****************************************************************
SGLang inference performance testing DeepSeek-R1-Distill-Qwen-32B
*****************************************************************
.. _sglang-benchmark-unified-docker:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-benchmark-models.yaml
{% set unified_docker = data.sglang_benchmark.unified_docker.latest %}
{% set docker = data.dockers[0] %}
`SGLang <https://docs.sglang.ai>`__ is a high-performance inference and
serving engine for large language models (LLMs) and vision models. The
ROCm-enabled `SGLang Docker image <{{ unified_docker.docker_hub_url }}>`__
ROCm-enabled `SGLang Docker image <{{ docker.docker_hub_url }}>`__
bundles SGLang with PyTorch, optimized for AMD Instinct MI300X series
accelerators. It includes the following software components:
@@ -24,14 +24,10 @@ SGLang inference performance testing
* - Software component
- Version
* - `ROCm <https://github.com/ROCm/ROCm>`__
- {{ unified_docker.rocm_version }}
* - `SGLang <https://docs.sglang.ai/index.html>`__
- {{ unified_docker.sglang_version }}
* - `PyTorch <https://github.com/pytorch/pytorch>`__
- {{ unified_docker.pytorch_version }}
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
System validation
=================
@@ -50,8 +46,8 @@ system's configuration.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-benchmark-models.yaml
{% set unified_docker = data.sglang_benchmark.unified_docker.latest %}
{% set model_groups = data.sglang_benchmark.model_groups %}
{% set unified_docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Pull the Docker image
=====================

304
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View File

@@ -7,14 +7,13 @@
vLLM inference performance testing
**********************************
.. _vllm-benchmark-unified-docker:
.. _vllm-benchmark-unified-docker-909:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
{% set docker = data.dockers[0] %}
The `ROCm vLLM Docker <{{ unified_docker.docker_hub_url }}>`_ image offers
The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers
a prebuilt, optimized environment for validating large language model (LLM)
inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
@@ -26,20 +25,13 @@ vLLM inference performance testing
* - Software component
- Version
* - `ROCm <https://github.com/ROCm/ROCm>`__
- {{ unified_docker.rocm_version }}
* - `vLLM <https://docs.vllm.ai/en/latest>`__
- {{ unified_docker.vllm_version }}
* - `PyTorch <https://github.com/ROCm/pytorch>`__
- {{ unified_docker.pytorch_version }}
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- {{ unified_docker.hipblaslt_version }}
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements>` for
inference performance numbers <vllm-benchmark-performance-measurements-909>` for
MI300X series accelerators.
What's new
@@ -47,27 +39,23 @@ What's new
The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <previous-versions/vllm-history>`.
* The ``--compilation-config-parameter`` is no longer required as its options are now enabled by default.
This parameter has been removed from the benchmarking script.
* Upgraded to vLLM v0.10.1.
* Resolved Llama 3.1 405 B custom all-reduce issue, eliminating the need for ``--disable-custom-all-reduce``.
This parameter has been removed from the benchmarking script.
* Set ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1`` by default for better performance.
* Fixed a ``+rms_norm`` custom kernel issue.
* Set ``VLLM_ROCM_USE_AITER_RMSNORM=0`` by default to avoid various issues with torch compile.
* Added quick reduce functionality. Set ``VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=FP`` to enable; supported modes are ``FP``, ``INT8``, ``INT6``, ``INT4``.
* Implemented a workaround to potentially mitigate GPU crashes experienced with the Command R+ model, pending a driver fix.
.. _vllm-benchmark-supported-models-909:
Supported models
================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
{% set docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
.. _vllm-benchmark-available-models:
.. _vllm-benchmark-available-models-909:
The following models are supported for inference performance benchmarking
with vLLM and ROCm. Some instructions, commands, and recommendations in this
@@ -76,25 +64,25 @@ Supported models
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model group</div>
<div class="row col-10">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
@@ -102,7 +90,7 @@ Supported models
</div>
</div>
.. _vllm-benchmark-vllm:
.. _vllm-benchmark-vllm-909:
{% for model_group in model_groups %}
{% for model in model_group.models %}
@@ -113,25 +101,21 @@ Supported models
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
Some models require access authorization prior to use via an external license agreement through a third party.
{% if model.precision == "float8" and model.model_repo.startswith("amd") %}
This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD accelerators.
{% endif %}
{% endfor %}
{% endfor %}
.. note::
vLLM is a toolkit and library for LLM inference and serving. AMD implements
high-performance custom kernels and modules in vLLM to enhance performance.
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements:
.. _vllm-benchmark-performance-measurements-909:
Performance measurements
========================
To evaluate performance, the
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
page provides reference throughput and latency measurements for inferencing popular AI models.
page provides reference throughput and serving measurements for inferencing popular AI models.
.. important::
@@ -157,18 +141,18 @@ system's configuration.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
{% set docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Pull the Docker image
=====================
Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
docker pull {{ docker.pull_tag }}
Benchmarking
============
@@ -176,7 +160,7 @@ system's configuration.
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-909:
{% for model_group in model_groups %}
{% for model in model_group.models %}
@@ -187,6 +171,9 @@ system's configuration.
.. tab-item:: MAD-integrated benchmarking
The following run command is tailored to {{ model.model }}.
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
@@ -209,12 +196,15 @@ system's configuration.
--timeout 28800
MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
and ``{{ model.mad_tag }}_serving.csv``.
Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
Although the :ref:`available models
<vllm-benchmark-available-models-909>` are preconfigured to collect
offline throughput and online serving performance data, you can
also change the benchmarking parameters. See the standalone
benchmarking tab for more information.
{% if model.tunableop %}
@@ -224,28 +214,38 @@ system's configuration.
TunableOp automatically explores different implementations and configurations of certain PyTorch
operators to find the fastest one for your hardware.
By default, ``{{model.mad_tag}}`` runs with TunableOp disabled
(see
`<https://github.com/ROCm/MAD/blob/develop/models.json>`__).
To enable it, include the ``--tunableop on`` argument in your
run.
By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
`<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
the ``--tunableop on`` argument in your run.
Enabling TunableOp triggers a two-pass run -- a warm-up followed
by the performance-collection run.
Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
performance-collection run.
{% endif %}
.. tab-item:: Standalone benchmarking
.. rubric:: Download the Docker image and required scripts
The following commands are optimized for {{ model.model }}.
See :ref:`vllm-benchmark-supported-models-909` to switch to another available model.
1. Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. seealso::
For more information on configuration, see the `config files
<https://github.com/ROCm/MAD-private/tree/develop/scripts/vllm/configs>`__
in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
for descriptions of available configuration options
and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
additional benchmarking information.
.. rubric:: Launch the container
You can run the vLLM benchmark tool independently by starting the
`Docker container <{{ docker.docker_hub_url }}>`_ as shown
in the following snippet.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
docker pull {{ docker.pull_tag }}
docker run -it \
--device=/dev/kfd \
--device=/dev/dri \
@@ -257,66 +257,102 @@ system's configuration.
-v $(pwd):/workspace \
--env HUGGINGFACE_HUB_CACHE=/workspace \
--name test \
{{ unified_docker.pull_tag }}
{{ docker.pull_tag }}
2. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
.. rubric:: Throughput command
Use the following command to start the throughput benchmark.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
model={{ model.model_repo }}
tp={{ model.config.tp }}
num_prompts=1024
in=128
out=128
dtype={{ model.config.dtype }}
kv_cache_dtype={{ model.config.kv_cache_dtype }}
max_num_seqs=1024
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
max_model_len={{ model.config.max_model_len }}
3. To start the benchmark, use the following command with the appropriate options.
vllm bench throughput --model $model \
-tp $tp \
--num-prompts $num_prompts \
--input-len $in \
--output-len $out \
--dtype $dtype \
--kv-cache-dtype $kv_cache_dtype \
--max-num-seqs $max_num_seqs \
--max-seq-len-to-capture $max_seq_len_to_capture \
--max-num-batched-tokens $max_num_batched_tokens \
--max-model-len $max_model_len \
--trust-remote-code \
--output-json ${model}_throughput.json \
--gpu-memory-utilization 0.9
.. dropdown:: Benchmark options
:open:
.. rubric:: Serving command
.. list-table::
:header-rows: 1
:align: center
1. Start the server using the following command:
* - Name
- Options
- Description
.. code-block:: shell
* - ``$test_option``
- latency
- Measure decoding token latency
model={{ model.model_repo }}
tp={{ model.config.tp }}
dtype={{ model.config.dtype }}
kv_cache_dtype={{ model.config.kv_cache_dtype }}
max_num_seqs=256
max_seq_len_to_capture={{ model.config.max_seq_len_to_capture }}
max_num_batched_tokens={{ model.config.max_num_batched_tokens }}
max_model_len={{ model.config.max_model_len }}
* -
- throughput
- Measure token generation throughput
vllm serve $model \
-tp $tp \
--dtype $dtype \
--kv-cache-dtype $kv_cache_dtype \
--max-num-seqs $max_num_seqs \
--max-seq-len-to-capture $max_seq_len_to_capture \
--max-num-batched-tokens $max_num_batched_tokens \
--max-model-len $max_model_len \
--no-enable-prefix-caching \
--swap-space 16 \
--disable-log-requests \
--trust-remote-code \
--gpu-memory-utilization 0.9
* -
- all
- Measure both throughput and latency
Wait until the model has loaded and the server is ready to accept requests.
* - ``$num_gpu``
- 1 or 8
- Number of GPUs
2. On another terminal on the same machine, run the benchmark:
* - ``$datatype``
- ``float16`` or ``float8``
- Data type
.. code-block:: shell
The input sequence length, output sequence length, and tensor parallel (TP) are
already configured. You don't need to specify them with this script.
# Connect to the container
docker exec -it test bash
Command:
# Wait for the server to start
until curl -s http://localhost:8000/v1/models; do sleep 30; done
.. code-block::
./vllm_benchmark_report.sh \
-s $test_option \
-m {{model.model_repo}} \
-g $num_gpu \
-d {{model.precision}}
# Run the benchmark
model={{ model.model_repo }}
max_concurrency=1
num_prompts=10
in=128
out=128
vllm bench serve --model $model \
--percentile-metrics "ttft,tpot,itl,e2el" \
--dataset-name random \
--ignore-eos \
--max-concurrency $max_concurrency \
--num-prompts $num_prompts \
--random-input-len $in \
--random-output-len $out \
--trust-remote-code \
--save-result \
--result-filename ${model}_serving.json
.. note::
For best performance, it's recommend to run with ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1``.
If you encounter the following error, pass your access-authorized Hugging
Face token to the gated models.
@@ -327,38 +363,6 @@ system's configuration.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. rubric:: Benchmarking examples
Here are some examples of running the benchmark with various options:
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
./vllm_benchmark_report.sh \
-s latency \
-m {{model.model_repo}} \
-g 8 \
-d {{model.precision}}
Find the latency report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
./vllm_benchmark_report.sh \
-s throughput \
-m {{model.model_repo}} \
-g 8 \
-d {{model.precision}}
Find the throughput report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
.. raw:: html
<style>
@@ -382,7 +386,7 @@ Advanced usage
==============
For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
see the developer's guide at `<https://github.com/ROCm/vllm/tree/f94ec9beeca1071cc34f9d1e206d8c7f3ac76129/docs/dev-docker>`__.
see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
Reproducing the Docker image
----------------------------
@@ -400,7 +404,7 @@ To reproduce this ROCm/vLLM Docker image release, follow these steps:
.. code-block:: shell
cd vllm
git checkout b432b7a285aa0dcb9677380936ffa74931bb6d6f
git checkout 6663000a391911eba96d7864a26ac42b07f6ef29
3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
@@ -408,11 +412,6 @@ To reproduce this ROCm/vLLM Docker image release, follow these steps:
docker build -f docker/Dockerfile.rocm -t vllm-rocm .
Known issues and workarounds
============================
AITER does not support FP8 KV cache yet.
Further reading
===============
@@ -424,15 +423,12 @@ Further reading
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
- See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
a brief introduction to vLLM and optimization strategies.
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run community models from Hugging Face on AMD GPUs, see
:doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
- To learn how to fine-tune LLMs and optimize inference, see
:doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
- For a list of other ready-made Docker images for AI with ROCm, see
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.

35
docs/how-to/rocm-for-ai/install.rst
View File

@@ -1,14 +1,14 @@
.. meta::
:description: How to install ROCm and popular machine learning frameworks.
:description: How to install ROCm and popular deep learning frameworks.
:keywords: ROCm, AI, LLM, train, fine-tune, FSDP, DeepSpeed, LLaMA, tutorial
.. _rocm-for-ai-install:
***********************************************
Installing ROCm and machine learning frameworks
***********************************************
********************************************
Installing ROCm and deep learning frameworks
********************************************
Before getting started, install ROCm and supported machine learning frameworks.
Before getting started, install ROCm and supported deep learning frameworks.
.. grid:: 1
@@ -22,7 +22,7 @@ If you’re new to ROCm, refer to the :doc:`ROCm quick start install guide for L
<rocm-install-on-linux:install/quick-start>`.
If you’re using a Radeon GPU for graphics-accelerated applications, refer to the
`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/docs-6.1.3/docs/install/native_linux/install-radeon.html>`_.
`Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/native_linux/howto_native_linux.html>`_.
You can install ROCm on :doc:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
distribution's package manager. See the following documentation resources to get started:
@@ -43,29 +43,16 @@ distribution's package manager. See the following documentation resources to get
If you encounter any issues during installation, refer to the
:doc:`Installation troubleshooting <rocm-install-on-linux:reference/install-faq>` guide.
Machine learning frameworks
===========================
Deep learning frameworks
========================
ROCm supports popular machine learning frameworks and libraries including `PyTorch
ROCm supports deep learning frameworks and libraries including `PyTorch
<https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package>`_, `TensorFlow
<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and `DeepSpeed
<https://cloudblogs.microsoft.com/opensource/2022/03/21/supporting-efficient-large-model-training-on-amd-instinct-gpus-with-deepspeed/>`_.
<https://tensorflow.org>`_, `JAX <https://jax.readthedocs.io/en/latest>`_, and more.
Review the framework installation documentation. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
Review the :doc:`framework installation documentation <../deep-learning-rocm>`. For ease-of-use, it's recommended to use official ROCm prebuilt Docker
images with the framework pre-installed.
* :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
* :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
* :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
Next steps
==========

445
docs/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
View File

@@ -2,9 +2,9 @@
:description: How to train a model using JAX MaxText for ROCm.
:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
**************************************
Training a model with MaxText for ROCm
**************************************
******************************************
Training a model with JAX MaxText for ROCm
******************************************
MaxText is a high-performance, open-source framework built on the Google JAX
machine learning library to train LLMs at scale. The MaxText framework for
@@ -12,70 +12,108 @@ ROCm is an optimized fork of the upstream
`<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads
on AMD MI300X series accelerators.
The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.5``) image
The MaxText for ROCm training Docker image
provides a prebuilt environment for training on AMD Instinct MI300X and MI325X accelerators,
including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
It includes the following software components:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| JAX | 0.4.35 |
+--------------------------+--------------------------------+
| Python | 3.10.12 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.12.0.dev0+b8b92dc |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-ae9c477a |
+--------------------------+--------------------------------+
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
Supported features and models
=============================
{% set dockers = data.dockers %}
.. tab-set::
MaxText provides the following key features to train large language models efficiently:
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
{% if jax_version == "0.6.0" %}
.. note::
Shardy is a new config in JAX 0.6.0. You might get related errors if it's
not configured correctly. For now you can turn it off by setting
``shardy=False`` during the training run. You can also follow the `migration
guide <https://docs.jax.dev/en/latest/shardy_jax_migration.html>`__ to enable
it.
The provided multi-node training scripts in this documentation are
not currently supported with JAX 0.6.0. For multi-node training, use the JAX 0.5.0
Docker image.
{% endif %}
{% endfor %}
MaxText with on ROCm provides the following key features to train large language models efficiently:
- Transformer Engine (TE)
- Flash Attention (FA) 3
- Flash Attention (FA) 3 -- with or without sequence input packing
- GEMM tuning
- Multi-node support
.. _amd-maxtext-model-support:
- NANOO FP8 quantization support
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
.. _amd-maxtext-model-support-v257:
* Llama 3.3 70B
Supported models
================
* Llama 3.1 8B
The following models are pre-optimized for performance on AMD Instinct MI300
series accelerators. Some instructions, commands, and available training
configurations in this documentation might vary by model -- select one to get
started.
* Llama 3.1 70B
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
* Llama 3 8B
{% set model_groups = data.model_groups %}
.. raw:: html
* Llama 3 70B
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
* Llama 2 7B
* Llama 2 70B
* DeepSeek-V2-Lite
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. note::
Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta).
Unsupported features
--------------------
Currently, MaxText's default packed input format is not supported. Using this format
with the current Docker image results in incorrect attention calculations
across different input sequences. Support for packed input format is planned for a future release.
System validation
=================
@@ -98,14 +136,14 @@ This Docker image is optimized for specific model configurations outlined
as follows. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
.. _amd-maxtext-multi-node-setup:
.. _amd-maxtext-multi-node-setup-v257:
Multi-node setup
----------------
For multi-node environments, ensure you have all the necessary packages for
your network device, such as, RDMA. If you're not using a multi-node setup
with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
with RDMA, skip ahead to :ref:`amd-maxtext-get-started-v257`.
1. Install the following packages to build and install the RDMA driver.
@@ -170,7 +208,7 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
e. RDMA interface
Ensure the :ref:`required packages <amd-maxtext-multi-node-setup>` are installed on all nodes.
Ensure the :ref:`required packages <amd-maxtext-multi-node-setup-v257>` are installed on all nodes.
Then, set the RDMA interfaces to use for communication.
.. code-block:: bash
@@ -180,196 +218,203 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
# If using Mellanox NIC
export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
.. _amd-maxtext-download-docker:
.. _amd-maxtext-get-started-v257:
Pull the Docker image
---------------------
Benchmarking
============
1. Use the following command to pull the Docker image from Docker Hub.
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
.. _vllm-benchmark-mad:
{% set dockers = data.dockers %}
{% set model_groups = data.model_groups %}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{model.mad_tag}}
.. tab-set::
{% if model.mad_tag and "single-node" in model.doc_options %}
.. tab-item:: MAD-integrated benchmarking
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
docker pull rocm/jax-training:maxtext-v25.5
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
2. Use the following command to launch the Docker container. Note that the benchmarking scripts
used in the :ref:`following section <amd-maxtext-get-started>` automatically launch the Docker container
and execute the benchmark.
2. Use this command to run the performance benchmark test on the {{ model.model }} model
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.5
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{model.mad_tag}} \
--keep-model-dir \
--live-output \
--timeout 28800
.. _amd-maxtext-get-started:
MAD launches a Docker container with the name
``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/perf.csv/``.
{% endif %}
Getting started
===============
.. tab-item:: Standalone benchmarking
The following examples demonstrate how to get started with single node
and multi-node training using the benchmarking scripts provided at
`<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__.
.. rubric:: Download the Docker image and required scripts
Run the JAX MaxText benchmark tool independently by starting the
Docker container as shown in the following snippet.
.. tab-set::
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. code-block:: shell
docker pull {{ docker.pull_tag }}
{% endfor %}
{% if model.model_repo and "single-node" in model.doc_options %}
.. rubric:: Single node training
1. Set up environment variables.
.. code-block:: shell
export MAD_SECRETS_HFTOKEN=<Your Hugging Face token>
export HF_HOME=<Location of saved/cached Hugging Face models>
``MAD_SECRETS_HFTOKEN`` is your Hugging Face access token to access models, tokenizers, and data.
See `User access tokens <https://huggingface.co/docs/hub/en/security-tokens>`__.
``HF_HOME`` is where ``huggingface_hub`` will store local data. See `huggingface_hub CLI <https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#huggingface-cli-download>`__.
If you already have downloaded or cached Hugging Face artifacts, set this variable to that path.
Downloaded files typically get cached to ``~/.cache/huggingface``.
2. Launch the Docker container.
.. tab-set::
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. code-block:: shell
docker run -it \
--device=/dev/dri \
--device=/dev/kfd \
--network host \
--ipc host \
--group-add video \
--cap-add=SYS_PTRACE \
--security-opt seccomp=unconfined \
--privileged \
-v $HOME:$HOME \
-v $HOME/.ssh:/root/.ssh \
-v $HF_HOME:/hf_cache \
-e HF_HOME=/hf_cache \
-e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
--shm-size 64G \
--name training_env \
{{ docker.pull_tag }}
{% endfor %}
3. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``MAD/scripts/jax-maxtext``.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD/scripts/jax-maxtext
4. Run the setup scripts to install libraries and datasets needed
for benchmarking.
.. code-block:: shell
./jax-maxtext_benchmark_setup.sh -m {{ model.model_repo }}
5. To run the training benchmark without quantization, use the following command:
.. code-block:: shell
./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }}
For quantized training, use the following command:
.. code-block:: shell
./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q nanoo_fp8
.. important::
The provided scripts launch a Docker container and execute a benchmark. Ensure you run these commands outside of any existing Docker container.
Quantized training is not supported with the JAX 0.6.0 Docker image; support
will be added in a future release. For quantized training, use the JAX 0.5.0
Docker image: ``rocm/jax-training:maxtext-v25.7``.
Before running any benchmarks, ensure the ``$HF_HOME`` environment variable is
set correctly and points to your Hugging Face cache directory. Refer to the
README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__
for more detailed instructions.
{% endif %}
{% if model.multinode_training_script and "multi-node" in model.doc_options %}
.. rubric:: Multi-node training
Single node training benchmarking examples
------------------------------------------
* Example 1: Single node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_7b.sh
* Example 2: Single node training with Llama 2 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_70b.sh
* Example 3: Single node training with Llama 3 8B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_8b.sh
* Example 4: Single node training with Llama 3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_70b.sh
* Example 5: Single node training with Llama 3.3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3.3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3.3_70b.sh
* Example 6: Single node training with DeepSeek V2 16B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/deepseek_v2_16b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./deepseek_v2_16b.sh
The following examples use SLURM to run on multiple nodes.
.. note::
The reported TFLOP/s by MaxText for DeepSeek is not accurate. Use
the tokens/s as a performance indicator.
The following scripts will launch the Docker container and run the
benchmark. Run them outside of any Docker container.
Multi-node training benchmarking examples
-----------------------------------------
1. Make sure ``$HF_HOME`` is set before running the test. See
`ROCm benchmarking <https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__
for more details on downloading the Llama models before running the
benchmark.
The following examples use SLURM for running on multiple nodes -- the commands might need to be adjusted for your
own cluster setup.
2. To run multi-node training for {{ model.model }},
use the
`multi-node training script <https://github.com/ROCm/MAD/blob/develop/scripts/jax-maxtext/gpu-rocm/{{ model.multinode_training_script }}>`__
under the ``scripts/jax-maxtext/gpu-rocm/`` directory.
* Example 1: Multi-node training with Llama 2 7B
Download the benchmarking script:
3. Run the multi-node training benchmark script.
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b_multinode.sh
sbatch -N <num_nodes> {{ model.multinode_training_script }}
Run the multi-node training benchmark. For example:
{% else %}
.. rubric:: Multi-node training
.. code-block:: shell
For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v257`
with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/gpu-rocm>`__.
{% endif %}
{% endfor %}
{% endfor %}
sbatch -N <num_nodes> llama2_7b_multinode.sh
Further reading
===============
* Example 2: Multi-node training with Llama 2 70B
- See the ROCm/maxtext benchmarking README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__.
Download the benchmarking script:
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
.. code-block:: shell
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_70b_multinode.sh
* Example 3: Multi-node training with Llama 3 8B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_8b_multinode.sh
* Example 4: Multi-node training with Llama 3 70B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_70b_multinode.sh
- For a list of other ready-made Docker images for AI with ROCm, see
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
Previous versions
=================

71
docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
View File

@@ -1,3 +1,5 @@
:orphan:
.. meta::
:description: How to train a model using Megatron-LM for ROCm.
:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
@@ -6,6 +8,14 @@
Training a model with Megatron-LM for ROCm
******************************************
.. caution::
The ROCm Megatron-LM framework now has limited support with this Docker
environment; it now focuses on Primus with Megatron-Core. See :doc:`primus-megatron`.
To learn how to migrate your existing workloads to Primus with Megatron-Core,
see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
a specialized fork of the robust Megatron-LM, designed to enable efficient
training of large-scale language models on AMD GPUs. By leveraging AMD
@@ -20,13 +30,17 @@ essential components, including PyTorch, ROCm libraries, and Megatron-LM
utilities. It contains the following software components to accelerate training
workloads:
.. note::
This Docker environment is based on Python 3.10 and Ubuntu 22.04. For an alternative environment with
Python 3.12 and Ubuntu 24.04, see the :doc:`previous ROCm Megatron-LM v25.6 Docker release <previous-versions/megatron-lm-v25.6>`.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
{% set dockers = data.dockers %}
{% if dockers|length > 1 %}
.. tab-set::
{% for docker in data.dockers %}
{% for docker in dockers %}
.. tab-item:: ``{{ docker.pull_tag }}``
:sync: {{ docker.pull_tag }}
@@ -42,28 +56,14 @@ workloads:
{% endfor %}
{% endfor %}
{% elif dockers|length == 1 %}
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
{% endif %}
.. _amd-megatron-lm-model-support:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
Supported models
================
The following models are supported for training performance benchmarking with Megatron-LM and ROCm.
The following models are supported for training performance benchmarking with Megatron-LM and ROCm
on AMD Instinct MI300X series accelerators.
Some instructions, commands, and training recommendations in this documentation might
vary by model -- select one to get started.
@@ -71,25 +71,25 @@ workloads:
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model variant</div>
<div class="row col-10">
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
@@ -177,7 +177,7 @@ Download the Docker image
{% if dockers|length > 1 %}
.. tab-set::
{% for docker in data.dockers %}
{% for docker in dockers %}
.. tab-item:: {{ docker.doc_name }}
:sync: {{ docker.pull_tag }}
@@ -227,10 +227,17 @@ Download the Docker image
docker start megatron_training_env
docker exec -it megatron_training_env bash
The Docker container includes a pre-installed, verified version of the ROCm
Megatron-LM development branch
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__, including necessary
training scripts.
4. **Megatron-LM backward compatibility setup** -- this Docker is primarily intended for use with Primus, but it maintains Megatron-LM compatibility with limited support.
To roll back to using Megatron-LM, follow these steps:
.. code-block:: shell
cd /workspace/Megatron-LM/
pip uninstall megatron-core
pip install -e .
The Docker container hosts
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__ at verified commit ``e8e9edc``.
.. _amd-megatron-lm-environment-setup:

13
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history.rst
View File

@@ -17,12 +17,21 @@ previous releases of the ``ROCm/jax-training`` Docker image on `Docker Hub <http
- Components
- Resources
* - 25.5 (latest)
* - 25.7 (latest)
-
* ROCm 6.4.1
* JAX 0.6.0, 0.5.0
-
* :doc:`Documentation <../jax-maxtext>`
* `Docker Hub (JAX 0.6.0) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7-jax060/images/sha256-7352212ae033a76dca2b9dceffc23c1b5f1a61a7a560082cf747a9bf1acfc9ce>`__
* `Docker Hub (JAX 0.5.0) <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.7/images/sha256-45f4c727d4019a63fc47313d3a5f5a5105569539294ddfd2d742218212ae9025>`__
* - 25.5
-
* ROCm 6.3.4
* JAX 0.4.35
-
* :doc:`Documentation <../jax-maxtext>`
* :doc:`Documentation <jax-maxtext-v25.5>`
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b>`__
* - 25.4

2
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4.rst
View File

@@ -51,7 +51,7 @@ MaxText provides the following key features to train large language models effic
- Multi-node support
.. _amd-maxtext-model-support:
.. _amd-maxtext-model-support-v254:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.

385
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.5.rst Normal file
View File

@@ -0,0 +1,385 @@
:orphan:
.. meta::
:description: How to train a model using JAX MaxText for ROCm.
:keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
**************************************
Training a model with MaxText for ROCm
**************************************
.. caution::
This documentation does not reflect the latest version of ROCm JAX MaxText
training performance documentation. See :doc:`../jax-maxtext` for the latest version.
MaxText is a high-performance, open-source framework built on the Google JAX
machine learning library to train LLMs at scale. The MaxText framework for
ROCm is an optimized fork of the upstream
`<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads
on AMD MI300X series accelerators.
The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.5``) image
provides a prebuilt environment for training on AMD Instinct MI300X and MI325X accelerators,
including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
It includes the following software components:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| JAX | 0.4.35 |
+--------------------------+--------------------------------+
| Python | 3.10.12 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.12.0.dev0+b8b92dc |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-ae9c477a |
+--------------------------+--------------------------------+
Supported features and models
=============================
MaxText provides the following key features to train large language models efficiently:
- Transformer Engine (TE)
- Flash Attention (FA) 3
- GEMM tuning
- Multi-node support
.. _amd-maxtext-model-support-v255:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
* Llama 3.3 70B
* Llama 3.1 8B
* Llama 3.1 70B
* Llama 3 8B
* Llama 3 70B
* Llama 2 7B
* Llama 2 70B
* DeepSeek-V2-Lite
.. note::
Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta).
Unsupported features
--------------------
Currently, MaxText's default packed input format is not supported. Using this format
with the current Docker image results in incorrect attention calculations
across different input sequences. Support for packed input format is planned for a future release.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting training.
To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.
Environment setup
=================
This Docker image is optimized for specific model configurations outlined
as follows. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
.. _amd-maxtext-multi-node-setup-v255:
Multi-node setup
----------------
For multi-node environments, ensure you have all the necessary packages for
your network device, such as, RDMA. If you're not using a multi-node setup
with RDMA, skip ahead to :ref:`amd-maxtext-download-docker-v255`.
1. Install the following packages to build and install the RDMA driver.
.. code-block:: shell
sudo apt install iproute2 -y
sudo apt install -y linux-headers-"$(uname-r)" libelf-dev
sudo apt install -y gcc make libtool autoconf librdmacm-dev rdmacm-utils infiniband-diags ibverbs-utils perftest ethtool libibverbs-dev rdma-core strace libibmad5 libibnetdisc5 ibverbs-providers libibumad-dev libibumad3 libibverbs1 libnl-3-dev libnl-route-3-dev
Refer to your NIC manufacturer's documentation for further steps on
compiling and installing the RoCE driver. For example, for Broadcom,
see `Compiling Broadcom NIC software from source <https://docs.broadcom.com/doc/957608-AN2XX#G3.484341>`_
in `Ethernet networking guide for AMD Instinct MI300X GPU clusters <https://docs.broadcom.com/doc/957608-AN2XX>`_.
2. Set the following environment variables.
a. Master address
Change ``localhost`` to the master node's resolvable hostname or IP address:
.. code-block:: bash
export MASTER_ADDR="${MASTER_ADDR:-localhost}"
b. Number of nodes
Set the number of nodes you want to train on (for example, ``2``, ``4``, or ``8``):
.. code-block:: bash
export NNODES="${NNODES:-1}"
c. Node ranks
Set the rank of each node (``0`` for master, ``1`` for the first worker node, and so on)
Node ranks should be unique across all nodes in the cluster.
.. code-block:: bash
export NODE_RANK="${NODE_RANK:-0}"
d. Network interface
Update the network interface in the script to match your system's network interface. To
find your network interface, run the following (outside of any Docker container):
.. code-block:: bash
ip a
Look for an active interface with an IP address in the same subnet as
your other nodes. Then, update the following variable in the script, for
example:
.. code-block:: bash
export NCCL_SOCKET_IFNAME=ens50f0np0
This variable specifies which network interface to use for inter-node communication.
Setting this variable to the incorrect interface can result in communication failures
or significantly reduced performance.
e. RDMA interface
Ensure the :ref:`required packages <amd-maxtext-multi-node-setup-v255>` are installed on all nodes.
Then, set the RDMA interfaces to use for communication.
.. code-block:: bash
# If using Broadcom NIC
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
# If using Mellanox NIC
export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
.. _amd-maxtext-download-docker-v255:
Pull the Docker image
---------------------
1. Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull rocm/jax-training:maxtext-v25.5
2. Use the following command to launch the Docker container. Note that the benchmarking scripts
used in the :ref:`following section <amd-maxtext-get-started-v255>` automatically launch the Docker container
and execute the benchmark.
.. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.5
.. _amd-maxtext-get-started-v255:
Getting started
===============
The following examples demonstrate how to get started with single node
and multi-node training using the benchmarking scripts provided at
`<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__.
.. important::
The provided scripts launch a Docker container and execute a benchmark. Ensure you run these commands outside of any existing Docker container.
Before running any benchmarks, ensure the ``$HF_HOME`` environment variable is
set correctly and points to your Hugging Face cache directory. Refer to the
README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/>`__
for more detailed instructions.
Single node training benchmarking examples
------------------------------------------
* Example 1: Single node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_7b.sh
* Example 2: Single node training with Llama 2 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_70b.sh
* Example 3: Single node training with Llama 3 8B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_8b.sh
* Example 4: Single node training with Llama 3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_70b.sh
* Example 5: Single node training with Llama 3.3 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3.3_70b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3.3_70b.sh
* Example 6: Single node training with DeepSeek V2 16B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/deepseek_v2_16b.sh
Run the single node training benchmark:
.. code-block:: shell
IMAGE="rocm/jax-training:maxtext-v25.5" bash ./deepseek_v2_16b.sh
.. note::
The reported TFLOP/s by MaxText for DeepSeek is not accurate. Use
the tokens/s as a performance indicator.
Multi-node training benchmarking examples
-----------------------------------------
The following examples use SLURM for running on multiple nodes -- the commands might need to be adjusted for your
own cluster setup.
* Example 1: Multi-node training with Llama 2 7B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_7b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_7b_multinode.sh
* Example 2: Multi-node training with Llama 2 70B
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama2_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama2_70b_multinode.sh
* Example 3: Multi-node training with Llama 3 8B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_8b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_8b_multinode.sh
* Example 4: Multi-node training with Llama 3 70B model
Download the benchmarking script:
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3_70b_multinode.sh
Run the multi-node training benchmark. For example:
.. code-block:: shell
sbatch -N <num_nodes> llama3_70b_multinode.sh
Previous versions
=================
See :doc:`jax-maxtext-history` to find documentation for previous releases
of the ``ROCm/jax-training`` Docker image.

12
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@@ -16,12 +16,20 @@ previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https
- Components
- Resources
* - v25.6 (latest)
* - v25.7 (latest)
-
* ROCm
* PyTorch
-
* :doc:`Documentation <../megatron-lm>`
* `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.7_py310/images/sha256-6189df849feeeee3ae31bb1e97aef5006d69d2b90c134e97708c19632e20ab5a>`__
* - v25.6
-
* ROCm 6.4.1
* PyTorch 2.8.0a0+git7d205b2
-
* :doc:`Documentation <../megatron-lm>`
* :doc:`Documentation <megatron-lm-v25.6>`
* `Docker Hub (py312) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0>`__
* `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6>`__

175
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@@ -0,0 +1,175 @@
:orphan:
**********************************************************************
Migrating workloads to Primus (Megatron-Core backend) from Megatron-LM
**********************************************************************
Primus supports Megatron-Core as backend optimization library,
replacing ROCm Megatron-LM. This document outlines the steps to migrate
workload from ROCm Megatron-LM to Primus with the Megatron-Core backend.
Model architecture
==================
ROCm Megatron-LM defines model architecture parameters in the training scripts;
for example, the Llama 3 8B model parameters are defined in
`examples/llama/train_llama3.sh <https://github.com/ROCm/Megatron-LM/blob/rocm_dev/examples/llama/train_llama3.sh#L117>`__
as shown below:
.. code-block:: bash
HIDDEN_SIZE=4096
FFN_HIDDEN_SIZE=14336
NUM_LAYERS=32
NUM_HEADS=32
NUM_KV_HEADS=8
Primus defines the model architecture through model YAML configuration files
inside the ``primus/configs/models/megatron/`` repository. For example, Llama 3 8B
model architecture parameters are defined in
`primus/configs/models/megatron/llama3_8B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/llama3_8B.yaml>`__
as shown below:
.. code-block:: yaml
bases:
- llama3_base.yaml
tokenizer_type: Llama3Tokenizer
tokenizer_model: meta-llama/Llama-3.1-8B
ffn_hidden_size: 14336
hidden_size: 4096
num_attention_heads: 32
num_layers: 32
num_query_groups: 8
Primus' model config files follow a hierarchical design, meaning that new model
config YAMLs can inherit existing model config files by importing them as
bases. For example,
`llama3.1_8B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
uses ``llama3_8B.yaml`` as a base config and overrides few parameters, as shown below.
In this example, ``llama3.1_8B`` overrides the ``max_position_embeddings`` value:
.. code-block:: yaml
bases:
- llama3_8B.yaml
tokenizer_type: Llama3Tokenizer
tokenizer_model: meta-llama/Llama-3.1-8B
max_position_embeddings: 131072
.. tip::
Primus provides ``llama_base.yaml`` as the base configuration, which can be
used as bases for additional model architectures. For example,
`mixtral_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/mixtral_base.yaml>`__
and
`deepseek_v3_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/deepseek_v3_base.yaml>`__
define ``llama_base.yaml`` as its base.
.. code-block:: yaml
# Example mixtral_base.yaml:
bases:
- llama_base.yaml
init_method_std: 0.01
rotary_base: 1000000
qk_layernorm: false
group_query_attention: true
num_query_groups: 8
# moe parameters
num_experts: 8
moe_router_topk: 2
moe_router_load_balancing_type: aux_loss
moe_aux_loss_coeff: 1e-2
moe_grouped_gemm: true
moe_token_dispatcher_type: alltoall
It is recommended to add a new ``${MODEL_NAME}_base.yaml`` to add a new
category of model and define new models on top of it. For example, to add
Qwen2.5 models in Primus, we define
`qwen2.5_base.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_base.yaml>`__
and build
`qwen2.5_7B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_7B.yaml>`__
and
`qwen2.5_72B.yaml <https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/primus/configs/models/megatron/qwen2.5_72B.yaml>`__
using ``qwen2.5_base.yaml`` as the base config.
Training parameters
===================
ROCm Megatron-LM also defines the training parameters, like batch size,
tensor-parallelism, precision, as so on, in the training scripts. For example,
Llama3 8B model parameters are defined in
`examples/llama/train_llama3.sh <https://github.com/ROCm/Megatron-LM/blob/rocm_dev/examples/llama/train_llama3.sh>`__
as shown below:
.. code-block:: bash
TP="${TP:-8}"
PP="${PP:-1}"
CP="${CP:-1}"
MBS="${MBS:-1}"
BS="${BS:-8}"
Primus defines the training parameters in top-level YAML files -- see
`examples/megatron/configs/
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
For example, the `llama3.1_8B-pretrain.yaml
<https://github.com/AMD-AIG-AIMA/Primus/blob/v0.1.0-rc1/examples/megatron/configs/llama3.1_8B-pretrain.yaml>`__
configuration imports the ``llama3.1_8B.yaml`` model architecture file. Users can then override
the default training parameters in ``llama3.1_8B-pretrain.yaml``.
.. code-block:: yaml
# model to run
model: llama3.1_8B.yaml # Model architecture yaml
overrides:
# log
# disable_wandb: false
# disable_tensorboard: false
stderr_sink_level: DEBUG
log_avg_skip_iterations: 2
log_avg_reset_interval: 50
train_iters: 50
micro_batch_size: 2
global_batch_size: 128
seq_length: 8192
max_position_embeddings: 8192
lr: 1.0e-5
min_lr: 0.0
lr_warmup_iters: 2
lr_decay_iters: null
lr_decay_style: cosine
weight_decay: 0.1
adam_beta1: 0.9
adam_beta2: 0.95
eod_mask_loss: true
init_method_std: 0.008
norm_epsilon: 1.0e-6
Backward compatibility with Megatron-LM
=======================================
The Dockerized environment used for Primus maintains compatibility with Megatron-LM with
limited support. To roll back to using Megatron-LM, follow these steps.
.. code-block:: shell
cd /workspace/Megatron-LM/
pip uninstall megatron-core
pip install -e .
Once Megatron-LM is installed, follow :doc:`the documentation <../megatron-lm>` to run workloads as
usual.

4
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@@ -18,7 +18,7 @@ Training a model with ROCm Megatron-LM
The ROCm Megatron-LM framework is a specialized fork of the robust Megatron-LM, designed to
enable efficient training of large-scale language models on AMD GPUs. By leveraging AMD Instinct™ MI300X
accelerators, AMD Megatron-LM delivers enhanced scalability, performance, and resource utilization for AI
workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support>`
workloads. It is purpose-built to :ref:`support models <amd-megatron-lm-model-support-24-12>`
like Meta's Llama 2, Llama 3, and Llama 3.1, enabling developers to train next-generation AI models with greater
efficiency. See the GitHub repository at `<https://github.com/ROCm/Megatron-LM>`__.
@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
- Pre-training
.. _amd-megatron-lm-model-support:
.. _amd-megatron-lm-model-support-24-12:
The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.

6
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@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
- Pre-training
.. _amd-megatron-lm-model-support:
.. _amd-megatron-lm-model-support-25-3:
The following models are pre-optimized for performance on the AMD Instinct MI300X accelerator.
@@ -278,7 +278,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
.. tab-item:: Llama
:sync: llama
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``.
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``Llama2Tokenizer``.
To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
Set the Hugging Face model link in the ``TOKENIZER_MODEL`` variable.
@@ -292,7 +292,7 @@ handle a variety of input sequences, including unseen words or domain-specific t
.. tab-item:: DeepSeek V2
:sync: deepseek
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-3>`, use the ``DeepSeekV2Tokenizer``.
Multi-node training
^^^^^^^^^^^^^^^^^^^

6
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@@ -67,7 +67,7 @@ Megatron-LM provides the following key features to train large language models e
- Pre-training
.. _amd-megatron-lm-model-support:
.. _amd-megatron-lm-model-support-25-4:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
@@ -291,7 +291,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
.. tab-item:: Llama
:sync: llama
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``Llama2Tokenizer``
To train any of the Llama 2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``Llama2Tokenizer``
or the default ``HuggingFaceTokenizer``.
To train any of Llama 3 and Llama 3.1 models that this Docker image supports, use the ``HuggingFaceTokenizer``.
@@ -320,7 +320,7 @@ or ``${DATA_DIR}/tokenizer_llama2``.
.. tab-item:: DeepSeek V2
:sync: deepseek
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support>`, use the ``DeepSeekV2Tokenizer``.
To train any of the DeepSeek V2 models that :ref:`this Docker image supports <amd-megatron-lm-model-support-25-4>`, use the ``DeepSeekV2Tokenizer``.
Multi-node training
^^^^^^^^^^^^^^^^^^^

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@@ -16,12 +16,20 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
- Components
- Resources
* - v25.7
-
* ROCm 6.4.2
* PyTorch 2.8.0a0+gitd06a406
-
* :doc:`Documentation <../pytorch-training>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712>`__
* - v25.6
-
* ROCm 6.3.4
* PyTorch 2.8.0a0+git7d205b2
-
* :doc:`Documentation <../pytorch-training>`
* :doc:`Documentation <pytorch-training-v25.6>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`__
* - v25.5

5
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@@ -437,3 +437,8 @@ Once the setup is complete, choose between two options to start benchmarking:
./pytorch_benchmark_report.sh -t HF_finetune_lora -p BF16 -m Llama-2-70B
Previous versions
=================
See :doc:`pytorch-training-history` to find documentation for previous releases
of the ``ROCm/pytorch-training`` Docker image.

456
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@@ -0,0 +1,456 @@
:orphan:
.. meta::
:description: How to train a model using PyTorch for ROCm.
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
**************************************
Training a model with PyTorch for ROCm
**************************************
.. caution::
This documentation does not reflect the latest version of ROCm vLLM
performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
PyTorch is an open-source machine learning framework that is widely used for
model training with GPU-optimized components for transformer-based models.
The `PyTorch for ROCm training Docker <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`_
(``rocm/pytorch-training:v25.6``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
training workloads:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| PyTorch | 2.8.0a0+git7d205b2 |
+--------------------------+--------------------------------+
| Python | 3.10.17 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.14.0+2f85f5f2 |
+--------------------------+--------------------------------+
| Flash Attention | 3.0.0.post1 |
+--------------------------+--------------------------------+
| hipBLASLt | 0.15.0-8c6919d |
+--------------------------+--------------------------------+
| Triton | 3.3.0 |
+--------------------------+--------------------------------+
.. _amd-pytorch-training-model-support-v256:
Supported models
================
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.6-benchmark-models.yaml
{% set unified_docker = data.unified_docker.latest %}
{% set model_groups = data.model_groups %}
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Workload</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. note::
Some models require an external license agreement through a third party (for example, Meta).
.. _amd-pytorch-training-performance-measurements-v256:
Performance measurements
========================
To evaluate performance, the
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
page provides reference throughput and latency measurements for training
popular AI models.
.. note::
The performance data presented in
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
should not be interpreted as the peak performance achievable by AMD
Instinct MI325X and MI300X accelerators or ROCm software.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting training.
To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.
This Docker image is optimized for specific model configurations outlined
below. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
Benchmarking
============
Once the setup is complete, choose between two options to start benchmarking:
.. tab-set::
.. tab-item:: MAD-integrated benchmarking
Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{ model.mad_tag }}
For example, use this command to run the performance benchmark test on the {{ model.model }} model
using one GPU with the {{ model.precision }} data type on the host machine.
.. code-block:: shell
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
madengine run \
--tags {{ model.mad_tag }} \
--keep-model-dir \
--live-output \
--timeout 28800
MAD launches a Docker container with the name
``container_ci-{{ model.mad_tag }}``, for example. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/perf.csv``.
{% endfor %}
{% endfor %}
.. tab-item:: Standalone benchmarking
.. rubric:: Download the Docker image and required packages
Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
Run the Docker container.
.. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env {{ unified_docker.pull_tag }}
Use these commands if you exit the ``training_env`` container and need to return to it.
.. code-block:: shell
docker start training_env
docker exec -it training_env bash
In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
repository and navigate to the benchmark scripts directory
``/workspace/MAD/scripts/pytorch_train``.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD/scripts/pytorch_train
.. rubric:: Prepare training datasets and dependencies
The following benchmarking examples require downloading models and datasets
from Hugging Face. To ensure successful access to gated repos, set your
``HF_TOKEN``.
.. code-block:: shell
export HF_TOKEN=$your_personal_hugging_face_access_token
Run the setup script to install libraries and datasets needed for benchmarking.
.. code-block:: shell
./pytorch_benchmark_setup.sh
.. container:: model-doc pyt_train_llama-3.1-8b
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
.. list-table::
:header-rows: 1
* - Library
- Reference
* - ``accelerate``
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
* - ``datasets``
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
.. container:: model-doc pyt_train_llama-3.1-70b
``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
.. list-table::
:header-rows: 1
* - Library
- Reference
* - ``datasets``
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
* - ``torchdata``
- `TorchData <https://pytorch.org/data/beta/index.html>`_
* - ``tomli``
- `Tomli <https://pypi.org/project/tomli/>`_
* - ``tiktoken``
- `tiktoken <https://github.com/openai/tiktoken>`_
* - ``blobfile``
- `blobfile <https://pypi.org/project/blobfile/>`_
* - ``tabulate``
- `tabulate <https://pypi.org/project/tabulate/>`_
* - ``wandb``
- `Weights & Biases <https://github.com/wandb/wandb>`_
* - ``sentencepiece``
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
* - ``tensorboard``
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
.. container:: model-doc pyt_train_flux
``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
.. list-table::
:header-rows: 1
* - Library
- Reference
* - ``accelerate``
- `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
* - ``datasets``
- `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
* - ``sentencepiece``
- `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
* - ``tensorboard``
- `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
* - ``csvkit``
- `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
* - ``deepspeed``
- `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
* - ``diffusers``
- `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
* - ``GitPython``
- `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
* - ``opencv-python-headless``
- `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
* - ``peft``
- `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
* - ``protobuf``
- `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
* - ``pytest``
- `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
* - ``python-dotenv``
- `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
* - ``seaborn``
- `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
* - ``transformers``
- `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
* `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
{% for model_group in model_groups %}
{% for model in model_group.models %}
{% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
.. container:: model-doc {{ model.mad_tag }}
.. rubric:: Pretraining
To start the pre-training benchmark, use the following command with the
appropriate options. See the following list of options and their descriptions.
.. code-block:: shell
./pytorch_benchmark_report.sh -t pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
.. list-table::
:header-rows: 1
* - Name
- Options
- Description
{% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
* - ``$datatype``
- ``BF16`` or ``FP8``
- Only Llama 3.1 8B supports FP8 precision.
{% else %}
* - ``$datatype``
- ``BF16``
- Only Llama 3.1 8B supports FP8 precision.
{% endif %}
* - ``$sequence_length``
- Sequence length for the language model.
- Between 2048 and 8192. 8192 by default.
{% if model.mad_tag == "pyt_train_flux" %}
.. container:: model-doc {{ model.mad_tag }}
.. note::
Occasionally, downloading the Flux dataset might fail. In the event of this
error, manually download it from Hugging Face at
`black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
the required dataset.
{% endif %}
{% endif %}
{% if model_group.tag == "fine-tuning" %}
.. container:: model-doc {{ model.mad_tag }}
.. rubric:: Fine-tuning
To start the fine-tuning benchmark, use the following command with the
appropriate options. See the following list of options and their descriptions.
.. code-block:: shell
./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
.. list-table::
:header-rows: 1
* - Name
- Options
- Description
* - ``$training_mode``
- ``finetune_fw``
- Full weight fine-tuning (BF16 supported)
* -
- ``finetune_lora``
- LoRA fine-tuning (BF16 supported)
* -
- ``finetune_qlora``
- QLoRA fine-tuning (BF16 supported)
* -
- ``HF_finetune_lora``
- LoRA fine-tuning with Hugging Face PEFT
* - ``$datatype``
- ``BF16``
- All models support BF16.
* - ``$sequence_length``
- Between 2048 and 16384.
- Sequence length for the language model.
.. note::
{{ model.model }} currently supports the following fine-tuning methods:
{% for method in model.training_modes %}
* ``{{ method }}``
{% endfor %}
{% if model.training_modes|length < 4 %}
The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
does not currently provide YAML configuration files for other combinations of
model to fine-tuning method
However, you can still configure your own YAML files to enable support for
fine-tuning methods not listed here by following existing patterns in the
``/workspace/torchtune/recipes/configs`` directory.
{% endif %}
{% endif %}
{% endfor %}
{% endfor %}
.. rubric:: Benchmarking examples
For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
Further reading
===============
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
- For a list of other ready-made Docker images for AI with ROCm, see
`AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
Previous versions
=================
See :doc:`pytorch-training-history` to find documentation for previous releases
of the ``ROCm/pytorch-training`` Docker image.

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.. meta::
:description: How to train a model using Megatron-LM for ROCm.
:keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
**********************************************
Training a model with Primus and Megatron-Core
**********************************************
`Primus <https://github.com/AMD-AIG-AIMA/Primus>`__ is a unified and flexible
LLM training framework designed to streamline training. It streamlines LLM
training on AMD Instinct accelerators using a modular, reproducible configuration paradigm.
Primus is backend-agnostic and supports multiple training engines -- including Megatron-Core.
.. note::
Primus with the Megatron-Core backend is intended to replace ROCm
Megatron-LM in this Dockerized training environment. To learn how to migrate
workloads from Megatron-LM to Primus with Megatron-Core, see
:doc:`previous-versions/megatron-lm-primus-migration-guide`.
For ease of use, AMD provides a ready-to-use Docker image for MI300 series accelerators
containing essential components for Primus and Megatron-Core.
.. note::
This Docker environment is based on Python 3.10 and Ubuntu 22.04. For an alternative environment with
Python 3.12 and Ubuntu 24.04, see the :doc:`previous ROCm Megatron-LM v25.6 Docker release <previous-versions/megatron-lm-v25.6>`.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
.. _amd-primus-megatron-lm-model-support:
Supported models
================
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
Some instructions, commands, and training examples in this documentation might
vary by model -- select one to get started.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set model_groups = data.model_groups %}
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
.. note::
Some models, such as Llama, require an external license agreement through
a third party (for example, Meta).
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
If you have already validated your system settings, including aspects like NUMA auto-balancing, you
can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
before starting training.
To test for optimal performance, consult the recommended :ref:`System health benchmarks
<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
system's configuration.
.. _mi300x-amd-primus-megatron-lm-training:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
Environment setup
=================
Use the following instructions to set up the environment, configure the script to train models, and
reproduce the benchmark results on MI300X series accelerators with the ``{{ docker.pull_tag }}`` image.
.. _amd-primus-megatron-lm-requirements:
Download the Docker image
-------------------------
1. Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ docker.pull_tag }}
2. Launch the Docker container.
.. code-block:: shell
docker run -it \
--device /dev/dri \
--device /dev/kfd \
--device /dev/infiniband \
--network host --ipc host \
--group-add video \
--cap-add SYS_PTRACE \
--security-opt seccomp=unconfined \
--privileged \
-v $HOME:$HOME \
--shm-size 128G \
--name primus_training_env \
{{ docker.pull_tag }}
3. Use these commands if you exit the ``primus_training_env`` container and need to return to it.
.. code-block:: shell
docker start primus_training_env
docker exec -it primus_training_env bash
The Docker container hosts verified release tag ``v0.1.0-rc1`` of the `Primus
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1>`__ repository.
.. _amd-primus-megatron-lm-environment-setup:
Configuration
=============
Primus defines a training configuration in YAML for each model in
`examples/megatron/configs <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/megatron/configs>`__.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set model_groups = data.model_groups %}
{% for model_group in model_groups %}
{% for model in model_group.models %}
.. container:: model-doc {{ model.mad_tag }}
To update training parameters for {{ model.model }}, you can update ``examples/megatron/configs/{{ model.config_name }}``.
Note that training configuration YAML files for other models follow this naming convention.
{% endfor %}
{% endfor %}
.. note::
See :ref:`Key options <amd-primus-megatron-lm-benchmark-test-vars>` for more information on configuration options.
Dataset options
---------------
You can use either mock data or real data for training.
* Mock data can be useful for testing and validation. Use the ``mock_data`` field to toggle between mock and real data. The default
value is ``true`` for enabled.
.. code-block:: yaml
mock_data: true
* If you're using a real dataset, update the ``train_data_path`` field to point to the location of your dataset.
.. code-block:: bash
mock_data: false
train_data_path: /path/to/your/dataset
Ensure that the files are accessible inside the Docker container.
.. _amd-primus-megatron-lm-tokenizer:
Tokenizer
---------
In Primus, each model uses a tokenizer from Hugging Face. For example, Llama
3.1 8B model uses ``tokenizer_model: meta-llama/Llama-3.1-8B`` and
``tokenizer_type: Llama3Tokenizer`` defined in the `llama3.1-8B model
<https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/primus/configs/models/megatron/llama3.1_8B.yaml>`__
definition. As such, you need to set the ``HF_TOKEN`` environment variable with
right permissions to access the tokenizer for each model.
.. code-block:: bash
# Export your HF_TOKEN in the workspace
export HF_TOKEN=<your_hftoken>
.. _amd-primus-megatron-lm-run-training:
Run training
============
Use the following example commands to set up the environment, configure
:ref:`key options <amd-primus-megatron-lm-benchmark-test-vars>`, and run training on
MI300X series accelerators with the AMD Megatron-LM environment.
Single node training
--------------------
To run training on a single node, navigate to ``/workspace/Primus`` and use the following setup command:
.. code-block:: shell
pip install -r requirements.txt
export HSA_NO_SCRATCH_RECLAIM=1
export NVTE_CK_USES_BWD_V3=1
Once setup is complete, run the appropriate training command.
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
To run pre-training for Llama 3.3 70B BF16, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
bash ./examples/run_pretrain.sh \
--micro_batch_size 2 \
--global_batch_size 16 \
--train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
To run pre-training for Llama 3.1 8B FP8, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
bash ./examples/run_pretrain.sh \
--train_iters 50 \
--fp8 hybrid
For Llama 3.1 8B BF16, use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
bash ./examples/run_pretrain.sh --train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
To run pre-training for Llama 3.1 70B BF16, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
bash ./examples/run_pretrain.sh \
--train_iters 50
To run the training on a single node for Llama 3.1 70B FP8 with proxy, use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
bash ./examples/run_pretrain.sh \
--train_iters 50 \
--num_layers 40 \
--fp8 hybrid \
--no_fp8_weight_transpose_cache true
.. note::
Use two or more nodes to run the *full* Llama 70B model with FP8 precision.
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
To run pre-training for Llama 2 7B FP8, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
bash ./examples/run_pretrain.sh \
--train_iters 50 \
--fp8 hybrid
To run pre-training for Llama 2 7B BF16, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
bash ./examples/run_pretrain.sh --train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
To run pre-training for Llama 2 70B BF16, run:
.. code-block:: shell
EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
bash ./examples/run_pretrain.sh --train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/deepseek_v3-pretrain.yaml \
bash examples/run_pretrain.sh \
--num_layers 3 \
--moe_layer_freq 1 \
--train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/deepseek_v2_lite-pretrain.yaml \
bash examples/run_pretrain.sh \
--global_batch_size 256 \
--train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
bash examples/run_pretrain.sh --train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
To run training on a single node for Mixtral 8x7B (MoE with expert parallel) with 4-layer proxy,
use the following command:
.. code-block:: shell
EXP=examples/megatron/configs/mixtral_8x22B_v0.1-pretrain.yaml \
bash examples/run_pretrain.sh \
--num_layers 4 \
--pipeline_model_parallel_size 1 \
--micro_batch_size 1 \
--global_batch_size 16 \
--train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
To run training on a single node for Qwen 2.5 7B BF16, use the following
command:
.. code-block:: shell
EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
bash examples/run_pretrain.sh --train_iters 50
For FP8, use the following command.
.. code-block:: shell
EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
bash examples/run_pretrain.sh \
--train_iters 50 \
--fp8 hybrid
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
.. code-block:: shell
EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
bash examples/run_pretrain.sh --train_iters 50
Multi-node training examples
----------------------------
To run training on multiple nodes, you can use the
`run_slurm_pretrain.sh <https://github.com/AMD-AIG-AIMA/Primus/tree/v0.1.0-rc1/examples/run_slurm_pretrain.sh>`__
to launch the multi-node workload. Use the following steps to setup your environment:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
.. code-block:: shell
cd /workspace/Primus/
export DOCKER_IMAGE={{ docker.pull_tag }}
export HF_TOKEN=<your_HF_token>
export HSA_NO_SCRATCH_RECLAIM=1
export NVTE_CK_USES_BWD_V3=1
export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
export NCCL_IB_GID_INDEX=3 # Set InfiniBand GID index for NCCL communication. Default is 3 for ROCE
.. note::
* Make sure correct network drivers are installed on the nodes. If inside a Docker, either install the drivers inside the Docker container or pass the network drivers from the host while creating Docker container.
* If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
* To find your network interface, you can use ``ip a``.
* To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB devices.
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
To train Llama 3.3 70B FP8 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 4 \
--global_batch_size 256 \
--recompute_num_layers 80 \
--no_fp8_weight_transpose_cache true \
--fp8 hybrid
To train Llama 3.3 70B BF16 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 1 \
--global_batch_size 256 \
--recompute_num_layers 12
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
To train Llama 3.1 8B FP8 on 8 nodes, run:
.. code-block:: shell
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
NNODES=8 EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
bash ./examples/run_slurm_pretrain.sh \
--global_batch_size 1024 \
--fp8 hybrid
.. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-70b
To train Llama 3.1 70B FP8 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 4 \
--global_batch_size 256 \
--recompute_num_layers 80 \
--no_fp8_weight_transpose_cache true \
--fp8 hybrid
To train Llama 3.1 70B BF16 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 1 \
--global_batch_size 256 \
--recompute_num_layers 12
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-7b
To train Llama 2 8B FP8 on 8 nodes, run:
.. code-block:: shell
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
NNODES=8 EXP=examples/megatron/configs/llama2_7B-pretrain.yaml bash ./examples/run_slurm_pretrain.sh --global_batch_size 2048 --fp8 hybrid
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
To train Llama 2 70B FP8 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 10 \
--global_batch_size 640 \
--recompute_num_layers 80 \
--no_fp8_weight_transpose_cache true \
--fp8 hybrid
To train Llama 2 70B BF16 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
bash ./examples/run_slurm_pretrain.sh \
--micro_batch_size 2 \
--global_batch_size 1536 \
--recompute_num_layers 12
.. container:: model-doc primus_pyt_megatron_lm_train_mixtral-8x7b
To train Mixtral 8x7B BF16 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 2 \
--global_batch_size 256
.. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-72b
To train Qwen2.5 72B FP8 on 8 nodes, run:
.. code-block:: shell
NNODES=8 EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
bash examples/run_slurm_pretrain.sh \
--micro_batch_size 8 \
--global_batch_size 512 \
--recompute_num_layers 80 \
--no_fp8_weight_transpose_cache true \
--fp8 hybrid
.. _amd-primus-megatron-lm-benchmark-test-vars:
Key options
-----------
The following are key options to take note of
fp8
``hybrid`` enables FP8 GEMMs.
use_torch_fsdp2
``use_torch_fsdp2: 1`` enables torch fsdp-v2. If FSDP is enabled,
set ``use_distributed_optimizer`` and ``overlap_param_gather`` to ``false``.
profile
To enable PyTorch profiling, set these parameters:
.. code-block:: yaml
profile: true
use_pytorch_profiler: true
profile_step_end: 7
profile_step_start: 6
train_iters
The total number of iterations (default: 50).
mock_data
True by default.
micro_batch_size
Micro batch size.
global_batch_size
Global batch size.
recompute_granularity
For activation checkpointing.
num_layers
For using a reduced number of layers as with proxy models.
Previous versions
=================
See :doc:`previous-versions/megatron-lm-history` to find documentation for previous releases
of the ``ROCm/megatron-lm`` Docker image.
This training environment now uses Primus with Megatron as the primary
configuration. Limited support for the legacy ROCm Megatron-LM is still
available. For instructions on using ROCm Megatron-LM, see the
:doc:`megatron-lm` document.

331
docs/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
View File

@@ -9,28 +9,25 @@ Training a model with PyTorch for ROCm
PyTorch is an open-source machine learning framework that is widely used for
model training with GPU-optimized components for transformer-based models.
The `PyTorch for ROCm training Docker <https://hub.docker.com/r/rocm/pytorch-training/tags>`_
(``rocm/pytorch-training:v25.6``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
The `PyTorch for ROCm training Docker <{{ docker.docker_hub_url }}>`__
(``{{ docker.pull_tag }}``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
training workloads:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| PyTorch | 2.8.0a0+git7d205b2 |
+--------------------------+--------------------------------+
| Python | 3.10.17 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.14.0+2f85f5f2 |
+--------------------------+--------------------------------+
| Flash Attention | 3.0.0.post1 |
+--------------------------+--------------------------------+
| hipBLASLt | 0.15.0-8c6919d |
+--------------------------+--------------------------------+
| Triton | 3.3.0 |
+--------------------------+--------------------------------+
.. list-table::
:header-rows: 1
* - Software component
- Version
{% for component_name, component_version in docker.components.items() %}
* - {{ component_name }}
- {{ component_version }}
{% endfor %}
.. _amd-pytorch-training-model-support:
@@ -38,34 +35,35 @@ Supported models
================
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
Some instructions, commands, and training recommendations in this documentation might
vary by model -- select one to get started.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
{% set unified_docker = data.unified_docker.latest %}
{% set unified_docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
<div class="row">
<div class="col-2 me-2 model-param-head">Workload</div>
<div class="row col-10">
<div class="row gx-0">
<div class="col-2 me-1 px-2 model-param-head">Model</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
<div class="col-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
{% endfor %}
</div>
</div>
<div class="row mt-1">
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
<div class="row gx-0 pt-1">
<div class="col-2 me-1 px-2 model-param-head">Variant</div>
<div class="row col-10 pe-0">
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
{% if models|length % 3 == 0 %}
<div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% else %}
<div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
<div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
{% endif %}
{% endfor %}
{% endfor %}
@@ -73,9 +71,36 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
</div>
</div>
.. _amd-pytorch-training-supported-training-modes:
The following table lists supported training modes per model.
.. dropdown:: Supported training modes
.. list-table::
:header-rows: 1
* - Model
- Supported training modes
{% for model_group in model_groups %}
{% set models = model_group.models %}
{% for model in models %}
* - {{ model.model }}
- ``{{ model.training_modes | join('``, ``') }}``
{% endfor %}
{% endfor %}
.. note::
Some models require an external license agreement through a third party (for example, Meta).
Some model and fine-tuning combinations are not listed. This is
because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
doesn't provide default YAML configurations for them.
For advanced usage, you can create a custom configuration to enable
unlisted fine-tuning methods by using an existing file in the
``/workspace/torchtune/recipes/configs`` directory as a template.
.. _amd-pytorch-training-performance-measurements:
@@ -111,18 +136,23 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
This Docker image is optimized for specific model configurations outlined
below. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
doesn’t test configurations and run conditions outside those described.
Benchmarking
Run training
============
Once the setup is complete, choose between two options to start benchmarking:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
{% set unified_docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Once the setup is complete, choose between two options to start benchmarking training:
.. tab-set::
.. tab-item:: MAD-integrated benchmarking
Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
directory and install the required packages on the host machine.
.. code-block:: shell
@@ -136,8 +166,8 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
.. container:: model-doc {{ model.mad_tag }}
For example, use this command to run the performance benchmark test on the {{ model.model }} model
using one GPU with the {{ model.precision }} data type on the host machine.
2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
using one node with the {{ model.precision }} data type on the host machine.
.. code-block:: shell
@@ -149,8 +179,8 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
--timeout 28800
MAD launches a Docker container with the name
``container_ci-{{ model.mad_tag }}``, for example. The latency and throughput reports of the
model are collected in the following path: ``~/MAD/perf.csv``.
``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
model are collected in ``~/MAD/perf.csv``.
{% endfor %}
{% endfor %}
@@ -159,17 +189,30 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
.. rubric:: Download the Docker image and required packages
Use the following command to pull the Docker image from Docker Hub.
1. Use the following command to pull the Docker image from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
Run the Docker container.
2. Run the Docker container.
.. code-block:: shell
docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v $HOME/.ssh:/root/.ssh --shm-size 64G --name training_env {{ unified_docker.pull_tag }}
docker run -it \
--device /dev/dri \
--device /dev/kfd \
--network host \
--ipc host \
--group-add video \
--cap-add SYS_PTRACE \
--security-opt seccomp=unconfined \
--privileged \
-v $HOME:$HOME \
-v $HOME/.ssh:/root/.ssh \
--shm-size 64G \
--name training_env \
{{ unified_docker.pull_tag }}
Use these commands if you exit the ``training_env`` container and need to return to it.
@@ -178,7 +221,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
docker start training_env
docker exec -it training_env bash
In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
repository and navigate to the benchmark scripts directory
``/workspace/MAD/scripts/pytorch_train``.
@@ -189,7 +232,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
.. rubric:: Prepare training datasets and dependencies
The following benchmarking examples require downloading models and datasets
1. The following benchmarking examples require downloading models and datasets
from Hugging Face. To ensure successful access to gated repos, set your
``HF_TOKEN``.
@@ -197,7 +240,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
export HF_TOKEN=$your_personal_hugging_face_access_token
Run the setup script to install libraries and datasets needed for benchmarking.
2. Run the setup script to install libraries and datasets needed for benchmarking.
.. code-block:: shell
@@ -317,64 +360,42 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
{% for model_group in model_groups %}
{% for model in model_group.models %}
{% if model_group.tag == "pre-training" and model.mad_tag in ["pyt_train_llama-3.1-8b", "pyt_train_llama-3.1-70b", "pyt_train_flux"] %}
{% set training_modes = model.training_modes %}
{% set training_mode_descs = {
"pretrain": "Benchmark pre-training.",
"HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
} %}
{% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
{% if available_modes %}
.. container:: model-doc {{ model.mad_tag }}
.. rubric:: Pretraining
.. rubric:: Pre-training
To start the pre-training benchmark, use the following command with the
appropriate options. See the following list of options and their descriptions.
.. code-block:: shell
./pytorch_benchmark_report.sh -t pretrain -m {{ model.model_repo }} -p $datatype -s $sequence_length
.. list-table::
:header-rows: 1
* - Name
- Options
- Description
{% if model.mad_tag == "pyt_train_llama-3.1-8b" %}
* - ``$datatype``
- ``BF16`` or ``FP8``
- Only Llama 3.1 8B supports FP8 precision.
{% else %}
* - ``$datatype``
- ``BF16``
- Only Llama 3.1 8B supports FP8 precision.
{% endif %}
* - ``$sequence_length``
- Sequence length for the language model.
- Between 2048 and 8192. 8192 by default.
./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
-m {{ model.model_repo }} \
-p $datatype \
-s $sequence_length
{% if model.mad_tag == "pyt_train_flux" %}
.. container:: model-doc {{ model.mad_tag }}
.. note::
Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
Occasionally, downloading the Flux dataset might fail. In the event of this
error, manually download it from Hugging Face at
`black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
the required dataset.
{% endif %}
{% endif %}
{% if model_group.tag == "fine-tuning" %}
.. container:: model-doc {{ model.mad_tag }}
.. rubric:: Fine-tuning
To start the fine-tuning benchmark, use the following command with the
appropriate options. See the following list of options and their descriptions.
.. code-block:: shell
./pytorch_benchmark_report.sh -t $training_mode -m {{ model.model_repo }} -p BF16 -s $sequence_length
.. list-table::
:header-rows: 1
@@ -383,45 +404,91 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
- Options
- Description
* - ``$training_mode``
- ``finetune_fw``
- Full weight fine-tuning (BF16 supported)
* -
- ``finetune_lora``
- LoRA fine-tuning (BF16 supported)
* -
- ``finetune_qlora``
- QLoRA fine-tuning (BF16 supported)
* -
- ``HF_finetune_lora``
- LoRA fine-tuning with Hugging Face PEFT
{% for mode in available_modes %}
* - {% if loop.first %}``$training_mode``{% endif %}
- ``{{ mode }}``
- {{ training_mode_descs[mode] }}
{% endfor %}
* - ``$datatype``
- ``BF16``
- All models support BF16.
- ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
- Only Llama 3.1 8B supports FP8 precision.
* - ``$sequence_length``
- Sequence length for the language model.
- Between 2048 and 8192. 8192 by default.
{% endif %}
{% set training_mode_descs = {
"finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
"finetune_lora": "LoRA fine-tuning (BF16 supported).",
"finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
"HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
} %}
{% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
{% if available_modes %}
.. container:: model-doc {{ model.mad_tag }}
.. rubric:: Fine-tuning
To start the fine-tuning benchmark, use the following command with the
appropriate options. See the following list of options and their descriptions.
See :ref:`supported training modes <amd-pytorch-training-supported-training-modes>`.
.. code-block:: shell
./pytorch_benchmark_report.sh -t $training_mode \
-m {{ model.model_repo }} \
-p $datatype \
-s $sequence_length
.. list-table::
:header-rows: 1
* - Name
- Options
- Description
{% for mode in available_modes %}
* - {% if loop.first %}``$training_mode``{% endif %}
- ``{{ mode }}``
- {{ training_mode_descs[mode] }}
{% endfor %}
* - ``$datatype``
- ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
- All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
* - ``$sequence_length``
- Between 2048 and 16384.
- Sequence length for the language model.
{% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
.. note::
{{ model.model }} currently supports the following fine-tuning methods:
For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
use the following torchtune commit for compatibility:
{% for method in model.training_modes %}
* ``{{ method }}``
{% endfor %}
{% if model.training_modes|length < 4 %}
.. code-block:: shell
git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
{% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
.. note::
You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
input tensor should be smaller than max_seq_len (4096)``.
This error indicates that an input sequence is longer than the model's maximum context window.
Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
tokens in this case). You can resolve this by truncating the input or splitting
it into smaller chunks before passing it to the model.
Note on reproducibility: The results in this guide are based on
commit ``b4c98ac`` from the upstream
`<https://github.com/pytorch/torchtune>`__ repository. For the
latest updates, you can use the main branch.
The upstream `torchtune <https://github.com/pytorch/torchtune>`_ repository
does not currently provide YAML configuration files for other combinations of
model to fine-tuning method
However, you can still configure your own YAML files to enable support for
fine-tuning methods not listed here by following existing patterns in the
``/workspace/torchtune/recipes/configs`` directory.
{% endif %}
{% endif %}
{% endfor %}
@@ -431,6 +498,50 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
Multi-node training
-------------------
Pre-training
~~~~~~~~~~~~
Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
.. code-block:: shell
# In the MAD repository
cd scripts/pytorch_train
sbatch run_slurm_train.sh
Fine-tuning
~~~~~~~~~~~
Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
.. code-block:: shell
huggingface-cli login # Get access to HF Llama model space
huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
# In the MAD repository
cd scripts/pytorch_train
sbatch Torchtune_Multinode.sh
.. note::
Information regarding benchmark setup:
* By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
* You can adjust the torchtune `YAML configuration file
<https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
if you're using a different model.
* The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
* Set the ``mounting_paths`` inside the SLURM script.
Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
Further reading
===============

2
docs/how-to/rocm-for-ai/training/index.rst
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@@ -21,6 +21,8 @@ In this guide, you'll learn about:
- Training a model
- :doc:`With Primus (Megatron-LM backend) <benchmark-docker/primus-megatron>`
- :doc:`With Megatron-LM <benchmark-docker/megatron-lm>`
- :doc:`With PyTorch <benchmark-docker/pytorch-training>`

2
docs/reference/gpu-arch-specs.rst
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@@ -319,7 +319,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
- Radeon AI PRO R9700
- RDNA4
- gfx1201
- 16
- 32
- 64
- 32 or 64
- 128

26
docs/sphinx/_toc.yml.in
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@@ -27,6 +27,28 @@ subtrees:
title: ROCm on Radeon GPUs
- file: how-to/deep-learning-rocm.md
title: Deep learning frameworks
subtrees:
- entries:
- file: compatibility/ml-compatibility/pytorch-compatibility.rst
title: PyTorch compatibility
- file: compatibility/ml-compatibility/tensorflow-compatibility.rst
title: TensorFlow compatibility
- file: compatibility/ml-compatibility/jax-compatibility.rst
title: JAX compatibility
- file: compatibility/ml-compatibility/verl-compatibility.rst
title: verl compatibility
- file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
title: Stanford Megatron-LM compatibility
- file: compatibility/ml-compatibility/dgl-compatibility.rst
title: DGL compatibility
- file: compatibility/ml-compatibility/megablocks-compatibility.rst
title: Megablocks compatibility
- file: compatibility/ml-compatibility/taichi-compatibility.rst
title: Taichi compatibility
- file: compatibility/ml-compatibility/ray-compatibility.rst
title: Ray compatibility
- file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
title: llama.cpp compatibility
- file: how-to/build-rocm.rst
title: Build ROCm from source
@@ -44,8 +66,8 @@ subtrees:
title: Training
subtrees:
- entries:
- file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
title: Train a model with Megatron-LM
- file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
title: Train a model with Primus and Megatron-Core
- file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
title: Train a model with PyTorch
- file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst

2
docs/sphinx/requirements.txt
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@@ -234,7 +234,7 @@ sphinx-notfound-page==1.1.0
# via rocm-docs-core
sphinx-reredirects==0.1.6
# via -r requirements.in
sphinx-sitemap==2.7.2
sphinx-sitemap==2.8.0
# via -r requirements.in
sphinxcontrib-applehelp==2.0.0
# via sphinx

46
docs/sphinx/static/css/vllm-benchmark.css
View File

@@ -7,15 +7,14 @@ html {
--compat-head-color: var(--pst-color-surface);
--compat-param-hover-color: var(--pst-color-link-hover);
--compat-param-selected-color: var(--pst-color-primary);
--compat-border-color: var(--pst-color-border);
}
html[data-theme="light"] {
--compat-border-color: var(--pst-gray-500);
--compat-param-disabled-color: var(--pst-gray-300);
}
html[data-theme="dark"] {
--compat-border-color: var(--pst-gray-600);
--compat-param-disabled-color: var(--pst-gray-600);
}
@@ -23,6 +22,7 @@ div#vllm-benchmark-ud-params-picker.container-fluid {
padding: 0 0 1rem 0;
}
div[data-param-k="model-group"],
div[data-param-k="model"] {
background-color: var(--compat-bg-color);
padding: 2px;
@@ -31,40 +31,19 @@ div[data-param-k="model"] {
cursor: pointer;
}
div[data-param-k="model-group"][data-param-state="selected"],
div[data-param-k="model"][data-param-state="selected"] {
background-color: var(--compat-param-selected-color);
color: var(--compat-fg-color);
}
div[data-param-k="model"][data-param-state="latest-version"] {
background-color: var(--compat-param-selected-color);
color: var(--compat-fg-color);
}
div[data-param-k="model"][data-param-state="disabled"] {
background-color: var(--compat-param-disabled-color);
text-decoration: line-through;
/* text-decoration-color: var(--pst-color-danger); */
cursor: auto;
}
div[data-param-k="model"]:not([data-param-state]):hover {
div[data-param-k="model-group"]:hover,
div[data-param-k="model"]:hover {
background-color: var(--compat-param-hover-color);
}
div[data-param-k="model-group"] {
background-color: var(--compat-bg-color);
padding: 2px;
border: solid 1px var(--compat-border-color);
font-weight: 500;
cursor: pointer;
}
div[data-param-k="model-group"][data-param-state="selected"] {
background-color: var(--compat-param-selected-color);
color: var(--compat-fg-color);
}
/*
div[data-param-k="model-group"][data-param-state="latest-version"] {
background-color: var(--compat-param-selected-color);
color: var(--compat-fg-color);
@@ -73,26 +52,19 @@ div[data-param-k="model-group"][data-param-state="latest-version"] {
div[data-param-k="model-group"][data-param-state="disabled"] {
background-color: var(--compat-param-disabled-color);
text-decoration: line-through;
/* text-decoration-color: var(--pst-color-danger); */
text-decoration-color: var(--pst-color-danger);
cursor: auto;
}
div[data-param-k="model-group"]:not([data-param-state]):hover {
background-color: var(--compat-param-hover-color);
}
*/
.model-param-head {
background-color: var(--compat-head-color);
padding: 0.15rem 0.15rem 0.15rem 0.67rem;
/* margin: 2px; */
border-right: solid 2px var(--compat-accent-color);
border-right: solid 4px var(--compat-accent-color);
font-weight: 600;
}
.model-param {
/* padding: 2px; */
/* margin: 0 2px 0 2px; */
/* margin: 2px; */
border: solid 1px var(--compat-border-color);
font-weight: 500;
}