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Merge remote-tracking branch 'external/develop' into sync-develop-from-external

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This commit is contained in:
Alex Xu
2025-07-21 14:55:59 -04:00
parent 2bbcfc8f92 5bcf3b0847
commit aa6f40e2e0
83 changed files with 3788 additions and 1193 deletions
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3
.azuredevops/components/ROCmValidationSuite.yml
View File

@@ -86,8 +86,7 @@ jobs:
value: $(Agent.BuildDirectory)/rocm
- name: HIP_INC_DIR
value: $(Agent.BuildDirectory)/rocm
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
pool: ${{ variables.MEDIUM_BUILD_POOL }}
workspace:
clean: all
steps:

3
.azuredevops/components/Tensile.yml
View File

@@ -33,8 +33,9 @@ parameters:
type: object
default:
- cmake
- libmsgpack-dev
- libboost-filesystem-dev
- libboost-program-options-dev
- libmsgpack-dev
- name: pipModules
type: object
default:

1
.azuredevops/components/composable_kernel.yml
View File

@@ -107,6 +107,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}
# if this artifact name is changed, please also update $ARTIFACT_URL inside miopen-get-ck-build.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
gpuTarget: ${{ job.target }}

2
.azuredevops/components/copyHIP.yml
View File

@@ -39,4 +39,6 @@ jobs:
parameters:
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
inputs:
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml

40
.azuredevops/components/hipBLAS-common.yml
View File

@@ -51,15 +51,15 @@ parameters:
buildJobs:
- { os: ubuntu2204, packageManager: apt }
- { os: almalinux8, packageManager: dnf }
# - name: downstreamComponentMatrix
# type: object
# default:
# - hipBLASLt:
# name: hipBLASLt
# sparseCheckoutDir: projects/hipblaslt
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - hipBLAS_common_build
- name: downstreamComponentMatrix
type: object
default:
- hipBLASLt:
name: hipBLASLt
sparseCheckoutDir: projects/hipblaslt
skipUnifiedBuild: 'false'
buildDependsOn:
- hipBLAS_common_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -122,14 +122,14 @@ jobs:
# extraEnvVars:
# - ROCM_PATH:::/home/user/workspace/rocm
# - ${{ 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 }}
- ${{ 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 }}

82
.azuredevops/components/hipBLASLt.yml
View File

@@ -77,28 +77,28 @@ parameters:
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- { os: ubuntu2204, packageManager: apt, target: gfx1201 }
# - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1030 }
# - { os: almalinux8, packageManager: dnf, target: gfx942 }
# - { os: almalinux8, packageManager: dnf, target: gfx90a }
# - { os: almalinux8, packageManager: dnf, target: gfx1201 }
# - { os: almalinux8, packageManager: dnf, target: gfx1100 }
# - { os: almalinux8, packageManager: dnf, target: gfx1030 }
- { pool: rocm-ci_ultra_build_pool, os: ubuntu2204, packageManager: apt, target: gfx942 }
- { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx90a }
- { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1201 }
- { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1030 }
- { pool: rocm-ci_ultra_build_pool, os: almalinux8, packageManager: dnf, target: gfx942 }
- { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx90a }
- { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1201 }
- { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1100 }
- { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1030 }
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
# - name: downstreamComponentMatrix
# type: object
# default:
# - rocBLAS:
# name: rocBLAS
# sparseCheckoutDir: projects/rocblas
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - hipBLASLt_build
- name: downstreamComponentMatrix
type: object
default:
- rocBLAS:
name: rocBLAS
sparseCheckoutDir: projects/rocblas
skipUnifiedBuild: 'false'
buildDependsOn:
- hipBLASLt_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -121,7 +121,7 @@ jobs:
value: $(Agent.BuildDirectory)/rocm
- name: DAY_STRING
value: $[format('{0:ddMMyyyy}', pipeline.startTime)]
pool: ${{ variables.ULTRA_BUILD_POOL }}
pool: ${{ job.pool }}
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
@@ -140,6 +140,10 @@ jobs:
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- gtest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
@@ -156,18 +160,15 @@ jobs:
script: |
echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/bin"
echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/llvm/bin"
# hipBLASLt has a script for gtest and lapack
# https://github.com/ROCm/hipBLASLt/blob/develop/deps/CMakeLists.txt
# $(Agent.BuildDirectory)/deps is a temporary folder for the build process
# $(Agent.BuildDirectory)/s/deps is part of the hipBLASLt repo
- task: Bash@3
displayName: Build and install external dependencies
displayName: Build and install LAPACK
inputs:
targetType: inline
script: |
mkdir -p $(Agent.BuildDirectory)/deps
cd $(Agent.BuildDirectory)/deps
cmake -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/s/deps
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
make
sudo make install
- script: |
@@ -187,7 +188,7 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_INCLUDE_PATH=$(Agent.BuildDirectory)/rocm/llvm/include
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
@@ -244,6 +245,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -280,14 +282,14 @@ jobs:
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 }}
- ${{ 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 }}

1
.azuredevops/components/hipFFT.yml
View File

@@ -156,6 +156,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

3
.azuredevops/components/hipSOLVER.yml
View File

@@ -70,8 +70,7 @@ jobs:
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
pool: ${{ variables.MEDIUM_BUILD_POOL }}
workspace:
clean: all
steps:

185
.azuredevops/components/hipSPARSE.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: hipSPARSE
- 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
@@ -14,13 +33,11 @@ parameters:
type: object
default:
- cmake
- ninja-build
- libboost-program-options-dev
- googletest
- libfftw3-dev
- git
- gfortran
- libgtest-dev
- git
- libboost-program-options-dev
- libfftw3-dev
- ninja-build
- python3-pip
- name: rocmDependencies
type: object
@@ -49,19 +66,31 @@ parameters:
type: object
default:
buildJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- { os: ubuntu2204, packageManager: apt, target: gfx1201 }
- { os: ubuntu2204, packageManager: apt, target: gfx1030 }
- { os: ubuntu2204, packageManager: apt, target: gfx1100 }
testJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- name: downstreamComponentMatrix
type: object
default:
- hipSPARSELt:
name: hipSPARSELt
sparseCheckoutDir: projects/hipsparselt
skipUnifiedBuild: 'false'
buildDependsOn:
- hipSPARSE_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: hipSPARSE_build_${{ job.target }}
- 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
@@ -73,42 +102,57 @@ jobs:
- 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/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- gtest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
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: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/rocm/share/rocm/cmake/
-DBUILD_CLIENTS_TESTS=ON
-DBUILD_CLIENTS_SAMPLES=OFF
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
artifactName: hipSPARSE
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
artifactName: hipSPARSE
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
publish: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
parameters:
sourceDir: $(Build.SourcesDirectory)/build/clients
sourceDir: $(Agent.BuildDirectory)/s/build/clients
contentsString: matrices/**
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
artifactName: testMatrices
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
# parameters:
@@ -116,44 +160,65 @@ jobs:
# environment: test
# gpuTarget: ${{ job.target }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipSPARSE_test_${{ job.target }}
dependsOn: hipSPARSE_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ 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/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: hipSPARSE
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsparse-test'
testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
environment: test
gpuTarget: ${{ job.target }}
- ${{ 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_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool: ${{ job.target }}_test_pool
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/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
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: ${{ parameters.componentName }}
os: ${{ job.os }}
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsparse-test'
testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
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 }}

163
.azuredevops/components/hipSPARSELt.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: hipSPARSELt
- 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
@@ -56,15 +75,17 @@ parameters:
type: object
default:
buildJobs:
- gfx942:
target: gfx942
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
testJobs:
- gfx942:
target: gfx942
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: hipSPARSELt_build_${{ job.target }}
- 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
@@ -86,17 +107,22 @@ jobs:
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 }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
# Build and install gtest and lapack
# $(Pipeline.Workspace)/deps is a temporary folder for the build process
# $(Pipeline.Workspace)/s/deps is part of the hipSPARSELt repo
@@ -115,6 +141,7 @@ jobs:
workingDirectory: $(Pipeline.Workspace)/deps
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
@@ -130,64 +157,80 @@ jobs:
-GNinja
- 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
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
gpuTarget: ${{ job.target }}
extraCopyDirectories:
- deps
extraPaths: /home/user/workspace/rocm/llvm/bin:/home/user/workspace/rocm/bin
extraEnvVars:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang
- CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/llvm/bin/hipcc
- TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
installLatestCMake: true
- ${{ if eq(job.os, 'ubuntu2204') }}:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
gpuTarget: ${{ job.target }}
extraCopyDirectories:
- deps
extraPaths: /home/user/workspace/rocm/llvm/bin:/home/user/workspace/rocm/bin
extraEnvVars:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang
- CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/llvm/bin/hipcc
- TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
installLatestCMake: true
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipSPARSELt_test_${{ job.target }}
dependsOn: hipSPARSELt_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ 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/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: hipSPARSELt
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsparselt-test'
testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes --gtest_filter=*pre_checkin*'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
environment: test
gpuTarget: ${{ job.target }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 120
dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool: ${{ job.target }}_test_pool
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/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
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: ${{ parameters.componentName }}
os: ${{ job.os }}
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsparselt-test'
testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes --gtest_filter=*pre_checkin*'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
environment: test
gpuTarget: ${{ job.target }}

1
.azuredevops/components/llvm-project.yml
View File

@@ -67,7 +67,6 @@ jobs:
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
skipLlvmSymlink: true
aggregatePipeline: ${{ parameters.aggregatePipeline }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml

3
.azuredevops/components/rocAL.yml
View File

@@ -86,8 +86,7 @@ jobs:
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
pool: ${{ variables.MEDIUM_BUILD_POOL }}
workspace:
clean: all
steps:

3
.azuredevops/components/rocALUTION.yml
View File

@@ -73,8 +73,7 @@ jobs:
- template: /.azuredevops/variables-global.yml
- name: HIP_ROCCLR_HOME
value: $(Build.BinariesDirectory)/rocm
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
pool: ${{ variables.MEDIUM_BUILD_POOL }}
workspace:
clean: all
steps:

130
.azuredevops/components/rocBLAS.yml
View File

@@ -33,17 +33,15 @@ parameters:
type: object
default:
- cmake
- ninja-build
- python3-venv
- git
- libmsgpack-dev
- gfortran
- libopenblas-dev
- googletest
- libgtest-dev
- wget
- python3-pip
- libdrm-dev
- libmsgpack-dev
- libopenblas-dev
- ninja-build
- python3-pip
- python3-venv
- wget
- name: pipModules
type: object
default:
@@ -52,18 +50,17 @@ parameters:
- name: rocmDependencies
type: object
default:
- rocm-cmake
- llvm-project
- ROCR-Runtime
- clr
- rocminfo
- rocprofiler-register
- rocm_smi_lib
- rocm-core
- aomp
- aomp-extras
- clr
- hipBLAS-common
- hipBLASLt
- llvm-project
- rocm-cmake
- rocm-core
- rocm_smi_lib
- rocminfo
- rocprofiler-register
- ROCR-Runtime
- roctracer
- name: rocmTestDependencies
type: object
@@ -86,32 +83,38 @@ parameters:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- { os: ubuntu2204, packageManager: apt, target: gfx1201 }
# - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1030 }
# - { os: almalinux8, packageManager: dnf, target: gfx942 }
# - { os: almalinux8, packageManager: dnf, target: gfx90a }
# - { os: almalinux8, packageManager: dnf, target: gfx1201 }
# - { os: almalinux8, packageManager: dnf, target: gfx1100 }
# - { os: almalinux8, packageManager: dnf, target: gfx1030 }
- { os: almalinux8, packageManager: dnf, target: gfx942 }
- { os: almalinux8, packageManager: dnf, target: gfx90a }
- { os: almalinux8, packageManager: dnf, target: gfx1201 }
- { os: almalinux8, packageManager: dnf, target: gfx1100 }
- { os: almalinux8, packageManager: dnf, target: gfx1030 }
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
# - name: downstreamComponentMatrix
# type: object
# default:
# # rocSOLVER depends on both rocBLAS and rocPRIM
# # for a unified build, rocBLAS will be the one to call rocSOLVER
# - rocSOLVER:
# name: rocSOLVER
# sparseCheckoutDir: projects/rocsolver
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - rocBLAS_build
# unifiedBuild:
# downstreamAggregateNames: rocBLAS+rocPRIM
# buildDependsOn:
# - rocBLAS_build
# - rocPRIM_build
- name: downstreamComponentMatrix
type: object
default:
- rocSPARSE:
name: rocSPARSE
sparseCheckoutDir: projects/rocsparse
skipUnifiedBuild: 'false'
buildDependsOn:
- rocBLAS_build
# rocSOLVER depends on both rocBLAS and rocPRIM
# for a unified build, rocBLAS will be the one to call rocSOLVER
# - rocSOLVER:
# name: rocSOLVER
# sparseCheckoutDir: projects/rocsolver
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - rocBLAS_build
# unifiedBuild:
# downstreamAggregateNames: rocBLAS+rocPRIM
# buildDependsOn:
# - rocBLAS_build
# - rocPRIM_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -151,6 +154,12 @@ jobs:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aocl.yml
parameters:
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- gtest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
@@ -164,21 +173,12 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_TOOLCHAIN_FILE=toolchain-linux.cmake
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
-DGPU_TARGETS=${{ job.target }}
-DTensile_CODE_OBJECT_VERSION=default
-DTensile_LOGIC=asm_full
-DTensile_SEPARATE_ARCHITECTURES=ON
-DTensile_LAZY_LIBRARY_LOADING=ON
-DTensile_LIBRARY_FORMAT=msgpack
-DBUILD_CLIENTS_TESTS=ON
-DBUILD_CLIENTS_BENCHMARKS=OFF
-DBUILD_CLIENTS_SAMPLES=OFF
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
@@ -208,6 +208,7 @@ jobs:
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 120
dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
condition:
and(succeeded(),
@@ -222,6 +223,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -258,18 +260,18 @@ jobs:
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 }}
# triggerDownstreamJobs: true
# unifiedBuild: ${{ parameters.unifiedBuild }}
# ${{ if parameters.unifiedBuild }}:
# buildDependsOn: ${{ component.unifiedBuild.buildDependsOn }}
# downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ component.unifiedBuild.downstreamAggregateNames }}
# ${{ else }}:
# buildDependsOn: ${{ component.buildDependsOn }}
# downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
- ${{ 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 }}
triggerDownstreamJobs: true
unifiedBuild: ${{ parameters.unifiedBuild }}
${{ if parameters.unifiedBuild }}:
buildDependsOn: ${{ component.unifiedBuild.buildDependsOn }}
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ component.unifiedBuild.downstreamAggregateNames }}
${{ else }}:
buildDependsOn: ${{ component.buildDependsOn }}
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}

1
.azuredevops/components/rocFFT.yml
View File

@@ -166,6 +166,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

1
.azuredevops/components/rocMLIR.yml
View File

@@ -27,6 +27,7 @@ parameters:
- numpy
- tomli
- scipy
- pybind11
- name: rocmDependencies
type: object
default:

2
.azuredevops/components/rocPRIM.yml
View File

@@ -210,7 +210,7 @@ jobs:
parameters:
componentName: ${{ parameters.componentName }}
testDir: '$(Agent.BuildDirectory)/rocm/bin/rocprim'
extraTestParameters: '-I ${{ job.shard }},,${{ job.shardCount }}'
extraTestParameters: '-I ${{ job.shard }},,${{ job.shardCount }} -E device_merge_inplace'
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:

19
.azuredevops/components/rocPyDecode.yml
View File

@@ -36,6 +36,7 @@ parameters:
- clr
- llvm-project
- rocDecode
- rocJPEG
- rocm-cmake
- rocm-core
- rocminfo
@@ -192,9 +193,9 @@ jobs:
inputs:
itemPattern: '**/*.whl'
targetPath: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
@@ -221,25 +222,17 @@ jobs:
- task: CMake@1
displayName: 'rocPyDecode Test CMake Flags'
inputs:
workingDirectory: $(Agent.BuildDirectory)/rocm/share/rocpydecode/tests
cmakeArgs: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(PYTHON_USER_SITE)/pybind11;$(PYTHON_DIST_PACKAGES)/pybind11;$(PYBIND11_PATH)
-DCMAKE_BUILD_TYPE=Release
-DGPU_TARGETS=${{ job.target }}
..
.
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocPyDecode
testDir: $(Build.SourcesDirectory)/build
# sudo required for pip install but screws up permissions for next pipeline run
- task: Bash@3
displayName: Clean up test environment
condition: always()
inputs:
targetType: inline
script: |
pip uninstall -y rocPyDecode
pip uninstall -y hip-python
testDir: $(Agent.BuildDirectory)/rocm/share/rocpydecode/tests
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

28
.azuredevops/components/rocSOLVER.yml
View File

@@ -33,13 +33,11 @@ parameters:
type: object
default:
- cmake
- ninja-build
- libsuitesparse-dev
- gfortran
- libfmt-dev
- git
- googletest
- libgtest-dev
- libfmt-dev
- libsuitesparse-dev
- ninja-build
- python3-pip
- name: rocmDependencies
type: object
@@ -75,13 +73,13 @@ parameters:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- { os: ubuntu2204, packageManager: apt, target: gfx1201 }
# - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1030 }
# - { os: almalinux8, packageManager: dnf, target: gfx942 }
# - { os: almalinux8, packageManager: dnf, target: gfx90a }
# - { os: almalinux8, packageManager: dnf, target: gfx1201 }
# - { os: almalinux8, packageManager: dnf, target: gfx1100 }
# - { os: almalinux8, packageManager: dnf, target: gfx1030 }
- { os: almalinux8, packageManager: dnf, target: gfx942 }
- { os: almalinux8, packageManager: dnf, target: gfx90a }
- { os: almalinux8, packageManager: dnf, target: gfx1201 }
- { os: almalinux8, packageManager: dnf, target: gfx1100 }
- { os: almalinux8, packageManager: dnf, target: gfx1030 }
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
@@ -119,6 +117,10 @@ jobs:
targetType: inline
script: git clone --depth 1 --branch v3.9.1 https://github.com/Reference-LAPACK/lapack
workingDirectory: '$(Build.SourcesDirectory)'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- gtest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
@@ -134,6 +136,7 @@ jobs:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_BUILD_TYPE=Release
-DCMAKE_POSITION_INDEPENDENT_CODE=ON
-DCMAKE_Fortran_FLAGS=-fno-optimize-sibling-calls
-DBUILD_TESTING=OFF
-DCBLAS=ON
@@ -146,7 +149,7 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Pipeline.Workspace)/deps-install
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Pipeline.Workspace)/deps-install;$(Agent.BuildDirectory)/vendor
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-DAMDGPU_TARGETS=${{ job.target }}
@@ -191,6 +194,7 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

219
.azuredevops/components/rocSPARSE.yml
View File

@@ -1,10 +1,29 @@
parameters:
- name: componentName
type: string
default: rocSPARSE
- 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
@@ -13,27 +32,25 @@ parameters:
- name: aptPackages
type: object
default:
- python3-pip
- cmake
- ninja-build
- libboost-program-options-dev
- googletest
- libfftw3-dev
- git
- gfortran
- libgtest-dev
- git
- libboost-program-options-dev
- libdrm-dev
- libfftw3-dev
- ninja-build
- python3-pip
- name: rocmDependencies
type: object
default:
- rocm-cmake
- llvm-project
- ROCR-Runtime
- clr
- llvm-project
- rocBLAS
- rocm-cmake
- rocminfo
- rocPRIM
- rocprofiler-register
- ROCR-Runtime
- roctracer
- name: rocmTestDependencies
type: object
@@ -52,19 +69,39 @@ parameters:
type: object
default:
buildJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- { os: ubuntu2204, packageManager: apt, target: gfx1201 }
- { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- { os: ubuntu2204, packageManager: apt, target: gfx1030 }
testJobs:
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- name: downstreamComponentMatrix
type: object
default:
- hipSPARSE:
name: hipSPARSE
sparseCheckoutDir: projects/hipsparse
skipUnifiedBuild: 'false'
buildDependsOn:
- rocSPARSE_build
# hipSOLVER depends on both rocSOLVER and rocSPARSE
# for a unified build, rocSOLVER will be the one to call hipSOLVER
# - hipSOLVER:
# name: hipSOLVER
# sparseCheckoutDir: projects/hipsolver
# skipUnifiedBuild: 'true'
# buildDependsOn:
# - rocSPARSE_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocSPARSE_build_${{ job.target }}
- 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
@@ -77,22 +114,32 @@ jobs:
- 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/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
parameters:
dependencyList:
- gtest
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
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: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_BUILD_TYPE=Release
-DAMDGPU_TARGETS=${{ job.target }}
@@ -103,68 +150,94 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
artifactName: rocSPARSE
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
artifactName: rocSPARSE
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
publish: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
parameters:
sourceDir: $(Build.SourcesDirectory)/build/clients
sourceDir: $(Agent.BuildDirectory)/s/build/clients
contentsString: matrices/**
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
artifactName: testMatrices
gpuTarget: ${{ job.target }}
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 }}
gpuTarget: ${{ job.target }}
extraEnvVars:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- ${{ if eq(job.os, 'ubuntu2204') }}:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
gpuTarget: ${{ job.target }}
extraEnvVars:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocSPARSE_test_${{ job.target }}
timeoutInMinutes: 90
dependsOn: rocSPARSE_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ 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/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: rocSPARSE
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './rocsparse-test'
testParameters: '--gtest_filter="*quick*" --gtest_output=xml:./test_output.xml --gtest_color=yes'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
environment: test
gpuTarget: ${{ job.target }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 120
dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool: ${{ job.target }}_test_pool
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/local-artifact-download.yml
parameters:
preTargetFilter: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
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: ${{ parameters.componentName }}
os: ${{ job.os }}
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './rocsparse-test'
testParameters: '--gtest_filter="*quick*" --gtest_output=xml:./test_output.xml --gtest_color=yes'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
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 }}

2
.azuredevops/components/rocm-examples.yml
View File

@@ -184,7 +184,7 @@ jobs:
parameters:
componentName: rocm-examples
testDir: $(Build.SourcesDirectory)/build
testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex "rocfft_callback"'
testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

1
.azuredevops/components/rocminfo.yml
View File

@@ -67,7 +67,6 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
skipLlvmSymlink: true
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:

1
.azuredevops/nightly/pytorch.yml
View File

@@ -407,7 +407,6 @@ jobs:
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: $(JOB_GPU_TARGET)
dependencySource: staging
skipLlvmSymlink: true
# get sources to run test scripts
- task: Bash@3
displayName: git clone upstream pytorch

1
.azuredevops/nightly/rocm-nightly.yml
View File

@@ -119,7 +119,6 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
skipLibraryLinking: true
- script: df -h
displayName: System disk space after ROCm
- script: du -sh $(Agent.BuildDirectory)/rocm

39
.azuredevops/templates/steps/artifact-download.yml
View File

@@ -12,6 +12,9 @@ parameters:
- name: fileFilter
type: string
default: ''
- name: extractAndDeleteFiles
type: boolean
default: true
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
@@ -22,34 +25,32 @@ steps:
- task: DownloadPipelineArtifact@2
displayName: Download ${{ parameters.componentName }}
inputs:
itemPattern: '**/*${{ parameters.componentName }}*${{ parameters.fileFilter }}*'
targetPath: '$(Pipeline.Workspace)/d'
allowPartiallySucceededBuilds: true
${{ if parameters.aggregatePipeline }}:
buildType: 'current'
itemPattern: '**/${{ parameters.componentName }}*${{ parameters.fileFilter }}*'
allowPartiallySucceededBuilds: true
targetPath: '$(Pipeline.Workspace)/d'
${{ else }}:
buildType: 'specific'
project: ROCm-CI
specificBuildWithTriggering: true
allowPartiallySucceededBuilds: true
definition: ${{ parameters.pipelineId }}
itemPattern: '**/*${{ parameters.fileFilter }}*'
targetPath: '$(Pipeline.Workspace)/d'
branchName: refs/heads/${{ parameters.branchName }}
${{ if eq(parameters.componentName, 'aomp') }}:
buildVersionToDownload: latest # aomp trigger lives in ROCm/ROCm, so cannot use ROCm/aomp branch names
${{ else }}:
buildVersionToDownload: latestFromBranch
- task: ExtractFiles@1
displayName: Extract ${{ parameters.componentName }}
inputs:
archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
destinationFolder: '$(Agent.BuildDirectory)/rocm'
cleanDestinationFolder: false
overwriteExistingFiles: true
- task: DeleteFiles@1
displayName: Cleanup Compressed ${{ parameters.componentName }}
inputs:
SourceFolder: '$(Pipeline.Workspace)/d'
Contents: '**/*.tar.gz'
RemoveDotFiles: true
- ${{ if eq(parameters.extractAndDeleteFiles, true) }}:
- task: ExtractFiles@1
displayName: Extract ${{ parameters.componentName }}
inputs:
archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
destinationFolder: '$(Agent.BuildDirectory)/rocm'
cleanDestinationFolder: false
overwriteExistingFiles: true
- task: DeleteFiles@1
displayName: Clean up Compressed ${{ parameters.componentName }}
inputs:
SourceFolder: '$(Pipeline.Workspace)/d'
Contents: '**/*.tar.gz'
RemoveDotFiles: true

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

@@ -15,8 +15,8 @@ steps:
URL_BEGIN="https://artprodcus3.artifacts.visualstudio.com/"
URL_MIDDLE="/_apis/artifact/"
URL_END="/content?format=file&subPath=%2F"
FORMATTED_JOB_NAME=$(echo $(Agent.JobName) | sed 's/ /./g; s/[-_]//g')
ARTIFACT_STRING="pipelineartifact://ROCm-CI/projectId/$(DOWNLOAD_PROJECT_ID)/buildId/$(Build.BuildId)/artifactName/${FORMATTED_JOB_NAME}"
ARTIFACT_NAME="$(Agent.JobName)_$(System.JobAttempt)"
ARTIFACT_STRING="pipelineartifact://ROCm-CI/projectId/$(DOWNLOAD_PROJECT_ID)/buildId/$(Build.BuildId)/artifactName/${ARTIFACT_NAME}"
ENCODED_STRING=$(echo -n "${ARTIFACT_STRING}" | base64 -w 0)
PADDING_COUNT=$(echo -n "${ENCODED_STRING}" | awk -F= '{print NF-1}')
if [ "$PADDING_COUNT" -gt 0 ]; then

3
.azuredevops/templates/steps/artifact-upload.yml
View File

@@ -46,5 +46,6 @@ steps:
displayName: '${{ parameters.artifactName }} Publish'
retryCountOnTaskFailure: 3
inputs:
artifactName: ${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}_$(System.JobAttempt)
# if this artifact name is changed, please also update $ARTIFACT_URL inside miopen-get-ck-build.yml
artifactName: $(Agent.JobName)_$(System.JobAttempt)
targetPath: '$(Build.ArtifactStagingDirectory)'

18
.azuredevops/templates/steps/dependencies-aocl.yml
View File

@@ -1,10 +1,15 @@
parameters:
- name: os
type: string
default: ubuntu2204
- name: repositoryUrl
type: string
default: https://download.amd.com/developer/eula/aocl/aocl-4-2
- name: packageName
type: string
default: aocl-linux-gcc-4.2.0_1_amd64.deb
type: object
default:
ubuntu2204: aocl-linux-gcc-4.2.0_1_amd64.deb
almalinux8: aocl-linux-gcc-4.2.0-1.x86_64.rpm
steps:
- task: Bash@3
@@ -12,16 +17,19 @@ steps:
inputs:
targetType: inline
workingDirectory: $(Pipeline.Workspace)
script: wget -nv ${{ parameters.repositoryUrl }}/${{ parameters.packageName }}
script: wget -nv ${{ parameters.repositoryUrl }}/${{ parameters.packageName[parameters.os] }}
- task: Bash@3
displayName: Install AOCL
inputs:
targetType: inline
workingDirectory: $(Pipeline.Workspace)
script: sudo apt install -y ./${{ parameters.packageName }}
${{ if eq(parameters.os, 'ubuntu2204') }}:
script: sudo apt install -y ./${{ parameters.packageName[parameters.os] }}
${{ elseif eq(parameters.os, 'almalinux8') }}:
script: sudo dnf install -y ./${{ parameters.packageName[parameters.os] }}
- task: Bash@3
displayName: Clean up AOCL
inputs:
targetType: inline
workingDirectory: $(Pipeline.Workspace)
script: rm -f ${{ parameters.packageName }}
script: rm -f ${{ parameters.packageName[parameters.os] }}

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

@@ -52,6 +52,7 @@ parameters:
libexpat-dev: expat-devel
libffi-dev: libffi-devel
libfftw3-dev: fftw-devel
libfmt-dev: fmt-devel
libgmp-dev: gmp-devel
liblzma-dev: xz-devel
libmpfr-dev: mpfr-devel

63
.azuredevops/templates/steps/dependencies-rocm.yml
View File

@@ -19,16 +19,6 @@ parameters:
- name: gpuTarget
type: string
default: ''
# set to true if you're calling this template file multiple files in same pipeline
# only leave last call false to optimize sequence
- name: skipLibraryLinking
type: boolean
default: false
# set to true if llvm-project is not downloaded in a particular call
# or if you just don't want the symlink
- name: skipLlvmSymlink
type: boolean
default: false
# set to true if dlopen calls for HIP libraries are causing failures
# because they do not follow shared library symlink convention
- name: setupHIPLibrarySymlinks
@@ -367,6 +357,7 @@ steps:
componentName: ${{ split(dependency, ':')[0] }}
pipelineId: ${{ parameters.componentVarList[split(dependency, ':')[0]].pipelineId }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
extractAndDeleteFiles: false
${{ if parameters.componentVarList[split(dependency, ':')[0]].hasGpuTarget }}:
fileFilter: "${{ split(dependency, ':')[1] }}*_${{ parameters.os }}_${{ parameters.gpuTarget }}"
# dependencySource = staging
@@ -405,6 +396,7 @@ steps:
componentName: ${{ dependency }}
pipelineId: ${{ parameters.componentVarList[dependency].pipelineId }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
extractAndDeleteFiles: false
${{ if parameters.componentVarList[dependency].hasGpuTarget }}:
fileFilter: ${{ parameters.os }}_${{ parameters.gpuTarget }}
${{ else }}:
@@ -430,8 +422,20 @@ steps:
# default = staging
${{ else }}:
branchName: ${{ parameters.componentVarList[dependency].stagingBranch }}
# Set link to redirect llvm folder
- ${{ if eq(parameters.skipLlvmSymlink, false) }}:
- task: ExtractFiles@1
displayName: Extract ROCm artifacts
inputs:
archiveFilePatterns: $(Pipeline.Workspace)/d/**/*.tar.gz
destinationFolder: $(Agent.BuildDirectory)/rocm
cleanDestinationFolder: false
overwriteExistingFiles: true
- task: DeleteFiles@1
displayName: Clean up ROCm artifacts
inputs:
SourceFolder: $(Pipeline.Workspace)/d
Contents: '**/*.tar.gz'
RemoveDotFiles: true
- ${{ if containsValue(parameters.dependencyList, 'llvm-project') }}:
- task: Bash@3
displayName: Symlink from rocm/llvm to rocm/lib/llvm
inputs:
@@ -439,6 +443,7 @@ steps:
script: |
sudo mkdir -p $(Agent.BuildDirectory)/rocm/lib
sudo ln -sr $(Agent.BuildDirectory)/rocm/llvm $(Agent.BuildDirectory)/rocm/lib/llvm
echo "Created symlink from rocm/llvm to rocm/lib/llvm"
- task: Bash@3
displayName: Symlink executables from rocm/llvm/bin to rocm/bin
inputs:
@@ -446,7 +451,14 @@ steps:
script: |
for file in amdclang amdclang++ amdclang-cl amdclang-cpp amdflang amdlld aompcc mygpu mycpu offload-arch; do
sudo ln -sr $(Agent.BuildDirectory)/rocm/llvm/bin/$file $(Agent.BuildDirectory)/rocm/bin/$file
echo "Created symlink from rocm/llvm/bin/$file to rocm/bin/$file"
done
- ${{ if containsValue(parameters.dependencyList, 'rocm-core') }}:
- task: Bash@3
displayName: Print rocm/.info/version
inputs:
targetType: inline
script: cat $(Agent.BuildDirectory)/rocm/.info/version
# dlopen calls within a ctest or pytest sequence runs into issues when shared library symlink convention is not followed
# the convention is as follows:
# unversioned .so is a symlink to major version .so
@@ -483,17 +495,16 @@ steps:
inputs:
targetType: inline
script: ls -la1R $(Agent.BuildDirectory)/rocm
- ${{ if eq(parameters.skipLibraryLinking, false) }}:
- task: Bash@3
displayName: 'Link ROCm shared libraries'
inputs:
targetType: inline
# OS ignores if the ROCm lib folder shows up more than once
script: |
echo $(Agent.BuildDirectory)/rocm/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/llvm/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/lib64 | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/llvm/lib64 | 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
- task: Bash@3
displayName: 'Link ROCm shared libraries'
inputs:
targetType: inline
# OS ignores if the ROCm lib folder shows up more than once
script: |
echo $(Agent.BuildDirectory)/rocm/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/llvm/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/lib64 | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
echo $(Agent.BuildDirectory)/rocm/llvm/lib64 | 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

29
.azuredevops/templates/steps/manifest.yml
View File

@@ -23,13 +23,14 @@ steps:
inputs:
targetType: inline
script: |
sudo apt-get install -y jq
${{ iif(or(eq(parameters.os, 'ubuntu2204'), eq(parameters.os, 'ubuntu2404')), 'sudo apt-get install -y jq', '') }}
# RESOURCES_REPOSITORIES is a runtime variable (not an env var!) that contains quotations and newlines
# So we need to save it to a file to properly preserve its formatting and contents
cat <<EOF > resources.repositories
$(RESOURCES_REPOSITORIES)
EOF
echo "Value of resources.repositories:"
cat resources.repositories
IS_TAG_BUILD=$(jq 'has("release_repo")' resources.repositories)
@@ -66,8 +67,6 @@ steps:
)
' resources.repositories)
manifest_json=$(Build.ArtifactStagingDirectory)/manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.json
dependencies=()
for manifest_file in $(Pipeline.Workspace)/d/**/manifest_*.json; do
echo "Processing $manifest_file"
@@ -78,6 +77,10 @@ steps:
done
dependencies_json=$(printf '%s\n' "${dependencies[@]}" | jq -s '.')
manifest_filename="manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}"
echo "##vso[task.setvariable variable=manifest_filename]$manifest_filename"
manifest_json=$(Build.ArtifactStagingDirectory)/$manifest_filename.json
jq -n \
--argjson current "$current" \
--argjson dependencies "$dependencies_json" \
@@ -111,8 +114,14 @@ steps:
')
dependencies_rows=$(echo $dependencies_rows)
echo "##vso[task.setvariable variable=dependencies_rows;]$dependencies_rows"
cat $manifest_json
- task: Bash@3
displayName: Print manifest.json
condition: always()
continueOnError: true
inputs:
targetType: inline
script: |
cat $(Build.ArtifactStagingDirectory)/$(manifest_filename).json
- task: Bash@3
displayName: Create manifest.html
condition: always()
@@ -120,10 +129,10 @@ steps:
inputs:
targetType: inline
script: |
manifest_html=$(Build.ArtifactStagingDirectory)/manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.html
manifest_html="$(Build.ArtifactStagingDirectory)/$(manifest_filename).html"
cat <<EOF > $manifest_html
<html>
<h1>Manifest</h1>
<h1>$(manifest_filename)</h1>
<h2>Current</h2>
<table border="1">
<tr>
@@ -163,7 +172,7 @@ steps:
continueOnError: true
inputs:
tabName: Manifest
reportDir: $(Build.ArtifactStagingDirectory)/manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.html
reportDir: $(Build.ArtifactStagingDirectory)/$(manifest_filename).html
- task: Bash@3
displayName: Save manifest artifact file name
condition: always()
@@ -172,5 +181,5 @@ steps:
workingDirectory: $(Pipeline.Workspace)
targetType: inline
script: |
echo "manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.html" >> pipelineArtifacts.txt
echo "manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.json" >> pipelineArtifacts.txt
echo "$(manifest_filename).html" >> pipelineArtifacts.txt
echo "$(manifest_filename).json" >> pipelineArtifacts.txt

27
.azuredevops/templates/steps/miopen-get-ck-build.yml
View File

@@ -17,7 +17,6 @@ steps:
script: |
AZ_API="https://dev.azure.com/ROCm-CI/ROCm-CI/_apis"
GH_API="https://api.github.com/repos/ROCm"
ARTIFACT_NAME="composablekernelbuild${{ parameters.gpuTarget }}"
EXIT_CODE=0
# Try to find an Azure build for the specific CK commit called out in MIOpen's requirements.txt
@@ -39,8 +38,15 @@ steps:
echo "Found specific CK build ID: $CK_BUILD_ID"
fi
AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?artifactName=$ARTIFACT_NAME&api-version=7.1"
ARTIFACT_URL=$(curl -s $AZURE_URL | jq '.resource.downloadUrl' | tr -d '"')
AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?api-version=7.1"
ARTIFACT_URL=$(curl -s $AZURE_URL | \
jq --arg gfx "${{ parameters.gpuTarget }}" '
.value
| map(select(.name | test($gfx)))
| max_by(.name | capture("_(?<dropNumber>\\d+)").dropNumber | tonumber)
| .resource.downloadUrl
' | \
tr -d '"')
# If using the specific CK commit and it doesn't have any valid artifacts, use latest successful CK build instead
if { [[ -z "$ARTIFACT_URL" ]] || [[ "$ARTIFACT_URL" == "null" ]]; } && [[ $EXIT_CODE -eq 0 ]]; then
@@ -48,8 +54,15 @@ steps:
LATEST_BUILD_URL="$AZ_API/build/builds?definitions=$(COMPOSABLE_KERNEL_PIPELINE_ID)&statusFilter=completed&resultFilter=succeeded&\$top=1&api-version=7.1"
CK_BUILD_ID=$(curl -s $LATEST_BUILD_URL | jq '.value[0].id')
echo "Found latest CK build ID: $CK_BUILD_ID"
AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?artifactName=$ARTIFACT_NAME&api-version=7.1"
ARTIFACT_URL=$(curl -s $AZURE_URL | jq '.resource.downloadUrl' | tr -d '"')
AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?api-version=7.1"
ARTIFACT_URL=$(curl -s $AZURE_URL | \
jq --arg os "ubuntu2204" --arg gfx "${{ parameters.gpuTarget }}" '
.value
| map(select(.name | test($os) and test($gfx)))
| max_by(.name | capture("_(?<dropNumber>\\d+)").dropNumber | tonumber)
| .resource.downloadUrl
' | \
tr -d '"')
EXIT_CODE=2
fi
@@ -57,8 +70,8 @@ steps:
wget --tries=5 --waitretry=10 --retry-connrefused -nv $ARTIFACT_URL -O $(System.ArtifactsDirectory)/ck.zip
unzip $(System.ArtifactsDirectory)/ck.zip -d $(System.ArtifactsDirectory)
mkdir -p $(Agent.BuildDirectory)/rocm
tar -zxvf $(System.ArtifactsDirectory)/$ARTIFACT_NAME/*.tar.gz -C $(Agent.BuildDirectory)/rocm
rm -r $(System.ArtifactsDirectory)/ck.zip $(System.ArtifactsDirectory)/$ARTIFACT_NAME
tar -zxvf $(System.ArtifactsDirectory)/composable_kernel*/*.tar.gz -C $(Agent.BuildDirectory)/rocm
rm -r $(System.ArtifactsDirectory)/ck.zip $(System.ArtifactsDirectory)/composable_kernel*
if [[ $EXIT_CODE -ne 0 ]]; then
BUILD_COMMIT=$(curl -s $AZ_API/build/builds/$CK_BUILD_ID | jq '.sourceVersion' | tr -d '"')

10
.azuredevops/variables-global.yml
View File

@@ -66,11 +66,11 @@ variables:
- name: HIP_TESTS_PIPELINE_ID
value: 233
- name: HIPBLAS_COMMON_PIPELINE_ID
value: 223
value: 300
- name: HIPBLAS_PIPELINE_ID
value: 87
- name: HIPBLASLT_PIPELINE_ID
value: 112
value: 301
- name: HIPCUB_PIPELINE_ID
value: 277
- name: HIPFFT_PIPELINE_ID
@@ -86,7 +86,7 @@ variables:
- name: HIPSPARSE_PIPELINE_ID
value: 83
- name: HIPSPARSELT_PIPELINE_ID
value: 104
value: 309
- name: HIPTENSOR_PIPELINE_ID
value: 105
- name: LLVM_PROJECT_PIPELINE_ID
@@ -104,7 +104,7 @@ variables:
- name: ROCALUTION_PIPELINE_ID
value: 89
- name: ROCBLAS_PIPELINE_ID
value: 85
value: 302
- name: ROCDBGAPI_PIPELINE_ID
value: 135
- name: ROCDECODE_PIPELINE_ID
@@ -154,7 +154,7 @@ variables:
- name: ROCSOLVER_PIPELINE_ID
value: 81
- name: ROCSPARSE_PIPELINE_ID
value: 98
value: 314
- name: ROCTHRUST_PIPELINE_ID
value: 276
- name: ROCTRACER_PIPELINE_ID

3
.wordlist.txt
View File

@@ -721,11 +721,13 @@ linearized
linter
linux
llvm
lm
localscratch
logits
lossy
macOS
matchers
megatron
microarchitecture
migraphx
migratable
@@ -797,6 +799,7 @@ quantile
quantizer
quasirandom
queueing
qwen
radeon
rccl
rdc

108
CHANGELOG.md
View File

@@ -190,9 +190,8 @@ for a complete overview of this release.
* When using the `--follow` flag with `amd-smi ras --cper`, CPER entries are not streamed continuously as intended. This will be fixed in an upcoming ROCm release.
```{note}
See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
```
> [!NOTE]
> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
### **HIP** (6.4.1)
@@ -273,9 +272,8 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
- Fixed partition enumeration. It now refers to the correct DRM Render and Card paths.
```{note}
See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
```
> [!NOTE]
> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
### **ROCm Systems Profiler** (1.0.1)
@@ -413,9 +411,8 @@ Some workaround options are as follows:
- The `pasid` field in struct `amdsmi_process_info_t` will be deprecated in a future ROCm release.
```{note}
See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
```
> [!NOTE]
> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
### **AMDMIGraphX** (2.12.0)
@@ -1023,9 +1020,8 @@ The following lists the backward incompatible changes planned for upcoming major
- Fixed `rsmi_dev_target_graphics_version_get`, `rocm-smi --showhw`, and `rocm-smi --showprod` not displaying graphics version correctly for Instinct MI200 series, MI100 series, and RDNA3-based GPUs.
```{note}
See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
```
> [!NOTE]
> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
### **ROCm Systems Profiler** (1.0.0)
@@ -1451,9 +1447,8 @@ for a complete overview of this release.
* Fixed `amd-smi monitor`'s reporting of encode and decode information. `VCLOCK` and `DCLOCK` are
now associated with both `ENC_UTIL` and `DEC_UTIL`.
```{note}
See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
```
> [!NOTE]
> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
### **HIP** (6.3.1)
@@ -1657,9 +1652,8 @@ for a complete overview of this release.
- The new partition command can display GPU information, including memory and accelerator partition information.
- The command will be at full functionality once additional partition information from `amdsmi_get_gpu_accelerator_partition_profile()` has been implemented.
```{note}
See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
```
> [!NOTE]
> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
### **HIP** (6.3.0)
@@ -1793,18 +1787,17 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
* Support for `fp8` data types
### **hipRAND** (2.11.0[*](#id22))
### **hipRAND** (2.11.0)
> [!NOTE]
> In ROCm 6.3.0, the hipRAND package version is incorrectly set to `2.11.0`.
> In ROCm 6.2.4, the hipRAND package version was `2.11.1`.
> The hipRAND version number will be corrected in a future ROCm release.
#### Changed
* Updated the default value for the `-a` argument from `rmake.py` to `gfx906:xnack-,gfx1030,gfx1100,gfx1101,gfx1102`.
#### Known issues
* In ROCm 6.3.0, the hipRAND package version is incorrectly set to `2.11.0`. In ROCm
6.2.4, the hipRAND package version was `2.11.1`. The hipRAND version number will be corrected in a
future ROCm release.
#### Resolved issues
* Fixed an issue in `rmake.py` where the list storing the CMake options would contain individual characters instead of a full string of options.
@@ -2005,7 +1998,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
#### Known issues
* See [MIVisionX memory access fault in Canny edge detection](#mivisionx-memory-access-fault-in-canny-edge-detection).
* See [MIVisionX memory access fault in Canny edge detection](https://github.com/ROCm/ROCm/issues/4086).
* Package installation requires the manual installation of OpenCV.
* Installation on CentOS/RedHat/SLES requires the manual installation of the `FFMPEG Dev` package.
* Hardware decode requires installation with `--usecase=graphics` in addition to `--usecase=rocm`.
@@ -2196,9 +2189,9 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
#### Known issues
- See [ROCm Compute Profiler post-upgrade](#rocm-compute-profiler-post-upgrade).
- See [ROCm Compute Profiler post-upgrade](https://github.com/ROCm/ROCm/issues/4082).
- See [ROCm Compute Profiler CTest failure in CI](#rocm-compute-profiler-ctest-failure-in-ci).
- See [ROCm Compute Profiler CTest failure in CI](https://github.com/ROCm/ROCm/issues/4085).
### **ROCm Data Center Tool** (0.3.0)
@@ -2211,7 +2204,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
#### Known issues
- See [ROCm Data Center Tool incorrect RHEL9 package version](#rocm-data-center-tool-incorrect-rhel9-package-version).
- See [ROCm Data Center Tool incorrect RHEL9 package version](https://github.com/ROCm/ROCm/issues/4089).
### **ROCm SMI** (7.4.0)
@@ -2249,9 +2242,8 @@ memory partition modes upon an invalid argument return from memory partition mod
- C++ tests for `memorypartition_read_write` are to be re-enabled in a future ROCm release.
```{note}
See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.x/CHANGELOG.md) for more details and examples.
```
> [!NOTE]
> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.x/CHANGELOG.md) for more details and examples.
### **ROCm Systems Profiler** (0.1.0)
@@ -2265,7 +2257,7 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.
#### Known issues
- See [ROCm Systems Profiler post-upgrade](#rocm-systems-profiler-post-upgrade).
- See [ROCm Systems Profiler post-upgrade](https://github.com/ROCm/ROCm/issues/4083).
### **ROCm Validation Suite** (1.1.0)
@@ -2279,7 +2271,7 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.
#### Known issues
- See [ROCm Validation Suite needs specified configuration file](#rocm-validation-suite-needs-specified-configuration-file).
- See [ROCm Validation Suite needs specified configuration file](https://github.com/ROCm/ROCm/issues/4090).
### **rocPRIM** (3.3.0)
@@ -3022,10 +3014,8 @@ for a complete overview of this release.
See [issue #3500](https://github.com/ROCm/ROCm/issues/3500) on GitHub.
```{note}
See the [detailed AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/docs/6.2.0/CHANGELOG.md)
on GitHub for more information.
```
> [!NOTE]
> See the [detailed AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/docs/6.2.0/CHANGELOG.md) on GitHub for more information.
### **Composable Kernel** (1.1.0)
@@ -3624,9 +3614,8 @@ The compiler may incorrectly compile a program that uses the
the function is undefined along some path to the function. For most functions,
uninitialized inputs cause undefined behavior.
```{note}
The ``-Wall`` compilation flag prompts the compiler to generate a warning if a variable is uninitialized along some path.
```
> [!NOTE]
> The ``-Wall`` compilation flag prompts the compiler to generate a warning if a variable is uninitialized along some path.
As a workaround, initialize the parameters to ``__shfl``. For example:
@@ -3947,10 +3936,8 @@ See [issue #3498](https://github.com/ROCm/ROCm/issues/3498) on GitHub.
- Fixed Partition ID CLI output.
```{note}
See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.2.0/CHANGELOG.md)
on GitHub for more information.
```
> [!NOTE]
> See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.2.0/CHANGELOG.md) on GitHub for more information.
### **ROCm Validation Suite** (1.0.0)
@@ -4320,9 +4307,8 @@ for a complete overview of this release.
* Fixed the `amdsmitstReadWrite.TestPowerCapReadWrite` test for RDNA3, RDNA2, and MI100 devices.
* Fixed an issue with the `amdsmi_get_gpu_memory_reserved_pages` and `amdsmi_get_gpu_bad_page_info` Python interface calls.
```{note}
See the AMD SMI [detailed changelog](https://github.com/ROCm/amdsmi/blob/rocm-6.1.x/CHANGELOG.md) with code samples for more information.
```
> [!NOTE]
> See the AMD SMI [detailed changelog](https://github.com/ROCm/amdsmi/blob/rocm-6.1.x/CHANGELOG.md) with code samples for more information.
### **RCCL** (2.18.6)
@@ -4402,9 +4388,8 @@ for a complete overview of this release.
- `amd-smi bad-pages` can result in a `ValueError: Null pointer access` error when using some PMU firmware versions.
```{note}
See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
```
> [!NOTE]
> See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
### **hipBLASLt** (0.7.0)
@@ -4473,9 +4458,8 @@ See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHAN
- ROCm SMI reports GPU utilization incorrectly for RDNA3 GPUs in some situations. See the issue on [GitHub](https://github.com/ROCm/ROCm/issues/3112).
```{note}
See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
```
> [!NOTE]
> See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
## ROCm 6.1.0
@@ -5192,16 +5176,16 @@ on GitHub for a complete overview of this release.
### **rocSPARSE** (2.5.4)
##### Added
#### Added
- Added more mixed precisions for SpMV, (matrix: float, vectors: double, calculation: double) and (matrix: rocsparse_float_complex, vectors: rocsparse_double_complex, calculation: rocsparse_double_complex)
- Added support for gfx940, gfx941 and gfx942
##### Optimized
#### Optimized
- Fixed a bug in csrsm and bsrsm
##### Known issues
#### Known issues
In csritlu0, the algorithm rocsparse_itilu0_alg_sync_split_fusion has some accuracy issues to investigate with XNACK enabled. The fallback is rocsparse_itilu0_alg_sync_split.
@@ -5287,7 +5271,7 @@ on GitHub for a complete overview of this release.
### **HIP** (5.6.0)
##### Added
#### Added
- Added hipRTC support for amd_hip_fp16
- Added hipStreamGetDevice implementation to get the device associated with the stream
@@ -5296,7 +5280,7 @@ on GitHub for a complete overview of this release.
- hipArrayGetDescriptor for getting 1D or 2D array descriptor
- hipArray3DGetDescriptor to get 3D array descriptor
##### Changed
#### Changed
- hipMallocAsync to return success for zero size allocation to match hipMalloc
- Separation of hipcc perl binaries from HIP project to hipcc project. hip-devel package depends on newly added hipcc package
@@ -5601,15 +5585,15 @@ $ gcc main.c -I/opt/rocm-5.6.0/include -L/opt/rocm-5.6.0/lib -lrocprofiler64-v2
The resulting `a.out` will depend on
`/opt/rocm-5.6.0/lib/librocprofiler64.so.2`.
##### Added
#### Added
- 'end_time' need to be disabled in roctx_trace.txt
##### Optimized
#### Optimized
- Improved Test Suite
##### Resolved issues
#### Resolved issues
- rocprof in ROcm/5.4.0 gpu selector broken.
- rocprof in ROCm/5.4.1 fails to generate kernel info.

3
docs/compatibility/compatibility-matrix-historical-6.0.csv
View File

@@ -30,6 +30,9 @@ ROCm Version,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,
:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
:doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>`,2.4.0,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.2,1.2,1.2,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
,,,,,,,,,,,,,,,,,
,,,,,,,,,,,,,,,,,
1 ROCm Version 6.4.2 6.4.1 6.4.0 6.3.3 6.3.2 6.3.1 6.3.0 6.2.4 6.2.2 6.2.1 6.2.0 6.1.5 6.1.2 6.1.1 6.1.0 6.0.2 6.0.0
30 :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>` 2.6, 2.5, 2.4, 2.3 2.6, 2.5, 2.4, 2.3 2.6, 2.5, 2.4, 2.3 2.4, 2.3, 2.2, 1.13 2.4, 2.3, 2.2, 1.13 2.4, 2.3, 2.2, 1.13 2.4, 2.3, 2.2, 2.1, 2.0, 1.13 2.3, 2.2, 2.1, 2.0, 1.13 2.3, 2.2, 2.1, 2.0, 1.13 2.3, 2.2, 2.1, 2.0, 1.13 2.3, 2.2, 2.1, 2.0, 1.13 2.1, 2.0, 1.13 2.1, 2.0, 1.13 2.1, 2.0, 1.13 2.1, 2.0, 1.13 2.1, 2.0, 1.13 2.1, 2.0, 1.13
31 :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>` 2.18.1, 2.17.1, 2.16.2 2.18.1, 2.17.1, 2.16.2 2.18.1, 2.17.1, 2.16.2 2.17.0, 2.16.2, 2.15.1 2.17.0, 2.16.2, 2.15.1 2.17.0, 2.16.2, 2.15.1 2.17.0, 2.16.2, 2.15.1 2.16.1, 2.15.1, 2.14.1 2.16.1, 2.15.1, 2.14.1 2.16.1, 2.15.1, 2.14.1 2.16.1, 2.15.1, 2.14.1 2.15.0, 2.14.0, 2.13.1 2.15.0, 2.14.0, 2.13.1 2.15.0, 2.14.0, 2.13.1 2.15.0, 2.14.0, 2.13.1 2.14.0, 2.13.1, 2.12.1 2.14.0, 2.13.1, 2.12.1
32 :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>` 0.4.35 0.4.35 0.4.35 0.4.31 0.4.31 0.4.31 0.4.31 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26 0.4.26
33 :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` N/A N/A N/A N/A N/A 85f95ae N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
34 :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` 2.4.0 2.4.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
35 :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat]_ N/A N/A N/A N/A N/A N/A N/A N/A N/A 0.3.0.post0 N/A N/A N/A N/A N/A N/A
36 `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_ 1.2 1.2 1.2 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.17.3 1.14.1 1.14.1
37
38

4
docs/compatibility/compatibility-matrix.rst
View File

@@ -54,8 +54,8 @@ compatibility and system requirements.
FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
:doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 2.1, 2.0, 1.13"
:doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1"
:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.35,0.4.35,0.4.31
:doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,`85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
:doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.35,0.4.35,0.4.31
:doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,85f95ae
:doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>`,2.4.0,2.4.0,N/A
`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.2,1.2,1.17.3
,,,

206
docs/compatibility/ml-compatibility/pytorch-compatibility.rst
View File

@@ -10,10 +10,10 @@
PyTorch compatibility
********************************************************************************
`PyTorch <https://pytorch.org/>`_ is an open-source tensor library designed for
`PyTorch <https://pytorch.org/>`__ is an open-source tensor library designed for
deep learning. PyTorch on ROCm provides mixed-precision and large-scale training
using `MIOpen <https://github.com/ROCm/MIOpen>`_ and
`RCCL <https://github.com/ROCm/rccl>`_ libraries.
using `MIOpen <https://github.com/ROCm/MIOpen>`__ and
`RCCL <https://github.com/ROCm/rccl>`__ libraries.
ROCm support for PyTorch is upstreamed into the official PyTorch repository. Due
to independent compatibility considerations, this results in two distinct
@@ -27,7 +27,7 @@ release cycles for PyTorch on ROCm:
- Offers :ref:`Docker images <pytorch-docker-compat>` with ROCm and PyTorch
preinstalled.
- ROCm PyTorch repository: `<https://github.com/ROCm/pytorch>`_
- ROCm PyTorch repository: `<https://github.com/ROCm/pytorch>`__
- See the :doc:`ROCm PyTorch installation guide <rocm-install-on-linux:install/3rd-party/pytorch-install>`
to get started.
@@ -37,10 +37,10 @@ release cycles for PyTorch on ROCm:
- Provides the latest stable version of PyTorch but might not necessarily
support the latest ROCm version.
- Official PyTorch repository: `<https://github.com/pytorch/pytorch>`_
- Official PyTorch repository: `<https://github.com/pytorch/pytorch>`__
- See the `Nightly and latest stable version installation guide <https://pytorch.org/get-started/locally/>`_
or `Previous versions <https://pytorch.org/get-started/previous-versions/>`_
- See the `Nightly and latest stable version installation guide <https://pytorch.org/get-started/locally/>`__
or `Previous versions <https://pytorch.org/get-started/previous-versions/>`__
to get started.
PyTorch includes tooling that generates HIP source code from the CUDA backend.
@@ -82,7 +82,7 @@ Use cases and recommendations
use of PyTorch on the ROCm platform and focuses on efficiently leveraging AMD
GPU hardware for training and inference tasks in AI applications.
For more use cases and recommendations, see `ROCm PyTorch blog posts <https://rocm.blogs.amd.com/blog/tag/pytorch.html>`_.
For more use cases and recommendations, see `ROCm PyTorch blog posts <https://rocm.blogs.amd.com/blog/tag/pytorch.html>`__.
.. _pytorch-docker-compat:
@@ -93,9 +93,9 @@ Docker image compatibility
<i class="fab fa-docker"></i>
AMD validates and publishes `PyTorch images <https://hub.docker.com/r/rocm/pytorch>`_
AMD validates and publishes `PyTorch images <https://hub.docker.com/r/rocm/pytorch>`__
with ROCm backends on Docker Hub. The following Docker image tags and associated
inventories were tested on `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_.
inventories were tested on `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
Click |docker-icon| to view the image on Docker Hub.
.. list-table:: PyTorch Docker image components
@@ -118,121 +118,121 @@ Click |docker-icon| to view the image on Docker Hub.
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-c76af9bfb1c25b0f40d4c29e8652105c57250bf018d23ff595b06bd79666fdd7"><i class="fab fa-docker fa-lg"></i></a>
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
- 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`_
- `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.16.0 <https://github.com/openucx/ucx/tree/v1.16.0>`_
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`__
- `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.16.0 <https://github.com/openucx/ucx/tree/v1.16.0>`__
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.6.0/images/sha256-f9d226135d51831c810dcb1251636ec61f85c65fcdda03e188c053a5d4f6585b"><i class="fab fa-docker fa-lg"></i></a>
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
- 22.04
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`_
- `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`_
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`__
- `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.5.1/images/sha256-3490e74d4f43dcdb3351dd334108d1ccd47e5a687c0523a2424ac1bcdd3dd6dd"><i class="fab fa-docker fa-lg"></i></a>
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
- 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`_
- `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.10.0>`_
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`__
- `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.10.0>`__
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.5.1/images/sha256-26c5dfffb4a54625884abca83166940f17dd27bc75f1b24f6e80fbcb7d4e9afb"><i class="fab fa-docker fa-lg"></i></a>
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
- 22.04
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`_
- `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`_
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`__
- `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-f378a24561fa6efc178b6dc93fc7d82e5b93653ecd59c89d4476674d29e1284d"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
- 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`_
- `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`_
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`__
- `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`__
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-2308dbd0e650b7bf8d548575cbb6e2bdc021f9386384ce570da16d58ee684d22"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
- 22.04
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`_
- `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`_
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`__
- `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13.0>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu24.04_py3.12_pytorch_release_2.3.0/images/sha256-eefd2ab019728f91f94c5e6a9463cb0ea900b3011458d18fe5d88e50c0b57d86"><i class="fab fa-docker fa-lg"></i></a>
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`__
- 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`_
- `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`_
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`__
- `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`__
- `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.1_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-473643226ab0e93a04720b256ed772619878abf9c42b9f84828cefed522696fd"><i class="fab fa-docker fa-lg"></i></a>
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`__
- 22.04
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`_
- `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`_
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13>`_
- `master <https://bitbucket.org/icl/magma/src/master/>`_
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`_
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`_
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`_
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`__
- `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`__
- `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13>`__
- `master <https://bitbucket.org/icl/magma/src/master/>`__
- `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
- `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
- `5.3-1.0.5.0 <https://content.mellanox.com/ofed/MLNX_OFED-5.3-1.0.5.0/MLNX_OFED_LINUX-5.3-1.0.5.0-ubuntu20.04-x86_64.tgz>`__
Key ROCm libraries for PyTorch
================================================================================
@@ -248,121 +248,121 @@ feature set available to developers.
- Version
- Purpose
- Used in
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`_
* - `Composable Kernel <https://github.com/ROCm/composable_kernel>`__
- :version-ref:`"Composable Kernel" rocm_version`
- Enables faster execution of core operations like matrix multiplication
(GEMM), convolutions and transformations.
- Speeds up ``torch.permute``, ``torch.view``, ``torch.matmul``,
``torch.mm``, ``torch.bmm``, ``torch.nn.Conv2d``, ``torch.nn.Conv3d``
and ``torch.nn.MultiheadAttention``.
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
* - `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>`_
* - `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.
- Accelerates operations such as ``torch.matmul``, ``torch.mm``, and the
matrix multiplications used in convolutional and linear layers.
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
* - `hipCUB <https://github.com/ROCm/hipCUB>`__
- :version-ref:`hipCUB rocm_version`
- Provides a C++ template library for parallel algorithms for reduction,
scan, sort and select.
- Supports operations such as ``torch.sum``, ``torch.cumsum``,
``torch.sort`` irregular shapes often involve scanning, sorting, and
filtering, which hipCUB handles efficiently.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
* - `hipFFT <https://github.com/ROCm/hipFFT>`__
- :version-ref:`hipFFT rocm_version`
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
- Used in functions like the ``torch.fft`` module.
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
* - `hipRAND <https://github.com/ROCm/hipRAND>`__
- :version-ref:`hipRAND rocm_version`
- Provides fast random number generation for GPUs.
- The ``torch.rand``, ``torch.randn``, and stochastic layers like
``torch.nn.Dropout`` rely on hipRAND.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
- :version-ref:`hipSOLVER rocm_version`
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
singular value decompositions (SVD).
- Supports functions like ``torch.linalg.solve``,
``torch.linalg.eig``, and ``torch.linalg.svd``.
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
- :version-ref:`hipSPARSE rocm_version`
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
- Sparse tensor operations ``torch.sparse``.
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`_
* - `hipSPARSELt <https://github.com/ROCm/hipSPARSELt>`__
- :version-ref:`hipSPARSELt rocm_version`
- Accelerates operations on sparse matrices, such as sparse matrix-vector
or matrix-matrix products.
- Sparse tensor operations ``torch.sparse``.
* - `hipTensor <https://github.com/ROCm/hipTensor>`_
* - `hipTensor <https://github.com/ROCm/hipTensor>`__
- :version-ref:`hipTensor rocm_version`
- Optimizes for high-performance tensor operations, such as contractions.
- Accelerates tensor algebra, especially in deep learning and scientific
computing.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
* - `MIOpen <https://github.com/ROCm/MIOpen>`__
- :version-ref:`MIOpen rocm_version`
- Optimizes deep learning primitives such as convolutions, pooling,
normalization, and activation functions.
- Speeds up convolutional neural networks (CNNs), recurrent neural
networks (RNNs), and other layers. Used in operations like
``torch.nn.Conv2d``, ``torch.nn.ReLU``, and ``torch.nn.LSTM``.
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`_
* - `MIGraphX <https://github.com/ROCm/AMDMIGraphX>`__
- :version-ref:`MIGraphX rocm_version`
- Adds graph-level optimizations, ONNX models and mixed precision support
and enable Ahead-of-Time (AOT) Compilation.
- Speeds up inference models and executes ONNX models for
compatibility with other frameworks.
``torch.nn.Conv2d``, ``torch.nn.ReLU``, and ``torch.nn.LSTM``.
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`__
- :version-ref:`MIVisionX rocm_version`
- Optimizes acceleration for computer vision and AI workloads like
preprocessing, augmentation, and inferencing.
- Faster data preprocessing and augmentation pipelines for datasets like
ImageNet or COCO and easy to integrate into PyTorch's ``torch.utils.data``
and ``torchvision`` workflows.
* - `rocAL <https://github.com/ROCm/rocAL>`_
* - `rocAL <https://github.com/ROCm/rocAL>`__
- :version-ref:`rocAL rocm_version`
- Accelerates the data pipeline by offloading intensive preprocessing and
augmentation tasks. rocAL is part of MIVisionX.
- Easy to integrate into PyTorch's ``torch.utils.data`` and
``torchvision`` data load workloads.
* - `RCCL <https://github.com/ROCm/rccl>`_
* - `RCCL <https://github.com/ROCm/rccl>`__
- :version-ref:`RCCL rocm_version`
- Optimizes for multi-GPU communication for operations like AllReduce and
Broadcast.
- Distributed data parallel training (``torch.nn.parallel.DistributedDataParallel``).
Handles communication in multi-GPU setups.
* - `rocDecode <https://github.com/ROCm/rocDecode>`_
* - `rocDecode <https://github.com/ROCm/rocDecode>`__
- :version-ref:`rocDecode rocm_version`
- Provides hardware-accelerated data decoding capabilities, particularly
for image, video, and other dataset formats.
- Can be integrated in ``torch.utils.data``, ``torchvision.transforms``
and ``torch.distributed``.
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`__
- :version-ref:`rocJPEG rocm_version`
- Provides hardware-accelerated JPEG image decoding and encoding.
- GPU accelerated ``torchvision.io.decode_jpeg`` and
``torchvision.io.encode_jpeg`` and can be integrated in
``torch.utils.data`` and ``torchvision``.
* - `RPP <https://github.com/ROCm/RPP>`_
* - `RPP <https://github.com/ROCm/RPP>`__
- :version-ref:`RPP rocm_version`
- Speeds up data augmentation, transformation, and other preprocessing steps.
- Easy to integrate into PyTorch's ``torch.utils.data`` and
``torchvision`` data load workloads to speed up data processing.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
* - `rocThrust <https://github.com/ROCm/rocThrust>`__
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
- Utilized in backend operations for tensor computations requiring
parallel processing.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
* - `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
@@ -383,7 +383,7 @@ Supported data types
The tensor data type is specified using the ``dtype`` attribute or argument.
PyTorch supports many data types for different use cases.
The following table lists `torch.Tensor <https://pytorch.org/docs/stable/tensors.html>`_
The following table lists `torch.Tensor <https://pytorch.org/docs/stable/tensors.html>`__
single data types:
.. list-table::

90
docs/compatibility/ml-compatibility/tensorflow-compatibility.rst
View File

@@ -10,16 +10,16 @@
TensorFlow compatibility
*******************************************************************************
`TensorFlow <https://www.tensorflow.org/>`_ is an open-source library for
`TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
solving machine learning, deep learning, and AI problems. It can solve many
problems across different sectors and industries but primarily focuses on
neural network training and inference. It is one of the most popular and
in-demand frameworks and is very active in open-source contribution and
development.
The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`_
The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
<http://github.com/rocm/tensorflow-upstream>`_ in order to quickly add bug
<http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
fixes, updates, and support for the latest ROCM versions.
- ROCm TensorFlow release:
@@ -27,16 +27,16 @@ fixes, updates, and support for the latest ROCM versions.
- Offers :ref:`Docker images <tensorflow-docker-compat>` with
ROCm and TensorFlow pre-installed.
- ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`_
- ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`__
- See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
to get started.
- Official TensorFlow release:
- Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`_
- Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`__
- See the `TensorFlow API versions <https://www.tensorflow.org/versions>`_ list.
- See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
.. note::
@@ -54,9 +54,9 @@ Docker image compatibility
<i class="fab fa-docker"></i>
AMD validates and publishes ready-made `TensorFlow images
<https://hub.docker.com/r/rocm/tensorflow>`_ with ROCm backends on
<https://hub.docker.com/r/rocm/tensorflow>`__ with ROCm backends on
Docker Hub. The following Docker image tags and associated inventories are
validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click
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:: TensorFlow Docker image components
@@ -76,8 +76,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- dev
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
@@ -86,8 +86,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- runtime
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
@@ -96,8 +96,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- dev
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
@@ -106,8 +106,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- runtime
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
@@ -116,8 +116,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- dev
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
@@ -126,8 +126,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- runtime
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-3124/>`_
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-3124/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
@@ -136,8 +136,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- dev
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
@@ -146,8 +146,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- runtime
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
@@ -156,8 +156,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- dev
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
* - .. raw:: html
@@ -166,8 +166,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- runtime
- 24.04
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
- `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
* - .. raw:: html
@@ -176,8 +176,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.16.2 <https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.10-tf2.16-dev/images/sha256-36c4fa047c86e2470ac473ec1429aea6d4b8934b90ffeb34d1afab40e7e5b377>`__
- dev
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
* - .. raw:: html
@@ -186,8 +186,8 @@ the |docker-icon| icon to view the image on Docker Hub.
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- runtime
- 22.04
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
- `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
Critical ROCm libraries for TensorFlow
@@ -207,43 +207,43 @@ are available in ROCm :version:`rocm_version`.
- Version
- Purpose
- Used in
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
- Accelerates operations like ``tf.matmul``, ``tf.linalg.matmul``, and
other matrix multiplications commonly used in neural network layers.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- :version-ref:`hipBLASLt rocm_version`
- Extends hipBLAS with additional optimizations like fused kernels and
integer tensor cores.
- Optimizes matrix multiplications and linear algebra operations used in
layers like dense, convolutional, and RNNs in TensorFlow.
* - `hipCUB <https://github.com/ROCm/hipCUB>`_
* - `hipCUB <https://github.com/ROCm/hipCUB>`__
- :version-ref:`hipCUB rocm_version`
- Provides a C++ template library for parallel algorithms for reduction,
scan, sort and select.
- Supports operations like ``tf.reduce_sum``, ``tf.cumsum``, ``tf.sort``
and other tensor operations in TensorFlow, especially those involving
scanning, sorting, and filtering.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
* - `hipFFT <https://github.com/ROCm/hipFFT>`__
- :version-ref:`hipFFT rocm_version`
- Accelerates Fast Fourier Transforms (FFT) for signal processing tasks.
- Used for operations like signal processing, image filtering, and
certain types of neural networks requiring FFT-based transformations.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
- :version-ref:`hipSOLVER rocm_version`
- Provides GPU-accelerated direct linear solvers for dense and sparse
systems.
- Optimizes linear algebra functions such as solving systems of linear
equations, often used in optimization and training tasks.
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
* - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
- :version-ref:`hipSPARSE rocm_version`
- Optimizes sparse matrix operations for efficient computations on sparse
data.
- Accelerates sparse matrix operations in models with sparse weight
matrices or activations, commonly used in neural networks.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
* - `MIOpen <https://github.com/ROCm/MIOpen>`__
- :version-ref:`MIOpen rocm_version`
- Provides optimized deep learning primitives such as convolutions,
pooling,
@@ -251,13 +251,13 @@ are available in ROCm :version:`rocm_version`.
- Speeds up convolutional neural networks (CNNs) and other layers. Used
in TensorFlow for layers like ``tf.nn.conv2d``, ``tf.nn.relu``, and
``tf.nn.lstm_cell``.
* - `RCCL <https://github.com/ROCm/rccl>`_
* - `RCCL <https://github.com/ROCm/rccl>`__
- :version-ref:`RCCL rocm_version`
- Optimizes for multi-GPU communication for operations like AllReduce and
Broadcast.
- Distributed data parallel training (``tf.distribute.MirroredStrategy``).
Handles communication in multi-GPU setups.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
* - `rocThrust <https://github.com/ROCm/rocThrust>`__
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
@@ -278,7 +278,7 @@ The data type of a tensor is specified using the ``dtype`` attribute or
argument, and TensorFlow supports a wide range of data types for different use
cases.
The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`_
The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`__
are as follows:
.. list-table::
@@ -550,7 +550,7 @@ Use cases and recommendations
===============================================================================
* The `Training a Neural Collaborative Filtering (NCF) Recommender on an AMD
GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`_
GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`__
blog post discusses training an NCF recommender system using TensorFlow. It
explains how NCF improves traditional collaborative filtering methods by
leveraging neural networks to model non-linear user-item interactions. The
@@ -559,7 +559,7 @@ Use cases and recommendations
purchasing) and how it addresses challenges like the lack of negative values.
* The `Creating a PyTorch/TensorFlow code environment on AMD GPUs
<https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`_
<https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`__
blog post provides instructions for creating a machine learning environment
for PyTorch and TensorFlow on AMD GPUs using ROCm. It covers steps like
installing the libraries, cloning code repositories, installing dependencies,
@@ -568,4 +568,4 @@ Use cases and recommendations
for a better experience on AMD GPUs. This guide aims to help data scientists
and ML practitioners adapt their code for AMD GPUs.
For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`_.
For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`__.

2
docs/compatibility/ml-compatibility/verl-compatibility.rst
View File

@@ -16,7 +16,7 @@ verl offers a scalable, open-source fine-tuning solution optimized for AMD Insti
* See the `verl documentation <https://verl.readthedocs.io/en/latest/>`_ for more information about verl.
* The official verl GitHub repository is `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`_.
* Use the AMD-validated :ref:`Docker images <verl-docker-compat>` with ROCm and verl preinstalled.
* See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>` to get started.
* See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` to get started.
.. note::

2
docs/conceptual/gpu-arch/mi300-mi200-performance-counters.rst
View File

@@ -8,7 +8,7 @@ MI300 and MI200 series performance counters and metrics
This document lists and describes the hardware performance counters and derived metrics available
for the AMD Instinct™ MI300 and MI200 GPU. You can also access this information using the
:doc:`ROCProfiler tool <rocprofiler:rocprofv1>`.
:doc:`ROCprofiler-SDK <rocprofiler-sdk:how-to/using-rocprofv3>`.
MI300 and MI200 series performance counters
===============================================================

76
docs/conf.py
View File

@@ -12,6 +12,54 @@ from pathlib import Path
shutil.copy2("../RELEASE.md", "./about/release-notes.md")
shutil.copy2("../CHANGELOG.md", "./release/changelog.md")
# Mark the consolidated changelog as orphan to prevent Sphinx from warning about missing toctree entries
with open("./release/changelog.md", "r+") 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:
lines = file.readlines()
modified_lines = []
in_admonition_section = False
# Map for matching the specific admonition type to its corresponding Sphinx markdown syntax
admonition_types = {
'> [!NOTE]': '```{note}',
'> [!TIP]': '```{tip}',
'> [!IMPORTANT]': '```{important}',
'> [!WARNING]': '```{warning}',
'> [!CAUTION]': '```{caution}'
}
for line in lines:
if any(line.startswith(k) for k in admonition_types):
for key in admonition_types:
if(line.startswith(key)):
modified_lines.append(admonition_types[key] + '\n')
break
in_admonition_section = True
elif in_admonition_section:
if line.strip() == '':
# If we encounter an empty line, close the admonition section
modified_lines.append('```\n\n') # Close the admonition block
in_admonition_section = False
else:
modified_lines.append(line.lstrip('> '))
else:
modified_lines.append(line)
# In case the file ended while still in a admonition section, close it
if in_admonition_section:
modified_lines.append('```')
file.close()
with open("./release/changelog.md", 'w') 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")
@@ -57,10 +105,22 @@ article_pages = [
{"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/prerequisite-system-validation", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/megatron-lm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/scale-model-training", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/megatron-lm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-history", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v24.12-dev", "os": ["linux"]},
{"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/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/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"]},
{"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/fine-tuning/overview", "os": ["linux"]},
@@ -72,7 +132,16 @@ article_pages = [
{"file": "how-to/rocm-for-ai/inference/hugging-face-models", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/llm-inference-frameworks", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/vllm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-history", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.4.3", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.4", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.6", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.7.3-20250325", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.3-20250415", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250513", "os": ["linux"]},
{"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/pytorch-inference", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
@@ -129,6 +198,7 @@ html_theme_options = {"link_main_doc": False}
redirects = {"reference/openmp/openmp": "../../about/compatibility/openmp.html"}
numfig = False
suppress_warnings = ["autosectionlabel.*"]
html_context = {
"project_path" : {project_path},

162
docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.0.1_20250605-benchmark-models.yaml Normal file
View File

@@ -0,0 +1,162 @@
vllm_benchmark:
unified_docker:
latest:
pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c
rocm_version: 6.4.1
vllm_version: 0.9.0.1 (0.9.0.2.dev108+g71faa1880.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

163
docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250702-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_20250702
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250702/images/sha256-45068a2079cb8df554ed777141bf0c67d6627c470a897256e60c9f262677faab
rocm_version: 6.4.1
vllm_version: 0.9.1 (0.9.2.dev206+gb335519f2.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

8
docs/data/how-to/rocm-for-ai/inference/pytorch-inference-benchmark-models.yaml
View File

@@ -31,3 +31,11 @@ pytorch_inference_benchmark:
model_repo: genmo/mochi-1-preview
url: https://huggingface.co/genmo/mochi-1-preview
precision: float16
- group: Wan2.1
tag: wan
models:
- model: Wan2.1
mad_tag: pyt_wan2.1_inference
model_repo: Wan-AI/Wan2.1-T2V-14B
url: https://huggingface.co/Wan-AI/Wan2.1-T2V-14B
precision: bfloat16

7
docs/data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
View File

@@ -1,10 +1,11 @@
vllm_benchmark:
unified_docker:
latest:
pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605
docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c
# 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: 0.9.0.1 (0.9.0.2.dev108+g71faa1880.rocm641)
vllm_version: 0.9.1 (0.9.2.dev364+gb432b7a28.rocm641)
pytorch_version: 2.7.0+gitf717b2a
hipblaslt_version: 0.15
model_groups:

57
docs/data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
View File

@@ -1,29 +1,60 @@
megatron-lm_benchmark:
model_groups:
- group: Meta Llama
tag: llama
models:
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
- 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:
- group: Mistral AI
tag: mistral
models:
- model: Mixtral 8x7B
mad_tag: pyt_megatron_lm_train_mixtral-8x7b
- model: Mixtral 8x22B
- 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

29
docs/data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.5-benchmark-models.yaml Normal file
View File

@@ -0,0 +1,29 @@
megatron-lm_benchmark:
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 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
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
mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy

16
docs/how-to/gpu-performance/mi300x.rst
View File

@@ -7,21 +7,21 @@ AMD Instinct MI300X performance guides
**************************************
The following performance guides provide essential guidance on the necessary
steps to properly :doc:`configure your system for AMD Instinct™ MI300X
accelerators <../system-optimization/mi300x>`. They include detailed
instructions on system settings and application :doc:`workload tuning
<../rocm-for-ai/inference-optimization/workload>` to help you
leverage the maximum capabilities of these accelerators and achieve superior
performance.
steps to properly `configure your system for AMD Instinct™ MI300X accelerators
<https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
They include detailed instructions on system settings and application
:doc:`workload tuning </how-to/rocm-for-ai/inference-optimization/workload>` to
help you leverage the maximum capabilities of these accelerators and achieve
superior performance.
* `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__
covers essential system settings and system management practices to configure
your AMD Instinct MI300X system for performance.
* :doc:`../rocm-for-ai/inference-optimization/workload` covers steps to
* :doc:`/how-to/rocm-for-ai/inference-optimization/workload` covers steps to
optimize the performance of AMD Instinct MI300X series accelerators for HPC
and deep learning operations.
* :doc:`../rocm-for-ai/inference/vllm-benchmark` introduces a preconfigured
* :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/vllm` introduces a preconfigured
environment for LLM inference, designed to help you test performance with
popular models on AMD Instinct MI300X series accelerators.

2
docs/how-to/rocm-for-ai/fine-tuning/index.rst
View File

@@ -24,5 +24,3 @@ training, fine-tuning, and inference. It leverages popular machine learning fram
- :doc:`Fine-tuning and inference <fine-tuning-and-inference>` using a
:doc:`single-accelerator <single-gpu-fine-tuning-and-inference>` or
:doc:`multi-accelerator <multi-gpu-fine-tuning-and-inference>` system.

2
docs/how-to/rocm-for-ai/index.rst
View File

@@ -6,7 +6,7 @@
Use ROCm for AI
**************************
ROCm™ is an open-source software platform that enables high-performance computing and machine learning applications. It features the ability to accelerate training, fine-tuning, and inference for AI application development. With ROCm, you can access the full power of AMD GPUs, which can significantly improve the performance and efficiency of AI workloads.
ROCm is an open-source software platform that enables high-performance computing and machine learning applications. It features the ability to accelerate training, fine-tuning, and inference for AI application development. With ROCm, you can access the full power of AMD GPUs, which can significantly improve the performance and efficiency of AI workloads.
You can use ROCm to perform distributed training, which enables you to train models across multiple GPUs or nodes simultaneously. Additionally, ROCm supports mixed-precision training, which can help reduce the memory and compute requirements of training workloads. For fine-tuning, ROCm provides access to various algorithms and optimization techniques. In terms of inference, ROCm provides several techniques that can help you optimize your models for deployment, such as quantization, GEMM tuning, and optimization with composable kernel.

22
docs/how-to/rocm-for-ai/inference-optimization/workload.rst
View File

@@ -151,8 +151,8 @@ desired effect. Continuous iteration helps refine the performance gains and
address any new bottlenecks that may emerge.
ROCm provides a prebuilt optimized Docker image that has everything required to implement
the tips in this section. It includes ROCm, vLLM, PyTorch, and tuning files in the CSV
format. For more information, see :doc:`../inference/vllm-benchmark`.
the LLM inference tips in this section. It includes ROCm, PyTorch, and vLLM.
For more information, see :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/vllm`.
.. _mi300x-profiling-tools:
@@ -343,9 +343,10 @@ The following performance tips are not *specific* to vLLM -- they are general
but relevant in this context. You can tune the following vLLM parameters to
achieve optimal request latency and throughput performance.
* As described in :ref:`mi300x-env-vars`, the environment
variable ``HIP_FORCE_DEV_KERNARG`` can improve vLLM performance. Set it to
``export HIP_FORCE_DEV_KERNARG=1``.
* As described in `Environment variables (MI300X)
<https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#environment-variables>`_,
the environment variable ``HIP_FORCE_DEV_KERNARG`` can improve vLLM
performance. Set it to ``export HIP_FORCE_DEV_KERNARG=1``.
* Set the :ref:`RCCL environment variable <mi300x-rccl>` ``NCCL_MIN_NCHANNELS``
to ``112`` to increase the number of channels on MI300X to potentially improve
@@ -410,9 +411,9 @@ for additional performance tips. :ref:`fine-tuning-llms-vllm` describes vLLM
usage with ROCm.
ROCm provides a prebuilt optimized Docker image for validating the performance
of LLM inference with vLLM on the MI300X accelerator. The Docker image includes
ROCm, vLLM, PyTorch, and tuning files in the CSV format. For more information,
see :doc:`../inference/vllm-benchmark`.
of LLM inference with vLLM on MI300X series accelerators. The Docker image includes
ROCm, vLLM, and PyTorch. For more information, see
:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/vllm`.
.. _mi300x-vllm-throughput-measurement:
@@ -1477,8 +1478,9 @@ following command: ``cat /proc/sys/kernel/numa_balancing`` and
checking whether the output is ``0``.
If the output is ``1``, you can disable NUMA auto-balancing by running the
following command: ``sudo sysctl kernel.numa_balancing=0``. For more
details, see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
following command: ``sudo sysctl kernel.numa_balancing=0``. For more details,
see `AMD Instinct MI300X system optimization
<https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#disable-numa-auto-balancing>`_.
.. _mi300x-rccl-disable-acs:

38
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.4.3.rst
View File

@@ -59,7 +59,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
@@ -80,11 +80,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
Once setup is complete, you can choose between two options to reproduce the
benchmark results:
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v043>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone-v043>`
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-v043:
MAD-integrated benchmarking
===========================
@@ -112,7 +112,7 @@ model are collected in the following path: ``~/MAD/reports_float16/``
Although the following eight models are pre-configured to collect latency and
throughput performance data, users can also change the benchmarking parameters.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v043>` section.
Available models
----------------
@@ -136,7 +136,7 @@ Available models
* ``pyt_vllm_jais-30b``
.. _vllm-benchmark-standalone:
.. _vllm-benchmark-standalone-v043:
Standalone benchmarking
=======================
@@ -167,14 +167,14 @@ Command
^^^^^^^^^^^^^^^^^^^^^^^^^
To start the benchmark, use the following command with the appropriate options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-options-v043>` for the list of
options and their descriptions.
.. code-block:: shell
./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
See the :ref:`examples <vllm-benchmark-run-benchmark-v043>` for more information.
.. note::
@@ -193,7 +193,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. _vllm-benchmark-standalone-options:
.. _vllm-benchmark-standalone-options-v043:
Options
^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -265,13 +265,13 @@ Options
- ``float16``
- Data type
.. _vllm-benchmark-run-benchmark:
.. _vllm-benchmark-run-benchmark-v043:
Running the benchmark on the MI300X accelerator
-----------------------------------------------
Here are some examples of running the benchmark with various options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-options-v043>` for the list of
options and their descriptions.
Latency benchmark example
@@ -322,22 +322,22 @@ Further reading
===============
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- 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 system settings and management practices to configure your system for
MI300X accelerators, see :doc:`/how-to/system-optimization/mi300x`.
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Using ROCm for AI </how-to/rocm-for-ai/index>`.
- 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 optimize inference on LLMs, see
:doc:`Fine-tuning LLMs and inference optimization </how-to/llm-fine-tuning-optimization/index>`.
- 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 ROCm, see the
:doc:`Docker image support matrix <rocm-install-on-linux:reference/docker-image-support-matrix>`.
- 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
=================

41
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.4.rst
View File

@@ -82,7 +82,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
@@ -103,11 +103,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
Once setup is complete, you can choose between two options to reproduce the
benchmark results:
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v064>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone-v064>`
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-v064:
MAD-integrated benchmarking
===========================
@@ -135,7 +135,7 @@ model are collected in the following path: ``~/MAD/reports_float16/``.
Although the following models are preconfigured to collect latency and
throughput performance data, you can also change the benchmarking parameters.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v064>` section.
Available models
----------------
@@ -177,7 +177,7 @@ Available models
* ``pyt_vllm_mixtral-8x22b_fp8``
.. _vllm-benchmark-standalone:
.. _vllm-benchmark-standalone-v064:
Standalone benchmarking
=======================
@@ -203,14 +203,14 @@ Command
-------
To start the benchmark, use the following command with the appropriate options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-v064-options>` for the list of
options and their descriptions.
.. code-block:: shell
./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
See the :ref:`examples <vllm-benchmark-run-benchmark-v064>` for more information.
.. note::
@@ -229,7 +229,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. _vllm-benchmark-standalone-options:
.. _vllm-benchmark-standalone-v064-options:
Options
-------
@@ -330,13 +330,13 @@ Options
- ``float16`` or ``float8``
- Data type
.. _vllm-benchmark-run-benchmark:
.. _vllm-benchmark-run-benchmark-v064:
Running the benchmark on the MI300X accelerator
-----------------------------------------------
Here are some examples of running the benchmark with various options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-v064-options>` for the list of
options and their descriptions.
Example 1: latency benchmark
@@ -392,25 +392,22 @@ Further reading
===============
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- 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 system settings and management practices to configure your system for
MI300X accelerators, see :doc:`/how-to/system-optimization/mi300x`.
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Using ROCm for AI </how-to/rocm-for-ai/index>`.
- 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 optimize inference on LLMs, see
:doc:`Fine-tuning LLMs and inference optimization </how-to/llm-fine-tuning-optimization/index>`.
- 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 ROCm, see the
:doc:`Docker image support matrix <rocm-install-on-linux:reference/docker-image-support-matrix>`.
- To compare with the previous version of the ROCm vLLM Docker image for performance validation, refer to
`LLM inference performance validation on AMD Instinct MI300X (ROCm 6.2.0) <https://rocm.docs.amd.com/en/docs-6.2.0/how-to/performance-validation/mi300x/vllm-benchmark.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
=================

52
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.6.rst
View File

@@ -31,8 +31,8 @@ accelerator and includes the following components:
With this Docker image, you can quickly validate the expected inference
performance numbers for the MI300X accelerator. This topic also provides tips on
optimizing performance with popular AI models. For more information, see the lists of
:ref:`available models for MAD-integrated benchmarking <vllm-benchmark-mad-models>`
and :ref:`standalone benchmarking <vllm-benchmark-standalone-options>`.
:ref:`available models for MAD-integrated benchmarking <vllm-benchmark-mad-v066-models>`
and :ref:`standalone benchmarking <vllm-benchmark-standalone-v066-options>`.
.. _vllm-benchmark-vllm:
@@ -55,7 +55,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
@@ -76,11 +76,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
Once the setup is complete, choose between two options to reproduce the
benchmark results:
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
- :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v066>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
- :ref:`Standalone benchmarking <vllm-benchmark-standalone-v066>`
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-v066:
MAD-integrated benchmarking
===========================
@@ -108,9 +108,9 @@ model are collected in the following path: ``~/MAD/reports_float16/``.
Although the following models are preconfigured to collect latency and
throughput performance data, you can also change the benchmarking parameters.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v066>` section.
.. _vllm-benchmark-mad-models:
.. _vllm-benchmark-mad-v066-models:
Available models
----------------
@@ -134,10 +134,10 @@ Available models
* - `Llama 3.2 11B Vision <https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct>`_
- ``pyt_vllm_llama-3.2-11b-vision-instruct``
* - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
* - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__
- ``pyt_vllm_llama-2-7b``
* - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
* - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`__
- ``pyt_vllm_llama-2-70b``
* - `Mixtral MoE 8x7B <https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1>`_
@@ -194,7 +194,7 @@ Available models
* - `C4AI Command R+ 08-2024 FP8 <https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV>`_
- ``pyt_vllm_command-r-plus_fp8``
.. _vllm-benchmark-standalone:
.. _vllm-benchmark-standalone-v066:
Standalone benchmarking
=======================
@@ -220,14 +220,14 @@ Command
-------
To start the benchmark, use the following command with the appropriate options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-v066-options>` for the list of
options and their descriptions.
.. code-block:: shell
./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
See the :ref:`examples <vllm-benchmark-run-benchmark-v066>` for more information.
.. note::
@@ -246,7 +246,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. _vllm-benchmark-standalone-options:
.. _vllm-benchmark-standalone-v066-options:
Options and available models
----------------------------
@@ -289,11 +289,11 @@ Options and available models
* -
- ``meta-llama/Llama-2-7b-chat-hf``
- `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
- `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__
* -
- ``meta-llama/Llama-2-70b-chat-hf``
- `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
- `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`__
* -
- ``mistralai/Mixtral-8x7B-Instruct-v0.1``
@@ -375,13 +375,13 @@ Options and available models
- ``float16`` or ``float8``
- Data type
.. _vllm-benchmark-run-benchmark:
.. _vllm-benchmark-run-benchmark-v066:
Running the benchmark on the MI300X accelerator
-----------------------------------------------
Here are some examples of running the benchmark with various options.
See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
See :ref:`Options <vllm-benchmark-standalone-v066-options>` for the list of
options and their descriptions.
Example 1: latency benchmark
@@ -437,22 +437,22 @@ Further reading
===============
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- 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 system settings and management practices to configure your system for
MI300X accelerators, see :doc:`../../system-optimization/mi300x`.
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../fine-tuning/index>`.
- 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
=================

36
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.7.3-20250325.rst
View File

@@ -36,10 +36,10 @@ vLLM inference performance testing
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
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-v073>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models:
.. _vllm-benchmark-available-models-v073:
Available models
================
@@ -95,7 +95,7 @@ vLLM inference performance testing
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements:
.. _vllm-benchmark-performance-measurements-v073:
Performance measurements
========================
@@ -109,7 +109,7 @@ vLLM inference performance testing
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 :doc:`latest version of this inference benchmarking environment <../vllm>`_.
only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
Advanced features and known issues
@@ -130,7 +130,7 @@ vLLM inference performance testing
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
@@ -154,7 +154,7 @@ vLLM inference performance testing
Once the setup is complete, choose between two options to reproduce the
benchmark results:
.. _vllm-benchmark-mad:
.. _vllm-benchmark-mad-v073:
{% for model_group in model_groups %}
{% for model in model_group.models %}
@@ -175,7 +175,7 @@ vLLM inference performance testing
pip install -r requirements.txt
Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the ``{{model.precision}}`` data type on the host machine.
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
@@ -186,7 +186,7 @@ vLLM inference performance testing
``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>` are preconfigured
Although the :ref:`available models <vllm-benchmark-available-models-v073>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
@@ -264,7 +264,7 @@ vLLM inference performance testing
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with the ``{{model.precision}}`` data type.
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with the :literal:`{{model.precision}}` data type.
.. code-block::
@@ -274,7 +274,7 @@ vLLM inference performance testing
* Throughput benchmark
Use this command to throughput the latency of the {{model.model}} model on eight GPUs with the ``{{model.precision}}`` data type.
Use this command to throughput the latency of the {{model.model}} model on eight GPUs with the :literal:`{{model.precision}}` data type.
.. code-block:: shell
@@ -305,22 +305,22 @@ Further reading
===============
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- 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 system settings and management practices to configure your system for
MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../fine-tuning/index>`.
- 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
=================

34
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.3-20250415.rst
View File

@@ -1,3 +1,5 @@
:orphan:
.. meta::
:description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
ROCm vLLM Docker image.
@@ -29,10 +31,10 @@ vLLM inference performance testing
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
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-v083>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models:
.. _vllm-benchmark-available-models-v083:
Supported models
================
@@ -88,7 +90,7 @@ vLLM inference performance testing
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements:
.. _vllm-benchmark-performance-measurements-v083:
Performance measurements
========================
@@ -102,7 +104,7 @@ vLLM inference performance testing
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 :doc:`latest version of this inference benchmarking environment <../vllm>`_.
only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
Advanced features and known issues
@@ -170,7 +172,7 @@ vLLM inference performance testing
pip install -r requirements.txt
Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the ``{{model.precision}}`` data type on the host machine.
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
@@ -181,7 +183,7 @@ vLLM inference performance testing
``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>` are preconfigured
Although the :ref:`available models <vllm-benchmark-available-models-v083>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
@@ -278,7 +280,7 @@ vLLM inference performance testing
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
@@ -288,7 +290,7 @@ vLLM inference performance testing
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
@@ -319,22 +321,22 @@ Further reading
===============
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- 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 system settings and management practices to configure your system for
MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../fine-tuning/index>`.
- 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
=================

32
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250513.rst
View File

@@ -36,10 +36,10 @@ vLLM inference performance testing
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
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-v085-20250513>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models:
.. _vllm-benchmark-available-models-v085-20250513:
Supported models
================
@@ -99,7 +99,7 @@ vLLM inference performance testing
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements:
.. _vllm-benchmark-performance-measurements-v085-20250513:
Performance measurements
========================
@@ -113,7 +113,7 @@ vLLM inference performance testing
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 :doc:`latest version of this inference benchmarking environment <../vllm>`_.
only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
Advanced features and known issues
@@ -181,7 +181,7 @@ vLLM inference performance testing
pip install -r requirements.txt
Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the ``{{model.precision}}`` data type on the host machine.
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
@@ -192,7 +192,7 @@ vLLM inference performance testing
``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>` are preconfigured
Although the :ref:`available models <vllm-benchmark-available-models-v085-20250513>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
@@ -289,7 +289,7 @@ vLLM inference performance testing
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
@@ -299,7 +299,7 @@ vLLM inference performance testing
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
@@ -333,19 +333,19 @@ Further reading
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
- To learn more about system settings and management practices to configure your system for
MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
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:`../../../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <../../hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../../../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../../../fine-tuning/index>`.
- 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
=================

31
docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250521.rst
View File

@@ -36,10 +36,10 @@ vLLM inference performance testing
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
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-v085-20250521>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models:
.. _vllm-benchmark-available-models-v085-20250521:
Supported models
================
@@ -99,7 +99,7 @@ vLLM inference performance testing
See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
more information.
.. _vllm-benchmark-performance-measurements:
.. _vllm-benchmark-performance-measurements-v085-20250521:
Performance measurements
========================
@@ -181,7 +181,7 @@ vLLM inference performance testing
pip install -r requirements.txt
Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the ``{{model.precision}}`` data type on the host machine.
using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
.. code-block:: shell
@@ -192,7 +192,7 @@ vLLM inference performance testing
``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>` are preconfigured
Although the :ref:`available models <vllm-benchmark-available-models-v085-20250521>` are preconfigured
to collect latency and throughput performance data, you can also change the benchmarking
parameters. See the standalone benchmarking tab for more information.
@@ -289,7 +289,7 @@ vLLM inference performance testing
* Latency benchmark
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block::
@@ -299,7 +299,7 @@ vLLM inference performance testing
* Throughput benchmark
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
.. code-block:: shell
@@ -333,22 +333,23 @@ Further reading
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
- To learn more about system settings and management practices to configure your system for
MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
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:`../../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <../hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../../fine-tuning/index>`.
- 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.

353
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View File

@@ -0,0 +1,353 @@
: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:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.0.1_20250605-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:
* `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
* `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
* `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements-v0901-20250605>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models-v0901-20250605:
Supported models
================
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:
{% 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-v0901-20250605:
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.
Advanced features and known issues
==================================
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/7bb0618b1fe725b7d4fad9e525aa44da12c94a8b/docs/dev-docker>`__.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
# disable automatic NUMA balancing
sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
# check if NUMA balancing is disabled (returns 0 if disabled)
cat /proc/sys/kernel/numa_balancing
0
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.
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:
{% 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
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
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"
python3 tools/run_models.py --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-v0901-20250605>` 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, edit the default run behavior in the ``models.json``
configuration before running inference -- update the model's run
``args`` by changing ``--tunableop off`` to ``--tunableop on``.
Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
{% endif %}
.. tab-item:: Standalone benchmarking
Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. code-block::
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 }}
In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
.. code-block::
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
To start the benchmark, use the following command with the appropriate options.
.. code-block::
./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
.. list-table::
:header-rows: 1
:align: center
* - Name
- Options
- Description
* - ``$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
.. note::
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::
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
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 %}
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 system settings and management practices to configure your system for
MI300X 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.

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@@ -0,0 +1,353 @@
: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:
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250702-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:
* `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
* `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
* `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements-20250702>` for
MI300X series accelerators.
.. _vllm-benchmark-available-models-20250702:
Supported models
================
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:
{% 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-20250702:
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.
Advanced features and known issues
==================================
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/5486e7bc8523be0324ccd68f221959445b56cc2a/docs/dev-docker>`__.
System validation
=================
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
# disable automatic NUMA balancing
sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
# check if NUMA balancing is disabled (returns 0 if disabled)
cat /proc/sys/kernel/numa_balancing
0
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.
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:
{% 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
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
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"
python3 tools/run_models.py --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-20250702>` 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, edit the default run behavior in the ``models.json``
configuration before running inference -- update the model's run
``args`` by changing ``--tunableop off`` to ``--tunableop on``.
Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
{% endif %}
.. tab-item:: Standalone benchmarking
Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. code-block::
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 }}
In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
.. code-block::
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
To start the benchmark, use the following command with the appropriate options.
.. code-block::
./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
.. list-table::
:header-rows: 1
:align: center
* - Name
- Options
- Description
* - ``$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
.. note::
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::
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
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 %}
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 system settings and management practices to configure your system for
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.

108
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View File

@@ -7,69 +7,103 @@ 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/r/rocm/vllm/tags>`__.
.. list-table::
:header-rows: 1
:stub-columns: 1
* - ROCm version
- vLLM version
- PyTorch version
* - Docker image tag
- Components
- Resources
* - 6.4.0
- 0.9.0.1
- 2.7.0
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715``
(latest)
-
* ROCm 6.4.1
* vLLM 0.9.1
* PyTorch 2.7.0
-
* :doc:`Documentation <../vllm>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250521/images/sha256-38410c51af7208897cd8b737c9bdfc126e9bc8952d4aa6b88c85482f03092a11>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea>`__
* - 6.3.1
- 0.8.5 (0.8.6.dev)
- 2.7.0
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.1_20250702``
-
* ROCm 6.4.1
* vLLM 0.9.1
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.9.1-20250702>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250702/images/sha256-45068a2079cb8df554ed777141bf0c67d6627c470a897256e60c9f262677faab>`__
* - ``rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605``
-
* ROCm 6.4.1
* vLLM 0.9.0.1
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.9.0.1-20250605>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c>`__
* - ``rocm/vllm:rocm6.3.1_vllm_0.8.5_20250521``
-
* ROCm 6.3.1
* 0.8.5 vLLM (0.8.6.dev)
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.8.5-20250521>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250521/images/sha256-38410c51af7208897cd8b737c9bdfc126e9bc8952d4aa6b88c85482f03092a11>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250521/images/sha256-38410c51af7208897cd8b737c9bdfc126e9bc8952d4aa6b88c85482f03092a11>`__
* - 6.3.1
- 0.8.5
- 2.7.0
* - ``rocm/vllm:rocm6.3.1_vllm_0.8.5_20250513``
-
* ROCm 6.3.1
* vLLM 0.8.5
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.8.5-20250513>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250513/images/sha256-5c8b4436dd0464119d9df2b44c745fadf81512f18ffb2f4b5dc235c71ebe26b4>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250513/images/sha256-5c8b4436dd0464119d9df2b44c745fadf81512f18ffb2f4b5dc235c71ebe26b4>`__
* - 6.3.1
- 0.8.3
- 2.7.0
* - ``rocm/vllm:rocm6.3.1_instinct_vllm0.8.3_20250415``
-
* ROCm 6.3.1
* vLLM 0.8.3
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.8.3-20250415>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.8.3_20250415/images/sha256-ad9062dea3483d59dedb17c67f7c49f30eebd6eb37c3fac0a171fb19696cc845>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.8.3_20250415/images/sha256-ad9062dea3483d59dedb17c67f7c49f30eebd6eb37c3fac0a171fb19696cc845>`__
* - 6.3.1
- 0.7.3
- 2.7.0
* - ``rocm/vllm:rocm6.3.1_instinct_vllm0.7.3_20250325``
-
* ROCm 6.3.1
* vLLM 0.7.3
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.7.3-20250325>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.7.3_20250325/images/sha256-25245924f61750b19be6dcd8e787e46088a496c1fe17ee9b9e397f3d84d35640>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.7.3_20250325/images/sha256-25245924f61750b19be6dcd8e787e46088a496c1fe17ee9b9e397f3d84d35640>`__
* - 6.3.1
- 0.6.6
- 2.7.0
* - ``rocm/vllm:rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6``
-
* ROCm 6.3.1
* vLLM 0.6.6
* PyTorch 2.7.0
-
* :doc:`Documentation <vllm-0.6.6>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6/images/sha256-9a12ef62bbbeb5a4c30a01f702c8e025061f575aa129f291a49fbd02d6b4d6c9>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_mi300_ubuntu22.04_py3.12_vllm_0.6.6/images/sha256-9a12ef62bbbeb5a4c30a01f702c8e025061f575aa129f291a49fbd02d6b4d6c9>`__
* - 6.2.1
- 0.6.4
- 2.5.0
* - ``rocm/vllm:rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4``
-
* ROCm 6.2.1
* vLLM 0.6.4
* PyTorch 2.5.0
-
* :doc:`Documentation <vllm-0.6.4>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4/images/sha256-ccbb74cc9e7adecb8f7bdab9555f7ac6fc73adb580836c2a35ca96ff471890d8>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu20.04_py3.9_vllm_0.6.4/images/sha256-ccbb74cc9e7adecb8f7bdab9555f7ac6fc73adb580836c2a35ca96ff471890d8>`__
* - 6.2.0
- 0.4.3
- 2.4.0
* - ``rocm/vllm:rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50``
-
* ROCm 6.2.0
* vLLM 0.4.3
* PyTorch 2.4.0
-
* :doc:`Documentation <vllm-0.4.3>`
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50/images/sha256-9e4dd4788a794c3d346d7d0ba452ae5e92d39b8dfac438b2af8efdc7f15d22c0>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/vllm/rocm6.2_mi300_ubuntu22.04_py3.9_vllm_7c5fd50/images/sha256-9e4dd4788a794c3d346d7d0ba452ae5e92d39b8dfac438b2af8efdc7f15d22c0>`__

10
docs/how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
View File

@@ -32,10 +32,10 @@ PyTorch inference performance testing
<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="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<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>
@@ -93,7 +93,7 @@ PyTorch inference performance testing
.. container:: model-doc pyt_chai1_inference
Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`_ from Docker Hub.
Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`__ from Docker Hub.
.. code-block:: shell
@@ -103,9 +103,9 @@ PyTorch inference performance testing
The Chai-1 benchmark uses a specifically selected Docker image using ROCm 6.2.3 and PyTorch 2.3.0 to address an accuracy issue.
.. container:: model-doc pyt_clip_inference pyt_mochi_video_inference
.. container:: model-doc pyt_clip_inference pyt_mochi_video_inference pyt_wan2.1_inference
Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`_ from Docker Hub.
Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`__ from Docker Hub.
.. code-block:: shell

355
docs/how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
View File

@@ -20,23 +20,55 @@ vLLM inference performance testing
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
accelerators and includes the following components:
* `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
.. list-table::
:header-rows: 1
* `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
* - Software component
- Version
* `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
* - `ROCm <https://github.com/ROCm/ROCm>`__
- {{ unified_docker.rocm_version }}
* `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
* - `vLLM <https://docs.vllm.ai/en/latest>`__
- {{ unified_docker.vllm_version }}
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements>` for
MI300X series accelerators.
* - `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>` for
MI300X series accelerators.
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.
* 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/vllm-benchmark-models.yaml
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
.. _vllm-benchmark-available-models:
Supported models
================
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.
@@ -44,18 +76,18 @@ vLLM 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 group</div>
<div class="row col-10">
<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>
</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 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 %}
@@ -66,8 +98,8 @@ vLLM inference performance testing
{% endif %}
{% endfor %}
{% endfor %}
</div>
</div>
</div>
</div>
</div>
.. _vllm-benchmark-vllm:
@@ -85,56 +117,48 @@ vLLM inference performance testing
{% endfor %}
{% endfor %}
.. note::
.. 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 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:
Performance measurements
========================
Performance measurements
========================
To evaluate performance, the
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>`_
page provides reference throughput and latency measurements for inferencing popular AI models.
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.
.. important::
System validation
=================
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.
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
Advanced features and known issues
==================================
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.
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/7bb0618b1fe725b7d4fad9e525aa44da12c94a8b/docs/dev-docker>`__.
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.
System validation
=================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
Before running AI workloads, it's important to validate that your AMD hardware is configured
correctly and performing optimally.
To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
might hang until the periodic balancing is finalized. For more information,
see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
.. code-block:: shell
# disable automatic NUMA balancing
sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
# check if NUMA balancing is disabled (returns 0 if disabled)
cat /proc/sys/kernel/numa_balancing
0
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.
{% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
{% set model_groups = data.vllm_benchmark.model_groups %}
Pull the Docker image
=====================
@@ -163,22 +187,26 @@ vLLM inference performance testing
.. 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.
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
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
git clone https://github.com/ROCm/MAD
cd MAD
pip install -r requirements.txt
Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
using one GPU with the ``{{model.precision}}`` data type on the host machine.
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
.. code-block:: shell
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
python3 tools/run_models.py --tags {{model.mad_tag}} --keep-model-dir --live-output --timeout 28800
export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
python3 tools/run_models.py \
--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
@@ -209,93 +237,125 @@ vLLM inference performance testing
.. tab-item:: Standalone benchmarking
Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
.. rubric:: Download the Docker image and required scripts
.. code-block::
1. Run the vLLM benchmark tool independently by starting the
`Docker container <{{ unified_docker.docker_hub_url }}>`_
as shown in the following snippet.
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 }}
.. code-block:: shell
In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
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 }}
.. code-block::
2. In the Docker container, clone the ROCm MAD repository and navigate to the
benchmark scripts directory at ``~/MAD/scripts/vllm``.
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
.. code-block:: shell
To start the benchmark, use the following command with the appropriate options.
git clone https://github.com/ROCm/MAD
cd MAD/scripts/vllm
.. code-block::
3. To start the benchmark, use the following command with the appropriate options.
./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
.. dropdown:: Benchmark options
:open:
.. list-table::
:header-rows: 1
:align: center
.. list-table::
:header-rows: 1
:align: center
* - Name
- Options
- Description
* - Name
- Options
- Description
* - ``$test_option``
- latency
- Measure decoding token latency
* - ``$test_option``
- latency
- Measure decoding token latency
* -
- throughput
- Measure token generation throughput
* -
- throughput
- Measure token generation throughput
* -
- all
- Measure both throughput and latency
* -
- all
- Measure both throughput and latency
* - ``$num_gpu``
- 1 or 8
- Number of GPUs
* - ``$num_gpu``
- 1 or 8
- Number of GPUs
* - ``$datatype``
- ``float16`` or ``float8``
- Data type
* - ``$datatype``
- ``float16`` or ``float8``
- Data type
.. note::
The input sequence length, output sequence length, and tensor parallel (TP) are
already configured. You don't need to specify them with this script.
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::
If you encounter the following error, pass your access-authorized Hugging
Face token to the gated models.
Command:
.. code-block::
OSError: You are trying to access a gated repo.
./vllm_benchmark_report.sh \
-s $test_option \
-m {{model.model_repo}} \
-g $num_gpu \
-d {{model.precision}}
# pass your HF_TOKEN
export HF_TOKEN=$your_personal_hf_token
.. note::
Here are some examples of running the benchmark with various options.
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 ``{{model.precision}}`` precision.
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}}
./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 ``{{model.precision}}`` precision.
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}}
./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``.
@@ -318,6 +378,41 @@ vLLM inference performance testing
{% 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
===============
@@ -325,22 +420,22 @@ Further reading
see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
- To learn more about system settings and management practices to configure your system for
MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
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:`../../inference-optimization/workload`.
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
- To learn how to run LLM models from Hugging Face or your own model, see
:doc:`Running models from Hugging Face <../hugging-face-models>`.
- 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 optimize inference on LLMs, see
:doc:`Inference optimization <../../inference-optimization/index>`.
- 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>`.
- To learn how to fine-tune LLMs, see
:doc:`Fine-tuning LLMs <../../fine-tuning/index>`.
- 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:`previous-versions/vllm-history` to find documentation for previous releases
of the ``ROCm/vllm`` Docker image.
of the ``ROCm/vllm`` Docker image.

6
docs/how-to/rocm-for-ai/inference/index.rst
View File

@@ -14,14 +14,14 @@ Throughout the following topics, this section provides a comprehensive guide to
The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
- :doc:`Installing ROCm and machine learning frameworks <install>`
- :doc:`Installing ROCm and machine learning frameworks <../install>`
- :doc:`Running models from Hugging Face <hugging-face-models>`
- :doc:`LLM inference frameworks <llm-inference-frameworks>`
- :doc:`vLLM inference performance testing <vllm-benchmark>`
- :doc:`vLLM inference performance testing <benchmark-docker/vllm>`
- :doc:`PyTorch inference performance testing <pytorch-inference-benchmark>`
- :doc:`PyTorch inference performance testing <benchmark-docker/pytorch-inference>`
- :doc:`Deploying your model <deploy-your-model>`

2
docs/how-to/rocm-for-ai/inference/llm-inference-frameworks.rst
View File

@@ -141,7 +141,7 @@ Installing vLLM
ROCm provides a prebuilt optimized Docker image for validating the performance of LLM inference with vLLM
on the MI300X accelerator. The Docker image includes ROCm, vLLM, and PyTorch.
For more information, see :doc:`vllm-benchmark`.
For more information, see :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/vllm`.
.. _fine-tuning-llms-tgi:

2
docs/how-to/rocm-for-ai/install.rst
View File

@@ -28,6 +28,8 @@ ROCm supports multiple :doc:`installation methods <rocm-install-on-linux:install
* :doc:`Using your Linux distribution's package manager <rocm-install-on-linux:install/install-methods/package-manager-index>`
* :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/install-methods/amdgpu-installer-index>`
* :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`
.. grid:: 1

481
docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
View File

@@ -15,57 +15,51 @@ purpose-built to support models like Llama, DeepSeek, and Mixtral,
enabling developers to train next-generation AI models more
efficiently.
AMD provides a ready-to-use Docker image for MI300X series accelerators containing
AMD provides ready-to-use Docker images for MI300X series accelerators containing
essential components, including PyTorch, ROCm libraries, and Megatron-LM
utilities. It contains the following software components to accelerate training
workloads:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| PyTorch | 2.8.0a0+gite2f9759 |
+--------------------------+--------------------------------+
| Python | 3.12 or 3.10 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.13.0+bb061ade |
+--------------------------+--------------------------------+
| Flash Attention | 3.0.0 |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-4f18bf6 |
+--------------------------+--------------------------------+
| Triton | 3.3.0 |
+--------------------------+--------------------------------+
| RCCL | 2.22.3 |
+--------------------------+--------------------------------+
Megatron-LM provides the following key features to train large language models efficiently:
- Transformer Engine (TE)
- APEX
- GEMM tuning
- Torch.compile
- 3D parallelism: TP + SP + CP
- Distributed optimizer
- Flash Attention (FA) 3
- Fused kernels
- Pre-training
.. _amd-megatron-lm-model-support:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
.. 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 %}
.. tab-item:: ``{{ docker.pull_tag }}``
: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 %}
{% 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
================
@@ -73,8 +67,7 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
Some instructions, commands, and training recommendations in this documentation might
vary by model -- select one to get started.
{% set model_groups = data["megatron-lm_benchmark"].model_groups %}
{% set model_groups = data.model_groups %}
.. raw:: html
<div id="vllm-benchmark-ud-params-picker" class="container-fluid">
@@ -82,7 +75,7 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
<div class="col-2 me-2 model-param-head">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
<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>
@@ -115,14 +108,14 @@ 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>`_
`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.
.. 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>`_
`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 training benchmarking environment.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
@@ -155,27 +148,77 @@ image.
Download the Docker image
-------------------------
1. Use the following command to pull the Docker image from Docker Hub.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
.. tab-set::
{% set dockers = data.dockers %}
1. Use the following command to pull the Docker image from Docker Hub.
.. tab-item:: Ubuntu 24.04 + Python 3.12
{% if dockers|length > 1 %}
.. tab-set::
.. code-block:: shell
{% for docker in data.dockers %}
.. tab-item:: {{ docker.doc_name }}
:sync: {{ docker.pull_tag }}
docker pull rocm/megatron-lm:v25.5_py312
.. code-block:: shell
.. tab-item:: Ubuntu 22.04 + Python 3.10
docker pull {{ docker.pull_tag }}
.. code-block:: shell
{% endfor %}
{% elif dockers|length == 1 %}
{% set docker = dockers[0] %}
.. code-block:: shell
docker pull rocm/megatron-lm:v25.5_py310
docker pull {{ docker.pull_tag }}
2. Launch the Docker container.
{% endif %}
2. Launch the Docker container.
.. code-block:: shell
{% if dockers|length > 1 %}
.. tab-set::
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 -v $HOME/.ssh:/root/.ssh --shm-size 64G --name megatron_training_env rocm/megatron-lm:v25.5
{% for docker in data.dockers %}
.. tab-item:: {{ docker.doc_name }}
:sync: {{ docker.pull_tag }}
.. 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 \
-v $HOME/.ssh:/root/.ssh \
--shm-size 128G \
--name megatron_training_env \
{{ docker.pull_tag }}
{% endfor %}
{% elif dockers|length == 1 %}
{% set docker = dockers[0] %}
.. 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 \
-v $HOME/.ssh:/root/.ssh \
--shm-size 128G \
--name megatron_training_env \
{{ docker.pull_tag }}
{% endif %}
3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
@@ -333,6 +376,22 @@ If the tokenizer is not found, it'll be downloaded if publicly available.
TOKENIZER_MODEL=tokenizer/tokenizer.model
.. container:: model-doc pyt_megatron_lm_train_qwen2.5-7b
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="Qwen/Qwen2.5-7B"
.. container:: model-doc pyt_megatron_lm_train_qwen2.5-72b
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="Qwen/Qwen2.5-72B"
Dataset options
---------------
@@ -358,7 +417,7 @@ You can use either mock data or real data for training.
Download the dataset
^^^^^^^^^^^^^^^^^^^^
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b pyt_megatron_lm_train_llama-3.1-70b-proxy
For Llama models, use the `prepare_dataset.sh
<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`_ script
@@ -397,8 +456,8 @@ Download the dataset
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
cd ..
bash tools/run_make_pretraining_dataset_megatron.sh deepseek-datasets/SlimPajama.json DeepSeekV3Tokenizer text deepseek-datasets deepseek-ai/DeepSeek-V3
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
@@ -422,8 +481,8 @@ Download the dataset
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
cd ..
bash tools/run_make_pretraining_dataset_megatron.sh deepseek-datasets/SlimPajama.json DeepSeekV3Tokenizer text deepseek-datasets deepseek-ai/DeepSeek-V3
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
@@ -433,8 +492,6 @@ Download the dataset
DATA_DIR="<path-to>/deepseek-datasets" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
If you don't already have the dataset, download the Mixtral dataset using the following
@@ -457,6 +514,27 @@ Download the dataset
Ensure that the files are accessible inside the Docker container.
.. container:: model-doc pyt_megatron_lm_train_qwen2.5-7b pyt_megatron_lm_train_qwen2.5-72b
If you don't already have the dataset, download the Mixtral dataset using the following
commands:
.. code-block:: shell
mkdir -p temp/qwen-datasets
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/qwen-datasets/wudao_qwenbpe_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/qwen-datasets/wudao_qwenbpe_text_document.idx
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
.. code-block:: bash
MOCK_DATA=0 # Train on real data
DATA_DIR="<path-to>/qwen-datasets" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
Multi-node configuration
------------------------
@@ -497,27 +575,17 @@ also be passed as command line arguments. Refer to the following example configu
# Specify which RDMA interfaces to use for communication
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
Getting started
===============
The prebuilt Megatron-LM with ROCm training environment allows users to quickly validate
system performance, conduct training benchmarks, and achieve superior
performance for models like Llama, DeepSeek, and Mixtral. This container should not be
expected to provide generalized performance across all training workloads. You
can expect the container to perform in the model configurations described in
the following section, but other configurations are not validated by AMD.
.. _amd-megatron-lm-run-training:
Run training
------------
============
Use the following example commands to set up the environment, configure
:ref:`key options <amd-megatron-lm-benchmark-test-vars>`, and run training on
MI300X series accelerators with the AMD Megatron-LM environment.
Single node training
^^^^^^^^^^^^^^^^^^^^
--------------------
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
@@ -526,7 +594,20 @@ Single node training
.. code-block:: shell
TEE_OUTPUT=1 RECOMPUTE=1 SEQ_LENGTH=8192 MBS=2 BS=16 TE_FP8=0 TP=1 PP=1 FSDP=1 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
TOKENIZER_MODEL=meta-llama/Llama-3.3-70B-Instruct \
CKPT_FORMAT=torch_dist \
TEE_OUTPUT=1 \
RECOMPUTE=1 \
SEQ_LENGTH=8192 \
MBS=2 \
BS=16 \
TE_FP8=0 \
TP=1 \
PP=1 \
FSDP=1 \
MODEL_SIZE=70 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama3.sh
.. note::
@@ -535,8 +616,6 @@ Single node training
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
To run training on a single node for Llama 3.1 8B FP8, navigate to the Megatron-LM folder and use the
@@ -544,13 +623,29 @@ Single node training
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
TEE_OUTPUT=1 \
MBS=2 \
BS=128 \
TP=1 \
TE_FP8=1 \
SEQ_LENGTH=8192 \
MODEL_SIZE=8 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama3.sh
For Llama 3.1 8B BF16, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=0 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
TEE_OUTPUT=1 \
MBS=2 \
BS=128 \
TP=1 \
TE_FP8=0 \
SEQ_LENGTH=8192 \
MODEL_SIZE=8 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama3.sh
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
@@ -559,7 +654,18 @@ Single node training
.. code-block:: shell
TEE_OUTPUT=1 MBS=3 BS=24 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=8192 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
CKPT_FORMAT=torch_dist \
TEE_OUTPUT=1 \
MBS=3 \
BS=24 \
TP=1 \
TE_FP8=0 \
FSDP=1 \
RECOMPUTE=1 \
SEQ_LENGTH=8192 \
MODEL_SIZE=70 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama3.sh
.. note::
@@ -568,7 +674,36 @@ Single node training
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b-proxy
To run the training on a single node for Llama 3.1 70B with proxy, use the following command.
.. code-block:: shell
CKPT_FORMAT=torch_dist \
TEE_OUTPUT=1 \
RECOMPUTE=1 \
MBS=3 \
BS=24 \
TP=1 \
TE_FP8=1 \
SEQ_LENGTH=8192 \
MODEL_SIZE=70 \
FSDP=1 \
TOTAL_ITERS=10 \
NUM_LAYERS=40 \
bash examples/llama/train_llama3.sh
.. note::
Use two or more nodes to run the *full* Llama 70B model with FP8 precision.
.. note::
It is suggested to use ``TP=1`` when FSDP is enabled for higher
throughput. FSDP-v2 is not supported with pipeline parallelism, expert
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
.. container:: model-doc pyt_megatron_lm_train_llama-2-7b
@@ -577,13 +712,29 @@ Single node training
.. code-block:: shell
TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
TEE_OUTPUT=1 \
MBS=4 \
BS=256 \
TP=1 \
TE_FP8=1 \
SEQ_LENGTH=4096 \
MODEL_SIZE=7 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama2.sh
For Llama 2 7B BF16, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=0 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
TEE_OUTPUT=1 \
MBS=4 \
BS=256 \
TP=1 \
TE_FP8=0 \
SEQ_LENGTH=4096 \
MODEL_SIZE=7 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama2.sh
.. container:: model-doc pyt_megatron_lm_train_llama-2-70b
@@ -592,7 +743,18 @@ Single node training
.. code-block:: shell
TEE_OUTPUT=1 MBS=7 BS=56 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=4096 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
CKPT_FORMAT=torch_dist \
TEE_OUTPUT=1 \
MBS=7 \
BS=56 \
TP=1 \
TE_FP8=0 \
FSDP=1 \
RECOMPUTE=1 \
SEQ_LENGTH=4096 \
MODEL_SIZE=70 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama2.sh
.. note::
@@ -601,8 +763,6 @@ Single node training
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc 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,
@@ -610,7 +770,8 @@ Single node training
.. code-block:: shell
FORCE_BANLANCE=true \
export NVTE_FUSED_ATTN_CK=0
FORCE_BALANCE=true \
RUN_ENV=cluster \
MODEL_SIZE=671B \
TRAIN_ITERS=50 \
@@ -632,7 +793,15 @@ Single node training
.. code-block:: shell
GEMM_TUNING=1 PR=bf16 MBS=4 AC=none SEQ_LEN=4096 PAD_LEN=4096 TRAIN_ITERS=50 bash examples/deepseek_v2/train_deepseekv2.sh
export NVTE_FUSED_ATTN_CK=0
GEMM_TUNING=1 \
PR=bf16 \
MBS=4 \
AC=none \
SEQ_LEN=4096 \
PAD_LEN=4096 \
TRAIN_ITERS=50 \
bash examples/deepseek_v2/train_deepseekv2.sh
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
@@ -641,7 +810,24 @@ Single node training
.. code-block:: shell
RECOMPUTE_NUM_LAYERS=0 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=none PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=4096 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x7B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
TOKENIZER_MODEL=<path/to/tokenizer/model>
RECOMPUTE_NUM_LAYERS=0 \
TEE_OUTPUT=1 \
MBS=1 \
GBS=16 \
TP_SIZE=1 \
PP_SIZE=1 \
AC=none \
PR=bf16 \
EP_SIZE=8 \
ETP_SIZE=1 \
SEQLEN=4096 \
FORCE_BALANCE=true \
MOCK_DATA=1 \
RUN_ENV=cluster \
MODEL_SIZE=8x7B \
TRAIN_ITERS=50 \
bash examples/mixtral/train_mixtral_moe.sh
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x22b-proxy
@@ -650,10 +836,85 @@ Single node training
.. code-block:: shell
RECOMPUTE_NUM_LAYERS=4 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=full NUM_LAYERS=4 PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=8192 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x22B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
TOKENIZER_MODEL=<path/to/tokenizer/model>
RECOMPUTE_NUM_LAYERS=4 \
TEE_OUTPUT=1 \
MBS=1 \
GBS=16 \
TP_SIZE=1 \
PP_SIZE=1 \
AC=full \
NUM_LAYERS=4 \
PR=bf16 \
EP_SIZE=8 \
ETP_SIZE=1 \
SEQLEN=8192 \
FORCE_BALANCE=true \
MOCK_DATA=1 \
RUN_ENV=cluster \
MODEL_SIZE=8x22B \
TRAIN_ITERS=50 \
bash examples/mixtral/train_mixtral_moe.sh
Multi-node training
^^^^^^^^^^^^^^^^^^^
.. container:: model-doc 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
bash examples/qwen/train_qwen2.sh TP=1 \
CP=1 \
PP=1 \
MBS=10 \
BS=640 \
TE_FP8=0 \
MODEL_SIZE=7 \
SEQ_LENGTH=2048 \
TOTAL_ITERS=50 \
MOCK_DATA=1 \
TOKENIZER_MODEL=Qwen/Qwen2.5-7B
For FP8, use the following command.
.. code-block:: shell
bash examples/qwen/train_qwen2.sh \
TP=1 \
CP=1 \
PP=1 \
MBS=10 \
BS=640 \
TE_FP8=1 \
MODEL_SIZE=7 \
SEQ_LENGTH=2048 \
TOTAL_ITERS=50 \
MOCK_DATA=1 \
TOKENIZER_MODEL=Qwen/Qwen2.5-7B
.. container:: model-doc 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
bash examples/qwen/train_qwen2.sh \
FSDP=1 \
CP=1 \
PP=1 \
MBS=3 \
BS=24 \
TE_FP8=0 \
MODEL_SIZE=72 \
SEQ_LENGTH=2048 \
TOTAL_ITERS=50 \
MOCK_DATA=1 \
TOKENIZER_MODEL=Qwen/Qwen2.5-72B \
RECOMPUTE_ACTIVATIONS=full \
CKPT_FORMAT=torch_dist
Multi-node training examples
----------------------------
To run training on multiple nodes, launch the Docker container on each node.
For example, for Llama 3 using a two node setup (``NODE0`` as the master node),
@@ -663,13 +924,33 @@ use these commands.
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=0 bash examples/llama/train_llama3.sh
TEE_OUTPUT=1 \
MBS=2 \
BS=256 \
TP=1 \
TE_FP8=1 \
SEQ_LENGTH=8192 \
MODEL_SIZE=8 \
MASTER_ADDR=IP_NODE0 \
NNODES=2 \
NODE_RANK=0 \
bash examples/llama/train_llama3.sh
* On the worker node ``NODE1``:
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=1 bash examples/llama/train_llama3.sh
TEE_OUTPUT=1 \
MBS=2 \
BS=256 \
TP=1 \
TE_FP8=1 \
SEQ_LENGTH=8192 \
MODEL_SIZE=8 \
MASTER_ADDR=IP_NODE0 \
NNODES=2 \
NODE_RANK=1 \
bash examples/llama/train_llama3.sh
Or, for DeepSeek-V3, an example script ``train_deepseek_v3_slurm.sh`` is
provided in

20
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history.rst
View File

@@ -12,23 +12,23 @@ previous releases of the ``ROCm/jax-training`` Docker image on `Docker Hub <http
.. list-table::
:header-rows: 1
:stub-columns: 1
* - Image version
- ROCm version
- JAX version
- Components
- Resources
* - 25.5
- 6.3.4
- 0.4.35
* - 25.5 (latest)
-
* ROCm 6.3.4
* JAX 0.4.35
-
* :doc:`Documentation <../jax-maxtext>`
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b>`__
* - 25.4
- 6.3.0
- 0.4.31
-
* ROCm 6.3.0
* JAX 0.4.31
-
* :doc:`Documentation <jax-maxtext-v25.4>`
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.4/images/sha256-fb3eb71cd74298a7b3044b7130cf84113f14d518ff05a2cd625c11ea5f6a7b01>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.4/images/sha256-fb3eb71cd74298a7b3044b7130cf84113f14d518ff05a2cd625c11ea5f6a7b01>`__

10
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4.rst
View File

@@ -96,14 +96,14 @@ 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-v254:
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-download-docker-v254`.
1. Install the following packages to build and install the RDMA driver.
@@ -168,7 +168,7 @@ 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-v254>` are installed on all nodes.
Then, set the RDMA interfaces to use for communication.
.. code-block:: bash
@@ -178,7 +178,7 @@ 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-download-docker-v254:
Download the Docker image
-------------------------
@@ -195,7 +195,7 @@ Download the Docker image
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-v25.4
.. _amd-maxtext-get-started:
.. _amd-maxtext-get-started-v254:
Getting started
===============

48
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-history.rst
View File

@@ -7,41 +7,53 @@ Megatron-LM training performance testing version history
This table lists previous versions of the ROCm Megatron-LM training 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/megatron-lm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`_.
previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`__.
.. list-table::
:header-rows: 1
:stub-columns: 1
* - Image version
- ROCm version
- PyTorch version
- Components
- Resources
* - v25.5
- 6.3.4
- 2.8.0a0+gite2f9759
* - v25.6 (latest)
-
* `Documentation <../megatron-lm>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py312/images/sha256-4506f18ba188d24189c6b1f95130b425f52c528a543bb3f420351824edceadc2>`_
* ROCm 6.4.1
* PyTorch 2.8.0a0+git7d205b2
-
* :doc:`Documentation <../megatron-lm>`
* `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>`__
* - v25.5
-
* ROCm 6.3.4
* PyTorch 2.8.0a0+gite2f9759
-
* :doc:`Documentation <megatron-lm-v25.5>`
* `Docker Hub (py312) <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py312/images/sha256-4506f18ba188d24189c6b1f95130b425f52c528a543bb3f420351824edceadc2>`__
* `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py310/images/sha256-743fbf1ceff7a44c4452f938d783a7abf143737d1c15b2b95f6f8a62e0fd048b>`__
* - v25.4
- 6.3.0
- 2.7.0a0+git637433
-
* ROCm 6.3.0
* PyTorch 2.7.0a0+git637433
-
* :doc:`Documentation <megatron-lm-v25.4>`
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.4/images/sha256-941aa5387918ea91c376c13083aa1e6c9cab40bb1875abbbb73bbb65d8736b3f>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.4/images/sha256-941aa5387918ea91c376c13083aa1e6c9cab40bb1875abbbb73bbb65d8736b3f>`__
* - v25.3
- 6.3.0
- 2.7.0a0+git637433
-
* ROCm 6.3.0
* PyTorch 2.7.0a0+git637433
-
* :doc:`Documentation <megatron-lm-v25.3>`
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.3/images/sha256-1e6ed9bdc3f4ca397300d5a9907e084ab5e8ad1519815ee1f868faf2af1e04e2>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.3/images/sha256-1e6ed9bdc3f4ca397300d5a9907e084ab5e8ad1519815ee1f868faf2af1e04e2>`__
* - v24.12-dev
- 6.1.0
- 2.4.0
-
* ROCm 6.1.0
* PyTorch 2.4.0
-
* :doc:`Documentation <megatron-lm-v24.12-dev>`
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/24.12-dev/images/sha256-5818c50334ce3d69deeeb8f589d83ec29003817da34158ebc9e2d112b929bf2e>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/24.12-dev/images/sha256-5818c50334ce3d69deeeb8f589d83ec29003817da34158ebc9e2d112b929bf2e>`__

35
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v24.12-dev.rst
View File

@@ -102,7 +102,8 @@ the output is ``1``, run the following command to disable NUMA auto-balancing.
sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
See :ref:`mi300x-disable-numa` for more information.
See :ref:`System validation and optimization <rocm-for-ai-system-optimization>`
for more information.
Hardware verification with ROCm
-------------------------------
@@ -118,7 +119,7 @@ Run the command:
rocm-smi --setperfdeterminism 1900
See :ref:`mi300x-hardware-verification-with-rocm` for more information.
See `Hardware verification with ROCm <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#hardware-verification-with-rocm>`_ for more information.
RCCL Bandwidth Test
-------------------
@@ -171,7 +172,7 @@ Run on 8 GPUs (``-g 8``), scanning from 8 bytes to 10 GB:
./build/all_reduce_perf -b 8 -e 10G -f 2 -g 8
.. image:: ../../data/how-to/rocm-for-ai/rccl-tests-8-gpu.png
.. image:: /data/how-to/rocm-for-ai/rccl-tests-8-gpu.png
:width: 800
Using one MPI process per GPU and ``-g 1`` for performance-oriented runs on both single-node and multi-node is
@@ -181,7 +182,7 @@ recommended. So, a run on 8 GPUs looks something like:
mpirun -np 8 --bind-to numa ./build/all_reduce_perf -b 8 -e 10G -f 2 -g 1
.. image:: ../../data/how-to/rocm-for-ai/rccl-tests-1-mpi-process-per-gpu.png
.. image:: /data/how-to/rocm-for-ai/rccl-tests-1-mpi-process-per-gpu.png
:width: 800
Running with one MPI process per GPU ensures a one-to-one mapping for CPUs and GPUs, which can be beneficial
@@ -202,10 +203,10 @@ Use the following script to run the RCCL test for four MI300X GPU nodes. Modify
-x NCCL_DEBUG=version \
$HOME/rccl-tests/build/all_reduce_perf -b 8 -e 8g -f 2 -g 1
.. image:: ../../data/how-to/rocm-for-ai/rccl-tests-4-mi300x-gpu-nodes.png
.. image:: /data/how-to/rocm-for-ai/rccl-tests-4-mi300x-gpu-nodes.png
:width: 800
.. _mi300x-amd-megatron-lm-training:
.. _mi300x-amd-megatron-lm-training-v2412:
Start training on MI300X accelerators
=====================================
@@ -217,7 +218,7 @@ Use the following instructions to set up the environment, configure the script t
reproduce the benchmark results on the MI300X accelerators with the AMD Megatron-LM Docker
image.
.. _amd-megatron-lm-requirements:
.. _amd-megatron-lm-requirements-v2412:
Download the Docker image and required packages
-----------------------------------------------
@@ -271,10 +272,10 @@ end-of-document token, remove sentence splitting, and use the tokenizer type.
In this case, the automatically generated output files are named ``my-gpt2_text_document.bin`` and
``my-gpt2_text_document.idx``.
.. image:: ../../data/how-to/rocm-for-ai/prep-training-datasets-my-gpt2-text-document.png
.. image:: /data/how-to/rocm-for-ai/prep-training-datasets-my-gpt2-text-document.png
:width: 800
.. _amd-megatron-lm-environment-setup:
.. _amd-megatron-lm-environment-setup-v2412:
Environment setup
-----------------
@@ -374,19 +375,19 @@ Run benchmark tests
NODE_RANK="${NODE_RANK:-0}"
* Use this command to run a performance benchmark test of any of the Llama 2 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars>`).
* Use this command to run a performance benchmark test of any of the Llama 2 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars-v2412>`).
.. code-block:: shell
{variables} bash examples/llama/train_llama2.sh
* Use this command to run a performance benchmark test of any of the Llama 3 and Llama 3.1 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars>`).
* Use this command to run a performance benchmark test of any of the Llama 3 and Llama 3.1 models that this Docker image supports (see :ref:`variables <amd-megatron-lm-benchmark-test-vars-v2412>`).
.. code-block:: shell
{variables} bash examples/llama/train_llama3.sh
.. _amd-megatron-lm-benchmark-test-vars:
.. _amd-megatron-lm-benchmark-test-vars-v2412:
The benchmark tests support the same set of variables:
@@ -465,11 +466,11 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v2412>`.
See the sample output:
.. image:: ../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
.. image:: /data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
:width: 800
.. tab-item:: Multi node training
@@ -494,18 +495,18 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v2412>`.
Sample output for 2-node training:
Master node:
.. image:: ../../data/how-to/rocm-for-ai/2-node-training-master.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-master.png
:width: 800
Worker node:
.. image:: ../../data/how-to/rocm-for-ai/2-node-training-worker.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-worker.png
:width: 800
Previous versions

21
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3.rst
View File

@@ -111,9 +111,10 @@ the output is ``1``, run the following command to disable NUMA auto-balancing.
sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
See :ref:`mi300x-disable-numa` for more information.
See :ref:`System validation and optimization <rocm-for-ai-system-optimization>`
for more information.
.. _mi300x-amd-megatron-lm-training:
.. _mi300x-amd-megatron-lm-training-v253:
Environment setup
=================
@@ -125,7 +126,7 @@ Use the following instructions to set up the environment, configure the script t
reproduce the benchmark results on the MI300X accelerators with the AMD Megatron-LM Docker
image.
.. _amd-megatron-lm-requirements:
.. _amd-megatron-lm-requirements-v253:
Download the Docker image
-------------------------
@@ -151,7 +152,7 @@ Download the Docker image
The Docker container includes a pre-installed, verified version of Megatron-LM from the `release branch <https://github.com/ROCm/Megatron-LM/tree/megatron_release_v25.3>`_.
.. _amd-megatron-lm-environment-setup:
.. _amd-megatron-lm-environment-setup-v253:
Configuration scripts
---------------------
@@ -395,7 +396,7 @@ accelerators with the AMD Megatron-LM Docker image.
Key options
-----------
.. _amd-megatron-lm-benchmark-test-vars:
.. _amd-megatron-lm-benchmark-test-vars-v253:
The benchmark tests support the following sets of variables:
@@ -485,11 +486,11 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v253>`.
See the sample output:
.. image:: ../../../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
.. image:: /data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
:width: 800
.. tab-item:: Multi-node training
@@ -514,18 +515,18 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v253>`.
Sample output for 2-node training:
Master node:
.. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-master.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-master.png
:width: 800
Worker node:
.. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-worker.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-worker.png
:width: 800
Previous versions

26
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4.rst
View File

@@ -90,21 +90,21 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
Some models, such as Llama, require an external license agreement through
a third party (for example, Meta).
.. _amd-megatron-lm-performance-measurements:
.. _amd-megatron-lm-performance-measurements-v254:
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>`_
`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.
.. 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 :doc:`latest version of this training benchmarking environment <../megatron-lm>`_.
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`__
only reflects the :doc:`latest version of this training benchmarking environment <../megatron-lm>`.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
System validation
@@ -115,7 +115,7 @@ auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
and optimization steps <train-a-model-system-validation>` to set up your system
before starting training.
.. _mi300x-amd-megatron-lm-training:
.. _mi300x-amd-megatron-lm-training-v254:
Environment setup
=================
@@ -127,7 +127,7 @@ Use the following instructions to set up the environment, configure the script t
reproduce the benchmark results on MI300X series accelerators with the AMD Megatron-LM Docker
image.
.. _amd-megatron-lm-requirements:
.. _amd-megatron-lm-requirements-v254:
Download the Docker image
-------------------------
@@ -154,7 +154,7 @@ Download the Docker image
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>`__
(commit `fd6f01 <https://github.com/ROCm/Megatron-LM/tree/fd6f0d11d7f9480ace32f22eb7e4dab5314fa350>`_).
.. _amd-megatron-lm-environment-setup:
.. _amd-megatron-lm-environment-setup-v254:
Configuration scripts
---------------------
@@ -468,7 +468,7 @@ accelerators with the AMD Megatron-LM Docker image.
Key options
-----------
.. _amd-megatron-lm-benchmark-test-vars:
.. _amd-megatron-lm-benchmark-test-vars-v254:
The benchmark tests support the following sets of variables:
@@ -568,11 +568,11 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v254>`.
See the sample output:
.. image:: ../../../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
.. image:: /data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
:width: 800
.. tab-item:: Multi-node training
@@ -597,18 +597,18 @@ Benchmarking examples
TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v254>`.
Sample output for 2-node training:
Master node:
.. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-master.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-master.png
:width: 800
Worker node:
.. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-worker.png
.. image:: /data/how-to/rocm-for-ai/2-node-training-worker.png
:width: 800
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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 Megatron-LM for ROCm
******************************************
.. caution::
This documentation does not reflect the latest version of ROCm Megatron-LM
training performance documentation. See :doc:`../megatron-lm` for the latest version.
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
Instinct™ MI300X series accelerators, Megatron-LM delivers enhanced
scalability, performance, and resource utilization for AI workloads. It is
purpose-built to support models like Llama, DeepSeek, and Mixtral,
enabling developers to train next-generation AI models more
efficiently.
AMD provides a ready-to-use Docker image for MI300X series accelerators containing
essential components, including PyTorch, ROCm libraries, and Megatron-LM
utilities. It contains the following software components to accelerate training
workloads:
+--------------------------+--------------------------------+
| Software component | Version |
+==========================+================================+
| ROCm | 6.3.4 |
+--------------------------+--------------------------------+
| PyTorch | 2.8.0a0+gite2f9759 |
+--------------------------+--------------------------------+
| Python | 3.12 or 3.10 |
+--------------------------+--------------------------------+
| Transformer Engine | 1.13.0+bb061ade |
+--------------------------+--------------------------------+
| Flash Attention | 3.0.0 |
+--------------------------+--------------------------------+
| hipBLASLt | 0.13.0-4f18bf6 |
+--------------------------+--------------------------------+
| Triton | 3.3.0 |
+--------------------------+--------------------------------+
| RCCL | 2.22.3 |
+--------------------------+--------------------------------+
Megatron-LM provides the following key features to train large language models efficiently:
- Transformer Engine (TE)
- APEX
- GEMM tuning
- Torch.compile
- 3D parallelism: TP + SP + CP
- Distributed optimizer
- Flash Attention (FA) 3
- Fused kernels
- Pre-training
.. _amd-megatron-lm-model-support-v255:
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.5-benchmark-models.yaml
Supported models
================
The following models are supported for training performance benchmarking with Megatron-LM and ROCm.
Some instructions, commands, and training recommendations in this documentation might
vary by model -- select one to get started.
{% set model_groups = data["megatron-lm_benchmark"].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">Model</div>
<div class="row col-10">
{% for model_group in model_groups %}
<div class="col-4 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">
{% 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, such as Llama, require an external license agreement through
a third party (for example, Meta).
.. _amd-megatron-lm-performance-measurements-v255:
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.
.. 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 training 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.
.. _mi300x-amd-megatron-lm-training-v255:
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 AMD Megatron-LM Docker
image.
.. _amd-megatron-lm-requirements-v255:
Download the Docker image
-------------------------
1. Use the following command to pull the Docker image from Docker Hub.
.. tab-set::
.. tab-item:: Ubuntu 24.04 + Python 3.12
:sync: py312
.. code-block:: shell
docker pull rocm/megatron-lm:v25.5_py312
.. tab-item:: Ubuntu 22.04 + Python 3.10
:sync: py310
.. code-block:: shell
docker pull rocm/megatron-lm:v25.5_py310
2. Launch the Docker container.
.. tab-set::
.. tab-item:: Ubuntu 24.04 + Python 3.12
:sync: py312
.. 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 -v $HOME/.ssh:/root/.ssh --shm-size 128G --name megatron_training_env rocm/megatron-lm:v25.5_py312
.. tab-item:: Ubuntu 22.04 + Python 3.10
:sync: py310
.. 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 -v $HOME/.ssh:/root/.ssh --shm-size 128G --name megatron_training_env rocm/megatron-lm:v25.5_py310
3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
.. code-block:: shell
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.
.. _amd-megatron-lm-environment-setup-v255:
Configuration
=============
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b
Update the ``train_llama3.sh`` configuration script in the ``examples/llama``
directory of
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
.. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
Update the ``train_llama2.sh`` configuration script in the ``examples/llama``
directory of
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
Update the ``train_deepseekv3.sh`` configuration script in the ``examples/deepseek_v3``
directory of
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to configure your training run.
Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
Update the ``train_deepseekv2.sh`` configuration script in the ``examples/deepseek_v2``
directory of
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v2>`__ to configure your training run.
Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
Update the ``train_mixtral_moe.sh`` configuration script in the ``examples/mixtral``
directory of
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/mixtral>`__ to configure your training run.
Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
.. note::
See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v255>` for more information on configuration options.
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 that has an IP address in the same subnet as
your other nodes. Then, update the following variables in the script, for
example:
.. code-block:: bash
export NCCL_SOCKET_IFNAME=ens50f0np0
export GLOO_SOCKET_IFNAME=ens50f0np0
.. _amd-megatron-lm-tokenizer-v255:
Tokenizer
---------
You can assign the path of an existing tokenizer to the ``TOKENIZER_MODEL`` as shown in the following examples.
If the tokenizer is not found, it'll be downloaded if publicly available.
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
If you do not have Llama 3.3 tokenizer locally, you need to use your
personal Hugging Face access token ``HF_TOKEN`` to download the tokenizer.
See `Llama-3.3-70B-Instruct
<https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct>`_. After you are
authorized, use your ``HF_TOKEN`` to download the tokenizer and set the
variable ``TOKENIZER_MODEL`` to the tokenizer path.
.. code-block:: shell
export HF_TOKEN=<Your personal Hugging Face access token>
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="meta-llama/Llama-3.3-70B-Instruct"
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="meta-llama/Llama-3.1-8B"
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="meta-llama/Llama-3.1-70B"
.. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
The training script uses either the ``Llama2Tokenizer`` or ``HuggingFaceTokenizer`` by default.
.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="deepseek-ai/DeepSeek-V3"
.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL="deepseek-ai/DeepSeek-V2-Lite"
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
Download the Mixtral tokenizer.
.. code-block:: shell
mkdir tokenizer
cd tokenizer
export HF_TOKEN=<Your personal Hugging Face access token>
wget --header="Authorization: Bearer $HF_TOKEN" -O ./tokenizer.model https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/resolve/main/tokenizer.model
Use the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
.. code-block:: shell
TOKENIZER_MODEL=tokenizer/tokenizer.model
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`` variable to toggle between mock and real data. The default
value is ``1`` for enabled.
.. code-block:: bash
MOCK_DATA=1
* If you're using a real dataset, update the ``DATA_PATH`` variable to point to the location of your dataset.
.. code-block:: bash
MOCK_DATA=0
DATA_PATH="/data/bookcorpus_text_sentence" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
Download the dataset
^^^^^^^^^^^^^^^^^^^^
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
For Llama models, use the `prepare_dataset.sh
<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`_ script
to prepare your dataset.
To download the dataset, set the ``DATASET`` variable to the dataset you'd
like to use. Three datasets are supported: ``DATASET=wiki``, ``DATASET=fineweb``, and
``DATASET=bookcorpus``.
.. code-block:: shell
DATASET=wiki TOKENIZER_MODEL=NousResearch/Llama-2-7b-chat-hf bash examples/llama/prepare_dataset.sh #for wiki-en dataset
DATASET=bookcorpus TOKENIZER_MODEL=NousResearch/Llama-2-7b-chat-hf bash examples/llama/prepare_dataset.sh #for bookcorpus dataset
``TOKENIZER_MODEL`` can be any accessible Hugging Face tokenizer.
Remember to either pre-download the tokenizer or setup Hugging Face access
otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v255>` section.
.. note::
When training set ``DATA_PATH`` to the specific file name prefix pointing to the ``.bin`` or ``.idx``
as in the following example:
.. code-block:: shell
DATA_PATH="data/bookcorpus_text_sentence" # Change to where your dataset is stored.
.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
If you don't already have the dataset, download the DeepSeek dataset using the following
commands:
.. code-block:: shell
mkdir deepseek-datasets
cd deepseek-datasets
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
.. code-block:: bash
MOCK_DATA=0 # Train on real data
DATA_DIR="<path-to>/deepseek-datasets" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
If you don't already have the dataset, download the DeepSeek dataset using the following
commands:
.. code-block:: shell
mkdir deepseek-datasets
cd deepseek-datasets
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
.. code-block:: bash
MOCK_DATA=0 # Train on real data
DATA_DIR="<path-to>/deepseek-datasets" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
If you don't already have the dataset, download the Mixtral dataset using the following
commands:
.. code-block:: shell
mkdir mixtral-datasets
cd mixtral-datasets
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/mistral-datasets/wudao_mistralbpe_content_document.bin
wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/mistral-datasets/wudao_mistralbpe_content_document.idx
To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
.. code-block:: bash
MOCK_DATA=0 # Train on real data
DATA_DIR="<path-to>/mixtral-datasets" # Change to where your dataset is stored
Ensure that the files are accessible inside the Docker container.
Multi-node configuration
------------------------
If you're running multi-node training, update the following environment variables. They can
also be passed as command line arguments. Refer to the following example configurations.
* Change ``localhost`` to the master node's hostname:
.. code-block:: shell
MASTER_ADDR="${MASTER_ADDR:-localhost}"
* Set the number of nodes you want to train on (for instance, ``2``, ``4``, ``8``):
.. code-block:: shell
NNODES="${NNODES:-1}"
* Set the rank of each node (0 for master, 1 for the first worker node, and so on):
.. code-block:: shell
NODE_RANK="${NODE_RANK:-0}"
* Set ``DATA_CACHE_PATH`` to a common directory accessible by all the nodes (for example, an
NFS directory) for multi-node runs:
.. code-block:: shell
DATA_CACHE_PATH=/root/cache # Set to a common directory for multi-node runs
* For multi-node runs, make sure the correct network drivers are installed on the nodes. If
inside a Docker container, either install the drivers inside the Docker container or pass the network
drivers from the host while creating the Docker container.
.. code-block:: shell
# Specify which RDMA interfaces to use for communication
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
Getting started
===============
The prebuilt Megatron-LM with ROCm training environment allows users to quickly validate
system performance, conduct training benchmarks, and achieve superior
performance for models like Llama, DeepSeek, and Mixtral. This container should not be
expected to provide generalized performance across all training workloads. You
can expect the container to perform in the model configurations described in
the following section, but other configurations are not validated by AMD.
.. _amd-megatron-lm-run-training-v255:
Run training
------------
Use the following example commands to set up the environment, configure
:ref:`key options <amd-megatron-lm-benchmark-test-vars-v255>`, and run training on
MI300X series accelerators with the AMD Megatron-LM environment.
Single node training
^^^^^^^^^^^^^^^^^^^^
.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
To run the training on a single node for Llama 3.3 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
For example, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 RECOMPUTE=1 SEQ_LENGTH=8192 MBS=2 BS=16 TE_FP8=0 TP=1 PP=1 FSDP=1 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
.. note::
It is suggested to use ``TP=1`` when FSDP is enabled for higher
throughput. FSDP-v2 is not supported with pipeline parallelism, expert
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
To run training on a single node for Llama 3.1 8B FP8, navigate to the Megatron-LM folder and use the
following command.
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
For Llama 3.1 8B BF16, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=0 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
To run the training on a single node for Llama 3.1 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
For example, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=3 BS=24 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=8192 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
.. note::
It is suggested to use ``TP=1`` when FSDP is enabled for higher
throughput. FSDP-v2 is not supported with pipeline parallelism, expert
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc pyt_megatron_lm_train_llama-2-7b
To run training on a single node for Llama 2 7B FP8, navigate to the Megatron-LM folder and use the
following command.
.. code-block:: shell
TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
For Llama 2 7B BF16, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=0 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
.. container:: model-doc pyt_megatron_lm_train_llama-2-70b
To run the training on a single node for Llama 2 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
For example, use the following command:
.. code-block:: shell
TEE_OUTPUT=1 MBS=7 BS=56 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=4096 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
.. note::
It is suggested to use ``TP=1`` when FSDP is enabled for higher
throughput. FSDP-v2 is not supported with pipeline parallelism, expert
parallelism, MCore's distributed optimizer, gradient accumulation fusion,
or FP16.
Currently, FSDP is only compatible with BF16 precision.
.. container:: model-doc 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,
navigate to the Megatron-LM folder and use the following command.
.. code-block:: shell
FORCE_BANLANCE=true \
RUN_ENV=cluster \
MODEL_SIZE=671B \
TRAIN_ITERS=50 \
SEQ_LEN=4096 \
NUM_LAYERS=3 \
MICRO_BATCH_SIZE=1 GLOBAL_BATCH_SIZE=32 \
PR=bf16 \
TP=1 PP=1 ETP=1 EP=8 \
GEMM_TUNING=1 \
NVTE_CK_USES_BWD_V3=1 \
USE_GROUPED_GEMM=true MOE_USE_LEGACY_GROUPED_GEMM=true \
GPT_LAYER_IN_TE=true \
bash examples/deepseek_v3/train_deepseekv3.sh
.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
navigate to the Megatron-LM folder and use the following command.
.. code-block:: shell
GEMM_TUNING=1 PR=bf16 MBS=4 AC=none SEQ_LEN=4096 PAD_LEN=4096 TRAIN_ITERS=50 bash examples/deepseek_v2/train_deepseekv2.sh
.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
navigate to the Megatron-LM folder and use the following command.
.. code-block:: shell
RECOMPUTE_NUM_LAYERS=0 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=none PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=4096 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x7B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
.. container:: model-doc 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,
navigate to the Megatron-LM folder and use the following command.
.. code-block:: shell
RECOMPUTE_NUM_LAYERS=4 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=full NUM_LAYERS=4 PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=8192 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x22B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
Multi-node training
^^^^^^^^^^^^^^^^^^^
To run training on multiple nodes, launch the Docker container on each node.
For example, for Llama 3 using a two node setup (``NODE0`` as the master node),
use these commands.
* On the master node ``NODE0``:
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=0 bash examples/llama/train_llama3.sh
* On the worker node ``NODE1``:
.. code-block:: shell
TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=1 bash examples/llama/train_llama3.sh
Or, for DeepSeek-V3, an example script ``train_deepseek_v3_slurm.sh`` is
provided in
`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to
enable training at scale under a SLURM environment. For example, to run
training on 16 nodes, try the following command:
.. code-block:: shell
sbatch examples/deepseek_v3/train_deepseek_v3_slurm.sh
.. _amd-megatron-lm-benchmark-test-vars-v255:
Key options
-----------
The benchmark tests support the following sets of variables.
``TEE_OUTPUT``
``1`` to enable training logs or ``0`` to disable.
``TE_FP8``
``0`` for B16 or ``1`` for FP8 -- ``0`` by default.
``GEMM_TUNING``
``1`` to enable GEMM tuning, which boosts performance by using the best GEMM kernels.
``USE_FLASH_ATTN``
``1`` to enable Flash Attention.
``FSDP``
``1`` to enable PyTorch FSDP2. If FSDP is enabled, ``--use-distributed-optimizer``,
``--overlap-param-gather``, and ``--sequence-parallel`` are automatically disabled.
``ENABLE_PROFILING``
``1`` to enable PyTorch profiling for performance analysis.
``transformer-impl``
``transformer_engine`` to use the Transformer Engine (TE) or ``local`` to disable TE.
``MODEL_SIZE``
``8B`` or ``70B`` for Llama 3 and 3.1. ``7B`` or ``70B`` for Llama 2, for example.
``TOTAL_ITERS``
The total number of iterations -- ``10`` by default.
``MOCK_DATA``
``1`` to use mock data or ``0`` to use real data you provide.
``MBS``
Micro batch size.
``BS``
Global batch size.
``TP`` / ``TP_SIZE``
Tensor parallel (``1``, ``2``, ``4``, ``8``). ``TP`` is disabled when ``FSDP`` is turned on.
``EP`` / ``EP_SIZE``
Expert parallel for MoE models.
``SEQ_LENGTH``
Input sequence length.
``PR``
Precision for training. ``bf16`` for BF16 (default) or ``fp8`` for FP8 GEMMs.
``AC``
Activation checkpointing (``none``, ``sel``, or ``full``) -- ``sel`` by default.
``NUM_LAYERS``
Use reduced number of layers as a proxy model.
``RECOMPUTE_NUM_LAYERS``
Number of layers used for checkpointing recompute.
Previous versions
=================
See :doc:`megatron-lm-history` to find documentation for previous releases
of the ``ROCm/megatron-lm`` Docker image.

32
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View File

@@ -11,37 +11,39 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
.. list-table::
:header-rows: 1
:stub-columns: 1
* - Image version
- ROCm version
- PyTorch version
- Components
- Resources
* - v25.6
- 6.3.4
- 2.8.0a0+git7d205b2
-
* ROCm 6.3.4
* PyTorch 2.8.0a0+git7d205b2
-
* :doc:`Documentation <../pytorch-training>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.6/images/sha256-a4cea3c493a4a03d199a3e81960ac071d79a4a7a391aa9866add3b30a7842661>`__
* - v25.5
- 6.3.4
- 2.7.0a0+git637433
-
* ROCm 6.3.4
* PyTorch 2.7.0a0+git637433
-
* :doc:`Documentation <pytorch-training-v25.5>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5>`__
* - v25.4
- 6.3.0
- 2.7.0a0+git637433
-
* ROCm 6.3.0
* PyTorch 2.7.0a0+git637433
-
* :doc:`Documentation <pytorch-training-v25.4>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.4/images/sha256-fa98a9aa69968e654466c06f05aaa12730db79b48b113c1ab4f7a5fe6920a20b>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.4/images/sha256-fa98a9aa69968e654466c06f05aaa12730db79b48b113c1ab4f7a5fe6920a20b>`__
* - v25.3
- 6.3.0
- 2.7.0a0+git637433
-
* ROCm 6.3.0
* PyTorch 2.7.0a0+git637433
-
* :doc:`Documentation <pytorch-training-v25.3>`
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.3/images/sha256-0ffdde1b590fd2787b1c7adf5686875b100980b0f314090901387c44253e709b>`_
* `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.3/images/sha256-0ffdde1b590fd2787b1c7adf5686875b100980b0f314090901387c44253e709b>`__

5
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3.rst
View File

@@ -39,7 +39,7 @@ software components to accelerate training workloads:
| Triton | 3.1 |
+--------------------------+--------------------------------+
.. _amd-pytorch-training-model-support:
.. _amd-pytorch-training-model-support-v253:
Supported models
================
@@ -80,7 +80,8 @@ the output is ``1``, run the following command to disable NUMA auto-balancing.
sudo sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
See :ref:`mi300x-disable-numa` for more information.
See :ref:`System validation and optimization <rocm-for-ai-system-optimization>`
for more information.
Environment setup
=================

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

@@ -39,7 +39,7 @@ software components to accelerate training workloads:
| Triton | 3.1 |
+--------------------------+--------------------------------+
.. _amd-pytorch-training-model-support:
.. _amd-pytorch-training-model-support-v254:
Supported models
================
@@ -61,7 +61,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta).
.. _amd-pytorch-training-performance-measurements:
.. _amd-pytorch-training-performance-measurements-v254:
Performance measurements
========================

6
docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5.rst
View File

@@ -1,3 +1,5 @@
:orphan:
.. meta::
:description: How to train a model using PyTorch for ROCm.
:keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
@@ -38,7 +40,7 @@ software components to accelerate training workloads:
| Triton | 3.2.0 |
+--------------------------+--------------------------------+
.. _amd-pytorch-training-model-support:
.. _amd-pytorch-training-model-support-v255:
Supported models
================
@@ -62,7 +64,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
Some models, such as Llama 3, require an external license agreement through
a third party (for example, Meta).
.. _amd-pytorch-training-performance-measurements:
.. _amd-pytorch-training-performance-measurements-v255:
Performance measurements
========================

1
docs/how-to/system-optimization/w6000-v620.md
View File

@@ -4,6 +4,7 @@ myst:
"description": "Learn about system settings and performance tuning for RDNA2-based GPUs."
"keywords": "RDNA2, workstation, desktop, BIOS, installation, Radeon, pro, v620, w6000"
---
:orphan:
# AMD RDNA2 system optimization

5
docs/how-to/tuning-guides/mi300x/index.rst
View File

@@ -1,3 +1,5 @@
:orphan:
.. meta::
:description: How to configure MI300X accelerators to fully leverage their capabilities and achieve optimal performance.
:keywords: ROCm, AI, machine learning, MI300X, LLM, usage, tutorial, optimization, tuning
@@ -12,8 +14,7 @@ accelerators. They include detailed instructions on system settings and
application tuning suggestions to help you fully leverage the capabilities of
these accelerators, thereby achieving optimal performance.
* :doc:`../../rocm-for-ai/inference/vllm-benchmark`
* :doc:`../../rocm-for-ai/inference-optimization/workload`
* :doc:`/how-to/rocm-for-ai/inference-optimization/workload`
* `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_

6
docs/sphinx/requirements.txt
View File

@@ -215,9 +215,9 @@ sphinx==8.1.3
# sphinx-copybutton
# sphinx-design
# sphinx-external-toc
# sphinx-last-updated-by-git
# sphinx-notfound-page
# sphinx-reredirects
# sphinx-sitemap
# sphinxcontrib-datatemplates
# sphinxcontrib-runcmd
sphinx-book-theme==1.1.4
@@ -228,11 +228,13 @@ sphinx-design==0.6.1
# via rocm-docs-core
sphinx-external-toc==1.0.1
# via rocm-docs-core
sphinx-last-updated-by-git==0.3.8
# via sphinx-sitemap
sphinx-notfound-page==1.1.0
# via rocm-docs-core
sphinx-reredirects==0.1.6
# via -r requirements.in
sphinx-sitemap==2.6.0
sphinx-sitemap==2.7.2
# via -r requirements.in
sphinxcontrib-applehelp==2.0.0
# via sphinx

2
docs/what-is-rocm.rst
View File

@@ -98,7 +98,7 @@ System Management
.. csv-table::
:header: "Component", "Description"
":doc:`AMD SMI <amdsmi:index>`", "C library for Linux that provides a user space interface for applications to monitor and control AMD devices"
":doc:`AMD SMI <amdsmi:index>`", "System management interface to control AMD GPU settings, monitor performance, and retrieve device and process information"
":doc:`ROCm Data Center Tool <rdc:index>`", "Simplifies administration and addresses key infrastructure challenges in AMD GPUs in cluster and data-center environments"
":doc:`rocminfo <rocminfo:index>`", "Reports system information"
":doc:`ROCm SMI <rocm_smi_lib:index>`", "C library for Linux that provides a user space interface for applications to monitor and control GPU applications"