Compare commits

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17 Commits

Author SHA1 Message Date
Peter Park
cef7e945be remove "mi300x" from fp4 inf page (#5385) 2025-09-18 12:29:42 -04:00
Peter Park
bc59cb8bef [docs/7.0-beta] Add pointers to latest documentation 2025-09-18 12:16:54 -04:00
Peter Park
fc8f998f8a [docs/7.0-beta] Add SGLang benchmark doc (#5242) 2025-09-02 09:17:25 -04:00
Peter Park
eb334335d0 Add training/inference user guides for 7.0 beta preview Dockers (#5234)
Update
docs/preview/benchmark-docker/inference-vllm-llama-3.1-405b-fp4.rst

Co-authored-by: dyokelso <dewi.yokelson@amd.com>

Update
docs/preview/benchmark-docker/inference-vllm-llama-3.1-405b-fp4.rst

Co-authored-by: dyokelso <dewi.yokelson@amd.com>

Update
docs/preview/benchmark-docker/inference-sglang-deepseek-r1-fp4.rst

Co-authored-by: dyokelso <dewi.yokelson@amd.com>
2025-08-28 23:24:44 -04:00
Peter Park
e7b032c3dc [docs/7.0-beta] Update preview versions list (#5169) 2025-08-08 10:11:05 -04:00
Peter Park
00a96635ae update wording 2025-07-24 17:42:19 -04:00
Peter Park
f8ce4a9113 [docs/7.0-beta] 7.0 beta preview docs
fix mi210 virtu OSes

update wording

words

improve look

update heading

fix preview versions list url
2025-07-24 16:48:38 -04:00
Peter Park
b5598e581d Remove extra files 2025-07-22 14:35:50 -04:00
Pratik Basyal
75216b8fcc Post 642 update in Alpha release documentation (#5076)
* Stable release version update

* Stable ROCm version updated
2025-07-21 17:39:29 -04:00
Peter Park
a94a616f42 Add Alpha 2 docs 2025-07-10 16:39:29 -04:00
Peter Park
2c60d2f776 remove extra xref in training.rst 2025-07-02 19:32:35 -04:00
Peter Park
f62339a857 update .wordlist.txt 2025-07-02 19:30:44 -04:00
Peter Park
dc5cac0fbf add alpha training docker docs 2025-07-02 19:28:26 -04:00
Alex Xu
8c7378ba71 use rocm-docs-core develop branch 2025-06-26 16:57:18 -04:00
Alex Xu
dcc949f441 upgrade rocm-docs-core to 1.21.1 2025-06-26 16:49:21 -04:00
Alex Xu
a4b1b2cc67 rocm-docs-core experiment 2025-06-26 15:58:18 -04:00
Peter Park
4f592f8949 [docs/7.0.0-alpha] Add docs for 7.0 alpha (#4978) 2025-06-26 15:47:42 -04:00
326 changed files with 3124 additions and 40625 deletions

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@@ -0,0 +1,42 @@
variables:
- group: common
- template: /.azuredevops/variables-global.yml
resources:
repositories:
- repository: aomp_repo
type: github
endpoint: ROCm
name: ROCm/aomp
ref: amd-mainline
- repository: aomp-extras_repo
type: github
endpoint: ROCm
name: ROCm/aomp-extras
ref: amd-mainline
- repository: flang_repo
type: github
endpoint: ROCm
name: ROCm/flang
ref: amd-mainline
- repository: llvm-project_repo
type: github
endpoint: ROCm
name: ROCm/llvm-project
ref: amd-mainline
pipelines:
- pipeline: rocr-runtime_pipeline
source: \ROCR-Runtime
trigger:
branches:
include:
- amd-mainline
# this job will only be triggered after successful build sequence of llvm-project and ROCR-Runtime
trigger: none
pr: none
jobs:
- template: ${{ variables.CI_COMPONENT_PATH }}/aomp.yml
parameters:
checkoutRepo: aomp_repo

View File

@@ -1,29 +1,10 @@
parameters:
- name: componentName
type: string
default: AMDMIGraphX
- 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
@@ -112,11 +93,7 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- job: AMDMIGraphX_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -144,8 +121,6 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
extraBuildFlags: >-
@@ -171,12 +146,12 @@ jobs:
gpuTarget: ${{ job.target }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: AMDMIGraphX_test_${{ job.target }}
dependsOn: AMDMIGraphX_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -208,8 +183,6 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: CMake@1
displayName: MIGraphXTest CMake Flags
inputs:
@@ -226,7 +199,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: ${{ parameters.componentName }}
componentName: AMDMIGraphX
testExecutable: make
testParameters: -j$(nproc) check
testPublishResults: false

View File

@@ -1,29 +1,10 @@
parameters:
- name: componentName
type: string
default: MIOpen
- 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
@@ -93,31 +74,10 @@ parameters:
target: gfx942
- gfx90a:
target: gfx90a
- name: downstreamComponentMatrix
type: object
default:
- MIVisionX:
name: MIVisionX
checkoutRepo: mivisionx_repo
sparseCheckoutDir: ''
skipUnifiedBuild: 'false'
buildDependsOn:
- MIOpen_build
- AMDMIGraphX:
name: AMDMIGraphX
checkoutRepo: amdmigraphx_repo
sparseCheckoutDir: ''
skipUnifiedBuild: 'false'
buildDependsOn:
- MIOpen_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- job: MIOpen_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -135,7 +95,6 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/miopen-get-ck-build.yml
parameters:
gpuTarget: ${{ job.target }}
@@ -145,13 +104,11 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Build and install other dependencies
inputs:
targetType: inline
workingDirectory: $(Agent.BuildDirectory)/s
workingDirectory: $(Build.SourcesDirectory)
script: |
sed -i '/composable_kernel/d' requirements.txt
mkdir -p $(Agent.BuildDirectory)/miopen-deps
@@ -173,10 +130,8 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -188,9 +143,9 @@ jobs:
- miopen-deps
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
- job: MIOpen_test_${{ job.target }}
timeoutInMinutes: 180
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
dependsOn: MIOpen_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
@@ -214,7 +169,6 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/miopen-get-ck-build.yml
parameters:
@@ -224,13 +178,11 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Build and install other dependencies
inputs:
targetType: inline
workingDirectory: $(Agent.BuildDirectory)/s
workingDirectory: $(Build.SourcesDirectory)
script: |
sed -i '/composable_kernel/d' requirements.txt
mkdir -p $(Agent.BuildDirectory)/miopen-deps
@@ -241,7 +193,7 @@ jobs:
displayName: 'MIOpen Test CMake Flags'
inputs:
cmakeArgs: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/s/bin;$(Agent.BuildDirectory)/miopen-deps
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Build.SourcesDirectory)/bin;$(Agent.BuildDirectory)/miopen-deps
-DCMAKE_INSTALL_PREFIX=$(Agent.BuildDirectory)/rocm
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
@@ -251,19 +203,19 @@ jobs:
-DBUILD_DEV=OFF
-DMIOPEN_USE_MLIR=ON
-DMIOPEN_GPU_SYNC=OFF
$(Agent.BuildDirectory)/s
..
- task: Bash@3
displayName: 'MIOpen Test Build'
inputs:
targetType: inline
workingDirectory: build
script: |
cmake --build . --target tests -- -j$(nproc)
workingDirectory: $(Build.SourcesDirectory)/build
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: ${{ parameters.componentName }}
testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex "test_rnn_seq_api|GPU_Conv2dTuningAsm_FP32|GPU_Conv2dTuningAsmBwdWrw_FP32"'
componentName: MIOpen
testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex "test_rnn_seq_api|GPU_Conv2dTuningAsm_FP32"'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -272,15 +224,3 @@ jobs:
gpuTarget: ${{ job.target }}
extraCopyDirectories:
- miopen-deps
# - ${{ 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: ${{ component.checkoutRepo }}
# # sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
# buildDependsOn: ${{ component.buildDependsOn }}
# downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
# triggerDownstreamJobs: true
# unifiedBuild: ${{ parameters.unifiedBuild }}

View File

@@ -1,29 +1,10 @@
parameters:
- name: componentName
type: string
default: MIVisionX
- 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
@@ -79,7 +60,6 @@ parameters:
- name: rocmTestDependencies
type: object
default:
- aomp
- clr
- half
- hipBLAS-common
@@ -108,11 +88,7 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- job: MIVisionX_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -134,8 +110,6 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
extraBuildFlags: >-
@@ -157,12 +131,12 @@ jobs:
# gpuTarget: ${{ job.target }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: MIVisionX_test_${{ job.target }}
dependsOn: MIVisionX_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -187,8 +161,6 @@ jobs:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmTestDependencies }}
gpuTarget: ${{ job.target }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- task: Bash@3
displayName: Build MIVisionX tests
inputs:
@@ -202,7 +174,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: ${{ parameters.componentName }}
componentName: MIVisionX
testDir: 'mivisionx-tests'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:

View File

@@ -28,8 +28,8 @@ parameters:
- name: rocmTestDependencies
type: object
default:
- amdsmi
- llvm-project
- rocm_smi_lib
- rocprofiler-register
- name: jobMatrix
@@ -111,6 +111,14 @@ jobs:
parameters:
aptPackages: ${{ parameters.aptPackages }}
packageManager: ${{ job.packageManager }}
- task: Bash@3
displayName: Install libhwloc5
inputs:
targetType: 'inline'
script: |
wget http://ftp.us.debian.org/debian/pool/main/h/hwloc/libhwloc5_1.11.12-3_amd64.deb
wget http://ftp.us.debian.org/debian/pool/main/h/hwloc/libhwloc-dev_1.11.12-3_amd64.deb
sudo apt install -y --allow-downgrades ./libhwloc5_1.11.12-3_amd64.deb ./libhwloc-dev_1.11.12-3_amd64.deb
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
@@ -153,10 +161,6 @@ jobs:
targetType: 'inline'
workingDirectory: $(Build.SourcesDirectory)/rocrtst/suites/test_common
script: |
echo $(Build.SourcesDirectory)/rocrtst/thirdparty/lib | sudo tee -a /etc/ld.so.conf.d/rocm-ci.conf
sudo cat /etc/ld.so.conf.d/rocm-ci.conf
sudo ldconfig -v
ldconfig -p
if [ -e /opt/rh/gcc-toolset-14/enable ]; then
source /opt/rh/gcc-toolset-14/enable
fi

View File

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

View File

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

View File

@@ -107,7 +107,6 @@ 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 }}

View File

@@ -39,6 +39,4 @@ 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

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 }}

View File

@@ -1,29 +1,10 @@
parameters:
- name: componentName
type: string
default: hipBLAS
- 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
@@ -88,30 +69,10 @@ parameters:
target: gfx942
- gfx90a:
target: gfx90a
# MIOpen depends on both rocRAND and hipBLAS
# for a unified build, hipBLAS will be the one to call MIOpen
- name: downstreamComponentMatrix
type: object
default:
- MIOpen:
name: MIOpen
sparseCheckoutDir: projects/miopen
skipUnifiedBuild: 'false'
buildDependsOn:
- hipBLAS_build
unifiedBuild:
downstreamAggregateNames: hipBLAS+rocRAND
buildDependsOn:
- hipBLAS_build
- rocRAND_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- job: hipBLAS_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -127,7 +88,6 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aocl.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
@@ -135,8 +95,6 @@ jobs:
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
extraBuildFlags: >-
@@ -151,12 +109,9 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -166,67 +121,46 @@ jobs:
installAOCL: true
gpuTarget: ${{ job.target }}
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ 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 }}
pipModules: ${{ parameters.pipModules }}
- 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 }}
- 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 }}
${{ 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 }}
testExecutable: $(Agent.BuildDirectory)/rocm/bin/hipblas-test
testParameters: '--yaml hipblas_smoke.yaml --gtest_output=xml:./test_output.xml --gtest_color=yes'
testDir: '$(Agent.BuildDirectory)/rocm/bin'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
environment: test
gpuTarget: ${{ job.target }}
- ${{ if parameters.triggerDownstreamJobs }}:
- ${{ each component in parameters.downstreamComponentMatrix }}:
- ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
- template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
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 }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipBLAS_test_${{ job.target }}
dependsOn: hipBLAS_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 }}
pipModules: ${{ parameters.pipModules }}
- 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: hipBLAS
testExecutable: $(Agent.BuildDirectory)/rocm/bin/hipblas-test
testParameters: '--yaml hipblas_smoke.yaml --gtest_output=xml:./test_output.xml --gtest_color=yes'
testDir: '$(Agent.BuildDirectory)/rocm/bin'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
environment: test
gpuTarget: ${{ job.target }}

View File

@@ -77,28 +77,28 @@ parameters:
type: object
default:
buildJobs:
- { 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 }
- { 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 }
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: ${{ job.pool }}
pool: ${{ variables.ULTRA_BUILD_POOL }}
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
@@ -140,10 +140,6 @@ 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: ${{ parameters.checkoutRef }}
@@ -160,15 +156,18 @@ jobs:
script: |
echo "##vso[task.prependpath]$(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 LAPACK
displayName: Build and install external dependencies
inputs:
targetType: inline
script: |
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
mkdir -p $(Agent.BuildDirectory)/deps
cd $(Agent.BuildDirectory)/deps
cmake -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/s/deps
make
sudo make install
- script: |
@@ -188,7 +187,7 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-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
@@ -245,7 +244,6 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -282,14 +280,14 @@ 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 }}

View File

@@ -80,11 +80,11 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: ${{ parameters.componentName }}_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- ${{ build }}_${{ job.target }} # todo: add OS
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -141,12 +141,12 @@ jobs:
# gpuTarget: ${{ job.target }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: ${{ parameters.componentName }}_test_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -156,7 +156,6 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

View File

@@ -72,15 +72,15 @@ parameters:
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- { os: ubuntu2204, packageManager: apt, target: gfx90a }
- name: downstreamComponentMatrix
type: object
default:
- rocFFT:
name: rocFFT
sparseCheckoutDir: projects/rocfft
skipUnifiedBuild: 'false'
buildDependsOn:
- hipRAND_build
# - name: downstreamComponentMatrix
# type: object
# default:
# - rocFFT:
# name: rocFFT
# sparseCheckoutDir: projects/rocfft
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - hipRAND_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -206,14 +206,14 @@ 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 }}

View File

@@ -1,29 +1,10 @@
parameters:
- name: componentName
type: string
default: hipSOLVER
- 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
@@ -85,15 +66,12 @@ parameters:
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_ubuntu2204_${{ job.target }}
- job: hipSOLVER_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
pool: ${{ variables.MEDIUM_BUILD_POOL }}
pool:
vmImage: ${{ variables.BASE_BUILD_POOL }}
workspace:
clean: all
steps:
@@ -104,21 +82,18 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
checkoutRef: ${{ parameters.checkoutRef }}
dependencyList: ${{ parameters.rocmDependencies }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
# build external gtest and lapack
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
componentName: external
cmakeBuildDir: '$(Agent.BuildDirectory)/s/deps/build'
cmakeSourceDir: '$(Agent.BuildDirectory)/s/deps'
cmakeBuildDir: '$(Build.SourcesDirectory)/deps/build'
cmakeSourceDir: '$(Build.SourcesDirectory)/deps'
installDir: '$(Pipeline.Workspace)/deps-install'
extraBuildFlags: >-
-DBUILD_BOOST=OFF
@@ -137,10 +112,8 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -150,49 +123,44 @@ jobs:
# extraCopyDirectories:
# - deps-install
- ${{ if eq(parameters.unifiedBuild, False) }}:
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ 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 }}
- 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 }}
- 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 }}
${{ 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: ${{ parameters.componentName }}
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsolver-test'
testParameters: '--gtest_filter="*checkin*" --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 }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipSOLVER_test_${{ job.target }}
dependsOn: hipSOLVER_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: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: hipSOLVER
testDir: '$(Agent.BuildDirectory)/rocm/bin'
testExecutable: './hipsolver-test'
testParameters: '--gtest_filter="*checkin*" --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 }}

View File

@@ -1,29 +1,10 @@
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
@@ -33,11 +14,13 @@ parameters:
type: object
default:
- cmake
- gfortran
- git
- libboost-program-options-dev
- libfftw3-dev
- ninja-build
- libboost-program-options-dev
- googletest
- libfftw3-dev
- git
- gfortran
- libgtest-dev
- python3-pip
- name: rocmDependencies
type: object
@@ -66,31 +49,19 @@ 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: gfx1030 }
- { os: ubuntu2204, packageManager: apt, target: gfx1100 }
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
testJobs:
- { 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
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
- job: hipSPARSE_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -102,57 +73,42 @@ 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:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
artifactName: hipSPARSE
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
artifactName: hipSPARSE
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
publish: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
parameters:
sourceDir: $(Agent.BuildDirectory)/s/build/clients
sourceDir: $(Build.SourcesDirectory)/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:
@@ -160,65 +116,44 @@ jobs:
# 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 }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipSPARSE_test_${{ 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 }}

View File

@@ -1,29 +1,10 @@
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
@@ -75,17 +56,15 @@ parameters:
type: object
default:
buildJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- gfx942:
target: gfx942
testJobs:
- { os: ubuntu2204, packageManager: apt, target: gfx942 }
- gfx942:
target: gfx942
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
- job: hipSPARSELt_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -107,22 +86,17 @@ 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
@@ -141,7 +115,6 @@ 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++
@@ -157,80 +130,64 @@ 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
- ${{ 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
- 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(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 }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: hipSPARSELt_test_${{ 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 }}

View File

@@ -67,6 +67,7 @@ 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

View File

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

View File

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

View File

@@ -33,15 +33,17 @@ parameters:
type: object
default:
- cmake
- git
- gfortran
- libdrm-dev
- libmsgpack-dev
- libopenblas-dev
- ninja-build
- python3-pip
- python3-venv
- git
- libmsgpack-dev
- gfortran
- libopenblas-dev
- googletest
- libgtest-dev
- wget
- python3-pip
- libdrm-dev
- name: pipModules
type: object
default:
@@ -50,17 +52,18 @@ parameters:
- name: rocmDependencies
type: object
default:
- aomp
- clr
- hipBLAS-common
- hipBLASLt
- llvm-project
- rocm-cmake
- rocm-core
- rocm_smi_lib
- llvm-project
- ROCR-Runtime
- clr
- rocminfo
- rocprofiler-register
- ROCR-Runtime
- rocm_smi_lib
- rocm-core
- aomp
- aomp-extras
- hipBLAS-common
- hipBLASLt
- roctracer
- name: rocmTestDependencies
type: object
@@ -83,38 +86,32 @@ 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:
- 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
# - 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
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -154,12 +151,6 @@ 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 }}
@@ -173,12 +164,21 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
-DCMAKE_TOOLCHAIN_FILE=toolchain-linux.cmake
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
-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:
@@ -208,7 +208,6 @@ 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(),
@@ -223,7 +222,6 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -260,18 +258,18 @@ 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 }}

View File

@@ -78,19 +78,19 @@ parameters:
target: gfx942
- gfx90a:
target: gfx90a
- name: downstreamComponentMatrix
type: object
default:
- hipFFT:
name: hipFFT
sparseCheckoutDir: projects/hipfft
skipUnifiedBuild: 'false'
buildDependsOn:
- rocFFT_build
# - name: downstreamComponentMatrix
# type: object
# default:
# - hipFFT:
# name: hipFFT
# sparseCheckoutDir: projects/hipfft
# skipUnifiedBuild: 'false'
# buildDependsOn:
# - rocFFT_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: ${{ parameters.componentName }}_build_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
@@ -151,12 +151,12 @@ jobs:
- HIP_ROCCLR_HOME:::/home/user/workspace/rocm
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: ${{ parameters.componentName }}_test_ubuntu2204_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_ubuntu2204_${{ job.target }}
- job: ${{ parameters.componentName }}_test_${{ job.target }}
dependsOn: ${{ parameters.componentName }}_build_${{ job.target }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
eq(${{ parameters.aggregatePipeline }}, False)
)
variables:
@@ -166,7 +166,6 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -196,14 +195,14 @@ 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 }}

View File

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

View File

@@ -91,12 +91,12 @@ parameters:
- rocPRIM_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: 'true'
buildDependsOn:
- rocPRIM_build
# - rocSOLVER:
# name: rocSOLVER
# sparseCheckoutDir: projects/rocsolver
# skipUnifiedBuild: 'true'
# buildDependsOn:
# - rocPRIM_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -210,7 +210,7 @@ jobs:
parameters:
componentName: ${{ parameters.componentName }}
testDir: '$(Agent.BuildDirectory)/rocm/bin/rocprim'
extraTestParameters: '-I ${{ job.shard }},,${{ job.shardCount }} -E device_merge_inplace'
extraTestParameters: '-I ${{ job.shard }},,${{ job.shardCount }}'
os: ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:

View File

@@ -36,7 +36,6 @@ parameters:
- clr
- llvm-project
- rocDecode
- rocJPEG
- rocm-cmake
- rocm-core
- rocminfo
@@ -193,9 +192,9 @@ jobs:
inputs:
itemPattern: '**/*.whl'
targetPath: $(Agent.BuildDirectory)
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
gpuTarget: ${{ job.target }}
checkoutRepo: ${{ parameters.checkoutRepo }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
@@ -222,17 +221,25 @@ 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: $(Agent.BuildDirectory)/rocm/share/rocpydecode/tests
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
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

View File

@@ -79,12 +79,6 @@ parameters:
skipUnifiedBuild: 'false'
buildDependsOn:
- rocRAND_build
- MIOpen:
name: MIOpen
sparseCheckoutDir: projects/miopen
skipUnifiedBuild: 'true'
buildDependsOn:
- rocRAND_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:

View File

@@ -33,11 +33,13 @@ parameters:
type: object
default:
- cmake
- gfortran
- git
- libfmt-dev
- libsuitesparse-dev
- ninja-build
- libsuitesparse-dev
- gfortran
- libfmt-dev
- git
- googletest
- libgtest-dev
- python3-pip
- name: rocmDependencies
type: object
@@ -73,38 +75,16 @@ 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:
- hipBLAS:
name: hipBLAS
sparseCheckoutDir: projects/hipblas
skipUnifiedBuild: 'false'
buildDependsOn:
- rocSOLVER_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: 'false'
buildDependsOn:
- rocSOLVER_build
unifiedBuild:
downstreamAggregateNames: rocSOLVER+rocSPARSE
buildDependsOn:
- rocSOLVER_build
- rocSPARSE_build
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -139,10 +119,6 @@ jobs:
targetType: inline
script: git clone --depth 1 --branch v3.9.1 https://github.com/Reference-LAPACK/lapack
workingDirectory: '$(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 }}
@@ -158,7 +134,6 @@ 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
@@ -171,7 +146,7 @@ jobs:
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Pipeline.Workspace)/deps-install;$(Agent.BuildDirectory)/vendor
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Pipeline.Workspace)/deps-install
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-DAMDGPU_TARGETS=${{ job.target }}
@@ -216,7 +191,6 @@ jobs:
workspace:
clean: all
steps:
- checkout: none
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
@@ -250,19 +224,3 @@ jobs:
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 }}
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 }}

View File

@@ -1,29 +1,10 @@
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
@@ -32,25 +13,27 @@ parameters:
- name: aptPackages
type: object
default:
- cmake
- gfortran
- git
- libboost-program-options-dev
- libdrm-dev
- libfftw3-dev
- ninja-build
- python3-pip
- cmake
- ninja-build
- libboost-program-options-dev
- googletest
- libfftw3-dev
- git
- gfortran
- libgtest-dev
- libdrm-dev
- name: rocmDependencies
type: object
default:
- clr
- llvm-project
- rocBLAS
- rocm-cmake
- llvm-project
- ROCR-Runtime
- clr
- rocBLAS
- rocminfo
- rocPRIM
- rocprofiler-register
- ROCR-Runtime
- roctracer
- name: rocmTestDependencies
type: object
@@ -69,39 +52,19 @@ 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 }
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
testJobs:
- { 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
- gfx942:
target: gfx942
- gfx90a:
target: gfx90a
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}_${{ job.target }}
- job: rocSPARSE_build_${{ job.target }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -114,32 +77,22 @@ 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/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/hipcc
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_BUILD_TYPE=Release
-DAMDGPU_TARGETS=${{ job.target }}
@@ -150,94 +103,68 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
artifactName: rocSPARSE
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
artifactName: rocSPARSE
gpuTarget: ${{ job.target }}
os: ${{ job.os }}
publish: false
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
parameters:
sourceDir: $(Agent.BuildDirectory)/s/build/clients
sourceDir: $(Build.SourcesDirectory)/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
- ${{ 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
- 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(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 }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocSPARSE_test_${{ 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 }}

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'
testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex "rocfft_callback"'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}

View File

@@ -1,163 +0,0 @@
parameters:
- name: componentName
type: string
default: rocm_libraries
- name: checkoutRepo
type: string
default: 'self'
- name: checkoutRef
type: string
default: ''
# monorepo related parameters
- name: sparseCheckoutDir
type: string
default: ''
- name: triggerDownstreamJobs
type: boolean
default: false
- name: downstreamAggregateNames
type: string
default: ''
- name: buildDependsOn
type: object
default: null
- name: unifiedBuild
type: boolean
default: false
# set to true if doing full build of ROCm stack
# and dependencies are pulled from same pipeline
- name: aggregatePipeline
type: boolean
default: false
- name: aptPackages
type: object
default:
- ccache
- gfortran
- git
- libdrm-dev
- libmsgpack-dev
- libnuma-dev
- ninja-build
- python3-pip
- python3-venv
- name: pipModules
type: object
default:
- joblib
- "packaging>=22.0"
- --upgrade
- name: rocmDependencies
type: object
default:
- aomp
- clr
- llvm-project
- rocminfo
- rocm-cmake
- rocm_smi_lib
- rocprofiler-register
- ROCR-Runtime
- roctracer
- name: rocmTestDependencies
type: object
default:
- aomp
- clr
- llvm-project
- rocminfo
- rocm_smi_lib
- rocprofiler-register
- ROCR-Runtime
- roctracer
- name: jobMatrix
type: object
default:
buildJobs:
- { pool: rocm-ci_ultra_build_pool, os: ubuntu2204, packageManager: apt, target: gfx942 }
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 300
${{ if parameters.buildDependsOn }}:
dependsOn:
- ${{ each build in parameters.buildDependsOn }}:
- ${{ build }}_${{ job.os }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
- name: DAY_STRING
value: $[format('{0:ddMMyyyy}', pipeline.startTime)]
pool: ${{ job.pool }}
${{ if eq(job.os, 'almalinux8') }}:
container:
image: rocmexternalcicd.azurecr.io/manylinux228:latest
endpoint: ContainerService3
workspace:
clean: all
steps:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
packageManager: ${{ job.packageManager }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
parameters:
checkoutRepo: ${{ parameters.checkoutRepo }}
- 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 }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
${{ if parameters.triggerDownstreamJobs }}:
downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
- script: |
mkdir -p $(CCACHE_DIR)
echo "##vso[task.prependpath]/usr/lib/ccache"
displayName: Update path for ccache
- task: Cache@2
displayName: Ccache caching
inputs:
key: rocm-libraries | ${{ job.os }} | ${{ job.target }} | $(DAY_STRING) | $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
path: $(CCACHE_DIR)
restoreKeys: |
rocm-libraries | ${{ job.os }} | ${{ job.target }} | $(DAY_STRING)
rocm-libraries | ${{ job.os }} | ${{ job.target }}
rocm-libraries | ${{ job.os }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
os: ${{ job.os }}
extraBuildFlags: >-
-DROCM_LIBRARIES_SUPERBUILD=ON
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
componentName: ${{ parameters.componentName }}
os: ${{ job.os }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- ${{ if eq(job.os, 'ubuntu2204') }}:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
parameters:
aptPackages: ${{ parameters.aptPackages }}
pipModules: ${{ parameters.pipModules }}
gpuTarget: ${{ job.target }}
extraPaths: /home/user/workspace/rocm/llvm/bin:/home/user/workspace/rocm/bin
installLatestCMake: true
extraCopyDirectories:
- deps

View File

@@ -67,6 +67,7 @@ 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:

View File

@@ -65,19 +65,43 @@ parameters:
type: object
default:
buildJobs:
- gfx942:
- gfx942-staging:
name: gfx942_staging
target: gfx942
- gfx90a:
dependencySource: staging
- gfx942-mainline:
name: gfx942_mainline
target: gfx942
dependencySource: mainline
- gfx90a-staging:
name: gfx90a_staging
target: gfx90a
dependencySource: staging
- gfx90a-mainline:
name: gfx90a_mainline
target: gfx90a
dependencySource: mainline
testJobs:
- gfx942:
- gfx942-staging:
name: gfx942_staging
target: gfx942
- gfx90a:
dependencySource: staging
- gfx942-mainline:
name: gfx942_mainline
target: gfx942
dependencySource: mainline
- gfx90a-staging:
name: gfx90a_staging
target: gfx90a
dependencySource: staging
- gfx90a-mainline:
name: gfx90a_mainline
target: gfx90a
dependencySource: mainline
jobs:
- ${{ each job in parameters.jobMatrix.buildJobs }}:
- job: rocprofiler_compute_build_${{ job.target }}
- job: rocprofiler_compute_build_${{ job.name }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -100,9 +124,11 @@ jobs:
-GNinja
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
artifactName: ${{ job.dependencySource }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
parameters:
artifactName: ${{ job.dependencySource }}
gpuTarget: ${{ job.target }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
# - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
@@ -112,9 +138,9 @@ jobs:
# gpuTarget: ${{ job.target }}
- ${{ each job in parameters.jobMatrix.testJobs }}:
- job: rocprofiler_compute_test_${{ job.target }}
- job: rocprofiler_compute_test_${{ job.name }}
timeoutInMinutes: 120
dependsOn: rocprofiler_compute_build_${{ job.target }}
dependsOn: rocprofiler_compute_build_${{ job.name }}
condition:
and(succeeded(),
eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
@@ -140,12 +166,14 @@ jobs:
checkoutRepo: ${{ parameters.checkoutRepo }}
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
parameters:
postTargetFilter: ${{ job.dependencySource }}
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 }}
dependencySource: ${{ job.dependencySource }}
gpuTarget: ${{ job.target }}
- task: Bash@3
displayName: Add en_US.UTF-8 locale

View File

@@ -37,7 +37,6 @@ parameters:
- libpfm4-dev
- libtool
- libopenmpi-dev
- libsqlite3-dev
- m4
- ninja-build
- openmpi-bin

View File

@@ -40,6 +40,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
dependencyList: ${{ parameters.rocmDependencies }}
dependencySource: staging
- task: Bash@3
displayName: Add ROCm binaries to PATH
inputs:

View File

@@ -219,6 +219,7 @@ jobs:
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
dependencyList: ${{ parameters.rocmDependencies }}
dependencySource: staging
gpuTarget: $(JOB_GPU_TARGET)
setupHIPLibrarySymlinks: true
- task: Bash@3
@@ -405,6 +406,8 @@ jobs:
parameters:
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

View File

@@ -3,21 +3,21 @@ parameters:
- name: jobList
type: object
default:
- { 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: ubuntu2404, packageManager: apt, target: gfx942 }
- { os: ubuntu2404, packageManager: apt, target: gfx90a }
- { os: ubuntu2404, packageManager: apt, target: gfx1201 }
- { os: ubuntu2404, packageManager: apt, target: gfx1100 }
- { os: ubuntu2404, 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: ubuntu2204, target: gfx942, source: staging }
- { os: ubuntu2204, target: gfx90a, source: staging }
- { os: ubuntu2204, target: gfx1201, source: staging }
- { os: ubuntu2204, target: gfx1100, source: staging }
- { os: ubuntu2204, target: gfx1030, source: staging }
- { os: ubuntu2404, target: gfx942, source: staging }
- { os: ubuntu2404, target: gfx90a, source: staging }
- { os: ubuntu2404, target: gfx1201, source: staging }
- { os: ubuntu2404, target: gfx1100, source: staging }
- { os: ubuntu2404, target: gfx1030, source: staging }
- { os: almalinux8, target: gfx942, source: staging }
- { os: almalinux8, target: gfx90a, source: staging }
- { os: almalinux8, target: gfx1201, source: staging }
- { os: almalinux8, target: gfx1100, source: staging }
- { os: almalinux8, target: gfx1030, source: staging }
- name: rocmDependencies
type: object
default:
@@ -92,8 +92,7 @@ schedules:
jobs:
- ${{ each job in parameters.jobList }}:
- job: nightly_${{ job.os }}_${{ job.target }}
timeoutInMinutes: 90
- job: rocm_nightly_${{ job.os }}_${{ job.target }}_${{ job.source }}
variables:
- group: common
- template: /.azuredevops/variables-global.yml
@@ -116,9 +115,11 @@ jobs:
displayName: System disk space before ROCm
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
parameters:
dependencySource: ${{ job.source }}
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
@@ -131,7 +132,7 @@ jobs:
includeRootFolder: false
archiveType: tar
tarCompression: gz
archiveFile: $(Build.ArtifactStagingDirectory)/$(Build.DefinitionName)_$(Build.BuildNumber)_${{ job.os }}_${{ job.target }}.tar.gz
archiveFile: $(Build.ArtifactStagingDirectory)/$(Build.DefinitionName)_$(Build.BuildNumber)_ubuntu2204_${{ job.target }}.tar.gz
- script: du -sh $(Build.ArtifactStagingDirectory)
displayName: Compressed ROCm size
- task: PublishPipelineArtifact@1
@@ -144,95 +145,5 @@ jobs:
inputs:
workingDirectory: $(Pipeline.Workspace)
targetType: inline
script: echo "$(Build.DefinitionName)_$(Build.BuildNumber)_${{ job.os }}_${{ job.target }}.tar.gz" >> pipelineArtifacts.txt
script: echo "$(Build.DefinitionName)_$(Build.BuildNumber)_ubuntu2204_${{ job.target }}.tar.gz" >> pipelineArtifacts.txt
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
- ${{ if eq(job.packageManager, 'apt') }}:
- task: Bash@3
displayName: Create Dockerfile
inputs:
workingDirectory: $(Agent.BuildDirectory)
targetType: inline
script: |
cat <<'EOF' > Dockerfile
${{ iif(eq(job.os, 'ubuntu2204'), 'FROM ubuntu:22.04', '') }}
${{ iif(eq(job.os, 'ubuntu2404'), 'FROM ubuntu:24.04', '') }}
WORKDIR /root
RUN mkdir rocm
RUN apt update \
&& apt upgrade -y \
&& apt install -y cmake curl git gcc g++ gpg lsb-release lsof ninja-build pkg-config python3 python3-pip wget zip libdrm-dev libelf-dev libgtest-dev libhsakmt-dev libhwloc-dev libnuma-dev libstdc++-12-dev libtbb-dev jq \
&& apt clean all
RUN PACKAGE_NAME=$(curl -s https://repo.radeon.com/rocm/apt/latest/pool/main/h/hsa-amd-aqlprofile/ | grep -oP "href=\"\K[^\"]*$(lsb_release -rs)[^\"]*\.deb") \
&& wget -nv --retry-connrefused https://repo.radeon.com/rocm/apt/latest/pool/main/h/hsa-amd-aqlprofile/$PACKAGE_NAME \
&& mkdir hsa-amd-aqlprofile \
&& dpkg-deb -R $PACKAGE_NAME hsa-amd-aqlprofile \
&& cp -R hsa-amd-aqlprofile/opt/rocm-*/* rocm
RUN ARTIFACT_URL="https://dev.azure.com/ROCm-CI/ROCm-CI/_apis/build/builds/$(Build.BuildId)/artifacts?artifactName=nightly${{ job.os }}${{ job.target }}&api-version=7.1" \
&& DOWNLOAD_URL=$(curl -s $ARTIFACT_URL | jq ".resource.downloadUrl" | tr -d '"') \
&& wget -nv --retry-connrefused $DOWNLOAD_URL -O nightly.zip \
&& unzip nightly.zip \
&& tar -xf nightly${{ job.os }}${{ job.target }}/rocm-nightly*${{ job.os }}*${{ job.target }}*.tar.gz -C rocm
RUN echo /root/rocm/lib | tee /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/llvm/lib | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/lib64 | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/llvm/lib64 | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN ldconfig -v
ENV PATH="$PATH:/root/rocm/bin"
ENTRYPOINT ["/bin/bash"]
EOF
cat Dockerfile
- ${{ elseif eq(job.packageManager, 'dnf') }}:
- task: Bash@3
displayName: Create Dockerfile
inputs:
workingDirectory: $(Agent.BuildDirectory)
targetType: inline
script: |
cat <<'EOF' > Dockerfile
${{ iif(eq(job.os, 'almalinux8'), 'FROM almalinux:8', '') }}
WORKDIR /root
RUN mkdir rocm
RUN dnf install -y cmake curl git gcc gcc-c++ gnupg2 redhat-lsb-core lsof pkgconf python3 python3-pip wget zip libdrm-devel elfutils-libelf-devel numactl-devel libstdc++-devel tbb-devel jq \
&& dnf clean all
RUN PACKAGE_NAME=$(curl -s https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/ | grep -oP "hsa-amd-aqlprofile-[^\"]+\.rpm" | head -n1) \
&& wget -nv --retry-connrefused https://repo.radeon.com/rocm/rhel8/$(REPO_RADEON_VERSION)/main/$PACKAGE_NAME \
&& mkdir hsa-amd-aqlprofile \
&& dnf -y install rpm-build cpio \
&& rpm2cpio $PACKAGE_NAME | (cd hsa-amd-aqlprofile && cpio -idmv) \
&& cp -R hsa-amd-aqlprofile/opt/rocm-*/* rocm
RUN ARTIFACT_URL="https://dev.azure.com/ROCm-CI/ROCm-CI/_apis/build/builds/$(Build.BuildId)/artifacts?artifactName=nightly${{ job.os }}${{ job.target }}&api-version=7.1" \
&& DOWNLOAD_URL=$(curl -s $ARTIFACT_URL | jq ".resource.downloadUrl" | tr -d '"') \
&& wget -nv --retry-connrefused $DOWNLOAD_URL -O nightly.zip \
&& UNZIP_DISABLE_ZIPBOMB_DETECTION=TRUE unzip nightly.zip \
&& tar -xf nightly${{ job.os }}${{ job.target }}/rocm-nightly*${{ job.os }}*${{ job.target }}*.tar.gz -C rocm
RUN echo /root/rocm/lib | tee /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/llvm/lib | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/lib64 | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN echo /root/rocm/llvm/lib64 | tee -a /etc/ld.so.conf.d/rocm-ci.conf
RUN ldconfig -v
ENV PATH="$PATH:/root/rocm/bin"
ENTRYPOINT ["/bin/bash"]
EOF
cat Dockerfile
- task: Docker@2
displayName: Build and upload Docker image
inputs:
containerRegistry: ContainerService3
repository: 'nightly-${{ job.os }}-${{ job.target }}'
Dockerfile: '$(Agent.BuildDirectory)/Dockerfile'
buildContext: '$(Agent.BuildDirectory)'
- task: Bash@3
displayName: '!! Docker Run Command !!'
inputs:
targetType: inline
script: echo "docker run -it --network=host --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined rocmexternalcicd.azurecr.io/nightly-${{ job.os }}-${{ job.target }}:$(Build.BuildId)" | tr '[:upper:]' '[:lower:]'

View File

@@ -12,9 +12,6 @@ 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
@@ -40,17 +37,16 @@ steps:
buildVersionToDownload: latest # aomp trigger lives in ROCm/ROCm, so cannot use ROCm/aomp branch names
${{ else }}:
buildVersionToDownload: latestFromBranch
- ${{ 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
- 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

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"
ARTIFACT_NAME="$(Agent.JobName)_$(System.JobAttempt)"
ARTIFACT_STRING="pipelineartifact://ROCm-CI/projectId/$(DOWNLOAD_PROJECT_ID)/buildId/$(Build.BuildId)/artifactName/${ARTIFACT_NAME}"
FORMATTED_JOB_NAME=$(echo $(Agent.JobName) | sed 's/ /./g; s/[-_]//g')
ARTIFACT_STRING="pipelineartifact://ROCm-CI/projectId/$(DOWNLOAD_PROJECT_ID)/buildId/$(Build.BuildId)/artifactName/${FORMATTED_JOB_NAME}"
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

View File

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

View File

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

View File

@@ -52,7 +52,6 @@ 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

View File

@@ -3,6 +3,13 @@ parameters:
- name: checkoutRef
type: string
default: ''
- name: dependencySource # optional, overrides checkoutRef
type: string
default: null
values:
- null # empty strings aren't allowed as values, use null instead
- staging
- mainline
- name: dependencyList
type: object
default: []
@@ -12,6 +19,16 @@ 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
@@ -31,240 +48,309 @@ parameters:
type: object
default:
AMDMIGraphX:
pipelineId: 113
developBranch: develop
pipelineId: $(AMDMIGRAPHX_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: master
hasGpuTarget: true
amdsmi:
pipelineId: 99
developBranch: amd-staging
pipelineId: $(AMDSMI_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
aomp-extras:
pipelineId: 111
developBranch: aomp-dev
pipelineId: $(AOMP_EXTRAS_PIPELINE_ID)
stagingBranch: aomp-dev
mainlineBranch: aomp-dev
hasGpuTarget: false
aomp:
pipelineId: 115
developBranch: aomp-dev
pipelineId: $(AOMP_PIPELINE_ID)
stagingBranch: aomp-dev
mainlineBranch: amd-mainline
hasGpuTarget: false
clr:
pipelineId: 145
developBranch: amd-staging
pipelineId: $(CLR_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
composable_kernel:
pipelineId: 86
developBranch: develop
pipelineId: $(COMPOSABLE_KERNEL_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
half:
pipelineId: 101
developBranch: rocm
pipelineId: $(HALF_PIPELINE_ID)
stagingBranch: rocm
mainlineBranch: rocm
hasGpuTarget: false
HIP:
pipelineId: 93
developBranch: amd-staging
pipelineId: $(HIP_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
hip-tests:
pipelineId: 233
developBranch: amd-staging
pipelineId: $(HIP_TESTS_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
hipBLAS:
pipelineId: 317
developBranch: develop
pipelineId: $(HIPBLAS_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipBLASLt:
pipelineId: 301
developBranch: develop
pipelineId: $(HIPBLASLT_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipBLAS-common:
pipelineId: 300
developBranch: develop
pipelineId: $(HIPBLAS_COMMON_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
hipCUB:
pipelineId: 277
developBranch: develop
pipelineId: $(HIPCUB_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: develop
hasGpuTarget: true
hipFFT:
pipelineId: 283
developBranch: develop
pipelineId: $(HIPFFT_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipfort:
pipelineId: 102
developBranch: develop
pipelineId: $(HIPFORT_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
HIPIFY:
pipelineId: 92
developBranch: amd-staging
pipelineId: $(HIPIFY_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
hipRAND:
pipelineId: 275
developBranch: develop
pipelineId: $(HIPRAND_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: develop
hasGpuTarget: true
hipSOLVER:
pipelineId: 84
developBranch: develop
pipelineId: $(HIPSOLVER_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipSPARSE:
pipelineId: 315
developBranch: develop
pipelineId: $(HIPSPARSE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipSPARSELt:
pipelineId: 309
developBranch: develop
pipelineId: $(HIPSPARSELT_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
hipTensor:
pipelineId: 105
developBranch: develop
pipelineId: $(HIPTENSOR_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
llvm-project:
pipelineId: 2
developBranch: amd-staging
pipelineId: $(LLVM_PROJECT_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
MIOpen:
pipelineId: 320
developBranch: develop
pipelineId: $(MIOpen_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: amd-master
hasGpuTarget: true
MIVisionX:
pipelineId: 80
developBranch: develop
pipelineId: $(MIVISIONX_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: master
hasGpuTarget: true
omnitrace: # deprecated
pipelineId: $(OMNITRACE_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: true
rccl:
pipelineId: 107
developBranch: develop
pipelineId: $(RCCL_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
rdc:
pipelineId: 100
developBranch: amd-staging
pipelineId: $(RDC_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocAL:
pipelineId: 151
developBranch: develop
pipelineId: $(ROCAL_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
rocALUTION:
pipelineId: 89
developBranch: develop
pipelineId: $(ROCALUTION_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
rocBLAS:
pipelineId: 302
developBranch: develop
pipelineId: $(ROCBLAS_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
ROCdbgapi:
pipelineId: 135
developBranch: amd-staging
pipelineId: $(ROCDBGAPI_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocDecode:
pipelineId: 79
developBranch: develop
pipelineId: $(ROCDECODE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
rocFFT:
pipelineId: 282
developBranch: develop
pipelineId: $(ROCFFT_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
ROCgdb:
pipelineId: 134
developBranch: amd-staging
pipelineId: $(ROCGDB_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline-rocgdb-15
hasGpuTarget: false
rocJPEG:
pipelineId: 262
developBranch: develop
pipelineId: $(ROCJPEG_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
rocm-cmake:
pipelineId: 6
developBranch: develop
pipelineId: $(ROCM_CMAKE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
rocm-core:
pipelineId: 103
developBranch: master
pipelineId: $(ROCM_CORE_PIPELINE_ID)
stagingBranch: master
mainlineBranch: amd-master
hasGpuTarget: false
rocm-examples:
pipelineId: 216
developBranch: amd-staging
pipelineId: $(ROCM_EXAMPLES_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: true
rocminfo:
pipelineId: 91
developBranch: amd-staging
pipelineId: $(ROCMINFO_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocMLIR:
pipelineId: 229
developBranch: develop
pipelineId: $(ROCMLIR_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: false
ROCmValidationSuite:
pipelineId: 106
developBranch: master
pipelineId: $(ROCMVALIDATIONSUITE_PIPELINE_ID)
stagingBranch: master
mainlineBranch: master
hasGpuTarget: true
rocm_bandwidth_test:
pipelineId: 88
developBranch: master
pipelineId: $(ROCM_BANDWIDTH_TEST_PIPELINE_ID)
stagingBranch: master
mainlineBranch: master
hasGpuTarget: false
rocm_smi_lib:
pipelineId: 96
developBranch: amd-staging
pipelineId: $(ROCM_SMI_LIB_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocPRIM:
pipelineId: 273
developBranch: develop
pipelineId: $(ROCPRIM_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: develop
hasGpuTarget: true
rocprofiler:
pipelineId: 143
developBranch: amd-staging
pipelineId: $(ROCPROFILER_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-master
hasGpuTarget: true
rocprofiler-compute:
pipelineId: 257
developBranch: develop
pipelineId: $(ROCPROFILER_COMPUTE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: amd-mainline
hasGpuTarget: true
rocprofiler-register:
pipelineId: 1
developBranch: amd-staging
pipelineId: $(ROCPROFILER_REGISTER_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocprofiler-sdk:
pipelineId: 246
developBranch: amd-staging
pipelineId: $(ROCPROFILER_SDK_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: true
rocprofiler-systems:
pipelineId: 255
developBranch: amd-staging
pipelineId: $(ROCPROFILER_SYSTEMS_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: true
rocPyDecode:
pipelineId: 239
developBranch: develop
pipelineId: $(ROCPYDECODE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
ROCR-Runtime:
pipelineId: 10
developBranch: amd-staging
pipelineId: $(ROCR_RUNTIME_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocRAND:
pipelineId: 274
developBranch: develop
pipelineId: $(ROCRAND_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: develop
hasGpuTarget: true
rocr_debug_agent:
pipelineId: 136
developBranch: amd-staging
pipelineId: $(ROCR_DEBUG_AGENT_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: false
rocSOLVER:
pipelineId: 81
developBranch: develop
pipelineId: $(ROCSOLVER_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
rocSPARSE:
pipelineId: 314
developBranch: develop
pipelineId: $(ROCSPARSE_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
ROCT-Thunk-Interface: # deprecated
pipelineId: $(ROCT_THUNK_INTERFACE_PIPELINE_ID)
stagingBranch: master
mainlineBranch: master
hasGpuTarget: false
rocThrust:
pipelineId: 276
developBranch: develop
pipelineId: $(ROCTHRUST_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: develop
hasGpuTarget: true
roctracer:
pipelineId: 141
developBranch: amd-staging
pipelineId: $(ROCTRACER_PIPELINE_ID)
stagingBranch: amd-staging
mainlineBranch: amd-mainline
hasGpuTarget: true
rocWMMA:
pipelineId: 109
developBranch: develop
pipelineId: $(ROCWMMA_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
rpp:
pipelineId: 78
developBranch: develop
pipelineId: $(RPP_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
TransferBench:
pipelineId: 265
developBranch: develop
pipelineId: $(TRANSFERBENCH_PIPELINE_ID)
stagingBranch: develop
mainlineBranch: mainline
hasGpuTarget: true
steps:
@@ -280,44 +366,72 @@ steps:
parameters:
componentName: ${{ split(dependency, ':')[0] }}
pipelineId: ${{ parameters.componentVarList[split(dependency, ':')[0]].pipelineId }}
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].developBranch }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
extractAndDeleteFiles: false
${{ if parameters.componentVarList[split(dependency, ':')[0]].hasGpuTarget }}:
fileFilter: "${{ split(dependency, ':')[1] }}*_${{ parameters.os }}_${{ parameters.gpuTarget }}"
# dependencySource = staging
${{ if eq(parameters.dependencySource, 'staging')}}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].stagingBranch }}
# dependencySource = mainline
${{ elseif eq(parameters.dependencySource, 'mainline')}}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].mainlineBranch }}
# checkoutRef = staging
${{ elseif eq(parameters.checkoutRef, parameters.componentVarList[variables['Build.DefinitionName']].stagingBranch) }}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].stagingBranch }}
# checkoutRef = mainline
${{ elseif eq(parameters.checkoutRef, parameters.componentVarList[variables['Build.DefinitionName']].mainlineBranch) }}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].mainlineBranch }}
# SourceBranchName = staging
${{ elseif eq(variables['Build.SourceBranchName'], parameters.componentVarlist[variables['Build.DefinitionName']].stagingBranch) }}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].stagingBranch }}
# SourceBranchName = mainline
${{ elseif eq(variables['Build.SourceBranchName'], parameters.componentVarlist[variables['Build.DefinitionName']].mainlineBranch) }}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].mainlineBranch }}
# default = staging
${{ else }}:
branchName: ${{ parameters.componentVarList[split(dependency, ':')[0]].stagingBranch }}
# no colon (:) found in this item in the list
- ${{ elseif containsValue(split(parameters.downstreamAggregateNames, '+'), dependency) }}:
- template: local-artifact-download.yml
parameters:
buildType: current
preTargetFilter: ${{ dependency }}
os: ${{ parameters.os }}
${{ if parameters.componentVarList[dependency].hasGpuTarget }}:
gpuTarget: ${{ parameters.gpuTarget }}
preTargetFilter: ${{ dependency }}
os: ${{ parameters.os }}
buildType: current
- ${{ else }}:
- template: artifact-download.yml
parameters:
componentName: ${{ dependency }}
pipelineId: ${{ parameters.componentVarList[dependency].pipelineId }}
branchName: ${{ parameters.componentVarList[dependency].developBranch }}
aggregatePipeline: ${{ parameters.aggregatePipeline }}
extractAndDeleteFiles: false
${{ if parameters.componentVarList[dependency].hasGpuTarget }}:
fileFilter: ${{ parameters.os }}_${{ parameters.gpuTarget }}
${{ else }}:
fileFilter: ${{ parameters.os }}
- 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') }}:
# dependencySource = staging
${{ if eq(parameters.dependencySource, 'staging')}}:
branchName: ${{ parameters.componentVarList[dependency].stagingBranch }}
# dependencySource = mainline
${{ elseif eq(parameters.dependencySource, 'mainline')}}:
branchName: ${{ parameters.componentVarList[dependency].mainlineBranch }}
# checkoutRef = staging
${{ elseif eq(parameters.checkoutRef, parameters.componentVarList[variables['Build.DefinitionName']].stagingBranch) }}:
branchName: ${{ parameters.componentVarList[dependency].stagingBranch }}
# checkoutRef = mainline
${{ elseif eq(parameters.checkoutRef, parameters.componentVarList[variables['Build.DefinitionName']].mainlineBranch) }}:
branchName: ${{ parameters.componentVarList[dependency].mainlineBranch }}
# SourceBranchName = staging
${{ elseif eq(variables['Build.SourceBranchName'], parameters.componentVarlist[variables['Build.DefinitionName']].stagingBranch) }}:
branchName: ${{ parameters.componentVarList[dependency].stagingBranch }}
# SourceBranchName = mainline
${{ elseif eq(variables['Build.SourceBranchName'], parameters.componentVarlist[variables['Build.DefinitionName']].mainlineBranch) }}:
branchName: ${{ parameters.componentVarList[dependency].mainlineBranch }}
# default = staging
${{ else }}:
branchName: ${{ parameters.componentVarList[dependency].stagingBranch }}
# Set link to redirect llvm folder
- ${{ if eq(parameters.skipLlvmSymlink, false) }}:
- task: Bash@3
displayName: Symlink from rocm/llvm to rocm/lib/llvm
inputs:
@@ -325,7 +439,6 @@ 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:
@@ -333,14 +446,7 @@ 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
@@ -377,16 +483,17 @@ steps:
inputs:
targetType: inline
script: ls -la1R $(Agent.BuildDirectory)/rocm
- 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
- ${{ 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

View File

@@ -23,14 +23,13 @@ steps:
inputs:
targetType: inline
script: |
${{ iif(or(eq(parameters.os, 'ubuntu2204'), eq(parameters.os, 'ubuntu2404')), 'sudo apt-get install -y jq', '') }}
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)
@@ -67,6 +66,8 @@ 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 $manifest_file"
@@ -77,10 +78,6 @@ steps:
done
dependencies_json=$(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" \
@@ -114,14 +111,8 @@ steps:
')
dependencies_rows=$(echo $dependencies_rows)
echo "##vso[task.setvariable variable=dependencies_rows;]$dependencies_rows"
- task: Bash@3
displayName: Print manifest.json
condition: always()
continueOnError: true
inputs:
targetType: inline
script: |
cat $(Build.ArtifactStagingDirectory)/$(manifest_filename).json
cat $manifest_json
- task: Bash@3
displayName: Create manifest.html
condition: always()
@@ -129,10 +120,10 @@ steps:
inputs:
targetType: inline
script: |
manifest_html="$(Build.ArtifactStagingDirectory)/$(manifest_filename).html"
manifest_html=$(Build.ArtifactStagingDirectory)/manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.html
cat <<EOF > $manifest_html
<html>
<h1>$(manifest_filename)</h1>
<h1>Manifest</h1>
<h2>Current</h2>
<table border="1">
<tr>
@@ -172,7 +163,7 @@ steps:
continueOnError: true
inputs:
tabName: Manifest
reportDir: $(Build.ArtifactStagingDirectory)/$(manifest_filename).html
reportDir: $(Build.ArtifactStagingDirectory)/manifest_${{ parameters.componentName }}_$(Build.BuildId)_$(Build.BuildNumber)_${{ parameters.os }}_${{ parameters.gpuTarget }}_${{ parameters.artifactName }}.html
- task: Bash@3
displayName: Save manifest artifact file name
condition: always()
@@ -181,5 +172,5 @@ steps:
workingDirectory: $(Pipeline.Workspace)
targetType: inline
script: |
echo "$(manifest_filename).html" >> pipelineArtifacts.txt
echo "$(manifest_filename).json" >> pipelineArtifacts.txt
echo "manifest_${{ 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

View File

@@ -13,10 +13,11 @@ steps:
CC: $(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
inputs:
targetType: inline
workingDirectory: $(Agent.BuildDirectory)/s
workingDirectory: $(Build.SourcesDirectory)
script: |
AZ_API="https://dev.azure.com/ROCm-CI/ROCm-CI/_apis"
GH_API="https://api.github.com/repos/ROCm"
ARTIFACT_NAME="composablekernelbuild${{ parameters.gpuTarget }}"
EXIT_CODE=0
# Try to find an Azure build for the specific CK commit called out in MIOpen's requirements.txt
@@ -38,15 +39,8 @@ steps:
echo "Found specific CK build ID: $CK_BUILD_ID"
fi
AZURE_URL="$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($gfx)))
| max_by(.name | capture("_(?<dropNumber>\\d+)").dropNumber | tonumber)
| .resource.downloadUrl
' | \
tr -d '"')
AZURE_URL="$AZ_API/build/builds/$CK_BUILD_ID/artifacts?artifactName=$ARTIFACT_NAME&api-version=7.1"
ARTIFACT_URL=$(curl -s $AZURE_URL | jq '.resource.downloadUrl' | tr -d '"')
# If using the specific CK commit and it doesn't have any valid artifacts, use latest successful CK build instead
if { [[ -z "$ARTIFACT_URL" ]] || [[ "$ARTIFACT_URL" == "null" ]]; } && [[ $EXIT_CODE -eq 0 ]]; then
@@ -54,15 +48,8 @@ steps:
LATEST_BUILD_URL="$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/$CK_BUILD_ID/artifacts?api-version=7.1"
ARTIFACT_URL=$(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 '"')
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 '"')
EXIT_CODE=2
fi
@@ -70,8 +57,8 @@ 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)/composable_kernel*/*.tar.gz -C $(Agent.BuildDirectory)/rocm
rm -r $(System.ArtifactsDirectory)/ck.zip $(System.ArtifactsDirectory)/composable_kernel*
tar -zxvf $(System.ArtifactsDirectory)/$ARTIFACT_NAME/*.tar.gz -C $(Agent.BuildDirectory)/rocm
rm -r $(System.ArtifactsDirectory)/ck.zip $(System.ArtifactsDirectory)/$ARTIFACT_NAME
if [[ $EXIT_CODE -ne 0 ]]; then
BUILD_COMMIT=$(curl -s $AZ_API/build/builds/$CK_BUILD_ID | jq '.sourceVersion' | tr -d '"')

View File

@@ -23,25 +23,145 @@ variables:
value: rocm-ci_high_build_pool
- name: ULTRA_BUILD_POOL
value: rocm-ci_ultra_build_pool
- name: ON_PREM_BUILD_POOL
value: rocm-ci_build_pool
- name: LARGE_DISK_BUILD_POOL
value: rocm-ci_larger_base_disk_pool
- name: GFX942_TEST_POOL
value: gfx942_test_pool
- name: GFX90A_TEST_POOL
value: gfx90a_test_pool
- name: LATEST_RELEASE_VERSION
value: 6.4.2
value: 6.4.1
- name: REPO_RADEON_VERSION
value: 6.4.2
value: 6.4.1
- name: NEXT_RELEASE_VERSION
value: 7.0.0
- name: LATEST_RELEASE_TAG
value: rocm-6.4.2
value: rocm-6.4.1
- name: DOCKER_SKIP_GFX
value: gfx90a
- name: AMDMIGRAPHX_PIPELINE_ID
value: 113
- name: AMDSMI_PIPELINE_ID
value: 99
- name: AOMP_EXTRAS_PIPELINE_ID
value: 111
- name: AOMP_PIPELINE_ID
value: 115
- name: CLR_PIPELINE_ID
value: 145
- name: COMPOSABLE_KERNEL_PIPELINE_ID
value: 86
- name: FLANG_LEGACY_PIPELINE_ID
value: 77
- name: HALF_PIPELINE_ID
value: 101
- name: HALF560_PIPELINE_ID
value: 68
- name: HALF560_BUILD_ID
value: 621
- name: HIP_PIPELINE_ID
value: 93
- name: HIP_TESTS_PIPELINE_ID
value: 233
- name: HIPBLAS_COMMON_PIPELINE_ID
value: 223
- name: HIPBLAS_PIPELINE_ID
value: 87
- name: HIPBLASLT_PIPELINE_ID
value: 112
- name: HIPCUB_PIPELINE_ID
value: 277
- name: HIPFFT_PIPELINE_ID
value: 121
- name: HIPFORT_PIPELINE_ID
value: 102
- name: HIPIFY_PIPELINE_ID
value: 92
- name: HIPRAND_PIPELINE_ID
value: 275
- name: HIPSOLVER_PIPELINE_ID
value: 84
- name: HIPSPARSE_PIPELINE_ID
value: 83
- name: HIPSPARSELT_PIPELINE_ID
value: 104
- name: HIPTENSOR_PIPELINE_ID
value: 105
- name: LLVM_PROJECT_PIPELINE_ID
value: 2
- name: MIOPEN_PIPELINE_ID
value: 108
- name: MIVISIONX_PIPELINE_ID
value: 80
- name: RCCL_PIPELINE_ID
value: 107
- name: RDC_PIPELINE_ID
value: 100
- name: ROCAL_PIPELINE_ID
value: 151
- name: ROCALUTION_PIPELINE_ID
value: 89
- name: ROCBLAS_PIPELINE_ID
value: 85
- name: ROCDBGAPI_PIPELINE_ID
value: 135
- name: ROCDECODE_PIPELINE_ID
value: 79
- name: ROCFFT_PIPELINE_ID
value: 120
- name: ROCGDB_PIPELINE_ID
value: 134
- name: ROCJPEG_PIPELINE_ID
value: 262
- name: ROCM_BANDWIDTH_TEST_PIPELINE_ID
value: 88
- name: ROCM_CMAKE_PIPELINE_ID
value: 6
- name: ROCM_CORE_PIPELINE_ID
value: 103
- name: ROCM_EXAMPLES_PIPELINE_ID
value: 216
- name: ROCM_SMI_LIB_PIPELINE_ID
value: 96
- name: ROCMINFO_PIPELINE_ID
value: 91
- name: ROCMLIR_PIPELINE_ID
value: 229
- name: ROCMVALIDATIONSUITE_PIPELINE_ID
value: 106
- name: ROCPRIM_PIPELINE_ID
value: 273
- name: ROCPROFILER_COMPUTE_PIPELINE_ID
value: 257
- name: ROCPROFILER_REGISTER_PIPELINE_ID
value: 1
- name: ROCPROFILER_SDK_PIPELINE_ID
value: 246
- name: ROCPROFILER_SYSTEMS_PIPELINE_ID
value: 255
- name: ROCPROFILER_PIPELINE_ID
value: 143
- name: ROCPYDECODE_PIPELINE_ID
value: 239
- name: ROCR_DEBUG_AGENT_PIPELINE_ID
value: 136
- name: ROCR_RUNTIME_PIPELINE_ID
value: 10
- name: ROCRAND_PIPELINE_ID
value: 274
- name: ROCSOLVER_PIPELINE_ID
value: 81
- name: ROCSPARSE_PIPELINE_ID
value: 98
- name: ROCTHRUST_PIPELINE_ID
value: 276
- name: ROCTRACER_PIPELINE_ID
value: 141
- name: ROCWMMA_PIPELINE_ID
value: 109
- name: RPP_PIPELINE_ID
value: 78
- name: TRANSFERBENCH_PIPELINE_ID
value: 265

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@@ -1,3 +1,38 @@
summarization
QKV
MLPerf
GovReport
oss
openai
gpt
SGLang
amd
MXFP
subproject
ROCpd
rocpd
STL
XCCs
chiplets
hipRTC
nvRTC
warpSize
Datacenter
GST
IET
LTO
MX
Microscaling
NANOO
ROCprof
affinitization
amdclang
benefitting
demangled
inlined
microscaling
roofline
torchtitan
AAC
ABI
ACE
@@ -6,7 +41,6 @@ ACS
AccVGPR
AccVGPRs
ALU
AllReduce
AMD
AMDGPU
AMDGPUs
@@ -14,7 +48,6 @@ AMDMIGraphX
AMI
AOCC
AOMP
AOT
AOTriton
APBDIS
APIC
@@ -34,7 +67,6 @@ Andrej
Arb
Autocast
BARs
BatchNorm
BLAS
BMC
BabelStream
@@ -45,7 +77,6 @@ Bootloader
CAS
CCD
CDNA
CGUI
CHTML
CIFAR
CLI
@@ -83,13 +114,10 @@ ConnectX
CuPy
da
Dashboarding
Dataloading
DBRX
DDR
DF
DGEMM
DGL
DGLGraph
dGPU
dGPUs
DIMM
@@ -107,7 +135,6 @@ DataFrame
DataLoader
DataParallel
Debian
decompositions
DeepSeek
DeepSpeed
Dependabot
@@ -116,7 +143,6 @@ DevCap
DirectX
Dockerfile
Doxygen
dropless
ELMo
ENDPGM
EPYC
@@ -134,12 +160,10 @@ FX
Filesystem
FindDb
Flang
FlashAttention
FluxBenchmark
Fortran
Fuyu
GALB
GAT
GCC
GCD
GCDs
@@ -167,8 +191,6 @@ GPT
GPU
GPU's
GPUs
Graphbolt
GraphSage
GRBM
GenAI
GenZ
@@ -178,11 +200,9 @@ HBM
HCA
HGX
HIPCC
hipDataType
HIPExtension
HIPIFY
HIPification
hipification
HIPify
HPC
HPCG
@@ -197,7 +217,6 @@ Higgs
Hyperparameters
Huggingface
ICD
ICT
ICV
IDE
IDEs
@@ -232,7 +251,6 @@ KV
KVM
Karpathy's
KiB
Kineto
Keras
Khronos
LAPACK
@@ -245,7 +263,6 @@ LM
LSAN
LSan
LTS
LSTMs
LanguageCrossEntropy
LoRA
MEM
@@ -273,7 +290,6 @@ Makefiles
Matplotlib
Matrox
MaxText
Megablocks
Megatrends
Megatron
Mellanox
@@ -283,8 +299,6 @@ Miniconda
MirroredStrategy
Mixtral
MosaicML
MoEs
Mpops
Multicore
Multithreaded
MyEnvironment
@@ -298,7 +312,6 @@ NIC
NICs
NLI
NLP
NN
NPKit
NPS
NSP
@@ -335,7 +348,6 @@ OpenMPI
OpenSSL
OpenVX
OpenXLA
Optim
Oversubscription
PagedAttention
Pallas
@@ -374,7 +386,6 @@ RDC's
RDMA
RDNA
README
Recomputation
RHEL
RMW
RNN
@@ -407,13 +418,11 @@ Ryzen
SALU
SBIOS
SCA
ScaledGEMM
SDK
SDMA
SDPA
SDRAM
SENDMSG
SGLang
SGPR
SGPRs
SHA
@@ -449,8 +458,6 @@ TCI
TCIU
TCP
TCR
TensorRT
TensorFloat
TF
TFLOPS
TP
@@ -458,8 +465,6 @@ TPS
TPU
TPUs
TSME
Taichi
Taichi's
Tagram
TensileLite
TensorBoard
@@ -539,7 +544,6 @@ allocator
allocators
amdgpu
api
aten
atmi
atomics
autogenerated
@@ -710,7 +714,6 @@ installable
interop
interprocedural
intra
intrinsics
invariants
invocating
ipo
@@ -729,13 +732,11 @@ linearized
linter
linux
llvm
lm
localscratch
logits
lossy
macOS
matchers
megatron
microarchitecture
migraphx
migratable
@@ -807,7 +808,6 @@ quantile
quantizer
quasirandom
queueing
qwen
radeon
rccl
rdc
@@ -816,7 +816,6 @@ reStructuredText
redirections
refactorization
reformats
reinforcememt
repo
repos
representativeness
@@ -824,7 +823,6 @@ req
resampling
rescaling
reusability
RLHF
roadmap
roc
rocAL
@@ -862,7 +860,6 @@ roctracer
rst
runtime
runtimes
ResNet
sL
scalability
scalable
@@ -871,7 +868,6 @@ seealso
sendmsg
seqs
serializers
sglang
shader
sharding
sigmoid
@@ -879,7 +875,6 @@ sm
smi
softmax
spack
spmm
src
stochastically
strided
@@ -888,7 +883,6 @@ subdirectory
subexpression
subfolder
subfolders
submatrix
submodule
submodules
subnet
@@ -913,7 +907,6 @@ torchvision
tqdm
tracebacks
txt
TopK
uarch
uncached
uncacheable
@@ -941,7 +934,6 @@ vectorize
vectorized
vectorizer
vectorizes
verl
virtualize
virtualized
vjxb

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@@ -23,6 +23,9 @@ source software compilers, debuggers, and libraries. ROCm is fully integrated in
> A new open source build platform for ROCm is under development at
> https://github.com/ROCm/TheRock, featuring a unified CMake build with bundled
> dependencies, Windows support, and more.
>
> The instructions below describe the prior process for building from source
> which will be replaced once TheRock is mature enough.
## Getting and Building ROCm from Source

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@@ -1,690 +0,0 @@
<!-- Do not edit this file! -->
<!-- This file is autogenerated with -->
<!-- tools/autotag/tag_script.py -->
<!-- Disable lints since this is an auto-generated file. -->
<!-- markdownlint-disable blanks-around-headers -->
<!-- markdownlint-disable no-duplicate-header -->
<!-- markdownlint-disable no-blanks-blockquote -->
<!-- markdownlint-disable ul-indent -->
<!-- markdownlint-disable no-trailing-spaces -->
<!-- markdownlint-disable reference-links-images -->
<!-- markdownlint-disable no-missing-space-atx -->
<!-- spellcheck-disable -->
# ROCm 6.4.2 release notes
The release notes provide a summary of notable changes since the previous ROCm release.
- [Release highlights](#release-highlights)
- [Operating system and hardware support changes](#operating-system-and-hardware-support-changes)
- [ROCm components versioning](#rocm-components)
- [Detailed component changes](#detailed-component-changes)
- [ROCm known issues](#rocm-known-issues)
- [ROCm resolved issues](#rocm-resolved-issues)
- [ROCm upcoming changes](#rocm-upcoming-changes)
```{note}
If youre using AMD Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
documentation to verify compatibility and system requirements.
```
## Release highlights
The following are notable new features and improvements in ROCm 6.4.2. For changes to individual components, see
[Detailed component changes](#detailed-component-changes).
### ROCm Compute Profiler enhancements
[ROCm Compute Profiler](https://rocm.docs.amd.com/projects/rocprofiler-compute/en/latest/index.html) includes the following changes:
* The ``--roofline-data-type`` option now supports FP8, FP16, BF16, FP32, FP64, I8, I32, and I64 data types. This is dependent on the GPU architecture. For more information, see [Roofline options](https://rocm.docs.amd.com/projects/rocprofiler-compute/en/docs-6.4.2/how-to/profile/mode.html#roofline-options).
* ROCm Compute Profiler now uses [AMD SMI](https://rocm.docs.amd.com/projects/amdsmi/en/latest/index.html) instead of [ROCm SMI](https://rocm.docs.amd.com/projects/rocm_smi_lib/en/latest/index.html). The AMD System Management Interface Library (AMD SMI) is a successor to ROCm SMI. It is a unified system management interface tool that provides a user-space interface for applications to monitor and control GPU applications and gives users the ability to query information about drivers and GPUs on the system. For more information, see [https://github.com/ROCm/amdsmi](https://github.com/ROCm/amdsmi) and the [AMD SMI documentation](https://rocm.docs.amd.com/projects/amdsmi/en/latest/index.html).
* ROCm Compute Profiler has added 8-bit floating point (FP8) metrics support for AMD Instinct MI300 series accelerators. For more information, see [System Speed-of-Light](https://rocm.docs.amd.com/projects/rocprofiler-compute/en/docs-6.4.2/conceptual/system-speed-of-light.html).
### rocSOLVER enhancements
rocSOLVER has improved the performance of eigensolvers and singular value decomposition (SVD). For more information, see [rocSOLVER documentation](https://rocm.docs.amd.com/projects/rocSOLVER/en/docs-6.4.2/index.html).
### ROCm Offline Installer Creator updates
The ROCm Offline Installer Creator 6.4.2 includes the following features and improvements:
* Added support for Oracle Linux 8.10 and 9.6, and SLES 15 SP7.
* Additional package options for the Offline Installer Creator, including `amd-smi`, `rocdecode`, `rocjpeg`, and `rdc`.
* ROCm meta packages are now used for selecting ROCm components and use cases.
* Improved separation of kernel/driver and ROCm prerequisite packages to reduce the size of ROCm-only or driver-only offline installers.
In addition, the option to build an offline installer based on ROCm version 5.7.3 has been removed. To build an offline installer for ROCm 5.7.3, use the Offline Installer Creator from version 6.4.1 or earlier. See [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.2/install/rocm-offline-installer.html) for more information.
### ROCm Runfile Installer updates
The ROCm Runfile Installer 6.4.2 adds support for Oracle Linux 8.10 and 9.6 (using the RHEL 8 or 9 .run files), Debian 12 (using the Ubuntu 22.04 .run file), and SLES 15 SP7. It also fixes permission settings issues during ROCm and AMDGPU driver installation. For more information, see [ROCm Runfile Installer](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.2/install/rocm-runfile-installer.html).
### ROCm documentation updates
ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider variety of user needs and use cases.
* [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been expanded with the following four new tutorials:
* Inference tutorial: [AI agent with MCPs using vLLM and PydanticAI](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/inference/build_airbnb_agent_mcp.html)
* GPU development and optimization tutorials:
* [Kernel development and optimization with Triton](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/gpu_dev_optimize/triton_kernel_dev.html)
* [Profiling Llama-4 inference with vLLM](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/gpu_dev_optimize/llama4_profiling_vllm.html)
* [FP8 quantization with AMD Quark for vLLM](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/gpu_dev_optimize/fp8_quantization_quark_vllm.html)
For more information about the changes, see [Changelog for the AI Developer Hub](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/changelog.html).
* ROCm provides a comprehensive ecosystem for deep learning development. For more details, see [Deep learning frameworks for ROCm](https://rocm.docs.amd.com/en/docs-6.4.2/how-to/deep-learning-rocm.html). As of July 2025, AMD ROCm provides support for the following additional deep learning frameworks:
* Deep Graph Library is an easy-to-use, high-performance, and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component in an end-to-end application, the rest of the logic is implemented using PyTorch. It is currently supported on ROCm 6.4.0. For more information, see [DGL compatibility](https://rocm.docs.amd.com/en/docs-6.4.2/compatibility/ml-compatibility/dgl-compatibility.html).
* Stanford Megatron-LM is a large-scale language model training framework. Its designed to train massive transformer-based language models efficiently by model and data parallelism. It is currently supported on ROCm 6.3.0. For more information, see [Stanford Megatron-LM compatibility](https://rocm.docs.amd.com/en/docs-6.4.2/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.html).
* Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs). verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support. It is currently supported on ROCm 6.2.0. For more information, see [verl compatibility](https://rocm.docs.amd.com/en/docs-6.4.2/compatibility/ml-compatibility/verl-compatibility.html).
* Documentation for the new [ROCprof Compute Viewer](https://rocm.docs.amd.com/projects/rocprof-compute-viewer/en/docs-6.4.2/) was added in May 2025. This tool is used to visualize and analyze GPU thread trace data collected using [rocprofv3](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/latest/index.html). Note that [ROCprof Compute Viewer](https://rocm.docs.amd.com/projects/rocprof-compute-viewer/en/docs-6.4.2/) is in an early access state. Running production workloads is not recommended.
* The AMDGPU installer documentation has been removed to encourage the use of the package manager for ROCm installation. While the package manager is the recommended method, you can still install ROCm using the AMDGPU installer by following the [legacy process](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.1/install/install-methods/amdgpu-installer-index.html). Ensure to update the command with the intended ROCm version before running it. For more information, see [Installation via native package manager](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.2/install/install-methods/package-manager-index.html).
## Operating system and hardware support changes
ROCm 6.4.2 adds support for SLES 15 SP7. For more information, see [SLES installation](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.2/install/install-methods/package-manager/package-manager-sles.html).
ROCm 6.4.2 marks the end of support (EoS) for RHEL 9.5.
ROCm 6.4.2 adds support for RDNA3 architecture-based [Radeon RX 7700 XT](https://www.amd.com/en/products/graphics/desktops/radeon/7000-series/amd-radeon-rx-7700-xt.html) GPU. This GPU is supported on Ubuntu 24.04.2 and RHEL 9.6.
For details, see the full list of [Supported GPUs
(Linux)](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.2/reference/system-requirements.html#supported-gpus).
See the [Compatibility
matrix](../../docs/compatibility/compatibility-matrix.rst)
for more information about operating system and hardware compatibility.
## ROCm components
The following table lists the versions of ROCm components for ROCm 6.4.2, including any version
changes from 6.4.1 to 6.4.2. Click the component's updated version to go to a list of its changes.
Click {fab}`github` to go to the component's source code on GitHub.
<div class="pst-scrollable-table-container">
<table id="rocm-rn-components" class="table">
<thead>
<tr>
<th>Category</th>
<th>Group</th>
<th>Name</th>
<th>Version</th>
<th></th>
</tr>
</thead>
<colgroup>
<col span="1">
<col span="1">
</colgroup>
<tbody class="rocm-components-libs rocm-components-ml">
<tr>
<th rowspan="9">Libraries</th>
<th rowspan="9">Machine learning and computer vision</th>
<td><a href="https://rocm.docs.amd.com/projects/composable_kernel/en/docs-6.4.2/index.html">Composable Kernel</a></td>
<td>1.1.0</td>
<td><a href="https://github.com/ROCm/composable_kernel"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/AMDMIGraphX/en/docs-6.4.2/index.html">MIGraphX</a></td>
<td>2.12.0</td>
<td><a href="https://github.com/ROCm/AMDMIGraphX"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/MIOpen/en/docs-6.4.2/index.html">MIOpen</a></td>
<td>3.4.0</td>
<td><a href="https://github.com/ROCm/MIOpen"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/MIVisionX/en/docs-6.4.2/index.html">MIVisionX</a></td>
<td>3.2.0</td>
<td><a href="https://github.com/ROCm/MIVisionX"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocAL/en/docs-6.4.2/index.html">rocAL</a></td>
<td>2.2.0</td>
<td><a href="https://github.com/ROCm/rocAL"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocDecode/en/docs-6.4.2/index.html">rocDecode</a></td>
<td>0.10.0</td>
<td><a href="https://github.com/ROCm/rocDecode"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocJPEG/en/docs-6.4.2/index.html">rocJPEG</a></td>
<td>0.8.0</td>
<td><a href="https://github.com/ROCm/rocJPEG"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocPyDecode/en/docs-6.4.2/index.html">rocPyDecode</a></td>
<td>0.3.1</td>
<td><a href="https://github.com/ROCm/rocPyDecode"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rpp/en/docs-6.4.2/index.html">RPP</a></td>
<td>1.9.10</td>
<td><a href="https://github.com/ROCm/rpp"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-libs rocm-components-communication tbody-reverse-zebra">
<tr>
<th rowspan="2"></th>
<th rowspan="2">Communication</th>
<td><a href="https://rocm.docs.amd.com/projects/rccl/en/docs-6.4.2/index.html">RCCL</a></td>
<td>2.22.3&nbsp;&Rightarrow;&nbsp;<a href="#rccl-2-22-3">2.22.3</td>
<td><a href="https://github.com/ROCm/rccl"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocSHMEM/en/docs-6.4.2/index.html">rocSHMEM</a></td>
<td>2.0.0&nbsp;&Rightarrow;&nbsp;<a href="#rocshmem-2-0-1">2.0.1</td>
<td><a href="https://github.com/ROCm/rocSHMEM"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-libs rocm-components-math tbody-reverse-zebra">
<tr>
<th rowspan="16"></th>
<th rowspan="16">Math</th>
<td><a href="https://rocm.docs.amd.com/projects/hipBLAS/en/docs-6.4.2/index.html">hipBLAS</a></td>
<td>2.4.0</td>
<td><a href="https://github.com/ROCm/hipBLAS"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipBLASLt/en/docs-6.4.2/index.html">hipBLASLt</a></td>
<td>0.12.1&nbsp;&Rightarrow;&nbsp;<a href="#hipblaslt-0-12-1">0.12.1</td>
<td><a href="https://github.com/ROCm/hipBLASLt"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipFFT/en/docs-6.4.2/index.html">hipFFT</a></td>
<td>1.0.18</td>
<td><a href="https://github.com/ROCm/hipFFT"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipfort/en/docs-6.4.2/index.html">hipfort</a></td>
<td>0.6.0</td>
<td><a href="https://github.com/ROCm/hipfort"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipRAND/en/docs-6.4.2/index.html">hipRAND</a></td>
<td>2.12.0</td>
<td><a href="https://github.com/ROCm/hipRAND"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipSOLVER/en/docs-6.4.2/index.html">hipSOLVER</a></td>
<td>2.4.0</td>
<td><a href="https://github.com/ROCm/hipSOLVER"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipSPARSE/en/docs-6.4.2/index.html">hipSPARSE</a></td>
<td>3.2.0</td>
<td><a href="https://github.com/ROCm/hipSPARSE"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipSPARSELt/en/docs-6.4.2/index.html">hipSPARSELt</a></td>
<td>0.2.3</td>
<td><a href="https://github.com/ROCm/hipSPARSELt"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocALUTION/en/docs-6.4.2/index.html">rocALUTION</a></td>
<td>3.2.3</td>
<td><a href="https://github.com/ROCm/rocALUTION"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocBLAS/en/docs-6.4.2/index.html">rocBLAS</a></td>
<td>4.4.0&nbsp;&Rightarrow;&nbsp;<a href="#rocblas-4-4-1">4.4.1</td></td>
<td><a href="https://github.com/ROCm/rocBLAS"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocFFT/en/docs-6.4.2/index.html">rocFFT</a></td>
<td>1.0.32</td>
<td><a href="https://github.com/ROCm/rocFFT"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocRAND/en/docs-6.4.2/index.html">rocRAND</a></td>
<td>3.3.0</td>
<td><a href="https://github.com/ROCm/rocRAND"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocSOLVER/en/docs-6.4.2/index.html">rocSOLVER</a></td>
<td>3.28.0&nbsp;&Rightarrow;&nbsp;<a href="#rocsolver-3-28-2">3.28.2</td>
<td><a href="https://github.com/ROCm/rocSOLVER"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocSPARSE/en/docs-6.4.2/index.html">rocSPARSE</a></td>
<td>3.4.0</td>
<td><a href="https://github.com/ROCm/rocSPARSE"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocWMMA/en/docs-6.4.2/index.html">rocWMMA</a></td>
<td>1.7.0</td>
<td><a href="https://github.com/ROCm/rocWMMA"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/Tensile/en/docs-6.4.2/src/index.html">Tensile</a></td>
<td>4.43.0</td>
<td><a href="https://github.com/ROCm/Tensile"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-libs rocm-components-primitives tbody-reverse-zebra">
<tr>
<th rowspan="4"></th>
<th rowspan="4">Primitives</th>
<td><a href="https://rocm.docs.amd.com/projects/hipCUB/en/docs-6.4.2/index.html">hipCUB</a></td>
<td>3.4.0</td>
<td><a href="https://github.com/ROCm/hipCUB"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/hipTensor/en/docs-6.4.2/index.html">hipTensor</a></td>
<td>1.5.0</td>
<td><a href="https://github.com/ROCm/hipTensor"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocPRIM/en/docs-6.4.2/index.html">rocPRIM</a></td>
<td>3.4.0&nbsp;&Rightarrow;&nbsp;<a href="#rocprim-3-4-1">3.4.1</td>
<td><a href="https://github.com/ROCm/rocPRIM"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocThrust/en/docs-6.4.2/index.html">rocThrust</a></td>
<td>3.3.0</td>
<td><a href="https://github.com/ROCm/rocThrust"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-tools rocm-components-system tbody-reverse-zebra">
<tr>
<th rowspan="7">Tools</th>
<th rowspan="7">System management</th>
<td><a href="https://rocm.docs.amd.com/projects/amdsmi/en/docs-6.4.2/index.html">AMD SMI</a></td>
<td>25.4.2&nbsp;&Rightarrow;&nbsp;<a href="#amd-smi-25-5-1">25.5.1</a></td>
<td><a href="https://github.com/ROCm/amdsmi"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rdc/en/docs-6.4.2/index.html">ROCm Data Center Tool</a></td>
<td>0.3.0</td>
<td><a href="https://github.com/ROCm/rdc"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocminfo/en/docs-6.4.2/index.html">rocminfo</a></td>
<td>1.0.0</td>
<td><a href="https://github.com/ROCm/rocminfo"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocm_smi_lib/en/docs-6.4.2/index.html">ROCm SMI</a></td>
<td>7.5.0</td>
<td><a href="https://github.com/ROCm/rocm_smi_lib"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/docs-6.4.2/index.html">ROCm Validation Suite</a></td>
<td>1.1.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-validation-suite-1-1-0">1.1.0</td>
<td><a href="https://github.com/ROCm/ROCmValidationSuite"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-tools rocm-components-perf">
<tr>
<th rowspan="6"></th>
<th rowspan="6">Performance</th>
<td><a href="https://rocm.docs.amd.com/projects/rocm_bandwidth_test/en/docs-6.4.2/index.html">ROCm Bandwidth
Test</a></td>
<td>1.4.0</td>
<td><a href="https://github.com/ROCm/rocm_bandwidth_test/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocprofiler-compute/en/docs-6.4.2/index.html">ROCm Compute Profiler</a></td>
<td>3.1.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-compute-profiler-3-1-1">3.1.1</td>
<td><a href="https://github.com/ROCm/rocprofiler-compute"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocprofiler-systems/en/docs-6.4.2/index.html">ROCm Systems Profiler</a></td>
<td>1.0.1&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-1-0-2">1.0.2</td>
<td><a href="https://github.com/ROCm/rocprofiler-systems"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.4.2/index.html">ROCProfiler</a></td>
<td>2.0.0</td>
<td><a href="https://github.com/ROCm/ROCProfiler/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/docs-6.4.2/index.html">ROCprofiler-SDK</a></td>
<td>0.6.0</td>
<td><a href="https://github.com/ROCm/rocprofiler-sdk/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr >
<td><a href="https://rocm.docs.amd.com/projects/roctracer/en/docs-6.4.2/index.html">ROCTracer</a></td>
<td>4.1.0</td>
<td><a href="https://github.com/ROCm/ROCTracer/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-tools rocm-components-dev">
<tr>
<th rowspan="5"></th>
<th rowspan="5">Development</th>
<td><a href="https://rocm.docs.amd.com/projects/HIPIFY/en/docs-6.4.2/index.html">HIPIFY</a></td>
<td>19.0.0</td>
<td><a href="https://github.com/ROCm/HIPIFY/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/ROCdbgapi/en/docs-6.4.2/index.html">ROCdbgapi</a></td>
<td>0.77.2</td>
<td><a href="https://github.com/ROCm/ROCdbgapi/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/ROCmCMakeBuildTools/en/docs-6.4.2/index.html">ROCm CMake</a></td>
<td>0.14.0</td>
<td><a href="https://github.com/ROCm/rocm-cmake/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/ROCgdb/en/docs-6.4.2/index.html">ROCm Debugger (ROCgdb)</a>
</td>
<td>15.2</td>
<td><a href="https://github.com/ROCm/ROCgdb/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/rocr_debug_agent/en/docs-6.4.2/index.html">ROCr Debug Agent</a>
</td>
<td>2.0.4</td>
<td><a href="https://github.com/ROCm/rocr_debug_agent/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-compilers tbody-reverse-zebra">
<tr>
<th rowspan="2" colspan="2">Compilers</th>
<td><a href="https://rocm.docs.amd.com/projects/HIPCC/en/docs-6.4.2/index.html">HIPCC</a></td>
<td>1.1.1</td>
<td><a href="https://github.com/ROCm/llvm-project/tree/amd-staging/amd/hipcc"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.4.2/index.html">llvm-project</a></td>
<td>19.0.0</td>
<td><a href="https://github.com/ROCm/llvm-project/"><i
class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
<tbody class="rocm-components-runtimes tbody-reverse-zebra">
<tr>
<th rowspan="2" colspan="2">Runtimes</th>
<td><a href="https://rocm.docs.amd.com/projects/HIP/en/docs-6.4.2/index.html">HIP</a></td>
<td>6.4.1&nbsp;&Rightarrow;&nbsp;<a href="#hip-6-4-2">6.4.2</td>
<td><a href="https://github.com/ROCm/HIP/"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
<tr>
<td><a href="https://rocm.docs.amd.com/projects/ROCR-Runtime/en/docs-6.4.2/index.html">ROCr Runtime</a></td>
<td>1.15.0</td>
<td><a href="https://github.com/ROCm/ROCR-Runtime/"><i class="fab fa-github fa-lg"></i></a></td>
</tr>
</tbody>
</table>
</div>
## Detailed component changes
The following sections describe key changes to ROCm components.
```{note}
For a historical overview of ROCm component updates, see the {doc}`ROCm consolidated changelog </release/changelog>`.
```
### **AMD SMI** (25.5.1)
#### Added
- Compute Unit Occupancy information per process.
- Support for getting the GPU Board voltage.
- New firmware PLDM_BUNDLE. `amd-smi firmware` can now show the PLDM Bundle on supported systems.
- `amd-smi ras --afid --cper-file <file_path>` to decode CPER records.
#### Changed
- Padded `asic_serial` in `amdsmi_get_asic_info` with 0s.
- Renamed field `COMPUTE_PARTITION` to `ACCELERATOR_PARTITION` in CLI call `amd-smi --partition`.
#### Resolved issues
- Corrected VRAM memory calculation in `amdsmi_get_gpu_process_list`. Previously, the VRAM memory usage reported by `amdsmi_get_gpu_process_list` was inaccurate and was calculated using KB instead of KiB.
```{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** (6.4.2)
#### Added
* HIP API implementation for `hipEventRecordWithFlags`, records an event in the specified stream with flags.
* Support for the pointer attribute `HIP_POINTER_ATTRIBUTE_CONTEXT`.
* Support for the flags `hipEventWaitDefault` and `hipEventWaitExternal`.
#### Optimized
* Improved implementation in `hipEventSynchronize`, HIP runtime now makes internal callbacks as non-blocking operations to improve performance.
#### Resolved issues
* Issue of dependency on `libgcc-s1` during rocm-dev install on Debian Buster. HIP runtime removed this Debian package dependency, and uses `libgcc1` instead for this distros.
* Building issue for `COMGR` dynamic load on Fedora and other Distros. HIP runtime now doesn't link against `libamd_comgr.so`.
* Failure in the API `hipStreamDestroy`, when stream type is `hipStreamLegacy`. The API now returns error code `hipErrorInvalidResourceHandle` on this condition.
* Kernel launch errors, such as `shared object initialization failed`, `invalid device function` or `kernel execution failure`. HIP runtime now loads `COMGR` properly considering the file with its name and mapped image.
* Memory access fault in some applications. HIP runtime fixed offset accumulation in memory address.
* The memory leak in virtual memory management (VMM). HIP runtime now uses the size of handle for allocated memory range instead of actual size for physical memory, which fixed the issue of address clash with VMM.
* Large memory allocation issue. HIP runtime now checks GPU video RAM and system RAM properly and sets size limits during memory allocation either on the host or the GPU device.
* Support of `hipDeviceMallocContiguous` flags in `hipExtMallocWithFlags()`. It now enables `HSA_AMD_MEMORY_POOL_CONTIGUOUS_FLAG` in the memory pool allocation on GPU device.
* Radom memory segmentation fault in handling `GraphExec` object release and `hipDeviceSyncronization`. HIP runtime now uses internal device synchronize function in `__hipUnregisterFatBinary`.
### **hipBLASLt** (0.12.1)
#### Added
* Support for gfx1151 on Linux, complementing the previous support in the HIP SDK for Windows.
### **RCCL** (2.22.3)
#### Added
* Added support for the LL128 protocol on gfx942.
### **rocBLAS** (4.4.1)
#### Resolved issues
* rocBLAS might have failed to produce correct results for cherk/zherk on gfx90a/gfx942 with problem sizes k > 500 due to the imaginary portion on the C matrix diagonal not being zeros. rocBLAS now zeros the imaginary portion.
### **ROCm Compute Profiler** (3.1.1)
#### Added
* 8-bit floating point (FP8) metrics support for AMD Instinct MI300 GPUs.
* Additional data types for roofline: FP8, FP16, BF16, FP32, FP64, I8, I32, I64 (dependent on the GPU architecture).
* Data type selection option ``--roofline-data-type / -R`` for roofline profiling. The default data type is FP32.
#### Changed
* Changed dependency from `rocm-smi` to `amd-smi`.
#### Resolved issues
* Fixed a crash related to Agent ID caused by the new format of the `rocprofv3` output CSV file.
### **ROCm Systems Profiler** (1.0.2)
#### Optimized
* Improved readability of the OpenMP target offload traces by showing on a single Perfetto track.
#### Resolved issues
* Fixed the file path to the script that merges Perfetto files from multi-process MPI runs. The script has also been renamed from `merge-multiprocess-output.sh` to `rocprof-sys-merge-output.sh`.
### **ROCm Validation Suite** (1.1.0)
#### Added
* NPS2/DPX and NPS4/CPX partition modes support for AMD Instinct MI300X.
### **rocPRIM** (3.4.1)
#### Upcoming changes
* Changes to the template parameters of warp and block algorithms will be made in an upcoming release.
* Due to an upcoming compiler change, the following symbols related to warp size have been marked as deprecated and will be removed in an upcoming major release:
* `rocprim::device_warp_size()`. This has been replaced by `rocprim::arch::wavefront::min_size()` and `rocprim::arch::wavefront::max_size()` for compile-time constants. Use these when allocating global or shared memory. For run-time constants, use `rocprim::arch::wavefront::size()`.
* `rocprim::warp_size()`
* `ROCPRIM_WAVEFRONT_SIZE`
* The default scan accumulator types for device-level scan algorithms will be changed in an upcoming release, resulting in a breaking change. Previously, the default accumulator type was set to the input type for the inclusive scans and to the initial value type for the exclusive scans. This could lead to unexpected overflow if the input or initial type was smaller than the output type when the accumulator type wasn't explicitly set using the `AccType` template parameter. The new default accumulator types will be set to the type that results when the input or initial value type is applied to the scan operator.
The following is the complete list of affected functions and how their default accumulator types are changing:
* `rocprim::inclusive_scan`
* current default: `class AccType = typename std::iterator_traits<InputIterator>::value_type>`
* future default: `class AccType = rocprim::invoke_result_binary_op_t<typename std::iterator_traits<InputIterator>::value_type, BinaryFunction>`
* `rocprim::deterministic_inclusive_scan`
* current default: `class AccType = typename std::iterator_traits<InputIterator>::value_type>`
* future default: `class AccType = rocprim::invoke_result_binary_op_t<typename std::iterator_traits<InputIterator>::value_type, BinaryFunction>`
* `rocprim::exclusive_scan`
* current default: `class AccType = detail::input_type_t<InitValueType>>`
* future default: `class AccType = rocprim::invoke_result_binary_op_t<rocprim::detail::input_type_t<InitValueType>, BinaryFunction>`
* `rocprim::deterministic_exclusive_scan`
* current default: `class AccType = detail::input_type_t<InitValueType>>`
* future default: `class AccType = rocprim::invoke_result_binary_op_t<rocprim::detail::input_type_t<InitValueType>, BinaryFunction>`
* `rocprim::load_cs` and `rocprim::store_cs` are deprecated and will be removed in an upcoming release. Alternatively, you can use `rocprim::load_nontemporal` and `rocprim::store_nontemporal` to load and store values in specific conditions (like bypassing the cache) for `rocprim::thread_load` and `rocprim::thread_store`.
### **rocSHMEM** (2.0.1)
#### Resolved issues
* Incorrect output for `rocshmem_ctx_my_pe` and `rocshmem_ctx_n_pes`.
* Multi-team errors by providing team specific buffers in `rocshmem_ctx_wg_team_sync`.
* Missing implementation of `rocshmem_g` for IPC conduit.
### **rocSOLVER** (3.28.2)
#### Added
* Hybrid computation support for existing routines, such as STERF.
* SVD for general matrices based on Cuppen's Divide and Conquer algorithm:
- GESDD (with batched and strided\_batched versions)
#### Optimized
* Reduced the device memory requirements for STEDC, SYEVD/HEEVD, and SYGVD/HEGVD.
* Improved the performance of STEDC and divide and conquer Eigensolvers.
* Improved the performance of SYTRD, the initial step of the Eigensolvers that start with the tridiagonalization of the input matrix.
## ROCm known issues
ROCm known issues are noted on {fab}`github` [GitHub](https://github.com/ROCm/ROCm/labels/Verified%20Issue). For known
issues related to individual components, review the [Detailed component changes](#detailed-component-changes).
## ROCm resolved issues
The following are previously known issues resolved in this release. For resolved issues related to
individual components, review the [Detailed component changes](#detailed-component-changes).
### AMD SMI CLI: CPER entries not dumped continuously when using follow flag
An issue where CPER entries were not streamed continuously as intended when using the `--follow` flag with `amd-smi ras --cper` has been resolved. See [GitHub issue #4768](https://github.com/ROCm/ROCm/issues/4768).
### Instinct MI300X reports incorrect raw GPU timestamps
An issue where the command processor firmware reported incorrect raw GPU timestamps on MI300X accelerators has been resolved. See [GitHub issue #4079](https://github.com/ROCm/ROCm/issues/4079).
### MIOpen generates incorrect results for particular input with FP32 data type
An issue where MIOpen generated incorrect results on the `conv2dbackward` function for a particular input with 32-bit floating point (FP32) data types has been resolved. The issue was only specific to FP32 data types with 2 * 2 kernel size and dilation 2 * 1. See [GitHub issue #4606](https://github.com/ROCm/ROCm/issues/4606).
## ROCm upcoming changes
The following changes to the ROCm software stack are anticipated for future releases.
### AMD SMI migration to AMDGPU driver repository
In a future release, [AMD SMI](https://github.com/ROCm/amdsmi) will be relocated from the ROCm organization repository to a new AMDTools repository to better align with its system-level functionality. `amd-smi-lib` will no longer be included in the `rocm-developer-tools` meta-package included with your standard ROCm installation. Instead, it will be packaged with the AMDGPU driver installation.
### ROCm SMI deprecation
[ROCm SMI](https://github.com/ROCm/rocm_smi_lib) will be phased out in an
upcoming ROCm release and will enter maintenance mode. After this transition,
only critical bug fixes will be addressed and no further feature development
will take place.
It's strongly recommended to transition your projects to [AMD
SMI](https://github.com/ROCm/amdsmi), the successor to ROCm SMI. AMD SMI
includes all the features of the ROCm SMI and will continue to receive regular
updates, new functionality, and ongoing support. For more information on AMD
SMI, see the [AMD SMI documentation](https://rocm.docs.amd.com/projects/amdsmi/en/latest/).
### ROCTracer, ROCProfiler, rocprof, and rocprofv2 deprecation
Development and support for ROCTracer, ROCProfiler, `rocprof`, and `rocprofv2` are being phased out in favor of ROCprofiler-SDK in upcoming ROCm releases. Starting with ROCm 6.4, only critical defect fixes will be addressed for older versions of the profiling tools and libraries. All users are encouraged to upgrade to the latest version of the ROCprofiler-SDK library and the (`rocprofv3`) tool to ensure continued support and access to new features. ROCprofiler-SDK is still in beta today and will be production-ready in a future ROCm release.
It's anticipated that ROCTracer, ROCProfiler, `rocprof`, and `rocprofv2` will reach end-of-life by future releases, aligning with Q1 of 2026.
### AMDGPU wavefront size compiler macro deprecation
Access to the wavefront size as a compile-time constant via the `__AMDGCN_WAVEFRONT_SIZE`
and `__AMDGCN_WAVEFRONT_SIZE__` macros or the `constexpr warpSize` variable is deprecated
and will be disabled in a future release.
* The `__AMDGCN_WAVEFRONT_SIZE__` macro and `__AMDGCN_WAVEFRONT_SIZE` alias will be removed in an upcoming release.
It is recommended to remove any use of this macro. For more information, see
[AMDGPU support](https://rocm.docs.amd.com/projects/llvm-project/en/docs-6.4.2/LLVM/clang/html/AMDGPUSupport.html).
* `warpSize` will only be available as a non-`constexpr` variable. Where required,
the wavefront size should be queried via the `warpSize` variable in device code,
or via `hipGetDeviceProperties` in host code. Neither of these will result in a compile-time constant. For more information, see [warpSize](https://rocm.docs.amd.com/projects/HIP/en/docs-6.4.2/how-to/hip_cpp_language_extensions.html#warpsize).
* For cases where compile-time evaluation of the wavefront size cannot be avoided,
uses of `__AMDGCN_WAVEFRONT_SIZE`, `__AMDGCN_WAVEFRONT_SIZE__`, or `warpSize`
can be replaced with a user-defined macro or `constexpr` variable with the wavefront
size(s) for the target hardware. For example:
```
#if defined(__GFX9__)
#define MY_MACRO_FOR_WAVEFRONT_SIZE 64
#else
#define MY_MACRO_FOR_WAVEFRONT_SIZE 32
#endif
```
### HIPCC Perl scripts deprecation
The HIPCC Perl scripts (`hipcc.pl` and `hipconfig.pl`) will be removed in an upcoming release.
### Changes to ROCm Object Tooling
ROCm Object Tooling tools ``roc-obj-ls``, ``roc-obj-extract``, and ``roc-obj`` are
deprecated in ROCm 6.4, and will be removed in a future release. Functionality
has been added to the ``llvm-objdump --offloading`` tool option to extract all
clang-offload-bundles into individual code objects found within the objects
or executables passed as input. The ``llvm-objdump --offloading`` tool option also
supports the ``--arch-name`` option, and only extracts code objects found with
the specified target architecture. See [llvm-objdump](https://llvm.org/docs/CommandGuide/llvm-objdump.html)
for more information.
### HIP runtime API changes
There are a number of upcoming changes planned for HIP runtime API in an upcoming major release
that are not backward compatible with prior releases. Most of these changes increase
alignment between HIP and CUDA APIs or behavior. Some of the upcoming changes are to
clean up header files, remove namespace collision, and have a clear separation between
`hipRTC` and HIP runtime. For more information, see [HIP 7.0 Is Coming: What You Need to Know to Stay Ahead](https://rocm.blogs.amd.com/ecosystems-and-partners/transition-to-hip-7.0-blog/README.html).

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<?xml version="1.0" encoding="UTF-8"?>
<manifest>
<remote name="rocm-org" fetch="https://github.com/ROCm/" />
<default revision="refs/tags/rocm-6.4.2"
<default revision="refs/tags/rocm-6.4.1"
remote="rocm-org"
sync-c="true"
sync-j="4" />

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<head>
<meta charset="UTF-8">
<meta name="description" content="ROCm licensing terms">
<meta name="keywords" content="license, licensing terms">
</head>
# ROCm license
```{include} ../../LICENSE
```
:::{note}
The preceding license applies to the [ROCm repository](https://github.com/ROCm/ROCm), which
primarily contains documentation. For licenses related to other ROCm components, refer to the
following section.
:::
## ROCm component licenses
ROCm is released by Advanced Micro Devices, Inc. (AMD) and is licensed per component separately.
The following table is a list of ROCm components with links to their respective license
terms. These components may include third party components subject to
additional licenses. Please review individual repositories for more information.
<!-- spellcheck-disable -->
| Component | License |
|:---------------------|:-------------------------|
| [AMD Compute Language Runtime (CLR)](https://github.com/ROCm/clr) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/LICENSE.txt) |
| [AMD SMI](https://github.com/ROCm/amdsmi) | [MIT](https://github.com/ROCm/amdsmi/blob/amd-staging/LICENSE) |
| [aomp](https://github.com/ROCm/aomp/) | [Apache 2.0](https://github.com/ROCm/aomp/blob/aomp-dev/LICENSE) |
| [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
| [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
| [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
| [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
| [HIP](https://github.com/ROCm/HIP/) | [MIT](https://github.com/ROCm/HIP/blob/amd-staging/LICENSE.txt) |
| [hipamd](https://github.com/ROCm/clr/tree/amd-staging/hipamd) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/hipamd/LICENSE.txt) |
| [hipBLAS](https://github.com/ROCm/hipBLAS/) | [MIT](https://github.com/ROCm/hipBLAS/blob/develop/LICENSE.md) |
| [hipBLASLt](https://github.com/ROCm/hipBLASLt/) | [MIT](https://github.com/ROCm/hipBLASLt/blob/develop/LICENSE.md) |
| [HIPCC](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/hipcc) | [MIT](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/hipcc/LICENSE.txt) |
| [hipCUB](https://github.com/ROCm/hipCUB/) | [Custom](https://github.com/ROCm/hipCUB/blob/develop/LICENSE.txt) |
| [hipFFT](https://github.com/ROCm/hipFFT/) | [MIT](https://github.com/ROCm/hipFFT/blob/develop/LICENSE.md) |
| [hipfort](https://github.com/ROCm/hipfort/) | [MIT](https://github.com/ROCm/hipfort/blob/develop/LICENSE) |
| [HIPIFY](https://github.com/ROCm/HIPIFY/) | [MIT](https://github.com/ROCm/HIPIFY/blob/amd-staging/LICENSE.txt) |
| [hipRAND](https://github.com/ROCm/hipRAND/) | [MIT](https://github.com/ROCm/hipRAND/blob/develop/LICENSE.txt) |
| [hipSOLVER](https://github.com/ROCm/hipSOLVER/) | [MIT](https://github.com/ROCm/hipSOLVER/blob/develop/LICENSE.md) |
| [hipSPARSE](https://github.com/ROCm/hipSPARSE/) | [MIT](https://github.com/ROCm/hipSPARSE/blob/develop/LICENSE.md) |
| [hipSPARSELt](https://github.com/ROCm/hipSPARSELt/) | [MIT](https://github.com/ROCm/hipSPARSELt/blob/develop/LICENSE.md) |
| [hipTensor](https://github.com/ROCm/hipTensor) | [MIT](https://github.com/ROCm/hipTensor/blob/develop/LICENSE) |
| hsa-amd-aqlprofile | [AMD Software EULA](https://www.amd.com/en/legal/eula/amd-software-eula.html) |
| [llvm-project](https://github.com/ROCm/llvm-project/) | [Apache](https://github.com/ROCm/llvm-project/blob/amd-staging/LICENSE.TXT) |
| [llvm-project/flang](https://github.com/ROCm/llvm-project/tree/amd-staging/flang) | [Apache 2.0](https://github.com/ROCm/llvm-project/blob/amd-staging/flang/LICENSE.TXT) |
| [MIGraphX](https://github.com/ROCm/AMDMIGraphX/) | [MIT](https://github.com/ROCm/AMDMIGraphX/blob/develop/LICENSE) |
| [MIOpen](https://github.com/ROCm/MIOpen/) | [MIT](https://github.com/ROCm/MIOpen/blob/develop/LICENSE.txt) |
| [MIVisionX](https://github.com/ROCm/MIVisionX/) | [MIT](https://github.com/ROCm/MIVisionX/blob/develop/LICENSE.txt) |
| [rocAL](https://github.com/ROCm/rocAL) | [MIT](https://github.com/ROCm/rocAL/blob/develop/LICENSE.txt) |
| [rocALUTION](https://github.com/ROCm/rocALUTION/) | [MIT](https://github.com/ROCm/rocALUTION/blob/develop/LICENSE.md) |
| [rocBLAS](https://github.com/ROCm/rocBLAS/) | [MIT](https://github.com/ROCm/rocBLAS/blob/develop/LICENSE.md) |
| [ROCdbgapi](https://github.com/ROCm/ROCdbgapi/) | [MIT](https://github.com/ROCm/ROCdbgapi/blob/amd-staging/LICENSE.txt) |
| [rocDecode](https://github.com/ROCm/rocDecode) | [MIT](https://github.com/ROCm/rocDecode/blob/develop/LICENSE) |
| [rocFFT](https://github.com/ROCm/rocFFT/) | [MIT](https://github.com/ROCm/rocFFT/blob/develop/LICENSE.md) |
| [ROCgdb](https://github.com/ROCm/ROCgdb/) | [GNU General Public License v3.0](https://github.com/ROCm/ROCgdb/blob/amd-staging/COPYING3) |
| [rocJPEG](https://github.com/ROCm/rocJPEG/) | [MIT](https://github.com/ROCm/rocJPEG/blob/develop/LICENSE) |
| [ROCK-Kernel-Driver](https://github.com/ROCm/ROCK-Kernel-Driver/) | [GPL 2.0 WITH Linux-syscall-note](https://github.com/ROCm/ROCK-Kernel-Driver/blob/master/COPYING) |
| [rocminfo](https://github.com/ROCm/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocminfo/blob/amd-staging/License.txt) |
| [ROCm Bandwidth Test](https://github.com/ROCm/rocm_bandwidth_test/) | [MIT](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
| [ROCm CMake](https://github.com/ROCm/rocm-cmake/) | [MIT](https://github.com/ROCm/rocm-cmake/blob/develop/LICENSE) |
| [ROCm Communication Collectives Library (RCCL)](https://github.com/ROCm/rccl/) | [Custom](https://github.com/ROCm/rccl/blob/develop/LICENSE.txt) |
| [ROCm-Core](https://github.com/ROCm/rocm-core) | [MIT](https://github.com/ROCm/rocm-core/blob/master/copyright) |
| [ROCm Compute Profiler](https://github.com/ROCm/rocprofiler-compute) | [MIT](https://github.com/ROCm/rocprofiler-compute/blob/amd-staging/LICENSE) |
| [ROCm Data Center (RDC)](https://github.com/ROCm/rdc/) | [MIT](https://github.com/ROCm/rdc/blob/amd-staging/LICENSE) |
| [ROCm-Device-Libs](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/device-libs) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/device-libs/LICENSE.TXT) |
| [ROCm-OpenCL-Runtime](https://github.com/ROCm/clr/tree/amd-staging/opencl) | [MIT](https://github.com/ROCm/clr/blob/amd-staging/opencl/LICENSE.txt) |
| [ROCm Performance Primitives (RPP)](https://github.com/ROCm/rpp) | [MIT](https://github.com/ROCm/rpp/blob/develop/LICENSE) |
| [ROCm SMI Lib](https://github.com/ROCm/rocm_smi_lib/) | [MIT](https://github.com/ROCm/rocm_smi_lib/blob/amd-staging/License.txt) |
| [ROCm Systems Profiler](https://github.com/ROCm/rocprofiler-systems) | [MIT](https://github.com/ROCm/rocprofiler-systems/blob/amd-staging/LICENSE) |
| [ROCm Validation Suite](https://github.com/ROCm/ROCmValidationSuite/) | [MIT](https://github.com/ROCm/ROCmValidationSuite/blob/master/LICENSE) |
| [rocPRIM](https://github.com/ROCm/rocPRIM/) | [MIT](https://github.com/ROCm/rocPRIM/blob/develop/LICENSE.txt) |
| [ROCProfiler](https://github.com/ROCm/rocprofiler/) | [MIT](https://github.com/ROCm/rocprofiler/blob/amd-staging/LICENSE) |
| [ROCprofiler-SDK](https://github.com/ROCm/rocprofiler-sdk) | [MIT](https://github.com/ROCm/rocprofiler-sdk/blob/amd-mainline/LICENSE) |
| [rocPyDecode](https://github.com/ROCm/rocPyDecode) | [MIT](https://github.com/ROCm/rocPyDecode/blob/develop/LICENSE.txt) |
| [rocRAND](https://github.com/ROCm/rocRAND/) | [MIT](https://github.com/ROCm/rocRAND/blob/develop/LICENSE.txt) |
| [ROCr Debug Agent](https://github.com/ROCm/rocr_debug_agent/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocr_debug_agent/blob/amd-staging/LICENSE.txt) |
| [ROCR-Runtime](https://github.com/ROCm/ROCR-Runtime/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCR-Runtime/blob/amd-staging/LICENSE.txt) |
| [rocSHMEM](https://github.com/ROCm/rocSHMEM/) | [MIT](https://github.com/ROCm/rocSHMEM/blob/develop/LICENSE.md) |
| [rocSOLVER](https://github.com/ROCm/rocSOLVER/) | [BSD-2-Clause](https://github.com/ROCm/rocSOLVER/blob/develop/LICENSE.md) |
| [rocSPARSE](https://github.com/ROCm/rocSPARSE/) | [MIT](https://github.com/ROCm/rocSPARSE/blob/develop/LICENSE.md) |
| [rocThrust](https://github.com/ROCm/rocThrust/) | [Apache 2.0](https://github.com/ROCm/rocThrust/blob/develop/LICENSE) |
| [ROCTracer](https://github.com/ROCm/roctracer/) | [MIT](https://github.com/ROCm/roctracer/blob/amd-master/LICENSE) |
| [rocWMMA](https://github.com/ROCm/rocWMMA/) | [MIT](https://github.com/ROCm/rocWMMA/blob/develop/LICENSE.md) |
| [Tensile](https://github.com/ROCm/Tensile/) | [MIT](https://github.com/ROCm/Tensile/blob/develop/LICENSE.md) |
| [TransferBench](https://github.com/ROCm/TransferBench) | [MIT](https://github.com/ROCm/TransferBench/blob/develop/LICENSE.md) |
Open sourced ROCm components are released via public GitHub
repositories, packages on [https://repo.radeon.com](https://repo.radeon.com) and other distribution channels.
Proprietary products are only available on [https://repo.radeon.com](https://repo.radeon.com).
Proprietary components are organized in a proprietary subdirectory in the package
repositories to distinguish from open sourced packages.
```{note}
The following additional terms and conditions apply to your use of ROCm technical documentation.
```
©2023 - 2025 Advanced Micro Devices, Inc. All rights reserved.
The information presented in this document is for informational purposes only
and may contain technical inaccuracies, omissions, and typographical errors. The
information contained herein is subject to change and may be rendered inaccurate
for many reasons, including but not limited to product and roadmap changes,
component and motherboard version changes, new model and/or product releases,
product differences between differing manufacturers, software changes, BIOS
flashes, firmware upgrades, or the like. Any computer system has risks of
security vulnerabilities that cannot be completely prevented or mitigated. AMD
assumes no obligation to update or otherwise correct or revise this information.
However, AMD reserves the right to revise this information and to make changes
from time to time to the content hereof without obligation of AMD to notify any
person of such revisions or changes.
THIS INFORMATION IS PROVIDED “AS IS.” AMD MAKES NO REPRESENTATIONS OR WARRANTIES
WITH RESPECT TO THE CONTENTS HEREOF AND ASSUMES NO RESPONSIBILITY FOR ANY
INACCURACIES, ERRORS, OR OMISSIONS THAT MAY APPEAR IN THIS INFORMATION. AMD
SPECIFICALLY DISCLAIMS ANY IMPLIED WARRANTIES OF NON-INFRINGEMENT,
MERCHANTABILITY, OR FITNESS FOR ANY PARTICULAR PURPOSE. IN NO EVENT WILL AMD BE
LIABLE TO ANY PERSON FOR ANY RELIANCE, DIRECT, INDIRECT, SPECIAL, OR OTHER
CONSEQUENTIAL DAMAGES ARISING FROM THE USE OF ANY INFORMATION CONTAINED HEREIN,
EVEN IF AMD IS EXPRESSLY ADVISED OF THE POSSIBILITY OF SUCH DAMAGES.
AMD, the AMD Arrow logo, ROCm, and combinations thereof are trademarks of
Advanced Micro Devices, Inc. Other product names used in this publication are
for identification purposes only and may be trademarks of their respective
companies.
### Package licensing
:::{attention}
AQL Profiler and AOCC CPU optimization are both provided in binary form, each
subject to the license agreement enclosed in the directory for the binary available
in `/opt/rocm/share/doc/hsa-amd-aqlprofile/EULA`. By using, installing,
copying or distributing AQL Profiler and/or AOCC CPU Optimizations, you agree to
the terms and conditions of this license agreement. If you do not agree to the
terms of this agreement, do not install, copy or use the AQL Profiler and/or the
AOCC CPU Optimizations.
:::
For the rest of the ROCm packages, you can find the licensing information at the
following location: `/opt/rocm/share/doc/<component-name>/` or in the locations
specified in the preceding table.
For example, you can fetch the licensing information of the `amd_comgr`
component (Code Object Manager) from the `/opt/rocm/share/doc/amd_comgr/LICENSE.txt` file.

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@@ -1,131 +0,0 @@
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
:ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
,"RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
,"SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
,"Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_",Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,,
,Azure Linux 3.0 [#mi300x-past-60]_,Azure Linux 3.0 [#mi300x-past-60]_,Azure Linux 3.0 [#mi300x-past-60]_,Azure Linux 3.0 [#mi300x-past-60]_,Azure Linux 3.0 [#mi300x-past-60]_,,,,,,,,,,,,
,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
,RDNA4,RDNA4,,,,,,,,,,,,,,,
,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
,,,,,,,,,,,,,,,,,
FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
: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:`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,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A
:doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,N/A,85f95ae,85f95ae,85f95ae,85f95ae,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>` [#dgl_compat]_,N/A,N/A,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
:doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`,N/A,N/A,N/A,0.7.0,0.7.0,0.7.0,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat]_,N/A,N/A,N/A,N/A,1.8.0b1,N/A,N/A,N/A,N/A,N/A,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
,,,,,,,,,,,,,,,,,
,,,,,,,,,,,,,,,,,
THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
`UCC <https://github.com/ROCm/ucc>`_,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
`UCX <https://github.com/ROCm/ucx>`_,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
,,,,,,,,,,,,,,,,,
THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
Thrust,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
CUB,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
,,,,,,,,,,,,,,,,,
KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
,,,,,,,,,,,,,,,,,
ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
:doc:`MIGraphX <amdmigraphx:index>`,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
:doc:`MIOpen <miopen:index>`,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
:doc:`MIVisionX <mivisionx:index>`,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
:doc:`rocAL <rocal:index>`,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
:doc:`rocDecode <rocdecode:index>`,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
:doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`RPP <rpp:index>`,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
,,,,,,,,,,,,,,,,,
COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`RCCL <rccl:index>`,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
:doc:`rocSHMEM <rocshmem:index>`,2.0.1,2.0.0,2.0.0,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
,,,,,,,,,,,,,,,,,
MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
`half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
:doc:`hipBLAS <hipblas:index>`,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
:doc:`hipBLASLt <hipblaslt:index>`,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
:doc:`hipFFT <hipfft:index>`,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
:doc:`hipfort <hipfort:index>`,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
:doc:`hipRAND <hiprand:index>`,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
:doc:`hipSOLVER <hipsolver:index>`,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
:doc:`hipSPARSE <hipsparse:index>`,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
:doc:`hipSPARSELt <hipsparselt:index>`,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
:doc:`rocALUTION <rocalution:index>`,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
:doc:`rocBLAS <rocblas:index>`,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
:doc:`rocFFT <rocfft:index>`,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
:doc:`rocRAND <rocrand:index>`,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
:doc:`rocSOLVER <rocsolver:index>`,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
:doc:`rocSPARSE <rocsparse:index>`,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
:doc:`rocWMMA <rocwmma:index>`,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
:doc:`Tensile <tensile:src/index>`,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
,,,,,,,,,,,,,,,,,
PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`hipCUB <hipcub:index>`,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
:doc:`hipTensor <hiptensor:index>`,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
:doc:`rocPRIM <rocprim:index>`,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
:doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
,,,,,,,,,,,,,,,,,
SUPPORT LIBS,,,,,,,,,,,,,,,,,
`hipother <https://github.com/ROCm/hipother>`_,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
`rocm-core <https://github.com/ROCm/rocm-core>`_,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
`ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
,,,,,,,,,,,,,,,,,
SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`AMD SMI <amdsmi:index>`,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
:doc:`ROCm Data Center Tool <rdc:index>`,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
:doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
:doc:`ROCm SMI <rocm_smi_lib:index>`,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
:doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
,,,,,,,,,,,,,,,,,
PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,
:doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
:doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`ROCProfiler <rocprofiler:index>`,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
:doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
:doc:`ROCTracer <roctracer:index>`,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
,,,,,,,,,,,,,,,,,
DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,
:doc:`HIPIFY <hipify:index>`,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
:doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
:doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
:doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
`rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
:doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
,,,,,,,,,,,,,,,,,
COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
`clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
:doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
`Flang <https://github.com/ROCm/flang>`_,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
:doc:`llvm-project <llvm-project:index>`,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
`OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
,,,,,,,,,,,,,,,,,
RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,
:doc:`AMD CLR <hip:understand/amd_clr>`,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
:doc:`HIP <hip:index>`,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
`OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
:doc:`ROCr Runtime <rocr-runtime:index>`,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
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
2 :ref:`Operating systems & kernels <OS-kernel-versions>` Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.2 Ubuntu 24.04.1, 24.04 Ubuntu 24.04.1, 24.04 Ubuntu 24.04.1, 24.04 Ubuntu 24.04
3 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5 Ubuntu 22.04.5, 22.04.4 Ubuntu 22.04.5, 22.04.4 Ubuntu 22.04.5, 22.04.4 Ubuntu 22.04.5, 22.04.4 Ubuntu 22.04.5, 22.04.4, 22.04.3 Ubuntu 22.04.4, 22.04.3 Ubuntu 22.04.4, 22.04.3 Ubuntu 22.04.4, 22.04.3 Ubuntu 22.04.4, 22.04.3, 22.04.2 Ubuntu 22.04.4, 22.04.3, 22.04.2
4 Ubuntu 20.04.6, 20.04.5 Ubuntu 20.04.6, 20.04.5 Ubuntu 20.04.6, 20.04.5 Ubuntu 20.04.6, 20.04.5 Ubuntu 20.04.6, 20.04.5 Ubuntu 20.04.6, 20.04.5
5 RHEL 9.6, 9.4 RHEL 9.6, 9.5, 9.4 RHEL 9.5, 9.4 RHEL 9.5, 9.4 RHEL 9.5, 9.4 RHEL 9.5, 9.4 RHEL 9.5, 9.4 RHEL 9.4, 9.3 RHEL 9.4, 9.3 RHEL 9.4, 9.3 RHEL 9.4, 9.3 RHEL 9.4, 9.3, 9.2 RHEL 9.4, 9.3, 9.2 RHEL 9.4, 9.3, 9.2 RHEL 9.4, 9.3, 9.2 RHEL 9.3, 9.2 RHEL 9.3, 9.2
6 RHEL 8.10 RHEL 8.10 RHEL 8.10 RHEL 8.10 RHEL 8.10 RHEL 8.10 RHEL 8.10 RHEL 8.10, 8.9 RHEL 8.10, 8.9 RHEL 8.10, 8.9 RHEL 8.10, 8.9 RHEL 8.9, 8.8 RHEL 8.9, 8.8 RHEL 8.9, 8.8 RHEL 8.9, 8.8 RHEL 8.9, 8.8 RHEL 8.9, 8.8
7 SLES 15 SP7, SP6 SLES 15 SP6 SLES 15 SP6 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP6, SP5 SLES 15 SP5, SP4 SLES 15 SP5, SP4 SLES 15 SP5, SP4 SLES 15 SP5, SP4 SLES 15 SP5, SP4 SLES 15 SP5, SP4
8 CentOS 7.9 CentOS 7.9 CentOS 7.9 CentOS 7.9 CentOS 7.9
9 Oracle Linux 9, 8 [#mi300x-past-60]_ Oracle Linux 9, 8 [#mi300x-past-60]_ Oracle Linux 9, 8 [#mi300x-past-60]_ Oracle Linux 8.10 [#mi300x-past-60]_ Oracle Linux 8.10 [#mi300x-past-60]_ Oracle Linux 8.10 [#mi300x-past-60]_ Oracle Linux 8.10 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_ Oracle Linux 8.9 [#mi300x-past-60]_
10 Debian 12 [#single-node-past-60]_ Debian 12 [#single-node-past-60]_ Debian 12 [#single-node-past-60]_ Debian 12 [#single-node-past-60]_ Debian 12 [#single-node-past-60]_ Debian 12 [#single-node-past-60]_
11 Azure Linux 3.0 [#mi300x-past-60]_ Azure Linux 3.0 [#mi300x-past-60]_ Azure Linux 3.0 [#mi300x-past-60]_ Azure Linux 3.0 [#mi300x-past-60]_ Azure Linux 3.0 [#mi300x-past-60]_
12 .. _architecture-support-compatibility-matrix-past-60:
13 :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>` CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3 CDNA3
14 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2 CDNA2
15 CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA CDNA
16 RDNA4 RDNA4
17 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3 RDNA3
18 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2 RDNA2
19 .. _gpu-support-compatibility-matrix-past-60:
20 :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>` gfx1201 [#RDNA-OS-past-60]_ gfx1201 [#RDNA-OS-past-60]_
21 gfx1200 [#RDNA-OS-past-60]_ gfx1200 [#RDNA-OS-past-60]_
22 gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_ gfx1101 [#RDNA-OS-past-60]_
23 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100 gfx1100
24 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030 gfx1030
25 gfx942 gfx942 gfx942 gfx942 gfx942 gfx942 gfx942 gfx942 [#mi300_624-past-60]_ gfx942 [#mi300_622-past-60]_ gfx942 [#mi300_621-past-60]_ gfx942 [#mi300_620-past-60]_ gfx942 [#mi300_612-past-60]_ gfx942 [#mi300_612-past-60]_ gfx942 [#mi300_611-past-60]_ gfx942 [#mi300_610-past-60]_ gfx942 [#mi300_602-past-60]_ gfx942 [#mi300_600-past-60]_
26 gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a gfx90a
27 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908 gfx908
28
29 FRAMEWORK SUPPORT .. _framework-support-compatibility-matrix-past-60:
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:`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 N/A 0.3.0.post0 N/A N/A N/A N/A N/A
34 :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` N/A N/A N/A 85f95ae 85f95ae 85f95ae 85f95ae N/A N/A N/A N/A N/A N/A N/A N/A N/A
35 :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_ N/A N/A 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
36 :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` N/A N/A N/A 0.7.0 0.7.0 0.7.0 0.7.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A
37 :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat]_ N/A N/A N/A N/A 1.8.0b1 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
38 `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
39
40
41 THIRD PARTY COMMS .. _thirdpartycomms-support-compatibility-matrix-past-60:
42 `UCC <https://github.com/ROCm/ucc>`_ >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.3.0 >=1.2.0 >=1.2.0
43 `UCX <https://github.com/ROCm/ucx>`_ >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.15.0 >=1.14.1 >=1.14.1 >=1.14.1 >=1.14.1 >=1.14.1 >=1.14.1
44
45 THIRD PARTY ALGORITHM .. _thirdpartyalgorithm-support-compatibility-matrix-past-60:
46 Thrust 2.5.0 2.5.0 2.5.0 2.3.2 2.3.2 2.3.2 2.3.2 2.2.0 2.2.0 2.2.0 2.2.0 2.1.0 2.1.0 2.1.0 2.1.0 2.0.1 2.0.1
47 CUB 2.5.0 2.5.0 2.5.0 2.3.2 2.3.2 2.3.2 2.3.2 2.2.0 2.2.0 2.2.0 2.2.0 2.1.0 2.1.0 2.1.0 2.1.0 2.0.1 2.0.1
48
49 KMD & USER SPACE [#kfd_support-past-60]_ .. _kfd-userspace-support-compatibility-matrix-past-60:
50 :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>` 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x 6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x 6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x 6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x
51
52 ML & COMPUTER VISION .. _mllibs-support-compatibility-matrix-past-60:
53 :doc:`Composable Kernel <composable_kernel:index>` 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0
54 :doc:`MIGraphX <amdmigraphx:index>` 2.12.0 2.12.0 2.12.0 2.11.0 2.11.0 2.11.0 2.11.0 2.10.0 2.10.0 2.10.0 2.10.0 2.9.0 2.9.0 2.9.0 2.9.0 2.8.0 2.8.0
55 :doc:`MIOpen <miopen:index>` 3.4.0 3.4.0 3.4.0 3.3.0 3.3.0 3.3.0 3.3.0 3.2.0 3.2.0 3.2.0 3.2.0 3.1.0 3.1.0 3.1.0 3.1.0 3.0.0 3.0.0
56 :doc:`MIVisionX <mivisionx:index>` 3.2.0 3.2.0 3.2.0 3.1.0 3.1.0 3.1.0 3.1.0 3.0.0 3.0.0 3.0.0 3.0.0 2.5.0 2.5.0 2.5.0 2.5.0 2.5.0 2.5.0
57 :doc:`rocAL <rocal:index>` 2.2.0 2.2.0 2.2.0 2.1.0 2.1.0 2.1.0 2.1.0 2.0.0 2.0.0 2.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0
58 :doc:`rocDecode <rocdecode:index>` 0.10.0 0.10.0 0.10.0 0.8.0 0.8.0 0.8.0 0.8.0 0.6.0 0.6.0 0.6.0 0.6.0 0.6.0 0.6.0 0.5.0 0.5.0 N/A N/A
59 :doc:`rocJPEG <rocjpeg:index>` 0.8.0 0.8.0 0.8.0 0.6.0 0.6.0 0.6.0 0.6.0 N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A
60 :doc:`rocPyDecode <rocpydecode:index>` 0.3.1 0.3.1 0.3.1 0.2.0 0.2.0 0.2.0 0.2.0 0.1.0 0.1.0 0.1.0 0.1.0 N/A N/A N/A N/A N/A N/A
61 :doc:`RPP <rpp:index>` 1.9.10 1.9.10 1.9.10 1.9.1 1.9.1 1.9.1 1.9.1 1.8.0 1.8.0 1.8.0 1.8.0 1.5.0 1.5.0 1.5.0 1.5.0 1.4.0 1.4.0
62
63 COMMUNICATION .. _commlibs-support-compatibility-matrix-past-60:
64 :doc:`RCCL <rccl:index>` 2.22.3 2.22.3 2.22.3 2.21.5 2.21.5 2.21.5 2.21.5 2.20.5 2.20.5 2.20.5 2.20.5 2.18.6 2.18.6 2.18.6 2.18.6 2.18.3 2.18.3
65 :doc:`rocSHMEM <rocshmem:index>` 2.0.1 2.0.0 2.0.0 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
66
67 MATH LIBS .. _mathlibs-support-compatibility-matrix-past-60:
68 `half <https://github.com/ROCm/half>`_ 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0 1.12.0
69 :doc:`hipBLAS <hipblas:index>` 2.4.0 2.4.0 2.4.0 2.3.0 2.3.0 2.3.0 2.3.0 2.2.0 2.2.0 2.2.0 2.2.0 2.1.0 2.1.0 2.1.0 2.1.0 2.0.0 2.0.0
70 :doc:`hipBLASLt <hipblaslt:index>` 0.12.1 0.12.1 0.12.0 0.10.0 0.10.0 0.10.0 0.10.0 0.8.0 0.8.0 0.8.0 0.8.0 0.7.0 0.7.0 0.7.0 0.7.0 0.6.0 0.6.0
71 :doc:`hipFFT <hipfft:index>` 1.0.18 1.0.18 1.0.18 1.0.17 1.0.17 1.0.17 1.0.17 1.0.16 1.0.15 1.0.15 1.0.14 1.0.14 1.0.14 1.0.14 1.0.14 1.0.13 1.0.13
72 :doc:`hipfort <hipfort:index>` 0.6.0 0.6.0 0.6.0 0.5.1 0.5.1 0.5.0 0.5.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0
73 :doc:`hipRAND <hiprand:index>` 2.12.0 2.12.0 2.12.0 2.11.1 2.11.1 2.11.1 2.11.0 2.11.1 2.11.0 2.11.0 2.11.0 2.10.16 2.10.16 2.10.16 2.10.16 2.10.16 2.10.16
74 :doc:`hipSOLVER <hipsolver:index>` 2.4.0 2.4.0 2.4.0 2.3.0 2.3.0 2.3.0 2.3.0 2.2.0 2.2.0 2.2.0 2.2.0 2.1.1 2.1.1 2.1.1 2.1.0 2.0.0 2.0.0
75 :doc:`hipSPARSE <hipsparse:index>` 3.2.0 3.2.0 3.2.0 3.1.2 3.1.2 3.1.2 3.1.2 3.1.1 3.1.1 3.1.1 3.1.1 3.0.1 3.0.1 3.0.1 3.0.1 3.0.0 3.0.0
76 :doc:`hipSPARSELt <hipsparselt:index>` 0.2.3 0.2.3 0.2.3 0.2.2 0.2.2 0.2.2 0.2.2 0.2.1 0.2.1 0.2.1 0.2.1 0.2.0 0.2.0 0.1.0 0.1.0 0.1.0 0.1.0
77 :doc:`rocALUTION <rocalution:index>` 3.2.3 3.2.3 3.2.2 3.2.1 3.2.1 3.2.1 3.2.1 3.2.1 3.2.0 3.2.0 3.2.0 3.1.1 3.1.1 3.1.1 3.1.1 3.0.3 3.0.3
78 :doc:`rocBLAS <rocblas:index>` 4.4.1 4.4.0 4.4.0 4.3.0 4.3.0 4.3.0 4.3.0 4.2.4 4.2.1 4.2.1 4.2.0 4.1.2 4.1.2 4.1.0 4.1.0 4.0.0 4.0.0
79 :doc:`rocFFT <rocfft:index>` 1.0.32 1.0.32 1.0.32 1.0.31 1.0.31 1.0.31 1.0.31 1.0.30 1.0.29 1.0.29 1.0.28 1.0.27 1.0.27 1.0.27 1.0.26 1.0.25 1.0.23
80 :doc:`rocRAND <rocrand:index>` 3.3.0 3.3.0 3.3.0 3.2.0 3.2.0 3.2.0 3.2.0 3.1.1 3.1.0 3.1.0 3.1.0 3.0.1 3.0.1 3.0.1 3.0.1 3.0.0 2.10.17
81 :doc:`rocSOLVER <rocsolver:index>` 3.28.2 3.28.0 3.28.0 3.27.0 3.27.0 3.27.0 3.27.0 3.26.2 3.26.0 3.26.0 3.26.0 3.25.0 3.25.0 3.25.0 3.25.0 3.24.0 3.24.0
82 :doc:`rocSPARSE <rocsparse:index>` 3.4.0 3.4.0 3.4.0 3.3.0 3.3.0 3.3.0 3.3.0 3.2.1 3.2.0 3.2.0 3.2.0 3.1.2 3.1.2 3.1.2 3.1.2 3.0.2 3.0.2
83 :doc:`rocWMMA <rocwmma:index>` 1.7.0 1.7.0 1.7.0 1.6.0 1.6.0 1.6.0 1.6.0 1.5.0 1.5.0 1.5.0 1.5.0 1.4.0 1.4.0 1.4.0 1.4.0 1.3.0 1.3.0
84 :doc:`Tensile <tensile:src/index>` 4.43.0 4.43.0 4.43.0 4.42.0 4.42.0 4.42.0 4.42.0 4.41.0 4.41.0 4.41.0 4.41.0 4.40.0 4.40.0 4.40.0 4.40.0 4.39.0 4.39.0
85
86 PRIMITIVES .. _primitivelibs-support-compatibility-matrix-past-60:
87 :doc:`hipCUB <hipcub:index>` 3.4.0 3.4.0 3.4.0 3.3.0 3.3.0 3.3.0 3.3.0 3.2.1 3.2.0 3.2.0 3.2.0 3.1.0 3.1.0 3.1.0 3.1.0 3.0.0 3.0.0
88 :doc:`hipTensor <hiptensor:index>` 1.5.0 1.5.0 1.5.0 1.4.0 1.4.0 1.4.0 1.4.0 1.3.0 1.3.0 1.3.0 1.3.0 1.2.0 1.2.0 1.2.0 1.2.0 1.1.0 1.1.0
89 :doc:`rocPRIM <rocprim:index>` 3.4.1 3.4.0 3.4.0 3.3.0 3.3.0 3.3.0 3.3.0 3.2.2 3.2.0 3.2.0 3.2.0 3.1.0 3.1.0 3.1.0 3.1.0 3.0.0 3.0.0
90 :doc:`rocThrust <rocthrust:index>` 3.3.0 3.3.0 3.3.0 3.3.0 3.3.0 3.3.0 3.3.0 3.1.1 3.1.0 3.1.0 3.0.1 3.0.1 3.0.1 3.0.1 3.0.1 3.0.0 3.0.0
91
92 SUPPORT LIBS
93 `hipother <https://github.com/ROCm/hipother>`_ 6.4.43483 6.4.43483 6.4.43482 6.3.42134 6.3.42134 6.3.42133 6.3.42131 6.2.41134 6.2.41134 6.2.41134 6.2.41133 6.1.40093 6.1.40093 6.1.40092 6.1.40091 6.1.32831 6.1.32830
94 `rocm-core <https://github.com/ROCm/rocm-core>`_ 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
95 `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ N/A [#ROCT-rocr-past-60]_ 20240607.5.7 20240607.5.7 20240607.4.05 20240607.1.4246 20240125.5.08 20240125.5.08 20240125.5.08 20240125.3.30 20231016.2.245 20231016.2.245
96
97 SYSTEM MGMT TOOLS .. _tools-support-compatibility-matrix-past-60:
98 :doc:`AMD SMI <amdsmi:index>` 25.5.1 25.4.2 25.3.0 24.7.1 24.7.1 24.7.1 24.7.1 24.6.3 24.6.3 24.6.3 24.6.2 24.5.1 24.5.1 24.5.1 24.4.1 23.4.2 23.4.2
99 :doc:`ROCm Data Center Tool <rdc:index>` 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0 0.3.0
100 :doc:`rocminfo <rocminfo:index>` 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0
101 :doc:`ROCm SMI <rocm_smi_lib:index>` 7.5.0 7.5.0 7.5.0 7.4.0 7.4.0 7.4.0 7.4.0 7.3.0 7.3.0 7.3.0 7.3.0 7.2.0 7.2.0 7.0.0 7.0.0 6.0.2 6.0.0
102 :doc:`ROCm Validation Suite <rocmvalidationsuite:index>` 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.1.0 1.0.60204 1.0.60202 1.0.60201 1.0.60200 1.0.60105 1.0.60102 1.0.60101 1.0.60100 1.0.60002 1.0.60000
103
104 PERFORMANCE TOOLS
105 :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>` 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0 1.4.0
106 :doc:`ROCm Compute Profiler <rocprofiler-compute:index>` 3.1.1 3.1.0 3.1.0 3.0.0 3.0.0 3.0.0 3.0.0 2.0.1 2.0.1 2.0.1 2.0.1 N/A N/A N/A N/A N/A N/A
107 :doc:`ROCm Systems Profiler <rocprofiler-systems:index>` 1.0.2 1.0.1 1.0.0 0.1.2 0.1.1 0.1.0 0.1.0 1.11.2 1.11.2 1.11.2 1.11.2 N/A N/A N/A N/A N/A N/A
108 :doc:`ROCProfiler <rocprofiler:index>` 2.0.60402 2.0.60401 2.0.60400 2.0.60303 2.0.60302 2.0.60301 2.0.60300 2.0.60204 2.0.60202 2.0.60201 2.0.60200 2.0.60105 2.0.60102 2.0.60101 2.0.60100 2.0.60002 2.0.60000
109 :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>` 0.6.0 0.6.0 0.6.0 0.5.0 0.5.0 0.5.0 0.5.0 0.4.0 0.4.0 0.4.0 0.4.0 N/A N/A N/A N/A N/A N/A
110 :doc:`ROCTracer <roctracer:index>` 4.1.60402 4.1.60401 4.1.60400 4.1.60303 4.1.60302 4.1.60301 4.1.60300 4.1.60204 4.1.60202 4.1.60201 4.1.60200 4.1.60105 4.1.60102 4.1.60101 4.1.60100 4.1.60002 4.1.60000
111
112 DEVELOPMENT TOOLS
113 :doc:`HIPIFY <hipify:index>` 19.0.0 19.0.0 19.0.0 18.0.0.25012 18.0.0.25012 18.0.0.24491 18.0.0.24455 18.0.0.24392 18.0.0.24355 18.0.0.24355 18.0.0.24232 17.0.0.24193 17.0.0.24193 17.0.0.24154 17.0.0.24103 17.0.0.24012 17.0.0.23483
114 :doc:`ROCm CMake <rocmcmakebuildtools:index>` 0.14.0 0.14.0 0.14.0 0.14.0 0.14.0 0.14.0 0.14.0 0.13.0 0.13.0 0.13.0 0.13.0 0.12.0 0.12.0 0.12.0 0.12.0 0.11.0 0.11.0
115 :doc:`ROCdbgapi <rocdbgapi:index>` 0.77.2 0.77.2 0.77.2 0.77.0 0.77.0 0.77.0 0.77.0 0.76.0 0.76.0 0.76.0 0.76.0 0.71.0 0.71.0 0.71.0 0.71.0 0.71.0 0.71.0
116 :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>` 15.2.0 15.2.0 15.2.0 15.2.0 15.2.0 15.2.0 15.2.0 14.2.0 14.2.0 14.2.0 14.2.0 14.1.0 14.1.0 14.1.0 14.1.0 13.2.0 13.2.0
117 `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_ 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.4.0 0.3.0 0.3.0 0.3.0 0.3.0 N/A N/A
118 :doc:`ROCr Debug Agent <rocr_debug_agent:index>` 2.0.4 2.0.4 2.0.4 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3 2.0.3
119
120 COMPILERS .. _compilers-support-compatibility-matrix-past-60:
121 `clang-ocl <https://github.com/ROCm/clang-ocl>`_ N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A 0.5.0 0.5.0 0.5.0 0.5.0 0.5.0 0.5.0
122 :doc:`hipCC <hipcc:index>` 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.1.1 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0 1.0.0
123 `Flang <https://github.com/ROCm/flang>`_ 19.0.0.25224 19.0.0.25184 19.0.0.25133 18.0.0.25012 18.0.0.25012 18.0.0.24491 18.0.0.24455 18.0.0.24392 18.0.0.24355 18.0.0.24355 18.0.0.24232 17.0.0.24193 17.0.0.24193 17.0.0.24154 17.0.0.24103 17.0.0.24012 17.0.0.23483
124 :doc:`llvm-project <llvm-project:index>` 19.0.0.25224 19.0.0.25184 19.0.0.25133 18.0.0.25012 18.0.0.25012 18.0.0.24491 18.0.0.24491 18.0.0.24392 18.0.0.24355 18.0.0.24355 18.0.0.24232 17.0.0.24193 17.0.0.24193 17.0.0.24154 17.0.0.24103 17.0.0.24012 17.0.0.23483
125 `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_ 19.0.0.25224 19.0.0.25184 19.0.0.25133 18.0.0.25012 18.0.0.25012 18.0.0.24491 18.0.0.24491 18.0.0.24392 18.0.0.24355 18.0.0.24355 18.0.0.24232 17.0.0.24193 17.0.0.24193 17.0.0.24154 17.0.0.24103 17.0.0.24012 17.0.0.23483
126
127 RUNTIMES .. _runtime-support-compatibility-matrix-past-60:
128 :doc:`AMD CLR <hip:understand/amd_clr>` 6.4.43484 6.4.43483 6.4.43482 6.3.42134 6.3.42134 6.3.42133 6.3.42131 6.2.41134 6.2.41134 6.2.41134 6.2.41133 6.1.40093 6.1.40093 6.1.40092 6.1.40091 6.1.32831 6.1.32830
129 :doc:`HIP <hip:index>` 6.4.43484 6.4.43483 6.4.43482 6.3.42134 6.3.42134 6.3.42133 6.3.42131 6.2.41134 6.2.41134 6.2.41134 6.2.41133 6.1.40093 6.1.40093 6.1.40092 6.1.40091 6.1.32831 6.1.32830
130 `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_ 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0 2.0.0
131 :doc:`ROCr Runtime <rocr-runtime:index>` 1.15.0 1.15.0 1.15.0 1.14.0 1.14.0 1.14.0 1.14.0 1.14.0 1.14.0 1.14.0 1.13.0 1.13.0 1.13.0 1.13.0 1.13.0 1.12.0 1.12.0

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@@ -1,248 +0,0 @@
.. meta::
:description: ROCm compatibility matrix
:keywords: GPU, architecture, hardware, compatibility, system, requirements, components, libraries
**************************************************************************************
Compatibility matrix
**************************************************************************************
Use this matrix to view the ROCm compatibility and system requirements across successive major and minor releases.
You can also refer to the :ref:`past versions of ROCm compatibility matrix<past-rocm-compatibility-matrix>`.
Accelerators and GPUs listed in the following table support compute workloads (no display
information or graphics). If youre using ROCm with AMD Radeon or Radeon Pro GPUs for graphics
workloads, see the `Use ROCm on Radeon GPU documentation
<https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility.html>`_ to verify
compatibility and system requirements.
.. |br| raw:: html
<br/>
.. container:: format-big-table
.. csv-table::
:header: "ROCm Version", "6.4.2", "6.4.1", "6.3.0"
:stub-columns: 1
:ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2
,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5
,"RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4"
,RHEL 8.10,RHEL 8.10,RHEL 8.10
,"SLES 15 SP7, SP6",SLES 15 SP6,"SLES 15 SP6, SP5"
,"Oracle Linux 9, 8 [#mi300x]_","Oracle Linux 9, 8 [#mi300x]_",Oracle Linux 8.10 [#mi300x]_
,Debian 12 [#single-node]_,Debian 12 [#single-node]_,
,Azure Linux 3.0 [#mi300x]_,Azure Linux 3.0 [#mi300x]_,
,.. _architecture-support-compatibility-matrix:,,
:doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3
,CDNA2,CDNA2,CDNA2
,CDNA,CDNA,CDNA
,RDNA4,RDNA4,
,RDNA3,RDNA3,RDNA3
,RDNA2,RDNA2,RDNA2
,.. _gpu-support-compatibility-matrix:,,
:doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1201 [#RDNA-OS]_,gfx1201 [#RDNA-OS]_,
,gfx1200 [#RDNA-OS]_,gfx1200 [#RDNA-OS]_,
,gfx1101 [#RDNA-OS]_ [#7700XT-OS]_,gfx1101 [#RDNA-OS]_,
,gfx1100,gfx1100,gfx1100
,gfx1030,gfx1030,gfx1030
,gfx942,gfx942,gfx942
,gfx90a,gfx90a,gfx90a
,gfx908,gfx908,gfx908
,,,
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
:doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`,N/A,N/A,0.7.0
`ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.2,1.2,1.17.3
,,,
THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
`UCC <https://github.com/ROCm/ucc>`_,>=1.3.0,>=1.3.0,>=1.3.0
`UCX <https://github.com/ROCm/ucx>`_,>=1.15.0,>=1.15.0,>=1.15.0
,,,
THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix:,,
Thrust,2.5.0,2.5.0,2.3.2
CUB,2.5.0,2.5.0,2.3.2
,,,
KMD & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
:doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
,,,
ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
:doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
:doc:`MIGraphX <amdmigraphx:index>`,2.12.0,2.12.0,2.11.0
:doc:`MIOpen <miopen:index>`,3.4.0,3.4.0,3.3.0
:doc:`MIVisionX <mivisionx:index>`,3.2.0,3.2.0,3.1.0
:doc:`rocAL <rocal:index>`,2.2.0,2.2.0,2.1.0
:doc:`rocDecode <rocdecode:index>`,0.10.0,0.10.0,0.8.0
:doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.8.0,0.6.0
:doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.3.1,0.2.0
:doc:`RPP <rpp:index>`,1.9.10,1.9.10,1.9.1
,,,
COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
:doc:`RCCL <rccl:index>`,2.22.3,2.22.3,2.21.5
:doc:`rocSHMEM <rocshmem:index>`,2.0.1,2.0.0,N/A
,,,
MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
`half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
:doc:`hipBLAS <hipblas:index>`,2.4.0,2.4.0,2.3.0
:doc:`hipBLASLt <hipblaslt:index>`,0.12.1,0.12.1,0.10.0
:doc:`hipFFT <hipfft:index>`,1.0.18,1.0.18,1.0.17
:doc:`hipfort <hipfort:index>`,0.6.0,0.6.0,0.5.0
:doc:`hipRAND <hiprand:index>`,2.12.0,2.12.0,2.11.0
:doc:`hipSOLVER <hipsolver:index>`,2.4.0,2.4.0,2.3.0
:doc:`hipSPARSE <hipsparse:index>`,3.2.0,3.2.0,3.1.2
:doc:`hipSPARSELt <hipsparselt:index>`,0.2.3,0.2.3,0.2.2
:doc:`rocALUTION <rocalution:index>`,3.2.3,3.2.3,3.2.1
:doc:`rocBLAS <rocblas:index>`,4.4.1,4.4.0,4.3.0
:doc:`rocFFT <rocfft:index>`,1.0.32,1.0.32,1.0.31
:doc:`rocRAND <rocrand:index>`,3.3.0,3.3.0,3.2.0
:doc:`rocSOLVER <rocsolver:index>`,3.28.2,3.28.0,3.27.0
:doc:`rocSPARSE <rocsparse:index>`,3.4.0,3.4.0,3.3.0
:doc:`rocWMMA <rocwmma:index>`,1.7.0,1.7.0,1.6.0
:doc:`Tensile <tensile:src/index>`,4.43.0,4.43.0,4.42.0
,,,
PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
:doc:`hipCUB <hipcub:index>`,3.4.0,3.4.0,3.3.0
:doc:`hipTensor <hiptensor:index>`,1.5.0,1.5.0,1.4.0
:doc:`rocPRIM <rocprim:index>`,3.4.1,3.4.0,3.3.0
:doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0
,,,
SUPPORT LIBS,,,
`hipother <https://github.com/ROCm/hipother>`_,6.4.43483,6.4.43483,6.3.42131
`rocm-core <https://github.com/ROCm/rocm-core>`_,6.4.2,6.4.1,6.3.0
`ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_
,,,
SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix:,,
:doc:`AMD SMI <amdsmi:index>`,25.5.1,25.4.2,24.7.1
:doc:`ROCm Data Center Tool <rdc:index>`,0.3.0,0.3.0,0.3.0
:doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0
:doc:`ROCm SMI <rocm_smi_lib:index>`,7.5.0,7.5.0,7.4.0
:doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.1.0
,,,
PERFORMANCE TOOLS,,,
:doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0
:doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.1.1,3.1.0,3.0.0
:doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.0.2,1.0.1,0.1.0
:doc:`ROCProfiler <rocprofiler:index>`,2.0.60402,2.0.60401,2.0.60300
:doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.6.0,0.6.0,0.5.0
:doc:`ROCTracer <roctracer:index>`,4.1.60402,4.1.60401,4.1.60300
,,,
DEVELOPMENT TOOLS,,,
:doc:`HIPIFY <hipify:index>`,19.0.0,19.0.0,18.0.0.24455
:doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0
:doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.2,0.77.0
:doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0
`rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0
:doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.4,2.0.3
,,,
COMPILERS,.. _compilers-support-compatibility-matrix:,,
`clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
:doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
`Flang <https://github.com/ROCm/flang>`_,19.0.0.25224,19.0.0.25184,18.0.0.24455
:doc:`llvm-project <llvm-project:index>`,19.0.0.25224,19.0.0.25184,18.0.0.24491
`OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25224,19.0.0.25184,18.0.0.24491
,,,
RUNTIMES,.. _runtime-support-compatibility-matrix:,,
:doc:`AMD CLR <hip:understand/amd_clr>`,6.4.43484,6.4.43483,6.3.42131
:doc:`HIP <hip:index>`,6.4.43484,6.4.43483,6.3.42131
`OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0
:doc:`ROCr Runtime <rocr-runtime:index>`,1.15.0,1.15.0,1.14.0
.. rubric:: Footnotes
.. [#mi300x] Oracle Linux and Azure Linux are supported only on AMD Instinct MI300X.
.. [#single-node] Debian 12 is supported only on AMD Instinct MI300X for single-node functionality.
.. [#RDNA-OS] Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
.. [#7700XT-OS] Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
.. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
.. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
.. _OS-kernel-versions:
Operating systems, kernel and Glibc versions
*********************************************
Use this lookup table to confirm which operating system and kernel versions are supported with ROCm.
.. csv-table::
:header: "OS", "Version", "Kernel", "Glibc"
:widths: 40, 20, 30, 20
:stub-columns: 1
`Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 24.04.2, "6.8 GA, 6.11 HWE", 2.39
,,
`Ubuntu <https://ubuntu.com/about/release-cycle#ubuntu-kernel-release-cycle>`_, 22.04.5, "5.15 GA, 6.8 HWE", 2.35
,,
`Red Hat Enterprise Linux (RHEL 9) <https://access.redhat.com/articles/3078#RHEL9>`_, 9.6, 5.14+, 2.34
,9.5, 5.14+, 2.34
,9.4, 5.14+, 2.34
,9.3, 5.14+, 2.34
,,
`Red Hat Enterprise Linux (RHEL 8) <https://access.redhat.com/articles/3078#RHEL8>`_, 8.10, 4.18.0+, 2.28
,8.9, 4.18.0, 2.28
,,
`SUSE Linux Enterprise Server (SLES) <https://www.suse.com/support/kb/doc/?id=000019587#SLE15SP4>`_, 15 SP7, 6.11.0+, 2.38
,15 SP6, "6.5.0+, 6.4.0", 2.38
,15 SP5, 5.14.21, 2.31
,,
`Oracle Linux <https://blogs.oracle.com/scoter/post/oracle-linux-and-unbreakable-enterprise-kernel-uek-releases>`_, 9, 5.15.0 (UEK), 2.35
,8, 5.15.0 (UEK), 2.28
,,
`Debian <https://www.debian.org/download>`_,12, 6.1, 2.36
,,
`Azure Linux <https://techcommunity.microsoft.com/blog/linuxandopensourceblog/azure-linux-3-0-now-in-preview-on-azure-kubernetes-service-v1-31/4287229>`_,3.0, 6.6.60, 2.38
,,
.. note::
* See `Red Hat Enterprise Linux Release Dates <https://access.redhat.com/articles/3078>`_ to learn about the specific kernel versions supported on Red Hat Enterprise Linux (RHEL).
* See `List of SUSE Linux Enterprise Server kernel <https://www.suse.com/support/kb/doc/?id=000019587>`_ to learn about the specific kernel version supported on SUSE Linux Enterprise Server (SLES).
..
Footnotes and ref anchors in below historical tables should be appended with "-past-60", to differentiate from the
footnote references in the above, latest, compatibility matrix. It also allows to easily find & replace.
An easy way to work is to download the historical.CSV file, and update open it in excel. Then when content is ready,
delete the columns you don't need, to build the current compatibility matrix to use in above table. Find & replace all
instances of "-past-60" to make it ready for above table.
.. _past-rocm-compatibility-matrix:
Past versions of ROCm compatibility matrix
***************************************************
Expand for full historical view of:
.. dropdown:: ROCm 6.0 - Present
You can `download the entire .csv <../downloads/compatibility-matrix-historical-6.0.csv>`_ for offline reference.
.. csv-table::
:file: compatibility-matrix-historical-6.0.csv
:header-rows: 1
:stub-columns: 1
.. rubric:: Footnotes
.. [#mi300x-past-60] Oracle Linux and Azure Linux are supported only on AMD Instinct MI300X.
.. [#single-node-past-60] Debian 12 is supported only on AMD Instinct MI300X for single-node functionality.
.. [#RDNA-OS-past-60] Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
.. [#7700XT-OS-past-60] Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
.. [#mi300_624-past-60] **For ROCm 6.2.4** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
.. [#mi300_622-past-60] **For ROCm 6.2.2** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
.. [#mi300_621-past-60] **For ROCm 6.2.1** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
.. [#mi300_620-past-60] **For ROCm 6.2.0** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
.. [#mi300_612-past-60] **For ROCm 6.1.2** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.4 and Oracle Linux.
.. [#mi300_611-past-60] **For ROCm 6.1.1** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.4 and Oracle Linux.
.. [#mi300_610-past-60] **For ROCm 6.1.0** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.4.
.. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
.. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
.. [#verl_compat] verl is only supported on ROCm 6.2.0.
.. [#dgl_compat] DGL is only supported on ROCm 6.4.0.
.. [#taichi_compat] Taichi is only supported on ROCm 6.3.2.
.. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
.. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.

View File

@@ -1,255 +0,0 @@
:orphan:
.. meta::
:description: Deep Graph Library (DGL) compatibility
:keywords: GPU, DGL compatibility
.. version-set:: rocm_version latest
********************************************************************************
DGL compatibility
********************************************************************************
Deep Graph Library `(DGL) <https://www.dgl.ai/>`_ is an easy-to-use, high-performance and scalable
Python package for deep learning on graphs. DGL is framework agnostic, meaning
if a deep graph model is a component in an end-to-end application, the rest of
the logic is implemented using PyTorch.
* ROCm support for DGL is hosted in the `https://github.com/ROCm/dgl <https://github.com/ROCm/dgl>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/dmlc/dgl <https://github.com/dmlc/dgl>`_ upstream repository.
* Use the prebuilt :ref:`Docker images <dgl-docker-compat>` with DGL, PyTorch, and ROCm preinstalled.
* See the :doc:`ROCm DGL installation guide <rocm-install-on-linux:install/3rd-party/dgl-install>`
to install and get started.
Supported devices
================================================================================
- **Officially Supported**: TF32 with AMD Instinct MI300X (through hipblaslt)
- **Partially Supported**: TF32 with AMD Instinct MI250X
.. _dgl-recommendations:
Use cases and recommendations
================================================================================
DGL can be used for Graph Learning, and building popular graph models like
GAT, GCN and GraphSage. Using these we can support a variety of use-cases such as:
- Recommender systems
- Network Optimization and Analysis
- 1D (Temporal) and 2D (Image) Classification
- Drug Discovery
Multiple use cases of DGL have been tested and verified.
However, a recommended example follows a drug discovery pipeline using the ``SE3Transformer``.
Refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_,
where you can search for DGL examples and best practices to optimize your training workflows on AMD GPUs.
Coverage includes:
- Single-GPU training/inference
- Multi-GPU training
.. _dgl-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `DGL images <https://hub.docker.com/r/rocm/dgl>`_
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_.
Click the |docker-icon| to view the image on Docker Hub.
.. list-table:: DGL Docker image components
:header-rows: 1
:class: docker-image-compatibility
* - Docker
- DGL
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-8ce2c3bcfaa137ab94a75f9e2ea711894748980f57417739138402a542dd5564"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-cf1683283b8eeda867b690229c8091c5bbf1edb9f52e8fb3da437c49a612ebe4"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-4834f178c3614e2d09e89e32041db8984c456d45dfd20286e377ca8635686554"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 22.04
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/dgl/dgl-2.4_rocm6.4_ubuntu22.04_py3.10_pytorch_release_2.3.0/images/sha256-88740a2c8ab4084b42b10c3c6ba984cab33dd3a044f479c6d7618e2b2cb05e69"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.0 <https://github.com/dmlc/dgl/releases/tag/v2.4.0>`_
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`_
- 22.04
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
Key ROCm libraries for DGL
================================================================================
DGL on ROCm depends on specific libraries that affect its features and performance.
Using the DGL Docker container or building it with the provided docker file or a ROCm base image is recommended.
If you prefer to build it yourself, ensure the following dependencies are installed:
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- Purpose
* - `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.
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
- :version-ref:`hipBLASLt rocm_version`
- hipBLASLt is an extension of the hipBLAS library, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
* - `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.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
- :version-ref:`hipFFT rocm_version`
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
- :version-ref:`hipRAND rocm_version`
- Provides fast random number generation for GPUs.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
- :version-ref:`hipSOLVER rocm_version`
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
singular value decompositions (SVD).
* - `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.
* - `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.
* - `hipTensor <https://github.com/ROCm/hipTensor>`_
- :version-ref:`hipTensor rocm_version`
- Optimizes for high-performance tensor operations, such as contractions.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
- :version-ref:`MIOpen rocm_version`
- Optimizes deep learning primitives such as convolutions, pooling,
normalization, and activation functions.
* - `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.
* - `MIVisionX <https://github.com/ROCm/MIVisionX>`_
- :version-ref:`MIVisionX rocm_version`
- Optimizes acceleration for computer vision and AI workloads like
preprocessing, augmentation, and inferencing.
* - `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.
* - `RCCL <https://github.com/ROCm/rccl>`_
- :version-ref:`RCCL rocm_version`
- Optimizes for multi-GPU communication for operations like AllReduce and
Broadcast.
* - `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.
* - `rocJPEG <https://github.com/ROCm/rocJPEG>`_
- :version-ref:`rocJPEG rocm_version`
- Provides hardware-accelerated JPEG image decoding and encoding.
* - `RPP <https://github.com/ROCm/RPP>`_
- :version-ref:`RPP rocm_version`
- Speeds up data augmentation, transformation, and other preprocessing steps.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
- :version-ref:`rocWMMA rocm_version`
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
multiplication (GEMM) and accumulation operations with mixed precision
support.
Supported features
================================================================================
Many functions and methods available in DGL Upstream are also supported in DGL ROCm.
Instead of listing them all, support is grouped into the following categories to provide a general overview.
* DGL Base
* DGL Backend
* DGL Data
* DGL Dataloading
* DGL DGLGraph
* DGL Function
* DGL Ops
* DGL Sampling
* DGL Transforms
* DGL Utils
* DGL Distributed
* DGL Geometry
* DGL Mpops
* DGL NN
* DGL Optim
* DGL Sparse
Unsupported features
================================================================================
* Graphbolt
* Partial TF32 Support (MI250x only)
* Kineto/ ROCTracer integration
Unsupported functions
================================================================================
* ``more_nnz``
* ``format``
* ``multiprocess_sparse_adam_state_dict``
* ``record_stream_ndarray``
* ``half_spmm``
* ``segment_mm``
* ``gather_mm_idx_b``
* ``pgexplainer``
* ``sample_labors_prob``
* ``sample_labors_noprob``

View File

@@ -1,314 +0,0 @@
:orphan:
.. meta::
:description: JAX compatibility
:keywords: GPU, JAX compatibility
.. version-set:: rocm_version latest
*******************************************************************************
JAX compatibility
*******************************************************************************
JAX provides a NumPy-like API, which combines automatic differentiation and the
Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine
learning at scale.
JAX uses composable transformations of Python and NumPy through just-in-time
(JIT) compilation, automatic vectorization, and parallelization. To learn about
JAX, including profiling and optimizations, see the official `JAX documentation
<https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
ROCm support for JAX is upstreamed, and users can build the official source code
with ROCm support:
- ROCm JAX release:
- Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
with ROCm and JAX preinstalled.
- ROCm JAX repository: `ROCm/jax <https://github.com/ROCm/jax>`_
- See the :doc:`ROCm JAX installation guide <rocm-install-on-linux:install/3rd-party/jax-install>`
to get started.
- Official JAX release:
- Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
- See the `AMD GPU (Linux) installation section
<https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
the JAX documentation.
.. note::
AMD releases official `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
quarterly alongside new ROCm releases. These images undergo full AMD testing.
`Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
follow upstream JAX releases and use the latest available ROCm version.
Use cases and recommendations
================================================================================
* The `nanoGPT in JAX <https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html>`_
blog explores the implementation and training of a Generative Pre-trained
Transformer (GPT) model in JAX, inspired by Andrej Karpathys JAX-based
nanoGPT. Comparing how essential GPT components—such as self-attention
mechanisms and optimizers—are realized in JAX and JAX, also highlights
JAXs unique features.
* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
blog post provides a comprehensive guide on enhancing the training efficiency
of GPT models by implementing mixed precision techniques in JAX, specifically
tailored for AMD GPUs utilizing the ROCm platform.
* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
blog demonstrates how to develop a custom fused dropout-activation kernel for
matrices using Triton, integrate it with JAX, and benchmark its performance
using ROCm.
* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
outlines the process of fine-tuning a Bidirectional Encoder Representations
from Transformers (BERT)-based large language model (LLM) using JAX for a text
classification task. The blog post discuss techniques for parallelizing the
fine-tuning across multiple AMD GPUs and assess the model's performance on a
holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
and the General Language Understanding Evaluation (GLUE) benchmark dataset was
used on a multi-GPU setup.
* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
accelerator using ROCm. The page is aimed at helping users achieve optimal
performance for deep learning and other high-performance computing tasks on
the MI300X GPU.
For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
.. _jax-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes ready-made `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
with ROCm backends on Docker Hub. The following Docker image tags and
associated inventories represent the latest JAX version from the official Docker Hub and are validated for
`ROCm 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`_. Click the |docker-icon|
icon to view the image on Docker Hub.
.. list-table:: JAX Docker image components
:header-rows: 1
* - Docker image
- JAX
- Linux
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.2-jax0.4.35-py3.12/images/sha256-8918fa806a172c1a10eb2f57131eb31b5d7c8fa1656b8729fe7d3d736112de83"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
- `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
- Ubuntu 24.04
- `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.2-jax0.4.35-py3.10/images/sha256-a394be13c67b7fc602216abee51233afd4b6cb7adaa57ca97e688fba82f9ad79"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
- `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
- Ubuntu 22.04
- `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
AMD publishes `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
with ROCm backends on Docker Hub. The following Docker image tags and
associated inventories are tested for `ROCm 6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_.
.. list-table:: JAX community Docker image components
:header-rows: 1
* - Docker image
- JAX
- Linux
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.12.8/images/sha256-25dfaa0183e274bd0a3554a309af3249c6f16a1793226cb5373f418e39d3146a"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
- `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
- Ubuntu 22.04
- `3.12.8 <https://www.python.org/downloads/release/python-3128/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.11.11/images/sha256-ff9baeca9067d13e6c279c911e5a9e5beed0817d24fafd424367cc3d5bd381d7"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
- `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
- Ubuntu 22.04
- `3.11.11 <https://www.python.org/downloads/release/python-31111/>`_
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.10.16/images/sha256-8bab484be1713655f74da51a191ed824bb9d03db1104fd63530a1ac3c37cf7b1"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
- `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
- Ubuntu 22.04
- `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
.. _key_rocm_libraries:
Key ROCm libraries for JAX
================================================================================
The following ROCm libraries represent potential targets that could be utilized
by JAX on ROCm for various computational tasks. The actual libraries used will
depend on the specific implementation and operations performed.
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- Purpose
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
- :version-ref:`hipBLASLt rocm_version`
- hipBLASLt is an extension of hipBLAS, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
* - `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.
* - `hipFFT <https://github.com/ROCm/hipFFT>`_
- :version-ref:`hipFFT rocm_version`
- Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
* - `hipRAND <https://github.com/ROCm/hipRAND>`_
- :version-ref:`hipRAND rocm_version`
- Provides fast random number generation for GPUs.
* - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
- :version-ref:`hipSOLVER rocm_version`
- Provides GPU-accelerated solvers for linear systems, eigenvalues, and
singular value decompositions (SVD).
* - `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.
* - `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.
* - `MIOpen <https://github.com/ROCm/MIOpen>`_
- :version-ref:`MIOpen rocm_version`
- Optimized for deep learning primitives such as convolutions, pooling,
normalization, and activation functions.
* - `RCCL <https://github.com/ROCm/rccl>`_
- :version-ref:`RCCL rocm_version`
- Optimized for multi-GPU communication for operations like all-reduce,
broadcast, and scatter.
* - `rocThrust <https://github.com/ROCm/rocThrust>`_
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
.. note::
This table shows ROCm libraries that could potentially be utilized by JAX. Not
all libraries may be used in every configuration, and the actual library usage
will depend on the specific operations and implementation details.
Supported data types and modules
===============================================================================
The following tables lists the supported public JAX API data types and modules.
Supported data types
--------------------------------------------------------------------------------
ROCm supports all the JAX data types of `jax.dtypes <https://docs.jax.dev/en/latest/jax.dtypes.html>`_
module, `jax.numpy.dtype <https://docs.jax.dev/en/latest/_autosummary/jax.numpy.dtype.html>`_
and `default_dtype <https://docs.jax.dev/en/latest/default_dtypes.html>`_ .
The ROCm supported data types in JAX are collected in the following table.
.. list-table::
:header-rows: 1
* - Data type
- Description
* - ``bfloat16``
- 16-bit bfloat (brain floating point).
* - ``bool``
- Boolean.
* - ``complex128``
- 128-bit complex.
* - ``complex64``
- 64-bit complex.
* - ``float16``
- 16-bit (half precision) floating-point.
* - ``float32``
- 32-bit (single precision) floating-point.
* - ``float64``
- 64-bit (double precision) floating-point.
* - ``half``
- 16-bit (half precision) floating-point.
* - ``int16``
- Signed 16-bit integer.
* - ``int32``
- Signed 32-bit integer.
* - ``int64``
- Signed 64-bit integer.
* - ``int8``
- Signed 8-bit integer.
* - ``uint16``
- Unsigned 16-bit (word) integer.
* - ``uint32``
- Unsigned 32-bit (dword) integer.
* - ``uint64``
- Unsigned 64-bit (qword) integer.
* - ``uint8``
- Unsigned 8-bit (byte) integer.
.. note::
JAX data type support is effected by the :ref:`key_rocm_libraries` and it's
collected on :doc:`ROCm data types and precision support <rocm:reference/precision-support>`
page.
Supported modules
--------------------------------------------------------------------------------
For a complete and up-to-date list of JAX public modules (for example, ``jax.numpy``,
``jax.scipy``, ``jax.lax``), their descriptions, and usage, please refer directly to the
`official JAX API documentation <https://jax.readthedocs.io/en/latest/jax.html>`_.
.. note::
Since version 0.1.56, JAX has full support for ROCm, and the
:ref:`Known issues and important notes <jax_comp_known_issues>` section
contains details about limitations specific to the ROCm backend. The list of
JAX API modules is maintained by the JAX project and is subject to change.
Refer to the official Jax documentation for the most up-to-date information.

View File

@@ -1,93 +0,0 @@
:orphan:
.. meta::
:description: Megablocks compatibility
:keywords: GPU, megablocks, compatibility
.. version-set:: rocm_version latest
********************************************************************************
Megablocks compatibility
********************************************************************************
Megablocks is a light-weight library for mixture-of-experts (MoE) training.
The core of the system is efficient "dropless-MoE" and standard MoE layers.
Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_,
where data and pipeline parallel training of MoEs is supported.
* ROCm support for Megablocks is hosted in the official `https://github.com/ROCm/megablocks <https://github.com/ROCm/megablocks>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository.
* Use the prebuilt :ref:`Docker image <megablocks-docker-compat>` with ROCm, PyTorch, and Megablocks preinstalled.
* See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` to install and get started.
.. note::
Megablocks is supported on ROCm 6.3.0.
Supported devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
Supported models and features
================================================================================
This section summarizes the Megablocks features supported by ROCm.
* Distributed Pre-training
* Activation Checkpointing and Recomputation
* Distributed Optimizer
* Mixture-of-Experts
* dropless-Mixture-of-Experts
.. _megablocks-recommendations:
Use cases and recommendations
================================================================================
The `ROCm Megablocks blog posts <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_
guide how to leverage the ROCm platform for pre-training using the Megablocks framework.
It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
* Single-GPU pre-training
* Multi-GPU pre-training
.. _megablocks-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `ROCm Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`_
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories represent the latest Megatron-LM version from the official Docker Hub.
The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
Click |docker-icon| to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- ROCm
- Megablocks
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/megablocks/megablocks-0.7.0_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-372ff89b96599019b8f5f9db469c84add2529b713456781fa62eb9a148659ab4"><i class="fab fa-docker fa-lg"></i> rocm/megablocks</a>
- `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
- `0.7.0 <https://github.com/databricks/megablocks/releases/tag/v0.7.0>`_
- `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_

View File

@@ -1,535 +0,0 @@
:orphan:
.. meta::
:description: PyTorch compatibility
:keywords: GPU, PyTorch compatibility
.. version-set:: rocm_version latest
********************************************************************************
PyTorch compatibility
********************************************************************************
`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.
ROCm support for PyTorch is upstreamed into the official PyTorch repository. Due
to independent compatibility considerations, this results in two distinct
release cycles for PyTorch on ROCm:
- ROCm PyTorch release:
- Provides the latest version of ROCm but might not necessarily support the
latest stable PyTorch version.
- Offers :ref:`Docker images <pytorch-docker-compat>` with ROCm and PyTorch
preinstalled.
- 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.
- Official PyTorch release:
- Provides the latest stable version of PyTorch but might not necessarily
support the latest ROCm version.
- 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/>`__
to get started.
PyTorch includes tooling that generates HIP source code from the CUDA backend.
This approach allows PyTorch to support ROCm without requiring manual code
modifications. For more information, see :doc:`HIPIFY <hipify:index>`.
ROCm development is aligned with the stable release of PyTorch, while upstream
PyTorch testing uses the stable release of ROCm to maintain consistency.
.. _pytorch-recommendations:
Use cases and recommendations
================================================================================
* :doc:`Using ROCm for AI: training a model </how-to/rocm-for-ai/training/benchmark-docker/pytorch-training>`
guides how to leverage the ROCm platform for training AI models. It covers the
steps, tools, and best practices for optimizing training workflows on AMD GPUs
using PyTorch features.
* :doc:`Single-GPU fine-tuning and inference </how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference>`
describes and demonstrates how to use the ROCm platform for the fine-tuning
and inference of machine learning models, particularly large language models
(LLMs), on systems with a single GPU. This topic provides a detailed guide for
setting up, optimizing, and executing fine-tuning and inference workflows in
such environments.
* :doc:`Multi-GPU fine-tuning and inference optimization </how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference>`
describes and demonstrates the fine-tuning and inference of machine learning
models on systems with multiple GPUs.
* The :doc:`Instinct MI300X workload optimization guide </how-to/rocm-for-ai/inference-optimization/workload>`
provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
accelerator using ROCm. This guide helps users achieve optimal performance for
deep learning and other high-performance computing tasks on the MI300X
accelerator.
* The :doc:`Inception with PyTorch documentation </conceptual/ai-pytorch-inception>`
describes how PyTorch integrates with ROCm for AI workloads It outlines the
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>`__.
.. _pytorch-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
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 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__.
Click |docker-icon| to view the image on Docker Hub.
.. list-table:: PyTorch Docker image components
:header-rows: 1
:class: docker-image-compatibility
* - Docker
- PyTorch
- Ubuntu
- Python
- Apex
- torchvision
- TensorBoard
- MAGMA
- UCX
- OMPI
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-6a287591500b4048a9556c1ecc92bc411fd3d552f6c8233bc399f18eb803e8d6"><i class="fab fa-docker fa-lg"></i></a>
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
- 24.04
- `3.12 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.6.0/images/sha256-06b967629ba6657709f04169832cd769a11e6b491e8b1394c361d42d7a0c8b43"><i class="fab fa-docker fa-lg"></i></a>
- `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
- 22.04
- `3.10 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.5.1/images/sha256-62022414217ef6de33ac5b1341e57db8a48e8573fa2ace12d48aa5edd4b99ef0"><i class="fab fa-docker fa-lg"></i></a>
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
- 24.04
- `3.12 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.11_pytorch_release_2.5.1/images/sha256-469a7f74fc149aff31797e011ee41978f6a190adc69fa423b3c6a718a77bd985"><i class="fab fa-docker fa-lg"></i></a>
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
- 22.04
- `3.11 <https://www.python.org/downloads/release/python-31113/>`__
- `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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.5.1/images/sha256-37f41a1cd94019688669a1b20d33ea74156e0c129ef6b8270076ef214a6a1a2c"><i class="fab fa-docker fa-lg"></i></a>
- `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
- 22.04
- `3.10 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-60824ba83dc1b9d94164925af1f81c0235c105dd555091ec04c57e05177ead1b"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
- 24.04
- `3.12 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-fe944fe083312f901be6891ab4d3ffebf2eaf2cf4f5f0f435ef0b76ec714fabd"><i class="fab fa-docker fa-lg"></i></a>
- `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
- 22.04
- `3.10 <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>`__
- `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
- `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>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.3.0/images/sha256-1d59251c47170c5b8960d1172a4dbe52f5793d8966edd778f168eaf32d56661a"><i class="fab fa-docker fa-lg"></i></a>
- `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`__
- 24.04
- `3.12 <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>`__
Key ROCm libraries for PyTorch
================================================================================
PyTorch functionality on ROCm is determined by its underlying library
dependencies. These ROCm components affect the capabilities, performance, and
feature set available to developers.
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- Purpose
- Used in
* - `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>`__
- :version-ref:`hipBLAS rocm_version`
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
- Supports operations such as matrix multiplication, matrix-vector
products, and tensor contractions. Utilized in both dense and batched
linear algebra operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- :version-ref:`hipBLASLt rocm_version`
- hipBLASLt is an extension of the hipBLAS library, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
- Accelerates operations such as ``torch.matmul``, ``torch.mm``, and the
matrix multiplications used in convolutional and linear layers.
* - `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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :version-ref:`rocWMMA rocm_version`
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
multiplication (GEMM) and accumulation operations with mixed precision
support.
- Linear layers (``torch.nn.Linear``), convolutional layers
(``torch.nn.Conv2d``), attention layers, general tensor operations that
involve matrix products, such as ``torch.matmul``, ``torch.bmm``, and
more.
Supported modules and data types
================================================================================
The following section outlines the supported data types, modules, and domain libraries available in PyTorch on ROCm.
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>`__
single data types:
.. list-table::
:header-rows: 1
* - Data type
- Description
* - ``torch.float8_e4m3fn``
- 8-bit floating point, e4m3
* - ``torch.float8_e5m2``
- 8-bit floating point, e5m2
* - ``torch.float16`` or ``torch.half``
- 16-bit floating point
* - ``torch.bfloat16``
- 16-bit floating point
* - ``torch.float32`` or ``torch.float``
- 32-bit floating point
* - ``torch.float64`` or ``torch.double``
- 64-bit floating point
* - ``torch.complex32`` or ``torch.chalf``
- 32-bit complex numbers
* - ``torch.complex64`` or ``torch.cfloat``
- 64-bit complex numbers
* - ``torch.complex128`` or ``torch.cdouble``
- 128-bit complex numbers
* - ``torch.uint8``
- 8-bit integer (unsigned)
* - ``torch.uint16``
- 16-bit integer (unsigned);
Not natively supported in ROCm
* - ``torch.uint32``
- 32-bit integer (unsigned);
Not natively supported in ROCm
* - ``torch.uint64``
- 64-bit integer (unsigned);
Not natively supported in ROCm
* - ``torch.int8``
- 8-bit integer (signed)
* - ``torch.int16`` or ``torch.short``
- 16-bit integer (signed)
* - ``torch.int32`` or ``torch.int``
- 32-bit integer (signed)
* - ``torch.int64`` or ``torch.long``
- 64-bit integer (signed)
* - ``torch.bool``
- Boolean
* - ``torch.quint8``
- Quantized 8-bit integer (unsigned)
* - ``torch.qint8``
- Quantized 8-bit integer (signed)
* - ``torch.qint32``
- Quantized 32-bit integer (signed)
* - ``torch.quint4x2``
- Quantized 4-bit integer (unsigned)
.. note::
Unsigned types, except ``uint8``, have limited support in eager mode. They
primarily exist to assist usage with ``torch.compile``.
See :doc:`ROCm precision support <rocm:reference/precision-support>` for the
native hardware support of data types.
Supported modules
--------------------------------------------------------------------------------
For a complete and up-to-date list of PyTorch core modules (for example., ``torch``,
``torch.nn``, ``torch.cuda``, ``torch.backends.cuda`` and
``torch.backends.cudnn``), their descriptions, and usage, please refer directly
to the `official PyTorch documentation <https://pytorch.org/docs/stable/index.html>`_.
Core PyTorch functionality on ROCm includes tensor operations, neural network
layers, automatic differentiation, distributed training, mixed-precision
training, compilation features, and domain-specific libraries for audio, vision,
text processing, and more.
Supported domain libraries
--------------------------------------------------------------------------------
PyTorch offers specialized `domain libraries <https://pytorch.org/domains/>`_ with
GPU acceleration that build on its core features to support specific application
areas. The table below lists the PyTorch domain libraries that are compatible
with ROCm.
.. list-table::
:header-rows: 1
* - Library
- Description
* - `torchaudio <https://docs.pytorch.org/audio/stable/index.html>`_
- Audio and signal processing library for PyTorch. Provides utilities for
audio I/O, signal and data processing functions, datasets, model
implementations, and application components for audio and speech
processing tasks.
**Note:** To ensure GPU-acceleration with ``torchaudio.transforms``,
you need to explicitly move audio data (waveform tensor) to GPU using
``.to('cuda')``.
* - `torchtune <https://docs.pytorch.org/torchtune/stable/index.html>`_
- PyTorch-native library designed for fine-tuning large language models
(LLMs). Provides supports the full fine-tuning workflow and offers
compatibility with popular production inference systems.
**Note:** Only official release exists.
* - `torchvision <https://docs.pytorch.org/vision/stable/index.html>`_
- Computer vision library that is part of the PyTorch project. Provides
popular datasets, model architectures, and common image transformations
for computer vision applications.
* - `torchtext <https://docs.pytorch.org/text/stable/index.html>`_
- Text processing library for PyTorch. Provides data processing utilities
and popular datasets for natural language processing, including
tokenization, vocabulary management, and text embeddings.
**Note:** ``torchtext`` does not implement ROCm-specific kernels.
ROCm acceleration is provided through the underlying PyTorch framework
and ROCm library integration. Only official release exists.
* - `torchdata <https://docs.pytorch.org/data/beta/index.html>`_
- Beta library of common modular data loading primitives for easily
constructing flexible and performant data pipelines, with features still
in prototype stage.
* - `torchrec <https://docs.pytorch.org/torchrec/>`_
- PyTorch domain library for common sparsity and parallelism primitives
needed for large-scale recommender systems, enabling authors to train
models with large embedding tables shared across many GPUs.
**Note:** ``torchrec`` does not implement ROCm-specific kernels. ROCm
acceleration is provided through the underlying PyTorch framework and
ROCm library integration.
* - `torchserve <https://docs.pytorch.org/serve/>`_
- Performant, flexible and easy-to-use tool for serving PyTorch models in
production, providing features for model management, batch processing,
and scalable deployment.
**Note:** `torchserve <https://docs.pytorch.org/serve/>`_ is no longer
actively maintained. Last official release is sent out with PyTorch 2.4.
* - `torchrl <https://docs.pytorch.org/rl/stable/index.html>`_
- Open-source, Python-first Reinforcement Learning library for PyTorch
with a focus on high modularity and good runtime performance, providing
low and high-level RL abstractions and reusable functionals for cost
functions, returns, and data processing.
**Note:** Only official release exists.
* - `tensordict <https://docs.pytorch.org/tensordict/stable/index.html>`_
- Dictionary-like class that simplifies operations on batches of tensors,
enhancing code readability, compactness, and modularity by abstracting
tailored operations and reducing errors through automatic operation
dispatching.
**Note:** Only official release exists.

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@@ -1,100 +0,0 @@
:orphan:
.. meta::
:description: Stanford Megatron-LM compatibility
:keywords: Stanford, Megatron-LM, compatibility
.. version-set:: rocm_version latest
********************************************************************************
Stanford Megatron-LM compatibility
********************************************************************************
Stanford Megatron-LM is a large-scale language model training framework developed by NVIDIA `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. It is
designed to train massive transformer-based language models efficiently by model and data parallelism.
* ROCm support for Stanford Megatron-LM is hosted in the official `https://github.com/ROCm/Stanford-Megatron-LM <https://github.com/ROCm/Stanford-Megatron-LM>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`_ upstream repository.
* Use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>` with ROCm, PyTorch, and Megatron-LM preinstalled.
* See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` to install and get started.
.. note::
Stanford Megatron-LM is supported on ROCm 6.3.0.
Supported Devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
Supported models and features
================================================================================
This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
Models:
* Bert
* GPT
* T5
* ICT
Features:
* Distributed Pre-training
* Activation Checkpointing and Recomputation
* Distributed Optimizer
* Mixture-of-Experts
.. _megatron-lm-recommendations:
Use cases and recommendations
================================================================================
See the `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs blog <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`_ post
to leverage the ROCm platform for pre-training by using the Stanford Megatron-LM framework of pre-processing datasets on AMD GPUs.
Coverage includes:
* Single-GPU pre-training
* Multi-GPU pre-training
.. _megatron-lm-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/megatron-lm>`_
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories represent the latest Megatron-LM version from the official Docker Hub.
The Docker images have been validated for `ROCm 6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_.
Click |docker-icon| to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- Stanford Megatron-LM
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i></a>
- `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
- `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
- 24.04
- `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_

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@@ -1,76 +0,0 @@
:orphan:
.. meta::
:description: Taichi compatibility
:keywords: GPU, Taichi compatibility
.. version-set:: rocm_version latest
*******************************************************************************
Taichi compatibility
*******************************************************************************
`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel
programming language designed for high-performance numerical computation.
Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate
compute-intensive Python code by compiling it to native GPU or CPU instructions.
Taichi is widely used across various domains, including real-time physical simulation,
numerical computing, augmented reality, artificial intelligence, computer vision, robotics,
visual effects in film and gaming, and general-purpose computing.
* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
.. note::
Taichi is supported on ROCm 6.3.2.
Supported devices and features
===============================================================================
There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X series GPUs with the exception of Taichis GPU rendering system, CGUI.
AMD Instinct MI300X series GPUs will be supported by November.
.. _taichi-recommendations:
Use cases and recommendations
================================================================================
To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators.
A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository,
providing practical insights and foundational knowledge for working with the Taichi programming language.
You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
.. _taichi-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
with ROCm backends on Docker Hub. The following Docker image tags and associated inventories
represent the latest Taichi version from the official Docker Hub.
The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_.
Click |docker-icon| to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- ROCm
- Taichi
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
- `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
- `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
- 22.04
- `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_

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@@ -1,504 +0,0 @@
:orphan:
.. meta::
:description: TensorFlow compatibility
:keywords: GPU, TensorFlow compatibility
.. version-set:: rocm_version latest
*******************************************************************************
TensorFlow compatibility
*******************************************************************************
`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>`__
includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
<http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
fixes, updates, and support for the latest ROCM versions.
- ROCm TensorFlow release:
- Offers :ref:`Docker images <tensorflow-docker-compat>` with
ROCm and TensorFlow pre-installed.
- 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>`__
- See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
.. note::
The official TensorFlow documentation does not cover ROCm support. Use the
ROCm documentation for installation instructions for Tensorflow on ROCm.
See :doc:`rocm-install-on-linux:install/3rd-party/tensorflow-install`.
.. _tensorflow-docker-compat:
Docker image compatibility
===============================================================================
.. |docker-icon| raw:: html
<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
Docker Hub. The following Docker image tags and associated inventories are
validated for `ROCm 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__. Click
the |docker-icon| icon to view the image on Docker Hub.
.. list-table:: TensorFlow Docker image components
:header-rows: 1
* - Docker image
- TensorFlow
- Ubuntu
- Python
- TensorBoard
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.18-dev/images/sha256-96754ce2d30f729e19b497279915b5212ba33d5e408e7e5dd3f2304d87e3441e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- 24.04
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.18-dev/images/sha256-fa741508d383858e86985a9efac85174529127408102558ae2e3a4ac894eea1e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- 22.04
- `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.17-dev/images/sha256-3a0aef09f2a8833c2b64b85874dd9449ffc2ad257351857338ff5b706c03a418"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- 24.04
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.17-dev/images/sha256-bc7341a41ebe7ab261aa100732874507c452421ef733e408ac4f05ed453b0bc5"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- 22.04
- `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
- `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.16-dev/images/sha256-4841a8df7c340dab79bf9362dad687797649a00d594e0832eb83ea6880a40d3b"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
- 24.04
- `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
- `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.16-dev/images/sha256-883fa95aba960c58a3e46fceaa18f03ede2c7df89b8e9fd603ab2d47e0852897"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
- `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`__
- 22.04
- `Python 3.10 <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
===============================================================================
TensorFlow depends on multiple components and the supported features of those
components can affect the TensorFlow ROCm supported feature set. The versions
in the following table refer to the first TensorFlow version where the ROCm
library was introduced as a dependency. The versions described
are available in ROCm :version:`rocm_version`.
.. list-table::
:widths: 25, 10, 35, 30
:header-rows: 1
* - ROCm library
- Version
- Purpose
- Used in
* - `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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :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>`__
- :version-ref:`MIOpen rocm_version`
- Provides optimized deep learning primitives such as convolutions,
pooling,
normalization, and activation functions.
- 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>`__
- :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>`__
- :version-ref:`rocThrust rocm_version`
- Provides a C++ template library for parallel algorithms like sorting,
reduction, and scanning.
- Reduction operations like ``tf.reduce_sum``, ``tf.cumsum`` for computing
the cumulative sum of elements along a given axis or ``tf.unique`` to
finds unique elements in a tensor can use rocThrust.
Supported and unsupported features
===============================================================================
The following section maps supported data types and GPU-accelerated TensorFlow
features to their minimum supported ROCm and TensorFlow versions.
Data types
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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>`__
are as follows:
.. list-table::
:header-rows: 1
* - Data type
- Description
- Since TensorFlow
- Since ROCm
* - ``bfloat16``
- 16-bit bfloat (brain floating point).
- 1.0.0
- 1.7
* - ``bool``
- Boolean.
- 1.0.0
- 1.7
* - ``complex128``
- 128-bit complex.
- 1.0.0
- 1.7
* - ``complex64``
- 64-bit complex.
- 1.0.0
- 1.7
* - ``double``
- 64-bit (double precision) floating-point.
- 1.0.0
- 1.7
* - ``float16``
- 16-bit (half precision) floating-point.
- 1.0.0
- 1.7
* - ``float32``
- 32-bit (single precision) floating-point.
- 1.0.0
- 1.7
* - ``float64``
- 64-bit (double precision) floating-point.
- 1.0.0
- 1.7
* - ``half``
- 16-bit (half precision) floating-point.
- 2.0.0
- 2.0
* - ``int16``
- Signed 16-bit integer.
- 1.0.0
- 1.7
* - ``int32``
- Signed 32-bit integer.
- 1.0.0
- 1.7
* - ``int64``
- Signed 64-bit integer.
- 1.0.0
- 1.7
* - ``int8``
- Signed 8-bit integer.
- 1.0.0
- 1.7
* - ``qint16``
- Signed quantized 16-bit integer.
- 1.0.0
- 1.7
* - ``qint32``
- Signed quantized 32-bit integer.
- 1.0.0
- 1.7
* - ``qint8``
- Signed quantized 8-bit integer.
- 1.0.0
- 1.7
* - ``quint16``
- Unsigned quantized 16-bit integer.
- 1.0.0
- 1.7
* - ``quint8``
- Unsigned quantized 8-bit integer.
- 1.0.0
- 1.7
* - ``resource``
- Handle to a mutable, dynamically allocated resource.
- 1.0.0
- 1.7
* - ``string``
- Variable-length string, represented as byte array.
- 1.0.0
- 1.7
* - ``uint16``
- Unsigned 16-bit (word) integer.
- 1.0.0
- 1.7
* - ``uint32``
- Unsigned 32-bit (dword) integer.
- 1.5.0
- 1.7
* - ``uint64``
- Unsigned 64-bit (qword) integer.
- 1.5.0
- 1.7
* - ``uint8``
- Unsigned 8-bit (byte) integer.
- 1.0.0
- 1.7
* - ``variant``
- Data of arbitrary type (known at runtime).
- 1.4.0
- 1.7
Features
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This table provides an overview of key features in TensorFlow and their
availability in ROCm.
.. list-table::
:header-rows: 1
* - Module
- Description
- Since TensorFlow
- Since ROCm
* - ``tf.linalg`` (Linear Algebra)
- Operations for matrix and tensor computations, such as
``tf.linalg.matmul`` (matrix multiplication), ``tf.linalg.inv``
(matrix inversion) and ``tf.linalg.cholesky`` (Cholesky decomposition).
These leverage GPUs for high-performance linear algebra operations.
- 1.4
- 1.8.2
* - ``tf.nn`` (Neural Network Operations)
- GPU-accelerated building blocks for deep learning models, such as 2D
convolutions with ``tf.nn.conv2d``, max pooling operations with
``tf.nn.max_pool``, activation functions like ``tf.nn.relu`` or softmax
for output layers with ``tf.nn.softmax``.
- 1.0
- 1.8.2
* - ``tf.image`` (Image Processing)
- GPU-accelerated functions for image preprocessing and augmentations,
such as resize images with ``tf.image.resize``, flip images horizontally
with ``tf.image.flip_left_right`` and adjust image brightness randomly
with ``tf.image.random_brightness``.
- 1.1
- 1.8.2
* - ``tf.keras`` (High-Level API)
- GPU acceleration for Keras layers and models, including dense layers
(``tf.keras.layers.Dense``), convolutional layers
(``tf.keras.layers.Conv2D``) and recurrent layers
(``tf.keras.layers.LSTM``).
- 1.4
- 1.8.2
* - ``tf.math`` (Mathematical Operations)
- GPU-accelerated mathematical operations, such as sum across dimensions
with ``tf.math.reduce_sum``, elementwise exponentiation with
``tf.math.exp`` and sigmoid activation (``tf.math.sigmoid``).
- 1.5
- 1.8.2
* - ``tf.signal`` (Signal Processing)
- Functions for spectral analysis and signal transformations.
- 1.13
- 2.1
* - ``tf.data`` (Data Input Pipeline)
- GPU-accelerated data preprocessing for efficient input pipelines,
Prefetching with ``tf.data.experimental.AUTOTUNE``. GPU-enabled
transformations like map and batch.
- 1.4
- 1.8.2
* - ``tf.distribute`` (Distributed Training)
- Enabling to scale computations across multiple devices on a single
machine or across multiple machines.
- 1.13
- 2.1
* - ``tf.random`` (Random Number Generation)
- GPU-accelerated random number generation
- 1.12
- 1.9.2
* - ``tf.TensorArray`` (Dynamic Array Operations)
- Enables dynamic tensor manipulation on GPUs.
- 1.0
- 1.8.2
* - ``tf.sparse`` (Sparse Tensor Operations)
- GPU-accelerated sparse matrix manipulations.
- 1.9
- 1.9.0
* - ``tf.experimental.numpy``
- GPU-accelerated NumPy-like API for numerical computations.
- 2.4
- 4.1.1
* - ``tf.RaggedTensor``
- Handling of variable-length sequences and ragged tensors with GPU
support.
- 1.13
- 2.1
* - ``tf.function`` with XLA (Accelerated Linear Algebra)
- Enable GPU-accelerated functions in optimization.
- 1.14
- 2.4
* - ``tf.quantization``
- Quantized operations for inference, accelerated on GPUs.
- 1.12
- 1.9.2
Distributed library features
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Enables developers to scale computations across multiple devices on a single machine or
across multiple machines.
.. list-table::
:header-rows: 1
* - Feature
- Description
- Since TensorFlow
- Since ROCm
* - ``MultiWorkerMirroredStrategy``
- Synchronous training across multiple workers using mirrored variables.
- 2.0
- 3.0
* - ``MirroredStrategy``
- Synchronous training across multiple GPUs on one machine.
- 1.5
- 2.5
* - ``TPUStrategy``
- Efficiently trains models on Google TPUs.
- 1.9
- ❌
* - ``ParameterServerStrategy``
- Asynchronous training using parameter servers for variable management.
- 2.1
- 4.0
* - ``CentralStorageStrategy``
- Keeps variables on a single device and performs computation on multiple
devices.
- 2.3
- 4.1
* - ``CollectiveAllReduceStrategy``
- Synchronous training across multiple devices and hosts.
- 1.14
- 3.5
* - Distribution Strategies API
- High-level API to simplify distributed training configuration and
execution.
- 1.10
- 3.0
Unsupported TensorFlow features
===============================================================================
The following are GPU-accelerated TensorFlow features not currently supported by
ROCm.
.. list-table::
:header-rows: 1
* - Feature
- Description
- Since TensorFlow
* - Mixed Precision with TF32
- Mixed precision with TF32 is used for matrix multiplications,
convolutions, and other linear algebra operations, particularly in
deep learning workloads like CNNs and transformers.
- 2.4
* - ``tf.distribute.TPUStrategy``
- Efficiently trains models on Google TPUs.
- 1.9
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>`__
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
post outlines the implementation using the recommenders library, focusing on
the use of implicit data (for example, user interactions like viewing or
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>`__
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,
and troubleshooting potential issues with CUDA-based code. Additionally, it
explains how to HIPify code (port CUDA code to HIP) and manage Docker images
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>`__.

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@@ -1,86 +0,0 @@
:orphan:
.. meta::
:description: verl compatibility
:keywords: GPU, verl compatibility
.. version-set:: rocm_version latest
*******************************************************************************
verl compatibility
*******************************************************************************
Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs).
verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support.
* 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/verl-install>` to install and get started.
.. note::
verl is supported on ROCm 6.2.0.
.. _verl-recommendations:
Use cases and recommendations
================================================================================
The benefits of verl in large-scale reinforcement learning from human feedback (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`_ blog.
.. _verl-supported_features:
Supported features
===============================================================================
The following table shows verl on ROCm support for GPU-accelerated modules.
.. list-table::
:header-rows: 1
* - Module
- Description
- verl version
- ROCm version
* - ``FSDP``
- Training engine
- 0.3.0.post0
- 6.2.0
* - ``vllm``
- Inference engine
- 0.3.0.post0
- 6.2.0
.. _verl-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes ready-made `ROCm verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
with ROCm backends on Docker Hub. The following Docker image tags and associated inventories represent the available verl versions from the official Docker Hub.
.. list-table::
:header-rows: 1
* - Docker image
- ROCm
- verl
- Ubuntu
- Pytorch
- Python
- vllm
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
- `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`_
- `0.3.0post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`_
- 20.04
- `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
- `3.9.19 <https://www.python.org/downloads/release/python-3919/>`_
- `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`_

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@@ -1,407 +0,0 @@
.. meta::
:description: Using CMake
:keywords: CMake, dependencies, HIP, C++, AMD, ROCm
*********************************
Using CMake
*********************************
Most components in ROCm support CMake. Projects depending on header-only or
library components typically require CMake 3.5 or higher whereas those wanting
to make use of the CMake HIP language support will require CMake 3.21 or higher.
Finding dependencies
====================
.. note::
For a complete
reference on how to deal with dependencies in CMake, refer to the CMake docs
on `find_package
<https://cmake.org/cmake/help/latest/command/find_package.html>`_ and the
`Using Dependencies Guide
<https://cmake.org/cmake/help/latest/guide/using-dependencies/index.html>`_
to get an overview of CMake related facilities.
In short, CMake supports finding dependencies in two ways:
* In Module mode, it consults a file ``Find<PackageName>.cmake`` which tries to find the component
in typical install locations and layouts. CMake ships a few dozen such scripts, but users and projects
may ship them as well.
* In Config mode, it locates a file named ``<packagename>-config.cmake`` or
``<PackageName>Config.cmake`` which describes the installed component in all regards needed to
consume it.
ROCm predominantly relies on Config mode, one notable exception being the Module
driving the compilation of HIP programs on NVIDIA runtimes. As such, when
dependencies are not found in standard system locations, one either has to
instruct CMake to search for package config files in additional folders using
the ``CMAKE_PREFIX_PATH`` variable (a semi-colon separated list of file system
paths), or using ``<PackageName>_ROOT`` variable on a project-specific basis.
There are nearly a dozen ways to set these variables. One may be more convenient
over the other depending on your workflow. Conceptually the simplest is adding
it to your CMake configuration command on the command line via
``-D CMAKE_PREFIX_PATH=....`` . AMD packaged ROCm installs can typically be
added to the config file search paths such as:
* Windows: ``-D CMAKE_PREFIX_PATH=${env:HIP_PATH}``
* Linux: ``-D CMAKE_PREFIX_PATH=/opt/rocm``
ROCm provides the respective *config-file* packages, and this enables
``find_package`` to be used directly. ROCm does not require any Find module as
the *config-file* packages are shipped with the upstream projects, such as
rocPRIM and other ROCm libraries.
For a complete guide on where and how ROCm may be installed on a system, refer
to the installation guides for
`Linux <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/tutorial/quick-start.html>`_
and
`Windows <https://rocm.docs.amd.com/projects/install-on-windows/en/latest/index.html>`_.
Using HIP in CMake
==================
ROCm components providing a C/C++ interface support consumption via any
C/C++ toolchain that CMake knows how to drive. ROCm also supports the CMake HIP
language features, allowing users to program using the HIP single-source
programming model. When a program (or translation-unit) uses the HIP API without
compiling any GPU device code, HIP can be treated in CMake as a simple C/C++
library.
Using the HIP single-source programming model
---------------------------------------------
Source code written in the HIP dialect of C++ typically uses the `.hip`
extension. When the HIP CMake language is enabled, it will automatically
associate such source files with the HIP toolchain being used.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.21) # HIP language support requires 3.21
cmake_policy(VERSION 3.21.3...3.27)
project(MyProj LANGUAGES HIP)
add_executable(MyApp Main.hip)
Should you have existing CUDA code that is from the source compatible subset of
HIP, you can tell CMake that despite their `.cu` extension, they're HIP sources.
Do note that this mostly facilitates compiling kernel code-only source files,
as host-side CUDA API won't compile in this fashion.
.. code-block:: cmake
add_library(MyLib MyLib.cu)
set_source_files_properties(MyLib.cu PROPERTIES LANGUAGE HIP)
CMake itself only hosts part of the HIP language support, such as defining
HIP-specific properties, etc. while the other half ships with the HIP
implementation, such as ROCm. CMake will search for a file
`hip-lang-config.cmake` describing how the the properties defined by CMake
translate to toolchain invocations. If one installs ROCm using non-standard
methods or layouts and CMake can't locate this file or detect parts of the SDK,
there's a catch-all, last resort variable consulted locating this file,
``-D CMAKE_HIP_COMPILER_ROCM_ROOT:PATH=`` which should be set the root of the
ROCm installation.
.. note::
Imported targets defined by `hip-lang-config.cmake` are for internal use
only.
If the user doesn't provide a semi-colon delimited list of device architectures
via ``CMAKE_HIP_ARCHITECTURES``, CMake will select some sensible default. It is
advised though that if a user knows what devices they wish to target, then set
this variable explicitly.
Consuming ROCm C/C++ libraries
------------------------------
Libraries such as rocBLAS, rocFFT, MIOpen, etc. behave as C/C++ libraries.
Illustrated in the example below is a C++ application using MIOpen from CMake.
It calls ``find_package(miopen)``, which provides the ``MIOpen`` imported
target. This can be linked with ``target_link_libraries``
.. code-block:: cmake
cmake_minimum_required(VERSION 3.5) # find_package(miopen) requires 3.5
cmake_policy(VERSION 3.5...3.27)
project(MyProj LANGUAGES CXX)
find_package(miopen)
add_library(MyLib ...)
target_link_libraries(MyLib PUBLIC MIOpen)
.. note::
Most libraries are designed as host-only API, so using a GPU device
compiler is not necessary for downstream projects unless they use GPU device
code.
Consuming the HIP API in C++ code
---------------------------------
Consuming the HIP API without compiling single-source GPU device code can be
done using any C++ compiler. The ``find_package(hip)`` provides the
``hip::host`` imported target to use HIP in this scenario.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.5) # find_package(hip) requires 3.5
cmake_policy(VERSION 3.5...3.27)
project(MyProj LANGUAGES CXX)
find_package(hip REQUIRED)
add_executable(MyApp ...)
target_link_libraries(MyApp PRIVATE hip::host)
When mixing such ``CXX`` sources with ``HIP`` sources holding device-code, link
only to `hip::host`. If HIP sources don't have `.hip` as their extension, use
`set_source_files_properties(<hip_sources>... PROPERTIES LANGUAGE HIP)` on them.
Linking to `hip::host` will set all the necessary flags for the ``CXX`` sources
while ``HIP`` sources inherit all flags from the built-in language support.
Having HIP sources in a target will turn the |LINK_LANG|_ into ``HIP``.
.. |LINK_LANG| replace:: ``LINKER_LANGUAGE``
.. _LINK_LANG: https://cmake.org/cmake/help/latest/prop_tgt/LINKER_LANGUAGE.html
Compiling device code in C++ language mode
------------------------------------------
.. attention::
The workflow detailed here is considered legacy and is shown for
understanding's sake. It pre-dates the existence of HIP language support in
CMake. If source code has HIP device code in it, it is a HIP source file
and should be compiled as such. Only resort to the method below if your
HIP-enabled CMake code path can't mandate CMake version 3.21.
If code uses the HIP API and compiles GPU device code, it requires using a
device compiler. The compiler for CMake can be set using either the
``CMAKE_C_COMPILER`` and ``CMAKE_CXX_COMPILER`` variable or using the ``CC``
and ``CXX`` environment variables. This can be set when configuring CMake or
put into a CMake toolchain file. The device compiler must be set to a
compiler that supports AMD GPU targets, which is usually Clang.
The ``find_package(hip)`` provides the ``hip::device`` imported target to add
all the flags necessary for device compilation.
.. code-block:: cmake
cmake_minimum_required(VERSION 3.8) # cxx_std_11 requires 3.8
cmake_policy(VERSION 3.8...3.27)
project(MyProj LANGUAGES CXX)
find_package(hip REQUIRED)
add_library(MyLib ...)
target_link_libraries(MyLib PRIVATE hip::device)
target_compile_features(MyLib PRIVATE cxx_std_11)
.. note::
Compiling for the GPU device requires at least C++11.
This project can then be configured with the following CMake commands:
* Windows: ``cmake -D CMAKE_CXX_COMPILER:PATH=${env:HIP_PATH}\bin\clang++.exe``
* Linux: ``cmake -D CMAKE_CXX_COMPILER:PATH=/opt/rocm/bin/amdclang++``
Which use the device compiler provided from the binary packages of
`ROCm HIP SDK <https://www.amd.com/en/developer/resources/rocm-hub/hip-sdk.html>`_ and
`repo.radeon.com <https://repo.radeon.com>`_ respectively.
When using the ``CXX`` language support to compile HIP device code, selecting the
target GPU architectures is done via setting the ``GPU_TARGETS`` variable.
``CMAKE_HIP_ARCHITECTURES`` only exists when the HIP language is enabled. By
default, this is set to some subset of the currently supported architectures of
AMD ROCm. It can be set to the CMake option ``-D GPU_TARGETS="gfx1032;gfx1035"``.
ROCm CMake packages
-------------------
+-----------+----------+--------------------------------------------------------+
| Component | Package | Targets |
+===========+==========+========================================================+
| HIP | hip | ``hip::host``, ``hip::device`` |
+-----------+----------+--------------------------------------------------------+
| rocPRIM | rocprim | ``roc::rocprim`` |
+-----------+----------+--------------------------------------------------------+
| rocThrust | rocthrust| ``roc::rocthrust`` |
+-----------+----------+--------------------------------------------------------+
| hipCUB | hipcub | ``hip::hipcub`` |
+-----------+----------+--------------------------------------------------------+
| rocRAND | rocrand | ``roc::rocrand`` |
+-----------+----------+--------------------------------------------------------+
| rocBLAS | rocblas | ``roc::rocblas`` |
+-----------+----------+--------------------------------------------------------+
| rocSOLVER | rocsolver| ``roc::rocsolver`` |
+-----------+----------+--------------------------------------------------------+
| hipBLAS | hipblas | ``roc::hipblas`` |
+-----------+----------+--------------------------------------------------------+
| rocFFT | rocfft | ``roc::rocfft`` |
+-----------+----------+--------------------------------------------------------+
| hipFFT | hipfft | ``hip::hipfft`` |
+-----------+----------+--------------------------------------------------------+
| rocSPARSE | rocsparse| ``roc::rocsparse`` |
+-----------+----------+--------------------------------------------------------+
| hipSPARSE | hipsparse| ``roc::hipsparse`` |
+-----------+----------+--------------------------------------------------------+
| rocALUTION|rocalution| ``roc::rocalution`` |
+-----------+----------+--------------------------------------------------------+
| RCCL | rccl | ``rccl`` |
+-----------+----------+--------------------------------------------------------+
| MIOpen | miopen | ``MIOpen`` |
+-----------+----------+--------------------------------------------------------+
| MIGraphX | migraphx | ``migraphx::migraphx``, ``migraphx::migraphx_c``, |
| | | ``migraphx::migraphx_cpu``, ``migraphx::migraphx_gpu``,|
| | | ``migraphx::migraphx_onnx``, ``migraphx::migraphx_tf`` |
+-----------+----------+--------------------------------------------------------+
Using CMake presets
===================
CMake command lines depending on how specific users like to be when compiling
code can grow to unwieldy lengths. This is the primary reason why projects tend
to bake script snippets into their build definitions controlling compiler
warning levels, changing CMake defaults (``CMAKE_BUILD_TYPE`` or
``BUILD_SHARED_LIBS`` just to name a few) and all sorts anti-patterns, all in
the name of convenience.
Load on the command-line interface (CLI) starts immediately by selecting a
toolchain, the set of utilities used to compile programs. To ease some of the
toolchain related pains, CMake does consult the ``CC`` and ``CXX`` environmental
variables when setting a default ``CMAKE_C[XX]_COMPILER`` respectively, but that
is just the tip of the iceberg. There's a fair number of variables related to
just the toolchain itself (typically supplied using
`toolchain files <https://cmake.org/cmake/help/latest/manual/cmake-toolchains.7.html>`_
), and then we still haven't talked about user preference or project-specific
options.
IDEs supporting CMake (Visual Studio, Visual Studio Code, CLion, etc.) all came
up with their own way to register command-line fragments of different purpose in
a setup-and-forget fashion for quick assembly using graphical front-ends. This is
all nice, but configurations aren't portable, nor can they be reused in
Continuous Integration (CI) pipelines. CMake has condensed existing practice
into a portable JSON format that works in all IDEs and can be invoked from any
command line. This is
`CMake Presets <https://cmake.org/cmake/help/latest/manual/cmake-presets.7.html>`_.
There are two types of preset files: one supplied by the project, called
``CMakePresets.json`` which is meant to be committed to version control,
typically used to drive CI; and one meant for the user to provide, called
``CMakeUserPresets.json``, typically used to house user preference and adapting
the build to the user's environment. These JSON files are allowed to include
other JSON files and the user presets always implicitly includes the non-user
variant.
Using HIP with presets
----------------------
Following is an example ``CMakeUserPresets.json`` file which actually compiles
the `amd/rocm-examples <https://github.com/amd/rocm-examples>`_ suite of sample
applications on a typical ROCm installation:
.. code-block:: json
{
"version": 3,
"cmakeMinimumRequired": {
"major": 3,
"minor": 21,
"patch": 0
},
"configurePresets": [
{
"name": "layout",
"hidden": true,
"binaryDir": "${sourceDir}/build/${presetName}",
"installDir": "${sourceDir}/install/${presetName}"
},
{
"name": "generator-ninja-multi-config",
"hidden": true,
"generator": "Ninja Multi-Config"
},
{
"name": "toolchain-makefiles-c/c++-amdclang",
"hidden": true,
"cacheVariables": {
"CMAKE_C_COMPILER": "/opt/rocm/bin/amdclang",
"CMAKE_CXX_COMPILER": "/opt/rocm/bin/amdclang++",
"CMAKE_HIP_COMPILER": "/opt/rocm/bin/amdclang++"
}
},
{
"name": "clang-strict-iso-high-warn",
"hidden": true,
"cacheVariables": {
"CMAKE_C_FLAGS": "-Wall -Wextra -pedantic",
"CMAKE_CXX_FLAGS": "-Wall -Wextra -pedantic",
"CMAKE_HIP_FLAGS": "-Wall -Wextra -pedantic"
}
},
{
"name": "ninja-mc-rocm",
"displayName": "Ninja Multi-Config ROCm",
"inherits": [
"layout",
"generator-ninja-multi-config",
"toolchain-makefiles-c/c++-amdclang",
"clang-strict-iso-high-warn"
]
}
],
"buildPresets": [
{
"name": "ninja-mc-rocm-debug",
"displayName": "Debug",
"configuration": "Debug",
"configurePreset": "ninja-mc-rocm"
},
{
"name": "ninja-mc-rocm-release",
"displayName": "Release",
"configuration": "Release",
"configurePreset": "ninja-mc-rocm"
},
{
"name": "ninja-mc-rocm-debug-verbose",
"displayName": "Debug (verbose)",
"configuration": "Debug",
"configurePreset": "ninja-mc-rocm",
"verbose": true
},
{
"name": "ninja-mc-rocm-release-verbose",
"displayName": "Release (verbose)",
"configuration": "Release",
"configurePreset": "ninja-mc-rocm",
"verbose": true
}
],
"testPresets": [
{
"name": "ninja-mc-rocm-debug",
"displayName": "Debug",
"configuration": "Debug",
"configurePreset": "ninja-mc-rocm",
"execution": {
"jobs": 0
}
},
{
"name": "ninja-mc-rocm-release",
"displayName": "Release",
"configuration": "Release",
"configurePreset": "ninja-mc-rocm",
"execution": {
"jobs": 0
}
}
]
}
.. note::
Getting presets to work reliably on Windows requires some CMake improvements
and/or support from compiler vendors. (Refer to
`Add support to the Visual Studio generators <https://gitlab.kitware.com/cmake/cmake/-/issues/24245>`_
and `Sourcing environment scripts <https://gitlab.kitware.com/cmake/cmake/-/issues/21619>`_
.)

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<head>
<meta charset="UTF-8">
<meta name="description" content="AMD ROCm documentation">
<meta name="keywords" content="documentation, guides, installation, compatibility, support,
reference, ROCm, AMD">
</head>
# Using compiler features
The following topics describe using specific features of the compilation tools:
* [ROCm compiler infrastructure](https://rocm.docs.amd.com/projects/llvm-project/en/latest/index.html)
* [Using AddressSanitizer](https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/using-gpu-sanitizer.html)
* [OpenMP support](https://rocm.docs.amd.com/projects/llvm-project/en/latest/conceptual/openmp.html)

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@@ -1,172 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="ROCm Linux Filesystem Hierarchy Standard reorganization">
<meta name="keywords" content="FHS, Linux Filesystem Hierarchy Standard, directory structure,
AMD, ROCm">
</head>
# ROCm Linux Filesystem Hierarchy Standard reorganization
## Introduction
The ROCm Software has adopted the Linux Filesystem Hierarchy Standard (FHS) [https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html](https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html) in order to to ensure ROCm is consistent with standard open source conventions. The following sections specify how current and future releases of ROCm adhere to FHS, how the previous ROCm file system is supported, and how improved versioning specifications are applied to ROCm.
## Adopting the FHS
In order to standardize ROCm directory structure and directory content layout ROCm has adopted the [FHS](https://refspecs.linuxfoundation.org/FHS_3.0/fhs/index.html), adhering to open source conventions for Linux-based distribution. FHS ensures internal consistency within the ROCm stack, as well as external consistency with other systems and distributions. The ROCm proposed file structure is outlined below:
```none
/opt/rocm-<ver>
| -- bin
| -- all public binaries
| -- lib
| -- lib<soname>.so->lib<soname>.so.major->lib<soname>.so.major.minor.patch
(public libaries to link with applications)
| -- <component>
| -- architecture dependent libraries and binaries used internally by components
| -- cmake
| -- <component>
| --<component>-config.cmake
| -- libexec
| -- <component>
| -- non ISA/architecture independent executables used internally by components
| -- include
| -- <component>
| -- public header files
| -- share
| -- html
| -- <component>
| -- html documentation
| -- info
| -- <component>
| -- info files
| -- man
| -- <component>
| -- man pages
| -- doc
| -- <component>
| -- license files
| -- <component>
| -- samples
| -- architecture independent misc files
```
## Changes from earlier ROCm versions
The following table provides a brief overview of the new ROCm FHS layout, compared to the layout of earlier ROCm versions. Note that /opt/ is used to denote the default rocm-installation-path and should be replaced in case of a non-standard installation location of the ROCm distribution.
```none
______________________________________________________
| New ROCm Layout | Previous ROCm Layout |
|_____________________________|________________________|
| /opt/rocm-<ver> | /opt/rocm-<ver> |
| | -- bin | | -- bin |
| | -- lib | | -- lib |
| | -- cmake | | -- include |
| | -- libexec | | -- <component_1> |
| | -- include | | -- bin |
| | -- <component_1> | | -- cmake |
| | -- share | | -- doc |
| | -- html | | -- lib |
| | -- info | | -- include |
| | -- man | | -- samples |
| | -- doc | | -- <component_n> |
| | -- <component_1> | | -- bin |
| | -- samples | | -- cmake |
| | -- .. | | -- doc |
| | -- <component_n> | | -- lib |
| | -- samples | | -- include |
| | -- .. | | -- samples |
|______________________________________________________|
```
## ROCm FHS reorganization: backward compatibility
The FHS file organization for ROCm was first introduced in the release of ROCm 5.2 . Backward compatibility was implemented to make sure users could still run their ROCm applications while transitioning to the new FHS. ROCm has moved header files and libraries to their new locations as indicated in the above structure, and included symbolic-links and wrapper header files in their old location for backward compatibility. The following sections detail ROCm backward compatibility implementation for wrapper header files, executable files, library files and CMake config files.
### Wrapper header files
Wrapper header files are placed in the old location (
`/opt/rocm-<ver>/<component>/include`) with a warning message to include files
from the new location (`/opt/rocm-<ver>/include`) as shown in the example below.
```cpp
#pragma message "This file is deprecated. Use file from include path /opt/rocm-ver/include/ and prefix with hip."
#include <hip/hip_runtime.h>
```
* Starting at ROCm 5.2 release, the deprecation for backward compatibility wrapper header files is: `#pragma` message announcing `#warning`.
* Starting from ROCm 6.0 (tentatively) backward compatibility for wrapper header files will be removed, and the `#pragma` message will be announcing `#error`.
### Executable files
Executable files are available in the `/opt/rocm-<ver>/bin` folder. For backward
compatibility, the old library location (`/opt/rocm-<ver>/<component>/bin`) has a
soft link to the library at the new location. Soft links will be removed in a
future release, tentatively ROCm v6.0.
```bash
$ ls -l /opt/rocm/hip/bin/
lrwxrwxrwx 1 root root 24 Jan 1 23:32 hipcc -> ../../bin/hipcc
```
### Library files
Library files are available in the `/opt/rocm-<ver>/lib` folder. For backward
compatibility, the old library location (`/opt/rocm-<ver>/<component>/lib`) has a
soft link to the library at the new location. Soft links will be removed in a
future release, tentatively ROCm v6.0.
```shell
$ ls -l /opt/rocm/hip/lib/
drwxr-xr-x 4 root root 4096 Jan 1 10:45 cmake
lrwxrwxrwx 1 root root 24 Jan 1 23:32 libamdhip64.so -> ../../lib/libamdhip64.so
```
### CMake config files
All CMake configuration files are available in the
`/opt/rocm-<ver>/lib/cmake/<component>` folder. For backward compatibility, the
old CMake locations (`/opt/rocm-<ver>/<component>/lib/cmake`) consist of a soft
link to the new CMake config. Soft links will be removed in a future release,
tentatively ROCm v6.0.
```shell
$ ls -l /opt/rocm/hip/lib/cmake/hip/
lrwxrwxrwx 1 root root 42 Jan 1 23:32 hip-config.cmake -> ../../../../lib/cmake/hip/hip-config.cmake
```
## Changes required in applications using ROCm
Applications using ROCm are advised to use the new file paths. As the old files
will be deprecated in a future release. Applications have to make sure to include
correct header file and use correct search paths.
1. `#include<header_file.h>` needs to be changed to
`#include <component/header_file.h>`
For example: `#include <hip.h>` needs to change
to `#include <hip/hip.h>`
2. Any variable in CMake or Makefiles pointing to component folder needs to
changed.
For example: `VAR1=/opt/rocm/hip` needs to be changed to `VAR1=/opt/rocm`
`VAR2=/opt/rocm/hsa` needs to be changed to `VAR2=/opt/rocm`
3. Any reference to `/opt/rocm/<component>/bin` or `/opt/rocm/<component>/lib`
needs to be changed to `/opt/rocm/bin` and `/opt/rocm/lib/`, respectively.
## Changes in versioning specifications
In order to better manage ROCm dependencies specification and allow smoother releases of ROCm while avoiding dependency conflicts, ROCm software shall adhere to the following scheme when numbering and incrementing ROCm files versions:
rocm-\<ver\>, where \<ver\> = \<x.y.z\>
x.y.z denote: MAJOR.MINOR.PATCH
z: PATCH - increment z when implementing backward compatible bug fixes.
y: MINOR - increment y when implementing minor changes that add functionality but are still backward compatible.
x: MAJOR - increment x when implementing major changes that are not backward compatible.

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@@ -1,72 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="GPU architecture">
<meta name="keywords" content="GPU architecture, architecture support, MI200, MI250, RDNA,
MI100, AMD Instinct">
</head>
(gpu-arch-documentation)=
# GPU architecture documentation
:::::{grid} 1 1 2 2
:gutter: 1
:::{grid-item-card}
**AMD Instinct MI300 series**
Review hardware aspects of the AMD Instinct™ MI300 series of GPU accelerators and the CDNA™ 3
architecture.
* [AMD Instinct™ MI300 microarchitecture](./gpu-arch/mi300.md)
* [AMD Instinct MI300/CDNA3 ISA](https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/instruction-set-architectures/amd-instinct-mi300-cdna3-instruction-set-architecture.pdf)
* [White paper](https://www.amd.com/content/dam/amd/en/documents/instinct-tech-docs/white-papers/amd-cdna-3-white-paper.pdf)
* [Performance counters](./gpu-arch/mi300-mi200-performance-counters.rst)
:::
:::{grid-item-card}
**AMD Instinct MI200 series**
Review hardware aspects of the AMD Instinct™ MI200 series of GPU accelerators and the CDNA™ 2
architecture.
* [AMD Instinct™ MI250 microarchitecture](./gpu-arch/mi250.md)
* [AMD Instinct MI200/CDNA2 ISA](https://www.amd.com/system/files/TechDocs/instinct-mi200-cdna2-instruction-set-architecture.pdf)
* [White paper](https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna2-white-paper.pdf)
* [Performance counters](./gpu-arch/mi300-mi200-performance-counters.rst)
:::
:::{grid-item-card}
**AMD Instinct MI100**
Review hardware aspects of the AMD Instinct™ MI100 series of GPU accelerators and the CDNA™ 1
architecture.
* [AMD Instinct™ MI100 microarchitecture](./gpu-arch/mi100.md)
* [AMD Instinct MI100/CDNA1 ISA](https://www.amd.com/system/files/TechDocs/instinct-mi100-cdna1-shader-instruction-set-architecture%C2%A0.pdf)
* [White paper](https://www.amd.com/content/dam/amd/en/documents/instinct-business-docs/white-papers/amd-cdna-white-paper.pdf)
:::
:::{grid-item-card}
**RDNA**
* [AMD RDNA4 ISA](https://www.amd.com/content/dam/amd/en/documents/radeon-tech-docs/instruction-set-architectures/rdna4-instruction-set-architecture.pdf)
* [AMD RDNA3 ISA](https://www.amd.com/system/files/TechDocs/rdna3-shader-instruction-set-architecture-feb-2023_0.pdf)
* [AMD RDNA2 ISA](https://www.amd.com/system/files/TechDocs/rdna2-shader-instruction-set-architecture.pdf)
* [AMD RDNA ISA](https://www.amd.com/system/files/TechDocs/rdna-shader-instruction-set-architecture.pdf)
:::
:::{grid-item-card}
**Older architectures**
* [AMD Instinct MI50/Vega 7nm ISA](https://www.amd.com/system/files/TechDocs/vega-7nm-shader-instruction-set-architecture.pdf)
* [AMD Instinct MI25/Vega ISA](https://www.amd.com/system/files/TechDocs/vega-shader-instruction-set-architecture.pdf)
* [AMD GCN3 ISA](https://www.amd.com/system/files/TechDocs/gcn3-instruction-set-architecture.pdf)
* [AMD Vega Architecture White Paper](https://en.wikichip.org/w/images/a/a1/vega-whitepaper.pdf)
:::
:::::

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@@ -1,95 +0,0 @@
---
myst:
html_meta:
"description lang=en": "Learn about the AMD Instinct MI100 series architecture."
"keywords": "Instinct, MI100, microarchitecture, AMD, ROCm"
---
# AMD Instinct™ MI100 microarchitecture
The following image shows the node-level architecture of a system that
comprises two AMD EPYC™ processors and (up to) eight AMD Instinct™ accelerators.
The two EPYC processors are connected to each other with the AMD Infinity™
fabric which provides a high-bandwidth (up to 18 GT/sec) and coherent links such
that each processor can access the available node memory as a single
shared-memory domain in a non-uniform memory architecture (NUMA) fashion. In a
2P, or dual-socket, configuration, three AMD Infinity™ fabric links are
available to connect the processors plus one PCIe Gen 4 x16 link per processor
can attach additional I/O devices such as the host adapters for the network
fabric.
![Structure of a single GCD in the AMD Instinct MI100 accelerator](../../data/conceptual/gpu-arch/image004.png "Node-level system architecture with two AMD EPYC™ processors and eight AMD Instinct™ accelerators.")
In a typical node configuration, each processor can host up to four AMD
Instinct™ accelerators that are attached using PCIe Gen 4 links at 16 GT/sec,
which corresponds to a peak bidirectional link bandwidth of 32 GB/sec. Each hive
of four accelerators can participate in a fully connected, coherent AMD
Instinct™ fabric that connects the four accelerators using 23 GT/sec AMD
Infinity fabric links that run at a higher frequency than the inter-processor
links. This inter-GPU link can be established in certified server systems if the
GPUs are mounted in neighboring PCIe slots by installing the AMD Infinity
Fabric™ bridge for the AMD Instinct™ accelerators.
## Microarchitecture
The microarchitecture of the AMD Instinct accelerators is based on the AMD CDNA
architecture, which targets compute applications such as high-performance
computing (HPC) and AI & machine learning (ML) that run on everything from
individual servers to the world's largest exascale supercomputers. The overall
system architecture is designed for extreme scalability and compute performance.
![Structure of the AMD Instinct accelerator (MI100 generation)](../../data/conceptual/gpu-arch/image005.png "Structure of the AMD Instinct accelerator (MI100 generation)")
The above image shows the AMD Instinct accelerator with its PCIe Gen 4 x16
link (16 GT/sec, at the bottom) that connects the GPU to (one of) the host
processor(s). It also shows the three AMD Infinity Fabric ports that provide
high-speed links (23 GT/sec, also at the bottom) to the other GPUs of the local
hive.
On the left and right of the floor plan, the High Bandwidth Memory (HBM)
attaches via the GPU memory controller. The MI100 generation of the AMD
Instinct accelerator offers four stacks of HBM generation 2 (HBM2) for a total
of 32GB with a 4,096bit-wide memory interface. The peak memory bandwidth of the
attached HBM2 is 1.228 TB/sec at a memory clock frequency of 1.2 GHz.
The execution units of the GPU are depicted in the above image as Compute
Units (CU). There are a total 120 compute units that are physically organized
into eight Shader Engines (SE) with fifteen compute units per shader engine.
Each compute unit is further sub-divided into four SIMD units that process SIMD
instructions of 16 data elements per instruction. This enables the CU to process
64 data elements (a so-called 'wavefront') at a peak clock frequency of 1.5 GHz.
Therefore, the theoretical maximum FP64 peak performance is 11.5 TFLOPS
(`4 [SIMD units] x 16 [elements per instruction] x 120 [CU] x 1.5 [GHz]`).
![Block diagram of an MI100 compute unit with detailed SIMD view of the AMD CDNA architecture](../../data/conceptual/gpu-arch/image006.png "An MI100 compute unit with detailed SIMD view of the AMD CDNA architecture")
The preceding image shows the block diagram of a single CU of an AMD Instinct™
MI100 accelerator and summarizes how instructions flow through the execution
engines. The CU fetches the instructions via a 32KB instruction cache and moves
them forward to execution via a dispatcher. The CU can handle up to ten
wavefronts at a time and feed their instructions into the execution unit. The
execution unit contains 256 vector general-purpose registers (VGPR) and 800
scalar general-purpose registers (SGPR). The VGPR and SGPR are dynamically
allocated to the executing wavefronts. A wavefront can access a maximum of 102
scalar registers. Excess scalar-register usage will cause register spilling and
thus may affect execution performance.
A wavefront can occupy any number of VGPRs from 0 to 256, directly affecting
occupancy; that is, the number of concurrently active wavefronts in the CU. For
instance, with 119 VGPRs used, only two wavefronts can be active in the CU at
the same time. With the instruction latency of four cycles per SIMD instruction,
the occupancy should be as high as possible such that the compute unit can
improve execution efficiency by scheduling instructions from multiple
wavefronts.
:::{table} Peak-performance capabilities of MI100 for different data types.
:name: mi100-perf
| Computation and Data Type | FLOPS/CLOCK/CU | Peak TFLOPS |
| :------------------------ | :------------: | ----------: |
| Vector FP64 | 64 | 11.5 |
| Matrix FP32 | 256 | 46.1 |
| Vector FP32 | 128 | 23.1 |
| Matrix FP16 | 1024 | 184.6 |
| Matrix BF16 | 512 | 92.3 |
:::

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@@ -1,134 +0,0 @@
---
myst:
html_meta:
"description lang=en": "Learn about the AMD Instinct MI250 series architecture."
"keywords": "Instinct, MI250, microarchitecture, AMD, ROCm"
---
# AMD Instinct™ MI250 microarchitecture
The microarchitecture of the AMD Instinct MI250 accelerators is based on the
AMD CDNA 2 architecture that targets compute applications such as HPC,
artificial intelligence (AI), and machine learning (ML) and that run on
everything from individual servers to the worlds largest exascale
supercomputers. The overall system architecture is designed for extreme
scalability and compute performance.
The following image shows the components of a single Graphics Compute Die (GCD) of the CDNA 2 architecture. On the top and the bottom are AMD Infinity Fabric™
interfaces and their physical links that are used to connect the GPU die to the
other system-level components of the node (see also Section 2.2). Both
interfaces can drive four AMD Infinity Fabric links. One of the AMD Infinity
Fabric links of the controller at the bottom can be configured as a PCIe link.
Each of the AMD Infinity Fabric links between GPUs can run at up to 25 GT/sec,
which correlates to a peak transfer bandwidth of 50 GB/sec for a 16-wide link (
two bytes per transaction). Section 2.2 has more details on the number of AMD
Infinity Fabric links and the resulting transfer rates between the system-level
components.
To the left and the right are memory controllers that attach the High Bandwidth
Memory (HBM) modules to the GCD. AMD Instinct MI250 GPUs use HBM2e, which offers
a peak memory bandwidth of 1.6 TB/sec per GCD.
The execution units of the GPU are depicted in the following image as Compute
Units (CU). The MI250 GCD has 104 active CUs. Each compute unit is further
subdivided into four SIMD units that process SIMD instructions of 16 data
elements per instruction (for the FP64 data type). This enables the CU to
process 64 work items (a so-called “wavefront”) at a peak clock frequency of 1.7
GHz. Therefore, the theoretical maximum FP64 peak performance per GCD is 22.6
TFLOPS for vector instructions. This equates to 45.3 TFLOPS for vector instructions for both GCDs together. The MI250 compute units also provide specialized
execution units (also called matrix cores), which are geared toward executing
matrix operations like matrix-matrix multiplications. For FP64, the peak
performance of these units amounts to 90.5 TFLOPS.
![Structure of a single GCD in the AMD Instinct MI250 accelerator.](../../data/conceptual/gpu-arch/image001.png "Structure of a single GCD in the AMD Instinct MI250 accelerator.")
```{list-table} Peak-performance capabilities of the MI250 OAM for different data types.
:header-rows: 1
:name: mi250-perf-table
*
- Computation and Data Type
- FLOPS/CLOCK/CU
- Peak TFLOPS
*
- Matrix FP64
- 256
- 90.5
*
- Vector FP64
- 128
- 45.3
*
- Matrix FP32
- 256
- 90.5
*
- Packed FP32
- 256
- 90.5
*
- Vector FP32
- 128
- 45.3
*
- Matrix FP16
- 1024
- 362.1
*
- Matrix BF16
- 1024
- 362.1
*
- Matrix INT8
- 1024
- 362.1
```
The above table summarizes the aggregated peak performance of the AMD
Instinct MI250 OCP Open Accelerator Modules (OAM, OCP is short for Open Compute
Platform) and its two GCDs for different data types and execution units. The
middle column lists the peak performance (number of data elements processed in a
single instruction) of a single compute unit if a SIMD (or matrix) instruction
is being retired in each clock cycle. The third column lists the theoretical
peak performance of the OAM module. The theoretical aggregated peak memory
bandwidth of the GPU is 3.2 TB/sec (1.6 TB/sec per GCD).
![Dual-GCD architecture of the AMD Instinct MI250 accelerators](../../data/conceptual/gpu-arch/image002.png "Dual-GCD architecture of the AMD Instinct MI250 accelerators")
The following image shows the block diagram of an OAM package that consists
of two GCDs, each of which constitutes one GPU device in the system. The two
GCDs in the package are connected via four AMD Infinity Fabric links running at
a theoretical peak rate of 25 GT/sec, giving 200 GB/sec peak transfer bandwidth
between the two GCDs of an OAM, or a bidirectional peak transfer bandwidth of
400 GB/sec for the same.
## Node-level architecture
The following image shows the node-level architecture of a system that is
based on the AMD Instinct MI250 accelerator. The MI250 OAMs attach to the host
system via PCIe Gen 4 x16 links (yellow lines). Each GCD maintains its own PCIe
x16 link to the host part of the system. Depending on the server platform, the
GCD can attach to the AMD EPYC processor directly or via an optional PCIe switch
. Note that some platforms may offer an x8 interface to the GCDs, which reduces
the available host-to-GPU bandwidth.
![Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation AMD EPYC processor](../../data/conceptual/gpu-arch/image003.png "Block diagram of AMD Instinct MI250 Accelerators with 3rd Generation AMD EPYC processor")
The preceding image shows the node-level architecture of a system with AMD
EPYC processors in a dual-socket configuration and four AMD Instinct MI250
accelerators. The MI250 OAMs attach to the host processors system via PCIe Gen 4
x16 links (yellow lines). Depending on the system design, a PCIe switch may
exist to make more PCIe lanes available for additional components like network
interfaces and/or storage devices. Each GCD maintains its own PCIe x16 link to
the host part of the system or to the PCIe switch. Please note, some platforms
may offer an x8 interface to the GCDs, which will reduce the available
host-to-GPU bandwidth.
Between the OAMs and their respective GCDs, a peer-to-peer (P2P) network allows
for direct data exchange between the GPU dies via AMD Infinity Fabric links (
black, green, and red lines). Each of these 16-wide links connects to one of the
two GPU dies in the MI250 OAM and operates at 25 GT/sec, which corresponds to a
theoretical peak transfer rate of 50 GB/sec per link (or 100 GB/sec
bidirectional peak transfer bandwidth). The GCD pairs 2 and 6 as well as GCDs 0
and 4 connect via two XGMI links, which is indicated by the thicker red line in
the preceding image.

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@@ -1,757 +0,0 @@
.. meta::
:description: MI300 and MI200 series performance counters and metrics
:keywords: MI300, MI200, performance counters, command processor counters
***************************************************************************************************
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-SDK <rocprofiler-sdk:how-to/using-rocprofv3>`.
MI300 and MI200 series performance counters
===============================================================
Series performance counters include the following categories:
* :ref:`command-processor-counters`
* :ref:`graphics-register-bus-manager-counters`
* :ref:`spi-counters`
* :ref:`compute-unit-counters`
* :ref:`l1i-and-sl1d-cache-counters`
* :ref:`vector-l1-cache-subsystem-counters`
* :ref:`l2-cache-access-counters`
The following sections provide additional details for each category.
.. note::
Preliminary validation of all MI300 and MI200 series performance counters is in progress. Those with
an asterisk (*) require further evaluation.
.. _command-processor-counters:
Command processor counters
---------------------------------------------------------------------------------------------------------------
Command processor counters are further classified into command processor-fetcher and command
processor-compute.
Command processor-fetcher counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``CPF_CMP_UTCL1_STALL_ON_TRANSLATION``", "Cycles", "Number of cycles one of the compute unified translation caches (L1) is stalled waiting on translation"
"``CPF_CPF_STAT_BUSY``", "Cycles", "Number of cycles command processor-fetcher is busy"
"``CPF_CPF_STAT_IDLE``", "Cycles", "Number of cycles command processor-fetcher is idle"
"``CPF_CPF_STAT_STALL``", "Cycles", "Number of cycles command processor-fetcher is stalled"
"``CPF_CPF_TCIU_BUSY``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is busy"
"``CPF_CPF_TCIU_IDLE``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is idle"
"``CPF_CPF_TCIU_STALL``", "Cycles", "Number of cycles command processor-fetcher texture cache interface unit interface is stalled waiting on free tags"
The texture cache interface unit is the interface between the command processor and the memory
system.
Command processor-compute counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``CPC_ME1_BUSY_FOR_PACKET_DECODE``", "Cycles", "Number of cycles command processor-compute micro engine is busy decoding packets"
"``CPC_UTCL1_STALL_ON_TRANSLATION``", "Cycles", "Number of cycles one of the unified translation caches (L1) is stalled waiting on translation"
"``CPC_CPC_STAT_BUSY``", "Cycles", "Number of cycles command processor-compute is busy"
"``CPC_CPC_STAT_IDLE``", "Cycles", "Number of cycles command processor-compute is idle"
"``CPC_CPC_STAT_STALL``", "Cycles", "Number of cycles command processor-compute is stalled"
"``CPC_CPC_TCIU_BUSY``", "Cycles", "Number of cycles command processor-compute texture cache interface unit interface is busy"
"``CPC_CPC_TCIU_IDLE``", "Cycles", "Number of cycles command processor-compute texture cache interface unit interface is idle"
"``CPC_CPC_UTCL2IU_BUSY``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is busy"
"``CPC_CPC_UTCL2IU_IDLE``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is idle"
"``CPC_CPC_UTCL2IU_STALL``", "Cycles", "Number of cycles command processor-compute unified translation cache (L2) interface is stalled"
"``CPC_ME1_DC0_SPI_BUSY``", "Cycles", "Number of cycles command processor-compute micro engine processor is busy"
The micro engine runs packet-processing firmware on the command processor-compute counter.
.. _graphics-register-bus-manager-counters:
Graphics register bus manager counters
---------------------------------------------------------------------------------------------------------------
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``GRBM_COUNT``", "Cycles","Number of free-running GPU cycles"
"``GRBM_GUI_ACTIVE``", "Cycles", "Number of GPU active cycles"
"``GRBM_CP_BUSY``", "Cycles", "Number of cycles any of the command processor blocks are busy"
"``GRBM_SPI_BUSY``", "Cycles", "Number of cycles any of the shader processor input is busy in the shader engines"
"``GRBM_TA_BUSY``", "Cycles", "Number of cycles any of the texture addressing unit is busy in the shader engines"
"``GRBM_TC_BUSY``", "Cycles", "Number of cycles any of the texture cache blocks are busy"
"``GRBM_CPC_BUSY``", "Cycles", "Number of cycles the command processor-compute is busy"
"``GRBM_CPF_BUSY``", "Cycles", "Number of cycles the command processor-fetcher is busy"
"``GRBM_UTCL2_BUSY``", "Cycles", "Number of cycles the unified translation cache (Level 2 [L2]) block is busy"
"``GRBM_EA_BUSY``", "Cycles", "Number of cycles the efficiency arbiter block is busy"
Texture cache blocks include:
* Texture cache arbiter
* Texture cache per pipe, also known as vector Level 1 (L1) cache
* Texture cache per channel, also known as known as L2 cache
* Texture cache interface
.. _spi-counters:
Shader processor input counters
---------------------------------------------------------------------------------------------------------------
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SPI_CSN_BUSY``", "Cycles", "Number of cycles with outstanding waves"
"``SPI_CSN_WINDOW_VALID``", "Cycles", "Number of cycles enabled by ``perfcounter_start`` event"
"``SPI_CSN_NUM_THREADGROUPS``", "Workgroups", "Number of dispatched workgroups"
"``SPI_CSN_WAVE``", "Wavefronts", "Number of dispatched wavefronts"
"``SPI_RA_REQ_NO_ALLOC``", "Cycles", "Number of arbiter cycles with requests but no allocation"
"``SPI_RA_REQ_NO_ALLOC_CSN``", "Cycles", "Number of arbiter cycles with compute shader (n\ :sup:`th` pipe) requests but no compute shader (n\ :sup:`th` pipe) allocation"
"``SPI_RA_RES_STALL_CSN``", "Cycles", "Number of arbiter stall cycles due to shortage of compute shader (n\ :sup:`th` pipe) pipeline slots"
"``SPI_RA_TMP_STALL_CSN``", "Cycles", "Number of stall cycles due to shortage of temp space"
"``SPI_RA_WAVE_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of single instruction, multiple data (SIMD) per cycle affected by shortage of wave slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
"``SPI_RA_VGPR_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of SIMDs per cycle affected by shortage of vector general-purpose register (VGPR) slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
"``SPI_RA_SGPR_SIMD_FULL_CSN``", "SIMD-cycles", "Accumulated number of SIMDs per cycle affected by shortage of scalar general-purpose register (SGPR) slots for compute shader (n\ :sup:`th` pipe) wave dispatch"
"``SPI_RA_LDS_CU_FULL_CSN``", "CU", "Number of compute units affected by shortage of local data share (LDS) space for compute shader (n\ :sup:`th` pipe) wave dispatch"
"``SPI_RA_BAR_CU_FULL_CSN``", "CU", "Number of compute units with compute shader (n\ :sup:`th` pipe) waves waiting at a BARRIER"
"``SPI_RA_BULKY_CU_FULL_CSN``", "CU", "Number of compute units with compute shader (n\ :sup:`th` pipe) waves waiting for BULKY resource"
"``SPI_RA_TGLIM_CU_FULL_CSN``", "Cycles", "Number of compute shader (n\ :sup:`th` pipe) wave stall cycles due to restriction of ``tg_limit`` for thread group size"
"``SPI_RA_WVLIM_STALL_CSN``", "Cycles", "Number of cycles compute shader (n\ :sup:`th` pipe) is stalled due to ``WAVE_LIMIT``"
"``SPI_VWC_CSC_WR``", "Qcycles", "Number of quad-cycles taken to initialize VGPRs when launching waves"
"``SPI_SWC_CSC_WR``", "Qcycles", "Number of quad-cycles taken to initialize SGPRs when launching waves"
.. _compute-unit-counters:
Compute unit counters
---------------------------------------------------------------------------------------------------------------
The compute unit counters are further classified into instruction mix, matrix fused multiply-add (FMA)
operation counters, level counters, wavefront counters, wavefront cycle counters, and LDS counters.
Instruction mix
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_INSTS``", "Instr", "Number of instructions issued"
"``SQ_INSTS_VALU``", "Instr", "Number of vector arithmetic logic unit (VALU) instructions including matrix FMA issued"
"``SQ_INSTS_VALU_ADD_F16``", "Instr", "Number of VALU half-precision floating-point (F16) ``ADD`` or ``SUB`` instructions issued"
"``SQ_INSTS_VALU_MUL_F16``", "Instr", "Number of VALU F16 Multiply instructions issued"
"``SQ_INSTS_VALU_FMA_F16``", "Instr", "Number of VALU F16 FMA or multiply-add instructions issued"
"``SQ_INSTS_VALU_TRANS_F16``", "Instr", "Number of VALU F16 Transcendental instructions issued"
"``SQ_INSTS_VALU_ADD_F32``", "Instr", "Number of VALU full-precision floating-point (F32) ``ADD`` or ``SUB`` instructions issued"
"``SQ_INSTS_VALU_MUL_F32``", "Instr", "Number of VALU F32 Multiply instructions issued"
"``SQ_INSTS_VALU_FMA_F32``", "Instr", "Number of VALU F32 FMAor multiply-add instructions issued"
"``SQ_INSTS_VALU_TRANS_F32``", "Instr", "Number of VALU F32 Transcendental instructions issued"
"``SQ_INSTS_VALU_ADD_F64``", "Instr", "Number of VALU F64 ``ADD`` or ``SUB`` instructions issued"
"``SQ_INSTS_VALU_MUL_F64``", "Instr", "Number of VALU F64 Multiply instructions issued"
"``SQ_INSTS_VALU_FMA_F64``", "Instr", "Number of VALU F64 FMA or multiply-add instructions issued"
"``SQ_INSTS_VALU_TRANS_F64``", "Instr", "Number of VALU F64 Transcendental instructions issued"
"``SQ_INSTS_VALU_INT32``", "Instr", "Number of VALU 32-bit integer instructions (signed or unsigned) issued"
"``SQ_INSTS_VALU_INT64``", "Instr", "Number of VALU 64-bit integer instructions (signed or unsigned) issued"
"``SQ_INSTS_VALU_CVT``", "Instr", "Number of VALU Conversion instructions issued"
"``SQ_INSTS_VALU_MFMA_I8``", "Instr", "Number of 8-bit Integer matrix FMA instructions issued"
"``SQ_INSTS_VALU_MFMA_F16``", "Instr", "Number of F16 matrix FMA instructions issued"
"``SQ_INSTS_VALU_MFMA_F32``", "Instr", "Number of F32 matrix FMA instructions issued"
"``SQ_INSTS_VALU_MFMA_F64``", "Instr", "Number of F64 matrix FMA instructions issued"
"``SQ_INSTS_MFMA``", "Instr", "Number of matrix FMA instructions issued"
"``SQ_INSTS_VMEM_WR``", "Instr", "Number of vector memory write instructions (including flat) issued"
"``SQ_INSTS_VMEM_RD``", "Instr", "Number of vector memory read instructions (including flat) issued"
"``SQ_INSTS_VMEM``", "Instr", "Number of vector memory instructions issued, including both flat and buffer instructions"
"``SQ_INSTS_SALU``", "Instr", "Number of scalar arithmetic logic unit (SALU) instructions issued"
"``SQ_INSTS_SMEM``", "Instr", "Number of scalar memory instructions issued"
"``SQ_INSTS_SMEM_NORM``", "Instr", "Number of scalar memory instructions normalized to match ``smem_level`` issued"
"``SQ_INSTS_FLAT``", "Instr", "Number of flat instructions issued"
"``SQ_INSTS_FLAT_LDS_ONLY``", "Instr", "**MI200 series only** Number of FLAT instructions that read/write only from/to LDS issued. Works only if ``EARLY_TA_DONE`` is enabled."
"``SQ_INSTS_LDS``", "Instr", "Number of LDS instructions issued **(MI200: includes flat; MI300: does not include flat)**"
"``SQ_INSTS_GDS``", "Instr", "Number of global data share instructions issued"
"``SQ_INSTS_EXP_GDS``", "Instr", "Number of EXP and global data share instructions excluding skipped export instructions issued"
"``SQ_INSTS_BRANCH``", "Instr", "Number of branch instructions issued"
"``SQ_INSTS_SENDMSG``", "Instr", "Number of ``SENDMSG`` instructions including ``s_endpgm`` issued"
"``SQ_INSTS_VSKIPPED``", "Instr", "Number of vector instructions skipped"
Flat instructions allow read, write, and atomic access to a generic memory address pointer that can
resolve to any of the following physical memories:
* Global Memory
* Scratch ("private")
* LDS ("shared")
* Invalid - ``MEM_VIOL`` TrapStatus
Matrix fused multiply-add operation counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_INSTS_VALU_MFMA_MOPS_I8``", "IOP", "Number of 8-bit integer matrix FMA ops in the unit of 512"
"``SQ_INSTS_VALU_MFMA_MOPS_F16``", "FLOP", "Number of F16 floating matrix FMA ops in the unit of 512"
"``SQ_INSTS_VALU_MFMA_MOPS_BF16``", "FLOP", "Number of BF16 floating matrix FMA ops in the unit of 512"
"``SQ_INSTS_VALU_MFMA_MOPS_F32``", "FLOP", "Number of F32 floating matrix FMA ops in the unit of 512"
"``SQ_INSTS_VALU_MFMA_MOPS_F64``", "FLOP", "Number of F64 floating matrix FMA ops in the unit of 512"
Level counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. note::
All level counters must be followed by ``SQ_ACCUM_PREV_HIRES`` counter to measure average latency.
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_ACCUM_PREV``", "Count", "Accumulated counter sample value where accumulation takes place once every four cycles"
"``SQ_ACCUM_PREV_HIRES``", "Count", "Accumulated counter sample value where accumulation takes place once every cycle"
"``SQ_LEVEL_WAVES``", "Waves", "Number of inflight waves"
"``SQ_INST_LEVEL_VMEM``", "Instr", "Number of inflight vector memory (including flat) instructions"
"``SQ_INST_LEVEL_SMEM``", "Instr", "Number of inflight scalar memory instructions"
"``SQ_INST_LEVEL_LDS``", "Instr", "Number of inflight LDS (including flat) instructions"
"``SQ_IFETCH_LEVEL``", "Instr", "Number of inflight instruction fetch requests from the cache"
Use the following formulae to calculate latencies:
* Vector memory latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_VMEM``
* Wave latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_WAVE``
* LDS latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_LDS``
* Scalar memory latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_INSTS_SMEM_NORM``
* Instruction fetch latency = ``SQ_ACCUM_PREV_HIRES`` divided by ``SQ_IFETCH``
Wavefront counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_WAVES``", "Waves", "Number of wavefronts dispatched to sequencers, including both new and restored wavefronts"
"``SQ_WAVES_SAVED``", "Waves", "Number of context-saved waves"
"``SQ_WAVES_RESTORED``", "Waves", "Number of context-restored waves sent to sequencers"
"``SQ_WAVES_EQ_64``", "Waves", "Number of wavefronts with exactly 64 active threads sent to sequencers"
"``SQ_WAVES_LT_64``", "Waves", "Number of wavefronts with less than 64 active threads sent to sequencers"
"``SQ_WAVES_LT_48``", "Waves", "Number of wavefronts with less than 48 active threads sent to sequencers"
"``SQ_WAVES_LT_32``", "Waves", "Number of wavefronts with less than 32 active threads sent to sequencers"
"``SQ_WAVES_LT_16``", "Waves", "Number of wavefronts with less than 16 active threads sent to sequencers"
Wavefront cycle counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_CYCLES``", "Cycles", "Clock cycles"
"``SQ_BUSY_CYCLES``", "Cycles", "Number of cycles while sequencers reports it to be busy"
"``SQ_BUSY_CU_CYCLES``", "Qcycles", "Number of quad-cycles each compute unit is busy"
"``SQ_VALU_MFMA_BUSY_CYCLES``", "Cycles", "Number of cycles the matrix FMA arithmetic logic unit (ALU) is busy"
"``SQ_WAVE_CYCLES``", "Qcycles", "Number of quad-cycles spent by waves in the compute units"
"``SQ_WAIT_ANY``", "Qcycles", "Number of quad-cycles spent waiting for anything"
"``SQ_WAIT_INST_ANY``", "Qcycles", "Number of quad-cycles spent waiting for any instruction to be issued"
"``SQ_ACTIVE_INST_ANY``", "Qcycles", "Number of quad-cycles spent by each wave to work on an instruction"
"``SQ_ACTIVE_INST_VMEM``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a vector memory instruction"
"``SQ_ACTIVE_INST_LDS``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on an LDS instruction"
"``SQ_ACTIVE_INST_VALU``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a VALU instruction"
"``SQ_ACTIVE_INST_SCA``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a SALU or scalar memory instruction"
"``SQ_ACTIVE_INST_EXP_GDS``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on an ``EXPORT`` or ``GDS`` instruction"
"``SQ_ACTIVE_INST_MISC``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a ``BRANCH`` or ``SENDMSG`` instruction"
"``SQ_ACTIVE_INST_FLAT``", "Qcycles", "Number of quad-cycles spent by the sequencer instruction arbiter to work on a flat instruction"
"``SQ_INST_CYCLES_VMEM_WR``", "Qcycles", "Number of quad-cycles spent to send addr and cmd data for vector memory write instructions"
"``SQ_INST_CYCLES_VMEM_RD``", "Qcycles", "Number of quad-cycles spent to send addr and cmd data for vector memory read instructions"
"``SQ_INST_CYCLES_SMEM``", "Qcycles", "Number of quad-cycles spent to execute scalar memory reads"
"``SQ_INST_CYCLES_SALU``", "Qcycles", "Number of quad-cycles spent to execute non-memory read scalar operations"
"``SQ_THREAD_CYCLES_VALU``", "Qcycles", "Number of quad-cycles spent to execute VALU operations on active threads"
"``SQ_WAIT_INST_LDS``", "Qcycles", "Number of quad-cycles spent waiting for LDS instruction to be issued"
``SQ_THREAD_CYCLES_VALU`` is similar to ``INST_CYCLES_VALU``, but it's multiplied by the number of
active threads.
LDS counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_LDS_ATOMIC_RETURN``", "Cycles", "Number of atomic return cycles in LDS"
"``SQ_LDS_BANK_CONFLICT``", "Cycles", "Number of cycles LDS is stalled by bank conflicts"
"``SQ_LDS_ADDR_CONFLICT``", "Cycles", "Number of cycles LDS is stalled by address conflicts"
"``SQ_LDS_UNALIGNED_STALL``", "Cycles", "Number of cycles LDS is stalled processing flat unaligned load or store operations"
"``SQ_LDS_MEM_VIOLATIONS``", "Count", "Number of threads that have a memory violation in the LDS"
"``SQ_LDS_IDX_ACTIVE``", "Cycles", "Number of cycles LDS is used for indexed operations"
Miscellaneous counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQ_IFETCH``", "Count", "Number of instruction fetch requests from L1i, in 32-byte width"
"``SQ_ITEMS``", "Threads", "Number of valid items per wave"
.. _l1i-and-sl1d-cache-counters:
L1 instruction cache (L1i) and scalar L1 data cache (L1d) counters
---------------------------------------------------------------------------------------------------------------
.. csv-table::
:header: "Hardware counter", "Unit", "Definition"
"``SQC_ICACHE_REQ``", "Req", "Number of L1 instruction (L1i) cache requests"
"``SQC_ICACHE_HITS``", "Count", "Number of L1i cache hits"
"``SQC_ICACHE_MISSES``", "Count", "Number of non-duplicate L1i cache misses including uncached requests"
"``SQC_ICACHE_MISSES_DUPLICATE``", "Count", "Number of duplicate L1i cache misses whose previous lookup miss on the same cache line is not fulfilled yet"
"``SQC_DCACHE_REQ``", "Req", "Number of scalar L1d requests"
"``SQC_DCACHE_INPUT_VALID_READYB``", "Cycles", "Number of cycles while sequencer input is valid but scalar L1d is not ready"
"``SQC_DCACHE_HITS``", "Count", "Number of scalar L1d hits"
"``SQC_DCACHE_MISSES``", "Count", "Number of non-duplicate scalar L1d misses including uncached requests"
"``SQC_DCACHE_MISSES_DUPLICATE``", "Count", "Number of duplicate scalar L1d misses"
"``SQC_DCACHE_REQ_READ_1``", "Req", "Number of constant cache read requests in a single 32-bit data word"
"``SQC_DCACHE_REQ_READ_2``", "Req", "Number of constant cache read requests in two 32-bit data words"
"``SQC_DCACHE_REQ_READ_4``", "Req", "Number of constant cache read requests in four 32-bit data words"
"``SQC_DCACHE_REQ_READ_8``", "Req", "Number of constant cache read requests in eight 32-bit data words"
"``SQC_DCACHE_REQ_READ_16``", "Req", "Number of constant cache read requests in 16 32-bit data words"
"``SQC_DCACHE_ATOMIC``", "Req", "Number of atomic requests"
"``SQC_TC_REQ``", "Req", "Number of texture cache requests that were issued by instruction and constant caches"
"``SQC_TC_INST_REQ``", "Req", "Number of instruction requests to the L2 cache"
"``SQC_TC_DATA_READ_REQ``", "Req", "Number of data Read requests to the L2 cache"
"``SQC_TC_DATA_WRITE_REQ``", "Req", "Number of data write requests to the L2 cache"
"``SQC_TC_DATA_ATOMIC_REQ``", "Req", "Number of data atomic requests to the L2 cache"
"``SQC_TC_STALL``", "Cycles", "Number of cycles while the valid requests to the L2 cache are stalled"
.. _vector-l1-cache-subsystem-counters:
Vector L1 cache subsystem counters
---------------------------------------------------------------------------------------------------------------
The vector L1 cache subsystem counters are further classified into texture addressing unit, texture data
unit, vector L1d or texture cache per pipe, and texture cache arbiter counters.
Texture addressing unit counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TA_TA_BUSY[n]``", "Cycles", "Texture addressing unit busy cycles", "0-15"
"``TA_TOTAL_WAVEFRONTS[n]``", "Instr", "Number of wavefronts processed by texture addressing unit", "0-15"
"``TA_BUFFER_WAVEFRONTS[n]``", "Instr", "Number of buffer wavefronts processed by texture addressing unit", "0-15"
"``TA_BUFFER_READ_WAVEFRONTS[n]``", "Instr", "Number of buffer read wavefronts processed by texture addressing unit", "0-15"
"``TA_BUFFER_WRITE_WAVEFRONTS[n]``", "Instr", "Number of buffer write wavefronts processed by texture addressing unit", "0-15"
"``TA_BUFFER_ATOMIC_WAVEFRONTS[n]``", "Instr", "Number of buffer atomic wavefronts processed by texture addressing unit", "0-15"
"``TA_BUFFER_TOTAL_CYCLES[n]``", "Cycles", "Number of buffer cycles (including read and write) issued to texture cache", "0-15"
"``TA_BUFFER_COALESCED_READ_CYCLES[n]``", "Cycles", "Number of coalesced buffer read cycles issued to texture cache", "0-15"
"``TA_BUFFER_COALESCED_WRITE_CYCLES[n]``", "Cycles", "Number of coalesced buffer write cycles issued to texture cache", "0-15"
"``TA_ADDR_STALLED_BY_TC_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit address path is stalled by texture cache", "0-15"
"``TA_DATA_STALLED_BY_TC_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit data path is stalled by texture cache", "0-15"
"``TA_ADDR_STALLED_BY_TD_CYCLES[n]``", "Cycles", "Number of cycles texture addressing unit address path is stalled by texture data unit", "0-15"
"``TA_FLAT_WAVEFRONTS[n]``", "Instr", "Number of flat opcode wavefronts processed by texture addressing unit", "0-15"
"``TA_FLAT_READ_WAVEFRONTS[n]``", "Instr", "Number of flat opcode read wavefronts processed by texture addressing unit", "0-15"
"``TA_FLAT_WRITE_WAVEFRONTS[n]``", "Instr", "Number of flat opcode write wavefronts processed by texture addressing unit", "0-15"
"``TA_FLAT_ATOMIC_WAVEFRONTS[n]``", "Instr", "Number of flat opcode atomic wavefronts processed by texture addressing unit", "0-15"
Texture data unit counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TD_TD_BUSY[n]``", "Cycle", "Texture data unit busy cycles while it is processing or waiting for data", "0-15"
"``TD_TC_STALL[n]``", "Cycle", "Number of cycles texture data unit is stalled waiting for texture cache data", "0-15"
"``TD_SPI_STALL[n]``", "Cycle", "Number of cycles texture data unit is stalled by shader processor input", "0-15"
"``TD_LOAD_WAVEFRONT[n]``", "Instr", "Number of wavefront instructions (read, write, atomic)", "0-15"
"``TD_STORE_WAVEFRONT[n]``", "Instr", "Number of write wavefront instructions", "0-15"
"``TD_ATOMIC_WAVEFRONT[n]``", "Instr", "Number of atomic wavefront instructions", "0-15"
"``TD_COALESCABLE_WAVEFRONT[n]``", "Instr", "Number of coalescable wavefronts according to texture addressing unit", "0-15"
Texture cache per pipe counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TCP_GATE_EN1[n]``", "Cycles", "Number of cycles vector L1d interface clocks are turned on", "0-15"
"``TCP_GATE_EN2[n]``", "Cycles", "Number of cycles vector L1d core clocks are turned on", "0-15"
"``TCP_TD_TCP_STALL_CYCLES[n]``", "Cycles", "Number of cycles texture data unit stalls vector L1d", "0-15"
"``TCP_TCR_TCP_STALL_CYCLES[n]``", "Cycles", "Number of cycles texture cache router stalls vector L1d", "0-15"
"``TCP_READ_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on a read", "0-15"
"``TCP_WRITE_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on a write", "0-15"
"``TCP_ATOMIC_TAGCONFLICT_STALL_CYCLES[n]``", "Cycles", "Number of cycles tag RAM conflict stalls on an atomic", "0-15"
"``TCP_PENDING_STALL_CYCLES[n]``", "Cycles", "Number of cycles vector L1d is stalled due to data pending from L2 Cache", "0-15"
"``TCP_TCP_TA_DATA_STALL_CYCLES``", "Cycles", "Number of cycles texture cache per pipe stalls texture addressing unit data interface", "NA"
"``TCP_TA_TCP_STATE_READ[n]``", "Req", "Number of state reads", "0-15"
"``TCP_VOLATILE[n]``", "Req", "Number of L1 volatile pixels or buffers from texture addressing unit", "0-15"
"``TCP_TOTAL_ACCESSES[n]``", "Req", "Number of vector L1d accesses. Equals ``TCP_PERF_SEL_TOTAL_READ`+`TCP_PERF_SEL_TOTAL_NONREAD``", "0-15"
"``TCP_TOTAL_READ[n]``", "Req", "Number of vector L1d read accesses", "0-15"
"``TCP_TOTAL_WRITE[n]``", "Req", "Number of vector L1d write accesses", "0-15"
"``TCP_TOTAL_ATOMIC_WITH_RET[n]``", "Req", "Number of vector L1d atomic requests with return", "0-15"
"``TCP_TOTAL_ATOMIC_WITHOUT_RET[n]``", "Req", "Number of vector L1d atomic without return", "0-15"
"``TCP_TOTAL_WRITEBACK_INVALIDATES[n]``", "Count", "Total number of vector L1d writebacks and invalidates", "0-15"
"``TCP_UTCL1_REQUEST[n]``", "Req", "Number of address translation requests to unified translation cache (L1)", "0-15"
"``TCP_UTCL1_TRANSLATION_HIT[n]``", "Req", "Number of unified translation cache (L1) translation hits", "0-15"
"``TCP_UTCL1_TRANSLATION_MISS[n]``", "Req", "Number of unified translation cache (L1) translation misses", "0-15"
"``TCP_UTCL1_PERMISSION_MISS[n]``", "Req", "Number of unified translation cache (L1) permission misses", "0-15"
"``TCP_TOTAL_CACHE_ACCESSES[n]``", "Req", "Number of vector L1d cache accesses including hits and misses", "0-15"
"``TCP_TCP_LATENCY[n]``", "Cycles", "**MI200 series only** Accumulated wave access latency to vL1D over all wavefronts", "0-15"
"``TCP_TCC_READ_REQ_LATENCY[n]``", "Cycles", "**MI200 series only** Total vL1D to L2 request latency over all wavefronts for reads and atomics with return", "0-15"
"``TCP_TCC_WRITE_REQ_LATENCY[n]``", "Cycles", "**MI200 series only** Total vL1D to L2 request latency over all wavefronts for writes and atomics without return", "0-15"
"``TCP_TCC_READ_REQ[n]``", "Req", "Number of read requests to L2 cache", "0-15"
"``TCP_TCC_WRITE_REQ[n]``", "Req", "Number of write requests to L2 cache", "0-15"
"``TCP_TCC_ATOMIC_WITH_RET_REQ[n]``", "Req", "Number of atomic requests to L2 cache with return", "0-15"
"``TCP_TCC_ATOMIC_WITHOUT_RET_REQ[n]``", "Req", "Number of atomic requests to L2 cache without return", "0-15"
"``TCP_TCC_NC_READ_REQ[n]``", "Req", "Number of non-coherently cached read requests to L2 cache", "0-15"
"``TCP_TCC_UC_READ_REQ[n]``", "Req", "Number of uncached read requests to L2 cache", "0-15"
"``TCP_TCC_CC_READ_REQ[n]``", "Req", "Number of coherently cached read requests to L2 cache", "0-15"
"``TCP_TCC_RW_READ_REQ[n]``", "Req", "Number of coherently cached with write read requests to L2 cache", "0-15"
"``TCP_TCC_NC_WRITE_REQ[n]``", "Req", "Number of non-coherently cached write requests to L2 cache", "0-15"
"``TCP_TCC_UC_WRITE_REQ[n]``", "Req", "Number of uncached write requests to L2 cache", "0-15"
"``TCP_TCC_CC_WRITE_REQ[n]``", "Req", "Number of coherently cached write requests to L2 cache", "0-15"
"``TCP_TCC_RW_WRITE_REQ[n]``", "Req", "Number of coherently cached with write write requests to L2 cache", "0-15"
"``TCP_TCC_NC_ATOMIC_REQ[n]``", "Req", "Number of non-coherently cached atomic requests to L2 cache", "0-15"
"``TCP_TCC_UC_ATOMIC_REQ[n]``", "Req", "Number of uncached atomic requests to L2 cache", "0-15"
"``TCP_TCC_CC_ATOMIC_REQ[n]``", "Req", "Number of coherently cached atomic requests to L2 cache", "0-15"
"``TCP_TCC_RW_ATOMIC_REQ[n]``", "Req", "Number of coherently cached with write atomic requests to L2 cache", "0-15"
Note that:
* ``TCP_TOTAL_READ[n]`` = ``TCP_PERF_SEL_TOTAL_HIT_LRU_READ`` + ``TCP_PERF_SEL_TOTAL_MISS_LRU_READ`` + ``TCP_PERF_SEL_TOTAL_MISS_EVICT_READ``
* ``TCP_TOTAL_WRITE[n]`` = ``TCP_PERF_SEL_TOTAL_MISS_LRU_WRITE``+ ``TCP_PERF_SEL_TOTAL_MISS_EVICT_WRITE``
* ``TCP_TOTAL_WRITEBACK_INVALIDATES[n]`` = ``TCP_PERF_SEL_TOTAL_WBINVL1``+ ``TCP_PERF_SEL_TOTAL_WBINVL1_VOL``+ ``TCP_PERF_SEL_CP_TCP_INVALIDATE``+ ``TCP_PERF_SEL_SQ_TCP_INVALIDATE_VOL``
Texture cache arbiter counters
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TCA_CYCLE[n]``", "Cycles", "Number of texture cache arbiter cycles", "0-31"
"``TCA_BUSY[n]``", "Cycles", "Number of cycles texture cache arbiter has a pending request", "0-31"
.. _l2-cache-access-counters:
L2 cache access counters
---------------------------------------------------------------------------------------------------------------
L2 cache is also known as texture cache per channel.
.. tab-set::
.. tab-item:: MI300 hardware counter
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TCC_CYCLE[n]``", "Cycles", "Number of L2 cache free-running clocks", "0-31"
"``TCC_BUSY[n]``", "Cycles", "Number of L2 cache busy cycles", "0-31"
"``TCC_REQ[n]``", "Req", "Number of L2 cache requests of all types (measured at the tag block)", "0-31"
"``TCC_STREAMING_REQ[n]``", "Req", "Number of L2 cache streaming requests (measured at the tag block)", "0-31"
"``TCC_NC_REQ[n]``", "Req", "Number of non-coherently cached requests (measured at the tag block)", "0-31"
"``TCC_UC_REQ[n]``", "Req", "Number of uncached requests. This is measured at the tag block", "0-31"
"``TCC_CC_REQ[n]``", "Req", "Number of coherently cached requests. This is measured at the tag block", "0-31"
"``TCC_RW_REQ[n]``", "Req", "Number of coherently cached with write requests. This is measured at the tag block", "0-31"
"``TCC_PROBE[n]``", "Req", "Number of probe requests", "0-31"
"``TCC_PROBE_ALL[n]``", "Req", "Number of external probe requests with ``EA_TCC_preq_all == 1``", "0-31"
"``TCC_READ[n]``", "Req", "Number of L2 cache read requests (includes compressed reads but not metadata reads)", "0-31"
"``TCC_WRITE[n]``", "Req", "Number of L2 cache write requests", "0-31"
"``TCC_ATOMIC[n]``", "Req", "Number of L2 cache atomic requests of all types", "0-31"
"``TCC_HIT[n]``", "Req", "Number of L2 cache hits", "0-31"
"``TCC_MISS[n]``", "Req", "Number of L2 cache misses", "0-31"
"``TCC_WRITEBACK[n]``", "Req", "Number of lines written back to the main memory, including writebacks of dirty lines and uncached write or atomic requests", "0-31"
"``TCC_EA0_WRREQ[n]``", "Req", "Number of 32-byte and 64-byte transactions going over the ``TC_EA_wrreq`` interface (doesn't include probe commands)", "0-31"
"``TCC_EA0_WRREQ_64B[n]``", "Req", "Total number of 64-byte transactions (write or ``CMPSWAP``) going over the ``TC_EA_wrreq`` interface", "0-31"
"``TCC_EA0_WR_UNCACHED_32B[n]``", "Req", "Number of 32 or 64-byte write or atomic going over the ``TC_EA_wrreq`` interface due to uncached traffic", "0-31"
"``TCC_EA0_WRREQ_STALL[n]``", "Cycles", "Number of cycles a write request is stalled", "0-31"
"``TCC_EA0_WRREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of input-output (IO) credits", "0-31"
"``TCC_EA0_WRREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits", "0-31"
"``TCC_EA0_WRREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits", "0-31"
"``TCC_TOO_MANY_EA_WRREQS_STALL[n]``", "Cycles", "Number of cycles the L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests", "0-31"
"``TCC_EA0_WRREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter write requests in flight", "0-31"
"``TCC_EA0_ATOMIC[n]``", "Req", "Number of 32-byte or 64-byte atomic requests going over the ``TC_EA_wrreq`` interface", "0-31"
"``TCC_EA0_ATOMIC_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter atomic requests in flight", "0-31"
"``TCC_EA0_RDREQ[n]``", "Req", "Number of 32-byte or 64-byte read requests to efficiency arbiter", "0-31"
"``TCC_EA0_RDREQ_32B[n]``", "Req", "Number of 32-byte read requests to efficiency arbiter", "0-31"
"``TCC_EA0_RD_UNCACHED_32B[n]``", "Req", "Number of 32-byte efficiency arbiter reads due to uncached traffic. A 64-byte request is counted as 2", "0-31"
"``TCC_EA0_RDREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of IO credits", "0-31"
"``TCC_EA0_RDREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of GMI credits", "0-31"
"``TCC_EA0_RDREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of DRAM credits", "0-31"
"``TCC_EA0_RDREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter read requests in flight", "0-31"
"``TCC_EA0_RDREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter read requests to High Bandwidth Memory (HBM)", "0-31"
"``TCC_EA0_WRREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter write requests to HBM", "0-31"
"``TCC_TAG_STALL[n]``", "Cycles", "Number of cycles the normal request pipeline in the tag is stalled for any reason", "0-31"
"``TCC_NORMAL_WRITEBACK[n]``", "Req", "Number of writebacks due to requests that are not writeback requests", "0-31"
"``TCC_ALL_TC_OP_WB_WRITEBACK[n]``", "Req", "Number of writebacks due to all ``TC_OP`` writeback requests", "0-31"
"``TCC_NORMAL_EVICT[n]``", "Req", "Number of evictions due to requests that are not invalidate or probe requests", "0-31"
"``TCC_ALL_TC_OP_INV_EVICT[n]``", "Req", "Number of evictions due to all ``TC_OP`` invalidate requests", "0-31"
.. tab-item:: MI200 hardware counter
.. csv-table::
:header: "Hardware counter", "Unit", "Definition", "Value range for ``n``"
"``TCC_CYCLE[n]``", "Cycles", "Number of L2 cache free-running clocks", "0-31"
"``TCC_BUSY[n]``", "Cycles", "Number of L2 cache busy cycles", "0-31"
"``TCC_REQ[n]``", "Req", "Number of L2 cache requests of all types (measured at the tag block)", "0-31"
"``TCC_STREAMING_REQ[n]``", "Req", "Number of L2 cache streaming requests (measured at the tag block)", "0-31"
"``TCC_NC_REQ[n]``", "Req", "Number of non-coherently cached requests (measured at the tag block)", "0-31"
"``TCC_UC_REQ[n]``", "Req", "Number of uncached requests. This is measured at the tag block", "0-31"
"``TCC_CC_REQ[n]``", "Req", "Number of coherently cached requests. This is measured at the tag block", "0-31"
"``TCC_RW_REQ[n]``", "Req", "Number of coherently cached with write requests. This is measured at the tag block", "0-31"
"``TCC_PROBE[n]``", "Req", "Number of probe requests", "0-31"
"``TCC_PROBE_ALL[n]``", "Req", "Number of external probe requests with ``EA_TCC_preq_all == 1``", "0-31"
"``TCC_READ[n]``", "Req", "Number of L2 cache read requests (includes compressed reads but not metadata reads)", "0-31"
"``TCC_WRITE[n]``", "Req", "Number of L2 cache write requests", "0-31"
"``TCC_ATOMIC[n]``", "Req", "Number of L2 cache atomic requests of all types", "0-31"
"``TCC_HIT[n]``", "Req", "Number of L2 cache hits", "0-31"
"``TCC_MISS[n]``", "Req", "Number of L2 cache misses", "0-31"
"``TCC_WRITEBACK[n]``", "Req", "Number of lines written back to the main memory, including writebacks of dirty lines and uncached write or atomic requests", "0-31"
"``TCC_EA_WRREQ[n]``", "Req", "Number of 32-byte and 64-byte transactions going over the ``TC_EA_wrreq`` interface (doesn't include probe commands)", "0-31"
"``TCC_EA_WRREQ_64B[n]``", "Req", "Total number of 64-byte transactions (write or ``CMPSWAP``) going over the ``TC_EA_wrreq`` interface", "0-31"
"``TCC_EA_WR_UNCACHED_32B[n]``", "Req", "Number of 32 write or atomic going over the ``TC_EA_wrreq`` interface due to uncached traffic. A 64-byte request will be counted as 2", "0-31"
"``TCC_EA_WRREQ_STALL[n]``", "Cycles", "Number of cycles a write request is stalled", "0-31"
"``TCC_EA_WRREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of input-output (IO) credits", "0-31"
"``TCC_EA_WRREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits", "0-31"
"``TCC_EA_WRREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits", "0-31"
"``TCC_TOO_MANY_EA_WRREQS_STALL[n]``", "Cycles", "Number of cycles the L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests", "0-31"
"``TCC_EA_WRREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter write requests in flight", "0-31"
"``TCC_EA_ATOMIC[n]``", "Req", "Number of 32-byte or 64-byte atomic requests going over the ``TC_EA_wrreq`` interface", "0-31"
"``TCC_EA_ATOMIC_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter atomic requests in flight", "0-31"
"``TCC_EA_RDREQ[n]``", "Req", "Number of 32-byte or 64-byte read requests to efficiency arbiter", "0-31"
"``TCC_EA_RDREQ_32B[n]``", "Req", "Number of 32-byte read requests to efficiency arbiter", "0-31"
"``TCC_EA_RD_UNCACHED_32B[n]``", "Req", "Number of 32-byte efficiency arbiter reads due to uncached traffic. A 64-byte request is counted as 2", "0-31"
"``TCC_EA_RDREQ_IO_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of IO credits", "0-31"
"``TCC_EA_RDREQ_GMI_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of GMI credits", "0-31"
"``TCC_EA_RDREQ_DRAM_CREDIT_STALL[n]``", "Cycles", "Number of cycles there is a stall due to the read request interface running out of DRAM credits", "0-31"
"``TCC_EA_RDREQ_LEVEL[n]``", "Req", "The accumulated number of efficiency arbiter read requests in flight", "0-31"
"``TCC_EA_RDREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter read requests to High Bandwidth Memory (HBM)", "0-31"
"``TCC_EA_WRREQ_DRAM[n]``", "Req", "Number of 32-byte or 64-byte efficiency arbiter write requests to HBM", "0-31"
"``TCC_TAG_STALL[n]``", "Cycles", "Number of cycles the normal request pipeline in the tag is stalled for any reason", "0-31"
"``TCC_NORMAL_WRITEBACK[n]``", "Req", "Number of writebacks due to requests that are not writeback requests", "0-31"
"``TCC_ALL_TC_OP_WB_WRITEBACK[n]``", "Req", "Number of writebacks due to all ``TC_OP`` writeback requests", "0-31"
"``TCC_NORMAL_EVICT[n]``", "Req", "Number of evictions due to requests that are not invalidate or probe requests", "0-31"
"``TCC_ALL_TC_OP_INV_EVICT[n]``", "Req", "Number of evictions due to all ``TC_OP`` invalidate requests", "0-31"
Note the following:
* ``TCC_REQ[n]`` may be more than the number of requests arriving at the texture cache per channel,
but it's a good indication of the total amount of work that needs to be performed.
* For ``TCC_EA0_WRREQ[n]``, atomics may travel over the same interface and are generally classified as
write requests.
* CC mtypes can produce uncached requests, and those are included in
``TCC_EA0_WR_UNCACHED_32B[n]``
* ``TCC_EA0_WRREQ_LEVEL[n]`` is primarily intended to measure average efficiency arbiter write latency.
* Average write latency = ``TCC_PERF_SEL_EA0_WRREQ_LEVEL`` divided by ``TCC_PERF_SEL_EA0_WRREQ``
* ``TCC_EA0_ATOMIC_LEVEL[n]`` is primarily intended to measure average efficiency arbiter atomic
latency
* Average atomic latency = ``TCC_PERF_SEL_EA0_WRREQ_ATOMIC_LEVEL`` divided by ``TCC_PERF_SEL_EA0_WRREQ_ATOMIC``
* ``TCC_EA0_RDREQ_LEVEL[n]`` is primarily intended to measure average efficiency arbiter read latency.
* Average read latency = ``TCC_PERF_SEL_EA0_RDREQ_LEVEL`` divided by ``TCC_PERF_SEL_EA0_RDREQ``
* Stalls can occur regardless of the need for a read to be performed
* Normally, stalls are measured exactly at one point in the pipeline however in the case of
``TCC_TAG_STALL[n]``, probes can stall the pipeline at a variety of places. There is no single point that
can accurately measure the total stalls
MI300 and MI200 series derived metrics list
==============================================================
.. csv-table::
:header: "Hardware counter", "Definition"
"``ALUStalledByLDS``", "Percentage of GPU time ALU units are stalled due to the LDS input queue being full or the output queue not being ready (value range: 0% (optimal) to 100%)"
"``FetchSize``", "Total kilobytes fetched from the video memory; measured with all extra fetches and any cache or memory effects taken into account"
"``FlatLDSInsts``", "Average number of flat instructions that read from or write to LDS, run per work item (affected by flow control)"
"``FlatVMemInsts``", "Average number of flat instructions that read from or write to the video memory, run per work item (affected by flow control). Includes flat instructions that read from or write to scratch"
"``GDSInsts``", "Average number of global data share read or write instructions run per work item (affected by flow control)"
"``GPUBusy``", "Percentage of time GPU is busy"
"``L2CacheHit``", "Percentage of fetch, write, atomic, and other instructions that hit the data in L2 cache (value range: 0% (no hit) to 100% (optimal))"
"``LDSBankConflict``", "Percentage of GPU time LDS is stalled by bank conflicts (value range: 0% (optimal) to 100%)"
"``LDSInsts``", "Average number of LDS read or write instructions run per work item (affected by flow control). Excludes flat instructions that read from or write to LDS."
"``MemUnitBusy``", "Percentage of GPU time the memory unit is active, which is measured with all extra fetches and writes and any cache or memory effects taken into account (value range: 0% to 100% (fetch-bound))"
"``MemUnitStalled``", "Percentage of GPU time the memory unit is stalled (value range: 0% (optimal) to 100%)"
"``MemWrites32B``", "Total number of effective 32B write transactions to the memory"
"``TCA_BUSY_sum``", "Total number of cycles texture cache arbiter has a pending request, over all texture cache arbiter instances"
"``TCA_CYCLE_sum``", "Total number of cycles over all texture cache arbiter instances"
"``SALUBusy``", "Percentage of GPU time scalar ALU instructions are processed (value range: 0% to 100% (optimal))"
"``SALUInsts``", "Average number of scalar ALU instructions run per work item (affected by flow control)"
"``SFetchInsts``", "Average number of scalar fetch instructions from the video memory run per work item (affected by flow control)"
"``VALUBusy``", "Percentage of GPU time vector ALU instructions are processed (value range: 0% to 100% (optimal))"
"``VALUInsts``", "Average number of vector ALU instructions run per work item (affected by flow control)"
"``VALUUtilization``", "Percentage of active vector ALU threads in a wave, where a lower number can mean either more thread divergence in a wave or that the work-group size is not a multiple of 64 (value range: 0%, 100% (optimal - no thread divergence))"
"``VFetchInsts``", "Average number of vector fetch instructions from the video memory run per work-item (affected by flow control); excludes flat instructions that fetch from video memory"
"``VWriteInsts``", "Average number of vector write instructions to the video memory run per work-item (affected by flow control); excludes flat instructions that write to video memory"
"``Wavefronts``", "Total wavefronts"
"``WRITE_REQ_32B``", "Total number of 32-byte effective memory writes"
"``WriteSize``", "Total kilobytes written to the video memory; measured with all extra fetches and any cache or memory effects taken into account"
"``WriteUnitStalled``", "Percentage of GPU time the write unit is stalled (value range: 0% (optimal) to 100%)"
You can lower ``ALUStalledByLDS`` by reducing LDS bank conflicts or number of LDS accesses.
You can lower ``MemUnitStalled`` by reducing the number or size of fetches and writes.
``MemUnitBusy`` includes the stall time (``MemUnitStalled``).
Hardware counters by and over all texture addressing unit instances
---------------------------------------------------------------------------------------------------------------
The following table shows the hardware counters *by* all texture addressing unit instances.
.. csv-table::
:header: "Hardware counter", "Definition"
"``TA_BUFFER_WAVEFRONTS_sum``", "Total number of buffer wavefronts processed"
"``TA_BUFFER_READ_WAVEFRONTS_sum``", "Total number of buffer read wavefronts processed"
"``TA_BUFFER_WRITE_WAVEFRONTS_sum``", "Total number of buffer write wavefronts processed"
"``TA_BUFFER_ATOMIC_WAVEFRONTS_sum``", "Total number of buffer atomic wavefronts processed"
"``TA_BUFFER_TOTAL_CYCLES_sum``", "Total number of buffer cycles (including read and write) issued to texture cache"
"``TA_BUFFER_COALESCED_READ_CYCLES_sum``", "Total number of coalesced buffer read cycles issued to texture cache"
"``TA_BUFFER_COALESCED_WRITE_CYCLES_sum``", "Total number of coalesced buffer write cycles issued to texture cache"
"``TA_FLAT_READ_WAVEFRONTS_sum``", "Sum of flat opcode reads processed"
"``TA_FLAT_WRITE_WAVEFRONTS_sum``", "Sum of flat opcode writes processed"
"``TA_FLAT_WAVEFRONTS_sum``", "Total number of flat opcode wavefronts processed"
"``TA_FLAT_ATOMIC_WAVEFRONTS_sum``", "Total number of flat opcode atomic wavefronts processed"
"``TA_TOTAL_WAVEFRONTS_sum``", "Total number of wavefronts processed"
The following table shows the hardware counters *over* all texture addressing unit instances.
.. csv-table::
:header: "Hardware counter", "Definition"
"``TA_ADDR_STALLED_BY_TC_CYCLES_sum``", "Total number of cycles texture addressing unit address path is stalled by texture cache"
"``TA_ADDR_STALLED_BY_TD_CYCLES_sum``", "Total number of cycles texture addressing unit address path is stalled by texture data unit"
"``TA_BUSY_avr``", "Average number of busy cycles"
"``TA_BUSY_max``", "Maximum number of texture addressing unit busy cycles"
"``TA_BUSY_min``", "Minimum number of texture addressing unit busy cycles"
"``TA_DATA_STALLED_BY_TC_CYCLES_sum``", "Total number of cycles texture addressing unit data path is stalled by texture cache"
"``TA_TA_BUSY_sum``", "Total number of texture addressing unit busy cycles"
Hardware counters over all texture cache per channel instances
---------------------------------------------------------------------------------------------------------------
.. csv-table::
:header: "Hardware counter", "Definition"
"``TCC_ALL_TC_OP_WB_WRITEBACK_sum``", "Total number of writebacks due to all ``TC_OP`` writeback requests."
"``TCC_ALL_TC_OP_INV_EVICT_sum``", "Total number of evictions due to all ``TC_OP`` invalidate requests."
"``TCC_ATOMIC_sum``", "Total number of L2 cache atomic requests of all types."
"``TCC_BUSY_avr``", "Average number of L2 cache busy cycles."
"``TCC_BUSY_sum``", "Total number of L2 cache busy cycles."
"``TCC_CC_REQ_sum``", "Total number of coherently cached requests."
"``TCC_CYCLE_sum``", "Total number of L2 cache free running clocks."
"``TCC_EA0_WRREQ_sum``", "Total number of 32-byte and 64-byte transactions going over the ``TC_EA0_wrreq`` interface. Atomics may travel over the same interface and are generally classified as write requests. This does not include probe commands."
"``TCC_EA0_WRREQ_64B_sum``", "Total number of 64-byte transactions (write or `CMPSWAP`) going over the ``TC_EA0_wrreq`` interface."
"``TCC_EA0_WR_UNCACHED_32B_sum``", "Total Number of 32-byte write or atomic going over the ``TC_EA0_wrreq`` interface due to uncached traffic. Note that coherently cached mtypes can produce uncached requests, and those are included in this. A 64-byte request is counted as 2."
"``TCC_EA0_WRREQ_STALL_sum``", "Total Number of cycles a write request is stalled, over all instances."
"``TCC_EA0_WRREQ_IO_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of IO credits, over all instances."
"``TCC_EA0_WRREQ_GMI_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of GMI credits, over all instances."
"``TCC_EA0_WRREQ_DRAM_CREDIT_STALL_sum``", "Total number of cycles an efficiency arbiter write request is stalled due to the interface running out of DRAM credits, over all instances."
"``TCC_EA0_WRREQ_LEVEL_sum``", "Total number of efficiency arbiter write requests in flight."
"``TCC_EA0_RDREQ_LEVEL_sum``", "Total number of efficiency arbiter read requests in flight."
"``TCC_EA0_ATOMIC_sum``", "Total Number of 32-byte or 64-byte atomic requests going over the ``TC_EA0_wrreq`` interface."
"``TCC_EA0_ATOMIC_LEVEL_sum``", "Total number of efficiency arbiter atomic requests in flight."
"``TCC_EA0_RDREQ_sum``", "Total number of 32-byte or 64-byte read requests to efficiency arbiter."
"``TCC_EA0_RDREQ_32B_sum``", "Total number of 32-byte read requests to efficiency arbiter."
"``TCC_EA0_RD_UNCACHED_32B_sum``", "Total number of 32-byte efficiency arbiter reads due to uncached traffic."
"``TCC_EA0_RDREQ_IO_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of IO credits."
"``TCC_EA0_RDREQ_GMI_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of GMI credits."
"``TCC_EA0_RDREQ_DRAM_CREDIT_STALL_sum``", "Total number of cycles there is a stall due to the read request interface running out of DRAM credits."
"``TCC_EA0_RDREQ_DRAM_sum``", "Total number of 32-byte or 64-byte efficiency arbiter read requests to HBM."
"``TCC_EA0_WRREQ_DRAM_sum``", "Total number of 32-byte or 64-byte efficiency arbiter write requests to HBM."
"``TCC_HIT_sum``", "Total number of L2 cache hits."
"``TCC_MISS_sum``", "Total number of L2 cache misses."
"``TCC_NC_REQ_sum``", "Total number of non-coherently cached requests."
"``TCC_NORMAL_WRITEBACK_sum``", "Total number of writebacks due to requests that are not writeback requests."
"``TCC_NORMAL_EVICT_sum``", "Total number of evictions due to requests that are not invalidate or probe requests."
"``TCC_PROBE_sum``", "Total number of probe requests."
"``TCC_PROBE_ALL_sum``", "Total number of external probe requests with ``EA0_TCC_preq_all == 1``."
"``TCC_READ_sum``", "Total number of L2 cache read requests (including compressed reads but not metadata reads)."
"``TCC_REQ_sum``", "Total number of all types of L2 cache requests."
"``TCC_RW_REQ_sum``", "Total number of coherently cached with write requests."
"``TCC_STREAMING_REQ_sum``", "Total number of L2 cache streaming requests."
"``TCC_TAG_STALL_sum``", "Total number of cycles the normal request pipeline in the tag is stalled for any reason."
"``TCC_TOO_MANY_EA0_WRREQS_STALL_sum``", "Total number of cycles L2 cache is unable to send an efficiency arbiter write request due to it reaching its maximum capacity of pending efficiency arbiter write requests."
"``TCC_UC_REQ_sum``", "Total number of uncached requests."
"``TCC_WRITE_sum``", "Total number of L2 cache write requests."
"``TCC_WRITEBACK_sum``", "Total number of lines written back to the main memory including writebacks of dirty lines and uncached write or atomic requests."
"``TCC_WRREQ_STALL_max``", "Maximum number of cycles a write request is stalled."
Hardware counters by, for, or over all texture cache per pipe instances
----------------------------------------------------------------------------------------------------------------
The following table shows the hardware counters *by* all texture cache per pipe instances.
.. csv-table::
:header: "Hardware counter", "Definition"
"``TCP_TA_TCP_STATE_READ_sum``", "Total number of state reads by ATCPPI"
"``TCP_TOTAL_CACHE_ACCESSES_sum``", "Total number of vector L1d accesses (including hits and misses)"
"``TCP_UTCL1_PERMISSION_MISS_sum``", "Total number of unified translation cache (L1) permission misses"
"``TCP_UTCL1_REQUEST_sum``", "Total number of address translation requests to unified translation cache (L1)"
"``TCP_UTCL1_TRANSLATION_MISS_sum``", "Total number of unified translation cache (L1) translation misses"
"``TCP_UTCL1_TRANSLATION_HIT_sum``", "Total number of unified translation cache (L1) translation hits"
The following table shows the hardware counters *for* all texture cache per pipe instances.
.. csv-table::
:header: "Hardware counter", "Definition"
"``TCP_TCC_READ_REQ_LATENCY_sum``", "Total vector L1d to L2 request latency over all wavefronts for reads and atomics with return"
"``TCP_TCC_WRITE_REQ_LATENCY_sum``", "Total vector L1d to L2 request latency over all wavefronts for writes and atomics without return"
"``TCP_TCP_LATENCY_sum``", "Total wave access latency to vector L1d over all wavefronts"
The following table shows the hardware counters *over* all texture cache per pipe instances.
.. csv-table::
:header: "Hardware counter", "Definition"
"``TCP_ATOMIC_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on an atomic"
"``TCP_GATE_EN1_sum``", "Total number of cycles vector L1d interface clocks are turned on"
"``TCP_GATE_EN2_sum``", "Total number of cycles vector L1d core clocks are turned on"
"``TCP_PENDING_STALL_CYCLES_sum``", "Total number of cycles vector L1d cache is stalled due to data pending from L2 Cache"
"``TCP_READ_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on a read"
"``TCP_TCC_ATOMIC_WITH_RET_REQ_sum``", "Total number of atomic requests to L2 cache with return"
"``TCP_TCC_ATOMIC_WITHOUT_RET_REQ_sum``", "Total number of atomic requests to L2 cache without return"
"``TCP_TCC_CC_READ_REQ_sum``", "Total number of coherently cached read requests to L2 cache"
"``TCP_TCC_CC_WRITE_REQ_sum``", "Total number of coherently cached write requests to L2 cache"
"``TCP_TCC_CC_ATOMIC_REQ_sum``", "Total number of coherently cached atomic requests to L2 cache"
"``TCP_TCC_NC_READ_REQ_sum``", "Total number of non-coherently cached read requests to L2 cache"
"``TCP_TCC_NC_WRITE_REQ_sum``", "Total number of non-coherently cached write requests to L2 cache"
"``TCP_TCC_NC_ATOMIC_REQ_sum``", "Total number of non-coherently cached atomic requests to L2 cache"
"``TCP_TCC_READ_REQ_sum``", "Total number of read requests to L2 cache"
"``TCP_TCC_RW_READ_REQ_sum``", "Total number of coherently cached with write read requests to L2 cache"
"``TCP_TCC_RW_WRITE_REQ_sum``", "Total number of coherently cached with write write requests to L2 cache"
"``TCP_TCC_RW_ATOMIC_REQ_sum``", "Total number of coherently cached with write atomic requests to L2 cache"
"``TCP_TCC_UC_READ_REQ_sum``", "Total number of uncached read requests to L2 cache"
"``TCP_TCC_UC_WRITE_REQ_sum``", "Total number of uncached write requests to L2 cache"
"``TCP_TCC_UC_ATOMIC_REQ_sum``", "Total number of uncached atomic requests to L2 cache"
"``TCP_TCC_WRITE_REQ_sum``", "Total number of write requests to L2 cache"
"``TCP_TCR_TCP_STALL_CYCLES_sum``", "Total number of cycles texture cache router stalls vector L1d"
"``TCP_TD_TCP_STALL_CYCLES_sum``", "Total number of cycles texture data unit stalls vector L1d"
"``TCP_TOTAL_ACCESSES_sum``", "Total number of vector L1d accesses"
"``TCP_TOTAL_READ_sum``", "Total number of vector L1d read accesses"
"``TCP_TOTAL_WRITE_sum``", "Total number of vector L1d write accesses"
"``TCP_TOTAL_ATOMIC_WITH_RET_sum``", "Total number of vector L1d atomic requests with return"
"``TCP_TOTAL_ATOMIC_WITHOUT_RET_sum``", "Total number of vector L1d atomic requests without return"
"``TCP_TOTAL_WRITEBACK_INVALIDATES_sum``", "Total number of vector L1d writebacks and invalidates"
"``TCP_VOLATILE_sum``", "Total number of L1 volatile pixels or buffers from texture addressing unit"
"``TCP_WRITE_TAGCONFLICT_STALL_CYCLES_sum``", "Total number of cycles tag RAM conflict stalls on a write"
Hardware counter over all texture data unit instances
--------------------------------------------------------
.. csv-table::
:header: "Hardware counter", "Definition"
"``TD_ATOMIC_WAVEFRONT_sum``", "Total number of atomic wavefront instructions"
"``TD_COALESCABLE_WAVEFRONT_sum``", "Total number of coalescable wavefronts according to texture addressing unit"
"``TD_LOAD_WAVEFRONT_sum``", "Total number of wavefront instructions (read, write, atomic)"
"``TD_SPI_STALL_sum``", "Total number of cycles texture data unit is stalled by shader processor input"
"``TD_STORE_WAVEFRONT_sum``", "Total number of write wavefront instructions"
"``TD_TC_STALL_sum``", "Total number of cycles texture data unit is stalled waiting for texture cache data"
"``TD_TD_BUSY_sum``", "Total number of texture data unit busy cycles while it is processing or waiting for data"

View File

@@ -1,129 +0,0 @@
---
myst:
html_meta:
"description lang=en": "Learn about the AMD Instinct MI300 series architecture."
"keywords": "Instinct, MI300X, MI300A, microarchitecture, AMD, ROCm"
---
# AMD Instinct™ MI300 series microarchitecture
The AMD Instinct MI300 series accelerators are based on the AMD CDNA 3
architecture which was designed to deliver leadership performance for HPC, artificial intelligence (AI), and machine
learning (ML) workloads. The AMD Instinct MI300 series accelerators are well-suited for extreme scalability and compute performance, running
on everything from individual servers to the worlds largest exascale supercomputers.
With the MI300 series, AMD is introducing the Accelerator Complex Die (XCD), which contains the
GPU computational elements of the processor along with the lower levels of the cache hierarchy.
The following image depicts the structure of a single XCD in the AMD Instinct MI300 accelerator series.
```{figure} ../../data/shared/xcd-sys-arch.png
---
name: mi300-xcd
align: center
---
XCD-level system architecture showing 40 Compute Units, each with 32 KB L1 cache, a Unified Compute System with 4 ACE Compute Accelerators, shared 4MB of L2 cache and an HWS Hardware Scheduler.
```
On the XCD, four Asynchronous Compute Engines (ACEs) send compute shader workgroups to the
Compute Units (CUs). The XCD has 40 CUs: 38 active CUs at the aggregate level and 2 disabled CUs for
yield management. The CUs all share a 4 MB L2 cache that serves to coalesce all memory traffic for the
die. With less than half of the CUs of the AMD Instinct MI200 Series compute die, the AMD CDNA™ 3
XCD die is a smaller building block. However, it uses more advanced packaging and the processor
can include 6 or 8 XCDs for up to 304 CUs, roughly 40% more than MI250X.
The MI300 Series integrate up to 8 vertically stacked XCDs, 8 stacks of
High-Bandwidth Memory 3 (HBM3) and 4 I/O dies (containing system
infrastructure) using the AMD Infinity Fabric™ technology as interconnect.
The Matrix Cores inside the CDNA 3 CUs have significant improvements, emphasizing AI and machine
learning, enhancing throughput of existing data types while adding support for new data types.
CDNA 2 Matrix Cores support FP16 and BF16, while offering INT8 for inference. Compared to MI250X
accelerators, CDNA 3 Matrix Cores triple the performance for FP16 and BF16, while providing a
performance gain of 6.8 times for INT8. FP8 has a performance gain of 16 times compared to FP32,
while TF32 has a gain of 4 times compared to FP32.
```{list-table} Peak-performance capabilities of the MI300X for different data types.
:header-rows: 1
:name: mi300x-perf-table
*
- Computation and Data Type
- FLOPS/CLOCK/CU
- Peak TFLOPS
*
- Matrix FP64
- 256
- 163.4
*
- Vector FP64
- 128
- 81.7
*
- Matrix FP32
- 256
- 163.4
*
- Vector FP32
- 256
- 163.4
*
- Vector TF32
- 1024
- 653.7
*
- Matrix FP16
- 2048
- 1307.4
*
- Matrix BF16
- 2048
- 1307.4
*
- Matrix FP8
- 4096
- 2614.9
*
- Matrix INT8
- 4096
- 2614.9
```
The above table summarizes the aggregated peak performance of the AMD Instinct MI300X Open
Compute Platform (OCP) Open Accelerator Modules (OAMs) for different data types and command
processors. The middle column lists the peak performance (number of data elements processed in a
single instruction) of a single compute unit if a SIMD (or matrix) instruction is submitted in each clock
cycle. The third column lists the theoretical peak performance of the OAM. The theoretical aggregated
peak memory bandwidth of the GPU is 5.3 TB per second.
The following image shows the block diagram of the APU (left) and the OAM package (right) both
connected via AMD Infinity Fabric™ network on-chip.
```{figure} ../../data/conceptual/gpu-arch/image008.png
---
name: mi300-arch
alt:
align: center
---
MI300 series system architecture showing MI300A (left) with 6 XCDs and 3 CCDs, while the MI300X (right) has 8 XCDs.
```
## Node-level architecture
```{figure} ../../data/shared/mi300-node-level-arch.png
---
name: mi300-node
align: center
---
MI300 series node-level architecture showing 8 fully interconnected MI300X OAM modules connected to (optional) PCIEe switches via retimers and HGX connectors.
```
The image above shows the node-level architecture of a system with AMD EPYC processors in a
dual-socket configuration and eight AMD Instinct MI300X accelerators. The MI300X OAMs attach to the
host system via PCIe Gen 5 x16 links (yellow lines). The GPUs are using seven high-bandwidth,
low-latency AMD Infinity Fabric™ links (red lines) to form a fully connected 8-GPU system.
<!---
We need performance data about the P2P communication here.
-->

View File

@@ -1,116 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="GPU isolation techniques">
<meta name="keywords" content="GPU isolation techniques, UUID, universally unique identifier,
environment variables, virtual machines, AMD, ROCm">
</head>
# GPU isolation techniques
Restricting the access of applications to a subset of GPUs, aka isolating
GPUs allows users to hide GPU resources from programs. The programs by default
will only use the "exposed" GPUs ignoring other (hidden) GPUs in the system.
There are multiple ways to achieve isolation of GPUs in the ROCm software stack,
differing in which applications they apply to and the security they provide.
This page serves as an overview of the techniques.
## Environment variables
The runtimes in the ROCm software stack read these environment variables to
select the exposed or default device to present to applications using them.
Environment variables shouldn't be used for isolating untrusted applications,
as an application can reset them before initializing the runtime.
### `ROCR_VISIBLE_DEVICES`
A list of device indices or {abbr}`UUID (universally unique identifier)`s
that will be exposed to applications.
Runtime
: ROCm Software Runtime. Applies to all applications using the user mode ROCm
software stack.
```{code-block} shell
:caption: Example to expose the 1. device and a device based on UUID.
export ROCR_VISIBLE_DEVICES="0,GPU-DEADBEEFDEADBEEF"
```
### `GPU_DEVICE_ORDINAL`
Devices indices exposed to OpenCL and HIP applications.
Runtime
: ROCm Compute Language Runtime (`ROCclr`). Applies to applications and runtimes
using the `ROCclr` abstraction layer including HIP and OpenCL applications.
```{code-block} shell
:caption: Example to expose the 1. and 3. device in the system.
export GPU_DEVICE_ORDINAL="0,2"
```
(hip_visible_devices)=
### `HIP_VISIBLE_DEVICES`
Device indices exposed to HIP applications.
Runtime: HIP runtime. Applies only to applications using HIP on the AMD platform.
```{code-block} shell
:caption: Example to expose the 1. and 3. devices in the system.
export HIP_VISIBLE_DEVICES="0,2"
```
### `CUDA_VISIBLE_DEVICES`
Provided for CUDA compatibility, has the same effect as `HIP_VISIBLE_DEVICES`
on the AMD platform.
Runtime
: HIP or CUDA Runtime. Applies to HIP applications on the AMD or NVIDIA platform
and CUDA applications.
### `OMP_DEFAULT_DEVICE`
Default device used for OpenMP target offloading.
Runtime
: OpenMP Runtime. Applies only to applications using OpenMP offloading.
```{code-block} shell
:caption: Example on setting the default device to the third device.
export OMP_DEFAULT_DEVICE="2"
```
## Docker
Docker uses Linux kernel namespaces to provide isolated environments for
applications. This isolation applies to most devices by default, including
GPUs. To access them in containers explicit access must be granted, please see
{ref}`docker-access-gpus-in-container` for details.
Specifically refer to {ref}`docker-restrict-gpus` on exposing just a subset
of all GPUs.
Docker isolation is more secure than environment variables, and applies
to all programs that use the `amdgpu` kernel module interfaces.
Even programs that don't use the ROCm runtime, like graphics applications
using OpenGL or Vulkan, can only access the GPUs exposed to the container.
## GPU passthrough to virtual machines
Virtual machines achieve the highest level of isolation, because even the kernel
of the virtual machine is isolated from the host. Devices physically installed
in the host system can be passed to the virtual machine using PCIe passthrough.
This allows for using the GPU with a different operating systems like a Windows
guest from a Linux host.
Setting up PCIe passthrough is specific to the hypervisor used. ROCm officially
supports [VMware ESXi](https://www.vmware.com/products/esxi-and-esx.html)
for select GPUs.
<!--
TODO: This should link to a page about virtualization that explains
pass-through and SR-IOV and how-tos for maybe `libvirt` and `VMWare`
-->

View File

@@ -5,64 +5,9 @@
# https://www.sphinx-doc.org/en/master/usage/configuration.html
import os
import shutil
import sys
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")
latex_engine = "xelatex"
latex_elements = {
"fontpkg": r"""
@@ -82,108 +27,26 @@ project = "ROCm Documentation"
project_path = os.path.abspath(".").replace("\\", "/")
author = "Advanced Micro Devices, Inc."
copyright = "Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved."
version = "6.4.2"
release = "6.4.2"
version = "7.0 Beta"
release = "7.0 Beta"
setting_all_article_info = True
all_article_info_os = ["linux", "windows"]
all_article_info_os = ["linux"]
all_article_info_author = ""
# pages with specific settings
article_pages = [
{"file": "about/release-notes", "os": ["linux"], "date": "2025-07-21"},
{"file": "release/changelog", "os": ["linux"],},
{"file": "compatibility/compatibility-matrix", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/tensorflow-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/jax-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/verl-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/stanford-megatron-lm-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/dgl-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/megablocks-compatibility", "os": ["linux"]},
{"file": "compatibility/ml-compatibility/taichi-compatibility", "os": ["linux"]},
{"file": "how-to/deep-learning-rocm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/install", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-health-check", "os": ["linux"]},
{"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/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"]},
{"file": "how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference/index", "os": ["linux"]},
{"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"]},
{"file": "how-to/rocm-for-ai/inference-optimization/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/model-quantization", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/model-acceleration-libraries", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/optimizing-with-composable-kernel", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/optimizing-triton-kernel", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/profiling-and-debugging", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/inference-optimization/workload", "os": ["linux"]},
{"file": "how-to/system-optimization/index", "os": ["linux"]},
{"file": "how-to/system-optimization/mi300x", "os": ["linux"]},
{"file": "how-to/system-optimization/mi200", "os": ["linux"]},
{"file": "how-to/system-optimization/mi100", "os": ["linux"]},
{"file": "how-to/system-optimization/w6000-v620", "os": ["linux"]},
{"file": "how-to/tuning-guides/mi300x/index", "os": ["linux"]},
{"file": "how-to/tuning-guides/mi300x/system", "os": ["linux"]},
{"file": "how-to/tuning-guides/mi300x/workload", "os": ["linux"]},
{"file": "how-to/system-debugging", "os": ["linux"]},
{"file": "how-to/gpu-enabled-mpi", "os": ["linux"]},
{"file": "preview/release", "date": "2025-07-24",},
]
external_toc_path = "./sphinx/_toc.yml"
external_projects_remote_repository = "" # don't fetch data to resolve intersphinx xrefs
# Add the _extensions directory to Python's search path
sys.path.append(str(Path(__file__).parent / 'extension'))
extensions = ["rocm_docs", "sphinx_reredirects", "sphinx_sitemap", "sphinxcontrib.datatemplates", "version-ref", "csv-to-list-table"]
compatibility_matrix_file = str(Path(__file__).parent / 'compatibility/compatibility-matrix-historical-6.0.csv')
external_projects_current_project = "rocm"
# Uncomment if facing rate limit exceed issue with local build
# external_projects_remote_repository = ""
html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "https://rocm-stg.amd.com/")
html_context = {}
if os.environ.get("READTHEDOCS", "") == "True":
@@ -192,18 +55,15 @@ if os.environ.get("READTHEDOCS", "") == "True":
html_theme = "rocm_docs_theme"
html_theme_options = {"flavor": "rocm-docs-home"}
html_static_path = ["sphinx/static/css", "extension/how-to/rocm-for-ai/inference"]
html_css_files = ["rocm_custom.css", "rocm_rn.css", "vllm-benchmark.css"]
html_js_files = ["vllm-benchmark.js"]
html_static_path = ["sphinx/static/css", "sphinx/static/js"]
html_css_files = ["rocm_custom.css", "rocm_rn.css"]
html_js_files = ["preview-version-list.js"]
html_title = "ROCm Documentation"
html_title = "ROCm 7.0 Beta documentation"
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},

View File

@@ -1,150 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="Building ROCm documentation">
<meta name="keywords" content="documentation, Visual Studio Code, GitHub, command line,
AMD, ROCm">
</head>
# Building documentation
## GitHub
If you open a pull request and scroll down to the summary panel,
there is a commit status section. Next to the line
`docs/readthedocs.com:advanced-micro-devices-demo`, there is a `Details` link.
If you click this, it takes you to the Read the Docs build for your pull request.
![GitHub PR commit status](../data/contribute/commit-status.png)
If you don't see this line, click `Show all checks` to get an itemized view.
## Command line
You can build our documentation via the command line using Python.
See the `build.tools.python` setting in the [Read the Docs configuration file](https://github.com/ROCm/ROCm/blob/develop/.readthedocs.yaml) for the Python version used by Read the Docs to build documentation.
See the [Python requirements file](https://github.com/ROCm/ROCm/blob/develop/docs/sphinx/requirements.txt) for Python packages needed to build the documentation.
Use the Python Virtual Environment (`venv`) and run the following commands from the project root:
```sh
python3 -mvenv .venv
.venv/bin/python -m pip install -r docs/sphinx/requirements.txt
.venv/bin/python -m sphinx -T -E -b html -d _build/doctrees -D language=en docs _build/html
```
Navigate to `_build/html/index.html` and open this file in a web browser.
## Visual Studio Code
With the help of a few extensions, you can create a productive environment to author and test
documentation locally using Visual Studio (VS) Code. Follow these steps to configure VS Code:
1. Install the required extensions:
* Python: `(ms-python.python)`
* Live Server: `(ritwickdey.LiveServer)`
2. Add the following entries to `.vscode/settings.json`.
```json
{
"liveServer.settings.root": "/.vscode/build/html",
"liveServer.settings.wait": 1000,
"python.terminal.activateEnvInCurrentTerminal": true
}
```
* `liveServer.settings.root`: Sets the root of the output website for live previews. Must be changed
alongside the `tasks.json` command.
* `liveServer.settings.wait`: Tells the live server to wait with the update in order to give Sphinx time to
regenerate the site contents and not refresh before the build is complete.
* `python.terminal.activateEnvInCurrentTerminal`: Activates the automatic virtual environment, so you
can build the site from the integrated terminal.
3. Add the following tasks to `.vscode/tasks.json`.
```json
{
"version": "2.0.0",
"tasks": [
{
"label": "Build Docs",
"type": "process",
"windows": {
"command": "${workspaceFolder}/.venv/Scripts/python.exe"
},
"command": "${workspaceFolder}/.venv/bin/python3",
"args": [
"-m",
"sphinx",
"-j",
"auto",
"-T",
"-b",
"html",
"-d",
"${workspaceFolder}/.vscode/build/doctrees",
"-D",
"language=en",
"${workspaceFolder}/docs",
"${workspaceFolder}/.vscode/build/html"
],
"problemMatcher": [
{
"owner": "sphinx",
"fileLocation": "absolute",
"pattern": {
"regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):(\\d+):\\s+(WARNING|ERROR):\\s+(.*)$",
"file": 1,
"line": 2,
"severity": 3,
"message": 4
}
},
{
"owner": "sphinx",
"fileLocation": "absolute",
"pattern": {
"regexp": "^(?:.*\\.{3}\\s+)?(\\/[^:]*|[a-zA-Z]:\\\\[^:]*):{1,2}\\s+(WARNING|ERROR):\\s+(.*)$",
"file": 1,
"severity": 2,
"message": 3
}
}
],
"group": {
"kind": "build",
"isDefault": true
}
}
]
}
```
> Implementation detail: two problem matchers were needed to be defined,
> because VS Code doesn't tolerate some problem information being potentially
> absent. While a single regex could match all types of errors, if a capture
> group remains empty (the line number doesn't show up in all warning/error
> messages) but the `pattern` references said empty capture group, VS Code
> discards the message completely.
4. Configure the Python virtual environment (`venv`).
From the Command Palette, run `Python: Create Environment`. Select `venv` environment and
`docs/sphinx/requirements.txt`.
5. Build the docs.
Launch the default build task using one of the following options:
* A hotkey (the default is `Ctrl+Shift+B`)
* Issuing the `Tasks: Run Build Task` from the Command Palette
6. Open the live preview.
Navigate to the site output within VS Code: right-click on `.vscode/build/html/index.html` and
select `Open with Live Server`. The contents should update on every rebuild without having to
refresh the browser.

View File

@@ -1,77 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="Contributing to ROCm">
<meta name="keywords" content="ROCm, contributing, contribute, maintainer, contributor">
</head>
# Contributing to the ROCm documentation
The ROCm documentation, like all of ROCm, is open source and available on GitHub. You can contribute to the ROCm documentation by forking the appropriate repository, making your changes, and opening a pull request.
To provide feedback on the ROCm documentation, including submitting an issue or suggesting a feature, see [Providing feedback about the ROCm documentation](./feedback.md).
## The ROCm repositories
The repositories for ROCm and all ROCm components are available on GitHub.
| Module | Documentation location |
| --- | --- |
| ROCm framework | [https://github.com/ROCm/ROCm/tree/develop/docs](https://github.com/ROCm/ROCm/tree/develop/docs) |
| ROCm installation for Linux | [https://github.com/ROCm/rocm-install-on-linux/tree/develop/docs](https://github.com/ROCm/rocm-install-on-linux/tree/develop/docs) |
| ROCm HIP SDK installation for Windows | [https://github.com/ROCm/rocm-install-on-windows/tree/develop/docs](https://github.com/ROCm/rocm-install-on-windows/tree/develop/docs) |
Individual components have their own repositories with their own documentation in their own `docs` folders.
The sub-folders within the `docs` folders across ROCm are typically structured as follows:
| Sub-folder name | Documentation type |
|-------|----------|
| `install` | Installation instructions, build instructions, and prerequisites |
| `conceptual` | Important concepts |
| `how-to` | How to implement specific use cases |
| `tutorials` | Tutorials |
| `reference` | API references and other reference resources |
## Editing and adding to the documentation
ROCm documentation follows the [Google developer documentation style guide](https://developers.google.com/style/highlights).
Most topics in the ROCm documentation are written in [reStructuredText (rst)](https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html), with some topics written in Markdown. Only use reStructuredText when adding new topics. Only use Markdown if the topic you are editing is already in Markdown.
To edit or add to the documentation:
1. Fork the repository you want to add to or edit.
2. Clone your fork locally.
3. Create a new local branch cut from the `develop` branch of the repository.
4. Make your changes to the documentation.
5. Optionally, build the documentation locally before creating a pull request by running the following commands from within the `docs` folder:
```bash
pip3 install -r sphinx/requirements.txt # You only need to run this command once
python3 -m sphinx -T -E -b html -d _build/doctrees -D language=en . _build/html
```
The output files will be located in the `docs/_build` folder. Open `docs/_build/html/index.html` to view the documentation.
For more information on ROCm build tools, see [Documentation toolchain](toolchain.md).
6. Push your changes. A GitHub link will be returned in the output of the `git push` command. Open this link in a browser to create the pull request.
The documentation is built as part of the checks on pull request, along with spell checking and linting. Scroll to the bottom of your pull request to view all the checks.
Verify that the linting and spell checking have passed, and that the documentation was built successfully. New words or acronyms can be added to the [wordlist file](https://github.com/ROCm/rocm-docs-core/blob/develop/.wordlist.txt). The wordlist is subject to approval by the ROCm documentation team.
The Read The Docs build of your pull request can be accessed by clicking on the Details link next to the Read The Docs build check. Verify that your changes are in the build and look as expected.
![The GitHub checks are collapsed by default and can be accessed by clicking on "Show All Checks".](../data/contribute/GitHubCheck-Highlight.png)
![The Read The Docs Build is accessed from the Details link in the Read The Docs check.](../data/contribute/GitHub-ReadThe-Docs-Highlight.png)
Your pull request will be reviewed by a member of the ROCm documentation team.
See the [GitHub documentation](https://docs.github.com/en) for information on how to fork and clone a repository, and how to create and push a local branch.
```{important}
By creating a pull request (PR), you agree to allow your contribution to be licensed under the terms of the
LICENSE.txt file in the corresponding repository. Different repositories can use different licenses.
```

View File

@@ -1,27 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="Providing feedback for ROCm documentation">
<meta name="keywords" content="documentation, pull request, GitHub, AMD, ROCm">
</head>
# Providing feedback about the ROCm documentation
Feedback about the ROCm documentation is welcome. You can provide feedback about the ROCm documentation either through GitHub Discussions or GitHub Issues.
## Participating in discussions through GitHub Discussions
You can ask questions, view announcements, suggest new features, and communicate with other members of the community through [GitHub Discussions](https://github.com/ROCm/ROCm/discussions).
## Submitting issues through GitHub Issues
You can submit issues through [GitHub Issues](https://github.com/ROCm/ROCm/issues).
When creating a new issue, follow the following guidelines:
1. Always do a search to see if the same issue already exists. If the issue already exists, upvote it, and comment or post to provide any additional details you might have.
2. If you find an issue that is similar to your issue, log your issue, then add a comment that includes a link to the similar issue, as well as its issue number.
3. Always provide as much information as possible. This helps reduce the time required to reproduce the issue.
After creating your issue, make sure to check it regularly for any requests for additional information.
For information about contributing content to the ROCm documentation, see [Contributing to the ROCm documentation](./contributing.md).

View File

@@ -1,46 +0,0 @@
<head>
<meta charset="UTF-8">
<meta name="description" content="ROCm documentation toolchain">
<meta name="keywords" content="documentation, toolchain, Sphinx, Doxygen, MyST, AMD, ROCm">
</head>
# ROCm documentation toolchain
The ROCm documentation relies on several open source toolchains and sites.
## rocm-docs-core
[rocm-docs-core](https://github.com/ROCm/rocm-docs-core) is an AMD-maintained
project that applies customizations for the ROCm documentation. This project is the tool most ROCm repositories use as part of their documentation build pipeline. It is available as a [pip package on PyPI](https://pypi.org/project/rocm-docs-core/).
See the user and developer guides for rocm-docs-core at
{doc}`rocm-docs-core documentation<rocm-docs-core:index>`.
## Sphinx
[Sphinx](https://www.sphinx-doc.org/en/master/) is a documentation generator originally used for Python. It is now widely used in the open source community.
### Sphinx External ToC
[Sphinx External ToC](https://sphinx-external-toc.readthedocs.io/en/latest/intro.html) is a Sphinx extension used for ROCm documentation navigation. This tool generates a navigation menu on the left
based on a YAML file (`_toc.yml.in`) that contains the table of contents.
### Sphinx-book-theme
[Sphinx-book-theme](https://sphinx-book-theme.readthedocs.io/en/latest/) is a Sphinx theme that defines the base appearance for ROCm documentation. ROCm documentation applies some customization, such as a custom header and footer, on top of the Sphinx Book Theme.
### Sphinx Design
[Sphinx design](https://sphinx-design.readthedocs.io/en/latest/index.html) is a Sphinx extension that adds design functionality. ROCm documentation uses Sphinx Design for grids, cards, and synchronized tabs.
## Doxygen
[Doxygen](https://www.doxygen.nl/) is a documentation generator that extracts information from in-code comments. It is used for API documentation.
## Breathe
[Breathe](https://www.breathe-doc.org/) is a Sphinx plugin for integrating Doxygen content.
## Read the Docs
[Read the Docs](https://docs.readthedocs.io/en/stable/) is the service that builds and hosts the HTML version of the ROCm documentation.

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