Taichi removed (#5802)

(cherry picked from commit c154b7e0a3)

.azuredevops/components/ROCmValidationSuite.yml

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

.azuredevops/components/Tensile.yml

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

.azuredevops/components/composable_kernel.yml

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

.azuredevops/components/copyHIP.yml

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

.azuredevops/components/hipBLAS-common.yml

@@ -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 }}

.azuredevops/components/hipBLASLt.yml

@@ -77,28 +77,28 @@ parameters:
   type: object
   default:
     buildJobs:
-      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
-      - { os: ubuntu2204, packageManager: apt, target: gfx90a }
-      - { os: ubuntu2204, packageManager: apt, target: gfx1201 }
-      # - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
-      - { os: ubuntu2204, packageManager: apt, target: gfx1030 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx942 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx90a }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1201 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1100 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1030 }
+      - { pool: rocm-ci_ultra_build_pool, os: ubuntu2204, packageManager: apt, target: gfx942 }
+      - { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx90a }
+      - { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1201 }
+      - { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1100 }
+      - { pool: rocm-ci_medium_build_pool, os: ubuntu2204, packageManager: apt, target: gfx1030 }
+      - { pool: rocm-ci_ultra_build_pool, os: almalinux8, packageManager: dnf, target: gfx942 }
+      - { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx90a }
+      - { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1201 }
+      - { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1100 }
+      - { pool: rocm-ci_medium_build_pool, os: almalinux8, packageManager: dnf, target: gfx1030 }
     testJobs:
       - { os: ubuntu2204, packageManager: apt, target: gfx942 }
       - { os: ubuntu2204, packageManager: apt, target: gfx90a }
-# - name: downstreamComponentMatrix
-#   type: object
-#   default:
-#     - rocBLAS:
-#       name: rocBLAS
-#       sparseCheckoutDir: projects/rocblas
-#       skipUnifiedBuild: 'false'
-#       buildDependsOn:
-#         - hipBLASLt_build
+- name: downstreamComponentMatrix
+  type: object
+  default:
+    - rocBLAS:
+      name: rocBLAS
+      sparseCheckoutDir: projects/rocblas
+      skipUnifiedBuild: 'false'
+      buildDependsOn:
+        - hipBLASLt_build
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -121,7 +121,7 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm
     - name: DAY_STRING
       value: $[format('{0:ddMMyyyy}', pipeline.startTime)]
-    pool: ${{ variables.ULTRA_BUILD_POOL }}
+    pool: ${{ job.pool }}
     ${{ if eq(job.os, 'almalinux8') }}:
       container:
         image: rocmexternalcicd.azurecr.io/manylinux228:latest
@@ -140,6 +140,10 @@ jobs:
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
         sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+      parameters:
+        dependencyList:
+          - gtest
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
@@ -156,18 +160,15 @@ jobs:
         script: |
           echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/bin"
           echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/llvm/bin"
-    # hipBLASLt has a script for gtest and lapack
-    # https://github.com/ROCm/hipBLASLt/blob/develop/deps/CMakeLists.txt
-    # $(Agent.BuildDirectory)/deps is a temporary folder for the build process
-    # $(Agent.BuildDirectory)/s/deps is part of the hipBLASLt repo
     - task: Bash@3
-      displayName: Build and install external dependencies
+      displayName: Build and install LAPACK
       inputs:
         targetType: inline
         script: |
-          mkdir -p $(Agent.BuildDirectory)/deps
-          cd $(Agent.BuildDirectory)/deps
-          cmake -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/s/deps
+          mkdir -p $(Agent.BuildDirectory)/temp-deps
+          cd $(Agent.BuildDirectory)/temp-deps
+          # position-independent LAPACK is required for almalinux8 builds
+          cmake -DBUILD_GTEST=OFF -DBUILD_LAPACK=ON -DCMAKE_POSITION_INDEPENDENT_CODE=ON $(Agent.BuildDirectory)/s/deps
           make
           sudo make install
     - script: |
@@ -187,7 +188,7 @@ jobs:
       parameters:
         os: ${{ job.os }}
         extraBuildFlags: >-
-          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
+          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
           -DCMAKE_INCLUDE_PATH=$(Agent.BuildDirectory)/rocm/llvm/include
           -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
           -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
@@ -244,6 +245,7 @@ jobs:
       workspace:
         clean: all
       steps:
+      - checkout: none
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
         parameters:
           aptPackages: ${{ parameters.aptPackages }}
@@ -280,14 +282,14 @@ jobs:
           environment: test
           gpuTarget: ${{ job.target }}
 
-# - ${{ if parameters.triggerDownstreamJobs }}:
-#   - ${{ each component in parameters.downstreamComponentMatrix }}:
-#     - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
-#       - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
-#         parameters:
-#           checkoutRepo: ${{ parameters.checkoutRepo }}
-#           sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
-#           buildDependsOn: ${{ component.buildDependsOn }}
-#           downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
-#           triggerDownstreamJobs: true
-#           unifiedBuild: ${{ parameters.unifiedBuild }}
+- ${{ if parameters.triggerDownstreamJobs }}:
+  - ${{ each component in parameters.downstreamComponentMatrix }}:
+    - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
+      - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
+          buildDependsOn: ${{ component.buildDependsOn }}
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
+          triggerDownstreamJobs: true
+          unifiedBuild: ${{ parameters.unifiedBuild }}

.azuredevops/components/hipFFT.yml

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

.azuredevops/components/hipSOLVER.yml

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

.azuredevops/components/hipSPARSE.yml

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

.azuredevops/components/hipSPARSELt.yml

@@ -1,10 +1,29 @@
 parameters:
+- name: componentName
+  type: string
+  default: hipSPARSELt
 - name: checkoutRepo
   type: string
   default: 'self'
 - name: checkoutRef
   type: string
   default: ''
+# monorepo related parameters
+- name: sparseCheckoutDir
+  type: string
+  default: ''
+- name: triggerDownstreamJobs
+  type: boolean
+  default: false
+- name: downstreamAggregateNames
+  type: string
+  default: ''
+- name: buildDependsOn
+  type: object
+  default: null
+- name: unifiedBuild
+  type: boolean
+  default: false
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -56,15 +75,17 @@ parameters:
   type: object
   default:
     buildJobs:
-      - gfx942:
-        target: gfx942
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
     testJobs:
-      - gfx942:
-        target: gfx942
+      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
-  - job: hipSPARSELt_build_${{ job.target }}
+  - job: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+    ${{ if parameters.buildDependsOn }}:
+      dependsOn:
+        - ${{ each build in parameters.buildDependsOn }}:
+          - ${{ build }}_${{ job.os }}_${{ job.target }}
     variables:
     - group: common
     - template: /.azuredevops/variables-global.yml
@@ -86,17 +107,22 @@ jobs:
       parameters:
         aptPackages: ${{ parameters.aptPackages }}
         pipModules: ${{ parameters.pipModules }}
+        packageManager: ${{ job.packageManager }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-latest.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
         dependencyList: ${{ parameters.rocmDependencies }}
         gpuTarget: ${{ job.target }}
+        os: ${{ job.os }}
         aggregatePipeline: ${{ parameters.aggregatePipeline }}
+        ${{ if parameters.triggerDownstreamJobs }}:
+          downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
   # Build and install gtest and lapack
   # $(Pipeline.Workspace)/deps is a temporary folder for the build process
   # $(Pipeline.Workspace)/s/deps is part of the hipSPARSELt repo
@@ -115,6 +141,7 @@ jobs:
       workingDirectory: $(Pipeline.Workspace)/deps
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
+        os: ${{ job.os }}
         extraBuildFlags: >-
           -DCMAKE_BUILD_TYPE=Release
           -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
@@ -130,64 +157,80 @@ jobs:
           -GNinja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
         gpuTarget: ${{ job.target }}
+        os: ${{ job.os }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
       parameters:
+        componentName: ${{ parameters.componentName }}
         gpuTarget: ${{ job.target }}
+        os: ${{ job.os }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        gpuTarget: ${{ job.target }}
-        extraCopyDirectories:
-          - deps
-        extraPaths: /home/user/workspace/rocm/llvm/bin:/home/user/workspace/rocm/bin
-        extraEnvVars:
-          - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
-          - TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang
-          - CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/llvm/bin/hipcc
-          - TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
-        installLatestCMake: true
+    - ${{ if eq(job.os, 'ubuntu2204') }}:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+          gpuTarget: ${{ job.target }}
+          extraCopyDirectories:
+            - deps
+          extraPaths: /home/user/workspace/rocm/llvm/bin:/home/user/workspace/rocm/bin
+          extraEnvVars:
+            - HIP_ROCCLR_HOME:::/home/user/workspace/rocm
+            - TENSILE_ROCM_ASSEMBLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang
+            - CMAKE_CXX_COMPILER:::/home/user/workspace/rocm/llvm/bin/hipcc
+            - TENSILE_ROCM_OFFLOAD_BUNDLER_PATH:::/home/user/workspace/rocm/llvm/bin/clang-offload-bundler
+          installLatestCMake: true
 
-- ${{ each job in parameters.jobMatrix.testJobs }}:
-  - job: hipSPARSELt_test_${{ job.target }}
-    dependsOn: hipSPARSELt_build_${{ job.target }}
-    condition:
-      and(succeeded(),
-        eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
-        not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
-        eq(${{ parameters.aggregatePipeline }}, False)
-      )
-    variables:
-    - group: common
-    - template: /.azuredevops/variables-global.yml
-    pool: ${{ job.target }}_test_pool
-    workspace:
-      clean: all
-    steps:
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
-      parameters:
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
-      parameters:
-        checkoutRef: ${{ parameters.checkoutRef }}
-        dependencyList: ${{ parameters.rocmTestDependencies }}
-        gpuTarget: ${{ job.target }}
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
-      parameters:
-        componentName: hipSPARSELt
-        testDir: '$(Agent.BuildDirectory)/rocm/bin'
-        testExecutable: './hipsparselt-test'
-        testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes --gtest_filter=*pre_checkin*'
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
-      parameters:
-        aptPackages: ${{ parameters.aptPackages }}
-        pipModules: ${{ parameters.pipModules }}
-        environment: test
-        gpuTarget: ${{ job.target }}
+- ${{ if eq(parameters.unifiedBuild, False) }}:
+  - ${{ each job in parameters.jobMatrix.testJobs }}:
+    - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
+      timeoutInMinutes: 120
+      dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
+      condition:
+        and(succeeded(),
+          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
+          not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
+          eq(${{ parameters.aggregatePipeline }}, False)
+        )
+      variables:
+      - group: common
+      - template: /.azuredevops/variables-global.yml
+      pool: ${{ job.target }}_test_pool
+      workspace:
+        clean: all
+      steps:
+      - checkout: none
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          packageManager: ${{ job.packageManager }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
+        parameters:
+          preTargetFilter: ${{ parameters.componentName }}
+          gpuTarget: ${{ job.target }}
+          os: ${{ job.os }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+        parameters:
+          checkoutRef: ${{ parameters.checkoutRef }}
+          dependencyList: ${{ parameters.rocmTestDependencies }}
+          gpuTarget: ${{ job.target }}
+          os: ${{ job.os }}
+          ${{ if parameters.triggerDownstreamJobs }}:
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
+        parameters:
+          componentName: ${{ parameters.componentName }}
+          os: ${{ job.os }}
+          testDir: '$(Agent.BuildDirectory)/rocm/bin'
+          testExecutable: './hipsparselt-test'
+          testParameters: '--gtest_output=xml:./test_output.xml --gtest_color=yes --gtest_filter=*pre_checkin*'
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
+        parameters:
+          aptPackages: ${{ parameters.aptPackages }}
+          pipModules: ${{ parameters.pipModules }}
+          environment: test
+          gpuTarget: ${{ job.target }}

.azuredevops/components/llvm-project.yml

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

.azuredevops/components/rocAL.yml

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

.azuredevops/components/rocALUTION.yml

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

.azuredevops/components/rocBLAS.yml

@@ -33,17 +33,15 @@ parameters:
   type: object
   default:
     - cmake
-    - ninja-build
-    - python3-venv
     - git
-    - libmsgpack-dev
     - gfortran
-    - libopenblas-dev
-    - googletest
-    - libgtest-dev
-    - wget
-    - python3-pip
     - libdrm-dev
+    - libmsgpack-dev
+    - libopenblas-dev
+    - ninja-build
+    - python3-pip
+    - python3-venv
+    - wget
 - name: pipModules
   type: object
   default:
@@ -52,18 +50,17 @@ parameters:
 - name: rocmDependencies
   type: object
   default:
-    - rocm-cmake
-    - llvm-project
-    - ROCR-Runtime
-    - clr
-    - rocminfo
-    - rocprofiler-register
-    - rocm_smi_lib
-    - rocm-core
     - aomp
-    - aomp-extras
+    - clr
     - hipBLAS-common
     - hipBLASLt
+    - llvm-project
+    - rocm-cmake
+    - rocm-core
+    - rocm_smi_lib
+    - rocminfo
+    - rocprofiler-register
+    - ROCR-Runtime
     - roctracer
 - name: rocmTestDependencies
   type: object
@@ -86,32 +83,38 @@ parameters:
       - { os: ubuntu2204, packageManager: apt, target: gfx942 }
       - { os: ubuntu2204, packageManager: apt, target: gfx90a }
       - { os: ubuntu2204, packageManager: apt, target: gfx1201 }
-      # - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
+      - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
       - { os: ubuntu2204, packageManager: apt, target: gfx1030 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx942 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx90a }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1201 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1100 }
-      # - { os: almalinux8, packageManager: dnf, target: gfx1030 }
+      - { os: almalinux8, packageManager: dnf, target: gfx942 }
+      - { os: almalinux8, packageManager: dnf, target: gfx90a }
+      - { os: almalinux8, packageManager: dnf, target: gfx1201 }
+      - { os: almalinux8, packageManager: dnf, target: gfx1100 }
+      - { os: almalinux8, packageManager: dnf, target: gfx1030 }
     testJobs:
       - { os: ubuntu2204, packageManager: apt, target: gfx942 }
       - { os: ubuntu2204, packageManager: apt, target: gfx90a }
-# - name: downstreamComponentMatrix
-#   type: object
-#   default:
-#     # rocSOLVER depends on both rocBLAS and rocPRIM
-#     # for a unified build, rocBLAS will be the one to call rocSOLVER
-#     - rocSOLVER:
-#       name: rocSOLVER
-#       sparseCheckoutDir: projects/rocsolver
-#       skipUnifiedBuild: 'false'
-#       buildDependsOn:
-#         - rocBLAS_build
-#       unifiedBuild:
-#         downstreamAggregateNames: rocBLAS+rocPRIM
-#         buildDependsOn:
-#           - rocBLAS_build
-#           - rocPRIM_build
+- name: downstreamComponentMatrix
+  type: object
+  default:
+    - rocSPARSE:
+      name: rocSPARSE
+      sparseCheckoutDir: projects/rocsparse
+      skipUnifiedBuild: 'false'
+      buildDependsOn:
+        - rocBLAS_build
+    # rocSOLVER depends on both rocBLAS and rocPRIM
+    # for a unified build, rocBLAS will be the one to call rocSOLVER
+    # - rocSOLVER:
+    #   name: rocSOLVER
+    #   sparseCheckoutDir: projects/rocsolver
+    #   skipUnifiedBuild: 'false'
+    #   buildDependsOn:
+    #     - rocBLAS_build
+    #   unifiedBuild:
+    #     downstreamAggregateNames: rocBLAS+rocPRIM
+    #     buildDependsOn:
+    #       - rocBLAS_build
+    #       - rocPRIM_build
 
 jobs:
 - ${{ each job in parameters.jobMatrix.buildJobs }}:
@@ -151,6 +154,12 @@ jobs:
         checkoutRepo: ${{ parameters.checkoutRepo }}
         sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aocl.yml
+      parameters:
+        os: ${{ job.os }}
+    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+      parameters:
+        dependencyList:
+          - gtest
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}
@@ -164,21 +173,12 @@ jobs:
       parameters:
         os: ${{ job.os }}
         extraBuildFlags: >-
-          -DCMAKE_TOOLCHAIN_FILE=toolchain-linux.cmake
-          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm
+          -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm/llvm;$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
           -DCMAKE_BUILD_TYPE=Release
           -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang++
           -DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/bin/amdclang
           -DGPU_TARGETS=${{ job.target }}
-          -DTensile_CODE_OBJECT_VERSION=default
-          -DTensile_LOGIC=asm_full
-          -DTensile_SEPARATE_ARCHITECTURES=ON
-          -DTensile_LAZY_LIBRARY_LOADING=ON
-          -DTensile_LIBRARY_FORMAT=msgpack
           -DBUILD_CLIENTS_TESTS=ON
-          -DBUILD_CLIENTS_BENCHMARKS=OFF
-          -DBUILD_CLIENTS_SAMPLES=OFF
-          -DROCM_PATH=$(Agent.BuildDirectory)/rocm
           -GNinja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
       parameters:
@@ -208,6 +208,7 @@ jobs:
 - ${{ if eq(parameters.unifiedBuild, False) }}:
   - ${{ each job in parameters.jobMatrix.testJobs }}:
     - job: ${{ parameters.componentName }}_test_${{ job.os }}_${{ job.target }}
+      timeoutInMinutes: 120
       dependsOn: ${{ parameters.componentName }}_build_${{ job.os }}_${{ job.target }}
       condition:
         and(succeeded(),
@@ -222,6 +223,7 @@ jobs:
       workspace:
         clean: all
       steps:
+      - checkout: none
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
         parameters:
           aptPackages: ${{ parameters.aptPackages }}
@@ -258,18 +260,18 @@ jobs:
           environment: test
           gpuTarget: ${{ job.target }}
 
-# - ${{ if parameters.triggerDownstreamJobs }}:
-#   - ${{ each component in parameters.downstreamComponentMatrix }}:
-#     - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
-#       - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
-#         parameters:
-#           checkoutRepo: ${{ parameters.checkoutRepo }}
-#           sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
-#           triggerDownstreamJobs: true
-#           unifiedBuild: ${{ parameters.unifiedBuild }}
-#           ${{ if parameters.unifiedBuild }}:
-#             buildDependsOn: ${{ component.unifiedBuild.buildDependsOn }}
-#             downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ component.unifiedBuild.downstreamAggregateNames }}
-#           ${{ else }}:
-#             buildDependsOn: ${{ component.buildDependsOn }}
-#             downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}
+- ${{ if parameters.triggerDownstreamJobs }}:
+  - ${{ each component in parameters.downstreamComponentMatrix }}:
+    - ${{ if not(and(parameters.unifiedBuild, eq(component.skipUnifiedBuild, 'true'))) }}:
+      - template: /.azuredevops/components/${{ component.name }}.yml@pipelines_repo
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ component.sparseCheckoutDir }}
+          triggerDownstreamJobs: true
+          unifiedBuild: ${{ parameters.unifiedBuild }}
+          ${{ if parameters.unifiedBuild }}:
+            buildDependsOn: ${{ component.unifiedBuild.buildDependsOn }}
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ component.unifiedBuild.downstreamAggregateNames }}
+          ${{ else }}:
+            buildDependsOn: ${{ component.buildDependsOn }}
+            downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}+${{ parameters.componentName }}

.azuredevops/components/rocFFT.yml

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

.azuredevops/components/rocMLIR.yml

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

.azuredevops/components/rocPRIM.yml

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

.azuredevops/components/rocPyDecode.yml

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

.azuredevops/components/rocSOLVER.yml

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

.azuredevops/components/rocSPARSE.yml

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

.azuredevops/components/rocm-examples.yml

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

.azuredevops/components/rocminfo.yml

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

.azuredevops/nightly/pytorch.yml

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

.azuredevops/nightly/rocm-nightly.yml

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

.azuredevops/templates/steps/artifact-download.yml

@@ -12,6 +12,9 @@ parameters:
 - name: fileFilter
   type: string
   default: ''
+- name: extractAndDeleteFiles
+  type: boolean
+  default: true
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -37,16 +40,17 @@ steps:
         buildVersionToDownload: latest # aomp trigger lives in ROCm/ROCm, so cannot use ROCm/aomp branch names
       ${{ else }}:
         buildVersionToDownload: latestFromBranch
-- task: ExtractFiles@1
-  displayName: Extract ${{ parameters.componentName }}
-  inputs:
-    archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
-    destinationFolder: '$(Agent.BuildDirectory)/rocm'
-    cleanDestinationFolder: false
-    overwriteExistingFiles: true
-- task: DeleteFiles@1
-  displayName: Cleanup Compressed ${{ parameters.componentName }}
-  inputs:
-    SourceFolder: '$(Pipeline.Workspace)/d'
-    Contents: '**/*.tar.gz'
-    RemoveDotFiles: true
+- ${{ if eq(parameters.extractAndDeleteFiles, true) }}:
+  - task: ExtractFiles@1
+    displayName: Extract ${{ parameters.componentName }}
+    inputs:
+      archiveFilePatterns: '$(Pipeline.Workspace)/d/**/*.tar.gz'
+      destinationFolder: '$(Agent.BuildDirectory)/rocm'
+      cleanDestinationFolder: false
+      overwriteExistingFiles: true
+  - task: DeleteFiles@1
+    displayName: Clean up Compressed ${{ parameters.componentName }}
+    inputs:
+      SourceFolder: '$(Pipeline.Workspace)/d'
+      Contents: '**/*.tar.gz'
+      RemoveDotFiles: true

.azuredevops/templates/steps/artifact-links.yml

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

.azuredevops/templates/steps/artifact-upload.yml

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

.azuredevops/templates/steps/dependencies-aocl.yml

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

.azuredevops/templates/steps/dependencies-dnf.yml

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

.azuredevops/templates/steps/dependencies-rocm.yml

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

.azuredevops/templates/steps/manifest.yml

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

.azuredevops/templates/steps/miopen-get-ck-build.yml

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

.azuredevops/variables-global.yml

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

.readthedocs.yaml

@@ -6,7 +6,7 @@ version: 2
 sphinx:
    configuration: docs/conf.py
 
-formats: [htmlzip]
+formats: [htmlzip, pdf]
 
 python:
    install:

.wordlist.txt

@@ -6,6 +6,7 @@ ACS
 AccVGPR
 AccVGPRs
 ALU
+AllReduce
 AMD
 AMDGPU
 AMDGPUs
@@ -13,6 +14,7 @@ AMDMIGraphX
 AMI
 AOCC
 AOMP
+AOT
 AOTriton
 APBDIS
 APIC
@@ -32,6 +34,7 @@ Andrej
 Arb
 Autocast
 BARs
+BatchNorm
 BLAS
 BMC
 BabelStream
@@ -42,6 +45,7 @@ Bootloader
 CAS
 CCD
 CDNA
+CGUI
 CHTML
 CIFAR
 CLI
@@ -79,10 +83,13 @@ ConnectX
 CuPy
 da
 Dashboarding
+Dataloading
 DBRX
 DDR
 DF
 DGEMM
+DGL
+DGLGraph
 dGPU
 dGPUs
 DIMM
@@ -100,6 +107,7 @@ DataFrame
 DataLoader
 DataParallel
 Debian
+decompositions
 DeepSeek
 DeepSpeed
 Dependabot
@@ -108,6 +116,7 @@ DevCap
 DirectX
 Dockerfile
 Doxygen
+dropless
 ELMo
 ENDPGM
 EPYC
@@ -125,10 +134,12 @@ FX
 Filesystem
 FindDb
 Flang
+FlashAttention
 FluxBenchmark
 Fortran
 Fuyu
 GALB
+GAT
 GCC
 GCD
 GCDs
@@ -156,6 +167,8 @@ GPT
 GPU
 GPU's
 GPUs
+Graphbolt
+GraphSage
 GRBM
 GenAI
 GenZ
@@ -168,6 +181,7 @@ HIPCC
 HIPExtension
 HIPIFY
 HIPification
+hipification
 HIPify
 HPC
 HPCG
@@ -182,6 +196,7 @@ Higgs
 Hyperparameters
 Huggingface
 ICD
+ICT
 ICV
 IDE
 IDEs
@@ -216,6 +231,7 @@ KV
 KVM
 Karpathy's
 KiB
+Kineto
 Keras
 Khronos
 LAPACK
@@ -228,6 +244,7 @@ LM
 LSAN
 LSan
 LTS
+LSTMs
 LanguageCrossEntropy
 LoRA
 MEM
@@ -255,6 +272,7 @@ Makefiles
 Matplotlib
 Matrox
 MaxText
+Megablocks
 Megatrends
 Megatron
 Mellanox
@@ -264,6 +282,8 @@ Miniconda
 MirroredStrategy
 Mixtral
 MosaicML
+MoEs
+Mpops
 Multicore
 Multithreaded
 MyEnvironment
@@ -277,6 +297,7 @@ NIC
 NICs
 NLI
 NLP
+NN
 NPKit
 NPS
 NSP
@@ -313,6 +334,7 @@ OpenMPI
 OpenSSL
 OpenVX
 OpenXLA
+Optim
 Oversubscription
 PagedAttention
 Pallas
@@ -351,6 +373,7 @@ RDC's
 RDMA
 RDNA
 README
+Recomputation
 RHEL
 RMW
 RNN
@@ -383,11 +406,13 @@ Ryzen
 SALU
 SBIOS
 SCA
+ScaledGEMM
 SDK
 SDMA
 SDPA
 SDRAM
 SENDMSG
+SGLang
 SGPR
 SGPRs
 SHA
@@ -423,6 +448,8 @@ TCI
 TCIU
 TCP
 TCR
+TensorRT
+TensorFloat
 TF
 TFLOPS
 TP
@@ -509,6 +536,7 @@ allocator
 allocators
 amdgpu
 api
+aten
 atmi
 atomics
 autogenerated
@@ -679,6 +707,7 @@ installable
 interop
 interprocedural
 intra
+intrinsics
 invariants
 invocating
 ipo
@@ -697,11 +726,13 @@ linearized
 linter
 linux
 llvm
+lm
 localscratch
 logits
 lossy
 macOS
 matchers
+megatron
 microarchitecture
 migraphx
 migratable
@@ -773,6 +804,7 @@ quantile
 quantizer
 quasirandom
 queueing
+qwen
 radeon
 rccl
 rdc
@@ -781,6 +813,7 @@ reStructuredText
 redirections
 refactorization
 reformats
+reinforcememt
 repo
 repos
 representativeness
@@ -788,6 +821,7 @@ req
 resampling
 rescaling
 reusability
+RLHF
 roadmap
 roc
 rocAL
@@ -825,6 +859,7 @@ roctracer
 rst
 runtime
 runtimes
+ResNet
 sL
 scalability
 scalable
@@ -833,6 +868,7 @@ seealso
 sendmsg
 seqs
 serializers
+sglang
 shader
 sharding
 sigmoid
@@ -840,6 +876,7 @@ sm
 smi
 softmax
 spack
+spmm
 src
 stochastically
 strided
@@ -848,6 +885,7 @@ subdirectory
 subexpression
 subfolder
 subfolders
+submatrix
 submodule
 submodules
 subnet
@@ -872,6 +910,7 @@ torchvision
 tqdm
 tracebacks
 txt
+TopK
 uarch
 uncached
 uncacheable
@@ -899,6 +938,7 @@ vectorize
 vectorized
 vectorizer
 vectorizes
+verl
 virtualize
 virtualized
 vjxb

CHANGELOG.md

@@ -4,6 +4,162 @@ This page is a historical overview of changes made to ROCm components. This
 consolidated changelog documents key modifications and improvements across
 different versions of the ROCm software stack and its components.
 
+## ROCm 6.4.2
+
+See the [ROCm 6.4.2 release notes](https://rocm.docs.amd.com/en/docs-6.4.2/about/release-notes.html)
+for a complete overview of this release.
+
+### **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 6.4.1
 
 See the [ROCm 6.4.1 release notes](https://rocm.docs.amd.com/en/docs-6.4.1/about/release-notes.html)
@@ -24,7 +180,7 @@ for a complete overview of this release.
 
 #### Optimized
 
-* Improved load times for CLI commands when the GPU has multiple parititons.
+* Improved load times for CLI commands when the GPU has multiple partitions.
 
 #### Resolved issues
 
@@ -34,9 +190,8 @@ for a complete overview of this release.
 
 * When using the `--follow` flag with `amd-smi ras --cper`, CPER entries are not streamed continuously as intended. This will be fixed in an upcoming ROCm release.
 
-```{note}
-See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
-```
+> [!NOTE]
+> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
 
 ### **HIP** (6.4.1)
 
@@ -117,9 +272,8 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/roc
 
 - Fixed partition enumeration. It now refers to the correct DRM Render and Card paths.
 
-```{note}
-See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
-```
+> [!NOTE]
+> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
 
 ### **ROCm Systems Profiler** (1.0.1)
 
@@ -257,9 +411,8 @@ Some workaround options are as follows:
 
 - The `pasid` field in struct `amdsmi_process_info_t` will be deprecated in a future ROCm release.
 
-```{note}
-See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
-```
+> [!NOTE]
+> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
 
 ### **AMDMIGraphX** (2.12.0)
 
@@ -867,9 +1020,8 @@ The following lists the backward incompatible changes planned for upcoming major
 
 - Fixed `rsmi_dev_target_graphics_version_get`, `rocm-smi --showhw`, and `rocm-smi --showprod` not displaying graphics version correctly for Instinct MI200 series, MI100 series, and RDNA3-based GPUs. 
 
-```{note}
-See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
-```
+> [!NOTE]
+> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
 
 ### **ROCm Systems Profiler** (1.0.0)
 
@@ -1295,9 +1447,8 @@ for a complete overview of this release.
 * Fixed `amd-smi monitor`'s reporting of encode and decode information. `VCLOCK` and `DCLOCK` are
   now associated with both `ENC_UTIL` and `DEC_UTIL`.
 
-```{note}
-See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
-```
+> [!NOTE]
+> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
 
 ### **HIP** (6.3.1)
 
@@ -1501,9 +1652,8 @@ for a complete overview of this release.
   - The new partition command can display GPU information, including memory and accelerator partition information.
   - The command will be at full functionality once additional partition information from `amdsmi_get_gpu_accelerator_partition_profile()` has been implemented.
 
-```{note}
-See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
-```
+> [!NOTE]
+> See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANGELOG.md) for more details and examples.
 
 ### **HIP** (6.3.0)
 
@@ -1637,18 +1787,17 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
 
 * Support for `fp8` data types
 
-### **hipRAND** (2.11.0[*](#id22))
+### **hipRAND** (2.11.0)
+
+> [!NOTE]
+> In ROCm 6.3.0, the hipRAND package version is incorrectly set to `2.11.0`.
+> In ROCm 6.2.4, the hipRAND package version was `2.11.1`.
+> The hipRAND version number will be corrected in a future ROCm release.
 
 #### Changed
 
 * Updated the default value for the `-a` argument from `rmake.py` to `gfx906:xnack-,gfx1030,gfx1100,gfx1101,gfx1102`.
 
-#### Known issues
-
-* In ROCm 6.3.0, the hipRAND package version is incorrectly set to `2.11.0`. In ROCm
-  6.2.4, the hipRAND package version was `2.11.1`. The hipRAND version number will be corrected in a
-  future ROCm release.
-
 #### Resolved issues
 
 * Fixed an issue in `rmake.py` where the list storing the CMake options would contain individual characters instead of a full string of options.
@@ -1849,7 +1998,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
 
 #### Known issues
 
-* See [MIVisionX memory access fault in Canny edge detection](#mivisionx-memory-access-fault-in-canny-edge-detection).
+* See [MIVisionX memory access fault in Canny edge detection](https://github.com/ROCm/ROCm/issues/4086).
 * Package installation requires the manual installation of OpenCV.
 * Installation on CentOS/RedHat/SLES requires the manual installation of the `FFMPEG Dev` package.
 * Hardware decode requires installation with `--usecase=graphics` in addition to `--usecase=rocm`.
@@ -2040,9 +2189,9 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
 
 #### Known issues
 
-- See [ROCm Compute Profiler post-upgrade](#rocm-compute-profiler-post-upgrade).
+- See [ROCm Compute Profiler post-upgrade](https://github.com/ROCm/ROCm/issues/4082).
 
-- See [ROCm Compute Profiler CTest failure in CI](#rocm-compute-profiler-ctest-failure-in-ci).
+- See [ROCm Compute Profiler CTest failure in CI](https://github.com/ROCm/ROCm/issues/4085).
 
 ### **ROCm Data Center Tool** (0.3.0)
 
@@ -2055,7 +2204,7 @@ See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/6.3.x/CHANG
 
 #### Known issues
 
-- See [ROCm Data Center Tool incorrect RHEL9 package version](#rocm-data-center-tool-incorrect-rhel9-package-version).
+- See [ROCm Data Center Tool incorrect RHEL9 package version](https://github.com/ROCm/ROCm/issues/4089).
 
 ### **ROCm SMI** (7.4.0)
 
@@ -2093,9 +2242,8 @@ memory partition modes upon an invalid argument return from memory partition mod
 
 - C++ tests for `memorypartition_read_write` are to be re-enabled in a future ROCm release.
 
-```{note}
-See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.x/CHANGELOG.md) for more details and examples.
-```
+> [!NOTE]
+> See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.x/CHANGELOG.md) for more details and examples.
 
 ### **ROCm Systems Profiler** (0.1.0)
 
@@ -2109,7 +2257,7 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.
 
 #### Known issues
 
-- See [ROCm Systems Profiler post-upgrade](#rocm-systems-profiler-post-upgrade).
+- See [ROCm Systems Profiler post-upgrade](https://github.com/ROCm/ROCm/issues/4083).
 
 ### **ROCm Validation Suite** (1.1.0)
 
@@ -2123,7 +2271,7 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/6.3.
 
 #### Known issues
 
-- See [ROCm Validation Suite needs specified configuration file](#rocm-validation-suite-needs-specified-configuration-file).
+- See [ROCm Validation Suite needs specified configuration file](https://github.com/ROCm/ROCm/issues/4090).
 
 ### **rocPRIM** (3.3.0)
 
@@ -2866,10 +3014,8 @@ for a complete overview of this release.
 
 See [issue #3500](https://github.com/ROCm/ROCm/issues/3500) on GitHub.
 
-```{note}
-See the [detailed AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/docs/6.2.0/CHANGELOG.md)
-on GitHub for more information.
-```
+> [!NOTE]
+> See the [detailed AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/docs/6.2.0/CHANGELOG.md) on GitHub for more information.
 
 ### **Composable Kernel** (1.1.0)
 
@@ -3468,9 +3614,8 @@ The compiler may incorrectly compile a program that uses the
 the function is undefined along some path to the function. For most functions,
 uninitialized inputs cause undefined behavior.
 
-```{note}
-The ``-Wall`` compilation flag prompts the compiler to generate a warning if a variable is uninitialized along some path.
-```
+> [!NOTE]
+> The ``-Wall`` compilation flag prompts the compiler to generate a warning if a variable is uninitialized along some path.
 
 As a workaround, initialize the parameters to ``__shfl``. For example:
 
@@ -3791,10 +3936,8 @@ See [issue #3498](https://github.com/ROCm/ROCm/issues/3498) on GitHub.
 
 - Fixed Partition ID CLI output.
 
-```{note}
-See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.2.0/CHANGELOG.md)
-on GitHub for more information.
-```
+> [!NOTE]
+> See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.2.0/CHANGELOG.md) on GitHub for more information.
 
 ### **ROCm Validation Suite** (1.0.0)
 
@@ -4164,9 +4307,8 @@ for a complete overview of this release.
 * Fixed the `amdsmitstReadWrite.TestPowerCapReadWrite` test for RDNA3, RDNA2, and MI100 devices.
 * Fixed an issue with the `amdsmi_get_gpu_memory_reserved_pages` and `amdsmi_get_gpu_bad_page_info` Python interface calls.
 
-```{note}
-See the AMD SMI [detailed changelog](https://github.com/ROCm/amdsmi/blob/rocm-6.1.x/CHANGELOG.md) with code samples for more information.
-```
+> [!NOTE]
+> See the AMD SMI [detailed changelog](https://github.com/ROCm/amdsmi/blob/rocm-6.1.x/CHANGELOG.md) with code samples for more information.
 
 ### **RCCL** (2.18.6)
 
@@ -4246,9 +4388,8 @@ for a complete overview of this release.
 
 - `amd-smi bad-pages` can result in a `ValueError: Null pointer access` error when using some PMU firmware versions.
 
-```{note}
-See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
-```
+> [!NOTE]
+> See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
 
 ### **hipBLASLt** (0.7.0)
 
@@ -4317,9 +4458,8 @@ See the [detailed changelog](https://github.com/ROCm/amdsmi/blob/docs/6.1.1/CHAN
 
 - ROCm SMI reports GPU utilization incorrectly for RDNA3 GPUs in some situations. See the issue on [GitHub](https://github.com/ROCm/ROCm/issues/3112).
 
-```{note}
-See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
-```
+> [!NOTE]
+> See the [detailed ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/docs/6.1.1/CHANGELOG.md) with code samples for more information.
 
 ## ROCm 6.1.0
 
@@ -5036,16 +5176,16 @@ on GitHub for a complete overview of this release.
 
 ### **rocSPARSE** (2.5.4)
 
-##### Added
+#### Added
 
 - Added more mixed precisions for SpMV, (matrix: float, vectors: double, calculation: double) and (matrix: rocsparse_float_complex, vectors: rocsparse_double_complex, calculation: rocsparse_double_complex)
 - Added support for gfx940, gfx941 and gfx942
 
-##### Optimized
+#### Optimized
 
 - Fixed a bug in csrsm and bsrsm
 
-##### Known issues
+#### Known issues
 
 In csritlu0, the algorithm rocsparse_itilu0_alg_sync_split_fusion has some accuracy issues to investigate with XNACK enabled. The fallback is rocsparse_itilu0_alg_sync_split.
 
@@ -5131,7 +5271,7 @@ on GitHub for a complete overview of this release.
 
 ### **HIP** (5.6.0)
 
-##### Added
+#### Added
 
 - Added hipRTC support for amd_hip_fp16
 - Added hipStreamGetDevice implementation to get the device associated with the stream
@@ -5140,7 +5280,7 @@ on GitHub for a complete overview of this release.
 - hipArrayGetDescriptor for getting 1D or 2D array descriptor
 - hipArray3DGetDescriptor to get 3D array descriptor
 
-##### Changed
+#### Changed
 
 - hipMallocAsync to return success for zero size allocation to match hipMalloc
 - Separation of hipcc perl binaries from HIP project to hipcc project. hip-devel package depends on newly added hipcc package
@@ -5445,15 +5585,15 @@ $ gcc main.c -I/opt/rocm-5.6.0/include -L/opt/rocm-5.6.0/lib -lrocprofiler64-v2
 The resulting `a.out` will depend on
 `/opt/rocm-5.6.0/lib/librocprofiler64.so.2`.
 
-##### Added
+#### Added
 
 - 'end_time' need to be disabled in roctx_trace.txt
 
-##### Optimized
+#### Optimized
 
 - Improved Test Suite
 
-##### Resolved issues
+#### Resolved issues
 
 - rocprof in ROcm/5.4.0 gpu selector broken.
 - rocprof in ROCm/5.4.1 fails to generate kernel info.

RELEASE.md

@@ -10,7 +10,7 @@
 <!-- markdownlint-disable reference-links-images            -->
 <!-- markdownlint-disable no-missing-space-atx              -->
 <!-- spellcheck-disable                                     -->
-# ROCm 6.4.1 release notes
+# ROCm 6.4.2 release notes
 
 The release notes provide a summary of notable changes since the previous ROCm release.
 
@@ -24,63 +24,81 @@ The release notes provide a summary of notable changes since the previous ROCm r
 
 - [ROCm known issues](#rocm-known-issues)
 
+- [ROCm resolved issues](#rocm-resolved-issues)
+
 - [ROCm upcoming changes](#rocm-upcoming-changes)
 
 ```{note}
-If you’re using 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)
+If you’re 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 6.4.1. For changes to individual components, see
+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).
 
-### Addition of DPX partition mode under NPS2 memory mode
- 
-AMD Instinct MI300X now supports DPX partition mode under NPS2 memory mode. For more partitioning information, see the [Deep dive into the MI300 compute and memory partition modes](https://rocm.blogs.amd.com/software-tools-optimization/compute-memory-modes/README.html) blog and [AMD Instinct MI300X system optimization](https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#change-gpu-partition-modes).
+### ROCm Compute Profiler enhancements
 
-### Introducing the ROCm Data Science toolkit
+[ROCm Compute Profiler](https://rocm.docs.amd.com/projects/rocprofiler-compute/en/latest/index.html) includes the following changes:
 
-The ROCm Data Science toolkit (or ROCm-DS) is an open-source software collection for high-performance data science applications built on the core ROCm platform. You can leverage ROCm-DS to accelerate both new and existing data science workloads, allowing you to execute intensive applications with larger datasets at lightning speed. ROCm-DS is in an early access state. Running production workloads is not recommended. For more information, see [AMD ROCm-DS Documentation](https://rocm.docs.amd.com/projects/rocm-ds/en/latest/index.html).
+* 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 6.4.1 now allows you to use the SPACEBAR or ENTER keys for menu item selection in the GUI. It also adds support for Debian 12 and fixes an issue for “full” mode RHEL offline installer creation, where GDM packages were uninstalled during offline installation. See [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html) for more information.
+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 6.4.1 adds the following improvements:
-- Relaxed version checks for installation on different distributions. Provided the dependencies are not installed by the Runfile Installer, you can target installation for a different path from the host system running the installer. For example, the installer can run on a system using Ubuntu 22.04 and install to a partition/system that is using Ubuntu 24.04.
-- Performance improvements for detecting a previous ROCm install. 
-- Removal of the extra `opt` directory created for the target during the ROCm installation.  For example, installing to `target=/home/amd` now installs ROCm to `/home/amd/rocm-6.4.1` and not `/home/amd/opt/rocm-6.4.1`. For installs using `target=/`, the installation will continue to use `/opt/`.
-- The Runfile Installer can be used to uninstall any Runfile-based installation of the driver.
-- In the CLI interface, the `postrocm` argument can now be run separately from the `rocm` argument.  In cases where `postrocm` was missed from the initial ROCm install, `postrocm` can now be run on the same target folder. For example, if you installed ROCm 6.4.1 using `install.run target=/myrocm rocm`, you can run the post-installation separately using the command `install.run target=/myrocm/rocm-6.4.1 postrocm`.
-
-For more information, see [ROCm Runfile Installer](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-runfile-installer.html).
+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 five new tutorials. These tutorials are Jupyter notebook-based, easy-to-follow documents. They are ideal for AI developers who want to learn about specific topics, including inference, fine-tuning, and training. 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).
-* The [Training a model with LLM Foundry](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.html) performance testing guide has been added. This guide describes how to use the preconfigured [ROCm/pytorch-training](https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5) training environment and [https://github.com/ROCm/MAD](https://github.com/ROCm/MAD) to test the training performance of the LLM Foundry framework on AMD Instinct MI325X and MI300X accelerators using the [MPT-30B](https://huggingface.co/mosaicml/mpt-30b) model.
-* The [Training a model with PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) performance testing guide has been updated to feature the latest [ROCm/pytorch-training](https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5) Docker image (a preconfigured training environment with ROCm and PyTorch). Support for [Llama 3.3 70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) has been added.
-* The [Training a model with JAX MaxText](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.html) performance testing guide has been updated to feature the latest [ROCm/jax-training](https://hub.docker.com/layers/rocm/jax-training/maxtext-v25.5/images/sha256-4e0516358a227cae8f552fb866ec07e2edcf244756f02e7b40212abfbab5217b) Docker image (a preconfigured training environment with ROCm, JAX, and [MaxText](https://github.com/AI-Hypercomputer/maxtext)). Support for [Llama 3.3 70B](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct) has been added.
-* The [vLLM inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/vllm-benchmark.html?model=pyt_vllm_qwq-32b) guide has been updated to feature the latest [ROCm/vLLM](https://hub.docker.com/layers/rocm/vllm/latest/images/sha256-5c8b4436dd0464119d9df2b44c745fadf81512f18ffb2f4b5dc235c71ebe26b4) Docker image (a preconfigured environment for inference with ROCm and [vLLM](https://docs.vllm.ai/en/latest/)). Support for the [QwQ-32B](https://huggingface.co/Qwen/QwQ-32B) model has been added.
-* The [PyTorch inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/pytorch-inference-benchmark.html?model=pyt_clip_inference) guide has been added, featuring the [ROCm/PyTorch](https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-ab1d350b818b90123cfda31363019d11c0d41a8f12a19e3cb2cb40cf0261137d) Docker image (a preconfigured inference environment with ROCm and PyTorch) with initial support for the [CLIP](https://huggingface.co/laion/CLIP-ViT-B-32-laion2B-s34B-b79K) and [Chai-1](https://huggingface.co/chaidiscovery/chai-1) models.
+* [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. It’s 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 6.4.1 introduces support for the RDNA4 architecture-based [Radeon AI PRO
-R9700](https://www.amd.com/en/products/graphics/workstations/radeon-ai-pro/ai-9000-series/amd-radeon-ai-pro-r9700.html),
-[Radeon RX 9070](https://www.amd.com/en/products/graphics/desktops/radeon/9000-series/amd-radeon-rx-9070.html),
-[Radeon RX 9070 XT](https://www.amd.com/en/products/graphics/desktops/radeon/9000-series/amd-radeon-rx-9070xt.html),
-Radeon RX 9070 GRE, and
-[Radeon RX 9060 XT](https://www.amd.com/en/products/graphics/desktops/radeon/9000-series/amd-radeon-rx-9060xt.html) GPUs
-for compute workloads. It also adds support for RDNA3 architecture-based [Radeon PRO W7700](https://www.amd.com/en/products/graphics/workstations/radeon-pro/w7700.html) and [Radeon RX 7800 XT](https://www.amd.com/en/products/graphics/desktops/radeon/7000-series/amd-radeon-rx-7800-xt.html) GPUs. These GPUs are supported on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.5, and RHEL 9.4.
+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/latest/reference/system-requirements.html#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)
@@ -88,8 +106,8 @@ for more information about operating system and hardware compatibility.
 
 ## ROCm components
 
-The following table lists the versions of ROCm components for ROCm 6.4.1, including any version
-changes from 6.4.0 to 6.4.1. Click the component's updated version to go to a list of its changes.
+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">
@@ -111,47 +129,47 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <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-6.4.1/index.html">Composable Kernel</a></td>
+                <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-6.4.1/index.html">MIGraphX</a></td>
+                <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-6.4.1/index.html">MIOpen</a></td>
+                <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-6.4.1/index.html">MIVisionX</a></td>
+                <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-6.4.1/index.html">rocAL</a></td>
+                <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-6.4.1/index.html">rocDecode</a></td>
+                <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-6.4.1/index.html">rocJPEG</a></td>
+                <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-6.4.1/index.html">rocPyDecode</a></td>
+                <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-6.4.1/index.html">RPP</a></td>
+                <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>
@@ -160,13 +178,13 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="2"></th>
                 <th rowspan="2">Communication</th>
-                <td><a href="https://rocm.docs.amd.com/projects/rccl/en/docs-6.4.1/index.html">RCCL</a></td>
+                <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-6.4.1/index.html">rocSHMEM</a></td>
-                <td>2.0.0</td>
+            <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>
@@ -174,82 +192,82 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="16"></th>
                 <th rowspan="16">Math</th>
-                <td><a href="https://rocm.docs.amd.com/projects/hipBLAS/en/docs-6.4.1/index.html">hipBLAS</a></td>
+                <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-6.4.1/index.html">hipBLASLt</a></td>
-                <td>0.12.0&nbsp;&Rightarrow;&nbsp;<a href="#hipblaslt-0-12-1">0.12.1</td>
+                <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-6.4.1/index.html">hipFFT</a></td>
+                <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-6.4.1/index.html">hipfort</a></td>
+                <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-6.4.1/index.html">hipRAND</a></td>
+                <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-6.4.1/index.html">hipSOLVER</a></td>
+                <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-6.4.1/index.html">hipSPARSE</a></td>
+                <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-6.4.1/index.html">hipSPARSELt</a></td>
+                <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-6.4.1/index.html">rocALUTION</a></td>
-                <td>3.2.2&nbsp;&Rightarrow;&nbsp;<a href="#rocalution-3-2-3">3.2.3</td></td>
+                <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-6.4.1/index.html">rocBLAS</a></td>
-                <td>4.4.0</td>
+                <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-6.4.1/index.html">rocFFT</a></td>
+                <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-6.4.1/index.html">rocRAND</a></td>
+                <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-6.4.1/index.html">rocSOLVER</a></td>
-                <td>3.28.0</td>
+                <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-6.4.1/index.html">rocSPARSE</a></td>
+                <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-6.4.1/index.html">rocWMMA</a></td>
+                <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-6.4.1/src/index.html">Tensile</a></td>
+                <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>
@@ -258,22 +276,22 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="4"></th>
                 <th rowspan="4">Primitives</th>
-                <td><a href="https://rocm.docs.amd.com/projects/hipCUB/en/docs-6.4.1/index.html">hipCUB</a></td>
+                <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-6.4.1/index.html">hipTensor</a></td>
+                <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-6.4.1/index.html">rocPRIM</a></td>
-                <td>3.4.0</td>
+                <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-6.4.1/index.html">rocThrust</a></td>
+                <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>
@@ -282,28 +300,28 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="7">Tools</th>
                 <th rowspan="7">System management</th>
-                <td><a href="https://rocm.docs.amd.com/projects/amdsmi/en/docs-6.4.1/index.html">AMD SMI</a></td>
-                <td>25.3.0&nbsp;&Rightarrow;&nbsp;<a href="#amd-smi-25-4-2">25.4.2</a></td>
+                <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-6.4.1/index.html">ROCm Data Center Tool</a></td>
-                <td>0.3.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-data-center-tool-0-3-0">0.3.0</td>
+                <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-6.4.1/index.html">rocminfo</a></td>
+                <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-6.4.1/index.html">ROCm SMI</a></td>
-                <td>7.5.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-smi-7-5-0">7.5.0</a></td>
+                <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-6.4.1/index.html">ROCmValidationSuite</a></td>
-                <td>1.1.0</td>
+                <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>
@@ -311,38 +329,38 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="6"></th>
                 <th rowspan="6">Performance</th>
-                <td><a href="https://rocm.docs.amd.com/projects/rocm_bandwidth_test/en/docs-6.4.1/index.html">ROCm Bandwidth
+                <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-6.4.1/index.html">ROCm Compute Profiler</a></td>
-                <td>3.1.0</td>
+                <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-6.4.1/index.html">ROCm Systems Profiler</a></td>
-                <td>1.0.0&nbsp;&Rightarrow;&nbsp;<a href="#rocm-systems-profiler-1-0-1">1.0.1</td>
+                <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-6.4.1/index.html">ROCProfiler</a></td>
+                <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-6.4.1/index.html">ROCprofiler-SDK</a></td>
+                <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-6.4.1/index.html">ROCTracer</a></td>
+                <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>
@@ -352,32 +370,32 @@ Click {fab}`github` to go to the component's source code on GitHub.
             <tr>
                 <th rowspan="5"></th>
                 <th rowspan="5">Development</th>
-                <td><a href="https://rocm.docs.amd.com/projects/HIPIFY/en/docs-6.4.1/index.html">HIPIFY</a></td>
+                <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-6.4.1/index.html">ROCdbgapi</a></td>
+                <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-6.4.1/index.html">ROCm CMake</a></td>
+                <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-6.4.1/index.html">ROCm Debugger (ROCgdb)</a>
+                <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-6.4.1/index.html">ROCr Debug Agent</a>
+                <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
@@ -387,13 +405,13 @@ Click {fab}`github` to go to the component's source code on GitHub.
         <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-6.4.1/index.html">HIPCC</a></td>
+                <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-6.4.1/index.html">llvm-project</a></td>
+                <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>
@@ -402,13 +420,13 @@ Click {fab}`github` to go to the component's source code on GitHub.
         <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-6.4.1/index.html">HIP</a></td>
-                <td>6.4.0&nbsp;&Rightarrow;&nbsp;<a href="#hip-6-4-1">6.4.1</td>
+                <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-6.4.1/index.html">ROCr Runtime</a></td>
-                <td>1.15.0&nbsp;&Rightarrow;&nbsp;<a href="#rocr-runtime-1-15-0">1.15.0</td>
+                <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>
@@ -423,172 +441,187 @@ The following sections describe key changes to ROCm components.
 For a historical overview of ROCm component updates, see the {doc}`ROCm consolidated changelog </release/changelog>`.
 ```
 
-### **AMD SMI** (25.4.2)
+### **AMD SMI** (25.5.1)
 
 #### Added
 
-* Dumping CPER entries from RAS tool `amdsmi_get_gpu_cper_entries()` to Python and C APIs.
-  - Dumping CPER entries consist of `amdsmi_cper_hdr_t`.
-  - Dumping CPER entries is also enabled in the CLI interface through `sudo amd-smi ras --cper`.
-* `amdsmi_get_gpu_busy_percent` to the C API.
+- 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
 
-* Modified VRAM display for amd-smi monitor -v. 
+- Padded `asic_serial` in `amdsmi_get_asic_info` with 0s.
 
-#### Optimized
-
-* Improved load times for CLI commands when the GPU has multiple parititons.
+- Renamed field `COMPUTE_PARTITION` to `ACCELERATOR_PARTITION` in CLI call `amd-smi --partition`.
 
 #### Resolved issues
 
-* Fixed partition enumeration in `amd-smi list -e`, `amdsmi_get_gpu_enumeration_info()`, `amdsmi_enumeration_info_t`, `drm_card`, and `drm_render` fields.
-
-#### Known issues
-
-* When using the `--follow` flag with `amd-smi ras --cper`, CPER entries are not streamed continuously as intended. This will be fixed in an upcoming ROCm release.
+- 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** (6.4.1)
+### **HIP** (6.4.2)
 
 #### Added
 
-* New log mask enumeration `LOG_COMGR` enables logging precise code object information.
-
-#### Changed
-
-* HIP runtime uses device bitcode before SPIRV.
-* The implementation of preventing `hipLaunchKernel` latency degradation with number of idle streams is reverted/disabled by default.
+* 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 kernel logging includes de-mangling shader names.
-* Refined implementation in HIP APIs `hipEventRecords` and `hipStreamWaitEvent` for performance improvement.
+* Improved implementation in `hipEventSynchronize`, HIP runtime now makes internal callbacks as non-blocking operations to improve performance.
 
 #### Resolved issues
 
-* Stale state during the graph capture. The return error was fixed, HIP runtime now always uses the latest dependent nodes during `hipEventRecord` capture.
-* Segmentation fault during kernel execution. HIP runtime now allows maximum stack size as per ISA on the GPU device.
+* 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)
 
-#### Resolved issues
+#### Added
 
-* Fixed an accuracy issue for some solutions using an `FP32` or `TF32` data type with a TT transpose.
+* Support for gfx1151 on Linux, complementing the previous support in the HIP SDK for Windows.
 
 ### **RCCL** (2.22.3)
 
-#### Changed
+#### Added
 
-* MSCCL++ is now disabled by default. To enable it, set `RCCL_MSCCLPP_ENABLE=1`.
+* Added support for the LL128 protocol on gfx942.
+
+### **rocBLAS** (4.4.1)
 
 #### Resolved issues
 
-* Fixed an issue where early termination, in rare circumstances, could cause the application to stop responding by adding synchronization before destroying a proxy thread.
-* Fixed the accuracy issue for the MSCCLPP `allreduce7` kernel in graph mode.
+* 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.
 
-#### Known issues
-
-* When splitting a communicator using `ncclCommSplit` in some GPU configurations, MSCCL initialization can cause a segmentation fault. The recommended workaround is to disable MSCCL with `export RCCL_MSCCL_ENABLE=0`.
-  This issue will be fixed in a future ROCm release.
-
-* Within the RCCL-UnitTests test suite, failures occur in tests ending with the
-  `.ManagedMem` and `.ManagedMemGraph` suffixes. These failures only affect the
-  test results and do not affect the RCCL component itself. This issue will be
-  resolved in a future ROCm release.
-
-### **rocALUTION** (3.2.3)
+### **ROCm Compute Profiler** (3.1.1)
 
 #### Added
 
-* The `-a` option has been added to the `rmake.py` build script. This option allows you to select specific architectures when building on Microsoft Windows.
-
-#### Resolved issues
-
-* Fixed an issue where the `HIP_PATH` environment variable was being ignored when compiling on Microsoft Windows.
-
-### **ROCm Data Center Tool** (0.3.0)
-
-#### Added
-
-- Support for GPU partitions.
-- `RDC_FI_GPU_BUSY_PERCENT` metric.
+* 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
 
-- Updated `rdc_field` to align with `rdc_bootstrap` for current metrics.
+* Changed dependency from `rocm-smi` to `amd-smi`.
 
 #### Resolved issues
 
-- Fixed [ROCProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.4.0/index.html) eval metrics and memory leaks.
+* Fixed a crash related to Agent ID caused by the new format of the `rocprofv3` output CSV file.
 
-### **ROCm SMI** (7.5.0)
+### **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 partition enumeration. It now refers to the correct DRM Render and Card paths.
+* 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`.
 
-```{note}
-See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/release/rocm-rel-6.4/CHANGELOG.md) for details, examples, and in-depth descriptions.
-```
+### **ROCm Validation Suite** (1.1.0)
 
-### **ROCm Systems Profiler** (1.0.1)
+#### Added
 
-#### Added 
+* NPS2/DPX and NPS4/CPX partition modes support for AMD Instinct MI300X.
 
-* How-to document for [network performance profiling](https://rocm.docs.amd.com/projects/rocprofiler-systems/en/latest/how-to/nic-profiling.html) for standard Network Interface Cards (NICs).
+### **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
 
-* Fixed a build issue with Dyninst on GCC 13.
+* 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.
 
-### **ROCr Runtime** (1.15.0)
+### **rocSOLVER** (3.28.2)
 
-#### Resolved issues
+#### Added
 
-* Fixed a rare occurrence issue on AMD Instinct MI25, MI50, and MI100 GPUs, where the `SDMA` copies might start before the dependent Kernel finishes and could cause memory corruption.
+* 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).
 
-### Radeon AI PRO R9700 hangs when running Stable Diffusion 2.1 at batch sizes above four
+## ROCm resolved issues
 
-Radeon AI PRO R9700 GPUs might hang when running [Stable Diffusion
-2.1](https://huggingface.co/stabilityai/stable-diffusion-2-1) with batch sizes
-greater than four. As a workaround, limit batch sizes to four or fewer. This issue
-will be addressed in a future ROCm release. See [issue #4770](https://github.com/ROCm/ROCm/issues/4770) on GitHub.
-
-### RCCL MSCCL initialization failure
-
-When splitting a communicator using `ncclCommSplit` in some GPU configurations, MSCCL initialization can cause a segmentation fault. The recommended workaround is to disable MSCCL with `export RCCL_MSCCL_ENABLE=0`.
-This issue will be fixed in a future ROCm release. See [issue #4769](https://github.com/ROCm/ROCm/issues/4769) on GitHub.
+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
 
-* When using the `--follow` flag with `amd-smi ras --cper`, CPER entries are not streamed continuously as intended. This will be fixed in an upcoming ROCm release.
-See [issue #4768](https://github.com/ROCm/ROCm/issues/4768) on GitHub.
+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).
 
-### ROCm SMI uninstallation issue on RHEL and SLES
+### Instinct MI300X reports incorrect raw GPU timestamps
 
-`rocm-smi-lib` does not get uninstalled and remains orphaned on RHEL and SLES systems when:
+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).
 
-* [Uninstalling ROCm using the AMDGPU installer](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/amdgpu-install.html#uninstalling-rocm) with `amdgpu-install --uninstall`
+### MIOpen generates incorrect results for particular input with FP32 data type
 
-* [Uninstalling via package manager](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/install-methods/package-manager/package-manager-rhel.html#uninstall-rocm-packages)
-  with `dnf remove rocm-core` on RHEL or `zypper remove rocm-core` on SLES.
-
-As a workaround, manually remove the `rocm-smi-lib` package using `sudo dnf remove rocm-smi-lib` or `sudo zypper remove rocm-smi-lib`.
-See [issue #4767](https://github.com/ROCm/ROCm/issues/4767) on GitHub.
+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
@@ -616,10 +649,10 @@ 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-6.4.0/LLVM/clang/html/AMDGPUSupport.html).
+  [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. 
+  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
@@ -654,4 +687,4 @@ There are a number of upcoming changes planned for HIP runtime API in an upcomin
 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.
+`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).

docs/compatibility/compatibility-matrix-historical-6.0.csv

@@ -1,126 +1,130 @@
-ROCm Version,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 24.04.1, 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, 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, 9.5, 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, 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 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 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]_,,,,,,,,,,,
-      ,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
-      ,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
-      ,RDNA4,,,,,,,,,,,,,,,
-      ,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
-      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,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
-      ,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
-      ,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","2.4, 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","2.17.0, 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>`,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,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,>=1.2.0,>=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,>=1.14.1,>=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,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      CUB,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-,,,,,,,,,,,,,,,,
-      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, 6.0.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
-      :doc:`MIGraphX <amdmigraphx:index>`,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>`,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`MIVisionX <mivisionx:index>`,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
-      :doc:`rocAL <rocal:index>`,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`rocDecode <rocdecode:index>`,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
-      :doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`RPP <rpp:index>`,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
-      ,,,,,,,,,,,,,,,,
-      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,
-      :doc:`RCCL <rccl:index>`,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      :doc:`rocSHMEM <rocshmem:index>`,2.0.0,2.0.0,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
-      :doc:`hipBLAS <hipblas:index>`,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>`,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
-      :doc:`hipFFT <hipfft:index>`,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
-      :doc:`hipfort <hipfort:index>`,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
-      :doc:`hipRAND <hiprand:index>`,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
-      :doc:`hipSOLVER <hipsolver:index>`,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
-      :doc:`hipSPARSE <hipsparse:index>`,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
-      :doc:`rocALUTION <rocalution:index>`,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
-      :doc:`rocBLAS <rocblas:index>`,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
-      :doc:`rocFFT <rocfft:index>`,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
-      :doc:`rocRAND <rocrand:index>`,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
-      :doc:`rocSOLVER <rocsolver:index>`,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
-      :doc:`rocSPARSE <rocsparse:index>`,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
-      :doc:`rocWMMA <rocwmma:index>`,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
-      :doc:`Tensile <tensile:src/index>`,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
-      ,,,,,,,,,,,,,,,,
-      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,
-      :doc:`hipCUB <hipcub:index>`,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`hipTensor <hiptensor:index>`,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
-      :doc:`rocPRIM <rocprim:index>`,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      ,,,,,,,,,,,,,,,,
-      SUPPORT LIBS,,,,,,,,,,,,,,,,
-      `hipother <https://github.com/ROCm/hipother>`_,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
-      `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]_,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>`,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
-      :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
-      :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
-      :doc:`ROCm SMI <rocm_smi_lib:index>`,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>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
-      ,,,,,,,,,,,,,,,,
-      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
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,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>`,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCTracer <roctracer:index>`,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
-      ,,,,,,,,,,,,,,,,
-      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,
-      :doc:`HIPIFY <hipify:index>`,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
-      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
-      ,,,,,,,,,,,,,,,,
-      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,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
-      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      `Flang <https://github.com/ROCm/flang>`_,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`llvm-project <llvm-project:index>`,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-,,,,,,,,,,,,,,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      :doc:`HIP <hip:index>`,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `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
-      :doc:`ROCr Runtime <rocr-runtime:index>`,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
+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>` [#stanford-megatron-lm_compat]_,N/A,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :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>` [#megablocks_compat]_,N/A,N/A,N/A,N/A,N/A,N/A,0.7.0,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

docs/compatibility/compatibility-matrix.rst

@@ -23,14 +23,14 @@ compatibility and system requirements.
 .. container:: format-big-table
 
   .. csv-table::
-      :header: "ROCm Version", "6.4.1", "6.4.0", "6.3.0"
+      :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, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4"
+      ,"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 SP6,SLES 15 SP6,"SLES 15 SP6, SP5"
+      ,"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]_,
@@ -38,13 +38,13 @@ compatibility and system requirements.
       :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3
       ,CDNA2,CDNA2,CDNA2
       ,CDNA,CDNA,CDNA
-      ,RDNA4,,
+      ,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]_,,
-      ,gfx1200 [#RDNA-OS]_,,
-      ,gfx1101 [#RDNA-OS]_,,
+      :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
@@ -54,7 +54,9 @@ compatibility and system requirements.
       FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
       :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 2.1, 2.0, 1.13"
       :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.35,0.4.35,0.4.31
+      :doc:`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:,,
@@ -81,23 +83,23 @@ compatibility and system requirements.
       ,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
       :doc:`RCCL <rccl:index>`,2.22.3,2.22.3,2.21.5
-      :doc:`rocSHMEM <rocshmem:index>`,2.0.0,2.0.0,N/A
+      :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>`,0.12.1,0.12.0,0.10.0
+      :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>`,3.2.3,3.2.2,3.2.1
-      :doc:`rocBLAS <rocblas:index>`,4.4.0,4.4.0,4.3.0
+      :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>`,3.28.0,3.28.0,3.27.0
+      :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
@@ -105,16 +107,16 @@ compatibility and system requirements.
       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>`,3.4.0,3.4.0,3.3.0
+      :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>`_,6.4.43483,6.4.43482,6.3.42131
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.4.1,6.4.0,6.3.0
+      `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>`,25.4.2,25.3.0,24.7.1
+      :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
@@ -122,11 +124,11 @@ compatibility and system requirements.
       ,,,
       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>`,3.1.0,3.1.0,3.0.0
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.0.1,1.0.0,0.1.0
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60401,2.0.60400,2.0.60300
+      :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>`,4.1.60401,4.1.60400,4.1.60300
+      :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
@@ -139,25 +141,25 @@ compatibility and system requirements.
       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>`_,19.0.0.25184,19.0.0.25133,18.0.0.24455
-      :doc:`llvm-project <llvm-project:index>`,19.0.0.25184,19.0.0.25133,18.0.0.24491
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25184,19.0.0.25133,18.0.0.24491
+      `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>`,6.4.43483,6.4.43482,6.3.42131
-      :doc:`HIP <hip:index>`,6.4.43483,6.4.43482,6.3.42131
+      :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. 
-.. [#mi300_620] **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].
-.. [#kfd_support] Starting from 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 (assuming hardware support is available in both). For earlier ROCm releases, the compatibility is provided for +/- 2 releases. These are the compatibility combinations that are currently supported.
+.. [#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.
-.. [#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, RHEL 9.5, and RHEL 9.4.
+
 
 .. _OS-kernel-versions:
 
@@ -176,14 +178,15 @@ Use this lookup table to confirm which operating system and kernel versions are
    `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
+   ,9.5, 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 SP6, "6.5.0+, 6.4.0", 2.38
+   `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
@@ -225,7 +228,9 @@ Expand for full historical view of:
    .. 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. 
+   .. [#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].
@@ -235,6 +240,10 @@ Expand for full historical view of:
    .. [#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.
-   .. [#kfd_support-past-60] Starting from 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 (assuming hardware support is available in both). For earlier ROCm releases, the compatibility is provided for +/- 2 releases. These are the compatibility combinations that are currently supported.
+   .. [#verl_compat] verl is only supported on ROCm 6.2.0.
+   .. [#stanford-megatron-lm_compat] Stanford Megatron-LM is only supported on ROCm 6.3.0.
+   .. [#dgl_compat] DGL is only supported on ROCm 6.4.0.
+   .. [#megablocks_compat] Megablocks is only supported on ROCm 6.3.0.
+   .. [#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.
-   .. [#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, RHEL 9.5, and RHEL 9.4.
+   

docs/compatibility/ml-compatibility/dgl-compatibility.rst

@@ -0,0 +1,255 @@
+: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``

docs/compatibility/ml-compatibility/jax-compatibility.rst

@@ -53,7 +53,7 @@ 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 Karpathy’s JAX-based
-  nanoGPT. Comparing how essential GPT components—such as self-attention 
+  nanoGPT. Comparing how essential GPT components—such as self-attention
   mechanisms and optimizers—are realized in JAX and JAX, also highlights
   JAX’s unique features.
 
@@ -97,7 +97,7 @@ Docker image compatibility
 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 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click the |docker-icon|
+`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
@@ -110,7 +110,7 @@ icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.1-jax0.4.35-py3.12/images/sha256-7a0745a2a2758bdf86397750bac00e9086cbf67d170cfdbb08af73f7c7d18a6a"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
+           <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
@@ -118,7 +118,7 @@ icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.1-jax0.4.35-py3.10/images/sha256-5f9e8d6e6e69fdc9a1a3f2ba3b1234c3f46c53b7468538c07fd18b00899da54f"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
+           <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
@@ -160,12 +160,14 @@ associated inventories are tested for `ROCm 6.3.2 <https://repo.radeon.com/rocm/
       - Ubuntu 22.04
       - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
 
+.. _key_rocm_libraries:
+
 Key ROCm libraries for JAX
 ================================================================================
 
-JAX functionality on ROCm is determined by its underlying library
-dependencies. These ROCm components affect the capabilities, performance, and
-feature set available to developers.
+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
@@ -173,347 +175,140 @@ feature set available to developers.
     * - 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.
-      - Matrix multiplication in ``jax.numpy.matmul``, ``jax.lax.dot`` and
-        ``jax.lax.dot_general``, operations like ``jax.numpy.dot``, which
-        involve vector and matrix computations and batch matrix multiplications
-        ``jax.numpy.einsum`` with matrix-multiplication patterns algebra
-        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.
-      - Matrix multiplication in ``jax.numpy.matmul`` or ``jax.lax.dot``, and
-        the XLA (Accelerated Linear Algebra) use hipBLASLt for optimized matrix
-        operations, mixed-precision support, and hardware-specific
-        optimizations.
     * - `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.
-      - Reduction functions (``jax.numpy.sum``, ``jax.numpy.mean``,
-        ``jax.numpy.prod``, ``jax.numpy.max`` and ``jax.numpy.min``), prefix sum
-        (``jax.numpy.cumsum``, ``jax.numpy.cumprod``) and sorting
-        (``jax.numpy.sort``, ``jax.numpy.argsort``).
     * - `hipFFT <https://github.com/ROCm/hipFFT>`_
       - :version-ref:`hipFFT rocm_version`
       - Provides GPU-accelerated Fast Fourier Transform (FFT) operations.
-      - Used in functions like ``jax.numpy.fft``.
     * - `hipRAND <https://github.com/ROCm/hipRAND>`_
       - :version-ref:`hipRAND rocm_version`
       - Provides fast random number generation for GPUs.
-      - The ``jax.random.uniform``, ``jax.random.normal``,
-        ``jax.random.randint`` and ``jax.random.split``.
     * - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
       - :version-ref:`hipSOLVER rocm_version`
       - Provides GPU-accelerated solvers for linear systems, eigenvalues, and
         singular value decompositions (SVD).
-      - Solving linear systems (``jax.numpy.linalg.solve``), matrix
-        factorizations, SVD (``jax.numpy.linalg.svd``) and eigenvalue problems
-        (``jax.numpy.linalg.eig``).
     * - `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 matrix multiplication (``jax.numpy.matmul``), sparse
-        matrix-vector and matrix-matrix products
-        (``jax.experimental.sparse.dot``), sparse linear system solvers and
-        sparse data handling.
     * - `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 matrix multiplication (``jax.numpy.matmul``), sparse
-        matrix-vector and matrix-matrix products
-        (``jax.experimental.sparse.dot``) and sparse linear system solvers.
     * - `MIOpen <https://github.com/ROCm/MIOpen>`_
       - :version-ref:`MIOpen rocm_version`
       - Optimized for 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
-        ``jax.nn.conv``, ``jax.nn.relu``, and ``jax.nn.batch_norm``.
     * - `RCCL <https://github.com/ROCm/rccl>`_
       - :version-ref:`RCCL rocm_version`
       - Optimized for multi-GPU communication for operations like  all-reduce,
         broadcast, and scatter.
-      - Distribute computations across multiple GPU with ``pmap`` and
-        ``jax.distributed``. XLA automatically uses rccl when executing
-        operations across multiple GPUs on AMD hardware.
     * - `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 ``jax.numpy.sum``, ``jax.pmap`` for
-        distributed training, which involves parallel reductions or
-        operations like ``jax.numpy.cumsum`` can use rocThrust.
 
-Supported features
+.. 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 table maps the public JAX API modules to their supported
-ROCm and JAX versions.
+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
 
-    * - Module
-      - Description
-      - As of JAX
-      - As of ROCm
-    * - ``jax.numpy``
-      - Implements the NumPy API, using the primitives in ``jax.lax``.
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy``
-      - Provides GPU-accelerated and differentiable implementations of many
-        functions from the SciPy library, leveraging JAX's transformations
-        (e.g., ``grad``, ``jit``, ``vmap``).
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.lax``
-      - A library of primitives operations that underpins libraries such as
-        ``jax.numpy.`` Transformation rules, such as Jacobian-vector product
-        (JVP) and batching rules, are typically defined as transformations on
-        ``jax.lax`` primitives.
-      - 0.1.57
-      - 5.0.0
-    * - ``jax.random``
-      - Provides a number of routines for deterministic generation of sequences
-        of pseudorandom numbers.
-      - 0.1.58
-      - 5.0.0
-    * - ``jax.sharding``
-      - Allows to define partitioning and distributing arrays across multiple
-        devices.
-      - 0.3.20
-      - 5.1.0
-    * - ``jax.distributed``
-      - Enables the scaling of computations across multiple devices on a single
-        machine or across multiple machines.
-      - 0.1.74
-      - 5.0.0
-    * - ``jax.image``
-      - Contains image manipulation functions like resize, scale and translation.
-      - 0.1.57
-      - 5.0.0
-    * - ``jax.nn``
-      - Contains common functions for neural network libraries.
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.ops``
-      - Computes the minimum, maximum, sum or product within segments of an
-        array.
-      - 0.1.57
-      - 5.0.0
-    * - ``jax.stages``
-      - Contains interfaces to stages of the compiled execution process.
-      - 0.3.4
-      - 5.0.0
-    * - ``jax.extend``
-      - Provides modules for access to JAX internal machinery module. The
-        ``jax.extend`` module defines a library view of some of JAX’s internal
-        components.
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.example_libraries``
-      - Serves as a collection of example code and libraries that demonstrate
-        various capabilities of JAX.
-      - 0.1.74
-      - 5.0.0
-    * - ``jax.experimental``
-      - Namespace for experimental features and APIs that are in development or
-        are not yet fully stable for production use.
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.lib``
-      - Set of internal tools and types for bridging between JAX’s Python
-        frontend and its XLA backend.
-      - 0.4.6
-      - 5.3.0
-    * - ``jax_triton``
-      - Library that integrates the Triton deep learning compiler with JAX.
-      - jax_triton 0.2.0
-      - 6.2.4
-
-jax.scipy module
--------------------------------------------------------------------------------
-
-A SciPy-like API for scientific computing.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - As of JAX
-      - As of ROCm
-    * - ``jax.scipy.cluster``
-      - 0.3.11
-      - 5.1.0
-    * - ``jax.scipy.fft``
-      - 0.1.71
-      - 5.0.0
-    * - ``jax.scipy.integrate``
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.scipy.interpolate``
-      - 0.1.76
-      - 5.0.0
-    * - ``jax.scipy.linalg``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy.ndimage``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy.optimize``
-      - 0.1.57
-      - 5.0.0
-    * - ``jax.scipy.signal``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy.spatial.transform``
-      - 0.4.12
-      - 5.4.0
-    * - ``jax.scipy.sparse.linalg``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy.special``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.scipy.stats``
-      - 0.1.56
-      - 5.0.0
-
-jax.scipy.stats module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-.. list-table::
-   :header-rows: 1
-
-   * - Module
-     - As of JAX
-     - As of ROCm
-   * - ``jax.scipy.stats.bernouli``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.beta``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.betabinom``
-     - 0.1.61
-     - 5.0.0
-   * - ``jax.scipy.stats.binom``
-     - 0.4.14
-     - 5.4.0
-   * - ``jax.scipy.stats.cauchy``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.chi2``
-     - 0.1.61
-     - 5.0.0
-   * - ``jax.scipy.stats.dirichlet``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.expon``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.gamma``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.gennorm``
-     - 0.3.15
-     - 5.2.0
-   * - ``jax.scipy.stats.geom``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.laplace``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.logistic``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.multinomial``
-     - 0.3.18
-     - 5.1.0
-   * - ``jax.scipy.stats.multivariate_normal``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.nbinom``
-     - 0.1.72
-     - 5.0.0
-   * - ``jax.scipy.stats.norm``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.pareto``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.poisson``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.t``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.truncnorm``
-     - 0.4.0
-     - 5.3.0
-   * - ``jax.scipy.stats.uniform``
-     - 0.1.56
-     - 5.0.0
-   * - ``jax.scipy.stats.vonmises``
-     - 0.4.2
-     - 5.3.0
-   * - ``jax.scipy.stats.wrapcauchy``
-     - 0.4.20
-     - 5.6.0
-
-jax.extend module
--------------------------------------------------------------------------------
-
-Modules for JAX extensions.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - As of JAX
-      - As of ROCm
-    * - ``jax.extend.ffi``
-      - 0.4.30
-      - 6.0.0
-    * - ``jax.extend.linear_util``
-      - 0.4.17
-      - 5.6.0
-    * - ``jax.extend.mlir``
-      - 0.4.26
-      - 5.6.0
-    * - ``jax.extend.random``
-      - 0.4.15
-      - 5.5.0
-
-Unsupported JAX features
-===============================================================================
-
-The following GPU-accelerated JAX features are not supported by ROCm for
-the listed supported JAX versions.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Feature
+    * - Data type
       - Description
 
-    * - 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.
+    * - ``bfloat16``
+      - 16-bit bfloat (brain floating point).
 
-    * - XLA int4 support
-      - 4-bit integer (int4) precision in the XLA compiler.
+    * - ``bool``
+      - Boolean.
 
-    * - MOSAIC (GPU)
-      - Mosaic is a library of kernel-building abstractions for JAX's Pallas system
+    * - ``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.

docs/compatibility/ml-compatibility/megablocks-compatibility.rst

@@ -0,0 +1,93 @@
+: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/>`_
+
+

docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst

@@ -0,0 +1,100 @@
+: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/>`_
+
+      
+

docs/compatibility/ml-compatibility/tensorflow-compatibility.rst

@@ -10,16 +10,16 @@
 TensorFlow compatibility
 *******************************************************************************
 
-`TensorFlow <https://www.tensorflow.org/>`_ is an open-source library for
+`TensorFlow <https://www.tensorflow.org/>`__ is an open-source library for
 solving machine learning, deep learning, and AI problems. It can solve many
 problems across different sectors and industries but primarily focuses on
 neural network training and inference. It is one of the most popular and
 in-demand frameworks and is very active in open-source contribution and
 development.
 
-The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`_
+The `official TensorFlow repository <http://github.com/tensorflow/tensorflow>`__
 includes full ROCm support. AMD maintains a TensorFlow `ROCm repository
-<http://github.com/rocm/tensorflow-upstream>`_ in order to quickly add bug
+<http://github.com/rocm/tensorflow-upstream>`__ in order to quickly add bug
 fixes, updates, and support for the latest ROCM versions.
 
 - ROCm TensorFlow release:
@@ -27,16 +27,16 @@ fixes, updates, and support for the latest ROCM versions.
   - Offers :ref:`Docker images <tensorflow-docker-compat>` with
     ROCm and TensorFlow pre-installed.
 
-  - ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`_
+  - ROCm TensorFlow repository: `<https://github.com/ROCm/tensorflow-upstream>`__
 
   - See the :doc:`ROCm TensorFlow installation guide <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
     to get started.
 
 - Official TensorFlow release:
 
-  - Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`_
+  - Official TensorFlow repository: `<https://github.com/tensorflow/tensorflow>`__
 
-  - See the `TensorFlow API versions <https://www.tensorflow.org/versions>`_ list.
+  - See the `TensorFlow API versions <https://www.tensorflow.org/versions>`__ list.
 
   .. note::
 
@@ -54,9 +54,9 @@ Docker image compatibility
    <i class="fab fa-docker"></i>
 
 AMD validates and publishes ready-made `TensorFlow images
-<https://hub.docker.com/r/rocm/tensorflow>`_ with ROCm backends on
+<https://hub.docker.com/r/rocm/tensorflow>`__ with ROCm backends on
 Docker Hub. The following Docker image tags and associated inventories are
-validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click
+validated for `ROCm 6.4.2 <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
@@ -65,129 +65,62 @@ the |docker-icon| icon to view the image on Docker Hub.
     * - Docker image
       - TensorFlow
       - Ubuntu
-      - Dev
       - Python
       - TensorBoard
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4-py3.12-tf2.18-dev/images/sha256-fa9cf5fa6c6079a7118727531ccd0056c6e3224a42c3d6e78a49e7781daafff4"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - dev
+      - `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 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
+      - `Python 3.12 <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/rocm6.4.1-py3.12-tf2.18-runtime/images/sha256-d14d8c4989e7c9a60f4e72461b9e349de72347c6162dcd6897e6f4f80ffbb440"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - runtime
+      - `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 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
+      - `Python 3.12 <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/rocm6.4.1-py3.10-tf2.18-dev/images/sha256-081e5bd6615a5dc17247ebd2ccc26895c3feeff086720400fa39b477e60a77c0"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - dev
+      - `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 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
+      - `Python 3.10 <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/rocm6.4.1-py3.10-tf2.18-runtime/images/sha256-bf369637378264f4af6ddad5ca8b8611d3e372ffbea9ab7a06f1e122f0a0867b"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - runtime
-      - 22.04
-      - `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.12-tf2.17-dev/images/sha256-5a502008c50d0b6508e6027f911bdff070a7493700ae064bed74e1d22b91ed50"><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/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - dev
+      - `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 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
+      - `Python 3.12 <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/rocm6.4.1-py3.12-tf2.17-runtime/images/sha256-1ee5dfffceb71ac66617ada33de3a10de0cb74199cc4b82441192e5e92fa2ddf"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - runtime
-      - 24.04
-      - `Python 3.12.10 <https://www.python.org/downloads/release/python-3124/>`_
-      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.10-tf2.17-dev/images/sha256-109218ad92bfae83bbd2710475f7502166e1ed54ca0b9748a9cbc3f5a1d75af1"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - dev
+      - `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 3.10.17 <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/rocm6.4.1-py3.10-tf2.17-runtime/images/sha256-5d78bd5918d394f92263daa2990e88d695d27200dd90ed83ec64d20c7661c9c1"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - runtime
-      - 22.04
-      - `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.12-tf2.16-dev/images/sha256-b09b1ad921c09c687b7c916141051e9fcf15539a5686e5aa67c689195a522719"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - dev
-      - 24.04
-      - `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.12-tf2.16-runtime/images/sha256-20dbd824e85558abfe33fc9283cc547d88cde3c623fe95322743a5082f883a64"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - runtime
-      - 24.04
-      - `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.10-tf2.16-dev/images/sha256-36c4fa047c86e2470ac473ec1429aea6d4b8934b90ffeb34d1afab40e7e5b377"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.10-tf2.16-dev/images/sha256-36c4fa047c86e2470ac473ec1429aea6d4b8934b90ffeb34d1afab40e7e5b377>`__
-      - dev
-      - 22.04
-      - `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.1-py3.10-tf2.16-runtime/images/sha256-a94150ffb81365234ebfa34e764db5474bc6ab7d141b56495eac349778dafcf3"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - runtime
-      - 22.04
-      - `Python 3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`_
+      - `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
+      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
 
 
 Critical ROCm libraries for TensorFlow
@@ -207,43 +140,43 @@ are available in ROCm :version:`rocm_version`.
       - Version
       - Purpose
       - Used in
-    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`_
+    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
       - :version-ref:`hipBLAS rocm_version`
       - Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
         matrix and vector operations.
       - Accelerates operations like ``tf.matmul``, ``tf.linalg.matmul``, and
         other matrix multiplications commonly used in neural network layers.
-    * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`_
+    * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
       - :version-ref:`hipBLASLt rocm_version`
       - Extends hipBLAS with additional optimizations like fused kernels and
         integer tensor cores.
       - Optimizes matrix multiplications and linear algebra operations used in
         layers like dense, convolutional, and RNNs in TensorFlow.
-    * - `hipCUB <https://github.com/ROCm/hipCUB>`_
+    * - `hipCUB <https://github.com/ROCm/hipCUB>`__
       - :version-ref:`hipCUB rocm_version`
       - Provides a C++ template library for parallel algorithms for reduction,
         scan, sort and select.
       - Supports operations like ``tf.reduce_sum``, ``tf.cumsum``, ``tf.sort``
         and other tensor operations in TensorFlow, especially those involving
         scanning, sorting, and filtering.
-    * - `hipFFT <https://github.com/ROCm/hipFFT>`_
+    * - `hipFFT <https://github.com/ROCm/hipFFT>`__
       - :version-ref:`hipFFT rocm_version`
       - Accelerates Fast Fourier Transforms (FFT) for signal processing tasks.
       - Used for operations like signal processing, image filtering, and
         certain types of neural networks requiring FFT-based transformations.
-    * - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`_
+    * - `hipSOLVER <https://github.com/ROCm/hipSOLVER>`__
       - :version-ref:`hipSOLVER rocm_version`
       - Provides GPU-accelerated direct linear solvers for dense and sparse
         systems.
       - Optimizes linear algebra functions such as solving systems of linear
         equations, often used in optimization and training tasks.
-    * - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`_
+    * - `hipSPARSE <https://github.com/ROCm/hipSPARSE>`__
       - :version-ref:`hipSPARSE rocm_version`
       - Optimizes sparse matrix operations for efficient computations on sparse
         data.
       - Accelerates sparse matrix operations in models with sparse weight
         matrices or activations, commonly used in neural networks.
-    * - `MIOpen <https://github.com/ROCm/MIOpen>`_
+    * - `MIOpen <https://github.com/ROCm/MIOpen>`__
       - :version-ref:`MIOpen rocm_version`
       - Provides optimized deep learning primitives such as convolutions,
         pooling,
@@ -251,13 +184,13 @@ are available in ROCm :version:`rocm_version`.
       - Speeds up convolutional neural networks (CNNs) and other layers. Used
         in TensorFlow for layers like ``tf.nn.conv2d``, ``tf.nn.relu``, and
         ``tf.nn.lstm_cell``.
-    * - `RCCL <https://github.com/ROCm/rccl>`_
+    * - `RCCL <https://github.com/ROCm/rccl>`__
       - :version-ref:`RCCL rocm_version`
       - Optimizes for multi-GPU communication for operations like AllReduce and
         Broadcast.
       - Distributed data parallel training (``tf.distribute.MirroredStrategy``).
         Handles communication in multi-GPU setups.
-    * - `rocThrust <https://github.com/ROCm/rocThrust>`_
+    * - `rocThrust <https://github.com/ROCm/rocThrust>`__
       - :version-ref:`rocThrust rocm_version`
       - Provides a C++ template library for parallel algorithms like sorting,
         reduction, and scanning.
@@ -278,7 +211,7 @@ The data type of a tensor is specified using the ``dtype`` attribute or
 argument, and TensorFlow supports a wide range of data types for different use
 cases.
 
-The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`_
+The basic, single data types of `tf.dtypes <https://www.tensorflow.org/api_docs/python/tf/dtypes>`__
 are as follows:
 
 .. list-table::
@@ -550,7 +483,7 @@ Use cases and recommendations
 ===============================================================================
 
 * The `Training a Neural Collaborative Filtering (NCF) Recommender on an AMD
-  GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`_
+  GPU <https://rocm.blogs.amd.com/artificial-intelligence/ncf/README.html>`__
   blog post discusses training an NCF recommender system using TensorFlow. It
   explains how NCF improves traditional collaborative filtering methods by
   leveraging neural networks to model non-linear user-item interactions. The
@@ -559,7 +492,7 @@ Use cases and recommendations
   purchasing) and how it addresses challenges like the lack of negative values.
 
 * The `Creating a PyTorch/TensorFlow code environment on AMD GPUs
-  <https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`_
+  <https://rocm.blogs.amd.com/software-tools-optimization/pytorch-tensorflow-env/README.html>`__
   blog post provides instructions for creating a machine learning environment
   for PyTorch and TensorFlow on AMD GPUs using ROCm. It covers steps like
   installing the libraries, cloning code repositories, installing dependencies,
@@ -568,4 +501,4 @@ Use cases and recommendations
   for a better experience on AMD GPUs. This guide aims to help data scientists
   and ML practitioners adapt their code for AMD GPUs.
 
-For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`_.
+For more use cases and recommendations, see the `ROCm Tensorflow blog posts <https://rocm.blogs.amd.com/blog/tag/tensorflow.html>`__.

docs/compatibility/ml-compatibility/verl-compatibility.rst

@@ -0,0 +1,86 @@
+: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>`_

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

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

docs/conf.py

@@ -12,6 +12,54 @@ from pathlib import Path
 shutil.copy2("../RELEASE.md", "./about/release-notes.md")
 shutil.copy2("../CHANGELOG.md", "./release/changelog.md")
 
+# Mark the consolidated changelog as orphan to prevent Sphinx from warning about missing toctree entries
+with open("./release/changelog.md", "r+") as file:
+    content = file.read()
+    file.seek(0)
+    file.write(":orphan:\n" + content)
+
+# Replace GitHub-style [!ADMONITION]s with Sphinx-compatible ```{admonition} blocks
+with open("./release/changelog.md", "r") as file:
+    lines = file.readlines()
+
+    modified_lines = []
+    in_admonition_section = False
+
+    # Map for matching the specific admonition type to its corresponding Sphinx markdown syntax
+    admonition_types = {
+        '> [!NOTE]': '```{note}',
+        '> [!TIP]': '```{tip}',
+        '> [!IMPORTANT]': '```{important}',
+        '> [!WARNING]': '```{warning}',
+        '> [!CAUTION]': '```{caution}'
+    }
+
+    for line in lines:
+        if any(line.startswith(k) for k in admonition_types):
+            for key in admonition_types:
+                if(line.startswith(key)):
+                    modified_lines.append(admonition_types[key] + '\n')
+                    break
+            in_admonition_section = True
+        elif in_admonition_section:
+            if line.strip() == '':
+                # If we encounter an empty line, close the admonition section
+                modified_lines.append('```\n\n')  # Close the admonition block
+                in_admonition_section = False
+            else:
+                modified_lines.append(line.lstrip('> '))
+        else:
+            modified_lines.append(line)
+
+    # In case the file ended while still in a admonition section, close it
+    if in_admonition_section:
+        modified_lines.append('```')
+
+    file.close()
+
+    with open("./release/changelog.md", 'w') as file:
+        file.writelines(modified_lines)
+
 os.system("mkdir -p ../_readthedocs/html/downloads")
 os.system("cp compatibility/compatibility-matrix-historical-6.0.csv ../_readthedocs/html/downloads/compatibility-matrix-historical-6.0.csv")
 
@@ -34,20 +82,24 @@ 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 = "6.4.1"
-release = "6.4.1"
+version = "6.4.2"
+release = "6.4.2"
 setting_all_article_info = True
 all_article_info_os = ["linux", "windows"]
 all_article_info_author = ""
 
 # pages with specific settings
 article_pages = [
-    {"file": "about/release-notes", "os": ["linux"], "date": "2025-05-07"},
+    {"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": "how-to/deep-learning-rocm", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
@@ -57,10 +109,22 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/prerequisite-system-validation", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/megatron-lm", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/scale-model-training", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/megatron-lm", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-history", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v24.12-dev", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.3", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.4", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.3", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/fine-tuning/overview", "os": ["linux"]},
@@ -72,7 +136,16 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/inference/hugging-face-models", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/llm-inference-frameworks", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/vllm", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-history", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.4.3", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.4", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.6.6", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.7.3-20250325", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.3-20250415", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250513", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.8.5-20250521", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250605", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250702", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
 
@@ -129,6 +202,7 @@ html_theme_options = {"link_main_doc": False}
 redirects = {"reference/openmp/openmp": "../../about/compatibility/openmp.html"}
 
 numfig = False
+suppress_warnings = ["autosectionlabel.*"]
 
 html_context = {
     "project_path" : {project_path},

docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.0.1_20250605-benchmark-models.yaml

@@ -0,0 +1,162 @@
+vllm_benchmark:
+  unified_docker:
+    latest:
+      pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605
+      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c
+      rocm_version: 6.4.1
+      vllm_version: 0.9.0.1 (0.9.0.2.dev108+g71faa1880.rocm641)
+      pytorch_version: 2.7.0+gitf717b2a
+      hipblaslt_version: 0.15
+  model_groups:
+    - group: Meta Llama
+      tag: llama
+      models:
+      - model: Llama 3.1 8B
+        mad_tag: pyt_vllm_llama-3.1-8b
+        model_repo: meta-llama/Llama-3.1-8B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: float16
+      - model: Llama 3.1 70B
+        mad_tag: pyt_vllm_llama-3.1-70b
+        model_repo: meta-llama/Llama-3.1-70B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+        precision: float16
+      - model: Llama 3.1 405B
+        mad_tag: pyt_vllm_llama-3.1-405b
+        model_repo: meta-llama/Llama-3.1-405B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
+        precision: float16
+      - model: Llama 2 7B
+        mad_tag: pyt_vllm_llama-2-7b
+        model_repo: meta-llama/Llama-2-7b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
+        precision: float16
+      - model: Llama 2 70B
+        mad_tag: pyt_vllm_llama-2-70b
+        model_repo: meta-llama/Llama-2-70b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+        precision: float16
+      - model: Llama 3.1 8B FP8
+        mad_tag: pyt_vllm_llama-3.1-8b_fp8
+        model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
+        precision: float8
+      - model: Llama 3.1 70B FP8
+        mad_tag: pyt_vllm_llama-3.1-70b_fp8
+        model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
+        precision: float8
+      - model: Llama 3.1 405B FP8
+        mad_tag: pyt_vllm_llama-3.1-405b_fp8
+        model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
+        precision: float8
+    - group: Mistral AI
+      tag: mistral
+      models:
+      - model: Mixtral MoE 8x7B
+        mad_tag: pyt_vllm_mixtral-8x7b
+        model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
+        precision: float16
+      - model: Mixtral MoE 8x22B
+        mad_tag: pyt_vllm_mixtral-8x22b
+        model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
+        precision: float16
+      - model: Mistral 7B
+        mad_tag: pyt_vllm_mistral-7b
+        model_repo: mistralai/Mistral-7B-Instruct-v0.3
+        url: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
+        precision: float16
+      - model: Mixtral MoE 8x7B FP8
+        mad_tag: pyt_vllm_mixtral-8x7b_fp8
+        model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        precision: float8
+      - model: Mixtral MoE 8x22B FP8
+        mad_tag: pyt_vllm_mixtral-8x22b_fp8
+        model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        precision: float8
+      - model: Mistral 7B FP8
+        mad_tag: pyt_vllm_mistral-7b_fp8
+        model_repo: amd/Mistral-7B-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV
+        precision: float8
+    - group: Qwen
+      tag: qwen
+      models:
+      - model: Qwen2 7B
+        mad_tag: pyt_vllm_qwen2-7b
+        model_repo: Qwen/Qwen2-7B-Instruct
+        url: https://huggingface.co/Qwen/Qwen2-7B-Instruct
+        precision: float16
+      - model: Qwen2 72B
+        mad_tag: pyt_vllm_qwen2-72b
+        model_repo: Qwen/Qwen2-72B-Instruct
+        url: https://huggingface.co/Qwen/Qwen2-72B-Instruct
+        precision: float16
+      - model: QwQ-32B
+        mad_tag: pyt_vllm_qwq-32b
+        model_repo: Qwen/QwQ-32B
+        url: https://huggingface.co/Qwen/QwQ-32B
+        precision: float16
+        tunableop: true
+    - group: Databricks DBRX
+      tag: dbrx
+      models:
+      - model: DBRX Instruct
+        mad_tag: pyt_vllm_dbrx-instruct
+        model_repo: databricks/dbrx-instruct
+        url: https://huggingface.co/databricks/dbrx-instruct
+        precision: float16
+      - model: DBRX Instruct FP8
+        mad_tag: pyt_vllm_dbrx_fp8
+        model_repo: amd/dbrx-instruct-FP8-KV
+        url: https://huggingface.co/amd/dbrx-instruct-FP8-KV
+        precision: float8
+    - group: Google Gemma
+      tag: gemma
+      models:
+      - model: Gemma 2 27B
+        mad_tag: pyt_vllm_gemma-2-27b
+        model_repo: google/gemma-2-27b
+        url: https://huggingface.co/google/gemma-2-27b
+        precision: float16
+    - group: Cohere
+      tag: cohere
+      models:
+      - model: C4AI Command R+ 08-2024
+        mad_tag: pyt_vllm_c4ai-command-r-plus-08-2024
+        model_repo: CohereForAI/c4ai-command-r-plus-08-2024
+        url: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024
+        precision: float16
+      - model: C4AI Command R+ 08-2024 FP8
+        mad_tag: pyt_vllm_command-r-plus_fp8
+        model_repo: amd/c4ai-command-r-plus-FP8-KV
+        url: https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV
+        precision: float8
+    - group: DeepSeek
+      tag: deepseek
+      models:
+      - model: DeepSeek MoE 16B
+        mad_tag: pyt_vllm_deepseek-moe-16b-chat
+        model_repo: deepseek-ai/deepseek-moe-16b-chat
+        url: https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat
+        precision: float16
+    - group: Microsoft Phi
+      tag: phi
+      models:
+      - model: Phi-4
+        mad_tag: pyt_vllm_phi-4
+        model_repo: microsoft/phi-4
+        url: https://huggingface.co/microsoft/phi-4
+    - group: TII Falcon
+      tag: falcon
+      models:
+      - model: Falcon 180B
+        mad_tag: pyt_vllm_falcon-180b
+        model_repo: tiiuae/falcon-180B
+        url: https://huggingface.co/tiiuae/falcon-180B
+        precision: float16

docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250702-benchmark-models.yaml

@@ -0,0 +1,163 @@
+vllm_benchmark:
+  unified_docker:
+    latest:
+      # TODO: update me
+      pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.1_20250702
+      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250702/images/sha256-45068a2079cb8df554ed777141bf0c67d6627c470a897256e60c9f262677faab
+      rocm_version: 6.4.1
+      vllm_version: 0.9.1 (0.9.2.dev206+gb335519f2.rocm641)
+      pytorch_version: 2.7.0+gitf717b2a
+      hipblaslt_version: 0.15
+  model_groups:
+    - group: Meta Llama
+      tag: llama
+      models:
+      - model: Llama 3.1 8B
+        mad_tag: pyt_vllm_llama-3.1-8b
+        model_repo: meta-llama/Llama-3.1-8B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: float16
+      - model: Llama 3.1 70B
+        mad_tag: pyt_vllm_llama-3.1-70b
+        model_repo: meta-llama/Llama-3.1-70B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+        precision: float16
+      - model: Llama 3.1 405B
+        mad_tag: pyt_vllm_llama-3.1-405b
+        model_repo: meta-llama/Llama-3.1-405B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-405B-Instruct
+        precision: float16
+      - model: Llama 2 7B
+        mad_tag: pyt_vllm_llama-2-7b
+        model_repo: meta-llama/Llama-2-7b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
+        precision: float16
+      - model: Llama 2 70B
+        mad_tag: pyt_vllm_llama-2-70b
+        model_repo: meta-llama/Llama-2-70b-chat-hf
+        url: https://huggingface.co/meta-llama/Llama-2-70b-chat-hf
+        precision: float16
+      - model: Llama 3.1 8B FP8
+        mad_tag: pyt_vllm_llama-3.1-8b_fp8
+        model_repo: amd/Llama-3.1-8B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-8B-Instruct-FP8-KV
+        precision: float8
+      - model: Llama 3.1 70B FP8
+        mad_tag: pyt_vllm_llama-3.1-70b_fp8
+        model_repo: amd/Llama-3.1-70B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-70B-Instruct-FP8-KV
+        precision: float8
+      - model: Llama 3.1 405B FP8
+        mad_tag: pyt_vllm_llama-3.1-405b_fp8
+        model_repo: amd/Llama-3.1-405B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
+        precision: float8
+    - group: Mistral AI
+      tag: mistral
+      models:
+      - model: Mixtral MoE 8x7B
+        mad_tag: pyt_vllm_mixtral-8x7b
+        model_repo: mistralai/Mixtral-8x7B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1
+        precision: float16
+      - model: Mixtral MoE 8x22B
+        mad_tag: pyt_vllm_mixtral-8x22b
+        model_repo: mistralai/Mixtral-8x22B-Instruct-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x22B-Instruct-v0.1
+        precision: float16
+      - model: Mistral 7B
+        mad_tag: pyt_vllm_mistral-7b
+        model_repo: mistralai/Mistral-7B-Instruct-v0.3
+        url: https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3
+        precision: float16
+      - model: Mixtral MoE 8x7B FP8
+        mad_tag: pyt_vllm_mixtral-8x7b_fp8
+        model_repo: amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV
+        precision: float8
+      - model: Mixtral MoE 8x22B FP8
+        mad_tag: pyt_vllm_mixtral-8x22b_fp8
+        model_repo: amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mixtral-8x22B-Instruct-v0.1-FP8-KV
+        precision: float8
+      - model: Mistral 7B FP8
+        mad_tag: pyt_vllm_mistral-7b_fp8
+        model_repo: amd/Mistral-7B-v0.1-FP8-KV
+        url: https://huggingface.co/amd/Mistral-7B-v0.1-FP8-KV
+        precision: float8
+    - group: Qwen
+      tag: qwen
+      models:
+      - model: Qwen2 7B
+        mad_tag: pyt_vllm_qwen2-7b
+        model_repo: Qwen/Qwen2-7B-Instruct
+        url: https://huggingface.co/Qwen/Qwen2-7B-Instruct
+        precision: float16
+      - model: Qwen2 72B
+        mad_tag: pyt_vllm_qwen2-72b
+        model_repo: Qwen/Qwen2-72B-Instruct
+        url: https://huggingface.co/Qwen/Qwen2-72B-Instruct
+        precision: float16
+      - model: QwQ-32B
+        mad_tag: pyt_vllm_qwq-32b
+        model_repo: Qwen/QwQ-32B
+        url: https://huggingface.co/Qwen/QwQ-32B
+        precision: float16
+        tunableop: true
+    - group: Databricks DBRX
+      tag: dbrx
+      models:
+      - model: DBRX Instruct
+        mad_tag: pyt_vllm_dbrx-instruct
+        model_repo: databricks/dbrx-instruct
+        url: https://huggingface.co/databricks/dbrx-instruct
+        precision: float16
+      - model: DBRX Instruct FP8
+        mad_tag: pyt_vllm_dbrx_fp8
+        model_repo: amd/dbrx-instruct-FP8-KV
+        url: https://huggingface.co/amd/dbrx-instruct-FP8-KV
+        precision: float8
+    - group: Google Gemma
+      tag: gemma
+      models:
+      - model: Gemma 2 27B
+        mad_tag: pyt_vllm_gemma-2-27b
+        model_repo: google/gemma-2-27b
+        url: https://huggingface.co/google/gemma-2-27b
+        precision: float16
+    - group: Cohere
+      tag: cohere
+      models:
+      - model: C4AI Command R+ 08-2024
+        mad_tag: pyt_vllm_c4ai-command-r-plus-08-2024
+        model_repo: CohereForAI/c4ai-command-r-plus-08-2024
+        url: https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024
+        precision: float16
+      - model: C4AI Command R+ 08-2024 FP8
+        mad_tag: pyt_vllm_command-r-plus_fp8
+        model_repo: amd/c4ai-command-r-plus-FP8-KV
+        url: https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV
+        precision: float8
+    - group: DeepSeek
+      tag: deepseek
+      models:
+      - model: DeepSeek MoE 16B
+        mad_tag: pyt_vllm_deepseek-moe-16b-chat
+        model_repo: deepseek-ai/deepseek-moe-16b-chat
+        url: https://huggingface.co/deepseek-ai/deepseek-moe-16b-chat
+        precision: float16
+    - group: Microsoft Phi
+      tag: phi
+      models:
+      - model: Phi-4
+        mad_tag: pyt_vllm_phi-4
+        model_repo: microsoft/phi-4
+        url: https://huggingface.co/microsoft/phi-4
+    - group: TII Falcon
+      tag: falcon
+      models:
+      - model: Falcon 180B
+        mad_tag: pyt_vllm_falcon-180b
+        model_repo: tiiuae/falcon-180B
+        url: https://huggingface.co/tiiuae/falcon-180B
+        precision: float16

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

@@ -1,6 +1,6 @@
 pytorch_inference_benchmark:
   unified_docker:
-    latest: &rocm-pytorch-docker-latest
+    latest:
       pull_tag: rocm/pytorch:latest
       docker_hub_url:
       rocm_version:
@@ -31,3 +31,19 @@ pytorch_inference_benchmark:
         model_repo: genmo/mochi-1-preview
         url: https://huggingface.co/genmo/mochi-1-preview
         precision: float16
+    - group: Wan2.1
+      tag: wan
+      models:
+      - model: Wan2.1
+        mad_tag: pyt_wan2.1_inference
+        model_repo: Wan-AI/Wan2.1-T2V-14B
+        url: https://huggingface.co/Wan-AI/Wan2.1-T2V-14B
+        precision: bfloat16
+    - group: Janus-Pro
+      tag: janus-pro
+      models:
+      - model: Janus Pro 7B
+        mad_tag: pyt_janus_pro_inference
+        model_repo: deepseek-ai/Janus-Pro-7B
+        url: https://huggingface.co/deepseek-ai/Janus-Pro-7B
+        precision: bfloat16

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

@@ -0,0 +1,17 @@
+sglang_benchmark:
+  unified_docker:
+    latest:
+      pull_tag: lmsysorg/sglang:v0.4.5-rocm630
+      docker_hub_url: https://hub.docker.com/layers/lmsysorg/sglang/v0.4.5-rocm630/images/sha256-63d2cb760a237125daf6612464cfe2f395c0784e21e8b0ea37d551cd10d3c951
+      rocm_version: 6.3.0
+      sglang_version: 0.4.5 (0.4.5-rocm)
+      pytorch_version: 2.6.0a0+git8d4926e
+  model_groups:
+    - group: DeepSeek
+      tag: deepseek
+      models:
+      - model: DeepSeek-R1-Distill-Qwen-32B
+        mad_tag: pyt_sglang_deepseek-r1-distill-qwen-32b
+        model_repo: deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+        url: https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B
+        precision: bfloat16

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

@@ -1,10 +1,11 @@
 vllm_benchmark:
   unified_docker:
     latest:
-      pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.0.1_20250605
-      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.0.1_20250605/images/sha256-f48beeb3d72663a93c77211eb45273d564451447c097e060befa713d565fa36c
+      # TODO: update me
+      pull_tag: rocm/vllm:rocm6.4.1_vllm_0.9.1_20250715
+      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.4.1_vllm_0.9.1_20250715/images/sha256-4a429705fa95a58f6d20aceab43b1b76fa769d57f32d5d28bd3f4e030e2a78ea
       rocm_version: 6.4.1
-      vllm_version: 0.9.0.1 (0.9.0.2.dev108+g71faa1880.rocm641)
+      vllm_version: 0.9.1 (0.9.2.dev364+gb432b7a28.rocm641)
       pytorch_version: 2.7.0+gitf717b2a
       hipblaslt_version: 0.15
   model_groups:

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

@@ -1,29 +1,60 @@
-megatron-lm_benchmark:
-  model_groups:
-    - group: Meta Llama
-      tag: llama
-      models:
+dockers:
+  - pull_tag: rocm/megatron-lm:v25.6_py312
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0
+    components:
+      ROCm: 6.4.1
+      PyTorch: 2.8.0a0+git7d205b2
+      Python: 3.12
+      Transformer Engine: 2.1.0.dev0+8c4a512
+      hipBLASLt: 393e413
+      Triton: 3.3.0
+      RCCL: 2.23.4.7a84c5d
+    doc_name: Ubuntu 24.04 + Python 3.12
+  - pull_tag: rocm/megatron-lm:v25.6_py310
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6
+    components:
+      ROCm: 6.4.1
+      PyTorch: 2.8.0a0+git7d205b2
+      Python: "3.10"
+      Transformer Engine: 2.1.0.dev0+8c4a512
+      hipBLASLt: 393e413
+      Triton: 3.3.0
+      RCCL: 2.23.4.7a84c5d
+    doc_name: Ubuntu 22.04 + Python 3.10
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
       - model: Llama 3.3 70B
         mad_tag: pyt_megatron_lm_train_llama-3.3-70b
       - model: Llama 3.1 8B
         mad_tag: pyt_megatron_lm_train_llama-3.1-8b
       - model: Llama 3.1 70B
         mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 3.1 70B (proxy)
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b-proxy
       - model: Llama 2 7B
         mad_tag: pyt_megatron_lm_train_llama-2-7b
       - model: Llama 2 70B
         mad_tag: pyt_megatron_lm_train_llama-2-70b
-    - group: DeepSeek
-      tag: deepseek
-      models:
-      - model: DeepSeek-V3
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
         mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
       - model: DeepSeek-V2-Lite
         mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
-    - group: Mistral AI
-      tag: mistral
-      models:
+  - group: Mistral AI
+    tag: mistral
+    models:
       - model: Mixtral 8x7B
         mad_tag: pyt_megatron_lm_train_mixtral-8x7b
-      - model: Mixtral 8x22B
+      - model: Mixtral 8x22B (proxy)
         mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b

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

@@ -0,0 +1,29 @@
+megatron-lm_benchmark:
+  model_groups:
+    - group: Meta Llama
+      tag: llama
+      models:
+      - model: Llama 3.3 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.3-70b
+      - model: Llama 3.1 8B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-8b
+      - model: Llama 3.1 70B
+        mad_tag: pyt_megatron_lm_train_llama-3.1-70b
+      - model: Llama 2 7B
+        mad_tag: pyt_megatron_lm_train_llama-2-7b
+      - model: Llama 2 70B
+        mad_tag: pyt_megatron_lm_train_llama-2-70b
+    - group: DeepSeek
+      tag: deepseek
+      models:
+      - model: DeepSeek-V3
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+    - group: Mistral AI
+      tag: mistral
+      models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy

docs/how-to/deep-learning-rocm.rst

@@ -17,6 +17,10 @@ features for these ROCm-enabled deep learning frameworks.
 * :doc:`PyTorch compatibility <../compatibility/ml-compatibility/pytorch-compatibility>`
 * :doc:`TensorFlow compatibility <../compatibility/ml-compatibility/tensorflow-compatibility>`
 * :doc:`JAX compatibility <../compatibility/ml-compatibility/jax-compatibility>`
+* :doc:`verl compatibility <../compatibility/ml-compatibility/verl-compatibility>`
+* :doc:`Stanford Megatron-LM compatibility <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
+* :doc:`DGL compatibility <../compatibility/ml-compatibility/dgl-compatibility>`
+* :doc:`Megablocks compatibility <../compatibility/ml-compatibility/megablocks-compatibility>`
 
 This chart steps through typical installation workflows for installing deep learning frameworks for ROCm.
 
@@ -29,6 +33,10 @@ See the installation instructions to get started.
 * :doc:`PyTorch for ROCm <rocm-install-on-linux:install/3rd-party/pytorch-install>`
 * :doc:`TensorFlow for ROCm <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
 * :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
+* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
+* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
+* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/dgl-install>`
+* :doc:`Megablocks for ROCm <rocm-install-on-linux:install/3rd-party/megablocks-install>`
 
 .. note::
 

docs/how-to/gpu-performance/mi300x.rst

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

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

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

docs/how-to/rocm-for-ai/index.rst

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

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

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

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/sglang-history.rst

@@ -0,0 +1,25 @@
+:orphan:
+
+****************************************************
+SGLang inference performance testing version history
+****************************************************
+
+This table lists previous versions of the ROCm SGLang inference performance
+testing environment. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Docker image tag
+     - Components
+     - Resources
+
+   * - ``lmsysorg/sglang:v0.4.5-rocm630``
+     - 
+       * ROCm 6.3.0
+       * SGLang 0.4.5
+       * PyTorch 2.6.0
+     - 
+       * :doc:`Documentation <../sglang>`
+       * `Docker Hub <https://hub.docker.com/layers/lmsysorg/sglang/v0.4.5-rocm630/images/sha256-63d2cb760a237125daf6612464cfe2f395c0784e21e8b0ea37d551cd10d3c951>`__

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

@@ -59,7 +59,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 
    To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
    might hang until the periodic balancing is finalized. For more information,
-   see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
    .. code-block:: shell
 
@@ -80,11 +80,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 Once setup is complete, you can choose between two options to reproduce the
 benchmark results:
 
--  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
+-  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v043>`
 
--  :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
+-  :ref:`Standalone benchmarking <vllm-benchmark-standalone-v043>`
 
-.. _vllm-benchmark-mad:
+.. _vllm-benchmark-mad-v043:
 
 MAD-integrated benchmarking
 ===========================
@@ -112,7 +112,7 @@ model are collected in the following path: ``~/MAD/reports_float16/``
 
 Although the following eight models are pre-configured to collect latency and
 throughput performance data, users can also change the benchmarking parameters.
-Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
+Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v043>` section.
 
 Available models
 ----------------
@@ -136,7 +136,7 @@ Available models
 
    * ``pyt_vllm_jais-30b``
 
-.. _vllm-benchmark-standalone:
+.. _vllm-benchmark-standalone-v043:
 
 Standalone benchmarking
 =======================
@@ -167,14 +167,14 @@ Command
 ^^^^^^^^^^^^^^^^^^^^^^^^^
 
 To start the benchmark, use the following command with the appropriate options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-options-v043>` for the list of
 options and their descriptions.
 
 .. code-block:: shell
 
    ./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
 
-See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
+See the :ref:`examples <vllm-benchmark-run-benchmark-v043>` for more information.
 
 .. note::
 
@@ -193,7 +193,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
       # pass your HF_TOKEN
       export HF_TOKEN=$your_personal_hf_token
 
-.. _vllm-benchmark-standalone-options:
+.. _vllm-benchmark-standalone-options-v043:
 
 Options
 ^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -265,13 +265,13 @@ Options
      - ``float16``
      - Data type
 
-.. _vllm-benchmark-run-benchmark:
+.. _vllm-benchmark-run-benchmark-v043:
 
 Running the benchmark on the MI300X accelerator
 -----------------------------------------------
 
 Here are some examples of running the benchmark with various options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-options-v043>` for the list of
 options and their descriptions.
 
 Latency benchmark example
@@ -322,22 +322,22 @@ Further reading
 ===============
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see :doc:`/how-to/system-optimization/mi300x`.
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Using ROCm for AI </how-to/rocm-for-ai/index>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Fine-tuning LLMs and inference optimization </how-to/llm-fine-tuning-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- For a list of other ready-made Docker images for ROCm, see the
-  :doc:`Docker image support matrix <rocm-install-on-linux:reference/docker-image-support-matrix>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -82,7 +82,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 
    To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
    might hang until the periodic balancing is finalized. For more information,
-   see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
    .. code-block:: shell
 
@@ -103,11 +103,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 Once setup is complete, you can choose between two options to reproduce the
 benchmark results:
 
--  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
+-  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v064>`
 
--  :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
+-  :ref:`Standalone benchmarking <vllm-benchmark-standalone-v064>`
 
-.. _vllm-benchmark-mad:
+.. _vllm-benchmark-mad-v064:
 
 MAD-integrated benchmarking
 ===========================
@@ -135,7 +135,7 @@ model are collected in the following path: ``~/MAD/reports_float16/``.
 
 Although the following models are preconfigured to collect latency and
 throughput performance data, you can also change the benchmarking parameters.
-Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
+Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v064>` section.
 
 Available models
 ----------------
@@ -177,7 +177,7 @@ Available models
 
    * ``pyt_vllm_mixtral-8x22b_fp8``
 
-.. _vllm-benchmark-standalone:
+.. _vllm-benchmark-standalone-v064:
 
 Standalone benchmarking
 =======================
@@ -203,14 +203,14 @@ Command
 -------
 
 To start the benchmark, use the following command with the appropriate options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-v064-options>` for the list of
 options and their descriptions.
 
 .. code-block:: shell
 
    ./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
 
-See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
+See the :ref:`examples <vllm-benchmark-run-benchmark-v064>` for more information.
 
 .. note::
 
@@ -229,7 +229,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
       # pass your HF_TOKEN
       export HF_TOKEN=$your_personal_hf_token
 
-.. _vllm-benchmark-standalone-options:
+.. _vllm-benchmark-standalone-v064-options:
 
 Options
 -------
@@ -330,13 +330,13 @@ Options
      - ``float16`` or ``float8``
      - Data type
 
-.. _vllm-benchmark-run-benchmark:
+.. _vllm-benchmark-run-benchmark-v064:
 
 Running the benchmark on the MI300X accelerator
 -----------------------------------------------
 
 Here are some examples of running the benchmark with various options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-v064-options>` for the list of
 options and their descriptions.
 
 Example 1: latency benchmark
@@ -392,25 +392,22 @@ Further reading
 ===============
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`/how-to/tuning-guides/mi300x/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see :doc:`/how-to/system-optimization/mi300x`.
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Using ROCm for AI </how-to/rocm-for-ai/index>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Fine-tuning LLMs and inference optimization </how-to/llm-fine-tuning-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- For a list of other ready-made Docker images for ROCm, see the
-  :doc:`Docker image support matrix <rocm-install-on-linux:reference/docker-image-support-matrix>`.
-
-- To compare with the previous version of the ROCm vLLM Docker image for performance validation, refer to
-  `LLM inference performance validation on AMD Instinct MI300X (ROCm 6.2.0) <https://rocm.docs.amd.com/en/docs-6.2.0/how-to/performance-validation/mi300x/vllm-benchmark.html>`_.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -31,8 +31,8 @@ accelerator and includes the following components:
 With this Docker image, you can quickly validate the expected inference
 performance numbers for the MI300X accelerator. This topic also provides tips on
 optimizing performance with popular AI models. For more information, see the lists of
-:ref:`available models for MAD-integrated benchmarking <vllm-benchmark-mad-models>`
-and :ref:`standalone benchmarking <vllm-benchmark-standalone-options>`.
+:ref:`available models for MAD-integrated benchmarking <vllm-benchmark-mad-v066-models>`
+and :ref:`standalone benchmarking <vllm-benchmark-standalone-v066-options>`.
 
 .. _vllm-benchmark-vllm:
 
@@ -55,7 +55,7 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 
    To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
    might hang until the periodic balancing is finalized. For more information,
-   see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
    .. code-block:: shell
 
@@ -76,11 +76,11 @@ MI300X accelerator with the prebuilt vLLM Docker image.
 Once the setup is complete, choose between two options to reproduce the
 benchmark results:
 
--  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad>`
+-  :ref:`MAD-integrated benchmarking <vllm-benchmark-mad-v066>`
 
--  :ref:`Standalone benchmarking <vllm-benchmark-standalone>`
+-  :ref:`Standalone benchmarking <vllm-benchmark-standalone-v066>`
 
-.. _vllm-benchmark-mad:
+.. _vllm-benchmark-mad-v066:
 
 MAD-integrated benchmarking
 ===========================
@@ -108,9 +108,9 @@ model are collected in the following path: ``~/MAD/reports_float16/``.
 
 Although the following models are preconfigured to collect latency and
 throughput performance data, you can also change the benchmarking parameters.
-Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone>` section.
+Refer to the :ref:`Standalone benchmarking <vllm-benchmark-standalone-v066>` section.
 
-.. _vllm-benchmark-mad-models:
+.. _vllm-benchmark-mad-v066-models:
 
 Available models
 ----------------
@@ -134,10 +134,10 @@ Available models
    * - `Llama 3.2 11B Vision <https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct>`_
      - ``pyt_vllm_llama-3.2-11b-vision-instruct``
 
-   * - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
+   * - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__
      - ``pyt_vllm_llama-2-7b``
 
-   * - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
+   * - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`__
      - ``pyt_vllm_llama-2-70b``
 
    * - `Mixtral MoE 8x7B <https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1>`_
@@ -194,7 +194,7 @@ Available models
    * - `C4AI Command R+ 08-2024 FP8 <https://huggingface.co/amd/c4ai-command-r-plus-FP8-KV>`_
      - ``pyt_vllm_command-r-plus_fp8``
 
-.. _vllm-benchmark-standalone:
+.. _vllm-benchmark-standalone-v066:
 
 Standalone benchmarking
 =======================
@@ -220,14 +220,14 @@ Command
 -------
 
 To start the benchmark, use the following command with the appropriate options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-v066-options>` for the list of
 options and their descriptions.
 
 .. code-block:: shell
 
    ./vllm_benchmark_report.sh -s $test_option -m $model_repo -g $num_gpu -d $datatype
 
-See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
+See the :ref:`examples <vllm-benchmark-run-benchmark-v066>` for more information.
 
 .. note::
 
@@ -246,7 +246,7 @@ See the :ref:`examples <vllm-benchmark-run-benchmark>` for more information.
       # pass your HF_TOKEN
       export HF_TOKEN=$your_personal_hf_token
 
-.. _vllm-benchmark-standalone-options:
+.. _vllm-benchmark-standalone-v066-options:
 
 Options and available models
 ----------------------------
@@ -289,11 +289,11 @@ Options and available models
 
    * -
      - ``meta-llama/Llama-2-7b-chat-hf``
-     - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`_
+     - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-7b-chat-hf>`__
 
    * -
      - ``meta-llama/Llama-2-70b-chat-hf``
-     - `Llama 2 7B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`_
+     - `Llama 2 70B <https://huggingface.co/meta-llama/Llama-2-70b-chat-hf>`__
 
    * -
      - ``mistralai/Mixtral-8x7B-Instruct-v0.1``
@@ -375,13 +375,13 @@ Options and available models
      - ``float16`` or ``float8``
      - Data type
 
-.. _vllm-benchmark-run-benchmark:
+.. _vllm-benchmark-run-benchmark-v066:
 
 Running the benchmark on the MI300X accelerator
 -----------------------------------------------
 
 Here are some examples of running the benchmark with various options.
-See :ref:`Options <vllm-benchmark-standalone-options>` for the list of
+See :ref:`Options <vllm-benchmark-standalone-v066-options>` for the list of
 options and their descriptions.
 
 Example 1: latency benchmark
@@ -437,22 +437,22 @@ Further reading
 ===============
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see :doc:`../../system-optimization/mi300x`.
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -36,10 +36,10 @@ vLLM inference performance testing
    * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
 
    With this Docker image, you can quickly test the :ref:`expected
-   inference performance numbers <vllm-benchmark-performance-measurements>` for
+   inference performance numbers <vllm-benchmark-performance-measurements-v073>` for
    MI300X series accelerators.
 
-   .. _vllm-benchmark-available-models:
+   .. _vllm-benchmark-available-models-v073:
 
    Available models
    ================
@@ -95,7 +95,7 @@ vLLM inference performance testing
       See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
       more information.
 
-   .. _vllm-benchmark-performance-measurements:
+   .. _vllm-benchmark-performance-measurements-v073:
 
    Performance measurements
    ========================
@@ -109,7 +109,7 @@ vLLM inference performance testing
 
       The performance data presented in
       `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`_.
+      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
       The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
    Advanced features and known issues
@@ -130,7 +130,7 @@ vLLM inference performance testing
 
       To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
       might hang until the periodic balancing is finalized. For more information,
-      see :ref:`AMD Instinct MI300X system optimization <mi300x-disable-numa>`.
+      see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
 
       .. code-block:: shell
 
@@ -154,7 +154,7 @@ vLLM inference performance testing
    Once the setup is complete, choose between two options to reproduce the
    benchmark results:
 
-   .. _vllm-benchmark-mad:
+   .. _vllm-benchmark-mad-v073:
 
    {% for model_group in model_groups %}
       {% for model in model_group.models %}
@@ -175,7 +175,7 @@ vLLM inference performance testing
                pip install -r requirements.txt
 
             Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
-            using one GPU with the ``{{model.precision}}`` data type on the host machine.
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
 
             .. code-block:: shell
 
@@ -186,7 +186,7 @@ vLLM inference performance testing
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
             model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
 
-            Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
+            Although the :ref:`available models <vllm-benchmark-available-models-v073>` are preconfigured
             to collect latency and throughput performance data, you can also change the benchmarking
             parameters. See the standalone benchmarking tab for more information.
 
@@ -264,7 +264,7 @@ vLLM inference performance testing
 
             * Latency benchmark
 
-              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with the ``{{model.precision}}`` data type.
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with the :literal:`{{model.precision}}` data type.
 
               .. code-block::
 
@@ -274,7 +274,7 @@ vLLM inference performance testing
 
             * Throughput benchmark
 
-              Use this command to throughput the latency of the {{model.model}} model on eight GPUs with the ``{{model.precision}}`` data type.
+              Use this command to throughput the latency of the {{model.model}} model on eight GPUs with the :literal:`{{model.precision}}` data type.
 
               .. code-block:: shell
 
@@ -305,22 +305,22 @@ Further reading
 ===============
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -1,3 +1,5 @@
+:orphan:
+
 .. meta::
    :description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
                  ROCm vLLM Docker image.
@@ -29,10 +31,10 @@ vLLM inference performance testing
    * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
 
    With this Docker image, you can quickly test the :ref:`expected
-   inference performance numbers <vllm-benchmark-performance-measurements>` for
+   inference performance numbers <vllm-benchmark-performance-measurements-v083>` for
    MI300X series accelerators.
 
-   .. _vllm-benchmark-available-models:
+   .. _vllm-benchmark-available-models-v083:
 
    Supported models
    ================
@@ -88,7 +90,7 @@ vLLM inference performance testing
       See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
       more information.
 
-   .. _vllm-benchmark-performance-measurements:
+   .. _vllm-benchmark-performance-measurements-v083:
 
    Performance measurements
    ========================
@@ -102,7 +104,7 @@ vLLM inference performance testing
 
       The performance data presented in
       `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`_.
+      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
       The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
    Advanced features and known issues
@@ -170,7 +172,7 @@ vLLM inference performance testing
                pip install -r requirements.txt
 
             Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
-            using one GPU with the ``{{model.precision}}`` data type on the host machine.
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
 
             .. code-block:: shell
 
@@ -181,7 +183,7 @@ vLLM inference performance testing
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
             model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
 
-            Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
+            Although the :ref:`available models <vllm-benchmark-available-models-v083>` are preconfigured
             to collect latency and throughput performance data, you can also change the benchmarking
             parameters. See the standalone benchmarking tab for more information.
 
@@ -278,7 +280,7 @@ vLLM inference performance testing
 
             * Latency benchmark
 
-              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block::
 
@@ -288,7 +290,7 @@ vLLM inference performance testing
 
             * Throughput benchmark
 
-              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block:: shell
 
@@ -319,22 +321,22 @@ Further reading
 ===============
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -36,10 +36,10 @@ vLLM inference performance testing
    * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
 
    With this Docker image, you can quickly test the :ref:`expected
-   inference performance numbers <vllm-benchmark-performance-measurements>` for
+   inference performance numbers <vllm-benchmark-performance-measurements-v085-20250513>` for
    MI300X series accelerators.
 
-   .. _vllm-benchmark-available-models:
+   .. _vllm-benchmark-available-models-v085-20250513:
 
    Supported models
    ================
@@ -99,7 +99,7 @@ vLLM inference performance testing
       See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
       more information.
 
-   .. _vllm-benchmark-performance-measurements:
+   .. _vllm-benchmark-performance-measurements-v085-20250513:
 
    Performance measurements
    ========================
@@ -113,7 +113,7 @@ vLLM inference performance testing
 
       The performance data presented in
       `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`_.
+      only reflects the :doc:`latest version of this inference benchmarking environment <../vllm>`.
       The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
    Advanced features and known issues
@@ -181,7 +181,7 @@ vLLM inference performance testing
                pip install -r requirements.txt
 
             Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
-            using one GPU with the ``{{model.precision}}`` data type on the host machine.
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
 
             .. code-block:: shell
 
@@ -192,7 +192,7 @@ vLLM inference performance testing
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
             model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
 
-            Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
+            Although the :ref:`available models <vllm-benchmark-available-models-v085-20250513>` are preconfigured
             to collect latency and throughput performance data, you can also change the benchmarking
             parameters. See the standalone benchmarking tab for more information.
 
@@ -289,7 +289,7 @@ vLLM inference performance testing
 
             * Latency benchmark
 
-              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block::
 
@@ -299,7 +299,7 @@ vLLM inference performance testing
 
             * Throughput benchmark
 
-              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block:: shell
 
@@ -333,19 +333,19 @@ Further reading
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../../../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <../../hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../../../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../../../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================

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

@@ -36,10 +36,10 @@ vLLM inference performance testing
    * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
 
    With this Docker image, you can quickly test the :ref:`expected
-   inference performance numbers <vllm-benchmark-performance-measurements>` for
+   inference performance numbers <vllm-benchmark-performance-measurements-v085-20250521>` for
    MI300X series accelerators.
 
-   .. _vllm-benchmark-available-models:
+   .. _vllm-benchmark-available-models-v085-20250521:
 
    Supported models
    ================
@@ -99,7 +99,7 @@ vLLM inference performance testing
       See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
       more information.
 
-   .. _vllm-benchmark-performance-measurements:
+   .. _vllm-benchmark-performance-measurements-v085-20250521:
 
    Performance measurements
    ========================
@@ -181,7 +181,7 @@ vLLM inference performance testing
                pip install -r requirements.txt
 
             Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
-            using one GPU with the ``{{model.precision}}`` data type on the host machine.
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
 
             .. code-block:: shell
 
@@ -192,7 +192,7 @@ vLLM inference performance testing
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
             model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
 
-            Although the :ref:`available models <vllm-benchmark-available-models>` are preconfigured
+            Although the :ref:`available models <vllm-benchmark-available-models-v085-20250521>` are preconfigured
             to collect latency and throughput performance data, you can also change the benchmarking
             parameters. See the standalone benchmarking tab for more information.
 
@@ -289,7 +289,7 @@ vLLM inference performance testing
 
             * Latency benchmark
 
-              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block::
 
@@ -299,7 +299,7 @@ vLLM inference performance testing
 
             * Throughput benchmark
 
-              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block:: shell
 
@@ -333,22 +333,23 @@ Further reading
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <../hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================
 
 See :doc:`vllm-history` to find documentation for previous releases
 of the ``ROCm/vllm`` Docker image.
+

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.0.1-20250605.rst

@@ -0,0 +1,353 @@
+:orphan:
+
+.. meta::
+   :description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
+                 ROCm vLLM Docker image.
+   :keywords: model, MAD, automation, dashboarding, validate
+
+**********************************
+vLLM inference performance testing
+**********************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   inference performance documentation. See :doc:`../vllm` for the latest version.
+
+.. _vllm-benchmark-unified-docker:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.0.1_20250605-benchmark-models.yaml
+
+   {% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
+   {% set model_groups = data.vllm_benchmark.model_groups %}
+
+   The `ROCm vLLM Docker <{{ unified_docker.docker_hub_url }}>`_ image offers
+   a prebuilt, optimized environment for validating large language model (LLM)
+   inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
+   Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
+   accelerators and includes the following components:
+
+   * `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
+
+   * `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
+
+   * `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
+
+   * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
+
+   With this Docker image, you can quickly test the :ref:`expected
+   inference performance numbers <vllm-benchmark-performance-measurements-v0901-20250605>` for
+   MI300X series accelerators.
+
+   .. _vllm-benchmark-available-models-v0901-20250605:
+
+   Supported models
+   ================
+
+   The following models are supported for inference performance benchmarking
+   with vLLM and ROCm. Some instructions, commands, and recommendations in this
+   documentation might vary by model -- select one to get started.
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+        <div class="row">
+          <div class="col-2 me-2 model-param-head">Model group</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+            <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+   {% endfor %}
+          </div>
+        </div>
+
+        <div class="row mt-1">
+          <div class="col-2 me-2 model-param-head">Model</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+      {% set models = model_group.models %}
+      {% for model in models %}
+         {% if models|length % 3 == 0 %}
+            <div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% else %}
+            <div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+          </div>
+        </div>
+      </div>
+
+   .. _vllm-benchmark-vllm:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. note::
+
+         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
+         Some models require access authorization prior to use via an external license agreement through a third party.
+
+      {% endfor %}
+   {% endfor %}
+
+   .. note::
+
+      vLLM is a toolkit and library for LLM inference and serving. AMD implements
+      high-performance custom kernels and modules in vLLM to enhance performance.
+      See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
+      more information.
+
+   .. _vllm-benchmark-performance-measurements-v0901-20250605:
+
+   Performance measurements
+   ========================
+
+   To evaluate performance, the
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+   page provides reference throughput and latency measurements for inferencing popular AI models.
+
+   .. important::
+
+      The performance data presented in
+      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+      only reflects the latest version of this inference benchmarking environment.
+      The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
+
+   Advanced features and known issues
+   ==================================
+
+   For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
+   see the developer's guide at `<https://github.com/ROCm/vllm/tree/7bb0618b1fe725b7d4fad9e525aa44da12c94a8b/docs/dev-docker>`__.
+
+   System validation
+   =================
+
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
+
+   To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
+   might hang until the periodic balancing is finalized. For more information,
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
+
+   .. code-block:: shell
+
+      # disable automatic NUMA balancing
+      sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+      # check if NUMA balancing is disabled (returns 0 if disabled)
+      cat /proc/sys/kernel/numa_balancing
+      0
+
+   To test for optimal performance, consult the recommended :ref:`System health benchmarks
+   <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+   system's configuration.
+
+   Pull the Docker image
+   =====================
+
+   Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ unified_docker.pull_tag }}
+
+   Benchmarking
+   ============
+
+   Once the setup is complete, choose between two options to reproduce the
+   benchmark results:
+
+   .. _vllm-benchmark-mad:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+            directory and install the required packages on the host machine.
+
+            .. code-block:: shell
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD
+               pip install -r requirements.txt
+
+            Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
+
+            .. code-block:: shell
+
+               export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+               python3 tools/run_models.py --tags {{model.mad_tag}} --keep-model-dir --live-output --timeout 28800
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
+            model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
+
+            Although the :ref:`available models <vllm-benchmark-available-models-v0901-20250605>` are preconfigured
+            to collect latency and throughput performance data, you can also change the benchmarking
+            parameters. See the standalone benchmarking tab for more information.
+
+            {% if model.tunableop %}
+
+            .. note::
+
+               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
+               TunableOp automatically explores different implementations and configurations of certain PyTorch
+               operators to find the fastest one for your hardware.
+
+               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled
+               (see
+               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To
+               enable it, edit the default run behavior in the ``models.json``
+               configuration before running inference -- update the model's run
+               ``args`` by changing ``--tunableop off`` to ``--tunableop on``.
+
+               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
+
+            {% endif %}
+
+         .. tab-item:: Standalone benchmarking
+
+            Run the vLLM benchmark tool independently by starting the
+            `Docker container <{{ unified_docker.docker_hub_url }}>`_
+            as shown in the following snippet.
+
+            .. code-block::
+
+               docker pull {{ unified_docker.pull_tag }}
+               docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 16G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name test {{ unified_docker.pull_tag }}
+
+            In the Docker container, clone the ROCm MAD repository and navigate to the
+            benchmark scripts directory at ``~/MAD/scripts/vllm``.
+
+            .. code-block::
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD/scripts/vllm
+
+            To start the benchmark, use the following command with the appropriate options.
+
+            .. code-block::
+
+               ./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
+
+            .. list-table::
+               :header-rows: 1
+               :align: center
+
+               * - Name
+                 - Options
+                 - Description
+
+               * - ``$test_option``
+                 - latency
+                 - Measure decoding token latency
+
+               * -
+                 - throughput
+                 - Measure token generation throughput
+
+               * -
+                 - all
+                 - Measure both throughput and latency
+
+               * - ``$num_gpu``
+                 - 1 or 8
+                 - Number of GPUs
+
+               * - ``$datatype``
+                 - ``float16`` or ``float8``
+                 - Data type
+
+            .. note::
+
+               The input sequence length, output sequence length, and tensor parallel (TP) are
+               already configured. You don't need to specify them with this script.
+
+            .. note::
+
+               If you encounter the following error, pass your access-authorized Hugging
+               Face token to the gated models.
+
+               .. code-block::
+
+                  OSError: You are trying to access a gated repo.
+
+                  # pass your HF_TOKEN
+                  export HF_TOKEN=$your_personal_hf_token
+
+            Here are some examples of running the benchmark with various options.
+
+            * Latency benchmark
+
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
+
+              .. code-block::
+
+                 ./vllm_benchmark_report.sh -s latency -m {{model.model_repo}} -g 8 -d {{model.precision}}
+
+              Find the latency report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
+
+            * Throughput benchmark
+
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
+
+              .. code-block:: shell
+
+                 ./vllm_benchmark_report.sh -s throughput -m {{model.model_repo}} -g 8 -d {{model.precision}}
+
+              Find the throughput report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
+
+            .. raw:: html
+
+               <style>
+               mjx-container[jax="CHTML"][display="true"] {
+                  text-align: left;
+                  margin: 0;
+               }
+               </style>
+
+            .. note::
+
+               Throughput is calculated as:
+
+               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
+
+               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
+      {% endfor %}
+   {% endfor %}
+
+Further reading
+===============
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
+
+- To learn more about system settings and management practices to configure your system for
+  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
+
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`vllm-history` to find documentation for previous releases
+of the ``ROCm/vllm`` Docker image.

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-0.9.1-20250702.rst

@@ -0,0 +1,353 @@
+:orphan:
+
+.. meta::
+   :description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and the
+                 ROCm vLLM Docker image.
+   :keywords: model, MAD, automation, dashboarding, validate
+
+**********************************
+vLLM inference performance testing
+**********************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   inference performance documentation. See :doc:`../vllm` for the latest version.
+
+.. _vllm-benchmark-unified-docker:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250702-benchmark-models.yaml
+
+   {% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
+   {% set model_groups = data.vllm_benchmark.model_groups %}
+
+   The `ROCm vLLM Docker <{{ unified_docker.docker_hub_url }}>`_ image offers
+   a prebuilt, optimized environment for validating large language model (LLM)
+   inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
+   Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
+   accelerators and includes the following components:
+
+   * `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
+
+   * `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
+
+   * `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
+
+   * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
+
+   With this Docker image, you can quickly test the :ref:`expected
+   inference performance numbers <vllm-benchmark-performance-measurements-20250702>` for
+   MI300X series accelerators.
+
+   .. _vllm-benchmark-available-models-20250702:
+
+   Supported models
+   ================
+
+   The following models are supported for inference performance benchmarking
+   with vLLM and ROCm. Some instructions, commands, and recommendations in this
+   documentation might vary by model -- select one to get started.
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+        <div class="row">
+          <div class="col-2 me-2 model-param-head">Model group</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+            <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+   {% endfor %}
+          </div>
+        </div>
+
+        <div class="row mt-1">
+          <div class="col-2 me-2 model-param-head">Model</div>
+          <div class="row col-10">
+   {% for model_group in model_groups %}
+      {% set models = model_group.models %}
+      {% for model in models %}
+         {% if models|length % 3 == 0 %}
+            <div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% else %}
+            <div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+          </div>
+        </div>
+      </div>
+
+   .. _vllm-benchmark-vllm:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. note::
+
+         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
+         Some models require access authorization prior to use via an external license agreement through a third party.
+
+      {% endfor %}
+   {% endfor %}
+
+   .. note::
+
+      vLLM is a toolkit and library for LLM inference and serving. AMD implements
+      high-performance custom kernels and modules in vLLM to enhance performance.
+      See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
+      more information.
+
+   .. _vllm-benchmark-performance-measurements-20250702:
+
+   Performance measurements
+   ========================
+
+   To evaluate performance, the
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+   page provides reference throughput and latency measurements for inferencing popular AI models.
+
+   .. important::
+
+      The performance data presented in
+      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+      only reflects the latest version of this inference benchmarking environment.
+      The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
+
+   Advanced features and known issues
+   ==================================
+
+   For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
+   see the developer's guide at `<https://github.com/ROCm/vllm/tree/5486e7bc8523be0324ccd68f221959445b56cc2a/docs/dev-docker>`__.
+
+   System validation
+   =================
+
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
+
+   To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
+   might hang until the periodic balancing is finalized. For more information,
+   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
+
+   .. code-block:: shell
+
+      # disable automatic NUMA balancing
+      sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
+      # check if NUMA balancing is disabled (returns 0 if disabled)
+      cat /proc/sys/kernel/numa_balancing
+      0
+
+   To test for optimal performance, consult the recommended :ref:`System health benchmarks
+   <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+   system's configuration.
+
+   Pull the Docker image
+   =====================
+
+   Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ unified_docker.pull_tag }}
+
+   Benchmarking
+   ============
+
+   Once the setup is complete, choose between two options to reproduce the
+   benchmark results:
+
+   .. _vllm-benchmark-mad:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+            directory and install the required packages on the host machine.
+
+            .. code-block:: shell
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD
+               pip install -r requirements.txt
+
+            Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
+            using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
+
+            .. code-block:: shell
+
+               export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+               python3 tools/run_models.py --tags {{model.mad_tag}} --keep-model-dir --live-output --timeout 28800
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
+            model are collected in the following path: ``~/MAD/reports_{{model.precision}}/``.
+
+            Although the :ref:`available models <vllm-benchmark-available-models-20250702>` are preconfigured
+            to collect latency and throughput performance data, you can also change the benchmarking
+            parameters. See the standalone benchmarking tab for more information.
+
+            {% if model.tunableop %}
+
+            .. note::
+
+               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
+               TunableOp automatically explores different implementations and configurations of certain PyTorch
+               operators to find the fastest one for your hardware.
+
+               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled
+               (see
+               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To
+               enable it, edit the default run behavior in the ``models.json``
+               configuration before running inference -- update the model's run
+               ``args`` by changing ``--tunableop off`` to ``--tunableop on``.
+
+               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
+
+            {% endif %}
+
+         .. tab-item:: Standalone benchmarking
+
+            Run the vLLM benchmark tool independently by starting the
+            `Docker container <{{ unified_docker.docker_hub_url }}>`_
+            as shown in the following snippet.
+
+            .. code-block::
+
+               docker pull {{ unified_docker.pull_tag }}
+               docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 16G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name test {{ unified_docker.pull_tag }}
+
+            In the Docker container, clone the ROCm MAD repository and navigate to the
+            benchmark scripts directory at ``~/MAD/scripts/vllm``.
+
+            .. code-block::
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD/scripts/vllm
+
+            To start the benchmark, use the following command with the appropriate options.
+
+            .. code-block::
+
+               ./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
+
+            .. list-table::
+               :header-rows: 1
+               :align: center
+
+               * - Name
+                 - Options
+                 - Description
+
+               * - ``$test_option``
+                 - latency
+                 - Measure decoding token latency
+
+               * -
+                 - throughput
+                 - Measure token generation throughput
+
+               * -
+                 - all
+                 - Measure both throughput and latency
+
+               * - ``$num_gpu``
+                 - 1 or 8
+                 - Number of GPUs
+
+               * - ``$datatype``
+                 - ``float16`` or ``float8``
+                 - Data type
+
+            .. note::
+
+               The input sequence length, output sequence length, and tensor parallel (TP) are
+               already configured. You don't need to specify them with this script.
+
+            .. note::
+
+               If you encounter the following error, pass your access-authorized Hugging
+               Face token to the gated models.
+
+               .. code-block::
+
+                  OSError: You are trying to access a gated repo.
+
+                  # pass your HF_TOKEN
+                  export HF_TOKEN=$your_personal_hf_token
+
+            Here are some examples of running the benchmark with various options.
+
+            * Latency benchmark
+
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal`{{model.precision}}` precision.
+
+              .. code-block::
+
+                 ./vllm_benchmark_report.sh -s latency -m {{model.model_repo}} -g 8 -d {{model.precision}}
+
+              Find the latency report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
+
+            * Throughput benchmark
+
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
+
+              .. code-block:: shell
+
+                 ./vllm_benchmark_report.sh -s throughput -m {{model.model_repo}} -g 8 -d {{model.precision}}
+
+              Find the throughput report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
+
+            .. raw:: html
+
+               <style>
+               mjx-container[jax="CHTML"][display="true"] {
+                  text-align: left;
+                  margin: 0;
+               }
+               </style>
+
+            .. note::
+
+               Throughput is calculated as:
+
+               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
+
+               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
+      {% endfor %}
+   {% endfor %}
+
+Further reading
+===============
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
+
+- To learn more about system settings and management practices to configure your system for
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
+
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`vllm-history` to find documentation for previous releases
+of the ``ROCm/vllm`` Docker image.

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/vllm-history.rst

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

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

@@ -32,10 +32,10 @@ PyTorch inference performance testing
 
       <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
         <div class="row">
-          <div class="col-2 me-2 model-param-head">Model group</div>
+          <div class="col-2 me-2 model-param-head">Model</div>
           <div class="row col-10">
    {% for model_group in model_groups %}
-            <div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+            <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
    {% endfor %}
           </div>
         </div>
@@ -93,7 +93,7 @@ PyTorch inference performance testing
 
    .. container:: model-doc pyt_chai1_inference
 
-      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`_ from Docker Hub.
+      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue/images/sha256-b736a4239ab38a9d0e448af6d4adca83b117debed00bfbe33846f99c4540f79b>`__ from Docker Hub.
 
       .. code-block:: shell
 
@@ -103,9 +103,9 @@ PyTorch inference performance testing
 
          The Chai-1 benchmark uses a specifically selected Docker image using ROCm 6.2.3 and PyTorch 2.3.0 to address an accuracy issue.
 
-   .. container:: model-doc pyt_clip_inference pyt_mochi_video_inference
+   .. container:: model-doc pyt_clip_inference pyt_mochi_video_inference pyt_wan2.1_inference pyt_janus_pro_inference
 
-      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`_ from Docker Hub.
+      Use the following command to pull the `ROCm PyTorch Docker image <https://hub.docker.com/layers/rocm/pytorch/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`__ from Docker Hub.
 
       .. code-block:: shell
 
@@ -140,22 +140,27 @@ PyTorch inference performance testing
       .. code-block:: shell
 
          export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-         python3 tools/run_models.py --tags {{model.mad_tag}} --keep-model-dir --live-output --timeout 28800
+         madengine run \
+             --tags {{model.mad_tag}} \
+             --keep-model-dir \
+             --live-output \
+             --timeout 28800
 
       MAD launches a Docker container with the name
       ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
-      model are collected in ``perf.csv``.
+      model are collected in ``perf_{{model.mad_tag}}.csv``.
 
+      {% if model.mad_tag != "pyt_janus_pro_inference" %}
       .. note::
 
          For improved performance, consider enabling TunableOp. By default,
          ``{{model.mad_tag}}`` runs with TunableOp disabled (see
          `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable
-         it, edit the default run behavior in the ``tools/run_models.py``-- update the model's
-         run ``args`` by changing ``--tunableop off`` to ``--tunableop on``.
+         it, include the ``--tunableop on`` argument in your run.
 
          Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
          Although this might increase the initial training time, it can result in a performance gain.
+      {% endif %}
 
       {% endfor %}
    {% endfor %}
@@ -163,8 +168,10 @@ PyTorch inference performance testing
 Further reading
 ===============
 
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+  AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
 
 - For application performance optimization strategies for HPC and AI workloads,
   including inference with vLLM, see :doc:`../../inference-optimization/workload`.

docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst

@@ -0,0 +1,280 @@
+.. meta::
+   :description: Learn how to validate LLM inference performance on MI300X accelerators using AMD MAD and SGLang
+   :keywords: model, MAD, automation, dashboarding, validate
+
+************************************
+SGLang inference performance testing
+************************************
+
+.. _sglang-benchmark-unified-docker:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-benchmark-models.yaml
+
+   {% set unified_docker = data.sglang_benchmark.unified_docker.latest %}
+
+   `SGLang <https://docs.sglang.ai>`__ is a high-performance inference and
+   serving engine for large language models (LLMs) and vision models. The
+   ROCm-enabled `SGLang Docker image <{{ unified_docker.docker_hub_url }}>`__
+   bundles SGLang with PyTorch, optimized for AMD Instinct MI300X series
+   accelerators. It includes the following software components:
+
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      * - `ROCm <https://github.com/ROCm/ROCm>`__
+        - {{ unified_docker.rocm_version }}
+
+      * - `SGLang <https://docs.sglang.ai/index.html>`__
+        - {{ unified_docker.sglang_version }} 
+
+      * - `PyTorch <https://github.com/pytorch/pytorch>`__
+        - {{ unified_docker.pytorch_version }} 
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-benchmark-models.yaml
+
+   {% set unified_docker = data.sglang_benchmark.unified_docker.latest %}
+   {% set model_groups = data.sglang_benchmark.model_groups %}
+
+   Pull the Docker image
+   =====================
+
+   Download the `SGLang Docker image <{{ unified_docker.docker_hub_url }}>`__.
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ unified_docker.pull_tag }}
+
+   Benchmarking
+   ============
+
+   Once the setup is complete, choose one of the following methods to benchmark inference performance with
+   `DeepSeek-R1-Distill-Qwen-32B <https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B>`__.
+
+   .. _sglang-benchmark-mad:
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
+               using one GPU with the ``{{model.precision}}`` data type on the host machine.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
+
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
+            model are collected in the following path: ``~/MAD/perf_DeepSeek-R1-Distill-Qwen-32B.csv``.
+
+            Although the DeepSeek-R1-Distill-Qwen-32B is preconfigured
+            to collect latency and throughput performance data, you can also change the benchmarking
+            parameters. See the standalone benchmarking tab for more information.
+
+         .. tab-item:: Standalone benchmarking
+
+            .. rubric:: Download the Docker image and required scripts
+
+            1. Run the SGLang benchmark script independently by starting the
+               `Docker container <{{ unified_docker.docker_hub_url }}>`__
+               as shown in the following snippet.
+
+               .. code-block:: shell
+
+                  docker pull {{ unified_docker.pull_tag }}
+                  docker run -it \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --shm-size 16G \
+                      --security-opt seccomp=unconfined \
+                      --security-opt apparmor=unconfined \
+                      --cap-add=SYS_PTRACE \
+                      -v $(pwd):/workspace \
+                      --env HUGGINGFACE_HUB_CACHE=/workspace \
+                      --name test \
+                      {{ unified_docker.pull_tag }}
+
+            2. In the Docker container, clone the ROCm MAD repository and navigate to the
+               benchmark scripts directory at ``~/MAD/scripts/sglang``.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD/scripts/sglang
+
+            3. To start the benchmark, use the following command with the appropriate options.
+
+               .. dropdown:: Benchmark options
+                  :open:
+
+                  .. list-table::
+                     :header-rows: 1
+                     :align: center
+
+                     * - Name
+                       - Options
+                       - Description
+
+                     * - ``$test_option``
+                       - latency
+                       - Measure decoding token latency
+
+                     * -
+                       - throughput
+                       - Measure token generation throughput
+
+                     * -
+                       - all
+                       - Measure both throughput and latency
+
+                     * - ``$num_gpu``
+                       - 8
+                       - Number of GPUs
+
+                     * - ``$datatype``
+                       - ``bfloat16``
+                       - Data type
+
+                     * - ``$dataset``
+                       - random
+                       - Dataset
+
+                  The input sequence length, output sequence length, and tensor parallel (TP) are
+                  already configured. You don't need to specify them with this script.
+
+               Command:
+
+               .. code-block:: shell
+
+                  ./sglang_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d $datatype [-a $dataset]
+
+            .. note::
+
+               If you encounter the following error, pass your access-authorized Hugging
+               Face token to the gated models.
+
+               .. code-block:: shell-session
+
+                  OSError: You are trying to access a gated repo.
+                  # pass your HF_TOKEN
+                  export HF_TOKEN=$your_personal_hf_token
+
+            .. rubric:: Benchmarking examples
+
+            Here are some examples of running the benchmark with various options:
+
+            * Latency benchmark
+
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+
+              .. code-block:: shell
+
+                 ./sglang_benchmark_report.sh \
+                     -s latency \
+                     -m {{model.model_repo}} \
+                     -g 8 \
+                     -d {{model.precision}}
+
+              Find the latency report at ``./reports_{{model.precision}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
+
+            * Throughput benchmark
+
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+
+              .. code-block:: shell
+
+                 ./sglang_benchmark_report.sh \
+                     -s throughput \
+                     -m {{model.model_repo}} \
+                     -g 8 \
+                     -d {{model.precision}} \
+                     -a random
+
+              Find the throughput report at ``./reports_{{model.precision}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
+
+            .. raw:: html
+
+               <style>
+               mjx-container[jax="CHTML"][display="true"] {
+                  text-align: left;
+                  margin: 0;
+               }
+               </style>
+
+            .. note::
+
+               Throughput is calculated as:
+
+               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
+
+               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
+      {% endfor %}
+   {% endfor %}
+
+Further reading
+===============
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/sgl-project/sglang/tree/main/benchmark/blog_v0_2>`__.
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__.
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
+
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`previous-versions/sglang-history` to find documentation for previous releases
+of SGLang inference performance testing.

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

@@ -20,23 +20,55 @@ vLLM inference performance testing
    Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
    accelerators and includes the following components:
 
-   * `ROCm {{ unified_docker.rocm_version }} <https://github.com/ROCm/ROCm>`_
+   .. list-table::
+      :header-rows: 1
 
-   * `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
+      * - Software component
+        - Version
 
-   * `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
+      * - `ROCm <https://github.com/ROCm/ROCm>`__
+        - {{ unified_docker.rocm_version }}
 
-   * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
+      * - `vLLM <https://docs.vllm.ai/en/latest>`__
+        - {{ unified_docker.vllm_version }}
 
-   With this Docker image, you can quickly test the :ref:`expected
-   inference performance numbers <vllm-benchmark-performance-measurements>` for
-   MI300X series accelerators.
+      * - `PyTorch <https://github.com/ROCm/pytorch>`__
+        - {{ unified_docker.pytorch_version }}
+
+      * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
+        - {{ unified_docker.hipblaslt_version }}
+
+With this Docker image, you can quickly test the :ref:`expected
+inference performance numbers <vllm-benchmark-performance-measurements>` for
+MI300X series accelerators.
+
+What's new
+==========
+
+The following is summary of notable changes since the :doc:`previous ROCm/vLLM Docker release <previous-versions/vllm-history>`.
+
+* The ``--compilation-config-parameter`` is no longer required as its options are now enabled by default.
+  This parameter has been removed from the benchmarking script.
+
+* Resolved Llama 3.1 405 B custom all-reduce issue, eliminating the need for ``--disable-custom-all-reduce``.
+  This parameter has been removed from the benchmarking script.
+
+* Fixed a ``+rms_norm`` custom kernel issue.
+
+* Added quick reduce functionality. Set ``VLLM_ROCM_QUICK_REDUCE_QUANTIZATION=FP`` to enable; supported modes are ``FP``, ``INT8``, ``INT6``, ``INT4``.
+
+* Implemented a workaround to potentially mitigate GPU crashes experienced with the Command R+ model, pending a driver fix.
+
+Supported models
+================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
+
+   {% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
+   {% set model_groups = data.vllm_benchmark.model_groups %}
 
    .. _vllm-benchmark-available-models:
 
-   Supported models
-   ================
-
    The following models are supported for inference performance benchmarking
    with vLLM and ROCm. Some instructions, commands, and recommendations in this
    documentation might vary by model -- select one to get started.
@@ -44,18 +76,18 @@ vLLM inference performance testing
    .. raw:: html
 
       <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
-        <div class="row">
-          <div class="col-2 me-2 model-param-head">Model group</div>
-          <div class="row col-10">
+      <div class="row">
+         <div class="col-2 me-2 model-param-head">Model group</div>
+         <div class="row col-10">
    {% for model_group in model_groups %}
             <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
    {% endfor %}
-          </div>
-        </div>
+         </div>
+      </div>
 
-        <div class="row mt-1">
-          <div class="col-2 me-2 model-param-head">Model</div>
-          <div class="row col-10">
+      <div class="row mt-1">
+         <div class="col-2 me-2 model-param-head">Model</div>
+         <div class="row col-10">
    {% for model_group in model_groups %}
       {% set models = model_group.models %}
       {% for model in models %}
@@ -66,8 +98,8 @@ vLLM inference performance testing
          {% endif %}
       {% endfor %}
    {% endfor %}
-          </div>
-        </div>
+         </div>
+      </div>
       </div>
 
    .. _vllm-benchmark-vllm:
@@ -85,56 +117,48 @@ vLLM inference performance testing
       {% endfor %}
    {% endfor %}
 
-   .. note::
+.. note::
 
-      vLLM is a toolkit and library for LLM inference and serving. AMD implements
-      high-performance custom kernels and modules in vLLM to enhance performance.
-      See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
-      more information.
+   vLLM is a toolkit and library for LLM inference and serving. AMD implements
+   high-performance custom kernels and modules in vLLM to enhance performance.
+   See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
+   more information.
 
-   .. _vllm-benchmark-performance-measurements:
+.. _vllm-benchmark-performance-measurements:
 
-   Performance measurements
-   ========================
+Performance measurements
+========================
 
-   To evaluate performance, the
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+page provides reference throughput and latency measurements for inferencing popular AI models.
+
+.. important::
+
+   The performance data presented in
    `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-   page provides reference throughput and latency measurements for inferencing popular AI models.
+   only reflects the latest version of this inference benchmarking environment.
+   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
-   .. important::
+System validation
+=================
 
-      The performance data presented in
-      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-      only reflects the latest version of this inference benchmarking environment.
-      The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
 
-   Advanced features and known issues
-   ==================================
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
 
-   For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
-   see the developer's guide at `<https://github.com/ROCm/vllm/tree/7bb0618b1fe725b7d4fad9e525aa44da12c94a8b/docs/dev-docker>`__.
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
 
-   System validation
-   =================
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
 
-   Before running AI workloads, it's important to validate that your AMD hardware is configured
-   correctly and performing optimally.
-
-   To optimize performance, disable automatic NUMA balancing. Otherwise, the GPU
-   might hang until the periodic balancing is finalized. For more information,
-   see the :ref:`system validation steps <rocm-for-ai-system-optimization>`.
-
-   .. code-block:: shell
-
-      # disable automatic NUMA balancing
-      sh -c 'echo 0 > /proc/sys/kernel/numa_balancing'
-      # check if NUMA balancing is disabled (returns 0 if disabled)
-      cat /proc/sys/kernel/numa_balancing
-      0
-
-   To test for optimal performance, consult the recommended :ref:`System health benchmarks
-   <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
-   system's configuration.
+   {% set unified_docker = data.vllm_benchmark.unified_docker.latest %}
+   {% set model_groups = data.vllm_benchmark.model_groups %}
 
    Pull the Docker image
    =====================
@@ -163,22 +187,26 @@ vLLM inference performance testing
 
          .. tab-item:: MAD-integrated benchmarking
 
-            Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
-            directory and install the required packages on the host machine.
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
 
-            .. code-block:: shell
+               .. code-block:: shell
 
-               git clone https://github.com/ROCm/MAD
-               cd MAD
-               pip install -r requirements.txt
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
 
-            Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
-            using one GPU with the ``{{model.precision}}`` data type on the host machine.
+            2. Use this command to run the performance benchmark test on the `{{model.model}} <{{ model.url }}>`_ model
+               using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
 
-            .. code-block:: shell
+               .. code-block:: shell
 
-               export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-               python3 tools/run_models.py --tags {{model.mad_tag}} --keep-model-dir --live-output --timeout 28800
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
 
             MAD launches a Docker container with the name
             ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
@@ -198,104 +226,136 @@ vLLM inference performance testing
 
                By default, ``{{model.mad_tag}}`` runs with TunableOp disabled
                (see
-               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To
-               enable it, edit the default run behavior in the ``models.json``
-               configuration before running inference -- update the model's run
-               ``args`` by changing ``--tunableop off`` to ``--tunableop on``.
+               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__).
+               To enable it, include the ``--tunableop on`` argument in your
+               run.
 
-               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the performance-collection run.
+               Enabling TunableOp triggers a two-pass run -- a warm-up followed
+               by the performance-collection run.
 
             {% endif %}
 
          .. tab-item:: Standalone benchmarking
 
-            Run the vLLM benchmark tool independently by starting the
-            `Docker container <{{ unified_docker.docker_hub_url }}>`_
-            as shown in the following snippet.
+            .. rubric:: Download the Docker image and required scripts
 
-            .. code-block::
+            1. Run the vLLM benchmark tool independently by starting the
+               `Docker container <{{ unified_docker.docker_hub_url }}>`_
+               as shown in the following snippet.
 
-               docker pull {{ unified_docker.pull_tag }}
-               docker run -it --device=/dev/kfd --device=/dev/dri --group-add video --shm-size 16G --security-opt seccomp=unconfined --security-opt apparmor=unconfined --cap-add=SYS_PTRACE -v $(pwd):/workspace --env HUGGINGFACE_HUB_CACHE=/workspace --name test {{ unified_docker.pull_tag }}
+               .. code-block:: shell
 
-            In the Docker container, clone the ROCm MAD repository and navigate to the
-            benchmark scripts directory at ``~/MAD/scripts/vllm``.
+                  docker pull {{ unified_docker.pull_tag }}
+                  docker run -it \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --shm-size 16G \
+                      --security-opt seccomp=unconfined \
+                      --security-opt apparmor=unconfined \
+                      --cap-add=SYS_PTRACE \
+                      -v $(pwd):/workspace \
+                      --env HUGGINGFACE_HUB_CACHE=/workspace \
+                      --name test \
+                      {{ unified_docker.pull_tag }}
 
-            .. code-block::
+            2. In the Docker container, clone the ROCm MAD repository and navigate to the
+               benchmark scripts directory at ``~/MAD/scripts/vllm``.
 
-               git clone https://github.com/ROCm/MAD
-               cd MAD/scripts/vllm
+               .. code-block:: shell
 
-            To start the benchmark, use the following command with the appropriate options.
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD/scripts/vllm
 
-            .. code-block::
+            3. To start the benchmark, use the following command with the appropriate options.
 
-               ./vllm_benchmark_report.sh -s $test_option -m {{model.model_repo}} -g $num_gpu -d {{model.precision}}
+               .. dropdown:: Benchmark options
+                  :open:
 
-            .. list-table::
-               :header-rows: 1
-               :align: center
+                  .. list-table::
+                     :header-rows: 1
+                     :align: center
 
-               * - Name
-                 - Options
-                 - Description
+                     * - Name
+                       - Options
+                       - Description
 
-               * - ``$test_option``
-                 - latency
-                 - Measure decoding token latency
+                     * - ``$test_option``
+                       - latency
+                       - Measure decoding token latency
 
-               * -
-                 - throughput
-                 - Measure token generation throughput
+                     * -
+                       - throughput
+                       - Measure token generation throughput
 
-               * -
-                 - all
-                 - Measure both throughput and latency
+                     * -
+                       - all
+                       - Measure both throughput and latency
 
-               * - ``$num_gpu``
-                 - 1 or 8
-                 - Number of GPUs
+                     * - ``$num_gpu``
+                       - 1 or 8
+                       - Number of GPUs
 
-               * - ``$datatype``
-                 - ``float16`` or ``float8``
-                 - Data type
+                     * - ``$datatype``
+                       - ``float16`` or ``float8``
+                       - Data type
 
-            .. note::
+                  The input sequence length, output sequence length, and tensor parallel (TP) are
+                  already configured. You don't need to specify them with this script.
 
-               The input sequence length, output sequence length, and tensor parallel (TP) are
-               already configured. You don't need to specify them with this script.
-
-            .. note::
-
-               If you encounter the following error, pass your access-authorized Hugging
-               Face token to the gated models.
+               Command:
 
                .. code-block::
 
-                  OSError: You are trying to access a gated repo.
+                  ./vllm_benchmark_report.sh \
+                      -s $test_option \
+                      -m {{model.model_repo}} \
+                      -g $num_gpu \
+                      -d {{model.precision}}
 
-                  # pass your HF_TOKEN
-                  export HF_TOKEN=$your_personal_hf_token
+               .. note::
 
-            Here are some examples of running the benchmark with various options.
+                  For best performance, it's recommend to run with ``VLLM_V1_USE_PREFILL_DECODE_ATTENTION=1``.
+
+                  If you encounter the following error, pass your access-authorized Hugging
+                  Face token to the gated models.
+
+                  .. code-block::
+
+                     OSError: You are trying to access a gated repo.
+
+                     # pass your HF_TOKEN
+                     export HF_TOKEN=$your_personal_hf_token
+
+            .. rubric:: Benchmarking examples
+
+            Here are some examples of running the benchmark with various options:
 
             * Latency benchmark
 
-              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the latency of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block::
 
-                 ./vllm_benchmark_report.sh -s latency -m {{model.model_repo}} -g 8 -d {{model.precision}}
+                 ./vllm_benchmark_report.sh \
+                     -s latency \
+                     -m {{model.model_repo}} \
+                     -g 8 \
+                     -d {{model.precision}}
 
               Find the latency report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_latency_report.csv``.
 
             * Throughput benchmark
 
-              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
+              Use this command to benchmark the throughput of the {{model.model}} model on eight GPUs with :literal:`{{model.precision}}` precision.
 
               .. code-block:: shell
 
-                 ./vllm_benchmark_report.sh -s throughput -m {{model.model_repo}} -g 8 -d {{model.precision}}
+                 ./vllm_benchmark_report.sh \
+                     -s throughput \
+                     -m {{model.model_repo}} \
+                     -g 8 \
+                     -d {{model.precision}}
 
               Find the throughput report at ``./reports_{{model.precision}}_vllm_rocm{{unified_docker.rocm_version}}/summary/{{model.model_repo.split('/', 1)[1] if '/' in model.model_repo else model.model_repo}}_throughput_report.csv``.
 
@@ -318,29 +378,66 @@ vLLM inference performance testing
       {% endfor %}
    {% endfor %}
 
+Advanced usage
+==============
+
+For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
+see the developer's guide at `<https://github.com/ROCm/vllm/tree/f94ec9beeca1071cc34f9d1e206d8c7f3ac76129/docs/dev-docker>`__.
+
+Reproducing the Docker image
+----------------------------
+
+To reproduce this ROCm/vLLM Docker image release, follow these steps:
+
+1. Clone the `vLLM repository <https://github.com/ROCm/vllm>`__.
+
+   .. code-block:: shell
+
+      git clone https://github.com/ROCm/vllm.git
+
+2. Checkout the specific release commit.
+
+   .. code-block:: shell
+
+      cd vllm
+      git checkout b432b7a285aa0dcb9677380936ffa74931bb6d6f
+
+3. Build the Docker image. Replace ``vllm-rocm`` with your desired image tag.
+
+   .. code-block:: shell
+
+      docker build -f docker/Dockerfile.rocm -t vllm-rocm .
+
+Known issues and workarounds
+============================
+
+AITER does not support FP8 KV cache yet.
+
 Further reading
 ===============
 
 - To learn more about the options for latency and throughput benchmark scripts,
   see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
 - To learn more about system settings and management practices to configure your system for
-  MI300X accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_
+  AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
 
 - For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../../inference-optimization/workload`.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
 
-- To learn how to run LLM models from Hugging Face or your own model, see
-  :doc:`Running models from Hugging Face <../hugging-face-models>`.
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
 
-- To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../../inference-optimization/index>`.
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
 
-- To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../../fine-tuning/index>`.
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 
 Previous versions
 =================
 
 See :doc:`previous-versions/vllm-history` to find documentation for previous releases
-of the ``ROCm/vllm`` Docker image.
+of the ``ROCm/vllm`` Docker image.

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

@@ -14,14 +14,16 @@ Throughout the following topics, this section provides a comprehensive guide to
 The AI Developer Hub contains `AMD ROCm tutorials <https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/>`_ for
 training, fine-tuning, and inference. It leverages popular machine learning frameworks on AMD GPUs.
 
-- :doc:`Installing ROCm and machine learning frameworks <install>`
+- :doc:`Installing ROCm and machine learning frameworks <../install>`
 
 - :doc:`Running models from Hugging Face <hugging-face-models>`
 
 - :doc:`LLM inference frameworks <llm-inference-frameworks>`
 
-- :doc:`vLLM inference performance testing <vllm-benchmark>`
+- :doc:`vLLM inference performance testing <benchmark-docker/vllm>`
 
-- :doc:`PyTorch inference performance testing <pytorch-inference-benchmark>`
+- :doc:`PyTorch inference performance testing <benchmark-docker/pytorch-inference>`
+
+- :doc:`SGLang inference performance testing <benchmark-docker/sglang>`
 
 - :doc:`Deploying your model <deploy-your-model>`

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

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

docs/how-to/rocm-for-ai/install.rst

@@ -24,12 +24,13 @@ If you’re new to ROCm, refer to the :doc:`ROCm quick start install guide for L
 If you’re using a Radeon GPU for graphics-accelerated applications, refer to the
 `Radeon installation instructions <https://rocm.docs.amd.com/projects/radeon/en/docs-6.1.3/docs/install/native_linux/install-radeon.html>`_.
 
-ROCm supports multiple :doc:`installation methods <rocm-install-on-linux:install/install-overview>`:
+You can install ROCm on :doc:`compatible systems <rocm-install-on-linux:reference/system-requirements>` via your Linux
+distribution's package manager. See the following documentation resources to get started:
+
+* :doc:`ROCm installation overview <rocm-install-on-linux:install/install-overview>`
 
 * :doc:`Using your Linux distribution's package manager <rocm-install-on-linux:install/install-methods/package-manager-index>`
 
-* :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/amdgpu-install>`
-
 * :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`
 
 .. grid:: 1
@@ -59,6 +60,12 @@ images with the framework pre-installed.
 
 * :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
 
+* :doc:`verl for ROCm <rocm-install-on-linux:install/3rd-party/verl-install>`
+
+* :doc:`Stanford Megatron-LM for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
+
+* :doc:`DGL for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
+
 Next steps
 ==========
 

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

@@ -15,57 +15,51 @@ purpose-built to support models like Llama, DeepSeek, and Mixtral,
 enabling developers to train next-generation AI models more
 efficiently.
 
-AMD provides a ready-to-use Docker image for MI300X series accelerators containing
+AMD provides ready-to-use Docker images for MI300X series accelerators containing
 essential components, including PyTorch, ROCm libraries, and Megatron-LM
 utilities. It contains the following software components to accelerate training
 workloads:
 
-+--------------------------+--------------------------------+
-| Software component       | Version                        |
-+==========================+================================+
-| ROCm                     | 6.3.4                          |
-+--------------------------+--------------------------------+
-| PyTorch                  | 2.8.0a0+gite2f9759             |
-+--------------------------+--------------------------------+
-| Python                   | 3.12 or 3.10                   |
-+--------------------------+--------------------------------+
-| Transformer Engine       | 1.13.0+bb061ade                |
-+--------------------------+--------------------------------+
-| Flash Attention          | 3.0.0                          |
-+--------------------------+--------------------------------+
-| hipBLASLt                | 0.13.0-4f18bf6                 |
-+--------------------------+--------------------------------+
-| Triton                   | 3.3.0                          |
-+--------------------------+--------------------------------+
-| RCCL                     | 2.22.3                         |
-+--------------------------+--------------------------------+
-
-Megatron-LM provides the following key features to train large language models efficiently:
-
-- Transformer Engine (TE)
-
-- APEX
-
-- GEMM tuning
-
-- Torch.compile
-
-- 3D parallelism: TP + SP + CP
-
-- Distributed optimizer
-
-- Flash Attention (FA) 3
-
-- Fused kernels
-
-- Pre-training
-
-.. _amd-megatron-lm-model-support:
-
-The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
-
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
+   {% set dockers = data.dockers %}
+   {% if dockers|length > 1 %}
+   .. tab-set::
+
+      {% for docker in data.dockers %}
+      .. tab-item:: ``{{ docker.pull_tag }}``
+         :sync: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+
+            {% endfor %}
+      {% endfor %}
+   {% elif dockers|length == 1 %}
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components %}
+      * - {{ component_name }}
+        - {{ component_version }}
+
+      {% endfor %}
+   {% endif %}
+
+   .. _amd-megatron-lm-model-support:
+
+   The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
+
    Supported models
    ================
 
@@ -73,8 +67,7 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
    Some instructions, commands, and training recommendations in this documentation might
    vary by model -- select one to get started.
 
-   {% set model_groups = data["megatron-lm_benchmark"].model_groups %}
-
+   {% set model_groups = data.model_groups %}
    .. raw:: html
 
          <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
@@ -82,7 +75,7 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
              <div class="col-2 me-2 model-param-head">Model</div>
              <div class="row col-10">
       {% for model_group in model_groups %}
-               <div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+               <div class="col-3 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
       {% endfor %}
              </div>
            </div>
@@ -115,14 +108,14 @@ Performance measurements
 ========================
 
 To evaluate performance, the
-`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`__
 page provides reference throughput and latency measurements for training
 popular AI models.
 
 .. important::
 
    The performance data presented in
-   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`__
    only reflects the latest version of this training benchmarking environment.
    The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
@@ -155,27 +148,77 @@ image.
 Download the Docker image
 -------------------------
 
-1. Use the following command to pull the Docker image from Docker Hub.
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
-   .. tab-set:: 
+   {% set dockers = data.dockers %}
+   1. Use the following command to pull the Docker image from Docker Hub.
 
-      .. tab-item:: Ubuntu 24.04 + Python 3.12
+      {% if dockers|length > 1 %}
+      .. tab-set:: 
 
-         .. code-block:: shell
+         {% for docker in data.dockers %}
+         .. tab-item:: {{ docker.doc_name }}
+            :sync: {{ docker.pull_tag }}
 
-            docker pull rocm/megatron-lm:v25.5_py312
+            .. code-block:: shell
 
-      .. tab-item:: Ubuntu 22.04 + Python 3.10
+               docker pull {{ docker.pull_tag }}
 
-         .. code-block:: shell
+         {% endfor %}
+      {% elif dockers|length == 1 %}
+      {% set docker = dockers[0] %}
+      .. code-block:: shell
 
-            docker pull rocm/megatron-lm:v25.5_py310
+         docker pull {{ docker.pull_tag }}
 
-2. Launch the Docker container.
+      {% endif %}
+   2. Launch the Docker container.
 
-   .. code-block:: shell
+      {% if dockers|length > 1 %}
+      .. tab-set::
 
-      docker run -it --device /dev/dri --device /dev/kfd --device /dev/infiniband --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 64G --name megatron_training_env rocm/megatron-lm:v25.5
+         {% for docker in data.dockers %}
+         .. tab-item:: {{ docker.doc_name }}
+            :sync: {{ docker.pull_tag }}
+
+            .. code-block:: shell
+
+               docker run -it \
+                   --device /dev/dri \
+                   --device /dev/kfd \
+                   --device /dev/infiniband \
+                   --network host --ipc host \
+                   --group-add video \
+                   --cap-add SYS_PTRACE \
+                   --security-opt seccomp=unconfined \
+                   --privileged \
+                   -v $HOME:$HOME \
+                   -v $HOME/.ssh:/root/.ssh \
+                   --shm-size 128G \
+                   --name megatron_training_env \
+                   {{ docker.pull_tag }}
+
+         {% endfor %}
+      {% elif dockers|length == 1 %}
+      {% set docker = dockers[0] %}
+      .. code-block:: shell
+
+         docker run -it \
+             --device /dev/dri \
+             --device /dev/kfd \
+             --device /dev/infiniband \
+             --network host --ipc host \
+             --group-add video \
+             --cap-add SYS_PTRACE \
+             --security-opt seccomp=unconfined \
+             --privileged \
+             -v $HOME:$HOME \
+             -v $HOME/.ssh:/root/.ssh \
+             --shm-size 128G \
+             --name megatron_training_env \
+             {{ docker.pull_tag }}
+
+      {% endif %}
 
 3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
 
@@ -333,6 +376,22 @@ If the tokenizer is not found, it'll be downloaded if publicly available.
 
       TOKENIZER_MODEL=tokenizer/tokenizer.model
 
+.. container:: model-doc pyt_megatron_lm_train_qwen2.5-7b
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="Qwen/Qwen2.5-7B"
+
+.. container:: model-doc pyt_megatron_lm_train_qwen2.5-72b
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="Qwen/Qwen2.5-72B"
+
 Dataset options
 ---------------
 
@@ -358,7 +417,7 @@ You can use either mock data or real data for training.
 Download the dataset
 ^^^^^^^^^^^^^^^^^^^^
 
-.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
+.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b pyt_megatron_lm_train_llama-3.1-70b-proxy
 
    For Llama models, use the `prepare_dataset.sh
    <https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`_ script
@@ -397,8 +456,8 @@ Download the dataset
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
-      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
-      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
+      cd ..
+      bash tools/run_make_pretraining_dataset_megatron.sh deepseek-datasets/SlimPajama.json DeepSeekV3Tokenizer text deepseek-datasets deepseek-ai/DeepSeek-V3
 
    To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
 
@@ -422,8 +481,8 @@ Download the dataset
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
       wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
-      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
-      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
+      cd ..
+      bash tools/run_make_pretraining_dataset_megatron.sh deepseek-datasets/SlimPajama.json DeepSeekV3Tokenizer text deepseek-datasets deepseek-ai/DeepSeek-V3
 
    To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
 
@@ -433,8 +492,6 @@ Download the dataset
 
       DATA_DIR="<path-to>/deepseek-datasets"  # Change to where your dataset is stored
 
-      Ensure that the files are accessible inside the Docker container.
-
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
 
    If you don't already have the dataset, download the Mixtral dataset using the following
@@ -457,6 +514,27 @@ Download the dataset
 
    Ensure that the files are accessible inside the Docker container.
 
+.. container:: model-doc pyt_megatron_lm_train_qwen2.5-7b pyt_megatron_lm_train_qwen2.5-72b
+
+   If you don't already have the dataset, download the Mixtral dataset using the following
+   commands:
+
+   .. code-block:: shell
+
+      mkdir -p temp/qwen-datasets
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/qwen-datasets/wudao_qwenbpe_text_document.bin
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/qwen-datasets/wudao_qwenbpe_text_document.idx
+
+   To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
+
+   .. code-block:: bash
+
+      MOCK_DATA=0 # Train on real data
+
+      DATA_DIR="<path-to>/qwen-datasets"  # Change to where your dataset is stored
+
+   Ensure that the files are accessible inside the Docker container.
+
 Multi-node configuration
 ------------------------
 
@@ -497,27 +575,17 @@ also be passed as command line arguments. Refer to the following example configu
      # Specify which RDMA interfaces to use for communication
      export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
 
-Getting started
-===============
-
-The prebuilt Megatron-LM with ROCm training environment allows users to quickly validate
-system performance, conduct training benchmarks, and achieve superior
-performance for models like Llama, DeepSeek, and Mixtral. This container should not be
-expected to provide generalized performance across all training workloads. You
-can expect the container to perform in the model configurations described in
-the following section, but other configurations are not validated by AMD.
-
 .. _amd-megatron-lm-run-training:
 
 Run training
-------------
+============
 
 Use the following example commands to set up the environment, configure
 :ref:`key options <amd-megatron-lm-benchmark-test-vars>`, and run training on
 MI300X series accelerators with the AMD Megatron-LM environment.
 
 Single node training
-^^^^^^^^^^^^^^^^^^^^
+--------------------
 
 .. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
 
@@ -526,7 +594,20 @@ Single node training
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 RECOMPUTE=1 SEQ_LENGTH=8192 MBS=2 BS=16 TE_FP8=0 TP=1 PP=1 FSDP=1 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh 
+      TOKENIZER_MODEL=meta-llama/Llama-3.3-70B-Instruct \
+      CKPT_FORMAT=torch_dist \
+      TEE_OUTPUT=1 \
+      RECOMPUTE=1 \
+      SEQ_LENGTH=8192 \
+      MBS=2 \
+      BS=16 \
+      TE_FP8=0 \
+      TP=1 \
+      PP=1 \
+      FSDP=1 \
+      MODEL_SIZE=70 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama3.sh 
 
    .. note::
 
@@ -535,8 +616,6 @@ Single node training
       parallelism, MCore's distributed optimizer, gradient accumulation fusion,
       or FP16.
 
-      Currently, FSDP is only compatible with BF16 precision.
-
 .. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
 
    To run training on a single node for Llama 3.1 8B FP8, navigate to the Megatron-LM folder and use the
@@ -544,13 +623,29 @@ Single node training
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+      TEE_OUTPUT=1 \
+      MBS=2 \
+      BS=128 \
+      TP=1 \
+      TE_FP8=1 \
+      SEQ_LENGTH=8192 \
+      MODEL_SIZE=8 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama3.sh
 
    For Llama 3.1 8B BF16, use the following command:
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=0 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+      TEE_OUTPUT=1 \
+      MBS=2 \
+      BS=128 \
+      TP=1 \
+      TE_FP8=0 \
+      SEQ_LENGTH=8192 \
+      MODEL_SIZE=8 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama3.sh
 
 .. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
 
@@ -559,7 +654,18 @@ Single node training
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=3 BS=24 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=8192 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+      CKPT_FORMAT=torch_dist \
+      TEE_OUTPUT=1 \
+      MBS=3 \
+      BS=24 \
+      TP=1 \
+      TE_FP8=0 \
+      FSDP=1 \
+      RECOMPUTE=1 \
+      SEQ_LENGTH=8192 \
+      MODEL_SIZE=70 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama3.sh
 
    .. note::
 
@@ -568,7 +674,36 @@ Single node training
       parallelism, MCore's distributed optimizer, gradient accumulation fusion,
       or FP16.
 
-      Currently, FSDP is only compatible with BF16 precision.
+.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b-proxy
+
+   To run the training on a single node for Llama 3.1 70B with proxy, use the following command.
+
+   .. code-block:: shell
+
+      CKPT_FORMAT=torch_dist \
+      TEE_OUTPUT=1 \
+      RECOMPUTE=1 \
+      MBS=3 \
+      BS=24 \
+      TP=1 \
+      TE_FP8=1 \
+      SEQ_LENGTH=8192 \
+      MODEL_SIZE=70 \
+      FSDP=1 \
+      TOTAL_ITERS=10 \
+      NUM_LAYERS=40 \
+      bash examples/llama/train_llama3.sh
+
+   .. note::
+
+      Use two or more nodes to run the *full* Llama 70B model with FP8 precision.
+
+   .. note::
+
+      It is suggested to use ``TP=1`` when FSDP is enabled for higher
+      throughput. FSDP-v2 is not supported with pipeline parallelism, expert
+      parallelism, MCore's distributed optimizer, gradient accumulation fusion,
+      or FP16.
 
 .. container:: model-doc pyt_megatron_lm_train_llama-2-7b
 
@@ -577,13 +712,29 @@ Single node training
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+      TEE_OUTPUT=1 \
+      MBS=4 \
+      BS=256 \
+      TP=1 \
+      TE_FP8=1 \
+      SEQ_LENGTH=4096 \
+      MODEL_SIZE=7 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama2.sh
 
    For Llama 2 7B BF16, use the following command:
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=0 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+      TEE_OUTPUT=1 \
+      MBS=4 \
+      BS=256 \
+      TP=1 \
+      TE_FP8=0 \
+      SEQ_LENGTH=4096 \
+      MODEL_SIZE=7 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama2.sh
 
 .. container:: model-doc pyt_megatron_lm_train_llama-2-70b
 
@@ -592,7 +743,18 @@ Single node training
 
    .. code-block:: shell
 
-      TEE_OUTPUT=1 MBS=7 BS=56 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=4096 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+      CKPT_FORMAT=torch_dist \
+      TEE_OUTPUT=1 \
+      MBS=7 \
+      BS=56 \
+      TP=1 \
+      TE_FP8=0 \
+      FSDP=1 \
+      RECOMPUTE=1 \
+      SEQ_LENGTH=4096 \
+      MODEL_SIZE=70 \
+      TOTAL_ITERS=50 \
+      bash examples/llama/train_llama2.sh
 
    .. note::
 
@@ -601,8 +763,6 @@ Single node training
       parallelism, MCore's distributed optimizer, gradient accumulation fusion,
       or FP16.
 
-      Currently, FSDP is only compatible with BF16 precision.
-
 .. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
 
    To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy, 
@@ -610,7 +770,8 @@ Single node training
 
    .. code-block:: shell
 
-      FORCE_BANLANCE=true \
+      export NVTE_FUSED_ATTN_CK=0
+      FORCE_BALANCE=true \
       RUN_ENV=cluster \
       MODEL_SIZE=671B \
       TRAIN_ITERS=50 \
@@ -632,7 +793,15 @@ Single node training
 
    .. code-block:: shell
 
-      GEMM_TUNING=1 PR=bf16 MBS=4 AC=none SEQ_LEN=4096 PAD_LEN=4096 TRAIN_ITERS=50 bash examples/deepseek_v2/train_deepseekv2.sh
+      export NVTE_FUSED_ATTN_CK=0
+      GEMM_TUNING=1 \
+      PR=bf16 \
+      MBS=4 \
+      AC=none \
+      SEQ_LEN=4096 \
+      PAD_LEN=4096 \
+      TRAIN_ITERS=50 \
+      bash examples/deepseek_v2/train_deepseekv2.sh
 
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
 
@@ -641,7 +810,24 @@ Single node training
 
    .. code-block:: shell
 
-      RECOMPUTE_NUM_LAYERS=0 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=none PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=4096 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x7B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
+      TOKENIZER_MODEL=<path/to/tokenizer/model>
+      RECOMPUTE_NUM_LAYERS=0 \
+      TEE_OUTPUT=1 \
+      MBS=1 \
+      GBS=16 \
+      TP_SIZE=1 \
+      PP_SIZE=1 \
+      AC=none \
+      PR=bf16 \
+      EP_SIZE=8 \
+      ETP_SIZE=1 \
+      SEQLEN=4096 \
+      FORCE_BALANCE=true \
+      MOCK_DATA=1 \
+      RUN_ENV=cluster \
+      MODEL_SIZE=8x7B \
+      TRAIN_ITERS=50 \
+      bash examples/mixtral/train_mixtral_moe.sh
 
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x22b-proxy
 
@@ -650,10 +836,85 @@ Single node training
 
    .. code-block:: shell
 
-      RECOMPUTE_NUM_LAYERS=4 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=full NUM_LAYERS=4 PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=8192 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x22B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
+      TOKENIZER_MODEL=<path/to/tokenizer/model>
+      RECOMPUTE_NUM_LAYERS=4 \
+      TEE_OUTPUT=1 \
+      MBS=1 \
+      GBS=16 \
+      TP_SIZE=1 \
+      PP_SIZE=1 \
+      AC=full \
+      NUM_LAYERS=4 \
+      PR=bf16 \
+      EP_SIZE=8 \
+      ETP_SIZE=1 \
+      SEQLEN=8192 \
+      FORCE_BALANCE=true \
+      MOCK_DATA=1 \
+      RUN_ENV=cluster \
+      MODEL_SIZE=8x22B \
+      TRAIN_ITERS=50 \
+      bash examples/mixtral/train_mixtral_moe.sh
 
-Multi-node training
-^^^^^^^^^^^^^^^^^^^
+.. container:: model-doc pyt_megatron_lm_train_qwen2.5-7b
+
+   To run training on a single node for Qwen 2.5 7B BF16, use the following
+   command.
+
+   .. code-block:: shell
+
+      bash examples/qwen/train_qwen2.sh TP=1 \
+          CP=1 \
+          PP=1 \
+          MBS=10 \
+          BS=640 \
+          TE_FP8=0 \
+          MODEL_SIZE=7 \
+          SEQ_LENGTH=2048 \
+          TOTAL_ITERS=50 \
+          MOCK_DATA=1 \
+          TOKENIZER_MODEL=Qwen/Qwen2.5-7B
+
+   For FP8, use the following command.
+
+   .. code-block:: shell
+
+      bash examples/qwen/train_qwen2.sh \
+          TP=1 \
+          CP=1 \
+          PP=1 \
+          MBS=10 \
+          BS=640 \
+          TE_FP8=1 \
+          MODEL_SIZE=7 \
+          SEQ_LENGTH=2048 \
+          TOTAL_ITERS=50 \
+          MOCK_DATA=1 \
+          TOKENIZER_MODEL=Qwen/Qwen2.5-7B
+
+.. container:: model-doc pyt_megatron_lm_train_qwen2.5-72b
+
+   To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
+
+   .. code-block:: shell
+
+      bash examples/qwen/train_qwen2.sh \
+          FSDP=1 \
+          CP=1 \
+          PP=1 \
+          MBS=3 \
+          BS=24 \
+          TE_FP8=0 \
+          MODEL_SIZE=72 \
+          SEQ_LENGTH=2048 \
+          TOTAL_ITERS=50 \
+          MOCK_DATA=1 \
+          TOKENIZER_MODEL=Qwen/Qwen2.5-72B \
+          RECOMPUTE_ACTIVATIONS=full \
+          CKPT_FORMAT=torch_dist
+
+Multi-node training examples
+----------------------------
 
 To run training on multiple nodes, launch the Docker container on each node.
 For example, for Llama 3 using a two node setup (``NODE0`` as the master node),
@@ -663,13 +924,33 @@ use these commands.
 
   .. code-block:: shell
 
-     TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8  MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=0 bash examples/llama/train_llama3.sh
+     TEE_OUTPUT=1 \
+     MBS=2 \
+     BS=256 \
+     TP=1 \
+     TE_FP8=1 \
+     SEQ_LENGTH=8192 \
+     MODEL_SIZE=8  \
+     MASTER_ADDR=IP_NODE0 \
+     NNODES=2 \
+     NODE_RANK=0 \
+     bash examples/llama/train_llama3.sh
 
 * On the worker node ``NODE1``:
 
   .. code-block:: shell
 
-     TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8  MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=1 bash examples/llama/train_llama3.sh
+     TEE_OUTPUT=1 \
+     MBS=2 \
+     BS=256 \
+     TP=1 \
+     TE_FP8=1 \
+     SEQ_LENGTH=8192 \
+     MODEL_SIZE=8  \
+     MASTER_ADDR=IP_NODE0 \
+     NNODES=2 \
+     NODE_RANK=1 \
+     bash examples/llama/train_llama3.sh
 
 Or, for DeepSeek-V3, an example script ``train_deepseek_v3_slurm.sh`` is
 provided in

docs/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.rst

@@ -73,7 +73,11 @@ document are not validated.
 
       .. code-block:: shell
 
-         python3 tools/run_models.py --tags pyt_mpt30b_training --keep-model-dir --live-output --clean-docker-cache
+         madengine run \
+             --tags pyt_mpt30b_training \
+             --keep-model-dir \
+             --live-output \
+             --clean-docker-cache
 
       .. tip::
 
@@ -90,7 +94,7 @@ document are not validated.
 
          For improved performance (training throughput), consider enabling TunableOp.
          By default, ``pyt_mpt30b_training`` runs with TunableOp disabled. To enable it,
-         run ``tools/run_models.py`` with the ``--tunableop on`` argument or edit the
+         run ``madengine run`` with the ``--tunableop on`` argument or edit the
          ``models.json`` configuration before running training.
 
          Although this might increase the initial training time, it can result in a performance gain.
@@ -172,4 +176,13 @@ Key performance metrics include:
 
     Overall training loss. A decreasing trend indicates the model is learning effectively.
 
+Further reading
+===============
 
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.

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

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

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

@@ -96,14 +96,14 @@ This Docker image is optimized for specific model configurations outlined
 as follows. Performance can vary for other training workloads, as AMD
 doesn’t validate configurations and run conditions outside those described.
 
-.. _amd-maxtext-multi-node-setup:
+.. _amd-maxtext-multi-node-setup-v254:
 
 Multi-node setup
 ----------------
 
 For multi-node environments, ensure you have all the necessary packages for
 your network device, such as, RDMA. If you're not using a multi-node setup
-with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
+with RDMA, skip ahead to :ref:`amd-maxtext-download-docker-v254`.
 
 1. Install the following packages to build and install the RDMA driver.
 
@@ -168,7 +168,7 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
 
    e. RDMA interface
 
-      Ensure the :ref:`required packages <amd-maxtext-multi-node-setup>` are installed on all nodes.
+      Ensure the :ref:`required packages <amd-maxtext-multi-node-setup-v254>` are installed on all nodes.
       Then, set the RDMA interfaces to use for communication.
 
       .. code-block:: bash
@@ -178,7 +178,7 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
          # If using Mellanox NIC
          export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
 
-.. _amd-maxtext-download-docker:
+.. _amd-maxtext-download-docker-v254:
 
 Download the Docker image
 -------------------------
@@ -195,7 +195,7 @@ Download the Docker image
 
       docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.4
 
-.. _amd-maxtext-get-started:
+.. _amd-maxtext-get-started-v254:
 
 Getting started
 ===============

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

@@ -7,41 +7,53 @@ Megatron-LM training performance testing version history
 This table lists previous versions of the ROCm Megatron-LM training Docker image for
 inference performance testing. For detailed information about available models
 for benchmarking, see the version-specific documentation. You can find tagged
-previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`_.
+previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`__.
 
 .. list-table::
    :header-rows: 1
-   :stub-columns: 1
 
    * - Image version
-     - ROCm version
-     - PyTorch version
+     - Components
      - Resources
 
-   * - v25.5
-     - 6.3.4
-     - 2.8.0a0+gite2f9759
+   * - v25.6 (latest)
      - 
-       * `Documentation <../megatron-lm>`_
-       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py312/images/sha256-4506f18ba188d24189c6b1f95130b425f52c528a543bb3f420351824edceadc2>`_
+       * ROCm 6.4.1
+       * PyTorch 2.8.0a0+git7d205b2
+     - 
+       * :doc:`Documentation <../megatron-lm>`
+       * `Docker Hub (py312) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py312/images/sha256-482ff906532285bceabdf2bda629bd32cb6174d2d07f4243a736378001b28df0>`__
+       * `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.6_py310/images/sha256-9627bd9378684fe26cb1a10c7dd817868f553b33402e49b058355b0f095568d6>`__
+
+   * - v25.5
+     - 
+       * ROCm 6.3.4
+       * PyTorch 2.8.0a0+gite2f9759
+     - 
+       * :doc:`Documentation <megatron-lm-v25.5>`
+       * `Docker Hub (py312) <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py312/images/sha256-4506f18ba188d24189c6b1f95130b425f52c528a543bb3f420351824edceadc2>`__
+       * `Docker Hub (py310) <https://hub.docker.com/layers/rocm/megatron-lm/v25.5_py310/images/sha256-743fbf1ceff7a44c4452f938d783a7abf143737d1c15b2b95f6f8a62e0fd048b>`__
 
    * - v25.4
-     - 6.3.0
-     - 2.7.0a0+git637433 
+     - 
+       * ROCm 6.3.0
+       * PyTorch 2.7.0a0+git637433 
      - 
        * :doc:`Documentation <megatron-lm-v25.4>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.4/images/sha256-941aa5387918ea91c376c13083aa1e6c9cab40bb1875abbbb73bbb65d8736b3f>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.4/images/sha256-941aa5387918ea91c376c13083aa1e6c9cab40bb1875abbbb73bbb65d8736b3f>`__
 
    * - v25.3
-     - 6.3.0
-     - 2.7.0a0+git637433 
+     - 
+       * ROCm 6.3.0
+       * PyTorch 2.7.0a0+git637433 
      - 
        * :doc:`Documentation <megatron-lm-v25.3>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.3/images/sha256-1e6ed9bdc3f4ca397300d5a9907e084ab5e8ad1519815ee1f868faf2af1e04e2>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/v25.3/images/sha256-1e6ed9bdc3f4ca397300d5a9907e084ab5e8ad1519815ee1f868faf2af1e04e2>`__
 
    * - v24.12-dev
-     - 6.1.0
-     - 2.4.0
+     - 
+       * ROCm 6.1.0
+       * PyTorch 2.4.0
      - 
        * :doc:`Documentation <megatron-lm-v24.12-dev>`
-       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/24.12-dev/images/sha256-5818c50334ce3d69deeeb8f589d83ec29003817da34158ebc9e2d112b929bf2e>`_
+       * `Docker Hub <https://hub.docker.com/layers/rocm/megatron-lm/24.12-dev/images/sha256-5818c50334ce3d69deeeb8f589d83ec29003817da34158ebc9e2d112b929bf2e>`__

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

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

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

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

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

@@ -90,21 +90,21 @@ The following models are pre-optimized for performance on AMD Instinct MI300X se
    Some models, such as Llama, require an external license agreement through
    a third party (for example, Meta).
 
-.. _amd-megatron-lm-performance-measurements:
+.. _amd-megatron-lm-performance-measurements-v254:
 
 Performance measurements
 ========================
 
 To evaluate performance, the
-`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`__
 page provides reference throughput and latency measurements for training
 popular AI models.
 
 .. important::
 
    The performance data presented in
-   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
-   only reflects the :doc:`latest version of this training benchmarking environment <../megatron-lm>`_.
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`__
+   only reflects the :doc:`latest version of this training benchmarking environment <../megatron-lm>`.
    The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
 
 System validation
@@ -115,7 +115,7 @@ auto-balancing, skip this step. Otherwise, complete the :ref:`system validation
 and optimization steps <train-a-model-system-validation>` to set up your system
 before starting training.
 
-.. _mi300x-amd-megatron-lm-training:
+.. _mi300x-amd-megatron-lm-training-v254:
 
 Environment setup
 =================
@@ -127,7 +127,7 @@ Use the following instructions to set up the environment, configure the script t
 reproduce the benchmark results on MI300X series accelerators with the AMD Megatron-LM Docker
 image.
 
-.. _amd-megatron-lm-requirements:
+.. _amd-megatron-lm-requirements-v254:
  
 Download the Docker image
 -------------------------
@@ -154,7 +154,7 @@ Download the Docker image
 The Docker container includes a pre-installed, verified version of the ROCm Megatron-LM development branch `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__
 (commit `fd6f01 <https://github.com/ROCm/Megatron-LM/tree/fd6f0d11d7f9480ace32f22eb7e4dab5314fa350>`_).
 
-.. _amd-megatron-lm-environment-setup:
+.. _amd-megatron-lm-environment-setup-v254:
 
 Configuration scripts
 ---------------------
@@ -468,7 +468,7 @@ accelerators with the AMD Megatron-LM Docker image.
 Key options
 -----------
 
-.. _amd-megatron-lm-benchmark-test-vars:
+.. _amd-megatron-lm-benchmark-test-vars-v254:
 
 The benchmark tests support the following sets of variables:
 
@@ -568,11 +568,11 @@ Benchmarking examples
                TEE_OUTPUT=1 MBS=5 BS=120 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
                SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
 
-            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
+            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v254>`.
 
             See the sample output:
 
-            .. image:: ../../../../data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
+            .. image:: /data/how-to/rocm-for-ai/llama2-7b-training-log-sample.png
                :width: 800
 
          .. tab-item:: Multi-node training
@@ -597,18 +597,18 @@ Benchmarking examples
                TEE_OUTPUT=1 MBS=4 BS=64 TP=8 TE_FP8=0 NO_TORCH_COMPILE=1
                SEQ_LENGTH=4096 bash examples/llama/train_llama2.sh
 
-            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup>`.
+            You can find the training logs at the location defined in ``$TRAIN_LOG`` in the :ref:`configuration script <amd-megatron-lm-environment-setup-v254>`.
 
             Sample output for 2-node training:
 
             Master node:
 
-            .. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-master.png
+            .. image:: /data/how-to/rocm-for-ai/2-node-training-master.png
                :width: 800
 
             Worker node:
 
-            .. image:: ../../../../data/how-to/rocm-for-ai/2-node-training-worker.png
+            .. image:: /data/how-to/rocm-for-ai/2-node-training-worker.png
                :width: 800
 
 Previous versions

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-v25.5.rst

@@ -0,0 +1,775 @@
+:orphan:
+
+.. meta::
+   :description: How to train a model using Megatron-LM for ROCm.
+   :keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch
+
+******************************************
+Training a model with Megatron-LM for ROCm
+******************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm Megatron-LM
+   training performance documentation. See :doc:`../megatron-lm` for the latest version.
+
+The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
+a specialized fork of the robust Megatron-LM, designed to enable efficient
+training of large-scale language models on AMD GPUs. By leveraging AMD
+Instinct™ MI300X series accelerators, Megatron-LM delivers enhanced
+scalability, performance, and resource utilization for AI workloads. It is
+purpose-built to support models like Llama, DeepSeek, and Mixtral,
+enabling developers to train next-generation AI models more
+efficiently.
+
+AMD provides a ready-to-use Docker image for MI300X series accelerators containing
+essential components, including PyTorch, ROCm libraries, and Megatron-LM
+utilities. It contains the following software components to accelerate training
+workloads:
+
++--------------------------+--------------------------------+
+| Software component       | Version                        |
++==========================+================================+
+| ROCm                     | 6.3.4                          |
++--------------------------+--------------------------------+
+| PyTorch                  | 2.8.0a0+gite2f9759             |
++--------------------------+--------------------------------+
+| Python                   | 3.12 or 3.10                   |
++--------------------------+--------------------------------+
+| Transformer Engine       | 1.13.0+bb061ade                |
++--------------------------+--------------------------------+
+| Flash Attention          | 3.0.0                          |
++--------------------------+--------------------------------+
+| hipBLASLt                | 0.13.0-4f18bf6                 |
++--------------------------+--------------------------------+
+| Triton                   | 3.3.0                          |
++--------------------------+--------------------------------+
+| RCCL                     | 2.22.3                         |
++--------------------------+--------------------------------+
+
+Megatron-LM provides the following key features to train large language models efficiently:
+
+- Transformer Engine (TE)
+
+- APEX
+
+- GEMM tuning
+
+- Torch.compile
+
+- 3D parallelism: TP + SP + CP
+
+- Distributed optimizer
+
+- Flash Attention (FA) 3
+
+- Fused kernels
+
+- Pre-training
+
+.. _amd-megatron-lm-model-support-v255:
+
+The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.5-benchmark-models.yaml
+
+   Supported models
+   ================
+
+   The following models are supported for training performance benchmarking with Megatron-LM and ROCm.
+   Some instructions, commands, and training recommendations in this documentation might
+   vary by model -- select one to get started.
+
+   {% set model_groups = data["megatron-lm_benchmark"].model_groups %}
+
+   .. raw:: html
+
+         <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+           <div class="row">
+             <div class="col-2 me-2 model-param-head">Model</div>
+             <div class="row col-10">
+      {% for model_group in model_groups %}
+               <div class="col-4 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+      {% endfor %}
+             </div>
+           </div>
+
+           <div class="row mt-1">
+             <div class="col-2 me-2 model-param-head">Model variant</div>
+             <div class="row col-10">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% else %}
+               <div class="col-6 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+             </div>
+           </div>
+         </div>
+
+.. note::
+
+   Some models, such as Llama, require an external license agreement through
+   a third party (for example, Meta).
+
+.. _amd-megatron-lm-performance-measurements-v255:
+
+Performance measurements
+========================
+
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`__
+page provides reference throughput and latency measurements for training
+popular AI models.
+
+.. important::
+
+   The performance data presented in
+   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`__
+   only reflects the latest version of this training benchmarking environment.
+   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+.. _mi300x-amd-megatron-lm-training-v255:
+
+Environment setup
+=================
+
+Use the following instructions to set up the environment, configure the script to train models, and
+reproduce the benchmark results on MI300X series accelerators with the AMD Megatron-LM Docker
+image.
+
+.. _amd-megatron-lm-requirements-v255:
+ 
+Download the Docker image
+-------------------------
+
+1. Use the following command to pull the Docker image from Docker Hub.
+
+   .. tab-set:: 
+
+      .. tab-item:: Ubuntu 24.04 + Python 3.12
+         :sync: py312
+
+         .. code-block:: shell
+
+            docker pull rocm/megatron-lm:v25.5_py312
+
+      .. tab-item:: Ubuntu 22.04 + Python 3.10
+         :sync: py310
+
+         .. code-block:: shell
+
+            docker pull rocm/megatron-lm:v25.5_py310
+
+2. Launch the Docker container.
+
+   .. tab-set::
+
+      .. tab-item:: Ubuntu 24.04 + Python 3.12
+         :sync: py312
+
+         .. code-block:: shell
+
+            docker run -it --device /dev/dri --device /dev/kfd --device /dev/infiniband --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 128G --name megatron_training_env rocm/megatron-lm:v25.5_py312
+
+
+      .. tab-item:: Ubuntu 22.04 + Python 3.10
+         :sync: py310
+
+         .. code-block:: shell
+
+            docker run -it --device /dev/dri --device /dev/kfd --device /dev/infiniband --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME:$HOME -v  $HOME/.ssh:/root/.ssh --shm-size 128G --name megatron_training_env rocm/megatron-lm:v25.5_py310
+
+3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
+
+   .. code-block:: shell
+
+      docker start megatron_training_env
+      docker exec -it megatron_training_env bash
+
+The Docker container includes a pre-installed, verified version of the ROCm
+Megatron-LM development branch
+`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__, including necessary
+training scripts.
+
+.. _amd-megatron-lm-environment-setup-v255:
+
+Configuration
+=============
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b
+
+   Update the ``train_llama3.sh`` configuration script in the ``examples/llama``
+   directory of
+   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
+
+.. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
+
+   Update the ``train_llama2.sh`` configuration script in the ``examples/llama``
+   directory of
+   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
+
+   Update the ``train_deepseekv3.sh`` configuration script in the ``examples/deepseek_v3``
+   directory of
+   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to configure your training run.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
+
+   Update the ``train_deepseekv2.sh`` configuration script in the ``examples/deepseek_v2``
+   directory of
+   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v2>`__ to configure your training run.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
+
+.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
+
+   Update the ``train_mixtral_moe.sh`` configuration script in the ``examples/mixtral``
+   directory of
+   `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/mixtral>`__ to configure your training run.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v255>`.
+
+.. note::
+
+   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v255>` for more information on configuration options.
+
+Network interface
+-----------------
+
+Update the network interface in the script to match your system's network interface. To
+find your network interface, run the following (outside of any Docker container):
+
+.. code-block:: bash
+
+   ip a
+
+Look for an active interface that has an IP address in the same subnet as
+your other nodes. Then, update the following variables in the script, for
+example:
+
+.. code-block:: bash
+
+   export NCCL_SOCKET_IFNAME=ens50f0np0
+
+   export GLOO_SOCKET_IFNAME=ens50f0np0
+
+.. _amd-megatron-lm-tokenizer-v255:
+
+Tokenizer
+---------
+
+You can assign the path of an existing tokenizer to the ``TOKENIZER_MODEL`` as shown in the following examples.
+If the tokenizer is not found, it'll be downloaded if publicly available.
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
+
+   If you do not have Llama 3.3 tokenizer locally, you need to use your
+   personal Hugging Face access token ``HF_TOKEN`` to download the tokenizer.
+   See `Llama-3.3-70B-Instruct
+   <https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct>`_. After you are
+   authorized, use your ``HF_TOKEN`` to download the tokenizer and set the
+   variable ``TOKENIZER_MODEL`` to the tokenizer path.
+
+   .. code-block:: shell
+
+      export HF_TOKEN=<Your personal Hugging Face access token>
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="meta-llama/Llama-3.3-70B-Instruct"
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="meta-llama/Llama-3.1-8B"
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="meta-llama/Llama-3.1-70B"
+
+.. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
+
+   The training script uses either the ``Llama2Tokenizer`` or ``HuggingFaceTokenizer`` by default.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="deepseek-ai/DeepSeek-V3"
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
+
+   The training script uses the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL="deepseek-ai/DeepSeek-V2-Lite"
+
+.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
+
+   Download the Mixtral tokenizer.
+
+   .. code-block:: shell
+
+      mkdir tokenizer
+      cd tokenizer
+      export HF_TOKEN=<Your personal Hugging Face access token>
+      wget --header="Authorization: Bearer $HF_TOKEN" -O ./tokenizer.model https://huggingface.co/mistralai/Mixtral-8x7B-v0.1/resolve/main/tokenizer.model
+
+   Use the ``HuggingFaceTokenizer``. Set ``TOKENIZER_MODEL`` to the appropriate Hugging Face model path.
+
+   .. code-block:: shell
+
+      TOKENIZER_MODEL=tokenizer/tokenizer.model
+
+Dataset options
+---------------
+
+You can use either mock data or real data for training.
+
+* Mock data can be useful for testing and validation. Use the ``MOCK_DATA`` variable to toggle between mock and real data. The default
+  value is ``1`` for enabled.
+
+  .. code-block:: bash
+
+     MOCK_DATA=1
+
+* If you're using a real dataset, update the ``DATA_PATH`` variable to point to the location of your dataset.
+
+  .. code-block:: bash
+
+     MOCK_DATA=0
+
+     DATA_PATH="/data/bookcorpus_text_sentence"  # Change to where your dataset is stored
+
+  Ensure that the files are accessible inside the Docker container.
+
+Download the dataset
+^^^^^^^^^^^^^^^^^^^^
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b pyt_megatron_lm_train_llama-3.1-8b pyt_megatron_lm_train_llama-3.1-70b pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
+
+   For Llama models, use the `prepare_dataset.sh
+   <https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`_ script
+   to prepare your dataset.
+   To download the dataset, set the ``DATASET`` variable to the dataset you'd
+   like to use. Three datasets are supported: ``DATASET=wiki``, ``DATASET=fineweb``, and
+   ``DATASET=bookcorpus``.
+
+   .. code-block:: shell
+
+      DATASET=wiki TOKENIZER_MODEL=NousResearch/Llama-2-7b-chat-hf bash examples/llama/prepare_dataset.sh #for wiki-en dataset
+      DATASET=bookcorpus TOKENIZER_MODEL=NousResearch/Llama-2-7b-chat-hf bash examples/llama/prepare_dataset.sh #for bookcorpus dataset
+
+   ``TOKENIZER_MODEL`` can be any accessible Hugging Face tokenizer.
+   Remember to either pre-download the tokenizer or setup Hugging Face access
+   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v255>` section.
+
+   .. note::
+
+      When training set ``DATA_PATH`` to the specific file name prefix pointing to the ``.bin`` or ``.idx``
+      as in the following example:
+
+      .. code-block:: shell
+
+         DATA_PATH="data/bookcorpus_text_sentence" # Change to where your dataset is stored.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
+
+   If you don't already have the dataset, download the DeepSeek dataset using the following
+   commands:
+
+   .. code-block:: shell
+
+      mkdir deepseek-datasets
+      cd deepseek-datasets
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
+
+   To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
+
+   .. code-block:: bash
+
+      MOCK_DATA=0 # Train on real data
+
+      DATA_DIR="<path-to>/deepseek-datasets"  # Change to where your dataset is stored
+
+      Ensure that the files are accessible inside the Docker container.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
+
+   If you don't already have the dataset, download the DeepSeek dataset using the following
+   commands:
+
+   .. code-block:: shell
+
+      mkdir deepseek-datasets
+      cd deepseek-datasets
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/SlimPajama.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-train.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/alpaca_zh-valid.json
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.bin
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/deepseek-datasets/mmap_deepseekv2_datasets_text_document.idx
+
+   To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
+
+   .. code-block:: bash
+
+      MOCK_DATA=0 # Train on real data
+
+      DATA_DIR="<path-to>/deepseek-datasets"  # Change to where your dataset is stored
+
+      Ensure that the files are accessible inside the Docker container.
+
+.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
+
+   If you don't already have the dataset, download the Mixtral dataset using the following
+   commands:
+
+   .. code-block:: shell
+
+      mkdir mixtral-datasets
+      cd mixtral-datasets
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/mistral-datasets/wudao_mistralbpe_content_document.bin
+      wget https://atp-modelzoo-wlcb-pai.oss-cn-wulanchabu.aliyuncs.com/release/models/pai-megatron-patch/mistral-datasets/wudao_mistralbpe_content_document.idx
+
+   To train on this data, update the ``DATA_DIR`` variable to point to the location of your dataset.
+
+   .. code-block:: bash
+
+      MOCK_DATA=0 # Train on real data
+
+      DATA_DIR="<path-to>/mixtral-datasets"  # Change to where your dataset is stored
+
+   Ensure that the files are accessible inside the Docker container.
+
+Multi-node configuration
+------------------------
+
+If you're running multi-node training, update the following environment variables. They can
+also be passed as command line arguments. Refer to the following example configurations.
+
+* Change ``localhost`` to the master node's hostname:
+
+  .. code-block:: shell
+
+     MASTER_ADDR="${MASTER_ADDR:-localhost}"
+
+* Set the number of nodes you want to train on (for instance, ``2``, ``4``, ``8``):
+
+  .. code-block:: shell
+
+     NNODES="${NNODES:-1}"
+
+* Set the rank of each node (0 for master, 1 for the first worker node, and so on):
+
+  .. code-block:: shell
+
+     NODE_RANK="${NODE_RANK:-0}"
+
+* Set ``DATA_CACHE_PATH`` to a common directory accessible by all the nodes (for example, an
+  NFS directory) for multi-node runs:
+
+  .. code-block:: shell
+
+     DATA_CACHE_PATH=/root/cache # Set to a common directory for multi-node runs
+
+* For multi-node runs, make sure the correct network drivers are installed on the nodes. If
+  inside a Docker container, either install the drivers inside the Docker container or pass the network
+  drivers from the host while creating the Docker container.
+
+  .. code-block:: shell
+
+     # Specify which RDMA interfaces to use for communication
+     export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
+
+Getting started
+===============
+
+The prebuilt Megatron-LM with ROCm training environment allows users to quickly validate
+system performance, conduct training benchmarks, and achieve superior
+performance for models like Llama, DeepSeek, and Mixtral. This container should not be
+expected to provide generalized performance across all training workloads. You
+can expect the container to perform in the model configurations described in
+the following section, but other configurations are not validated by AMD.
+
+.. _amd-megatron-lm-run-training-v255:
+
+Run training
+------------
+
+Use the following example commands to set up the environment, configure
+:ref:`key options <amd-megatron-lm-benchmark-test-vars-v255>`, and run training on
+MI300X series accelerators with the AMD Megatron-LM environment.
+
+Single node training
+^^^^^^^^^^^^^^^^^^^^
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.3-70b
+
+   To run the training on a single node for Llama 3.3 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
+   For example, use the following command:
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 RECOMPUTE=1 SEQ_LENGTH=8192 MBS=2 BS=16 TE_FP8=0 TP=1 PP=1 FSDP=1 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh 
+
+   .. note::
+
+      It is suggested to use ``TP=1`` when FSDP is enabled for higher
+      throughput. FSDP-v2 is not supported with pipeline parallelism, expert
+      parallelism, MCore's distributed optimizer, gradient accumulation fusion,
+      or FP16.
+
+      Currently, FSDP is only compatible with BF16 precision.
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.1-8b
+
+   To run training on a single node for Llama 3.1 8B FP8, navigate to the Megatron-LM folder and use the
+   following command.
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+
+   For Llama 3.1 8B BF16, use the following command:
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=2 BS=128 TP=1 TE_FP8=0 SEQ_LENGTH=8192 MODEL_SIZE=8 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+
+.. container:: model-doc pyt_megatron_lm_train_llama-3.1-70b
+
+   To run the training on a single node for Llama 3.1 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
+   For example, use the following command:
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=3 BS=24 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=8192 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama3.sh
+
+   .. note::
+
+      It is suggested to use ``TP=1`` when FSDP is enabled for higher
+      throughput. FSDP-v2 is not supported with pipeline parallelism, expert
+      parallelism, MCore's distributed optimizer, gradient accumulation fusion,
+      or FP16.
+
+      Currently, FSDP is only compatible with BF16 precision.
+
+.. container:: model-doc pyt_megatron_lm_train_llama-2-7b
+
+   To run training on a single node for Llama 2 7B FP8, navigate to the Megatron-LM folder and use the
+   following command.
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+
+   For Llama 2 7B BF16, use the following command:
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=4 BS=256 TP=1 TE_FP8=0 SEQ_LENGTH=4096 MODEL_SIZE=7 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+
+.. container:: model-doc pyt_megatron_lm_train_llama-2-70b
+
+   To run the training on a single node for Llama 2 70B BF16 with FSDP-v2 enabled, add the ``FSDP=1`` argument.
+   For example, use the following command:
+
+   .. code-block:: shell
+
+      TEE_OUTPUT=1 MBS=7 BS=56 TP=1 TE_FP8=0 FSDP=1 RECOMPUTE=1 SEQ_LENGTH=4096 MODEL_SIZE=70 TOTAL_ITERS=50 bash examples/llama/train_llama2.sh
+
+   .. note::
+
+      It is suggested to use ``TP=1`` when FSDP is enabled for higher
+      throughput. FSDP-v2 is not supported with pipeline parallelism, expert
+      parallelism, MCore's distributed optimizer, gradient accumulation fusion,
+      or FP16.
+
+      Currently, FSDP is only compatible with BF16 precision.
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
+
+   To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy, 
+   navigate to the Megatron-LM folder and use the following command.
+
+   .. code-block:: shell
+
+      FORCE_BANLANCE=true \
+      RUN_ENV=cluster \
+      MODEL_SIZE=671B \
+      TRAIN_ITERS=50 \
+      SEQ_LEN=4096 \
+      NUM_LAYERS=3 \
+      MICRO_BATCH_SIZE=1 GLOBAL_BATCH_SIZE=32 \
+      PR=bf16 \
+      TP=1 PP=1 ETP=1 EP=8 \
+      GEMM_TUNING=1 \
+      NVTE_CK_USES_BWD_V3=1 \
+      USE_GROUPED_GEMM=true MOE_USE_LEGACY_GROUPED_GEMM=true \
+      GPT_LAYER_IN_TE=true \
+      bash examples/deepseek_v3/train_deepseekv3.sh
+
+.. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
+
+   To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel),
+   navigate to the Megatron-LM folder and use the following command.
+
+   .. code-block:: shell
+
+      GEMM_TUNING=1 PR=bf16 MBS=4 AC=none SEQ_LEN=4096 PAD_LEN=4096 TRAIN_ITERS=50 bash examples/deepseek_v2/train_deepseekv2.sh
+
+.. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b
+
+   To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
+   navigate to the Megatron-LM folder and use the following command.
+
+   .. code-block:: shell
+
+      RECOMPUTE_NUM_LAYERS=0 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=none PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=4096 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x7B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
+
+.. container:: model-doc pyt_megatron_lm_train_mixtral-8x22b-proxy
+
+   To run training on a single node for Mixtral 8x7B (MoE with expert parallel) with 4-layer proxy,
+   navigate to the Megatron-LM folder and use the following command.
+
+   .. code-block:: shell
+
+      RECOMPUTE_NUM_LAYERS=4 TEE_OUTPUT=1 MBS=1 GBS=16 TP_SIZE=1 PP_SIZE=1 AC=full NUM_LAYERS=4 PR=bf16 EP_SIZE=8 ETP_SIZE=1 SEQLEN=8192 FORCE_BALANCE=true MOCK_DATA=1 RUN_ENV=cluster MODEL_SIZE=8x22B TRAIN_ITERS=50 bash examples/mixtral/train_mixtral_moe.sh
+
+Multi-node training
+^^^^^^^^^^^^^^^^^^^
+
+To run training on multiple nodes, launch the Docker container on each node.
+For example, for Llama 3 using a two node setup (``NODE0`` as the master node),
+use these commands.
+
+* On the master node ``NODE0``:
+
+  .. code-block:: shell
+
+     TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8  MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=0 bash examples/llama/train_llama3.sh
+
+* On the worker node ``NODE1``:
+
+  .. code-block:: shell
+
+     TEE_OUTPUT=1 MBS=2 BS=256 TP=1 TE_FP8=1 SEQ_LENGTH=8192 MODEL_SIZE=8  MASTER_ADDR=IP_NODE0 NNODES=2 NODE_RANK=1 bash examples/llama/train_llama3.sh
+
+Or, for DeepSeek-V3, an example script ``train_deepseek_v3_slurm.sh`` is
+provided in
+`<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to
+enable training at scale under a SLURM environment. For example, to run
+training on 16 nodes, try the following command:
+
+.. code-block:: shell
+
+   sbatch examples/deepseek_v3/train_deepseek_v3_slurm.sh
+
+.. _amd-megatron-lm-benchmark-test-vars-v255:
+
+Key options
+-----------
+
+The benchmark tests support the following sets of variables.
+
+``TEE_OUTPUT``
+  ``1`` to enable training logs or ``0`` to disable.
+
+``TE_FP8``
+  ``0`` for B16 or ``1`` for FP8 -- ``0`` by default.
+
+``GEMM_TUNING``
+  ``1`` to enable GEMM tuning, which boosts performance by using the best GEMM kernels.
+
+``USE_FLASH_ATTN``
+  ``1`` to enable Flash Attention.
+
+``FSDP``
+  ``1`` to enable PyTorch FSDP2. If FSDP is enabled, ``--use-distributed-optimizer``,
+  ``--overlap-param-gather``, and ``--sequence-parallel`` are automatically disabled.
+
+``ENABLE_PROFILING``
+  ``1`` to enable PyTorch profiling for performance analysis.
+
+``transformer-impl``
+  ``transformer_engine`` to use the Transformer Engine (TE) or ``local`` to disable TE.
+
+``MODEL_SIZE``
+  ``8B`` or ``70B`` for Llama 3 and 3.1. ``7B`` or ``70B`` for Llama 2, for example.
+
+``TOTAL_ITERS``
+  The total number of iterations -- ``10`` by default.
+
+``MOCK_DATA``
+  ``1`` to use mock data or ``0`` to use real data you provide.
+
+``MBS``
+  Micro batch size.
+
+``BS``
+  Global batch size.
+
+``TP`` / ``TP_SIZE``
+  Tensor parallel (``1``, ``2``, ``4``, ``8``). ``TP`` is disabled when ``FSDP`` is turned on.
+
+``EP`` / ``EP_SIZE``
+  Expert parallel for MoE models.
+
+``SEQ_LENGTH``
+  Input sequence length.
+
+``PR``
+  Precision for training. ``bf16`` for BF16 (default) or ``fp8`` for FP8 GEMMs.
+
+``AC``
+  Activation checkpointing (``none``, ``sel``, or ``full``) -- ``sel`` by default.
+
+``NUM_LAYERS``
+  Use reduced number of layers as a proxy model.
+
+``RECOMPUTE_NUM_LAYERS``
+  Number of layers used for checkpointing recompute.
+
+Previous versions
+=================
+
+See :doc:`megatron-lm-history` to find documentation for previous releases
+of the ``ROCm/megatron-lm`` Docker image.

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history.rst

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

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

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

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

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

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

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

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

@@ -142,7 +142,11 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
             .. code-block:: shell
 
                export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
-               python3 tools/run_models.py --tags {{ model.mad_tag }} --keep-model-dir --live-output --timeout 28800
+               madengine run \
+                   --tags {{ model.mad_tag }} \
+                   --keep-model-dir \
+                   --live-output \
+                   --timeout 28800
 
             MAD launches a Docker container with the name
             ``container_ci-{{ model.mad_tag }}``, for example. The latency and throughput reports of the
@@ -427,6 +431,17 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
 
                For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
 
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
 Previous versions
 =================
 

docs/how-to/rocm-for-hpc/index.rst

@@ -76,14 +76,6 @@ Ubuntu versions.
           single node workstations, multi and many-core nodes, clusters of nodes via
           QMP, and classic vector computers.
 
-      * -
-        - `Grid <https://github.com/amd/InfinityHub-CI/tree/main/grid/>`_
-        - Grid is a library for lattice QCD calculations that employs a high-level data parallel
-          approach while using a number of techniques to target multiple types of parallelism.
-          The library currently supports MPI, OpenMP and short vector parallelism. The SIMD
-          instructions sets covered include SSE, AVX, AVX2, FMA4, IMCI and AVX512. Recent
-          releases expanded this support to include GPU offloading.
-
       * -
         - `MILC <https://github.com/amd/InfinityHub-CI/tree/main/milc/>`_
         - The MILC Code is a set of research codes developed by MIMD Lattice Computation
@@ -237,12 +229,18 @@ Ubuntu versions.
           of these applications.
 
       * - Tools and libraries
-        - `ROCm with GPU-aware MPI container <https://github.com/amd/InfinityHub-CI/tree/main/base-gpu-mpi-rocm-docker>`_
+        - `AMD ROCm with OpenMPI container <https://github.com/amd/InfinityHub-CI/tree/main/base-gpu-mpi-rocm-docker>`_
         - Base container for GPU-aware MPI with ROCm for HPC applications. This
           project provides a boilerplate for building and running a Docker
           container with ROCm supporting GPU-aware MPI implementations using
           OpenMPI or UCX.
-
+      
+      * - 
+        - `AMD ROCm with MPICH container <https://github.com/amd/InfinityHub-CI/tree/main/base-mpich-rocm-docker>`_
+        - Base container for GPU-aware MPI with ROCm for HPC applications. This
+          project provides a boilerplate for building and running a Docker
+          container with ROCm supporting GPU-aware MPI implementations using MPICH.
+      
       * -
         - `Kokkos <https://github.com/amd/InfinityHub-CI/tree/main/kokkos>`_
         - Kokkos is a programming model in C++ for writing performance portable

docs/how-to/system-optimization/w6000-v620.md

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

docs/how-to/tuning-guides/mi300x/index.rst

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

docs/reference/gpu-arch-specs.rst

@@ -285,7 +285,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - Radeon AI PRO R9700
           - RDNA4
           - gfx1201
-          - 16
+          - 32
           - 64
           - 32 or 64
           - 128

docs/reference/graph-safe-support.rst

@@ -93,7 +93,7 @@ The following table shows whether a ROCm library is graph-safe.
       - ⚠️ (experimental)
     * 
       - `rocThrust <https://github.com/ROCm/rocThrust>`_
-      - ❌ (see :doc:`details <rocthrust:hipgraph-support>`)
+      - ❌ (see :doc:`details <rocthrust:reference/rocThrust-hipgraph-support>`)
     * 
       - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
       - ❌

docs/release/versions.md

@@ -10,6 +10,7 @@
 
 | Version | Release date |
 | ------- | ------------ |
+| [6.4.2](https://rocm.docs.amd.com/en/docs-6.4.2/) | July 21, 2025 |
 | [6.4.1](https://rocm.docs.amd.com/en/docs-6.4.1/) | May 21, 2025 |
 | [6.4.0](https://rocm.docs.amd.com/en/docs-6.4.0/) | April 11, 2025 |
 | [6.3.3](https://rocm.docs.amd.com/en/docs-6.3.3/) | February 19, 2025 |

docs/sphinx/_toc.yml.in

@@ -19,9 +19,9 @@ subtrees:
 
 - caption: Install
   entries:
-  - url: https://rocm.docs.amd.com/projects/install-on-linux/en/latest/
+  - url: https://rocm.docs.amd.com/projects/install-on-linux/en/${branch}/
     title: ROCm on Linux
-  - url: https://rocm.docs.amd.com/projects/install-on-windows/en/${branch}/
+  - url: https://rocm.docs.amd.com/projects/install-on-windows/en/latest/
     title: HIP SDK on Windows
   - url: https://rocm.docs.amd.com/projects/radeon/en/latest/index.html
     title: ROCm on Radeon GPUs
@@ -82,6 +82,8 @@ subtrees:
             title: vLLM inference performance testing
           - file: how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
             title: PyTorch inference performance testing
+          - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
+            title: SGLang inference performance testing
           - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
             title: Deploy your model
 

docs/sphinx/requirements.in

@@ -1,4 +1,4 @@
-rocm-docs-core==1.20.1
+rocm-docs-core==1.26.0
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0