Docs: Pytorch compatibility page update

[docs/6.4.1] Fix Sphinx issue in vllm-benchmark 0.8.5-20250513 previous version (#…

.azuredevops/components/HIP.yml

@@ -77,7 +77,8 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       componentName: clr
-      cmakeBuildDir: 'clr/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
       extraBuildFlags: >-
         -DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
         -DHIP_PLATFORM=amd
@@ -138,7 +139,8 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       componentName: clr
-      cmakeBuildDir: 'clr/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/clr/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/clr'
       extraBuildFlags: >-
         -DHIP_COMMON_DIR=$(Build.SourcesDirectory)/HIP
         -DHIP_PLATFORM=nvidia

.azuredevops/components/HIPIFY.yml

@@ -73,6 +73,7 @@ jobs:
     parameters:
       componentName: upstream-llvm
       cmakeBuildDir: $(Pipeline.Workspace)/llvm-project/llvm/build
+      cmakeSourceDir: $(Pipeline.Workspace)/llvm-project/llvm
       installDir: $(Pipeline.Workspace)/llvm
       extraBuildFlags: >-
         -DCMAKE_BUILD_TYPE=Release

.azuredevops/components/MIOpen.yml

@@ -214,7 +214,7 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
       parameters:
         componentName: MIOpen
-        testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex test_rnn_seq_api'
+        testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml --exclude-regex "test_rnn_seq_api|GPU_Conv2dTuningAsm_FP32"'
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
       parameters:
         aptPackages: ${{ parameters.aptPackages }}

.azuredevops/components/ROCgdb.yml

@@ -15,6 +15,7 @@ parameters:
   type: object
   default:
     - bison
+    - cmake
     - dejagnu
     - flex
     - libbabeltrace-dev
@@ -39,17 +40,69 @@ parameters:
 - name: jobMatrix
   type: object
   default:
-    buildTestJobs:
+    testJobs:
       - gfx942:
         target: gfx942
       - gfx90a:
         target: gfx90a
 
 jobs:
-- ${{ each job in parameters.jobMatrix.buildTestJobs }}:
-  - job: ROCgdb_build_test_${{ job.target }}
+- job: ROCgdb
+  variables:
+  - group: common
+  - template: /.azuredevops/variables-global.yml
+  - name: PKG_CONFIG_PATH
+    value: $(Agent.BuildDirectory)/rocm/share/pkgconfig
+  pool:
+    vmImage: ${{ variables.BASE_BUILD_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/checkout.yml
+    parameters:
+      checkoutRepo: ${{ parameters.checkoutRepo }}
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-aqlprofile.yml
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
+    parameters:
+      checkoutRef: ${{ parameters.checkoutRef }}
+      dependencyList: ${{ parameters.rocmDependencies }}
+      aggregatePipeline: ${{ parameters.aggregatePipeline }}
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-autotools.yml
+    parameters:
+      configureFlags: >-
+        --program-prefix=roc
+        --enable-64-bit-bfd
+        --enable-targets="x86_64-linux-gnu,amdgcn-amd-amdhsa"
+        --disable-ld
+        --disable-gas
+        --disable-gdbserver
+        --disable-sim
+        --enable-tui
+        --disable-gdbtk
+        --disable-shared
+        --disable-gprofng
+        --with-expat
+        --with-system-zlib
+        --without-guile
+        --with-babeltrace
+        --with-lzma
+        --with-python=python3
+        --with-rocm-dbgapi=$(Agent.BuildDirectory)/rocm
+        LDFLAGS="-Wl,--enable-new-dtags,-rpath=$(Agent.BuildDirectory)/rocm/lib"
+      makeCallPrefix: LD_RUN_PATH='${ORIGIN}/../lib'
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
+  - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
+
+- ${{ each job in parameters.jobMatrix.testJobs }}:
+  - job: ROCgdb_test_${{ job.target }}
+    dependsOn: ROCgdb
     condition:
-      and(
+      and(succeeded(),
         eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
         not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), variables['Build.DefinitionName'])),
         eq(${{ parameters.aggregatePipeline }}, False)
@@ -99,8 +152,6 @@ jobs:
           --with-rocm-dbgapi=$(Agent.BuildDirectory)/rocm
           LDFLAGS="-Wl,--enable-new-dtags,-rpath=$(Agent.BuildDirectory)/rocm/lib"
         makeCallPrefix: LD_RUN_PATH='${ORIGIN}/../lib'
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
     - task: Bash@3
       displayName: Setup test environment
       inputs:
@@ -109,7 +160,6 @@ jobs:
           # Assuming that /opt is no longer persistent across runs, test environments are fully ephemeral
           sudo ln -s $(Agent.BuildDirectory)/rocm /opt/rocm
           echo "##vso[task.prependpath]/opt/rocm/bin"
-    - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
     - task: Bash@3
       displayName: check-gdb

.azuredevops/components/ROCmValidationSuite.yml

@@ -27,6 +27,7 @@ parameters:
   type: object
   default:
     - amdsmi
+    - aomp
     - clr
     - hipBLAS-common
     - hipBLASLt
@@ -43,6 +44,7 @@ parameters:
   type: object
   default:
     - amdsmi
+    - aomp
     - clr
     - hipBLAS-common
     - hipBLASLt
@@ -108,6 +110,7 @@ jobs:
           -DROCM_PATH=$(Agent.BuildDirectory)/rocm
           -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/clang++
           -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm
+          -DCMAKE_CXX_FLAGS=-I$(Agent.BuildDirectory)/rocm/llvm/include
           -DCPACK_PACKAGING_INSTALL_PREFIX=$(Build.BinariesDirectory)
           -GNinja
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml

.azuredevops/components/aomp.yml

@@ -118,6 +118,7 @@ jobs:
     parameters:
       componentName: extras
       cmakeBuildDir: '$(Build.SourcesDirectory)/aomp-extras/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/aomp-extras'
       installDir: '$(Build.BinariesDirectory)/llvm'
       extraBuildFlags: >-
         -DLLVM_DIR=$(Agent.BuildDirectory)/rocm/llvm
@@ -129,6 +130,7 @@ jobs:
     parameters:
       componentName: openmp
       cmakeBuildDir: '$(Build.SourcesDirectory)/llvm-project/openmp/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/llvm-project/openmp'
       installDir: '$(Build.BinariesDirectory)/llvm'
       extraBuildFlags: >-
         -DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm;$(Build.BinariesDirectory)"
@@ -155,6 +157,7 @@ jobs:
     parameters:
       componentName: offload
       cmakeBuildDir: '$(Build.SourcesDirectory)/llvm-project/offload/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/llvm-project/offload'
       installDir: '$(Build.BinariesDirectory)/llvm'
       extraBuildFlags: >-
         -DCMAKE_PREFIX_PATH="$(Agent.BuildDirectory)/rocm;$(Build.BinariesDirectory)"

.azuredevops/components/copyHIP.yml

@@ -26,9 +26,11 @@ jobs:
     parameters:
       componentName: HIP
       pipelineId: $(HIP_PIPELINE_ID)
-  - template:  ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-prepare-package.yml
-    parameters:
-      sourceDir: $(Agent.BuildDirectory)/rocm
+  - task: Bash@3
+    displayName: Copy HIP artifacts
+    inputs:
+      targetType: inline
+      script: cp -a $(Agent.BuildDirectory)/rocm/* $(Build.BinariesDirectory)/
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml

.azuredevops/components/hipSOLVER.yml

@@ -92,7 +92,8 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         componentName: external
-        cmakeBuildDir: 'deps/build'
+        cmakeBuildDir: '$(Build.SourcesDirectory)/deps/build'
+        cmakeSourceDir: '$(Build.SourcesDirectory)/deps'
         installDir: '$(Pipeline.Workspace)/deps-install'
         extraBuildFlags: >-
           -DBUILD_BOOST=OFF

.azuredevops/components/llvm-project.yml

@@ -83,7 +83,8 @@ jobs:
         -DROCM_LLVM_BACKWARD_COMPAT_LINK=$(Build.BinariesDirectory)/llvm
         -DROCM_LLVM_BACKWARD_COMPAT_LINK_TARGET=./lib/llvm
         -GNinja
-      cmakeBuildDir: 'llvm/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/llvm/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/llvm'
       installDir: '$(Build.BinariesDirectory)/llvm'
 # use llvm-lit to run unit tests for llvm, clang, and lld
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
@@ -121,7 +122,8 @@ jobs:
       extraBuildFlags: >-
         -DCMAKE_PREFIX_PATH="$(Build.SourcesDirectory)/llvm/build"
         -DCMAKE_BUILD_TYPE=Release
-      cmakeBuildDir: 'amd/device-libs/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/amd/device-libs/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/amd/device-libs'
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       componentName: comgr
@@ -129,7 +131,8 @@ jobs:
         -DCMAKE_PREFIX_PATH="$(Build.SourcesDirectory)/llvm/build;$(Build.SourcesDirectory)/amd/device-libs/build"
         -DCOMGR_DISABLE_SPIRV=1
         -DCMAKE_BUILD_TYPE=Release
-      cmakeBuildDir: 'amd/comgr/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/amd/comgr/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/amd/comgr'
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
     parameters:
       componentName: comgr
@@ -142,7 +145,8 @@ jobs:
       extraBuildFlags: >-
         -DCMAKE_BUILD_TYPE=Release
         -DHIPCC_BACKWARD_COMPATIBILITY=OFF
-      cmakeBuildDir: 'amd/hipcc/build'
+      cmakeBuildDir: '$(Build.SourcesDirectory)/amd/hipcc/build'
+      cmakeSourceDir: '$(Build.SourcesDirectory)/amd/hipcc'
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-upload.yml
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/artifact-links.yml

.azuredevops/components/rdc.yml

@@ -105,6 +105,7 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:
         cmakeBuildDir: $(Build.SourcesDirectory)/grpc/build
+        cmakeSourceDir: $(Build.SourcesDirectory)/grpc
         installDir: $(Build.SourcesDirectory)/bin
         extraBuildFlags: >-
           -DgRPC_INSTALL=ON

.azuredevops/components/rocAL.yml

@@ -125,6 +125,7 @@ jobs:
       parameters:
         componentName: PyBind11
         cmakeBuildDir: '$(Build.SourcesDirectory)/pybind11/build'
+        cmakeSourceDir: '$(Build.SourcesDirectory)/pybind11'
         customInstallPath: false
         installEnabled: false
         extraBuildFlags: >-
@@ -141,6 +142,7 @@ jobs:
       parameters:
         componentName: RapidJSON
         cmakeBuildDir: '$(Build.SourcesDirectory)/rapidjson/build'
+        cmakeSourceDir: '$(Build.SourcesDirectory)/rapidjson'
         customInstallPath: false
         installEnabled: false
         extraBuildFlags: >-
@@ -200,7 +202,6 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm/include/rocal
     pool:
       name: ${{ job.target }}_test_pool
-      demands: firstRenderDeviceAccess
     workspace:
       clean: all
     steps:

.azuredevops/components/rocDecode.yml

@@ -108,7 +108,6 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm
     pool:
       name: ${{ job.target }}_test_pool
-      demands: firstRenderDeviceAccess
     workspace:
       clean: all
     steps:

.azuredevops/components/rocJPEG.yml

@@ -114,7 +114,6 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm
     pool:
       name: ${{ job.target }}_test_pool
-      demands: firstRenderDeviceAccess
     workspace:
       clean: all
     steps:

.azuredevops/components/rocPRIM.yml

@@ -5,6 +5,12 @@ parameters:
 - name: checkoutRef
   type: string
   default: ''
+- name: sparseCheckout
+  type: boolean
+  default: false
+- name: sparseCheckoutDir
+  type: string
+  default: ''
 # set to true if doing full build of ROCm stack
 # and dependencies are pulled from same pipeline
 - name: aggregatePipeline
@@ -66,6 +72,8 @@ jobs:
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
+        sparseCheckout: ${{ parameters.sparseCheckout }}
+        sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
       parameters:
         checkoutRef: ${{ parameters.checkoutRef }}

.azuredevops/components/rocPyDecode.yml

@@ -168,7 +168,6 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm
     pool:
       name: ${{ job.target }}_test_pool
-      demands: firstRenderDeviceAccess
     workspace:
       clean: all
     steps:

.azuredevops/components/rocSOLVER.yml

@@ -105,6 +105,7 @@ jobs:
           -DLAPACKE=OFF
           -GNinja
         cmakeBuildDir: '$(Build.SourcesDirectory)/lapack/build'
+        cmakeSourceDir: '$(Build.SourcesDirectory)/lapack'
         installDir: '$(Pipeline.Workspace)/deps-install'
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
       parameters:

.azuredevops/components/rocm-examples.yml

@@ -183,6 +183,7 @@ 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"'
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
       parameters:
         aptPackages: ${{ parameters.aptPackages }}

.azuredevops/components/rocprofiler-systems.yml

@@ -67,6 +67,7 @@ parameters:
     - rocprofiler-register
     - rocprofiler-sdk
     - ROCR-Runtime
+    - roctracer
 
 - name: jobMatrix
   type: object
@@ -166,7 +167,6 @@ jobs:
       value: $(Agent.BuildDirectory)/rocm
     pool:
       name: ${{ job.target }}_test_pool
-      demands: firstRenderDeviceAccess
     workspace:
       clean: all
     steps:

.azuredevops/dependencies/grpc.yml

@@ -38,6 +38,7 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       cmakeBuildDir: $(Agent.BuildDirectory)/grpc/build
+      cmakeSourceDir: $(Agent.BuildDirectory)/grpc
       extraBuildFlags: >-
         -DgRPC_INSTALL=ON
         -DgRPC_BUILD_TESTS=OFF

.azuredevops/dependencies/gtest.yml

@@ -38,6 +38,7 @@ jobs:
   - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
     parameters:
       cmakeBuildDir: $(Agent.BuildDirectory)/googletest/build
+      cmakeSourceDir: $(Agent.BuildDirectory)/googletest
       extraBuildFlags: >-
         -DGTEST_FORCE_SHARED_CRT=ON
         -DCMAKE_DEBUG_POSTFIX=d

.azuredevops/templates/steps/build-cmake.yml

@@ -10,10 +10,10 @@ parameters:
   default: ''
 - name: cmakeBuildDir
   type: string
-  default: 'build'
+  default: $(Agent.BuildDirectory)/s/build
 - name: cmakeSourceDir
   type: string
-  default: '..'
+  default: $(Agent.BuildDirectory)/s
 - name: customBuildTarget
   type: string
   default: ''
@@ -46,7 +46,7 @@ steps:
     ${{ if eq(parameters.customInstallPath, true) }}:
       cmakeArgs: -DCMAKE_INSTALL_PREFIX=${{ parameters.installDir }} ${{ parameters.extraBuildFlags }} ${{ parameters.cmakeSourceDir }}
     ${{ else }}:
-      cmakeArgs: ${{ parameters.extraBuildFlags }} ..
+      cmakeArgs: ${{ parameters.extraBuildFlags }} ${{ parameters.cmakeSourceDir }}
 - ${{ if parameters.printDiskSpace }}:
   - script: df -h
     displayName: Disk space before build

.azuredevops/templates/steps/checkout.yml

@@ -4,6 +4,12 @@ parameters:
 - name: checkoutRepo
   type: string
   default: 'self'
+- name: sparseCheckout
+  type: boolean
+  default: false
+- name: sparseCheckoutDir
+  type: string
+  default: ''
 # submodule download behaviour
 # change to 'recursive' for repos with submodules
 - name: submoduleBehaviour
@@ -15,3 +21,13 @@ steps:
     clean: true
     submodules: ${{ parameters.submoduleBehaviour }}
     retryCountOnTaskFailure: 3
+    fetchFilter: blob:none
+    ${{ if eq(parameters.sparseCheckout, true) }}:
+      sparseCheckoutDirectories: ${{ parameters.sparseCheckoutDir }}
+      path: sparse
+  - ${{ if eq(parameters.sparseCheckout, true) }}:
+    - task: Bash@3
+      displayName: Symlink sparse checkout
+      inputs:
+        targetType: inline
+        script: ln -s $(Agent.BuildDirectory)/sparse/${{ parameters.sparseCheckoutDir }} $(Agent.BuildDirectory)/s

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

@@ -463,7 +463,7 @@ steps:
   displayName: 'List downloaded ROCm files'
   inputs:
     targetType: inline
-    script: ls -1R $(Agent.BuildDirectory)/rocm
+    script: ls -la1R $(Agent.BuildDirectory)/rocm
 - ${{ if eq(parameters.skipLibraryLinking, false) }}:
   - task: Bash@3
     displayName: 'Link ROCm shared libraries'

.azuredevops/templates/steps/docker-container.yml

@@ -106,6 +106,7 @@ parameters:
   type: object
   default:
     - gfx90a
+    - gfx942
 
 steps:
 # these steps should only be run if there was a failure or warning

.wordlist.txt

@@ -32,8 +32,10 @@ Andrej
 Arb
 Autocast
 BARs
+BatchNorm
 BLAS
 BMC
+BabelStream
 Blit
 Blockwise
 Bluefield
@@ -124,6 +126,7 @@ FX
 Filesystem
 FindDb
 Flang
+FlashAttention
 FluxBenchmark
 Fortran
 Fuyu
@@ -138,6 +141,7 @@ GDR
 GDS
 GEMM
 GEMMs
+GFLOPS
 GFortran
 GFXIP
 Gemma
@@ -226,6 +230,8 @@ LM
 LSAN
 LSan
 LTS
+LSTMs
+LanguageCrossEntropy
 LoRA
 MEM
 MERCHANTABILITY
@@ -243,6 +249,7 @@ MMIOH
 MMU
 MNIST
 MPI
+MPT
 MSVC
 MVAPICH
 MVFFR
@@ -259,6 +266,7 @@ Meta's
 Miniconda
 MirroredStrategy
 Mixtral
+MosaicML
 Multicore
 Multithreaded
 MyEnvironment
@@ -267,6 +275,7 @@ NBIO
 NBIOs
 NCCL
 NCF
+NFS
 NIC
 NICs
 NLI
@@ -329,6 +338,7 @@ PipelineParallel
 PnP
 PowerEdge
 PowerShell
+Pretrained
 Pretraining
 Profiler's
 PyPi
@@ -376,6 +386,7 @@ Ryzen
 SALU
 SBIOS
 SCA
+ScaledGEMM
 SDK
 SDMA
 SDPA
@@ -416,6 +427,8 @@ TCI
 TCIU
 TCP
 TCR
+TensorRT
+TensorFloat
 TF
 TFLOPS
 TP
@@ -494,6 +507,7 @@ ZenDNN
 accuracies
 activations
 addr
+ade
 ai
 alloc
 allocatable
@@ -501,6 +515,7 @@ allocator
 allocators
 amdgpu
 api
+aten
 atmi
 atomics
 autogenerated
@@ -509,6 +524,7 @@ avx
 awk
 backend
 backends
+bb
 benchmarked
 benchmarking
 bfloat
@@ -532,6 +548,7 @@ cd
 centos
 centric
 changelog
+checkpointing
 chiplet
 cmake
 cmd
@@ -572,6 +589,7 @@ de
 deallocation
 debuggability
 debian
+deepseek
 denoise
 denoised
 denoises
@@ -595,6 +613,7 @@ embeddings
 enablement
 encodings
 endfor
+endif
 endpgm
 enqueue
 env
@@ -637,6 +656,7 @@ hipSPARSELt
 hipTensor
 hipamd
 hipblas
+hipcc
 hipcub
 hipfft
 hipfort
@@ -666,6 +686,7 @@ installable
 interop
 interprocedural
 intra
+intrinsics
 invariants
 invocating
 ipo
@@ -695,6 +716,7 @@ migratable
 miopen
 miopengemm
 mivisionx
+mixtral
 mjx
 mkdir
 mlirmiopen
@@ -811,6 +833,7 @@ roctracer
 rst
 runtime
 runtimes
+ResNet
 sL
 scalability
 scalable
@@ -826,6 +849,7 @@ sm
 smi
 softmax
 spack
+spmm
 src
 stochastically
 strided
@@ -834,8 +858,10 @@ subdirectory
 subexpression
 subfolder
 subfolders
+submatrix
 submodule
 submodules
+subnet
 supercomputing
 symlink
 symlinks
@@ -857,6 +883,7 @@ torchvision
 tqdm
 tracebacks
 txt
+TopK
 uarch
 uncached
 uncacheable

CHANGELOG.md

@@ -4,9 +4,142 @@ 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 6.4.1
+
+See the [ROCm 6.4.1 release notes](https://rocm.docs.amd.com/en/docs-6.4.1/about/release-notes.html)
+for a complete overview of this release.
+
+### **AMD SMI** (25.4.2)
+
+#### 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.
+
+#### Changed
+
+* Modified VRAM display for `amd-smi monitor -v`. 
+
+#### Optimized
+
+* Improved load times for CLI commands when the GPU has multiple parititons.
+
+#### 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.
+
+```{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)
+
+#### 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 or disabled by default.
+
+#### Optimized
+
+* Improved kernel logging includes de-mangling shader names.
+* Refined implementation in HIP APIs `hipEventRecords` and `hipStreamWaitEvent` for performance improvement.
+
+#### 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.
+
+### **hipBLASLt** (0.12.1)
+
+#### Resolved issues
+
+* Fixed an accuracy issue for some solutions using an `FP32` or `TF32` data type with a TT transpose.
+
+### **RCCL** (2.22.3)
+
+#### Changed
+
+* MSCCL++ is now disabled by default. To enable it, set `RCCL_MSCCLPP_ENABLE=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.
+
+#### 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)
+
+#### 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.
+
+#### Changed
+
+- Updated `rdc_field` to align with `rdc_bootstrap` for current metrics.
+
+#### Resolved issues
+
+- Fixed [ROCProfiler](https://rocm.docs.amd.com/projects/rocprofiler/en/docs-6.4.0/index.html) eval metrics and memory leaks.
+
+### **ROCm SMI** (7.5.0)
+
+#### Resolved issues
+
+- 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.
+```
+
+### **ROCm Systems Profiler** (1.0.1)
+
+#### Added 
+
+* 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).
+
+#### Resolved issues
+
+* Fixed a build issue with Dyninst on GCC 13.
+
+### **ROCr Runtime** (1.15.0)
+
+#### Resolved issues
+
+* 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.
+
 ## ROCm 6.4.0
 
-See the [ROCm 6.4.0 release notes](https://rocm-stg.amd.com/en/latest/about/release-notes.html)
+See the [ROCm 6.4.0 release notes](https://rocm.docs.amd.com/en/docs-6.4.0/about/release-notes.html)
 for a complete overview of this release.
 
 ### **AMD SMI** (25.3.0)
@@ -743,6 +876,10 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/rele
 #### Added 
 
 - Support for VA-API and rocDecode tracing.
+- Aggregation of MPI data collected across distributed nodes and ranks. The data is concatenated into a single proto file.
+
+#### Changed
+- Backend refactored to use [ROCprofiler-SDK](https://github.com/ROCm/rocprofiler-sdk) rather than [ROCProfiler](https://github.com/ROCm/rocprofiler) and [ROCTracer](https://github.com/ROCm/ROCTracer).
 
 #### Resolved issues
 
@@ -753,9 +890,21 @@ See the full [ROCm SMI changelog](https://github.com/ROCm/rocm_smi_lib/blob/rele
 - Fixed interruption in config file generation.
 
 - Fixed segmentation fault while running rocprof-sys-instrument.
+- Fixed an issue where running `rocprof-sys-causal` or using the `-I all` option with `rocprof-sys-sample` caused the system to become non-responsive.
+
+- Fixed an issue where sampling multi-GPU Python workloads caused the system to stop responding.
+
+### **ROCm Validation Suite** (1.1.0)
+
+#### Added
+
+* Configuration files for MI210.
+* Support for OCP fp8 data type.
+* GPU index-based CLI execution.
 
 #### Changed
-- Backend refactored to use [ROCprofiler-SDK](https://github.com/ROCm/rocprofiler-sdk) rather than [ROCProfiler](https://github.com/ROCm/rocprofiler) and [ROCTracer](https://github.com/ROCm/ROCTracer).
+
+* JSON logging with updated schema.
 
 ### **rocPRIM** (3.4.0)
 

README.md

@@ -50,7 +50,7 @@ The following example shows how to use the repo tool to download the ROCm source
 ```bash
 mkdir -p ~/ROCm/
 cd ~/ROCm/
-export ROCM_VERSION=6.4.0
+export ROCM_VERSION=6.4.1
 ~/bin/repo init -u http://github.com/ROCm/ROCm.git -b roc-6.4.x -m tools/rocm-build/rocm-${ROCM_VERSION}.xml
 ~/bin/repo sync
 ```
@@ -77,7 +77,7 @@ The Build time will reduce significantly if we limit the GPU Architecture/s agai
 
 mkdir -p ~/WORKSPACE/      # Or any folder name other than WORKSPACE
 cd ~/WORKSPACE/
-export ROCM_VERSION=6.4.0
+export ROCM_VERSION=6.4.1
 ~/bin/repo init -u http://github.com/ROCm/ROCm.git -b roc-6.4.x -m tools/rocm-build/rocm-${ROCM_VERSION}.xml
 ~/bin/repo sync
 
@@ -127,6 +127,7 @@ bash install-prerequisites.sh
 export GPU_ARCHS="gfx942"               # Example
 export GPU_ARCHS="gfx940;gfx941;gfx942" # Example
 
+cd ~/WORKSPACE/
 # Pick and run build commands in the docker container:
 # Build rocm-dev packages
 make -f ROCm/tools/rocm-build/ROCm.mk -j ${NPROC:-$(nproc)} rocm-dev

default.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
     <remote name="rocm-org" fetch="https://github.com/ROCm/" />
-    <default revision="refs/tags/rocm-6.4.0"
+    <default revision="refs/tags/rocm-6.4.1"
      remote="rocm-org"
      sync-c="true"
      sync-j="4" />

docs/about/license.md

@@ -81,6 +81,7 @@ additional licenses. Please review individual repositories for more information.
 | [rocRAND](https://github.com/ROCm/rocRAND/) | [MIT](https://github.com/ROCm/rocRAND/blob/develop/LICENSE.txt) |
 | [ROCr Debug Agent](https://github.com/ROCm/rocr_debug_agent/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocr_debug_agent/blob/amd-staging/LICENSE.txt) |
 | [ROCR-Runtime](https://github.com/ROCm/ROCR-Runtime/) | [The University of Illinois/NCSA](https://github.com/ROCm/ROCR-Runtime/blob/amd-staging/LICENSE.txt) |
+| [rocSHMEM](https://github.com/ROCm/rocSHMEM/) | [MIT](https://github.com/ROCm/rocSHMEM/blob/develop/LICENSE.md) |
 | [rocSOLVER](https://github.com/ROCm/rocSOLVER/) | [BSD-2-Clause](https://github.com/ROCm/rocSOLVER/blob/develop/LICENSE.md) |
 | [rocSPARSE](https://github.com/ROCm/rocSPARSE/) | [MIT](https://github.com/ROCm/rocSPARSE/blob/develop/LICENSE.md) |
 | [rocThrust](https://github.com/ROCm/rocThrust/) | [Apache 2.0](https://github.com/ROCm/rocThrust/blob/develop/LICENSE) |

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

@@ -1,121 +1,126 @@
-ROCm Version,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 24.04.1, 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, 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 9.5, 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, 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, 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 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]_,,,,,,,,,,,
-,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
-      ,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
-      ,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
-      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,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
-      ,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
-      ,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","2.4, 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","2.17.0, 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>`,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.2,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
-,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,
-      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,>=1.2.0,>=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,>=1.14.1,>=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,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      CUB,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-,,,,,,,,,,,,,,,
-      KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      KMD versions,"6.4.x, 6.3.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
-      :doc:`MIGraphX <amdmigraphx:index>`,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>`,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`MIVisionX <mivisionx:index>`,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
-      :doc:`rocAL <rocal:index>`,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`rocDecode <rocdecode:index>`,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
-      :doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`RPP <rpp:index>`,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
-      ,,,,,,,,,,,,,,,
-      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      :doc:`RCCL <rccl:index>`,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      ,,,,,,,,,,,,,,,
-      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
-      :doc:`hipBLAS <hipblas:index>`,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>`,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
-      :doc:`hipFFT <hipfft:index>`,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
-      :doc:`hipfort <hipfort:index>`,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
-      :doc:`hipRAND <hiprand:index>`,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
-      :doc:`hipSOLVER <hipsolver:index>`,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
-      :doc:`hipSPARSE <hipsparse:index>`,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
-      :doc:`rocALUTION <rocalution:index>`,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
-      :doc:`rocBLAS <rocblas:index>`,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
-      :doc:`rocFFT <rocfft:index>`,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
-      :doc:`rocRAND <rocrand:index>`,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
-      :doc:`rocSOLVER <rocsolver:index>`,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
-      :doc:`rocSPARSE <rocsparse:index>`,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
-      :doc:`rocWMMA <rocwmma:index>`,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
-      :doc:`Tensile <tensile:src/index>`,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
-      ,,,,,,,,,,,,,,,
-      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      :doc:`hipCUB <hipcub:index>`,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`hipTensor <hiptensor:index>`,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
-      :doc:`rocPRIM <rocprim:index>`,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      ,,,,,,,,,,,,,,,
-      SUPPORT LIBS,,,,,,,,,,,,,,,
-      `hipother <https://github.com/ROCm/hipother>`_,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
-      `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]_,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>`,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
-      :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
-      :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
-      :doc:`ROCm SMI <rocm_smi_lib:index>`,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>`,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
-      ,,,,,,,,,,,,,,,
-      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
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,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>`,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCTracer <roctracer:index>`,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
-      ,,,,,,,,,,,,,,,
-      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,
-      :doc:`HIPIFY <hipify:index>`,19.0.0.25104,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
-      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
-      ,,,,,,,,,,,,,,,
-      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,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
-      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      `Flang <https://github.com/ROCm/flang>`_,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`llvm-project <llvm-project:index>`,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-,,,,,,,,,,,,,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      :doc:`HIP <hip:index>`,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      `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
-      :doc:`ROCr Runtime <rocr-runtime:index>`,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,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 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

docs/compatibility/compatibility-matrix.rst

@@ -23,126 +23,131 @@ compatibility and system requirements.
 .. container:: format-big-table
 
   .. csv-table::
-      :header: "ROCm Version", "6.4.0", "6.3.3", "6.2.0"
+      :header: "ROCm Version", "6.4.1", "6.4.0", "6.3.0"
       :stub-columns: 1
 
-      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04
-      ,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4"
-      ,"RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3"
-      ,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9"
-      ,"SLES 15 SP6","SLES 15 SP6, SP5","SLES 15 SP6, SP5"
-      ,"Oracle Linux 9, 8 [#mi300x]_",Oracle Linux 8.10 [#mi300x]_,Oracle Linux 8.9 [#mi300x]_
+      :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 8.10,RHEL 8.10,RHEL 8.10
+      ,SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5"
+      ,"Oracle Linux 9, 8 [#mi300x]_","Oracle Linux 9, 8 [#mi300x]_",Oracle Linux 8.10 [#mi300x]_
       ,Debian 12 [#single-node]_,Debian 12 [#single-node]_,
       ,Azure Linux 3.0 [#mi300x]_,Azure Linux 3.0 [#mi300x]_,
       ,.. _architecture-support-compatibility-matrix:,,
       :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA3,CDNA3,CDNA3
       ,CDNA2,CDNA2,CDNA2
       ,CDNA,CDNA,CDNA
+      ,RDNA4,,
       ,RDNA3,RDNA3,RDNA3
       ,RDNA2,RDNA2,RDNA2
       ,.. _gpu-support-compatibility-matrix:,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1100,gfx1100,gfx1100
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx1201 [#RDNA-OS]_,,
+      ,gfx1200 [#RDNA-OS]_,,
+      ,gfx1101 [#RDNA-OS]_,,
+      ,gfx1100,gfx1100,gfx1100
       ,gfx1030,gfx1030,gfx1030
-      ,gfx942,gfx942,gfx942 [#mi300_620]_
+      ,gfx942,gfx942,gfx942
       ,gfx90a,gfx90a,gfx90a
       ,gfx908,gfx908,gfx908
       ,,,
       FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","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","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.4.35,0.4.31,0.4.26
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.20,1.17.3,1.17.3
+      :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
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.2,1.2,1.17.3
       ,,,
       THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
       `UCC <https://github.com/ROCm/ucc>`_,>=1.3.0,>=1.3.0,>=1.3.0
       `UCX <https://github.com/ROCm/ucx>`_,>=1.15.0,>=1.15.0,>=1.15.0
       ,,,
       THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix:,,
-      Thrust,2.5.0,2.3.2,2.2.0
-      CUB,2.5.0,2.3.2,2.2.0
+      Thrust,2.5.0,2.5.0,2.3.2
+      CUB,2.5.0,2.5.0,2.3.2
       ,,,
       KMD & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
-      KMD versions,"6.4.x, 6.3.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"
+      :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
       ,,,
       ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
       :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
-      :doc:`MIGraphX <amdmigraphx:index>`,2.12.0,2.11.0,2.10.0
-      :doc:`MIOpen <miopen:index>`,3.4.0,3.3.0,3.2.0
-      :doc:`MIVisionX <mivisionx:index>`,3.2.0,3.1.0,3.0.0
-      :doc:`rocAL <rocal:index>`,2.2.0,2.1.0,1.0.0
-      :doc:`rocDecode <rocdecode:index>`,0.10.0,0.8.0,0.6.0
-      :doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.6.0,N/A
-      :doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.2.0,0.1.0
-      :doc:`RPP <rpp:index>`,1.9.10,1.9.1,1.8.0
+      :doc:`MIGraphX <amdmigraphx:index>`,2.12.0,2.12.0,2.11.0
+      :doc:`MIOpen <miopen:index>`,3.4.0,3.4.0,3.3.0
+      :doc:`MIVisionX <mivisionx:index>`,3.2.0,3.2.0,3.1.0
+      :doc:`rocAL <rocal:index>`,2.2.0,2.2.0,2.1.0
+      :doc:`rocDecode <rocdecode:index>`,0.10.0,0.10.0,0.8.0
+      :doc:`rocJPEG <rocjpeg:index>`,0.8.0,0.8.0,0.6.0
+      :doc:`rocPyDecode <rocpydecode:index>`,0.3.1,0.3.1,0.2.0
+      :doc:`RPP <rpp:index>`,1.9.10,1.9.10,1.9.1
       ,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
-      :doc:`RCCL <rccl:index>`,2.22.3,2.21.5,2.20.5
+      :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
       ,,,
       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>`,2.4.0,2.3.0,2.2.0
-      :doc:`hipBLASLt <hipblaslt:index>`,0.12.0,0.10.0,0.8.0
-      :doc:`hipFFT <hipfft:index>`,1.0.18,1.0.17,1.0.14
-      :doc:`hipfort <hipfort:index>`,0.6.0,0.5.1,0.4.0
-      :doc:`hipRAND <hiprand:index>`,2.12.0,2.11.1,2.11.0
-      :doc:`hipSOLVER <hipsolver:index>`,2.4.0,2.3.0,2.2.0
-      :doc:`hipSPARSE <hipsparse:index>`,3.2.0,3.1.2,3.1.1
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.3,0.2.2,0.2.1
-      :doc:`rocALUTION <rocalution:index>`,3.2.2,3.2.1,3.2.0
-      :doc:`rocBLAS <rocblas:index>`,4.4.0,4.3.0,4.2.0
-      :doc:`rocFFT <rocfft:index>`,1.0.32,1.0.31,1.0.28
-      :doc:`rocRAND <rocrand:index>`,3.3.0,3.2.0,3.1.0
-      :doc:`rocSOLVER <rocsolver:index>`,3.28.0,3.27.0,3.26.0
-      :doc:`rocSPARSE <rocsparse:index>`,3.4.0,3.3.0,3.2.0
-      :doc:`rocWMMA <rocwmma:index>`,1.7.0,1.6.0,1.5.0
-      :doc:`Tensile <tensile:src/index>`,4.43.0,4.42.0,4.41.0
+      :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:`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:`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:`rocSPARSE <rocsparse:index>`,3.4.0,3.4.0,3.3.0
+      :doc:`rocWMMA <rocwmma:index>`,1.7.0,1.7.0,1.6.0
+      :doc:`Tensile <tensile:src/index>`,4.43.0,4.43.0,4.42.0
       ,,,
       PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
-      :doc:`hipCUB <hipcub:index>`,3.4.0,3.3.0,3.2.0
-      :doc:`hipTensor <hiptensor:index>`,1.5.0,1.4.0,1.3.0
-      :doc:`rocPRIM <rocprim:index>`,3.4.0,3.3.0,3.2.0
-      :doc:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.0.1
+      :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:`rocThrust <rocthrust:index>`,3.3.0,3.3.0,3.3.0
       ,,,
       SUPPORT LIBS,,,
-      `hipother <https://github.com/ROCm/hipother>`_,6.4.43482,6.3.42134,6.2.41133
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,6.4.0,6.3.3,6.2.0
-      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,20240607.1.4246
+      `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
+      `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>`,25.3.0,24.7.1,24.6.2
+      :doc:`AMD SMI <amdsmi:index>`,25.4.2,25.3.0,24.7.1
       :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>`,7.5.0,7.4.0,7.3.0
-      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.0.60200
+      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.5.0,7.5.0,7.4.0
+      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.1.0,1.1.0,1.1.0
       ,,,
       PERFORMANCE TOOLS,,,
       :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,1.4.0,1.4.0,1.4.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.1.0,3.0.0,2.0.1
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.0.0,0.1.2,1.11.2
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.60400,2.0.60303,2.0.60200
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,0.6.0,0.5.0,0.4.0
-      :doc:`ROCTracer <roctracer:index>`,4.1.60400,4.1.60303,4.1.60200
+      :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:`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
       ,,,
       DEVELOPMENT TOOLS,,,
-      :doc:`HIPIFY <hipify:index>`,19.0.0.25133,18.0.0.25012,18.0.0.24232
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.13.0
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.0,0.76.0
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,14.2.0
+      :doc:`HIPIFY <hipify:index>`,19.0.0,19.0.0,18.0.0.24455
+      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0
+      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.2,0.77.2,0.77.0
+      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,15.2.0,15.2.0,15.2.0
       `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.4.0,0.4.0,0.4.0
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.3,2.0.3
+      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.0.4,2.0.4,2.0.3
       ,,,
       COMPILERS,.. _compilers-support-compatibility-matrix:,,
       `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
       :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
-      `Flang <https://github.com/ROCm/flang>`_,19.0.0.25133,18.0.0.25012,18.0.0.24232
-      :doc:`llvm-project <llvm-project:index>`,19.0.0.25133,18.0.0.25012,18.0.0.24232
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,19.0.0.25133,18.0.0.25012,18.0.0.24232
+      `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
       ,,,
       RUNTIMES,.. _runtime-support-compatibility-matrix:,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,6.4.43482,6.3.42134,6.2.41133
-      :doc:`HIP <hip:index>`,6.4.43482,6.3.42134,6.2.41133
+      :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
       `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>`,1.15.0,1.14.0,1.13.0
+      :doc:`ROCr Runtime <rocr-runtime:index>`,1.15.0,1.15.0,1.14.0
 
 
 .. rubric:: Footnotes
@@ -152,6 +157,7 @@ compatibility and system requirements.
 .. [#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.
 .. [#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:
 
@@ -169,7 +175,8 @@ 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>`_, 9.5, 5.14+, 2.34
+   `Red Hat Enterprise Linux (RHEL 9) <https://access.redhat.com/articles/3078#RHEL9>`_, 9.6, 5.14+, 2.34
+   , 9.5, 5.14+, 2.34
    ,9.4, 5.14+, 2.34
    ,9.3, 5.14+, 2.34
    ,,
@@ -230,3 +237,4 @@ Expand for full historical view of:
    .. [#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.
    .. [#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/jax-compatibility.rst

@@ -14,17 +14,18 @@ JAX provides a NumPy-like API, which combines automatic differentiation and the
 Accelerated Linear Algebra (XLA) compiler to achieve high-performance machine
 learning at scale.
 
-JAX uses composable transformations of Python and NumPy through just-in-time (JIT) compilation,
-automatic vectorization, and parallelization. To learn about JAX, including profiling and
-optimizations, see the official `JAX documentation
+JAX uses composable transformations of Python and NumPy through just-in-time
+(JIT) compilation, automatic vectorization, and parallelization. To learn about
+JAX, including profiling and optimizations, see the official `JAX documentation
 <https://jax.readthedocs.io/en/latest/notebooks/quickstart.html>`_.
 
-ROCm support for JAX is upstreamed and users can build the official source code with ROCm
-support:
+ROCm support for JAX is upstreamed, and users can build the official source code
+with ROCm support:
 
 - ROCm JAX release:
 
-  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>` with ROCm and JAX pre-installed.
+  - Offers AMD-validated and community :ref:`Docker images <jax-docker-compat>`
+    with ROCm and JAX preinstalled.
 
   - ROCm JAX repository: `ROCm/jax <https://github.com/ROCm/jax>`_
 
@@ -36,8 +37,8 @@ support:
   - Official JAX repository: `jax-ml/jax <https://github.com/jax-ml/jax>`_
 
   - See the `AMD GPU (Linux) installation section
-    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in the JAX
-    documentation.
+    <https://jax.readthedocs.io/en/latest/installation.html#amd-gpu-linux>`_ in
+    the JAX documentation.
 
 .. note::
 
@@ -46,6 +47,44 @@ support:
    `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
    follow upstream JAX releases and use the latest available ROCm version.
 
+Use cases and recommendations
+================================================================================
+
+* The `nanoGPT in JAX <https://rocm.blogs.amd.com/artificial-intelligence/nanoGPT-JAX/README.html>`_
+  blog explores the implementation and training of a Generative Pre-trained
+  Transformer (GPT) model in JAX, inspired by Andrej Karpathy’s JAX-based
+  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.
+
+* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
+  ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
+  blog post provides a comprehensive guide on enhancing the training efficiency
+  of GPT models by implementing mixed precision techniques in JAX, specifically
+  tailored for AMD GPUs utilizing the ROCm platform.
+
+* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
+  blog demonstrates how to develop a custom fused dropout-activation kernel for
+  matrices using Triton, integrate it with JAX, and benchmark its performance
+  using ROCm.
+
+* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
+  outlines the process of fine-tuning a Bidirectional Encoder Representations
+  from Transformers (BERT)-based large language model (LLM) using JAX for a text
+  classification task. The blog post discuss techniques for parallelizing the
+  fine-tuning across multiple AMD GPUs and assess the model's performance on a
+  holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
+  and the General Language Understanding Evaluation (GLUE) benchmark dataset was
+  used on a multi-GPU setup.
+
+* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
+  provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
+  accelerator using ROCm. The page is aimed at helping users achieve optimal
+  performance for deep learning and other high-performance computing tasks on
+  the MI300X GPU.
+
+For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
+
 .. _jax-docker-compat:
 
 Docker image compatibility
@@ -57,8 +96,8 @@ 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 are validated for
-`ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_. Click the |docker-icon|
+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|
 icon to view the image on Docker Hub.
 
 .. list-table:: JAX Docker image components
@@ -71,19 +110,19 @@ icon to view the image on Docker Hub.
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4-jax0.4.35-py3.12/images/sha256-4069398229078f3311128b6d276c6af377c7e97d3363d020b0bf7154fae619ca"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
+           <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>
 
       - `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
       - Ubuntu 24.04
-      - `3.12.7 <https://www.python.org/downloads/release/python-3127/>`_
+      - `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4-jax0.4.35-py3.10/images/sha256-a137f901f91ce6c13b424c40a6cf535248d4d20fd36d5daf5eee0570190a4a11"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
+           <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>
 
       - `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
       - Ubuntu 22.04
-      - `3.10.14 <https://www.python.org/downloads/release/python-31014/>`_
+      - `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
 
 AMD publishes `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
 with ROCm backends on Docker Hub. The following Docker image tags and
@@ -121,13 +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/>`_
 
-Critical ROCm libraries for JAX
+.. _key_rocm_libraries:
+
+Key ROCm libraries for JAX
 ================================================================================
 
-The functionality of JAX with ROCm is determined by its underlying library
-dependencies. These critical ROCm components affect the capabilities,
-performance, and feature set available to developers. The versions described
-are available in ROCm :version:`rocm_version`.
+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
@@ -135,539 +175,140 @@ are available in ROCm :version:`rocm_version`.
     * - 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 and unsupported 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 GPU-accelerated JAX 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
+    * - Data type
       - Description
-      - Since JAX
-      - Since 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.dlpack``
-      - For exchanging tensor data between JAX and other libraries that support the
-        DLPack standard.
-      - 0.1.57
-      - 5.0.0
-    * - ``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.dtypes``
-      - Provides utilities for working with and managing data types in JAX
-        arrays and computations.
-      - 0.1.66
-      - 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.profiler``
-      - Contains JAX’s tracing and time profiling features.
-      - 0.1.57
-      - 5.0.0
-    * - ``jax.stages``
-      - Contains interfaces to stages of the compiled execution process.
-      - 0.3.4
-      - 5.0.0
-    * - ``jax.tree``
-      - Provides utilities for working with tree-like container data structures.
-      - 0.4.26
-      - 5.6.0
-    * - ``jax.tree_util``
-      - Provides utilities for working with nested data structures, or
-        ``pytrees``.
-      - 0.1.65
-      - 5.0.0
-    * - ``jax.typing``
-      - Provides JAX-specific static type annotations.
-      - 0.3.18
-      - 5.1.0
-    * - ``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
--------------------------------------------------------------------------------
+    * - ``bfloat16``
+      - 16-bit bfloat (brain floating point).
 
-A SciPy-like API for scientific computing.
+    * - ``bool``
+      - Boolean.
 
-.. list-table::
-    :header-rows: 1
+    * - ``complex128``
+      - 128-bit complex.
 
-    * - Module
-      - Since JAX
-      - Since 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
+    * - ``complex64``
+      - 64-bit complex.
 
-jax.scipy.stats module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+    * - ``float16``
+      - 16-bit (half precision) floating-point.
 
-.. list-table::
-   :header-rows: 1
+    * - ``float32``
+      - 32-bit (single precision) floating-point.
 
-   * - Module
-     - Since JAX
-     - Since 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
+    * - ``float64``
+      - 64-bit (double precision) floating-point.
 
-jax.extend module
--------------------------------------------------------------------------------
+    * - ``half``
+      - 16-bit (half precision) floating-point.
 
-Modules for JAX extensions.
+    * - ``int16``
+      - Signed 16-bit integer.
 
-.. list-table::
-    :header-rows: 1
+    * - ``int32``
+      - Signed 32-bit integer.
 
-    * - Module
-      - Since JAX
-      - Since 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
+    * - ``int64``
+      - Signed 64-bit integer.
 
-jax.experimental module
--------------------------------------------------------------------------------
+    * - ``int8``
+      - Signed 8-bit integer.
 
-Experimental modules and APIs.
+    * - ``uint16``
+      - Unsigned 16-bit (word) integer.
 
-.. list-table::
-    :header-rows: 1
+    * - ``uint32``
+      - Unsigned 32-bit (dword) integer.
 
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.checkify``
-      - 0.1.75
-      - 5.0.0
-    * - ``jax.experimental.compilation_cache.compilation_cache``
-      - 0.1.68
-      - 5.0.0
-    * - ``jax.experimental.custom_partitioning``
-      - 0.4.0
-      - 5.3.0
-    * - ``jax.experimental.jet``
-      - 0.1.56
-      - 5.0.0
-    * - ``jax.experimental.key_reuse``
-      - 0.4.26
-      - 5.6.0
-    * - ``jax.experimental.mesh_utils``
-      - 0.1.76
-      - 5.0.0
-    * - ``jax.experimental.multihost_utils``
-      - 0.3.2
-      - 5.0.0
-    * - ``jax.experimental.pallas``
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.experimental.pjit``
-      - 0.1.61
-      - 5.0.0
-    * - ``jax.experimental.serialize_executable``
-      - 0.4.0
-      - 5.3.0
-    * - ``jax.experimental.shard_map``
-      - 0.4.3
-      - 5.3.0
-    * - ``jax.experimental.sparse``
-      - 0.1.75
-      - 5.0.0
+    * - ``uint64``
+      - Unsigned 64-bit (qword) integer.
 
-.. list-table::
-    :header-rows: 1
+    * - ``uint8``
+      - Unsigned 8-bit (byte) integer.
 
-    * - API
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.enable_x64``
-      - 0.1.60
-      - 5.0.0
-    * - ``jax.experimental.disable_x64``
-      - 0.1.60
-      - 5.0.0
+.. note::
 
-jax.experimental.pallas module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  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.
 
-Module for Pallas, a JAX extension for custom kernels.
+Supported modules
+--------------------------------------------------------------------------------
 
-.. list-table::
-    :header-rows: 1
+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>`_.
 
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.pallas.mosaic_gpu``
-      - 0.4.31
-      - 6.1.3
-    * - ``jax.experimental.pallas.tpu``
-      - 0.4.15
-      - 5.5.0
-    * - ``jax.experimental.pallas.triton``
-      - 0.4.32
-      - 6.1.3
+.. note::
 
-jax.experimental.sparse module
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Experimental support for sparse matrix operations.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Module
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.sparse.linalg``
-      - 0.3.15
-      - 5.2.0
-    * - ``jax.experimental.sparse.sparsify``
-      - 0.3.25
-      - ❌
-
-.. list-table::
-    :header-rows: 1
-
-    * - ``sparse`` data structure API
-      - Since JAX
-      - Since ROCm
-    * - ``jax.experimental.sparse.BCOO``
-      - 0.1.72
-      - 5.0.0
-    * - ``jax.experimental.sparse.BCSR``
-      - 0.3.20
-      - 5.1.0
-    * - ``jax.experimental.sparse.CSR``
-      - 0.1.75
-      - 5.0.0
-    * - ``jax.experimental.sparse.NM``
-      - 0.4.27
-      - 5.6.0
-    * - ``jax.experimental.sparse.COO``
-      - 0.1.75
-      - 5.0.0
-
-Unsupported JAX features
-------------------------
-
-The following are GPU-accelerated JAX features not currently supported by
-ROCm.
-
-.. list-table::
-    :header-rows: 1
-
-    * - Feature
-      - Description
-      - Since JAX
-    * - 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.
-      - 0.2.25
-    * - RNN support
-      - Currently only LSTM with double bias is supported with float32 input
-        and weight.
-      - 0.3.25
-    * - XLA int4 support
-      - 4-bit integer (int4) precision in the XLA compiler.
-      - 0.4.0
-    * - ``jax.experimental.sparsify``
-      - Converts a dense matrix to a sparse matrix representation.
-      - Experimental
-
-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 PyTorch-based
-  nanoGPT. By comparing how essential GPT components—such as self-attention
-  mechanisms and optimizers—are realized in PyTorch and JAX, also highlight
-  JAX’s unique features.
-
-* The `Optimize GPT Training: Enabling Mixed Precision Training in JAX using
-  ROCm on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-mixed-precision/README.html>`_
-  blog post provides a comprehensive guide on enhancing the training efficiency
-  of GPT models by implementing mixed precision techniques in JAX, specifically
-  tailored for AMD GPUs utilizing the ROCm platform.
-
-* The `Supercharging JAX with Triton Kernels on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/jax-triton/README.html>`_
-  blog demonstrates how to develop a custom fused dropout-activation kernel for
-  matrices using Triton, integrate it with JAX, and benchmark its performance
-  using ROCm.
-
-* The `Distributed fine-tuning with JAX on AMD GPUs <https://rocm.blogs.amd.com/artificial-intelligence/distributed-sft-jax/README.html>`_
-  outlines the process of fine-tuning a Bidirectional Encoder Representations
-  from Transformers (BERT)-based large language model (LLM) using JAX for a text
-  classification task. The blog post discuss techniques for parallelizing the
-  fine-tuning across multiple AMD GPUs and assess the model's performance on a
-  holdout dataset. During the fine-tuning, a BERT-base-cased transformer model
-  and the General Language Understanding Evaluation (GLUE) benchmark dataset was
-  used on a multi-GPU setup.
-
-* The `MI300X workload optimization guide <https://rocm.docs.amd.com/en/latest/how-to/tuning-guides/mi300x/workload.html>`_
-  provides detailed guidance on optimizing workloads for the AMD Instinct MI300X
-  accelerator using ROCm. The page is aimed at helping users achieve optimal
-  performance for deep learning and other high-performance computing tasks on
-  the MI300X GPU.
-
-For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.blogs.amd.com/blog/tag/jax.html>`_.
+  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/tensorflow-compatibility.rst

@@ -56,7 +56,7 @@ Docker image compatibility
 AMD validates and publishes ready-made `TensorFlow images
 <https://hub.docker.com/r/rocm/tensorflow>`_ with ROCm backends on
 Docker Hub. The following Docker image tags and associated inventories are
-validated for `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`_. Click
+validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`_. Click
 the |docker-icon| icon to view the image on Docker Hub.
 
 .. list-table:: TensorFlow Docker image components
@@ -73,82 +73,122 @@ the |docker-icon| icon to view the image on Docker Hub.
 
            <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>
 
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
       - dev
       - 24.04
-      - `Python 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
+      - `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
       - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4-py3.12-tf2.18-runtime/images/sha256-14addca4b92a47c806b83ebaeed593fc6672cd99f0017ed8dad759fe72ed0309"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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>
 
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
       - runtime
       - 24.04
-      - `Python 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
+      - `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
       - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4-py3.10-tf2.18-dev/images/sha256-f5e151060df04ff5fb59f5604b49cd371931bbe75b06aec9fe7781397c4be0ce"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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>
 
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
       - dev
       - 22.04
-      - `Python 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `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-py3.10-tf2.18-runtime/images/sha256-5cd4c03fdb1036570c0d4929da60a65c4466998dc80f1dc8a5a0b173eae017fb"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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>
 
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `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 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `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-py3.12-tf2.17-dev/images/sha256-b3add80e374a2db2d1088d746e740afa89d439aca02cacba959ad298f5cd2b3f"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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
       - 24.04
-      - `Python 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
+      - `Python 3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
       - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`_
 
     * - .. raw:: html
 
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4-py3.12-tf2.17-runtime/images/sha256-3a244f026c32177eff7958ffbad390de85b438b2b48b455cc39f15d70fa1270d"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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>
 
       - `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 3.12.4 <https://www.python.org/downloads/release/python-3124/>`_
+      - `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-py3.10-tf2.17-dev/images/sha256-e0cecdfacb59169335049983cdab6da578c209bb9f4d08aad97e184ae59171a6"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.1/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
       - dev
       - 22.04
-      - `Python 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `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-py3.10-tf2.17-runtime/images/sha256-6f43de12f7eb202791b698ac51d28b72098de90034dbcd48486629b0125f7707"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
+           <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/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
+      - `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 3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+      - `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>`_
+
 
 Critical ROCm libraries for TensorFlow
 ===============================================================================

docs/conf.py

@@ -34,15 +34,15 @@ 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 = "6.4.0"
-release = "6.4.0"
+version = "6.4.1"
+release = "6.4.1"
 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": "2025-04-11"},
+    {"file": "about/release-notes", "os": ["linux"], "date": "2025-05-07"},
     {"file": "release/changelog", "os": ["linux"],},
     {"file": "compatibility/compatibility-matrix", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
@@ -51,12 +51,15 @@ article_pages = [
     {"file": "how-to/deep-learning-rocm", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/install", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/system-health-check", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/prerequisite-system-validation", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/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/fine-tuning/index", "os": ["linux"]},
@@ -66,11 +69,11 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/inference/index", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/inference/install", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/hugging-face-models", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/llm-inference-frameworks", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/inference/vllm-benchmark", "os": ["linux"]},
-    {"file": "how-to/rocm-for-ai/inference/pytorch-inference-benchmark", "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-0.8.5-20250513", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
 
     {"file": "how-to/rocm-for-ai/inference-optimization/index", "os": ["linux"]},

docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.8.5_20250513-benchmark-models.yaml

@@ -0,0 +1,152 @@
+vllm_benchmark:
+  unified_docker:
+    latest:
+      pull_tag: rocm/vllm:rocm6.3.1_vllm0.8.5_20250513
+      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250513/images/sha256-5c8b4436dd0464119d9df2b44c745fadf81512f18ffb2f4b5dc235c71ebe26b4
+      rocm_version: 6.3.1
+      vllm_version: 0.8.5
+      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 3.2 11B Vision
+        mad_tag: pyt_vllm_llama-3.2-11b-vision-instruct
+        model_repo: meta-llama/Llama-3.2-11B-Vision-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision-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

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

@@ -23,3 +23,11 @@ pytorch_inference_benchmark:
         model_repo: meta-llama/Llama-3.1-8B-Instruct
         url: https://huggingface.co/chaidiscovery/chai-1
         precision: float16
+    - group: Mochi Video
+      tag: mochi
+      models:
+      - model: Mochi 1
+        mad_tag: pyt_mochi_video_inference
+        model_repo: genmo/mochi-1-preview
+        url: https://huggingface.co/genmo/mochi-1-preview
+        precision: float16

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

@@ -1,14 +1,14 @@
 vllm_benchmark:
   unified_docker:
     latest:
-      pull_tag: rocm/vllm:rocm6.3.1_instinct_vllm0.8.3_20250410
-      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_instinct_vllm0.8.3_20250410/images/sha256-a0b55c6c0f3fa5d437fb54a66e32a108306c36d4776e570dfd0ae902719bd190
+      pull_tag: rocm/vllm:rocm6.3.1_vllm0.8.5_20250521
+      docker_hub_url: https://hub.docker.com/layers/rocm/vllm/rocm6.3.1_vllm_0.8.5_20250521/images/sha256-38410c51af7208897cd8b737c9bdfc126e9bc8952d4aa6b88c85482f03092a11
       rocm_version: 6.3.1
-      vllm_version: 0.8.3
-      pytorch_version: 2.7.0 (dev nightly)
-      hipblaslt_version: 0.13
+      vllm_version: 0.8.5 (0.8.6.dev315+g91a560098.rocm631)
+      pytorch_version: 2.7.0+gitf717b2a
+      hipblaslt_version: 0.15
   model_groups:
-    - group: Llama
+    - group: Meta Llama
       tag: llama
       models:
       - model: Llama 3.1 8B
@@ -56,7 +56,7 @@ vllm_benchmark:
         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
+    - group: Mistral AI
       tag: mistral
       models:
       - model: Mixtral MoE 8x7B
@@ -108,7 +108,7 @@ vllm_benchmark:
         url: https://huggingface.co/Qwen/QwQ-32B
         precision: float16
         tunableop: true
-    - group: DBRX
+    - group: Databricks DBRX
       tag: dbrx
       models:
       - model: DBRX Instruct
@@ -121,7 +121,7 @@ vllm_benchmark:
         model_repo: amd/dbrx-instruct-FP8-KV
         url: https://huggingface.co/amd/dbrx-instruct-FP8-KV
         precision: float8
-    - group: Gemma
+    - group: Google Gemma
       tag: gemma
       models:
       - model: Gemma 2 27B
@@ -150,3 +150,18 @@ vllm_benchmark:
         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/training/megatron-lm-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/rocm-for-ai/inference-optimization/model-quantization.rst

@@ -1,15 +1,178 @@
 .. meta::
    :description: How to use model quantization techniques to speed up inference.
-   :keywords: ROCm, LLM, fine-tuning, usage, tutorial, quantization, GPTQ, transformers, bitsandbytes
+   :keywords: ROCm, LLM, fine-tuning, usage, tutorial, quantization, Quark, GPTQ, transformers, bitsandbytes
 
 *****************************
 Model quantization techniques
 *****************************
 
 Quantization reduces the model size compared to its native full-precision version, making it easier to fit large models
-onto accelerators or GPUs with limited memory usage. This section explains how to perform LLM quantization using GPTQ
+onto accelerators or GPUs with limited memory usage. This section explains how to perform LLM quantization using AMD Quark, GPTQ
 and bitsandbytes on AMD Instinct hardware.
 
+.. _quantize-llms-quark:
+
+AMD Quark
+=========
+
+`AMD Quark <https://quark.docs.amd.com/latest/>`_ offers the leading efficient and scalable quantization solution tailored to AMD Instinct GPUs. It supports ``FP8`` and ``INT8`` quantization for activations, weights, and KV cache, 
+including ``FP8`` attention. For very large models, it employs a two-level ``INT4-FP8`` scheme—storing weights in ``INT4`` while computing with ``FP8``—for nearly 4× compression without sacrificing accuracy. 
+Quark scales efficiently across multiple GPUs, efficiently handling ultra-large models like Llama-3.1-405B. Quantized ``FP8`` models like Llama, Mixtral, and Grok-1 are available under the `AMD organization on Hugging Face <https://huggingface.co/collections/amd/quark-quantized-ocp-fp8-models-66db7936d18fcbaf95d4405c>`_, and can be deployed directly via `vLLM <https://github.com/vllm-project/vllm/tree/main/vllm>`_.
+
+Installing Quark
+-------------------
+
+The latest release of Quark can be installed with pip
+
+.. code-block:: shell
+
+    pip install amd-quark
+
+For detailed installation instructions, refer to the `Quark documentation <https://quark.docs.amd.com/latest/install.html>`_.
+
+
+Using Quark for quantization
+-----------------------------
+
+#. First, load the pre-trained model and its corresponding tokenizer using the Hugging Face ``transformers`` library.
+
+   .. code-block:: python
+
+      from transformers import AutoTokenizer, AutoModelForCausalLM
+
+      MODEL_ID = "meta-llama/Llama-2-70b-chat-hf"
+      MAX_SEQ_LEN = 512
+
+      model = AutoModelForCausalLM.from_pretrained(
+          MODEL_ID, device_map="auto", torch_dtype="auto",
+      )
+      model.eval()
+
+      tokenizer = AutoTokenizer.from_pretrained(MODEL_ID, model_max_length=MAX_SEQ_LEN)
+      tokenizer.pad_token = tokenizer.eos_token
+
+#. Prepare the calibration DataLoader (static quantization requires calibration data).
+
+   .. code-block:: python
+
+      from datasets import load_dataset
+      from torch.utils.data import DataLoader
+
+      BATCH_SIZE = 1
+      NUM_CALIBRATION_DATA = 512
+
+      dataset = load_dataset("mit-han-lab/pile-val-backup", split="validation")
+      text_data = dataset["text"][:NUM_CALIBRATION_DATA]
+
+      tokenized_outputs = tokenizer(
+      text_data, return_tensors="pt", padding=True, truncation=True, max_length=MAX_SEQ_LEN
+      )
+      calib_dataloader = DataLoader(
+      tokenized_outputs['input_ids'], batch_size=BATCH_SIZE, drop_last=True
+      )
+
+#. Define the quantization configuration. See the comments in the following code snippet for descriptions of each configuration option.
+
+   .. code-block:: python
+
+      from quark.torch.quantization import (Config, QuantizationConfig,
+                                           FP8E4M3PerTensorSpec)
+
+      # Define fp8/per-tensor/static spec.
+      FP8_PER_TENSOR_SPEC = FP8E4M3PerTensorSpec(observer_method="min_max",
+          is_dynamic=False).to_quantization_spec()
+
+      # Define global quantization config, input tensors and weight apply FP8_PER_TENSOR_SPEC.
+      global_quant_config = QuantizationConfig(input_tensors=FP8_PER_TENSOR_SPEC,
+          weight=FP8_PER_TENSOR_SPEC)
+
+      # Define quantization config for kv-cache layers, output tensors apply FP8_PER_TENSOR_SPEC.
+      KV_CACHE_SPEC = FP8_PER_TENSOR_SPEC
+      kv_cache_layer_names_for_llama = ["*k_proj", "*v_proj"]
+      kv_cache_quant_config = {name :
+          QuantizationConfig(input_tensors=global_quant_config.input_tensors,
+                             weight=global_quant_config.weight,
+                             output_tensors=KV_CACHE_SPEC)
+          for name in kv_cache_layer_names_for_llama}
+      layer_quant_config = kv_cache_quant_config.copy()
+
+      EXCLUDE_LAYERS = ["lm_head"]
+      quant_config = Config(
+          global_quant_config=global_quant_config,
+          layer_quant_config=layer_quant_config,
+          kv_cache_quant_config=kv_cache_quant_config,
+          exclude=EXCLUDE_LAYERS)
+
+#. Quantize the model and export
+
+   .. code-block:: python
+
+      import torch
+      from quark.torch import ModelQuantizer, ModelExporter
+      from quark.torch.export import ExporterConfig, JsonExporterConfig
+
+      # Apply quantization.
+      quantizer = ModelQuantizer(quant_config)
+      quant_model = quantizer.quantize_model(model, calib_dataloader)
+
+      # Freeze quantized model to export.
+      freezed_model = quantizer.freeze(model)
+
+      # Define export config.
+      LLAMA_KV_CACHE_GROUP = ["*k_proj", "*v_proj"]
+      export_config = ExporterConfig(json_export_config=JsonExporterConfig())
+      export_config.json_export_config.kv_cache_group = LLAMA_KV_CACHE_GROUP
+
+      EXPORT_DIR = MODEL_ID.split("/")[1] + "-w-fp8-a-fp8-kvcache-fp8-pertensor"
+      exporter = ModelExporter(config=export_config, export_dir=EXPORT_DIR)
+      with torch.no_grad():
+          exporter.export_safetensors_model(freezed_model,
+              quant_config=quant_config, tokenizer=tokenizer)
+
+Evaluating the quantized model with vLLM
+----------------------------------------
+
+The exported Quark-quantized model can be loaded directly by vLLM for inference. You need to specify the model path and inform vLLM about the quantization method (``quantization='quark'``) and the KV cache data type (``kv_cache_dtype='fp8'``).
+Use the ``LLM`` interface to load the model:
+
+.. code-block:: python
+
+   from vllm import LLM, SamplingParamsinterface
+
+   # Sample prompts.
+   prompts = [
+       "Hello, my name is",
+       "The president of the United States is",
+       "The capital of France is",
+       "The future of AI is",
+   ]
+   # Create a sampling params object.
+   sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+
+   # Create an LLM.
+   llm = LLM(model="Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor",
+             kv_cache_dtype='fp8',quantization='quark')
+   # Generate texts from the prompts. The output is a list of RequestOutput objects
+   # that contain the prompt, generated text, and other information.
+   outputs = llm.generate(prompts, sampling_params)
+   # Print the outputs.
+   print("\nGenerated Outputs:\n" + "-" * 60)
+   for output in outputs:
+       prompt = output.prompt
+       generated_text = output.outputs[0].text
+       print(f"Prompt:    {prompt!r}")
+       print(f"Output:    {generated_text!r}")
+       print("-" * 60)
+
+You can also evaluate the quantized model's accuracy on standard benchmarks using the `lm-evaluation-harness <https://github.com/EleutherAI/lm-evaluation-harness>`_. Pass the necessary vLLM arguments to ``lm_eval`` via ``--model_args``.
+
+.. code-block:: shell
+
+   lm_eval --model vllm \
+     --model_args pretrained=Llama-2-70b-chat-hf-w-fp8-a-fp8-kvcache-fp8-pertensor,kv_cache_dtype='fp8',quantization='quark' \
+     --tasks gsm8k
+
+This provides a standardized way to measure the performance impact of quantization.
 .. _fine-tune-llms-gptq:
 
 GPTQ
@@ -33,7 +196,7 @@ The AutoGPTQ library implements the GPTQ algorithm.
    .. code-block:: shell
 
       # This will install pre-built wheel for a specific ROCm version.
-      
+
       pip install auto-gptq --no-build-isolation --extra-index-url https://huggingface.github.io/autogptq-index/whl/rocm573/
 
    Or, install AutoGPTQ from source for the appropriate ROCm version (for example, ROCm 6.1).
@@ -43,10 +206,10 @@ The AutoGPTQ library implements the GPTQ algorithm.
       # Clone the source code.
       git clone https://github.com/AutoGPTQ/AutoGPTQ.git
       cd AutoGPTQ
-      
+
       # Speed up the compilation by specifying PYTORCH_ROCM_ARCH to target device.
       PYTORCH_ROCM_ARCH=gfx942 ROCM_VERSION=6.1 pip install .
-      
+
       # Show the package after the installation 
 
 #. Run ``pip show auto-gptq`` to print information for the installed ``auto-gptq`` package. Its output should look like
@@ -112,7 +275,7 @@ Using GPTQ with Hugging Face Transformers
    .. code-block:: python
 
       from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
-      
+
       base_model_name = " NousResearch/Llama-2-7b-hf"
       tokenizer = AutoTokenizer.from_pretrained(base_model_name)
       gptq_config = GPTQConfig(bits=4, dataset="c4", tokenizer=tokenizer)
@@ -212,10 +375,10 @@ To get started with bitsandbytes primitives, use the following code as reference
 .. code-block:: python
 
    import bitsandbytes as bnb
-   
+
    # Use Int8 Matrix Multiplication
    bnb.matmul(..., threshold=6.0)
-   
+
    # Use bitsandbytes 8-bit Optimizers
    adam = bnb.optim.Adam8bit(model.parameters(), lr=0.001, betas=(0.9, 0.995))
 
@@ -227,14 +390,14 @@ To load a Transformers model in 4-bit, set ``load_in_4bit=true`` in ``BitsAndByt
 .. code-block:: python
 
    from transformers import AutoModelForCausalLM, BitsAndBytesConfig
-   
+
    base_model_name = "NousResearch/Llama-2-7b-hf"
    quantization_config = BitsAndBytesConfig(load_in_4bit=True)
    bnb_model_4bit = AutoModelForCausalLM.from_pretrained(
            base_model_name, 
            device_map="auto", 
            quantization_config=quantization_config)
-   
+
    # Check the memory footprint with get_memory_footprint method
    print(bnb_model_4bit.get_memory_footprint())
 
@@ -243,9 +406,9 @@ To load a model in 8-bit for inference, use the ``load_in_8bit`` option.
 .. code-block:: python
 
    from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-   
+
    base_model_name = "NousResearch/Llama-2-7b-hf"
-   
+
    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
    quantization_config = BitsAndBytesConfig(load_in_8bit=True)
    tokenizer = AutoTokenizer.from_pretrained(base_model_name)
@@ -253,7 +416,7 @@ To load a model in 8-bit for inference, use the ``load_in_8bit`` option.
            base_model_name, 
            device_map="auto", 
            quantization_config=quantization_config)
-   
+
    prompt = "What is a large language model?"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    generated_ids = model.generate(**inputs)

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

@@ -678,7 +678,7 @@ To specify the quantization scaling config, use the
 ``--quantization-param-path`` parameter. If the parameter is not specified,
 the default scaling factor of ``1`` is used, which can lead to less accurate
 results. To generate ``kv-cache`` scaling JSON file, see `FP8 KV
-Cache <https://github.com/vllm-project/vllm/blob/main/examples/fp8/README.md>`__
+Cache <https://github.com/vllm-project/llm-compressor/blob/main/examples/quantization_kv_cache/README.md>`__
 in the vLLM GitHub repository.
 
 Two sample Llama scaling configuration files are in vLLM for ``llama2-70b`` and

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

@@ -0,0 +1,348 @@
+: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
+   performance benchmark documentation. See :doc:`../vllm` for the latest version.
+
+.. _vllm-benchmark-unified-docker:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.8.5_20250513-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/pytorch/pytorch>`_
+
+   * `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
+   MI300X series accelerators.
+
+   .. _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.
+
+   .. 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:
+
+   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.
+
+   .. note::
+
+      The performance data presented in
+      `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
+      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/blob/7bb0618b1fe725b7d4fad9e525aa44da12c94a8b/docs/dev-docker/README.md>`__.
+
+   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 ``{{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>` 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 ``{{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 ``{{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:`../../../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 optimize inference on LLMs, see
+  :doc:`Inference optimization <../../../inference-optimization/index>`.
+
+- To learn how to fine-tune LLMs, see
+  :doc:`Fine-tuning LLMs <../../../fine-tuning/index>`.

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

@@ -24,20 +24,24 @@ PyTorch inference performance testing
    Supported models
    ================
 
+   The following models are supported for inference performance benchmarking
+   with PyTorch 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</div>
+          <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-6 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+            <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" style="display: none;">
-          <div class="col-2 me-2 model-param-head">Model variant</div>
+          <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 %}
@@ -62,47 +66,52 @@ PyTorch inference performance testing
       {% endfor %}
    {% endfor %}
 
-   Getting started
-   ===============
+   System validation
+   =================
 
-   Use the following procedures to reproduce the benchmark results on an
-   MI300X series accelerator with the prebuilt PyTorch Docker image.
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
 
-   .. _pytorch-benchmark-get-started:
+   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>`.
 
-   1. Disable NUMA auto-balancing.
+   .. code-block:: shell
 
-      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>`.
+      # 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
 
-      .. code-block:: shell
+   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.
 
-         # 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
+   Pull the Docker image
+   =====================
 
    .. container:: model-doc pyt_chai1_inference
 
-      2. 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/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
+      .. code-block:: shell
 
-            docker pull rocm/pytorch:rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue
+         docker pull rocm/pytorch:rocm6.2.3_ubuntu22.04_py3.10_pytorch_release_2.3.0_triton_llvm_reg_issue
 
-         .. note::
+      .. note::
 
-            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.
+         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
+   .. container:: model-doc pyt_clip_inference pyt_mochi_video_inference
 
-      2. 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/latest/images/sha256-05b55983e5154f46e7441897d0908d79877370adca4d1fff4899d9539d6c4969>`_ from Docker Hub.
 
-         .. code-block:: shell
+      .. code-block:: shell
 
-            docker pull rocm/pytorch:latest
+         docker pull rocm/pytorch:latest
+
+   .. _pytorch-benchmark-get-started:
 
    Benchmarking
    ============
@@ -157,11 +166,14 @@ Further reading
 - 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:`../../inference-optimization/workload`.
+
 - To learn how to run LLM models from Hugging Face or your model, see
-  :doc:`Running models from Hugging Face <hugging-face-models>`.
+  :doc:`Running models from Hugging Face <../hugging-face-models>`.
 
 - To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../inference-optimization/index>`.
+  :doc:`Inference optimization <../../inference-optimization/index>`.
 
 - To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
+  :doc:`Fine-tuning LLMs <../../fine-tuning/index>`.

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

@@ -24,7 +24,7 @@ vLLM inference performance testing
 
    * `vLLM {{ unified_docker.vllm_version }} <https://docs.vllm.ai/en/latest>`_
 
-   * `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/pytorch/pytorch>`_
+   * `PyTorch {{ unified_docker.pytorch_version }} <https://github.com/ROCm/pytorch.git>`_
 
    * `hipBLASLt {{ unified_docker.hipblaslt_version }} <https://github.com/ROCm/hipBLASLt>`_
 
@@ -37,11 +37,15 @@ vLLM inference performance testing
    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</div>
+          <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>
@@ -50,7 +54,7 @@ vLLM inference performance testing
         </div>
 
         <div class="row mt-1">
-          <div class="col-2 me-2 model-param-head">Model variant</div>
+          <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 %}
@@ -111,35 +115,37 @@ vLLM inference performance testing
    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/blob/main/docs/dev-docker/README.md>`__.
 
-   Getting started
-   ===============
+   System validation
+   =================
 
-   Use the following procedures to reproduce the benchmark results on an
-   MI300X accelerator with the prebuilt vLLM Docker image.
+   Before running AI workloads, it's important to validate that your AMD hardware is configured
+   correctly and performing optimally.
 
-   .. _vllm-benchmark-get-started:
+   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>`.
 
-   1. Disable NUMA auto-balancing.
+   .. code-block:: shell
 
-      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>`.
+      # 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
 
-      .. code-block:: shell
+   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.
 
-         # 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
+   Pull the Docker image
+   =====================
 
-   2. Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Download the `ROCm vLLM Docker image <{{ unified_docker.docker_hub_url }}>`_.
+   Use the following command to pull the Docker image from Docker Hub.
 
-      Use the following command to pull the Docker image from Docker Hub.
+   .. code-block:: shell
 
-      .. code-block:: shell
-
-         docker pull {{ unified_docker.pull_tag }}
+      docker pull {{ unified_docker.pull_tag }}
 
    Benchmarking
    ============
@@ -276,7 +282,7 @@ 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 ``{{model.precision}}`` precision.
 
               .. code-block::
 
@@ -286,11 +292,11 @@ 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 benchmark the throughput of the {{model.model}} model on eight GPUs with ``{{model.precision}}`` precision.
 
               .. code-block:: shell
 
-                 ./vllm_benchmark_report.sh -s latency -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``.
 
@@ -316,23 +322,23 @@ vLLM inference performance testing
 Further reading
 ===============
 
-- For application performance optimization strategies for HPC and AI workloads,
-  including inference with vLLM, see :doc:`../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>`_
 
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`../../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>`.
+  :doc:`Running models from Hugging Face <../hugging-face-models>`.
 
 - To learn how to optimize inference on LLMs, see
-  :doc:`Inference optimization <../inference-optimization/index>`.
+  :doc:`Inference optimization <../../inference-optimization/index>`.
 
 - To learn how to fine-tune LLMs, see
-  :doc:`Fine-tuning LLMs <../fine-tuning/index>`.
+  :doc:`Fine-tuning LLMs <../../fine-tuning/index>`.
 
 Previous versions
 =================
@@ -350,6 +356,20 @@ for benchmarking, see the version-specific documentation.
      - PyTorch version
      - Resources
 
+   * - 6.3.1
+     - 0.8.5
+     - 2.7.0
+     - 
+       * :doc:`Documentation <previous-versions/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>`_
+
+   * - 6.3.1
+     - 0.8.3
+     - 2.7.0
+     - 
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.4.0/how-to/rocm-for-ai/inference/vllm-benchmark.html>`_
+       * `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

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

@@ -20,6 +20,8 @@ training, fine-tuning, and inference. It leverages popular machine learning fram
 
 - :doc:`LLM inference frameworks <llm-inference-frameworks>`
 
-- :doc:`Performance testing <vllm-benchmark>`
+- :doc:`vLLM inference performance testing <vllm-benchmark>`
+
+- :doc:`PyTorch inference performance testing <pytorch-inference-benchmark>`
 
 - :doc:`Deploying your model <deploy-your-model>`

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

@@ -30,7 +30,7 @@ ROCm supports multiple :doc:`installation methods <rocm-install-on-linux:install
 
 * :doc:`Using the AMDGPU installer <rocm-install-on-linux:install/amdgpu-install>`
 
-* :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`.
+* :ref:`Multi-version installation <rocm-install-on-linux:installation-types>`
 
 .. grid:: 1
 
@@ -59,4 +59,8 @@ images with the framework pre-installed.
 
 * :doc:`JAX for ROCm <rocm-install-on-linux:install/3rd-party/jax-install>`
 
-The sections that follow in :doc:`Training a model <../training/train-a-model>` are geared for a ROCm with PyTorch installation.
+Next steps
+==========
+
+After installing ROCm and your desired ML libraries -- and before running AI workloads -- conduct system health benchmarks
+to test the optimal performance of your AMD hardware. See :doc:`system-health-check` to get started.

docs/how-to/rocm-for-ai/system-health-check.rst

@@ -0,0 +1,104 @@
+.. meta::
+   :description: System health checks with RVS, RCCL tests, BabelStream, and TransferBench to validate AMD hardware performance running AI workloads.
+   :keywords: gpu, accelerator, system, health, validation, bench, perf, performance, rvs, rccl, babel, mi300x, mi325x, flops, bandwidth, rbt, training, inference
+
+.. _rocm-for-ai-system-health-bench:
+
+************************
+System health benchmarks
+************************
+
+Before running AI workloads, it is important to validate that your AMD hardware is configured correctly and is performing optimally. This topic outlines several system health benchmarks you can use to test key aspects like GPU compute capabilities (FLOPS), memory bandwidth, and interconnect performance. Many of these tests are part of the ROCm Validation Suite (RVS).
+
+ROCm Validation Suite (RVS) tests
+=================================
+
+RVS provides a collection of tests, benchmarks, and qualification tools, each
+targeting a specific subsystem of the system under test. It includes tests for
+GPU stress and memory bandwidth.
+
+.. _healthcheck-install-rvs:
+
+Install ROCm Validation Suite
+-----------------------------
+
+To get started, install RVS. For example, on an Ubuntu system with ROCm already
+installed, run the following command:
+
+.. code-block:: shell
+
+   sudo apt update
+   sudo apt install rocm-validation-suite
+
+See the `ROCm Validation Suite installation instructions <https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/install/installation.html>`_,
+and `System validation tests <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/system-validation.html#system-validation-tests>`_
+in the Instinct documentation for more detailed instructions.
+
+Benchmark, stress, and qualification tests
+------------------------------------------
+
+The GPU stress test runs various GEMM computations as workloads to stress the GPU FLOPS performance and check whether it
+meets the configured target GFLOPS.
+
+Run the benchmark, stress, and qualification tests included with RVS. See the `Benchmark, stress, qualification
+<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/system-validation.html#benchmark-stress-qualification>`_
+section of the Instinct documentation for usage instructions.
+
+BabelStream test
+----------------
+
+BabelStream is a synthetic GPU benchmark based on the STREAM benchmark for
+CPUs, measuring memory transfer rates to and from global device memory.
+BabelStream tests are included with the RVS package as part of the `BABEL module
+<https://rocm.docs.amd.com/projects/ROCmValidationSuite/en/latest/conceptual/rvs-modules.html#babel-benchmark-test-babel-module>`_.
+
+For more information, see `Performance benchmarking
+<https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#babelstream-benchmarking-results>`_
+in the Instinct documentation.
+
+RCCL tests
+==========
+
+The ROCm Communication Collectives Library (RCCL) enables efficient multi-GPU
+communication. The `<https://github.com/ROCm/rccl-tests>`__ suite benchmarks
+the performance and verifies the correctness of these collective operations.
+This helps ensure optimal scaling for multi-accelerator tasks.
+
+1. To get started, build RCCL-tests using the official instructions in the README at
+   `<https://github.com/ROCm/rccl-tests?tab=readme-ov-file#build>`__ or use the
+   following commands:
+
+   .. code-block:: shell
+
+      git clone https://github.com/ROCm/rccl-tests.git
+      cd rccl-tests
+      make
+
+2. Run the suggested RCCL tests -- see `RCCL benchmarking
+   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#rccl-benchmarking-results>`_
+   in the Instinct performance benchmarking documentation for instructions.
+
+TransferBench test
+==================
+
+TransferBench is a standalone utility for benchmarking simultaneous data
+transfer performance between various devices in the system, including
+CPU-to-GPU and GPU-to-GPU (peer-to-peer). This helps identify potential
+bottlenecks in data movement between the host system and the GPUs, or between
+GPUs, which can impact end-to-end latency.
+
+.. _healthcheck-install-transferbench:
+
+1. To get started, use the instructions in the `TransferBench documentation
+   <https://rocm.docs.amd.com/projects/TransferBench/en/latest/install/install.html#install-transferbench>`_
+   or use the following commands:
+
+   .. code:: shell
+
+      git clone https://github.com/ROCm/TransferBench.git
+      cd TransferBench
+      CC=hipcc make
+
+2. Run the suggested TransferBench tests -- see `TransferBench benchmarking
+   <https://instinct.docs.amd.com/projects/system-acceptance/en/latest/mi300x/performance-bench.html#transferbench-benchmarking-results>`_
+   in the Instinct performance benchmarking documentation for instructions.

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

@@ -12,7 +12,7 @@ ROCm is an optimized fork of the upstream
 `<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads
 on AMD MI300X series accelerators.
 
-The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.4``) image
+The MaxText for ROCm training Docker (``rocm/jax-training:maxtext-v25.5``) image
 provides a prebuilt environment for training on AMD Instinct MI300X and MI325X accelerators,
 including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
 It includes the following software components:
@@ -20,15 +20,15 @@ It includes the following software components:
 +--------------------------+--------------------------------+
 | Software component       | Version                        |
 +==========================+================================+
-| ROCm                     | 6.3.0                          |
+| ROCm                     | 6.3.4                          |
 +--------------------------+--------------------------------+
-| JAX                      | 0.4.31                         |
+| JAX                      | 0.4.35                         |
 +--------------------------+--------------------------------+
-| Python                   | 3.10                           |
+| Python                   | 3.10.12                        |
 +--------------------------+--------------------------------+
-| Transformer Engine       | 1.12.0.dev0+f81a3eb            |
+| Transformer Engine       | 1.12.0.dev0+b8b92dc            |
 +--------------------------+--------------------------------+
-| hipBLASLt                | git78ec8622                    |
+| hipBLASLt                | 0.13.0-ae9c477a                |
 +--------------------------+--------------------------------+
 
 Supported features and models
@@ -48,6 +48,8 @@ MaxText provides the following key features to train large language models effic
 
 The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
 
+* Llama 3.3 70B
+
 * Llama 3.1 8B
 
 * Llama 3.1 70B
@@ -77,11 +79,18 @@ across different input sequences. Support for packed input format is planned for
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-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 running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 Environment setup
 =================
 
@@ -115,7 +124,7 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
 
    a. Master address
 
-      Change `localhost` to the master node's resolvable hostname or IP address:
+      Change ``localhost`` to the master node's resolvable hostname or IP address:
 
       .. code-block:: bash
 
@@ -173,20 +182,22 @@ with RDMA, skip ahead to :ref:`amd-maxtext-download-docker`.
 
 .. _amd-maxtext-download-docker:
 
-Download the Docker image
--------------------------
+Pull the Docker image
+---------------------
 
 1. Use the following command to pull the Docker image from Docker Hub.
 
    .. code-block:: shell
 
-      docker pull rocm/jax-training:maxtext-v25.4
+      docker pull rocm/jax-training:maxtext-v25.5
 
-2. Run the Docker container.
+2. Use the following command to launch the Docker container. Note that the benchmarking scripts
+   used in the :ref:`following section <amd-maxtext-get-started>` automatically launch the Docker container
+   and execute the benchmark.
 
    .. code-block:: shell
 
-      docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.4
+      docker run -it --device /dev/dri --device /dev/kfd --network host --ipc host --group-add video --cap-add SYS_PTRACE --security-opt seccomp=unconfined --privileged -v $HOME/.ssh:/root/.ssh --shm-size 128G --name maxtext_training rocm/jax-training:maxtext-v25.5
 
 .. _amd-maxtext-get-started:
 
@@ -219,7 +230,9 @@ Single node training benchmarking examples
 
   Run the single node training benchmark:
 
-  IMAGE="rocm/jax-training:maxtext-v25.4" bash ./llama2_7b.sh
+  .. code-block:: shell
+
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_7b.sh
 
 * Example 2: Single node training with Llama 2 70B
 
@@ -233,7 +246,7 @@ Single node training benchmarking examples
 
   .. code-block:: shell
 
-     IMAGE="rocm/jax-training:maxtext-v25.4" bash ./llama2_70b.sh
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama2_70b.sh
 
 * Example 3: Single node training with Llama 3 8B
 
@@ -247,7 +260,7 @@ Single node training benchmarking examples
 
   .. code-block:: shell
 
-     IMAGE="rocm/jax-training:maxtext-v25.4" bash ./llama3_8b.sh
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_8b.sh
 
 * Example 4: Single node training with Llama 3 70B
 
@@ -261,9 +274,23 @@ Single node training benchmarking examples
 
   .. code-block:: shell
 
-     IMAGE="rocm/jax-training:maxtext-v25.4" bash ./llama3_70b.sh
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3_70b.sh
 
-* Example 5: Single node training with DeepSeek V2 16B
+* Example 5: Single node training with Llama 3.3 70B
+
+  Download the benchmarking script:
+
+  .. code-block:: shell
+
+     wget https://raw.githubusercontent.com/ROCm/maxtext/refs/heads/main/benchmarks/gpu-rocm/llama3.3_70b.sh
+
+  Run the single node training benchmark:
+
+  .. code-block:: shell
+
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./llama3.3_70b.sh
+
+* Example 6: Single node training with DeepSeek V2 16B
 
   Download the benchmarking script:
 
@@ -275,7 +302,7 @@ Single node training benchmarking examples
 
   .. code-block:: shell
 
-     IMAGE="rocm/jax-training:maxtext-v25.4" bash ./deepseek_v2_16b.sh
+     IMAGE="rocm/jax-training:maxtext-v25.5" bash ./deepseek_v2_16b.sh
 
   .. note::
 
@@ -343,3 +370,26 @@ own cluster setup.
   .. code-block:: shell
 
      sbatch -N <num_nodes> llama3_70b_multinode.sh
+
+Previous versions
+=================
+
+This table lists previous versions of the ROCm JAX MaxText Docker image for training
+performance testing. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+   :stub-columns: 1
+
+   * - Image version
+     - ROCm version
+     - JAX version
+     - Resources
+
+   * - 25.4
+     - 6.3.0
+     - 0.4.31
+     - 
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.3.3/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.html>`_
+       * `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/mpt-llm-foundry.rst

@@ -0,0 +1,175 @@
+.. meta::
+   :description: How to train a model using LLM Foundry for ROCm.
+   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
+
+******************************************
+Training MPT-30B with LLM Foundry and ROCm
+******************************************
+
+MPT-30B is a 30-billion parameter decoder-style transformer-based model from
+the Mosaic Pretrained Transformer (MPT) family -- learn more about it in
+MosaicML's research blog `MPT-30B: Raising the bar for open-source foundation
+models <https://www.databricks.com/blog/mpt-30b>`_.
+
+ROCm and `<https://github.com/ROCm/MAD>`__ provide a pre-configured training
+environment for the MPT-30B model using the ``rocm/pytorch-training:v25.5``
+base `Docker image <https://hub.docker.com/layers/rocm/pytorch-training/v25.5/images/sha256-d47850a9b25b4a7151f796a8d24d55ea17bba545573f0d50d54d3852f96ecde5>`_
+and the `LLM Foundry <https://github.com/mosaicml/llm-foundry>`_ framework.
+This environment packages the following software components to train
+on AMD Instinct MI300X series accelerators:
+
++--------------------------+--------------------------------+
+| Software component       | Version                        |
++==========================+================================+
+| ROCm                     | 6.3.4                          |
++--------------------------+--------------------------------+
+| PyTorch                  | 2.7.0a0+git6374332             |
++--------------------------+--------------------------------+
+| Flash Attention          | 3.0.0.post1                    |
++--------------------------+--------------------------------+
+
+Using this image, you can build, run, and test the training process
+for MPT-30B with access to detailed logs and performance metrics.
+
+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.
+
+Getting started
+===============
+
+The following procedures help you set up the training environment in a
+reproducible Docker container. This training environment is tailored for
+training MPT-30B using LLM Foundry and the specific model configurations outlined.
+Other configurations and run conditions outside those described in this
+document are not validated.
+
+.. tab-set::
+
+   .. tab-item:: MAD-integrated benchmarking
+
+      On your host machine, clone the ROCm Model Automation and Dashboarding
+      (`<https://github.com/ROCm/MAD>`__) repository to a local directory and
+      install the required packages.
+
+      .. code-block:: shell
+
+         git clone https://github.com/ROCm/MAD
+         cd MAD
+         pip install -r requirements.txt
+
+      Use this command to initiate the MPT-30B training benchmark.
+
+      .. code-block:: shell
+
+         python3 tools/run_models.py --tags pyt_mpt30b_training --keep-model-dir --live-output --clean-docker-cache
+
+      .. tip::
+
+         If you experience data download failures, set the
+         ``MAD_SECRETS_HFTOKEN`` variable to your Hugging Face access token. See
+         `User access tokens <https://huggingface.co/docs/hub/security-tokens>`_
+         for details.
+
+         .. code-block:: shell
+
+            export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+
+      .. note::
+
+         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
+         ``models.json`` configuration before running training.
+
+         Although this might increase the initial training time, it can result in a performance gain.
+
+   .. tab-item:: Standalone benchmarking
+
+      To set up the training environment, clone the
+      `<https://github.com/ROCm/MAD>`__ repo and build the Docker image. In
+      this snippet, the image is named ``mosaic_mpt30_image``.
+
+      .. code-block:: shell
+
+         git clone https://github.com/ROCm/MAD
+         cd MAD
+
+         docker build --build-arg MAD_SYSTEM_GPU_ARCHITECTURE=gfx942 -f docker/pyt_mpt30b_training.ubuntu.amd.Dockerfile -t mosaic_mpt30_image .
+
+      Start a ``mosaic_mpt30_image`` container using the following command.
+
+      .. code-block:: shell
+
+         docker run -it --device=/dev/kfd --device=/dev/dri --group-add=video --ipc=host --shm-size=8G mosaic_mpt30_image
+
+      In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+      repository and navigate to the benchmark scripts directory at
+      ``/workspace/MAD/scripts/pyt_mpt30b_training``.
+
+      .. code-block:: shell
+
+         git clone https://github.com/ROCm/MAD
+         cd MAD/scripts/pyt_mpt30b_training
+
+      To initiate the training process, use the following command. This script uses the hyperparameters defined in
+      ``mpt-30b-instruct.yaml``.
+
+      .. code-block:: shell
+
+         source run.sh
+
+      .. note::
+
+         For improved performance (training throughput), consider enabling TunableOp.
+         To enable it, add the ``--tunableop on`` flag.
+
+         .. code-block:: shell
+
+            source run.sh --tunableop on
+
+         Although this might increase the initial training time, it can result in a performance gain.
+
+Interpreting the output
+=======================
+
+The training output will be displayed in the terminal and simultaneously saved
+to the ``output.txt`` file in the current directory. Key performance metrics will
+also be extracted and appended to the ``perf_pyt_mpt30b_training.csv`` file.
+
+Key performance metrics include:
+
+- Training logs: Real-time display of loss metrics, accuracy, and training progress.
+
+- Model checkpoints: Periodically saved model snapshots for potential resume or evaluation.
+
+- Performance metrics: Detailed summaries of training speed and training loss metrics.
+
+  - Performance (throughput/samples_per_sec)
+
+    Overall throughput, measuring the total samples processed per second. Higher values indicate better hardware utilization.
+
+  - Performance per device (throughput/samples_per_sec)
+
+    Throughput on a per-device basis, showing how each GPU or CPU is performing.
+
+  - Language Cross Entropy (metrics/train/LanguageCrossEntropy)
+
+    Measures prediction accuracy. Lower cross entropy suggests the model’s output is closer to the expected distribution.
+
+  - Training loss (loss/train/total)
+
+    Overall training loss. A decreasing trend indicates the model is learning effectively.
+
+

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

@@ -77,11 +77,18 @@ popular AI models.
 System validation
 =================
 
-If you have already validated your system settings, including NUMA
-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 running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
 This Docker image is optimized for specific model configurations outlined
 below. Performance can vary for other training workloads, as AMD 
 doesn’t validate configurations and run conditions outside those described.
@@ -443,7 +450,7 @@ benchmarking, see the version-specific documentation.
      - 6.3.0
      - 2.7.0a0+git637433
      - 
-       * `Documentation <https://rocm.docs.amd.com/en/docs-6.3.4/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html>`_
+       * `Documentation <https://rocm.docs.amd.com/en/docs-6.3.3/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html>`_
        * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.4/images/sha256-fa98a9aa69968e654466c06f05aaa12730db79b48b113c1ab4f7a5fe6920a20b>`_
 
    * - v25.3

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

@@ -21,8 +21,12 @@ In this guide, you'll learn about:
 
 - Training a model
 
-  - :doc:`Train a model with Megatron-LM <benchmark-docker/megatron-lm>`
+  - :doc:`With Megatron-LM <benchmark-docker/megatron-lm>`
 
-  - :doc:`Train a model with PyTorch <benchmark-docker/pytorch-training>`
+  - :doc:`With PyTorch <benchmark-docker/pytorch-training>`
+
+  - :doc:`With JAX MaxText <benchmark-docker/jax-maxtext>`
+
+  - :doc:`With LLM Foundry <benchmark-docker/mpt-llm-foundry>`
 
 - :doc:`Scaling model training <scale-model-training>`

docs/how-to/rocm-for-ai/training/prerequisite-system-validation.rst

@@ -5,12 +5,13 @@
    :keywords: ROCm, AI, LLM, train, megatron, Llama, tutorial, docker, torch, pytorch, jax
 
 .. _train-a-model-system-validation:
+.. _rocm-for-ai-system-optimization:
 
-**********************************************
-Prerequisite system validation before training
-**********************************************
+**********************************************************
+Prerequisite system validation before running AI workloads
+**********************************************************
 
-Complete the following system validation and optimization steps to set up your system before starting training.
+Complete the following system validation and optimization steps to set up your system before starting training and inference.
 
 Disable NUMA auto-balancing
 ---------------------------
@@ -26,7 +27,8 @@ 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 `Disable NUMA auto-balancing <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#disable-numa-auto-balancing>`_
+in the Instinct documentation for more information.
 
 Hardware verification with ROCm
 -------------------------------
@@ -42,7 +44,8 @@ Run the command:
 
    rocm-smi --setperfdeterminism 1900
 
-See :ref:`mi300x-hardware-verification-with-rocm` for more information.
+See `Hardware verfication for ROCm <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#hardware-verification-with-rocm>`_
+in the Instinct documentation for more information.
 
 RCCL Bandwidth Test for multi-node setups
 -----------------------------------------

docs/reference/api-libraries.md

@@ -45,6 +45,7 @@
 (communication-libraries)=
 
 * {doc}`RCCL <rccl:index>`
+* {doc}`rocSHMEM <rocshmem:index>`
 :::
 
 :::{grid-item-card} Math

docs/reference/gpu-arch-specs.rst

@@ -281,13 +281,31 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - SGPR File (KiB)
           - GFXIP Major version
           - GFXIP Minor version
+        *
+          - Radeon AI PRO R9700
+          - RDNA4
+          - gfx1201
+          - 16
+          - 64
+          - 32 or 64
+          - 128
+          - 64
+          - 8
+          - N/A
+          - 32
+          - 16
+          - 32
+          - 768
+          - 32
+          - 12
+          - 0
         *
           - Radeon PRO V710
           - RDNA3
           - gfx1101
           - 28
           - 54
-          - 32
+          - 32 or 64
           - 128
           - 56
           - 4
@@ -296,7 +314,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -305,7 +323,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 48
           - 96
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 6
@@ -314,7 +332,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -323,7 +341,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 48
           - 96
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 6
@@ -332,7 +350,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -341,7 +359,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 48
           - 70
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 6
@@ -350,7 +368,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -359,7 +377,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 32
           - 70
-          - 32
+          - 32 or 64
           - 128
           - 64
           - 6
@@ -368,7 +386,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -377,7 +395,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1101
           - 16
           - 48
-          - 32
+          - 32 or 64
           - 128
           - 64
           - 4
@@ -386,7 +404,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -395,7 +413,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 32
           - 60
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -404,7 +422,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -413,7 +431,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1032
           - 8
           - 28
-          - 32
+          - 32 or 64
           - 128
           - 32
           - 2
@@ -422,7 +440,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -431,7 +449,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 32
           - 72
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -440,7 +458,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -449,7 +467,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1012
           - 8
           - 22
-          - 32
+          - 32 or 64
           - 128
           -
           - 4
@@ -504,13 +522,85 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - SGPR File (KiB)
           - GFXIP Major version
           - GFXIP Minor version
+        *
+          - Radeon RX 9070 XT
+          - RDNA4
+          - gfx1201
+          - 16
+          - 64
+          - 32 or 64
+          - 128
+          - 64
+          - 8
+          - N/A
+          - 32
+          - 16
+          - 32
+          - 768
+          - 32
+          - 12
+          - 0
+        *
+          - Radeon RX 9070 GRE
+          - RDNA4
+          - gfx1201
+          - 16
+          - 48
+          - 32 or 64
+          - 128
+          - 48
+          - 6
+          - N/A
+          - 32
+          - 16
+          - 32
+          - 768
+          - 32
+          - 12
+          - 0
+        *
+          - Radeon RX 9070
+          - RDNA4
+          - gfx1201
+          - 16
+          - 56
+          - 32 or 64
+          - 128
+          - 64
+          - 8
+          - N/A
+          - 32
+          - 16
+          - 32
+          - 768
+          - 32
+          - 12
+          - 0
+        *
+          - Radeon RX 9060 XT
+          - RDNA4
+          - gfx1200
+          - 16
+          - 32
+          - 32 or 64
+          - 128
+          - 32
+          - 4
+          - N/A
+          - 32
+          - 16
+          - 32
+          - 768
+          - 32
+          - 12
+          - 0
         *
           - Radeon RX 7900 XTX
           - RDNA3
           - gfx1100
           - 24
           - 96
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 6
@@ -519,7 +609,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -528,7 +618,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 20
           - 84
-          - 32
+          - 32 or 64
           - 128
           - 80
           - 6
@@ -537,7 +627,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -546,7 +636,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1100
           - 16
           - 80
-          - 32
+          - 32 or 64
           - 128
           - 64
           - 6
@@ -555,7 +645,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -564,7 +654,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1101
           - 16
           - 60
-          - 32
+          - 32 or 64
           - 128
           - 64
           - 4
@@ -573,7 +663,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -582,7 +672,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1101
           - 12
           - 54
-          - 32
+          - 32 or 64
           - 128
           - 48
           - 4
@@ -591,7 +681,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 768
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -600,7 +690,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1102
           - 8
           - 32
-          - 32
+          - 32 or 64
           - 128
           - 32
           - 2
@@ -609,7 +699,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 11
           - 0
         *
@@ -618,7 +708,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 16
           - 80
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -627,7 +717,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -636,7 +726,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 16
           - 80
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -645,7 +735,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -654,7 +744,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 16
           - 72
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -663,7 +753,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -672,7 +762,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1030
           - 16
           - 60
-          - 32
+          - 32 or 64
           - 128
           - 128
           - 4
@@ -681,7 +771,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -690,7 +780,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1031
           - 12
           - 40
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 3
@@ -699,7 +789,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -708,7 +798,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1031
           - 12
           - 40
-          - 32
+          - 32 or 64
           - 128
           - 96
           - 3
@@ -717,7 +807,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -726,7 +816,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1031
           - 10
           - 36
-          - 32
+          - 32 or 64
           - 128
           - 80
           - 3
@@ -735,7 +825,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -744,7 +834,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1032
           - 8
           - 32
-          - 32
+          - 32 or 64
           - 128
           - 32
           - 2
@@ -753,7 +843,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -762,7 +852,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1032
           - 8
           - 32
-          - 32
+          - 32 or 64
           - 128
           - 32
           - 2
@@ -771,7 +861,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *
@@ -780,7 +870,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - gfx1032
           - 8
           - 28
-          - 32
+          - 32 or 64
           - 128
           - 32
           - 2
@@ -789,7 +879,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 16
           - 32
           - 512
-          - 16
+          - 32
           - 10
           - 3
         *

docs/release/versions.md

@@ -10,6 +10,7 @@
 
 | Version | Release date |
 | ------- | ------------ |
+| [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 |
 | [6.3.2](https://rocm.docs.amd.com/en/docs-6.3.2/) | January 28, 2025 |

docs/sphinx/_toc.yml.in

@@ -36,16 +36,22 @@ subtrees:
     title: Use ROCm for AI
     subtrees:
     - entries:
+      - file: how-to/rocm-for-ai/install.rst
+        title: Installation
+      - file: how-to/rocm-for-ai/system-health-check.rst
+        title: System health benchmarks
       - file: how-to/rocm-for-ai/training/index.rst
         title: Training
         subtrees:
         - entries:
-          - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm
+          - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
             title: Train a model with Megatron-LM
-          - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training
+          - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
             title: Train a model with PyTorch
-          - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext
+          - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
             title: Train a model with JAX MaxText
+          - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
+            title: Train a model with LLM Foundry
           - file: how-to/rocm-for-ai/training/scale-model-training.rst
             title: Scale model training
 
@@ -68,15 +74,13 @@ subtrees:
         title: Inference
         subtrees:
         - entries:
-          - file: how-to/rocm-for-ai/inference/install.rst
-            title: Installation
           - file: how-to/rocm-for-ai/inference/hugging-face-models.rst
             title: Run models from Hugging Face
           - file: how-to/rocm-for-ai/inference/llm-inference-frameworks.rst
             title: LLM inference frameworks
-          - file: how-to/rocm-for-ai/inference/vllm-benchmark.rst
+          - file: how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
             title: vLLM inference performance testing
-          - file: how-to/rocm-for-ai/inference/pytorch-inference-benchmark.rst
+          - file: how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
             title: PyTorch 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==1.18.2
+rocm-docs-core==1.20.1
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0

docs/sphinx/requirements.txt

@@ -2,7 +2,7 @@
 # This file is autogenerated by pip-compile with Python 3.10
 # by the following command:
 #
-#    pip-compile docs/sphinx/requirements.in
+#    pip-compile requirements.in
 #
 accessible-pygments==0.0.5
     # via pydata-sphinx-theme
@@ -10,74 +10,73 @@ alabaster==1.0.0
     # via sphinx
 asttokens==3.0.0
     # via stack-data
-attrs==25.1.0
+attrs==25.3.0
     # via
     #   jsonschema
     #   jupyter-cache
     #   referencing
-babel==2.16.0
+babel==2.17.0
     # via
     #   pydata-sphinx-theme
     #   sphinx
-beautifulsoup4==4.12.3
+beautifulsoup4==4.13.4
     # via pydata-sphinx-theme
-breathe==4.35.0
+breathe==4.36.0
     # via rocm-docs-core
-certifi==2024.8.30
+certifi==2025.4.26
     # via requests
 cffi==1.17.1
     # via
     #   cryptography
     #   pynacl
-charset-normalizer==3.4.0
+charset-normalizer==3.4.2
     # via requests
-click==8.1.7
+click==8.2.1
     # via
     #   jupyter-cache
     #   sphinx-external-toc
 comm==0.2.2
     # via ipykernel
-cryptography==44.0.1
+cryptography==45.0.3
     # via pyjwt
-debugpy==1.8.12
+debugpy==1.8.14
     # via ipykernel
-decorator==5.1.1
+decorator==5.2.1
     # via ipython
 defusedxml==0.7.1
     # via sphinxcontrib-datatemplates
-deprecated==1.2.15
+deprecated==1.2.18
     # via pygithub
 docutils==0.21.2
     # via
-    #   breathe
     #   myst-parser
     #   pydata-sphinx-theme
     #   sphinx
-exceptiongroup==1.2.2
+exceptiongroup==1.3.0
     # via ipython
 executing==2.2.0
     # via stack-data
-fastjsonschema==2.20.0
+fastjsonschema==2.21.1
     # via
     #   nbformat
     #   rocm-docs-core
-gitdb==4.0.11
+gitdb==4.0.12
     # via gitpython
-gitpython==3.1.43
+gitpython==3.1.44
     # via rocm-docs-core
-greenlet==3.1.1
+greenlet==3.2.3
     # via sqlalchemy
 idna==3.10
     # via requests
 imagesize==1.4.1
     # via sphinx
-importlib-metadata==8.6.1
+importlib-metadata==8.7.0
     # via
     #   jupyter-cache
     #   myst-nb
 ipykernel==6.29.5
     # via myst-nb
-ipython==8.31.0
+ipython==8.37.0
     # via
     #   ipykernel
     #   myst-nb
@@ -87,9 +86,9 @@ jinja2==3.1.6
     # via
     #   myst-parser
     #   sphinx
-jsonschema==4.23.0
+jsonschema==4.24.0
     # via nbformat
-jsonschema-specifications==2024.10.1
+jsonschema-specifications==2025.4.1
     # via jsonschema
 jupyter-cache==1.0.1
     # via myst-nb
@@ -97,7 +96,7 @@ jupyter-client==8.6.3
     # via
     #   ipykernel
     #   nbclient
-jupyter-core==5.7.2
+jupyter-core==5.8.1
     # via
     #   ipykernel
     #   jupyter-client
@@ -117,9 +116,9 @@ mdit-py-plugins==0.4.2
     # via myst-parser
 mdurl==0.1.2
     # via markdown-it-py
-myst-nb==1.1.2
+myst-nb==1.2.0
     # via rocm-docs-core
-myst-parser==4.0.0
+myst-parser==4.0.1
     # via myst-nb
 nbclient==0.10.2
     # via
@@ -132,19 +131,20 @@ nbformat==5.10.4
     #   nbclient
 nest-asyncio==1.6.0
     # via ipykernel
-packaging==24.2
+packaging==25.0
     # via
     #   ipykernel
+    #   pydata-sphinx-theme
     #   sphinx
 parso==0.8.4
     # via jedi
 pexpect==4.9.0
     # via ipython
-platformdirs==4.3.6
+platformdirs==4.3.8
     # via jupyter-core
-prompt-toolkit==3.0.50
+prompt-toolkit==3.0.51
     # via ipython
-psutil==6.1.1
+psutil==7.0.0
     # via ipykernel
 ptyprocess==0.7.0
     # via pexpect
@@ -152,19 +152,19 @@ pure-eval==0.2.3
     # via stack-data
 pycparser==2.22
     # via cffi
-pydata-sphinx-theme==0.16.0
+pydata-sphinx-theme==0.15.4
     # via
     #   rocm-docs-core
     #   sphinx-book-theme
-pygithub==2.5.0
+pygithub==2.6.1
     # via rocm-docs-core
-pygments==2.18.0
+pygments==2.19.1
     # via
     #   accessible-pygments
     #   ipython
     #   pydata-sphinx-theme
     #   sphinx
-pyjwt[crypto]==2.10.0
+pyjwt[crypto]==2.10.1
     # via pygithub
 pynacl==1.5.0
     # via pygithub
@@ -178,7 +178,7 @@ pyyaml==6.0.2
     #   rocm-docs-core
     #   sphinx-external-toc
     #   sphinxcontrib-datatemplates
-pyzmq==26.2.0
+pyzmq==26.4.0
     # via
     #   ipykernel
     #   jupyter-client
@@ -186,23 +186,23 @@ referencing==0.36.2
     # via
     #   jsonschema
     #   jsonschema-specifications
-requests==2.32.3
+requests==2.32.4
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==1.18.2
+rocm-docs-core==1.20.1
     # via -r requirements.in
-rpds-py==0.22.3
+rpds-py==0.25.1
     # via
     #   jsonschema
     #   referencing
 six==1.17.0
     # via python-dateutil
-smmap==5.0.1
+smmap==5.0.2
     # via gitdb
-snowballstemmer==2.2.0
+snowballstemmer==3.0.1
     # via sphinx
-soupsieve==2.6
+soupsieve==2.7
     # via beautifulsoup4
 sphinx==8.1.3
     # via
@@ -220,7 +220,7 @@ sphinx==8.1.3
     #   sphinx-sitemap
     #   sphinxcontrib-datatemplates
     #   sphinxcontrib-runcmd
-sphinx-book-theme==1.1.3
+sphinx-book-theme==1.1.4
     # via rocm-docs-core
 sphinx-copybutton==0.5.2
     # via rocm-docs-core
@@ -228,7 +228,7 @@ sphinx-design==0.6.1
     # via rocm-docs-core
 sphinx-external-toc==1.0.1
     # via rocm-docs-core
-sphinx-notfound-page==1.0.4
+sphinx-notfound-page==1.1.0
     # via rocm-docs-core
 sphinx-reredirects==0.1.6
     # via -r requirements.in
@@ -250,13 +250,13 @@ sphinxcontrib-runcmd==0.2.0
     # via sphinxcontrib-datatemplates
 sphinxcontrib-serializinghtml==2.0.0
     # via sphinx
-sqlalchemy==2.0.37
+sqlalchemy==2.0.41
     # via jupyter-cache
 stack-data==0.6.3
     # via ipython
 tabulate==0.9.0
     # via jupyter-cache
-tomli==2.1.0
+tomli==2.2.1
     # via sphinx
 tornado==6.4.2
     # via
@@ -272,21 +272,23 @@ traitlets==5.14.3
     #   matplotlib-inline
     #   nbclient
     #   nbformat
-typing-extensions==4.12.2
+typing-extensions==4.14.0
     # via
+    #   beautifulsoup4
+    #   exceptiongroup
     #   ipython
     #   myst-nb
     #   pydata-sphinx-theme
     #   pygithub
     #   referencing
     #   sqlalchemy
-urllib3==2.2.3
+urllib3==2.4.0
     # via
     #   pygithub
     #   requests
 wcwidth==0.2.13
     # via prompt-toolkit
-wrapt==1.17.0
+wrapt==1.17.2
     # via deprecated
-zipp==3.21.0
+zipp==3.23.0
     # via importlib-metadata

docs/what-is-rocm.rst

@@ -10,7 +10,7 @@ ROCm is a software stack, composed primarily of open-source software, that
 provides the tools for programming AMD Graphics Processing Units (GPUs), from
 low-level kernels to high-level end-user applications.
 
-.. image:: data/rocm-software-stack-6_3_2.jpg
+.. image:: data/rocm-software-stack-6_4_0.jpg
   :width: 800
   :alt: AMD's ROCm software stack and enabling technologies.
   :align: center
@@ -52,6 +52,7 @@ Communication
   :header: "Component", "Description"
 
   ":doc:`RCCL <rccl:index>`", "Standalone library that provides multi-GPU and multi-node collective communication primitives"
+  ":doc:`rocSHMEM <rocshmem:index>`", "An intra-kernel networking library that provides GPU-centric networking through an OpenSHMEM-like interface"
 
 Math
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
@@ -116,6 +117,11 @@ Performance
   ":doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`", "Toolkit for developing analysis tools for profiling and tracing GPU compute applications. This toolkit is in beta and subject to change"
   ":doc:`ROCTracer <roctracer:index>`", "Intercepts runtime API calls and traces asynchronous activity"
 
+.. note::
+
+  `ROCprof Compute Viewer <https://rocm.docs.amd.com/projects/rocprof-compute-viewer/en/amd-mainline/>`_ is a tool for visualizing and analyzing GPU thread trace data collected using :doc:`rocprofv3 <rocprofiler-sdk:index>`.
+  Note that `ROCprof Compute Viewer <https://rocm.docs.amd.com/projects/rocprof-compute-viewer/en/amd-mainline/>`_ is in an early access state. Running production workloads is not recommended.
+
 Development
 ^^^^^^^^^^^
 

tools/autotag/components.xml

@@ -1,7 +1,7 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <manifest>
     <remote name="rocm-org" fetch="https://github.com/ROCm/" />
-    <default revision="refs/tags/rocm-6.4.0"
+    <default revision="refs/tags/rocm-6.4.1"
      remote="rocm-org"
      sync-c="true"
      sync-j="4" />

tools/rocm-build/ROCm.mk

@@ -87,7 +87,6 @@ endef
 
 $(call adddep,amd_smi_lib,${ASAN_DEP})
 $(call adddep,aqlprofile,${ASAN_DEP} rocr)
-$(call adddep,aqlprofiletest,lightning rocminfo aqlprofile opencl_on_rocclr hip_on_rocclr)
 $(call adddep,comgr,lightning devicelibs)
 $(call adddep,dbgapi,rocr comgr)
 $(call adddep,devicelibs,lightning)
@@ -115,7 +114,7 @@ $(call adddep,roctracer,${ASAN_DEP} rocr hip_on_rocclr)
 
 
 # rocm-dev points to all possible last finish components of Stage1 build.
-rocm-dev-components :=amd_smi_lib aqlprofile aqlprofiletest comgr dbgapi devicelibs hip_on_rocclr hipcc hipify_clang \
+rocm-dev-components :=amd_smi_lib aqlprofile comgr dbgapi devicelibs hip_on_rocclr hipcc hipify_clang \
 	lightning rocprofiler-compute opencl_on_rocclr openmp_extras rocm_bandwidth_test rocm_smi_lib \
 	rocm-cmake rocm-core rocm-gdb rocminfo rocprofiler-register rocprofiler-sdk rocprofiler-systems \
 	rocprofiler rocr rocr_debug_agent rocrsamples roctracer

tools/rocm-build/build_rocr.sh

@@ -255,8 +255,8 @@ print_output_directory() {
 # Common variables
 target="build"
 
-kfdtest_target="yes"
-rocrtst_target="yes"
+kfdtest_target="no"
+rocrtst_target="no"
 rocr_target="ON"
 
 package_root="$(getPackageRoot)"

tools/rocm-build/docker/ubuntu22/packages

@@ -60,7 +60,6 @@ libfile-find-rule-perl
 libgflags-dev
 libglew-dev
 libgmp-dev
-libgoogle-glog-dev
 libgtk2.0-dev
 libhdf5-serial-dev
 libjpeg-dev
@@ -90,7 +89,6 @@ libsuitesparse-dev
 libsystemd-dev
 libtinfo-dev
 libtool
-libunwind-dev
 liburi-encode-perl
 libva-dev
 libvirt-clients
@@ -98,7 +96,6 @@ libvirt-daemon-system
 libyaml-cpp-dev
 libzstd-dev
 llvm
-llvm-6.0-dev
 llvm-dev
 llvm-runtime
 mesa-common-dev
@@ -112,8 +109,7 @@ pigz
 pkg-config
 protobuf-compiler
 python-is-python3
-python-pip-whl
-python-yaml
+python3-pip-whl
 python3-dev
 python3-pip
 python3-venv

tools/rocm-build/docker/ubuntu24/install-prerequisites.sh

@@ -17,7 +17,7 @@ git --version
 
 # venv for python to be able to run pip3 without --break-system-packages
 python3 -m venv /opt/venv
-
+source /opt/venv/bin/activate
 pip3 install CppHeaderParser argparse lxml recommonmark jinja2==3.0.0 \
     websockets matplotlib numpy scipy minimal msgpack pytest sphinx joblib PyYAML rocm-docs-core cmake==3.25.2 pandas \
     myst-parser setuptools lit

tools/rocm-build/envsetup.sh

@@ -217,7 +217,7 @@ export RCCL_ROOT=$WORK_ROOT/rccl
 export ROCM_DBGAPI_ROOT=$WORK_ROOT/ROCdbgapi
 export ROCM_GDB_ROOT=$WORK_ROOT/ROCgdb
 # export ROCclr_ROOT=$WORK_ROOT/vdi
-export HIP_ON_ROCclr_ROOT=$WORK_ROOT/HIP
+export HIP_ON_ROCclr_ROOT=$WORK_ROOT/hip
 export HIPAMD_ROOT=$WORK_ROOT/hipamd
 export HIP_CATCH_TESTS_ROOT=$WORK_ROOT/hip-tests
 # export OPENCL_ON_ROCclr_ROOT=$WORK_ROOT/opencl-on-vdi

tools/rocm-build/rocm-6.4.1.xml

@@ -0,0 +1,79 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<manifest>
+    <remote name="rocm-org" fetch="https://github.com/ROCm/" />
+    <default revision="refs/tags/rocm-6.4.1"
+     remote="rocm-org"
+     sync-c="true"
+     sync-j="4" />
+<!--list of projects for ROCm-->
+    <project name="ROCm" revision="roc-6.4.x" />
+    <project name="ROCK-Kernel-Driver" />
+    <project name="ROCR-Runtime" />
+    <project name="amdsmi" />
+    <project name="rdc" />
+    <project name="rocm_bandwidth_test" />
+    <project name="rocm_smi_lib" />
+    <project name="rocm-core" />
+    <project name="rocm-examples" />
+    <project name="rocminfo" />
+    <project name="rocprofiler" />
+    <project name="rocprofiler-register" />
+    <project name="rocprofiler-sdk" />
+    <project name="rocprofiler-compute" />
+    <project name="rocprofiler-systems" />
+    <project name="roctracer" />
+<!--HIP Projects-->
+    <project name="HIP" />
+    <project name="hip-tests" />
+    <project name="HIPIFY" />
+    <project name="clr" />
+    <project name="hipother" />
+<!-- The following projects are all associated with the AMDGPU LLVM compiler -->
+    <project name="half" />
+    <project name="llvm-project" />
+    <project name="spirv-llvm-translator" />
+<!-- gdb projects -->
+    <project name="ROCdbgapi" />
+    <project name="ROCgdb" />
+    <project name="rocr_debug_agent" />
+<!-- ROCm Libraries -->
+    <project groups="mathlibs" name="AMDMIGraphX" />
+    <project groups="mathlibs" name="MIOpen" />
+    <project groups="mathlibs" name="MIVisionX" />
+    <project groups="mathlibs" name="ROCmValidationSuite" />
+    <project groups="mathlibs" name="Tensile" />
+    <project groups="mathlibs" name="composable_kernel" />
+    <project groups="mathlibs" name="hipBLAS-common" />
+    <project groups="mathlibs" name="hipBLAS" />
+    <project groups="mathlibs" name="hipBLASLt" />
+    <project groups="mathlibs" name="hipCUB" />
+    <project groups="mathlibs" name="hipFFT" />
+    <project groups="mathlibs" name="hipRAND" />
+    <project groups="mathlibs" name="hipSOLVER" />
+    <project groups="mathlibs" name="hipSPARSE" />
+    <project groups="mathlibs" name="hipSPARSELt" />
+    <project groups="mathlibs" name="hipTensor" />
+    <project groups="mathlibs" name="hipfort" />
+    <project groups="mathlibs" name="rccl" />
+    <project groups="mathlibs" name="rocAL" />
+    <project groups="mathlibs" name="rocALUTION" />
+    <project groups="mathlibs" name="rocBLAS" />
+    <project groups="mathlibs" name="rocDecode" />
+    <project groups="mathlibs" name="rocJPEG" />
+    <project groups="mathlibs" name="rocPyDecode" />
+    <project groups="mathlibs" name="rocFFT" />
+    <project groups="mathlibs" name="rocPRIM" />
+    <project groups="mathlibs" name="rocRAND" />
+    <project groups="mathlibs" name="rocSHMEM" />
+    <project groups="mathlibs" name="rocSOLVER" />
+    <project groups="mathlibs" name="rocSPARSE" />
+    <project groups="mathlibs" name="rocThrust" />
+    <project groups="mathlibs" name="rocWMMA" />
+    <project groups="mathlibs" name="rocm-cmake" />
+    <project groups="mathlibs" name="rpp" />
+    <project groups="mathlibs" name="TransferBench" />
+<!-- Projects for OpenMP-Extras -->
+    <project name="aomp" path="openmp-extras/aomp" />
+    <project name="aomp-extras" path="openmp-extras/aomp-extras" />
+    <project name="flang" path="openmp-extras/flang" />
+</manifest>