Apply suggestion from @randyh62

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.azuredevops/components/HIP.yml

@@ -34,6 +34,7 @@ parameters:
   default:
     - cmake
     - libnuma-dev
+    - libsimde-dev
     - mesa-common-dev
     - ninja-build
     - ocl-icd-libopencl1

.azuredevops/components/origami.yml

@@ -39,6 +39,7 @@ parameters:
     - python3
     - python3-dev
     - python3-pip
+    - python3-venv
     - libgtest-dev
     - libboost-filesystem-dev
     - libboost-program-options-dev
@@ -46,6 +47,8 @@ parameters:
   type: object
   default:
     - nanobind>=2.0.0
+    - pytest
+    - pytest-cov
 - name: rocmDependencies
   type: object
   default:
@@ -72,8 +75,10 @@ parameters:
       - { os: ubuntu2204, packageManager: apt }
       - { os: almalinux8, packageManager: dnf }
     testJobs:
-      - { os: ubuntu2204, packageManager: apt, target: gfx942 }
       - { os: ubuntu2204, packageManager: apt, target: gfx90a }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1100 }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1151 }
+      # - { os: ubuntu2204, packageManager: apt, target: gfx1201 }
 - name: downstreamComponentMatrix
   type: object
   default:
@@ -116,6 +121,11 @@ jobs:
       parameters:
         dependencyList:
           - gtest
+    - ${{ if ne(job.os, 'almalinux8') }}:
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+        parameters:
+          dependencyList:
+            - catch2
     - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
       parameters:
         checkoutRepo: ${{ parameters.checkoutRepo }}
@@ -137,6 +147,7 @@ jobs:
           -DORIGAMI_BUILD_SHARED_LIBS=ON
           -DORIGAMI_ENABLE_PYTHON=ON
           -DORIGAMI_BUILD_TESTING=ON
+          -DORIGAMI_ENABLE_FETCH=ON
           -GNinja
     - ${{ if ne(job.os, 'almalinux8') }}:
       - task: PublishPipelineArtifact@1
@@ -169,7 +180,6 @@ jobs:
       dependsOn: origami_build_${{ job.os }}
       condition:
         and(succeeded(),
-          eq(variables['ENABLE_${{ upper(job.target) }}_TESTS'], 'true'),
           not(containsValue(split(variables['DISABLED_${{ upper(job.target) }}_TESTS'], ','), '${{ parameters.componentName }}')),
           eq(${{ parameters.aggregatePipeline }}, False)
         )
@@ -180,30 +190,30 @@ jobs:
       workspace:
         clean: all
       steps:
-      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
-        parameters:
-          checkoutRepo: ${{ parameters.checkoutRepo }}
-          sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-other.yml
         parameters:
           aptPackages: ${{ parameters.aptPackages }}
           pipModules: ${{ parameters.pipModules }}
           packageManager: ${{ job.packageManager }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-cmake-custom.yml
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/preamble.yml
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/checkout.yml
+        parameters:
+          checkoutRepo: ${{ parameters.checkoutRepo }}
+          sparseCheckoutDir: ${{ parameters.sparseCheckoutDir }}
+      - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+        parameters:
+          dependencyList:
+            - gtest
+      - ${{ if ne(job.os, 'almalinux8') }}:
+        - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-vendor.yml
+          parameters:
+            dependencyList:
+              - catch2
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/local-artifact-download.yml
         parameters:
           preTargetFilter: ${{ parameters.componentName }}
           os: ${{ job.os }}
-      - task: DownloadPipelineArtifact@2
-        displayName: 'Download Build Directory Artifact'
-        inputs:
-          artifact: '${{ parameters.componentName }}_${{ job.os }}_build_dir'
-          path: '$(Agent.BuildDirectory)/s/build'
-      - task: DownloadPipelineArtifact@2
-        displayName: 'Download Python Source Artifact'
-        inputs:
-          artifact: '${{ parameters.componentName }}_${{ job.os }}_python_src'
-          path: '$(Agent.BuildDirectory)/s/python'
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/dependencies-rocm.yml
         parameters:
           checkoutRef: ${{ parameters.checkoutRef }}
@@ -212,25 +222,72 @@ jobs:
           gpuTarget: ${{ job.target }}
           ${{ if parameters.triggerDownstreamJobs }}:
             downstreamAggregateNames: ${{ parameters.downstreamAggregateNames }}
+      - task: CMake@1
+        displayName: 'Origami Test CMake Configuration'
+        inputs:
+          cmakeArgs: >-
+            -DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/vendor
+            -DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
+            -DORIGAMI_BUILD_SHARED_LIBS=ON
+            -DORIGAMI_ENABLE_PYTHON=ON
+            -DORIGAMI_BUILD_TESTING=ON
+            -GNinja
+            $(Agent.BuildDirectory)/s
+      - task: Bash@3
+        displayName: 'Build Origami Tests and Python Bindings'
+        inputs:
+          targetType: inline
+          workingDirectory: build
+          script: |
+            cmake --build . --target origami-tests origami_python -- -j$(nproc)
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/gpu-diagnostics.yml
+      # Run tests using CTest (discovers and runs both C++ and Python tests)
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
         parameters:
           componentName: ${{ parameters.componentName }}
           os: ${{ job.os }}
-          testDir: '$(Agent.BuildDirectory)/rocm/bin'
+          testDir: 'build'
-          testExecutable: './origami-tests'
+          testParameters: '--output-on-failure --force-new-ctest-process --output-junit test_output.xml'
-          testParameters: '--yaml origami-tests.yaml --gtest_output=xml:./test_output.xml --gtest_color=yes'
+      # Test pip install workflow
-      - script: |
+      # - task: Bash@3
-          set -e
+      #   displayName: 'Test Pip Install'
-          export PYTHONPATH=$(Agent.BuildDirectory)/s/build/python:$PYTHONPATH
+      #   inputs:
-
+      #     targetType: inline
-          echo "--- Running origami_test.py ---"
+      #     script: |
-          python3 $(Agent.BuildDirectory)/s/python/origami_test.py
+      #       set -e
-          
+            
-          echo "--- Running origami_grid_test.py ---"
+      #       echo "==================================================================="
-          python3 $(Agent.BuildDirectory)/s/python/origami_grid_test.py
+      #       echo "Testing pip install workflow (pip install -e .)"
-        displayName: 'Run Python Binding Tests'
+      #       echo "==================================================================="
-        condition: succeeded()
+            
+      #       # Set environment variables for pip install CMake build
+      #       export ROCM_PATH=$(Agent.BuildDirectory)/rocm
+      #       export CMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm:$(Agent.BuildDirectory)/vendor
+      #       export CMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
+            
+      #       echo "ROCM_PATH: $ROCM_PATH"
+      #       echo "CMAKE_PREFIX_PATH: $CMAKE_PREFIX_PATH"
+      #       echo "CMAKE_CXX_COMPILER: $CMAKE_CXX_COMPILER"
+      #       echo ""
+            
+      #       # Install from source directory
+      #       cd "$(Agent.BuildDirectory)/s/python"
+      #       pip install -e .
+            
+      #       # Verify import works
+      #       echo ""
+      #       echo "Verifying origami can be imported..."
+      #       python3 -c "import origami; print('✓ Successfully imported origami')"
+            
+      #       # Run pytest on installed package
+      #       echo ""
+      #       echo "Running pytest tests..."
+      #       python3 -m pytest tests/ -v -m "not slow" --tb=short
+            
+      #       echo ""
+      #       echo "==================================================================="
+      #       echo "Pip install test completed successfully"
+      #       echo "==================================================================="
       - template: ${{ variables.CI_TEMPLATE_PATH }}/steps/docker-container.yml
         parameters:
           aptPackages: ${{ parameters.aptPackages }}

.azuredevops/components/rocm-examples.yml

@@ -30,6 +30,7 @@ parameters:
     - python3-pip
     - protobuf-compiler
     - libprotoc-dev
+    - libopencv-dev
 - name: pipModules
   type: object
   default:
@@ -64,6 +65,7 @@ parameters:
     - MIVisionX
     - rocm_smi_lib
     - rccl
+    - rocAL
     - rocALUTION
     - rocBLAS
     - rocDecode
@@ -103,6 +105,7 @@ parameters:
     - MIVisionX
     - rocm_smi_lib
     - rccl
+    - rocAL
     - rocALUTION
     - rocBLAS
     - rocDecode

.wordlist.txt

@@ -36,7 +36,6 @@ Andrej
 Arb
 Autocast
 autograd
-Backported
 BARs
 BatchNorm
 BLAS
@@ -204,11 +203,9 @@ GenAI
 GenZ
 GitHub
 Gitpod
-hardcoded
 HBM
 HCA
 HGX
-HLO
 HIPCC
 hipDataType
 HIPExtension
@@ -336,7 +333,6 @@ MoEs
 Mooncake
 Mpops
 Multicore
-multihost
 Multithreaded
 mx
 MXFP
@@ -1031,7 +1027,6 @@ uncacheable
 uncorrectable
 underoptimized
 unhandled
-unfused
 uninstallation
 unmapped
 unsqueeze

RELEASE.md

@@ -270,26 +270,26 @@ The [ROCm examples repository](https://github.com/ROCm/rocm-examples) has been e
 :margin: auto 0 auto auto
 :::{grid}
 :margin: auto 0 auto auto
-* [hipBLASLt](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/hipBLASLt)
+* [hipBLASLt](https://rocm.docs.amd.com/projects/hipBLASLt/en/latest/)
-* [hipSPARSE](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/hipSPARSE)
+* [hipSPARSE](https://rocm.docs.amd.com/projects/hipSPARSE/en/latest/)
-* [hipSPARSELt](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/hipSPARSELt)
+* [hipSPARSELt](https://rocm.docs.amd.com/projects/hipSPARSELt/en/latest/)
-* [hipTensor](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/hipTensor)
+* [hipTensor](https://rocm.docs.amd.com/projects/hipTensor/en/latest/)
 :::
 :::{grid}
 :margin: auto 0 auto auto
-* [rocALUTION](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/rocALUTION)
+* [rocALUTION](https://rocm.docs.amd.com/projects/rocALUTION/en/latest/)
-* [ROCprofiler-SDK](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/rocProfiler-SDK)
+* [ROCprofiler-SDK](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/latest/)
-* [rocWMMA](https://github.com/ROCm/rocm-examples/tree/amd-staging/Libraries/rocWMMA)
+* [rocWMMA](https://rocm.docs.amd.com/projects/rocWMMA/en/latest/)
 :::
 ::::
 
 Usage examples are now available for the following performance analysis tools:
 
-* [ROCm Compute Profiler](https://github.com/ROCm/rocm-examples/tree/amd-staging/Tools/rocprof-compute)
+* [ROCm Compute Profiler](https://rocm.docs.amd.com/projects/rocprofiler-compute/en/latest/index.html)
-* [ROCm Systems Profiler](https://github.com/ROCm/rocm-examples/tree/amd-staging/Tools/rocprof-systems)
+* [ROCm Systems Profiler](https://rocm.docs.amd.com/projects/rocprofiler-systems/en/latest/index.html)
-* [rocprofv3](https://github.com/ROCm/rocm-examples/tree/amd-staging/Tools/rocprofv3)
+* [rocprofv3](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/latest/how-to/using-rocprofv3.html)
 
-The complete source code for the [HIP Graph Tutorial](https://github.com/ROCm/rocm-examples/tree/amd-staging/HIP-Doc/Tutorials/graph_api) is also available as part of the ROCm examples.
+The complete source code for the [HIP Graph Tutorial](https://rocm.docs.amd.com/projects/HIP/en/latest/tutorial/graph_api.html) is also available as part of the ROCm examples.
 
 ### ROCm documentation updates
 

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

@@ -269,33 +269,6 @@ For a complete and up-to-date list of JAX public modules (for example, ``jax.num
   JAX API modules are maintained by the JAX project and is subject to change.
   Refer to the official Jax documentation for the most up-to-date information.
 
-Key features and enhancements for ROCm 7.1
-===============================================================================
-
-- Enabled compilation of multihost HLO runner Python bindings.
-
-  - Backported multihost HLO runner bindings and some related changes to
-    :code:`FunctionalHloRunner`.
-
-  - Added :code:`requirements_lock_3_12` to enable building for Python 3.12.
-
-- Removed hardcoded NHWC convolution layout for ``fp16`` precision to address the performance drops for ``fp16`` precision on gfx12xx GPUs.
-
-
-- ROCprofiler-SDK integration:
-
-  - Integrated ROCprofiler-SDK (v3) to XLA to improve profiling of GPU events,
-    support both time-based and step-based profiling.
-
-  - Added unit tests for :code:`rocm_collector` and :code:`rocm_tracer`.
-
-- Added Triton unsupported conversion from ``f8E4M3FNUZ`` to ``fp16`` with
-  rounding mode.
-
-- Introduced :code:`CudnnFusedConvDecomposer` to revert fused convolutions
-  when :code:`ConvAlgorithmPicker` fails to find a fused algorithm, and removed
-  unfused fallback paths from :code:`RocmFusedConvRunner`.
-
 Key features and enhancements for ROCm 7.0
 ===============================================================================
 

docs/conf.py

@@ -268,3 +268,6 @@ html_context = {
     "granularity_type" : [('Coarse-grained', 'coarse-grained'), ('Fine-grained', 'fine-grained')],
     "scope_type" : [('Device', 'device'), ('System', 'system')]
 }
+
+# Disable figure and table numbering
+numfig = False

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

@@ -24,7 +24,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
       - GitHub
 
     * - :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/pytorch-install>`
+      - :doc:`Pytorch install <rocm-install-on-linux:install/3rd-party/pytorch-install>`
       - 
         - Docker image
         - Wheels package
@@ -35,7 +35,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/pytorch"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
+      - :doc:`TensorFlow install <rocm-install-on-linux:install/3rd-party/tensorflow-install>`
       - 
         - Docker image
         - Wheels package
@@ -45,7 +45,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/tensorflow-upstream"><i class="fab fa-github fa-lg"></i></a> 
 
     * - :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/jax-install>`
+      - :doc:`JAX install <rocm-install-on-linux:install/3rd-party/jax-install>`
       - 
         - Docker image
       - .. raw:: html
@@ -53,7 +53,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/verl-install>`
+      - :doc:`verl install <rocm-install-on-linux:install/3rd-party/verl-install>`
       - 
         - Docker image
       - .. raw:: html
@@ -61,7 +61,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
+      - :doc:`Stanford Megatron-LM install <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
       - 
         - Docker image
       - .. raw:: html
@@ -69,7 +69,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/dgl-install>`
+      - :doc:`DGL install <rocm-install-on-linux:install/3rd-party/dgl-install>`
       - 
         - Docker image
       - .. raw:: html
@@ -77,15 +77,24 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a> 
 
     * - :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/megablocks-install>`
+      - :doc:`Megablocks install <rocm-install-on-linux:install/3rd-party/megablocks-install>`
       - 
         - Docker image
       - .. raw:: html
 
           <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
 
+    * - :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>`
+      - `Taichi install <https://rocm.docs.amd.com/projects/taichi/en/latest/install/taichi-install.html>`__
+      - 
+        - Docker image
+        - Wheels package
+      - .. raw:: html
+
+          <a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
+
     * - :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/ray-install>`
+      - :doc:`Ray install <rocm-install-on-linux:install/3rd-party/ray-install>`
       - 
         - Docker image
         - Wheels package
@@ -95,7 +104,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/ray"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/llama-cpp-install>`
+      - :doc:`llama.cpp install <rocm-install-on-linux:install/3rd-party/llama-cpp-install>`
       - 
         - Docker image
         - ROCm Base Docker image
@@ -104,7 +113,7 @@ The table below summarizes information about ROCm-enabled deep learning framewor
           <a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>
 
     * - :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>`
-      - :doc:`link <rocm-install-on-linux:install/3rd-party/flashinfer-install>`
+      - :doc:`FlashInfer install <rocm-install-on-linux:install/3rd-party/flashinfer-install>`
       - 
         - Docker image
         - ROCm Base Docker image

docs/how-to/rocm-for-ai/fine-tuning/multi-gpu-fine-tuning-and-inference.rst

@@ -44,7 +44,7 @@ Setting up the base implementation environment
 
    .. code-block:: shell
 
-      amd-smi static --board
+      rocm-smi --showproductname
 
 #. Check that your GPUs are available to PyTorch.
 
@@ -65,8 +65,8 @@ Setting up the base implementation environment
 
 .. tip::
 
-   During training and inference, you can check the memory usage by running the ``amd-smi`` command in your terminal.
+   During training and inference, you can check the memory usage by running the ``rocm-smi`` command in your terminal.
-   This tool helps you see which GPUs are involved.
+   This tool helps you see shows which GPUs are involved.
 
 
 .. _fine-tuning-llms-multi-gpu-hugging-face-accelerate:
@@ -91,10 +91,10 @@ Now, it's important to adjust how you load the model. Add the ``device_map`` par
 
    ...
    base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-
+   
    # Load base model to GPU memory
    base_model = AutoModelForCausalLM.from_pretrained(
-           base_model_name,
+           base_model_name, 
            device_map = "auto",
            trust_remote_code = True)
    ...
@@ -130,7 +130,7 @@ After loading the model in this way, the model is fully ready to use the resourc
 torchtune for fine-tuning and inference
 =============================================
 
-`torchtune <https://pytorch.org/torchtune/main/>`_ is a PyTorch-native library for easy single and multi-GPU
+`torchtune <https://meta-pytorch.org/torchtune/main/>`_ is a PyTorch-native library for easy single and multi-GPU 
 model fine-tuning and inference with LLMs.
 
 #. Install torchtune using pip.
@@ -139,7 +139,7 @@ model fine-tuning and inference with LLMs.
 
       # Install torchtune with PyTorch release 2.2.2+
       pip install torchtune
-
+      
       # To confirm that the package is installed correctly
       tune --help
 
@@ -148,12 +148,12 @@ model fine-tuning and inference with LLMs.
    .. code-block:: shell
 
       usage: tune [-h] {download,ls,cp,run,validate} ...
-
+      
       Welcome to the TorchTune CLI!
-
+      
       options:
         -h, --help            show this help message and exit
-
+      
       subcommands:
         {download,ls,cp,run,validate}
 
@@ -194,11 +194,11 @@ model fine-tuning and inference with LLMs.
         apply_lora_to_output: False
         lora_rank: 8
         lora_alpha: 16
-
+      
       tokenizer:
         _component_: torchtune.models.llama2.llama2_tokenizer
         path: /tmp/Llama-2-7b-hf/tokenizer.model
-
+      
       # Dataset and sampler
       dataset:
         _component_: torchtune.datasets.alpaca_cleaned_dataset

docs/how-to/rocm-for-ai/fine-tuning/single-gpu-fine-tuning-and-inference.rst

@@ -44,19 +44,20 @@ Setting up the base implementation environment
 
    .. code-block:: shell
 
-      amd-smi static --board
+      rocm-smi --showproductname
 
    Your output should look like this:
 
    .. code-block:: shell
 
-      GPU: 0
+      ============================ ROCm System Management Interface ============================
-         BOARD:
+      ====================================== Product Info ======================================
-            MODEL_NUMBER: 102-G39203-0B
+      GPU[0]          : Card Series:          AMD Instinct MI300X OAM
-            PRODUCT_SERIAL: PCB079220-1150
+      GPU[0]          : Card model:           0x74a1
-            FRU_ID: 113-AMDG392030B04-100-300000097H
+      GPU[0]          : Card vendor:          Advanced Micro Devices, Inc. [AMD/ATI]
-            PRODUCT_NAME: AMD Instinct MI325 OAM
+      GPU[0]          : Card SKU:             MI3SRIOV
-            MANUFACTURER_NAME: AMD
+      ==========================================================================================
+      ================================== End of ROCm SMI Log ===================================
 
 #. Check that your GPUs are available to PyTorch.
 
@@ -93,13 +94,13 @@ Setting up the base implementation environment
       pip install -r requirements-dev.txt
       cmake -DBNB_ROCM_ARCH="gfx942" -DCOMPUTE_BACKEND=hip -S .
       python setup.py install
-
+      
       # To leverage the SFTTrainer in TRL for model fine-tuning.
       pip install trl
-
+      
       # To leverage PEFT for efficiently adapting pre-trained language models .
       pip install peft
-
+      
       # Install the other dependencies.
       pip install transformers datasets huggingface-hub scipy
 
@@ -131,7 +132,7 @@ Download the base model and fine-tuning dataset
 
    .. note::
 
-      You can also use the `NousResearch Llama-2-7b-chat-hf <https://huggingface.co/NousResearch/Llama-2-7b-chat-hf>`_
+      You can also use the `NousResearch Llama-2-7b-chat-hf <https://huggingface.co/NousResearch/Llama-2-7b-chat-hf>`_ 
       as a substitute. It has the same model weights as the original.
 
 #. Run the following code to load the base model and tokenizer.
@@ -140,14 +141,14 @@ Download the base model and fine-tuning dataset
 
       # Base model and tokenizer names.
       base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-
+      
       # Load base model to GPU memory.
       device = "cuda:0"
       base_model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code = True).to(device)
-
+      
       # Load tokenizer.
       tokenizer = AutoTokenizer.from_pretrained(
-              base_model_name,
+              base_model_name, 
               trust_remote_code = True)
       tokenizer.pad_token = tokenizer.eos_token
       tokenizer.padding_side = "right"
@@ -161,10 +162,10 @@ Download the base model and fine-tuning dataset
       # Dataset for fine-tuning.
       training_dataset_name = "mlabonne/guanaco-llama2-1k"
       training_dataset = load_dataset(training_dataset_name, split = "train")
-
+      
       # Check the data.
       print(training_dataset)
-
+      
       # Dataset 11 is a QA sample in English.
       print(training_dataset[11])
 
@@ -251,8 +252,8 @@ Compare the number of trainable parameters and training time under the two diffe
                     dataset_text_field = "text",
                     tokenizer = tokenizer,
                     args = training_arguments
-            )
+            ) 
-
+            
             # Run the trainer.
             sft_trainer.train()
 
@@ -285,7 +286,7 @@ Compare the number of trainable parameters and training time under the two diffe
                     if param.requires_grad:
                         trainable_params += param.numel()
                 print(f"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param:.2f}")
-
+            
             sft_trainer.peft_config = None
             print_trainable_parameters(sft_trainer.model)
 
@@ -308,8 +309,8 @@ Compare the number of trainable parameters and training time under the two diffe
                     dataset_text_field = "text",
                     tokenizer = tokenizer,
                     args = training_arguments
-            )
+            ) 
-
+            
             # Training.
             trainer_full.train()
 
@@ -348,7 +349,7 @@ store, and load.
 
          # PEFT adapter name.
          adapter_name = "llama-2-7b-enhanced-adapter"
-
+         
          # Save PEFT adapter.
          sft_trainer.model.save_pretrained(adapter_name)
 
@@ -358,21 +359,21 @@ store, and load.
 
          # Access adapter directory.
          cd llama-2-7b-enhanced-adapter
-
+         
          # List all adapter files.
          README.md  adapter_config.json  adapter_model.safetensors
 
    .. tab-item:: Saving a fully fine-tuned model
       :sync: without
 
-      If you're not using LoRA and PEFT so there is no PEFT LoRA configuration used for training, use the following code
+      If you're not using LoRA and PEFT so there is no PEFT LoRA configuration used for training, use the following code 
       to save your fine-tuned model to your system.
 
       .. code-block:: python
 
          # Fully fine-tuned model name.
          new_model_name = "llama-2-7b-enhanced"
-
+         
          # Save the fully fine-tuned model.
          full_trainer.model.save_pretrained(new_model_name)
 
@@ -382,7 +383,7 @@ store, and load.
 
          # Access new model directory.
          cd llama-2-7b-enhanced
-
+         
          # List all model files.
          config.json                       model-00002-of-00006.safetensors  model-00005-of-00006.safetensors
          generation_config.json            model-00003-of-00006.safetensors  model-00006-of-00006.safetensors
@@ -411,26 +412,26 @@ Let's look at achieving model inference using these types of models.
 
    .. tab-item:: Inference using PEFT adapters
 
-      To use PEFT adapters like a normal transformer model, you can run the generation by loading a base model along with PEFT
+      To use PEFT adapters like a normal transformer model, you can run the generation by loading a base model along with PEFT 
       adapters as follows.
 
       .. code-block:: python
 
          from peft import PeftModel
          from transformers import AutoModelForCausalLM
-
+         
          # Set the path of the model or the name on Hugging face hub
          base_model_name = "meta-llama/Llama-2-7b-chat-hf"
-
+         
          # Set the path of the adapter
          adapter_name = "Llama-2-7b-enhanced-adpater"
-
+         
-         # Load base model
+         # Load base model 
          base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
-
+         
-         # Adapt the base model with the adapter
+         # Adapt the base model with the adapter 
          new_model = PeftModel.from_pretrained(base_model, adapter_name)
-
+         
          # Then, run generation as the same with a normal model outlined in 2.1
 
       The PEFT library provides a ``merge_and_unload`` method, which merges the adapter layers into the base model. This is
@@ -438,13 +439,13 @@ Let's look at achieving model inference using these types of models.
 
       .. code-block:: python
 
-         # Load base model
+         # Load base model 
          base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
-
+         
-         # Adapt the base model with the adapter
+         # Adapt the base model with the adapter 
          new_model = PeftModel.from_pretrained(base_model, adapter_name)
-
+         
-         # Merge adapter
+         # Merge adapter 
          model = model.merge_and_unload()
 
          # Save the merged model into local
@@ -460,25 +461,25 @@ Let's look at achieving model inference using these types of models.
 
          # Import relevant class for loading model and tokenizer
          from transformers import AutoTokenizer, AutoModelForCausalLM
-
+         
          # Set the pre-trained model name on Hugging face hub
          model_name = "meta-llama/Llama-2-7b-chat-hf"
-
+         
-         # Set device type
+         # Set device type 
          device = "cuda:0"
-
+         
-         # Load model and tokenizer
+         # Load model and tokenizer 
          model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
          tokenizer = AutoTokenizer.from_pretrained(model_name)
-
+         
-         # Input prompt encoding
+         # Input prompt encoding 
          query = "What is a large language model?"
          inputs = tokenizer.encode(query, return_tensors="pt").to(device)
-
+         
-         # Token generation
+         # Token generation  
-         outputs = model.generate(inputs)
+         outputs = model.generate(inputs) 
-
+         
-         # Outputs decoding
+         # Outputs decoding 
          print(tokenizer.decode(outputs[0]))
 
       In addition, pipelines from Transformers offer simple APIs to use pre-trained models for different tasks, including
@@ -489,14 +490,14 @@ Let's look at achieving model inference using these types of models.
 
          # Import relevant class for loading model and tokenizer
          from transformers import pipeline
-
+         
          # Set the path of your model or the name on Hugging face hub
          model_name_or_path = "meta-llama/Llama-2-7b-chat-hf"
-
+         
-         # Set pipeline
+         # Set pipeline 
          # A positive device value will run the model on associated CUDA device id
          pipe = pipeline("text-generation", model=model_name_or_path, device=0)
-
+         
          # Token generation
          print(pipe("What is a large language model?")[0]["generated_text"])
 

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

@@ -31,16 +31,16 @@ in the Instinct documentation for more information.
 Hardware verification with ROCm
 -------------------------------
 
-Use the command ``amd-smi set --perf-determinism 1900`` to set the max clock speed up to 1900 MHz
+Use the command ``rocm-smi --setperfdeterminism 1900`` to set the max clock speed up to 1900 MHz
 instead of the default 2100 MHz. This can reduce the chance of a PCC event lowering the attainable
 GPU clocks. This setting will not be required for new IFWI releases with the production PRC feature.
-You can restore this setting to its default value with the ``amd-smi reset --clocks`` command.
+You can restore this setting to its default value with the ``rocm-smi -r`` command.
 
 Run the command:
 
 .. code-block:: shell
 
-   amd-smi set --perf-determinism 1900
+   rocm-smi --setperfdeterminism 1900
 
 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.

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

@@ -108,16 +108,16 @@ for more information.
 Hardware verification with ROCm
 -------------------------------
 
-Use the command ``amd-smi set --perf-determinism 1900`` to set the max clock speed up to 1900 MHz
+Use the command ``rocm-smi --setperfdeterminism 1900`` to set the max clock speed up to 1900 MHz
 instead of the default 2100 MHz. This can reduce the chance of a PCC event lowering the attainable
 GPU clocks. This setting will not be required for new IFWI releases with the production PRC feature.
-You can restore this setting to its default value with the ``amd-smi reset --clocks`` command.
+You can restore this setting to its default value with the ``rocm-smi -r`` command.
 
 Run the command:
 
 .. code-block:: shell
 
-   amd-smi set --perf-determinism 1900
+   rocm-smi --setperfdeterminism 1900
 
 See `Hardware verification with ROCm <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html#hardware-verification-with-rocm>`_ for more information.
 
@@ -248,7 +248,7 @@ Download the Docker image and required packages
       Checking out this specific commit is recommended for a stable and reproducible environment.
 
       .. code-block:: shell
-
+         
          git checkout bb93ccbfeae6363c67b361a97a27c74ab86e7e92
 
 Prepare training datasets

docs/reference/gpu-arch-specs.rst

@@ -5,7 +5,7 @@
 GPU hardware specifications
 ===========================================
 
-The following tables provide an overview of the hardware specifications for AMD Instinct™ GPUs, AMD Radeon™ PRO and Radeon™ GPUs, and AMD Ryzen™ APUs.
+The following tables provide an overview of the hardware specifications for AMD Instinct™ GPUs, and AMD Radeon™ PRO and Radeon™ GPUs.
 
 For more information about ROCm hardware compatibility, see the ROCm `Compatibility matrix <https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html>`_.
 
@@ -18,7 +18,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
         :name: instinct-arch-spec-table
 
         *
-          - Name
+          - Model
           - Architecture
           - LLVM target name
           - VRAM (GiB)
@@ -297,7 +297,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
         :name: radeon-pro-arch-spec-table
 
         *
-          - Name
+          - Model
           - Architecture
           - LLVM target name
 
@@ -539,7 +539,7 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
         :name: radeon-arch-spec-table
 
         *
-          - Name
+          - Model
           - Architecture
           - LLVM target name
           - VRAM (GiB)
@@ -953,127 +953,6 @@ For more information about ROCm hardware compatibility, see the ROCm `Compatibil
           - 9
           - 0
 
-  .. tab-item:: AMD Ryzen APUs
-
-    .. list-table::
-        :header-rows: 1
-        :name: ryzen-arch-spec-table
-
-        *
-          - Name
-          - Graphics model
-          - Architecture
-          - LLVM target name
-          - VRAM (GiB)
-          - Compute Units
-          - Wavefront Size
-          - LDS (KiB)
-          - Infinity Cache (MiB)
-          - L2 Cache (MiB)
-          - Graphics L1 Cache (KiB)
-          - L0 Vector Cache (KiB)
-          - L0 Scalar Cache (KiB)
-          - L0 Instruction Cache (KiB)
-          - VGPR File (KiB)
-          - SGPR File (KiB)
-          - GFXIP Major version
-          - GFXIP Minor version
-        *
-          - AMD Ryzen 7 7840U
-          - Radeon 780M
-          - RDNA3
-          - gfx1103
-          - Dynamic + carveout
-          - 12
-          - 32 or 64
-          - 128
-          - N/A
-          - 2
-          - 256
-          - 32
-          - 16
-          - 32
-          - 512
-          - 32
-          - 11
-          - 0
-        *
-          - AMD Ryzen 9 270
-          - Radeon 780M
-          - RDNA3
-          - gfx1103
-          - Dynamic + carveout
-          - 12
-          - 32 or 64
-          - 128
-          - N/A
-          - 2
-          - 256
-          - 32
-          - 16
-          - 32
-          - 512
-          - 32
-          - 11
-          - 0
-        *
-          - AMD Ryzen AI 9 HX 375
-          - Radeon 890M
-          - RDNA3.5
-          - gfx1150
-          - Dynamic + carveout
-          - 16
-          - 32 or 64
-          - 128
-          - N/A
-          - 2
-          - 256
-          - 32
-          - 16
-          - 32
-          - 512
-          - 32
-          - 11
-          - 5
-        *
-          - AMD Ryzen AI Max+ PRO 395
-          - Radeon 8060S
-          - RDNA3.5
-          - gfx1151
-          - Dynamic + carveout
-          - 40
-          - 32 or 64
-          - 128
-          - 32
-          - 2
-          - 256
-          - 32
-          - 16
-          - 32
-          - 768
-          - 32
-          - 11
-          - 5
-        *
-          - AMD Ryzen Al 7 350
-          - Radeon 860M
-          - RDNA3.5
-          - gfx1152
-          - Dynamic + carveout
-          - 8
-          - 32 or 64
-          - 128
-          - N/A
-          - 1
-          - 256
-          - 32
-          - 16
-          - 32
-          - 512
-          - 32
-          - 11
-          - 5
-
 Glossary
 ========
 

docs/sphinx/_toc.yml.in

@@ -29,25 +29,27 @@ subtrees:
     title: Deep learning frameworks
     subtrees:
     - entries:
-      - file: compatibility/ml-compatibility/pytorch-compatibility.rst
+      - file: compatibility/ml-compatibility/pytorch-compatibility
         title: PyTorch compatibility
-      - file: compatibility/ml-compatibility/tensorflow-compatibility.rst
+      - file: compatibility/ml-compatibility/tensorflow-compatibility
         title: TensorFlow compatibility
-      - file: compatibility/ml-compatibility/jax-compatibility.rst
+      - file: compatibility/ml-compatibility/jax-compatibility
         title: JAX compatibility
-      - file: compatibility/ml-compatibility/verl-compatibility.rst
+      - file: compatibility/ml-compatibility/verl-compatibility
         title: verl compatibility
-      - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
+      - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility
         title: Stanford Megatron-LM compatibility
-      - file: compatibility/ml-compatibility/dgl-compatibility.rst
+      - file: compatibility/ml-compatibility/dgl-compatibility
         title: DGL compatibility
-      - file: compatibility/ml-compatibility/megablocks-compatibility.rst
+      - file: compatibility/ml-compatibility/megablocks-compatibility
         title: Megablocks compatibility
-      - file: compatibility/ml-compatibility/ray-compatibility.rst
+      - file: compatibility/ml-compatibility/taichi-compatibility
+        title: Taichi compatibility
+      - file: compatibility/ml-compatibility/ray-compatibility
         title: Ray compatibility
-      - file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
+      - file: compatibility/ml-compatibility/llama-cpp-compatibility
         title: llama.cpp compatibility
-      - file: compatibility/ml-compatibility/flashinfer-compatibility.rst
+      - file: compatibility/ml-compatibility/flashinfer-compatibility
         title: FlashInfer compatibility
   - file: how-to/build-rocm.rst
     title: Build ROCm from source
@@ -75,14 +77,8 @@ subtrees:
         - entries:
           - file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
             title: Train a model with Primus and Megatron-LM
-            entries:
-            - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
-              title: Train a model with Megatron-LM
           - file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
             title: Train a model with Primus and PyTorch
-            entries:
-            - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
-              title: Train a model with PyTorch
           - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
             title: Train a model with JAX MaxText
           - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
@@ -121,8 +117,6 @@ subtrees:
             title: SGLang inference performance testing
           - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
             title: SGLang distributed inference with Mooncake
-          - file: how-to/rocm-for-ai/inference/xdit-diffusion-inference.rst
-            title: xDiT diffusion inference
           - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
             title: Deploy your model
 
@@ -140,8 +134,6 @@ subtrees:
             title: Profile and debug
           - file: how-to/rocm-for-ai/inference-optimization/workload.rst
             title: Workload optimization
-          - file: how-to/rocm-for-ai/inference-optimization/vllm-optimization.rst
-            title: vLLM V1 performance optimization
 
       - url: https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/
         title: AI tutorials
@@ -188,7 +180,7 @@ subtrees:
           - file: conceptual/gpu-arch/mi300-mi200-performance-counters.rst
             title: MI300 and MI200 performance counters
           - file: conceptual/gpu-arch/mi350-performance-counters.rst
-            title: MI350 Series performance counters
+            title: MI350 series performance counters
       - file: conceptual/gpu-arch/mi250.md
         title: MI250 microarchitecture
         subtrees:
@@ -222,8 +214,6 @@ subtrees:
     title: ROCm tools, compilers, and runtimes
   - file: reference/gpu-arch-specs.rst
   - file: reference/gpu-atomics-operation.rst
-  - file: reference/env-variables.rst
-    title: Environment variables
   - file: reference/precision-support.rst
     title: Data types and precision support
   - file: reference/graph-safe-support.rst

docs/what-is-rocm.rst

@@ -123,8 +123,7 @@ Performance
 
 .. 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>`.
+  `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.
-  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
 ^^^^^^^^^^^