Fix skipping of origami tests

* Taichi removed from ROCm docs

* Warnings fixed

.azuredevops/components/origami.yml

@@ -169,7 +169,6 @@ 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)
         )

.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

@@ -138,6 +138,7 @@ ESXi
 EP
 EoS
 etcd
+equalto
 fas
 FBGEMM
 FiLM
@@ -226,6 +227,8 @@ href
 Hyperparameters
 HybridEngine
 Huggingface
+Hunyuan
+HunyuanVideo
 IB
 ICD
 ICT
@@ -517,8 +520,6 @@ TPS
 TPU
 TPUs
 TSME
-Taichi
-Taichi's
 Tagram
 TensileLite
 TensorBoard
@@ -541,6 +542,7 @@ UAC
 UC
 UCC
 UCX
+ud
 UE
 UIF
 UMC
@@ -852,6 +854,7 @@ pallas
 parallelization
 parallelizing
 param
+params
 parameterization
 passthrough
 pe
@@ -898,6 +901,7 @@ querySelectorAll
 queueing
 qwen
 radeon
+rc
 rccl
 rdc
 rdma
@@ -959,6 +963,7 @@ scalability
 scalable
 scipy
 seealso
+selectattr
 selectedTag
 sendmsg
 seqs
@@ -1062,6 +1067,8 @@ writebacks
 wrreq
 wzo
 xargs
+xdit
+xDiT
 xGMI
 xPacked
 xz

CHANGELOG.md

@@ -39,7 +39,11 @@ for a complete overview of this release.
   - VMs were incorrectly reporting `AMDSMI_STATUS_API_FAILED` when unable to get the power cap within the `amdsmi_get_power_info`.
   - The API now returns `N/A` or `UINT_MAX` for values that can't be retrieved, instead of failing.
 
-- Fixed output for `amd-smi xgmi -l --json`.  
+- Fixed output for `amd-smi xgmi -l --json`.
+
+```{note}
+See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-7.1/CHANGELOG.md#amd_smi_lib-for-rocm-711) for details, examples, and in-depth descriptions.
+```
 
 ### **Composable Kernel** (1.1.0)
 
@@ -1730,8 +1734,8 @@ HIP runtime has the following functional improvements which improves runtime per
 
 #### Upcoming changes
 
-* `__AMDGCN_WAVEFRONT_SIZE__` macro and HIP’s `warpSize` variable as `constexpr` are deprecated and will be disabled in a future release. Users are encouraged to update their code if needed to ensure future compatibility. For more information, see [AMDGCN_WAVEFRONT_SIZE deprecation](#amdgpu-wavefront-size-compiler-macro-deprecation).
-* The `roc-obj-ls` and `roc-obj-extract` tools are  deprecated. To extract all Clang offload bundles into separate code objects use `llvm-objdump --offloading <file>`. For more information, see [Changes to ROCm Object Tooling](#changes-to-rocm-object-tooling). 
+* `__AMDGCN_WAVEFRONT_SIZE__` macro and HIP’s `warpSize` variable as `constexpr` are deprecated and will be disabled in a future release. Users are encouraged to update their code if needed to ensure future compatibility. For more information, see [AMDGCN_WAVEFRONT_SIZE deprecation](https://rocm.docs.amd.com/en/docs-7.0.0/about/release-notes.html#amdgpu-wavefront-size-compiler-macro-deprecation).
+* The `roc-obj-ls` and `roc-obj-extract` tools are  deprecated. To extract all Clang offload bundles into separate code objects use `llvm-objdump --offloading <file>`. For more information, see [Changes to ROCm Object Tooling](https://rocm.docs.amd.com/en/docs-7.0.0/about/release-notes.html#changes-to-rocm-object-tooling). 
 
 ### **MIGraphX** (2.13.0)
 

RELEASE.md

@@ -100,12 +100,13 @@ firmware, AMD GPU drivers, and the ROCm user space software.
               01.25.16.03<br>
               01.25.15.04
           </td>
-          <td rowspan="2" style="vertical-align: middle;">
+          <td>
               30.20.1<br>
               30.20.0<br>
               30.10.2<br>
               30.10.1<br>
-              30.10</td>
+              30.10
+            </td>
           <td rowspan="3" style="vertical-align: middle;">8.6.0.K</td>
       </tr>
       <tr>
@@ -114,6 +115,13 @@ firmware, AMD GPU drivers, and the ROCm user space software.
               01.25.16.03<br>
               01.25.15.04
           </td>
+          <td>
+              30.20.1<br>
+              30.20.0<br>
+              30.10.2<br>
+              30.10.1<br>
+              30.10
+            </td>
       </tr>
       <tr>
           <td>MI325X<a href="#footnote1"><sup>[1]</sup></a></td>
@@ -674,7 +682,7 @@ For a historical overview of ROCm component updates, see the {doc}`ROCm consolid
 - Fixed output for `amd-smi xgmi -l --json`.  
 
 ```{note}
-See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-7.1/CHANGELOG.md#amd_smi_lib-for-rocm-710) for details, examples, and in-depth descriptions.
+See the full [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-7.1/CHANGELOG.md#amd_smi_lib-for-rocm-711) for details, examples, and in-depth descriptions.
 ```
 
 ### **Composable Kernel** (1.1.0)
@@ -831,7 +839,7 @@ issues related to individual components, review the [Detailed component changes]
 
 ### RCCL performance degradation on AMD Instinct MI300X GPU with AMD Pollara AI NIC
 
-If you’re using RCCL on AMD Instinct MI300X GPUs with AMD Pollara AI NIC, you might observe performance degradation for specific collectives and message sizes. The affected collectives are `Scatter`, `AllToAll`, and `AlltoAllv`. It's recommended to avoid using RCCL packaged with ROCm 7.1.1. As a workaround, use the {fab}`github`[RCCL `develop` branch](https://github.com/ROCm/rccl/tree/develop), which contains the fix and will be included in a future ROCm release.
+If you’re using RCCL on AMD Instinct MI300X GPUs with AMD Pollara AI NIC, you might observe performance degradation for specific collectives and message sizes. The affected collectives are `Scatter`, `AllToAll`, and `AlltoAllv`. It's recommended to avoid using RCCL packaged with ROCm 7.1.1. As a workaround, use the {fab}`github`[RCCL `develop` branch](https://github.com/ROCm/rccl/tree/develop), which contains the fix and will be included in a future ROCm release. See [GitHub issue #5717](https://github.com/ROCm/ROCm/issues/5717).
 
 ### Segmentation fault in training models using TensorFlow 2.20.0 Docker images
 
@@ -839,7 +847,7 @@ Training models `tf2_tfm_resnet50_fp16_train` and `tf2_tfm_resnet50_fp32_train`
 might fail with a segmentation fault when run on the TensorFlow 2.20.0 Docker
 image with ROCm 7.1.1. As a workaround, use TensorFlow 2.19.x Docker image for
 training the models in ROCm 7.1.1. This issue will be fixed in a future ROCm
-release.
+release. See [GitHub issue #5718](https://github.com/ROCm/ROCm/issues/5718).
 
 ### AMD SMI CLI triggers repeated kernel errors on GPUs with partitioning support
 
@@ -858,27 +866,19 @@ amdgpu 0000:15:00.0: amdgpu: renderD153 partition 1 not valid!
 These repeated kernel logs can clutter the system logs and may cause
 unnecessary concern about GPU health. However, this is a non-functional issue
 and does not affect AMD SMI functionality or GPU performance. This issue will
-be fixed in a future ROCm release.
+be fixed in a future ROCm release. See [GitHub issue #5720](https://github.com/ROCm/ROCm/issues/5720).
 
 ### Excessive bad page logs in AMD GPU Driver (amdgpu)
 
-Due to partial data corruption of Electrically Erasable Programmable Read-Only Memory (EEPROM) and limited error handling in the AMD GPU Driver(amdgpu), excessive log output might result when querying the reliability, availability, and serviceability (RAS) bad pages. This issue will be fixed in a future AMD GPU Driver(amdgpu) and ROCm release.
+Due to partial data corruption in the Electrically Erasable Programmable Read-Only Memory (EEPROM) and limited error handling in the AMD GPU Driver (amdgpu), excessive log output might occur when querying the reliability, availability, and serviceability (RAS) bad pages. This issue will be fixed in a future AMD GPU Driver (amdgpu) and ROCm release. See [GitHub issue #5719](https://github.com/ROCm/ROCm/issues/5719).
 
-### OpenBLAS runtime dependency for hipblastlt-test and hipblaslt-bench
+### Incorrect results in gemm_ex operations for rocBLAS and hipBLAS
 
-Running `hipblaslt-test` or `hipblaslt-bench` without installing the OpenBLAS development package results in the following error:
-```
-libopenblas.so.0: cannot open shared object file: No such file or directory
-```
-As a workaround, first install `libopenblas-dev` or `libopenblas-deve`, depending on the package manager used. The issue will be fixed in a future ROCm release. See [GitHub issue #5639](https://github.com/ROCm/ROCm/issues/5639).
+Some `gemm_ex` operations with 8-bit input data types (`int8`, `float8`, `bfloat8`) for specific matrix dimensions (K = 1 and number of workgroups > 1) might yield incorrect results. The issue results from incorrect tailloop code that fails to consider workgroup index when calculating valid element size. The issue will be fixed in a future ROCm release. See [GitHub issue #5722](https://github.com/ROCm/ROCm/issues/5722).
 
-### Reduced precision in gemm_ex operations for rocBLAS and hipBLAS
+### hipBLASLt performance variation for a particular FP8 GEMM operation on AMD Instinct MI325X GPUs
 
-Some `gemm_ex` operations with `half` or `f32_r` data types might yield 16-bit precision results instead of the expected 32-bit precision when matrix dimensions are m=1 or n=1. The issue results from the optimization that enables `_ex` APIs to use lower precision multiples. It limits the high-precision matrix operations performed in PyTorch with rocBLAS and hipBLAS. The issue will be fixed in a future ROCm release. See [GitHub issue #5640](https://github.com/ROCm/ROCm/issues/5640).
-
-### RCCL profiler plugin failure with AllToAll operations
-
-The RCCL profiler plugin `librccl-profiler.so` might fail with a segmentation fault during `AllToAll` collective operations due to improperly assigned point-to-point task function pointers. This leads to invalid memory access and prevents profiling of `AllToAll` performance. Other operations, like `AllReduce`, are unaffected. It's recommended to avoid using the RCCL profiler plugin with `AllToAll` operations until the fix is available. This issue is resolved in the {fab}`github`[RCCL `develop` branch](https://github.com/ROCm/rccl/tree/develop) and will be part of a future ROCm release. See [GitHub issue #5653](https://github.com/ROCm/ROCm/issues/5653).
+If you’re using hipBLASLt on AMD Instinct MI325X GPUs for large FP8 GEMM operations (such as 9728x8192x65536), you might observe a noticeable performance variation. The issue is currently under investigation and will be fixed in a future ROCm release. See [GitHub issue #5734](https://github.com/ROCm/ROCm/issues/5734).
 
 ## ROCm resolved issues
 

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

@@ -33,15 +33,14 @@ ROCm Version,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6
       :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9, 2.8, 2.7","2.8, 2.7, 2.6","2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 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>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 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>`,0.7.1,0.7.1,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat-past-60]_,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,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat-past-60]_,N/A,N/A,N/A,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
       :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` [#stanford-megatron-lm_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,2.4.0,2.4.0,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` [#megablocks_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,1.8.0b1,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>` [#ray_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,2.48.0.post0,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,N/A
       :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat-past-60]_,N/A,N/A,N/A,b6652,b6356,b6356,b6356,b5997,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,v0.2.5,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,N/A
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.1,1.22.0,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
       ,,,,,,,,,,,,,,,,,,,,,,
       ,,,,,,,,,,,,,,,,,,,,,,
       THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,
@@ -68,7 +67,7 @@ ROCm Version,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6
       ,,,,,,,,,,,,,,,,,,,,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,
       :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      :doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocSHMEM <rocshmem:index>`,3.1.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
       ,,,,,,,,,,,,,,,,,,,,,,
       MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,
       `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0

docs/compatibility/compatibility-matrix.rst

@@ -59,7 +59,7 @@ compatibility and system requirements.
       :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.7.1,0.7.1,0.4.35
       :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_,N/A,N/A,2.4.0
       :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat]_,N/A,N/A,b5997
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.22.0,1.20.0
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.1,1.22.0,1.20.0
       ,,,
       THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
       `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0
@@ -85,7 +85,7 @@ compatibility and system requirements.
       ,,,
       COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
       :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.22.3
-      :doc:`rocSHMEM <rocshmem:index>`,3.0.0,3.0.0,2.0.0
+      :doc:`rocSHMEM <rocshmem:index>`,3.1.0,3.0.0,2.0.0
       ,,,
       MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
       `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
@@ -155,8 +155,8 @@ compatibility and system requirements.
 
 .. rubric:: Footnotes
 
-.. [#os-compatibility] Some operating systems are supported on limited GPUs. For detailed information, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`_, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-operating-systems>`_, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`_.
-.. [#gpu-compatibility] Some GPUs have limited operating system support. For detailed information, see the latest :ref:`supported_GPUs`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-gpus>`_, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-gpus>`_, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-gpus>`_.
+.. [#os-compatibility] Some operating systems are supported on limited GPUs. For detailed information, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`__, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
+.. [#gpu-compatibility] Some GPUs have limited operating system support. For detailed information, see the latest :ref:`supported_GPUs`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-gpus>`__, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-gpus>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-gpus>`__.
 .. [#dgl_compat] DGL is supported only on ROCm 7.0.0, ROCm 6.4.3 and ROCm 6.4.0.
 .. [#llama-cpp_compat] llama.cpp is supported only on ROCm 7.0.0 and ROCm 6.4.x.
 .. [#mi325x_KVM] For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.
@@ -241,11 +241,10 @@ Expand for full historical view of:
    .. [#os-compatibility-past-60] Some operating systems are supported on limited GPUs. For detailed information, see :ref:`supported_distributions` and select the required ROCm version for version specific support.
    .. [#gpu-compatibility-past-60] Some GPUs have limited operating system support. For detailed information, see :ref:`supported_GPUs` and select the required ROCm version for version specific support.
    .. [#tf-mi350-past-60] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
-   .. [#verl_compat-past-60] verl is supported only on ROCm 6.2.0.
+   .. [#verl_compat-past-60] verl is supported only on ROCm 7.0.0 and 6.2.0.
    .. [#stanford-megatron-lm_compat-past-60] Stanford Megatron-LM is supported only on ROCm 6.3.0.
    .. [#dgl_compat-past-60] DGL is supported only on ROCm 7.0.0, ROCm 6.4.3 and ROCm 6.4.0.
    .. [#megablocks_compat-past-60] Megablocks is supported only on ROCm 6.3.0.
-   .. [#taichi_compat-past-60] Taichi is supported only on ROCm 6.3.2.
    .. [#ray_compat-past-60] Ray is supported only on ROCm 6.4.1.
    .. [#llama-cpp_compat-past-60] llama.cpp is supported only on ROCm 7.0.0 and 6.4.x.
    .. [#flashinfer_compat-past-60] FlashInfer is supported only on ROCm 6.4.1.

docs/compatibility/ml-compatibility/pytorch-compatibility.rst

@@ -349,7 +349,7 @@ with ROCm.
         you need to explicitly move audio data (waveform tensor) to GPU using
         ``.to('cuda')``.
 
-    * - `torchtune <https://docs.pytorch.org/torchtune/stable/index.html>`_
+    * - `torchtune <https://meta-pytorch.org/torchtune/stable/index.html>`_
       - PyTorch-native library designed for fine-tuning large language models
         (LLMs). Provides supports the full fine-tuning workflow and offers
         compatibility with popular production inference systems.
@@ -366,7 +366,7 @@ with ROCm.
         constructing flexible and performant data pipelines, with features still
         in prototype stage.
 
-    * - `torchrec <https://docs.pytorch.org/torchrec/>`_
+    * - `torchrec <https://meta-pytorch.org/torchrec/>`_
       - PyTorch domain library for common sparsity and parallelism primitives
         needed for large-scale recommender systems, enabling authors to train
         models with large embedding tables shared across many GPUs.
@@ -401,25 +401,25 @@ with ROCm.
 
 Key features and enhancements for PyTorch 2.9 with ROCm 7.1.1
 ================================================================================
-- Scaled Dot Product Attention (SDPA) upgraded to use AOTriton version 0.11b
+- Scaled Dot Product Attention (SDPA) upgraded to use AOTriton version 0.11b.
 
-- Default hipBLASLt support enabled for gfx908 architecture on ROCm 6.3 and later
+- Default hipBLASLt support enabled for gfx908 architecture on ROCm 6.3 and later.
 
-- MIOpen now supports channels last memory format for 3D convolutions and batch normalization
+- MIOpen now supports channels last memory format for 3D convolutions and batch normalization.
 
-- NHWC convolution operations in MIOpen optimized by eliminating unnecessary transpose operations
+- NHWC convolution operations in MIOpen optimized by eliminating unnecessary transpose operations.
 
-- Improved tensor.item() performance by removing redundant synchronization
+- Improved tensor.item() performance by removing redundant synchronization.
 
-- Enhanced performance for element-wise operations and reduction kernels
+- Enhanced performance for element-wise operations and reduction kernels.
 
-- Added support for grouped GEMM operations through fbgemm_gpu generative AI components
+- Added support for grouped GEMM operations through fbgemm_gpu generative AI components.
 
-- Resolved device error in Inductor when using CUDA graph trees with HIP
+- Resolved device error in Inductor when using CUDA graph trees with HIP.
 
-- Corrected logsumexp scaling in AOTriton-based SDPA implementation
+- Corrected logsumexp scaling in AOTriton-based SDPA implementation.
 
-- Added stream graph capture status validation in memory copy synchronization functions
+- Added stream graph capture status validation in memory copy synchronization functions.
 
 Key features and enhancements for PyTorch 2.8 with ROCm 7.1
 ================================================================================

docs/compatibility/ml-compatibility/taichi-compatibility.rst

@@ -1,99 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Taichi compatibility
-    :keywords: GPU, Taichi, deep learning, framework compatibility
-
-.. version-set:: rocm_version latest
-
-*******************************************************************************
-Taichi compatibility
-*******************************************************************************
-
-`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel 
-programming language designed for high-performance numerical computation. 
-Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate 
-compute-intensive Python code by compiling it to native GPU or CPU instructions.
-
-Taichi is widely used across various domains, including real-time physical simulation, 
-numerical computing, augmented reality, artificial intelligence, computer vision, robotics, 
-visual effects in film and gaming, and general-purpose computing.
-
-Support overview
-================================================================================
-
-- The ROCm-supported version of Taichi is maintained in the official `https://github.com/ROCm/taichi 
-  <https://github.com/ROCm/taichi>`__ repository, which differs from the 
-  `https://github.com/taichi-dev/taichi <https://github.com/taichi-dev/taichi>`__ upstream repository.
-
-- To get started and install Taichi on ROCm, use the prebuilt :ref:`Docker image <taichi-docker-compat>`, 
-  which includes ROCm, Taichi, and all required dependencies.
-
-  - See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` 
-    for installation and setup instructions.
-
-  - You can also consult the upstream `Installation guide <https://github.com/taichi-dev/taichi>`__ 
-    for additional context.
-
-Version support
---------------------------------------------------------------------------------
-
-Taichi is supported on `ROCm 6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`__.
-
-Supported devices
---------------------------------------------------------------------------------
-
-- **Officially Supported**: AMD Instinct™ MI250X, MI210X (with the exception of Taichi’s GPU rendering system, CGUI)
-- **Upcoming Support**: AMD Instinct™ MI300X
-
-.. _taichi-recommendations:
-
-Use cases and recommendations
-================================================================================
-
-* The `Accelerating Parallel Programming in Python with Taichi Lang on AMD GPUs 
-  <https://rocm.blogs.amd.com/artificial-intelligence/taichi/README.html>`__
-  blog highlights Taichi as an open-source programming language designed for high-performance 
-  numerical computation, particularly in domains like real-time physical simulation, 
-  artificial intelligence, computer vision, robotics, and visual effects. Taichi 
-  is embedded in Python and uses just-in-time (JIT) compilation frameworks like 
-  LLVM to optimize execution on GPUs and CPUs. The blog emphasizes the versatility 
-  of Taichi in enabling complex simulations and numerical algorithms, making 
-  it ideal for developers working on compute-intensive tasks. Developers are 
-  encouraged to follow recommended coding patterns and utilize Taichi decorators 
-  for performance optimization, with examples available in the `https://github.com/ROCm/taichi_examples 
-  <https://github.com/ROCm/taichi_examples>`_ repository. Prebuilt Docker images 
-  integrating ROCm, PyTorch, and Taichi are provided for simplified installation 
-  and deployment, making it easier to leverage Taichi for advanced computational workloads.
-
-.. _taichi-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tag and associated inventories 
-represent the latest Taichi version from the official Docker Hub.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - ROCm
-      - Taichi
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
-      - `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
-      - `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
-      - 22.04
-      - `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_

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

@@ -31,7 +31,7 @@ Support overview
 - To get started and install verl on ROCm, use the prebuilt :ref:`Docker image <verl-docker-compat>`, 
   which includes ROCm, verl, and all required dependencies.
 
-  - See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>` 
+  - See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>`
     for installation and setup instructions.
 
   - You can also consult the upstream `verl documentation <https://verl.readthedocs.io/en/latest/>`__ 
@@ -40,7 +40,8 @@ Support overview
 Version support
 --------------------------------------------------------------------------------
 
-verl is supported on `ROCm 6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`__.
+verl is supported on `ROCm 7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__ and
+`ROCm 6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`__.
 
 Supported devices
 --------------------------------------------------------------------------------
@@ -57,7 +58,7 @@ Use cases and recommendations
   GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__ 
   blog. The blog post outlines how the Volcano Engine Reinforcement Learning 
   (verl) framework integrates with the AMD ROCm platform to optimize training on 
-  Instinct™ MI300X GPUs. The guide details the process of building a Docker image, 
+  AMD Instinct™ GPUs. The guide details the process of building a Docker image, 
   setting up single-node and multi-node training environments, and highlights 
   performance benchmarks demonstrating improved throughput and convergence accuracy. 
   This resource serves as a comprehensive starting point for deploying verl on AMD GPUs, 
@@ -79,12 +80,20 @@ The following table shows verl on ROCm support for GPU-accelerated modules.
       - ROCm version
     * - ``FSDP``
       - Training engine
-      - 0.3.0.post0
-      - 6.2.0
+      - 
+       * 0.6.0
+       * 0.3.0.post0
+      - 
+       * 7.0.0
+       * 6.2.0
     * - ``vllm``
       - Inference engine
-      - 0.3.0.post0
-      - 6.2.0
+      - 
+       * 0.6.0
+       * 0.3.0.post0
+      - 
+       * 7.0.0
+       * 6.2.0
 
 .. _verl-docker-compat:
 
@@ -95,28 +104,45 @@ Docker image compatibility
 
    <i class="fab fa-docker"></i>
 
-AMD validates and publishes ready-made `verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
+AMD validates and publishes `verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
 with ROCm backends on Docker Hub. The following Docker image tag and associated inventories 
 represent the latest verl version from the official Docker Hub. 
 Click |docker-icon| to view the image on Docker Hub.
 
-.. list-table:: 
-    :header-rows: 1
+.. list-table::
+   :header-rows: 1
+   :class: docker-image-compatibility
 
-    *   - Docker image
-        - ROCm
-        - verl
-        - Ubuntu
-        - Pytorch
-        - Python
-        - vllm
+   * - Docker image
+     - ROCm
+     - verl
+     - Ubuntu
+     - PyTorch
+     - Python
+     - vllm
 
-    *   - .. raw:: html
+   * - .. raw:: html
 
-            <a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
-        - `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`_
-        - `0.3.0post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`_
-        - 20.04
-        - `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`_
-        - `3.9.19 <https://www.python.org/downloads/release/python-3919/>`_
-        - `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`_
+        <a href="https://hub.docker.com/layers/rocm/verl/verl-0.6.0.amd0_rocm7.0_vllm0.11.0.dev/images/sha256-f70a3ebc94c1f66de42a2fcc3f8a6a8d6d0881eb0e65b6958d7d6d24b3eecb0d"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
+     - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+     - `0.6.0 <https://github.com/volcengine/verl/releases/tag/v0.6.0>`__
+     - 22.04
+     - `2.9.0 <https://github.com/ROCm/pytorch/tree/release/2.9-rocm7.x-gfx115x>`__
+     - `3.12.11 <https://www.python.org/downloads/release/python-31211/>`__
+     - `0.11.0 <https://github.com/vllm-project/vllm/releases/tag/v0.11.0>`__
+
+   * - .. raw:: html
+
+        <a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
+     - `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`__
+     - `0.3.0.post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`__
+     - 20.04
+     - `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
+     - `3.9.19 <https://www.python.org/downloads/release/python-3919/>`__
+     - `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`__
+
+
+Previous versions
+===============================================================================
+See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/verl-history` to find documentation for previous releases
+of the ``ROCm/verl`` Docker image.

docs/conf.py

@@ -111,7 +111,6 @@ article_pages = [
     {"file": "compatibility/ml-compatibility/stanford-megatron-lm-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/dgl-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/megablocks-compatibility", "os": ["linux"]},
-    {"file": "compatibility/ml-compatibility/taichi-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/ray-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/llama-cpp-compatibility", "os": ["linux"]},
     {"file": "compatibility/ml-compatibility/flashinfer-compatibility", "os": ["linux"]},
@@ -145,6 +144,7 @@ article_pages = [
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/inference/xdit-diffusion-inference", "os": ["linux"]},    
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
     {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
@@ -249,3 +249,6 @@ 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/data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml

@@ -0,0 +1,55 @@
+xdit_diffusion_inference:
+  docker:
+    pull_tag: rocm/pytorch-xdit:v25.10
+    docker_hub_url: https://hub.docker.com/r/rocm/pytorch-xdit
+    ROCm: 7.9.0
+    components:
+      TheRock: 7afbe45
+      rccl: 9b04b2a
+      composable_kernel: b7a806f
+      rocm-libraries: f104555
+      rocm-systems: 25922d0
+      torch: 2.10.0a0+gite9c9017
+      torchvision: 0.22.0a0+966da7e
+      triton: 3.5.0+git52e49c12
+      accelerate: 1.11.0.dev0
+      aiter: 0.1.5.post4.dev20+ga25e55e79
+      diffusers: 0.36.0.dev0
+      xfuser: 0.4.4
+      yunchang: 0.6.3.post1
+
+  model_groups:
+    - group: Hunyuan Video
+      tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          model_name: hunyuanvideo
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+    - group: Wan-AI
+      tag: wan
+      models:
+        - model: Wan2.1
+          model_name: wan2_1-i2v-14b-720p
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+        - model: Wan2.2
+          model_name: wan2_2-i2v-a14b
+          model_repo: Wan-AI/Wan2.2-I2V-A14B
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+    - group: FLUX
+      tag: flux
+      models:
+        - model: FLUX.1
+          model_name: FLUX.1-dev
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux

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

@@ -0,0 +1,109 @@
+xdit_diffusion_inference:
+  docker:
+    - version: v25-11
+      pull_tag: rocm/pytorch-xdit:v25.11
+      docker_hub_url: https://hub.docker.com/r/rocm/pytorch-xdit
+      ROCm: 7.10.0
+      supported_models:
+        - group: Hunyuan Video
+          models:
+            - Hunyuan Video
+        - group: Wan-AI
+          models:
+            - Wan2.1
+            - Wan2.2
+        - group: FLUX
+          models:
+            - FLUX.1
+      whats_new:
+        - "Minor bug fixes and clarifications to READMEs."
+        - "Bumps TheRock, AITER, Diffusers, xDiT versions."
+        - "Changes Aiter rounding mode for faster gfx942 FWD Attention."
+      components:
+        TheRock: 3e3f834
+        rccl: d23d18f
+        composable_kernel: 2570462
+        rocm-libraries: 0588f07
+        rocm-systems: 473025a
+        torch: 73adac
+        torchvision: f5c6c2e
+        triton: 7416ffc
+        accelerate: 34c1779
+        aiter: de14bec
+        diffusers: 40528e9
+        xfuser: 83978b5
+        yunchang: 2c9b712
+
+    - version: v25-10
+      pull_tag: rocm/pytorch-xdit:v25.10
+      docker_hub_url: https://hub.docker.com/r/rocm/pytorch-xdit
+      ROCm: 7.9.0
+      supported_models:
+        - group: Hunyuan Video
+          models:
+            - Hunyuan Video
+        - group: Wan-AI
+          models:
+            - Wan2.1
+            - Wan2.2
+        - group: FLUX
+          models:
+            - FLUX.1
+      whats_new:
+        - "First official xDiT Docker Release for Diffusion Inference."
+        - "Supports gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X)."
+        - "Support Wan 2.1, Wan 2.2, HunyuanVideo and Flux workloads."
+      components:
+        TheRock: 7afbe45
+        rccl: 9b04b2a
+        composable_kernel: b7a806f
+        rocm-libraries: f104555
+        rocm-systems: 25922d0
+        torch: 2.10.0a0+gite9c9017
+        torchvision: 0.22.0a0+966da7e
+        triton: 3.5.0+git52e49c12
+        accelerate: 1.11.0.dev0
+        aiter: 0.1.5.post4.dev20+ga25e55e79
+        diffusers: 0.36.0.dev0
+        xfuser: 0.4.4
+        yunchang: 0.6.3.post1
+
+  model_groups:
+    - group: Hunyuan Video
+      tag: hunyuan
+      models:
+        - model: Hunyuan Video
+          page_tag: hunyuan_tag
+          model_name: hunyuanvideo
+          model_repo: tencent/HunyuanVideo
+          revision: refs/pr/18
+          url: https://huggingface.co/tencent/HunyuanVideo
+          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
+          mad_tag: pyt_xdit_hunyuanvideo
+    - group: Wan-AI
+      tag: wan
+      models:
+        - model: Wan2.1
+          page_tag: wan_21_tag
+          model_name: wan2_1-i2v-14b-720p
+          model_repo: Wan-AI/Wan2.1-I2V-14B-720P
+          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P
+          github: https://github.com/Wan-Video/Wan2.1
+          mad_tag: pyt_xdit_wan_2_1
+        - model: Wan2.2
+          page_tag: wan_22_tag
+          model_name: wan2_2-i2v-a14b
+          model_repo: Wan-AI/Wan2.2-I2V-A14B
+          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B
+          github: https://github.com/Wan-Video/Wan2.2
+          mad_tag: pyt_xdit_wan_2_2
+    - group: FLUX
+      tag: flux
+      models:
+        - model: FLUX.1
+          page_tag: flux_1_tag
+          model_name: FLUX.1-dev
+          model_repo: black-forest-labs/FLUX.1-dev
+          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+          github: https://github.com/black-forest-labs/flux
+          mad_tag: pyt_xdit_flux

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

@@ -1,21 +1,17 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/megatron-lm:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: aab4234
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/megatron-lm:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
 model_groups:
   - group: Meta Llama
     tag: llama

docs/data/how-to/rocm-for-ai/training/previous-versions/megatron-lm-v25.9-benchmark-models.yaml

@@ -0,0 +1,53 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/megatron-lm:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: aab4234
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/megatron-lm:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/megatron-lm/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+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 (proxy)
+        mad_tag: pyt_megatron_lm_train_deepseek-v3-proxy
+      - model: DeepSeek-V2-Lite
+        mad_tag: pyt_megatron_lm_train_deepseek-v2-lite-16b
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: pyt_megatron_lm_train_mixtral-8x7b
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: pyt_megatron_lm_train_mixtral-8x22b-proxy
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-7b
+      - model: Qwen 2.5 72B
+        mad_tag: pyt_megatron_lm_train_qwen2.5-72b

docs/data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v25.9-benchmark-models.yaml

@@ -0,0 +1,65 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/primus:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: 0.3.0
+      Primus Turbo: 0.1.1
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/primus:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+      - model: Llama 3.3 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
+        config_name: llama3.3_70B-pretrain.yaml
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
+        config_name: llama3.1_70B-pretrain.yaml
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
+        config_name: llama3.1_8B-pretrain.yaml
+      - model: Llama 2 7B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
+        config_name: llama2_7B-pretrain.yaml
+      - model: Llama 2 70B
+        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
+        config_name: llama2_70B-pretrain.yaml
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek-V3 (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
+        config_name: deepseek_v3-pretrain.yaml
+      - model: DeepSeek-V2-Lite
+        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
+        config_name: deepseek_v2_lite-pretrain.yaml
+  - group: Mistral AI
+    tag: mistral
+    models:
+      - model: Mixtral 8x7B
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
+        config_name: mixtral_8x7B_v0.1-pretrain.yaml
+      - model: Mixtral 8x22B (proxy)
+        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
+        config_name: mixtral_8x22B_v0.1-pretrain.yaml
+  - group: Qwen
+    tag: qwen
+    models:
+      - model: Qwen 2.5 7B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
+        config_name: primus_qwen2.5_7B-pretrain.yaml
+      - model: Qwen 2.5 72B
+        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
+        config_name: qwen2.5_72B-pretrain.yaml

docs/data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.9-benchmark-models.yaml

@@ -0,0 +1,39 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/primus:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: 0.3.0
+      Primus Turbo: 0.1.1
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/primus:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 3.1 8B
+      mad_tag: primus_pyt_train_llama-3.1-8b
+      model_repo: meta-llama/Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      config_file:
+        bf16: "./llama3_8b_fsdp_bf16.toml"
+        fp8: "./llama3_8b_fsdp_fp8.toml"
+    - model: Llama 3.1 70B
+      mad_tag: primus_pyt_train_llama-3.1-70b
+      model_repo: meta-llama/Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B
+      precision: BF16
+      config_file:
+        bf16: "./llama3_70b_fsdp_bf16.toml"
+        fp8: "./llama3_70b_fsdp_fp8.toml"

docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.9-benchmark-models.yaml

@@ -0,0 +1,186 @@
+dockers:
+  MI355X and MI350X:
+    pull_tag: rocm/pytorch-training:v25.9_gfx950
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
+    components: &docker_components
+      ROCm: 7.0.0
+      Primus: aab4234
+      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
+      Python: "3.10"
+      Transformer Engine: 2.2.0.dev0+54dd2bdc
+      Flash Attention: 2.8.3
+      hipBLASLt: 911283acd1
+      Triton: 3.4.0+rocm7.0.0.git56765e8c
+      RCCL: 2.26.6
+  MI325X and MI300X:
+    pull_tag: rocm/pytorch-training:v25.9_gfx942
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
+    components: *docker_components
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Stable Diffusion
+    tag: sd
+    models:
+    - model: Stable Diffusion XL
+      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
+      model_repo: SDXL
+      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+      precision: BF16
+      training_modes: [posttrain-p]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [posttrain-p]
+  - group: NCF
+    tag: ncf
+    models:
+    - model: NCF
+      mad_tag: pyt_ncf_training
+      model_repo:
+      url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
+      precision: FP32

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

@@ -1,22 +1,15 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/primus:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: 0.3.0
-      Primus Turbo: 0.1.1
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/primus:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
+    Triton: 3.4.0
+    RCCL: 2.27.7
 model_groups:
   - group: Meta Llama
     tag: llama

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

@@ -1,39 +1,32 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/primus:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: 0.3.0
-      Primus Turbo: 0.1.1
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/primus:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
 model_groups:
   - group: Meta Llama
     tag: llama
     models:
-    - model: Llama 3.1 8B
-      mad_tag: primus_pyt_train_llama-3.1-8b
-      model_repo: meta-llama/Llama-3.1-8B
-      url: https://huggingface.co/meta-llama/Llama-3.1-8B
-      precision: BF16
-      config_file:
-        bf16: "./llama3_8b_fsdp_bf16.toml"
-        fp8: "./llama3_8b_fsdp_fp8.toml"
-    - model: Llama 3.1 70B
-      mad_tag: primus_pyt_train_llama-3.1-70b
-      model_repo: meta-llama/Llama-3.1-70B
-      url: https://huggingface.co/meta-llama/Llama-3.1-70B
-      precision: BF16
-      config_file:
-        bf16: "./llama3_70b_fsdp_bf16.toml"
-        fp8: "./llama3_70b_fsdp_fp8.toml"
+      - model: Llama 3.1 8B
+        mad_tag: primus_pyt_train_llama-3.1-8b
+        model_repo: Llama-3.1-8B
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B
+        precision: BF16
+      - model: Llama 3.1 70B
+        mad_tag: primus_pyt_train_llama-3.1-70b
+        model_repo: Llama-3.1-70B
+        url: https://huggingface.co/meta-llama/Llama-3.1-70B
+        precision: BF16
+  - group: DeepSeek
+    tag: deepseek
+    models:
+      - model: DeepSeek V2 16B
+        mad_tag: primus_pyt_train_deepseek-v2
+        model_repo: DeepSeek-V2
+        url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+        precision: BF16

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

@@ -1,21 +1,15 @@
-dockers:
-  MI355X and MI350X:
-    pull_tag: rocm/pytorch-training:v25.9_gfx950
-    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6
-    components: &docker_components
-      ROCm: 7.0.0
-      Primus: aab4234
-      PyTorch: 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
-      Python: "3.10"
-      Transformer Engine: 2.2.0.dev0+54dd2bdc
-      Flash Attention: 2.8.3
-      hipBLASLt: 911283acd1
-      Triton: 3.4.0+rocm7.0.0.git56765e8c
-      RCCL: 2.26.6
-  MI325X and MI300X:
-    pull_tag: rocm/pytorch-training:v25.9_gfx942
-    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357
-    components: *docker_components
+docker:
+  pull_tag: rocm/primus:v25.10
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197
+  components:
+    ROCm: 7.1.0
+    Primus: 0.3.0
+    Primus Turbo: 0.1.1
+    PyTorch: 2.10.0.dev20251112+rocm7.1
+    Python: "3.10"
+    Transformer Engine: 2.4.0.dev0+32e2d1d4
+    Flash Attention: 2.8.3
+    hipBLASLt: 1.2.0-09ab7153e2
 model_groups:
   - group: Meta Llama
     tag: llama
@@ -119,6 +113,15 @@ model_groups:
       url: https://huggingface.co/openai/gpt-oss-120b
       precision: BF16
       training_modes: [HF_finetune_lora]
+  - group: DeepSeek
+    tag: deepseek
+    models:
+    - model: DeepSeek V2 16B
+      mad_tag: primus_pyt_train_deepseek-v2
+      model_repo: DeepSeek-V2
+      url: https://huggingface.co/deepseek-ai/DeepSeek-V2
+      precision: BF16
+      training_modes: [pretrain]
   - group: Qwen
     tag: qwen
     models:
@@ -166,7 +169,7 @@ model_groups:
       model_repo: SDXL
       url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
       precision: BF16
-      training_modes: [posttrain-p]
+      training_modes: [posttrain]
   - group: Flux
     tag: flux
     models:
@@ -175,12 +178,20 @@ model_groups:
       model_repo: Flux
       url: https://huggingface.co/black-forest-labs/FLUX.1-dev
       precision: BF16
-      training_modes: [posttrain-p]
+      training_modes: [posttrain]
   - group: NCF
     tag: ncf
     models:
     - model: NCF
       mad_tag: pyt_ncf_training
       model_repo:
-      url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
+      url: https://github.com/ROCm/FluxBenchmark
       precision: FP32
+  - group: DLRM
+    tag: dlrm
+    models:
+    - model: DLRM v2
+      mad_tag: pyt_train_dlrm
+      model_repo: DLRM
+      url: https://github.com/AMD-AGI/DLRMBenchmark
+      training_modes: [pretrain]

docs/data/reference/precision-support/precision-support.yaml

@@ -32,7 +32,7 @@ library_groups:
 
       - name: "MIGraphX"
         tag: "migraphx"
-        doc_link: "amdmigraphx:reference/cpp"
+        doc_link: "amdmigraphx:reference/MIGraphX-cpp"
         data_types:
           - type: "int8"
             support: "⚠️"
@@ -290,7 +290,7 @@ library_groups:
 
       - name: "Tensile"
         tag: "tensile"
-        doc_link: "tensile:reference/precision-support"
+        doc_link: "tensile:src/reference/precision-support"
         data_types:
           - type: "int8"
             support: "✅"

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

@@ -100,18 +100,6 @@ The table below summarizes information about ROCm-enabled deep learning framewor
 
           <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
 
-    * - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
-
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
-
     * - `Ray <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/ray-compatibility.html>`__
       - .. raw:: html
 

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

@@ -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.

docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.10.rst

@@ -0,0 +1,396 @@
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. _xdit-video-diffusion:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
+   a prebuilt, optimized inference environment based on `xDiT
+   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion-based
+   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
+   and MI300X (gfx942) GPUs.
+   This image is based on ROCm {{docker.ROCm}} preview release via `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following software components:
+
+   .. tab-set::
+
+      .. tab-item:: {{ docker.pull_tag }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+
+- Initial ROCm-enabled xDiT Docker release for diffusion inference.
+- Supported architectures: gfx942 and gfx950 (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X).
+- Supported workloads: Wan 2.1, Wan 2.2, HunyuanVideo, and Flux models.
+
+.. _xdit-video-diffusion-supported-models-2510:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+                  <div class="col-4 px-2 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 gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if models|length == 1 %}
+                <div class="col-12 px-2 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 px-2 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>
+
+   {% for model_group in model_groups %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+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 `System validation and optimization
+<https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/system-setup/prerequisite-system-validation.html>`__
+guide to properly configure your system settings before starting.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+Once the image has been downloaded you can follow these steps to
+run benchmarks and generate outputs.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups %}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.mad_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models-2510` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+.. _xdit-video-diffusion-setup:
+
+Prepare the model
+-----------------
+
+.. note::
+
+   If you're using ROCm MAD to :ref:`run your model
+   <xdit-video-diffusion-run>`, you can skip this section. MAD will handle
+   starting the container and downloading required models inside the container.
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.mad_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+.. _xdit-video-diffusion-run:
+
+Run inference
+=============
+
+You can benchmark models through `MAD <https://github.com/ROCm/MAD>`__-integrated automation or standalone
+torchrun commands.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.10-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.mad_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+                     
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --model tencent/HunyuanVideo \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd Wan2.1
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
+                  --image "/app/Wan2.1/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd Wan2.2
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-A14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
+                  --image "/app/Wan2.2/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+
+            {% if model.model == "FLUX.1" %}
+               cd Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model black-forest-labs/FLUX.1-dev \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 1 \
+                  --benchmark_output_directory results
+
+            {% endif %}
+
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% endif %}
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- For a list of other ready-made Docker images for AI with ROCm, see `AMD
+  Infinity Hub
+  <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`__.
+
+Previous versions
+=================
+
+See :doc:`xdit-history` to find documentation for previous releases
+of xDiT diffusion inference performance testing.

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

@@ -0,0 +1,56 @@
+:orphan:
+
+************************************************************
+xDiT diffusion inference performance testing version history
+************************************************************
+
+This table lists previous versions of the ROCm xDiT diffusion inference performance
+testing environment. For detailed information about available models for
+benchmarking, see the version-specific documentation.
+
+.. list-table::
+   :header-rows: 1
+
+   * - Docker image tag
+     - Components
+     - Resources
+
+   * - ``rocm/pytorch-xdit:v25.11`` (latest)
+     - 
+       * ROCm 7.10.0 preview
+       * TheRock 3e3f834
+       * rccl d23d18f
+       * composable_kernel 2570462
+       * rocm-libraries 0588f07
+       * rocm-systems 473025a
+       * torch 73adac
+       * torchvision f5c6c2e
+       * triton 7416ffc
+       * accelerate 34c1779
+       * aiter de14bec
+       * diffusers 40528e9
+       * xfuser 83978b5
+       * yunchang 2c9b712
+     - 
+       * :doc:`Documentation <../../xdit-diffusion-inference>`
+       * `Docker Hub <https://hub.docker.com/r/rocm/pytorch-xdit>`__
+
+   * - ``rocm/pytorch-xdit:v25.10``
+     - 
+       * ROCm 7.9.0 preview
+       * TheRock 7afbe45
+       * rccl 9b04b2a
+       * composable_kernel b7a806f
+       * rocm-libraries f104555
+       * rocm-systems 25922d0
+       * torch 2.10.0a0+gite9c9017
+       * torchvision 0.22.0a0+966da7e
+       * triton 3.5.0+git52e49c12
+       * accelerate 1.11.0.dev0
+       * aiter 0.1.5.post4.dev20+ga25e55e79
+       * diffusers 0.36.0.dev0
+       * xfuser 0.4.4
+       * yunchang 0.6.3.post1
+     - 
+       * :doc:`Documentation <xdit-25.10>`
+       * `Docker Hub <https://hub.docker.com/r/rocm/pytorch-xdit>`__

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

@@ -27,3 +27,6 @@ training, fine-tuning, and inference. It leverages popular machine learning fram
 - :doc:`SGLang inference performance testing <benchmark-docker/sglang>`
 
 - :doc:`Deploying your model <deploy-your-model>`
+
+- :doc:`xDiT diffusion inference <xdit-diffusion-inference>`
+

docs/how-to/rocm-for-ai/inference/xdit-diffusion-inference.rst

@@ -0,0 +1,388 @@
+.. meta::
+   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
+                 prebuilt and optimized docker images.
+   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
+
+************************
+xDiT diffusion inference
+************************
+
+.. _xdit-video-diffusion:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   and includes the following components:
+
+   .. dropdown:: Software components
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Software component
+           - Version
+
+         {% for component_name, component_version in docker.components.items() %}
+         * - {{ component_name }}
+           - {{ component_version }}
+         {% endfor %}
+
+Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
+For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
+
+What's new
+==========
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for item in docker.whats_new %}
+   * {{ item }}
+   {% endfor %}
+
+.. _xdit-video-diffusion-supported-models:
+
+Supported models
+================
+
+The following models are supported for inference performance benchmarking.
+Some instructions, commands, and recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups %}
+   
+   {# Create a lookup for supported models #}
+   {% set supported_lookup = {} %}
+   {% for supported in docker.supported_models %}
+   {% set _ = supported_lookup.update({supported.group: supported.models}) %}
+   {% endfor %}
+
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+          <div class="row gx-0">
+              <div class="col-2 me-1 px-2 model-param-head">Model</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% if model_group.group in supported_lookup %}
+                  <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+            {% endif %}
+        {% endfor %}
+              </div>
+          </div>
+
+          <div class="row gx-0 pt-1">
+              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+              <div class="row col-10 pe-0">
+        {% for model_group in model_groups %}
+            {% if model_group.group in supported_lookup %}
+            {% set supported_models = supported_lookup[model_group.group] %}
+            {% set models = model_group.models %}
+            {% for model in models %}
+                {% if model.model in supported_models %}
+                {% if models|length % 3 == 0 %}
+                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.page_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% else %}
+                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.page_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+                {% endif %}
+                {% endif %}
+            {% endfor %}
+            {% endif %}
+        {% endfor %}
+              </div>
+          </div>
+      </div>
+
+   {% for model_group in model_groups %}
+       {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.page_tag }}
+
+      .. note::
+
+         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
+         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
+         external license agreement through a third party.
+
+       {% endfor %}
+   {% endfor %}
+
+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.
+
+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
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+
+   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
+   Pull the image using the following command:
+
+   .. code-block:: shell
+
+      docker pull {{ docker.pull_tag }}
+
+Validate and benchmark
+======================
+
+Once the image has been downloaded you can follow these steps to
+run benchmarks and generate outputs.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{model.page_tag}}
+
+      The following commands are written for {{ model.model }}.
+      See :ref:`xdit-video-diffusion-supported-models` to switch to another available model.
+
+     {% endfor %}
+   {% endfor %}
+
+Choose your setup method
+------------------------
+
+You can either use an existing Hugging Face cache or download the model fresh inside the container.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set docker = data.xdit_diffusion_inference.docker | selectattr("version", "equalto", "v25-11") | first %}
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+   .. container:: model-doc {{model.page_tag}}
+
+      .. tab-set::
+
+         .. tab-item:: Option 1: Use existing Hugging Face cache
+
+            If you already have models downloaded on your host system, you can mount your existing cache.
+
+            1. Set your Hugging Face cache location.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/your/hf_cache/location
+
+            2. Download the model (if not already cached).
+
+               .. code-block:: shell
+
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+            3. Launch the container with mounted cache.
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      -e HF_HOME=/app/huggingface_models \
+                      -v $HF_HOME:/app/huggingface_models \
+                      {{ docker.pull_tag }}
+
+         .. tab-item:: Option 2: Download inside container
+
+            If you prefer to keep the container self-contained or don't have an existing cache.
+
+            1. Launch the container
+
+               .. code-block:: shell
+
+                  docker run \
+                      -it --rm \
+                      --cap-add=SYS_PTRACE \
+                      --security-opt seccomp=unconfined \
+                      --user root \
+                      --device=/dev/kfd \
+                      --device=/dev/dri \
+                      --group-add video \
+                      --ipc=host \
+                      --network host \
+                      --privileged \
+                      --shm-size 128G \
+                      --name pytorch-xdit \
+                      -e HSA_NO_SCRATCH_RECLAIM=1 \
+                      -e OMP_NUM_THREADS=16 \
+                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+                      {{ docker.pull_tag }}
+
+            2. Inside the container, set the Hugging Face cache location and download the model.
+
+               .. code-block:: shell
+
+                  export HF_HOME=/app/huggingface_models
+                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
+
+               .. warning::
+
+                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
+     {% endfor %}
+   {% endfor %}
+
+Run inference
+=============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+
+   {% set model_groups = data.xdit_diffusion_inference.model_groups%}
+   {% for model_group in model_groups %}
+     {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.page_tag }}
+
+      .. tab-set::
+
+         .. tab-item:: MAD-integrated benchmarking
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. On the host machine, use this command to run the performance benchmark test on
+               the `{{model.model}} <{{ model.url }}>`_ model using one node.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{model.mad_tag}} \
+                      --keep-model-dir \
+                      --live-output
+                     
+            MAD launches a Docker container with the name
+            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
+            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
+            and ``{{ model.mad_tag }}_serving.csv``.
+
+         .. tab-item:: Standalone benchmarking
+
+            To run the benchmarks for {{ model.model }}, use the following command:
+
+            .. code-block:: shell
+            {% if model.model == "Hunyuan Video" %}
+               cd /app/Hunyuanvideo
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --model tencent/HunyuanVideo \
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
+                  --height 720 --width 1280 --num_frames 129 \
+                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
+                  --ulysses_degree 8 \
+                  --enable_tiling --enable_slicing \
+                  --use_torch_compile \
+                  --bench_output results
+            {% endif %}
+            {% if model.model == "Wan2.1" %}
+               cd Wan2.1
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.1-I2V-14B-720P/snapshots/8823af45fcc58a8aa999a54b04be9abc7d2aac98/" \
+                  --image "/app/Wan2.1/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+            {% if model.model == "Wan2.2" %}
+               cd Wan2.2
+               mkdir results
+
+               torchrun --nproc_per_node=8 run.py \
+                  --task i2v-A14B \
+                  --size 720*1280 --frame_num 81 \
+                  --ckpt_dir "${HF_HOME}/hub/models--Wan-AI--Wan2.2-I2V-A14B/snapshots/206a9ee1b7bfaaf8f7e4d81335650533490646a3/" \
+                  --image "/app/Wan2.2/examples/i2v_input.JPG" \
+                  --ulysses_size 8 --ring_size 1 \
+                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
+                  --benchmark_output_directory results --save_file video.mp4 --num_benchmark_steps 1 \
+                  --offload_model 0 \
+                  --vae_dtype bfloat16 \
+                  --allow_tf32 \
+                  --compile
+            {% endif %}
+
+            {% if model.model == "FLUX.1" %}
+               cd Flux
+               mkdir results
+
+               torchrun --nproc_per_node=8 /app/Flux/run.py \
+                  --model black-forest-labs/FLUX.1-dev \
+                  --seed 42 \
+                  --prompt "A small cat" \
+                  --height 1024 \
+                  --width 1024 \
+                  --num_inference_steps 25 \
+                  --max_sequence_length 256 \
+                  --warmup_steps 5 \
+                  --no_use_resolution_binning \
+                  --ulysses_degree 8 \
+                  --use_torch_compile \
+                  --num_repetitions 1 \
+                  --benchmark_output_directory results
+
+            {% endif %}
+
+            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model == "FLUX.1" %}results/timing.json{% endif %}
+
+            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
+
+      {% endfor %}
+    {% endfor %}
+
+Previous versions
+=================
+
+See :doc:`benchmark-docker/previous-versions/xdit-history` to find documentation for previous releases
+of xDiT diffusion inference performance testing.

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

@@ -36,12 +36,10 @@ accelerate training workloads:
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
    .. tab-set::
 
-   {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      .. tab-item:: {{ data.docker.pull_tag }}
+         :sync: {{ data.docker.pull_tag }}
 
          .. list-table::
             :header-rows: 1
@@ -49,12 +47,12 @@ accelerate training workloads:
             * - Software component
               - Version
 
-            {% for component_name, component_version in docker.components.items() %}
+            {% for component_name, component_version in data.docker.components.items() %}
             * - {{ component_name }}
               - {{ component_version }}
             {% endfor %}
-   {% endfor %}
-   .. _amd-megatron-lm-model-support:
+
+   .. _amd-megatron-lm-model-support-v2510:
 
    Supported models
    ================
@@ -99,7 +97,7 @@ accelerate training workloads:
    Some models, such as Llama, require an external license agreement through
    a third party (for example, Meta).
 
-.. _amd-megatron-lm-performance-measurements:
+.. _amd-megatron-lm-performance-measurements-v2510:
 
 Performance measurements
 ========================
@@ -131,7 +129,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 
-.. _mi300x-amd-megatron-lm-training:
+.. _mi300x-amd-megatron-lm-training-v2510:
 
 Environment setup
 =================
@@ -140,52 +138,38 @@ Use the following instructions to set up the environment, configure the script t
 reproduce the benchmark results on MI300X Series GPUs with the AMD Megatron-LM Docker
 image.
 
-.. _amd-megatron-lm-requirements:
+.. _amd-megatron-lm-requirements-v2510:
 
 Download the Docker image
 -------------------------
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
+   {% set docker = data.docker %}
    1. Use the following command to pull the Docker image from Docker Hub.
 
-      .. tab-set::
+      .. code-block:: shell
 
-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
-
-            .. code-block:: shell
-
-               docker pull {{ docker.pull_tag }}
-         {% endfor %}
+         docker pull {{ docker.pull_tag }}
 
    2. Launch the Docker container.
 
-      .. tab-set::
+      .. code-block:: shell
 
-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
-
-            .. code-block:: shell
-
-               docker run -it \
-                   --device /dev/dri \
-                   --device /dev/kfd \
-                   --device /dev/infiniband \
-                   --network host --ipc host \
-                   --group-add video \
-                   --cap-add SYS_PTRACE \
-                   --security-opt seccomp=unconfined \
-                   --privileged \
-                   -v $HOME:$HOME \
-                   -v $HOME/.ssh:/root/.ssh \
-                   --shm-size 128G \
-                   --name megatron_training_env \
-                   {{ docker.pull_tag }}
-         {% endfor %}
+         docker run -it \
+             --device /dev/dri \
+             --device /dev/kfd \
+             --device /dev/infiniband \
+             --network host --ipc host \
+             --group-add video \
+             --cap-add SYS_PTRACE \
+             --security-opt seccomp=unconfined \
+             --privileged \
+             -v $HOME:$HOME \
+             -v $HOME/.ssh:/root/.ssh \
+             --shm-size 128G \
+             --name megatron_training_env \
+             {{ docker.pull_tag }}
 
 3. Use these commands if you exit the ``megatron_training_env`` container and need to return to it.
 
@@ -206,7 +190,7 @@ Download the Docker image
 The Docker container hosts a verified commit of
 `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev>`__.
 
-.. _amd-megatron-lm-environment-setup:
+.. _amd-megatron-lm-environment-setup-v2510:
 
 Configuration
 =============
@@ -216,39 +200,39 @@ Configuration
    Update the ``train_llama3.sh`` configuration script in the ``examples/llama``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v2510>`.
 
 .. container:: model-doc pyt_megatron_lm_train_llama-2-7b pyt_megatron_lm_train_llama-2-70b
 
    Update the ``train_llama2.sh`` configuration script in the ``examples/llama``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/llama>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v2510>`.
 
 .. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
 
    Update the ``train_deepseekv3.sh`` configuration script in the ``examples/deepseek_v3``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v3>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v2510>`.
 
 .. container:: model-doc pyt_megatron_lm_train_deepseek-v2-lite-16b
 
    Update the ``train_deepseekv2.sh`` configuration script in the ``examples/deepseek_v2``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/deepseek_v2>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v2510>`.
 
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b pyt_megatron_lm_train_mixtral-8x22b-proxy
 
    Update the ``train_mixtral_moe.sh`` configuration script in the ``examples/mixtral``
    directory of
    `<https://github.com/ROCm/Megatron-LM/tree/rocm_dev/examples/mixtral>`__ to configure your training run.
-   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training>`.
+   Options can also be passed as command line arguments as described in :ref:`Run training <amd-megatron-lm-run-training-v2510>`.
 
 .. note::
 
-   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars>` for more information on configuration options.
+   See :ref:`Key options <amd-megatron-lm-benchmark-test-vars-v2510>` for more information on configuration options.
 
 Multi-node configuration
 ------------------------
@@ -256,7 +240,7 @@ Multi-node configuration
 Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
 training. See :ref:`amd-megatron-lm-multi-node-examples` for example run commands.
 
-.. _amd-megatron-lm-tokenizer:
+.. _amd-megatron-lm-tokenizer-v2510:
 
 Tokenizer
 ---------
@@ -393,7 +377,7 @@ Download the dataset
 
    ``TOKENIZER_MODEL`` can be any accessible Hugging Face tokenizer.
    Remember to either pre-download the tokenizer or setup Hugging Face access
-   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer>` section.
+   otherwise when needed -- see the :ref:`Tokenizer <amd-megatron-lm-tokenizer-v2510>` section.
 
    .. note::
 
@@ -495,15 +479,38 @@ Download the dataset
 
    Ensure that the files are accessible inside the Docker container.
 
-.. _amd-megatron-lm-run-training:
+.. _amd-megatron-lm-run-training-v2510:
 
 Run training
 ============
 
 Use the following example commands to set up the environment, configure
-:ref:`key options <amd-megatron-lm-benchmark-test-vars>`, and run training on
+:ref:`key options <amd-megatron-lm-benchmark-test-vars-v2510>`, and run training on
 MI300X Series GPUs with the AMD Megatron-LM environment.
 
+Before starting training, export the following environment variables.
+
+.. tab-set::
+
+   .. tab-item:: MI355X and MI350X
+
+      .. code-block:: shell
+
+         export HSA_NO_SCRATCH_RECLAIM=1
+         export NVTE_CK_USES_BWD_V3=1
+         export NVTE_CK_USES_BWD_V3=1
+
+   .. tab-item:: MI325X and MI300X
+
+      .. code-block:: shell
+
+         export HSA_NO_SCRATCH_RECLAIM=1
+         export NVTE_CK_USES_BWD_V3=1
+         export NVTE_CK_USES_BWD_V3=1
+
+         # Set this on MI325X/MI300X only
+         export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
 Single node training
 --------------------
 
@@ -913,7 +920,7 @@ Single node training
           RECOMPUTE_ACTIVATIONS=full \
           CKPT_FORMAT=torch_dist
 
-.. _amd-megatron-lm-multi-node-examples:
+.. _amd-megatron-lm-multi-node-examples-v2510:
 
 Multi-node training examples
 ----------------------------
@@ -964,7 +971,7 @@ training on 16 nodes, try the following command:
 
    sbatch examples/deepseek_v3/train_deepseek_v3_slurm.sh
 
-.. _amd-megatron-lm-benchmark-test-vars:
+.. _amd-megatron-lm-benchmark-test-vars-v2510:
 
 Key options
 -----------
@@ -1029,11 +1036,6 @@ The benchmark tests support the following sets of variables.
 ``RECOMPUTE_NUM_LAYERS``
   Number of layers used for checkpointing recompute.
 
-Known issues
-============
-
-PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
-
 Previous versions
 =================
 

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

@@ -16,14 +16,23 @@ previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https
      - Components
      - Resources
 
-   * - v25.9 (latest)
+   * - v25.10 (latest)
+     -
+       * ROCm 7.1.0
+       * PyTorch 2.10.0.dev20251112+rocm7.1
+     -
+       * :doc:`Primus Megatron documentation <../primus-megatron>`
+       * :doc:`Megatron-LM (legacy) documentation <../megatron-lm>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197>`__
+
+   * - v25.9
      -
        * ROCm 7.0.0
        * Primus 0.3.0
        * PyTorch 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
      -
-       * :doc:`Primus Megatron documentation <../primus-megatron>`
-       * :doc:`Megatron-LM (legacy) documentation <../megatron-lm>`
+       * :doc:`Primus Megatron documentation <primus-megatron-v25.9>`
+       * :doc:`Megatron-LM (legacy) documentation <megatron-lm-v25.9>`
        * `Docker Hub (gfx950) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6>`__
        * `Docker Hub (gfx942) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357>`__
 

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v25.9.rst

@@ -0,0 +1,574 @@
+:orphan:
+
+.. meta::
+   :description: How to train a model using PyTorch for ROCm.
+   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
+
+****************************************
+Training a model with Primus and PyTorch
+****************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm Primus PyTorch training
+   performance benchmark documentation. See :doc:`../primus-pytorch` for the latest version.
+
+`Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
+LLM training framework designed to streamline training. It streamlines LLM
+training on AMD Instinct GPUs using a modular, reproducible configuration paradigm.
+Primus now supports the PyTorch torchtitan backend.
+
+.. note::
+
+   For a unified training solution on AMD GPUs with ROCm, the `rocm/pytorch-training
+   <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
+   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
+   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
+   including torchtitan and :doc:`Megatron-LM <../primus-megatron>`.
+
+   Primus with the PyTorch torchtitan backend is designed to replace the
+   :doc:`ROCm PyTorch training <../pytorch-training>` workflow. See
+   :doc:`../pytorch-training` to see steps to run workloads without Primus.
+
+AMD provides a ready-to-use Docker image for MI355X, MI350X, MI325X, and
+MI300X GPUs containing essential components for Primus and PyTorch training
+with Primus Turbo optimizations.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.9-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   .. tab-set::
+
+   {% for supported_gpus, docker in dockers.items() %}
+      .. tab-item:: {{ supported_gpus }}
+         :sync: {{ supported_gpus }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+   {% endfor %}
+
+.. _amd-primus-pytorch-model-support-v259:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X GPUs.
+Some instructions, commands, and training recommendations in this documentation might
+vary by model -- select one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.9-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0">
+            <div class="col-2 me-1 px-2 model-param-head">Model</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+               <div class="col-12 px-2 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 gx-0 pt-1">
+            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 px-2 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 px-2 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>
+
+.. seealso::
+
+   For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
+   see the documentation :doc:`../pytorch-training` (without Primus)
+
+.. _amd-primus-pytorch-performance-measurements-v259:
+
+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.
+
+This Docker image is optimized for specific model configurations outlined
+below. Performance can vary for other training workloads, as AMD
+doesn’t test configurations and run conditions outside those described.
+
+Pull the Docker image
+=====================
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.9-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+
+   Use the following command to pull the Docker image from Docker Hub.
+
+   .. tab-set::
+
+      {% for supported_gpus, docker in dockers.items() %}
+      .. tab-item:: {{ supported_gpus }}
+         :sync: {{ supported_gpus }}
+
+         .. code-block:: shell
+
+            docker pull {{ docker.pull_tag }}
+      {% endfor %}
+
+Run training
+============
+
+Once the setup is complete, choose between the following two workflows to start benchmarking training.
+For fine-tuning workloads and multi-node training examples, see :doc:`../pytorch-training` (without Primus).
+For best performance on MI325X, MI350X, and MI355X GPUs, you might need to
+tweak some configurations (such as batch sizes).
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v25.9-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set model_groups = data.model_groups %}
+
+   .. tab-set::
+
+      .. tab-item:: MAD-integrated benchmarking
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following run command is tailored to {{ model.model }}.
+            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
+               using one node with the {{ model.precision }} data type on the host machine.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{ model.mad_tag }} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
+
+               MAD launches a Docker container with the name
+               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
+               model are collected in ``~/MAD/perf.csv``.
+
+               .. note::
+
+                  Currently, Primus torchtitan models are run with Primus Turbo
+                  enabled for enhanced performance. To disable Primus Turbo,
+                  modify respective configuration file
+                  ``scripts/primus/pytorch_train/primus_torchtitan_scripts/llama3_[8B|70B]-[BF16|FP8].yaml``.
+
+      {% endfor %}
+   {% endfor %}
+
+      .. tab-item:: Primus benchmarking
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following run commands are tailored to {{ model.model }}.
+            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
+
+            .. rubric:: Download the Docker image and required packages
+
+            1. Pull the appropriate Docker image for your AMD GPU architecture from Docker Hub.
+
+               .. tab-set::
+
+                  {% for supported_gpus, docker in dockers.items() %}
+                  .. tab-item:: {{ supported_gpus }}
+                     :sync: {{ supported_gpus }}
+
+                     .. code-block:: shell
+
+                        docker pull {{ docker.pull_tag }}
+                  {% endfor %}
+
+            2. Run the Docker container.
+
+               .. tab-set::
+
+                  {% for supported_gpus, docker in dockers.items() %}
+                  .. tab-item:: {{ supported_gpus }}
+                     :sync: {{ supported_gpus }}
+
+                     .. 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 $HOME:$HOME \
+                            -v $HOME/.ssh:/root/.ssh \
+                            --shm-size 64G \
+                            --name training_env \
+                            {{ docker.pull_tag }}
+                  {% endfor %}
+
+               Use these commands if you exit the ``training_env`` container and need to return to it.
+
+               .. code-block:: shell
+
+                  docker start training_env
+                  docker exec -it training_env bash
+
+            .. rubric:: Prepare training datasets and dependencies
+
+            The following benchmarking examples require downloading models and datasets
+            from Hugging Face. To ensure successful access to gated repos, set your
+            ``HF_TOKEN``.
+
+            .. code-block:: shell
+
+               export HF_TOKEN=$your_personal_hugging_face_access_token
+
+            .. rubric:: Pretraining
+
+            To get started, navigate to the ``Primus`` directory in your container.
+
+            .. code-block::
+
+               cd /workspace/Primus
+
+            Now, to start the pretraining benchmark, use the ``run_pretrain.sh`` script
+            included with Primus with the appropriate options.
+
+            .. rubric:: Benchmarking examples
+
+            .. container:: model-doc primus_pyt_train_llama-3.1-8b
+
+               Use the following command to run train Llama 3.1 8B with BF16 precision using Primus torchtitan.
+
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 5
+
+                  .. tab-item:: MI325X
+                     :sync: MI325X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 6
+
+                  .. tab-item:: MI300X
+                     :sync: MI325X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 4
+
+
+               To train Llama 3.1 8B with FP8 precision, use the following command.
+
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 8
+
+                  .. tab-item:: MI325X
+                     :sync: MI325X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 7
+
+                  .. tab-item:: MI300X
+                     :sync: MI325X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_8B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 5
+
+            .. container:: model-doc primus_pyt_train_llama-3.1-70b
+
+               Use the following command to run train Llama 3.1 70B with BF16 precision using Primus torchtitan.
+
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 8
+
+                  .. tab-item:: MI325X
+                     :sync: MI325X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 6
+
+                  .. tab-item:: MI300X
+                     :sync: MI325X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 4
+
+               To train Llama 3.1 70B with FP8 precision, use the following command.
+
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 6
+
+                  .. tab-item:: MI325X
+                     :sync: MI325X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 5
+
+                  .. tab-item:: MI300X
+                     :sync: MI325X and MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh \
+                            --metrics.enable_tensorboard false \
+                            --profiling.enable_profiling false \
+                            --training.batch_size 3
+      {% endfor %}
+   {% endfor %}
+
+      .. tab-item:: Standalone torchtitan benchmarking
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following run commands are tailored to {{ model.model }}.
+            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
+
+            .. rubric:: Download the Docker image and required packages
+
+            1. Pull the appropriate Docker image for your AMD GPU architecture from Docker Hub.
+
+               .. tab-set::
+
+                  {% for supported_gpus, docker in dockers.items() %}
+                  .. tab-item:: {{ supported_gpus }}
+                     :sync: {{ supported_gpus }}
+
+                     .. code-block:: shell
+
+                        docker pull {{ docker.pull_tag }}
+                  {% endfor %}
+
+            2. Run the Docker container.
+
+               .. tab-set::
+
+                  {% for supported_gpus, docker in dockers.items() %}
+                  .. tab-item:: {{ supported_gpus }}
+                     :sync: {{ supported_gpus }}
+
+                     .. 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 $HOME:$HOME \
+                            -v $HOME/.ssh:/root/.ssh \
+                            --shm-size 64G \
+                            --name training_env \
+                            {{ docker.pull_tag }}
+                  {% endfor %}
+
+               Use these commands if you exit the ``training_env`` container and need to return to it.
+
+               .. code-block:: shell
+
+                  docker start training_env
+                  docker exec -it training_env bash
+
+            3. Navigate to the ``torchtitan`` workspace directory.
+
+               .. code-block:: shell
+
+                  cd /workspace/torchtitan
+
+            .. rubric:: Download the tokenizer
+
+            1. The following benchmarking examples require downloading models and datasets
+               from Hugging Face. To ensure successful access to gated repos, set your
+               ``HF_TOKEN``.
+
+               .. code-block:: shell
+
+                  export HF_TOKEN=$your_personal_hugging_face_access_token
+
+            2. Download the tokenizer for your model.
+
+               .. container:: model-doc {{ model.mad_tag }}
+
+                  .. code-block:: shell
+
+                     python3 scripts/download_tokenizer.py \
+                        --repo_id {{ model.model_repo }} \
+                        --tokenizer_path "original" \
+                        --hf_token=${HF_TOKEN}
+
+            .. rubric:: Pretraining examples
+
+            Run the training script with the appropriate configuration file.
+
+            For train with BF16 precicion, use the following command:
+
+            .. container:: model-doc {{ model.mad_tag }}
+
+               .. code-block:: shell
+
+                  CONFIG_FILE={{ model.config_file.bf16 }} \
+                  .run_train.sh
+
+            For train with BF16 precicion, use the following command:
+
+            .. container:: model-doc {{ model.mad_tag }}
+
+               .. code-block:: shell
+
+                  CONFIG_FILE={{ model.config_file.fp8 }} \
+                  .run_train.sh
+      {% endfor %}
+   {% endfor %}
+
+Known issues
+============
+
+PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
+
+
+Further reading
+===============
+
+- For an introduction to Primus, see `Primus: A Lightweight, Unified Training
+  Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`pytorch-training-history` to find documentation for previous releases
+of the ``ROCm/pytorch-training`` Docker image.

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

@@ -16,14 +16,23 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
      - Components
      - Resources
 
-   * - v25.9 (latest)
+   * - v25.10 (latest)
+     -
+       * ROCm 7.1.0
+       * PyTorch 2.10.0.dev20251112+rocm7.1
+     -
+       * :doc:`Primus PyTorch Training documentation <../primus-pytorch>`
+       * :doc:`PyTorch training (legacy) documentation <../pytorch-training>`
+       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.10/images/sha256-140c37cd2eeeb183759b9622543fc03cc210dc97cbfa18eeefdcbda84420c197>`__
+
+   * - v25.9
      -
        * ROCm 7.0.0
        * Primus 0.3.0
        * PyTorch 2.9.0.dev20250821+rocm7.0.0.lw.git125803b7
      -
-       * :doc:`Primus PyTorch Training documentation <../primus-pytorch>`
-       * :doc:`PyTorch training (legacy) documentation <../pytorch-training>`
+       * :doc:`Primus PyTorch Training documentation <primus-pytorch-v25.9>`
+       * :doc:`PyTorch training (legacy) documentation <pytorch-training-v25.9>`
        * `Docker Hub (gfx950) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx950/images/sha256-1a198be32f49efd66d0ff82066b44bd99b3e6b04c8e0e9b36b2c481e13bff7b6>`__
        * `Docker Hub (gfx942) <https://hub.docker.com/layers/rocm/primus/v25.9_gfx942/images/sha256-df6ab8f45b4b9ceb100fb24e19b2019a364e351ee3b324dbe54466a1d67f8357>`__
 

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

@@ -240,7 +240,7 @@ The following models are pre-optimized for performance on the AMD Instinct MI325
                  - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
 
                * - ``torchdata``
-                 - `TorchData <https://pytorch.org/data/beta/index.html>`_
+                 - `TorchData <https://meta-pytorch.org/data/beta/index.html>`_
 
                * - ``tomli``
                  - `Tomli <https://pypi.org/project/tomli/>`_

docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.9.rst

@@ -0,0 +1,667 @@
+:orphan:
+
+.. meta::
+   :description: How to train a model using PyTorch for ROCm.
+   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
+
+**************************************
+Training a model with PyTorch on ROCm
+**************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm PyTorch training
+   performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
+
+.. note::
+
+   For a unified training solution on AMD GPUs with ROCm, the `rocm/pytorch-training
+   <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
+   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
+   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
+   including torchtitan and :doc:`Megatron-LM <../primus-megatron>`.
+
+   See :doc:`../primus-pytorch` for details.
+
+PyTorch is an open-source machine learning framework that is widely used for
+model training with GPU-optimized components for transformer-based models.
+The PyTorch for ROCm training Docker image provides a prebuilt optimized
+environment for fine-tuning and pretraining a model on AMD Instinct MI325X
+and MI300X GPUs. It includes the following software components to accelerate
+training workloads:
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.9-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   .. tab-set::
+
+   {% for supported_gpus, docker in dockers.items() %}
+      .. tab-item:: {{ supported_gpus }}
+         :sync: {{ supported_gpus }}
+
+         .. list-table::
+            :header-rows: 1
+
+            * - Software component
+              - Version
+
+            {% for component_name, component_version in docker.components.items() %}
+            * - {{ component_name }}
+              - {{ component_version }}
+            {% endfor %}
+   {% endfor %}
+
+.. _amd-pytorch-training-model-support-v259:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct
+MI355X, MI350X, MI325X, and MI300X GPUs. Some instructions, commands, and
+training recommendations in this documentation might vary by model -- select
+one to get started.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.9-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0">
+            <div class="col-2 me-1 px-2 model-param-head">Model</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+               <div class="col-4 px-2 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 gx-0 pt-1">
+            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
+            <div class="row col-10 pe-0">
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+            {% if models|length % 3 == 0 %}
+               <div class="col-4 px-2 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 px-2 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>
+
+.. _amd-pytorch-training-supported-training-modes-v259:
+
+The following table lists supported training modes per model.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.9-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   .. dropdown:: Supported training modes
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Model
+           - Supported training modes
+
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+         {% if model.training_modes %}
+         * - {{ model.model }}
+           - ``{{ model.training_modes | join('``, ``') }}``
+
+         {% endif %}
+         {% endfor %}
+      {% endfor %}
+
+      .. note::
+
+         Some model and fine-tuning combinations are not listed. This is
+         because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
+         doesn't provide default YAML configurations for them.
+         For advanced usage, you can create a custom configuration to enable
+         unlisted fine-tuning methods by using an existing file in the
+         ``/workspace/torchtune/recipes/configs`` directory as a template.
+
+.. _amd-pytorch-training-performance-measurements-v259:
+
+Performance measurements
+========================
+
+To evaluate performance, the
+`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8deaeb413-item-21cea50186-tab>`_
+page provides reference throughput and latency measurements for training
+popular AI models.
+
+.. 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#tabs-a8deaeb413-item-21cea50186-tab>`_
+   should not be interpreted as the peak performance achievable by AMD
+   Instinct MI325X and MI300X GPUs or ROCm software.
+
+System validation
+=================
+
+Before running AI workloads, it's important to validate that your AMD hardware is configured
+correctly and performing optimally.
+
+If you have already validated your system settings, including aspects like NUMA auto-balancing, you
+can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
+optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
+before starting training.
+
+To test for optimal performance, consult the recommended :ref:`System health benchmarks
+<rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
+system's configuration.
+
+This Docker image is optimized for specific model configurations outlined
+below. Performance can vary for other training workloads, as AMD
+doesn’t test configurations and run conditions outside those described.
+
+Run training
+============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.9-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set model_groups = data.model_groups %}
+
+   Once the setup is complete, choose between two options to start benchmarking training:
+
+   .. tab-set::
+
+      .. tab-item:: MAD-integrated benchmarking
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following run command is tailored to {{ model.model }}.
+            See :ref:`amd-pytorch-training-model-support-v259` to switch to another available model.
+
+            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
+               directory and install the required packages on the host machine.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD
+                  pip install -r requirements.txt
+
+            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
+               using one node with the {{ model.precision }} data type on the host machine.
+
+               .. code-block:: shell
+
+                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
+                  madengine run \
+                      --tags {{ model.mad_tag }} \
+                      --keep-model-dir \
+                      --live-output \
+                      --timeout 28800
+
+               MAD launches a Docker container with the name
+               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
+               model are collected in ``~/MAD/perf.csv``.
+
+      {% endfor %}
+   {% endfor %}
+
+      .. tab-item:: Standalone benchmarking
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following commands are tailored to {{ model.model }}.
+            See :ref:`amd-pytorch-training-model-support-v259` to switch to another available model.
+
+      {% endfor %}
+   {% endfor %}
+
+         .. rubric:: Download the Docker image and required packages
+
+         1. Use the following command to pull the Docker image from Docker Hub.
+
+            .. tab-set::
+
+               {% for supported_gpus, docker in dockers.items() %}
+               .. tab-item:: {{ supported_gpus }}
+                  :sync: {{ supported_gpus }}
+
+                  .. code-block:: shell
+
+                     docker pull {{ docker.pull_tag }}
+               {% endfor %}
+
+         2. Launch the Docker container.
+
+            .. tab-set::
+
+               {% for supported_gpus, docker in dockers.items() %}
+               .. tab-item:: {{ supported_gpus }}
+                  :sync: {{ supported_gpus }}
+
+                  .. 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 $HOME:$HOME \
+                         -v $HOME/.ssh:/root/.ssh \
+                         --shm-size 64G \
+                         --name training_env \
+                         {{ docker.pull_tag }}
+               {% endfor %}
+
+            Use these commands if you exit the ``training_env`` container and need to return to it.
+
+            .. code-block:: shell
+
+               docker start training_env
+               docker exec -it training_env bash
+
+         3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+            repository and navigate to the benchmark scripts directory
+            ``/workspace/MAD/scripts/pytorch_train``.
+
+            .. code-block:: shell
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD/scripts/pytorch_train
+
+         .. rubric:: Prepare training datasets and dependencies
+
+         1. The following benchmarking examples require downloading models and datasets
+            from Hugging Face. To ensure successful access to gated repos, set your
+            ``HF_TOKEN``.
+
+            .. code-block:: shell
+
+               export HF_TOKEN=$your_personal_hugging_face_access_token
+
+         2. Run the setup script to install libraries and datasets needed for benchmarking.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_setup.sh
+
+            .. container:: model-doc pyt_train_llama-3.1-8b
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+            .. container:: model-doc pyt_train_llama-3.1-70b
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+                  * - ``torchdata``
+                    - `TorchData <https://meta-pytorch.org/data/beta/index.html#torchdata>`__
+
+                  * - ``tomli``
+                    - `Tomli <https://pypi.org/project/tomli/>`__
+
+                  * - ``tiktoken``
+                    - `tiktoken <https://github.com/openai/tiktoken>`__
+
+                  * - ``blobfile``
+                    - `blobfile <https://pypi.org/project/blobfile/>`__
+
+                  * - ``tabulate``
+                    - `tabulate <https://pypi.org/project/tabulate/>`__
+
+                  * - ``wandb``
+                    - `Weights & Biases <https://github.com/wandb/wandb>`__
+
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
+
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
+
+            .. container:: model-doc pyt_train_flux
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`__ 3.2.0
+
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`__ 0.2.0
+
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`__ 2.18.0
+
+                  * - ``csvkit``
+                    - `csvkit <https://csvkit.readthedocs.io/en/latest/>`__ 2.0.1
+
+                  * - ``deepspeed``
+                    - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`__ 0.16.2
+
+                  * - ``diffusers``
+                    - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`__ 0.31.0
+
+                  * - ``GitPython``
+                    - `GitPython <https://github.com/gitpython-developers/GitPython>`__ 3.1.44
+
+                  * - ``opencv-python-headless``
+                    - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`__ 4.10.0.84
+
+                  * - ``peft``
+                    - `PEFT <https://huggingface.co/docs/peft/en/index>`__ 0.14.0
+
+                  * - ``protobuf``
+                    - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`__ 5.29.2
+
+                  * - ``pytest``
+                    - `PyTest <https://docs.pytest.org/en/stable/>`__ 8.3.4
+
+                  * - ``python-dotenv``
+                    - `python-dotenv <https://pypi.org/project/python-dotenv/>`__ 1.0.1
+
+                  * - ``seaborn``
+                    - `Seaborn <https://seaborn.pydata.org/>`__ 0.13.2
+
+                  * - ``transformers``
+                    - `Transformers <https://huggingface.co/docs/transformers/en/index>`__ 4.47.0
+
+            ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
+
+            * `frank-chieng/chinese_architecture_siheyuan <https://huggingface.co/datasets/frank-chieng/chinese_architecture_siheyuan>`__
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+         {% set training_modes = model.training_modes %}
+         {% set training_mode_descs = {
+            "pretrain": "Benchmark pre-training.",
+            "HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
+         } %}
+         {% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
+         {% if available_modes %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Pre-training
+
+            To start the pre-training benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            {% if model.mad_tag == "pyt_train_flux" %}
+            .. container:: model-doc {{ model.mad_tag }}
+
+               .. note::
+
+                  Currently, FLUX models are not supported out-of-the-box on this Docker.
+                  To use FLUX, refer to ``rocm/pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
+
+                  Occasionally, downloading the Flux dataset might fail. In the event of this
+                  error, manually download it from Hugging Face at
+                  `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
+                  and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
+                  the required dataset.
+            {% endif %}
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
+                 - Only Llama 3.1 8B supports FP8 precision.
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+         {% endif %}
+
+         {% set training_modes = model.training_modes %}
+         {% set training_mode_descs = {
+            "posttrain": "Benchmark post-training.",
+         } %}
+         {% set available_modes = training_modes | select("in", ["posttrain"]) | list %}
+         {% if available_modes %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Post-training
+
+            To start the post-training benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
+                 - Only Llama 3.1 8B supports FP8 precision.
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+         {% endif %}
+
+         {% set training_mode_descs = {
+            "finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
+            "finetune_lora": "LoRA fine-tuning (BF16 supported).",
+            "finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
+            "HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
+         } %}
+         {% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
+         {% if available_modes %}
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Fine-tuning
+
+            To start the fine-tuning benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v259>`.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t $training_mode \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
+                 - All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
+
+               * - ``$sequence_length``
+                 - Between 2048 and 16384.
+                 - Sequence length for the language model.
+
+            {% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
+            .. note::
+
+               For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
+               use the following torchtune commit for compatibility:
+
+               .. code-block:: shell
+
+                  git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
+
+            {% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
+            .. note::
+
+               You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
+               input tensor should be smaller than max_seq_len (4096)``.
+               This error indicates that an input sequence is longer than the model's maximum context window.
+
+               Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
+               tokens in this case). You can resolve this by truncating the input or splitting
+               it into smaller chunks before passing it to the model.
+
+               Note on reproducibility: The results in this guide are based on
+               commit ``b4c98ac`` from the upstream
+               `<https://github.com/pytorch/torchtune>`__ repository. For the
+               latest updates, you can use the main branch.
+
+            {% endif %}
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+
+            .. rubric:: Benchmarking examples
+
+            For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
+
+.. _amd-pytorch-training-multinode-examples-v259:
+
+Multi-node training
+-------------------
+
+Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
+training. See :ref:`rocm-for-ai-multi-node-setup-pyt-train-example` for example Slurm run commands.
+
+Pre-training
+~~~~~~~~~~~~
+
+Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch run_slurm_train.sh
+
+Fine-tuning
+~~~~~~~~~~~
+
+Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   huggingface-cli login # Get access to HF Llama model space
+   huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch Torchtune_Multinode.sh
+
+.. note::
+
+   Information regarding benchmark setup:
+
+   * By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
+   * You can adjust the torchtune `YAML configuration file
+     <https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
+     if you're using a different model.
+   * The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
+   * Set the ``mounting_paths`` inside the SLURM script.
+
+Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
+
+Known issues
+============
+
+PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
+
+Further reading
+===============
+
+- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
+
+- For a list of other ready-made Docker images for AI with ROCm, see
+  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
+
+Previous versions
+=================
+
+See :doc:`pytorch-training-history` to find documentation for previous releases
+of the ``ROCm/pytorch-training`` Docker image.

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

@@ -31,12 +31,10 @@ Megatron-LM.
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
    .. tab-set::
 
-   {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      .. tab-item:: {{ data.docker.pull_tag }}
+         :sync: {{ data.docker.pull_tag }}
 
          .. list-table::
             :header-rows: 1
@@ -44,13 +42,12 @@ Megatron-LM.
             * - Software component
               - Version
 
-            {% for component_name, component_version in docker.components.items() %}
+            {% for component_name, component_version in data.docker.components.items() %}
             * - {{ component_name }}
               - {{ component_version }}
             {% endfor %}
-   {% endfor %}
 
-.. _amd-primus-megatron-lm-model-support-v259:
+.. _amd-primus-megatron-lm-model-support-v2510:
 
 Supported models
 ================
@@ -111,7 +108,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 
-.. _mi300x-amd-primus-megatron-lm-training-v259:
+.. _mi300x-amd-primus-megatron-lm-training-v2510:
 
 Environment setup
 =================
@@ -121,63 +118,49 @@ Environment setup
    Use the following instructions to set up the environment, configure the script to train models, and
    reproduce the benchmark results on AMD Instinct GPUs.
 
-.. _amd-primus-megatron-lm-requirements-v259:
+.. _amd-primus-megatron-lm-requirements-v2510:
 
 Pull the Docker image
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
+   {% set docker = data.docker %}
 
-   1. Pull the appropriate Docker image for your AMD GPU architecture from Docker Hub.
+   1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
 
-      .. tab-set::
+      .. code-block:: shell
 
-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
-
-            .. code-block:: shell
-
-               docker pull {{ docker.pull_tag }}
-         {% endfor %}
+         docker pull {{ docker.pull_tag }}
 
    2. Launch the Docker container.
 
-      .. tab-set::
+      .. code-block:: shell
 
-         {% for supported_gpus, docker in dockers.items() %}
-         .. tab-item:: {{ supported_gpus }}
-            :sync: {{ supported_gpus }}
+         docker run -it \
+             --device /dev/dri \
+             --device /dev/kfd \
+             --device /dev/infiniband \
+             --network host --ipc host \
+             --group-add video \
+             --cap-add SYS_PTRACE \
+             --security-opt seccomp=unconfined \
+             --privileged \
+             -v $HOME:$HOME \
+             --shm-size 128G \
+             --name primus_training_env \
+             {{ docker.pull_tag }}
 
-            .. code-block:: shell
+      Use these commands if you exit the ``primus_training_env`` container and need to return to it.
 
-               docker run -it \
-                   --device /dev/dri \
-                   --device /dev/kfd \
-                   --device /dev/infiniband \
-                   --network host --ipc host \
-                   --group-add video \
-                   --cap-add SYS_PTRACE \
-                   --security-opt seccomp=unconfined \
-                   --privileged \
-                   -v $HOME:$HOME \
-                   --shm-size 128G \
-                   --name primus_training_env \
-                   {{ docker.pull_tag }}
-         {% endfor %}
+      .. code-block:: shell
 
-3. Use these commands if you exit the ``primus_training_env`` container and need to return to it.
+         docker start primus_training_env
+         docker exec -it primus_training_env bash
 
-   .. code-block:: shell
+The Docker container hosts verified branch ``release/v25.10`` of the `Primus
+<https://github.com/AMD-AGI/Primus/tree/release/v25.10>`__ repository.
 
-      docker start primus_training_env
-      docker exec -it primus_training_env bash
-
-The Docker container hosts verified commit ``e16b27b`` of the `Primus
-<https://github.com/AMD-AGI/Primus/tree/e16b27b>`__ repository.
-
-.. _amd-primus-megatron-lm-environment-setup-v259:
+.. _amd-primus-megatron-lm-environment-setup-v2510:
 
 Configuration
 =============
@@ -224,7 +207,7 @@ You can use either mock data or real data for training.
 
   Ensure that the files are accessible inside the Docker container.
 
-.. _amd-primus-megatron-lm-tokenizer-v259:
+.. _amd-primus-megatron-lm-tokenizer-v2510:
 
 Tokenizer
 ---------
@@ -245,7 +228,7 @@ right permissions to access the tokenizer for each model.
    <https://github.com/AMD-AGI/Primus/blob/e16b27bf6c1b2798f38848fc574fee60d9a9b902/examples/megatron/configs/llama3.1_8B-pretrain.yaml>`__
    definition.
 
-.. _amd-primus-megatron-lm-run-training-v259:
+.. _amd-primus-megatron-lm-run-training-v2510:
 
 Run training
 ============
@@ -269,7 +252,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run pre-training for Llama 3.3 70B BF16, run:
 
@@ -280,7 +263,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama3.3_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 6 \
@@ -291,7 +274,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.3_70B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama3.3_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 2 \
@@ -301,7 +289,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run pre-training for Llama 3.1 8B FP8, run:
 
@@ -312,7 +300,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama3.1_8B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid \
@@ -324,7 +312,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama3.1_8B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid
@@ -338,7 +331,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama3.1_8B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 4 \
@@ -349,7 +342,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama3.1_8B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50
 
@@ -357,7 +355,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run pre-training for Llama 3.1 70B BF16, run:
 
@@ -368,7 +366,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama3.1_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                  --train_iters 50 \
                  --micro_batch_size 4 \
@@ -379,7 +377,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama3.1_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                  --train_iters 50
 
@@ -398,7 +401,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama3.1_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid \
@@ -411,7 +414,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama3.1_70B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama3.1_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --num_layers 40 \
@@ -422,7 +430,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run pre-training for Llama 2 7B FP8, run:
 
@@ -433,7 +441,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama2_7B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid \
@@ -445,7 +453,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama2_7B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid
@@ -459,7 +472,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama2_7B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 10 \
@@ -470,7 +483,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_7B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama2_7B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50
 
@@ -478,7 +496,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run pre-training for Llama 2 70B BF16, run:
 
@@ -489,7 +507,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/llama2_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 17 \
@@ -500,7 +518,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/llama2_70B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/llama2_70B-pretrain.yaml \
             bash ./examples/run_pretrain.sh \
                 --train_iters 50
 
@@ -508,7 +531,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to DeepSeek-V3.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run training on a single node for DeepSeek-V3 (MoE with expert parallel) BF16 with 3-layer proxy,
    use the following command:
@@ -520,7 +543,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/deepseek_v3-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/deepseek_v3-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --num_layers 3 \
                 --moe_layer_freq 1 \
@@ -533,17 +556,24 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/deepseek_v3-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/deepseek_v3-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --num_layers 3 \
                 --moe_layer_freq 1 \
+                --micro_batch_size 3 \
+                --global_batch_size 192 \
                 --train_iters 50
 
 .. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to DeepSeek-V2-Lite.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel) BF16,
    use the following command:
@@ -555,7 +585,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/deepseek_v2_lite-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/deepseek_v2_lite-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 12 \
@@ -566,7 +596,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/deepseek_v2_lite-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/deepseek_v2_lite-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --global_batch_size 256
@@ -575,7 +610,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Mixtral 8x7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
    use the following command:
@@ -587,7 +622,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/mixtral_8x7B_v0.1-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 4 \
@@ -598,7 +633,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/mixtral_8x7B_v0.1-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/mixtral_8x7B_v0.1-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50
 
@@ -606,7 +646,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Mixtral 8x22B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run training on a single node for Mixtral 8x22B BF16 (MoE with expert parallel) 4-layer proxy,
    use the following command:
@@ -618,7 +658,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/mixtral_8x22B_v0.1-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/mixtral_8x22B_v0.1-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --num_layers 4 \
@@ -631,7 +671,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/mixtral_8x22B_v0.1-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/mixtral_8x22B_v0.1-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --num_layers 4 \
@@ -643,7 +688,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Qwen 2.5 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run training on a single node for Qwen 2.5 7B BF16, use the following
    command:
@@ -655,7 +700,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_7B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 16 \
@@ -666,7 +711,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/qwen2.5_7B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50
 
@@ -679,7 +729,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_7B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid
@@ -691,7 +741,12 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_7B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/qwen2.5_7B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --fp8 hybrid
@@ -700,7 +755,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Qwen 2.5 72B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
 
@@ -711,7 +766,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
+            EXP=examples/megatron/configs/MI355X/qwen2.5_72B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50 \
                 --micro_batch_size 16 \
@@ -722,11 +777,16 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 
          .. code-block:: shell
 
-            EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
+            # Set the variables for better performance
+            # only on MI325X and MI300X
+            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+            export NVTE_CK_IS_V3_ATOMIC_FP32=1
+
+            EXP=examples/megatron/configs/MI300X/qwen2.5_72B-pretrain.yaml \
             bash examples/run_pretrain.sh \
                 --train_iters 50
 
-.. _amd-primus-megatron-multi-node-examples-v259:
+.. _amd-primus-megatron-multi-node-examples-v2510:
 
 Multi-node training examples
 ----------------------------
@@ -740,28 +800,27 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
-   .. tab-set::
+   {% set docker = data.docker %}
+   .. code-block:: shell
 
-      {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      git clone --recurse-submodules https://github.com/AMD-AGI/Primus.git
+      cd Primus
+      git checkout release/v25.10
+      git submodule update --init --recursive
 
-         .. code-block:: shell
+      export DOCKER_IMAGE={{ docker.pull_tag }}
+      export HF_TOKEN=<your_HF_token>
+      export HSA_NO_SCRATCH_RECLAIM=1
+      export NVTE_CK_USES_BWD_V3=1
+      export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
+      export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
+      export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
+      export NCCL_IB_GID_INDEX=3 # Set InfiniBand GID index for NCCL communication. Default is 3 for ROCE
 
-            git clone --recurse-submodules https://github.com/AMD-AGI/Primus.git
-            cd Primus
-            git checkout e16b27b
-
-            export DOCKER_IMAGE={{ docker.pull_tag }}
-            export HF_TOKEN=<your_HF_token>
-            export HSA_NO_SCRATCH_RECLAIM=1
-            export NVTE_CK_USES_BWD_V3=1
-            export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
-            export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
-            export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
-            export NCCL_IB_GID_INDEX=3 # Set InfiniBand GID index for NCCL communication. Default is 3 for ROCE
-      {% endfor %}
+      # Set the variables for better performance
+      # only on MI325X and MI300X
+      export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
+      export NVTE_CK_IS_V3_ATOMIC_FP32=1
 
 .. note::
 
@@ -769,13 +828,13 @@ to launch the multi-node workload. Use the following steps to setup your environ
    * If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
    * To find your network interface, you can use ``ip a``.
    * To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB  devices.
-   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v259`) as appropriate.
+   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v2510`) as appropriate.
 
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Llama 3.1 8B FP8 on 8 nodes, run:
 
@@ -793,7 +852,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Llama 2 7B FP8 on 8 nodes, run:
 
@@ -811,7 +870,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Llama 3.1 70B FP8 on 8 nodes, run:
 
@@ -843,7 +902,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Llama 2 70B FP8 on 8 nodes, run:
 
@@ -875,7 +934,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Llama 3.3 70B FP8 on 8 nodes, run:
 
@@ -907,7 +966,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Mixtral 8x7B BF16 on 8 nodes, run:
 
@@ -925,7 +984,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
 
    Once setup is complete, run the appropriate training command.
    The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v259` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v2510` to switch to another available model.
 
    To train Qwen2.5 72B FP8 on 8 nodes, run:
 
@@ -942,7 +1001,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
           --no_fp8_weight_transpose_cache true \
           --fp8 hybrid
 
-.. _amd-primus-megatron-lm-benchmark-test-vars-v259:
+.. _amd-primus-megatron-lm-benchmark-test-vars-v2510:
 
 Key options
 -----------
@@ -987,7 +1046,10 @@ num_layers
 Known issues
 ============
 
-PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
+DeepSeekV3 proxy model and Mixtral 8x22B proxy model may exit with an error
+due to a memory free issue. However, this does not impacts training runs. All
+iterations, in this case 50, should have been completed before the exit and
+the results should be available in the end.
 
 Further reading
 ===============

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

@@ -29,12 +29,10 @@ with Primus Turbo optimizations.
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
    .. tab-set::
 
-   {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      .. tab-item:: {{ data.docker.pull_tag }}
+         :sync: {{ data.docker.pull_tag }}
 
          .. list-table::
             :header-rows: 1
@@ -42,13 +40,12 @@ with Primus Turbo optimizations.
             * - Software component
               - Version
 
-            {% for component_name, component_version in docker.components.items() %}
+            {% for component_name, component_version in data.docker.components.items() %}
             * - {{ component_name }}
               - {{ component_version }}
             {% endfor %}
-   {% endfor %}
 
-.. _amd-primus-pytorch-model-support-v259:
+.. _amd-primus-pytorch-model-support-v2510:
 
 Supported models
 ================
@@ -67,7 +64,7 @@ vary by model -- select one to get started.
             <div class="col-2 me-1 px-2 model-param-head">Model</div>
             <div class="row col-10 pe-0">
       {% for model_group in model_groups %}
-               <div class="col-12 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
+               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
       {% endfor %}
             </div>
          </div>
@@ -94,7 +91,7 @@ vary by model -- select one to get started.
    For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
    see the documentation :doc:`pytorch-training` (without Primus)
 
-.. _amd-primus-pytorch-performance-measurements-v259:
+.. _amd-primus-pytorch-performance-measurements-v2510:
 
 System validation
 =================
@@ -120,20 +117,11 @@ Pull the Docker image
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
-
    Use the following command to pull the Docker image from Docker Hub.
 
-   .. tab-set::
+   .. code-block:: shell
 
-      {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
-
-         .. code-block:: shell
-
-            docker pull {{ docker.pull_tag }}
-      {% endfor %}
+      docker pull {{ data.docker.pull_tag }}
 
 Run training
 ============
@@ -145,7 +133,7 @@ tweak some configurations (such as batch sizes).
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
+   {% set docker = data.docker %}
    {% set model_groups = data.model_groups %}
 
    .. tab-set::
@@ -158,7 +146,7 @@ tweak some configurations (such as batch sizes).
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
+            See :ref:`amd-primus-pytorch-model-support-v2510` to switch to another available model.
 
             1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
                directory and install the required packages on the host machine.
@@ -185,13 +173,6 @@ tweak some configurations (such as batch sizes).
                ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
                model are collected in ``~/MAD/perf.csv``.
 
-               .. note::
-
-                  Currently, Primus torchtitan models are run with Primus Turbo
-                  enabled for enhanced performance. To disable Primus Turbo,
-                  modify respective configuration file
-                  ``scripts/primus/pytorch_train/primus_torchtitan_scripts/llama3_[8B|70B]-[BF16|FP8].yaml``.
-
       {% endfor %}
    {% endfor %}
 
@@ -203,48 +184,34 @@ tweak some configurations (such as batch sizes).
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run commands are tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
+            See :ref:`amd-primus-pytorch-model-support-v2510` to switch to another available model.
 
             .. rubric:: Download the Docker image and required packages
 
-            1. Pull the appropriate Docker image for your AMD GPU architecture from Docker Hub.
+            1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
 
-               .. tab-set::
+               .. code-block:: shell
 
-                  {% for supported_gpus, docker in dockers.items() %}
-                  .. tab-item:: {{ supported_gpus }}
-                     :sync: {{ supported_gpus }}
-
-                     .. code-block:: shell
-
-                        docker pull {{ docker.pull_tag }}
-                  {% endfor %}
+                  docker pull {{ docker.pull_tag }}
 
             2. Run the Docker container.
 
-               .. tab-set::
+               .. code-block:: shell
 
-                  {% for supported_gpus, docker in dockers.items() %}
-                  .. tab-item:: {{ supported_gpus }}
-                     :sync: {{ supported_gpus }}
-
-                     .. 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 $HOME:$HOME \
-                            -v $HOME/.ssh:/root/.ssh \
-                            --shm-size 64G \
-                            --name training_env \
-                            {{ docker.pull_tag }}
-                  {% endfor %}
+                  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 $HOME:$HOME \
+                      -v $HOME/.ssh:/root/.ssh \
+                      --shm-size 64G \
+                      --name training_env \
+                      {{ docker.pull_tag }}
 
                Use these commands if you exit the ``training_env`` container and need to return to it.
 
@@ -283,37 +250,28 @@ tweak some configurations (such as batch sizes).
                .. tab-set::
 
                   .. tab-item:: MI355X and MI350X
-                     :sync: MI355X and MI300X
+                     :sync: MI355X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 5
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 6
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6
 
                   .. tab-item:: MI300X
-                     :sync: MI325X and MI300X
+                     :sync: MI300X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 4
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 4
 
 
                To train Llama 3.1 8B with FP8 precision, use the following command.
@@ -321,37 +279,28 @@ tweak some configurations (such as batch sizes).
                .. tab-set::
 
                   .. tab-item:: MI355X and MI350X
-                     :sync: MI355X and MI300X
+                     :sync: MI355X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 8
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 8
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 7
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 7
 
                   .. tab-item:: MI300X
-                     :sync: MI325X and MI300X
+                     :sync: MI300X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_8B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 5
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 5
 
             .. container:: model-doc primus_pyt_train_llama-3.1-70b
 
@@ -364,36 +313,57 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 8
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 8
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 6
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6
 
                   .. tab-item:: MI300X
-                     :sync: MI325X and MI300X
+                     :sync: MI300X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-BF16-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 4
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 4
 
                To train Llama 3.1 70B with FP8 precision, use the following command.
 
+               .. tab-set::
+
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 6
+
+                  .. tab-item:: MI325X
+                     :sync: MI325X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 5
+
+                  .. tab-item:: MI300X
+                     :sync: MI300X
+
+                     .. code-block:: shell
+
+                        EXP=examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 3
+
+            .. container:: model-doc primus_pyt_train_deepseek-v2
+
+               Use the following command to run train DeepSeek V2 16B with BF16 precision using Primus torchtitan.
+
                .. tab-set::
 
                   .. tab-item:: MI355X and MI350X
@@ -401,151 +371,55 @@ tweak some configurations (such as batch sizes).
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 6
+                        EXP=examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 16
 
                   .. tab-item:: MI325X
                      :sync: MI325X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 5
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 10
 
                   .. tab-item:: MI300X
-                     :sync: MI325X and MI300X
+                     :sync: MI300X
 
                      .. code-block:: shell
 
-                        EXP=examples/torchtitan/configs/llama3.1_70B-FP8-pretrain.yaml \
-                        bash examples/run_pretrain.sh \
-                            --metrics.enable_tensorboard false \
-                            --profiling.enable_profiling false \
-                            --training.batch_size 3
-      {% endfor %}
-   {% endfor %}
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 8
 
-      .. tab-item:: Standalone torchtitan benchmarking
-
-   {% for model_group in model_groups %}
-      {% for model in model_group.models %}
-
-         .. container:: model-doc {{ model.mad_tag }}
-
-            The following run commands are tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v259` to switch to another available model.
-
-            .. rubric:: Download the Docker image and required packages
-
-            1. Pull the appropriate Docker image for your AMD GPU architecture from Docker Hub.
+               To train DeepSeek V2 16B with FP8 precision, use the following command.
 
                .. tab-set::
 
-                  {% for supported_gpus, docker in dockers.items() %}
-                  .. tab-item:: {{ supported_gpus }}
-                     :sync: {{ supported_gpus }}
+                  .. tab-item:: MI355X and MI350X
+                     :sync: MI355X
 
                      .. code-block:: shell
 
-                        docker pull {{ docker.pull_tag }}
-                  {% endfor %}
+                        EXP=examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 16
 
-            2. Run the Docker container.
-
-               .. tab-set::
-
-                  {% for supported_gpus, docker in dockers.items() %}
-                  .. tab-item:: {{ supported_gpus }}
-                     :sync: {{ supported_gpus }}
+                  .. tab-item:: MI325X
+                     :sync: MI325X
 
                      .. 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 $HOME:$HOME \
-                            -v $HOME/.ssh:/root/.ssh \
-                            --shm-size 64G \
-                            --name training_env \
-                            {{ docker.pull_tag }}
-                  {% endfor %}
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 8
 
-               Use these commands if you exit the ``training_env`` container and need to return to it.
+                  .. tab-item:: MI300X
+                     :sync: MI300X
 
-               .. code-block:: shell
+                     .. code-block:: shell
 
-                  docker start training_env
-                  docker exec -it training_env bash
-
-            3. Navigate to the ``torchtitan`` workspace directory.
-
-               .. code-block:: shell
-
-                  cd /workspace/torchtitan
-
-            .. rubric:: Download the tokenizer
-
-            1. The following benchmarking examples require downloading models and datasets
-               from Hugging Face. To ensure successful access to gated repos, set your
-               ``HF_TOKEN``.
-
-               .. code-block:: shell
-
-                  export HF_TOKEN=$your_personal_hugging_face_access_token
-
-            2. Download the tokenizer for your model.
-
-               .. container:: model-doc {{ model.mad_tag }}
-
-                  .. code-block:: shell
-
-                     python3 scripts/download_tokenizer.py \
-                        --repo_id {{ model.model_repo }} \
-                        --tokenizer_path "original" \
-                        --hf_token=${HF_TOKEN}
-
-            .. rubric:: Pretraining examples
-
-            Run the training script with the appropriate configuration file.
-
-            For train with BF16 precicion, use the following command:
-
-            .. container:: model-doc {{ model.mad_tag }}
-
-               .. code-block:: shell
-
-                  CONFIG_FILE={{ model.config_file.bf16 }} \
-                  .run_train.sh
-
-            For train with BF16 precicion, use the following command:
-
-            .. container:: model-doc {{ model.mad_tag }}
-
-               .. code-block:: shell
-
-                  CONFIG_FILE={{ model.config_file.fp8 }} \
-                  .run_train.sh
+                        EXP=examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
+                        bash examples/run_pretrain.sh --training.local_batch_size 8
       {% endfor %}
    {% endfor %}
 
-Known issues
-============
-
-PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
-
-
 Further reading
 ===============
 

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

@@ -27,12 +27,10 @@ training workloads:
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
    .. tab-set::
 
-   {% for supported_gpus, docker in dockers.items() %}
-      .. tab-item:: {{ supported_gpus }}
-         :sync: {{ supported_gpus }}
+      .. tab-item:: {{ data.docker.pull_tag }}
+         :sync: {{ data.docker.pull_tag }}
 
          .. list-table::
             :header-rows: 1
@@ -40,13 +38,12 @@ training workloads:
             * - Software component
               - Version
 
-            {% for component_name, component_version in docker.components.items() %}
+            {% for component_name, component_version in data.docker.components.items() %}
             * - {{ component_name }}
               - {{ component_version }}
             {% endfor %}
-   {% endfor %}
 
-.. _amd-pytorch-training-model-support-v259:
+.. _amd-pytorch-training-model-support-v2510:
 
 Supported models
 ================
@@ -88,7 +85,7 @@ one to get started.
          </div>
       </div>
 
-.. _amd-pytorch-training-supported-training-modes-v259:
+.. _amd-pytorch-training-supported-training-modes-v2510:
 
 The following table lists supported training modes per model.
 
@@ -123,7 +120,7 @@ The following table lists supported training modes per model.
          unlisted fine-tuning methods by using an existing file in the
          ``/workspace/torchtune/recipes/configs`` directory as a template.
 
-.. _amd-pytorch-training-performance-measurements-v259:
+.. _amd-pytorch-training-performance-measurements-v2510:
 
 Performance measurements
 ========================
@@ -164,7 +161,7 @@ Run training
 
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
 
-   {% set dockers = data.dockers %}
+   {% set docker = data.docker %}
    {% set model_groups = data.model_groups %}
 
    Once the setup is complete, choose between two options to start benchmarking training:
@@ -179,7 +176,7 @@ Run training
          .. container:: model-doc {{ model.mad_tag }}
 
             The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v259` to switch to another available model.
+            See :ref:`amd-pytorch-training-model-support-v2510` to switch to another available model.
 
             1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
                directory and install the required packages on the host machine.
@@ -217,7 +214,7 @@ Run training
          .. container:: model-doc {{ model.mad_tag }}
 
             The following commands are tailored to {{ model.model }}.
-            See :ref:`amd-pytorch-training-model-support-v259` to switch to another available model.
+            See :ref:`amd-pytorch-training-model-support-v2510` to switch to another available model.
 
       {% endfor %}
    {% endfor %}
@@ -226,42 +223,28 @@ Run training
 
          1. Use the following command to pull the Docker image from Docker Hub.
 
-            .. tab-set::
+            .. code-block:: shell
 
-               {% for supported_gpus, docker in dockers.items() %}
-               .. tab-item:: {{ supported_gpus }}
-                  :sync: {{ supported_gpus }}
-
-                  .. code-block:: shell
-
-                     docker pull {{ docker.pull_tag }}
-               {% endfor %}
+               docker pull {{ docker.pull_tag }}
 
          2. Launch the Docker container.
 
-            .. tab-set::
+            .. code-block:: shell
 
-               {% for supported_gpus, docker in dockers.items() %}
-               .. tab-item:: {{ supported_gpus }}
-                  :sync: {{ supported_gpus }}
-
-                  .. 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 $HOME:$HOME \
-                         -v $HOME/.ssh:/root/.ssh \
-                         --shm-size 64G \
-                         --name training_env \
-                         {{ docker.pull_tag }}
-               {% endfor %}
+               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 $HOME:$HOME \
+                   -v $HOME/.ssh:/root/.ssh \
+                   --shm-size 64G \
+                   --name training_env \
+                   {{ docker.pull_tag }}
 
             Use these commands if you exit the ``training_env`` container and need to return to it.
 
@@ -419,11 +402,34 @@ Run training
 
          .. container:: model-doc {{ model.mad_tag }}
 
-            .. rubric:: Pre-training
+            .. rubric:: Pretraining
 
             To start the pre-training benchmark, use the following command with the
             appropriate options. See the following list of options and their descriptions.
 
+            {% if model.mad_tag == "pyt_train_dlrm" %}
+
+            1. Go to the DLRM directory.
+
+               .. code-block:: shell
+
+                  cd /workspace/DLRMBenchmark
+
+            2. To run the single node training benchmark for DLRM-v2 with TF32 precision,
+               run the following script.
+
+               .. code-block:: shell
+
+                  ./launch_training_single_node.sh
+
+               To run with MAD within the Docker container, use the following command.
+
+               .. code-block:: shell
+
+                  ./pytorch_benchmark_report.sh -t pretrain -m DLRM
+
+            {% else %}
+
             .. code-block:: shell
 
                ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
@@ -466,6 +472,7 @@ Run training
                * - ``$sequence_length``
                  - Sequence length for the language model.
                  - Between 2048 and 8192. 8192 by default.
+            {% endif %}
          {% endif %}
 
          {% set training_modes = model.training_modes %}
@@ -525,7 +532,7 @@ Run training
 
             To start the fine-tuning benchmark, use the following command with the
             appropriate options. See the following list of options and their descriptions.
-            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v259>`.
+            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v2510>`.
 
             .. code-block:: shell
 
@@ -590,7 +597,7 @@ Run training
 
             For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
 
-.. _amd-pytorch-training-multinode-examples-v259:
+.. _amd-pytorch-training-multinode-examples-v2510:
 
 Multi-node training
 -------------------
@@ -639,11 +646,6 @@ To launch the training job on a SLURM cluster for Llama 3.3 70B, run the followi
 
 Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
 
-Known issues
-============
-
-PyTorch Profiler may produce inaccurate traces when CPU activity profiling is enabled.
-
 Further reading
 ===============
 

docs/sphinx/_toc.yml.in

@@ -43,8 +43,6 @@ subtrees:
         title: DGL compatibility
       - file: compatibility/ml-compatibility/megablocks-compatibility.rst
         title: Megablocks compatibility
-      - file: compatibility/ml-compatibility/taichi-compatibility.rst
-        title: Taichi compatibility
       - file: compatibility/ml-compatibility/ray-compatibility.rst
         title: Ray compatibility
       - file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
@@ -117,6 +115,8 @@ 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
 

docs/sphinx/requirements.in

@@ -1,4 +1,4 @@
-rocm-docs-core==1.29.0
+rocm-docs-core==1.31.1
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0

docs/sphinx/requirements.txt

@@ -132,6 +132,7 @@ nest-asyncio==1.6.0
 packaging==25.0
     # via
     #   ipykernel
+    #   pydata-sphinx-theme
     #   sphinx
 parso==0.8.5
     # via jedi
@@ -149,7 +150,7 @@ pure-eval==0.2.3
     # via stack-data
 pycparser==2.23
     # via cffi
-pydata-sphinx-theme==0.16.1
+pydata-sphinx-theme==0.15.4
     # via
     #   rocm-docs-core
     #   sphinx-book-theme
@@ -187,7 +188,7 @@ requests==2.32.5
     # via
     #   pygithub
     #   sphinx
-rocm-docs-core==1.29.0
+rocm-docs-core==1.31.1
     # via -r requirements.in
 rpds-py==0.29.0
     # via
@@ -217,7 +218,7 @@ sphinx==8.1.3
     #   sphinx-reredirects
     #   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

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>`.
-  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.
+  `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
 ^^^^^^^^^^^