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Merge pull request #577 from ROCm/sync-develop-from-external

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Sync develop from external
This commit is contained in:
alexxu-amd
2025-10-08 14:25:03 -04:00
committed by GitHub
parent 4c3e33c291 a366048b64
commit 978c58d196
27 changed files with 771 additions and 228 deletions
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1
.azuredevops/components/hipBLASLt.yml
View File

@@ -77,6 +77,7 @@ parameters:
- clr
- hipBLAS-common
- llvm-project
- rocm-cmake
- rocminfo
- rocm_smi_lib
- rocprofiler-register

2
.azuredevops/components/hipSPARSELt.yml
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@@ -54,6 +54,7 @@ parameters:
- hipSPARSE
- llvm-project
- rocBLAS
- rocm-cmake
- rocm_smi_lib
- rocminfo
- rocprofiler-register
@@ -67,6 +68,7 @@ parameters:
- llvm-project
- hipBLAS-common
- hipBLASLt
- rocm-cmake
- rocBLAS
- rocminfo
- rocprofiler-register

1
.azuredevops/components/hipTensor.yml
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@@ -77,6 +77,7 @@ jobs:
extraBuildFlags: >-
-DCMAKE_PREFIX_PATH=$(Agent.BuildDirectory)/rocm;$(Agent.BuildDirectory)/rocm/llvm
-DCMAKE_CXX_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang++
-DCMAKE_C_COMPILER=$(Agent.BuildDirectory)/rocm/llvm/bin/amdclang
-DROCM_PATH=$(Agent.BuildDirectory)/rocm
-DCMAKE_BUILD_TYPE=Release
-DHIPTENSOR_BUILD_TESTS=ON

1
.azuredevops/components/rocBLAS.yml
View File

@@ -70,6 +70,7 @@ parameters:
- hipBLAS-common
- hipBLASLt
- llvm-project
- rocm-cmake
- rocminfo
- rocprofiler-register
- rocm_smi_lib

9
.azuredevops/components/rocprofiler-systems.yml
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@@ -226,8 +226,11 @@ jobs:
echo "##vso[task.prependpath]$(Agent.BuildDirectory)/rocm/llvm/bin"
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/build-cmake.yml
parameters:
cmakeSourceDir: $(Agent.BuildDirectory)/s/projects/rocprofiler-systems
# build flags reference: https://rocm.docs.amd.com/projects/omnitrace/en/latest/install/install.html
extraBuildFlags: >-
-DCMAKE_INSTALL_PREFIX=$(Agent.BuildDirectory)/rocprofiler-systems
-DROCPROFSYS_USE_PYTHON=ON
-DROCPROFSYS_BUILD_TESTING=ON
-DROCPROFSYS_BUILD_DYNINST=ON
-DROCPROFSYS_BUILD_LIBUNWIND=ON
@@ -245,11 +248,13 @@ jobs:
displayName: Set up rocprofiler-systems env
inputs:
targetType: inline
script: source share/rocprofiler-systems/setup-env.sh
workingDirectory: build
script: source $(Agent.BuildDirectory)/rocprofiler-systems/share/rocprofiler-systems/setup-env.sh
workingDirectory: $(Agent.BuildDirectory)/rocprofiler-systems/share/rocprofiler-systems
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/test.yml
parameters:
componentName: ${{ parameters.componentName }}
testDir: $(Agent.BuildDirectory)/s/build/tests/
testParameters: '--output-on-failure'
- template: ${{ variables.CI_TEMPLATE_PATH }}/steps/manifest.yml
parameters:
gpuTarget: ${{ job.target }}

4
.azuredevops/templates/steps/dependencies-rocm.yml
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@@ -227,8 +227,8 @@ parameters:
developBranch: develop
hasGpuTarget: true
rocprofiler-systems:
pipelineId: 255
developBranch: amd-staging
pipelineId: 345
developBranch: develop
hasGpuTarget: true
rocPyDecode:
pipelineId: 239

4
.wordlist.txt
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@@ -43,6 +43,7 @@ Blit
Blockwise
Bluefield
Bootloader
Broadcom
CAS
CCD
CDNA
@@ -146,6 +147,8 @@ Filesystem
FindDb
Flang
FlashAttention
FlashInfer’s
FlashInfer
FluxBenchmark
Fortran
Fuyu
@@ -480,6 +483,7 @@ TCI
TCIU
TCP
TCR
TVM
THREADGROUPS
threadgroups
TensorRT

107
docs/compatibility/ml-compatibility/flashinfer-compatibility.rst Normal file
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@@ -0,0 +1,107 @@
:orphan:
.. meta::
:description: FlashInfer deep learning framework compatibility
:keywords: GPU, LLM, FlashInfer, compatibility
.. version-set:: rocm_version latest
********************************************************************************
FlashInfer compatibility
********************************************************************************
`FlashInfer <https://docs.flashinfer.ai/index.html>`__ is a library and kernel generator
for Large Language Models (LLMs) that provides high-performance implementation of graphics
processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well
as advanced performance across diverse scenarios.
FlashInfer features highly efficient attention kernels, load-balanced scheduling, and memory-optimized
techniques, while supporting customized attention variants. It’s compatible with ``torch.compile``, and
offers high-performance LLM-specific operators, with easy integration through PyTorch, and C++ APIs.
.. note::
The ROCm port of FlashInfer is under active development, and some features are not yet available.
For the latest feature compatibility matrix, refer to the ``README`` of the
`https://github.com/ROCm/flashinfer <https://github.com/ROCm/flashinfer>`__ repository.
Support for the ROCm port of FlashInfer is available as follows:
- ROCm support for FlashInfer is hosted in the `https://github.com/ROCm/flashinfer
<https://github.com/ROCm/flashinfer>`__ repository. This location differs from the
`https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`_
upstream repository.
- To install FlashInfer, use the prebuilt :ref:`Docker image <flashinfer-docker-compat>`,
which includes ROCm, FlashInfer, and all required dependencies.
- See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>`
to install and get started.
- See the `Installation guide <https://docs.flashinfer.ai/installation.html>`__
in the upstream FlashInfer documentation.
.. note::
Flashinfer is supported on ROCm 6.4.1.
Supported devices
================================================================================
**Officially Supported**: AMD Instinct™ MI300X
.. _flashinfer-recommendations:
Use cases and recommendations
================================================================================
This release of FlashInfer on ROCm provides the decode functionality for LLM inferencing.
In the decode phase, tokens are generated sequentially, with the model predicting each new
token based on the previously generated tokens and the input context.
FlashInfer on ROCm brings over upstream features such as load balancing, sparse and dense
attention optimizations, and batching support, enabling efficient execution on AMD Instinct™ MI300X GPUs.
Because large LLMs often require substantial KV caches or long context windows, FlashInfer on ROCm
also implements cascade attention from upstream to reduce memory usage.
For currently supported use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
where you can search for examples and best practices to optimize your workloads on AMD GPUs.
.. _flashinfer-docker-compat:
Docker image compatibility
================================================================================
.. |docker-icon| raw:: html
<i class="fab fa-docker"></i>
AMD validates and publishes `ROCm FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
inventories represent the FlashInfer version from the official Docker Hub.
The Docker images have been validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
Click |docker-icon| to view the image on Docker Hub.
.. list-table::
:header-rows: 1
:class: docker-image-compatibility
* - Docker image
- ROCm
- FlashInfer
- PyTorch
- Ubuntu
- Python
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/flashinfer/flashinfer-0.2.5_rocm6.4_ubuntu24.04_py3.12_pytorch2.7/images/sha256-558914838821c88c557fb6d42cfbc1bdb67d79d19759f37c764a9ee801f93313"><i class="fab fa-docker fa-lg"></i> rocm/flashinfer</a>
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
- `v0.2.5 <https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.2.5>`__
- `2.7.1 <https://github.com/ROCm/pytorch/releases/tag/v2.7.1>`__
- 24.04
- `3.12 <https://www.python.org/downloads/release/python-3129/>`__

143
docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst
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@@ -16,7 +16,7 @@ for Large Language Model (LLM) inference that runs on both central processing un
a simple, dependency-free setup.
The framework supports multiple quantization options, from 1.5-bit to 8-bit integers,
to speed up inference and reduce memory usage. Originally built as a CPU-first library,
to accelerate inference and reduce memory usage. Originally built as a CPU-first library,
llama.cpp is easy to integrate with other programming environments and is widely
adopted across diverse platforms, including consumer devices.
@@ -40,12 +40,12 @@ with ROCm support:
.. note::
llama.cpp is supported on ROCm 6.4.0.
llama.cpp is supported on ROCm 7.0.0 and ROCm 6.4.x.
Supported devices
================================================================================
**Officially Supported**: AMD Instinct™ MI300X, MI210
**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210
Use cases and recommendations
@@ -70,7 +70,7 @@ llama.cpp is also used in a range of real-world applications, including:
For more use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__,
where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs.
- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__,
- The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__
blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``,
server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for
AMD Instinct GPUs within the ROCm ecosystem.
@@ -84,9 +84,9 @@ Docker image compatibility
<i class="fab fa-docker"></i>
AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp>`__
AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
with ROCm backends on Docker Hub. The following Docker image tags and associated
inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
inventories represent the available llama.cpp versions from the official Docker Hub.
Click |docker-icon| to view the image on Docker Hub.
.. important::
@@ -105,8 +105,115 @@ Click |docker-icon| to view the image on Docker Hub.
- Server Docker
- Light Docker
- llama.cpp
- ROCm
- Ubuntu
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_full/images/sha256-a2ecd635eaa65bb289a9041330128677f3ae88bee6fee0597424b17e38d4903c"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_server/images/sha256-cb46b47df415addb5ceb6e6fdf0be70bf9d7f6863bbe6e10c2441ecb84246d52"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_light/images/sha256-8f8536eec4b05c0ff1c022f9fc6c527ad1c89e6c1ca0906e4d39e4de73edbde9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
- 24.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_full/images/sha256-f36de2a3b03ae53e81c85422cb3780368c9891e1ac7884b04403a921fe2ea45d"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_server/images/sha256-df15e8ab11a6837cd3736644fec1e047465d49e37d610ab0b79df000371327df"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_light/images/sha256-4ea2d5bb7964f0ee3ea9b30ba7f343edd6ddfab1b1037669ca7eafad2e3c2bd7"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
- 22.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_full/images/sha256-5960fc850024a8a76451f9eaadd89b7e59981ae9f393b407310c1ddf18892577"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_server/images/sha256-1b79775d9f546065a6aaf9ca426e1dd4ed4de0b8f6ee83687758cc05af6538e6"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_light/images/sha256-8f863c4c2857ae42bebd64e4f1a0a1e7cc3ec4503f243e32b4a4dcad070ec361"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
- 24.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_full/images/sha256-888879b3ee208f9247076d7984524b8d1701ac72611689e89854a1588bec9867"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_server/images/sha256-90e4ff99a66743e33fd00728cd71a768588e5f5ef355aaa196669fe65ac70672"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_light/images/sha256-bd447a049939cb99054f8fbf3f2352870fe906a75e2dc3339c845c08b9c53f9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
- 22.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_full/images/sha256-5b3a1bc4889c1fcade434b937fbf9cc1c22ff7dc0317c130339b0c9238bc88c4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_server/images/sha256-5228ff99d0f627a9032d668f4381b2e80dc1e301adc3e0821f26d8354b175271"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_light/images/sha256-b12723b332a826a89b7252dddf868cbe4d1a869562fc4aa4032f59e1a683b968"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
- 24.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_full/images/sha256-cd6e21a6a73f59b35dd5309b09dd77654a94d783bf13a55c14eb8dbf8e9c2615"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_server/images/sha256-c2b4689ab2c47e6626e8fea22d7a63eb03d47c0fde9f5ef8c9f158d15c423e58"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_light/images/sha256-1acc28f29ed87db9cbda629cb29e1989b8219884afe05f9105522be929e94da4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
- 22.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_full/images/sha256-2f8ae8a44510d96d52dea6cb398b224f7edeb7802df7ec488c6f63d206b3cdc9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_server/images/sha256-fece497ff9f4a28b12f645de52766941da8ead8471aa1ea84b61d4b4568e51f2"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_light/images/sha256-3e14352fa6f8c6128b23cf9342531c20dbfb522550b626e09d83b260a1947022"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
- 24.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_full/images/sha256-80763062ef0bec15038c35fd01267f1fc99a5dd171d4b48583cc668b15efad69"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_server/images/sha256-db2a6c957555ed83b819bbc54aea884a93192da0fb512dae63d32e0dc4e8ab8f"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_light/images/sha256-c6dbb07cc655fb079d5216e4b77451cb64a9daa0585d23b6fb8b32cb22021197"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
- `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
- 22.04
* - .. raw:: html
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_full/images/sha256-f78f6c81ab2f8e957469415fe2370a1334fe969c381d1fe46050c85effaee9d5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
@@ -117,40 +224,52 @@ Click |docker-icon| to view the image on Docker Hub.
<a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_light/images/sha256-cc324e6faeedf0e400011f07b49d2dc41a16bae257b2b7befa0f4e2e97231320"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
- `b5997 <https://github.com/ROCm/llama.cpp/tree/release/b5997>`__
- `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
- 24.04
Key ROCm libraries for llama.cpp
================================================================================
llama.cpp functionality on ROCm is determined by its underlying library
dependencies. These ROCm components affect the capabilities, performance, and
feature set available to developers.
feature set available to developers. Ensure you have the required libraries for
your corresponding ROCm version.
.. list-table::
:header-rows: 1
* - ROCm library
- Version
- ROCm 7.0.0 version
- ROCm 6.4.x version
- Purpose
- Usage
* - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
- :version-ref:`hipBLAS rocm_version`
- 3.0.0
- 2.4.0
- Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
matrix and vector operations.
- Supports operations such as matrix multiplication, matrix-vector
products, and tensor contractions. Utilized in both dense and batched
linear algebra operations.
* - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
- :version-ref:`hipBLASLt rocm_version`
- 1.0.0
- 0.12.0
- hipBLASLt is an extension of the hipBLAS library, providing additional
features like epilogues fused into the matrix multiplication kernel or
use of integer tensor cores.
- By setting the flag ``ROCBLAS_USE_HIPBLASLT``, you can dispatch hipblasLt
kernels where possible.
* - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
- :version-ref:`rocWMMA rocm_version`
- 2.0.0
- 1.7.0
- Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
multiplication (GEMM) and accumulation operations with mixed precision
support.
- Can be used to enhance the flash attention performance on AMD compute, by enabling
the flag during compile time.
the flag during compile time.
Previous versions
===============================================================================
See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/llama-cpp-history` to find documentation for previous releases
of the ``ROCm/llama.cpp`` Docker image.

2
docs/compatibility/ml-compatibility/megablocks-compatibility.rst
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@@ -28,7 +28,7 @@ Supported devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
Supported models and features
================================================================================

2
docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
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@@ -27,7 +27,7 @@ Supported Devices
================================================================================
- **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210
Supported models and features

6
docs/conf.py
View File

@@ -110,11 +110,15 @@ article_pages = [
{"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"]},
{"file": "how-to/deep-learning-rocm", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/install", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-health-check", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-setup/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-setup/multi-node-setup", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-setup/prerequisite-system-validation", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/system-setup/system-health-check", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/index", "os": ["linux"]},
{"file": "how-to/rocm-for-ai/training/train-a-model", "os": ["linux"]},

12
docs/how-to/deep-learning-rocm.rst
View File

@@ -128,10 +128,22 @@ The table below summarizes information about ROCm-enabled deep learning framewor
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-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/llama-cpp-install.html#use-a-prebuilt-docker-image-with-llama-cpp-pre-installed>`__
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#build-your-own-docker-image>`__
- .. raw:: html
<a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>
* - `FlashInfer <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/flashinfer-compatibility.html>`__
- .. raw:: html
<a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-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/flashinfer-install.html#use-a-prebuilt-docker-image-with-flashinfer-pre-installed>`__
- `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html#build-your-own-docker-image>`__
- .. raw:: html
<a href="https://github.com/ROCm/flashinfer"><i class="fab fa-github fa-lg"></i></a>
Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
through the following guides.

4
docs/how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
View File

@@ -16,7 +16,7 @@ PyTorch inference performance testing
The `ROCm PyTorch Docker <https://hub.docker.com/r/rocm/pytorch/tags>`_ image offers a prebuilt,
optimized environment for testing model inference performance on AMD Instinct™ MI300X series
accelerators. This guide demonstrates how to use the AMD Model Automation and Dashboarding (MAD)
GPUs. This guide demonstrates how to use the AMD Model Automation and Dashboarding (MAD)
tool with the ROCm PyTorch container to test inference performance on various models efficiently.
.. _pytorch-inference-benchmark-available-models:
@@ -175,7 +175,7 @@ Further reading
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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 application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`../../inference-optimization/workload`.

6
docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
View File

@@ -23,7 +23,7 @@ improved efficiency and throughput.
serving engine for large language models (LLMs) and vision models. The
ROCm-enabled `SGLang base Docker image <{{ docker.docker_hub_url }}>`__
bundles SGLang with PyTorch, which is optimized for AMD Instinct MI300X series
accelerators. It includes the following software components:
GPUs. It includes the following software components:
.. list-table::
:header-rows: 1
@@ -37,7 +37,7 @@ improved efficiency and throughput.
{% endfor %}
The following guides on setting up and running SGLang and Mooncake for disaggregated
distributed inference on a Slurm cluster using AMD Instinct MI300X series accelerators backed by
distributed inference on a Slurm cluster using AMD Instinct MI300X series GPUs backed by
Mellanox CX-7 NICs.
Prerequisites
@@ -236,7 +236,7 @@ Further reading
- See the base upstream Docker image on `Docker Hub <https://hub.docker.com/layers/lmsysorg/sglang/v0.5.2rc1-rocm700-mi30x/images/sha256-10c4ee502ddba44dd8c13325e6e03868bfe7f43d23d0a44780a8ee8b393f4729>`__.
- To learn more about system settings and management practices to configure your system for
MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__.
MI300X series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__.
- For application performance optimization strategies for HPC and AI workloads,
including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.

28
docs/how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
View File

@@ -14,9 +14,9 @@ vLLM inference performance testing
The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers
a prebuilt, optimized environment for validating large language model (LLM)
inference performance on AMD Instinct™ MI300X series accelerators. This ROCm vLLM
inference performance on AMD Instinct™ MI300X series GPUs. This ROCm vLLM
Docker image integrates vLLM and PyTorch tailored specifically for MI300X series
accelerators and includes the following components:
GPUs and includes the following components:
.. list-table::
:header-rows: 1
@@ -31,7 +31,7 @@ vLLM inference performance testing
With this Docker image, you can quickly test the :ref:`expected
inference performance numbers <vllm-benchmark-performance-measurements-909>` for
MI300X series accelerators.
MI300X series GPUs.
What's new
==========
@@ -101,7 +101,7 @@ Supported models
See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
Some models require access authorization prior to use via an external license agreement through a third party.
{% if model.precision == "float8" and model.model_repo.startswith("amd") %}
This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD accelerators.
This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
{% endif %}
{% endfor %}
@@ -121,7 +121,7 @@ page provides reference throughput and serving measurements for inferencing popu
The performance data presented in
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
only reflects the latest version of this inference benchmarking environment.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X GPUs or ROCm software.
System validation
=================
@@ -138,13 +138,12 @@ 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.
Pull the Docker image
=====================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
{% set docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Pull the Docker image
=====================
Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
Use the following command to pull the Docker image from Docker Hub.
@@ -153,8 +152,13 @@ system's configuration.
docker pull {{ docker.pull_tag }}
Benchmarking
============
Benchmarking
============
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
{% set docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Once the setup is complete, choose between two options to reproduce the
benchmark results:
@@ -423,7 +427,7 @@ Further reading
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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>`_.
- See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
a brief introduction to vLLM and optimization strategies.

2
docs/how-to/rocm-for-ai/install.rst
View File

@@ -57,4 +57,4 @@ Next steps
==========
After installing ROCm and your desired ML libraries -- and before running AI workloads -- conduct system health benchmarks
to test the optimal performance of your AMD hardware. See :doc:`system-health-check` to get started.
to test the optimal performance of your AMD hardware. See :doc:`system-setup/index` to get started.

40
docs/how-to/rocm-for-ai/system-setup/index.rst Normal file
View File

@@ -0,0 +1,40 @@
.. meta::
:description: System setup and validation steps for AI training and inference on ROCm
:keywords: AMD Instinct, ROCm, GPU, AI, training, inference, benchmarking, performance, validation
*************************************
System setup for AI workloads on ROCm
*************************************
Before you begin training or inference on AMD Instinct™ GPUs, complete
the following system setup and validation steps to ensure optimal performance.
Prerequisite system validation
==============================
First, confirm that your system meets all software and hardware prerequisites.
See :doc:`prerequisite-system-validation`.
Docker images for AMD Instinct GPUs
===================================
AMD provides prebuilt Docker images for AMD Instinct™ MI300X and MI325X
GPUs. These images include ROCm-enabled deep learning frameworks and
essential software components. They support single-node and multi-node configurations
and are ready for training and inference workloads out of the box.
Multi-node training
-------------------
For instructions on enabling multi-node training, see :doc:`multi-node-setup`.
System optimization and validation
==================================
Before running workloads, verify that the system is configured correctly and
operating at peak efficiency. Recommended steps include:
- Disabling NUMA auto-balancing
- Running system benchmarks to validate hardware performance
For details on running system health checks, see :doc:`system-health-check`.

320
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@@ -0,0 +1,320 @@
.. meta::
:description: Multi-node setup for AI training
:keywords: gpu, accelerator, system, health, validation, bench, perf, performance, rvs, rccl, babel, mi300x, mi325x, flops, bandwidth, rbt, training
.. _rocm-for-ai-multi-node-setup:
*********************************
Multi-node setup for AI workloads
*********************************
AMD provides ready-to-use Docker images for AMD Instinct™ MI300X and MI325X
GPUs containing ROCm-capable deep learning frameworks and essential
software components. These Docker images can run and leverage multiple nodes if
they are available. This page describes how to enable the multi-node training
of AI workloads on AMD Instinct GPUs.
Prerequisites
=============
Before starting, ensure your environment meets the following requirements:
* Multi-node networking: your cluster should have a configured multi-node network. For setup
instructions, see the `Multi-node network configuration for AMD Instinct
accelerators
<https://instinct.docs.amd.com/projects/gpu-cluster-networking/en/latest/how-to/multi-node-config.html>`__
guide in the Instinct documentation.
* ROCm Docker container to simplify environment setup for AI workloads. See the following resources to get started:
* :doc:`Training a model with Megatron-LM and ROCm <../training/benchmark-docker/megatron-lm>`
* :doc:`Training a model with PyTorch and ROCm <../training/benchmark-docker/pytorch-training>`
* :doc:`Training a model with JAX MaxText and ROCm <../training/benchmark-docker/jax-maxtext>`
* Slurm workload manager to run the :ref:`provided examples <multi-node-setup-training-examples>`.
Install required packages
=========================
To run multi-node workloads, ensure you have all the required packages installed based on your
network device. For example, on Ubuntu systems:
.. code-block:: shell
apt install -y iproute2
apt install -y linux-headers-"$(uname -r)" libelf-dev
apt install -y gcc make libtool autoconf librdmacm-dev rdmacm-utils infiniband-diags ibverbs-utils perftest ethtool libibverbs-dev rdma-core strace libibmad5 libibnetdisc5 ibverbs-providers libibumad-dev libibumad3 libibverbs1 libnl-3-dev libnl-route-3-dev
Compile and install the RoCE library
------------------------------------
If you're using Broadcom NICs, you need to compile and install the RoCE (RDMA
over Converged Ethernet) library. See `RoCE cluster network configuration guide
for AMD Instinct accelerators
<https://instinct.docs.amd.com/projects/gpu-cluster-networking/en/latest/how-to/roce-network-config.html#roce-cluster-network-configuration-guide-for-amd-instinct-accelerators>`__
for more information.
See the `Ethernet networking guide for AMD
Instinct MI300X GPU clusters: Compiling Broadcom NIC software from source
<https://docs.broadcom.com/doc/957608-AN2XX#page=81>`_ for more details.
.. important::
It is crucial to install the exact same version of the RoCE library that
is installed on your host system. Also, ensure that the path to these
libraries on the host is correctly mounted into your Docker container.
Failure to do so can lead to compatibility issues and communication
failures.
1. Set ``BUILD_DIR`` to the path on the host system where the Broadcom drivers and ``bnxt_rocelib`` source are located.
Then, navigate to the ``bnxt_rocelib`` directory.
.. code-block:: shell
export BUILD_DIR=/path/to/your/broadcom_drivers_on_host
cd $BUILD_DIR/drivers_linux/bnxt_rocelib/
2. The ``bnxt_rocelib`` directory contains a version of ``libbnxt_re`` in a zipped ``.tar.gz`` file.
.. code-block:: shell
tar -xf libbnxt_re-a.b.c.d.tar.gz
cd libbnxt_re-a.b.c.d
3. Compile and install the RoCE library.
.. code-block:: shell
sh autogen.sh
./configure
make
find /usr/lib64/ /usr/lib -name "libbnxt_re-rdmav*.so" -exec mv {} {}.inbox \;
make install all
sh -c "echo /usr/local/lib >> /etc/ld.so.conf"
ldconfig
cp -f bnxt_re.driver /etc/libibverbs.d/
find . -name "*.so" -exec md5sum {} \;
BUILT_MD5SUM=$(find . -name "libbnxt_re-rdmav*.so" -exec md5sum {} \; | cut -d " " -f 1)
Environment setup
=================
Before running multi-node workloads, set these essential environment variables:
Master address
--------------
By default, ``localhost`` is used for single-node configurations. Change
``localhost`` to the master node's resolvable hostname or IP address:
.. code-block:: bash
export MASTER_ADDR="${MASTER_ADDR:-localhost}"
Number of nodes
---------------
Set the number of nodes you want to train on (for example, ``2``, ``4``, or ``8``):
.. code-block:: bash
export NNODES="${NNODES:-<num_nodes>}"
Node ranks
----------
Set the rank of each node (``0`` for master, ``1`` for the first worker node, and so on).
Node ranks should be unique across all nodes in the cluster.
.. code-block:: bash
export NODE_RANK="${NODE_RANK:-<node_rank>}"
Network interface
-----------------
Update the network interface in the script to match your system's network interface. To
find your network interface, run the following (outside of any Docker container):
.. code-block:: bash
ip a
Look for an active interface (status "UP") with an IP address in the same subnet as
your other nodes. Then, update the following variable in the script, for
example:
.. code-block:: bash
export NCCL_SOCKET_IFNAME=ens50f0np0
This variable specifies which network interface to use for inter-node communication.
Setting this variable to the incorrect interface can result in communication failures
or significantly reduced performance.
.. tip::
This command sets ``NCCL_SOCKET_IFNAME``'s value to the last RDMA interface.
.. code-block:: bash
export NCCL_SOCKET_IFNAME=$(rdma link show | awk '{print $NF}' | sort | tail -n1)
RDMA/IB interface
-----------------
Set the RDMA interfaces to be used for communication. NICs can come from different vendors and the names of the RDMA interface can be different. To get the list of all the RDMA/IB devices, run:
.. code-block:: bash
ibv_devices
The command below gets the list of all RDMA/IB devices and puts them in a
comma-separated format. If
(``rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7``) are your RDMA
interfaces, then set:
.. code-block:: bash
# If using Broadcom NIC
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
# If using Mellanox NIC
# export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
.. tip::
Alternatively, if you want to choose the RDMA interface automatically, you
can use the following. This command will sort the RDMA interfaces and then
select the first eight RDMA interfaces.
.. code-block:: bash
export NCCL_IB_HCA=$(ibv_devices | awk 'NR>2 {print $1}' | sort | head -n 8 | paste -sd,)
Global ID index
---------------
Update the global ID index if you're using RoCE.
.. code-block:: bash
export NCCL_IB_GID_INDEX=3
.. _multi-node-setup-training-examples:
Multi-node training examples
============================
The following examples use the Slurm workload manager to launch jobs on
multiple nodes. To run these scripts as-is, you must have a Slurm environment
configured. The scripts are designed to work with both Broadcom Thor 2 and
Mellanox NICs by automatically installing the required libraries and setting
the necessary environment variables. For systems with Broadcom NICs, the
scripts assume the host's RoCE library is located in the ``/opt`` directory.
The following benchmarking examples demonstrate the training of a Llama 3 8B model
across multiple 8-GPU nodes, using FSDP for intra-node parallelism and DP for
inter-node parallelism.
.. _rocm-for-ai-multi-node-setup-jax-train-example:
JAX MaxText
-----------
1. Download the desired multi-node benchmarking script from `<https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/gpu-rocm>`__.
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/MAD/refs/heads/develop/scripts/jax-maxtext/gpu-rocm/llama3_8b_multinode.sh
Or clone the `<https://github.com/ROCm/MAD>`__ repository.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd scripts/jax-maxtext/gpu-rocm
2. Run the benchmark for multi-node training.
.. code-block:: shell
sbatch -N <num_nodes> llama3_8b_multinode.sh
.. _rocm-for-ai-multi-node-setup-pyt-train-example:
PyTorch training
----------------
.. note::
The ROCm PyTorch Training Docker image now focuses on :doc:`Training a model
with Primus and PyTorch <../training/benchmark-docker/primus-pytorch>`. The
following example refers to the legacy workflow :ref:`Training a
model with PyTorch <amd-pytorch-training-multinode-examples>`.
1. Download the ``run_multinode_train.sh`` benchmarking script from `<https://github.com/ROCm/MAD/tree/develop/scripts/pytorch_train>`__.
.. code-block:: shell
wget https://raw.githubusercontent.com/ROCm/MAD/refs/heads/develop/scripts/pytorch_train/run_multinode_train.sh
Or clone the `<https://github.com/ROCm/MAD>`__ repository.
.. code-block:: shell
git clone https://github.com/ROCm/MAD
cd scripts/pytorch_train
2. Run the benchmark for multi-node training.
.. code-block:: shell
sbatch -N <num_nodes> run_multinode_train.sh
.. seealso::
See :ref:`Training a model with PyTorch <amd-pytorch-multinode-examples>` for more examples and information.
Megatron-LM
-----------
.. note::
The Megatron-LM Docker image now focuses on :ref:`Training a model with
Primus and Megatron <amd-primus-megatron-multi-node-examples>`. The
following example refers to the legacy Megatron-LM :ref:`Training a model
with Megatron-LM <amd-megatron-lm-multi-node-examples>` and might have
limited support.
1. Download the ``train_llama_slurm.sh`` benchmarking script from
`<https://github.com/ROCm/Megatron-LM/blob/rocm_dev/examples/llama/train_llama_slurm.sh>`__.
2. Set the network interface parameters as per the above guidelines and run the script.
.. code-block:: shell
cd </path/to/your/Megatron-LM>
export NETWORK_INTERFACE=$NCCL_SOCKET_IFNAME
export NCCL_IB_HCA=$NCCL_IB_HCA
export IMAGE=docker.io/rocm/megatron-lm:latest OR your preferred image
export DATA_CACHE_PATH=/nfs/mounted/repo
sbatch –N <num_nodes> examples/llama/train_llama_slurm.sh <MODEL_SIZE> <MBS> <GBS> <SEQ_LENGTH> <FSDP> <RECOMPUTE>
2. For example, to run a Llama 3 8B workload in BF16 precision, use the following command.
.. code-block:: shell
MODEL_NAME=llama3 sbatch –N 8 examples/llama/train_llama_slurm.sh 8 2 128 8192 0 0
# Other parameters, such as TP, FP8 datatype, can be adjusted in the script.
Further reading
===============
* `Multi-node network configuration for AMD Instinct accelerators <https://instinct.docs.amd.com/projects/gpu-cluster-networking/en/latest/how-to/multi-node-config.html>`__
* `Ethernet networking guide for AMD Instinct MI300X GPU clusters: Compiling Broadcom NIC software from source <https://docs.broadcom.com/doc/957608-AN2XX#page=81>`__

2
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View File

@@ -1,5 +1,3 @@
:orphan:
.. meta::
:description: Prerequisite system validation before using ROCm for AI.
:keywords: ROCm, AI, LLM, train, megatron, Llama, tutorial, docker, torch, pytorch, jax

10
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View File

@@ -1,12 +1,14 @@
:orphan:
.. meta::
:description: System health checks with RVS, RCCL tests, BabelStream, and TransferBench to validate AMD hardware performance running AI workloads.
:keywords: gpu, accelerator, system, health, validation, bench, perf, performance, rvs, rccl, babel, mi300x, mi325x, flops, bandwidth, rbt, training, inference
.. _rocm-for-ai-system-health-bench:
************************
System health benchmarks
************************
*****************************************
System health benchmarks for AI workloads
*****************************************
Before running AI workloads, it is important to validate that your AMD hardware is configured correctly and is performing optimally. This topic outlines several system health benchmarks you can use to test key aspects like GPU compute capabilities (FLOPS), memory bandwidth, and interconnect performance. Many of these tests are part of the ROCm Validation Suite (RVS).
@@ -62,7 +64,7 @@ RCCL tests
The ROCm Communication Collectives Library (RCCL) enables efficient multi-GPU
communication. The `<https://github.com/ROCm/rccl-tests>`__ suite benchmarks
the performance and verifies the correctness of these collective operations.
This helps ensure optimal scaling for multi-accelerator tasks.
This helps ensure optimal scaling for multi-GPU tasks.
1. To get started, build RCCL-tests using the official instructions in the README at
`<https://github.com/ROCm/rccl-tests?tab=readme-ov-file#build>`__ or use the

114
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@@ -10,10 +10,10 @@ MaxText is a high-performance, open-source framework built on the Google JAX
machine learning library to train LLMs at scale. The MaxText framework for
ROCm is an optimized fork of the upstream
`<https://github.com/AI-Hypercomputer/maxtext>`__ enabling efficient AI workloads
on AMD MI300X series accelerators.
on AMD MI300X series GPUs.
The MaxText for ROCm training Docker image
provides a prebuilt environment for training on AMD Instinct MI300X and MI325X accelerators,
provides a prebuilt environment for training on AMD Instinct MI300X and MI325X GPUs,
including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
It includes the following software components:
@@ -25,7 +25,7 @@ It includes the following software components:
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
.. tab-item:: ``{{ docker.pull_tag }}``
:sync: {{ docker.pull_tag }}
.. list-table::
@@ -69,7 +69,7 @@ Supported models
================
The following models are pre-optimized for performance on AMD Instinct MI300
series accelerators. Some instructions, commands, and available training
series GPUs. Some instructions, commands, and available training
configurations in this documentation might vary by model -- select one to get
started.
@@ -132,87 +132,35 @@ This Docker image is optimized for specific model configurations outlined
as follows. Performance can vary for other training workloads, as AMD
doesn’t validate configurations and run conditions outside those described.
Pull the Docker image
---------------------
Use the following command to pull the Docker image from Docker Hub.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
{% set dockers = data.dockers %}
.. tab-set::
{% for docker in dockers %}
{% set jax_version = docker.components["JAX"] %}
.. tab-item:: JAX {{ jax_version }}
:sync: {{ docker.pull_tag }}
.. code-block:: shell
docker pull {{ docker.pull_tag }}
{% endfor %}
.. _amd-maxtext-multi-node-setup-v257:
Multi-node setup
----------------
Multi-node configuration
------------------------
For multi-node environments, ensure you have all the necessary packages for
your network device, such as, RDMA. If you're not using a multi-node setup
with RDMA, skip ahead to :ref:`amd-maxtext-get-started-v257`.
1. Install the following packages to build and install the RDMA driver.
.. code-block:: shell
sudo apt install iproute2 -y
sudo apt install -y linux-headers-"$(uname-r)" libelf-dev
sudo apt install -y gcc make libtool autoconf librdmacm-dev rdmacm-utils infiniband-diags ibverbs-utils perftest ethtool libibverbs-dev rdma-core strace libibmad5 libibnetdisc5 ibverbs-providers libibumad-dev libibumad3 libibverbs1 libnl-3-dev libnl-route-3-dev
Refer to your NIC manufacturer's documentation for further steps on
compiling and installing the RoCE driver. For example, for Broadcom,
see `Compiling Broadcom NIC software from source <https://docs.broadcom.com/doc/957608-AN2XX#G3.484341>`_
in `Ethernet networking guide for AMD Instinct MI300X GPU clusters <https://docs.broadcom.com/doc/957608-AN2XX>`_.
2. Set the following environment variables.
a. Master address
Change ``localhost`` to the master node's resolvable hostname or IP address:
.. code-block:: bash
export MASTER_ADDR="${MASTER_ADDR:-localhost}"
b. Number of nodes
Set the number of nodes you want to train on (for example, ``2``, ``4``, or ``8``):
.. code-block:: bash
export NNODES="${NNODES:-1}"
c. Node ranks
Set the rank of each node (``0`` for master, ``1`` for the first worker node, and so on)
Node ranks should be unique across all nodes in the cluster.
.. code-block:: bash
export NODE_RANK="${NODE_RANK:-0}"
d. Network interface
Update the network interface in the script to match your system's network interface. To
find your network interface, run the following (outside of any Docker container):
.. code-block:: bash
ip a
Look for an active interface with an IP address in the same subnet as
your other nodes. Then, update the following variable in the script, for
example:
.. code-block:: bash
export NCCL_SOCKET_IFNAME=ens50f0np0
This variable specifies which network interface to use for inter-node communication.
Setting this variable to the incorrect interface can result in communication failures
or significantly reduced performance.
e. RDMA interface
Ensure the :ref:`required packages <amd-maxtext-multi-node-setup-v257>` are installed on all nodes.
Then, set the RDMA interfaces to use for communication.
.. code-block:: bash
# If using Broadcom NIC
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
# If using Mellanox NIC
export NCCL_IB_HCA=mlx5_0,mlx5_1,mlx5_2,mlx5_3,mlx5_4,mlx5_5,mlx5_8,mlx5_9
See :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your
environment for multi-node training.
.. _amd-maxtext-get-started-v257:
@@ -399,7 +347,7 @@ Further reading
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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>`_.

86
docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
View File

@@ -10,20 +10,20 @@ Training a model with Megatron-LM on ROCm
.. caution::
Primus with Megatron supersedes this ROCm Megatron-LM training workflow.
Primus with Megatron is designed to replace this ROCm Megatron-LM training workflow.
To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
a specialized fork of the robust Megatron-LM, designed to enable efficient
training of large-scale language models on AMD GPUs. By leveraging AMD
Instinct™ MI300X series accelerators, Megatron-LM delivers enhanced
Instinct™ MI300X series GPUs, Megatron-LM delivers enhanced
scalability, performance, and resource utilization for AI workloads. It is
purpose-built to support models like Llama, DeepSeek, and Mixtral,
enabling developers to train next-generation AI models more
efficiently.
AMD provides ready-to-use Docker images for MI300X series accelerators containing
AMD provides ready-to-use Docker images for MI300X series GPUs containing
essential components, including PyTorch, ROCm libraries, and Megatron-LM
utilities. It contains the following software components to accelerate training
workloads:
@@ -61,7 +61,7 @@ workloads:
================
The following models are supported for training performance benchmarking with Megatron-LM and ROCm
on AMD Instinct MI300X series accelerators.
on AMD Instinct MI300X series GPUs.
Some instructions, commands, and training recommendations in this documentation might
vary by model -- select one to get started.
@@ -115,7 +115,7 @@ popular AI models.
The performance data presented in
`Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`__
only reflects the latest version of this training benchmarking environment.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X accelerators or ROCm software.
The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct MI325X and MI300X GPUs or ROCm software.
System validation
=================
@@ -138,11 +138,11 @@ Environment setup
=================
Use the following instructions to set up the environment, configure the script to train models, and
reproduce the benchmark results on MI300X series accelerators with the AMD Megatron-LM Docker
reproduce the benchmark results on MI300X series GPUs with the AMD Megatron-LM Docker
image.
.. _amd-megatron-lm-requirements:
Download the Docker image
-------------------------
@@ -152,7 +152,7 @@ Download the Docker image
1. Use the following command to pull the Docker image from Docker Hub.
{% if dockers|length > 1 %}
.. tab-set::
.. tab-set::
{% for docker in data.dockers %}
.. tab-item:: {{ docker.doc_name }}
@@ -281,25 +281,11 @@ Configuration
See :ref:`Key options <amd-megatron-lm-benchmark-test-vars>` for more information on configuration options.
Network interface
-----------------
Multi-node configuration
------------------------
Update the network interface in the script to match your system's network interface. To
find your network interface, run the following (outside of any Docker container):
.. code-block:: bash
ip a
Look for an active interface that has an IP address in the same subnet as
your other nodes. Then, update the following variables in the script, for
example:
.. code-block:: bash
export NCCL_SOCKET_IFNAME=ens50f0np0
export GLOO_SOCKET_IFNAME=ens50f0np0
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:
@@ -540,46 +526,6 @@ Download the dataset
Ensure that the files are accessible inside the Docker container.
Multi-node configuration
------------------------
If you're running multi-node training, update the following environment variables. They can
also be passed as command line arguments. Refer to the following example configurations.
* Change ``localhost`` to the master node's hostname:
.. code-block:: shell
MASTER_ADDR="${MASTER_ADDR:-localhost}"
* Set the number of nodes you want to train on (for instance, ``2``, ``4``, ``8``):
.. code-block:: shell
NNODES="${NNODES:-1}"
* Set the rank of each node (0 for master, 1 for the first worker node, and so on):
.. code-block:: shell
NODE_RANK="${NODE_RANK:-0}"
* Set ``DATA_CACHE_PATH`` to a common directory accessible by all the nodes (for example, an
NFS directory) for multi-node runs:
.. code-block:: shell
DATA_CACHE_PATH=/root/cache # Set to a common directory for multi-node runs
* For multi-node runs, make sure the correct network drivers are installed on the nodes. If
inside a Docker container, either install the drivers inside the Docker container or pass the network
drivers from the host while creating the Docker container.
.. code-block:: shell
# Specify which RDMA interfaces to use for communication
export NCCL_IB_HCA=rdma0,rdma1,rdma2,rdma3,rdma4,rdma5,rdma6,rdma7
.. _amd-megatron-lm-run-training:
Run training
@@ -587,7 +533,7 @@ Run training
Use the following example commands to set up the environment, configure
:ref:`key options <amd-megatron-lm-benchmark-test-vars>`, and run training on
MI300X series accelerators with the AMD Megatron-LM environment.
MI300X series GPUs with the AMD Megatron-LM environment.
Single node training
--------------------
@@ -612,7 +558,7 @@ Single node training
FSDP=1 \
MODEL_SIZE=70 \
TOTAL_ITERS=50 \
bash examples/llama/train_llama3.sh
bash examples/llama/train_llama3.sh
.. note::
@@ -770,7 +716,7 @@ Single node training
.. container:: model-doc pyt_megatron_lm_train_deepseek-v3-proxy
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
navigate to the Megatron-LM folder and use the following command.
.. code-block:: shell
@@ -925,6 +871,8 @@ Single node training
RECOMPUTE_ACTIVATIONS=full \
CKPT_FORMAT=torch_dist
.. _amd-megatron-lm-multi-node-examples:
Multi-node training examples
----------------------------

42
docs/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
View File

@@ -8,16 +8,16 @@ Training a model with Primus and Megatron-LM
`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 accelerators using a modular, reproducible configuration paradigm.
training on AMD Instinct GPUs using a modular, reproducible configuration paradigm.
Primus is backend-agnostic and supports multiple training engines -- including Megatron.
.. note::
Primus with Megatron supersedes the :doc:`ROCm Megatron-LM training <megatron-lm>` workflow.
Primus with Megatron is designed to replace the :doc:`ROCm Megatron-LM training <megatron-lm>` workflow.
To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
see :doc:`previous-versions/megatron-lm-primus-migration-guide`.
For ease of use, AMD provides a ready-to-use Docker image for MI300 series accelerators
For ease of use, AMD provides a ready-to-use Docker image for MI300 series GPUs
containing essential components for Primus and Megatron-LM. This Docker is powered by Primus
Turbo optimizations for performance; this release adds support for Primus Turbo
with optimized attention and grouped GEMM kernels.
@@ -47,7 +47,7 @@ with optimized attention and grouped GEMM kernels.
Supported models
================
The following models are pre-optimized for performance on AMD Instinct MI300X series accelerators.
The following models are pre-optimized for performance on AMD Instinct MI300X series GPUs.
Some instructions, commands, and training examples in this documentation might
vary by model -- select one to get started.
@@ -105,21 +105,26 @@ system's configuration.
.. _mi300x-amd-primus-megatron-lm-training:
Environment setup
=================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
Environment setup
=================
Use the following instructions to set up the environment, configure the script to train models, and
reproduce the benchmark results on MI300X series accelerators with the ``{{ docker.pull_tag }}`` image.
reproduce the benchmark results on MI300X series GPUs with the ``{{ docker.pull_tag }}`` image.
.. _amd-primus-megatron-lm-requirements:
Download the Docker image
-------------------------
Pull the Docker image
=====================
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
1. Use the following command to pull the Docker image from Docker Hub.
@@ -229,7 +234,7 @@ Run training
Use the following example commands to set up the environment, configure
:ref:`key options <amd-primus-megatron-lm-benchmark-test-vars>`, and run training on
MI300X series accelerators with the AMD Megatron-LM environment.
MI300X series GPUs with the AMD Megatron-LM environment.
Single node training
--------------------
@@ -341,7 +346,7 @@ 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 \
bash ./examples/run_pretrain.sh --train_iters 50
bash ./examples/run_pretrain.sh --train_iters 50
.. container:: model-doc primus_pyt_megatron_lm_train_deepseek-v3-proxy
@@ -349,7 +354,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
The following run commands are tailored to DeepSeek-V3.
See :ref:`amd-primus-megatron-lm-model-support` to switch to another available model.
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
To run training on a single node for DeepSeek-V3 (MoE with expert parallel) with 3-layer proxy,
use the following command:
.. code-block:: shell
@@ -445,9 +450,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
EXP=examples/megatron/configs/qwen2.5_72B-pretrain.yaml \
bash examples/run_pretrain.sh --train_iters 50
.. _amd-primus-megatron-multi-node-examples:
Multi-node training examples
----------------------------
Refer to :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your environment for multi-node
training.
To run training on multiple nodes, you can use the
`run_slurm_pretrain.sh <https://github.com/AMD-AGI/Primus/blob/927a71702784347a311ca48fd45f0f308c6ef6dd/examples/run_slurm_pretrain.sh>`__
to launch the multi-node workload. Use the following steps to setup your environment:
@@ -505,7 +515,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
.. code-block:: shell
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
NNODES=8 EXP=examples/megatron/configs/llama3.1_8B-pretrain.yaml \
bash ./examples/run_slurm_pretrain.sh \
--global_batch_size 1024 \
@@ -540,7 +550,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
.. code-block:: shell
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
# Adjust the training parameters. For e.g., `global_batch_size: 8 * #single_node_bs` for 8 nodes in this case
NNODES=8 EXP=examples/megatron/configs/llama2_7B-pretrain.yaml bash ./examples/run_slurm_pretrain.sh --global_batch_size 2048 --fp8 hybrid
.. container:: model-doc primus_pyt_megatron_lm_train_llama-2-70b
@@ -639,7 +649,7 @@ Further reading
Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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>`_.

23
docs/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
View File

@@ -8,12 +8,12 @@ Training a model with Primus and PyTorch
`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 accelerators using a modular, reproducible configuration paradigm.
training on AMD Instinct GPUs using a modular, reproducible configuration paradigm.
Primus now supports the PyTorch torchtitan backend.
.. note::
Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
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.
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
@@ -21,7 +21,7 @@ Primus now supports the PyTorch torchtitan backend.
{% set dockers = data.dockers %}
{% set docker = dockers[0] %}
For ease of use, AMD provides a ready-to-use Docker image -- ``{{
docker.pull_tag }}`` -- for MI300X series accelerators containing essential
docker.pull_tag }}`` -- for MI300X series GPUs containing essential
components for Primus and PyTorch training with
Primus Turbo optimizations.
@@ -41,7 +41,7 @@ Primus now supports the PyTorch torchtitan backend.
Supported models
================
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
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.
@@ -104,22 +104,25 @@ 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/primus-pytorch-benchmark-models.yaml
{% set unified_docker = data.dockers[0] %}
Pull the Docker image
=====================
Use the following command to pull the `Docker image <{{ unified_docker.docker_hub_url }}>`_ from Docker Hub.
.. code-block:: shell
docker pull {{ unified_docker.pull_tag }}
Run training
============
Run training
============
.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
{% set unified_docker = data.dockers[0] %}
{% set model_groups = data.model_groups %}
Once the setup is complete, choose between the following two workflows to start benchmarking training.
@@ -293,7 +296,7 @@ Further reading
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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>`_.

15
docs/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
View File

@@ -10,7 +10,7 @@ Training a model with PyTorch on ROCm
.. note::
Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
Primus with the PyTorch torchtitan backend is designed to replace :doc:`ROCm PyTorch training <pytorch-training>` workflow.
See :doc:`primus-pytorch` for details.
PyTorch is an open-source machine learning framework that is widely used for
@@ -22,7 +22,7 @@ model training with GPU-optimized components for transformer-based models.
{% set docker = dockers[0] %}
The `PyTorch for ROCm training Docker <{{ docker.docker_hub_url }}>`__
(``{{ docker.pull_tag }}``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
model on AMD Instinct MI325X and MI300X GPUs. It includes the following software components to accelerate
training workloads:
.. list-table::
@@ -41,7 +41,7 @@ model training with GPU-optimized components for transformer-based models.
Supported models
================
The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
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.
@@ -126,7 +126,7 @@ popular AI models.
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 accelerators or ROCm software.
Instinct MI325X and MI300X GPUs or ROCm software.
System validation
=================
@@ -521,9 +521,14 @@ 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:
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
~~~~~~~~~~~~
@@ -571,7 +576,7 @@ Further reading
- To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
- To learn more about system settings and management practices to configure your system for
AMD Instinct MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
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>`_.

13
docs/sphinx/_toc.yml.in
View File

@@ -49,6 +49,8 @@ subtrees:
title: Ray compatibility
- file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
title: llama.cpp compatibility
- file: compatibility/ml-compatibility/flashinfer-compatibility.rst
title: FlashInfer compatibility
- file: how-to/build-rocm.rst
title: Build ROCm from source
@@ -60,8 +62,15 @@ subtrees:
- entries:
- file: how-to/rocm-for-ai/install.rst
title: Installation
- file: how-to/rocm-for-ai/system-health-check.rst
title: System health benchmarks
- file: how-to/rocm-for-ai/system-setup/index.rst
title: System setup
entries:
- file: how-to/rocm-for-ai/system-setup/prerequisite-system-validation.rst
title: System validation
- file: how-to/rocm-for-ai/system-setup/multi-node-setup.rst
title: Multi-node setup
- file: how-to/rocm-for-ai/system-setup/system-health-check.rst
title: System health benchmarks
- file: how-to/rocm-for-ai/training/index.rst
title: Training
subtrees: